LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes
BackgroundChanges in certain metabolites are linked to an increased risk of type I diabetes (T1D), making metabolite analysis a valuable tool for T1D diagnosis and treatment. This study aimed to identify a metabolic signature linked with T1D.MethodsUntargeted metabolomic profiling was performed usin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1588718/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849393121483292672 |
|---|---|
| author | Muhadasi Tuerxunyiming Muhadasi Tuerxunyiming Qing Zhao Qiaosheng Hu Ping Zhu Shiting Zhu |
| author_facet | Muhadasi Tuerxunyiming Muhadasi Tuerxunyiming Qing Zhao Qiaosheng Hu Ping Zhu Shiting Zhu |
| author_sort | Muhadasi Tuerxunyiming |
| collection | DOAJ |
| description | BackgroundChanges in certain metabolites are linked to an increased risk of type I diabetes (T1D), making metabolite analysis a valuable tool for T1D diagnosis and treatment. This study aimed to identify a metabolic signature linked with T1D.MethodsUntargeted metabolomic profiling was performed using liquid chromatography–mass spectrometry (LC-MS) on peripheral blood samples from T1D patients (n = 45) and healthy controls (n = 40). Data preprocessing and quality control were conducted using MetaboAnalyst 4.0. Differential metabolites (DMs) were identified via Wilcoxon rank-sum test (P< 0.05), and key diagnostic markers were selected using least absolute shrinkage and selection operator (LASSO) regression. A streptozotocin (STZ)-induced diabetic rat model was used for in vivo validation.ResultsA total of 157 annotated metabolites were detected (58 in ESI− and 99 in ESI+ mode). Twenty-six DMs were identified, including 25 upregulated and 1 downregulated in the T1D group, mainly involving Acylcarnitines and xanthine metabolites. LASSO regression selected Hydroxyhexadecanoyl carnitine, Propionylcarnitine, and Valerylcarnitine as candidate markers. In the rat model, Hydroxyhexadecanoyl carnitine and Valerylcarnitine demonstrated strong diagnostic performance, with AUCs of 0.9383 (95% CI: 0.8786–0.9980) and 0.8395 (95% CI: 0.7451–0.9338), respectively (P< 0.01).ConclusionHydroxyhexadecanoyl carnitine and Valerylcarnitine are closely linked to altered lipid oxidation in T1D and show strong potential as diagnostic biomarkers. These findings provide new insights into the metabolic basis of T1D and offer promising targets for early detection. |
| format | Article |
| id | doaj-art-03fcaaa41e01458ba32be79b2eb345f7 |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-03fcaaa41e01458ba32be79b2eb345f72025-08-20T03:40:31ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-08-011610.3389/fendo.2025.15887181588718LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetesMuhadasi Tuerxunyiming0Muhadasi Tuerxunyiming1Qing Zhao2Qiaosheng Hu3Ping Zhu4Shiting Zhu5Zhejiang University School of Medicine, Hangzhou, ChinaSchool of Medicine, Hangzhou City University, Hangzhou, ChinaEndocrinology Department, Lianshui County People’s Hospital, Huai’an, Jiangsu, ChinaEndocrinology Department, Lianshui County People’s Hospital, Huai’an, Jiangsu, ChinaEndocrinology Department, The Affiliated Chuzhou Hospital of Traditional Chinese Medicine of Jiangsu College of Nursing, Huai’an, Jiangsu, ChinaRehabilitation Medicine Department, Lianshui County People’s Hospital, Huai’an, Jiangsu, ChinaBackgroundChanges in certain metabolites are linked to an increased risk of type I diabetes (T1D), making metabolite analysis a valuable tool for T1D diagnosis and treatment. This study aimed to identify a metabolic signature linked with T1D.MethodsUntargeted metabolomic profiling was performed using liquid chromatography–mass spectrometry (LC-MS) on peripheral blood samples from T1D patients (n = 45) and healthy controls (n = 40). Data preprocessing and quality control were conducted using MetaboAnalyst 4.0. Differential metabolites (DMs) were identified via Wilcoxon rank-sum test (P< 0.05), and key diagnostic markers were selected using least absolute shrinkage and selection operator (LASSO) regression. A streptozotocin (STZ)-induced diabetic rat model was used for in vivo validation.ResultsA total of 157 annotated metabolites were detected (58 in ESI− and 99 in ESI+ mode). Twenty-six DMs were identified, including 25 upregulated and 1 downregulated in the T1D group, mainly involving Acylcarnitines and xanthine metabolites. LASSO regression selected Hydroxyhexadecanoyl carnitine, Propionylcarnitine, and Valerylcarnitine as candidate markers. In the rat model, Hydroxyhexadecanoyl carnitine and Valerylcarnitine demonstrated strong diagnostic performance, with AUCs of 0.9383 (95% CI: 0.8786–0.9980) and 0.8395 (95% CI: 0.7451–0.9338), respectively (P< 0.01).ConclusionHydroxyhexadecanoyl carnitine and Valerylcarnitine are closely linked to altered lipid oxidation in T1D and show strong potential as diagnostic biomarkers. These findings provide new insights into the metabolic basis of T1D and offer promising targets for early detection.https://www.frontiersin.org/articles/10.3389/fendo.2025.1588718/fulltype 1 diabetesmetabolomicsLC-MSmetabolic markersLASSO |
| spellingShingle | Muhadasi Tuerxunyiming Muhadasi Tuerxunyiming Qing Zhao Qiaosheng Hu Ping Zhu Shiting Zhu LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes Frontiers in Endocrinology type 1 diabetes metabolomics LC-MS metabolic markers LASSO |
| title | LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes |
| title_full | LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes |
| title_fullStr | LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes |
| title_full_unstemmed | LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes |
| title_short | LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes |
| title_sort | lc ms based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type i diabetes |
| topic | type 1 diabetes metabolomics LC-MS metabolic markers LASSO |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1588718/full |
| work_keys_str_mv | AT muhadasituerxunyiming lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes AT muhadasituerxunyiming lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes AT qingzhao lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes AT qiaoshenghu lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes AT pingzhu lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes AT shitingzhu lcmsbasedconventionalmetabolomicscombinedwithmachinelearningmodelstoidentifymetabolicmarkersforthediagnosisoftypeidiabetes |