The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease

Zhen Cheng,&ast; Chunyu Hu,&ast; Yalan Zhang, Jie Zhou, Jiayang Shi, Li Sun, Zongtao Chen Health Management Centre, First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zongtao Chen; Li Sun, Health Management Centre, First Affiliated Hospital of Army Medical University, No. 30, Gaotanyan Street, Shapingba District, Chongqing, People’s Republic of China, Tel +86 2368765191 ; +86 2368765228, Email [email protected]; [email protected]: The aim of this study was to comprehensively compare the predictive effect of 10 body fat indexes on MAFLD in different sex, age and BMI subgroups.Patients and Methods: A total of 5403 physical examination data were included and divided into the MAFLD group (N=2632) and non-MAFLD group (N=2771). The differences and correlation of 10 promising indicators between the two groups were compared, including fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral fat index (VAI), cardiometabolic index (CMI), body adiposity index (BAI), and triglyceride-glucose index (TyG), waist circumference index (WC), body mass index (BMI), waist to height ratio (WHtR). Logistic regression was used to analyze the risk of MAFLD under different adjustment conditions. The operating characteristic curve of different genders, BMI levels and age subgroups was plotted.Results: Male gender, smoking, alcohol drinking, and higher age are risk factors for MAFLD. In addition to BAI, the other 9 indicators had a high correlation with MAFLD, the area under the curve (AUC) value was > 0.7, and the prediction effect was better in females, BMI< 24 kg/m2, age < 35 years subgroup, among which FLI (AUC: 0.912, 95% CI: 0.905– 0.920), LAP (AUC: 0.894, 95% CI: 0.8866– 0.903), and HSI (AUC: 0.881, 95% CI: 0.872– 0.890) have better prediction effects.Conclusion: Our study confirmed the accuracy of body fat-related indexes in predicting MAFLD in people of different sexes, ages, and BMI levels. Among them, FLI, LAP and HSI have high predictive value and can be utilized as simple and cost-effective tools for screening MAFLD in clinical settings.Keywords: metabolic dysfunction-associated fatty liver disease, body fat index, metabolic index, cross-sectional study

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-10-01
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease

SHILAP Revista de lepidopterología
Magnetic confinement fusion research
article

The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease

article en

Abstract

Zhen Cheng,&ast; Chunyu Hu,&ast; Yalan Zhang, Jie Zhou, Jiayang Shi, Li Sun, Zongtao Chen Health Management Centre, First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zongtao Chen; Li Sun, Health Management Centre, First Affiliated Hospital of Army Medical University, No. 30, Gaotanyan Street, Shapingba District, Chongqing, People’s Republic of China, Tel +86 2368765191 ; +86 2368765228, Email [email protected]; [email protected]: The aim of this study was to comprehensively compare the predictive effect of 10 body fat indexes on MAFLD in different sex, age and BMI subgroups.Patients and Methods: A total of 5403 physical examination data were included and divided into the MAFLD group (N=2632) and non-MAFLD group (N=2771). The differences and correlation of 10 promising indicators between the two groups were compared, including fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral fat index (VAI), cardiometabolic index (CMI), body adiposity index (BAI), and triglyceride-glucose index (TyG), waist circumference index (WC), body mass index (BMI), waist to height ratio (WHtR). Logistic regression was used to analyze the risk of MAFLD under different adjustment conditions. The operating characteristic curve of different genders, BMI levels and age subgroups was plotted.Results: Male gender, smoking, alcohol drinking, and higher age are risk factors for MAFLD. In addition to BAI, the other 9 indicators had a high correlation with MAFLD, the area under the curve (AUC) value was > 0.7, and the prediction effect was better in females, BMI< 24 kg/m2, age < 35 years subgroup, among which FLI (AUC: 0.912, 95% CI: 0.905– 0.920), LAP (AUC: 0.894, 95% CI: 0.8866– 0.903), and HSI (AUC: 0.881, 95% CI: 0.872– 0.890) have better prediction effects.Conclusion: Our study confirmed the accuracy of body fat-related indexes in predicting MAFLD in people of different sexes, ages, and BMI levels. Among them, FLI, LAP and HSI have high predictive value and can be utilized as simple and cost-effective tools for screening MAFLD in clinical settings.Keywords: metabolic dysfunction-associated fatty liver disease, body fat index, metabolic index, cross-sectional study

SHILAP Revista de lepidopterología
Good health and well-being
Openalex Percentile: Top 79%
Magnetic confinement fusion research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease · SHILAP Revista de lepidopterología (2024) | TGRS Research Map | TGRS