Association of systemic immune biomarkers with metabolic dysfunction-associated steatotic liver disease: a cross-sectional study of NHANES 2007-2018

Objective: Numerous studies emphasize the pivotal role of inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD) development. Some link specific systemic immune biomarkers (e.g., systemic immuno-inflammatory index [SII], neutrophil-to-albumin ratio [NPAR] and neutrophil-to-lymphocyte ratio [NLR]) to hepatic steatosis risk. However, the relevance of other markers like systemic immune-inflammation index [SIRI], platelet-to-lymphocyte ratio [PLR] and lymphocyte/monocyte ratio [LMR] in MASLD remains unclear. Limited literature covers all six markers together. This study aims to investigate the association between SII, SIRI, LMR, NLR, PLR, and NPAR and MASLD, assessing their predictive value. Methods: In this cross-sectional analysis of adults from NHANES (2007-2018), we investigated the relationship between six systemic immune biomarkers, stratified by quartiles: quartile1 (Q1), quartile2 (Q2), quartile3 (Q3) and quartile4 (Q4), and the outcome of MASLD assessed by Fatty Liver Index (FLI) and United States Fatty Liver Index (USFLI). Logistic regression and restricted cubic splines (RCS) were employed to assess the association between systemic immune biomarkers and MASLD risks. Propensity score matching controlled for potential confounders, and receiver operating characteristic (ROC) curve analysis evaluated the biomarkers' predictive performances for MASLD. Subgroup and interaction analysis were conducted to explore the effects of systemic immune biomarkers on MASLD risks. Multicollinearity was quantified using the variance inflation factor. Results: In total, 14,413 participants were included and 6,518 had MASLD. Compared with non-MASLD, participants with MASLD had higher SII, SIRI, NLR, PLR, and NPAR (p < 0.001). SII, SIRI, NLR, and NPAR were further validated in the restricted cubic splines (RCS) regression model and identified as positive linear relationships (p for nonlinear >0.05). The prevalence of MASLD increased with the Q4 of SII [OR = 1.47, 95%CI (1.24, 1.74)], SIRI [OR = 1.30, 95%CI (1.09, 1.54)], NLR [OR = 1.25, 95%CI (1.04, 1.49)], PLR [OR = 1.29, 95%CI (1.09, 1.53)] and NPAR [OR = 1.29, 95%CI (1.09, 1.54)] compared to the Q1 after adjusting for the bias caused by potential confounders. However, the propensity score matching analysis only supported an association between the highest SII, SIRI, NLR NPAR and the risk of MASLD. The results of the subgroup analysis showed considerable robustness in the relationship. Conclusion: Higher SII, SIRI, NLR and NPAR were positively associated with a heightened risk of MASLD. NPAR showed the superior predictive value, followed by SII, SIRI and NLR. This needs to be validated in additional longitudinal studies and clinical trials.