
==== Front
J Clin Endocrinol Metab
J Clin Endocrinol Metab
jcem
The Journal of Clinical Endocrinology and Metabolism
0021-972X
1945-7197
Oxford University Press US

38709677
10.1210/clinem/dgae303
dgae303
Meta-Analysis
AcademicSubjects/MED00250
Association Between Visceral Obesity Index and Diabetes: A Systematic Review and Meta-analysis
Deng Ruixue College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Chen Weijie College of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Zhang Zepeng Research Center of Traditional Chinese Medicine, College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Zhang Jingzhou College of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Wang Ying College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Sun Baichuan College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Yin Kai College of Integrated Chinese and Western Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Cao Jingsi College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Fan Xuechun College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Zhang Yuan College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Liu Huan College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Fang Jinxu College of Acupuncture and Moxibustion Massage, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Song Jiamei College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

Yu Bin College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

https://orcid.org/0000-0002-1374-9809
Mi Jia Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000, Jilin, China

https://orcid.org/0000-0001-6780-6314
Li Xiangyan Northeast 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

Correspondence: Jia Mi, PhD, Department of Endocrinology, The First Affiliated Hospital of Changchun University of Chinese Medicine, 1478 Gongnong Rd, Changchun 130000, Jilin, China. Email: mijia0101@126.com; or Xiangyan Li, Postdoctoral fellow, Northeast 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. Email: xiangyan_li1981@163.com.
Ruixue Deng and Weijie Chen contributed equally to this work and should be considered as co-first authors.

Jia Mi and Xiangyan Li contributed equally to this work and should be considered as co-corresponding authors.

10 2024
06 5 2024
06 5 2024
109 10 26922707
12 3 2024
28 4 2024
23 5 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

Abstract

Content

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

Objective

This 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.

Conclusion

VAI 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.

VAI
diabetes mellitus
systematic review
meta-analysis
The National Natural Science Foundation of China 10.13039/501100001809 82205039 Chinese Medicine Innovation Team talent Support Program of the State Administration of Traditional Chinese Medicine ZYYCXTD-D-202001
==== Body
pmcDiabetes mellitus (DM) has emerged as a prominent chronic disease, affecting approximately 463 million individuals worldwide, which imposes substantial public health and economic burdens (1). Type 2 diabetes mellitus (T2DM) is the predominant form of DM, accounting for approximately 90% of all diabetes cases globally (2). The occurrence of DM is often closely related to health status indicators. Therefore, research on the early risk prediction based on DM-related indicators is if clinical significance, which holds the potential to reverse the outcomes of DM (3). Among related measurement indicators such as body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI), fasting blood glucose, glycated hemoglobin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (4), VAI is a clinically available metabolic parameter with a predictive value for DM and cardiovascular disease (5). Previous reviews and studies have shown a significant correlation between VAI and a series of chronic diseases, such as nonalcoholic fatty liver disease and liver fibrosis (6), chronic kidney disease (7), and obstructive sleep apnea (8).

The hallmark of DM is insulin resistance (9). Obesity has been recognized as a risk factor for T2DM and its complications, even all-cause mortality (10). VAI is a novel marker of fat distribution and function in obese patients (11) and has been recognized as an important indicator of insulin-related alterations in fat function (12). Increased VAI is significantly associated with decreased insulin sensitivity (13). Some studies have explored and discussed the correlation between VAI and DM.

Currently, because of the absence of long-term prospective studies, we cannot assess the predictive ability of VAI for the risk of DM. There is a deficiency of comprehensive, systematic evidence-based arguments regarding the association between VAI and DM. Furthermore, comprehensive and systematic studies on the relevance and early predictive value of VAI for DM in different sexes are scarce. It remains unclear whether it is reasonable to use VAI as a screening tool for DM to broaden an effective method for early identification of diabetes risk. Therefore, this systematic review and meta-analysis aims to elucidate the correlation between VAI and DM, clarify its early predictive accuracy for DM, and provide a reliable evidence-based argument for subsequent DM prevention efforts.

Materials and Methods

Study Registration

This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (2020) and as duly registered in PROSPERO (ID: CRD42023480506) in a prospective manner.

Eligibility Criteria

Inclusion criteria

Population (P): Adults aged ≥ 18 years were included

Exposure (E): High VAI was defined as the exposure factor in this systematic review according to original studies.

Comparison (C): Low VAI in various cohort, case-control, as well as cross-sectional studies was deemed as the control group. The formula for calculating VAI is as follows (14):

Males=(WC39.68+(1.88×BMI))×(TGs1.03)×(1.31HDL−C)

Female=(WC36.58+(1.89×BMI))×(TGs0.81)×(1.52HDL−C)

Outcome (O):

The outcome indicators of this systematic review are the odds ratios (ORs) and hazard ratios (HRs) associated with high VAI and DM, adjusted for other confounding factors using logistic regression or Cox regression analysis.

The ORs associated with high VAI and DM after grouping by propensity score matching in case-control studies.

The area under the receiver operating characteristic (ROC) curve (ROC area under the curve [AUC]) reflecting the predictive value of VAI for the occurrence of DM in cohort studies.

The sensitivity, as well as the specificity of VAI for the prediction accuracy of DM.

Study design (S): This systematic review incorporated case-control studies, cross-sectional studies, and cohort studies.

Exclusion criteria

Population (P): Individuals aged <18 years and patients with other underlying diseases.

Exposure (E): None.

Comparison (C): None.

Outcome (O): Cohort studies that only conducted univariate analyses to explore the correlation between VAI and DM.

Study design (S): The following studies were excluded:

Abstracts of meetings published without peer review

For repeatedly published studies of the same dataset, the earliest published article was selected

Since multivariate logistic regression or Cox regression models were used when discussing the correlation between VAI and DM, the robustness of the regression coefficient was challenging when the number of cases is small, so studies with <30 cases were excluded.

Data Sources and Search Strategy

We systematically searched the PubMed, Cochrane, Embase, and Web of Science databases for all data available up to Oct. 17, 2023. The search was conducted using MeSH + free text terms without restrictions on region or year of publication. (For the detailed search strategy, see Table 1).

Table 1. Literature search strategy

1. PubMed	
Search number	Query	Results	
#1	“Diabetes Mellitus”[Mesh]	511 596	
#2	((((((((((((((((((((((((Diabetes Mellitus[Title/Abstract]) OR (Scleredema Adultorum[Title/Abstract])) OR (Glucose Intolerance[Title/Abstract])) OR (Gastroparesis[Title/Abstract])) OR (IDDM[Title/Abstract])) OR (DIDMOAD[Title/Abstract])) OR (NIDDM[Title/Abstract])) OR (MODY[Title/Abstract])) OR (Fetal Macrosomias[Title/Abstract])) OR (Nonketotic Hyperosmolar Coma[Title/Abstract])) OR (Hyperosmolar Hyperglycemic State[Title/Abstract])) OR (Intracapillary Glomerulosclerosis[Title/Abstract])) OR (Kimmelstiel Wilson Syndrome[Title/Abstract])) OR (Kimmelstiel Wilson Disease[Title/Abstract])) OR (impaired glucose tolerance[Title/Abstract])) OR (Wolfram syndrome[Title/Abstract])) OR (genetic prediabetes[Title/Abstract])) OR (prediabetic stage[Title/Abstract])) OR (Prediabetic State[Title/Abstract])) OR (Diabete[Title/Abstract])) OR (Diabetes[Title/Abstract])) OR (Diabetic[Title/Abstract])) OR (Prediabetic States[Title/Abstract])) OR (Prediabetes[Title/Abstract])) OR (T2DM[Title/Abstract])	795 275	
#3	(“Diabetes Mellitus”[Mesh]) OR (((((((((((((((((((((((((Diabetes Mellitus[Title/Abstract]) OR (Scleredema Adultorum[Title/Abstract])) OR (Glucose Intolerance[Title/Abstract])) OR (Gastroparesis[Title/Abstract])) OR (IDDM[Title/Abstract])) OR (DIDMOAD[Title/Abstract])) OR (NIDDM[Title/Abstract])) OR (MODY[Title/Abstract])) OR (Fetal Macrosomias[Title/Abstract])) OR (Nonketotic Hyperosmolar Coma[Title/Abstract])) OR (Hyperosmolar Hyperglycemic State[Title/Abstract])) OR (Intracapillary Glomerulosclerosis[Title/Abstract])) OR (Kimmelstiel Wilson Syndrome[Title/Abstract])) OR (Kimmelstiel Wilson Disease[Title/Abstract])) OR (impaired glucose tolerance[Title/Abstract])) OR (Wolfram syndrome[Title/Abstract])) OR (genetic prediabetes[Title/Abstract])) OR (prediabetic stage[Title/Abstract])) OR (Prediabetic State[Title/Abstract])) OR (Diabete[Title/Abstract])) OR (Diabetes[Title/Abstract])) OR (Diabetic[Title/Abstract])) OR (Prediabetic States[Title/Abstract])) OR (Prediabetes[Title/Abstract])) OR (T2DM[Title/Abstract]))	858 684	
#4	(((visceral adiposity index[Title/Abstract]) OR (visceral adipose index[Title/Abstract])) OR (VAI[Title/Abstract])) OR (Visceral adiposity[Title/Abstract])	3737	
#5	((“Diabetes Mellitus”[Mesh]) OR (((((((((((((((((((((((((Diabetes Mellitus[Title/Abstract]) OR (Scleredema Adultorum[Title/Abstract])) OR (Glucose Intolerance[Title/Abstract])) OR (Gastroparesis[Title/Abstract])) OR (IDDM[Title/Abstract])) OR (DIDMOAD[Title/Abstract])) OR (NIDDM[Title/Abstract])) OR (MODY[Title/Abstract])) OR (Fetal Macrosomias[Title/Abstract])) OR (Nonketotic Hyperosmolar Coma[Title/Abstract])) OR (Hyperosmolar Hyperglycemic State[Title/Abstract])) OR (Intracapillary Glomerulosclerosis[Title/Abstract])) OR (Kimmelstiel Wilson Syndrome[Title/Abstract])) OR (Kimmelstiel Wilson Disease[Title/Abstract])) OR (impaired glucose tolerance[Title/Abstract])) OR (Wolfram syndrome[Title/Abstract])) OR (genetic prediabetes[Title/Abstract])) OR (prediabetic stage[Title/Abstract])) OR (Prediabetic State[Title/Abstract])) OR (Diabete[Title/Abstract])) OR (Diabetes[Title/Abstract])) OR (Diabetic[Title/Abstract])) OR (Prediabetic States[Title/Abstract])) OR (Prediabetes[Title/Abstract])) OR (T2DM[Title/Abstract]))) AND ((((visceral adiposity index[Title/Abstract]) OR (visceral adipose index[Title/Abstract])) OR (VAI[Title/Abstract])) OR (Visceral adiposity[Title/Abstract]))	1096	
2. Cochrane	
Search number	Query	Results	
#1	MeSH descriptor: [Diabetes Mellitus] explode all trees	46685	
#2	(Diabetes Mellitus):ti,ab,kw OR (Scleredema Adultorum):ti,ab,kw OR (Glucose Intolerance):ti,ab,kw OR (Gastroparesis):ti,ab,kw OR (IDDM):ti,ab,kw	81884	
#3	(DIDMOAD):ti,ab,kw OR (NIDDM):ti,ab,kw OR (MODY):ti,ab,kw OR (Fetal Macrosomias):ti,ab,kw OR (Nonketotic Hyperosmolar Coma):ti,ab,kw	1181	
#4	(Hyperosmolar Hyperglycemic State):ti,ab,kw OR (Intracapillary Glomerulosclerosis):ti,ab,kw OR (Kimmelstiel Wilson Syndrome):ti,ab,kw OR (Kimmelstiel Wilson Disease):ti,ab,kw OR (impaired glucose tolerance):ti,ab,kw	4242	
#5	(Wolfram syndrome):ti,ab,kw OR (genetic prediabetes):ti,ab,kw OR (prediabetic stage):ti,ab,kw OR (Prediabetic State):ti,ab,kw OR (Diabete):ti,ab,kw	2313	
#6	(Diabetes):ti,ab,kw OR (Diabetic):ti,ab,kw OR (Prediabetic States):ti,ab,kw OR (Prediabetes):ti,ab,kw OR (T2DM):ti,ab,kw	115978	
#7	#1 OR #2 OR #3 OR #4 OR #5 OR #6	118019	
#8	(visceral adiposity index):ti,ab,kw OR (visceral adipose index):ti,ab,kw OR (VAI):ti,ab,kw OR (Visceral adiposity):ti,ab,kw	1165	
#9	#7 AND #8	405	
3. Embase	
Search number	Query	Results	
#1	'diabetes mellitus’/exp	1283784	
#2	(‘diabetes mellitus’:ab,ti OR ‘scleredema adultorum’:ab,ti OR ‘glucose intolerance’:ab,ti OR gastroparesis:ab,ti OR iddm:ab,ti OR didmoad:ab,ti OR niddm:ab,ti OR mody:ab,ti OR ‘fetal macrosomias’:ab,ti OR ‘nonketotic hyperosmolar coma’:ab,ti OR ‘hyperosmolar hyperglycemic state’:ab,ti OR ‘intracapillary glomerulosclerosis’:ab,ti OR ‘kimmelstiel wilson syndrome’:ab,ti OR ‘kimmelstiel wilson disease’:ab,ti) AND ‘impaired glucose tolerance’:ab,ti OR ‘wolfram syndrome’:ab,ti OR ‘genetic prediabetes’:ab,ti OR ‘prediabetic stage’:ab,ti OR ‘prediabetic state’:ab,ti OR diabete:ab,ti OR diabetes:ab,ti OR diabetic:ab,ti OR ‘prediabetic states’:ab,ti OR prediabetes:ab,ti OR t2dm:ab,ti	1190305	
#3	#1 AND #2	989538	
#4	‘visceral adiposity index’/exp	619	
#5	‘visceral adiposity index’:ab,ti OR ‘visceral adipose index’:ab,ti OR vai:ab,ti OR ‘visceral adiposity’:ab,ti	5616	
#6	#4 OR #5	5705	
#7	#3 AND #6	1364	
4. Web of science	
Search number	Query	Results	
#1	Diabetes Mellitus (Topic) OR Scleredema Adultorum (Topic) OR Glucose Intolerance (Topic) OR Gastroparesis (Topic) OR IDDM (Topic) OR DIDMOAD (Topic) OR NIDDM (Topic) OR MODY (Topic) OR Fetal Macrosomias (Topic) OR Nonketotic Hyperosmolar Coma (Topic) OR Hyperosmolar Hyperglycemic State (Topic) OR Intracapillary Glomerulosclerosis (Topic) OR Kimmelstiel Wilson Syndrome (Topic) OR Kimmelstiel Wilson Disease (Topic) OR impaired glucose tolerance (Topic) OR Wolfram syndrome (Topic) OR genetic prediabetes (Topic) OR prediabetic stage (Topic) OR Prediabetic State (Topic) OR Diabete (Topic) OR Diabetes (Topic) OR Diabetic (Topic) OR Prediabetic States (Topic) OR Prediabetes (Topic) OR T2DM (Topic)	697280	
#2	visceral adiposity index (Topic) OR visceral adipose index (Topic) OR VAI (Topic) OR Visceral adiposity (Topic)	10381	
#3	#1 AND #2	3142	

Study Selection and Data Extraction

All obtained articles were imported into EndNote, where titles and abstracts were read to eliminate duplicates or studies that did not meet the criteria. Studies that matched the criteria were selected, and their full texts were downloaded for thorough review. Ultimately, original study data that conformed to the criteria for this systematic review were included. Before extracting data, a standard data extraction spreadsheet was rigorously prepared. The extracted content included serial number, title, first author, year of publication, PMID/DOI, study type (cohort study, case-control study, cross-sectional study, nested case-control study, case-cohort study), follow-up duration, author's country, patient source, population, outcome events (prediabetes, T2DM), number of outcome events, total case number, gender (male/female), age, biomarker cutoff points, and adjusted confounders.

The screening of literature and extraction of data were independently conducted by 2 researchers in the field of DM: M.J. (a doctor with 10 years of clinical experience in DM) and Z.J.Z. (a doctor with 5 years of clinical experience in gastroenterological diseases). After completion, a cross-check was performed, and any disagreements were resolved with the assistance of a third researcher, C.W.J. (a doctor with 5 years of clinical experience in nephrology).

Assessment of Study Quality

The studies included case-control, cohort, as well as cross-sectional studies. Therefore, 2 assessment tools are needed for bias risk evaluation or quality assessment. For cohort and case-control studies, Newcastle-Ottawa Scale (NOS) (NOS) is a commonly used quality assessment tool for case-control and cohort studies) (15) was employed, and for cross-sectional studies, the The Journal of Biomedical Informatics (JBI) (JBI) document quality Assessment scale is a tool for assessing the quality of biomedical literature) scale (16) was adopted. Two researchers assessed the quality of original data from the included cohort, case-control, as well as cross-sectional studies using NOS and JBI to evaluate the quality of the literature. Afterward, an interactive check was conducted. A third researcher was invited to make decisions in case of disagreements.

Synthesis Methods

The data was meta-analyzed using Stata 15.0. The heterogeneity among the results of the included studies was analyzed via the Q test and quantitatively determined by the heterogeneity index, I2. In the meta-analysis elucidating the correlation between VAI and DM because the original studies used different cutoffs for VAI, we summarized the highest ORs or HRs in a dose-response meta-analytic approach (17). During the meta-analysis, a fixed-effects model was employed when I2 < 50%, and a random-effects model was adopted when I2 > 50%. Subsequent subgroup analysis, sensitivity analysis, and meta-regression analysis were systematically undertaken to delve into potential sources of heterogeneity. Additionally, when discussing the predictive value of the VAI for DM, we only conducted a meta-analysis of cohort studies via a bivariate mixed-effects model.

Funnel plots were drawn based on the obtained data to visually display publication bias. Concurrently, statistical tests for publication bias, namely Egger's and Begg's tests, were executed. P < .05 indicated publication bias, and the trim-and-fill method was implemented to assess the impact of such bias on the meta-analysis. In this study, a P value less than .05 was deemed statistically significant.

Results

Study Selection

A total of 6007 articles were retrieved, including 950 duplicates. Following the exclusion process, 64 relevant articles were screened through the reading of titles and abstracts.

Three articles that could not be downloaded were therefore deleted, 2 articles were removed because the included population was already diagnosed with DM, 2 articles were excluded for involving populations with metabolic syndrome, insulin resistance, and other diseases, and 4 conference abstracts with incomplete information were also excluded. Ultimately, 53 studies (4, 5, 14, 18-26) were included in the study (Fig. 1).

Figure 1. Literature screening flowchart.

Study Characteristics

Among the 53 articles included, there were 31 cohort studies (4, 5, 14, 20, 23, 24, 26-31), 19 cross-sectional studies (18, 21, 22, 32-47), 2 case-control studies (19, 48), and 1 nested case-control study (which is a part of cohort study) (25).

The total number of cases across the included cohort, cross-sectional, case-control, as well as nested case-control studies, were 225 530, 240 381, 15 004, and 2556, respectively, amounting to 594 939 cases in total. The follow-up duration in cohort studies ranged from 3 to 24 years, covering 14 countries, with 34 studies originating from China (4, 5, 14, 18-23, 28, 29, 33, 34). The top 3 countries with the highest number of cases were Poland, Brazil, and Iran (29, 32, 40, 43, 44, 49, 50). The included patients mainly came from communities, hospitals, health examination centers, and public research databases. Confounders such as age, gender, race, residence, drinking and smoking status, systolic blood pressure, diastolic blood pressure, BMI, WC, waist-to-hip ratio, total cholesterol, TG, HDL-C, low-density lipoprotein cholesterol, fasting plasma glucose, 2-hour postprandial glucose, and oral glucose tolerance test were adjusted. Among these, 37 articles divided VAI into quartiles (4, 5, 14, 18-22, 25, 27, 28, 32, 34-36, 38, 40, 42, 43, 45-48, 50-64).

Assessment of Study Quality

In 31 cohort studies, NOS was adopted for analysis. Among these, 10 articles (5, 20, 24, 28, 52, 53, 57, 60, 63, 65) had insufficient follow-up time (for DM follow-up duration, a duration less than 5 years was considered insufficient in this study; however, it is noteworthy that there is currently a lack of universally recognized standards to precisely define this item), but all were high-quality studies with total scores between 7 and 9 points. In the 2 case-control studies, 1 article (48) had insufficient control source communities/hospitals, but all were high-quality studies with total scores between 7 and 9 points. In the cross-sectional studies, the JBI scale was employed for evaluation. The assessment results showed that 6 articles (18, 32, 34, 37, 40, 44) had insufficient sample size (a sample size less than 2000 was considered insufficient in this study; however, it is noteworthy that there is currently a lack of universally recognized standards to precisely define this item); additionally, 2 articles (18, 37) had insufficient response rate (Tables 2 and 3).

Table 2. Results of quality assessment for cohort studies and case-control studies employing the NOS

No.	Author	Year	Study type	V1	V2	V3	V4	V5	V6	V7	V8	
2	Junxiang Wei	2019	Cohort study	1	1	1	1	2	1	1	1	
3	Chun Zhou	2021	Case-control study	1	1	1	1	1	1	1	1	
4	Meilin Zhang	2016	Cohort study	1	1	1	1	2	1	0	1	
7	Y. Wang	2014	Cohort study	1	1	1	1	2	1	1	1	
9	Randy Nusrianto	2019	Cohort study	1	1	1	1	2	1	0	1	
10	Gertraud Maskarinec	2021	Nested case-control study	1	1	1	1	2	1	1	1	
12	E. Koloverou	2019	Cohort study	1	1	1	1	2	1	1	1	
13	Minghui Han	2020	Cohort study	1	1	1	1	2	1	1	1	
14	Chen Chen	2014	Cohort study	1	1	1	1	2	1	0	1	
15	Mohammadreza Bozorgmanesh	2011	Cohort study	1	1	1	1	2	1	1	1	
22	Ming-Feng Xia	2018	Cohort study	1	1	1	1	2	1	0	1	
24	Mohsen Janghorbani	2016	Cohort study	1	1	1	1	2	1	1	0	
26	Aysha Alkhalaqi	2020	Cohort study	1	1	1	1	2	1	1	1	
27	Nayeon Ahn	2019	Cohort study	1	1	1	1	2	1	1	1	
33	Zheng, S.	2016	Cohort study	1	1	1	1	2	1	1	1	
34	Qianyuan Yang	2022	Cohort study	1	1	1	1	2	1	0	1	
35	Ming-Feng Xia	2016	Cohort study	1	1	1	1	2	1	0	1	
36	Bingyuan Wang	2018	Cohort study	1	1	1	1	2	1	1	1	
41	Vineetha K. Ramdas Nayak	2020	Case-control study	1	1	0	1	1	1	1	1	
44	Jing Yang	2018	Cohort study	1	1	1	1	2	1	1	1	
47	Emily W. Harville	2014	Cohort study	1	1	1	1	2	1	1	1	
48	Tadeusz Derezińsk	2020	Cohort study	1	1	1	1	0	1	1	1	
49	Samira Behboudi-Gandevani	2019	Cohort study	1	1	1	1	2	1	1	1	
52	Jirapitcha Boonpor	2023	Cohort study	1	1	1	1	2	1	1	1	
54	Liang Pan	2022	Cohort study	1	1	1	1	2	1	0	1	
56	Meng-Ting Tsou	2021	Cohort study	1	1	1	1	2	1	1	1	
57	Ning Chen	2023	Cohort study	1	1	1	1	2	1	1	1	
58	Ning Chen	2024	Cohort study	1	1	1	1	2	1	0	1	
59	Camila Maciel de Oliveira	2022	Cohort study	1	1	1	1	2	1	1	1	
62	Liang Pan	2022	Cohort study	1	1	1	1	2	1	0	1	
63	Xiaotong Li	2022	Cohort study	1	1	1	1	2	1	1	1	
68	Tengfei Yang	2021	Cohort study	1	1	1	1	2	1	1	1	
69	Yibo Zhang	2022	Cohort study	1	1	1	1	2	1	0	1	
1 = The maximum score is 9, with higher scores indicating higher study quality. Each item can score up to 1 point, except for comparability, which can score up to 2 points.

2 = When using the NOS for quality assessment of cohort studies, V1 = representativeness of the exposed cohort; V2 = selection of the nonexposed cohort; V3 = ascertainment of exposure; V4 = demonstration that the outcome of interest was not present at the start of the study; V5 = comparability of cohorts based on the design or analysis; V6 = assessment of outcome; V7 = was follow-up long enough for outcomes to occur? (eg, 5 years); V8 = adequacy of follow-up.

3 = When using the NOS for quality assessment of case-control studies, V1 = adequate definition of cases; V2 = representativeness of cases; V3 = selection of controls; V4 = definition of controls; V5 = comparability of cases and controls based on the design or analysis; V6 = ascertainment of exposure; V7 = same method of exposure ascertainment for cases and controls; V8 = nonresponse rate.

Abbreviation: NOS, Newcastle-Ottawa Scale.

Table 3. Results of quality assessment for cross-sectional studies employing the JBI scale

No.	Author	Year	Study type	V1	V2	V3	V4	V5	V6	V7	V8	V9	
1	Chen-Ying Lin	2023	Cross-sectional study	1	1	0	1	1	1	1	1	0	
5	Yumei Yang	2015	Cross-sectional study	1	1	1	1	1	1	1	1	0	
6	Jinshan Wu	2017	Cross-sectional study	1	1	1	1	1	1	1	1	0	
18	Denise Rosso Tenório Wanderley Rocha	2017	Cross-sectional study	1	1	0	1	1	1	1	1	1	
19	Peng Ju Liu	2016	Cross-sectional study	1	1	1	1	1	1	1	1	1	
20	Nebojsa Kavaric	2017	Cross-sectional study	1	1	0	1	1	1	1	1	1	
21	Dongfeng Gu	2017	Cross-sectional study	1	1	1	1	1	1	1	1	1	
28	TDu	2014	Cross-sectional study	1	1	1	1	1	1	1	1	0	
31	M.C. Amato	2015	Cross-sectional study	1	1	0	1	1	1	1	1	0	
37	Yi-Ting Qian	2022	Cross-sectional study	1	1	1	1	1	1	1	1	0	
38	Pan Ke	2022	Cross-sectional study	1	1	1	1	1	1	1	1	0	
40	Tadeusz Dereziński	2019	Cross-sectional study	1	1	0	1	1	1	1	1	1	
42	Wenning Fu	2021	Cross-sectional study	1	1	1	1	1	1	1	1	0	
45	Kan Sun	2019	Cross-sectional study	1	1	1	1	1	1	1	1	1	
50	Amir Bagheri	2022	Cross-sectional study	1	1	1	1	1	1	1	1	0	
51	Amir Bagheri	2023	Cross-sectional study	1	1	1	1	1	1	1	1	0	
53	A.M. Cybulska	2023	Cross-sectional study	1	1	0	1	1	1	1	1	1	
60	Hai Deng	2022	Cross-sectional study	1	1	1	1	1	1	1	1	0	
61	Xiaoyan Feng	2023	Cross-sectional study	1	1	1	1	1	1	1	1	0	
1 = The maximum score is 9, with higher scores indicating higher research quality. Each item can score up to 1 point.

2 = When using the JBI scale for quality assessment of cross-sectional studies, V1 = Was the sample frame appropriate to address the target population? V2 = Were study participants sampled in an appropriate way? V3 = Was the sample size adequate? V4 = Were the study subjects and the setting described in detail? V5 = Was the data analysis conducted with sufficient coverage of the identified sample? V6 = Were valid methods used for the identification of the condition? V7 = Was the condition measured in a standard, reliable way for all participants? V8 = Was there appropriate statistical analysis? V9 = Was the response rate adequate, and if not, was the low response rate managed appropriately?

Abbreviation: JBI, Journal of Biomedical Informatics.

Meta-analysis

Correlation Analysis

Synthesized results and sensitivity analysis and publication bias of cohort studies

Six studies (4, 20, 52, 54, 58, 66) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in males. The results of the random-effects model (I2 = 57.9%) suggested that an increase in VAI is correlated with an increased DM occurrence risk in males (OR = 2.83 [95% CI, 2.30-3.49]). Five studies (20, 52, 54, 58, 66) encompassing 6 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results (I2 = 65.1%) showed that an increase in VAI is associated with an increased DM occurrence risk in females (OR = 3.32 [95% CI, 2.48-4.45]). Thirteen studies (14, 23, 26, 27-29, 51, 57-62) encompassing 19 cohorts expounded on the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model (I2 = 90.6%) showed that an increase in VAI is correlated with an increased risk of DM irrespective of gender (OR = 2.52 [95% CI, 2.01-3.15]) (Fig. 2).

Figure 2. Forest plot of the meta-analysis of ORs for the correlation between high VAI and DM risk in cohort studies.

Sensitivity analysis of all cohort studies indicated that the meta-analysis results had no significant changes on the sequential exclusion of individual studies, underscoring the stability of our meta-analysis. The evaluation of publication bias through funnel plots revealed indications of bias, further supported by Egger's test results (P = .025), as shown in Fig. 3.

Figure 3. Sensitivity analysis and funnel plot for publication bias of the relationship between high level of VAI and diabetes in cohort studies.

Synthesized Results and Sensitivity Analysis and Publication Bias of Cross-sectional or Case-control Studies

Five studies (33, 36, 38, 41, 47) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in males. The results of the random-effects model (I2 = 81.9%) suggested that an increase in VAI is correlated with an increased risk of DM in males (OR = 2.66 [95% CI, 1.91-3.70]). Five studies (33, 36, 38, 41, 47) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results (I2 = 77.6%) showed that an increase in VAI is correlated with an increased DM occurrence risk in females (OR = 2.32 [95% CI, 1.77-3.04]). Eight studies (18, 21, 22, 34, 38, 41, 42, 46) encompassing 10 cohorts reported the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model (I2 = 95%) indicated that an increase in VAI is correlated with an increased risk of DM irrespective of gender (OR = 1.99 [95% CI, 1.57-2.54]) (Fig. 4).

Figure 4. Forest plot of the meta-analysis of ORs for the correlation between high VAI and DM risk in cross-sectional studies and case-control studies.

The sensitivity analysis of all studies without gender distinction indicated that the meta-analysis results had no significant changes on the sequential exclusion of individual studies, underscoring the stability of our meta-analysis. The evaluation of publication bias through funnel plots revealed indications of bias, further supported by Egger's test results (P = .336) (Fig. 5).

Figure 5. Sensitivity analysis and funnel plot for publication bias of the relationship between high level of VAI and diabetes in cross-sectional or case-control studies.

Area Under ROC Curve

In cohort studies, 18 studies (4, 5, 14, 20, 24, 27, 31, 51-54) encompassing 20 cohorts reported on the correlation between VAI and DM occurrence risk in males. The results of the random-effects model (I2 =93.8%) showed that the VAI had a predictive value for DM in males of [AUC = 0.64 (95% CI: .62-.66)]. Fifteen studies (4, 20, 27, 31, 47, 51-54, 56-58, 63, 65, 66) encompassing 17 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results (I2 = 97.3%) showed that the VAI had a predictive value for DM in females of (AUC = 0.67 [95% CI, .64-.69]). Eight studies (29, 31, 51, 53, 58, 60, 62, 63) encompassing 9 cohorts reported the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model (I2 = 98.5%) indicated that the VAI had a predictive value for DM irrespective of gender of (AUC = 0.67 [95% CI, .62-.71]) (Figs. 6-8).

Figure 6. Forest plot of ROC AUC for the correlation between high VAI and DM in males in cohort studies.

Figure 7. Forest plot of ROC AUC for the correlation between high VAI and DM in females in cohort studies.

Figure 8. Forest plot of ROC AUC for the correlation between high VAI and DM without distinguishing by gender in cohort studies.

Sensitivity and Specificity

A meta-analysis of multiple different trials of the same index can be expressed by a ROC curve according to their OR weight, called SROC, from which the specificity and sensitivity of the group of studies can be obtained. Sixteen studies (4, 5, 20, 24, 27, 30, 31, 51-54, 57, 58, 63, 65, 66) encompassing 19 cohorts reported the 2×2 tables for predicting DM in males using VAI, the results of the meta-analysis via a bivariate mixed-effects model showed the following for the prediction of DM in males using VAI: sensitivity 0.57 (95% CI, .53-.61), specificity 0.65 (95% CI, .61-.69), positive likelihood ratio 1.6 (95% CI, 1.5-1.8), negative likelihood ratio 0.66 (95% CI, .62-.70), diagnostic OR 2 (95% CI, 2-3), and SROC curve 0.64 (95% CI, .15-.95).

Fifteen studies (4, 20, 24, 27, 30, 31, 51-54, 58, 60, 63, 65, 66) encompassing 18 cohorts reported the 2×2 tables for predicting DM in females using VAI, and the results of the meta-analysis by a bivariate mixed-effects model showed the following for the prediction of DM in females using VAI: sensitivity 0.66 (95% CI, .60-.71), specificity 0.61 (95% CI, .57-.66), positive likelihood ratio 1.7 (95% CI, 1.5-1.9), negative likelihood ratio 0.56 (95% CI, .49–.64), diagnostic odds ratio 3 (95% CI, 2-4), and SROC curve 0.67 (95% CI, .16-.96).

Six studies (29, 31, 51, 60, 62, 63) encompassing 6 cohorts reported the 2×2 tables for predicting DM without distinguishing gender using VAI, and the results of the meta-analysis through a bivariate mixed-effects model showed the following for the prediction of DM without distinguishing gender using VAI: sensitivity 0.60 (95% CI, .51-.69), specificity 0.63 (95% CI, .56-.69), positive likelihood ratio 1.6 (95% CI, 1.4-1.9), negative likelihood ratio 0.63 (95% CI, .51-.78), diagnostic odds ratio 3 (95% CI, 2-4), and SROC curve 0.65 (95% CI, .45-.81) (Figs. 9-11).

Figure 9. Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM in males in cohort studies.

Figure 10. Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM in females in cohort studies.

Figure 11. Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM without distinguishing by gender in cohort studies.

Discussion

Summary of the Main Findings

In cohort studies, we found that high VAI was associated with an augmented risk of DM in both males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]) with DM. In cross-sectional or case-control studies, a high VAI was correlated with an augmented risk of DM in both males (OR = 2.66 [95% CI, 1.91-3.70]) and females (OR = 2.32 [95% CI, 1.77-3.04]). Although there is an independent correlation, the predictive value for the risk of DM occurrence remains limited. In cohort studies, the predictive value of VAI for the occurrence of DM in males was determined through the ROC curve, resulting in a C-index of 0.64 (95% CI, .62-.66), with a sensitivity of 0.57 (95% CI, .53-.60) and a specificity of 0.66 (95% CI, .62-.69), respectively; for females, the predictive value of VAI for the occurrence of DM was also determined through the ROC curve, resulting in a C-index of 0.67 (95% CI, .65-.69), with a sensitivity of 0.66 (95% CI, .61-.71) and a specificity of 0.61 (95% CI, .57-.65), respectively; for DM occurrence without distinguishing by gender, the predictive value of VAI was calculated through the ROC curve, resulting in a C-index of 0.67 (95% CI, .62-.71), with sensitivity and specificity of 0.60 (95% CI, .51-.69) and 0.63 (95% CI, .56-.69).

Comparison With Previous Other Reviews

We have noticed that other researchers have also provided evidence-based arguments for the correlation between VAI and human health and diseases. Early systematic reviews have found a significant correlation between VAI and the occurrence of prediabetes (67); other related measurement indicators besides VAI are also worthy of attention, such as fasting blood glucose (68), glycated hemoglobin (69), BMI (70), WC (71), TG (72), and HDL (73). Unfortunately, the composite indicator we constructed based on these indicators did not significantly improve the predictive value for the risk of DM occurrence. As mentioned previously, VAI is composed of the related indicators mentioned; however, there are significant differences in weight, waist circumference, etc., across different regions or countries, ethnicities, and economic levels (74, 75). In our study, 5 cohort studies and 1 cross-sectional study (5, 27, 47, 57, 58, 60) developed the correlation between the Chinese VAI and the risk of DM occurrence (OR) on the basis of VAI, with its predictive value for the risk of DM occurrence being (AUC = 0.62 [95% CI, .52-.71]). Hence, the contemplation of the predictive efficacy of VAI for the occurrence of DM risk across diverse populations is imperative.

Given the differences in VAI calculations between males and females, we believe that males and females should not be integrated to discuss the correlation between VAI and the risk of DM occurrence. However, in the original studies we included, a notable number did not distinguish between the correlation of VAI with the risk of DM occurrence in males and females (14, 18, 21-23, 26, 27-29, 34, 38, 41, 42, 46, 50, 51, 57-59, 61, 62). Therefore, we consider there to be certain limitations in their studies regarding the correlation analysis, and achieving comprehensiveness in considering related factors after correcting for confounders remains a challenge.

Limitations of the Study

Limitations are presented in this study. First, in our registered protocol, we planned to discuss the variability of VAI cutoff values used in different studies and attempted to explore its predictive accuracy for early DM. However, the limited number of original studies included did not provide detailed results on the correlation between the VAI and the risk of DM at various levels. Additionally, very few previous original studies examined the VAI across multiple groups; therefore, an effective dose-response analysis was not performed. Variations exist among different ethnicities and countries, resulting in a lack of consensus on the definition of VAI cutoffs, which prevents us from providing effective cutoffs to offer useful insights for clinicians. Comprehensive, large-scale studies involving multiple ethnicities are needed to further discuss the cutoffs for VAI. Furthermore, we summarized the association of high-level VAI with DM obtained from cohort studies. However, very few studies used HRs; thus, effective subgroup analyses were not performed.

Conclusions

This systematic review and meta-analysis aimed to discuss the association between elevated level of VAI and the risk of DM occurrence and to confirm its early predictive accuracy for the risk of DM. VAI appears to be an effective factor for early prediction of DM when combined with other relevant indicators, although challenges in the prediction persist. Specialized screening regimens should be established in future for obese DM populations. Additionally, we consider combining VAI with other indicators as a scoring tool for assessing the risk of DM, thereby enhancing the predictive accuracy of DM in the general population.

Acknowledgments

Not applicable.

Funding

This study was supported by The National Natural Science Foundation of China (No: 82205039), Chinese Medicine Innovation Team talent Support Program of the State Administration of Traditional Chinese Medicine (No: ZYYCXTD-D-202001).

Author Contributions

Conceptualization: J.M., X.L.; methodology: J.F., B.Y., J.M., X.L.; formal analysis: J.S., K.Y., J.F.; investigation: J.S., K.Y., J.F.; funding acquisition: J.M.; resources: J.S., K.Y., J.F., B.Y.; data curation: X.F., Y.Z., H.L.; writing—original draft preparation: R.D., W.C.; writing—review and editing: Z.Z., J.Z., Y.W., B.S., J.C., J.M., X.L.; visualization: R.D., W.C.; supervision: J.M., X.L.; project administration: J.F., B.Y. All authors have read and agreed to the published version of the manuscript.

Disclosures

The authors have nothing to disclose.

Data Availability

Original data generated and analyzed during this study are included in this published article.

Abbreviations

AUC area under the curve

BMI body mass index

DM diabetes mellitus

HDL-C high-density lipoprotein cholesterol

HR hazard ratio

OR odds ratio

ROC receiver operating characteristic

TG triglyceride

VAI visceral adiposity index

WC waist circumference
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