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Deep biomarkers of aging are population-dependent
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.ebiom.2015.07.029openalex-snapshot
Inflammation, But Not Telomere Length, Predicts Successful Ageing at Extreme Old Age: A Longitudinal Study of Semi-supercentenarians
inferredcites →https://doi.org/10.1016/j.exger.2014.04.016openalex-snapshot
Cross-population validation of statistical distance as a measure of physiological dysregulation during aging
inferredcites →https://doi.org/10.1016/j.mad.2013.01.004openalex-snapshot
A novel statistical approach shows evidence for multi-system physiological dysregulation during aging
inferredcites →https://doi.org/10.1016/j.mad.2014.10.001openalex-snapshot
Trajectories of physiological dysregulation predicts mortality and health outcomes in a consistent manner across three populations
inferredcites →https://doi.org/10.1016/j.molcel.2012.10.016openalex-snapshot
Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates
inferredcites →https://doi.org/10.1016/s0140-6736(14)61682-2openalex-snapshot
Global, regional, and national age–sex specifi c all-cause andcause-specifi c mortality for 240 causes of death, 1990–2013:a systematic analysis for the Global Burden of DiseaseStudy 2013
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.5b00982openalex-snapshot
Applications of Deep Learning in Biomedicine
inferredcites →https://doi.org/10.1073/pnas.1506264112openalex-snapshot
Quantification of biological aging in young adults
inferredcites →https://doi.org/10.1093/ajcn/77.5.1146openalex-snapshot
Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials
inferredcites →https://doi.org/10.1093/gerona/gls233openalex-snapshot
Modeling the Rate of Senescence: Can Estimated Biological Age Predict Mortality More Accurately Than Chronological Age?
inferredcites →https://doi.org/10.1093/jn/134.5.1181openalex-snapshot
Dietary Fiber and C-Reactive Protein: Findings from National Health and Nutrition Examination Survey Data
inferredcites →https://doi.org/10.1111/j.0894-0959.2004.17603.xopenalex-snapshot
POOR NUTRITIONAL STATUS AND INFLAMMATION: Serum Albumin: Relationship to Inflammation and Nutrition
inferredcites →https://doi.org/10.1111/j.1532-5415.2000.tb03873.xopenalex-snapshot
Subsystems Contributing to the Decline in Ability to Walk: Bridging the Gap Between Epidemiology and Geriatric Practice in the InCHIANTI Study
inferredcites →https://doi.org/10.1111/mec.13357openalex-snapshot
Molecular biomarkers for chronological age in animal ecology
inferredcites →https://doi.org/10.1186/gb-2013-14-10-r115openalex-snapshot
DNA methylation age of human tissues and cell types
inferredcites →https://doi.org/10.1186/s12916-015-0400-xopenalex-snapshot
Age-related frailty and its association with biological markers of ageing
inferredcites →https://doi.org/10.13016/sclw-iccqopenalex-snapshot
Normal Human Aging: The Baltimore Longitudinal Study on Aging
inferredcites →https://doi.org/10.1371/journal.pone.0122541openalex-snapshot
Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
inferredcites →https://doi.org/10.18632/aging.100894openalex-snapshot
Aging of immune system: immune signature from peripheral blood lymphocyte subsets in 1068 healthy adults
inferredcites →https://doi.org/10.18632/aging.100968openalex-snapshot
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
inferredcites →https://doi.org/10.18632/aging.100995openalex-snapshot
Longitudinal decline of leukocyte telomere length in old age and the association with sex and genetic risk
inferredcites →https://doi.org/10.3389/fpubh.2016.00003openalex-snapshot
Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10522-020-09890-yopenalex-snapshot
AI-based investigation of molecular biomarkers of longevity
inferred← citeshttps://doi.org/10.1007/s11357-025-01784-6openalex-snapshot
A simple blood biomarker based on gene expression describes cardiovascular health-related biological age
inferred← citeshttps://doi.org/10.1016/j.arr.2018.11.003openalex-snapshot
Artificial intelligence for aging and longevity research: Recent advances and perspectives
inferred← citeshttps://doi.org/10.1016/j.exger.2017.07.011openalex-snapshot
The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations
inferred← citeshttps://doi.org/10.1016/j.exger.2024.112421openalex-snapshot
Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT
inferred← citeshttps://doi.org/10.1038/s41598-018-23534-9openalex-snapshot
Extracting biological age from biomedical data via deep learning: too much of a good thing?
inferred← citeshttps://doi.org/10.1038/s41598-025-27478-9openalex-snapshot
A sex-adjusted 7-biomarker clinical aging clock for translational preventative medicine
inferred← citeshttps://doi.org/10.1093/gerona/glaa238openalex-snapshot
Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies
inferred← citeshttps://doi.org/10.1093/gerona/glac161openalex-snapshot
Development of a Novel Multidimensional Measure of Aging to Predict Mortality and Morbidity in the Prospective MJ Cohort
inferred← citeshttps://doi.org/10.1093/gerona/gly005openalex-snapshot
Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations
inferred← citeshttps://doi.org/10.1093/gerona/glz174openalex-snapshot
Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative
inferred← citeshttps://doi.org/10.1093/gerona/glz223openalex-snapshot
Testing Proposed Quantifications of Biological Aging in Taiwanese Older Adults
inferred← citeshttps://doi.org/10.1101/095653openalex-snapshot
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
inferred← citeshttps://doi.org/10.1111/acel.13080openalex-snapshot
Measuring biological aging in humans: A quest
inferred← citeshttps://doi.org/10.1111/acel.13872openalex-snapshot
Explainable machine learning framework to predict personalized physiological aging
inferred← citeshttps://doi.org/10.1177/2058739218780058openalex-snapshot
Blood biomarkers in ischemic stroke: Role of biomarkers in differentiation of clinical phenotype
inferred← citeshttps://doi.org/10.18632/aging.202816openalex-snapshot
Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience
inferred← citeshttps://doi.org/10.18632/aging.204787openalex-snapshot
Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis
inferred← citeshttps://doi.org/10.26102/2310-6018/2021.33.2.028openalex-snapshot
Modeling the biological age of the patients based on their functional indicators
inferred← citeshttps://doi.org/10.3389/fpubh.2023.1074274openalex-snapshot
Progress in biological age research
inferred← citeshttps://doi.org/10.3390/ijms232315103openalex-snapshot
Biological Age Predictors: The Status Quo and Future Trends
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