Evidence map›Paper›PMID 38191206›Full record

ArticleBMJ open diabetes research & care2024

Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models.

Huan Hu, Tohru Nakagawa, Toru Honda, Shuichiro Yamamoto, Tetsuya Mizoue

Abstract read
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Article in BMJ open diabetes research & care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
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25citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

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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.

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Huan HuResearch Center for Prevention from Radiation Hazards of Workers, National Institute of Occupational Safety and Health, Kawasaki, Kanagawa, Japan hu.huanhuan@yahoo.com.ORCID 0000-0001-8558-2895
Tohru NakagawaHitachi Health Care Center, Hitachi, Ltd, Hitachi, Ibaraki, Japan.
Toru HondaHitachi Health Care Center, Hitachi, Ltd, Hitachi, Ibaraki, Japan.
Shuichiro YamamotoHitachi Health Care Center, Hitachi, Ltd, Hitachi, Ibaraki, Japan.
Tetsuya MizoueDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Heath and Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInsulin resistance and defects in pancreatic beta cells are the two major pathophysiologic abnormalities that underlie type 2 diabetes. In addition, visceral fat area (VFA) is reported to be a stronger predictor for diabetes than body mass index (BMI). Here, we tested whether the performance of diabetes prediction models could be improved by adding HOMA-IR and HOMA-β and replacing BMI with VFA. RESEARCH DESIGN AND

methodsWe developed five prediction models using data from a cohort study (5578 individuals, of whom 94.7% were male, and 943 had incident diabetes). We conducted a baseline model (model 1) including age, sex, BMI, smoking, dyslipidemia, hypertension, and HbA1c. Subsequently, we developed another four models: model 2, predictors in model 1 plus fasting plasma glucose (FPG); model 3, predictors in model 1 plus HOMA-IR and HOMA-β; model 4, predictors in model 1 plus FPG, HOMA-IR, and HOMA-β; model 5, replaced BMI with VFA in model 2. We assessed model discrimination and calibration for the first 10 years of follow-up.

resultsThe addition of FPG to model 1 obviously increased the value of the area under the receiver operating characteristic curve from 0.79 (95% CI 0.78, 0.81) to 0.84 (0.83, 0.85). Compared with model 1, model 2 also significantly improved the risk reclassification and discrimination, with a continuous net reclassification improvement index of 0.61 (0.56, 0.70) and an integrated discrimination improvement index of 0.09 (0.08, 0.10). Adding HOMA-IR and HOMA-β (models 3 and 4) or replacing BMI with VFA (model 5) did not further materially improve the performance.

conclusionsThis cohort study, primarily composed of male workers, suggests that a model with BMI, FPG, and HbA1c effectively identifies those at high diabetes risk. However, adding HOMA-IR, HOMA-β, or replacing BMI with VFA does not significantly improve the model. Further studies are needed to confirm our findings.

Indexed as

Diabetes Mellitus, Type 2Insulin ResistanceCohort StudiesFemaleGlycated HemoglobinHumansInsulin SecretionIntra-Abdominal FatMaleGlycated HemoglobinDiabetes Mellitus, Type 2Risk Assessment

Identifiers

PMID38191206
PMCPMC10806829

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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.