Evidence map›Paper›PMID 38709677›Full record

SynthesisThe Journal of clinical endocrinology and metabolism2024

Association Between Visceral Obesity Index and Diabetes: A Systematic Review and Meta-analysis.

Ruixue Deng, Weijie Chen, Zepeng Zhang, Jingzhou Zhang, Ying Wang, Baichuan Sun, Kai Yin, Jingsi Cao, Xuechun Fan, Yuan Zhang and 6 more

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in The Journal of clinical endocrinology and metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Atherosclerosis index and diabetic foot ulcers: an NHANES database investigation.International journal of surgery (London, England) · 2025
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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

16 authors.

Ruixue DengCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Weijie ChenCollege of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Zepeng ZhangResearch Center of Traditional Chinese Medicine, College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Jingzhou ZhangCollege of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Ying WangCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Baichuan SunCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Kai YinCollege of Integrated Chinese and Western Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Jingsi CaoCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Xuechun FanCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Yuan ZhangCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Huan LiuCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Jinxu FangCollege of Acupuncture and Moxibustion Massage, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Jiamei SongCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Bin YuCollege of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.
Jia MiDepartment of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China.ORCID 0000-0002-1374-9809
Xiangyan LiNortheast Asia Research Institute of Traditional Chinese Medicine, Key Laboratory of Active Substances and Biological Mechanisms of Ginseng Efficacy, Ministry of Education, Jilin Provincial Key Laboratory of Bio-Macromolecules of Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130117, Jilin, China.ORCID 0000-0001-6780-6314

Funding

Chinese Medicine Innovation Team talent Support Program of the State Administration of Traditional Chinese Medicine ZYYCXTD-D-202001The National Natural Science Foundation of China 82205039
6 · The paper itself

Abstract

contentThe correlation between visceral obesity index (VAI) and diabetes and accuracy of early prediction of diabetes are still controversial.

objectiveThis study aims to review the relationship between high level of VAI and diabetes and early predictive value of diabetes. DATA SOURCES: The databases of PubMed, Cochrane, Embase, and Web of Science were searched until October 17, 2023. STUDY SELECTION: After adjusting for confounding factors, the original study on the association between VAI and diabetes was analyzed. DATA EXTRACTION: We extracted odds ratio (OR) between VAI and diabetes management after controlling for mixed factors, and the sensitivity, specificity, and diagnostic 4-grid table for early prediction of diabetes. DATA SYNTHESIS: Fifty-three studies comprising 595 946 participants were included. The findings of the meta-analysis elucidated that in cohort studies, a high VAI significantly increased the risk of diabetes mellitus in males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]). The receiver operating characteristic, sensitivity, and specificity of VAI for early prediction of diabetes in males were 0.64 (95% CI, .62-.66), 0.57 (95% CI, .53-.61), and 0.65 (95% CI, .61-.69), respectively, and 0.67 (95% CI, .65-.69), 0.66 (95% CI, .60-.71), and 0.61 (95% CI, .57-.66) in females, respectively.

conclusionVAI is an independent predictor of the risk of diabetes, yet its predictive accuracy remains limited. In future studies, determine whether VAI can be used in conjunction with other related indicators to early predict the risk of diabetes, to enhance the accuracy of prediction of the risk of diabetes.

Indexed as

Diabetes MellitusObesity, AbdominalAdiposityFemaleHumansMaleRisk Factorsdiabetes mellitusmeta-analysissystematic reviewVAI

Identifiers

PMID38709677
PMCPMC11403314

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.