Search PubMedSearch

PubMed · 41424121

Global epidemiology of diabetes and prediabetes in lean or non-obese patients with NAFLD: a systematic review and meta-analysis.

Abstract

BACKGROUND: The presence of diabetes increases the risk of adverse outcomes of patients with non-alcoholic fatty liver disease (NAFLD) even in those with lean or non-obese NAFLD. However, the epidemiological data regarding the prevalence of diabetes and prediabetes in lean or non-obese NAFLD populations remain limited. We assessed the global epidemiology of diabetes and prediabetes in lean or non-obese patients with NAFLD. METHODS: Published studies were searched in PubMed, EMBASE, Cochrane Library, and Web of Science databases from the inception of the databases to October 2024. The pooled global prevalence of diabetes or prediabetes in patients with NAFLD was evaluated using random-effects meta-analysis. Subgroup meta-analysis and meta-regression were used to investigate potential sources of heterogeneity. RESULTS: A total of 54 studies involving 146,714 patients with non-obese or lean NAFLD were included. The pooled global prevalence of diabetes among patients with lean or non-obese NAFLD was 15.6% (95% CI 10.8%-22.7%). Studies from South America reported the highest prevalence (41.3%, CI 39.1%-43.5%). Meta-regression models showed that geographic region and mean age (p&#x2009;<&#x2009;0.05) were associated with the were associated with the prevalence of diabetes, jointly accounting for 51.61% of the heterogeneity. The global prevalence of prediabetes among patients with lean or non-obese NAFLD was 22.9% (95% CI 12.5%-41.9%) with the highest prevalence reported in studies from Europe (34.4%, CI 23.0%-51.4%). Meta-regression models showed that geographic region and country (p&#x2009;<&#x2009;0.05) were associated with the prevalence of prediabetes, jointly accounting for 73.65% of the heterogeneity. CONCLUSION: The pooled global prevalence of diabetes and prediabetes were 15.6% and 22.9% in lean or non-obese patients with NAFLD, respectively. These findings suggest the importance of diabetes screening in these patients.

Explore related subjects

Keep this discovery

BibTeXRIS

Yuan-Yuan Li, Yuan Yu, Hai Bo Jing, Yi Hao Gu, Hui-Feng Zhang, Wei-Ping Bao, Chao Liu, Lin Cao, Yao-Fu Fan. 2025-12-22. Global epidemiology of diabetes and prediabetes in lean or non-obese patients with NAFLD: a systematic review and meta-analysis.. https://doi.org/10.1080/07853890.2025.2602995

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

Incidence of Cirrhosis in Fibrotic Metabolic Dysfunction-Associated Steatohepatitis: A Meta-Analysis of Placebo Arms from Randomized Clinical Trials.

BACKGROUNDS AND AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) with stage F2-F3 fibrosis represents the main target population for emerging pharmacotherapies. However, data on short-term progression to cirrhosis (F4) in this group remain limited. We aimed to evaluate the incidence of cirrhosis in placebo-treated patients with fibrotic MASH in randomized controlled trials (RCTs). METHODS: In this single-arm meta-analysis, we systematically searched PubMed and Cochrane Library from inception to December 13, 2024, for pharmacological Phase&#x2009;&#x2265;&#x2009;2 RCTs reporting cirrhosis events (detected in liver biopsy or clinical signs) among patients with fibrotic MASH receiving placebo. Incidence rates were pooled using generalized linear mixed models with Clopper-Pearson confidence intervals (CIs). RESULTS: We identified a total of 11 RCTs, including 586 patients with fibrotic MASH. Total follow-up was 657.23 person-years (PYs), with 83 cirrhosis events reported. The pooled incidence rate was 13.09 per 100 PYs (95% CI 7.81 to 21.12, I2&#x2009;=&#x2009;75.6%, &#x3c4;2&#x2009;=&#x2009;0.682). In subgroup analysis, the incidence of cirrhosis was 3.40 per 100 PYs in MASH F2 (95% CI 1.10 to 10.02, I2&#x2009;=&#x2009;0%, &#x3c4;2&#x2009;=&#x2009;0) and 17.90 per 100 PYs (95% CI 10.63 to 28.55, I2&#x2009;=&#x2009;70.2%, &#x3c4;2&#x2009;=&#x2009;0.561) in MASH F3, with significant differences between stages (p&#x2009;=&#x2009;0.006). Sensitivity analyses showed consistent estimates. Most RCTs were judged to have a low risk of bias. CONCLUSIONS: This study provides stage-specific data on cirrhosis incidence in fibrotic MASH, highlighting the high short-term risk associated with MASH F3 in trial settings. These data may inform benchmarks to guide event expectations, enrichment strategies, sample size assumptions, and the interpretation of future MASH clinical trials.

Humans

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans