Intrinsic Epigenetic Age Acceleration Research Articles

A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study.

Accelerated biological aging is an increasingly popular way to track the acceleration of biology over time that may not be captured by calendar time. Biological aging has been linked to external and internal chronic stressors and has the potential to be used clinically to understand a person's personalized functioning and predict future disease. We compared the association of different measures of biological aging and incident cardiovascular disease (CVD) overall and by race. We used multiple informants models to compare the strength of clinical marker-derived age acceleration, 5 measures of epigenetic age acceleration (intrinsic and extrinsic epigenetic age acceleration, GrimAge acceleration, and PhenoAge acceleration), and 1 established clinical predictor of future CVD, Framingham 10-year risk score, with incident CVD over an 11-year period (2007-2018). Participants were 913 self-identified Black or White (41% and 59%, respectively) female or male (51% and 49%, respectively) individuals enrolled in the US-based CARDIA (Coronary Artery Risk Development in Young Adults) cohort study. The analytic baseline for this study was the 20-year follow-up examination (2005-2006; median age 45 years). We also included race-specific analysis. We found that all measures were modestly correlated with one another. However, clinical marker-derived age acceleration and Framingham 10-year risk score were more strongly associated with incident CVD than all the epigenetic measures. Clinical marker-derived age acceleration and Framingham 10-year risk score were not significantly different than one another in their association with incident CVD. The type of accelerated aging measure should be taken into consideration when comparing their association with clinical outcomes. A multisystem clinical composite shows associations with incident CVD equally to a well-known clinical predictor.

Abstract P413: Epigenetic Age Acceleration Measures Predict Cardiovascular Outcomes in Diabetic Kidney Disease

Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality in the US, highlighting the need for novel approaches to its prediction, prevention, and management. Epigenetic age acceleration (EAA), or increased DNA methylation-based age relative to chronological age, is a promising biomarker of disease risk. While previous studies have prospectively linked EAA to CVD, recent work in patients with end-stage kidney disease showed no association between EAA and mortality from CVD, underscoring a need for more research across patient populations. Hypothesis: EAA measures, specifically intrinsic epigenetic age acceleration (IEAA), extrinsic epigenetic age acceleration (EEAA), phenotypic epigenetic age acceleration (PhenoAA), and Grim epigenetic age acceleration (GrimAA), are associated with incident CVD in diabetic kidney disease (DKD). Methods: In a subset of 496 participants from the Chronic Renal Insufficiency Cohort with DKD, selected based on kidney function decline over follow-up (large versus small), we examined four EAA measures: IEAA, EEAA, PhenoAA, and GrimAA. GrimAA is comprised of eight DNA methylation-based biomarkers, including plasminogen activator inhibitor-1 age acceleration (PAI-1 AA ) and tissue inhibitor of matrix metalloproteinase (TIMP-1 AA ), which were also assessed. Primary outcomes were incident CVD, atherosclerotic CVD, and CVD mortality; secondary outcomes included myocardial infarction, congestive heart failure (CHF), cerebrovascular accident, and peripheral artery disease (PAD). Cox proportional models tested associations between the EAA measures and CVD endpoints after adjusting for baseline demographics, clinical center, kidney function decline as a study design feature, lifestyle, and clinical risk factors. Results: Participants were predominantly male (63%), with a median age of 57 years, median eGFR of 44 mL/min/1.73m 2 , and mean hemoglobin A1c of 8.0%. After adjusting for multiple testing, each standard deviation increase in GrimAA was associated with incident CVD (HR=1.27; 95% confidence interval [CI], 1.08-1.50; p=0.012) and PAD (HR=1.65; 95% CI, 1.15-2.37; p=0.006). Likewise, PAI-1 AA was significantly associated with incident CVD (HR=1.24; 95% CI, 1.06-1.44; p=0.006) and PAD (HR=1.46; 95% CI, 1.17-1.81; p=0.001), while TIMP-1 AA was associated with CHF (HR=1.32; 95% CI, 1.09-1.60; p=0.005). Nominally significant associations between IEAA and atherosclerotic CVD were also identified (HR=1.24; 95% CI, 1.00-1.53; p=0.049). Similarly, EEAA was nominally associated with incident CVD (HR 1.22; 95% CI, 1.01-1.47, p=0.039). Conclusion: EAA measures, notably GrimAA, are promising CVD biomarkers in DKD patients. Associations of GrimAA components PAI-1 AA and TIMP-1 AA with incident CVD add to the accumulating evidence of their predictive and therapeutic potential.

GrimAge is elevated in older adults with mild COVID-19 an exploratory analysis.

COVID-19 has been contained; however, the side effects associated with its infection continue to be a challenge for public health, particularly for older adults. On the other hand, epigenetic status contributes to the inter-individual health status and is associated with COVID-19 severity. Nevertheless, current studies focus only on severe COVID-19. Considering that most of the worldwide population developed mild COVID-19 infection. In the present exploratory study, we aim to analyze the association of mild COVID-19 with epigenetic ages (HorvathAge, HannumAge, GrimAge, PhenoAge, SkinAge, and DNAmTL) and clinical variables obtained from a Mexican cohort of older adults. We found that all epigenetic ages significantly differ from the chronological age, but only GrimAge is elevated. Additionally, both the intrinsic epigenetic age acceleration (IEAA) and the extrinsic epigenetic age acceleration (EEAA) are accelerated in all patients. Moreover, we found that immunological estimators and DNA damage were associated with PhenoAge, SkinBloodHorvathAge, and HorvathAge, suggesting that the effects of mild COVID-19 on the epigenetic clocks are mainly associated with inflammation and immunology changes. In conclusion, our results show that the effects of mild COVID-19 on the epigenetic clock are mainly associated with the immune system and an increase in GrimAge, IEAA, and EEAA.

The Relationship between Telomere Length and Epigenetic Aging with Hematologic Diseases Risk: A Multivariable Mendelian Randomisation Study

Introduction: Aging is a complex phenomenon that involves multifaceted changes, including significant alterations in telomere length and epigenetic aging. When aging affects the hematopoietic system, it leads to a gradual increase in mutations in hematopoietic stem cells, making individuals more susceptible to the development of hematologic diseases. However, due to the limitations of observational studies, such as potential confounding factors and reverse causality, the precise causal direction and magnitude of the relationship between telomere length, epigenetic aging, and the incidence of hematologic diseases remain uncertain. Therefore, we conducted an investigation to determine the causal relevance of telomere length and epigenetic aging in relation to hematologic diseases using Mendelian randomization (MR). Methods: Firstly, we conducted a 2-sample single-variable Mendelian randomization (SVMR) study to examine the genetically predicted effects of telomere length and epigenetic age acceleration, as measured by HannumAge, Horvath Intrinsic Age, PhenoAge, GrimAge, and DNAm PAI-1 (DNA methylation-estimated plasminogen activator inhibitor-1) levels, on multiple hematologic diseases, including anemia, lymphoma, leukemia, myeloproliferative diseases, and hemostasis and coagulation diseases. Additionally, we used lasso regression to exclude multicollinearity in SNPs and subsequently performed multivariable MR using the screened SNPs to adjust for statistically significant risk factors, and MR Bayesian model averaging was performed to rank the significant risk factors based on their genetic evidence (Figure A). Results: Summary data were available for 19 malignant hematological neoplasms and 35 non-malignant hematological disorders, corresponding to 70,813 cases and 12,253,012 controls. Increased telomere length due to germline genetic variation was generally associated with increased risk for hematologic diseases. The strongest associations were observed for lymphoma, lymphoid leukaemia, Hodgkin lymphoma, chronic lymphocytic leukaemia, essential thrombocythaemia, unspecified types of non-Hodgkin lymphoma, multiple myeloma, Non-follicular lymphoma, unspecified iron deficiency, leukaemia, while higher levels of telomere length was associated with a reduced risk of pernicious anaemia (Figure B). Meta-analyzed IVW MR findings suggested that higher PhenoAge acceleration was associated with increased risks of myeloid leukemia, chronic lymphocytic leukemia, and lymphoid leukemia. Conversely, higher PhenoAge acceleration was linked to reduced risks of unspecified disorders of white blood cells and unspecified coagulation defects. Similarly, SVMR showed genetic predisposition for GrimAge acceleration to be associated with increased risks of amyloidosis, unspecified anemias, and myeloid leukemia. Conversely, genetically predicted GrimAge acceleration was associated with reduced risks of hemorrhagic anemia, hemolytic anemias, and idiopathic thrombocytopenic purpura. Furthermore, our findings suggested that elevated HannumAge acceleration increased the risks of lymphoma, leukemia, and lymphoid leukemia.Conversely, higher HannumAge acceleration was associated with a reduced risk of chronic myeloid leukemia. Additionally, higher levels of DNAm PAI-1 were found to be associated with an increased risk of chronic myeloid leukemia. However, there was no evidence of causality between genetically predicted Intrinsic epigenetic age acceleration and the 19 malignant hematological neoplasms and 35 non-malignant hematological disorders mentioned (Figure C). We utilized MVMR-LASSO to estimate the combined effects of telomere length and five epigenetic clocks on hematologic diseases. After adjusting for epigenetic aging, we observed a significant positive association between telomere length and various hematologic diseases, including leukemia, lymphoid leukemia, chronic lymphocytic leukemia, lymphomas, Hodgkin lymphoma, non-follicular lymphoma, unspecified types of non-Hodgkin lymphoma, essential thrombocythemia, and multiple myeloma (Figure D&E). Conclusion: Longer telomeres and epigenetic aging may increase the risk of developing most hematologic malignancies, while simultaneously raising or decreasing the risk of certain non-malignant hematologic diseases.

Genetic relationship between ageing and coronary heart disease: a Mendelian randomization study.

Genetic relationship between ageing and coronary heart disease has not been well investigated. The aim of the study was to explore the association of several ageing biomarkers with the risk of several types of coronary heart disease using the Mendelian randomization approach. Summary data for telomere length, four epigenetic clocks (such as intrinsic epigenetic age acceleration), four types of coronary heart disease (such as myocardial infarction) were collected from the most updated and available genome-wide association studies. Instrumental variables were extracted from the exposure-related summary data according to correlation, independence and exclusivity assumptions. Three Mendelian randomization methods (such as inverse variance weighted) were used for causal inference. Four sensitivity analyses (such as MR-Egger intercept) were performed to prevent horizontal pleiotropy. Inverse variance weighted reported that longer telomere length was related to the lower risk of myocardial infarction, angina pectoris, unstable angina pectoris and coronary atherosclerosis (P = 8.840e-11, P = 9.830e-04, P = 1.539e-05, P = 2.607e-09). Inverse variance weighted also reported that four epigenetic clocks might be not implicated in the risk of these coronary heart diseases. Furthermore, there was not enough evidence to confirm the effect of coronary heart disease on these ageing biomarkers. Longer telomere length, but not the epigenetic clock changes, genetically decreased the risk of coronary heart disease. Considering that telomere length and epigenetic clocks were two independent ageing biomarkers, the correlation between ageing and coronary heart disease might be redefined at the genetic level.

Abstract 13528: A Comparison of Five Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study

Background: Accelerated biological aging is an increasingly popular way to track the effects of chronic stress over time. Biological aging is linked to external and internal chronic stressors and has the potential to be used clinically to understand a person’s personalized functioning and to predict future disease. Aims: The primary aim was to investigate the similarities and differences in the association of different measures of biological aging and incident cardiovascular disease (CVD). Hypotheses: Our novel predictor, Clinical Marker-Derived Age Acceleration (CAA), and the Framingham Risk Score (FRS) will be most robustly associated with incident CVD, and the associations will be stronger among Black participants. The latter is based on previous findings that Black participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study have, on average, more adverse social determinants of health. Methods: We used Multiple Informants Models (MIM) to compare the strength of five measures of accelerated aging (Intrinsic and Extrinsic Epigenetic Age Acceleration, GrimAge Acceleration, PhenoAge Acceleration, CAA (including clinical biomarkers, e.g., CRP, lung function, glucose, etc.), and one established clinical predictor of future CVD, FRS, to predict incident CVD over 11 years (2007 – 2018). Participants were 913 Black or White individuals examined by CARDIA in 2005-2006, when their mean age was 45 years. We also included race-specific analysis. Measures were standardized (M = 0, SD=1) to facilitate comparison. Results: The incidence rate of CVD within our sample was 7.8 per 1,000 person years and the prevalence of CVD was 7.5%. CAA and FRS significantly predicted incident CVD in the fully adjusted model (OR = 1.31, 95% CI: 1.06 – 1.63) and (OR = 1.28, 95% CI: 1.05 – 1.56), respectively. CAA and FRS were the best predictors of CVD, but there was no significant difference in the predictive value of CAA and FRS (Chi2 = 0.05, p = 0.828). In race-stratified models, CAA was the strongest and most consistent predictor in the Black-only models. Conclusions: Our findings support the association of CAA, a multi-system clinical composite, with incident CVD. Further, this composite may account for some of the well-known racial variation in incident CVD.

Air pollution and epigenetic aging among Black and White women in the US

BackgroundDNA methylation-based measures of biological aging have been associated with air pollution and may link pollutant exposures to aging-related health outcomes. However, evidence is inconsistent and there is little information for Black women. ObjectiveWe examined associations of ambient particulate matter <2.5 μm and <10 μm in diameter (PM2.5 and PM10) and nitrogen dioxide (NO2) with DNA methylation, including epigenetic aging and individual CpG sites, and evaluated whether associations differ between Black and non-Hispanic White (NHW) women. MethodsValidated models were used to estimate annual average outdoor residential exposure to PM2.5, PM10, and NO2 in a sample of self-identified Black (n=633) and NHW (n=3493) women residing in the contiguous US. We used sampling-weighted generalized linear regression to examine the effects of pollutants on six epigenetic aging measures (primary: DunedinPACE, GrimAgeAccel, and PhenoAgeAccel; secondary: Horvath intrinsic epigenetic age acceleration [EAA], Hannum extrinsic EAA, and skin & blood EAA) and epigenome-wide associations for individual CpG sites. Wald tests of nested models with and without interaction terms were used to examine effect measure modification by race/ethnicity. ResultsBlack participants had higher median air pollution exposure than NHW participants. GrimAgeAccel was associated with both PM10 and NO2 among Black participants, (Q4 versus Q1, PM10: β=1.09, 95% CI: 0.16–2.03; NO2: β=1.01, 95% CI 0.08–1.94) but not NHW participants (p-for-heterogeneity: PM10=0.10, NO2=0.20). In Black participants, we also observed a monotonic exposure–response relationship between NO2 and DunedinPACE (Q4 versus Q1, NO2: β=0.029, 95% CI: 0.004–0.055; p-for-trend=0.03), which was not observed in NHW participants (p-for-heterogeneity=0.09). In the EWAS, pollutants were significantly associated with differential methylation at 19 CpG sites in Black women and one in NHW women. ConclusionsIn a US-wide cohort study, our findings suggest that air pollution is associated with DNA methylation alterations consistent with higher epigenetic aging among Black, but not NHW, women.

Association of Adverse Childhood Experiences With Accelerated Epigenetic Aging in Midlife

Adverse childhood experiences (ACEs) are associated with the risk of poorer health, and identifying molecular mechanisms may lay the foundation for health promotion in people with ACEs. To investigate the associations of ACEs with changes in epigenetic age acceleration (EAA), a biomarker associated with various health outcomes in middle-aged adults, in a population with balanced race and sex demographics. Data for this cohort study were from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants in CARDIA underwent 8 follow-up exams from baseline (year 0 [Y0]; 1985-1986) to Y30 (2015-2016), and participant blood DNA methylation information was obtained at Y15 (2000-2001) and Y20 (2005-2006). Individuals from Y15 and Y20 with available DNA methylation data and complete variables for ACEs and covariates were included. Data were analyzed from September 2021 to August 2022. Participant ACEs (general negligence, emotional negligence, physical violence, physical negligence, household substance abuse, verbal and emotional abuse, and household dysfunction) were obtained at Y15. The primary outcome consisted of results from 5 DNA methylation-based EAA measurements known to be associated with biological aging and long-term health: intrinsic EAA (IEAA), extrinsic EAA (EEAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), measured at Y15 and Y20. Linear regression and generalized estimating equations were used to assess associations of the burden of ACEs (≥4 vs <4 ACEs) with EAA adjusting for demographics, health-related behaviors, and early life and adult socioeconomic status. A total of 895 participants for Y15 (mean [SD] age, 40.4 [3.5] years; 450 males [50.3%] and 445 females [49.7%]; 319 Black [35.6%] and 576 White [64.4%]) and 867 participants for Y20 (mean [SD] age, 45.4 [3.5] years; 432 males [49.8%] and 435 females [50.2%]; 306 Black [35.3%] and 561 White [64.7%]) were included after excluding participants with missing data. There were 185 participants with (20.7%) vs 710 participants without (79.3%) 4 or more ACEs at Y15 and 179 participants with (20.6%) vs 688 participants without (79.4%) 4 or more ACEs at Y20. Having 4 or more ACEs was positively associated with EAA in years at Y15 (EEAA: β = 0.60 years; 95% CI, 0.18-1.02 years; PhenoAA: β = 0.62 years; 95% CI = 0.13-1.11 years; GrimAA: β = 0.71 years; 95% CI, 0.42-1.00 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) and Y20 (IEAA: β = 0.41 years; 95% CI, 0.05-0.77 years; EEAA: β = 1.05 years; 95% CI, 0.66-1.44 years; PhenoAA: β = 0.57 years; 95% CI, 0.08-1.05 years; GrimAA: β = 0.57 years; 95% CI, 0.28-0.87 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) after adjusting for demographics, health-related behaviors, and socioeconomic status. In this cohort study, ACEs were associated with EAA among middle-aged adults after controlling for demographics, behavior, and socioeconomic status. These findings of the associations between early life experience and the biological aging process in midlife may contribute to health promotion in a life course perspective.

Time to relapse in chronic lymphocytic leukemia and DNA-methylation-based biological age

Chronic lymphocytic leukemia (CLL) is a mature B cell neoplasm with a predilection for older individuals. While previous studies have identified epigenetic signatures associated with CLL, whether age-related DNA methylation changes modulate CLL relapse remains elusive. In this study, we examined the association between epigenetic age acceleration and time to CLL relapse in a publicly available dataset. DNA methylation profiling of 35 CLL patients prior to initiating chemoimmunotherapy was performed using the Infinium HumanMethylation450 BeadChip. Four epigenetic age acceleration metrics (intrinsic epigenetic age acceleration [IEAA], extrinsic epigenetic age acceleration [EEAA], PhenoAge acceleration [PhenoAA], and GrimAge acceleration [GrimAA]) were estimated from blood DNA methylation levels. Linear, quantile, and logistic regression and receiver operating characteristic curve analyses were conducted to assess the association between each epigenetic age metric and time to CLL relapse. EEAA (p = 0.011) and PhenoAA (p = 0.046) were negatively and GrimAA (p = 0.040) was positively associated with time to CLL relapse. Simultaneous assessment of EEAA and GrimAA in male patients distinguished patients who relapsed early from patients who relapsed later (p = 0.039). No associations were observed with IEAA. These findings suggest epigenetic age acceleration prior to chemoimmunotherapy initiation is associated with time to CLL relapse. Our results provide novel insight into the association between age-related DNA methylation changes and CLL relapse and may serve has biomarkers for treatment relapse, and potentially, treatment selection.

Associations between prenatal phthalate exposure and childhood epigenetic age acceleration

Phthalates, a group of pervasive endocrine-disrupting chemicals found in plastics and personal care products, have been associated with a wide range of developmental and health outcomes. However, their impact on biomarkers of aging has not been characterized. We tested associations between prenatal exposure to 11 phthalate metabolites on epigenetic aging in children at birth, 7, 9, and 14 years of age. We hypothesized that prenatal phthalate exposure will be associated with epigenetic age acceleration measures at birth and in early childhood, with patterns dependent on sex and timing of DNAm measurement. Among 385 mother-child pairs from the CHAMACOS cohort, we measured DNAm at birth, 7, 9, and 14 years of age, and utilized adjusted linear regression to assess the association between prenatal phthalate exposure and Bohlin's Gestational Age Acceleration (GAA) at birth and Intrinsic Epigenetic Age Acceleration (IEAA) throughout childhood. Additionally, quantile g-computation was utilized to assess the effect of the phthalate mixture on GAA at birth and IEAA throughout childhood. We found a negative association between prenatal di (2-ethylhexyl) phthalate (DEHP) exposure and IEAA among males at age 7 (-0.62 years; 95% CI:-1.06 to -0.18), and a marginal negative association between the whole phthalate mixture and GAA among males at birth (-1.54 days, 95% CI: -2.79 to -0.28), while most other associations were nonsignificant. Our results suggest that prenatal exposure to certain phthalates is associated with epigenetic aging in children. Additionally, our findings suggest that the influence of prenatal exposures on epigenetic age may only manifest during specific periods of child development, and studies relying on DNAm measurements solely from cord blood or single time points may overlook potential relationships.

Appraising the Associations Between Systemic Iron Status and Epigenetic Clocks: A Genetic Correlation and Bidirectional Mendelian Randomization Study

BackgroundGenetic correlations (Rg) and bidirectional causal effects between systemic iron status and epigenetic clocks have not been fully investigated, although observational studies have suggested systemic iron status is associated with human aging. ObjectivesWe explored the genetic correlations and bidirectional causal effects between systemic iron status and epigenetic clocks. MethodsLeveraging large-scale genome-wide association study summary-level statistics for 4 systemic iron status biomarkers (ferritin, serum iron, transferrin, and transferrin saturation) (N = 48,972) and 4 measures for epigenetic age (GrimAge, PhenoAge, intrinsic epigenetic age acceleration (IEAA), and HannumAge) (N = 34,710), genetic correlations, and bidirectional causal effects were estimated between them mainly by applying linkage disequilibrium score (LDSC) regression, Mendelian randomization (MR), and MR based on Bayesian model averaging. The main analyses were conducted employing multiplicative random-effects inverse-variance weighted MR. MR-Egger, weighted median, weighted mode, and MR-PRESSO were performed as sensitivity analyses to support the robustness of causal effects. ResultsThe LDSC results illustrated Rg between serum iron and PhenoAge (Rg = 0.1971, P = 0.048) and between transferrin saturation and PhenoAge (Rg = 0.196, P = 0.0469). We found that increased ferritin and transferrin saturation significantly increased all 4 measures of epigenetic age acceleration (all P < 0.0125, β > 0). Each standard deviation genetically increases in serum iron only significantly associated with increased IEAA (β: 0.36; 95% CI: 0.16, 0.57; P = 6.01 × 10−4) and increased HannumAge acceleration (β: 0.32; 95% CI: 0.11, 0.52; P = 2.69 × 10−3). Evidence showed a suggestively significant causal effect of transferrin on epigenetic age acceleration (all 0.0125 <P < 0.05). Additionally, reverse MR study indicated no significant causal effect of epigenetic clocks on systemic iron status. ConclusionsAll 4 iron status biomarkers had a significant or suggestively significant causal effect on epigenetic clocks, whereas reverse MR studies did not.

Effects of epigenetic age acceleration on kidney function: a Mendelian randomization study

BackgroundPrevious studies have reported cross-sectional associations between measures of epigenetic age acceleration (EAA) and kidney function phenotypes. However, the temporal and potentially causal relationships between these variables remain unclear. We conducted a bidirectional two-sample Mendelian randomization study of EAA and kidney function. Genetic instruments for EAA and estimate glomerular filtration rate (eGFR) were identified from previous genome-wide association study (GWAS) meta-analyses of European-ancestry participants. Causal effects of EAA on kidney function and kidney function on EAA were assessed through summary-based Mendelian randomization utilizing data from the CKDGen GWAS meta-analysis of log-transformed estimated glomerular filtration rate (log-eGFR; n = 5,67,460) and GWAS meta-analyses of EAA (n = 34,710). An allele score-based Mendelian randomization leveraging individual-level data from UK Biobank participants (n = 4,33,462) further examined the effects of EAA on kidney function.ResultsUsing summary-based Mendelian randomization, we found that each 5 year increase in intrinsic EAA (IEAA) and GrimAge acceleration (GrimAA) was associated with − 0.01 and − 0.02 unit decreases in log-eGFR, respectively (P = 0.02 and P = 0.09, respectively), findings which were strongly supported by allele-based Mendelian randomization study (both P < 0.001). Summary-based Mendelian randomization identified 24% increased odds of CKD with each 5-unit increase in IEAA (P = 0.05), with consistent findings observed in allele score-based analysis (P = 0.07). Reverse-direction Mendelian randomization identified potentially causal effects of decreased kidney function on HannumAge acceleration (HannumAA), GrimAA, and PhenoAge acceleration (PhenoAA), conferring 3.14, 1.99, and 2.88 year decreases in HanumAA, GrimAA, and PhenoAA, respectively (P = 0.003, 0.05, and 0.002, respectively) with each 1-unit increase in log-eGFR.ConclusionThis study supports bidirectional causal relationships between EAA and kidney function, pointing to potential prevention and therapeutic strategies.

Childhood sleep health and epigenetic age acceleration in late adolescence: Cross-sectional and longitudinal analyses.

Investigate if childhood measures of sleep health are associated with epigenetic age acceleration in late adolescence. Parent-reported sleep trajectories from age 5 to 17, self-reported sleep problems at age 17, and six measures of epigenetic age acceleration at age 17 were studied in 1192 young Australians from the Raine Study Gen2. There was no evidence for a relationship between the parent-reported sleep trajectories and epigenetic age acceleration (p ≥ 0.17). There was a positive cross-sectional relationship between self-reported sleep problem score and intrinsic epigenetic age acceleration at age 17 (b=0.14, p=0.04), which was attenuated after controlling for depressive symptom score at the same age (b=0.08, p=0.34). Follow-up analyses suggested this finding may represent greater overtiredness and intrinsic epigenetic age acceleration in adolescents with higher depressive symptoms. There was no evidence for a relationship between self- or parent-reported sleep health and epigenetic age acceleration in late adolescence after adjusting for depressive symptoms. Mental health should be considered as a potential confounding variable in future research on sleep and epigenetic age acceleration, particularly if subjective measures of sleep are used.

Abstract P304: Association of Epigenetic Age Acceleration With Cognition in the Bogalusa Heart Study

Background: Despite known associations of epigenetic age acceleration (EAA), or increased DNA methylation (DNAm)-based age relative to chronological age, with Alzheimer’s disease, few studies have investigated the relation of EAA with midlife cognitive function. We examined cross-sectional associations of EAA with cognition among the 1,252 middle-aged participants of the Bogalusa Heart Study 2013-2016 study visit. Methods: Whole-blood DNA methylation data was generated using the Illumina HumanMethylation450 BeadChip. Four EAA measures, including extrinsic EAA (EEAA), intrinsic EAA (IEAA), PhenoAA and GrimAA, were derived from the methylation data. Cognitive function was assessed by a standard battery of eight neurocognitive tests covering five cognition domains, and a global cognition score was calculated. Associations of EAA measures with cognitive function were assessed by 3 multivariable models sequentially adding (i) demographics (age, sex, race, education) and vocabulary; (ii) lifestyle behaviors (smoking, alcohol drinking, and depression), and (iii) clinical measures (blood pressure, lipids, body mass index, glycated hemoglobin, medication, and white blood cell count. Results: After Bonferroni correction (α=1.39E-3), PhenoAA and GrimAA were significantly associated with decreased processing speed (lower Digit coding test score) and decreased working memory (higher trail making test A score). GrimAA was also associated with lower global cognition score ( Table ). EEAA was associated with decreased processing speed (lower Digit coding test). After adjusting for lifestyle and clinical measures, such associations were slightly attenuated but remained at least nominally significant. IEAA was not associated with any cognitive test results. Conclusions: EAA measured by PhenoAA, GrimAA, and EEAA were significantly associated with decreased processing speed, working memory, and/or global cognition. The associations were independent of known cognitive decline risk factors.

Associations Between Epigenetic Age Acceleration and microRNA Expression Among U.S. Firefighters.

Epigenetic changes may be biomarkers of health. Epigenetic age acceleration (EAA), the discrepancy between epigenetic age measured via epigenetic clocks and chronological age, is associated with morbidity and mortality. However, the intersection of epigenetic clocks with microRNAs (miRNAs) and corresponding miRNA-based health implications have not been evaluated. We analyzed DNA methylation and miRNA profiles from blood sampled among 332 individuals enrolled across 2 U.S.-based firefighter occupational studies (2015-2018 and 2018-2020). We considered 7 measures of EAA in leukocytes (PhenoAge, GrimAge, Horvath, skin-blood, and Hannum epigenetic clocks, and extrinsic and intrinsic epigenetic age acceleration). We identified miRNAs associated with EAA using individual linear regression models, adjusted for sex, race/ethnicity, chronological age, and cell type estimates, and investigated downstream effects of associated miRNAs with miRNA enrichment analyses and genomic annotations. On average, participants were 38 years old, 88% male, and 75% non-Hispanic white. We identified 183 of 798 miRNAs associated with EAA (FDR q < 0.05); 126 with PhenoAge, 59 with GrimAge, 1 with Horvath, and 1 with the skin-blood clock. Among miRNAs associated with Horvath and GrimAge, there were 61 significantly enriched disease annotations including age-related metabolic and cardiovascular conditions and several cancers. Enriched pathways included those related to proteins and protein modification. We identified miRNAs associated with EAA of multiple epigenetic clocks. PhenoAge had more associations with individual miRNAs, but GrimAge and Horvath had greater implications for miRNA-associated pathways. Understanding the relationship between these epigenetic markers could contribute to our understanding of the molecular underpinnings of aging and aging-related diseases.

Genetic Variants in Telomerase Reverse Transcriptase Contribute to Solar Lentigines

Solar lentigines (SLs) are a hallmark of human skin aging. They result from chronic exposure to sunlight and other environmental stressors. Recent studies also imply genetic factors, but findings are partially conflicting and lack of replication. Through a multi-trait based analysis strategy, we discovered that genetic variants in telomerase reverse transcriptase were significantly associated with non-facial SL in two EastAsian (Taizhou longitudinal cohort, n= 2,964 and National Survey of Physical Traits, n= 2,954) and one Caucasian population (SALIA, n= 462), top SNP rs2853672 (P-value for Taizhou longitudinal cohort= 1.32× 10‒28 and P-value for National Survey of Physical Traits= 3.66× 10‒17 and P-value for SALIA= 0.0007 and Pmeta= 4.93× 10‒44). The same variants were nominally associated with facial SL but not with other skin aging or skin pigmentation traits. The SL-enhanced allele/haplotype upregulated the transcription of the telomerase reverse transcriptase gene. Of note, well-known telomerase reverse transcriptase‒related aging markers such as leukocyte telomere length and intrinsic epigenetic age acceleration were not associated with SL. Our results indicate a previously unrecognized role of telomerase reverse transcriptase in skin aging‒related lentigines formation.

Exploring the relationship between DNA methylation age measures and psychoneurological symptoms in women with early-stage breast cancer.

The epigenetic clock has been acknowledged as an indicator for molecular aging, but few studies have examined possible associations of DNA methylation (DNAm) age or age acceleration (AA) with symptom burden in individuals who are treated for cancer. This study explored the association of DNAm age or AA with psychoneurological (PN) symptoms, including cognitive impairment, fatigue, sleep disturbances, pain, and depressive symptoms, in breast cancer survivors over a 2-year period. We measured PN symptoms using reliable instruments and DNAm levels by Infinium HumanMethylation450K BeadChip (N = 72). DNAm age was calculated by the Horvath, Grim, and Hannum-based intrinsic and extrinsic age estimations. AA was defined by the residual regressing estimated epigenetic age on chronological age. Mixed regression models were fitted for AA and changes in AA to study the association over time. Separate linear regression models and a mixed-effects model were fitted for AA at each time point. Horvath-AA, Grim-AA, and extrinsic epigenetic AA were significantly changed over time, while intrinsic epigenetic AA did not exhibit any temporal changes. Increased AA was associated with greater anxiety and fatigue, as well as worse cognitive memory, adjusting for race, BMI, income, chemotherapy, radiation therapy, and chronological age. Increased DNAm age was associated with greater anxiety over 2years. Our findings suggest DNAm age and AA may be associated with PN symptoms over the course of cancer treatment and survivorship. Some PN symptoms may be amenable to preventive interventions targeted to epigenetic clocks that influence aging-associated processes.

Increased epigenetic age acceleration in the hidradenitis suppurativa skin.

Epigenetic (or DNA methylation) age is calculated based on methylation of certain cytosine-guanine (CpG) repeats, and it can accurately estimate one's chronologic age. Importantly, epigenetic age acceleration (EAA) is highly predictive of age-associated morbidity and all-cause mortality. Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease with significant systemic disease burden. Here, we performed a pilot study to calculate EAA from formalin-fixed paraffin-embedded skin samples using Illumina Infinium MethylationEpic BeadChip arrays. Our results demonstrated no significant difference in intrinsic EAA among HS compared to controls (- 1.00years, p-value = 0.52), significant increases in both extrinsic EAA (13.72years, p-value < 0.001) and PhenoAge acceleration (7.72years, p-value = 0.003), and asignificant decrease in GrimAge acceleration (- 5.14years, p-value < 0.001). Our findings suggest that the acceleration of epigenetic age in the HS skin may be associated with extrinsic immune-related changes and can potentially serve as a biomarker of the present and/or future disease burden in HS patients.

Marijuana use and DNA methylation-based biological age in young adults

BackgroundMarijuana is the third most commonly used drug in the USA and efforts to legalize it for medical and recreational use are growing. Despite the increase in use, marijuana’s effect on aging remains understudied and understanding the effects of marijuana on molecular aging may provide novel insights into the role of marijuana in the aging process. We therefore sought to investigate the association between cumulative and recent use of marijuana with epigenetic age acceleration (EAA) as estimated from blood DNA methylation.ResultsA random subset of participants from The Coronary Artery Risk Development in Young Adults (CARDIA) Study with available whole blood at examination years (Y) 15 and Y20 underwent epigenomic profiling. Four EAA estimates (intrinsic epigenetic age acceleration, extrinsic epigenetic age acceleration, PhenoAge acceleration, and GrimAge acceleration) were calculated from DNA methylation levels measured at Y15 and Y20. Ever use and cumulative marijuana-years were calculated from the baseline visit to Y15 and Y20, and recent marijuana use (both any and number of days of use in the last 30 days) were calculated at Y15 and Y20. Ever use of marijuana and each additional marijuana-year were associated with a 6-month (P < 0.001) and a 2.5-month (P < 0.001) higher average in GrimAge acceleration (GAA) using generalized estimating equations, respectively. Recent use and each additional day of recent use were associated with a 20-month (P < 0.001) and a 1-month (P < 0.001) higher GAA, respectively. A statistical interaction between marijuana-years and alcohol consumption on GAA was observed (P = 0.011), with nondrinkers exhibiting a higher GAA (β = 0.21 [95% CI 0.05, 0.36], P = 0.008) compared to heavy drinkers (β = 0.05 [95% CI − 0.09, 0.18], P = 0.500) per each additional marijuana-year. No associations were observed for the remaining EAA estimates.ConclusionsThese findings suggest cumulative and recent marijuana use are associated with age-related epigenetic changes that are related to lifespan. These observed associations may be modified by alcohol consumption. Given the increase in use and legalization, these findings provide novel insight on the effect of marijuana use on the aging process as captured through blood DNA methylation.

Association of Accelerometer-Measured Physical Activity and Sedentary Time with Epigenetic Markers of Aging.

Physical activity may influence chronic disease risk, in part, through epigenetic mechanisms. Previous studies have demonstrated that an acute bout of physical activity can influence DNA methylation status. Few studies have explored the relationship between habitual, accelerometer-measured physical activity or sedentary time with epigenetic markers of aging. We used linear regression to examine cross-sectional associations of accelerometer-measured physical activity and sedentary time with extrinsic and intrinsic epigenetic age acceleration (EEAA and IEAA) models and GrimAge measured from blood samples from Framingham Heart Study participants with accelerometry and DNA methylation data ( n = 2435; mean age, 54.9 ± 14.3; 46.0% men). Residuals of Hannum-, Horvath-, and GrimAge-predicted epigenetic age were calculated by regressing epigenetic age on chronological age. We took into account blood cell composition for EEAA, IEAA, and AdjGrimAge. Moderate to vigorous physical activity was log-transformed to normalize its distribution. Adjustment models accounted for family structure, age, sex, smoking status, cohort-laboratory indicator, and accelerometer wear time. We additionally explored adjustment for body mass index (BMI). Walking 1500 more steps per day or spending 3 fewer hours sedentary was associated with >10 months lower GrimAge biological age (or ~1 month lower AdjGrimAge, after adjusting for blood cells, P < 0.05). Every 5 min·d -1 more moderate to vigorous physical activity was associated with 19-79 d of lower GrimAge (4-23 d lower using EEAA or AdjGrimAge, P < 0.01). Adjusting for BMI attenuated these results, but all statistically significant associations with AdjGrimAge remained. Greater habitual physical activity and lower sedentary time were associated with lower epigenetic age, which was partially explained by BMI. Further research should explore whether changes in physical activity influence methylation status and whether those modifications influence chronic disease risk.