Estimated global prevalence of metabolic syndrome: a proportional meta-analysis

Metabolic syndrome is a major global health burden. However, existing global prevalence estimates are largely based on studies conducted before the COVID-19 pandemic and do not fully account for contemporary epidemiological and structural determinants. This systematic review and meta-analysis updated global metabolic syndrome prevalence estimates and examined associated socioeconomic and healthcare system correlates using the most recent available evidence.

Six databases were searched from inception to October 27, 2025, for studies reporting crude metabolic syndrome prevalence among adults. Two reviewers independently screened the studies and extracted data. Random-effects proportional meta-analyses and meta-regression were performed to estimate the prevalence of metabolic syndrome and explore associated determinants, respectively. A total of 684 unique observational studies involving 44,979,527 participants were included in quantitative data synthesis. The global metabolic syndrome prevalence ranged from 19% (95% CI: 15%-24%) to 31% (95% CI: 30%-33%), with substantial heterogeneity across analyses. Subgroup analysis according to World Bank region indicated that studies conducted in South Asia (range: 30%-35%) and Latin America and the Caribbean (range: 33%-55%) reported higher prevalence rates than studies in other regions. A higher metabolic syndrome prevalence was significantly associated with an older age, higher proportion of females, greater income inequality, and higher Universal Health Coverage. Pre- and post-pandemic subgroup analyses showed no statistically significant difference in prevalence overall.

Metabolic syndrome remains a substantial global health challenge in the post-pandemic era. Beyond estimating prevalence, this review suggests that structural socioeconomic factors and health system coverage are meaningfully associated with prevalence patterns, supporting targeted prevention strategies in socioeconomically disadvantaged regions and populations.