Sex differences in risk of incident microvascular and macrovascular complications: a population-based data-linkage study among 25 713 people with diabetes Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.1136/jech-2023-221759
Background The global prevalence of diabetes is similar in men and women; however, there is conflicting evidence regarding sex differences in diabetes-related complications. The aim of this study was to investigate sex differences in incident microvascular and macrovascular complications among adults with diabetes. Methods This prospective cohort study linked data from the 45 and Up Study, Australia, to administrative health records. The study sample included 25 713 individuals (57% men), aged ≥45 years, with diabetes at baseline. Incident cardiovascular disease (CVD), eye, lower limb, and kidney complications were determined using hospitalisation data and claims for medical services. Multivariable Cox proportional hazards models were used to assess the association between sex and incident complications. Results Age-adjusted incidence rates per 1000 person years for CVD, eye, lower limb, and kidney complications were 37, 52, 21, and 32, respectively. Men had a greater risk of CVD (adjusted hazard ratio (aHR) 1.51, 95% CI 1.43 to 1.59), lower limb (aHR 1.47, 95% CI 1.38 to 1.57), and kidney complications (aHR 1.55, 95% CI 1.47 to 1.64) than women, and a greater risk of diabetic retinopathy (aHR 1.14, 95% CI 1.03 to 1.26). Over 10 years, 44%, 57%, 25%, and 35% of men experienced a CVD, eye, lower limb, or kidney complication, respectively, compared with 31%, 61%, 18%, and 25% of women. Diabetes duration (<10 years vs ≥10 years) had no substantial effect on sex differences in complications. Conclusions Men with diabetes are at greater risk of complications, irrespective of diabetes duration. High rates of complications in both sexes highlight the importance of targeted complication screening and prevention strategies from diagnosis.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/jech-2023-221759
- https://jech.bmj.com/content/jech/early/2024/04/17/jech-2023-221759.full.pdf
- OA Status
- hybrid
- Cited By
- 13
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396945842
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396945842Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1136/jech-2023-221759Digital Object Identifier
- Title
-
Sex differences in risk of incident microvascular and macrovascular complications: a population-based data-linkage study among 25 713 people with diabetesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-16Full publication date if available
- Authors
-
Alice A. Gibson, Emma Cox, Francisco J. Schneuer, Jacob Humphries, Crystal Lee, Joanne Gale, Steven J. Chadban, Mark C. Gillies, Clara K Chow, Stephen Colagiuri, Natasha NassarList of authors in order
- Landing page
-
https://doi.org/10.1136/jech-2023-221759Publisher landing page
- PDF URL
-
https://jech.bmj.com/content/jech/early/2024/04/17/jech-2023-221759.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://jech.bmj.com/content/jech/early/2024/04/17/jech-2023-221759.full.pdfDirect OA link when available
- Concepts
-
Medicine, Diabetes mellitus, Hazard ratio, Internal medicine, Population, Proportional hazards model, Cohort study, Prospective cohort study, Incidence (geometry), Diabetic retinopathy, Record linkage, Kidney disease, Cohort, Retinopathy, Confidence interval, Endocrinology, Environmental health, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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