García Rodríguez, María

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García Rodríguez, María
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García Rodríguez, María
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    Item type:Publication,
    Evaluation of the predictive capacity of metabolic syndrome severity scores and anthropometric indicators in a Spanish working cohort
    (Elsevier BV, 2026-04-28)
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    Molina-Luque, Rafael
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    Molina-Recio, Guillermo
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    Alcaide-Leyva, José Manuel
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    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.
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    Item type:Publication,
    Development and validation of a food group system for intake control in people with diabetes: SMARTCLOTH-Database
    (Public Library of Science (PLoS), 2026-07-09)
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    León-García, Fernando
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    Molina-Luque, Rafael
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    Villena-Esponera, María Pilar
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    Romero-Saldaña, Manuel
    This study presents the development and validation of the SMARTCLOTH-Database, a structured food group system designed to support dietary management for individuals with diabetes. The database organises foods into 20 nutritionally coherent groups, distinguishing between raw and cooked forms, based on Spanish dietary patterns and statistical criteria. Nutritional values were calculated using weighted averages derived from national food consumption data and multiple international food composition databases. To validate the system, a computational tool was developed to generate daily menus automatically and compare nutritional outputs against a gold standard reference (BDCA). The comparison showed small mean differences (less than ±22 kcal, < 2 g carbohydrates, < 0.6 g protein, and <1.5 g lipids), with high concordance demonstrated through intraclass correlation coefficients (ICC ≥ 0.75) for carbohydrates and proteins across most analyses. These results indicate that the SMARTCLOTHDatabase provides accurate and reliable estimates of macronutrient content, making it suitable for integration into digital tools aimed at improving dietary self-management in people with diabetes.