Nomogram models for predicting sarcopenia in elderly Asian patients with type 2 diabetes

Area of Science:

• Gerontology
• Endocrinology
• Metabolic Diseases

Background:

• Sarcopenia is a frequent complication in elderly patients with Type 2 Diabetes Mellitus (T2DM).
• Early diagnosis and intervention are limited by sarcopenia's complex etiology.
• This study focused on developing a tool for individualized risk assessment.

Purpose of the Study:

• To develop and internally validate a nomogram for assessing sarcopenia risk.
• To identify key predictors of sarcopenia in elderly T2DM patients.
• To support personalized intervention strategies.

Main Methods:

• A cross-sectional study included 300 elderly T2DM patients.
• Sarcopenia was diagnosed using Asian Working Group for Sarcopenia (AWGS) criteria (DXA and handgrip strength).
• LASSO regression and multivariable logistic regression identified independent predictors for nomogram construction.

Main Results:

• Sarcopenia prevalence was 22%.
• Independent predictors included age, sex, HbA1c, serum albumin, thyroid dysfunction, and physical activity.
• The nomogram showed high predictive accuracy (AUC 0.891 development, 0.868 validation) and clinical utility.

Conclusions:

• The nomogram facilitates early sarcopenia risk identification in elderly T2DM patients.
• It supports tailored intervention strategies.
• Further validation in diverse populations is recommended.