%0 Journal Article
%A Guasch-Ferré, Marta
%A Ruiz-Canela, Miguel
%A Li, Jun
%A Zheng, Yan
%A Bulló, Mònica
%A Wang, Dong D
%A Toledo, Estefanía
%A Clish, Clary
%A Corella, Dolores
%A Estruch, Ramon
%A Ros, Emilio
%A Fitó, Montserrat
%A Arós, Fernando
%A Fiol, Miquel
%A Lapetra, José
%A Serra-Majem, Lluís
%A Liang, Liming
%A Papandreou, Christopher
%A Dennis, Courtney
%A Martínez-González, Miguel A
%A Hu, Frank B
%A Salas-Salvadó, Jordi
%T Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk.
%D 2019
%U http://hdl.handle.net/10668/13180
%X The potential associations between acylcarnitine profiles and incidence of type 2 diabetes (T2D) and whether acylcarnitines can be used to improve diabetes prediction remain unclear. To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors. We designed a case-cohort study within the PREDIMED Study including all incident cases of T2D (n = 251) and 694 randomly selected participants at baseline (follow-up, 3.8 years). Plasma acylcarnitines were measured using a targeted approach by liquid chromatography-tandem mass spectrometry. We tested the associations between baseline and 1-year changes in individual acylcarnitines and T2D risk using weighted Cox regression models. We used elastic net regressions to select acylcarnitines for T2D prediction and compute a weighted score using a cross-validation approach. An acylcarnitine profile, especially including short- and long-chain acylcarnitines, was significantly associated with a higher risk of T2D independent of traditional risk factors. The relative risks of T2D per SD increment of the predictive model scores were 4.03 (95% CI, 3.00 to 5.42; P  An acylcarnitine profile, mainly including short- and long-chain acylcarnitines, was significantly associated with higher T2D risk in participants at high cardiovascular risk. The inclusion of acylcarnitines into the model did not significantly improve the T2D prediction C-statistics beyond traditional risk factors, including fasting glucose.
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