Brain and behavior · 2025
Exploring the Link Between Inflammatory Biomarkers (SII, SIRI, PLR, NLR, LMR) and Migraine in Young and Early Middle-Aged US Adults: Evidence From NHANES 1999-2004 and Machine Learning Models.
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Ha Guodong, Yan Zixuan, Wu Jiawei, Wang Xun, Hu Jing, Duan Lincheng, Zhao Zhengyu, Cai Dingjun
Аннотация
BACKGROUND: Migraines are a prevalent neurological condition that significantly impacts quality of life, but the underlying pathophysiology remains unclear. This study aims to explore the relationship between inflammatory biomarkers and migraine prevalence in young and early middle-aged Americans. The inflammatory biomarkers considered include the Systemic Immune-Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), Platelet-to-Lymphocyte Ratio (PLR), Neutrophil-to-Lymphocyte Ratio (NLR), and Lymphocyte-to-Monocyte Ratio (LMR).
METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004 were utilized for this investigation. Subgroup analysis, smooth curve fitting, and multivariable logistic regression were employed to evaluate associations. Boruta's algorithm, alongside nine machine learning models, was applied to identify key features. SHapley Additive Explanations (SHAP) values were used to interpret the leading models and highlight influential features.
RESULTS: The study revealed no significant differences in SII, SIRI, NLR, or PLR between individuals with and without migraines. However, a significantly higher LMR was observed in individuals with migraines (mean difference: 0.37, p < 0.001). Multivariable logistic regression analysis demonstrated a strong positive correlation between LMR and migraine risk across multiple models (OR = 1.51, 95% CI: 1.14-2.00, p = 0.009). No significant associations were found for the other inflammatory biomarkers. Subgroup analyses further confirmed that the positive correlation between LMR and migraine risk remained consistent across different strata. Threshold effect analysis revealed a stable linear relationship between LMR and migraine risk up to a value of 1.61. Among the nine machine learning models, the LightGBM model exhibited the highest AUROC (0.9198), recall (93.3%), F1-score (0.896), and MCC (0.702).
CONCLUSIONS: LMR may serve as a potential biomarker for assessing migraine risk, offering support for early diagnosis and personalized intervention strategies.
METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004 were utilized for this investigation. Subgroup analysis, smooth curve fitting, and multivariable logistic regression were employed to evaluate associations. Boruta's algorithm, alongside nine machine learning models, was applied to identify key features. SHapley Additive Explanations (SHAP) values were used to interpret the leading models and highlight influential features.
RESULTS: The study revealed no significant differences in SII, SIRI, NLR, or PLR between individuals with and without migraines. However, a significantly higher LMR was observed in individuals with migraines (mean difference: 0.37, p < 0.001). Multivariable logistic regression analysis demonstrated a strong positive correlation between LMR and migraine risk across multiple models (OR = 1.51, 95% CI: 1.14-2.00, p = 0.009). No significant associations were found for the other inflammatory biomarkers. Subgroup analyses further confirmed that the positive correlation between LMR and migraine risk remained consistent across different strata. Threshold effect analysis revealed a stable linear relationship between LMR and migraine risk up to a value of 1.61. Among the nine machine learning models, the LightGBM model exhibited the highest AUROC (0.9198), recall (93.3%), F1-score (0.896), and MCC (0.702).
CONCLUSIONS: LMR may serve as a potential biomarker for assessing migraine risk, offering support for early diagnosis and personalized intervention strategies.