DataSheet_2_Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population.docx

BackgroundIdentifying the metabolite profile of individuals with prediabetes who turned to type 2 diabetes (T2D) may give novel insights into early T2D interception. The purpose of this study was to identify metabolic markers that predict the development of T2D from prediabetes in a Chinese population.
MethodsWe used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who turned to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline [age: 56 ± 7 years old, body mass index (BMI): 24.2 ± 2.8 kg/m2] and at a 5-year follow-up [age: 61 ± 7 years old, BMI: 24.5 ± 3.1 kg/m2]. Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors.
FindingsMetabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine [odds ratio (OR) = 19.00; 95% confidence interval (CI): 4.23-85.37] and carvacrol (OR = 17.63; 95% CI: 4.98-62.34). Thirteen metabolites were found to improve T2D risk prediction beyond eight conventional T2D risk factors [area under the curve (AUC) was 0.98 for risk factors + metabolites vs 0.72 for risk factors, P < 0.05].
InterpretationsUse of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes.

研究背景：明确进展为2型糖尿病（type 2 diabetes, T2D）的前驱糖尿病（prediabetes）患者的代谢谱，可为早期干预2型糖尿病提供全新的研究视角。本研究旨在在中国人群中筛选可预测前驱糖尿病进展为2型糖尿病的代谢标志物。
研究方法：本研究采用非靶向代谢组学（untargeted metabolomics）方法，在中国人群（REACTION研究）中探究血清代谢物与进展为显性2型糖尿病的前驱糖尿病患者（n=153，平均随访5年）的发病风险之间的关联。以基线及5年随访时均维持前驱糖尿病状态的匹配对照人群作为参照：基线时年龄为56±7岁，体质量指数（body mass index, BMI）为24.2±2.8 kg/m²；5年随访时年龄为61±7岁，BMI为24.5±3.1 kg/m²。本研究校正了混杂因素，采用多因素logistic回归分析评估代谢物与糖尿病发病风险之间的关联。此外，通过10折交叉验证随机森林分类（random forest classification, RFC）模型筛选用于预测糖尿病发病的最优代谢物组合，并在内部验证相较于传统临床风险因素，所选代谢物的区分能力。
研究结果：包括氨基酸代谢和脂质代谢相关改变在内的代谢异常，与前驱糖尿病进展为糖尿病的风险升高相关。其中最重要的代谢物为肌苷（inosine）[比值比（odds ratio, OR）=19.00；95%置信区间（confidence interval, CI）：4.23~85.37]和香芹酚（carvacrol）[OR=17.63；95% CI：4.98~62.34]。相较于仅纳入8项传统2型糖尿病风险因素的模型，新增13种代谢物后可显著提升糖尿病风险预测效能：风险因素+代谢物模型的曲线下面积（area under the curve, AUC）为0.98，仅风险因素模型的AUC为0.72（P<0.05）。
研究解读：本研究筛选得到的代谢物有助于识别前驱糖尿病中进展为糖尿病风险最高的人群。