Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies.

Background: Age is one of the major risk factors for a wide range of diseases. Nevertheless, some individuals can better cope with these changes and become centenarians. We hypothesize that their blood transcriptome may provide insights into the mechanisms contributing to healthy aging, as well as enable the discovery of candidate therapeutic targets. The Long-Life Family Study (LLFS), which includes participants from families enriched with long-lived individuals, serves as a valuable dataset for achieving these objectives. Methods: To identify transcripts associated with age, we analyzed the association between age at blood draw and 16,284 RNAseq-based blood transcriptomic data from 2,167 LLFS participants with ages ranging from 18 to 107. We used linear mixed-effect models controlling for familial relatedness and adjusted for genetic, socioeconomic, and technical confounders. We validated results in a dataset of 20,884 RNAseq-based blood transcriptomic data from 434 participants of the Integrative Longevity Omics Study, and compared findings to a published reference aging signature. We integrated the results by building a transcriptomic aging clock. We also identified transcripts associated with mortality risk using a Cox-proportional hazard model. Results: We identified 4,227 transcripts increasing and 4,044 transcripts decreasing with age. Age-associated expression patterns were significantly replicated in external datasets, with high correlation (R = 0.78 - 0.94). Enrichment analysis revealed age-related upregulation of inflammatory and senescence-related pathways (e.g., IFN-γ response, TNF-α/NF-κB signaling), and downregulation of MYC and Wnt/β-catenin targets, among others. WGCNA identified co-expression modules reflecting inflammation, immune signaling, and decreased protein synthesis. We also identified 314 transcripts significantly associated with mortality risk and found that pro-survival gene sets included NK cell-mediated cytotoxicity and GPCR signaling. A subset of transcripts showed age associations unique to longevity-enriched cohorts and not present in non-longevity populations, implicating IL6-Jak-Stat3, mitotic spindle, and p53 pathways. Finally, transcriptomic age (delta-age) was strongly associated with increased mortality (HR = 1.108, p = 3.33e-18), with significant survival differences between delta-age groups. Conclusions: This study identified robust transcriptomic signatures of aging and mortality in a longevity-enriched population, highlighting key biological pathways such as immune modulation, inflammation, and senescence. Age-associated expression profiles that are unique to long-lived individuals may represent resilience mechanisms distinct from general aging trends. Transcriptomic age acceleration is a strong predictor of mortality, reinforcing its utility as a molecular biomarker of biological aging.