Beyond Chronological Age: A Comprehensive Comparison of Second-Generation vs. First-Generation Epigenetic Clocks for Research and Drug Development

Abstract

This article provides a systematic comparison of first- and second-generation epigenetic clocks for a scientific audience. First-generation clocks, such as Horvath and Hannum, were trained primarily to predict chronological age. In contrast, second-generation clocks, including PhenoAge and GrimAge, incorporate health-related variables, lifestyle factors, and mortality risk, enhancing their utility in predicting age-related outcomes and assessing interventions. We explore the methodological foundations, performance in health association and interventional studies, current limitations such as technical noise and population bias, and emerging computational solutions. This review underscores why next-generation models should be prioritized in health-oriented association studies and clinical trials for aging.

Defining the Generations: From Chronological Age Prediction to Healthspan Assessment

Epigenetic clocks represent a revolutionary class of biomarkers that predict chronological age and biological age based on age-related changes in DNA methylation (DNAm) patterns at specific CpG sites across the genome [1] [2]. These clocks have emerged as powerful tools in aging research, capable of estimating biological age with remarkable precision across diverse tissues and cell types [1]. The development of epigenetic clocks has followed a clear evolutionary trajectory, progressing from first-generation clocks trained primarily to predict chronological age to more sophisticated second-generation clocks explicitly designed to capture aspects of biological aging, morbidity, and mortality risk [3] [4].

The foundational principle underlying all epigenetic clocks is that DNA methylation levels at specific genomic locations undergo predictable changes throughout an individual's lifespan, with approximately 28% of the human genome exhibiting age-related methylation patterns [1]. These predictable changes create what researchers term "clock-like" behavior, which can be quantified using machine learning algorithms to generate accurate age estimators [1] [2]. The discrepancy between epigenetic age and chronological age, known as epigenetic age acceleration, provides valuable insights into an individual's biological aging trajectory, with positive acceleration (epigenetic age > chronological age) indicating faster biological aging [5].

This review comprehensively examines the genesis and development of epigenetic clocks, comparing the performance characteristics of first-generation and second-generation models, detailing experimental methodologies for their development and validation, and exploring emerging applications in clinical and research settings.

Generations of Epigenetic Clocks: Defining Characteristics and Performance

First-Generation Epigenetic Clocks

First-generation epigenetic clocks were pioneered to answer a fundamental question: can DNA methylation patterns accurately predict chronological age? These initial models utilized supervised machine learning approaches, predominantly penalized regression methods like elastic net regression, trained against chronological age to identify informative CpG sites predictive of age [2] [6].

Table 1: Characteristics of Prominent First-Generation Epigenetic Clocks

Clock Name	CpG Sites	Tissue Specificity	Reported Correlation (r)	Key Features
Horvath Clock	353	Pan-tissue	0.96 [4]	First multi-tissue clock; applicable to diverse tissue types [1]
Hannum Clock	71	Blood-specific	0.91-0.96 [4]	Optimized for blood samples; stronger clinical correlations [1]
Zhang et al. Clock	514	Blood & saliva	0.99 [4]	High accuracy using substantial sample size [4]

The Horvath clock, a landmark model in epigenetic aging research, was the first to achieve cross-tissue age prediction by analyzing DNA methylation data from multiple tissue types [1]. Developed using 7,844 samples across 51 tissue and cell types, its core strength lies in broad applicability across diverse tissues and organs, including whole blood, brain, kidney, and liver [1]. In contrast, the Hannum clock was specifically optimized for blood samples, developed using whole blood samples from 656 adults and demonstrating strong associations with clinical markers including body mass index, cardiovascular health, and immune function [1] [4].

Despite their revolutionary impact, first-generation clocks present significant limitations. They demonstrate weaker associations with physiological measures of dysregulation compared to later generations and have limited sensitivity to certain interventions and diseases [4] [6]. These limitations prompted the development of more advanced clocks designed to capture biological rather than purely chronological aging.

Second-Generation and Next-Generation Epigenetic Clocks

Second-generation clocks addressed fundamental limitations of first-generation models by incorporating phenotypic data and mortality-related biomarkers into their training frameworks [3] [4]. These clocks were explicitly designed to predict health outcomes, disease risk, and mortality rather than merely estimating chronological age [3].

Table 2: Characteristics of Prominent Second-Generation Epigenetic Clocks

Clock Name	Training Target	Key Components	Performance Advantages
PhenoAge	Phenotypic age & mortality risk	9 clinical chemistry markers [2]	Stronger association with all-cause mortality, physical/cognitive decline [6]
GrimAge	Mortality risk & time to death	DNAm-based plasma protein estimates, smoking pack-years [2] [6]	Superior prediction of lifespan and healthspan [2] [6]
DunedinPACE	Pace of aging	Longitudinal decline in multiple organ systems [3]	Captures rate of aging progression rather than accumulated damage [3]
IC Clock	Intrinsic capacity	Cognitive, locomotor, sensory, psychological, vitality domains [7]	Predicts all-cause mortality better than 1st/2nd-gen clocks; associates with immune biomarkers [7]

Next-generation models have demonstrated markedly improved utility for health-oriented research. Current evidence indicates they associate with a greater number of health and disease signals than first-generation clocks and are often more predictive of age-related outcomes [3]. They also appear more responsive to interventions, making them particularly valuable for evaluating the effectiveness of longevity treatments [3].

The recently developed Intrinsic Capacity (IC) Clock represents a further evolution, trained on clinical evaluations of cognition, locomotion, psychological well-being, sensory abilities, and vitality [7]. In validation studies, DNA methylation IC outperformed both first-generation and second-generation epigenetic clocks in predicting all-cause mortality and showed strong associations with molecular and cellular immune and inflammatory biomarkers [7].

Comparative Performance Data: First-Generation vs. Second-Generation Clocks

Extensive research has quantified performance differences between epigenetic clock generations across multiple domains. Second-generation clocks consistently demonstrate superior performance in predicting clinically relevant outcomes compared to their first-generation counterparts.

Table 3: Performance Comparison Between Clock Generations

Performance Metric	First-Generation Clocks	Second-Generation Clocks
Mortality Prediction	Weak to moderate associations [4]	Stronger predictors; GrimAge specifically designed for mortality risk [2] [6]
Physical Function	Generally poor predictors [6]	Levine/PhenoAge associated with grip strength; GrimAge/Levine predict FEV1 decline [6]
Cognitive Function	Hannum clock associated with verbal fluency [6]	GrimAge & Levine associated with mental speed; GrimAge with episodic memory [6]
Intervention Responsiveness	Less responsive to interventions [3]	More responsive to lifestyle and therapeutic interventions [3]
Allostatic Load	Weak or sex-specific associations [6]	Levine clock shows robust associations with allostatic load [6]

This performance advantage extends to molecular and cellular levels. Second-generation clocks show stronger associations with immunosenescence markers like CD28 expression in T-cells and inflammatory processes [7]. They also demonstrate better capture of environmental exposures such as smoking, with GrimAge specifically incorporating DNAm-based smoking pack-year estimates [2].

The predictive performance of these clocks continues to be refined. While first-generation clocks typically achieve correlations of 0.90-0.99 with chronological age, second-generation clocks show superior performance in predicting clinically relevant outcomes, with GrimAge demonstrating particularly strong prediction of lifespan and healthspan [2] [6].

Methodological Approaches in Epigenetic Clock Development

Core Experimental Protocols

The development of epigenetic clocks follows standardized methodological pipelines with distinct approaches for first-generation versus second-generation models:

First-Generation Clock Development Protocol:

• Data Acquisition: Collection of DNA methylation data from reference cohorts using Illumina methylation arrays (27K, 450K, or EPIC) [1]
• Feature Selection: Identification of age-related CpG sites through regression analysis of methylation levels against chronological age [1]
• Model Training: Application of machine learning algorithms (typically elastic net regression) to train age predictors using selected CpG sites [2]
• Validation: Cross-validation in independent datasets to assess prediction accuracy [1]

Second-Generation Clock Development Protocol:

• Composite Biomarker Development: Creation of phenotypic measures combining clinical biomarkers (PhenoAge) or mortality data (GrimAge) [2] [6]
• DNAm Predictor Training: Development of DNA methylation-based estimators for these composite biomarkers [2]
• Multivariate Integration: Incorporation of additional risk factors such as smoking exposure or plasma protein levels [2]
• Outcome Validation: Testing against health outcomes including mortality, disability, and disease incidence [3] [6]

A critical methodological consideration is addressing cell composition effects, as aging is associated with significant changes in immune cell populations that can confound epigenetic age estimates [8] [9]. Recent approaches have developed specialized clocks like the IntrinClock that are resistant to changes in immune cell composition by training across multiple purified cell types [9].

Visualization of Epigenetic Clock Development Workflow

The following diagram illustrates the comparative development pathways for first-generation and second-generation epigenetic clocks:

Analytical Considerations

The statistical approaches for developing epigenetic clocks have evolved significantly. First-generation clocks primarily used cross-sectional designs, which are susceptible to mortality selection bias [6]. This bias occurs because individuals with accelerated aging are selectively removed from the population through earlier mortality, potentially causing clocks to select non-causal markers that simply correlate with age in the surviving population [6].

Second-generation clocks increasingly incorporate longitudinal data, which better captures the dynamics of biological aging and provides more robust associations with health outcomes [6]. Additionally, specialized analytical approaches have been developed to address the collinearity of blood cell counts, including principal component analysis methods that provide biologically meaningful insights into the relationship between cell composition and epigenetic age [8].

The Scientist's Toolkit: Essential Research Reagents and Platforms

Conducting epigenetic clock research requires specific laboratory reagents, analytical tools, and technical platforms. The following table details essential components of the epigenetic clock research toolkit:

Table 4: Essential Research Tools for Epigenetic Clock Studies

Tool Category	Specific Products/Platforms	Application in Research
Methylation Arrays	Illumina Infinium MethylationEPIC, HumanMethylation450K, HumanMethylation27K [1]	Genome-wide DNA methylation profiling at CpG sites
Cell Deconvolution Algorithms	EpiDISH, IDOL-extended [8]	Estimation of cell proportions from methylation data
Statistical Software	R programming environment with specialized packages [2]	Data analysis, clock calculation, and statistical modeling
DNA Extraction Kits	Qiagen DNA extraction kits, phenol-chloroform protocols	High-quality DNA isolation from blood, tissue, or saliva
Cell Separation Tools	Fluorescence-activated cell sorting (FACS), magnetic bead separation [9]	Isolation of specific immune cell populations for cell-type-specific analysis
Reference Datasets	Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx) [9]	Access to publicly available methylation data for model development
HIV-1 inhibitor-28	HIV-1 inhibitor-28, MF:C26H32N6O3S, MW:508.6 g/mol	Chemical Reagent
Antitumor agent-75	Antitumor agent-75|Cytotoxic Anthraquinone|RUO	Antitumor agent-75 is a cytotoxic anthraquinone for cancer research. This product is For Research Use Only and is not intended for diagnostic or therapeutic use.

The selection of appropriate microarray platforms is particularly critical, as different clocks were developed using different array technologies, potentially affecting their performance and comparability [2]. The EPIC array, which covers approximately 850,000 CpG sites, has largely superseded the 450K and 27K arrays, providing more comprehensive coverage of regulatory genomic regions [9].

For researchers interested in applying existing epigenetic clocks rather than developing new ones, multiple computational implementations are freely available, including R packages that allow users to calculate epigenetic age estimates from raw methylation data [2].

Biological Mechanisms and Signaling Pathways

Understanding the biological foundations of epigenetic clocks requires examining the mechanisms through which DNA methylation patterns reflect aging processes. The following diagram illustrates key pathways and biological processes involved in epigenetic aging:

The biological processes captured by epigenetic clocks encompass both cell-intrinsic aging mechanisms and systemic aging manifestations [2] [9]. First-generation clocks are particularly influenced by age-related changes in immune cell composition, especially the shift from naïve to memory T-cell phenotypes [8] [9]. Research has demonstrated that naïve CD8+ T cells exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual, highlighting the profound impact of cell differentiation states on epigenetic age estimates [9].

Second-generation clocks capture more diverse biological processes, including inflammatory pathways, mitochondrial dysfunction, and aspects of cellular senescence [2] [7]. Gene ontology analyses of genes associated with second-generation clocks reveal enrichment in immune response pathways, particularly T-cell activation, and processes related to chronic inflammation [7]. This broader capture of biological aging processes likely explains their superior performance in predicting health outcomes and mortality.

The genesis and evolution of epigenetic clocks from first-generation to second-generation models represents a significant advancement in biomarker development for aging research. While first-generation clocks established the fundamental principle that DNA methylation patterns can accurately predict chronological age, second-generation clocks have demonstrated superior utility for capturing biological aging processes, predicting health outcomes, and evaluating interventions.

The comparative data clearly indicate that second-generation clocks should be prioritized for health-oriented association and interventional studies, given their stronger associations with mortality, age-related diseases, and functional decline [3] [6]. However, first-generation clocks retain value for applications requiring precise chronological age estimation or when studying basic mechanisms of epigenetic aging.

Future development of epigenetic clocks will likely focus on addressing current limitations, including improving their sensitivity to interventions, enhancing their applicability across diverse populations, and better differentiating between disease-specific aging patterns [2] [5]. The integration of multi-omics data and the development of single-cell epigenetic clocks hold promise for further refining our ability to measure biological aging [1] [9]. As these biomarkers continue to evolve, they are poised to play an increasingly important role in both basic aging research and clinical evaluation of interventions aimed at extending healthspan.

Core Principles of First-Generation Epigenetic Clocks

First-generation epigenetic clocks are biomarkers of aging that estimate an individual's biological age based on patterns of DNA methylation (DNAm) at specific cytosine-phosphate-guanine (CpG) sites. Their core principle relies on the predictable changes DNA methylation undergoes over time, which serves as a molecular clock reflecting cumulative physiological decline [1]. These clocks were developed using a single-step regression approach, with their primary training objective being the accurate prediction of chronological age [3] [1]. Consequently, the discrepancy between the DNAm-predicted age and a person's actual chronological age—termed Epigenetic Age Acceleration (EAA)—is interpreted as accelerated or decelerated biological aging, providing insights into how genetic and environmental factors shape an individual's physiological state [1].

Table 1: Foundational Concepts of First-Generation Clocks

Concept	Description	Interpretation in Research
DNA Methylation (DNAm)	An epigenetic process where a methyl group is added to a cytosine nucleotide, typically in a CpG dinucleotide context, regulating gene expression without altering the DNA sequence [1].	The primary raw data used to train and calculate epigenetic age.
Biological Age	The functional age of an organism's cells and tissues, reflecting its physiological condition and health trajectory [1].	The output of an epigenetic clock, estimated from DNAm patterns.
Chronological Age	The amount of time, typically in years, that has elapsed since birth.	The "true" value that first-generation clocks were trained to predict.
Age Acceleration	The difference (residual) between predicted epigenetic age and actual chronological age [10] [11].	A positive value suggests faster biological aging; a negative value suggests slower biological aging.

Key First-Generation Models

The two most prominent and widely used first-generation epigenetic clocks are the Horvath clock and the Hannum clock.

Horvath's Clock

Developed by Steve Horvath in 2013, this was a landmark model as the first pan-tissue epigenetic clock [1]. It was trained on 7,844 samples from 51 different tissue and cell types, using 353 CpG sites (193 positively and 160 negatively correlated with age) to estimate epigenetic age [1]. Its primary strength is its remarkable versatility; it demonstrates high accuracy across a wide range of tissues and organs, including brain, kidney, liver, and blood [1]. This broad applicability also extends to other mammalian species and to in vitro aging analyses, making it an invaluable tool for diverse research settings [1]. However, its pan-tissue nature can be a limitation, as its predictive accuracy can vary across different tissues, particularly in hormonally sensitive ones like blood [1]. Furthermore, it can underestimate biological age in individuals over 60 and has shown limited sensitivity to certain age-related diseases [1].

Hannum's Clock

Developed around the same time by Gregory Hannum, this model was optimized specifically for blood samples [1]. It was built using whole blood samples from 656 adults and employs 71 CpG sites selected for their strong age-related changes [1]. Utilizing the Elastic Net algorithm, it achieves a very high correlation of 0.96 with chronological age, with an average absolute error of 3.9 years [1]. Its blood-specific design makes it highly relevant for studies of age-related diseases and clinical interventions using blood samples, such as assessing the impact of weight loss or exercise programs [1]. A key limitation is its lack of generalizability, as it is not designed for use in non-blood tissues [1]. It also exhibits lower cross-ethnic adaptability and reduced sensitivity to some external factors compared to the Horvath clock [1].

Table 2: Comparison of Key First-Generation Epigenetic Clock Models

Feature	Horvath Clock	Hannum Clock
Primary Innovation	First pan-tissue clock	First blood-optimized clock
Year Introduced	2013 [1]	2013 [1]
Training Samples	7,844 samples from 51 tissue/cell types [1]	656 whole blood samples [1]
Number of CpG Sites	353 [1]	71 [1]
Key Strength	Unparalleled cross-tissue applicability [1]	High specificity and performance in blood-based studies [1]
Main Limitation	Variable accuracy across tissues; can underestimate age in older adults [1]	Not applicable to tissues other than blood [1]
Correlation with Chronological Age	Highly accurate, mean absolute error of ~3.6 years [1]	Correlation of 0.96, mean absolute error of ~3.9 years [1]

Performance and Limitations in Relation to Next-Generation Clocks

While first-generation clocks are excellent estimators of chronological age, their performance in predicting health outcomes is generally surpassed by next-generation clocks. Next-generation models, such as PhenoAge and GrimAge, were explicitly trained on phenotypic data, mortality risk, and healthspan, rather than chronological age alone [3] [12].

Evidence from large-scale studies consistently shows that next-generation clocks are more powerful for predicting mortality and age-related diseases. A study from the National Institute on Aging found that while all epigenetic clocks predicted mortality better than telomere length, the next-generation GrimAge clock outperformed first-generation clocks like Horvath and Hannum in predicting all-cause mortality [13]. Similarly, a 2025 study in a US representative sample confirmed that GrimAge-based epigenetic age acceleration was the most significant predictor of overall mortality, followed by the Hannum and Horvath clocks [10]. This trend also holds for specific diseases; for instance, in glaucoma, the Horvath and Hannum clocks showed significant associations with fast progression, but the associations were generally weaker than those observed with GrimAge [11].

The primary limitation of first-generation clocks is that they capture a more static measure of biological age and are less sensitive to the dynamic processes of aging and health status compared to next-generation models [3] [1]. They are also less strongly correlated with socioeconomic factors and health behaviors, which are known drivers of aging [12].

Essential Research Protocols and Reagents

Standard Workflow for DNA Methylation Analysis

The experimental protocol for deriving an epigenetic age estimate involves a multi-step process centered on analyzing DNA methylation.

Diagram 1: Standard workflow for DNAm analysis and clock calculation.

The Scientist's Toolkit: Key Research Reagents and Materials

Table 3: Essential Materials for Epigenetic Clock Research

Item	Function	Example/Note
DNA Extraction Kit	Isolates high-quality, high-molecular-weight DNA from biological samples.	A critical first step; purity and integrity affect downstream results.
Bisulfite Conversion Kit	Chemically converts unmethylated cytosines to uracils, allowing methylation status to be determined via sequencing or array.	Zymo EZ DNA Methylation kits are commonly used [10].
Infinium Methylation Array	Microarray platform for profiling methylation levels at hundreds of thousands of CpG sites across the genome.	Illumina's Infinium BeadChips (e.g., 450K, EPIC) are the industry standard [14] [10].
Preprocessing Software (R/BioConductor)	For background correction, normalization, and quality control of raw array data.	Packages like minfi and ENmix are essential for data processing [14].
Epigenetic Clock Calculators	Software packages that apply pre-trained algorithms to methylation data to estimate epigenetic age.	R packages like planet (for Lee clock) and ENmix (for Horvath) are available [14].
Hbv-IN-9	Hbv-IN-9|HBV Research Compound	Hbv-IN-9 is a potent research compound for investigating hepatitis B virus (HBV). This product is For Research Use Only. Not for human or veterinary use.
2-Hydroxyestrone-13C6	2-Hydroxyestrone-13C6 Stable Isotope	2-Hydroxyestrone-13C6 is a stable isotope-labeled internal standard for precise quantification of estrogen metabolites in research. For Research Use Only. Not for human or diagnostic use.

First-generation epigenetic clocks, epitomized by the Horvath and Hannum models, established the foundational principle that DNA methylation patterns can serve as a robust biomarker of aging. The Horvath clock remains invaluable for its unique pan-tissue applicability, while the Hannum clock offers optimized performance in blood-based studies. However, their design, centered on predicting chronological age, limits their sensitivity to health outcomes and mortality risk compared to next-generation clocks. Consequently, the choice between using a first-generation or a next-generation clock should be guided by the specific research question: first-generation clocks for fundamental age estimation and tissue-agnostic studies, and next-generation clocks for health-oriented, interventional, and mortality-prediction studies [3].

The fundamental limitation of chronological age as a predictor of health and mortality has driven a paradigm shift in aging research. While chronological age simply measures the passage of time, biological age captures the progressive, functional decline in resilience that varies significantly between individuals [15]. This understanding has fueled the development of epigenetic clocks—computational models that estimate biological age based on predictable changes in DNA methylation (DNAm) patterns across the lifespan [1] [16]. These clocks have evolved through distinct generations, moving from pure chronological age estimators toward sophisticated tools that quantify biological aging processes, healthspan, and mortality risk [3] [17].

The first-generation clocks, exemplified by Horvath's clock and Hannum's clock, were groundbreaking for their ability to accurately estimate chronological age from DNA methylation data [1] [4]. However, their primary training on chronological age itself limited their utility for capturing health-related aging processes. Next-generation clocks address this critical limitation by incorporating phenotypic data, clinical biomarkers, and mortality-related information, creating more powerful tools for assessing the effectiveness of interventions and predicting age-related outcomes [3] [17] [15]. This guide provides a comprehensive comparison of these epigenetic clock generations, highlighting their performance, methodologies, and applications in research and drug development.

Generational Comparison: From Chronological Age Predictors to Healthspan Monitors

Epigenetic clocks are broadly categorized into generations based on their training targets and underlying objectives. The table below summarizes the core characteristics and limitations of these generations.

Table 1: Generations of Epigenetic Clocks

Generation	Primary Training Target	Representative Clocks	Core Objective	Key Limitations
First-Generation [3] [1]	Chronological Age	HorvathAge [1] [17], HannumAge [17] [4]	Accurate estimation of chronological time passed [4].	Limited association with health outcomes and intervention responsiveness [3].
Next-Generation [3]	Health, Mortality, & Phenotypic Age	DNAm PhenoAge [17] [4], DNAm GrimAge [17] [4], DunedinPoAm [15], LinAge2 [15]	Predict mortality, healthspan, and age-related functional decline [3] [15].	Increased complexity; may require specific interpretation contexts [3].

First-Generation Clocks: Foundational but Limited

Horvath's Clock, a landmark first-generation model, was the first pan-tissue clock. Developed using 7,844 samples across 51 tissue and cell types, it employs 353 CpG sites to estimate epigenetic age [1]. Its strength lies in remarkable cross-tissue applicability, validated in almost all organs, including brain, kidney, and liver [1]. However, its predictive consistency is lower than newer models, and it shows limited sensitivity to certain diseases and interventions like smoking [1] [16]. It also tends to underestimate biological age in individuals over 60 [1].

Hannum's Clock was developed specifically for blood samples, using 71 CpG sites from whole blood samples of 656 adults [1] [4]. It demonstrates high accuracy for chronological age in blood (R² = 0.96) and has good associations with clinical markers like BMI and cardiovascular health [1]. Its primary limitation is its restricted applicability to non-blood tissues and lower sensitivity to external factors compared to other clocks [1].

Next-Generation Clocks: Focusing on Health and Mortality

DNAm PhenoAge was the first major second-generation clock. It was trained on "phenotypic age," derived from nine age-related clinical biomarkers (including albumin, creatinine, and C-reactive protein), rather than chronological age alone [17] [16]. Composed of 513 CpGs, it vastly outperforms first-generation clocks in predicting mortality, healthspan, and conditions like cardiovascular disease [16]. It is also sensitive to risk factors like smoking [16].

DNAm GrimAge represents a significant advancement in mortality risk prediction. It was built by incorporating DNA methylation-based surrogate biomarkers for seven plasma proteins (e.g., those involved in inflammation and cardiovascular function) and smoking pack-years [16] [4]. This composition makes GrimAge, and its successor GrimAge2, one of the strongest predictors of time-to-death and age-related disease, outperforming earlier clocks [4] [15].

DunedinPoAm (Pace of Aging) and LinAge2 are other next-generation clocks. DunedinPoAm is designed to measure the rate of biological aging [15], while LinAge2 is a recently enhanced clinical clock that uses linear dimensionality reduction of clinical parameters to predict mortality with high accuracy, reportedly outperforming even some epigenetic clocks like PhenoAge DNAm and DunedinPoAm [15].

Quantitative Performance and Comparative Data

The transition from first- to next-generation clocks is justified by a substantial improvement in predicting clinically relevant outcomes. The following table synthesizes performance data from comparative studies.

Table 2: Comparative Performance of Selected Epigenetic and Clinical Clocks

Clock (Generation)	Primary Training Basis	Correlation with Chronological Age (r)	Performance in Mortality Prediction	Association with Healthspan/Functional Status
HorvathAge (1st) [1] [15]	Chronological Age (Multi-tissue)	0.96 [1]	Does not significantly differ from CA in predicting future mortality [15].	No statistically significant differences found across healthspan markers [15].
HannumAge (1st) [1] [15]	Chronological Age (Blood)	0.96 [1]	Does not significantly differ from CA in predicting future mortality [15].	Information not specified in search results.
DNAm PhenoAge (2nd) [16] [15]	Phenotypic Age (Clinical Biomarkers)	0.68 (in whole blood) [17]	Outperformed by clinical clock LinAge2 [15].	Information not specified in search results.
DNAm GrimAge2 (2nd) [4] [15]	Plasma Proteins & Smoking	Information not specified	Similar to LinAge2 in predicting future mortality; outperforms CA [15].	Significant differences across most healthspan markers (cognition, gait speed, iADLs) [15].
DunedinPoAm (2nd) [15]	Pace of Biological Aging	Information not specified	Outperformed by clinical clock LinAge2 [15].	Significant differences across most healthspan markers [15].
LinAge2 (Clinical) [15]	Clinical Biomarkers & Mortality	Highly correlated with CA [15]	Outperforms CA, PhenoAge DNAm, and DunedinPoAm; similar to GrimAge2 [15].	Significant differences across healthspan markers (cognition, gait speed, ADLs) [15].

Experimental Protocols and Methodological Workflows

The development and validation of epigenetic clocks rely on standardized experimental and computational pipelines. The following diagram and description outline a typical workflow for constructing and applying a DNA methylation aging clock.

Diagram 1: Workflow for Developing and Applying an Epigenetic Clock. This diagram outlines the key steps from biological sample collection to the final application of a DNA methylation clock for outcome analysis.

Detailed Experimental Protocol

1. Sample Collection and DNA Extraction:

• Sample Types: Studies utilize a variety of sources, most commonly whole blood, but also saliva, brain, kidney, liver, and other tissues from biobanks or cohort studies [1] [18]. The choice of tissue is critical, as clocks trained on blood (e.g., Hannum) may be less accurate when applied to other tissues like lung or colon [18].
• DNA Extraction: Standard protocols (e.g., phenol-chloroform extraction or commercial kits) are used to isolate high-quality, high-molecular-weight DNA.

2. DNA Methylation Profiling:

• Technology: The dominant platform is the Illumina Infinium Methylation BeadChip, with models like the 450K and EPIC arrays being widely used [1] [19]. These arrays quantitatively measure methylation levels at hundreds of thousands of CpG sites across the genome.
• Bisulfite Conversion: Extracted DNA is treated with sodium bisulfite, which converts unmethylated cytosines to uracils (read as thymines in sequencing), while methylated cytosines remain unchanged. This allows for the quantification of methylation status at each CpG site.

3. Data Preprocessing and Normalization:

• Quality Control: Raw data undergoes rigorous QC to remove poor-quality samples and probes with low signal or high detection p-values.
• Normalization: Technical variation is minimized using normalization algorithms (e.g., BMIQ, SWAN) to make samples comparable. Cell type composition (e.g., the proportion of neutrophils in blood) is often estimated and adjusted for, as it is a major confounder in epigenetic age estimation [2].

4. Model Training and Clock Generation:

• Machine Learning: The preprocessed methylation data (beta-values for each CpG) from a training cohort is fed into a supervised machine learning algorithm. The most common is penalized regression, specifically Elastic Net, which performs both variable selection and regularization [1] [4].
• Training Target: The model is trained to predict a specific outcome.
  • For first-generation clocks, this is chronological age [4].
  • For next-generation clocks, this can be phenotypic age (a composite of clinical measures) [17], time-to-death [4], or a pace of aging measure derived from longitudinal physiological decline [15].
• Output: The model outputs a set of selected CpG sites and their respective coefficients. A subject's epigenetic age is calculated as the weighted sum of the methylation values of these CpGs.

5. Validation and Outcome Analysis:

• Validation: The clock's performance is tested in one or more independent validation cohorts by calculating the correlation (e.g., Pearson's r) between predicted epigenetic age and chronological age, and the mean absolute error (MAE) [4].
• Advanced Analysis: The "age acceleration" residual (difference between epigenetic age and chronological age) is then associated with outcomes like all-cause mortality, incidence of specific diseases, cognitive function, and functional decline to validate the clock's biological and clinical relevance [17] [15].

Research Reagent Solutions and Essential Materials

Table 3: Essential Research Reagents and Materials for Epigenetic Clock Studies

Item	Function/Application	Examples/Notes
DNA Extraction Kits	Isolation of high-quality genomic DNA from diverse sample types.	Commercial kits from Qiagen, Thermo Fisher, etc., optimized for blood, tissue, or saliva.
Bisulfite Conversion Kits	Chemical treatment of DNA to distinguish methylated and unmethylated cytosines.	Kits from Zymo Research, Qiagen (EpiTect), etc. Efficiency of conversion is critical.
Infinium Methylation BeadChips	Genome-wide methylation profiling at single-base resolution.	Illumina Infinium HM450K, MethylationEPIC, and EPIC v2.0 arrays [19].
Bioinformatics Software	Data preprocessing, normalization, cell type deconvolution, and clock calculation.	R packages (minfi, ewastools, ENmix), Horvath's online calculator, custom scripts.
Reference Datasets	For model training, validation, and cell type composition estimation.	Publicly available datasets from GTEx [18], NHANES [15], and other large biobanks.

The evidence demonstrates a clear trajectory in the evolution of epigenetic clocks: models trained directly on health and mortality outcomes, such as DNAm GrimAge and LinAge2, provide superior predictive power for clinically relevant endpoints compared to first-generation clocks trained purely on chronological age [3] [15]. This underscores the fundamental limitation of using chronological age as a proxy for biological aging and highlights the necessity of next-generation clocks for evaluating healthspan-extending interventions.

Future advancements are likely to focus on several key areas. First, the development of tissue-specific and cell-type-specific clocks will improve accuracy and biological interpretability, as current pan-tissue and blood-based clocks show variability across different organs [1] [18]. Second, the integration of multi-omics data (transcriptomics, proteomics, metabolomics) with methylation data promises to create more holistic and powerful models of aging [1] [20]. Finally, enhancing the interpretability and actionability of clocks, as attempted with LinAge2's principal component analysis, will be crucial for translating these research tools into clinical practice for risk stratification and personalized intervention strategies [15]. For researchers and drug developers, selecting an epigenetic clock must be guided by the specific research question, with current evidence strongly favoring next-generation clocks for any study focused on health outcomes and therapeutic efficacy [3].

Epigenetic clocks have revolutionized the field of aging research by providing powerful biomarkers for quantifying biological age. First-generation epigenetic clocks, such as Horvath's pan-tissue clock and HannumAge, were primarily trained to predict chronological age using DNA methylation (DNAm) patterns [3] [4]. While these clocks achieved remarkable accuracy in estimating chronological age (with correlation coefficients often exceeding 0.96), they demonstrated only moderate associations with age-related health outcomes and mortality risk [3] [21]. This limitation prompted the development of second-generation clocks, which are explicitly trained to predict health outcomes, mortality risk, and phenotypic aging rather than merely chronological age [3] [22]. These next-generation biomarkers, including GrimAge and PhenoAge, integrate phenotypic data and mortality-associated plasma protein surrogates, resulting in significantly enhanced predictive performance for lifespan, healthspan, and age-related disease outcomes compared to their first-generation predecessors [3] [23] [21].

Table 1: Comparison of First-Generation vs. Second-Generation Epigenetic Clocks

Feature	First-Generation Clocks	Second-Generation Clocks
Primary Training Target	Chronological age	Mortality risk, healthspan, phenotypic age
Predictive Focus	Age estimation	Disease risk, mortality, functional decline
Examples	HorvathAge, HannumAge	GrimAge, PhenoAge, DunedinPACE
Strength	High chronological age accuracy	Superior health outcome prediction
Typical Applications	Basic aging research, forensic age estimation	Clinical risk stratification, intervention studies

GrimAge: A Composite Biomarker of Mortality Risk

Development and Algorithm Architecture

GrimAge represents a paradigm shift in epigenetic clock design through its innovative two-stage development approach [23]. Unlike first-generation clocks that directly regress DNAm data against chronological age, GrimAge was constructed by first developing DNAm-based surrogate biomarkers for physiological risk factors and then combining these surrogates into a composite mortality risk predictor [23]. The algorithm was trained using data from the Framingham Heart Study (FHS) Offspring Cohort, involving 2,356 individuals with both DNAm data and plasma protein measurements [23].

The first stage involved creating DNAm-based estimators for smoking pack-years and 12 plasma proteins previously associated with mortality risk, including:

• Plasminogen activator inhibitor 1 (PAI-1)
• Growth differentiation factor 15 (GDF15)
• Adrenomedullin (ADM)
• C-reactive protein
• Leptin

Elastic net regression models were used to identify optimal CpG combinations for predicting each protein, typically selecting fewer than 200 CpGs per surrogate [23]. The second stage combined these DNAm surrogates with chronological age and sex in a Cox proportional hazards model for time-to-death, ultimately selecting eight key components: DNAm pack-years, DNAm ADM, DNAm B2M, DNAm Cystatin C, DNAm GDF15, DNAm Leptin, DNAm PAI-1, and DNAm TIMP1 [23]. The resulting mortality risk estimate was linearly transformed into age units (years), creating the DNAm GrimAge biomarker.

Predictive Performance and Clinical Validation

GrimAge has demonstrated exceptional predictive power for mortality and age-related conditions across multiple validation studies. In the original 2019 publication, GrimAge achieved a Cox regression p-value of 2.0E-75 for time-to-death, significantly outperforming existing epigenetic clocks [23]. A 2025 retrospective cohort study based on 1,942 NHANES participants with median 208-month follow-up confirmed that GrimAge age acceleration (AA) shows approximately linear positive associations with all-cause, cancer-specific, and cardiac mortality [24]. The study reported that GrimAge and its updated version GrimAge2 showed very similar performance in predicting these outcomes, with only small differences in Akaike Information Criterion values and concordance index scores [24].

A 2024 systematic review and meta-analysis further established GrimAge's strong association with frailty, demonstrating both cross-sectional (standardized β=0.11, 95% CI 0.06-0.15) and longitudinal (standardized β=0.02, 95% CI 0.00-0.05) relationships [25]. This consistency across diverse populations and health outcomes underscores GrimAge's robustness as a biomarker of biological aging.

Table 2: GrimAge Performance Across Health Outcomes

Health Outcome	Study Design	Performance Metrics	Source
All-cause mortality	Cohort (N=1,942)	Linear positive association, C-index similar to GrimAge2	[24]
Cardiac mortality	Cohort (N=1,942)	Significant association, consistent across subgroups	[24]
Cancer mortality	Cohort (N=1,942)	Significant association, consistent across subgroups	[24]
Frailty	Meta-analysis (N=10,371)	Cross-sectional β=0.11 (0.06-0.15)	[25]
Frailty progression	Meta-analysis (N=6,143)	Longitudinal β=0.02 (0.00-0.05)	[25]
Coronary heart disease	Original validation	Cox P=6.2E-24	[23]
Cancer incidence	Original validation	Cox P=1.3E-12	[23]

PhenoAge: Bridging Clinical Biomarkers and Epigenetics

Conceptual Framework and Development

PhenoAge (DNAm phenotypic age) represents another pioneering second-generation epigenetic clock that incorporates clinical biomarkers into its framework. Developed by Levine et al. in 2018, PhenoAge was trained to predict a composite phenotypic measure of mortality risk derived from nine clinical chemistry biomarkers plus chronological age [4] [26]. The approach effectively bridges conventional clinical biomarkers with epigenetic profiling, creating a multidimensional measure of biological age.

The phenotypic age used as the training target for DNAm PhenoAge was calculated from biomarkers including:

• Albumin
• Creatinine
• Glucose
• C-reactive protein
• Lymphocyte percentage
• Mean cell volume
• Red blood cell distribution width
• Alkaline phosphatase
• White blood cell count

This combination captures multiple physiological systems relevant to aging, including inflammation, metabolic function, renal function, and immune parameters. The DNAm version of PhenoAge was developed by regressing this phenotypic age measure on DNAm data, creating an epigenetic biomarker that reflects the underlying clinical phenotype [4] [26].

Predictive Utility and Comparative Performance

PhenoAge demonstrates strong predictive performance for mortality and age-related conditions, though evidence suggests GrimAge may have superior performance for certain outcomes. The 2025 NHANES-based study found that while several epigenetic clocks showed non-linear associations with mortality outcomes, only GrimAge and GrimAge2 demonstrated approximately linear positive associations with all-cause, cancer, and cardiac mortality [24].

However, PhenoAge remains a valuable tool in aging research, particularly for studies interested in the clinical biomarkers that constitute its training target. A 2024 systematic review of phenotypic and epigenetic clocks highlighted that phenotypic clocks like PhenoAge can predict mortality better than chronological age alone and offer advantages in clinical interpretability [4]. The relationship between PhenoAge acceleration and frailty has been established in meta-analyses, showing a cross-sectional association (standardized β=0.07, 95% CI 0.03-0.11) though longitudinal associations were not statistically significant [25].

Comparative Performance Analysis: GrimAge vs. PhenoAge

Head-to-Head Comparisons in Large Cohorts

Direct comparisons between GrimAge and PhenoAge in large population studies provide valuable insights for researchers selecting appropriate epigenetic clocks. A comprehensive 2025 analysis comparing 14 epigenetic clocks in relation to 174 disease outcomes in 18,859 individuals found that second-generation clocks collectively outperformed first-generation clocks, with GrimAge showing particularly strong performance [21]. The study demonstrated that second-generation clocks have significant applications in disease prediction, especially for respiratory, liver, and metabolic conditions.

The 2025 NHANES study provided detailed performance metrics for both clocks, reporting that GrimAge and GrimAge2 showed very similar performance in predicting all-cause, cancer, and cardiac mortality, with only small differences in AIC values and C-index scores [24]. This suggests that while both clocks are effective mortality predictors, GrimAge may have a slight advantage in consistency across outcomes.

Methodological Considerations for Research Applications

The choice between GrimAge and PhenoAge depends on specific research questions and methodological considerations. GrimAge's unique two-stage approach incorporating plasma protein surrogates may make it particularly sensitive to cardiovascular and metabolic aging processes [23]. Its strong performance in predicting frailty progression also suggests utility in geriatric and functional decline research [25].

PhenoAge, with its foundation in routine clinical chemistry markers, may offer advantages in studies focused on clinical interpretability and translation [4] [22]. The direct relationship between its component biomarkers and physiological systems facilitates biological interpretation of findings.

Figure 1: Methodological and Conceptual Differences Between Epigenetic Clock Generations

Experimental Protocols and Methodological Guidelines

DNA Methylation Measurement and Data Processing

Standardized protocols for DNA methylation assessment are crucial for generating comparable epigenetic clock measurements across studies. Most validation studies, including the recent NHANES analyses, utilized the Illumina Infinium MethylationEPIC BeadChip v1.0 platform, which quantifies methylation at over 850,000 CpG sites [24] [26]. Raw IDAT image files typically undergo comprehensive preprocessing including chromatic aberration correction, background subtraction, and BMIQ normalization following established pipelines [24].

The 2025 NHANES study detailed their quality control procedure, which resulted in exclusion of individuals with missing covariate data (PIR, education, marital status, BMI, diabetes, smoking, alcohol consumption, stroke, and CHD history) from an initial pool of 2,532 to a final analysis set of 1,942 participants [24]. This highlights the importance of complete covariate data for robust association analyses.

For researchers without in-house bioinformatics capabilities, several resources are available for epigenetic clock calculation. The Clock Foundation and the Horvath lab (http://dnamage.genetics.ucla.edu/) provide analysis services for raw methylation data [26]. These services typically include quality control measures and calculation of multiple epigenetic clocks from the same methylation array data.

Age Acceleration Calculation and Statistical Analysis

The calculation of age acceleration (AA) - the deviation of epigenetic age from chronological age - requires appropriate statistical adjustment. The standard approach involves fitting a linear regression model with epigenetic age as the dependent variable and chronological age as the independent variable, with the residuals defined as AA [24]. This metric can then be used in association studies with health outcomes.

For mortality analyses, studies typically employ Cox proportional hazards regression with comprehensive covariate adjustment. The 2025 NHANES study adjusted for age, sex, race, BMI, poverty income ratio, education level, marital status, smoking status, alcohol consumption, hypertension, diabetes, stroke, and coronary heart disease [24]. Restricted cubic spline models can assess the shape of associations between AA and mortality risk, addressing potential non-linear relationships [24].

Model performance comparisons often utilize the Akaike Information Criterion for model fit and the concordance index for predictive accuracy [24]. These metrics allow objective comparison of different epigenetic clocks' performance for specific outcomes.

Table 3: Essential Research Reagents and Resources for Epigenetic Clock Studies

Resource Category	Specific Products/Platforms	Application Notes
Methylation Array	Illumina Infinium MethylationEPIC BeadChip	Covers >850,000 CpGs; enables calculation of major clocks
Analysis Software	R packages: nhanesR, dplyr, tableone, survival, rcssci	Data processing, survival analyses, visualization
Calculation Services	Clock Foundation, Horvath Lab Online Calculator	Accessible analysis for teams without bioinformatics support
Sample Collection	EDTA whole blood, buffy coat, PBMCs	Standard blood collection methods; batch processing recommended
Reference Datasets	NHANES, Framingham Heart Study, Generation Scotland	Validation cohorts with mortality follow-up

Implications for Clinical Trials and Therapeutic Development

The enhanced predictive validity of second-generation epigenetic clocks, particularly GrimAge, opens new possibilities for clinical trials targeting aging processes. A key advantage is the potential to use these biomarkers as intermediate endpoints, potentially reducing the duration and cost of trials focused on healthy aging [26]. The Clock Foundation recommends collecting at least two baseline samples prior to treatment and two samples after treatment to account for biological variability and enhance reliability of intervention effect estimates [26].

Recent evidence suggests these clocks are responsive to interventions. Analysis of CALERIE participants indicated that 2 years of mild caloric restriction significantly reduced biological age as measured by clinical aging clocks [22]. This intervention-responsiveness, combined with their strong predictive validity for hard endpoints like mortality, positions second-generation clocks as valuable tools for evaluating anti-aging interventions.

For drug development professionals, epigenetic clocks offer a composite measure of biological aging that may be more sensitive to intervention effects than individual clinical biomarkers [26]. Their ability to integrate signals across multiple physiological systems makes them particularly suitable for evaluating broad-spectrum anti-aging therapies rather than disease-specific treatments.

Figure 2: Research Workflow for Epigenetic Clock Studies in Clinical Trials

Second-generation epigenetic clocks represent a significant advancement over first-generation models by directly incorporating phenotypic data and mortality risk into their training frameworks. GrimAge, with its unique two-stage approach combining DNAm surrogates of plasma proteins and smoking exposure, has demonstrated superior performance for predicting mortality, age-related diseases, and functional decline across multiple large validation studies [24] [23] [25]. PhenoAge provides a valuable alternative with strong ties to clinical biomarkers, facilitating biological interpretation [4] [22].

For researchers and drug development professionals, selecting between these clocks should be guided by specific research questions, with GrimAge preferred for mortality-related outcomes and PhenoAge offering advantages in clinical translatability. The growing evidence base supports the integration of these biomarkers into clinical trials as intermediate endpoints for evaluating interventions targeting human aging. As the field advances, further refinement and validation of these powerful biomarkers will continue to enhance their utility in both research and clinical applications.

Epigenetic clocks are biomarkers of aging that estimate biological age by measuring predictable, age-associated changes in DNA methylation patterns. These computational models have rapidly evolved, with each generation reflecting a significant paradigm shift in training methodology and conceptual understanding of the aging process. The progression from first- to next-generation clocks represents a fundamental transition from correlative age estimation to the prediction of healthspan, mortality risk, and the pace of biological aging [3] [12]. This evolution has critical implications for their application in geroscience research, clinical trials, and drug development. First-generation clocks were trained exclusively to predict chronological age, treating aging as a temporal phenomenon. In contrast, subsequent generations were explicitly trained to associate with health, lifestyle, and age-related clinical outcomes, thereby capturing the physiological dimensions of aging [3] [27]. This comparative guide provides a structured analysis of the training goals, fundamental outputs, and performance characteristics of different epigenetic clock generations, synthesizing current evidence to inform their appropriate research application.

Generational Classification and Defining Characteristics

Table 1: Generational Classification of Epigenetic Clocks

Generation	Defining Training Goal	Exemplary Clocks	Fundamental Output
First	Predict chronological age	Horvath, Hannum	Epigenetic age estimate (highly correlated with chronological age)
Second	Predict mortality risk & phenotypic health	PhenoAge, GrimAge, GrimAge2	Estimate of biological age/health status linked to mortality & morbidity
Third	Measure pace of aging	DunedinPACE, DunedinPoAm	Pace of biological aging (rate of change per chronological year)
Fourth	Identify causal elements in aging	CausAge, AdaptAge, DamAge	Biological age using putatively causal CpG sites for aging processes

The classification of epigenetic clocks into generations is primarily defined by their distinct training goals, which directly dictate their fundamental outputs and research applications.

• First-Generation Clocks: These pioneering clocks, such as the multi-tissue Horvath clock and the blood-based Hannum clock, were developed using machine learning models trained on DNA methylation data from thousands of individuals with the singular objective of accurately predicting chronological age [12] [27] [28]. Their output is an epigenetic age estimate, and the difference between this epigenetic age and chronological age is termed "age acceleration" [12]. While these clocks demonstrated that methylation patterns could serve as a powerful molecular timestamp, their grounding in chronological age inherently limits their utility for predicting health-related outcomes [3].

• Second-Generation Clocks: This generation marked a conceptual leap by training clocks to predict health outcomes directly. PhenoAge was developed by first creating a measure of phenotypic age based on clinical chemistry markers and then identifying DNA methylation sites that predict this composite measure [12] [27]. GrimAge was trained on time-to-death (mortality), incorporating DNAm-based surrogates for plasma proteins and smoking history into its algorithm [12] [29]. The output is an estimate of biological age that is more strongly linked to healthspan, mortality risk, and age-related disease susceptibility than first-generation clocks [12].

• Third-Generation Clocks: Moving from a static age estimate to a dynamic measure of rate, third-generation clocks like DunedinPACE (Pace of Aging Calculated from the Epigenome) were trained on longitudinal data tracking the decline of multiple organ systems over time [12] [28]. The output is a single-score estimate of the pace of biological aging, representing how rapidly an individual's body is deteriorating per chronological year [12]. A value of 1.0 represents the average pace of aging in the training population.

• Fourth-Generation Clocks: The most recent advance involves "causal clocks" (e.g., CausAge, AdaptAge, DamAge). These utilize Mendelian randomization to select CpG sites putatively causally linked to aging processes, healthspan, and lifespan, rather than merely correlated with age [12] [28]. This approach aims to distinguish between methylation changes representing damage from those representing adaptation to aging [12].

Figure 1: Training Goals and Outputs by Epigenetic Clock Generation. The fundamental output of an epigenetic clock is directly determined by its training goal, leading to distinct types of estimates across generations.

Comparative Performance in Health Outcome Prediction

Recent large-scale, systematic comparisons provide robust evidence for the superior performance of later-generation clocks in predicting age-related health outcomes, mortality, and morbidity.

Table 2: Performance Comparison Across Generations Based on Large-Scale Studies

Clock Generation	Exemplary Clock	Association with All-Cause Mortality (Hazard Ratio per SD)	Number of Bonferroni-Significant Disease Associations (out of 174)	Key Associated Health Outcomes
First	Horvath	Not specified / Weaker	~5% of total significant findings	Limited applications in disease settings [29]
Second	GrimAge v2	1.54 (95% CI 1.46-1.62) [29]	37 (Max among all clocks) [29]	Respiratory diseases, liver cirrhosis, lung cancer, diabetes [21] [29]
Second	PhenoAge	Significant but smaller than GrimAge v2	Not specified	Cardiovascular disease, Crohn's disease, Parkinson's disease [29]
Third	DunedinPACE	Significant, comparable to GrimAge v2 [29]	Not specified	Diabetes, ischemic stroke [29]

A landmark unbiased comparison of 14 epigenetic clocks in relation to 10-year onset of 174 disease outcomes in 18,859 individuals from the Generation Scotland cohort yielded critical insights [21] [29]. The study adjusted for age, sex, body mass index, smoking, alcohol consumption, education, and socioeconomic deprivation in its analyses.

• Performance Gap: Second-generation clocks significantly outperformed first-generation clocks, which demonstrated limited applications in disease settings. Of 176 Bonferroni-significant associations detected, first-generation clocks accounted for only approximately 5% of the findings [29].
• Mortality Prediction: GrimAge v2 showed the strongest and most significant association with all-cause mortality (Hazard Ratio per standard deviation = 1.54) [29].
• Disease-Specific Prediction: The study identified 27 diseases where the hazard ratio for a specific clock exceeded its association with all-cause mortality. Later-generation clocks showed particularly strong links to respiratory (e.g., primary lung cancer, COPD) and liver-related outcomes (e.g., cirrhosis), as well as diabetes, Crohn's disease, and delirium [29].
• Clinical Utility: There were 35 instances where adding a second or third-generation clock to a model with traditional risk factors increased the classification accuracy by more than 1% while maintaining a clinically meaningful AUC (Area Under the Curve) of over 0.80 [29].

This performance differential is consistent with a separate analysis showing that second-generation clocks like GrimAge and PhenoAge are more accurate predictors of long-term cognitive change in midlife compared to first-generation models, particularly in individuals who experienced childhood socioeconomic disadvantage [30].

Experimental Protocols for Clock Validation and Application

Large-Scale Comparative Study Workflow

The protocol for a comprehensive clock comparison, as executed by Mavrommatis et al. (2025), involves a standardized pipeline from data preparation to statistical analysis [21] [29].

Figure 2: Workflow for Large-Scale Clock Comparison. This unbiased protocol systematically evaluates the predictive performance of multiple clocks against a wide array of incident diseases [29].

Detailed Methodology:

• Cohort and Phenotypic Data: The study utilized the Generation Scotland cohort (n=18,859), a large, deeply phenotyped, DNA-methylation profiled population resource [29]. The 174 disease outcomes were defined using ICD-10 codes from linked national hospital inpatient and mortality records, with a minimum of 30 incident cases per disease.
• DNA Methylation Profiling and Clock Calculation: DNA methylation was profiled from blood samples using array-based technologies (e.g., Illumina EPIC array). The 14 selected clocks, representing first-, second-, and third-generation models, were calculated from the normalized methylation beta-values according to their respective published algorithms [29].
• Statistical Analysis - Survival Models: For each clock-disease pair, a Cox proportional hazards regression model was run for 10-year disease onset. The models were adjusted for chronological age, sex, body mass index, smoking status, alcohol consumption, education, and socioeconomic deprivation (Scottish Index of Multiple Deprivation) [29]. Age, estimated cell-type proportions, and genetic relatedness were regressed out of each clock prior to analysis.
• Statistical Analysis - Classification Models: Logistic regression models were run for 10-year disease classification. A "null" model included only the covariates listed above. A "full" model added the epigenetic clock as a predictor. The difference in Area Under the Curve (AUC) between the null and full models was calculated to quantify the added predictive value of the clock [29].
• Significance Thresholding: A Bonferroni correction for multiple testing was applied, setting the significance threshold at P < 0.05 / 174 ≈ 2.9 x 10⁻⁴ [29].

Protocol for Novel Clock Development with Long-Read Sequencing

Emerging methodologies are leveraging new sequencing technologies to create more accurate and inclusive clocks. A 2025 preprint demonstrates a protocol for developing epigenetic clocks from Oxford Nanopore Technologies (ONT) long-read sequencing data [31].

Detailed Methodology:

• Sample and Sequencing: The study used postmortem prefrontal cortex tissue from 187 neurologically healthy individuals of European ancestry (NABEC cohort) and 130 of African ancestry (HBCC cohort). Genomic DNA was subjected to ONT PromethION long-read whole-genome sequencing [31].
• Methylation Calling and Aggregation: Methylation status was called from the long-read sequencing data. Instead of analyzing individual CpG sites, the methylation signal was aggregated across entire promoter regions (defined using the Eukaryotic Promoter Database). This promoter-level aggregation was found to substantially improve prediction accuracy and cross-cohort generalizability by reducing stochastic noise [31].
• Feature Selection and Model Training: The automated machine learning platform GenoML was employed. After quality control filtering to remove highly correlated promoters, an unbiased feature selection process identified promoters most predictive of age within each cohort (yielding 3,260 promoters for NABEC and 5,996 for HBCC) [31].
• Model Validation: The final models were validated on data withheld during model fitting, achieving high accuracy in both the HBCC (R² = 0.946) and NABEC (R² = 0.901) cohorts, demonstrating the power of inclusive training datasets and long-read sequencing for ancestry-aware aging clocks [31].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for Epigenetic Clock Research

Item	Function/Application	Examples/Notes
DNA Methylation Profiling Platforms	Quantifying methylation levels at CpG sites	Illumina Infinium Methylation EPIC BeadChip (~850,000 sites); Oxford Nanopore long-read sequencing (genome-wide) [31] [12]
Bioinformatics Pipelines	Preprocessing raw data, normalizing signals, calculating clock ages	minfi (R package for array data); Guppy (for ONT basecalling/methylation calling); custom scripts for specific clocks [31]
Epigenetic Clock Algorithms	Translating methylation data into age estimates	Published formulas for Horvath, Hannum, PhenoAge, GrimAge, DunedinPACE, etc. [12] [29]
Cohort Data with Linked Health Records	Validating clocks against longitudinal health outcomes	Generation Scotland, Health and Retirement Study, NHANES (with DNAm data) [12] [29]
Automated Machine Learning Platforms	Developing and optimizing new clock models	GenoML for competing algorithms and fine-tuning best performers [31]
Functional Interpretation Tools	Understanding biological meaning of clock outputs	SHAP (SHapley Additive exPlanations) for feature importance; gene ontology enrichment analysis [31]
Egfr-IN-56	Egfr-IN-56, MF:C23H22N4O3S, MW:434.5 g/mol	Chemical Reagent
Cdc7-IN-14	Cdc7-IN-14|CDC7 Kinase Inhibitor|For Research Use

The evidence clearly demonstrates that the training goal of an epigenetic clock fundamentally determines its output and predictive utility. First-generation clocks, trained on chronological age, remain useful for estimating cellular age but have limited value in predicting health outcomes. Second-generation clocks (e.g., GrimAge, PhenoAge), trained on mortality and phenotypic health, and third-generation clocks (e.g., DunedinPACE), trained on the pace of aging, show superior and more clinically relevant performance in associating with and predicting a wide spectrum of age-related diseases, functional decline, and mortality [3] [21] [29].

For researchers and drug development professionals, this dictates a context-dependent choice of tool. First-generation clocks may still be appropriate for studies focused on fundamental, cellular aging processes. However, for the vast majority of applications in health-oriented association studies, clinical trial endpoints (e.g., to monitor intervention efficacy), and disease risk prediction, the current evidence strongly supports prioritizing second- and third-generation clocks [3]. Future directions will likely involve greater use of genome-wide sequencing technologies like long-read sequencing, the development of more causally informative and interpretable clocks, and a continued emphasis on inclusive training datasets that ensure equitable performance across diverse populations [31].

Methodologies, Mechanisms, and Research Applications in Preclinical and Clinical Settings

Epigenetic clocks have emerged as powerful biomarkers for aging, estimating biological age based on DNA methylation patterns. Their development relies heavily on statistical models that can handle high-dimensional data, where the number of DNA methylation sites (CpGs) vastly exceeds the number of observations. First-generation clocks were primarily trained to predict chronological age using linear regression models, while next-generation clocks have incorporated health covariates and mortality-related outcomes to better capture biological aging processes [3]. The evolution from first to second-generation clocks represents not just a biological advancement but a statistical one, where the choice of modeling technique significantly impacts predictive accuracy and clinical utility.

The performance gap between these generations is substantial. Second-generation clocks demonstrate superior performance in predicting age-related diseases and mortality, with a recent large-scale study of 18,859 individuals finding they "significantly outperformed first-generation clocks, which have limited applications in disease settings" [21]. This performance advantage stems from both the incorporation of health covariates in training and the sophisticated statistical methods that enable stable parameter estimation amidst high predictor correlation. This guide examines how elastic net regression has become a foundational tool in this field, comparing its performance against alternative methods and providing experimental protocols for researchers developing epigenetic clocks.

Methodological Foundations: Elastic Net Regression

Core Mathematical Framework

Elastic net regression is a regularized linear modeling technique that combines the strengths of both L1 (lasso) and L2 (ridge) regularization. Its objective function is given by:

minβ{12n∑i=1n(yi−xiTβ)2+λ(α∥β∥1+1−α2∥β∥22)} [32]

Where:

• β represents the vector of coefficients
• λ is the overall regularization parameter controlling penalty strength
• α is the mixing parameter (0 ≤ α ≤ 1) balancing L1 and L2 penalties
• yi is the response variable (e.g., chronological age, mortality risk)
• xi represents the predictor variables (DNA methylation values)

The L1 penalty (α∥β∥₁) promotes sparsity by driving coefficients of irrelevant predictors to zero, effectively performing variable selection. The L2 penalty ((1-α)/2∥β∥₂²) handles multicollinearity by shrinking coefficients of correlated predictors toward each other [32]. This dual functionality makes elastic net particularly suited for epigenetic data, where DNA methylation sites often exhibit strong correlation structures.

Advantages for Epigenetic Clock Development

Elastic net addresses several critical challenges in epigenetic clock development:

• High-dimensional data: Epigenome-wide association studies typically analyze >400,000 CpG sites with sample sizes orders of magnitude smaller [7]. Elastic net maintains stability in these "p > n" scenarios where traditional regression fails.

• Correlated predictors: DNA methylation sites often exist in correlated clusters. While lasso might arbitrarily select one CpG from a correlated block, elastic net retains entire groups, providing more biologically plausible models [32] [33].

• Automatic feature selection: The L1 component efficiently zeroes out non-informative CpGs, yielding sparse models with enhanced interpretability. The recently developed IC clock, for instance, uses just 91 CpGs out of thousands available [7].

• Prevention of overfitting: Regularization shrinks coefficient estimates, reducing model variance and improving generalizability to new datasets [32].

Comparative Performance Analysis

Elastic Net vs. Alternative Statistical Methods

Table 1: Performance comparison of statistical methods for high-dimensional biological data

Method	Key Characteristics	Advantages	Limitations	Reported Performance
Elastic Net	Combines L1 & L2 regularization	Handles multicollinearity; Grouping effect; Feature selection	Requires parameter tuning (α, λ)	Best performance for cognitive decline prediction (RMSE=3.520, R²=0.435) [34]
Lasso Regression	L1 regularization only	Sparse solutions; Feature selection	Arbitrary selection from correlated features	Similar C-index to elastic net for hypertension (0.78) [35]
Ridge Regression	L2 regularization only	Handles multicollinearity well	No feature selection; All features retained	Similar C-index to elastic net for hypertension (0.78) [35]
Random Survival Forest	Tree-based ensemble method	Handles nonlinearities; No distributional assumptions	Lower interpretability; Computational intensity	Lower performance for cognitive decline (RMSE >3.520) [34]
Conventional Cox PH	Proportional hazards model	Highly interpretable; Standard in clinical research	Limited with high-dimensional data	C-index=0.77 for hypertension prediction [35]

A systematic comparison of linear regression-based methods for exposome-health associations found that elastic net and sparse partial least-squares showed a sensitivity of 76% with a false discovery proportion of 44%, outperforming environment-wide association study approaches which had a false discovery proportion of 86% despite higher sensitivity [33].

Impact on Epigenetic Clock Generations

Table 2: Performance comparison of epigenetic clock generations

Clock Characteristic	First-Generation Clocks	Second-Generation Clocks
Training Target	Chronological age [3]	Healthspan, mortality, clinical biomarkers [3]
Representative Examples	Horvath, Hannum [36]	PhenoAge, GrimAge, DunedinPACE [36]
Disease Association	Limited applications in disease settings [21]	Strongly associated with disease outcomes [21]
Mortality Prediction	Moderate predictive ability	Superior prediction; IC clock outperforms others for all-cause mortality [7]
Typical Modeling Approach	Linear regression [3]	Regularized regression (including elastic net) [7]

The recent intrinsic capacity (IC) clock, developed using elastic net regression, demonstrates the power of this approach. Trained on clinical evaluations across five domains (cognition, locomotion, psychological well-being, sensory abilities, and vitality), the IC clock outperformed both first-generation and second-generation epigenetic clocks in predicting all-cause mortality in the Framingham Heart Study [7]. This demonstrates how combining relevant health covariates with appropriate statistical methods yields biologically meaningful biomarkers.

Experimental Protocols and Implementation

Standardized Workflow for Epigenetic Clock Development

The following diagram illustrates the typical experimental workflow for developing an epigenetic clock using elastic net regression:

Detailed Methodological Protocols

Data Collection and Preprocessing

The development of the intrinsic capacity (IC) clock exemplifies proper protocol implementation. Researchers collected DNA methylation data using the Infinium EPIC array from 933 INSPIRE-T participants aged 20-102 years [7]. Preprocessing included:

• Quality control: Removal of probes with detection p-value > 0.01, samples with poor bisulfite conversion, and sex-mismatched samples
• Normalization: Application of quantile normalization to reduce technical variability
• Batch effect correction: Using ComBat or similar methods to account for processing batches
• Cell type composition: Estimation and adjustment for blood cell counts using reference-based methods

Phenotypic data encompassed five domains of intrinsic capacity: cognition, locomotion, psychological well-being, sensory abilities, and vitality. Each domain was operationalized using standardized clinical assessments [7].

Model Training with Cross-Validation

The IC clock development employed elastic net regression with tenfold cross-validation [7]. The specific implementation included:

• Parameter grid: Testing multiple α values (mixing parameter) from 0 to 1 in increments of 0.1
• λ selection: Using cross-validation to identify the optimal regularization strength
• Model selection criteria: Ranking models based on correlation between observed and cross-validated predicted values, model error, and number of CpG sites
• Final model: Selected the model with optimal balance across all three criteria, resulting in a clock with 91 CpGs

This approach mirrors that used in other successful epigenetic clocks, including those trained on mortality outcomes (GrimAge) and phenotypic age (PhenoAge) [3].

Validation Protocols

Robust validation is essential for epigenetic clocks. The recommended approach includes:

• Internal validation: Using bootstrap resampling (≥100 iterations) to obtain optimism-corrected performance estimates [37]
• External validation: Application to completely independent cohorts with different demographic characteristics
• Clinical validation: Assessing association with relevant clinical outcomes, functional measures, and mortality

For the IC clock, external validation in the Framingham Heart Study demonstrated its superior performance in predicting all-cause mortality compared to existing epigenetic clocks [7].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential research reagents and computational tools for epigenetic clock development

Category	Item	Specification/Version	Function/Purpose
Wet Lab Reagents	Infinium MethylationEPIC Kit	Illumina EPIC array	Genome-wide DNA methylation profiling (≈850,000 CpG sites)
	QIAamp DNA Blood Mini Kit	Qiagen 51104	DNA extraction from whole blood samples
	EZ-96 DNA Methylation Kit	Zymo Research D5004	Bisulfite conversion of genomic DNA
Bioinformatic Tools	R Programming Language	Version 4.1.0+	Statistical analysis and model development
	glmnet Package	Version 4.1-3+	Elastic net regression implementation
	minfi Package	Version 1.38.0+	DNA methylation data preprocessing and normalization
	SeSAMe Package	Version 3.0+	Improved preprocessing for Illumina methylation arrays
Data Resources	Reference Methylomes	...	Cell-type specific methylation references for deconvolution
	Bioconductor Annotation	org.Hs.eg.db	Mapping between CpG sites and genomic contexts
Validation Tools	Independent Cohort Data	Framingham Heart Study, etc.	External validation of clock performance
Brd4-BD1-IN-1	Brd4-BD1-IN-1|BRD4 Bromodomain Inhibitor|Research Compound		Bench Chemicals
Antibacterial agent 123	Antibacterial agent 123, MF:C17H8F9N3O, MW:441.25 g/mol	Chemical Reagent	Bench Chemicals

Biological Interpretation and Pathway Analysis

Beyond prediction accuracy, understanding the biological meaning captured by epigenetic clocks is essential. The development of the IC clock revealed strong associations with immune function, particularly T-cell activation [7]. Key findings included:

• CD28 upregulation: Higher IC clock values associated with increased expression of CD28, a critical T-cell costimulatory molecule whose loss defines immunosenescence
• Inflammatory pathways: Poor IC clock performance tracked with elevated expression of CDK14/PFTK1, a regulator of Wnt signaling and inflammatory mediator
• Cellular senescence: Gene Ontology enrichment analysis connected IC clock signatures to cellular senescence and chronic inflammation pathways

These biological insights validate that elastic net-derived clocks capture meaningful biological processes beyond statistical abstraction, providing researchers with actionable insights into aging mechanisms.

Elastic net regression has established itself as a foundational method for developing epigenetic clocks, particularly second-generation clocks that incorporate health covariates. Its ability to handle high-dimensional, correlated data while performing feature selection makes it ideally suited for DNA methylation-based biomarker development. The performance advantage of elastic net over both traditional statistical methods and other machine learning approaches has been demonstrated across multiple studies, from cognitive decline prediction to epigenetic clock development [34] [7].

The integration of health covariates through elastic net has enabled the transition from clocks that merely estimate chronological age to those capturing biological aging processes relevant to disease risk and mortality. As the field advances toward interventional applications, including clinical trials of aging interventions, the robustness and interpretability of elastic net-derived models will be increasingly valuable [36].

Future methodological developments may integrate elastic net with more complex deep learning architectures or extend it to handle longitudinal data. However, its current position as a workhorse for epigenetic clock development remains secure, offering an optimal balance of predictive performance, computational efficiency, and biological interpretability for aging researchers and drug development professionals.

Epigenetic clocks have revolutionized the quantification of biological aging, evolving from first-generation models that predict chronological age to second-generation models that capture morbidity and mortality risk. Among these, DNA methylation GrimAge stands out for its unique multi-component architecture and superior predictive performance. This review deconstructs GrimAge's two-stage biomarker framework, comparing its predictive validity against other epigenetic clocks across mortality, age-related diseases, and functional decline. We present comprehensive quantitative analyses demonstrating that GrimAge and its successor, GrimAge2, consistently outperform first-generation and other second-generation clocks in predicting all-cause mortality, cardiac outcomes, cancer incidence, and cognitive decline. The architectural innovation of leveraging DNA methylation surrogates for plasma proteins and smoking exposure represents a paradigm shift in epigenetic age estimation, offering researchers and clinical professionals enhanced tools for interventional studies and therapeutic development.

The development of epigenetic clocks represents a significant milestone in biogerontology, providing powerful biomarkers for quantifying biological aging processes. First-generation epigenetic clocks, including Horvath's pan-tissue clock and Hannum's blood-specific clock, were primarily trained to predict chronological age based on DNA methylation patterns at specific CpG sites [3]. While these clocks correlate strongly with calendar age, they demonstrate limited associations with clinical measures of physiological dysregulation, functional decline, and mortality risk [38]. This limitation prompted the development of second-generation clocks, which incorporate phenotypic measures of healthspan and mortality risk into their training algorithms [3].

The distinction between these generations is fundamental: first-generation clocks answer "How old is this organism?" while second-generation clocks address "What is this organism's risk of aging-related decline and death?" [3]. GrimAge epitomizes this paradigm shift through its innovative multi-component architecture that integrates DNA methylation surrogates of plasma proteins and smoking exposure into a composite mortality risk predictor [23] [39]. This architectural innovation has established GrimAge as the leading epigenetic clock for predicting age-related clinical phenotypes and intervention outcomes [38].

Table 1: Generations of Epigenetic Clocks

Clock Generation	Representative Clocks	Training Target	Primary Applications
First-Generation	HorvathAge, HannumAge, SkinBloodAge	Chronological age	Age estimation in tissues and cells
Second-Generation	PhenoAge, GrimAge, GrimAge2, DunedinPoAm	Mortality risk, phenotypic age, clinical biomarkers	Disease risk prediction, mortality assessment, intervention studies

Architectural Framework: Deconstructing GrimAge's Multi-Component Design

GrimAge employs a sophisticated two-stage algorithmic approach that fundamentally differs from single-stage mortality predictors [23] [39]. This architecture enables the integration of multiple physiological systems into a unified aging biomarker, capturing complex pathophysiology beyond what single-dimensional clocks can assess.

Stage 1: DNA Methylation Surrogates of Plasma Proteins and Smoking Exposure

The first stage involves developing DNA methylation-based estimators for circulating plasma proteins and smoking-related exposure. Researchers analyzed 88 plasma protein variables measured alongside DNA methylation arrays in the Framingham Heart Study (FHS) Offspring Cohort [23] [39]. Through elastic net regression models, they identified CpG sites whose methylation levels collectively predict plasma protein concentrations. Only 12 of the 88 plasma proteins exhibited correlation coefficients (r > 0.35) between measured levels and their DNA methylation surrogates in test datasets, indicating the specificity of these epigenetic proxies [39].

Concurrently, the team developed a DNA methylation-based estimator of smoking pack-years (DNAm PACKYRS) using a linear combination of 172 CpGs [23]. Surprisingly, this DNAm-based smoking surrogate sometimes outperformed self-reported smoking history in predicting mortality, possibly due to more objective quantification of exposure and its biological effects [39].

Figure 1: GrimAge Stage 1 Architecture - Development of DNA methylation surrogates for plasma proteins and smoking exposure using elastic net regression on Framingham Heart Study data.

Stage 2: Integration into a Mortality Risk Predictor

The second stage transforms these DNA methylation surrogates into a mortality risk estimator. Researchers performed Cox proportional hazards regression of time-to-death due to all-cause mortality on the DNAm-based biomarkers, chronological age, and sex [23]. The elastic net algorithm selected the most predictive subset of biomarkers: chronological age, sex, DNAm PACKYRS, and seven DNAm-based plasma protein surrogates [39].

The final GrimAge algorithm incorporates these eight DNAm biomarkers:

• DNAm PACKYRS (smoking exposure surrogate)
• DNAm ADM (adrenomedullin, associated with vascular stress)
• DNAm B2M (beta-2-microglobulin, related to immune function)
• DNAm Cystatin C (kidney function marker)
• DNAm GDF-15 (growth differentiation factor 15, associated with inflammation and oxidative stress)
• DNAm Leptin (adipokine related to fat mass)
• DNAm PAI-1 (plasminogen activation inhibitor 1, involved in fibrinolysis)
• DNAm TIMP-1 (tissue inhibitor metalloproteinase 1, involved in extracellular matrix remodeling)

The linear combination of these covariate values was transformed into years to create DNAm GrimAge [23] [39]. The resulting biomarker can be adjusted for chronological age to generate Age Acceleration Grim (AgeAccelGrim), which represents biological age acceleration independent of calendar age.

Figure 2: GrimAge Stage 2 Architecture - Integration of DNA methylation surrogates into a mortality risk predictor using Cox proportional hazards regression.

GrimAge2: Enhanced Architecture with Expanded Biomarkers

Building on GrimAge's success, researchers developed GrimAge version 2 with additional DNA methylation-based estimators for high-sensitivity C-reactive protein (logCRP) and hemoglobin A1C (logA1C) [40]. This enhanced architecture improves predictive performance for metabolic conditions and inflammation-related pathologies. GrimAge2 was trained on individuals aged 40-92 years and validated across 13,399 blood samples from nine study cohorts, demonstrating robust performance across multiple racial/ethnic groups [40].

Performance Comparison: GrimAge Versus Alternative Epigenetic Clocks

Predictive Validity for Mortality Outcomes

Comprehensive validation studies demonstrate GrimAge's superior performance in predicting mortality compared to other epigenetic clocks. A 2025 retrospective cohort study of 1,942 NHANES participants with 208 months median follow-up found that only GrimAge and GrimAge2 demonstrated approximately linear and positive associations with all-cause, cancer-specific, and cardiac mortality [24]. The GrimAge clocks showed very similar performance, with only small differences in Akaike Information Criterion (AIC) values and concordance index (C-index) scores [24].

Table 2: Mortality Prediction Performance Across Epigenetic Clocks

Epigenetic Clock	All-Cause Mortality	Cancer Mortality	Cardiac Mortality	Key Evidence
GrimAge	Strongest predictor (HR per SD: 1.81) [41]	Significant association [24]	Significant association [24]	outperforms others in TILDA study [38]
GrimAge2	Comparable to GrimAge (P=3.6×10⁻¹⁶⁷) [40]	Significant association [24]	Significant association [24]	Enhanced multi-ethnic prediction [40]
PhenoAge	Moderate predictor	Limited association [24]	Limited association [24]	Weaker than GrimAge in NHANES [24]
First-Generation Clocks	Weak or non-significant	Limited association [24]	Limited association [24]	Poor mortality prediction [38]

The original GrimAge development study demonstrated unprecedented predictive power for time-to-death (Cox regression P=2.0E-75), substantially outperforming existing epigenetic clocks [23]. In the Lothian Birth Cohort 1936, each standard deviation increase in GrimAge was associated with an 81% higher hazard of all-cause mortality (P < 2.0 × 10⁻¹⁶) over the eighth decade of life [41].

Predictive Validity for Age-Related Diseases and Functional Decline

GrimAge's architectural advantages translate to superior prediction of diverse age-related conditions. In the Irish Longitudinal Study on Ageing (TILDA), GrimAge acceleration was associated with 8 of 9 clinical phenotypes including walking speed, polypharmacy, frailty, and cognitive performance, while first-generation clocks showed no significant associations and PhenoAge showed more limited associations [38].

A comprehensive 2025 comparison of 14 epigenetic clocks across 174 disease outcomes in 18,859 individuals confirmed that second-generation clocks significantly outperformed first-generation clocks in disease prediction [21]. The study identified 27 diseases where epigenetic clock hazard ratios exceeded their association with all-cause mortality, highlighting disease-specific predictive patterns [21].

Table 3: Disease and Functional Outcome Prediction Across Epigenetic Clocks

Health Domain	GrimAge Performance	Comparative Performance	Key Evidence
Cognitive Function	Associated with lower age 73 general cognitive ability (β = -0.18) [41]	Outperforms first-generation clocks [41] [38]	Predicts cognitive decline independently of childhood intelligence [41]
Brain Health	Associated with decreased brain volume (β = -0.25), increased white matter hyperintensities (β = 0.17) [41]	Superior to other clocks for brain structure [41]	Associates with vascular brain lesions [41]
Cardiovascular Disease	Significant predictor of time-to-coronary heart disease (Cox P=6.2E-24) [23]	Stronger association than other clocks [23]	Validated in large-scale meta-analysis [23]
Physical Function	Predicts walking speed, frailty, polypharmacy [38]	Outperforms PhenoAge and first-generation clocks [38]	Maintains significance after full covariate adjustment [38]

GrimAge also demonstrates strong associations with computed tomography measures of fatty liver disease and excess visceral fat, outperforming other epigenetic clocks in capturing these metabolically significant fat depots [23]. The DNAm-based surrogate for PAI-1 alone shows remarkable predictive validity, associating with lifespan (P=5.4E-28), comorbidity count (P=7.3E-56), and type 2 diabetes (P=2.0E-26) [23].

Experimental Protocols and Methodological Considerations

DNA Methylation Measurement and Quality Control

Standardized protocols for DNA methylation assessment are critical for reliable GrimAge estimation. The following methodological framework derives from multiple large-scale studies [24] [23] [38]:

Sample Processing Protocol:

• DNA Extraction: Isolate DNA from whole blood using standardized extraction kits
• Bisulfite Conversion: Treat DNA with bisulfite using EZ-96 DNA Methylation kits (Zymo Research) or equivalent
• Methylation Array Processing: Process samples using Illumina Infinium MethylationEPIC BeadChip v1.0 or 850k BeadChip
• Quality Control: Exclude samples with poor bisulfite conversion, low signal intensity, or detection P-values > 0.05
• Normalization: Apply preprocessing pipeline with background subtraction, dye bias correction, and BMIQ normalization

Data Processing Pipeline:

• Raw Data Output: Obtain IDAT files from methylation arrays
• Preprocessing: Use the normalization pipeline and reference set developed by Horvath
• Probe Filtering: Exclude cross-reactive probes, SNPs, and sex chromosome probes if pan-tissue application required
• Cell Composition Estimation: Calculate estimated blood cell counts using the Houseman algorithm for intrinsic epigenetic age acceleration calculation [38]

GrimAge Calculation Protocol

The precise computational steps for deriving GrimAge include:

• DNAm Surrogate Estimation: Calculate DNAm-based estimators for plasma proteins and smoking pack-years using established CpG weights and coefficients
• Mortality Risk Score: Compute linear combination of selected biomarkers using GrimAge coefficients
• Age Acceleration Calculation: Regress DNAm GrimAge on chronological age; residuals represent AgeAccelGrim
• Batch Effect Correction: Adjust for technical covariates when processing data across multiple batches or arrays

For the updated GrimAge2 algorithm, additional steps include calculating DNAm-based estimators for logCRP and logA1C before computing the composite biomarker [40].

Table 4: Essential Research Resources for GrimAge Analysis

Resource Category	Specific Tools/Reagents	Application in GrimAge Research
DNA Methylation Arrays	Illumina Infinium MethylationEPIC BeadChip v1.0, Illumina 850k BeadChip	Genome-wide methylation profiling for GrimAge calculation [24]
Analysis Platforms	R Statistical Environment (version 4.4.1+) with packages: nhanesR, dplyr, survival, rcssci	Data processing, survival analysis, and restricted cubic spline modeling [24]
Online Calculators	Horvath DNAm Age Calculator (https://dnamage.genetics.ucla.edu/)	Web-based GrimAge estimation from methylation data [41]
Reference Datasets	Framingham Heart Study, NHANES, Lothian Birth Cohorts, TILDA	Validation cohorts for GrimAge performance assessment [23] [41] [38]
Commercial Services	GrimAge Analysis (Clock Foundation), TruDiagnostic (DunedinPACE)	Commercial GrimAge testing for research applications [42]

The multi-component architecture of GrimAge represents a significant advancement in epigenetic clock technology, demonstrating consistently superior performance in predicting mortality, age-related diseases, and functional decline compared to both first-generation and other second-generation clocks. Its two-stage design, incorporating DNA methylation surrogates of plasma proteins and smoking exposure, captures complex pathophysiology underlying biological aging.

For researchers and drug development professionals, GrimAge offers several key advantages:

• Enhanced Predictive Validity for diverse age-related outcomes across multiple organ systems
• Responsiveness to Interventions, making it valuable for clinical trials of anti-aging therapies
• Cross-Tissue Applicability with validated performance in blood, saliva, and other tissues [40]
• Multi-Ethnic Validity with demonstrated performance across diverse populations [40]

Future directions include continued refinement of GrimAge algorithms, development of tissue-specific versions, and exploration of its utility for monitoring intervention efficacy. The recent development of cell-type specific epigenetic clocks promises even greater precision in aging research [43], potentially leading to next-generation clocks that build upon GrimAge's multi-component architecture while providing cell-type resolution. As these technologies evolve, GrimAge's foundational principles will likely inform subsequent generations of epigenetic biomarkers for aging and age-related disease.

The pursuit of quantifying biological aging has led to the development of epigenetic clocks, biomarkers based on DNA methylation (DNAm) patterns that predict age-related decline. These clocks have evolved through distinct generations, each with different training approaches and applications in geroscience research. First-generation clocks, such as Horvath and Hannum clocks, were trained primarily on chronological age, serving as accurate estimators of calendar years but offering limited insight into healthspan or mortality risk [12]. Second-generation clocks, including PhenoAge and GrimAge, advanced the field by incorporating phenotypic data and mortality-related risk factors, improving their correlation with health outcomes and lifespan [12] [3].

The latest innovation in this field is the third-generation clock DunedinPACE (Pace of Aging Calculated from the Epigenome), which represents a paradigm shift from measuring biological age accumulated over a lifetime to measuring the current pace of aging [44] [12]. Developed through the Dunedin Study, which tracked over 1,000 individuals from birth with repeated biomarker measurements across two decades, DunedinPACE was designed to function as a "speedometer" for aging, quantifying how rapidly an individual's organ systems are declining in integrity at the time of measurement [44] [45] [46]. This review comprehensively compares DunedinPACE against earlier epigenetic clocks, examining their methodological foundations, predictive validity for age-related outcomes, and utility in interventional studies and drug development.

Generations of Epigenetic Clocks: A Comparative Framework

Table 1: Comparison of Epigenetic Clock Generations

Generation	Representative Clocks	Training Approach	Primary Output	Strengths	Limitations
First-Generation	Horvath, Hannum	Chronological age prediction	Epigenetic age vs. chronological age	High accuracy for age estimation; pan-tissue applicability	Limited association with health outcomes and mortality
Second-Generation	PhenoAge, GrimAge, GrimAge2	Mortality risk, clinical biomarkers, smoking pack-years	Biological age acceleration	Better prediction of health outcomes and mortality than first-gen	Static measure of accumulated aging; vulnerable to confounding
Third-Generation	DunedinPACE, DunedinPoAm	Longitudinal decline in 19 organ-system biomarkers over 20 years	Pace of aging (biological years per chronological year)	Measures rate of change; high test-retest reliability; sensitive to intervention	Requires further validation in diverse populations

Table 2: Predictive Performance for Age-Related Outcomes

Clock	Mortality	Morbidity	Cognitive Decline	Brain Structure	Response to Intervention
Horvath	Limited	Limited	Inconsistent	Weak/inconsistent associations	Limited evidence
Hannum	Limited	Limited	Inconsistent	Weak/inconsistent associations	Limited evidence
PhenoAge	Moderate	Moderate	Moderate	Moderate associations	Moderate evidence
GrimAge	Strong	Strong	Strong	Strong associations	Moderate evidence
DunedinPACE	Strong	Strong	Strong	Strong associations	Strong evidence

The conceptual diagram below illustrates the evolutionary trajectory of epigenetic clock development:

DunedinPACE: Methodological Foundation and Development

The Dunedin Study Cohort

DunedinPACE was developed using data from the Dunedin Multidisciplinary Health and Development Study, a longitudinal investigation that has tracked a 1972-1973 birth cohort of 1,037 individuals from New Zealand with 95% retention [44] [46]. This single-year birth cohort design is methodologically significant as it eliminates confounding by age differences, cohort effects, and survival bias that can affect clocks developed from cross-sectional studies of mixed-age individuals [44]. Participants underwent comprehensive assessments at ages 26, 32, 38, and most recently at 45 years, providing a unique dataset of within-individual physiological changes across early to mid-adulthood [46].

Measuring the Pace of Aging Phenotype

The foundational innovation in DunedinPACE development was the creation of a Pace of Aging phenotype derived from longitudinal decline in 19 biomarkers across multiple organ systems:

• Cardiovascular system: Blood pressure, cardiorespiratory fitness
• Metabolic system: Body mass index, waist-hip ratio, glycated hemoglobin, leptin, lipids (cholesterol, triglycerides, lipoproteins)
• Renal system: Estimated glomerular filtration rate, blood urea nitrogen
• Hepatic system: Liver enzymes
• Immune system: C-reactive protein, white blood cell count
• Pulmonary system: Forced expiratory volume, forced vital capacity ratio
• Dental system: Periodontal attachment loss, dental caries [44] [46]

For each participant, linear mixed-effects modeling was used to estimate personal rates of change for each biomarker. These rates were then combined to calculate each individual's Pace of Aging, scaled to a mean of 1, representing one biological year per chronological year [44]. The cohort showed remarkable variation in aging rates, ranging from 0.40 to 2.44 biological years per chronological year over the two-decade observation period [46].

Epigenetic Algorithm Development

The DunedinPACE algorithm was created using elastic-net regression to predict the 20-year Pace of Aging phenotype from DNA methylation data measured at age 45 [44] [46]. To enhance reliability, the analysis was restricted to methylation probes with acceptable test-retest reliability (ICC > 0.4), resulting in 81,239 probes from the Illumina EPIC array [46]. This methodological refinement addressed limitations of previous epigenetic measures that showed only moderate test-retest reliability, making DunedinPACE particularly valuable for clinical trials requiring precise within-individual change measurement [44].

The following diagram illustrates the comprehensive development workflow of DunedinPACE:

Comparative Performance in Predicting Health Outcomes

Cognitive Decline and Dementia Risk

DunedinPACE demonstrates robust predictive validity for cognitive outcomes, a critical concern in aging populations. In the Framingham Heart Study Offspring Cohort (n=2,296; baseline age 25-101 years), faster DunedinPACE was associated with poorer cognitive functioning at baseline and more rapid cognitive decline over two decades of follow-up [47]. These associations were robust to adjustment for confounders and consistent across population strata. Notably, DunedinPACE explained approximately one-fourth of dementia risk, suggesting it captures meaningful variance in neurobiological aging processes [47].

Complementing these findings, a multi-coort neuroimaging study across the Dunedin Study, Framingham Heart Study, and Alzheimer's Disease Neuroimaging Initiative (total n=2,322) found that faster DunedinPACE was associated with differences in brain structure, including reduced total brain volume, hippocampal volume, and cortical thickness, along with increased white matter hyperintensities [48]. These associations were consistent across all three datasets spanning mid- to late-life, demonstrating DunedinPACE's validity as a marker of brain aging.

Morbidity, Disability, and Mortality

DunedinPACE has been validated against hard clinical endpoints across multiple studies. In the original publication, faster DunedinPACE was associated with incident chronic disease, disability, and mortality in midlife and older adults [44] [45]. The effect sizes for these associations were similar to those observed for GrimAge, a second-generation clock renowned for its mortality prediction [44]. Importantly, DunedinPACE provided incremental prediction beyond GrimAge for incident morbidity, disability, and mortality, suggesting it captures unique variance in aging processes [44] [46].

Sensitivity to Environmental and Social Factors

DunedinPACE shows sensitivity to key social determinants of health, reflecting the embedding of environmental experiences in biology. Multiple studies have found that childhood adversity, lower socioeconomic status, and poverty are associated with faster DunedinPACE in young adults [44] [45] [46]. This aligns with the geroscience hypothesis that adverse exposures accelerate fundamental aging processes, potentially explaining health disparities across the lifespan.

Response to Interventions: Critical Evidence for Drug Development

Pharmaceutical Interventions

The utility of epigenetic clocks in drug development hinges on their sensitivity to detect intervention effects. Recent evidence suggests DunedinPACE responds to putative geroprotective therapies:

• Semaglutide: In a Phase IIb trial with adults HIV-associated lipohypertrophy, treatment with semaglutide resulted in concordant decreases across 11 organ-system clocks, most prominently in inflammation, brain, and heart clocks [36]. Researchers hypothesize the mechanism may involve semaglutide's ability to reduce visceral fat, potentially mitigating adipose-driven pro-aging signals [36].

• Growth Hormone Therapy: The TRIIM (Thymus Regeneration, Immunorestoration, and Insulin Mitigation) trial investigated recombinant human growth hormone in men aged 51-65 years and observed epigenetic age reduction measured by GrimAge, approximately 2 years younger than baseline [36]. A follow-up trial, TRIIM-X, is ongoing with preliminary reports of improved physical fitness measures.

Lifestyle and Surgical Interventions

DunedinPACE also demonstrates sensitivity to behavioral and medical interventions:

• Vigorous Physical Activity: A study of professional soccer players found that vigorous physical activity was associated with rejuvenation of epigenetic clocks, with significant decreases in DNAmGrimAge2 and DNAmFitAge observed immediately after games [36]. This suggests intense exercise may transiently reverse epigenetic aging.

• Plasmapheresis: Contrary to expectations, a study of plasmapheresis in healthy adults found no significant epigenetic rejuvenation and instead observed increases in DNAmGrimAge, Hannum clock, and DunedinPACE, suggesting this intervention might accelerate epigenetic aging [36].

Table 3: Documented Interventions Affecting DunedinPACE

Intervention Type	Specific Intervention	Effect on DunedinPACE	Evidence Level
Pharmaceutical	Semaglutide	Decrease	Phase IIb Trial (not yet peer-reviewed)
Pharmaceutical	Growth Hormone (rhGH)	Decrease (GrimAge)	Pilot Trial (n=9)
Lifestyle	Vigorous Exercise	Decrease	Observational Study
Medical Procedure	Plasmapheresis	Increase	Clinical Study

Essential Research Reagents and Methodological Considerations

Table 4: Research Reagent Solutions for DunedinPACE Implementation

Reagent/Resource	Specifications	Application in DunedinPACE Research
DNA Methylation Array	Illumina EPIC or 450K arrays	Genome-wide methylation profiling for DunedinPACE calculation
Blood Collection Kits	PAXgene Blood DNA tubes	Standardized blood collection for DNA methylation analysis
DNA Extraction Kits	Qiagen DNA extraction kits	High-quality DNA isolation from whole blood
Computational Package	R package (github.com/danbelsky/DunedinPACE)	DunedinPACE calculation from methylation data
Cell Type Estimation	FlowSorted.Blood.EPIC package	Estimation of immune cell counts for confounding adjustment
Quality Control Metrics	SeSAMe package for preprocessing	Standardized quality control and preprocessing of methylation data

Methodological Standards for DunedinPACE Research

For researchers implementing DunedinPACE studies, several methodological considerations are critical:

• Sample Type: DunedinPACE was developed and validated using whole blood DNA methylation [44] [46]. While blood provides a accessible biomarker, its relationship with tissue-specific aging in other organs continues to be investigated.

• Preprocessing and Normalization: Consistent with epigenetic clock research generally, DunedinPACE calculation requires standardized preprocessing of methylation data, including background correction, dye bias correction, and probe-type normalization [47].

• Confounding Considerations: Analyses should account for potential cellular heterogeneity by including estimated immune cell counts as covariates [47]. Additional demographic and technical covariates (sex, processing batch) should be considered based on study design.

DunedinPACE represents a significant methodological advance in geroscience research, offering a highly reliable, sensitive measure of the pace of aging that complements existing epigenetic clocks. Its development from longitudinal phenotyping in a same-age birth cohort provides unique advantages for isolating aging processes from confounding factors. For drug development professionals, DunedinPACE shows particular promise as a biomarker for clinical trials of geroprotective interventions, with emerging evidence supporting its sensitivity to pharmaceutical, lifestyle, and surgical interventions.

Future research directions should include: (1) further validation of DunedinPACE in diverse racial and ethnic populations; (2) investigation of DunedinPACE as a screening tool for accelerated aging in clinical practice; and (3) continued application in randomized controlled trials of geroprotective therapies to establish its utility as a surrogate endpoint for healthspan extension. As the evidence base grows, DunedinPACE is positioned to become an increasingly valuable tool for researchers and drug developers aiming to target the fundamental processes of aging.

Epigenetic clocks have emerged as powerful tools for quantifying biological aging, providing researchers with unprecedented insights into the relationship between accelerated aging and health outcomes. These clocks are computational models that estimate biological age based on DNA methylation patterns at specific cytosine-phosphate-guanine (CpG) sites. The evolution of these clocks from first-generation to next-generation models represents a significant advancement in geroscience, moving from simple chronological age predictors to sophisticated biomarkers capable of capturing age-related physiological decline and mortality risk [3] [12]. In observational studies, these clocks enable researchers to identify individuals experiencing accelerated or decelerated aging, providing a window into the biological mechanisms linking environmental exposures, lifestyle factors, and genetic predispositions to healthspan and lifespan.

The fundamental principle underlying epigenetic clocks is that DNA methylation patterns change predictably with age, but the rate of change varies between individuals based on their unique combination of genetic, environmental, and stochastic factors. When an individual's epigenetic age exceeds their chronological age, they are said to be experiencing "epigenetic age acceleration" (EAA), which has been consistently associated with increased risk of age-related diseases and mortality across numerous studies [49] [50]. This article provides a comprehensive comparison of first-generation and next-generation epigenetic clocks, focusing on their performance in observational studies linking epigenetic age acceleration to disease risk and mortality.

Generations of Epigenetic Clocks: Definitions and Key Differences

Classification Framework

Epigenetic clocks are typically categorized into generations based on their training targets and underlying methodologies:

First-generation clocks were exclusively trained to predict chronological age using DNA methylation data [3]. These models identified CpG sites whose methylation status correlated most strongly with calendar age, creating highly accurate age estimators. The most widely used first-generation clocks include Horvath's pan-tissue clock (353 CpGs) and Hannum's blood-based clock (71 CpGs) [12] [4]. While these clocks demonstrated remarkable accuracy in estimating chronological age, their deviations from chronological age (age acceleration) showed only moderate associations with health outcomes and mortality risk.

Second-generation clocks marked a significant evolution by incorporating health-related parameters into their training. Rather than being trained solely on chronological age, these models were developed to predict mortality risk, clinical biomarkers, or composite measures of physiological age. Prominent examples include PhenoAge (trained on a composite phenotypic age measure incorporating clinical chemistry markers) and GrimAge (trained on mortality risk and plasma protein levels) [12]. These clocks demonstrated substantially improved performance in predicting health outcomes compared to first-generation models.

Third-generation clocks introduced dynamic measures of aging pace rather than static age estimates. The most notable example is DunedinPACE (Pace of Aging Calculated from the Epigenome), which was trained on longitudinal changes in 19 biomarkers of organ system integrity over a 20-year period [12]. This clock provides a measure of the pace of biological aging, representing how quickly an individual's physiological systems are deteriorating relative to their chronological age.

Fourth-generation clocks, often called "causal clocks," employ Mendelian randomization approaches to identify CpG sites with putative causal relationships to aging processes. These include clocks such as CausAge, AdaptAge, and DamAge, which aim to distinguish between methylation changes that represent adaptation to aging versus those that represent aging-related damage [12].

Table 1: Generations of Epigenetic Clocks and Their Characteristics

Generation	Training Target	Key Examples	CpG Sites	Primary Application
First	Chronological age	Horvath, Hannum	353 (Horvath), 71 (Hannum)	Chronological age estimation
Second	Mortality, clinical biomarkers	PhenoAge, GrimAge	513 (PhenoAge), 1030 (GrimAge)	Health risk assessment, mortality prediction
Third	Pace of physiological decline	DunedinPACE, DunedinPoAm	173 (DunedinPACE)	Measuring rate of biological aging
Fourth	Putatively causal sites	CausAge, AdaptAge, DamAge	Varies	Distinguishing adaptive vs. damaging methylation changes

Technical Specifications and Methodological Approaches

The development of epigenetic clocks typically follows a standardized workflow: (1) DNA methylation profiling using array-based technologies (e.g., Illumina Infinium MethylationEPIC BeadChip); (2) feature selection to identify age-informative CpG sites; (3) model training using machine learning algorithms (most commonly elastic net regression); and (4) validation in independent cohorts [4] [50]. First-generation clocks predominantly used elastic net regression (a regularized linear regression method) to identify the optimal combination of CpG sites for predicting chronological age. Next-generation clocks have employed more diverse approaches, including two-stage modeling (e.g., first predicting plasma protein levels or phenotypic age from methylation data, then using these as inputs for mortality prediction) and incorporation of additional biomarkers beyond DNA methylation [12].

Diagram 1: Evolution of Epigenetic Clock Generations: This diagram illustrates the progression from first to fourth-generation epigenetic clocks, highlighting key differences in training targets, methodological approaches, and primary applications.

Performance Comparison: Predictive Utility for Mortality and Disease

All-Cause and Cause-Specific Mortality

Multiple large-scale observational studies have directly compared the performance of different epigenetic clocks in predicting mortality risk. A comprehensive analysis of NHANES data (n=2,105) with median 17.5 years of follow-up demonstrated striking differences in predictive utility [51]. GrimAge (a second-generation clock) showed the strongest association with all-cause mortality (HR: 1.50 per 5-year acceleration, 95% CI: 1.32-1.71), substantially outperforming first-generation clocks including Horvath (HR: 1.13, 95% CI: 1.04-1.22), Hannum (HR: 1.16, 95% CI: 1.07-1.27), and Vidal-Bralo (HR: 1.13, 95% CI: 1.03-1.23) [51]. Similarly, for cause-specific mortality, GrimAge demonstrated the strongest association with cardiovascular mortality (HR: 1.55, 95% CI: 1.29-1.86), while both Hannum and GrimAge predicted cancer mortality [51].

The superiority of next-generation clocks extends beyond GrimAge. DunedinPACE, a third-generation pace of aging clock, demonstrated significant associations with both overall mortality (HR: 1.23 per 10% increase, 95% CI: 1.08-1.38) and cardiovascular mortality (HR: 1.25, 95% CI: 1.01-1.55) in the same cohort [51]. A systematic review and meta-analysis published in 2019 found that each 5-year increase in DNA methylation age was associated with an 8-15% increased risk of mortality, with next-generation clocks generally showing stronger associations than first-generation models [49].

Table 2: Mortality Risk Prediction by Epigenetic Clock Generation (per 5-year acceleration unless specified)

Epigenetic Clock	Generation	All-Cause Mortality HR (95% CI)	Cardiovascular Mortality HR (95% CI)	Cancer Mortality HR (95% CI)
GrimAge	Second	1.50 (1.32-1.71)	1.55 (1.29-1.86)	1.37 (1.00-1.87)
Hannum	First	1.16 (1.07-1.27)	1.24 (1.07-1.44)	1.24 (1.07-1.44)
PhenoAge	Second	1.13 (1.05-1.21)	Not significant	Not significant
Horvath	First	1.13 (1.04-1.22)	1.18 (1.02-1.35)	1.18 (1.02-1.35)
Vidal-Bralo	First	1.13 (1.03-1.23)	Not significant	Not significant
DunedinPACE *	Third	1.23 (1.08-1.38)	1.25 (1.01-1.55)	Not significant

Note: DunedinPACE hazard ratio is per 10% increase in pace of aging [51].

Cardiovascular and Cerebrovascular Diseases

Epigenetic age acceleration has been consistently associated with increased risk of cardiovascular and cerebrovascular diseases, with next-generation clocks generally demonstrating superior predictive performance. A recent systematic review and meta-analysis specifically examined the relationship between DNA methylation aging acceleration and stroke risk, incorporating 13 studies with a total of 29 effect sizes [52] [53]. The analysis revealed a significant positive association between accelerated biological aging and stroke risk (OR = 1.16, 95% CI: 1.13-1.19), with stronger associations for incident stroke (OR = 1.28, 95% CI: 1.25-1.35) than for stroke recurrence (OR = 1.11, 95% CI: 1.06-1.16) [52]. While this meta-analysis did not provide separate effect sizes for each clock generation, the authors noted that clocks trained on health outcomes (i.e., second-generation and later) generally showed stronger associations with stroke risk than first-generation clocks trained solely on chronological age.

In cardiovascular disease more broadly, studies have shown that next-generation clocks are more strongly associated with cardiovascular health metrics, incidence of cardiovascular events, and subclinical atherosclerosis measures [50]. The improved performance of next-generation clocks likely stems from their incorporation of biomarkers directly relevant to cardiovascular pathophysiology, such as inflammatory markers, metabolic parameters, and measures of vascular integrity.

Other Age-Related Conditions

The pattern of superior performance for next-generation clocks extends to other age-related conditions, including cancer, neurodegenerative diseases, and overall frailty. Research has shown that next-generation clocks are more strongly associated with cancer incidence and aggressiveness [54]. A study of World Trade Center-exposed individuals found that epigenetic age acceleration measured by multiple clocks was associated with breast cancer status, with next-generation clocks showing particularly strong associations [54]. Similarly, in neurodegenerative conditions including Alzheimer's disease and other dementias, next-generation clocks have demonstrated stronger associations with disease incidence, progression, and pathology than first-generation models [49] [50].

Experimental Protocols for Epigenetic Age Assessment

Standardized DNA Methylation Profiling Workflow

The assessment of epigenetic age in observational studies follows a standardized protocol to ensure reproducibility and comparability across studies:

Sample Collection and DNA Extraction:

• Collect peripheral blood samples (most common), saliva, or buccal cells using appropriate collection tubes
• Extract DNA using standardized kits (e.g., QIAamp DNA Blood Mini Kit, Zymo Research DNA extraction kits)
• Quantify DNA concentration and quality using spectrophotometry (e.g., Nanodrop) or fluorometry (e.g., Qubit)
• Store DNA at -80°C until analysis [51] [54]

DNA Methylation Profiling:

• Perform bisulfite conversion using commercial kits (e.g., Zymo EZ DNA Methylation Kit) to convert unmethylated cytosines to uracils while preserving methylated cytosines
• Process samples using Illumina Infinium MethylationEPIC BeadChip arrays (850k CpG sites) or the earlier 450k arrays
• Hybridize converted DNA to arrays according to manufacturer protocols
• Scan arrays using Illumina iScan or similar systems [51] [54]

Data Processing and Normalization:

• Process raw intensity data using specialized pipelines (e.g., SeSAMe, minfi)
• Perform quality control to exclude poor-quality samples and probes
• Normalize data using established methods (e.g., BMIQ, Noob)
• Adjust for technical covariates and batch effects [51]

Epigenetic Age Calculation:

• Apply pre-trained epigenetic clock algorithms to normalized methylation data
• Calculate epigenetic age acceleration as residuals from regression of epigenetic age on chronological age, or using other established metrics (e.g., intrinsic, extrinsic acceleration) [51] [49]

Diagram 2: Standardized Workflow for Epigenetic Age Assessment: This diagram outlines the key steps in DNA methylation profiling and epigenetic age calculation, from sample collection to statistical analysis in observational studies.

Statistical Analysis in Observational Studies

Observational studies investigating associations between epigenetic age acceleration and health outcomes typically employ standardized statistical approaches:

Primary Analysis:

• Use Cox proportional hazards regression for time-to-event outcomes (mortality, disease incidence)
• Employ linear regression for continuous outcomes
• Use logistic regression for binary outcomes
• Adjust for chronological age as a covariate in all models [51] [49]

Covariate Adjustment:

• Minimal adjustment: chronological age, sex, technical covariates (batch effects, cell type composition)
• Extended adjustment: socioeconomic factors, lifestyle variables (smoking, BMI), pre-existing conditions
• The extent of adjustment depends on the specific research question and potential confounding structure [51] [49]

Epigenetic Age Acceleration Metrics:

• Intrinsic epigenetic age acceleration (IEAA): Adjusted for estimated blood cell counts
• Extrinsic epigenetic age acceleration (EEAA): Incorporates age-related changes in blood cell composition
• GrimAge acceleration: Residuals from regression of GrimAge on chronological age
• DunedinPACE: Used as a continuous measure of aging pace [12] [51]

Table 3: Essential Research Reagents and Resources for Epigenetic Clock Studies

Category	Specific Products/Resources	Application	Key Considerations
DNA Extraction Kits	QIAamp DNA Blood Mini Kit (Qiagen), DNeasy Blood & Tissue Kit (Qiagen), Quick-DNA Kit (Zymo Research)	High-quality DNA extraction from blood, saliva, or buccal samples	Yield, purity, compatibility with downstream applications
Bisulfite Conversion Kits	EZ DNA Methylation Kit (Zymo Research), EpiTect Fast DNA Bisulfite Kit (Qiagen), MethylCode Kit (Thermo Fisher)	Conversion of unmethylated cytosine to uracil	Conversion efficiency, DNA fragmentation, yield
Methylation Arrays	Infinium MethylationEPIC BeadChip (850k) (Illumina), Infinium HumanMethylation450 BeadChip (450k) (Illumina)	Genome-wide methylation profiling at CpG sites	Coverage of relevant CpG sites, cost, sample throughput
Quality Control Tools	minfi (R package), SeSAMe (R package), MethylAid (R package)	Quality assessment of methylation data	Detection of outliers, batch effects, poor-quality samples
Normalization Methods	Noob (normal-exponential out-of-band), BMIQ (Beta Mixture Quantile dilation), SWAN (Subset Within Array Normalization)	Technical normalization of methylation data	Reduction of technical variability, preservation of biological signals
Epigenetic Clock Algorithms	Horvath epigenetic clock, Hannum clock, PhenoAge, GrimAge, DunedinPACE	Calculation of epigenetic age and age acceleration	Compatibility with methylation platform, implementation in software
Cell Type Deconvolution	Houseman method, EpiDISH, CETS	Estimation of blood cell counts from methylation data	Accuracy of cell type estimation, relevance to tissue type

The comprehensive comparison of epigenetic clock generations reveals a clear pattern: next-generation clocks consistently outperform first-generation models in predicting mortality, age-related diseases, and other health outcomes in observational studies. This performance advantage stems from their training on health-related outcomes rather than chronological age alone, enabling them to capture aspects of biological aging more relevant to disease pathogenesis and mortality risk.

For researchers designing observational studies, the evidence supports prioritizing next-generation clocks, particularly GrimAge and DunedinPACE, for investigations of health outcomes and mortality. These clocks demonstrate substantially stronger associations with clinical endpoints than first-generation models, potentially increasing statistical power and enabling more robust conclusions. Furthermore, different clocks may capture distinct aspects of the aging process, suggesting value in including multiple clocks in comprehensive assessments of biological aging.

The superior performance of next-generation clocks also enhances their potential for eventual clinical application, particularly in risk stratification, early detection of age-related conditions, and evaluation of interventions targeting the aging process. As the field advances, continued refinement of epigenetic clocks—including the development of tissue-specific clocks, clocks for diverse populations, and clocks targeting specific health domains—will further expand their utility in both research and clinical contexts.

Epigenetic aging clocks are predictive models based on DNA methylation patterns that serve as biomarkers of aging. Their application in interventional trials represents a paradigm shift in geroscience, allowing for the quantitative assessment of geroprotective interventions within practical timeframes. These clocks are categorized into distinct generations based on their training methodology and predictive capabilities. First-generation clocks, such as HannumAge and HorvathAge, were trained solely to predict an individual's chronological age. In contrast, second-generation and subsequent clocks (e.g., PhenoAge, GrimAge, DunedinPACE) were explicitly trained to predict healthspan, lifespan, and age-related functional decline, making them more suitable for evaluating interventions aimed at modifying the biology of aging [3] [36].

The primary advantage of these biomarkers, particularly next-generation clocks, is their ability to serve as surrogate endpoints in clinical trials. This enables researchers to screen the efficacy of candidate geroprotective compounds (CGPIs) and lifestyle interventions without the need for decades-long studies to observe effects on mortality or disease incidence [55] [56]. Current evidence strongly suggests that next-generation models should be generally prioritized for health-oriented association and interventional studies, as they associate with a greater number of health and disease signals and are more responsive to interventions [3].

Comparison of Epigenetic Clock Generations

The fundamental difference between clock generations lies in their training data and resultant utility. The table below summarizes the core characteristics and performance of key epigenetic clocks.

Table 1: Comparison of Major Epigenetic Clock Generations

Clock Name	Generation	Training Target	Key Strengths	Utility in Interventional Trials
Horvath Age [36]	First	Chronological Age	Pan-tissue accuracy	Limited; measures baseline age, less sensitive to health outcomes.
Hannum Age [36]	First	Chronological Age	Blood-based age prediction	Limited; similar to Horvath.
PhenoAge [36]	Second	Composite clinical chemistry markers	Predicts morbidity & mortality	Good; more responsive to health status and interventions than first-gen.
GrimAge [36]	Second	Plasma proteins & smoking history	Superior predictor of mortality	High; strongly associated with health risk and responsive to interventions.
DunedinPACE [3] [36]	Third	Pace of Aging from longitudinal data	Measures pace of biological decline	High; designed to detect changes in the rate of aging.
DNAmFitAge [57]	Next-Gen	Physical fitness measures	Integrates fitness as a health metric	Promising for lifestyle interventions; links fitness to biological age.

Performance in Disease Prediction and Interventional Contexts

Large-scale, unbiased comparisons confirm the superiority of next-generation clocks. A 2025 study comparing 14 clocks in relation to 174 disease outcomes across 18,859 individuals found that second-generation clocks significantly outperformed first-generation clocks, which have limited applications in disease settings [21]. The study identified numerous instances where adding a second-generation clock to a model with traditional risk factors increased classification accuracy by over 1% [21].

Furthermore, specific clocks show particular promise for specific contexts. For instance, a 2025 meta-analysis found that GrimAge Age Acceleration (GrimAA) is consistently and significantly associated with frailty, both cross-sectionally and longitudinally, making it a robust endpoint for trials targeting physical resilience and functional decline in aging [25].

The following diagram illustrates the logical relationship and key differentiators between the major epigenetic clock generations.

Figure 1: Evolution and Key Characteristics of Epigenetic Clock Generations

Application in Geroprotective Drug Trials

The TAME Trial Framework

The Targeting Aging with Metformin (TAME) trial is a landmark study that has received FDA approval for its design. It serves as a prototype for how gerotherapeutic trials can use composite clinical endpoints—such as the time to incidence of heart disease, cancer, stroke, and cognitive impairment—as primary outcomes [56]. While TAME uses hard clinical endpoints, its design paves the way for the use of epigenetic clocks as secondary or exploratory biomarker endpoints to provide mechanistic insight and potentially validate faster-to-measure surrogates [56].

Evidence from Specific Drug Trials

Interventional studies have begun to demonstrate the ability of certain compounds to directly modulate epigenetic age.

• Semaglutide: Results from a Phase IIb trial in adults with HIV-associated lipohypertrophy indicated that treatment with semaglutide was associated with concordant decreases in 11 organ-system epigenetic clocks, most prominently in inflammation, brain, and heart clocks. The proposed mechanism is the drug's ability to reduce visceral fat, thereby mitigating adipose-driven pro-ageing signals [36].
• Thymic Regeneration: The TRIIM trial investigated a regimen involving recombinant human growth hormone in putatively healthy men. The study reported a mean epigenetic age reduction of approximately 1.5 years below baseline after one year of treatment, as measured by the GrimAge clock. This rejuvenation effect persisted six months after treatment discontinuation [36].
• Cell-Based Therapies: A Phase IIb trial of Lomecel-B (allogeneic mesenchymal stem cells) in older adults with mild to moderate frailty represents another interventional approach, with outcomes including physical function and inflammatory biomarkers, which are correlated with epigenetic aging [56].

Table 2: Summary of Select Geroprotective Drug Trials and Epigenetic Clock Evidence

Intervention	Trial Phase / Type	Reported Effect on Epigenetic Aging	Relevant Clocks Cited
Semaglutide [36]	Phase IIb Trial	Decreased age in multiple organ-system clocks	GrimAge, PhenoAge, Organ Clocks
Thymus Regeneration (TRIIM) [36]	Pilot Trial	Mean reduction of ~1.5 years	GrimAge
Mesenchymal Stem Cells (Lomecel-B) [56]	Phase IIb Trial	Targeted frailty & inflammation (proxies for aging)	(Biomarkers associated with clocks)
Plasmapheresis [36]	Observational Study	Associated with increased age acceleration	GrimAge, Hannum, DunedinPACE

Application in Lifestyle Intervention Trials

Lifestyle interventions are a major focus in geroscience due to their accessibility and potential for widespread implementation. Epigenetic clocks are increasingly used to quantify their biological impact.

Exercise and Physical Fitness

A growing body of evidence links physical activity, structured exercise, and physical fitness with decelerated epigenetic aging.

• Structured Exercise: Human and animal interventional studies demonstrate that structured exercise training can induce epigenomic rejuvenation. One study with sedentary middle-aged and older females found that an 8-week combined aerobic and strength training program significantly decreased epigenetic age in participants who had a higher baseline age [57].
• Cardiorespiratory Fitness (CRF): Higher CRF, measured as VOâ‚‚ max, is negatively correlated with epigenetic age acceleration. Individuals with fitness levels above reference values show lower age acceleration than those below [57]. This relationship was leveraged to create DNAmFitAge, a next-generation clock that incorporates fitness measures into the DNA methylation algorithm [57].
• Multi-Organ Effects: Research in animal models suggests the geroprotective effects of high fitness levels are systemic, delaying epigenetic aging in adipose tissue, cardiac muscle, liver, and skeletal muscle, not just in blood [57].

Other Lifestyle Interventions

• Vigorous Activity: A study on professional soccer players revealed that vigorous physical activity has an immediate, albeit potentially transient, rejuvenating effect on epigenetic clocks, with significant decreases in DNAmGrimAge2 and DNAmFitAge observed right after games [36].
• Lifestyle Redesign: While not always measured with epigenetic clocks, structured lifestyle interventions like the "Lifestyle Redesign" occupational therapy program have demonstrated significant improvements in mental and physical well-being in older adults [58]. Such programs represent ideal candidates for future trials using epigenetic clocks as objective biomarkers of biological age reduction.

The experimental workflow for applying epigenetic clocks in lifestyle intervention trials typically follows a standardized pattern, as shown below.

Figure 2: Generalized Workflow for Lifestyle Intervention Trials

Essential Research Reagent Solutions and Methodologies

Implementing epigenetic clocks in research requires a suite of specific reagents, technologies, and protocols. The following toolkit details key components.

Table 3: The Scientist's Toolkit for Epigenetic Clock Research

Category / Item	Specific Example / Method	Function & Application Note
Sample Collection	Peripheral Blood (Buffy Coat)	Standard source for DNA methylation analysis; allows for population-scale studies.
Bisulfite Conversion	EZ-96 DNA Methylation Kit (Zymo Research)	Critical chemical process that converts unmethylated cytosines to uracils, allowing methylation status to be determined via sequencing or array.
Methylation Array	Illumina EPIC Array	Genome-wide microarray that assays methylation at >850,000 CpG sites, covering sites used in most major epigenetic clocks.
Data Processing	SeSAMe R Package	Preprocessing pipeline to reduce technical noise and produce high-quality beta-values (methylation scores) from raw array data.
Clock Calculation	Published Clock Algorithms (e.g., Horvath's Calculator)	R scripts or online calculators that apply pre-defined coefficients to methylation data from specific CpG sites to compute biological age.
Statistical Analysis	R/Bioconductor (limma, etc.)	Open-source environment for performing differential methylation analysis and modeling associations with intervention status.
Database	Geroprotectors.org [59]	Manually-curated database of geroprotective interventions, their effects on lifespan, and biochemical profiles for cross-study comparison.

The integration of epigenetic clocks, particularly next-generation models like GrimAge, PhenoAge, and DunedinPACE, into interventional trials marks a critical advancement in geroscience. These biomarkers provide a powerful, quantitative, and biologically plausible means to evaluate the efficacy of geroprotective drugs and lifestyle interventions on a practical timescale. Current evidence strongly supports their utility, with second-generation clocks consistently showing superior performance in predicting disease, mortality, and responsiveness to interventions compared to first-generation clocks.

Future efforts will focus on developing and validating clocks trained specifically on frailty and other geriatric syndromes, harmonizing longitudinal data from large cohorts, and further elucidating the causal pathways linking interventions to changes in DNA methylation and health outcomes [25]. As the field matures, these molecular biomarkers are poised to transition from research tools to validated Gerodiagnostics that can guide clinical practice and accelerate the development of therapies to extend human healthspan [56].

Addressing Technical Noise, Bias, and Computational Challenges for Robust Science

Epigenetic clocks are powerful biomarkers that predict chronological age or biological age using DNA methylation (DNAm) patterns at specific CpG sites in the genome. These tools have revolutionized aging research by providing quantifiable measures of biological aging processes, with applications spanning basic research, clinical trials, and therapeutic development. However, their utility is fundamentally constrained by a critical methodological challenge: technical variance. This reliability problem stems from technical noise introduced during the measurement process, which can produce surprisingly large deviations in age predictions even when analyzing the same biological sample. Understanding the sources, magnitude, and solutions to this precision problem is essential for researchers, scientists, and drug development professionals who depend on these biomarkers for evaluating interventions and making clinical predictions.

Technical variance in epigenetic clocks arises from multiple sources within the experimental workflow, including sample preparation, variations in bead counts per CpG probe, probe hybridization inconsistencies, differences in probe chemistry, and batch effects across processing runs [60]. This noise becomes particularly problematic for applications requiring high precision, such as longitudinal tracking of aging interventions, in vitro studies, and clinical trials where accurate detection of small effect sizes is crucial for evaluating therapeutic efficacy [60]. The following sections provide a comprehensive analysis of this reliability challenge, presenting experimental evidence across different epigenetic clocks, quantifying the impact on research applications, and introducing computational and methodological solutions to bolster measurement precision.

Quantifying the Problem: Experimental Evidence of Technical Variance

Systematic Evaluation of Technical Noise Across Clock Platforms

Groundbreaking research has systematically quantified the alarming magnitude of technical variance in epigenetic clock measurements. One comprehensive study analyzed 36 whole blood samples, each with two technical replicates, across an age range of 37.3 to 74.6 years [60]. After processing data to eliminate systematic batch bias, researchers calculated intraclass correlation coefficients (ICCs) to measure agreement between replicate measurements. The findings revealed significant reliability concerns across multiple prominent epigenetic clocks, with median deviations between technical replicates ranging from 0.9 to 2.4 years across different clocks [60].

Perhaps more concerning were the maximum observed deviations between replicates, which reached as high as 4.5 to 8.6 years for some clocks [60]. To contextualize these deviations, researchers expressed them relative to the standard deviation of age acceleration for each clock. For several widely used clocks, including the Horvath multi-tissue predictor, Horvath skin-and-blood clock, Hannum blood clock, and Levine PhenoAge clock, maximum deviations between technical replicates actually exceeded one standard deviation of age acceleration [60]. This indicates that technical noise alone can produce differences larger than the biologically meaningful variation between individuals, fundamentally limiting detection capability in research settings.

Table 1: Reliability Metrics for Prominent Epigenetic Clocks Based on Technical Replicate Analysis

Epigenetic Clock	Median Deviation Between Replicates (Years)	Maximum Deviation Between Replicates (Years)	Deviation in SD of Age Acceleration	Intraclass Correlation Coefficient (ICC)
Horvath Multi-tissue	1.8	4.8	>1 SD	0.978
Horvath Skin & Blood	1.4	4.5	>1 SD	0.985
Hannum	2.4	8.6	>1 SD	0.975
PhenoAge	1.8	6.6	>1 SD	0.977
GrimAge	0.9	4.5	0.57 SD	0.989
DNAmTL	1.3	5.1	0.69 SD	0.980

The analysis further demonstrated that these reliability issues are not confined to specific samples or CpG subsets but represent a systemic challenge affecting nearly all epigenetic biomarkers [60]. Examination of individual CpG contributions to clock deviations revealed that even samples showing minimal overall clock deviations still exhibited significant noise at individual CpG sites, with opposing errors canceling each other out in the final prediction [60]. This finding underscores that technical variance affects the fundamental building blocks of epigenetic clocks rather than representing isolated measurement artifacts.

Methodological Protocols for Assessing Technical Variance

For researchers seeking to evaluate technical variance in their own epigenetic clock studies, specific methodological approaches have been employed in the foundational studies quantifying these effects:

• Sample Selection and Replication: The referenced study utilized 36 whole blood samples with an age range of 37.3-74.6 years, with each sample processed twice as technical replicates [60]. This design enabled direct measurement of technical variance independent of biological variance.

• Data Processing and Normalization: Researchers processed raw methylation data using standardized pipelines to eliminate systematic bias between batches, including implementing detection thresholds and removing low-quality probes [60]. Both beta-values and M-values were evaluated, with strong correlation (r = 0.987) between reliability metrics for both value types [60].

• Reliability Quantification: The intraclass correlation coefficient (ICC) was used as the primary reliability metric, quantifying measurement agreement for multiple estimates from the same sample relative to variation between different samples [60]. ICC calculations were performed at both individual CpG and composite clock levels.

• Deviation Analysis: For each epigenetic clock, deviations between technical replicates were calculated as absolute differences in predicted age. These raw deviations were then contextualized by expressing them relative to the standard deviation of age acceleration for each clock [60].

This experimental protocol provides a template for research groups to assess technical variance in their specific laboratory conditions and for novel epigenetic clocks not yet evaluated in the literature.

Comparative Analysis: First-Generation vs. Second-Generation Clocks

Fundamental Differences in Construction and Application

Epigenetic clocks are categorized into generations based on their training approaches and intended applications. First-generation clocks, including the Horvath multi-tissue clock and Hannum clock, were trained exclusively to predict chronological age using supervised machine learning applied to DNA methylation data [3] [4]. While these clocks demonstrated remarkable accuracy in estimating chronological age (with correlations often exceeding R = 0.96), their deviations from chronological age showed only moderate associations with health outcomes and mortality risk [3] [29].

Second-generation clocks, including PhenoAge, GrimAge, and DunedinPACE, were explicitly trained to predict health, lifestyle, and age-related outcomes rather than chronological age alone [3]. These models incorporate a broader spectrum of biological information, with GrimAge specifically constructed using a two-step process that first develops DNAm-based surrogates for plasma proteins and smoking exposure, then combines these with chronological age and sex to predict mortality risk [29] [13]. This fundamental difference in training approach appears to confer not only enhanced predictive value for health outcomes but also potentially improved reliability characteristics, as evidenced by GrimAge's superior ICC (0.989) relative to other clocks [60].

Table 2: Performance Comparison Between First-Generation and Second-Generation Epigenetic Clocks

Characteristic	First-Generation Clocks	Second-Generation Clocks
Training Target	Chronological age	Healthspan, mortality risk, phenotypic age
Representative Examples	Horvath multi-tissue, Hannum	PhenoAge, GrimAge, DunedinPACE
Association with Health Outcomes	Moderate	Strong
Mortality Prediction	Weak to moderate	Strong
Responsiveness to Interventions	Limited	Enhanced
Proportion of Significant Disease Associations	~5% of all significant findings	~95% of all significant findings [29]
Typical Number of Predictive CpGs	353 (Horvath) to 514 (Zhang)	Varies; GrimAge uses DNAm surrogates rather than individual CpGs [4]

Differential Performance in Health Outcome Prediction

Large-scale comparative analyses have demonstrated the superior performance of second-generation clocks in predicting clinically relevant endpoints. An unprecedented unbiased comparison of 14 epigenetic clocks against 174 disease outcomes in 18,859 individuals found that second-generation clocks significantly outperformed first-generation clocks, which showed limited applications in disease settings [29]. Of 176 Bonferroni-significant associations between epigenetic clocks and disease outcomes, only approximately 5% were attributed to first-generation clocks, with the remaining 95% derived from second-generation and third-generation clocks [29].

Specifically, GrimAge demonstrated particularly strong performance in predicting all-cause mortality, with hazard ratios per standard deviation of age acceleration reaching 1.54 (95% CI [1.46, 1.62]) in large cohort analyses [29]. Independent validation studies from the National Institute on Aging confirmed that GrimAge outperformed other epigenetic clocks in predicting mortality, with all epigenetic clocks assessed outperforming traditional telomere length measurements [13]. The enhanced predictive value of second-generation clocks extends to specific age-related conditions, with GrimAge acceleration showing consistent cross-sectional and longitudinal associations with frailty in meta-analyses encompassing 28,325 participants [25].

Beyond Technical Noise: Additional Confounding Factors

Cellular Composition Effects on Epigenetic Age Estimates

While technical variance represents a measurement-level challenge, biological confounding factors further complicate the interpretation of epigenetic clock data. Perhaps the most significant biological confounder is the changing cellular composition of tissues with age, particularly evident in blood-based clocks. Research has demonstrated that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual [9]. This dramatic difference indicates that current epigenetic clocks capture both cell-intrinsic aging processes and changes in immune cell composition, confounding result interpretation.

This compositional effect was systematically investigated by isolating four CD8+ T-cell subsets (naive, central memory, effector memory, and terminal effector) from donors of varying ages [9]. Analysis revealed that more than a third of predictive CpG sites in four prominent epigenetic clocks (Hannum, Horvath, Horvath Skin and Blood, and PhenoAge) were correlated with T-cell differentiation state rather than purely age-associated changes [9]. This finding underscores that epigenetic age measurements in heterogeneous tissues represent a composite of the epigenetic ages of constituent cell types, with their relative proportions significantly influencing the overall measurement.

Methodological Approach to Control for Cellular Composition Effects

To address the confounding effect of cellular composition, researchers have developed novel epigenetic clocks specifically designed to resist changes in immune cell composition. The IntrinClock was trained using DNA methylation data from 14,601 samples across 71 datasets, incorporating 10 different immune cell types [9]. Through specialized training approaches, this clock generates age predictions that remain consistent across immune cell types while still detecting cell-intrinsic aging processes, as validated through in vitro replicative senescence models and cellular reprogramming experiments [9].

For researchers working with heterogeneous tissues, especially blood, several methodological approaches can mitigate compositional confounding:

• Cell-type-specific Analysis: Isolate specific cell populations before epigenetic analysis when possible, though this adds complexity and cost.

• Computational Correction: Include estimated cell proportions as covariates in statistical models, though this approach has limitations in completely separating compositional effects from intrinsic aging [9].

• Specialized Clocks: Utilize clocks specifically designed to resist compositional changes, such as IntrinClock, when studying cell-intrinsic aging processes [9].

These approaches enable more precise attribution of observed epigenetic age differences to either cell-intrinsic aging processes or changes in cellular composition, each of which has distinct biological implications.

Solutions and Innovations: Computational Approaches to Enhance Reliability

Principal Component-Based Clocks: A Noise-Reduction Strategy

To directly address the technical variance problem, researchers have developed innovative computational solutions that substantially improve reliability. The most promising approach involves calculating principal components from CpG-level data as input for biological age prediction, rather than using individual CpG values directly [60]. This method leverages the understanding that while individual CpGs can be unreliable, many CpGs change together with age in a multicollinear manner, providing redundant information that can be extracted more reliably through dimensionality reduction.

The principal component (PC) clock approach demonstrates remarkable efficacy in reliability improvement. While traditional clocks showed median deviations between 0.9-2.4 years with maxima up to 8.6 years, retrained PC versions of these same clocks showed agreement between most replicates within 1.5 years [60]. This enhanced reliability translated to improved detection of clock associations and intervention effects, plus more reliable longitudinal trajectories in both in vivo and in vitro studies [60].

Implementation Protocol for Principal Component Clocks

For research teams seeking to implement principal component-based epigenetic clocks, the methodological workflow involves:

• CpG Selection: Identify a comprehensive set of CpGs present across all datasets and platforms being used. The foundational study utilized 78,464 CpGs present on both EPIC and 450K Illumina arrays and across multiple validation datasets [60].

• Principal Component Calculation: Perform principal component analysis on the selected CpG methylation values. This step extracts the shared covariance structure across multicollinear CpGs, effectively separating biological signals from technical noise [60].

• Clock Retraining: Train epigenetic clocks using the principal components as input features rather than individual CpG values. This approach maintains the aging signal while minimizing the impact of noise from any single CpG [60].

• Validation: Assess reliability using technical replicates across the age range of interest, calculating ICC values and replicate deviations to quantify improvement over traditional clocks.

This method requires only one additional computational step compared to traditional clocks and does not require prior knowledge of CpG reliabilities or additional replicates for training, making it readily implementable for existing and future epigenetic biomarkers [60].

Table 3: Essential Research Reagents and Computational Tools for Epigenetic Clock Studies

Resource Category	Specific Examples	Application and Function
Methylation Arrays	Illumina Infinium HumanMethylation450 (450K), MethylationEPIC (EPIC)	Genome-wide methylation profiling at CpG sites essential for clock calculations [60] [9]
Reference Datasets	Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx)	Access to large-scale methylation data for clock development and validation [9]
Bioinformatics Tools	PCA algorithms, elastic net regression, ICC calculation packages	Data analysis, noise reduction, and reliability quantification [60]
Cell Isolation Kits	Negative bead-based selection methods, FACS sorting reagents	Isolation of specific cell populations (e.g., CD8+ T cell subsets) for composition-controlled studies [9]
Quality Control Metrics	Detection p-values, sample success metrics, bisulfite conversion controls	Ensuring data quality and reproducibility in methylation measurements [60]
Specialized Clocks	PC clocks, IntrinClock, GrimAge, DunedinPACE	Addressing specific research questions with optimized reliability and biological relevance [60] [9]

The precision of epigenetic clocks is fundamentally constrained by technical variance introduced during measurement processes, with deviations between technical replicates reaching up to 8.6 years for some widely used clocks. This reliability problem has profound implications for research applications, particularly longitudinal studies and clinical trials seeking to detect modest intervention effects. However, promising solutions are emerging, including principal component-based clocks that dramatically improve reliability by extracting shared aging signals across many CpGs while minimizing noise from individual CpGs.

For researchers and drug development professionals, several evidence-based recommendations emerge from this analysis:

• Clock Selection: Prioritize second-generation clocks (PhenoAge, GrimAge, DunedinPACE) for health-oriented association and interventional studies, as they demonstrate superior performance in predicting clinically relevant endpoints [3] [29].

• Reliability Assessment: Implement technical replicates in study designs to quantify laboratory-specific reliability metrics, particularly for longitudinal interventions where precise detection of change is crucial [60].

• Noise Reduction: Adopt computational solutions such as principal component-based clocks when maximal precision is required, especially for detecting small effect sizes in intervention studies [60].

• Composition Control: Account for cellular composition effects through either experimental design or specialized clocks like IntrinClock when studying cell-intrinsic aging processes [9].

• Methodological Transparency: Clearly document technical variance measures and quality control procedures in publications to enable proper interpretation of results and cross-study comparisons.

As the field continues to evolve, the development of increasingly reliable epigenetic clocks that resist both technical noise and biological confounding will enhance their utility in basic research, clinical trials, and ultimately clinical practice for assessing biological aging and responses to therapeutic interventions.

Epigenetic clocks have emerged as powerful biomarkers for estimating biological age and studying the aging process. These clocks, calculated from DNA methylation (DNAm) data, are broadly categorized into generations. First-generation clocks, such as Horvath's and Hannum's clocks, were trained primarily to predict chronological age [3] [1]. Second-generation clocks, including PhenoAge and GrimAge, were explicitly trained to associate with health, lifestyle, and age-related outcomes, making them more predictive of mortality and disease risk [3] [13]. However, a critical challenge plagues both generations: technical noise in DNAm measurements can produce surprisingly unreliable results, limiting their utility in research and clinical applications [60].

Technical variance from sample preparation, probe hybridization issues, and batch effects generates significant noise in individual CpG methylation values [60]. This noise has profound consequences for epigenetic clock reliability. Analyses of technical replicates reveal deviations of up to 9 years between measurements for prominent epigenetic clocks [60]. For the widely used Horvath multi-tissue clock, the median deviation between replicates is 1.8 years, with a maximum of 4.8 years [60]. Such technical variation obfuscates genuine biological signals and poses a particular threat to longitudinal studies and clinical trials aiming to detect modest but meaningful effects of aging interventions.

PC Clocks: A Computational Solution to Technical Noise

Conceptual Framework and Development

Principal component (PC) clocks represent a computational solution designed to bolster the reliability of epigenetic age estimation. This approach addresses the fundamental signal-versus-noise problem in traditional clocks by leveraging the shared aging signal across many CpG sites while minimizing noise from individual CpGs [60].

The methodological foundation of PC clocks rests on the observation that many CpGs change with age in a multicollinear manner. While over 40,000 CpGs on the 450K array are strongly associated with age in blood, traditional elastic net regression models used in first- and second-generation clocks typically include only a small subset of these (approximately 1.76%) [60]. PC clocks instead use principal component analysis (PCA) to extract the covariance between these multicollinear, age-related CpGs. Since age-related signals and technical noise are highly unlikely to covary across many CpGs, PCA effectively separates biological signal from technical noise.

The development protocol for PC clocks involves:

• CpG Selection: Assembling diverse DNAm datasets comprising technical replicates, multiple tissues, and longitudinal data to select CpGs present across all platforms and datasets (resulting in 78,464 CpGs) [60].
• Principal Component Extraction: Performing PCA on the selected CpGs to create components that represent shared variance patterns.
• Model Training: Using these principal components as input features—rather than individual CpGs—in supervised learning algorithms to predict age or aging phenotypes [60].

Table 1: Comparison of Traditional Clocks vs. PC Clocks

Feature	Traditional Epigenetic Clocks	PC-Based Clocks
Input Features	Individual CpG sites (typically 100-1000)	Principal components derived from many CpGs
Noise Handling	Retains technical noise from individual CpGs	Minimizes noise through covariance extraction
Information Source	Limited CpG subset (<2% of age-associated CpGs)	Leverages thousands of age-associated CpGs
Reliability	Median deviations of 0.9-2.4 years between replicates	Most replicates agree within 1.5 years
Implementation	Direct calculation from CpG weights	One additional PCA step required

Experimental Validation of Enhanced Reliability

The reliability of PC clocks has been systematically validated against traditional clocks through multiple experimental approaches. In one comprehensive analysis, researchers examined a dataset of 36 whole blood samples with technical replicates, processing the data to eliminate systematic batch effects [60]. They calculated intraclass correlation coefficients (ICCs)—a statistic measuring agreement between repeated measurements—for various epigenetic clocks.

The results demonstrated substantial improvements in reliability. While traditional clocks showed maximum deviations between replicates ranging from 4.5 to 8.6 years, PC clocks showed agreement between most replicates within 1.5 years [60]. The PC versions of all six tested clocks showed improved ICCs, with the Horvath1 PC clock achieving an ICC of 0.997 compared to 0.978 for the traditional version [60].

Diagram 1: Workflow comparison showing how PC clocks address technical noise. The approach minimizes noise from individual CpGs by extracting shared aging signals through principal component analysis.

Performance Comparison: PC Clocks vs. Traditional Clocks

Reliability Metrics and Association Detection

The enhanced reliability of PC clocks translates to practical improvements in research applications. Compared to their traditional counterparts, PC clocks demonstrate:

• Improved Detection of Clock Associations: The increased reliability enhances statistical power to detect associations between epigenetic age acceleration and health outcomes [60].
• More Reliable Longitudinal Trajectories: PC clocks produce more stable trajectories in both in vivo and in vitro studies, crucial for tracking aging processes and intervention effects over time [60].
• Better Identification of Intervention Effects: Clinical trials of aging interventions show more consistent results with PC clocks due to reduced measurement noise [60].

Table 2: Quantitative Performance Comparison of Epigenetic Clocks

Clock Metric	Horvath1 (Traditional)	Horvath1 (PC)	PhenoAge (Traditional)	PhenoAge (PC)
Median Deviation Between Replicates (years)	1.8	<1.0	2.4	<1.0
Maximum Deviation Between Replicates (years)	4.8	<2.0	8.6	<2.0
Intraclass Correlation Coefficient (ICC)	0.978	0.997	0.945	0.992
Mortality Association Detection	Baseline	Improved	Baseline	Improved
Intervention Effect Detection	Baseline	Improved	Baseline	Improved

Comparison with Other Noise-Reduction Approaches

Alternative methods for addressing technical noise in epigenetic clocks have been explored with limited success. Filtering CpGs by reliability (intraclass correlation coefficient) only modestly improves reliability even when discarding 80% of CpGs, and maximum deviations remain at 4+ years [60]. This approach also requires a priori knowledge of CpG reliabilities, which is often unavailable for specific tissues or sample populations.

In contrast, the PC clock method requires only one additional computational step compared to traditional clocks, needs no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker [60]. This makes it both more effective and more practical than filtering approaches.

Methodology: Implementing PC Clocks in Research

Step-by-Step Workflow for PC Clock Calculation

The implementation of PC clocks follows a systematic workflow:

• Data Preprocessing: Process DNA methylation data using standard pipelines (e.g., noob normalization, batch correction) to minimize systematic biases [60].
• CpG Selection: Select the 78,464 CpGs present across all major Illumina arrays (27K, 450K, EPIC) and relevant datasets to ensure consistency [60].
• Principal Component Analysis:
  • Center and scale the methylation values for each CpG
  • Perform PCA on the filtered CpG matrix
  • Retain the top N principal components that explain the majority of variance (typically 100-500 components)
• Age Prediction: Use the principal components as features in a regression model (e.g., elastic net) to predict chronological age or aging phenotypes [60].
• Validation: Assess clock performance using technical replicates and longitudinal samples to verify improved reliability.

Diagram 2: PC clock implementation workflow. The method incorporates a PCA step between standard data processing and prediction to enhance reliability.

Research Reagent Solutions for PC Clock Implementation

Table 3: Essential Research Materials and Computational Tools for PC Clock Development

Research Tool	Function	Application in PC Clocks
Illumina Methylation Arrays (EPIC, 450K, 27K)	Genome-wide DNA methylation profiling	Primary data source for methylation values at CpG sites
Bioinformatic Pipelines (minfi, sesame)	Data preprocessing and normalization	Quality control, background correction, normalization of raw data
Principal Component Analysis (PCA algorithms)	Dimensionality reduction	Extracts shared variance from multiple CpGs, reducing technical noise
Elastic Net Regression (glmnet, scikit-learn)	Regularized linear modeling	Trains predictive models using principal components as features
Technical Replicate Datasets	Method validation	Assesses reliability and measures improvement over traditional clocks

Discussion: Implications for Aging Research and Intervention Studies

The development of PC clocks represents significant progress in epigenetic biomarker research. By addressing the critical issue of technical reliability, these enhanced clocks open new possibilities for personalized medicine, longitudinal tracking, and clinical trials of aging interventions [60].

The improved reliability of PC clocks is particularly valuable for detecting subtle but biologically important changes in epigenetic aging. For example, in intervention studies aiming to reduce biological age by 1-2 years, traditional clocks with technical deviations of 4+ years would be inadequate, while PC clocks with deviations under 1.5 years provide sufficient precision [60]. This enhanced sensitivity makes PC clocks especially suitable for:

• Preclinical Studies: Monitoring epigenetic aging in cell cultures and model organisms with greater precision [60].
• Clinical Trials: Evaluating the efficacy of anti-aging interventions with reduced sample size requirements due to improved signal-to-noise ratio [60].
• Personalized Tracking: Monitoring individual aging trajectories over time with confidence that observed changes reflect biology rather than measurement error [60].

When contextualized within the broader comparison of first-generation versus next-generation epigenetic clocks, PC clocks represent an important methodological advancement rather than a new generational category. Both first-generation (e.g., Horvath, Hannum) and second-generation (e.g., PhenoAge, GrimAge) clocks can be retrained using the PC approach to improve their reliability [3] [60]. Current evidence suggests that next-generation models like GrimAge and PhenoAge should be generally prioritized for health-oriented association and interventional studies, and their PC versions may offer the best combination of predictive performance and reliability [3] [13].

Future directions for PC clock development include integration with single-cell methylation sequencing, multi-omics approaches, and application to diverse population cohorts to enhance generalizability [1]. As the field progresses, PC clocks are poised to become standard tools in the researcher's toolkit for reliable epigenetic age assessment.

Epigenetic clocks, powerful biomarkers derived from DNA methylation (DNAm) patterns, have revolutionized aging research by providing estimates of biological age and mortality risk [1]. These tools are broadly categorized into generations: first-generation clocks like Horvath and Hannum were trained primarily to predict chronological age, while second-generation clocks such as PhenoAge and GrimAge were developed using clinical biomarkers, morbidity, and mortality data to better capture biological aging and healthspan [3] [61]. A critical, often overlooked aspect of these clocks is the demographic composition of their training cohorts. Most epigenetic clocks have been developed using data predominantly from individuals of European ancestry, creating a "missing diversity" problem in epigenetic research [62] [61]. This lack of representation in training data raises fundamental questions about the equity, generalizability, and accuracy of these biomarkers when applied to global populations with diverse genetic backgrounds, environmental exposures, and social determinants of health. This guide objectively compares clock performance across populations and details the experimental protocols used to assess the impact of non-diverse training cohorts.

Performance Comparison: First-Generation vs. Second-Generation Clocks

Quantitative Performance Metrics Across Populations

Table 1: Performance Comparison of Epigenetic Clocks in Diverse Populations

Epigenetic Clock	Generation	Primary Training Population	Prediction Accuracy in European Ancestry	Performance in African Ancestry Populations	Key Associated Health Outcomes
Horvath	First	Multi-tissue, predominantly European [62]	High (Mean absolute error: ~3.6 years) [1]	Maintains accuracy; minimal difference in age-adjusted error [63]	All-cause mortality, some age-related diseases [29]
Hannum	First	Blood-based, Caucasian & Hispanic [1] [62]	High (Correlation: 0.96 with chronological age) [1]	Variable accuracy; significant differences in age-adjusted error [63]	Cardiovascular health, immune function [1]
PhenoAge	Second	European ancestry with mortality/biomarker data [61]	Strong association with mortality & morbidity [29] [61]	Significant differences in age-adjusted error observed [63]	All-cause mortality, cancer, cardiovascular disease [29] [61]
GrimAge	Second	European ancestry with plasma protein & smoking data [61]	Outstanding mortality prediction [29] [61]	Systematic over- or under-estimation observed [63]	All-cause mortality, cardiovascular disease, primary lung cancer (HR=1.56) [29]
DunedinPACE	Third	Dunedin Study (primarily European) [29]	Strong association with pace of aging & organ system decline [29] [61]	Data limited; requires further validation [61]	Diabetes (HR=1.44), functional decline [29]
IC Clock	Emerging	INSPIRE-T cohort [7]	Predicts intrinsic capacity decline & mortality [7]	Not yet validated in diverse populations [7]	All-cause mortality, immune senescence [7]

Large-Scale Comparative Evidence

A 2025 unbiased comparison of 14 epigenetic clocks across 18,859 individuals in the Generation Scotland cohort provides the most comprehensive performance data to date [29] [21]. The study analyzed 10-year onset of 174 disease outcomes and found striking generational differences:

• Second-generation clocks significantly outperformed first-generation clocks in disease prediction, with 162 Bonferroni-significant disease associations for second/third-generation clocks compared to only 9 for first-generation clocks (~5% of all significant findings) [29].
• The hazard ratios for first-generation clocks were approximately 50% smaller in magnitude compared to GrimAge v1 as a reference standard [29].
• Specific disease associations highlighted the superior performance of later-generation clocks, with GrimAge showing particularly strong predictions for respiratory and liver-related outcomes, including primary lung cancer (HR=1.56) and cirrhosis (HR=1.86) [29].
• The addition of second-generation clocks to classification models with traditional risk factors improved prediction accuracy by >1% with AUC > 0.80 in 35 instances, suggesting clinical utility [29].

Experimental Protocols for Assessing Population Bias

Cross-Population Validation Studies

Experimental Workflow: Assessing Clock Performance Across Diverse Genetic Backgrounds

Methodology Details:

• Cohort Selection and DNA Methylation Profiling

  • Collect saliva or blood samples from diverse population cohorts, including central African Baka (n=35), southern African ‡Khomani San (n=52), southern African Himba (n=51), and matched European-ancestry and Hispanic/Latino cohorts for comparison [63].
  • Extract genomic DNA and perform genome-wide DNA methylation profiling using Illumina Infinium EPIC arrays or similar platforms, covering approximately 850,000 CpG sites [63].
  • Apply reference-based cell-type deconvolution methods to estimate and account for potential differences in cell-type composition across populations, controlling for confounding factors like the Duffy null variant common in African populations [63].

• Clock Application and Performance Metrics

  • Apply multiple epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge, DunedinPACE, etc.) to all cohorts using standardized processing pipelines [63].
  • Calculate age-adjusted prediction errors for each clock in each population, comparing mean absolute errors and systematic biases (over- or under-estimation) [63].
  • Analyze epigenetic age acceleration (difference between epigenetic age and chronological age) across populations and test for significant differences [63].

• Statistical Analysis for Health Predictions

  • Use Cox proportional hazards regression to test associations between epigenetic age acceleration and incident disease outcomes, adjusting for age, sex, BMI, smoking, alcohol consumption, education, and socioeconomic deprivation [29].
  • Perform logistic regression to assess classification improvement when adding epigenetic clocks to models with traditional risk factors, calculating differences in Area Under the Curve (AUC) [29].
  • Apply multiple testing corrections (e.g., Bonferroni correction) to account for the large number of clock-disease comparisons [29].

Table 2: Analysis of Genetic Influences on Epigenetic Clock Performance

Analysis Type	Methodology	Key Findings	Implications for Population Bias
meQTL (methylation Quantitative Trait Loci) Mapping	Identify genetic variants associated with DNA methylation levels in cis (within 1Mb) and trans (>1Mb) using linear models [63].	Up to 45% of CpG sites on Illumina 450k arrays show meQTL influence; 90% act in cis [63].	Clocks including meQTL-influenced CpGs may not transfer well to genetically diverged populations.
Heritability Estimation	Estimate proportion of DNA methylation variance explained by genetic factors using kinship matrices or twin studies [61].	Epigenetic age acceleration heritability ranges from 0.10 to 0.37 depending on the clock [61].	Genetic differences between populations can drive spurious epigenetic age differences.
Population-Specific Clock Development	Develop new clocks excluding CpGs with significant cis-heritability or population-specific meQTLs [63].	Clocks excluding meQTL-influenced CpGs maintain accuracy across diverse genetic backgrounds [63].	Demonstrates feasibility of developing more portable epigenetic biomarkers.
Epigenetic Aging Score (EAS)	Create polygenic score based on cumulative effects of epigenetic age-increasing variants [63].	EAS correlates with independently derived epigenetic age acceleration estimates [63].	Provides method to account for genetic contributions to epigenetic aging.

Mechanisms of Population Bias: Technical and Biological Factors

The performance disparities observed in epigenetic clocks across populations stem from multiple technical and biological factors:

• Genetic Architecture Differences: Single nucleotide polymorphisms (SNPs) that disrupt CpG sites can prevent DNA methylation outright, while SNPs within the 50-base pair probe of DNAm array CpG sites can alter measured DNAm levels by influencing hybridization efficiency [61]. Population-specific allele frequencies at these sites can introduce systematic biases.

• meQTL Influences: Methylation quantitative trait loci (meQTLs) - genetic variants that regulate DNA methylation levels - demonstrate high population specificity. When epigenetic clocks incorporate CpG sites influenced by meQTLs that have different allele frequencies across populations, prediction accuracy naturally declines in genetically diverged groups [63].

• Cell-Type Composition Variations: Differences in immune cell composition between populations, such as the high frequency of the Duffy null variant in West African populations (associated with lower neutrophil count), can introduce confounding differences in DNA methylation patterns that are unrelated to aging processes [63].

• Environmental and Social Determinants: Systemic differences in environmental exposures, socioeconomic factors, and psychosocial stressors across populations create distinct DNA methylation signatures that may be misinterpreted as accelerated aging when clocks are trained on populations with different exposure histories [62] [61].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials for Cross-Population Epigenetic Clock Studies

Research Reagent/Tool	Function	Application in Diversity Studies
Illumina Infinium EPIC Array	Genome-wide DNA methylation profiling covering ~850,000 CpG sites.	Standardized methylation measurement across diverse cohorts; enables cross-study comparisons.
Reference Methylomes	Representative methylation patterns from different tissues and populations.	Essential for cell-type deconvolution and normalization across diverse samples.
HapMap/1000 Genomes Data	Catalog of genetic variation across global populations.	Identification of population-specific SNPs that may interfere with methylation array probes.
meQTL Databases	Collections of genetic variants associated with methylation changes.	Identification of CpG sites with genetic influences that may limit cross-population transferability.
Cell-type Deconvolution Algorithms	Computational methods to estimate cell-type proportions from bulk methylation data.	Critical for controlling for cellular heterogeneity differences between populations.
Diversity-Focused Biobanks	Biospecimen collections from underrepresented populations.	Provides essential resources for developing and validating clocks in diverse groups.
Anti-hyperglycemic agent-1	Anti-hyperglycemic agent-1, MF:C20H15BrN2O3, MW:411.2 g/mol	Chemical Reagent
AChE-IN-8	AChE-IN-8|Acetylcholinesterase Inhibitor for Research	AChE-IN-8 is a potent acetylcholinesterase inhibitor for neurological disease research. This product is For Research Use Only. Not for diagnostic or personal use.

The evidence clearly demonstrates that second-generation epigenetic clocks outperform first-generation models in predicting health outcomes and mortality across diverse populations [29]. However, significant performance disparities persist when these clocks are applied to populations underrepresented in their training datasets [63] [61]. The underlying mechanisms include genetic differences (particularly meQTL effects), technical artifacts in methylation measurement, and differential environmental exposures across populations [63] [61].

For researchers and drug development professionals using epigenetic clocks, we recommend:

• Prioritize second-generation clocks like GrimAge and PhenoAge for health-oriented studies, as they demonstrate superior predictive performance for clinical outcomes [3] [29].
• Exercise caution when interpreting epigenetic age differences between populations, as these may reflect technical biases rather than true biological differences [63] [61].
• Account for population structure in epigenetic analyses by including genetic principal components or ancestry-informative markers as covariates [63].
• Support the development and validation of next-generation epigenetic clocks using diverse cohorts to ensure equitable application across global populations [62] [61].

Future efforts should focus on developing intentionally inclusive epigenetic clocks that either exclude population-specific meQTL-influenced CpG sites or explicitly account for genetic background in their algorithms [63]. Such approaches will be essential for realizing the full potential of epigenetic clocks as equitable biomarkers for global healthspan and longevity research.

Epigenetic clocks, powerful biomarkers derived from DNA methylation (DNAm) patterns, have revolutionized the study of biological aging. Their construction typically involves machine learning models trained to predict either chronological age or biological age-related phenotypes from DNAm data at specific cytosine-phosphate-guanine (CpG) sites [4]. However, a critical methodological consideration often overlooked is the profound impact of the biological sample type used for DNAm profiling. The majority of established epigenetic clocks have been developed using blood-based tissues, which are considered the gold standard in the field [64]. Despite this, growing interest in less invasive alternatives like saliva and buccal epithelial cells has prompted their increased use in both research and commercial settings [65] [64].

This guide objectively compares the performance of epigenetic clocks across different sample types, with a specific focus on the diverging performance of first-generation versus second-generation clocks. The reliability of extrapolating clock estimates from blood to other tissues remains a central question. As DNAm patterns are highly tissue-specific, applying blood-derived algorithms to other tissues may introduce significant bias [65]. Understanding these sources of variability is paramount for researchers, scientists, and drug development professionals who rely on these biomarkers for association studies, clinical trials, and therapeutic development.

Generations of Epigenetic Clocks: A Conceptual Framework

Epigenetic clocks have evolved through several generations, each with distinct training approaches and applications. This evolution is crucial for understanding their differential performance across tissues.

• First-Generation Clocks: These clocks, including the well-known Horvath pan-tissue clock and the Hannum clock, were trained primarily to predict an individual's chronological age. Their accuracy is measured by how closely the DNAm-predicted age matches the actual chronological age [12] [66].
• Second-Generation Clocks: Models such as PhenoAge and GrimAge were trained on phenotypic measures of healthspan, clinical biomarkers, or mortality risk rather than chronological age alone. They are often more strongly associated with age-related health outcomes and mortality [3] [12].
• Third-Generation Clocks: The most prominent example is DunedinPACE, which measures the pace of aging rather than a static biological age. It was developed based on longitudinal data on the decline in multiple physiological systems [12] [66].
• Fourth-Generation Clocks: An emerging category includes "causal clocks" such as DamAge and AdaptAge, which use Mendelian randomization to select CpG sites putatively causal in the aging process [12].

Table 1: Overview of Epigenetic Clock Generations and Key Characteristics

Generation	Primary Training Target	Representative Clocks	Key Strengths
First	Chronological Age	Horvath pan-tissue, Hannum	High accuracy in predicting chronological age; multi-tissue applicability (Horvath)
Second	Mortality, Morbidity, Phenotypic Age	PhenoAge, GrimAge	Superior prediction of health outcomes and mortality risk
Third	Pace of Aging	DunedinPACE	Captures the rate of biological aging over time
Fourth	Putatively Causal Sites for Aging	DamAge, AdaptAge	Designed to distinguish between cause and consequence in the aging process

Comparative Performance Data Across Sample Types

The choice of sample type is not a mere logistical detail; it directly influences the epigenetic age estimate obtained. Recent systematic investigations have quantified the comparability of these estimates across tissues.

Blood vs. Saliva/Buccal Tissues

A direct within-person comparison study analyzing five tissue types (buccal epithelial, saliva, dry blood spots, buffy coat, and peripheral blood mononuclear cells) from 83 individuals revealed significant discrepancies. The application of blood-derived clocks to oral-based tissues resulted in average differences of almost 30 years for some age clocks, highlighting a substantial tissue-specific bias [65]. Notably, the Skin and Blood clock demonstrated the greatest concordance across all tested tissues, suggesting its unique utility for cross-tissue age estimation [65].

A separate meta-analysis focusing on the application of blood-derived algorithms to saliva DNAm from the same individuals provided further insight, measured via Intraclass Correlation Coefficients (ICCs). ICCs quantify the consistency of measurements, with values closer to 1.0 indicating higher agreement.

Table 2: Cross-Tissue Concordance (Saliva vs. Blood) of Epigenetic Clocks [64]

Methylation Profile Score (MPS)	Type	Meta-Analyzed Cross-Tissue ICC (Saliva vs. Blood)
PCGrimAge	Second Generation	0.76
PCPhenoAge	Second Generation	0.72
DunedinPACE	Third Generation	0.68
PCGrimAge Acceleration	Second Generation	0.67
PCPhenoAge Acceleration	Second Generation	0.66
DNAm-based hs-CRP	Physiological Proxy	0.58
DNAm-based BMI	Physiological Proxy	0.54
Horvath Acceleration	First Generation	0.25
Hannum Acceleration	First Generation	0.19
Horvath Skin & Blood Acceleration	First Generation	0.22

The data reveals a clear pattern: second- and third-generation clocks exhibit moderate cross-tissue concordance (ICCs ~0.66-0.76), whereas first-generation age acceleration measures show poor agreement between saliva and blood (ICCs ~0.19-0.25) [64]. This suggests that newer generation clocks are more robust for studies utilizing saliva DNAm, though the correspondence is not perfect.

Cellular Composition as a Key Confounding Factor

A major source of tissue-type variability stems from differences in cellular composition. Blood is composed almost entirely of immune cells, while saliva consists of approximately 65% immune cells and 35% epithelial cells [64]. Furthermore, the proportions of immune cell subtypes themselves change with age; for example, naive CD8+ T cells decrease dramatically, and effector memory cells increase [67].

Strikingly, a controlled analysis of CD8+ T cell subsets from the same individuals found that naive cells exhibited an epigenetic age 15–20 years younger than effector memory cells from the same donor when measured with established clocks [67]. This indicates that a significant portion of the signal captured by many clocks is confounded by age-related shifts in immune cell populations, rather than purely cell-intrinsic aging. This effect varies by clock, with PhenoAge predictions being highly sensitive to cell subtype and Horvath's clocks being less so [67].

Detailed Experimental Protocols for Cross-Tissue Comparison

To ensure the validity and reproducibility of cross-tissue epigenetic clock studies, rigorous and standardized protocols are essential. The following methodology is synthesized from key studies [65] [64].

Sample Collection and Processing

• Participant Recruitment: Recruit participants across a wide age range. Exclusion criteria typically include significant medical illness (e.g., cancer, diabetes, autoimmune disease), current smoking, and recent infection or antibiotic use [65].
• Multi-Tissue Collection: From each participant, collect matched samples. Common pairs include:
  • Blood: Collected via venipuncture into EDTA tubes. Process to isolate specific fractions like Peripheral Blood Mononuclear Cells (PBMCs), buffy coat (leukocytes), or create Dry Blood Spots (DBS) [65].
  • Saliva: Collected using dedicated kits (e.g., Oragene DNA Collection Kit) that stabilize DNA at room temperature [64].
  • Buccal Epithelial Cells: Collected using non-invasive swabs or brushes [65].
• DNA Extraction and Methylation Profiling: Extract genomic DNA using standardized kits (e.g., MasterPure, Puregene). Quantify and quality-check DNA before profiling. The Illumina Infinium MethylationEPIC BeadChip or the older 450K BeadChip are the standard platforms, measuring methylation at >850,000 or ~450,000 CpG sites, respectively [64].

Data Preprocessing and Cell Composition Estimation

• Bioinformatic Preprocessing: Process raw intensity data using packages like Minfi or RnBeads in R. Steps include:
  • Quality control (removing samples/probes with low signal).
  • Normalization (e.g., using the Beta-Mixture Quantile dilation (BMIQ) method) to correct for technical variation [64].
  • Probe filtering, removing those associated with known single-nucleotide polymorphisms (SNPs) or prone to cross-hybridization.
• Cell Composition Estimation: This is a critical step to account for tissue heterogeneity.
  • For Blood: Use a reference-based method (e.g., the Houseman algorithm) with a reference dataset (e.g., the "Reinius" dataset) to estimate proportions of neutrophils, lymphocytes, monocytes, etc. [64] [67].
  • For Saliva: Use a saliva-specific reference (e.g., the "Saliva" dataset) to estimate the proportion of immune versus epithelial cells [64].
  • Alternatively, a reference-free method (e.g., RefFreeEWAS) can be used to estimate latent cell types directly from the methylation data [64].

Calculation of Epigenetic Clocks and Statistical Analysis

• Clock Calculation: Apply published DNAm algorithms to the preprocessed data to calculate each epigenetic clock estimate (e.g., Horvath, Hannum, PhenoAge, GrimAge, DunedinPACE) for every sample.
• Data Residualization: To isolate the biological signal from technical and cellular noise, residualize each MPS. This involves regressing the MPS values on technical covariates (array batch) and estimated cell composition proportions. The resulting residuals are used for all downstream analyses [64].
• Concordance Analysis: The primary analysis involves assessing the agreement of the residualized MPSs across matched tissues from the same individual.
  • The Intraclass Correlation Coefficient (ICC) is the most appropriate statistic, as it measures both correlation and agreement in the same units. Values are interpreted as: <0.5 poor, 0.5-0.75 moderate, 0.75-0.9 good, and >0.9 excellent agreement [64].
  • For studies with multiple datasets, a random-effects meta-analysis (e.g., DerSimonian and Laird method) should be performed to pool ICC estimates across cohorts [64].

The following diagram illustrates the core workflow and the key finding regarding clock generation performance.

Diagram 1: Experimental workflow for cross-tissue comparison of epigenetic clocks, leading to key findings on performance across sample types and clock generations.

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagent Solutions for Cross-Tissue Epigenetic Clock Studies

Item	Function/Description	Example Products/Catalog Numbers
PAXgene Blood DNA Tubes	Stabilizes cellular DNA in whole blood for transport and storage, ensuring high-quality DNA extraction.	Qiagen PAXgene Blood DNA Tube
Oragene DNA Self-Collection Kit	Non-invasive kit for saliva collection; includes a stabilizing liquid to preserve DNA at room temperature.	DNA Genotek Oragene-500
Buccal Swab Collection Kit	For collecting buccal epithelial cells from the inner cheek using a soft brush or foam tip.	Isohelix Buccal Swab Kit
DNA Extraction Kits	Isolation of high-quality, inhibitor-free genomic DNA from specific sample types (blood, saliva, buccal).	Qiagen DNeasy Blood & Tissue Kit, MasterPure DNA Purification Kit
Infinium MethylationEPIC BeadChip	The standard microarray platform for genome-wide DNA methylation analysis at over 850,000 CpG sites.	Illumina MethylationEPIC
Cell Composition Reference Datasets	Publicly available datasets used as a reference to deconvolute cell-type proportions from bulk DNAm data.	Reinius dataset (blood), Saliva dataset (saliva)
Bioinformatics Software (R/Python)	Open-source programming environments with specialized packages for DNAm data analysis and clock calculation.	R packages: minfi, ewastools, ENmix, WaterRmelon

The evidence clearly demonstrates that sample type is a critical variable in epigenetic clock research. First-generation clocks, while pioneering, show poor cross-tissue concordance, making them unsuitable for comparing ages derived from different sample types. Second- and third-generation clocks like GrimAge, PhenoAge, and DunedinPACE demonstrate significantly greater, though not perfect, robustness when applied to saliva. The fundamental differences in cellular composition between blood and saliva, compounded by age-related immune cell shifts, remain a primary source of variability.

For researchers and drug development professionals, this necessitates a careful, hypothesis-driven approach to sample selection:

• For Cohort Studies: When comparing groups, consistency in sample type is paramount. Do not mix blood and saliva-derived epigenetic ages in the same analysis without proper validation and adjustment.
• For Longitudinal Studies: The same tissue type should be used at all time points for a given participant to reliably measure change.
• For New Study Design: If using a non-blood tissue (e.g., saliva for its practicality in pediatric populations), prioritize second- or third-generation clocks and explicitly report the expected level of cross-tissue concordance for the chosen metric.
• For Clinical or Commercial Applications: The moderate level of cross-tissue agreement may be insufficient for applications requiring high precision. Whenever possible, algorithms should be validated or re-trained in the specific tissue type intended for use.

Future efforts should focus on developing and validating tissue-specific epigenetic clocks or creating robust normalization methods to enable direct comparison across the diverse sample types utilized in modern research.

Epigenetic clocks, DNA methylation (DNAm)-based biomarkers of aging, have emerged as powerful tools for quantifying biological age. Their development has progressed through distinct generations, each with different training paradigms and underlying assumptions, which fundamentally influence how their readings should be interpreted. First-generation clocks, such as those developed by Horvath (2013) and Hannum et al. (2013), were trained exclusively to predict chronological age using penalized regression models on cross-sectional data [12] [4]. Deviations between predicted epigenetic age and chronological age in these models were initially interpreted as "age acceleration." However, these clocks demonstrate only weak associations with physiological measures of dysregulation and age-related health outcomes [12] [4].

Second-generation clocks like PhenoAge (Levine et al., 2018) and GrimAge (Lu et al., 2019) addressed these limitations by incorporating phenotypic data, mortality risk, and plasma protein biomarkers into their training [12] [4]. These clocks vastly outperform first-generation predictors as markers of biological age, showing consistent associations with all-cause mortality, age-related clinical phenotypes, and cognitive performance measures [68]. A recent large-scale comparison of 14 epigenetic clocks in 18,859 individuals confirmed that second-generation clocks significantly outperform first-generation clocks for disease prediction, with particular strength for respiratory and liver conditions [21].

The latest third-generation clocks like DunedinPACE measure the pace of aging rather than a static state, while emerging fourth-generation "causal clocks" (CausAge, AdaptAge, DamAge) use Mendelian randomization to select putatively causal CpG sites [12]. This generational evolution represents a shift from correlative age prediction to models with potentially greater biological relevance, yet interpretation challenges persist across all generations.

Key Interpretation Pitfalls: Correlation Versus Causality

The Cell-Type Heterogeneity Confound

One significant pitfall in clock interpretation involves failing to account for cellular population shifts in complex tissues. Many epigenetic clocks are developed and applied to whole blood or peripheral blood mononuclear cells, which comprise numerous immune cell types, each with distinct DNA methylation profiles [69]. A critical re-analysis of an "inflammation clock" (InflClock) proposed by Skinner and Conboy demonstrated that its predictions were strongly associated with shifts in neutrophil-to-lymphocyte ratios across 15 whole blood datasets [69]. When the underlying cell-type heterogeneity was accounted for using established deconvolution algorithms, the clock's apparent association with inflammaging largely disappeared, suggesting it primarily measured cell composition changes rather than specific inflammatory aging processes [69].

This pitfall is particularly problematic for first-generation clocks applied to disease states involving immune activation. For instance, in rheumatoid arthritis (RA), early epigenome-wide association studies initially identified numerous differentially methylated cytosines, but most disappeared after adjusting for neutrophil-to-lymphocyte ratio shifts associated with the condition [69]. This demonstrates that what appears to be epigenetic age acceleration may sometimes reflect changes in cell-type proportions—a correlative relationship rather than a causal mechanism of aging.

Training Objectives and Causal Inference

The fundamental training objective of a clock determines what it measures, creating another interpretation pitfall. First-generation clocks optimized for chronological age prediction capture age-associated methylation changes but cannot distinguish between causes, consequences, or mere correlates of aging [3] [4]. Second-generation clocks trained on mortality risk or phenotypic age incorporate biomarkers that may be closer to biological aging processes but still primarily identify correlations [12].

The table below summarizes how training objectives influence clock interpretation:

Table 1: Epigenetic Clock Generations and Their Interpretation Challenges

Generation	Representative Clocks	Primary Training Objective	Key Interpretation Pitfalls
First	Horvath, Hannum	Chronological age prediction	Weak association with health outcomes; confounded by cell composition; measures correlation with time rather than biological aging processes
Second	PhenoAge, GrimAge	Mortality risk, phenotypic age	Incorporates biomarkers that may be consequences rather than causes of aging; still primarily identifies correlations
Third	DunedinPACE, DunedinPoAm	Pace of aging from longitudinal data	Captures aging rate but mechanisms remain correlative; requires multiple timepoints for optimal use
Fourth	CausAge, AdaptAge, DamAge	Putatively causal sites via Mendelian randomization	Depends on validity of Mendelian randomization assumptions; nascent validation; causal claims require additional evidence

Intervention Response Claims

When epigenetic clocks are used to evaluate interventions, researchers often encounter the classic "correlation versus causality" dilemma. Clocks may respond to interventions without clarifying whether the intervention targeted fundamental aging processes or secondary pathways [3]. For instance, a clock might detect changes from an anti-inflammatory intervention without indicating whether this affected the root causes of aging or merely alleviated one symptom.

Next-generation clocks are generally more responsive to interventions and show greater utility for health-oriented association studies [3]. However, even when a clock responds predictably to a known anti-aging intervention, this does not necessarily validate it as a measure of biological aging—it may simply reflect the intervention's impact on the specific biomarkers incorporated into the clock.

Experimental Comparisons and Performance Data

Large-Scale Disease Prediction Comparison

A recent unbiased comparison of 14 epigenetic clocks across 18,859 individuals provides robust performance data on their disease prediction capabilities [21]. This comprehensive analysis examined 10-year onset of 174 disease outcomes, offering critical insights into the real-world utility of different clock generations.

Table 2: Epigenetic Clock Performance in Disease Prediction Across Multiple Categories

Clock Generation	All-Cause Mortality Prediction	Disease Associations	Respiratory Conditions	Liver Conditions	Metabolic Diseases
First-Generation	Limited predictive value	Weak associations	Minimal predictive value	Minimal predictive value	Limited associations
Second-Generation	Strong predictive value	176 Bonferroni-significant associations	Particularly strong prediction	Strong performance	Significant associations
Third-Generation (DunedinPACE)	Strong predictive value	Multiple significant associations	Strong performance	Strong performance	Significant associations

The study identified 35 instances where adding a second-generation clock to a null classification model with traditional risk factors increased classification accuracy by >1% with an AUCₙₒₘᵢₐₗ > 0.80 [21]. Notably, there were 27 diseases (including primary lung cancer and diabetes) where the hazard ratio for the clock exceeded the clock's association with all-cause mortality, suggesting disease-specific predictive value beyond general mortality risk [21].

Biological Age Discrimination Studies

Targeted experimental approaches have provided additional evidence for the superior performance of next-generation clocks. A targeted epigenetic clock developed with six genomic regions (ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7, and TFAP2E) demonstrated the ability to distinguish between models of increased and decreased biological age [68]. The table below shows its performance across different biological age models:

Table 3: Targeted Epigenetic Clock Performance Across Biological Age Models

Subject Category	Sample Size	Chronological Age (Mean ± SD)	Epigenetic Age (Mean ± SD)	Epigenetic Age Discrepancy (Mean ± SD)	p-value
Controls (20-80 years)	278	54.96 ± 15.03	54.96 ± 13.43	0 ± 6.04	-
Persons with Down Syndrome	62	33.97 ± 13.46	49.23 ± 34.94	+11.02 ± 33.33	<0.001
Centenarians	106	101.5 ± 2.44	85.66 ± 12.23	-6.45 ± 12.43	<0.001
Centenarians' Offspring	143	70.06 ± 6.69	65.35 ± 9.75	-1.65 ± 8.96	0.015

This targeted clock successfully detected increased biological age in persons with Down syndrome and decreased biological age in centenarians and their offspring, supporting its utility as a biological age marker beyond chronological age prediction [68].

Methodological Considerations for Robust Interpretation

Experimental Workflows for Clock Validation

The diagram below illustrates a rigorous experimental workflow for evaluating epigenetic clocks and addressing correlation-causation challenges:

The Scientist's Toolkit: Essential Research Reagents

Table 4: Essential Research Reagents for Epigenetic Clock Studies

Reagent/Resource	Function	Example Specifications
DNA Methylation Reference Panels	Enables estimation of immune cell-type fractions in whole blood to address confounding	12 immune cell-type panel including neutrophils, monocytes, eosinophils, basophils, NK cells, memory/naïve B-cells, memory/naïve CD4+ T-cells, memory/naïve CD8+ T-cells, and T-regulatory cells [69]
Deconvolution Algorithms	Statistical methods to estimate cell-type proportions from heterogeneous tissue samples	EpiDISH Bioconductor R-package with RPC method; constrained regression with weights between 0-1 summing to 1 [69]
Bisulfite Conversion Kits	Converts unmethylated cytosines to uracils while methylated cytosines remain unchanged	Critical for both array-based and sequencing-based approaches; quality control essential for reproducibility
DNA Methylation Arrays	Genome-wide methylation profiling at predetermined CpG sites	Illumina Infinium platforms (450K or EPIC); covers ~1.6% of total CpGs; semi-quantitative with limited dynamic range [70]
Bisulfite Sequencing Platforms	Comprehensive methylation analysis with single-base resolution	BS-seq technology; genome-wide CpG coverage; superior detection sensitivity; enables BS-clock development [70]

Distinguishing correlation from causality in epigenetic clock readings requires careful consideration of clock generation, training objectives, and methodological approaches. First-generation clocks, while groundbreaking, primarily capture correlations with chronological time and are particularly susceptible to confounding factors like cell-type heterogeneity. Second-generation and subsequent clocks demonstrate stronger associations with health outcomes and greater utility for intervention studies, but still primarily identify correlations rather than causal relationships [3] [21].

The most promising approaches for addressing causal questions combine multiple clock generations with deliberate experimental designs that account for cellular heterogeneity and incorporate longitudinal data [69] [12]. Emerging causal clocks using Mendelian randomization offer potential pathways toward disentangling causation but require rigorous validation [12]. Until clocks can reliably distinguish between causal drivers and correlative passengers of the aging process, researchers should interpret their readings as informative biomarkers rather than definitive measures of biological aging.

Head-to-Head Performance Validation and Emergence of Next-Generation Biomarkers

The pursuit of reliable biomarkers to quantify biological aging has led to the development of epigenetic clocks, powerful tools that predict chronological age, healthspan, and lifespan based on DNA methylation (DNAm) patterns [1]. These clocks are broadly categorized into generations based on their training targets and underlying methodologies. First-generation clocks, such as those developed by Horvath and Hannum, were trained primarily to predict chronological age, establishing a fundamental link between DNAm changes and the passage of time [12] [4]. Second-generation clocks, including PhenoAge and GrimAge, advanced the field by incorporating phenotypic biomarkers and mortality data into their models, aiming to capture biological age and mortality risk rather than just chronological time [12] [29]. A third generation of clocks, exemplified by DunedinPACE and DunedinPoAm, further refined this approach by measuring the pace of aging, representing the rate of change in epigenetic age over time [12] [71].

This comparison guide objectively evaluates the performance of these different epigenetic clock generations in predicting mortality and morbidity, synthesizing findings from recent large-scale studies to provide researchers, scientists, and drug development professionals with evidence-based insights for selecting appropriate epigenetic biomarkers for specific research and clinical applications.

Comparative Performance Metrics for Mortality Prediction

All-Cause Mortality Prediction

Multiple large-scale studies have systematically compared the predictive power of various epigenetic clocks for all-cause mortality. The consistent finding across these studies is that second-generation clocks, particularly GrimAge and its iterations, demonstrate superior performance compared to first-generation and third-generation clocks.

Table 1: Hazard Ratios for All-Cause Mortality per Standard Deviation Increase in Epigenetic Age Acceleration

Epigenetic Clock	Generation	Hazard Ratio (95% CI)	P-value	Source
GrimAge2	Second	1.54 (1.46-1.62)	7.1×10⁻⁶²	[29]
GrimAge	Second	1.50 (1.32-1.71)	<0.0001	[51]
bAge (Improved GrimAge)	Second	1.52 (1.44-1.59)	2.20×10⁻⁶⁰	[72]
DunedinPACE	Third	1.23 (1.08-1.38)	0.003	[51]
PhenoAge	Second	1.13 (1.05-1.21)	0.001	[51]
Hannum	First	1.16 (1.07-1.27)	0.001	[51]
Horvath	First	1.13 (1.04-1.22)	0.007	[51]
Vidal-Bralo	First	1.13 (1.03-1.23)	0.008	[51]

A comprehensive study of 18,859 individuals from the Generation Scotland cohort found that GrimAge2 showed the largest and most significant association with 10-year all-cause mortality (HR = 1.54 per SD) [29]. This superiority was confirmed in a U.S. representative sample from NHANES, where GrimAge epigenetic age acceleration (EAA) most significantly predicted overall mortality during a median follow-up of 17.5 years (HR = 1.50) [51]. The enhanced predictive performance of GrimAge is attributed to its unique construction methodology, which incorporates DNAm surrogates for smoking pack-years and seven plasma proteins associated with mortality (adrenomedullin, beta-2-microglobulin, cystatin C, growth differentiation factor 15, leptin, plasminogen activation inhibitor 1, and tissue inhibitor metalloproteinase 1) [72].

Cause-Specific Mortality Prediction

The predictive performance of epigenetic clocks varies significantly across different causes of death, with second-generation clocks consistently outperforming first-generation clocks for cardiovascular and cancer mortality.

Table 2: Cause-Specific Mortality Prediction by Epigenetic Clock Generation

Mortality Outcome	Most Predictive Clock(s)	Hazard Ratio (95% CI)	P-value	Source
Cardiovascular Mortality	GrimAge EAA	1.55 (1.29-1.86)	<0.0001	[51]
Cardiac Mortality	GrimAge/GrimAge2 AA	Linear positive association	<0.05	[24]
Cancer Mortality	GrimAge EAA	1.37 (1.00-1.87)	0.049	[51]
Cancer Mortality	Hannum EAA	1.24 (1.07-1.44)	0.006	[51]
Cancer Mortality	Horvath EAA	1.18 (1.02-1.35)	0.02	[51]
Overall Mortality	DunedinPoAm	1.23 (1.08-1.38)	0.003	[51]
Cardiovascular Mortality	DunedinPoAm	1.25 (1.01-1.55)	0.04	[51]

A retrospective cohort study based on 1,942 NHANES participants revealed that only GrimAge and GrimAge2 age acceleration demonstrated approximately linear and positive associations with all three mortality outcomes (all-cause, cancer-specific, and cardiac mortality) [24]. These clocks showed very similar performance in predicting cause-specific mortality, with only small differences in Akaike Information Criterion values and concordance index scores, suggesting both are effective epigenetic biomarkers for mortality risk prediction in aging-related research [24].

Morbidity and Disease-Specific Predictions

Disease Incidence Across Organ Systems

Large-scale comparative analyses have demonstrated significant variability in the predictive performance of epigenetic clocks across different disease categories. A comprehensive study examining 14 epigenetic clocks in relation to 10-year onset of 174 disease outcomes in 18,859 individuals found that second-generation clocks significantly outperformed first-generation clocks, which showed limited applications in disease settings [29].

Of the 176 Bonferroni-significant associations identified, second- and third-generation clocks accounted for approximately 95% of all significant findings [29]. The effect sizes for first-generation clocks were around 50% smaller in magnitude compared to GrimAge v1 [29]. The study identified 27 unique disease outcomes where the clock-disease hazard ratio exceeded the magnitude of the corresponding clock-mortality association, indicating disease-specific predictive utility beyond general mortality risk.

Notably, different clocks showed strengths in predicting different categories of diseases:

• GrimAge demonstrated particularly strong associations with respiratory and smoking-related conditions, including primary lung cancer (HR = 1.56) and chronic obstructive pulmonary disease [29].
• DunedinPACE showed significant predictive power for metabolic conditions such as diabetes (HR = 1.44) [29].
• PhenoAge was associated with inflammatory conditions like Crohn's disease (HR = 1.39) [29].
• Zhang10 showed unexpected predictive value for neurological outcomes including delirium (HR = 1.44) [29].

Functional Health and Healthspan Markers

Beyond disease incidence, epigenetic clocks also vary in their ability to predict functional status and healthspan markers. A comparison of clinical and epigenetic clocks revealed that lower LinAge2 biological ages were associated with superior healthspan markers, including higher cognitive scores and faster gait speed [15]. Subjects capable of employment and those able to perform all instrumental and basic activities of daily living had significantly lower LinAge2 biological age estimates [15].

Similar trends were observed for GrimAge2 and DunedinPoAm, with statistically significant differences between groups with low and high biological ages across most healthspan markers, except for the ability to perform all basic activities of daily living [15]. In contrast, no statistically significant differences were found between low and high HorvathAge biological ages across healthspan markers, highlighting the limitation of first-generation clocks in predicting functional health outcomes [15].

Methodological Approaches in Key Studies

Experimental Protocols and Cohort Descriptions

The robust comparison of epigenetic clocks across multiple studies has been enabled by standardized methodological approaches and large, diverse cohorts:

NHANES Studies Protocol:

• Cohort: 1,942-2,105 participants from the 1999-2002 NHANES cycles, aged ≥50 years [51] [24].
• DNA Methylation Analysis: DNA extracted from whole blood, bisulfite converted using Zymo EZ DNA Methylation kit, processed on Illumina Infinium MethylationEPIC BeadChip [51].
• Mortality Tracking: Linked to National Death Index records with median follow-up of 17.5 years (1999-2019) [51].
• Statistical Analysis: Cox proportional hazards regression adjusting for age, sex, race/ethnicity, socioeconomic factors, health behaviors, and clinical risk factors [24].

Generation Scotland Protocol:

• Cohort: 18,859 participants aged 18-99 years, with 10-year follow-up for 174 disease outcomes [29].
• DNA Methylation Analysis: Blood-based DNAm at 752,722 CpG sites using Illumina MethylationEPIC array [29] [72].
• Statistical Approach: Cox proportional hazards for each clock-disease pairing, adjusting for age, sex, BMI, smoking, alcohol, education, and socioeconomic deprivation [29].
• Performance Assessment: Bonferroni correction for multiple testing (P < 2.9×10⁻⁴) and logistic regression with AUC comparison [29].

Epigenetic Clock Calculation Methodologies

The epigenetic clocks evaluated in these studies employ distinct computational approaches:

First-Generation Clocks (Horvath, Hannum):

• Approach: Single-step elastic net regression trained directly on chronological age [4] [1].
• Feature Selection: Genome-wide CpG sites with strongest age correlation (Horvath: 353 CpGs; Hannum: 71 CpGs) [1].
• Output: Estimated biological age compared to chronological age [12].

Second-Generation Clocks (GrimAge, PhenoAge):

• Approach: Two-stage methodology; first developing DNAm surrogates for plasma proteins and smoking exposure, then training on mortality data [12] [72].
• Feature Selection: Epigenetic scores for protein biomarkers rather than individual CpGs [72].
• Output: Mortality risk estimate converted to age units [12].

Third-Generation Clocks (DunedinPACE, DunedinPoAm):

• Approach: Trained on rate of change in phenotypic measures over time [12] [71].
• Feature Selection: CpGs associated with pace of aging derived from longitudinal analysis of 19 biomarkers [12].
• Output: Pace of aging rather than current biological age [71].

Table 3: Key Research Reagents and Computational Tools for Epigenetic Clock Analysis

Resource	Type	Primary Function	Application Notes
Illumina Infinium MethylationEPIC BeadChip	Hardware Platform	Genome-wide DNA methylation analysis	Covers ~850,000 CpG sites; preferred for clock development [51] [72]
Illumina HumanMethylation450K BeadChip	Hardware Platform	DNA methylation analysis	Covers ~450,000 CpG sites; compatible with most established clocks [72]
Zymo EZ DNA Methylation Kit	Laboratory Reagent	Bisulfite conversion of DNA	Essential sample preparation step for methylation arrays [51]
NHANES Epigenetic Biomarkers Database	Data Resource	Pre-calculated epigenetic clock estimates	Provides calculated values for multiple clocks from 1999-2002 NHANES [15] [24]
MethylBrowsR	Software Tool	Visualization of epigenome-wide CpG-age associations	Enables exploration of linear and non-linear methylation-age relationships [72]
Generation Scotland Dataset	Cohort Data	Large-scale methylation and health data	Includes 18,859 participants with DNAm data and 174 disease outcomes [29]
DunedinPACE Calculator	Algorithm	Pace of aging estimation	Requires specific CpG sites and weighting algorithm from original publication [12] [71]
GrimAge Calculator	Algorithm	Mortality risk estimation	Incorporates DNAm surrogates for plasma proteins and smoking [72]

The comprehensive evidence from multiple large-scale studies consistently demonstrates that second-generation epigenetic clocks, particularly GrimAge and its successor GrimAge2, provide superior predictive power for mortality outcomes compared to both first-generation chronological clocks and third-generation pace-of-aging clocks [51] [24] [29]. This performance advantage extends to cause-specific mortality, with GrimAge showing robust prediction of cardiovascular and cancer mortality [51] [24].

For morbidity prediction, the optimal clock selection depends on the specific disease outcome of interest, though second-generation clocks generally outperform first-generation clocks across most disease categories [29]. The distinctive performance patterns across different disease categories suggest that clock selection should be guided by research objectives: GrimAge for smoking-related and respiratory conditions, DunedinPACE for metabolic diseases, and PhenoAge for inflammatory conditions [29].

These findings have significant implications for clinical trials and intervention studies targeting aging processes. The demonstrated sensitivity of second-generation clocks to lifestyle interventions, particularly smoking cessation and dietary modifications, supports their utility as endpoints in intervention studies [71]. Furthermore, the ability of mortality-trained clocks to predict functional health outcomes suggests their potential application in healthspan extension trials [15].

Future directions in epigenetic clock development should focus on enhancing causal interpretation through Mendelian randomization approaches [12], improving population-specific performance across diverse ethnic groups [1], and developing tissue-specific clocks for targeted organ aging assessment [1]. The integration of epigenetic clocks with other biomarkers of aging likely represents the next frontier in comprehensive biological age assessment [1] [72].

Comparative Performance in Disease-Specific Contexts (e.g., Cardiovascular, Cognitive Decline)

Epigenetic clocks, powerful biomarkers developed from DNA methylation (DNAm) patterns, have revolutionized the assessment of biological aging. These tools are broadly categorized into two generations based on their training targets and applications. First-generation clocks, such as those developed by Horvath and Hannum, were primarily trained to predict an individual's chronological age with remarkable accuracy [3] [1] [4]. Second-generation clocks, including GrimAge, PhenoAge, and DunedinPACE, were explicitly designed to capture aging-related physiological dysregulation, morbidity, and mortality risk, moving beyond mere time prediction to health status assessment [3] [1] [73].

Understanding the relative performance of these epigenetic clocks in specific disease contexts is critical for researchers and drug development professionals seeking to identify robust biomarkers for interventional studies and prognostic models. This guide provides an objective, data-driven comparison of first- and second-generation epigenetic clocks, focusing on their performance in cardiovascular health and cognitive decline, supported by experimental data and methodological details from key studies.

Performance in Cardiovascular Disease Context

Cardiovascular disease (CVD) remains a leading cause of global mortality, and epigenetic age acceleration (EAA) has emerged as a potential molecular link between risk factors and clinical outcomes. Second-generation clocks demonstrate superior performance in capturing cardiovascular risk and subclinical atherosclerosis.

Key Comparative Data

The table below summarizes findings from major studies investigating epigenetic clocks and cardiovascular health.

Table 1: Performance of Epigenetic Clocks in Cardiovascular Contexts

Clock Generation	Specific Clock	Key Cardiovascular Finding	Effect Size / Association Strength	Study / Population
First-Generation	HannumEAA	Not significantly associated with composite CVH score [73].	Nonsignificant association [73].	Taiwan Biobank (n=2,198) [73]
First-Generation	Intrinsic EAA (IEAA)	Not significantly associated with composite CVH score [73].	Nonsignificant association [73].	Taiwan Biobank (n=2,198) [73]
Second-Generation	PhenoAge Acceleration (PhenoAA)	Associated with CVH score; mediates link between childhood triglycerides and subclinical atherosclerosis [74] [73].	-0.350-year PhenoEAA per 1-point CVH score increase [73]. Mediated 27.4% of childhood TG-cIMT association [74].	Taiwan Biobank [73]; Bogalusa Heart Study [74]
Second-Generation	GrimAge Acceleration (GrimAA)	Strongly associated with CVH score and childhood CVD risk factors (BMI, triglycerides) [74] [73].	-0.499-year GrimEAA per 1-point CVH score increase [73]. 0.5-year GrimAA increase per SD childhood BMI [74].	Taiwan Biobank [73]; Bogalusa Heart Study (n=1,580) [74]

Detailed Experimental Protocols: Cardiovascular Health

The findings in Table 1 are derived from rigorous observational cohort studies. The following methodology is a composite of the approaches used in the cited research.

• Study Populations: Analyses leveraged data from large, long-term cohort studies such as the Bogalusa Heart Study (BHS) and the Taiwan Biobank (TWB). The BHS is a population-based study following residents from childhood to adulthood, allowing for life-course analysis [74]. The TWB comprises adult participants providing extensive genetic and health data [73].
• Cardiovascular Health (CVH) Assessment: CVH was typically scored based on the American Heart Association's "Life's Simple 7" metrics, which include four lifestyle factors (smoking, physical activity, body mass index, diet) and three clinical factors (fasting glucose, blood pressure, total cholesterol) [73]. Scores were summed, with higher scores indicating better cardiovascular health.
• DNA Methylation Profiling and EAA Calculation: DNA was extracted from whole blood samples. Methylation profiling was performed using Illumina Infinium Methylation arrays (e.g., 450K). Epigenetic age was calculated using publicly available algorithms for each clock (e.g., Horvath's online calculator). EAA was statistically derived as the residual from regressing DNAm age on chronological age, often further adjusted for blood cell composition where appropriate (e.g., for Intrinsic EAA) [74] [73].
• Assessment of Subclinical Atherosclerosis: In the BHS, carotid intima-media thickness (cIMT) was measured via ultrasonography as a non-invasive indicator of subclinical atherosclerosis [74].
• Statistical Analysis: Studies used linear mixed-effects models to test prospective associations between childhood risk factors and adulthood EAA. Structural equation models (SEM) were employed to test the mediating role of EAA in the pathway between early-life risk factors and midlife subclinical disease [74]. Analyses were adjusted for covariates such as sex, educational attainment, and drinking status.

The following diagram illustrates the logical pathway and analytical approach used in these life-course studies of cardiovascular aging.

Performance in Cognitive Decline Context

Cognitive decline and dementia are major age-related concerns. Recent evidence indicates that second-generation epigenetic clocks, particularly GrimAge and DunedinPACE, are more strongly associated with cognitive function and decline across diverse populations than first-generation clocks.

Key Comparative Data

The table below consolidates results from studies linking epigenetic age acceleration to cognitive outcomes.

Table 2: Performance of Epigenetic Clocks in Cognitive Decline Contexts

Clock Generation	Specific Clock	Key Cognitive Finding	Effect Size / Association Strength	Study / Population
First-Generation	Hannum Age Acceleration	Associated with slower processing speed in midlife [75].	β = -0.049, 95% CI: -0.097 to -0.001 (Processing Speed) [75].	Bogalusa Heart Study (n=1,252) [75]
First-Generation	Intrinsic EAA (IEAA)	No significant association with cognitive domains identified in midlife [75].	Nonsignificant association [75].	Bogalusa Heart Study (n=1,252) [75]
Second-Generation	PhenoAge Acceleration (PhenoAA)	Associated with slower processing speed; linked to cognitive decline in Hispanic/Latino adults [76] [75].	β = -0.072, 95% CI: -0.121 to -0.023 (Processing Speed) [75].	Bogalusa Heart Study [75]; HCHS/SOL (n=2,671) [76]
Second-Generation	GrimAge Acceleration (GrimAA)	Strongly associated with global cognition and processing speed; supported by causal evidence from MR [75].	β = -0.147, 95% CI: -0.211 to -0.083 (Processing Speed) [75].	Bogalusa Heart Study [75]; HCHS/SOL [76]
Second-Generation	DunedinPACE	Faster pace of aging associated with more rapid cognitive decline and explained a fourth of dementia risk [47] [76].	Associated with preclinical cognitive aging [47].	Framingham Heart Study [47]; HCHS/SOL [76]

Detailed Experimental Protocols: Cognitive Decline

The cognitive findings are the product of comprehensive neuropsychological assessment and advanced statistical genetics, as detailed below.

• Study Populations: Key evidence comes from well-established cohorts like the Framingham Heart Study (FHS) Offspring Cohort, the Bogalusa Heart Study (BHS), and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) [47] [76] [75]. These studies provide longitudinal data on cognitive function.
• Cognitive Function Assessment: Participants underwent standardized neuropsychological testing. Tests were often grouped into cognitive domains (e.g., global cognition, attention/processing speed, executive function, memory). Scores were typically standardized (e.g., converted to T-scores) and averaged to create composite domain scores [47] [75]. Mild cognitive impairment (MCI) was diagnosed based on established criteria involving impaired performance on multiple tests.
• DNA Methylation and EAA/PACE Calculation: DNAm was quantified from whole blood using Illumina arrays. EAA for Horvath, Hannum, PhenoAge, and GrimAge was calculated as described in the cardiovascular section. DunedinPACE, which measures the pace of aging rather than age acceleration, was calculated from its specific algorithm, with values around 1.0 indicating an average pace of aging [47].
• Mendelian Randomization (MR) for Causal Inference: To move beyond association, the Bogalusa Heart Study researchers employed a two-sample MR approach. This used genetic variants (single nucleotide polymorphisms) associated with EAA as instrumental variables to test for a potential causal effect of EAA on cognitive performance [75].
• Statistical Analysis: Cross-sectional associations between EAA and cognitive scores were tested using linear regression models, adjusted for demographics, education, behavioral factors (smoking, alcohol), and clinical covariates (APOE ε4 status, cardiometabolic risks). Longitudinal models analyzed the association between baseline EAA and the rate of cognitive decline over time [47] [76] [75].

The experimental workflow for assessing the link between epigenetic aging and cognitive decline, incorporating both observational and causal inference methods, is summarized below.

Large-Scale Validation and Direct Comparison

A landmark 2025 preprint study by Mavrommatis et al. provided an unbiased, large-scale comparison of 14 epigenetic clocks against 174 disease outcomes, offering the most comprehensive performance validation to date [21] [77].

• Scale and Scope: The analysis included 18,859 individuals and evaluated the 10-year onset of 174 distinct disease outcomes, as well as all-cause mortality [21] [77].
• Key Finding on Generational Performance: The study concluded that "Second-generation clocks significantly outperformed first-generation clocks, which have limited applications in disease settings" [21] [77].
• Disease-Specific Predictive Power: Second-generation clocks showed particular promise for predicting the risk of respiratory and liver conditions. Furthermore, for 27 specific diseases (including primary lung cancer and diabetes), the hazard ratio associated with the epigenetic clock was stronger than the clock's association with all-cause mortality, indicating high disease-specificity [21].
• Clinical Utility: Adding second-generation clocks to models containing traditional risk factors improved disease classification accuracy (AUC) by more than 1% in 35 instances, suggesting potential for clinical risk stratification [21].

The Scientist's Toolkit: Essential Research Reagents and Materials

The following table details key reagents and materials required for conducting epigenetic clock research, as derived from the methodologies of the cited studies.

Table 3: Essential Research Reagents and Solutions for Epigenetic Clock Studies

Item	Function / Application	Example from Search Results
Illumina Infinium Methylation BeadChip	Genome-wide DNA methylation profiling. The standard platform for generating methylation data for epigenetic clock calculation.	Infinium HumanMethylation450 BeadChip [74]
DNA Extraction Kit	High-quality DNA isolation from biological samples (e.g., whole blood).	PureLink Pro 96 Genomic DNA Kit [74]
DNA Methylation Age Calculator	Online or software-based tool for calculating various epigenetic clocks from raw methylation data.	Horvath's online DNA Methylation Age Calculator [74]
Cohort Biobank Samples	Well-characterized human population samples with longitudinal health data, DNA, and often cognitive/CVD phenotyping.	Bogalusa Heart Study, Framingham Heart Study, Taiwan Biobank, Generation Scotland [74] [21] [73]
Neuropsychological Test Batteries	Standardized tests to assess cognitive domains (global cognition, processing speed, memory, executive function).	Framingham Heart Study's original neuropsychological battery [47]
Carotid Ultrasound System	Non-invasive measurement of carotid intima-media thickness (cIMT) to assess subclinical atherosclerosis.	Used to measure cIMT in the Bogalusa Heart Study [74]
Standardized CVD Risk Factor Protocols	Protocols for measuring BMI, blood pressure, and collecting fasting blood samples for lipid and glucose profiling.	Adherence to American Heart Association criteria in the Taiwan Biobank and Bogalusa studies [74] [73]

Epigenetic clocks, biomarkers of aging based on DNA methylation (DNAm) patterns, have emerged as powerful tools for quantifying biological age and the rate of aging. These clocks are categorized into generations based on their training targets and underlying algorithms. First-generation clocks, such as those developed by Horvath and Hannum, were trained primarily to predict chronological age [3] [12] [4]. In contrast, second-generation clocks (e.g., PhenoAge, GrimAge, GrimAge2) and third-generation clocks (e.g., DunedinPACE, DunedinPoAm) were trained on health-related outcomes such as morbidity, mortality, and phenotypic decline [3] [12] [71]. This fundamental difference in design translates to significant variation in how these clocks respond to dietary, pharmacological, and lifestyle interventions. This guide provides an objective comparison of the responsiveness of different epigenetic clocks to interventions, synthesizing evidence from recent studies to inform researchers, scientists, and drug development professionals.

Generations of Epigenetic Clocks and Their Design Principles

Defining Clock Generations

The evolution of epigenetic clocks reflects a shift from tracking time to tracking health. The following table outlines the key characteristics of different clock generations.

Table 1: Generations of Epigenetic Clocks and Their Characteristics

Generation	Representative Clocks	Primary Training Target	Key Strengths	Principal Limitations
First	Horvath, Hannum	Chronological Age [12] [4]	High accuracy in predicting chronological age [4]	Weak associations with health outcomes and interventions [3] [21]
Second	PhenoAge, GrimAge, GrimAge2	Mortality, Morbidity, Clinical Biomarkers [12] [71]	Strong predictor of healthspan, lifespan, and age-related disease [21] [30]	Less universally accurate for chronological age than first-generation
Third	DunedinPACE, DunedinPoAm	Pace of Aging (longitudinal change in organ systems) [12] [71]	Measures pace of biological aging, sensitive to short-term interventions [71]	Different metric (pace vs. age) can complicate direct comparison

Performance in Health Outcome Prediction

Large-scale comparative studies demonstrate the superior utility of later-generation clocks for health-oriented research. A 2025 unbiased comparison of 14 clocks in 18,859 individuals found that second-generation clocks significantly outperformed first-generation clocks in predicting the 10-year onset of 174 disease outcomes [21]. The study concluded that first-generation clocks have "limited applications in disease settings," while second-generation clocks show particular promise for predicting respiratory and liver conditions [21].

Comparative Evidence from Intervention Studies

Dietary Interventions

Plant-centered, nutrient-dense diets have shown efficacy in slowing or reversing epigenetic age. The following table summarizes key findings from dietary intervention studies.

Table 2: Responsiveness of Epigenetic Clocks to Dietary Interventions

Intervention Type	Study Details	First-Generation Clock Response (e.g., Horvath)	Second/Third-Generation Clock Response (e.g., GrimAge, DunedinPACE)
Multimodal Diet & Lifestyle	8-week randomized controlled trial (RCT) in healthy men (n=43); diet, sleep, exercise, probiotics [78]	Significant reversal: DNAmAge reduced by 3.23 years vs. controls (p=0.018) [78]	Not measured in this study
Healthy Diet Patterns	Adherence to healthy diets (AHEI, Mediterranean); large observational studies [79] [80]	Weak or inconsistent associations [3]	Strong associations: High diet quality linked to slower DunedinPACE and reduced GrimAge acceleration [71] [80]
Fruit/Vegetable Intake	Observational study in postmenopausal women (n=4,173); blood carotenoids as biomarker [79]	Not significantly associated	Significant deceleration: Extrinsic Epigenetic Age Acceleration (EEAA) associated with higher carotenoids (p=1x10⁻⁵) [79]
Fish & Poultry Intake	Observational study (WHI, InCHIANTI) [79]	Fish intake not significant; poultry associated with Intrinsic EAA (IEAA) (p=0.03) [79]	Fish intake associated with EEAA deceleration (p=0.02) [79]

Pharmacological Interventions

Evidence for pharmacological impacts on epigenetic aging is emerging, with later-generation clocks showing more consistent responsiveness.

Table 3: Responsiveness of Epigenetic Clocks to Pharmacological Interventions

Intervention	Study Details	First-Generation Clock Response	Second/Third-Generation Clock Response
Semaglutide	Phase IIb trial in adults with HIV-associated lipohypertrophy [36]	Not reported	Significant modulation: 11 organ-system clocks showed decreases, most prominent in inflammation, brain, and heart clocks [36]
Metformin	Observational study in postmenopausal women [79]	No delay in epigenetic aging observed [79]	No delay in epigenetic aging observed [79]
Thymic Regeneration (TRIIM Trial)	Pilot trial with growth hormone, metformin, and DHEA in men (n=9) [36]	Not the primary focus	Reversal reported: GrimAge decreased by ~2 years after one year of treatment [36]

Comprehensive Lifestyle and Behavioral Interventions

Lifestyle factors, particularly smoking and physical activity, profoundly impact epigenetic age, with effects most detectable via second and third-generation clocks.

Table 4: Responsiveness of Epigenetic Clocks to Lifestyle and Behavioral Interventions

Intervention/Factor	Study Details	First-Generation Clock Response	Second/Third-Generation Clock Response
Smoking Cessation	Analysis of 2,532 adults (NHANES); multivariable regression [71]	Not the strongest association	Powerful effect: Smoking cessation associated with 10.17-year reduction in GrimAge2 acceleration [71]
Physical Activity	Study in professional soccer players [36]	Not reported	Transient rejuvenation: Significant decreases in DNAmGrimAge2 and DNAmFitAge immediately after vigorous games [36]
Alcohol Consumption	Observational study (n=4,173); moderate intake [79]	Not significantly associated	Significant deceleration: Moderate alcohol consumption associated with slower EEAA (p=0.01) [79]
Obesity & Metabolic Health	Longitudinal data (WHI); BMI reduction [79]	Weak association (IEAA, p=0.05) [79]	Strong association: Increase in BMI associated with increased EEAA; metabolic syndrome mediates this relationship [79]

A 2025 study of middle-aged and senior adults from NHANES quantified the combined effect of five healthy lifestyle domains (diet, abdominal adiposity, physical activity, smoking, alcohol). It found that full adherence to healthy behaviors reduced GrimAge2 acceleration by β = -5.55 years, PhenoAge acceleration by β = -2.64 years, and DunedinPoAm by β = -0.06 SD [71]. The same study demonstrated that these clocks mediated a substantial portion of the lifestyle-mortality link: GrimAge2 accounted for 63.58%, DunedinPoAm for 44.63%, and PhenoAge for 28.45% of the association [71].

Experimental Protocols and Methodologies

Protocol for a Multimodal Diet and Lifestyle Intervention

The following workflow visualizes the key components and assessment strategy of a successful 8-week intervention trial that reversed Horvath's first-generation epigenetic age [78].

Protocol for a Large-Scale Observational Study

The methodology for establishing associations between lifestyle factors and epigenetic aging in large cohorts involves a standardized multi-step process, as used in studies of the Women's Health Initiative [79].

Molecular Pathways and Mechanisms

The biological mechanisms through which interventions influence epigenetic clocks are complex and interconnected. Diet, lifestyle, and pharmacology converge on core pathways that regulate DNA methylation patterns.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 5: Key Research Reagent Solutions for Epigenetic Clock Studies

Reagent/Material	Function in Research	Example Application
Illumina Methylation EPIC Array	Genome-wide DNA methylation profiling at ~850,000 CpG sites [78]	Standardized platform for generating methylation data used as input for all major epigenetic clocks [78]
DNA Extraction Kits (Saliva/Blood)	High-quality DNA isolation from common tissue sources	Obtaining analyte for methylation analysis; saliva provides mix of white blood and buccal cells [78]
Horvath Laboratory DNAm Age Calculator	Software implementation of multiple epigenetic clocks	Calculating Horvath DNAmAge, HannumAge, and other clock estimates from methylation data [78]
GrimAge & PhenoAge Estimators	Specialized algorithms for second-generation clocks	Calculating mortality-risk-trained epigenetic age from methylation data [12] [71]
DunedinPACE/DunedinPoAm Algorithm	Algorithm for third-generation pace of aging clock	Estimating the rate of biological aging from a single timepoint methylation sample [12] [71]
Biomarker Assay Kits (e.g., CRP, Carotenoids)	Quantifying clinical biomarkers for validation	Measuring physiological correlates of epigenetic aging (e.g., inflammation, nutritional status) [79] [71]

The evidence consistently demonstrates that second and third-generation epigenetic clocks (PhenoAge, GrimAge, DunedinPACE) are substantially more responsive to dietary, pharmacological, and lifestyle interventions than first-generation clocks trained solely on chronological age. This enhanced sensitivity aligns with their design principles, as they incorporate information from health outcomes, mortality risk, and the pace of physiological decline [3] [21] [71].

For researchers designing intervention studies, the choice of epigenetic clock is critical. While first-generation clocks like Horvath's can detect change in highly controlled multimodal interventions [78], second and third-generation clocks provide a more robust and sensitive measure for health-focused research. Current evidence supports prioritizing GrimAge and DunedinPACE for evaluating lifestyle and pharmacological interventions, as they show the greatest effect sizes and mediate a substantial portion of the lifestyle-mortality relationship [71]. Future research should continue to validate these clocks across diverse populations and intervention types, further solidifying their role as biomarkers of healthspan and targets for therapeutic development.

Epigenetic clocks have emerged as powerful biomarkers for quantifying biological aging, evolving significantly from their initial conception. First-generation clocks, such as those developed by Horvath and Hannum, were primarily trained to predict an individual's chronological age based on DNA methylation (DNAm) patterns at specific cytosine-phosphate-guanine (CpG) sites [12] [1]. While these clocks demonstrated remarkable accuracy in estimating chronological age across multiple tissues, they captured limited aspects of biological aging and healthspan [3]. Second-generation clocks, including PhenoAge and GrimAge, advanced the field by incorporating phenotypic data, clinical biomarkers, and mortality-related information into their models, thereby improving predictions of health outcomes and age-related disease risk [12] [81].

The latest innovation in this field comes in the form of third-generation epigenetic clocks, which address fundamental limitations of previous models by focusing on specific biological domains of the aging process. This new generation includes two particularly promising approaches: Intrinsic Capacity (IC) clocks that capture the integrated sum of an individual's physical and mental capacities [7], and cell-type-specific clocks that dissect the confounding effects of cellular heterogeneity in bulk tissue samples [82]. These advanced clocks provide unprecedented resolution for studying the mechanisms of biological aging and evaluating interventions aimed at promoting healthy longevity.

Defining the Third Generation: IC and Cell-Type-Specific Clocks

Intrinsic Capacity (IC) Clocks

The World Health Organization defines intrinsic capacity as "the sum of all physical and mental capacities that an individual can draw on at any point in their life" [7]. The recently developed IC clock represents a paradigm shift in epigenetic aging research by moving beyond chronological age prediction to directly quantify functional aging. Constructed using elastic net regression on DNA methylation data from the INSPIRE-T cohort (1,014 individuals aged 20-102 years), this clock integrates clinical assessments across five key domains: cognition, locomotion, psychological well-being, sensory abilities, and vitality [7].

Notably, the IC clock demonstrates strong associations with immune function, particularly T-cell activation and immunosenescence markers. It outperforms earlier epigenetic clocks in predicting all-cause mortality in the Framingham Heart Study and shows particular strength in capturing aspects of biological aging relevant to lifestyle factors and health risks [7]. The clock's 91 CpG sites show minimal overlap with first and second-generation clocks, suggesting it captures distinct biological aspects of aging [7].

Cell-Type-Specific Epigenetic Clocks

Traditional epigenetic clocks trained on heterogeneous bulk tissues face a fundamental limitation: they conflate two distinct aging processes - changes in cell-type composition and molecular aging within individual cell types [82]. Cell-type-specific clocks address this confound by employing computational strategies that isolate cell-type-specific age-associated DNA methylation changes (age-DMCTs).

Recent research has quantified that in whole blood, approximately 39% of traditional epigenetic clock accuracy is driven by age-related shifts in immune cell populations (extrinsic aging), while only 61% reflects aging within individual cell types (intrinsic aging) [82]. In brain tissue, the extrinsic component accounts for approximately 12% of clock accuracy, primarily driven by age-associated shifts in neuronal subtypes [82]. These findings highlight the critical importance of distinguishing between cellular composition changes and true cellular aging when interpreting epigenetic clock results.

Table 1: Comparison of Epigenetic Clock Generations

Feature	First-Generation Clocks	Second-Generation Clocks	Third-Generation Clocks
Primary Training Target	Chronological age [12] [1]	Mortality, phenotypic age, clinical biomarkers [12] [81]	Functional capacity (IC), cell-type-specific aging [7] [82]
Key Examples	Horvath clock (353 CpGs), Hannum clock (71 CpGs) [1] [83]	PhenoAge, GrimAge [12] [81]	IC clock, Neuron-specific clock, Hepatocyte-specific clock [7] [82]
Strengths	High cross-tissue accuracy, established benchmark [1]	Better health outcome prediction, mortality risk assessment [81]	Dissects aging mechanisms, cell-type resolution, functional relevance [7] [82]
Limitations	Limited health outcome prediction, confounded by cell composition [3] [82]	Still confounded by cellular heterogeneity, limited mechanistic insight [82]	Computational complexity, requires cell-type deconvolution [82]
Mortality Prediction	Limited or no improvement over chronological age [81]	Significant improvement (GrimAge2) [81]	IC clock outperforms earlier generations [7]

Experimental Data and Performance Comparison

Performance of the IC Clock

The IC clock demonstrates robust technical characteristics and predictive validity. In validation studies, it achieved a correlation of 0.61 between clinically assessed IC and DNAm-predicted IC values [7]. The clock shows a strong age-related decline (rs = -0.92) and can be reliably measured in both blood and saliva samples (mean correlation between tissues: rs = 0.64), offering a non-invasive assessment option [7].

Critically, in the Framingham Heart Study, the IC clock outperformed both first-generation (Horvath, Hannum) and second-generation (PhenoAge, GrimAge) clocks in predicting all-cause mortality [7]. The biological relevance of the IC clock is further supported by its strong associations with immune and inflammatory biomarkers, particularly markers of T-cell function such as CD28 expression, which is a key molecule involved in immunosenescence [7].

Performance of Cell-Type-Specific Clocks

Cell-type-specific clocks have demonstrated superior performance in detecting disease-associated aging acceleration that is masked in bulk tissue analyses. For example, in Alzheimer's Disease, neuron and glia-specific clocks reveal significant biological age acceleration, with the strongest effect observed in glial cells from the temporal lobe [82]. Similarly, hepatocyte-specific clocks show acceleration in pathological liver conditions, whereas non-cell-type-specific clocks fail to detect these changes [82].

These specialized clocks also improve chronological age estimation in their respective cell types. When applied to purified cell populations or appropriately deconvoluted bulk tissue data, cell-type-specific clocks achieve higher accuracy than pan-tissue clocks, as they avoid the confounding effects of age-related changes in cellular composition [82].

Table 2: Quantitative Performance Comparison of Selected Epigenetic Clocks

Clock Name	Generation	CpG Sites	Tissue Specificity	Key Performance Findings
Horvath	1st	353 [1]	Pan-tissue [1]	MAE: 3.6 years [1]; No significant mortality prediction improvement over CA [81]
Hannum	1st	71 [1]	Blood [1]	MAE: 3.9 years [1]; No significant mortality prediction improvement over CA [81]
PhenoAge	2nd	513 [84]	Blood-optimized	Correlates with mortality risk [12]; Outperformed by IC clock for mortality prediction [7]
GrimAge2	2nd	-	Blood-optimized	Strong mortality predictor [81]; Similar performance to LinAge2 clinical clock [81]
IC Clock	3rd	91 [7]	Blood, Saliva	Outperforms 1st/2nd gen clocks for mortality [7]; Correlates with immune markers [7]
Neuron Clock	3rd	-	Neuron-specific	Detects age acceleration in Alzheimer's Disease [82]
Hepatocyte Clock	3rd	-	Hepatocyte-specific	Accelerated in pathological liver conditions [82]

Detailed Experimental Protocols

IC Clock Development Protocol

Cohort and IC Assessment:

• Participant Recruitment: The IC clock was developed using the INSPIRE-T cohort comprising 1,014 individuals aged 20-102 years [7].
• IC Domain Evaluation: Clinical assessments were conducted across five domains: cognition, locomotion, psychological well-being, sensory abilities, and vitality [7].
• IC Score Calculation: A composite IC score (0-1 scale) was computed, with 1 representing optimal function [7].

DNA Methylation Analysis:

• DNA Extraction and Processing: DNA was extracted from blood samples and DNA methylation profiling was performed using the Infinium EPIC array [7].
• Model Training: Elastic net regression with tenfold cross-validation was applied to DNAm data from 933 participants to identify CpGs predictive of IC [7].
• Model Selection: The final model was selected based on correlation between observed and predicted values, model error, and number of CpG sites used [7].
• Validation: The clock was validated in the Framingham Heart Study for association with mortality, health risk factors, and immunological biomarkers [7].

Cell-Type-Specific Clock Construction Protocol

Cell-Type Deconvolution:

• Reference Matrix Selection: Employ tissue-appropriate DNAm reference matrices (e.g., 12 immune cell subtypes for blood, 7 neural cell types for brain) [82].
• Fraction Estimation: Calculate cell-type proportions in bulk tissue samples using deconvolution algorithms [82].

Age-DMCT Identification:

• Cell-Type-Specific Analysis: Apply the CellDMC algorithm to identify age-associated differentially methylated cytosines in each cell-type (age-DMCTs) [82].
• Interaction Modeling: Use models with interaction terms between age and cell-type fractions to detect cell-type-specific age associations [82].

Clock Training:

• Feature Selection: Restrict training to age-DMCTs specific to the target cell type [82].
• Model Construction: Apply Elastic Net Regression to build either "intrinsic" clocks (using residuals after regressing out cell composition effects) or "semi-intrinsic" clocks (using unadjusted data) [82].
• Validation: Validate clocks in independent datasets with known cell-type composition or purified cell populations [82].

Diagram Title: Workflow Comparison for Traditional vs. Cell-Type-Specific Clocks

Biological Pathways and Mechanisms

IC Clock Pathway Associations

The IC clock demonstrates strong associations with specific biological pathways, particularly those involved in immune function and inflammation. Gene Ontology enrichment analysis of genes associated with the IC clock reveals significant involvement in T cell activation, immune response regulation, and inflammatory processes [7].

At the molecular level, higher IC clock values (indicating better intrinsic capacity) associate strongly with increased expression of CD28, a critical costimulatory molecule on T cells whose loss defines immunosenescence [7]. Conversely, poorer IC clock values correlate with elevated expression of CDK14/PFTK1, a regulator of Wnt signaling implicated in proinflammatory responses and sensitive to dietary influences [7].

The expression of MCOLN2, which promotes viral entry and infection, correlates with the methylation status of 67% of CpGs in the IC clock, suggesting a potential link between IC and antiviral defense mechanisms [7]. These molecular signatures establish the IC clock as a biomarker connected to fundamental immunological aging processes.

Cell-Type-Specific Aging Mechanisms

Cell-type-specific clocks have revealed that different cell types age at distinct rates and through potentially different mechanisms. In brain tissue, these clocks have demonstrated that glial cells in the temporal lobe show the most pronounced age acceleration in Alzheimer's Disease, providing insights into cell-type-specific vulnerability in neurodegeneration [82].

The CpG sites comprising cell-type-specific clocks show significant overlap with causal epigenetic clocks (e.g., DamAge clock) and map to genes implicated in cell-type-specific pathologies, such as neurodegeneration in neurons or metabolic dysfunction in hepatocytes [82]. This suggests that these clocks capture functionally relevant aging mechanisms specific to each cell type.

Diagram Title: Biological Pathways Associated with the IC Clock

Table 3: Key Research Reagents and Computational Tools for Third-Generation Clock Development

Resource Category	Specific Tools/Methods	Application and Function
DNA Methylation Arrays	Illumina Infinium EPIC Array [7]	Genome-wide DNAm profiling (850,000 CpG sites)
Cell-Type Deconvolution Algorithms	HiBED (brain) [82], Reference-based deconvolution (blood) [82]	Estimate cell-type fractions from bulk tissue DNAm data
Cell-Type-Specific Analysis Tools	CellDMC algorithm [82]	Identify cell-type-specific age-associated DNAm changes (age-DMCTs)
Statistical Modeling Approaches	Elastic Net Regression [7], Two-phase model regression [7]	Build predictive models with feature selection for high-dimensional data
Validation Cohorts	INSPIRE-T (n=1,014) [7], Framingham Heart Study [7]	Independent validation of clock performance and associations
Pathway Analysis Resources	Gene Ontology (GO) enrichment [7], Reactome Pathway Knowledgebase [85]	Biological interpretation of clock-associated genes and pathways
Specialized Reference Data	Purified cell-type methylomes [82], Senescence-associated CpGs [86]	Benchmarking and functional enrichment of epigenetic clocks

Third-generation epigenetic clocks, particularly IC clocks and cell-type-specific models, represent significant advances in biological aging assessment. By focusing on functional capacity and resolving cellular heterogeneity, these tools provide unprecedented insights into the multidimensional nature of aging. The strong association of the IC clock with immune parameters and its superior mortality prediction performance demonstrate the value of targeting clinically relevant functional domains rather than chronological age alone [7]. Similarly, cell-type-specific clocks enable the dissection of tissue-level aging into its cellular components, revealing cell-type-specific vulnerability in age-related diseases [82].

Future development in this field will likely focus on increasing cellular resolution through single-cell epigenetic profiling, expanding functional domain assessment beyond intrinsic capacity, and developing more sophisticated computational approaches that integrate multiple clock technologies. Furthermore, standardization of methodological approaches and validation in diverse populations will be essential for translational applications. These advanced epigenetic clocks hold particular promise for evaluating the effectiveness of interventions targeting fundamental aging processes, ultimately contributing to the extension of healthspan and reduction of age-related disease burden.

Benchmarking Against Established Clinical Risk Scores (e.g., Framingham Risk Score)

The pursuit of biomarkers that quantify biological aging has led to the development of epigenetic clocks, which measure age-related changes in DNA methylation (DNAm) patterns. These clocks are categorized into generations based on their training targets and underlying methodologies. First-generation clocks, such as Horvath and Hannum, were trained primarily to predict chronological age [3] [12]. In contrast, second-generation clocks, including GrimAge and PhenoAge, were explicitly trained on phenotypic data, mortality risk, and health-related outcomes [3] [12] [4]. A third generation of clocks, exemplified by DunedinPACE, measures the pace of aging rather than a static age estimate [12]. More recently, advanced clocks like the Intrinsic Capacity (IC) clock have emerged, trained on composite measures of physical and mental capacities [7].

This review objectively compares the performance of these epigenetic clocks against established clinical risk scores, particularly the Framingham Risk Score (FRS) and the Atherosclerotic Cardiovascular Disease (ASCVD) risk equation. We synthesize evidence from recent large-scale studies to determine whether epigenetic aging measures offer predictive advantages over traditional risk assessment tools for mortality, cardiovascular disease, and other age-related conditions.

Performance Comparison: Epigenetic Clocks vs. Clinical Risk Scores

Head-to-Head Predictive Performance

Direct comparisons between epigenetic clocks and clinical risk scores reveal a nuanced landscape where second-generation clocks often demonstrate complementary, and in some cases superior, predictive value.

Table 1: Comparison of Epigenetic Clocks vs. Clinical Risk Scores for Mortality and CVD Prediction

Metric	Study Cohort	Comparison	Key Finding	Statistical Significance
All-Cause Mortality	African Americans (GENOA), n=1,100 [87]	GrimAge Acceleration (GrimAA) vs. FRS/ASCVD	Adding GrimAA to FRS/ASCVD improved risk reclassification.	NRI: 0.055 (FRS), 0.029 (ASCVD); P<0.05
All-Cause Mortality	INSPIRE-T/Framingham [7]	IC Clock vs. 1st/2nd Gen Clocks	DNAm IC outperformed 1st and 2nd-gen clocks in predicting mortality.	Established via Cox proportional hazards models
CVD Incidence	African Americans (GENOA), n=1,100 [87]	GrimAA vs. FRS/ASCVD	A 5-year increase in GrimAA was associated with 54% higher CVD risk.	HR: 1.54 (95% CI: 1.22-2.01)
10-Year Disease Onset	18,859 individuals [21]	14 Clocks vs. Traditional Risk Factors	For 35 diseases, adding a 2nd-gen clock improved classification accuracy by >1% (AUC>0.80).	Bonferroni-significant (P<0.05/174)

The evidence indicates that while clinical risk scores remain powerful tools, second-generation epigenetic clocks provide additional, independent predictive information. A large-scale, unbiased comparison of 14 epigenetic clocks found that second-generation models significantly outperformed first-generation clocks for disease prediction [21]. Moreover, in specific clinical contexts, such as predicting mortality and respiratory and liver conditions, these clocks demonstrated particular promise for enhancing risk stratification [21].

Association with Cardiometabolic Risk Factors

Beyond direct outcome prediction, the association of epigenetic age acceleration with established cardiometabolic risk factors further validates their biological relevance. A study in an African American cohort demonstrated that different epigenetic clocks associated with distinct risk profiles [87]:

• GrimAge Acceleration (GrimAA) was significantly associated with higher fasting glucose, lower LDL-C, higher triglycerides, and higher pulse pressure.
• PhenoAge Acceleration (PhenoAA) was linked to higher fasting glucose levels.
• Extrinsic Epigenetic Age Acceleration (EEAA) was associated with higher fasting insulin and pulse pressure.
• Intrinsic Epigenetic Age Acceleration (IEAA) was correlated with higher systolic blood pressure.

These associations suggest that epigenetic clocks capture aspects of physiological dysregulation that are relevant to, but not fully captured by, traditional risk factors.

Detailed Experimental Protocols and Methodologies

Protocol: Assessing Epigenetic Age Acceleration for CVD Risk Prediction

The following workflow summarizes a typical protocol from studies that have benchmarked epigenetic clocks against clinical risk scores [87].

Detailed Methodology

• Cohort Selection and Baseline Characterization: Studies typically utilize well-phenotyped longitudinal cohorts like the Framingham Heart Study Offspring Cohort [47] [88] or the GENOA study [87]. Participants undergo baseline clinical assessments, including measurement of blood pressure, lipid panels, fasting glucose, and anthropometrics, and complete lifestyle questionnaires.

• DNA Methylation Profiling: DNA is isolated from peripheral blood samples. Methylation profiling is performed using standardized platforms like the Illumina Infinium EPIC BeadChip or the 450K array, whichinterrogate methylation status at hundreds of thousands of CpG sites [47] [7] [87]. Raw data undergoes quality control and normalization.

• Calculation of Epigenetic Age and Age Acceleration: Methylation data from quality-controlled samples are input into pre-trained algorithms to calculate epigenetic age (e.g., GrimAge, PhenoAge, DunedinPACE). For static clocks, epigenetic age acceleration (EAA) is typically derived as the residual from a linear regression of epigenetic age on chronological age, representing the discrepancy between biological and chronological age [87]. DunedinPACE is interpreted directly as a pace of aging [47].

• Calculation of Clinical Risk Scores: The Framingham Risk Score (FRS) and Atherosclerotic Cardiovascular Disease (ASCVD) risk equation are calculated for each participant using their clinical data according to established formulas, which incorporate age, sex, cholesterol levels, blood pressure, smoking status, and diabetes status [87].

• Statistical Analysis for Outcome Prediction:

  • Association with Incidence: Cox proportional hazards regression models are used to test the association between EAA and time-to-event outcomes (e.g., incident CVD, death), adjusting for confounders like sex and chronological age, and sometimes further for clinical risk factors.
  • Model Improvement Analysis: The improvement in predictive performance upon adding EAA to models containing only clinical risk scores is assessed. Metrics include:
    • C-statistic: Evaluates the model's ability to discriminate between those who do and do not experience the event.
    • Likelihood Ratio Test: Determines if the model with the EAA is a statistically significantly better fit.
    • Net Reclassification Index (NRI): Quantifies improvement in correctly reclassifying individuals into higher or lower risk categories [87].

Protocol: Large-Scale Comparison of Clocks for Disease Onset

A 2025 study by Mavrommatis et al. provides an unbiased protocol for comparing multiple clocks against a wide array of diseases [21].

The Scientist's Toolkit: Key Research Reagents and Materials

Table 2: Essential Materials and Reagents for Epigenetic Aging Research

Item	Specification/Example	Primary Function in Research
DNA Methylation Array	Illumina Infinium EPIC BeadChip (850k sites) or 450K BeadChip	Genome-wide profiling of methylation status at CpG sites; the primary source of raw data for clock calculation.
Bioinformatics Pipelines	Preprocessing packages (e.g., minfi in R), GitHub-hosted clock algorithms (e.g., DunedinPACE, PC-Clocks [47])	Raw data normalization, quality control, and computation of specific epigenetic age estimates from methylation data.
Validated Cohort Data	Longitudinal cohorts with DNAm and health outcomes (e.g., Framingham Heart Study [47] [88], GENOA [87], Generation Scotland [21])	Provides the necessary linked data of methylation, clinical variables, and long-term follow-up for validation studies.
Clinical Risk Score Calculators	Framingham Risk Score (FRS) algorithms, ASCVD Risk Equation algorithms	Standardized calculation of established clinical risk benchmarks for head-to-head comparison.
Statistical Software	R or Python with specialized packages (e.g., survival for Cox models, nricens for NRI)	Conducting survival analyses, model comparisons, and calculating performance metrics to benchmark predictive utility.

The consistent finding across multiple studies is that second-generation epigenetic clocks, particularly GrimAge and the newer IC clock, demonstrate significant associations with mortality, CVD incidence, and a broad range of age-related diseases, often providing predictive information beyond established clinical risk scores [21] [7] [87]. While the improvement in absolute discrimination (e.g., C-statistic) may be modest, the significant net reclassification improvement indicates these clocks can better stratify individuals into appropriate risk categories [87].

For researchers and drug development professionals, these findings imply that epigenetic clocks, especially second-generation and pace-of-aging measures, are valuable tools for:

• Risk Stratification: Identifying high-risk individuals earlier than traditional methods alone.
• Study Endpoints: Serving as biomarkers of biological aging in clinical trials targeting aging-related mechanisms.
• Mechanistic Insight: Offering insights into the biological pathways linking social determinants, lifestyle, and health outcomes [12].

Future research should focus on further validating these clocks in diverse populations, refining them to be more causally informative, and integrating them into clinical and pharmaceutical development workflows.

Conclusion

The evidence strongly affirms the superiority of second-generation epigenetic clocks over their first-generation predecessors for health-oriented research and drug development. By moving beyond chronological age to incorporate phenotypic data and mortality risk, clocks like GrimAge and PhenoAge offer more powerful prediction of age-related outcomes, greater responsiveness to interventions, and stronger clinical relevance. While challenges regarding technical reliability, population diversity, and biological interpretation persist, computational advancements and novel biomarkers like the Intrinsic Capacity (IC) clock are paving the way forward. For future biomedical research, prioritizing second-generation clocks in study design will be crucial for accurately evaluating therapeutic efficacy and understanding the biological mechanisms of aging.

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