Association of geriatric nutritional risk index with metabolic dysfunction-associated steatotic liver disease and subtypes in Chinese elderly: identification of an overnutrition risk threshold and implications for extended risk stratification - Guo, Meiyan; Mao, Lifang; Kang, Qiuyun; Lyu, Haixin; Chen, Haiyan; Qin, Yangyang; Zhan, Zegeng; Shen, Tianran (Frontiers in Nutrition, 2026)
BackgroundCurrent evidence on the geriatric nutritional risk index (GNRI) and metabolic dysfunction-associated steatotic liver disease (MASLD) in the elderly is inconsistent, with limited data from Chinese studies. Notably, few research has explored the association between GNRI and MASLD subtypes. Therefore, this study aimed to investigate the association between GNRI and the risk of MASLD and subtypes in a Chinese elderly population.MethodsThis cross-sectional study recruited 7,628 Chinese adults aged≥60 years from Zhongshan during 2020-2021. Binary and multinomial logistic regression were used to analyze the associations between GNRI and MASLD prevalence and subtypes, respectively. Restricted cubic splines (RCS) were employed to explore non-linear relationship. Receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate GNRI's predictive accuracy. Mediation and stratified analyses were conducted to explore underlying mechanisms and subgroup effects.ResultsAmong 7,628 participants (40.4% male; MASLD prevalence: 38.8%), 96.6% were classified as “no-risk” (GNRI≥98) according to traditional criteria, highlighting the limitation of current risk stratification. Fully adjusted model demonstrated that each unit increase in GNRI elevated MASLD risk by 12% (OR = 1.12, 95% CI: 1.10–1.13), with quartile analysis revealing a dose-dependent increase (Q1: reference; Q2: OR = 1.56, 95% CI: 1.31–1.84; Q3: OR = 1.93, 95% CI: 1.64–2.28; Q4: OR = 2.70, 95% CI: 2.28–3.20; P−trend < 0.001). RCS identified a nonlinear inflection at GNRI≥107.59 (P−overall < 0.001; P−nonlinear = 0.008), where the risk of MASLD escalated substantially. GNRI showed moderate predictive accuracy (AUC = 0.802, 95% CI: 0.792–0.812), while mediation analysis indicated BMI accounted for the largest proportion of the total effect of GNRI on MASLD (21.1%, 95% CI: 6.4%-29.2%). Using the non-MASLD population as a reference, as GNRI levels increased, the risks of MASLD subtypes increased significantly, following a gradient: overweight/obesity subtype>diabetes subtype>lean metabolic disorder subtype (all P<0.05).ConclusionElevated GNRI significantly increases MASLD and subtype risks in Chinese elderly, with GNRI ≥107.59 identified as a critical threshold for escalating the risk of MASLD. These findings support extending GNRI's risk stratification to overnutrition monitoring, enabling prioritized screening for metabolic hazards. Future research should validate this threshold's clinical utility, establish evidence-based upper limits for overnutrition risk, and explore GNRI's role in MASLD pathogenesis.