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International journal of obesity (2005)

October 17, 2026 · via Europe PMC

Metabolic health is a stronger determinant than obesity for cardiovascular disease incidence: a meta-analysis of longitudinal cohort studies.

Ghorbani Z, Soltani S, Aune D, Khandouzi N, Noormohammadi M, Kazemi A.

ReviewAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Metabolic health and BMI each independently relate to cardiovascular risk, and metabolically unhealthy obesity carries roughly double the risk of metabolically healthy normal weight.

What was studied

Systematic review and random-effects meta-analysis of cohort studies (PubMed and Scopus to January 2025; 51 publications, 44 studies) comparing BMI-metabolic health phenotypes for CVD, CHD, MI, stroke and heart failure.

Key findings

  • Metabolically unhealthy obesity versus healthy normal weight: HR 2.10 for CVD, 1.97 for CHD, 2.20 for MI, 1.55 for stroke and 2.05 for HF
  • Dose-response relationships were positive for both higher BMI and poorer metabolic health
  • Risk in metabolically unhealthy normal weight often exceeded that in metabolically healthy obesity
  • For stroke, poor metabolic health appeared to be the primary determinant

Clinical relevance

Supports assessing metabolic health in people of all BMI categories rather than relying on BMI alone.

Who should care

Endocrinology

Limitations

Observational cohorts with heterogeneous phenotype definitions; the abstract does not report heterogeneity statistics or bias assessment.

Both BMI and metabolic health matter, and metabolic health is at least as important.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Overweight and obesity are established risk factors for cardiovascular diseases (CVDs), yet heterogeneity in metabolic health among individuals with varied body mass index (BMI) complicates risk assessment. This systematic-review and meta-analysis of cohort studies investigated the risk of combined BMI-metabolic health phenotypes in relation to CVDs. PubMed and Scopus were searched up to January 2025. Metabolic phenotypes were defined as individuals with metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), and their metabolically unhealthy counterparts, were analyzed for associations with CVD, coronary heart disease (CHD), myocardial infarction (MI), stroke, and heart failure (HF). Hazard ratios (HRs) and 95%confidence intervals (95% CIs) were pooled using random-effects models. Fifty-one publications (44 studies) were included. Compared with MHNW individuals, individuals with metabolically unhealthy obesity exhibited significantly higher risks across all cardiovascular outcomes with HRs of 2.10 (1.85-2.40) for CVD, 1.97 (1.71-2.27) for CHD, 2.20 (1.75-2.76) for MI, 1.55 (1.38-1.78) for stroke, and 2.05 (1.65-2.54) for HF. There was a positive dose-response relationship between both higher BMI and poor metabolic health and risk of CVD, CHD, MI, and HF incidence, however, associations for metabolically unhealthy normal weight subjects often exceeded that of metabolically healthy obese subjects. For stroke, poor metabolic health appears to be a primary determinant of risk. In conclusion, worsening metabolic health and higher BMI independently associated with increased CVD, CHD, MI, and HF risk, with their combination posing the greatest risk, while stroke risk was mainly associated with metabolic health.

DOI

10.1038/s41366-026-02237-6

PMID

42892649

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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