1- This study aimed to explore plasma extracellular microRNAs as a biological aging indicator and their associations with health outcomes using population-level data. We quantified plasma expression levels of 2083 extracellular microRNAs using targeted RNA-sequencing in 2684 participants from the population-based Rotterdam Study cohort. The training and test sets included 1930 participants from the advanced-aged initial and second subcohort (RS-I/RS-II; median age: 70.6), while the validation set comprised 754 participants from the middle-aged fourth subcohort (RS-IV; median age: 53.5). Based on 591 microRNAs well-expressed in plasma, we examined differential expression of microRNAs with chronological age, PhenoAge-a composite score of age and nine multi-system blood biomarkers-the frailty index, and mortality. Next, elastic net models were employed to construct composite microRNA-based aging biomarkers predicting chronological age (mirAge), PhenoAge (mirPA), frailty index (mirFI), and mortality (mirMort). We identified 188 microRNAs differentially expressed with chronological age within the RS-I/RS-II advanced-aged population (ntraining = 1158, ntest = 772), of which 177 microRNAs (94.1%) were replicated in the middle-aged RS-IV subcohort (nvalidation = 754). Moreover, 227 miRNAs showed robust associations with PhenoAge, 61 with FI, and 16 with 10-year mortality independent of chronological age. Subsequently, we constructed four plasma microRNA-based aging biomarkers: mirAge with 108, mirPA with 153, mirFI with 81, and mirMort with 50 miRNAs. Elevated scores on these microRNA-based aging biomarkers were associated with unfavorable health outcomes, including lower subjective physical functioning and self-reported health and increased mortality and frailty risk, but not with first- or multi-morbidity. 

Plasma microRNA signatures of aging and their links to health outcomes and mortality: findings from a population-based cohort study Kuiper LM, Mens MMJ, Wu JW, Goudsmit J, Ma Y, Liang L, Hofman A, Voortman T, Ikram MA, van Rooij JGJ, van Meurs JBJ, Ghanbari M. Genome Medicine 2025 Jun 25;17(1):70. doi: 10.1186/s13073-025-01437-5.

2- Application of biological age as a measure of an individual´s health status offers new perspectives into extension of both lifespan and healthspan. We present a community-based cohort study of 1930 participants with a mean age of 72 years and a follow-up period of over 7 years, using two variants of a phenotypic blood-based algorithm that either excludes (BioAge1) or includes (BioAge2) neurofilament light chain (NfL) as a neurodegenerative marker. BioAge1 and BioAge2 predict dementia equally well, as well as lifespan and healthspan. We additionally tested the association of microRNAs with age and identified 263 microRNAs significantly associated with biological and chronological age alike. Top differentially expressed microRNAs based on biological age had a higher significance level than those based on chronological age, suggesting that biological age captures aspects of aging signals at the epigenetic level.

Biological age in healthy elderly predicts aging-related diseases including dementia. Wu JW, Yaqub A, Ma Y, Koudstaal W, Hofman A, Ikram MA, Ghanbari M, Goudsmit J. Sci Rep. 2021 Aug 5;11(1):15929. doi: 10.1038/s41598-021-95425-5.