An Approach to Early Detection of Metabolic Syndrome through Non-Invasive Methods in Obese Children

Bibliographic Data

ID15718294
AuthorsRafael Molina-Luque (0000-0002-5223-7400, Instituto Maimónides de Investigación Biomédica de Córdoba), Natalia Ulloa (0000-0002-0437-5016, University of Concepción, corresponding author), Andrea Gleisner (University of Concepción), Martin Zilic (University of Concepción), Manuel Romero‐Saldaña (0000-0002-6146-4402, Instituto Maimónides de Investigación Biomédica de Córdoba), Guillermo Molina-Recio (0000-0002-0005-4901, Instituto Maimónides de Investigación Biomédica de Córdoba)
Year2020
Volume7
Issue12
Pages304-304
Publication date2020-12-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueChildren (JOURNAL)
Journal identifiersISSN: 2227-9067 • E-ISSN: 2227-9067
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/children7120304
PMID33348633
OpenAlexW3110872570
LanguageEN
Citations received2
References cited6

Early detection of MetS is possible through non-invasive methods in overweight and obese children. Two models (Clinical decision trees) based on anthropometric (non-invasive) variables with acceptable validity indexes have been presented. Clinical decision trees can be applied in different clinical and non-clinical settings, adapting to the tools available, being an economical and easy to measurement option. These methods reduce the use of blood tests to those patients who require confirmation

Anthropometry · Body mass index · Decision tree · Logistic regression · Machine learning · Metabolic syndrome · Obesity · Overweight · Receiver operating characteristic · Waist · Waist-to-height ratio · Computer Science · Diabetes, Cardiovascular Risks, and Lipoproteins · Medicine · Nutritional Studies and Diet · Obesity, Physical Activity, Diet · Internal Medicine

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Open DOIOpen Access