ArticleAging2020
PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Biomarkers of ageing of humans and non-human primates.Nature reviews. Molecular cell biology · 2025Review
- Association of clinical biomarker-based biological age and aging trajectory with cardiovascular disease and all-cause mortality in Chinese adults: a population-based cohort study.BMC public health · 2025Article
- Article
- Psychogenic Aging: A Novel Prospect to Integrate Psychobiological Hallmarks of Aging.Translational psychiatry · 2024Review
- Progress in the study of aging marker criteria in human populations.Frontiers in public health · 2024Review
- Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis.Aging · 2023Article
- An evaluation of aging measures: from biomarkers to clocks.Biogerontology · 2023Review
- Biomarkers of aging.Science China. Life sciences · 2023Review
- Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications.Brain sciences · 2023Article
- The landscape of aging.Science China. Life sciences · 2022Review
- Biological Age Predictors: The Status Quo and Future Trends.International journal of molecular sciences · 2022Review
- Article
- Article
- Increased Pace of Aging in COVID-Related Mortality.Life (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of "deep aging clocks". In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor. Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.
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