Epigenetic Clock Analysis: Accurate Biological Age Insights for Aging Research
CD Genomics' Epigenetic Clock Analysis service provides DNA methylation–based biological age estimates and age-acceleration metrics for cohorts in aging and age-related disease research. High-precision, configurable clock models help quantify aging processes and evaluate intervention effects at the study or cohort level.
By combining validated methylation platforms with dedicated bioinformatics pipelines, this service supports biomarker discovery, characterization of age-related disease mechanisms, and prioritization of aging-targeted interventions in preclinical and translational research.
Designed for academic, biomedical, and pharmaceutical research teams, our epigenetic age analysis service is a robust, data-driven tool for aging research.
For research use only. Not for clinical or personal use.
Why Epigenetic Clock Analysis is Critical for Aging Research
For anyone studying aging, chronological age—just how many years someone's been alive—doesn't tell the whole story. What really matters is biological age: how their cells, tissues, and organs are actually aging at a molecular level. That's where epigenetic clock analysis comes in—it's the most honest, detailed way to get to the heart of aging by looking at DNA methylation patterns, which reveal the hidden clues traditional age metrics miss.
Epigenetic clock analysis isn't just a tool for calculating a number. It's a window into the why behind aging and disease. It shows you what's driving these processes at a molecular level—something you can't get from regular tests. This is a game-changer for anyone researching aging, chronic illnesses, or potential treatments.
Find the biomarkers that matter
Epigenetic clocks help you spot the exact molecular signs of aging linked to diseases like cancer, neurodegeneration, or heart issues. They're like a compass pointing to the signals traditional methods overlook—signals that could be the key to your next breakthrough.
Understand how aging really works
This analysis lets you see how genes, environment, and lifestyle interact to shape aging. It's not abstract—it's real, actionable data that makes your research more meaningful. You're not guessing about what affects aging; you're seeing it firsthand.
It fits your work
Whether you're unpacking aging mechanisms, testing a new therapy, or tracking disease progression, epigenetic clock analysis adapts to your focus. From oncology to immunology to regenerative medicine, it's a tool that works for almost any research area.
When you use epigenetic clock analysis, you're not just doing research—you're unlocking insights that change how you think about aging. It's the kind of information that helps your research team design better prevention trials, evaluate aging-targeted therapies, and prioritize drug development strategies. For anyone serious about aging research, this isn't an add-on—it's a must-have.
It's simple: If you want to understand aging at a deeper level, epigenetic clock analysis is the key. It's the difference between guessing how someone's aging and knowing—and that's what makes all the difference in your work.
Popular Epigenetic Clock Models: Matching Strengths to Your Research Goals
Let's break down the most popular epigenetic clock models—each has its own strength, so picking the right one depends on what you're researching. Here's the lowdown:
At a glance, different epigenetic clock models are suited to different study designs:
Horvath's clock: best for investigating aging mechanisms across multiple tissues or cell types.
Hannum's clock: optimized for blood- and immune-related studies using blood-based DNA methylation markers.
PhenoAge: integrates DNA methylation with phenotypic and biochemical indicators to link epigenetic aging to overall physiological status and age-related outcomes.
GrimAge: focuses on lifespan, mortality risk, and age-related disease progression, making it suitable for studies of healthspan and survival.
Horvath's Clock
This is the true workhorse of epigenetic clocks. It uses specific CpG sites to estimate biological age, and it works for any tissue—blood, brain, liver, kidney, you name it. If your research looks at how different organs or cell types age, this is your go-to. It's flexible, widely trusted, and adapts to almost any study focus—no wonder it's so popular.
Hannum's Clock
Hannum's clock is all about blood. It's designed to predict biological age using methylation patterns in blood cells, so it's perfect if your work focuses on blood-related tissues or the immune system. Whether you're studying how the immune system changes with age or looking for blood-based aging biomarkers, this model fits like a glove. It's tailored to blood, so you get precise insights for that specific focus.
PhenoAge
PhenoAge goes beyond just a number. It combines DNA methylation data with clinical markers—like albumin or CRP—to give you a fuller picture of aging. It doesn't just tell you biological age; it helps you see how genetic markers tie to real physiological health, and even assesses age-related disease risk. If you want a comprehensive view of aging (not just a score), this is the model for you.
GrimAge
GrimAge is the new kid on the block, but it's already making waves. It uses DNA methylation and plasma protein markers to predict two big things: lifespan (how long someone lives) and healthspan (how long they live well). It's great for evaluating disease risk—whether you're studying heart health, cognitive decline, or cancer—or how aging impacts different systems. It's accurate and针对性, so it's perfect if you're researching lifespan or disease progression.
Comparing Epigenetic Clocks: Choosing the Right Model for Your Research
In Epigenetic Clock Analysis, different clock models use varying numbers of CpG sites and apply distinct training methods and prediction targets. Understanding these differences is crucial for selecting the right model for your specific research needs. Below is a detailed comparison of the key epigenetic clock models commonly used in aging and disease research:
Comparison of Key Epigenetic Clocks
Metric
Horvath's Clock
Hannum's Clock
PhenoAge
GrimAge
Number of CpGs
353
71
513
1030
Illumina Array
27 K, 450 K
450 K
27 K, 450 K, 850 K
450 K, EPIC
Number of Subjects
7844
482
9926
6935
Age Range
0–100
19–101
0–100
46–78
Tissues Used
51 healthy tissues and cell types
Whole blood
Whole blood
Whole blood
Training Phenotype
Chronological age
Chronological age
Lifespan
Lifespan
Regression Model
Penalized regression model (elastic net)
Penalized regression model
Cox penalized regression model
Elastic net Cox regression model
Prediction Accuracy (r)
0.960
0.905
N/A
N/A
Applications
How Epigenetic Clock Analysis Drives Insights into Aging and Disease
Epigenetic Clock Analysis provides key insights into the biological processes of aging and how they relate to disease development. By analyzing DNA methylation patterns, this tool offers a deeper understanding of how aging occurs at the molecular level and how it impacts health outcomes.
Understanding Aging Mechanisms
Aging is a complex process influenced by various factors. Epigenetic Clock Analysis helps uncover how these factors affect biological age by identifying:
Age-related Epigenetic Changes: Analyzing how specific DNA methylation patterns evolve with age, shedding light on the molecular processes behind aging.
Biomarkers of Aging: Identifying markers that indicate biological age, allowing for a better understanding of how tissues and organs age.
This analysis provides valuable data for aging research, highlighting how biological age can be influenced by environmental and lifestyle factors.
Disease Progression and Aging
Many diseases are linked to aging, and Epigenetic Clock Analysis is essential for understanding how aging influences disease development. It allows researchers to:
Track Disease Mechanisms: Study how accelerated biological aging contributes to the progression of diseases like cancer, neurodegeneration, and metabolic disorders.
Connect Aging to Disease: Identify how shifts in biological age can correlate with disease onset and severity, providing insights into disease pathways.
This approach helps identify epigenetic markers related to specific diseases, advancing understanding of disease mechanisms.
Evaluating Interventions and Research Outcomes
Epigenetic Clock Analysis is also valuable for evaluating the impact of various interventions aimed at slowing aging or reversing age-related damage. Researchers can:
Monitor Intervention Effects: Assess the effectiveness of drugs, lifestyle changes, or other interventions by observing their impact on biological age.
Optimize Research Outcomes: Use biological age data to refine research methods and understand the broader effects of interventions on aging.
By providing an accurate measure of biological age, this analysis helps researchers gauge the success of their interventions and improve their understanding of aging and disease.
Advantages
At CD Genomics, Your Epigenetic Clock Research is Covered
From custom models to high-precision analysis, we're here to support your aging studies with data and insights that drive your research forward.
High-Precision DNA Methylation Data
We use advanced high-throughput sequencing to profile DNA methylation across thousands of CpG sites, capturing even the smallest methylation shifts for accurate biological age predictions. Whether you're working with small cohorts or large multi-site projects, we handle big data efficiently and deliver reliable results.
Custom Epigenetic Clock Models
Aging research isn't one-size-fits-all, and neither are our clocks. We specialize in creating custom models tailored to your research needs:
Species/Tissue-Specific Models: Whether you need a model for humans, animals, or specific tissue types, we can build one to match your focus.
Disease-Focused Models: For age-related diseases like cancer or neurodegeneration, we build models focused on the biomarkers that matter to your study.
Expert Bioinformatics Support
Raw data is just the beginning. Our expert bioinformatics team uses advanced algorithms and machine learning to interpret methylation data, turning it into actionable insights. We integrate data from multiple sources—clinical information, experimental variables, and methylation data—to give you a complete picture of biological age and aging-related risks.
Flexible Sample Handling
We understand that each project has different sample needs. Whether you're working with clinical samples or large cohorts, we accommodate all sample types, from blood to tissue to saliva. We handle sample collection, shipment, and processing for researchers worldwide, ensuring a seamless experience.
Why Choose Us?
Accurate Predictions: Our models have high correlation with biological age, providing reliable results you can trust.
Scientific Foundation: We use DNA methylation, the most reliable biomarker for biological age, to ensure robust, scientifically-backed results.
Impact of Interventions: Our analysis helps quantify age-related disease risk at the cohort level and assess the impact of interventions in research, so you can test what truly slows aging in your models or trials.
At CD Genomics, we're not just a service provider—we're your partner in aging research. We'll work with you every step of the way to deliver the data, models, and insights needed to unlock the secrets of biological age.
Service Workflow
Our Epigenetic Clock Analysis Workflow
Our epigenetic clock projects follow a streamlined, research-focused workflow:
1. Project consultation – Align research goals, sample types, and epigenetic clock models.
2. Sample receipt & QC – Receive DNA samples and verify integrity and quantity.
3. DNA methylation profiling – Generate high-quality methylation data using validated platforms.
4. Clock computation – Apply selected clock models to estimate biological age and related metrics.
5. Data analysis – Summarize age measures and associations at the cohort or group level.
6. Reporting – Deliver research-ready results, figures, and a concise methods overview.
