Evidence map›Paper›PMID 38569189›Full record

ArticleJMIR public health and surveillance2024

Metabolic Score for Insulin Resistance and New-Onset Type 2 Diabetes in a Middle-Aged and Older Adult Population: Nationwide Prospective Cohort Study and Implications for Primary Care.

Hui Cheng, Zhihui Jia, Yu Ting Li, Xiao Yu, Jia Ji Wang, Yao Jie Xie, Jose Hernandez, Harry H X Wang

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 4% of its field
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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.

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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

8 authors at 4 institutions in 3 countries.

Hui Cheng *School of Public Health, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0003-1535-3979
Zhihui Jia *School of Public Health, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0002-5983-9041
Yu Ting LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0002-6716-358X
Xiao YuSchool of Public Health, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0003-4078-042X
Jia Ji WangSchool of Public Health, Guangzhou Medical University, Guangzhou, China.ORCID 0000-0003-2061-0327
Yao Jie XieSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, China (Hong Kong).ORCID 0000-0001-9289-4985
Jose HernandezFaculty of Medicine and Health, EDU, Digital Education Holdings Ltd, Kalkara, Malta.ORCID 0009-0005-1019-1511
Harry H X WangSchool of Public Health, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0002-0361-6527
Sun Yat-sen University · CNGuangzhou Medical University · CNHong Kong Polytechnic University · HKUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe metabolic score for insulin resistance (METS-IR) has emerged as a noninsulin-based index for the approximation of insulin resistance (IR), yet longitudinal evidence supporting the utility of METS-IR in the primary prevention of type 2 diabetes mellitus (T2DM) remains limited.

objectiveWe aimed to investigate the longitudinal association between METS-IR, which combines fasting plasma glucose (FPG), lipid profiles, and anthropometrics that can be routinely obtained in resource-limited primary care settings, and the incidence of new-onset T2DM.

methodsWe conducted a closed-cohort analysis of a nationwide, prospective cohort of 7583 Chinese middle-aged and older adults who were free of T2DM at baseline, sampled from 28 out of 31 provinces in China. We examined the characteristics of participants stratified by elevated blood pressure (BP) at baseline and new-onset T2DM at follow-up. We performed Cox proportional hazard regression analysis to explore associations of baseline METS-IR with incident T2DM in participants overall and in participants stratified by baseline BP. We also applied net reclassification improvement and integrated discrimination improvement to examine the incremental value of METS-IR.

resultsDuring a mean follow-up period of 6.3 years, T2DM occurred in 527 participants, among which two-thirds (332/527, 62.9%; 95% CI 58.7%-67.1%) had baseline FPG<110 mg/dL. A SD unit increase in baseline METS-IR was associated with the first incidence of T2DM (adjusted hazard ratio [aHR] 1.33, 95% CI 1.22-1.45; P<.001) in all participants. We obtained similar results in participants with normal baseline BP (aHR 1.41, 95% CI 1.22-1.62; P<.001) and elevated baseline BP (aHR 1.29, 95% CI 1.16-1.44; P<.001). The predictive capability for incident T2DM was improved by adding METS-IR to FPG. In study participants with new-onset T2DM whose baseline FPG was <126 mg/dL and <110 mg/dL, 62.9% (332/527; 95% CI 60%-65.9%) and 58.1% (193/332; 95% CI 54.3%-61.9%) of participants had baseline METS-IR above the cutoff values, respectively.

conclusionsMETS-IR was significantly associated with new-onset T2DM, regardless of baseline BP level. Regular monitoring of METS-IR on top of routine blood glucose in clinical practice may add to the ability to enhance the early identification of primary care populations at risk for T2DM.

Indexed as

Diabetes Mellitus, Type 2Insulin ResistancePrimary Health CareAgedBlood GlucoseChinaFemaleHumansIncidenceMaleMiddle AgedProspective StudiesRisk FactorsBlood Glucoseblood pressurelongitudinal studymetabolic score for insulin resistanceprimary caretype 2 diabetes mellitus

Identifiers

PMID38569189
PMCPMC11184265
OpenAlexW4394762551

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.