| Title: | Cardio-cerebrovascular Outcomes in MODY, Type 1 Diabetes, and Type 2 Diabetes: A Prospective Cohort Study |
| Journal: | The Journal of Clinical Endocrinology & Metabolism |
| Published: | 24 Apr 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37093977/ |
| DOI: | https://doi.org/10.1210/clinem/dgad233 |
Publication 11376
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Abstract
CONTEXT: Cardio-cerebrovascular events are severe complications of diabetes.</p>
OBJECTIVE: We aim to compare the incident risk of cardio-cerebrovascular events in maturity onset diabetes of the young (MODY), type 1 diabetes, and type 2 diabetes.</p>
METHODS: Type 1 diabetes, type 2 diabetes, and MODY were diagnosed by whole exome sequencing. The primary endpoint was the occurrence of the first major adverse cardiovascular event (MACE), including acute myocardial infarction, heart failure, stroke, unstable angina pectoris, and cardio-cerebrovascular-related mortality. Cox proportional hazards models were applied and adjusted to calculate hazard ratios (HRs) and 95% CIs for the incident risk of MACE in type 1 diabetes, type 2 diabetes, MODY, and MODY subgroups compared with people without diabetes (control group).</p>
RESULTS: Type 1 diabetes, type 2 diabetes, and MODY accounted for 2.7%, 68.1%, and 11.4% of 26 198 participants with diabetes from UK Biobank. During a median follow-up of 13 years, 1028 MACEs occurred in the control group, contrasting with 70 events in patients with type 1 diabetes (HR 2.15, 95% CI 1.69-2.74, P < .05), 5020 events in patients with type 2 diabetes (HR 7.02, 95% CI 6.56-7.51, P < .05), and 717 events in MODY (HR 5.79, 95% CI 5.26-6.37, P < .05). The hazard of MACE in HNF1B-MODY was highest among MODY subgroups (HR 11.00, 95% CI 5.47-22.00, P = 1.5 × 10-11).</p>
CONCLUSION: MODY diagnosed by genetic analysis represents higher prevalence than the clinical diagnosis in UK Biobank. The risk of incident cardio-cerebrovascular events in MODY ranks between type 1 diabetes and type 2 diabetes.</p>
7 Authors
- Hui-Xuan Wu
- Tian-Yao Chu
- Junaid Iqbal
- Hong-Li Jiang
- Long Li
- Yan-Xuan Wu
- Hou-De Zhou
1 Application
| Application ID | Title |
|---|---|
| 87286 | Improvement and validation of comprehensive predictive and precise diagnostic model of diabetes |
Enabling scientific discoveries that improve human health