Greater than the sum of its parts: combining epigenetic clocks to characterize the association of biological age acceleration and adiposity in young Filipino adults

This study demonstrates that combining multiple epigenetic clocks into a holistic biological age acceleration factor reveals significant positive associations between adiposity measures (BMI, waist circumference, and waist-to-height ratio) and accelerated biological aging in a young Filipino adult cohort.

The Gist

Imagine your body has two different clocks running on the wall.

The first is the Wall Clock (Chronological Age). It simply counts the years you've been alive. If you were born in 2003, it says you are 21 years old. This clock is perfect, but it doesn't tell you how your body is actually feeling.

The second is the Wear-and-Tear Clock (Biological Age). This clock measures how fast your cells are aging based on your lifestyle, stress, and health. If you eat well and exercise, this clock might tick slower than the Wall Clock. If you have high stress or carry extra weight, it might tick faster.

This paper is about trying to get the most accurate reading from that second "Wear-and-Tear" clock, specifically in young adults in the Philippines, and seeing how carrying extra weight affects it.

The Problem: Too Many Clocks, Too Much Confusion

Scientists have invented many different "Wear-and-Tear" clocks using DNA (the instruction manual inside your cells). Some clocks look at your blood sugar, some at your smoking history, and some at how fast your cells are dividing.

The problem is that these clocks often disagree. One might say you are aging fast, while another says you are aging slowly. It's like asking five different weather forecasters if it's going to rain, and they all give you different answers. This makes it hard to know what's really going on.

The Solution: The "Super-Weatherman"

The researchers in this paper had a clever idea. Instead of picking just one clock, they decided to combine them all into one "Super-Weatherman."

They used a statistical tool called Factor Analysis. Think of this as a smart filter. It looks at all the different clocks and asks: "What part of the story do all these clocks agree on?"

By combining the data from six different clocks, they created a single, super-accurate measure called FactorAge. This new measure filters out the "noise" (the parts where the clocks disagree or make mistakes) and focuses on the shared truth: how fast your body is actually aging.

The Experiment: The "Young Filipino" Test

The researchers tested this new "Super-Weatherman" on 1,745 young adults (average age 21) in Cebu, Philippines. This is important because most previous studies were done on older people or in wealthy Western countries. These young Filipinos are just starting their adult lives, but the country is changing rapidly, with more people eating processed foods and becoming less active.

They measured three things about their bodies:

1. BMI: A general score of how heavy you are for your height.
2. Waist Circumference: How big your belly is.
3. Waist-to-Height Ratio: A specific way to see if your belly fat is too big for your frame.

The Findings: The Belly Connection

Here is what they discovered, using simple terms:

• Fat Speeds Up the Clock: Just like a car engine runs hotter when it's overloaded, the more fat a person carried (especially around the waist), the faster their "Super-Weatherman" clock said they were aging. Even though these people were young, those with higher waist measurements showed signs of being biologically older than their actual age.
• The "Super-Weatherman" is the Best Tool: The combined clock (FactorAge) was better at detecting these signs of aging than any single clock on its own. It was like having a team of experts instead of just one; they caught the problem more clearly.
• The "PC" Upgrade: The researchers also tested a newer, "cleaned-up" version of these clocks (called PC-based clocks). These were even better at spotting the link between belly fat and aging, because they are less confused by random biological noise.
• Men vs. Women: There was an interesting twist. For women, belly fat was strongly linked to aging faster. For men, the link was weaker. The researchers suspect this might be because men in this group were more likely to be physically active (doing manual labor or sports), which might have protected their cells even if they had a bit more weight.

Why This Matters

This study is like a warning light on a car dashboard. It shows that even in young people, carrying extra weight—especially around the middle—starts to wear down the body's engine earlier than expected.

By combining multiple DNA clocks into one "Super-Weatherman," scientists now have a better tool to spot these risks early. This is crucial for countries like the Philippines and other developing nations, where obesity is rising fast. If we can detect biological aging early, we can intervene sooner with diet and lifestyle changes to prevent heart disease and diabetes later in life.

In short: The researchers built a smarter way to measure how fast your body is aging. They found that carrying extra weight, especially around the waist, makes your body age faster, even when you are young. And the best way to see this is to listen to the "consensus" of all the clocks, not just one.

Technical Summary

1. Problem Statement

• Research Gap: While diverse epigenetic clocks capture health risks associated with increased adiposity, their estimates are rarely combined to form a holistic measure of biological age acceleration (BAA). Furthermore, there is a significant lack of research using these clocks in lower-middle-income Asian countries (LMICs), where obesity rates are rising rapidly but environmental and genetic contexts differ from high-income nations.
• Methodological Limitations: Current studies often analyze multiple epigenetic clocks as separate outcomes, leading to issues with inconsistent false discovery adjustments, unclear biological interpretation due to overlapping but distinct mechanisms, and biological noise inherent in individual clock estimates.
• Objective: The study aims to create a robust, latent variable representing BAA by combining multiple epigenetic clocks using factor analysis and to test its association with adiposity (BMI, waist circumference, waist-to-height ratio) in a young Filipino cohort.

2. Methodology

• Study Population: Data were drawn from the Cebu Longitudinal Health and Nutrition Survey (CLHNS), a birth cohort of individuals born in Metro Cebu, Philippines (1983–1984). The analysis included 1,745 participants (mean age 21.7 ± 0.3 years; 45% female). Pregnant women and twins were excluded.
• Exposure Variables (Adiposity):
  • Body Mass Index (BMI).
  • Waist Circumference (WC).
  • Waist-to-Height Ratio (WHtR).
  • Note: Measures were standardized using sex-specific z-scores. Analyses accounted for underweight status (BMI < 18.5) via interaction terms.
• Outcome Variables (Epigenetic Clocks):
  • Six clocks were utilized: Horvath, Hannum, PhenoAge, GrimAge, DNAmTL, and DunedinPACE.
  • Both original versions and Principal Component (PC)-based versions (e.g., PCHorvath 2, PCPhenoAge) were analyzed to mitigate biological noise.
  • Clocks were residualized on chronological age (except DunedinPACE, which represents the pace of aging) to derive BAA measures.
• Statistical Approach:
  • Factor Analysis: Exploratory Factor Analysis (EFA) was performed to create two composite latent variables: FactorAge (based on original clocks) and PCFactorAge (based on PC-corrected clocks). Suitability was confirmed via Kaiser-Meyer-Olkin (KMO) tests (0.81 for PC clocks).
  • Regression Modeling: Multivariate linear regression tested associations between adiposity measures and BAA outcomes. Models were adjusted for sex, underweight status, blood cell proportions, lifetime household wealth, smoking, and female-specific reproductive factors (parity, breastfeeding).
  • Multiple Testing: Benjamini-Hochberg correction was applied for individual clock analyses.

3. Key Contributions

• Novel Methodological Framework: The study introduces a factor analysis approach to combine multiple epigenetic clocks into a single latent variable (FactorAge/PCFactorAge). This addresses the "noise" issue in individual clocks and provides a unified metric of biological aging.
• LMIC Context: It provides the first comprehensive analysis of adiposity-epigenetic age associations in a young adult cohort from a lower-middle-income Asian country, addressing a critical geographic and demographic gap in the literature.
• Comparative Analysis: The paper rigorously compares original clocks, PC-corrected clocks, and the combined factor scores, demonstrating that PC-based versions generally offer higher reliability and stronger associations with adiposity.

4. Key Results

• Factor Analysis Validity:
  • Data was highly suitable for factor analysis (KMO = 0.81 for PC clocks).
  • A single factor solution was identified. In the PC-based model, PCHorvath 2, PCHannum, and PCPhenoAge had the highest factor loadings (>0.78), indicating they drive the latent variable most strongly.
• Adiposity Associations:
  • Combined Measures: Both FactorAge and PCFactorAge showed statistically significant positive associations with all adiposity measures (BMI, WC, WHtR).
    • Example: A 0.1 increase in WHtR corresponded to a 0.164 SD increase in FactorAge ($p=0.001$) and 0.163 SD increase in PCFactorAge ($p<0.001$).
  • Individual Clocks:
    • PC-based clocks generally showed stronger associations with adiposity than their original counterparts.
    • PCPhenoAge and PCHannum were the most sensitive, showing significant associations with all three adiposity measures.
    • DNAmTL (and PCDNAmTL) showed no significant association with any adiposity measure, consistent with previous findings that telomere length estimators are less sensitive to adiposity than other clocks.
  • DunedinPACE: Showed unique sex-specific associations. It was strongly associated with adiposity in females but showed weaker or non-significant associations in males (except for WHtR). This suggests males with higher BMI may have higher muscle mass rather than adiposity, which aligns with DunedinPACE's training on organ system integrity (including fitness markers).
• Model Fit: Models using PC-based clocks and the combined FactorAge variables demonstrated better goodness-of-fit ($R^2 > 0.4$) compared to most individual original clocks.

5. Significance

• Robustness of Latent Variables: The study validates that combining epigenetic clocks via factor analysis creates a more robust and reliable measure of biological age acceleration than any single clock, effectively filtering out biological noise.
• Early Detection in LMICs: The findings demonstrate that even in a young (21-year-old), relatively lean population with high rates of early-life undernutrition, excess adiposity is already detectable as accelerated biological aging. This highlights the potential for early intervention strategies in LMICs facing an obesity transition.
• Sex-Specific Nuances: The identification of sex-specific patterns (particularly with DunedinPACE) underscores the need for stratified analyses in aging research, as biological aging mechanisms may respond differently to adiposity in men and women.
• Future Directions: The proposed factor analysis framework offers a scalable method for future studies to integrate new or emerging epigenetic clocks, improving the generalizability of biological age research across diverse populations.