Sleep chart of biological aging clocks across organs and omics.

Optimal sleep plays a vital role in promoting healthy aging and enhancing longevity. This study proposes a Sleep Chart to assess the relationship between sleep duration and 23 biological aging clocks across 17 organ systems or tissues and 3 omics data types (imaging, proteomics, and metabolomics). First, a systemic, U-shaped pattern shows that both short (<6 hours) and long (>8 hours) sleep duration are linked to elevated biological age gaps (BAGs) across 9 brain and body systems and 3 omics types, with optimal sleep time varying by organ and sex ([6.

No winners or losers: clinical chemistry-based biological aging metrics perform similarly across cohorts and health outcomes.

Aging is the leading risk factor for most chronic disease. However, disease risk varies substantially between individuals of the same age. Biological aging measures attempt to quantify this difference using biomarkers; such measures have amassed substantial evidence as reliable correlates of morbidity and mortality.

An unbiased comparison of 14 epigenetic clocks in relation to 10-year onset of 174 disease outcomes in 18,859 individuals.

Epigenetic Clocks have been trained to predict chronological age, healthspan and lifespan. Such clocks are often analysed in relation to disease outcomes - typically using small datasets and a limited number of clocks. Here, we present the first large-scale (n=18,849), unbiased comparison of 14 widely used clocks as predictors of 174 incident disease outcomes and all-cause mortality.

A 2-year calorie restriction intervention may reduce glycomic biological age biomarkers - a pilot study.

In this pilot study, a subset of CALERIE Phase 2 (No. NCT00427193, registered 25th Jan 2007) participants (n = 26) were evaluated for the effects of 2 years of 25% calorie restriction (CR) on N-glycosylation of IgG, plasma, and complement C3, as well as IgG-based biological age (GlycAge). Plasma samples were collected at baseline (BL), 12 (12mo), and 24 months (24mo).

Multi-organ MRI digitizes biological aging clocks across proteomics, metabolomics, and genetics.

Leveraging clinical phenotypes, neuroimaging, proteomics, metabolomics, and epigenetics, biological aging clocks across organ systems and tissues have advanced our understanding of human aging and disease. In this study, we expand this biological aging clock framework to multi-organ magnetic resonance imaging (MRI) by developing 7 organ-specific MRI-based biological age gaps (MRIBAGs), including the brain, heart, liver, adipose tissue, spleen, kidney, and pancreas. Leveraging imaging, genetic, proteomic, and metabolomic data from 313,645 individuals curated by the MULTI consortium, we link the 7 MRIBAGs to 2,923 plasma proteins, 327 metabolites, and 6,477,810 common genetic variants.