The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models
Abstract Background This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity. Methods Cross–sectional data were acquired from the Nat...
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BMC
2025-07-01
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| Series: | Lipids in Health and Disease |
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| Online Access: | https://doi.org/10.1186/s12944-025-02648-w |
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| author | Zixuan Yan Lincheng Duan Hong Yin Muchen Wang Jingwen Li Chenghua Li Xiao Wang Dingjun Cai Fanrong Liang Wenchuan Qi |
| author_facet | Zixuan Yan Lincheng Duan Hong Yin Muchen Wang Jingwen Li Chenghua Li Xiao Wang Dingjun Cai Fanrong Liang Wenchuan Qi |
| author_sort | Zixuan Yan |
| collection | DOAJ |
| description | Abstract Background This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity. Methods Cross–sectional data were acquired from the National Health and Nutrition Examination Survey conducted between 1999 and 2004, as well as from the China Longitudinal Study of Health and Retirement (CHARLS) performed from 2011 to 2012. Weighted logistic regression analysis, subgroup analysis, smooth curve fitting and threshold effect analysis were used to ascertain the intricate relationships among triglyceride glucose–body mass index (TyG–BMI), triglyceride glucose–waist circumference (TyG–WC), triglyceride glucose–waist height ratio (TyG–WHtR) and migraine. Boruta’s algorithm and nine machine learning models were applied. SHapley Additive Explanations (SHAP) values were used to analyze leading models, highlighting influential features. Results The analysis included 6,204 U.S. participants and 9,401 Chinese participants. TyG–BMI as well as TyG–WHtR were shown to be strongly correlated with the incidence of migraine among U.S. adults (TyG–BMI: OR = 1.28, 95% CI 1.14–1.44, P < 0.001; TyG–WHtR: OR = 1.17, 95% CI 1.09–1.26, P < 0.001). However, this correlation was not detected in Chinese adults. TyG–BMI indicated a strong positive association beyond the threshold of 206, while TyG–WHtR demonstrated a significant positive link below the cutoff of 7.4. In addition, age was an important interaction factor between TyG–BMI and TyG–WHtR and migraine. The XGBoost model showed excellent performance, with higher AUC values for TyG–BMI than for TyG–WHtR (0.929/0.926). Conclusions The TyG–BMI, relative to the TyG–WHtR, may provide clinicians with information about patients’ insulin sensitivity, thus helping to develop individualized treatment strategies. These findings contribute to population-level health interventions aimed at mitigating metabolic and neurological disease burdens, ensuring healthy lives and promoting well-being. |
| format | Article |
| id | doaj-art-d7ec281cfcc24d3b8b354a1c6fcd802a |
| institution | Kabale University |
| issn | 1476-511X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | Lipids in Health and Disease |
| spelling | doaj-art-d7ec281cfcc24d3b8b354a1c6fcd802a2025-08-20T04:01:40ZengBMCLipids in Health and Disease1476-511X2025-07-0124111510.1186/s12944-025-02648-wThe value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning modelsZixuan Yan0Lincheng Duan1Hong Yin2Muchen Wang3Jingwen Li4Chenghua Li5Xiao Wang6Dingjun Cai7Fanrong Liang8Wenchuan Qi9Chengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu Fifth People’s HospitalChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineAbstract Background This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity. Methods Cross–sectional data were acquired from the National Health and Nutrition Examination Survey conducted between 1999 and 2004, as well as from the China Longitudinal Study of Health and Retirement (CHARLS) performed from 2011 to 2012. Weighted logistic regression analysis, subgroup analysis, smooth curve fitting and threshold effect analysis were used to ascertain the intricate relationships among triglyceride glucose–body mass index (TyG–BMI), triglyceride glucose–waist circumference (TyG–WC), triglyceride glucose–waist height ratio (TyG–WHtR) and migraine. Boruta’s algorithm and nine machine learning models were applied. SHapley Additive Explanations (SHAP) values were used to analyze leading models, highlighting influential features. Results The analysis included 6,204 U.S. participants and 9,401 Chinese participants. TyG–BMI as well as TyG–WHtR were shown to be strongly correlated with the incidence of migraine among U.S. adults (TyG–BMI: OR = 1.28, 95% CI 1.14–1.44, P < 0.001; TyG–WHtR: OR = 1.17, 95% CI 1.09–1.26, P < 0.001). However, this correlation was not detected in Chinese adults. TyG–BMI indicated a strong positive association beyond the threshold of 206, while TyG–WHtR demonstrated a significant positive link below the cutoff of 7.4. In addition, age was an important interaction factor between TyG–BMI and TyG–WHtR and migraine. The XGBoost model showed excellent performance, with higher AUC values for TyG–BMI than for TyG–WHtR (0.929/0.926). Conclusions The TyG–BMI, relative to the TyG–WHtR, may provide clinicians with information about patients’ insulin sensitivity, thus helping to develop individualized treatment strategies. These findings contribute to population-level health interventions aimed at mitigating metabolic and neurological disease burdens, ensuring healthy lives and promoting well-being.https://doi.org/10.1186/s12944-025-02648-wTriglyceride glucose–body mass indexTriglyceride glucose–waist circumference indexTriglyceride glucose–waist high ratio indexMigraineCross–sectional studyMachine learning |
| spellingShingle | Zixuan Yan Lincheng Duan Hong Yin Muchen Wang Jingwen Li Chenghua Li Xiao Wang Dingjun Cai Fanrong Liang Wenchuan Qi The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models Lipids in Health and Disease Triglyceride glucose–body mass index Triglyceride glucose–waist circumference index Triglyceride glucose–waist high ratio index Migraine Cross–sectional study Machine learning |
| title | The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models |
| title_full | The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models |
| title_fullStr | The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models |
| title_full_unstemmed | The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models |
| title_short | The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models |
| title_sort | value of triglyceride glucose index related indices in evaluating migraine perspectives from multi centre cross sectional studies and machine learning models |
| topic | Triglyceride glucose–body mass index Triglyceride glucose–waist circumference index Triglyceride glucose–waist high ratio index Migraine Cross–sectional study Machine learning |
| url | https://doi.org/10.1186/s12944-025-02648-w |
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