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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| Items | – Name: Title Label: Title Group: Ti Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sakane%2C+Naoki%22">Sakane, Naoki</searchLink><br /><searchLink fieldCode="AR" term="%22Kato%2C+Ken%22">Kato, Ken</searchLink><br /><searchLink fieldCode="AR" term="%22Hata%2C+Sonyun%22">Hata, Sonyun</searchLink><br /><searchLink fieldCode="AR" term="%22Nishimura%2C+Erika%22">Nishimura, Erika</searchLink><br /><searchLink fieldCode="AR" term="%22Araki%2C+Rika%22">Araki, Rika</searchLink><br /><searchLink fieldCode="AR" term="%22Kouyama%2C+Kunichi%22">Kouyama, Kunichi</searchLink><br /><searchLink fieldCode="AR" term="%22Hatao%2C+Masako%22">Hatao, Masako</searchLink><br /><searchLink fieldCode="AR" term="%22Matoba%2C+Yuka%22">Matoba, Yuka</searchLink><br /><searchLink fieldCode="AR" term="%22Matsushita%2C+Yuichi%22">Matsushita, Yuichi</searchLink><br /><searchLink fieldCode="AR" term="%22Domichi%2C+Masayuki%22">Domichi, Masayuki</searchLink><br /><searchLink fieldCode="AR" term="%22Suganuma%2C+Akiko%22">Suganuma, Akiko</searchLink><br /><searchLink fieldCode="AR" term="%22Sakane%2C+Seiko%22">Sakane, Seiko</searchLink><br /><searchLink fieldCode="AR" term="%22Murata%2C+Takashi%22">Murata, Takashi</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Fei+Ling%22">Wu, Fei Ling</searchLink> – Name: TitleSource Label: Source Group: Src Data: Diabetology International; Apr2025, Vol. 16 Issue 2, p294-302, 9p – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Diabetology International is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13340-024-00788-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 294 Titles: – TitleFull: 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. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sakane, Naoki – PersonEntity: Name: NameFull: Kato, Ken – PersonEntity: Name: NameFull: Hata, Sonyun – PersonEntity: Name: NameFull: Nishimura, Erika – PersonEntity: Name: NameFull: Araki, Rika – PersonEntity: Name: NameFull: Kouyama, Kunichi – PersonEntity: Name: NameFull: Hatao, Masako – PersonEntity: Name: NameFull: Matoba, Yuka – PersonEntity: Name: NameFull: Matsushita, Yuichi – PersonEntity: Name: NameFull: Domichi, Masayuki – PersonEntity: Name: NameFull: Suganuma, Akiko – PersonEntity: Name: NameFull: Sakane, Seiko – PersonEntity: Name: NameFull: Murata, Takashi – PersonEntity: Name: NameFull: Wu, Fei Ling IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 21901678 Numbering: – Type: volume Value: 16 – Type: issue Value: 2 Titles: – TitleFull: Diabetology International Type: main |
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