Evidence map›Paper›PMID 42565426›Full record

ArticleEndocrine, metabolic & immune disorders drug targets2026

Relationship Between Comprehensive Inflammatory Indicators and Gout Risk: A Cross-sectional Study Based on NHANES 2015-2018.

Fan Zeng, Ou Mei, Min Lu, Li Wang, Gao-Yan Kuang

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Article in Endocrine, metabolic & immune disorders drug targets, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Fan ZengThe Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang 330006, Jiangxi, China.
Ou MeiThe Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang 330006, Jiangxi, China.
Min LuThe First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha 410007, Hunan, China.
Li WangThe Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang 330006, Jiangxi, China.
Gao-Yan KuangThe First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha 410007, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGout development and progression are closely linked to inflammatory processes. Six novel systemic inflammation indices, including the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), inflammation score, systemic inflammation response index (SIRI), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) have shown significant value in assessing systemic inflammation severity and related comorbidities. This study aims to explore the relationship between these systemic inflammation indices and the risk of gout.

methodsData from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed in this study to explore the link between systemic inflammation indices and gout risk. Demographic information of all participants was collected, and multivariable logistic regression was applied to evaluate the relationship between the inflammation indices and gout risk. Restricted cubic splines (RCS) were used to investigate the nonlinear association between inflammation scores and gout risk. Stratified and interaction analyses were also conducted to examine the relationships in different populations.

resultsA total of 7,681 participants were included in the analysis. After adjusting for all covariates in the multivariable logistic regression model, the inflammation score demonstrated the most significant association with gout risk. Further RCS analysis revealed a nonlinear relationship (P=0.0134) with a turning point at 1.641. When the inflammation score was below 1.641, an increase in the score was significantly associated with a higher risk of gout. Stratified analyses indicated that age, race, and smoking status interacted with the relationship between inflammation scores and gout risk. DISCUSSION: This study identified a significant association between the inflammation score and gout risk, particularly at low-to-moderate inflammation levels, suggesting that systemic inflammation may play an important role in the early stages of gout development. The inflammation score remained independently associated with gout after full adjustment, indicating its potential utility for early risk identification.

conclusionThe findings suggest that the inflammation score is a robust and independent predictor of gout risk, particularly at low-to-moderate levels, highlighting its potential for the early identification of high-risk individuals.

Indexed as

GoutInflammationInflammation MediatorsAdultAgedBiomarkersCross-Sectional StudiesFemaleHumansMaleMiddle AgedNutrition SurveysRisk FactorsUnited StatesBiomarkersInflammation Mediatorscrosssectional studyGoutinflammation scoreinflammatory indicatorsnational health and nutrition examination surveyrisk identification

Identifiers

PMID42565426
PMCPMC13635963

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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 OpenQuestion graph.