Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention (The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University), Wuxi 214023, Jiangsu, China.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China.
J Clin Endocrinol Metab. 2022 Nov 23;107(11):3120-3127. doi: 10.1210/clinem/dgac487.
It is essential to improve the current predictive ability for type 2 diabetes (T2D) risk.
We aimed to identify novel metabolic markers for future T2D in Chinese individuals of Han ethnicity and to determine whether the combined effect of metabolic and genetic markers improves the accuracy of prediction models containing clinical factors.
A nested case-control study containing 220 incident T2D patients and 220 age- and sex- matched controls from normoglycemic Chinese individuals of Han ethnicity was conducted within the Wuxi Non-Communicable Disease cohort with a 12-year follow-up. Metabolic profiling detection was performed by high-performance liquid chromatography‒mass spectrometry (HPLC-MS) by an untargeted strategy and 20 single nucleotide polymorphisms (SNPs) associated with T2D were genotyped using the Iplex Sequenom MassARRAY platform. Machine learning methods were used to identify metabolites associated with future T2D risk.
We found that abnormal levels of 5 metabolites were associated with increased risk of future T2D: riboflavin, cnidioside A, 2-methoxy-5-(1H-1, 2, 4-triazol-5-yl)- 4-(trifluoromethyl) pyridine, 7-methylxanthine, and mestranol. The genetic risk score (GRS) based on 20 SNPs was significantly associated with T2D risk (OR = 1.35; 95% CI, 1.08-1.70 per SD). The area under the receiver operating characteristic curve (AUC) was greater for the model containing metabolites, GRS, and clinical traits than for the model containing clinical traits only (0.960 vs 0.798, P = 7.91 × 10-16).
In individuals with normal fasting glucose levels, abnormal levels of 5 metabolites were associated with future T2D. The combination of newly discovered metabolic markers and genetic markers could improve the prediction of incident T2D.
提高 2 型糖尿病（T2D）风险的现有预测能力至关重要。
我们旨在确定汉族中国人群中未来 T2D 的新代谢标志物，并确定代谢和遗传标志物的联合效应对包含临床因素的预测模型的准确性是否有所提高。
在无锡非传染性疾病队列中进行了一项嵌套病例对照研究，该研究包含 220 例新发生的 T2D 患者和 220 例年龄和性别匹配的血糖正常汉族中国人群对照，随访时间为 12 年。通过高分辨液相色谱-质谱（HPLC-MS）以非靶向策略进行代谢谱检测，并使用 Iplex Sequenom MassARRAY 平台对与 T2D 相关的 20 个单核苷酸多态性（SNP）进行基因分型。使用机器学习方法来确定与未来 T2D 风险相关的代谢物。
我们发现，5 种代谢物的异常水平与未来 T2D 风险增加相关：核黄素、香菇多糖 A、2-甲氧基-5-（1H-1,2,4-三唑-5-基）-4-（三氟甲基）吡啶、7-甲基黄嘌呤和炔雌醇。基于 20 个 SNP 的遗传风险评分（GRS）与 T2D 风险显著相关（OR=1.35；95%CI，每 SD 增加 1.08-1.70）。包含代谢物、GRS 和临床特征的模型的受试者工作特征曲线（ROC）下面积大于仅包含临床特征的模型（0.960 与 0.798，P=7.91×10-16）。
在空腹血糖正常的个体中，5 种代谢物的异常水平与未来 T2D 相关。新发现的代谢标志物和遗传标志物的组合可以提高对 T2D 发病的预测。
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