Evidence map›Paper›PMID 42342685›Full record

ArticleNPJ science of food2026

Urinary caffeine and caffeine metabolites, biological aging, and cardiovascular-kidney-metabolic syndrome: deciphering association, mediation, and mechanism via epidemiology and bioinformatics.

Jie Liu, Wenshan Shi, Shuhui Wan, Da Shi, Jiahao Song, Le Hong, Feifei Wang, Yani Xiong, Wendi Shi, Qing Liu and 7 more

Abstract read
In one paragraph

Article in NPJ science of food, 2026. 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

17 authors.

Jie LiuDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wenshan ShiDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shuhui WanDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Da ShiFaculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada.
Jiahao SongDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Le HongDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Feifei WangDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yani XiongDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wendi ShiLucy Cavendish College, University of Cambridge, Cambridge, UK.
Qing LiuDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhiying HuoDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jinqiang LiDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Pengfei ChenDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yongfang ZhangDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiamin XuDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Weihong ChenDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. wchen@mails.tjmu.edu.cn.
Bin WangDepartment of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. gentwong@163.com.

Funding

China Postdoctoral Science Foundation 2022T150230Hubei Young sci-tech Talent Funding Project E Kexieban 2025[67]National Natural Science Foundation of China 82574050the Hubei Provincial Natural Science Foundation of China 2025AFB591the Science & Technology Fundamental Resources Investigation Program 2023FY100602
6 · The paper itself

Abstract

Associations of caffeine (the most routinely consumed bioactive compound worldwide) and caffeine metabolites (CAF/CAFMs) with cardiovascular-kidney-metabolic (CKM) syndrome (a newly-introduced high-burden clinical condition) and role of biological aging and underlying mechanisms are uncharted territories warranting pressing decryption. Among 3020 adults, 15 urinary CAF/CAFMs were determined to investigate their single-analyte (by weighted logistic regression) and mixture (by Bayesian kernel machine regression [BKMR] and weighted quantile sum [WQS]) relationships between advanced CKM syndrome. Role of emerging biological aging metric-Gompertz-Law-Based Biological Age Difference (GOLD BioAgeDiff)-was explored by mediation analysis. Underlying mechanisms were explored by network pharmacology. Specific CAF/CAFMs (1-methylxanthine, theophylline, paraxanthine, and theobromine) were negatively linked to advanced CKM syndrome (P and false discovery rate < 0.05). WQS and BKMR uncovered negative CAF/CAFMs mixture-advanced CKM syndrome association (odds ratio = 0.782; 95% confidential interval: 0.670, 0.912). Exploratorily, GOLD BioAgeDiff mediated 13.85-20.06% of the CAF/CAFMs-advanced CKM syndrome associations. Metabolic, mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase-protein kinase B (PI3K-Akt), and apoptosis signaling pathways were exploratorily enriched in the CAF/CAFMs-CKM syndrome relationships. Overall, several CAF/CAFMs and, especially, their mixture showed protective associations with CKM syndrome, which was exploratorily mediated by delayed biological aging, while metabolic, MAPK, PI3K-Akt, and apoptosis signaling pathways might be involved in the underlying mechanisms.

Identifiers

PMID42342685
PMCPMC13658184

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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.