A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging

Abstract

Why people age at different rates is a fundamental unsolved problem in biology. We created a model that predicts an individual's age, taking as input physiological traits that change with age in the large UK Biobank dataset, such as blood pressure, blood metabolites, strength, and stimulus-reaction time. The model's Root Mean Square Error of age prediction (RMSE) is less than 5 years. We argue that the difference between calculated 'biological' age and actual age (delta-Age) reflects an individual's relative youthfulness and possibly their rate of aging. Validating this interpretation, people predicted to be physiologically young for their age have a lower subsequent mortality rate and a higher parental age at death, even though no mortality data were used to calculate delta-Age. A Genome-Wide Association Study (GWAS) of delta-Age, and analysis of environmental factors associated with delta-Age identified known as well as new factors that may influence human aging, including genes involved in synapse biology and a tendency to play computer games. We identify 12 readily-measured physiological traits that together assess a person's biological age and may be used clinically to evaluate therapeutics designed to slow aging and extend healthy life.

The C. elegans Observatory: High-throughput exploration of behavioral aging

“…Although the C. elegans Observatory has the potential throughput to enable an unbiased screen, we were curious whether a candidate-gene approach could reveal novel behavioral trajectory phenotypes. Via computational analysis of human health data ( Libert et al, 2022 ) and literature search, a variety of candidate mammalian genes were selected. We picked C. elegans genes corresponding to some of these based on the existence of orthologs, paralogs, or at least reasonably closely related gene families and the presence of corresponding RNAi constructs in the Ahringer RNAi library ( Kamath et al, 2003 ).…”

A Year at the Forefront of Proteostasis and Aging

“…systolic blood pressure, IGF-1 levels, etc.) that when tracked collectively serve as a reliable predictor of biological age ( Libert et al, 2022 preprint). The continued development of reliable biomarkers will be essential for clinical trials evaluating interventions that can lengthen human healthspan in the coming years.…”

The principal component-based clinical aging clock (PCAge) identifies signatures of healthy aging and provides normative targets for clinical intervention

“…Attempts to construct classifiers (biological aging clocks) to determine BA from observable physical features (biomarkers) have a long history [2][3][4] . These can be constructed based on a wide range of biological features, including clinical parameters [5][6][7][8][9][10][11][12][13] , DNA methylation (DNAm) [14][15][16][17][18][19][20][21] , and -omics data [22][23][24][25][26] .…”

“…Second-generation clock construction requires large cohort data with subjects for whom both data on biological features and decades of disease and mortality follow-up have been collected 34,35 . For standard clinical chemistry and physiological features, datasets meeting these criteria have recently become available, enabling generation of "clinical clocks" (CCs), which predict future mortality and morbidity directly from clinical features and biomarkers 6,16,18,19,30,[36][37][38] . Equivalent historic data are not yet available for most types of -omics data, including DNAm.…”