Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain.
CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.
Am J Clin Nutr. 2019 Mar 1;109(3):626-634. doi: 10.1093/ajcn/nqy262.
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.
胰岛素抵抗是一种复杂的代谢紊乱，常与 2 型糖尿病（T2D）有关。
本研究旨在检验基线代谢物是否可以在经典危险因素之外进一步改善胰岛素抵抗的预测。此外，我们还研究了预测非糖尿病患者胰岛素抵抗的多代谢物模型是否也可以预测 2 型糖尿病的发生。
我们在 Prevención con Dieta Mediterránea（PREDIMED）试验的亚组中进行了病例对照研究，纳入了 700、500 和 256 名基线时无 T2D 且在 1 年和 3 年时无 T2D 的参与者。使用液相色谱-串联质谱法对空腹血浆代谢物进行半定量分析。我们通过弹性网络回归分析评估了代谢物浓度与胰岛素抵抗的稳态模型（HOMA-IR）之间的相关性。随后，我们通过加权 Cox 比例风险模型检验了基线 HOMA-IR 相关多代谢物模型与 T2D 发生率之间的相关性。
我们确定了一组与 HOMA-IR 相关的基线代谢物。代谢物的 1 年变化也与 HOMA-IR 显著相关。与仅包含经典危险因素相比，包含经典危险因素和代谢物的模型在基线时（0.81 [95%CI：0.79，0.84] 与 0.69 [95%CI：0.66，0.73]）和在 1 年期间（0.69 [95%CI：0.66，0.72] 与 0.57 [95%CI：0.53，0.62]）的曲线下面积显著更大。在所有时间段，代谢物与经典危险因素组合解释的 HOMA-IR 方差也更高。在预测基线时高 HOMA-IR（中位数或更高）或 HOMA-IR（连续）的多代谢物评分（模型 3）中，T2D 风险因素校正后，新发 T2D 的估计 HR 分别为 2.00（95%CI：1.58，2.55）和 2.24（95%CI：1.72，2.90）。
本研究中确定的多代谢物模型显著提高了经典危险因素之外预测 HOMA-IR 的能力，并显著预测了 T2D 的风险。
