Unveiling the Correlation between Inadequate Energy/Macronutrient Intake and Clinical Alterations in Volunteers at Risk of Metabolic Syndrome by a Predictive Model
2021
Danesi, Francesca | Mengucci, Carlo | Vita, Simona | Bub, Achim | Seifert, Stephanie | Malpuech Brugère, Corinne | Richard, Ruddy | Orfila, Caroline | Sutulic, Samantha | Ricciardiello, Luigi | Marcato, Elisa | Capozzi, Francesco | Bordoni, Alessandra | Alma Mater Studiorum Università di Bologna = University of Bologna (UNIBO) | Max Rubner-Institut | Unité de Nutrition Humaine (UNH) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA) | Centre de Recherche en Nutrition Humaine Auvergne [CHU Clermont-Ferrand] (CRNH A) ; Direction de la Recherche Clinique et de l’Innovation [CHU Clermont-Ferrand] (DRCI) ; CHU Clermont-Ferrand-CHU Clermont-Ferrand | University of Leeds | European Commission311876: PATHWAY-27
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Afficher plus [+] Moins [-]anglais. Although lifestyle-based interventions are the most effective to prevent metabolic syndrome (MetS), there is no definitive agreement on which nutritional approach is the best. The aim of the present retrospective analysis was to identify a multivariate model linking energy and macronutrient intake to the clinical features of MetS. Volunteers at risk of MetS (F = 77, M = 80) were recruited in four European centres and finally eligible for analysis. For each subject, the daily energy and nutrient intake was estimated using the EPIC questionnaire and a 24-h dietary recall, and it was compared with the dietary reference values. Then we built a predictive model for a set of clinical outcomes computing shifts from recommended intake thresholds. The use of the ridge regression, which optimises prediction performances while retaining information about the role of all the nutritional variables, allowed us to assess if a clinical outcome was manly dependent on a single nutritional variable, or if its prediction was characterised by more complex interactions between the variables. The model appeared suitable for shedding light on the complexity of nutritional variables, which effects could be not evident with univariate analysis and must be considered in the framework of the reciprocal influence of the other variables.
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