Interactions Between Eleven Sleep-Related Characteristics and Diabetic Nephropathy: A Bidirectional Mendelian Randomization Study in European Population
- Affiliations
-
- 1Department of Nephrology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
- 2Department of Dermatology, The Fifth People’s Hospital of Hainan Province, Haikou, China
- 3Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- 4Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
- 5Department of Nephrology, The Affiliated Lianyungang Municipal Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang Municipal Oriental Hospital, Lianyungang, China
Abstract
Objective
Observational studies often report disturbed sleep patterns in individuals with diabetic nephropathy (DN). The possible causal relationship behind these connections remains unknown. This research assessed the possible cause-and-effect relationship between eleven sleep-related characteristics and the risk of developing DN using a two-sample Mendelian randomization (MR) study.
Methods
This study employed a two-sample bidirectional MR analytical approach. Genetic data for eleven sleep-related characteristics were acquired from the genome-wide association studies (GWAS) database of individuals of European ancestry which involve scanning complete sets of DNA, or genomes. GWAS summary data for DN included 4,111 DN cases and 308,539 controls. Instrumental variables were single nucleotide polymorphisms strongly linked to sleep-related characteristics. The main analysis used the random-effects inverse variance weighted (IVW) approach, with validation through sensitivity testing.
Results
MR analysis revealed that a higher genetic predisposition for sleep efficiency reduced the chance of developing DN (odds ratio [OR]: 0.384; 95% confidence interval [CI] 0.205–0.717; p=0.003). Genetic susceptibility to DN was associated with a higher likelihood of experiencing more sleep episodes (OR: 1.015; 95% CI 1.003–1.028; p=0.016). Sensitivity analysis confirmed the robustness of these correlations. No significant connections were found between other genetically predicted sleep characteristics and the likelihood of developing DN.
Conclusion
Our research indicates that a genetic predisposition for better sleep efficiency is linked to a lower risk of developing DN. There is also evidence suggesting that genetic predisposition to DN may directly impact sleep episodes. Further research is needed to explore the molecular mechanisms underlying these findings.