Evaluation of the predictive capacity of metabolic syndrome severity scores and anthropometric indicators in a Spanish working cohort
Year: 2026
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Metabolic syndrome (MetS) comprises cardiometabolic risk factors linked to higher morbidity and mortality. Traditional binary definitions, such as the Harmonised Definition, enable diagnosis but may underestimate risk progression. Severity scores like MetS-Z and MetSSS aim to quantify burden more precisely. OBJECTIVES: To assess the predictive capacity of MetS-Z and MetSSS severity scores for MetS incidence in a Spanish working cohort and compare them with classic anthropometric indicators. METHODS: A longitudinal study (2006-2019) included 630 municipal workers from Córdoba, Spain, free of MetS at baseline. Participants underwent periodic health examinations. Variables were classified using international guidelines and the Harmonised Definition. Predictive ability was evaluated through survival analysis, Cox regression, and ROC curves. RESULTS: Over a mean follow-up of 9.1 years (5728 worker-years), 65 new MetS cases occurred, with an incidence density of 11.3 per 1000 worker-years (95% CI: 8.8-14.5). Incidence was higher in men (13.1) than women (8.0). Among anthropometric indicators, waist-to-height ratio (WHtR) had the highest discriminatory capacity (AUC = 0.82), followed by BMI and waist circumference (both AUC = 0.80). In multivariate Cox models, the best-performing model (AUC = 0.74) included age, BMI, leukocytes, eosinophils, and MetS-Z. The MetS-Z score was independently associated with MetS incidence (HR = 3.2; 95% CI: 1.8-5.7). CONCLUSIONS: MetS incidence in this occupational cohort was lower than in the general Spanish population. WHtR was the strongest anthropometric predictor, while BMI showed greater robustness in adjusted models. The MetS-Z score added independent predictive value, supporting its use as a continuous marker of cardiometabolic risk in occupational health.
Source: Nutrition
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,870
OpenAlex ID: W7127305461