Evidence mapPaperPMID 40977107Full record

ArticleBrain and behavior2025

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.

Guodong Ha, Zixuan Yan, Jiawei Wu, Xun Wang, Jing Hu, Lincheng Duan, Zhengyu Zhao, Dingjun Cai

Abstract read
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Article in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Guodong HaAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.ORCID https://orcid.org/0000-0003-0271-0877
Zixuan YanAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Jiawei WuAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Xun WangAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Jing HuAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Lincheng DuanAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.ORCID https://orcid.org/0000-0003-1937-1605
Zhengyu ZhaoAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Dingjun CaiAcupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.

Funding

National Natural Science Foundation of China 82441055
6 · The paper itself

Abstract

backgroundMigraines 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).

methodsData 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.

resultsThe 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).

conclusionsLMR may serve as a potential biomarker for assessing migraine risk, offering support for early diagnosis and personalized intervention strategies.

Indexed as

InflammationMigraine DisordersAdultBiomarkersBlood PlateletsFemaleHumansLymphocytesMachine LearningMaleMiddle AgedMonocytesNeutrophilsNutrition SurveysUnited StatesYoung AdultBiomarkersInflammatory BiomarkersLMRMigraineNHANES

Identifiers

PMID40977107
PMCPMC12451049

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.