Guodong Ha, Zixuan Yan, Jiawei Wu, Xun Wang, Jing Hu, Lincheng Duan, Zhengyu Zhao, Dingjun Cai
{"title":"探索炎症生物标志物（SII， SIRI， PLR， NLR， LMR）与美国中青年偏头痛之间的联系：来自NHANES 1999-2004和机器学习模型的证据","authors":"Guodong Ha, Zixuan Yan, Jiawei Wu, Xun Wang, Jing Hu, Lincheng Duan, Zhengyu Zhao, Dingjun Cai","doi":"10.1002/brb3.70886","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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, <i>p </i>< 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, <i>p </i>= 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%), <i>F</i>1-score (0.896), and MCC (0.702).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>LMR may serve as a potential biomarker for assessing migraine risk, offering support for early diagnosis and personalized intervention strategies.</p>\n </section>\n </div>","PeriodicalId":9081,"journal":{"name":"Brain and Behavior","volume":"15 9","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brb3.70886","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Guodong Ha, Zixuan Yan, Jiawei Wu, Xun Wang, Jing Hu, Lincheng Duan, Zhengyu Zhao, Dingjun Cai\",\"doi\":\"10.1002/brb3.70886\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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, <i>p </i>< 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, <i>p </i>= 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%), <i>F</i>1-score (0.896), and MCC (0.702).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>LMR may serve as a potential biomarker for assessing migraine risk, offering support for early diagnosis and personalized intervention strategies.</p>\\n </section>\\n </div>\",\"PeriodicalId\":9081,\"journal\":{\"name\":\"Brain and Behavior\",\"volume\":\"15 9\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brb3.70886\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain and Behavior\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/brb3.70886\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain and Behavior","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/brb3.70886","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
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
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.
期刊介绍：
Brain and Behavior is supported by other journals published by Wiley, including a number of society-owned journals. The journals listed below support Brain and Behavior and participate in the Manuscript Transfer Program by referring articles of suitable quality and offering authors the option to have their paper, with any peer review reports, automatically transferred to Brain and Behavior.
* [Acta Psychiatrica Scandinavica](https://publons.com/journal/1366/acta-psychiatrica-scandinavica)
* [Addiction Biology](https://publons.com/journal/1523/addiction-biology)
* [Aggressive Behavior](https://publons.com/journal/3611/aggressive-behavior)
* [Brain Pathology](https://publons.com/journal/1787/brain-pathology)
* [Child: Care, Health and Development](https://publons.com/journal/6111/child-care-health-and-development)
* [Criminal Behaviour and Mental Health](https://publons.com/journal/3839/criminal-behaviour-and-mental-health)
* [Depression and Anxiety](https://publons.com/journal/1528/depression-and-anxiety)
* Developmental Neurobiology
* [Developmental Science](https://publons.com/journal/1069/developmental-science)
* [European Journal of Neuroscience](https://publons.com/journal/1441/european-journal-of-neuroscience)
* [Genes, Brain and Behavior](https://publons.com/journal/1635/genes-brain-and-behavior)
* [GLIA](https://publons.com/journal/1287/glia)
* [Hippocampus](https://publons.com/journal/1056/hippocampus)
* [Human Brain Mapping](https://publons.com/journal/500/human-brain-mapping)
* [Journal for the Theory of Social Behaviour](https://publons.com/journal/7330/journal-for-the-theory-of-social-behaviour)
* [Journal of Comparative Neurology](https://publons.com/journal/1306/journal-of-comparative-neurology)
* [Journal of Neuroimaging](https://publons.com/journal/6379/journal-of-neuroimaging)
* [Journal of Neuroscience Research](https://publons.com/journal/2778/journal-of-neuroscience-research)
* [Journal of Organizational Behavior](https://publons.com/journal/1123/journal-of-organizational-behavior)
* [Journal of the Peripheral Nervous System](https://publons.com/journal/3929/journal-of-the-peripheral-nervous-system)
* [Muscle & Nerve](https://publons.com/journal/4448/muscle-and-nerve)
* [Neural Pathology and Applied Neurobiology](https://publons.com/journal/2401/neuropathology-and-applied-neurobiology)
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
