Evidence map›Paper›PMID 40051521›Full record

ArticleFrontiers in public health2025

Association of metabolic score for insulin resistance with incident metabolic syndrome: a cohort study in middle-aged and older adult Chinese population.

Qiuling Zhang, Yushuang Wei, Shengzhu Huang, YeMei Mo, Boteng Yan, Xihui Jin, Mingjie Xu, Xiaoyou Mai, Chaoyan Tang, Haiyun Lan and 4 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

14 authors.

Qiuling Zhang *The First People's Hospital of Yulin, Yulin, Guangxi, China.
Yushuang Wei *School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Shengzhu Huang *Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
YeMei MoThe First People's Hospital of Yulin, Yulin, Guangxi, China.
Boteng YanInstitute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
Xihui JinInstitute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
Mingjie XuInstitute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
Xiaoyou MaiSchool of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Chaoyan TangThe First People's Hospital of Yulin, Yulin, Guangxi, China.
Haiyun LanThe First People's Hospital of Yulin, Yulin, Guangxi, China.
Rongrong LiuThe First People's Hospital of Yulin, Yulin, Guangxi, China.
Mingli LiGuangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Zengnan MoInstitute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
Wenchao XieThe First People's Hospital of Yulin, Yulin, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recent studies suggest that the metabolic score for insulin resistance (MetS-IR) is an effective indicator of metabolic disorders. However, evidence on the relationship between MetS-IR and metabolic syndrome (MetS) among the Chinese middle-aged and older adult population is limited. Objective: This cohort study aims to assess the associations of MetS-IR levels with MetS risk and its components. Methods: Data used in this study from the National Basic Public Health Service Project Management System (2020-2023). Multivariable Cox proportional hazards model and restricted cubic spline (RCS) were employed to evaluate the associations of baseline MetS-IR levels with MetS risk and its components, receiver operating characteristic (ROC) curves were further utilized to assess the efficacy of MetS-IR in predicting the risk of MetS and its component. Results: Of 1,498 subjects without MetS at baseline, 392 incident MetS cases were observed during a median of 27.70 months of follow-up. The adjusted multivariable Cox regression analysis indicated an elevated 15% risk of developing MetS for 1-SD increment of MetS-IR [hazard ratios (HRs) and 95% confidence intervals: 1.16 (1.13-1.18)]. Compared to the first tertile of MetS-IR, the HRs of the third tertile and second tertile were 6.31 (95% CI 4.55-8.76) and 2.72 (95% CI 1.92-3.85), respectively. Consistent findings were further detected across subgroups. Moreover, nonlinear associations were observed between MetS-IR and the risk of MetS, abdominal obesity, and reduced high-density lipoprotein concentration (HDL-C) ( Conclusion: Our cohort study indicates a positive nonlinear association between MetS-IR with incident MetS, abdominal obesity, and reduced HDL-C, but positive linear associations of MetS-IR and elevated blood pressure (BP), elevated fasting blood glucose (FBG), elevated triglycerides (TG) in middle-aged and older adult people, more studies are warranted to verify our findings.

Indexed as

Insulin ResistanceMetabolic SyndromeAgedChinaCohort StudiesEast Asian PeopleFemaleHumansIncidenceMaleMiddle AgedProportional Hazards ModelsRisk Factorsabdominal obesityHDL-C (high density lipoprotein)metabolic syndromeMETS-IR indexolder adult

Identifiers

PMID40051521
PMCPMC11883690

What Socratic holds

Textmetadata
LicenceCC BY
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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.