A unified framework for systematic curation and evaluation of aging biomarkers.

Kejun Ying, Seth Paulson, Alec Eames, Alexander Tyshkovskiy, Siyuan Li, Nir Eynon, Macsue Jacques, Robin Grolaux, Kirsten Seale, Erik Jacques, Ludger J E Goeminne, Andrea Cipriano, Martin Perez-Guevara, Mehrnoosh Emamifar, Maximiliano Casas Martínez, Dayoon Kwon, Anna Kosheleva, Michael Snyder, Dane Gobel, Chiara Herzog, Daniel L McCartney, Riccardo E Marioni, Jessica Lasky-Su, Jesse R Poganik, Mahdi Moqri, Vadim N Gladyshev

Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. · pubmed

Paper

Abstract

Aging biomarkers are essential tools for quantifying biological aging, but systematic validation has been hindered by methodological inconsistencies and fragmented datasets. Here we show that the ability of traditional aging clocks to predict chronological age does not correlate with mortality prediction capacity (R = 0.12, P = 0.67), suggesting that these metrics capture distinct biological processes. We developed Biolearn, an open-source framework enabling standardized evaluation of 39 biomarkers across over 20,000 individuals from diverse cohorts. The Horvath skin and blood clock achieved the highest chronological age accuracy (R

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Research summary

The paper claims that traditional aging clocks do not correlate with mortality prediction capacity, highlighting the need for a unified framework for evaluating aging biomarkers. This research addresses the systematic validation of aging biomarkers, which is crucial for understanding biological aging and developing interventions that could potentially extend lifespan or mitigate age-related decline.

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