Background & Aims: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new terminology updated from non-alcoholic fatty liver disease (NAFLD). In this study, we aim to estimate the global prevalence of MAFLD specifically in overweight and obese adults from the general population by performing a systematic review and meta-analysis through mining the existing epidemiological data on fatty liver disease. Methods: We searched Medline, Embase, Web of Science, Cochrane and google scholar database from inception to November, 2020. DerSimonian-Laird random-effects model with Logit transformation was performed for data analysis. Sensitivity analysis and meta-regression were used to explore predictors of MAFLD prevalence in pooled statistics with high heterogeneity. Results: We identified 116 relevant studies comprised of 2,667,052 participants in general population with an estimated global MAFLD prevalence as 50.7% (95% CI 46.9-54.4) among overweight/obese adults regardless of diagnostic techniques. Ultrasound was the most commonly used diagnostic technique generating prevalence rate of 51.3% (95% CI, 49.1-53.4). Male (59.0%; 95% CI, 52.0-65.6) had a significantly higher MAFLD prevalence than female (47.5%; 95% CI, 40.7-54.5). Interestingly, MAFLD prevalence rates are comparable based on classical NAFLD and non-NAFLD studies in general population. The pooled estimate prevalence of comorbidities such as type 2 diabetes and metabolic syndrome was 19.7% (95% CI, 12.8-29.0) and 57.5% (95% CI, 49.9-64.8), respectively. Conclusions: MAFLD has an astonishingly high prevalence rate in overweight and obese adults. This calls for attention and dedicated action from primary care physicians, specialists, health policy makers and the general public alike.
Bibliographical noteFunding Information:
Funding Supported by the KWF (Dutch Cancer Society) Young Investigator grant (no. 10140 ) and a VIDI grant (no. 91719300 ) from the Netherlands Organisation for Scientific Research (NWO) to Q. Pan, Sichuan Science and Technology Support Projects (2020YJ0237) to Z. Li, the National Science and Technology Key Projects ( 2017ZX10203207-003-002 ) to T. Wen, the Changjiang Scholars and Innovative Research Team in University (no. IRT_17R88 ) to Z. Ma, and the China Scholarship Council for funding PhD fellowship to J. Liu ( 201606240079 ).
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