Associations between clustering of hypoglycemic symptoms, psychological traits, and problem-solving abilities in adults with type 1 diabetes: baseline data analysis of the PR-IAH study.
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| Titel: | Associations between clustering of hypoglycemic symptoms, psychological traits, and problem-solving abilities in adults with type 1 diabetes: baseline data analysis of the PR-IAH study. |
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| Autoren: | Sakane, Naoki, Kato, Ken, Hata, Sonyun, Nishimura, Erika, Araki, Rika, Kouyama, Kunichi, Hatao, Masako, Matoba, Yuka, Matsushita, Yuichi, Domichi, Masayuki, Suganuma, Akiko, Sakane, Seiko, Murata, Takashi, Wu, Fei Ling |
| Quelle: | Diabetology International; Apr2025, Vol. 16 Issue 2, p294-302, 9p |
| Abstract: | Background: Precision medicine in diabetes care requires a dedicated focus on hypoglycemic symptoms. This study explored the associations between clustering of hypoglycemic symptoms, psychological characteristics, and problem-solving capabilities in adults with type 1 diabetes (T1D). Methods: A total of 251 adults with T1D participated in this survey. Hierarchical clustering was used to analyze 11 hypoglycemic symptoms (Edinburgh scale). The data included diabetic complications, fear of hypoglycemia, depressive symptoms, hypoglycemia problem-solving scale (HPSS), and treatment details. For predicting clusters and identifying feature importance, we utilized a machine learning approach. Results: Three distinct clusters were observed; individuals not sensitive to autonomic or neuroglycopenic symptoms (cluster 1, n = 138), those sensitive to both autonomic and neuroglycopenic symptoms (cluster 2, n = 19), and those sensitive to autonomic but not neuroglycopenic symptoms (cluster 3, n = 94). Compared to cluster 1, individuals from clusters 2 and 3 were of younger age, had higher fear of hypoglycemia, increased depressive symptoms, and greater use of continuous subcutaneous insulin infusion. Cluster 2 displayed enhanced HPSS scores, indicating better detection control and a more proactive approach to seeking preventive strategies than cluster 1. The accuracy for classifying into 3 clusters using machine learning was 88.2%. The feature importance of random forest model indicated that hunger, shaking, palpitation, sweating, and confusion were the top five important factors for predicting clusters. Conclusion: This study identified three distinct clusters of adults with T1D. These findings may provide valuable insights for diabetes professionals seeking to educate these individuals on how to manage hypoglycemia effectively. Trial registration: University Hospital Medical Information Network (UMIN) Center: UMIN000039475); approval date: February 13, 2020. [ABSTRACT FROM AUTHOR] |
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| Datenbank: | Biomedical Index |
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