Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D).
The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D.
We used a case-cohort study nested within the Prevención con Dieta Mediterránea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography–tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR–related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models.
We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors.
The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.