A Simple Clinical Score Using Hemoglobin, Age, and BMI Effectively Predicts Possible Sarcopenia

Objective To evaluate whether a simple biomarker can identify possible sarcopenia (PS) in a community-dwelling elderly population. Methods This study included 289 individuals ≥65 years of age who underwent medical examinations in Sobetsu in 2017. The validation cohort consisted of 183 individuals ≥65 years of age who were surveyed in the same town in 2018, 2019, 2022, and 2023, without participating in the 2017 survey. The predictive factors for PS were identified, and a simple scoring system was developed. Results The mean ages of the 2017 and validation cohorts were 74 years and 72 years, respectively. The prevalence of PS did not differ between the 2017 and validation cohorts, with rates of 31.5% and 32.8%, respectively. A univariate logistic regression analysis, using the presence of PS as the dependent variable, identified age [odds ratio (OR) 1.13], anemia (OR 4.54), and hemoglobin (OR 0.63) as significant factors (p<0.001). A multivariate logistic regression model, adjusted for age, BMI, and hemoglobin level and selected based on the lowest Akaike's Information Criterion, achieved an area under the curve (AUC) of 0.747 for predicting PS. External validation of this model yielded an AUC of 0.733. A simplified prediction score, derived from cutoffs for age, BMI, and hemoglobin, demonstrated an AUC of 0.711 for PS prediction in the initial cohort, with an AUC of 0.658 in the external validation cohort. Conclusion A simple score based on age, BMI, and hemoglobin level effectively estimated PS in a community-dwelling elderly population, offering a practical and accessible tool for screening.