Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis
Objects: Our aim was to identify changes in the metabolome in dilated cardiomyopathy (DCM) as well as to construct a metabolic diagnostic model for DCM. Methods: We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data w...
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402500307X |
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| author | Xiao-Lei Li Aibibanmu Aizezi Yan-Peng Li Yan-Hong Li Fen Liu Qian Zhao Xiang Ma Dilare Adi Yi-Tong Ma |
| author_facet | Xiao-Lei Li Aibibanmu Aizezi Yan-Peng Li Yan-Hong Li Fen Liu Qian Zhao Xiang Ma Dilare Adi Yi-Tong Ma |
| author_sort | Xiao-Lei Li |
| collection | DOAJ |
| description | Objects: Our aim was to identify changes in the metabolome in dilated cardiomyopathy (DCM) as well as to construct a metabolic diagnostic model for DCM. Methods: We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis. It was validated in combination with transcriptome sequencing data from public databases. Machine learning models were used for the diagnosis of DCM. Results: Using multiple analytical techniques, 130 metabolite alterations were identified in DCM compared to healthy controls. Perturbations in glycerophospholipid metabolism (GPL) were identified and validated as a characteristic metabolic pathway in DCM. Through the least absolute shrinkage and selection operator (LASSO), we identified the 7 most important GPL metabolites, including LysoPA (16:0/0:0), LysoPA (18:1(9Z)/0:0), PC (20:3(8Z,11Z,14Z)/20:1(11Z)), PC (20:0/14:0), LysoPC (16:0), PS(15:0/18:0), and PE(16:0/20:4 (5Z,8Z,11Z,14Z)). The machine learning models based on the seven metabolites all had good accuracy in distinguishing DCM [All area under the curve (AUC) > 0.900], and the artificial neural network (ANN) model performed the most consistently (AUC: 0.919 ± 0.075). Conclusions: This study demonstrates that GPL metabolism may play a contributing role in the pathophysiological mechanisms of DCM. The 7-GPL metabolite model may help for early diagnosis of DCM. |
| format | Article |
| id | doaj-art-3026ed611dba479aaae3287f63fe73e8 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3026ed611dba479aaae3287f63fe73e82025-02-06T05:12:33ZengElsevierHeliyon2405-84402025-02-01113e41927Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysisXiao-Lei Li0Aibibanmu Aizezi1Yan-Peng Li2Yan-Hong Li3Fen Liu4Qian Zhao5Xiang Ma6Dilare Adi7Yi-Tong Ma8State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China; Hubei University of Medicine, Sinopharm Dongfeng General Hospital (Hubei Clinical Research Center of Hypertension), Shiyan, Hubei, 442008, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaLaboratory and Equipment Management, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaState Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China; Corresponding author. State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia,Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China.State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China; Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China; Corresponding author. State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia,Department of Cardiology,First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China.Objects: Our aim was to identify changes in the metabolome in dilated cardiomyopathy (DCM) as well as to construct a metabolic diagnostic model for DCM. Methods: We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis. It was validated in combination with transcriptome sequencing data from public databases. Machine learning models were used for the diagnosis of DCM. Results: Using multiple analytical techniques, 130 metabolite alterations were identified in DCM compared to healthy controls. Perturbations in glycerophospholipid metabolism (GPL) were identified and validated as a characteristic metabolic pathway in DCM. Through the least absolute shrinkage and selection operator (LASSO), we identified the 7 most important GPL metabolites, including LysoPA (16:0/0:0), LysoPA (18:1(9Z)/0:0), PC (20:3(8Z,11Z,14Z)/20:1(11Z)), PC (20:0/14:0), LysoPC (16:0), PS(15:0/18:0), and PE(16:0/20:4 (5Z,8Z,11Z,14Z)). The machine learning models based on the seven metabolites all had good accuracy in distinguishing DCM [All area under the curve (AUC) > 0.900], and the artificial neural network (ANN) model performed the most consistently (AUC: 0.919 ± 0.075). Conclusions: This study demonstrates that GPL metabolism may play a contributing role in the pathophysiological mechanisms of DCM. The 7-GPL metabolite model may help for early diagnosis of DCM.http://www.sciencedirect.com/science/article/pii/S240584402500307XMetabolomicDilated cardiomyopathyGlycerophospholipidMachine learning |
| spellingShingle | Xiao-Lei Li Aibibanmu Aizezi Yan-Peng Li Yan-Hong Li Fen Liu Qian Zhao Xiang Ma Dilare Adi Yi-Tong Ma Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis Heliyon Metabolomic Dilated cardiomyopathy Glycerophospholipid Machine learning |
| title | Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis |
| title_full | Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis |
| title_fullStr | Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis |
| title_full_unstemmed | Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis |
| title_short | Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis |
| title_sort | dilated cardiomyopathy signature metabolic marker screening machine learning and multi omics analysis |
| topic | Metabolomic Dilated cardiomyopathy Glycerophospholipid Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S240584402500307X |
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