Sarcopenia is associated with significantly higher mortality risk, and earlier detection of sarcopenia has remarkable public health benefits. However, the model that predicts sarcopenia in the community has yet to be well identified. The study aimed to develop a nomogram for predicting the risk of sarcopenia and compare the performance with 3 sarcopenia screen models in community-dwelling older adults in China.Cross-sectional study.A total of 966 community-dwelling older adults.A total of 966 community-dwelling older adults were enrolled in the study, with 678 participants grouped into the Training Set and 288 participants grouped into the Validation Set according to a 7:3 randomization. Predictors were identified in the Training Set by univariate and multivariate logistic regression and then combined into a nomogram to predict the risk of sarcopenia. The performance of this nomogram was assessed by calibration, discrimination, and clinical utility.Age, body mass index, calf circumference, congestive heart failure, and chronic obstructive pulmonary disease were demonstrated to be predictors for sarcopenia. The nomogram (named as AB3C model) that was constructed based on these predictors showed excellent calibration and discrimination in the Training Set with an area under the receiver operating characteristic curve (AUC) of 0.930. The nomogram also showed perfect calibration and discrimination in the Validation Set with an AUC of 0.897. The clinical utility of the nomogram was supported by decision curve analysis. Comparing the performance with 3 sarcopenia screen models (SARC-F, Ishii, and Calf circumference), the AB3C model outperformed the other models regarding sensitivity and AUC.AB3C model, an easy-to-apply and cost-effective nomogram, was developed to predict the risk of sarcopenia, which may contribute to optimizing sarcopenia screening in community settings.Copyright © 2023 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.