Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, 400076, India.
Industrial Engineering and Operations Research, Indian Institute of Technology Bombay, Mumbai, Maharashtra, 400076, India.
Metabolomics. 2023 Nov 28;20(1):1. doi: 10.1007/s11306-023-02066-y.
To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms.
87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels.
Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores.
Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.
在新诊断的糖尿病患者和长期糖尿病患者的印度亚群中，确定糖尿病的代谢物和脂质生物标志物。利用全球极性代谢组学和脂质组学图谱，利用机器学习算法预测个体患糖尿病的易感性。
本研究纳入了 87 名个体，包括健康对照者、新诊断的糖尿病患者和长期接受药物治疗的糖尿病患者。在获得同意后，使用他们的血清分离极性代谢组和脂质组。分别使用 NMR 和 LCMS 鉴定极性代谢物和脂质。进行统计分析以确定显著改变的分子。利用 NMR 和 LCMS 综合数据，使用机器学习算法生成糖尿病模型。另外招募了 10 名（前驱糖尿病）个体，生成他们的极性代谢组和脂质组图谱。然后利用前驱糖尿病的代谢组和脂质组谱，在血糖水平之外预测代谢组和脂质组的糖尿病状态。
甘露糖、甜菜碱、黄嘌呤、三酰甘油（38:1）、神经鞘磷脂（d63:7）和磷脂酸（37:2）是糖尿病的一些关键生物标志物。生成的预测模型在测试和验证数据上的接收者操作特征曲线（ROC-AUC）均为 1，表明准确性非常高。该模型根据概率评分预测前驱糖尿病个体代谢的糖尿病接近程度。
糖尿病患者的极性代谢和脂质谱与健康个体非常不同。在易感个体中，脂质谱在糖尿病发生前发生改变。不考虑葡萄糖，可以确定任何个体的代谢的糖尿病接近程度。
