已发表论文

中国老年2型糖尿病患者肌少症风险预测模型的建立和验证

 

Authors Wang X, Gao S 

Received 30 August 2024

Accepted for publication 7 November 2024

Published 30 November 2024 Volume 2024:17 Pages 4611—4626

DOI https://doi.org/10.2147/DMSO.S493903

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Konstantinos Tziomalos

Xinming Wang,1 Shengnan Gao2 

1Department of the Endoscope Center, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, People’s Republic of China; 2Hunnan International Department VIP Ward Section, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, People’s Republic of China

Correspondence: Shengnan Gao, Hunnan International Department VIP Ward Section, The First Affiliated Hospital of China Medical University, No. 210, Baita Street, Hunnan District, Shenyang City, Liaoning Province, 110000, People’s Republic of China, Email gaosn@cmu1h.com

Purpose: Sarcopenia is a common prevalent age-related disorder among older patients with type 2 diabetes mellitus (T2DM). This study aimed to develop and validate a nomogram model to assess the risk of incident sarcopenia among older patients with T2DM.
Patients and methods: A total of 1434 older patients (≥ 60 years) diagnosed with T2DM between May 2020 and November 2023 were recruited. The study cohort was randomly divided into a training set (n = 1006) and a validation set (n = 428) at the ratio of 7:3. The best-matching predictors of sarcopenia were incorporated into the nomogram model. The accuracy and applicability of the nomogram model were measured by using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA).
Results: 571 out of 1434 participants (39.8%) had sarcopenia. Nine best-matching factors, including age, body mass index (BMI), diabetic duration, glycated hemoglobin A1c (HbA1c), 25 (OH)Vitamin D, nephropathy, neuropathy, nutrition status, and osteoporosis were selected to construct the nomogram prediction model. The AUC values for training and validation sets were 0.800 (95% CI = 0.773– 0.828) and 0.796 (95% CI = 0.755– 0.838), respectively. Furthermore, the agreement between predicted and actual clinical probability of sarcopenia was demonstrated by calibration curves, the Hosmer-Lemeshow test (P > 0.05), and DCA.
Conclusion: Sarcopenia was prevalent among older patients with T2DM. A visual nomogram prediction model was verified effectively to evaluate incident sarcopenia in older patients with T2DM, allowing targeted interventions to be implemented timely to combat sarcopenia in geriatric population with T2DM.

Keywords: sarcopenia, type 2 diabetes mellitus, older adults, nomogram, prediction model