ArticleAging2021
Predicting physiological aging rates from a range of quantitative traits using machine learning.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Proteomic Markers of Aging and Longevity: A Systematic Review.International journal of molecular sciences · 2024Pooled it
- Association of biological age acceleration with cardiovascular disease and premature mortality: a population-based prospective cohort study.Frontiers in public health · 2026Article
- Impact of cardiometabolic risk factors and its management on the reversion and progression of arterial stiffness.NPJ cardiovascular health · 2025Review
- Development of an emergency department triage tool to predict admission or discharge for older adults.International journal of emergency medicine · 2025Article
- Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging.Frontiers in aging · 2025Review
- Endogenous mitochondrial NAD(P)H fluorescence can predict lifespan.Communications biology · 2024Article
- Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation.bioRxiv : the preprint server for biology · 2024Article
- Measuring healthy ageing: current and future tools.Biogerontology · 2023Review
- Explainable machine learning framework to predict personalized physiological aging.Aging cell · 2023Article
- An evaluation of aging measures: from biomarkers to clocks.Biogerontology · 2023Review
- How general practitioners perceive the aging trajectory of oldest-old - A qualitative study.BMC primary care · 2023Article
- Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain.Nature aging · 2023Article
- Minimum standards for evaluating machine-learned models of high-dimensional data.Frontiers in aging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 6 institutions in 2 countries.
Funding
Abstract
It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual's physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.