Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks
ABSTRACT Aims/Introduction This study aimed to identify low‐ and high‐risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering‐based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventio...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Diabetes Investigation |
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| Online Access: | https://doi.org/10.1111/jdi.14328 |
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| author | Djeane Debora Onthoni Ying‐Erh Chen Yi‐Hsuan Lai Guo‐Hung Li Yong‐Sheng Zhuang Hong‐Ming Lin Yu‐Ping Hsiao Ade Indra Onthoni Hung‐Yi Chiou Ren‐Hua Chung |
| author_facet | Djeane Debora Onthoni Ying‐Erh Chen Yi‐Hsuan Lai Guo‐Hung Li Yong‐Sheng Zhuang Hong‐Ming Lin Yu‐Ping Hsiao Ade Indra Onthoni Hung‐Yi Chiou Ren‐Hua Chung |
| author_sort | Djeane Debora Onthoni |
| collection | DOAJ |
| description | ABSTRACT Aims/Introduction This study aimed to identify low‐ and high‐risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering‐based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions. Materials and Methods Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow‐up data. K‐means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method. Results We identified two stable clusters representing high‐ and low‐risk diabetes groups in both biobanks. The high‐risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low‐risk clusters, respectively. Notably, males were predominant in the high‐risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high‐risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high‐risk group (P < 0.001). Kaplan–Meier curves indicated significant differences in diabetes complication incidences between clusters. Conclusions UL effectively identified risk‐specific groups within prediabetes populations, with high‐risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk. |
| format | Article |
| id | doaj-art-50a2e27a694e4dbcbc0652d1e5d4b3ca |
| institution | OA Journals |
| issn | 2040-1116 2040-1124 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Investigation |
| spelling | doaj-art-50a2e27a694e4dbcbc0652d1e5d4b3ca2025-08-20T01:47:43ZengWileyJournal of Diabetes Investigation2040-11162040-11242025-01-01161253510.1111/jdi.14328Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK BiobanksDjeane Debora Onthoni0Ying‐Erh Chen1Yi‐Hsuan Lai2Guo‐Hung Li3Yong‐Sheng Zhuang4Hong‐Ming Lin5Yu‐Ping Hsiao6Ade Indra Onthoni7Hung‐Yi Chiou8Ren‐Hua Chung9Institute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanDepartment of Risk Management and Insurance Tamkang University New Taipei City TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanInstitute of Population Health Sciences National Health Research Institutes Miaoli County TaiwanABSTRACT Aims/Introduction This study aimed to identify low‐ and high‐risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering‐based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions. Materials and Methods Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow‐up data. K‐means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method. Results We identified two stable clusters representing high‐ and low‐risk diabetes groups in both biobanks. The high‐risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low‐risk clusters, respectively. Notably, males were predominant in the high‐risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high‐risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high‐risk group (P < 0.001). Kaplan–Meier curves indicated significant differences in diabetes complication incidences between clusters. Conclusions UL effectively identified risk‐specific groups within prediabetes populations, with high‐risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.https://doi.org/10.1111/jdi.14328Machine learningPrediabetesRisk stratification |
| spellingShingle | Djeane Debora Onthoni Ying‐Erh Chen Yi‐Hsuan Lai Guo‐Hung Li Yong‐Sheng Zhuang Hong‐Ming Lin Yu‐Ping Hsiao Ade Indra Onthoni Hung‐Yi Chiou Ren‐Hua Chung Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks Journal of Diabetes Investigation Machine learning Prediabetes Risk stratification |
| title | Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks |
| title_full | Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks |
| title_fullStr | Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks |
| title_full_unstemmed | Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks |
| title_short | Clustering‐based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks |
| title_sort | clustering based risk stratification of prediabetes populations insights from the taiwan and uk biobanks |
| topic | Machine learning Prediabetes Risk stratification |
| url | https://doi.org/10.1111/jdi.14328 |
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