Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease
Depressive symptoms are prevalent in individuals with Parkinson’s disease. Previous research has demonstrated a significant association between the triglyceride glucose (TyG) index and depression. Leveraging multicenter clinical data, the present study evaluates the predictive capacity of the TyG in...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1584005/full |
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| author | Haijun Zhang Yifan Zhang Guihua Li Guihua Li |
| author_facet | Haijun Zhang Yifan Zhang Guihua Li Guihua Li |
| author_sort | Haijun Zhang |
| collection | DOAJ |
| description | Depressive symptoms are prevalent in individuals with Parkinson’s disease. Previous research has demonstrated a significant association between the triglyceride glucose (TyG) index and depression. Leveraging multicenter clinical data, the present study evaluates the predictive capacity of the TyG index for depressive symptoms in PD patients, aiming to establish its potential role in identifying individuals at risk for depression. A comparative analysis of multiple machine learning models was conducted to predict depression in PD patients, ultimately selecting the most effective model. Key predictive variables, including diabetes status, sex, cholesterol levels, triglycerides, blood glucose, and sleep disturbances, were incorporated into a support vector machine (SVM)-based nomogram to assess depression risk in PD patients. Additionally, a genome-wide association study (GWAS) utilizing external databases confirmed a causal relationship between the TyG index and depression. Furthermore, this study explores the biological functions and molecular mechanisms underlying shared transcriptomic proteins between PD and depression, providing insights into potential pathophysiological links between the two conditions. |
| format | Article |
| id | doaj-art-2c03d7e647dd491bb666a91ea0b2a4d1 |
| institution | OA Journals |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-2c03d7e647dd491bb666a91ea0b2a4d12025-08-20T02:08:27ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-04-011710.3389/fnagi.2025.15840051584005Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s diseaseHaijun Zhang0Yifan Zhang1Guihua Li2Guihua Li3Department of Neurology, ShenzhenBaoan People’s Hospital, Shenzhen, ChinaDepartment of Neurology, ShenzhenBaoan People’s Hospital, Shenzhen, ChinaDepartment of Neurology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, ChinaThe Second Clinical Medical College of Southern Medical University, Guangzhou, Guangdong, ChinaDepressive symptoms are prevalent in individuals with Parkinson’s disease. Previous research has demonstrated a significant association between the triglyceride glucose (TyG) index and depression. Leveraging multicenter clinical data, the present study evaluates the predictive capacity of the TyG index for depressive symptoms in PD patients, aiming to establish its potential role in identifying individuals at risk for depression. A comparative analysis of multiple machine learning models was conducted to predict depression in PD patients, ultimately selecting the most effective model. Key predictive variables, including diabetes status, sex, cholesterol levels, triglycerides, blood glucose, and sleep disturbances, were incorporated into a support vector machine (SVM)-based nomogram to assess depression risk in PD patients. Additionally, a genome-wide association study (GWAS) utilizing external databases confirmed a causal relationship between the TyG index and depression. Furthermore, this study explores the biological functions and molecular mechanisms underlying shared transcriptomic proteins between PD and depression, providing insights into potential pathophysiological links between the two conditions.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1584005/fullmachine learningclinical predictiongenetic correlationsParkinson’s diseasedepression |
| spellingShingle | Haijun Zhang Yifan Zhang Guihua Li Guihua Li Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease Frontiers in Aging Neuroscience machine learning clinical prediction genetic correlations Parkinson’s disease depression |
| title | Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease |
| title_full | Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease |
| title_fullStr | Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease |
| title_full_unstemmed | Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease |
| title_short | Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease |
| title_sort | machine learning study on predicting depressive symptoms and genetic correlations in parkinson s disease |
| topic | machine learning clinical prediction genetic correlations Parkinson’s disease depression |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1584005/full |
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