Diabetes, metabolic syndrome and obesity : targets and therapy · 2026
Machine Learning-Based Identification of Serum Metabolic Signatures in Adult Patients with Type 1 Diabetes.
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Wang Chaofan, Lan Yuhe, Zhao Minzhe, Zhu Jiangyu, Tan Huiling, Li Xingyu, Ding Yu, Zheng Xueying, Liu Sihua, Gu Zhaohe
Аннотация
BACKGROUND: Metabolomic profiling via machine learning can reveal signatures of host metabolism and identify useful biomarkers. We aimed to investigate metabolomic profiles and biomarkers in adult patients with type 1 diabetes (T1D) via machine learning.
METHODS: We recruited 29 adult patients with T1D and matched them with 29 healthy controls on the basis of age, sex, and body mass index (BMI). We collected serum samples from both groups and performed nontargeted metabolomics with liquid chromatography‒mass spectrometry (LC‒MS). Four machine learning algorithms (logistic regression, support vector machine, Gaussian naive Bayes, and random forest) were used to screen potential T1D-related biomarkers.
RESULTS: We identified 328 differently abundant metabolites between the T1D group and the control group that were significantly enriched in three metabolic pathways (purine metabolism, ketone body synthesis and degradation, and methyl butyrate metabolism), with P values less than 0.05. Ten metabolites were identified as T1D-related indicators, including L-fucopyranose, hept-2-ulose, L-rhamnose, docosahexaenoic acid, pumiliotoxin 251d, 9,12-octadecadienal, oleamide, estrane, (e,e)-2,4-heptadienal, and hexadecanamide. The predictive value of the ten candidate metabolites, as measured by the area under the curve (AUC), ranged from 0.86 to 0.95.
CONCLUSION: In this study, we identified purine metabolism, synthesis and degradation of ketone bodies, and impaired methyl butyrate metabolism as metabolic pathways that are altered in adult patients with T1D. Our findings present an extensive profile of metabolic changes in adult patients with T1D, and the identified biomarkers may have important clinical significance in the diagnosis of T1D and the monitoring of responses to therapeutic interventions.
METHODS: We recruited 29 adult patients with T1D and matched them with 29 healthy controls on the basis of age, sex, and body mass index (BMI). We collected serum samples from both groups and performed nontargeted metabolomics with liquid chromatography‒mass spectrometry (LC‒MS). Four machine learning algorithms (logistic regression, support vector machine, Gaussian naive Bayes, and random forest) were used to screen potential T1D-related biomarkers.
RESULTS: We identified 328 differently abundant metabolites between the T1D group and the control group that were significantly enriched in three metabolic pathways (purine metabolism, ketone body synthesis and degradation, and methyl butyrate metabolism), with P values less than 0.05. Ten metabolites were identified as T1D-related indicators, including L-fucopyranose, hept-2-ulose, L-rhamnose, docosahexaenoic acid, pumiliotoxin 251d, 9,12-octadecadienal, oleamide, estrane, (e,e)-2,4-heptadienal, and hexadecanamide. The predictive value of the ten candidate metabolites, as measured by the area under the curve (AUC), ranged from 0.86 to 0.95.
CONCLUSION: In this study, we identified purine metabolism, synthesis and degradation of ketone bodies, and impaired methyl butyrate metabolism as metabolic pathways that are altered in adult patients with T1D. Our findings present an extensive profile of metabolic changes in adult patients with T1D, and the identified biomarkers may have important clinical significance in the diagnosis of T1D and the monitoring of responses to therapeutic interventions.