Eric A. Whitsel’s research while affiliated with University of North Carolina at Chapel Hill and other places

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased mortality and malignancy risk, yet the determinants of clonal expansion remain poorly understood. We performed sequencing at >4,000x depth of coverage for CHIP mutations in 6,986 postmenopausal women from the Women's Health Initiative at two timepoints approximately 15 years apart. Among 3,685 mutations detected at baseline (VAF ≥ 0.5%), 50% progressed to CHIP (VAF ≥ 2%) at follow-up. We confirmed that clonal expansion is highly dependent on initial clone size and CHIP driver gene, with SF3B1 and JAK2 mutations exhibiting the fastest growth rate. We identified germline variants in TERT, IL6R, TCL1A, and MSI2 that modulate clonal expansion rate. Measured baseline leukocyte telomere length showed differential effects on incident CHIP risk, with shorter baseline leukocyte telomere length predisposing to incident PPM1D mutations and longer baseline leukocyte telomere length favoring incident DNMT3A mutations. We discovered that the IL6R missense variant p.Asp358Ala specifically impairs TET2 clonal expansion, supported by direct measurements of soluble interleukin-6 receptor and interleukin-6. Faster clonal growth rate was associated with increased risk of cytopenia, leukemia, and all-cause mortality. Notably, CHIP clonal expansion rate mediated 34.4% and 43.7% of the Clonal Hematopoiesis Risk Score's predictive value for leukemia and all-cause mortality, respectively. These findings reveal key biological determinants of CHIP progression and suggest that incorporating growth rate measurements could enhance risk stratification.

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women’s Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women’s health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Rationale: Although COPD prevalence and exacerbations have been linked to ambient pollutants, evidence on the impact of ambient pollutants on COPD incidence is relatively sparse. Objectives: To evaluate the associations of long-term ambient particulate matter (PM2.5; PM10), nitrogen dioxide (NO2), and incident self-reported COPD in the Women’s Health Initiative (WHI), a large prospective cohort study of post-menopausal women across the United States. Methods: We estimated annual average residential pollutant concentrations using validated spatiotemporal models and monitored data. We estimated pollutant-COPD associations as hazard ratios (HRs) and 95% confidence intervals (CI) per inter-quartile range (IQR) increase in pollutant using time-varying Cox proportional hazards models adjusted for potential confounders including sociodemographic characteristics, lifestyle and health factors, and WHI Clinical Center at baseline. Finally, we assessed the joint impact of exposure to multiple pollutants using quantile-based G-computation for survival outcomes. Measurements and Main Results: During the median follow-up time of 11.1 years, the study participants experienced 3532 cases of COPD. HRs ranged from 1.20 (95% CI:1.15, 1.26) per IQR increase in PM2.5, to 1.19 (95% CI:1.13, 1.26) per IQR increase in NO2, to 1.10 (95% CI:1.06, 1.15) per IQR increase in PM10. In our multi-pollutant model, a quartile increase in PM2.5 and NO2 was associated with a HR of 1.16 (95% CI:1.11, 1.20). Conclusions: In this national cohort of post-menopausal women, the long-term residential concentrations of ambient particulate matter (PM2.5 and PM10), and NO2 were associated with a higher risk of incident COPD.

Background Understanding the impact of clonal hematopoiesis of indeterminate potential (CHIP) and mosaic chromosomal alterations (mCAs) on solid tumor risk and mortality can shed light on novel cancer pathways. Methods The authors analyzed whole genome sequencing data from the Trans‐Omics for Precision Medicine Women’s Health Initiative study (n = 10,866). They investigated the presence of CHIP and mCA and their association with the development and mortality of breast, lung, and colorectal cancers. Results CHIP was associated with higher risk of breast (hazard ratio [HR], 1.30; 95% confidence interval [CI], 1.03–1.64; p = .02) but not colorectal (p = .77) or lung cancer (p = .32). CHIP carriers who developed colorectal cancer also had a greater risk for advanced‐stage (p = .01), but this was not seen in breast or lung cancer. CHIP was associated with increased colorectal cancer mortality both with (HR, 3.99; 95% CI, 2.41–6.62; p < .001) and without adjustment (HR, 2.50; 95% CI, 1.32–4.72; p = .004) for advanced‐stage and a borderline higher breast cancer mortality (HR, 1.53; 95% CI, 0.98–2.41; p = .06). Conversely, mCA (cell fraction [CF] >3%) did not correlate with cancer risk. With higher CFs (mCA >5%), autosomal mCA was associated with increased breast cancer risk (HR, 1.39; 95% CI, 1.06–1.83; p = .01). There was no association of mCA (>3%) with breast, colorectal, or lung mortality except higher colon cancer mortality (HR, 2.19; 95% CI, 1.11–4.3; p = .02) with mCA >5%. Conclusions CHIP and mCA (CF >5%) were associated with higher breast cancer risk and colorectal cancer mortality individually. These data could inform on novel pathways that impact cancer risk and lead to better risk stratification.

Background: Evidence linking gaseous air pollution to late-life brain health is mixed. Objective: We explored associations between exposure to gaseous pollutants and brain magnetic resonance imaging (MRI) markers among Atherosclerosis Risk in Communities (ARIC) Study participants, with attention to the influence of exposure estimation method and confounding by site. Methods: We considered data from 1,665 eligible ARIC participants recruited from four US sites in the period 1987-1989 with valid brain MRI data from Visit 5 (2011-2013). We estimated 10-y (2001-2010) mean carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxides (NOx), and 8- and 24-h ozone (O3) concentrations at participant addresses, using multiple exposure estimation methods. We estimated site-specific associations between pollutant exposures and brain MRI outcomes (total and regional volumes; presence of microhemorrhages, infarcts, lacunes, and severe white matter hyperintensities), using adjusted linear and logistic regression models. We compared meta-analytically combined site-specific associations to analyses that did not account for site. Results: Within-site exposure distributions varied across exposure estimation methods. Meta-analytic associations were generally not statistically significant regardless of exposure, outcome, or exposure estimation method; point estimates often suggested associations between higher NO2 and NOx and smaller temporal lobe, deep gray, hippocampal, frontal lobe, and Alzheimer disease signature region of interest volumes and between higher CO and smaller temporal and frontal lobe volumes. Analyses that did not account for study site more often yielded significant associations and sometimes different direction of associations. Discussion: Patterns of local variation in estimated air pollution concentrations differ by estimation method. Although we did not find strong evidence supporting impact of gaseous pollutants on brain changes detectable by MRI, point estimates suggested associations between higher exposure to CO, NOx, and NO2 and smaller regional brain volumes. Analyses of air pollution and dementia-related outcomes that do not adjust for location likely underestimate uncertainty and may be susceptible to confounding bias. https://doi.org/10.1289/EHP13906.

Background and objectives: Epigenetic age estimators indicating faster/slower biological aging vs chronological age independently associate with several age-related outcomes; however, longitudinal associations with cognitive function are understudied. We examined associations of epigenetic age estimators with cognitive function measured annually. Methods: This longitudinal study consisted of older women enrolled in the Women's Health Initiative Memory Study with DNA methylation (DNAm) collected at baseline (1995-1998) from 3 ancillary studies and were followed up to 13 years. Global cognitive function was measured annually by Modified Mini-Mental State Examination (3MS; baseline-2007) and by modified Telephone Interview for Cognitive Status (TICS-m, 2008-2021). We calculated 5 epigenetic age estimators: extrinsic AgeAccel, intrinsic AgeAccel, AgeAccelPheno, AgeAccelGrim2, Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), and AgeAccelGrim2 components (DNA-based plasma protein surrogates). We estimated longitudinal epigenetic age estimator-cognitive function associations using linear mixed-effects models containing age, education, race or ethnicity, and subsequently alcohol, smoking, body mass index, and comorbidities. We examined effect modification by APOE ε4 carriage. Results: A total of 795 participants were enrolled. The mean baseline age was 70.8 ± 4 years (10.7% Black, 3.9% Hispanic or Latina, 85.4% White), A 1-SD (0.12) increment in DunedinPACE associated with faster annual declines in TICS-m scores in minimally adjusted (β = -0.118, 95% CI -0.202 to -0.034; p = 0.0006) and fully adjusted (β = -0.123, 95% CI -0.211 to -0.036; p = 0.006) models. AgeAccelPheno associated with faster annual declines in TICS-m with minimal adjustment (β = -0.091, 95% CI -0.176 to -0.006; p = 0.035) but not with full adjustment. No other epigenetic age estimators associated with changes in 3MS or TICS-m. Higher values of DNAm-based surrogates of growth differentiation factor 15, beta-2 microglobulin, Cystatin C, tissue inhibitor metalloproteinase 1, and adrenomedullin associated with faster annual declines in 3MS and TICS-m. Higher DNAm log A1c associated with faster annual declines in TICS-m only. DunedinPACE associated with faster annual declines in 3MS among APOE ε4 carriers but not among noncarriers (p-interaction = 0.020). Discussion: Higher DunedinPACE associated with faster declines in TICS-m and 3MS scores among APOE ε4 carriers. DunedinPACE may help identify older women at risk of future cognitive decline. Limitations include the ancillary studies that collected epigenetic data not designed to study epigenetics and cognitive function. We examined epigenetic age estimators with global cognitive function and not specific cognitive domains. Findings may not generalize to men and more diverse populations.