Geriatric Nutritional Risk Index as a Predictor for Osteoporosis Risk in Elderly Patients with Type 2 Diabetes Mellitus: A Hospital-Based Study

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Authors: Abdalla M. Abdelrahman, Michael Edwar Farg, Hanaa Abu Elazayem Nofal, Shaherah Yousef Andargeery, Dina Sameh Elrafey, Wesam M. R. Ashour, Ahmed Ibrahim Gad
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References: 18

Abstract

Background: Osteoporosis is a major complication in older adults with type 2 diabetes mellitus (T2DM). Malnutrition contributes to bone loss, and the Geriatric Nutritional Risk Index (GNRI) has emerged as a simple tool for assessing nutritional status. Evidence on the predictive value of the GNRI for osteoporosis in elderly patients with T2DM remains limited. Objective: To evaluate the association between GNRI scores and osteoporosis and determine its predictive performance in elderly patients with T2DM. Methods: A cross-sectional study was conducted on 200 elderly patients with T2DM attending the internal medicine outpatient clinics at Zagazig university hospitals between January and October 2025. Clinical data, biochemical parameters, and bone mineral density (BMD) at the lumbar spine, femoral neck, and total hip were assessed. GNRI scores were calculated using standard formulas. Participants were classified into osteoporosis and non-osteoporosis groups according to WHO criteria. Correlations and ROC curve analysis were performed to assess the predictive ability of the GNRI in comparison with age, BMI, and serum albumin. Results: Osteoporosis was present in 15% of the cohort. Patients with osteoporosis had significantly lower GNRI scores and lower BMD values at all measured sites (p < 0.05). The GNRI showed significant positive correlations with BMD parameters in both sexes. ROC analysis demonstrated that the GNRI had the highest predictive performance for osteoporosis (AUC = 0.80 for all patients; AUC = 0.85 in males; AUC = 0.77 in females). Optimal GNRI cutoff values were

Fields of Study & Subjects

Bone health and osteoporosis research Nutrition and Health in Aging Body Composition Measurement Techniques

Publication details

Journal / source
Diagnostics
Volume
16
Issue
3
Pages
408–408
Publication date
27 January 2026
Language
EN