Author, as appears in the article.: Ozcariz, E; Guardiola, M; Amigó, N; Rojo-Martínez, G; Valdés, S; Rehues, P; Masana, L; Ribalta, J
Department: Medicina i Cirurgia
URV's Author/s: Guardiola Guionnet, Montserrat / Masana Marín, Luis / Rehues Masip, Pere / Ribalta Vives, Josep
Keywords: 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
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: luis.masana@urv.cat pere.rehues@urv.cat pere.rehues@urv.cat montse.guardiola@urv.cat josep.ribalta@urv.cat
Author identifier: 0000-0002-0789-4954 0000-0002-9696-7384 0000-0002-8879-4719
Record's date: 2024-08-03
Papper version: info:eu-repo/semantics/submittedVersion
Link to the original source: https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(23)00535-1/fulltext#%20
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Diabetes Research And Clinical Practice. 202 110772-110772
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
Article's DOI: 10.1016/j.diabres.2023.110772
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications
Repositori URV