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- Title
Detecting Sarcopenia Risk by Diabetes Clustering: A Japanese Prospective Cohort Study.
- Authors
Hayato Tanabe; Hiroyuki Hirai; Haruka Saito; Kenichi Tanaka; Hiroaki Masuzaki; Kazama, Junichiro J.; Michio Shimabukuro
- Abstract
Context: Previous studies have assessed the usefulness of data-driven clustering for predicting complications in patients with diabetes mellitus. However, whether the diabetes clustering is useful in predicting sarcopenia remains unclear. Objective: To evaluate the predictive power of diabetes clustering for the incidence of sarcopenia in a prospective Japanese cohort. Design: Three-year prospective cohort study Setting and Patients: We recruited Japanese patients with type 1 or type 2 diabetes mellitus (n = 659) between January 2018 and February 2020 from the Fukushima Diabetes, Endocrinology, and Metabolism cohort. Interventions: Kaplan-Meier and Cox proportional hazards models were used to measure the predictive values of the conventional and clustering-based classification of diabetes mellitus for the onset of sarcopenia. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update. Main Outcome Measures: Onset of sarcopenia. Results: Cluster analysis of a Japanese population revealed 5 diabetes clusters: cluster 1 [severe autoimmune diabetes (SAID)], cluster 2 [severe insulin-deficient diabetes (SIDD)], cluster 3 (severe insulin-resistant diabetes, cluster 4 (mild obesity-related diabetes), and cluster 5 (mild age-related diabetes). At baseline, 38 (6.5%) patients met the AWGS sarcopenia criteria, and 55 had newly developed sarcopenia within 3 years. The SAID and SIDD clusters were at high risk of developing sarcopenia after correction for known risk factors. Conclusions: This study reveals that among the 5 diabetes clusters, the SAID and SIDD clusters are at a high risk for developing sarcopenia. Clustering-based stratification may be beneficial for predicting and preventing sarcopenia in patients with diabetes.
- Subjects
SARCOPENIA; DISEASE complications; DIABETES; PATIENT satisfaction; INSULIN resistance
- Publication
Journal of Clinical Endocrinology & Metabolism, 2022, Vol 107, Issue 10, p2729
- ISSN
0021-972X
- Publication type
Article
- DOI
10.1210/clinem/dgac430