Autor segons l'article: Ozcariz, E; Guardiola, M; Amigó, N; Rojo-Martínez, G; Valdés, S; Rehues, P; Masana, L; Ribalta, J
Departament: Medicina i Cirurgia
Autor/s de la URV: Guardiola Guionnet, Montserrat / Masana Marín, Luis / Rehues Masip, Pere / Ribalta Vives, Josep
Paraules clau: Nmr Metabolomics Machine learning Lipoproteins Insulin-resistance Glycoproteins Glucose serum creatinine risk particle nmr machine learning lipoproteins lipoprotein glycoproteins glucose glomerular hyperfiltration disease branched-chain acid
Resum: The aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study.The study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained.Logistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510-0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant.The model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia.Copyright © 2023. Published by Elsevier B.V.
Àrees temàtiques: Saúde coletiva Psicología Odontología Nutrição Medicine (miscellaneous) Medicina iii Medicina ii Medicina i Internal medicine Interdisciplinar Farmacia Engenharias iv Enfermagem Endocrinology, diabetes and metabolism Endocrinology & metabolism Endocrinology Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciência de alimentos Biotecnología
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: luis.masana@urv.cat pere.rehues@urv.cat pere.rehues@urv.cat montse.guardiola@urv.cat josep.ribalta@urv.cat
Identificador de l'autor: 0000-0002-0789-4954 0000-0002-9696-7384 0000-0002-8879-4719
Data d'alta del registre: 2024-08-03
Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Diabetes Research And Clinical Practice. 202 110772-110772
Referència de l'ítem segons les normes APA: Ozcariz, E; Guardiola, M; Amigó, N; Rojo-Martínez, G; Valdés, S; Rehues, P; Masana, L; Ribalta, J (2023). NMR-based metabolomic profiling identifies inflammation and muscle-related metabolites as predictors of incident type 2 diabetes mellitus beyond glucose: the Di@bet. Diabetes Research And Clinical Practice, 202(), 110772-110772. DOI: 10.1016/j.diabres.2023.110772
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2023
Tipus de publicació: Journal Publications
Repositori URV