Evidence map›Paper›PMID 41254261›Full record

ArticleGeroScience2025

Glycemic status-dependent proteomic signatures of biological aging for health risk prediction.

Jiang Li, Jie Li, Xiaoqin Xu, Yuefeng Yu, Wenqi Shen, Ying Sun, Yanqi Fu, Xiao Tan, Ningjian Wang, Yingli Lu and 1 more

Abstract read
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In one paragraph

Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Jiang Li *Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Jie Li *Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Xiaoqin XuInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Yuefeng YuInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Wenqi ShenInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Ying SunInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Yanqi FuInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Xiao TanDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Ningjian WangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Yingli LuInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. luyingli@sjtu.edu.cn.
Bin WangInstitute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. binwang1126@163.com.ORCID http://orcid.org/0000-0002-4869-1352

Funding

Major Science and Technology Innovation Program of Shanghai Municipal Education Commission 2019-01-07-00-01-E00059National Natural Science Foundation of China 82120108008National Natural Science Foundation of China 82170870Science and Technology Commission of Shanghai Municipality 22015810500
6 · The paper itself

Abstract

Existing proteomic aging clocks have been derived from the overall population, with little consideration of extended models tailored to individuals with different glycemic status. We aimed to quantify glycemic status-dependent proteomic signatures of aging and developed proteomic aging scores (ProAS) for health risk prediction. A total of 2923 plasma proteins were measured using Olink in 46,047 UK Biobank participants, including 37,353 with normoglycemia, 5977 with prediabetes, and 2717 with diabetes. Using a three-step screening approach, we identified 11, 23, and 21 representative protein biomarkers associated with all-cause mortality among individuals with normoglycemia, prediabetes, and diabetes, respectively. Three proteins (GDF15, EDA2R, and WFDC2) were shared across all groups, with GDF15 emerging as the top-ranked important protein in normoglycemia and prediabetes and WFDC2 in diabetes. The protein-based ProAS according to glycemic status showed significant associations with diverse health outcomes. Adding the ProAS in the models improved the predictive accuracy of mortality and incident diseases beyond conventional risk factors, but the performance progressively diminished as glycemic status deteriorated. In addition, 72, 51, and 36 out of 102 modifiable factors spanning seven categories were identified as determinants for ProRS in normoglycemia, prediabetes, and diabetes, respectively. Our findings extend the current proteomic clocks by revealing glycemic status-specific aging patterns and their ability to predict age-related outcomes, potentially refining risk stratification and targeted interventions for healthy aging.

Indexed as

Biological agingGlycemic statusHealth outcomesProteome

Identifiers

PMID41254261

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.