Multi-omic underpinnings of epigenetic aging and human longevity

Abstract

Biological aging is accompanied by increasing morbidity, mortality, and healthcare costs; however, its molecular mechanisms are poorly understood. Here, we use multi-omic methods to integrate genomic, transcriptomic, and metabolomic data and identify biological associations with four measures of epigenetic age acceleration and a human longevity phenotype comprising healthspan, lifespan, and exceptional longevity (multivariate longevity). Using transcriptomic imputation, fine-mapping, and conditional analysis, we identify 22 high confidence associations with epigenetic age acceleration and seven with multivariate longevity. FLOT1, KPNA4, and TMX2 are novel, high confidence genes associated with epigenetic age acceleration. In parallel, cis-instrument Mendelian randomization of the druggable genome associates TPMT and NHLRC1 with epigenetic aging, supporting transcriptomic imputation findings. Metabolomics Mendelian randomization identifies a negative effect of non-high-density lipoprotein cholesterol and associated lipoproteins on multivariate longevity, but not epigenetic age acceleration. Finally, cell-type enrichment analysis implicates immune cells and precursors in epigenetic age acceleration and, more modestly, multivariate longevity. Follow-up Mendelian randomization of immune cell traits suggests lymphocyte subpopulations and lymphocytic surface molecules affect multivariate longevity and epigenetic age acceleration. Our results highlight druggable targets and biological pathways involved in aging and facilitate multi-omic comparisons of epigenetic clocks and human longevity.

Fig. 1. Study overview.

An overview of this study’s data sources, analytical flow, and methodology. Created with BioRender.com. IEAA intrinsic epigenetic age acceleration, TWAS transcriptome-wide association study, SNP single nucleotide polymorphism, eQTL expression quantitative trait loci, GTEx Genotype-Tissue Expression Project, IVW inverse variance weighted, CELLECT CELL-type Expression-specific integration for Complex Traits, FOCUS Fine-mapping Of CaUsal gene Sets, MAGMA Multi-marker Analysis of GenoMic Annotation, S-LDSC stratified linkage disequilibrium score regression, PrismEXP Prediction of gene Insights from Stratified Mammalian gene co-EXPression.

Fig. 2. Results of TWASs of EAA and multivariate longevity.

a–e Manhattan plots of gene-traits associations for aging-related traits (IEAA, GrimAge, HannumAge, PhenoAge, multivariate longevity). X axes represent genomic position. Blue lines represent Z = 4.837, which corresponds to a Bonferroni-corrected significance threshold of P = 1.32 × 10⁻⁶. Red circles represent statistically significant gene-trait associations. Statistical analyses were conducted using two-sided t-tests. f Venn diagram quantifying the overlapping genes shared by two or more aging-related phenotypes. Encircled numbers represent the number of significant genes shared between two or more phenotypes. TWAS transcriptome-wide association study, EAA epigenetic age acceleration, IEAA intrinsic epigenetic age acceleration.

Fig. 3. Results of metabolome-wide MR analysis on multivariate longevity.

MR effects of metabolic phenotypes on multivariate longevity. Metabolic phenotypes with significant, positive Z scores (beta/standard error) are predicted to increase multivariate longevity and vice versa. The eight most significant positive and negative associations are labeled with abbreviated codes, and the full name corresponding to each code is contained in Supplementary Data 23. Green circles represent metabolic phenotypes that increase multivariate longevity and blue circles represent metabolic phenotypes that decrease multivariate longevity. The dotted line corresponds to a Bonferroni-adjusted significance threshold of P = 0.00122 (0.05/41 principal components). The full results of the metabolome-wide MR analysis, including estimates on EAA (all null), are contained in Supplementary Data 25–29. Statistical analyses were conducted using two-sided t-tests. MR Mendelian randomization, EAA epigenetic age acceleration.

Fig. 4. CELLECT-MAGMA cellular associations with EAA.

a–d Results from CELLECT-MAGMA cell-type enrichment analysis of four EAA traits significant at a FDR of 0.05. Bars represent negative, log-transformed, unadjusted P values. The scRNA-seq data used in this analysis comes from the Tabula Muris database and encompasses 115 cell types from 20 Mus musculus tissues. The full results of the cell-type enrichment analyses, including CELLECT-LDSC results and cellular associations with multivariate longevity (all null), are contained in Supplementary Data 30–34. Statistical analyses were conducted using one-sided t-test tests. CELLECT CELL-type Expression-specific integration for Complex Traits; MAGMA Multi-marker Analysis of GenoMic Annotation, FDR false discovery rate, scRNA-seq single-cell RNA sequencing, LDSC linkage disequilibrium score regression.