Biomedical subjects

Steve Horvath

Publications and source records attributed to Steve Horvath.

3 recordsLinked to original sources

PubMed2026

Epigenetic Clocks of Biological Aging and Cognitively Healthy Longevity: The Women's Health Initiative Memory Study.

BACKGROUND: Little is known about whether epigenetic age acceleration (EAA) clocks are capable of predicting exceptional longevity with or without preserved cognitive function. METHODS: We examined 5844 women from the Women's Health Initiative Memory Study. Fifteen epigenetic clocks were measured at baseline (1996-1999). Longevity outcomes were defined as: 1) survival to age 90 with preserved cognition (n = 1726, 29.5%); or 2) survival to age 90 with cognitive impairment (n = 956, 16.4%); vs. 3) death before age 90 (n = 2611, 44.7%). Logistic regression models examined associations between the 15 clocks and survival to age 90 (vs. death before age 90), adjusting for covariates. Multinomial logistic regression models examined associations with survival to age 90 without cognitive impairment and survival to age 90 with cognitive impairment (each vs. death before age 90), also adjusting for covariates. RESULTS: Each standard deviation increase in EAA for the first-generation clocks was associated with 7%-18% reduced odds of survival to age 90 vs. earlier death. Stronger associations were observed for second- and third-generation clocks, including AgeAccelGrim2 (OR = 0.66; 95% CI 0.61-0.71), PCGrimAge (OR = 0.64; 95% CI 0.59-0.69), PCPhenoAge (OR = 0.73; 95% CI 0.68-0.78) and DunedinPACE (OR = 0.77; 95% CI 0.72-0.82). None of the clocks was more strongly associated with survival to age 90 with preserved cognition than with survival to age 90 with cognitive impairment, relative to death before age 90. CONCLUSION: All epigenetic clocks were associated with exceptional longevity, but none were associated with cognitive healthspan. Developing clocks that can differentiate long survival with and without preserved cognitive function is critical.

Healthspan↗

PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

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.

Humans↗

PubMed2022

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

DNA Methylation↗