Autor segons l'article: Hernandez-Alonso, Pablo; Garcia-Gavilan, Jesus; Camacho-Barcia, Lucia; Sjodin, Anders; Hansen, Thea T; Harrold, Jo; Salas-Salvado, Jordi; Halford, Jason C G; Canudas, Silvia; Bullo, Monica
Departament: Bioquímica i Biotecnologia
Autor/s de la URV: Bulló Bonet, Mònica / García Gavilán, Jesús Francisco / Salas Salvadó, Jorge
Paraules clau: Young adult Vitamin-e Sensitivity Roc curve Risk Plasma Overweight Obese Middle aged Metabolomics Male Linoleic-acid Insulin resistance Humans Homeostasis Glucose Female Cross-sectional studies Branched-chain Body mass index Area under curve Aged Adult
Resum: Different plasma metabolites have been related to insulin resistance (IR). However, there is a lack of metabolite models predicting IR with external validation. The aim of this study is to identify a multi-metabolite model associated to the homeostatic model assessment (HOMA)-IR values. We performed a cross-sectional metabolomics analysis of samples collected from overweight and obese subjects from two independent studies. The training step was performed in 236 subjects from the SATIN study and validated in 102 subjects from the GLYNDIET study. Plasma metabolomics profile was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance (NMR). Associations between metabolites and HOMA-IR were assessed using elastic net regression analysis with a leave-one-out cross validation (CV) and 100 CV runs. HOMA-IR was analyzed both as linear and categorical (median or lower versus higher than the median). Receiver operating characteristic curves were constructed based on metabolites' weighted models. A set of 30 metabolites discriminating extremes of HOMA-IR were consistently selected. These metabolites comprised some amino acids, lipid species and different organic acids. The area under the curve (AUC) for the discrimination between HOMA-IR extreme categories was 0.82 (95% CI: 0.74-0.90), based on the multi-metabolite model weighted with the regression coefficients of metabolites in the validation dataset. We identified a set of metabolites discriminating between extremes of HOMA-IR and able to predict HOMA-IR with high accuracy.
Àrees temàtiques: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Letras / linguística Interdisciplinar Geografía Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Enfermagem Educação física Educação Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Biodiversidade Astronomia / física
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 20452322
Adreça de correu electrònic de l'autor: jesusfrancisco.garcia@urv.cat jesusfrancisco.garcia@urv.cat monica.bullo@urv.cat jordi.salas@urv.cat
Identificador de l'autor: 0000-0002-0218-7046 0000-0003-2700-7459
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.nature.com/articles/s41598-019-50260-7
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Scientific Reports. 9 (1): 13895-13895
Referència de l'ítem segons les normes APA: Hernandez-Alonso, Pablo; Garcia-Gavilan, Jesus; Camacho-Barcia, Lucia; Sjodin, Anders; Hansen, Thea T; Harrold, Jo; Salas-Salvado, Jordi; Halford, Jas (2019). Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation. Scientific Reports, 9(1), 13895-13895. DOI: 10.1038/s41598-019-50260-7
DOI de l'article: 10.1038/s41598-019-50260-7
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2019
Tipus de publicació: Journal Publications