Evidence map›Paper›PMID 40600465›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Gompertz Law-Based Biological Age (GOLD BioAge): A Simple and Practical Measurement of Biological Ageing to Capture Morbidity and Mortality Risks.

Meng Hao, Hui Zhang, Jingyi Wu, Yaqi Huang, Xiangnan Li, Meijia Wang, Shuming Wang, Jiaofeng Wang, Jie Chen, Zhi Jun Bao and 5 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. [Application of biological age for cardiovascular risk prediction in a community-based Chinese cohort].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. A Residual Approach to Estimate Biological Age from Gompertz Modeling.medRxiv : the preprint server for health sciences · 2025
    Article
  17. Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Meng HaoDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.ORCID https://orcid.org/0000-0002-5543-5373
Hui ZhangDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Jingyi WuDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Yaqi HuangDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Xiangnan LiArtificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai Academy of Artificial Intelligence for Science, Shanghai, China, Fudan University, Shanghai, 200438, China.
Meijia WangDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Shuming WangDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Jiaofeng WangDepartment of Gerontology, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Jie ChenDepartment of Gerontology, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Zhi Jun BaoDepartment of Gerontology, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Li JinState Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China.
Xiaofeng WangDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
Zixin HuArtificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai Academy of Artificial Intelligence for Science, Shanghai, China, Fudan University, Shanghai, 200438, China.
Shuai JiangDepartment of Vascular Surgery, Shanghai Key Laboratory of Vascular Lesion Regulation and Remodeling, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
Yi LiDepartment of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.

Funding

National Natural Science Foundation of China-Youth Science Fund 32200536National Natural Science Foundation of China-Youth Science Fund 32288101National Natural Science Foundation of China-Youth Science Fund 32300533National Natural Science Foundation of China-Youth Science Fund 82301768Shanghai Municipal Science and Technology Major Project 2023SHZDZX02,2017SHZDZX01Shanghai Sailing Program 23YF1430500
6 · The paper itself

Abstract

Biological age reflects actual ageing and overall health, but current ageing clocks are often complex and difficult to interpret, which limits their clinical application. This study introduces a Gompertz law-based biological age (GOLD BioAge) model designed to simplify the assessment of ageing. We calculated GOLD BioAge using clinical biomarkers and found significant associations between the difference from chronological age (BioAgeDiff) and the risks of morbidity and mortality in the NHANES and UK Biobank. Using proteomics and metabolomics data, we developed GOLD ProtAge and MetAge, which outperformed the clinical biomarker models in predicting mortality and chronic disease risk in UK Biobank. Benchmark analyses demonstrated that the models outperformed common ageing clocks in predicting mortality across diverse age groups in both the NHANES and UK Biobank cohorts. Additionally, a simplified version called Light BioAge is created, which uses three biomarkers to assess ageing. The Light model reliably captured the mortality risk across three validation cohorts (CHARLS, RuLAS, and CLHLS). It significantly predicted the onset of frailty, stratified frail individuals, and collectively identified individuals at high risk of mortality. In summary, the GOLD BioAge algorithm provides a valuable framework for the assessment of ageing in public health and clinical practice.

Indexed as

AgingMortalityAdultAgedAged, 80 and overBiomarkersFemaleHumansMaleMiddle AgedMorbidityUnited KingdomBiomarkersaging clocksbiological agefrailty indexmetabolomicsproteomics

Identifiers

PMID40600465
PMCPMC12407260

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Registered trials

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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.