INFLA score: a novel inflammatory marker for assessing cardiometabolic disease risk in obese individuals

INFLA评分:一种用于评估肥胖个体心血管代谢疾病风险的新型炎症标志物

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Abstract

BACKGROUND: The low-grade inflammation score (INFLA-score) is a composite index that assesses chronic inflammatory status using multiple inflammatory markers. However, its correlation with cardiometabolic diseases (CMDs) in obese populations remains unclear. METHODS: We conducted a prospective cohort study involving 79,160 participants with obesity (BMI ≥ 30 kg/m(2)) from the UK Biobank. The INFLA-score was calculated based on high-sensitivity C-reactive protein, leukocyte count, platelet count and granulocyte/lymphocyte ratio. We employed Kaplan-Meier survival curves, multivariable Cox regression, restricted cubic splines and accelerated time-to-failure models to analyse the association between the INFLA-score and CMDs risk, including coronary heart disease (CAD), stroke and type 2 diabetes mellitus (T2DM). RESULTS: Over a median follow-up of 161.41 months, we recorded 14,903 CMDs events, comprising 7184 CAD cases, 1914 strokes and 7924 T2DM cases. Cox regression analysis revealed that each unit increase in the INFLA-score corresponded to a 1.5%, 1.1%, 1.2% and 2.4% increase CMDs risk (HR: 1.015, 95% CI 1.013-1.018), CAD risk (HR: 1.011, 95% CI 1.007-1.015), stroke risk (HR: 1.012, 95% CI 1.004-1.020) and T2DM risk (HR: 1.024, 95% CI 1.020-1.028), respectively. Restricted cubic spline analysis indicated a non-linear relationship between cumulative INFLA-score and CMDs risk (P = 0.044). Subgroup analysis revealed interactions between sex, age, history of lipid-lowering drug use, and INFLA-score regarding CMDs risk. Sensitivity analysis corroborated the main findings. CONCLUSION: Our findings strongly support the close association between INFLA-score and CMDs risk, particularly notable in women, those aged < 55, and individuals with a history of lipid-lowering drug use. These findings offer new insights into the role of inflammation in obesity-related CMDs, suggesting potential applications for prevention and identification of high-risk populations.

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