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Unveiling the Correlation between Inadequate Energy/Macronutrient Intake and Clinical Alterations in Volunteers at Risk of Metabolic Syndrome by a Predictive Model

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

Abstract (englisch):

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000131838
Veröffentlicht am 27.04.2021
Originalveröffentlichung
DOI: 10.3390/nu13041377
Scopus
Zitationen: 2
Web of Science
Zitationen: 1
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Sport und Sportwissenschaft (IfSS)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2072-6643
KITopen-ID: 1000131838
Erschienen in Nutrients
Verlag MDPI
Band 13
Heft 4
Seiten Article no: 1377
Vorab online veröffentlicht am 20.04.2021
Schlagwörter metabolic syndrome; energy intake; macronutrient intake; penalised models; feature shrinkage; prevention
Nachgewiesen in Dimensions
Web of Science
Scopus
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