Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk

Abstract

Context: 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.

Objective: To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors.

Design, setting, and participants: 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.

Results: 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 < 0.001) for the conventional model and 4.85 (95% CI, 3.65 to 6.45; P < 0.001) for the model including acylcarnitines, with a hazard ratio of 1.33 (95% CI, 1.08 to 1.63; P < 0.001) attributed to the acylcarnitines. Including the acylcarnitines into the model did not significantly improve the area under the receiver operator characteristic curve (0.86 to 0.88, P = 0.61). A 1-year increase in C4OH-carnitine was associated with higher risk of T2D [per SD increment, 1.44 (1.03 to 2.01)].

Conclusions: 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.

Figure 1.

Receiver operator characteristic curves for prediction of incident T2D. The black curve indicates the conventional model including age, sex, BMI, smoking, baseline hypertension, physical activity, fasting glucose, and stratification by intervention group and recruitment centers. The red curve indicates the conventional model plus acylcarnitines selected from the elastic net model. Robust standard errors to account for intracluster correlations were used. Statistics of both models were built based on the leave-one-out cross-validation approach. The table below shows the C-statistics, reclassification index (NRI), IDI, and Akaike information criteria for the conventional and the conventional model plus acylcarnitines.