Evidence map›Paper›PMID 39902164›Full record

ArticleFrontiers in endocrinology2024

Value of triglyceride glucose-body mass index in predicting nonalcoholic fatty liver disease in individuals with type 2 diabetes mellitus.

Xiaoyi Qian, Wenwen Wu, Boyang Chen, Simin Zhang, Chunmei Xiao, Long Chen, Jun Chen, Lingli Ke, Meian He, Xiulou Li

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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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

10 authors.

Xiaoyi QianDepartment of Public Health, Hubei University of Medicine, Shiyan, Hubei, China.
Wenwen WuDepartment of Public Health, Hubei University of Medicine, Shiyan, Hubei, China.
Boyang ChenDepartment of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Simin ZhangDepartment of Public Health, Hubei University of Medicine, Shiyan, Hubei, China.
Chunmei XiaoDepartment of Health Examination Center, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Long ChenDepartment of Health Examination Center, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Jun ChenDepartment of Public Health, Hubei University of Medicine, Shiyan, Hubei, China.
Lingli KeDepartment of Health Examination Center, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Meian HeDepartment of Occupational and Environmental Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiulou LiDepartment of Public Health, Hubei University of Medicine, Shiyan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There is limited data on the association between TyG-BMI and NAFLD in patients with Type 2 Diabetes Mellitus (T2DM). The magnitude of risk prediction and predictive efficacy of TyG-BMI for T2DM with NAFLD remains unclear. Objective: To examine the association of TyG-BMI with NAFLD in T2DM patients and assess the effectiveness of screening using the TyG-BMI index. Methods: We conducted a retrospective analysis of clinical data from 602 T2DM patients at an enterprise health lodge from September 2021 to November 2022. Patients were categorized into two groups: T2DM alone (n=250) and T2DM with NAFLD (n=352). The Mann-Whitney U test was used for comparing non-normally distributed continuous data between groups, while the Chi-square test was used for categorical data. Logistic regression analysis was performed to evaluate the effect of BMI, TyG index, and TyG-BMI index on NAFLD. The ROC curve was used to assess the predictive efficacy of the TyG-BMI index for NAFLD in T2DM patients. Results: BMI predicted the development of NAFLD in T2DM patients with an area under the receiver operating characteristic (ROC) curve of 0.792 (95% CI 0.757-0.828), and the optimal cutoff value was 25.22, with 72.2% sensitivity and 71.6% specificity; The area under the receiver operating characteristic (ROC) curve of the TyG index to predict the development of NAFLD in patients with T2DM was 0.755 (95% CI 0.716-0.794), and the optimal cutoff value was 8. 945, with a sensitivity of 80.1% and a specificity of 59.2%; The area under the receiver operating characteristic (ROC) curve of TyG-BMI index to predict the development of NAFLD in T2DM patients was 0.852, (95% CI 0.822-0.882), and the optimal cutoff value was 227.385, with a sensitivity and specificity of 80.1% and 59.2%, respectively. Conclusions: The TyG-BMI index is a significant predictor of comorbid NAFLD in T2DM patients and provides better screening performance than BMI alone. The TyG-BMI index shows promise as an early screening tool for NAFLD in T2DM patients.

Indexed as

Blood GlucoseBody Mass IndexDiabetes Mellitus, Type 2Non-alcoholic Fatty Liver DiseaseTriglyceridesAdultAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesROC CurveBlood GlucoseTriglyceridesbody mass indexnonalcoholic fatty liver diseasereceiver operating characteristic curvetriglyceride glucose body mass indextype 2 diabetes mellitus

Identifiers

PMID39902164
PMCPMC11788176

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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.