Accelerated Epigenetic Aging Is Associated With Multiple Cardiometabolic, Hematologic, and Renal Abnormalities: A Project Baseline Health Substudy
Circulation: Genomic and Precision Medicine
Abstract
Background:
Epigenetic clocks estimate chronologic age using methylation levels at specific loci. We tested the hypothesis that accelerated epigenetic aging is associated with abnormal values in a range of clinical, imaging, and laboratory characteristics.
Methods:
The Project Baseline Health Study recruited 2502 participants, including 1661 with epigenetic age estimates from the Horvath pan-tissue clock. We classified individuals with extreme values as having epigenetic age acceleration (EAA) or epigenetic age deceleration. A subset of participants with longitudinal methylation profiling was categorized as accelerated versus nonaccelerated. Using principal components analysis, we created phenoclusters using 122 phenotypic variables and compared individuals with EAA versus epigenetic age deceleration, and at one year of follow-up, using logistic regression models adjusted for sex (false discovery rate [Q] <0.10); in secondary exploratory analyses, we tested individual clinical variables.
Results:
The EAA (n=188) and epigenetic age deceleration (n=195) groups were identified as having EAA estimates ≥5 years or ≤−5 years, respectively. In primary analyses, individuals with EAA had higher values for phenoclusters summarizing lung function and lipids, and lower values for a phenocluster representing physical function. In secondary analyses of individual variables, neutrophils, body mass index, and waist circumference were significantly higher in individuals with EAA (Q<0.10). No phenoclusters were significantly different between participants with accelerated (n=148) versus nonaccelerated (n=112) longitudinal aging.
Conclusions:
We report multiple cardiometabolic, hematologic, and physical function features characterizing individuals with EAA. These highlight factors that may mediate the adverse effects of aging and identify potential targets for study of mitigation of these effects.
Registration:
URL: https://www.clinicaltrials.gov; Unique identifier: NCT03154346.
DNA methylation (DNAm) is an epigenetic mechanism used by mammalian cells to regulate gene expression through silencing of gene sequences.1,2 DNAm is an important regulatory mechanism for processes such as X-inactivation in females and cell differentiation. Over the last decade, studies have shown an association between DNAm and the aging process.3–7 Specifically, DNAm clocks, or epigenetic clocks, are mathematical models that use DNAm levels at particular cytosine-phosphate-guanine dinucleotides to estimate age; a variety of such clocks, including the Horvath clock,8 have been well-validated.9 Epigenetic age acceleration (EAA) describes the phenomenon in which an individual’s epigenetic age, calculated by epigenetic clocks, is greater than expected based on his or her chronologic age. Interestingly, while this epigenetic age (or biologic age) is highly correlated with chronologic age, epigenetic age can vary widely across individuals with the same chronologic age. Identifying the underlying differences between chronologic age and epigenetic age is critical to understanding pathology associated with adverse aging.
The discordance between chronologic and epigenetic age has morbidity and mortality implications. Within the realm of modifiable risk factors, EAA has been associated with environmental exposures,10 smoking and education,11 and body mass index (BMI).12 At a disease level, EAA has been associated with lung cancer,13 cardiovascular heart disease risk factors,14 and neuronal white matter intensity.15 Importantly, EAA has been associated with mortality, including cardiovascular mortality16 and all-cause mortality.16,17
Although many studies have demonstrated physiological and pathological relationships between clinical data and EAA, few have integrated these data across multiple biologic systems in the context of a contemporary big data approach to provide a more holistic analysis of adverse aging. We leveraged the PBHS (Project Baseline Health Study), which used a deep clinical phenotyping approach to map human health and disease in a contemporary context, to understand the relationships between a wide variety of established and novel clinical factors and EAA.
Methods
The deidentified PBHS data corresponding to this study are available upon request for the purpose of examining its reproducibility. Requests are subject to approval by PBHS governance. This study was approved by the Duke University Institutional Review Board, and all participants were appropriately consented for use of biospecimens and data. The full methods are available in the Supplemental Material.
Results
Baseline Characteristics of the Study Population
Among the 1719 PBHS participants with data available for the methylation assays, 58 who were <20 or >80 years of age were excluded to restrict the analysis to ages expected to have a linear relationship between epigenetic and chronologic age. Of the remaining 1661 participants, 47.7% were male; 61.7% were self-reported White; 16.9% were self-reported Black; and the mean age (SD) was 48.2 years (16.1; Table 1). A high prevalence of disease risk factors was observed, reflecting 60% of the overall PBHS cohort who were recruited based on a high risk for cardiovascular disease or cancer. This subset was similar to the overall PBHS cohort (N=2502) across demographics and risk factors (Table S2).
| n=1661 | |
|---|---|
| Enrollment site, n (%) | |
| CHLI | 387 (23.3) |
| Durham | 351 (21.1) |
| Kannapolis | 361 (21.7) |
| Stanford | 562 (33.8) |
| Age, mean (SD)* | 48.2 (16.1) |
| Age, n (%)* | |
| ≤45 | 739 (44.5) |
| 46–55 | 316 (19.0) |
| 56–65 | 302 (18.2) |
| >65 | 304 (18.3) |
| Female, n (%) | 869 (52.3) |
| Self-reported race, n (%) | |
| Asian | 189 (11.4) |
| Black | 281 (16.9) |
| Other | 166 (10.0) |
| White | 1025 (61.7) |
| Current smoker, n (%) | 193 (11.6) |
| BMI, mean (SD) | 28.6 (7.0) |
| History of hypertension, n (%) | 423 (25.5) |
| History of hyperlipidemia, n (%) | 307 (18.5) |
| Diabetes, n (%) | |
| Diabetes | 242 (14.6) |
| Prediabetes | 325 (19.6) |
| No diabetes | 1094 (65.9) |
| History of cancer, n (%) | 194 (11.7) |
| High-risk CVD cohort, n (%) | 306 (18.4) |
| High-risk lung cancer cohort, n (%) | 128 (7.7) |
| High-risk breast/ovarian cancer cohort, n (%) | 213 (24.5) |
BMI indicates body mass index; and CVD, cardiovascular disease.
*
Chronologic age based on date of birth.
Epigenetic Age and EAA
Of the 1661 PBHS individuals with methylation data at enrollment, 195 (11.7%) showed epigenetic age deceleration (EAD;, that is, residuals from −14.9 to −5.0 years) while 188 (11.3%) had EAA (ie, 5.0–17.3 years). Biologic age and chronologic age were strongly correlated (r=0.95, P<10−16; Figure [A]). Because our measure of difference between biologic and chronologic age was based on regression residuals, its mean was zero by definition; in this sample, it was normally distributed with a median of 0.05 and an SD of 4.2 (Figure [B]). EAA was highly associated with sex: 61% of individuals with EAA were male compared with 39% of individuals with EAD (P=4×10−5).
Distribution of Epigenetic Age, Chronologic Age, and Epigenetic Age Acceleration (EAA) and Epigenetic Age Deceleration (EAD). A, Calculated Horvath epigenetic age versus chronologic age at enrollment in PBHS (Project Baseline Health Study) participants. Darker colors reflect larger absolute estimates of EAA (above the regression line) or EAD (below the regression line). B, Distribution of epigenetic age acceleration estimates (years) in all participants. Colored sections represent participants defined as EAA or EAD and included in the analysis.
PCA Phenocluster Factors and EAA
Unsupervised principal components analysis (PCA) identified 33 orthogonal factors with an eigenvalue >1, each of which represents a set of correlated clinical variables. Each PCA factor, or phenocluster, is a weighted sum of each of the 109 clinical variables with sufficient data for inclusion in PCA; Table S3 presents a summary of these phenoclusters, the clinical variables with an absolute value loading >0.4 in each, and the percentage of the variance explained by each phenocluster. The full set of loadings of all variables on all phenoclusters, not restricted to those with high loadings, are given in Table S4. Bivariate plots of the relationship between each PCA factor and EAA/EAD estimates are included in Figure S1.
In logistic regression analyses, 3 factors were significantly associated with EAA versus EAD after controlling the FDR at Q<0.10 (Table 2). Factor 1 (odds ratio [OR], 0.73 for EAA [95% CI, 0.59–0.89]; P=0.0025, Q=0.048) was composed of variables related to physical function (sit/rise score, leg balance, 6-minute walk distance) with negative loads for measures of obesity (BMI, waist circumference). These results suggest that individuals with EAA have adverse functional measures and relatedly, higher BMI and waist circumference. Factor 2 (OR for EAA, 1.60 [95% CI, 1.18–2.20]; P=0.0029, Q=0.048) was composed of variables related to exercise and pulmonary function, with positive loadings for pulmonary function tests (forced vital capacity, carbon monoxide diffusing capacity, and forced expiratory volume), grip strength, hemoglobin, hematocrit, absolute red blood cells, 6-minute walk distance, and sex. Finally, factor 12 (OR for EAA, 1.32 [95% CI, 1.07–1.62]; P=0.0091, Q=0.0997) contains total cholesterol and LDL (low-density lipoprotein), suggesting that individuals with EAA are more likely to have worse lipids. For each of these factors, we included an interaction term in the model to test for differential associations in males and females; none of the factors had significant interactions with sex. We also examined sex-stratified models, which showed consistent directions of effect for males and females for all 3 factors (Table S5).
| Factor | Description | Odds ratio (95% CI) | P value | Q value |
|---|---|---|---|---|
| 1 | Sit/rise score, mean leg balance, 6-minute walk distance, body mass index (−), waist circumference (−) | 0.73 (0.59–0.89) | 0.0025 | 0.048 |
| 2 | Pulmonary function tests (forced vital capacity, Dlco, forced expiratory volume), grip strength, hemoglobin, hematocrit, red blood cells (absolute), 6-minute walk distance, sex | 1.60 (1.18–2.20) | 0.0029 | 0.048 |
| 12 | Total cholesterol, low-density lipoprotein | 1.32 (1.07–1.62) | 0.0091 | 0.0997 |
ORs from a logistic regression model adjusted for sex; ORs >1 indicate an increase in EAA with an increase in the factor; ORs <1 indicate a decrease in EAA with an increase in the factor. Components of each factor with absolute loadings <0.4 are shown; variables designated (−) are negatively loaded on the factors while all others are positively loaded. DLCO indicates carbon monoxide diffusing capacity; EAA, epigenetic age acceleration; FDR, false discovery rate; and OR, odds ratio.
Although factor 7 does not meet significance after adjustment for multiple tests, we note that it was nominally associated with EAA (OR, 0.82 [95% CI, 0.67–0.99]; P=0.04, Q=0.3) and is composed of variables related to ocular health, including retinal thickness measurements and total macular volume, suggesting that individuals with high EAA may have lower ocular function measures. No other PCA factors were nominally associated with EAA.
Association Between Individual Clinical Variables and EAA
In exploratory secondary comparisons of individual clinical variables, we found that a wide range of clinical parameters were associated with EAA: BMI, waist circumference, and total neutrophils displayed the most significant association with EAA versus EAD (Q<0.1), but a range of other individual variables were nominally associated (P<0.05; Table 3). Given the wide array of results in this exploratory analysis, the subsequent results are presented by overall organ system category, including cardiometabolic, hematologic, and renal traits in addition to digital health measures and physical function markers.
| EAD* | EAA* | Odds ratio (95% CI) | P value | |
|---|---|---|---|---|
| Laboratory | ||||
| Triglycerides, mg/dL | 128.3 (83.8) | 158.3 (122.8) | 1.07 (1.01–1.13)† | 0.02 |
| High-density lipoprotein, mg/dL | 59.7 (18.6) | 53.4 (17.0) | 0.88 (0.77–0.99)‡ | 0.04 |
| Total neutrophils, k/mcl | 3.7 (1.3) | 4.1 (1.6) | 1.26 (1.09–1.47) | 0.002§ |
| Total neutrophils (% WBC) | 58.8 (9.3) | 60.6 (10.1) | 1.02 (1–1.04) | 0.05 |
| Neutrophil:lymphocyte ratio, k/mcL | 2.1 (1.0) | 2.4 (1.2) | 1.21 (1–1.48) | 0.05 |
| White blood cell count | 6.1 (1.7) | 6.6 (2.0) | 1.18 (1.05–1.33) | 0.005 |
| Albumin, g/dL | 4.43 (0.30) | 4.39 (0.30) | 0.42 (0.20–0.87) | 0.02 |
| Glomerular filtration rate, mL/min/1.73 m2, based on MDRD equation | 90.3 (23.0) | 85.3 (19.6) | 0.989 (0.979–0.999) | 0.03 |
| Uric acid | 5.0 (1.2) | 5.5 (1.4) | 1.29 (1.08–1.54) | 0.005 |
| Clinical | ||||
| History of hypertension, n (%) | 52 (26.7) | 70 (37.2) | 1.7 (1.09–2.67) | 0.02 |
| Body mass index | 27.8 (6.3) | 30.7 (8.1) | 1.06 (1.03–1.1) | 0.0001§ |
| Waist circumference, cm | 91.3 (15.3) | 99.2 (18.7) | 1.02 (1.01–1.04) | 0.0002§ |
| Average daily number of steps in first 30 days | 8558 (3674) | 7786 (3599) | 0.91 (0.85–0.98)‖ | 0.009 |
| Hand-grip strength | 72.5 (23.0) | 84.5 (25.9) | 1.014 (1.001–1.026) | 0.03 |
| Selection for high-risk lung cancer cohort, n (%) | 13 (6.7) | 28 (14.9) | 2.58 (1.3–5.4) | 0.009 |
| Selection for high-risk CVD cohort, n (%) | 34 (17.4) | 51 (27.1) | 2.02 (1.22–3.39) | 0.007 |
| Imaging | ||||
| Average RNFL thickness, μm | 93.1 (11.8) | 89.8 (12.6) | 0.979 (0.961–0.996) | 0.02 |
| RNFL superior quad thickness, μm | 115.3 (18.3) | 111.3 (19.4) | 0.988 (0.976–0.999) | 0.04 |
Except where indicated, the results are expressed as the mean (SD). ORs from a logistic regression model adjusted for sex; ORs >1 indicate an increase in epigenetic age acceleration with an increase in the variable; ORs <1 indicate a decrease in epigenetic age acceleration with an increase in the variable. CVD indicates cardiovascular disease; EAA, epigenetic age acceleration; EAD, epigenetic age deceleration; FDR, false discovery rate; MDRD, modification of diet in renal disease; OR, odds ratio; RNFL, retinal nerve fiber layer; and WBC, white blood cell.
*
Age acceleration categories are determined using ±5-year cutoffs.
†
OR and CI are for an increase of 25 mg/dL.
‡
OR and CI are for an increase of 10 mg/dL.
§
Variables with FDR Q<0.1.
‖
OR and CI are for an increase of 1000 steps/d.
In the cardiometabolic trait category, in addition to the significant association with BMI (OR, 1.06 [95% CI, 1.03–1.1]; P=8×10−5, Q=0.01) and waist circumference (OR, 1.02 [95% CI, 1.01–1.04]; P=2×10−4, Q=0.02), triglycerides, HDL (high-density lipoprotein), and history of hypertension were nominally associated with EAA versus EAD (OR, 1.07 [95% CI, 1.01–1.13]; P=0.02 for a 25 mg/dL increase in triglycerides, OR, 0.88 [95% CI, 0.77–0.99]; P=0.04 for a 10 mg/dL increase in HDL, and OR, 1.7 [95% CI, 1.09–2.67]; P=0.02, respectively). Specifically, individuals with EAA had higher BMI (difference of 2.9 kg/cm2, standard error [SE]=0.8), waist circumference (difference of 7.9 cm, SE=1.8), and triglycerides (difference of 30.0 mg/dL, SE=10.8) and lower HDL (difference of 6.4 mg/dL, SE=1.8). These individual variable results support results from the primary analyses of PCA factor 1 (physical function) and factor 12 (lipids).
In the hematologic trait category, neutrophils displayed a significant association with EAA versus EAD (OR, 1.26 [95% CI, 1.09–1.47]; P=0.002, Q=0.09) after FDR adjustment. Other individual variables in this category that were nominally associated included white blood cell count and neutrophil-to-lymphocyte ratio (OR, 1.18 [95% CI, 1.05–1.33]; P=0.005 and OR, 1.21 [95% CI, 1.00–1.48]; P=0.05, respectively).
In the renal trait category, uric acid displayed the most significant association with EAA (OR, 1.29 [95% CI, 1.08–1.54]; P=0.005), as individuals with high EAA had nominally higher mean uric acid levels compared with individuals with EAD. Albumin and estimated glomerular filtration rate showed suggestive associations with EAA (OR, 0.42 [95% CI, 0.20–0.87]; P=0.02 and OR, 0.989 [95% CI, 0.979–0.999]; P=0.03, respectively).
With regards to digital health parameters and physical function, the average daily step count in the first 30 days was associated with EAA (OR, 0.91 [95% CI, 0.85–0.98]; P=0.009 for a 1000 step/d increase), with lower mean step counts in individuals with EAA (average step count 772 steps fewer [SE=421] than that of participants with EAD). In addition, grip strength was nominally associated with EAA (OR, 1.014 [95% CI, 1.001–1.026]; P=0.03). Participants with EAA had a mean grip strength 12 lbs higher (SE=2.6) than that of individuals with EAD.
The PBHS study was enriched for individuals considered to be high-risk for lung cancer, breast cancer, and cardiovascular disease. Belonging to the high-risk lung cancer category or the high-risk cardiovascular disease cohort was associated with high versus EAD (OR, 2.58 [95% CI, 1.30–5.40]; P=0.009 and OR, 2.02 [95% CI, 1.22–3.39]; P=0.007, respectively). Both the average retinal nerve fiber layer thickness and superior quadrant retinal nerve fiber layer thickness (measures of retinal ganglion cell axonal loss), which decrease in diseased states (ie, glaucoma18), were lower by an average of 3.29 µm (SE=1.3) and 4.02 µm (SE=2.0) in participants with EAA (OR, 0.979 [95% CI, 0.961–0.996]; P=0.02 and OR, 0.988 [95% CI, 0.976–0.999]; P=0.04, respectively).
Longitudinal Epigenetic Age Changes
A subset of 260 individuals had both enrollment methylation data and a repeat methylation array at year 1 of follow-up. In this subset, the range of difference between change in epigenetic age and one year (chronologic difference in age) was −6.4 to 12.7 years: 148 individuals had a difference of >1 year (accelerated aging), whereas 112 individuals had a difference of <1 year (nonaccelerated aging). No PCA factors were significantly associated with accelerated aging. In the secondary individual variable analysis, few clinical variables from enrollment were nominally associated with accelerated aging (Table 4): diastolic blood pressure (OR, 1.03 [95% CI, 1–1.05]; P=0.04), blood glucose (OR, 1.01 [95% CI, 1–1.01]; P=0.04), mean corpuscular hemoglobin concentration (OR, 0.61 [95% CI, 0.44–0.84]; P=0.003), and center subfield thickness (OR, 0.987 [95% CI, 0.977–0.997]; P=0.01). Specifically, individuals with accelerated aging had higher mean diastolic blood pressure and blood glucose readings, and lower values for mean corpuscular hemoglobin concentration and center subfield thickness, compared with individuals with nonaccelerated aging.
| Clinical measure | Nonaccelerated aging | Accelerated aging | OR (95% CI) | P value |
|---|---|---|---|---|
| Diastolic blood pressure, mm Hg | 78.0 (9.7) | 80.6 (10.7) | 1.03 (1.0–1.05) | 0.04 |
| Blood glucose, mg/dL | 107 (44) | 123 (67) | 1.01 (1.0–1.01) | 0.04 |
| Mean corpuscular hemoglobin concentration (% RBC) | 33.0 (0.8) | 32.7 (0.9) | 0.61 (0.44–0.84) | 0.003 |
| Center subfield thickness (center 1 mm) | 263 (32) | 254 (25) | 0.99 (0.98–1.0) | 0.01 |
Results are presented as mean (SD) for each measure. ORs from a logistic regression model adjusted for sex. ORs >1 indicate an increase in accelerated aging with an increase in the clinical measure; ORs <1 indicate a decrease in accelerated aging with an increase in the clinical measure. FDR indicates false discovery rate; OR, odds ratio; and RBC, red blood cell. No results have FDR Q<0.1.
Discussion
In one of the most comprehensive studies of clinical, imaging, and laboratory variables associated with epigenetic aging, we found that a wide array of cardiometabolic, physical function, and laboratory characteristics are associated with EAA. These results have implications for better understanding the risk factors and effects of accelerated biologic aging.
These findings support a growing body of literature that suggests that age acceleration is associated with a variety of cardiometabolic, hematologic, and renal pathologies.19–24 Within the cardiometabolic context, several studies have shown a relationship between EAA and higher BMI,12,25 worse lipid function,26 and a higher prevalence of hypertension.27 Yet, few studies have integrated these associations to provide a multidimensional characterization of individuals with EAA.
In our primary analysis of factors created using unsupervised PCA, we found that EAA was most significantly associated with phenoclusters related to poor cardiometabolic risk factor health. Specifically, EAA was associated with phenoclusters characterized by BMI, waist circumference, total cholesterol, and LDL (all higher in individuals with EAA than in individuals with EAD) along with physical function parameters (lower in individuals with EAA). This finding was bolstered by our exploratory analysis of individual clinical variables, in which many cardiometabolic traits were nominally associated with EAA. Specifically, we found that individuals with EAA had higher BMI, higher triglycerides, lower HDL, a higher prevalence of hypertension, and a greater likelihood of being in the high-risk cardiovascular group than individuals with EAD. These relationships are consistent with the existing literature that suggests epigenetic involvement in metabolic diseases, such as hyperlipidemia.18,28,29 More recently, del C. Gomez-Alonso et al30 demonstrated through an epigenome-wide association study analysis that DNAm is intricately involved in lipid composition and concentrations along the lipid synthesis pathway.
Our PCA phenocluster analysis also identified a significant association between factor 2 (composed of hematologic markers, pulmonary function tests, and physical function parameters) and EAA. This phenocluster represents a clinically coherent relationship, with a cluster of hematologic markers of oxygen delivery (ie, hemoglobin and hematocrit), pulmonary function parameters and markers of physical function (grip strength and 6-minute walk distance). However, the direction of individual variables represents a counterintuitive relationship between exercise-related markers and EAA, with hemoglobin, hematocrit, pulmonary function test parameters, and physical function parameters all higher in individuals with EAA, corroborated by analyses of individual variables. This is in contradistinction to prior studies that demonstrated that exercise can slow epigenetic aging.25,31 Our somewhat counterintuitive results from this phenocluster may be due to a complex relationship with sex (which had a high factor loading in the factor), although our results were all adjusted for sex, and post hoc analyses showed no interaction between sex and this factor.
Exploratory secondary analyses of individual variables primarily corroborated results for PCA factors significantly associated with EAA after adjustment for multiple comparisons; however, these exploratory analyses also highlighted other complementary associations in hematologic, renal and ocular traits. For example, neutrophil count, neutrophil percentage, neutrophil-to-lymphocyte ratio, and white blood cell count were higher in individuals with EAA. Given that hematologic markers can approximate inflammation or stress in one’s body, the higher levels of hematologic markers in individuals with EAA could suggest that such individuals suffer from more chronic stress and inflammation than individuals with EAD. In addition, renal markers also displayed a potential relationship with epigenetic aging, consistent with and corroborating existing studies, which have linked kidney disease to biologic aging.32,33 Although exploratory, our imaging analysis of ocular health measurements establishes, for the first time, a putative association between retinal nerve fiber layer thickness and EAA. A decrease in ocular health measures has been associated with pathologies, including glaucoma,18,34 neuronal loss in multiple sclerosis,35 and cognitive impairments in schizophrenia.36
Finally, the nominally significant results of our longitudinal analysis recapitulate some of the overarching results of this study and contribute to our understanding of epigenetic trends over time. This analysis extends the results of our initial methylation assay to include diastolic blood pressure and mean corpuscular hemoglobin concentration, specifically, as relevant cardiometabolic and hematologic parameters, respectively. These results also highlight the importance of center subfield thickness as a potentially important signal of ocular function and are supported by the known association of center subfield thickness with retinopathy severity.37 Lastly, we showed that blood glucose may be an important factor in the longitudinal assessments of participants. Our data suggest a relationship between higher glucose and accelerated epigenetic aging and corroborates with our finding that BMI and lipids are related to epigenetic aging.
Epigenetic age in our analyses was calculated using the validated Horvath pan-tissue method,8 which uses methylation levels at 353 cytosine-phosphate-guanines across the genome, although other epigenetic biologic clocks have been developed. The underlying biology of genes nearest these cytosine-phosphate-guanine sites has been previously evaluated using Ingenuity Pathway Analysis8 and revealed significant networks that included the following functions: hematologic system development, cell death and survival, cellular growth and proliferation, hematopoiesis, hepatocellular peroxisome proliferation, cellular development, and cardiovascular disease. These networks are consistent with the phenoclusters observed in our study, broadly reporting on key cellular functions. Some of the genes with the greatest contribution in this clock include CHST3, involved in extracellular matrix; TEX264, involved in reticulophagy and protein-DNA covalent cross-linking repair; and CCL7, involved in immunoregulatory and inflammatory processes, all processes relevant to the traits we have identified. In reviewing the GWAS catalogue (https://www.ebi.ac.uk/gwas/), 8 of the top 20 most influential clock-related genes have variants associated with relevant traits (ie, TEX264 is associated with BMI-adjusted waist circumference), suggesting that both genetic and epigenetic variation in these genes are concordantly relevant to biologic aging and disease processes.
The strengths of this study include the large number of clinical, laboratory, and imaging parameters analyzed and the study cohort with good racial, sex, and age distribution. Our primary analysis uses a dimension reduction approach to assess the association of weighted clusters of variables, which allows us to explore relationships between disparate variables in an unbiased fashion while maintaining FDR control at 10%. However, study limitations should be noted. Enrollment analyses were cross-sectional and as such, cause and effect relationships could not be assessed. Further, we were limited by a smaller sample size for the longitudinal cohort with only one year of follow-up; the lack of significant results may be due to the fact that the entire sample was divided into accelerated and nonaccelerated aging groups, instead of selecting true extremes for comparison, as we did for the EAA versus EAD analysis. Finally, we did not adjust for multiple comparisons in the secondary analysis of individual variables, given the exploratory nature of this analysis vis-à-vis the analysis of the PCA factors and the desire to look at a broad array of clinical abnormalities in a relatively small sample. Although BMI, neutrophil count, and waist circumference do meet an FDR adjustment for multiple comparisons at the 10% threshold, the other individual variables discussed here should be considered as preliminary results for further study.
Conclusions
In the PBHS study, we found that individuals with EAA were more likely to have metabolic syndrome (a higher lipid profile, hypertension, obesity, a higher waist circumference), in addition to higher inflammatory markers, more abnormal renal markers, lower step counts, a higher risk of lung cancer and cardiovascular disease, and lower ocular markers compared with individuals who had EAD. Further work is needed to better understand the relationship between individuals with EAA versus EAD and, more importantly, how this variability changes over time. Such analysis may provide a framework for targeted interventional therapy to reverse pathological biologic aging.
Article Information
Supplemental Material
Supplemental Methods
Tables S1–S5
Figure S1
References 38–41
Project Baseline Health Study Team
Acknowledgments
The authors would like to thank the participants of the PBHS (Project Baseline Health Study) and acknowledge the dedication and commitment of researchers and study staff.
Footnote
Nonstandard Abbreviations and Acronyms
- BMI
- body mass index
- DNAm
- DNA methylation
- EAA
- epigenetic age acceleration
- EAD
- epigenetic age deceleration
- HDL
- high-density lipoprotein
- LDL
- low-density lipoprotein
- OR
- odds ratio
- PBHS
- Project Baseline Health Study
- PCA
- principal components analysis
- SE
- standard error
Supplemental Material
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Received: 11 March 2022
Accepted: 30 January 2023
Published online: 11 April 2023
Published in print: June 2023
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Disclosures All authors acknowledge institutional research grants from Verily Life Sciences. Dr Haddad received an institutional research grant from Actelion Ltd within the last 2 years and an institutional research grant from Precordior Ltd. Dr Mahaffey reports grants from Verily, Afferent, the American Heart Association (AHA), Cardiva Medical Inc, Gilead, Luitpold, Medtronic, Merck, Eidos, Ferring, Apple Inc, Sanifit, and St. Jude; grants and personal fees from Amgen, AstraZeneca, Bayer, CSL Behring, Johnson & Johnson, Novartis, and Sanofi; and personal fees from Anthos, Applied Therapeutics, Elsevier, Inova, Intermountain Health, Medscape, Mount Sinai, Mundi Pharma, Myokardia, Novo Nordisk, Otsuka, Portola, SmartMedics, and Theravance outside the submitted work. Dr Hernandez reports grants from Verily; grants and personal fees from AstraZeneca, Amgen, Bayer, Merck, and Novartis; and personal fees from Boston Scientific outside the submitted work. The other authors report no conflicts.
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The Baseline Health Study and this analysis were funded by Verily Life Sciences, San Francisco, CA.
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