- 2025-11
- Archives of gerontology and geriatrics 138
- Dunan Xie
- Tianjin Huang
- Chuhua Yang
- Chen Li
- Faxiu Chen
Study Design
- Type
- Meta-Analysis
- Sample size
- n = 9,707
- Population
- 11 observational studies involving 9707 patients
- Methods
- PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively retrieved. NOS and AHRQ tools used for risk of bias. Statistical analyses using STATA 15.1. Publication bias evaluated via funnel plots and Egger's test.
Background
Sarcopenia (SP) is an age-related syndrome characterized by a decrease in the mass, strength, and functional capacity of muscles. It noticeably impacts the quality of life and prognosis of older individuals. The Geriatric Nutritional Risk Index (GNRI) is a convenient tool for nutritional assessment. However, its predictive performance for SP remains unclear.Method
PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively retrieved. The NOS and AHRQ tools were used to assess the risk of bias. Statistical analyses were performed using STATA version 15.1. The publication bias among studies was evaluated via funnel plots and Egger's test. Then, the link of GNRI with SP was evaluated utilizing odds ratios (OR) and 95% confidence intervals (CIs).Results
In total, 11 observational studies involving 9707 patients were included. According to the meta-analysis, with GNRI as a categorical variable, SP risks were markedly lower in the high GNRI group compared to the low GNRI group (OR = 0.22, 95% CI: 0.13-0.35). With GNRI considered as a continuous variable, a 1-unit rise in GNRI resulted in a 6.9% decrease in the risk of SP. The subgroup analysis revealed that GNRI thresholds might be an important source of heterogeneity. The dose-response analysis results indicated that a decreased SP risk was linked with increased GNRI.Conclusion
GNRI can serve as an effective predictive indicator for SP, facilitating the early detection of high-risk populations and the formulation of intervention strategies. Future research should further optimize the application of GNRI and validate its predictive performance.