Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study

Abstract

Background Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). Objectives 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. Methods 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. Results 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. Conclusions 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.

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

“…A recent report of the PREDIMED Trial study indicates a non-targeted metabolomics approach. This investigation found that plasma metabolites predict insulin resistance and incident T2DM 26 . Metabolite profile of individuals with normal fasting glucose is also available in this report.…”

“…A large number of metabolomics studies have been conducted in patients with T2DM [24][25][26][27][28] . Metabolomics analysis has, however, not been widely used to identify altered metabolism in healthy people who are at risk for T2DM 6,29 .…”

NMR-Based Metabolomic Approach Tracks Potential Serum Biomarkers of Disease Progression in Patients with Type 2 Diabetes Mellitus

“…Recently, several metabolomic techniques were applied to the identification of metabolic signatures associated with T2DM [10,11,12,13,14,15,16,17]. Data in this research field were extensive, revealing new diagnostic/prognostic disease biomarkers [14], or metabolic markers of response to targeted therapies [15,16].…”

Blood Metabolite Signatures of Metabolic Syndrome in Two Cross-Cultural Older Adult Cohorts

“…Elevated levels of sphingomyelins have been shown to play a critical role in cardiovascular dysfunction including insulin resistance, atherosclerosis, and cardiomyopathy [36]. While levels of SM C16:0 were not shown to be associated with the clinical features of MetS in a young Japanese sample [37], SM C16:0 was shown to be associated with insulin resistance in a Southern European cohort at high risk for cardiovascular disease [38]. Sphingomyelins likely play a significant role in chronic inflammation and may therefore be implicated in multiple metabolic diseases, including those of old age such as dementia [34,[39][40][41].…”