Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method
Background Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide pol...
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2519679 |
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| author | Tao Huang Yuanyuan Li Simin Wang Shijie Qiao Xiujuan Zheng Wenhui Xiong Menghan Yang Xirui Huang Bizhen Gao |
| author_facet | Tao Huang Yuanyuan Li Simin Wang Shijie Qiao Xiujuan Zheng Wenhui Xiong Menghan Yang Xirui Huang Bizhen Gao |
| author_sort | Tao Huang |
| collection | DOAJ |
| description | Background Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application.Methods Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model.Results A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959).Conclusion This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence. |
| format | Article |
| id | doaj-art-d7d5c81051424da2a0193a2ed63621a2 |
| institution | DOAJ |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
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| series | Annals of Medicine |
| spelling | doaj-art-d7d5c81051424da2a0193a2ed63621a22025-08-20T03:22:39ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2519679Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning methodTao Huang0Yuanyuan Li1Simin Wang2Shijie Qiao3Xiujuan Zheng4Wenhui Xiong5Menghan Yang6Xirui Huang7Bizhen Gao8College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaCollege of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, ChinaBackground Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application.Methods Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model.Results A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959).Conclusion This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.https://www.tandfonline.com/doi/10.1080/07853890.2025.2519679Metabolic syndromemachine learninggenetic risk scoreprediction modelSHAP |
| spellingShingle | Tao Huang Yuanyuan Li Simin Wang Shijie Qiao Xiujuan Zheng Wenhui Xiong Menghan Yang Xirui Huang Bizhen Gao Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method Annals of Medicine Metabolic syndrome machine learning genetic risk score prediction model SHAP |
| title | Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method |
| title_full | Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method |
| title_fullStr | Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method |
| title_full_unstemmed | Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method |
| title_short | Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method |
| title_sort | assessing individual genetic susceptibility to metabolic syndrome interpretable machine learning method |
| topic | Metabolic syndrome machine learning genetic risk score prediction model SHAP |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2519679 |
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