Interactions Between Eleven Sleep-Related Characteristics and Diabetic Nephropathy: A Bidirectional Mendelian Randomization Study in European Population

Article information

Psychiatry Investig. 2024;21(10):1083-1093
Publication date (electronic) : 2024 October 17
doi : https://doi.org/10.30773/pi.2024.0192
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
Correspondence: Xinfang Tang, MD Department of Nephrology, The Affiliated Lianyungang Municipal Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang Municipal Oriental Hospital, Lianyungang 222042, China Tel: +86-518-80683889, E-mail: 39023830@qq.com
Correspondence: Feng Jiang, MD Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China Tel: +86-21-33189900, E-mail: dxyjiang@163.com
Correspondence: Hong Wang, MM Department of Nephrology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China Tel: +86-851-27596113, E-mail: 673207757@qq.com
*These authors contributed equally to this work.
Received 2024 June 6; Revised 2024 July 8; Accepted 2024 July 15.

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.

INTRODUCTION

Diabetic nephropathy (DN) is a condition marked by a consistent rise in proteinuria and a gradual increase in blood pressure. It is also considered one of the most severe long-term microvascular consequences of diabetes mellitus [1,2]. Approximately half of individuals diagnosed with DN ultimately progress to end-stage renal disease (ESRD), a condition characterized by complete kidney failure [3]. This risk is further compounded by the ongoing worldwide prevalence of diabetes. DN has progressively supplanted other kidney disorders as the primary cause of ESRD [4,5]. DN has a gradual and insidious onset, with initial symptoms that are not readily apparent, making it difficult to identify based solely on straightforward clinical indicators. When DN advances to ESRD, the only viable therapeutic options for patients are dialysis and kidney transplantation [6]. However, these two therapies do not significantly enhance the long-term survival outlook of patients. Hence, it is crucial to determine the possible causal factors in order to prevent and treat DN.

Sleep is an innate activity common to all people and fulfills several roles at both the cellular and organismal levels. As a result of changes in lifestyle and circadian rhythms, sleep has developed into several patterns, including evening or morning chronotypes [7,8]. Recent evidence suggests that irregular sleep patterns significantly increase the susceptibility of people to several disorders, including diabetes [9]. A meta-analysis of prospective research revealed a significant correlation between inadequate sleep habits and an increased susceptibility to diabetes. Poor sleeping habits have been associated with the development of microvascular and macrovascular complications [10,11], including diabetic kidney disease. Nevertheless, the evaluation of sleep-related characteristics in most research has mostly depended on self-reported information, raising concerns about its potential limitations in accurately reflecting real sleep patterns and susceptibility to memory bias. Hence, it is crucial to extensively investigate the correlation between sleep habits and DN.

Conventional observational studies, even if they are well-planned, prospective, and include a large number of participants, are susceptible to bias from lingering confounding effects and reverse causality. Mendelian randomization (MR) leverages genetic variants as instrumental variables (IVs) to infer causality, minimizing confounding and reverse causation biases that are often present in observational studies [12]. This approach is especially beneficial in studying the possible causal relationships between sleep characteristics and DN because sleep characteristics and DN both have significant genetic components. By using genetic variants associated with sleep traits, MR allows us to disentangle the possible causal effects from potential confounders such as lifestyle and environmental factors. Additionally, MR establishes the temporal order of exposure and outcome by using genetic variants that are determined at conception, ensuring that the observed associations reflect the effect of sleep characteristics on DN rather than the reverse. Traditional observational studies may be influenced by reverse causation and confounding variables. MR reduces these biases by using genetic instruments, providing more robust and reliable estimates of the possible causal effect. These advantages make MR an ideal method for our study, allowing us to investigate the possible causal pathways between sleep characteristics and DN with greater confidence. As far as we know, no research has been conducted to investigate the possible connections between sleep-related characteristics and DN.

The aim of this research was to comprehensively investigate the possible causal links between sleep-related variables and DN. The analysis included the use of two-sample MR on a dataset consisting of seven sleep-related parameters. These characteristics were gathered from genome-wide association studies (GWAS) conducted using the FinnGen and UK Biobank databases.

METHODS

Study design

Utilizing the most extensive publicly accessible GWAS datasets, we conducted a two-sample MR study to examine the correlation between sleep characteristics and the likelihood of developing DN. For MR research to ensure robust possible causal inference, three prerequisites must be fulfilled: 1) genetic instruments must have strong associations with the exposure, 2) genetic instruments must not be associated with potential confounding variables, and 3) genetic instruments should only influence the outcome through the exposure, excluding alternative mechanisms [13]. Figure 1 depicts a schematic for a bidirectional MR analysis.

Figure 1.

The study design of MR analysis on the possible causal associations between eleven sleep-related characteristics and the risk of DN. The steps include collecting GWAS data for sleep characteristics and DN, selecting independent SNPs, performing bidirectional MR analysis using IVW, MR-Egger, and weighted median methods, conducting sensitivity analyses to ensure robustness, and interpreting the results in the context of biological mechanisms linking sleep efficiency and DN. SNP, single nucleotide polymorphism; LD, linkage disequilibrium; L5 timing, timing of least activity during a 5-hour period; IEU, Integrative Epidemiology Unit; GWAS, genome-wide association studies; MR, Mendelian randomization; DN, diabetic nephropathy; IVW, inverse variance weighted.

Data sources

This research conducted a two-sample MR analysis using summary data on the relationship between single nucleotide polymorphisms (SNPs) and phenotypes. The data were gathered from published GWAS conducted on the European population in the UK Biobank (https://www.ukbiobank.ac.uk/) and FinnGen (https://www.finngen.fi/en). The present study incorporated eleven sleep-related characteristics as factors of interest, including self-reported total sleep duration [14], short sleep (≤6 h) [14], long sleep (≥9 h) [14], insomnia [15], chronotype [16], daytime napping [17], daytime sleepiness [18], sleep duration measured by an accelerometer [19], timing of least activity during a 5-hour period [19], sleep efficiency [19], and the number of sleep episodes [19]. The research outcome for DN was derived from the ninth data release of the FinnGen collaboration [20]. The sample included 4,111 cases and 308,539 controls for DN. This study utilized data from FinnGen and UK Biobank. Ethical approval for the FinnGen study was obtained from the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS), with the ethical approval number HUS/990/2017. For the UK Biobank, ethical approval was granted by the North West Multi-centre Research Ethics Committee (MREC), with the approval reference number 11/NW/0382. All participants provided informed consent prior to participation in both FinnGen and UK Biobank studies. The GWAS datasets used in this study are publicly available and can be freely accessed by researchers. Therefore, no additional ethical approval was required for this secondary analysis.

IVs

IVs were selected based on a GWAS significance threshold of p<5×10-8. SNPs that met this criterion were considered strong candidates for IVs. To ensure the independence of the selected SNPs and to avoid bias due to linkage disequilibrium (LD), we utilized PLINK software (https://www.cog-genomics.org/plink/) to prune the SNPs, excluding those with a pairwise LD r2 <0.001 and ensuring a distance of more than 10,000 kb between SNPs. Additionally, SNPs were selected if they had an F-statistic>10 to ensure strong instrument strength. The F-statistic is calculated using the formula: F=R2 (N-K-1)/(K[1-R2]), where K is the number of instrument, N is the sample size, and R2 is the proportion of variance in the exposure explained by the SNP [21].

MR analysis

MR analyses were conducted using the TwoSampleMR package in R (https://github.com/MRCIEU/TwoSampleMR). We employed five different methods to ensure robust possible causal inference: inverse variance weighted (IVW), MR-Egger, simple mode, weighted mode, and weighted median [22-25]. The IVW method is our primary analysis tool as it combines the Wald ratios of individual SNPs into a single causal estimate, providing high statistical power when the instrumental variables are valid and there is no horizontal pleiotropy. We used MR-Egger regression to assess and correct for potential horizontal pleiotropy, which can bias the IVW estimates. MR-Egger provides a valid causal estimate even in the presence of pleiotropy, albeit with reduced statistical power. Simple mode and weighted mode methods offer alternative approaches to account for pleiotropy by identifying the most frequent causal estimate among the instruments. The weighted median method offers robustness to invalid instruments by providing a consistent causal estimate if at least 50% of the weight comes from valid instruments, thus offering protection against outliers and pleiotropic effects. To assess the presence of heterogeneity, which can indicate variability in the causal estimates derived from different SNPs, we used Cochran’s Q test. A significant Cochran’s Q value suggests heterogeneity among the SNPs, implying potential violations of MR assumptions. Conversely, a p-value >0.05 from Cochran’s Q test indicates no significant heterogeneity. Pleiotropy was evaluated using MR-Egger’s intercept; a non-significant intercept (p>0.05) suggests no evidence of directional pleiotropy. Sensitivity analyses were performed to examine the robustness of our findings. These included leave-one-out analyses, where each SNP is removed in turn to assess the influence of individual SNPs on the overall causal estimate. A consistent estimate across these analyses further supports the robustness of the causal inference.

RESULTS

Basic information about instrumental variables, exposures and outcome

Following the application of certain criteria, a total of 455 SNPs associated with eleven sleep-related characteristics were selected from the GWAS database. These SNPs were then used as IVs in the analysis (Supplementary Table 1). The details about the eleven sleep-related characteristics and DN are succinctly outlined in Supplementary Table 2. The SNPs used in the outcomes of both the forward and reverse MR analyses are shown in Supplementary Tables 3 and 4. All SNPs exhibited F statistics over 10, suggesting the absence of weak instrumental bias.

The causative impact of eleven sleep-related characteristics on DN

The results of the MR study are shown in Figures 2 and 3. The main IVW analysis revealed a possible causal link between genetically determined sleep efficiency and a reduced risk of DN (odds ratio [OR]: 0.384; 95% confidence interval [CI] 0.205 to 0.717; p=0.003) (Figures 3 and 4). Similarly, the weighted median approach yielded similar findings with an OR of 0.409, a 95% CI ranging from 0.190 to 0.881, and a p-value of 0.022. Heterogeneity was identified using the heterogeneity test. The test results for MR-Egger showed a Cochran’s Q value of 0.950 and a p-value of 0.813, while the results for IVW showed a Q value of 2.571 and a p-value of 0.631, as shown in Table 1. However, despite the use of the random-effects IVW technique as the main strategy, the findings produced from the method were not affected by the presence of heterogeneity. The SNPs for DN did not exhibit any signs of imbalanced horizontal pleiotropy. This is evident from the intercept of MR-Egger, which was -0.037 with a p-value of 0.292, as seen in Table 2. The funnel plots demonstrated the lack of directional pleiotropy for the outcomes, as seen in Figure 4D. The leave-one-out analysis conclusively showed that the MR estimates were not affected by any specific SNP (Figure 4A). Supplementary Table 5 presents the results of the IVW, weighted median, MR-Egger, simple mode, and weighted mode analyses for all eleven sleep-related characteristics on DN. A OR value greater than 1 suggests a higher odds of the outcome with the exposure, while a value less than 1 suggests lower odds. The CI provides a range of values within which the true OR is likely to fall, with a 95% CI indicating that we are 95% confident that the true OR lies within this range. If the CI does not include 1, the result is considered statistically significant.

Figure 2.

Possible causal effects of eleven sleep-related characteristics on DN. From the inner circle to the outer circle, four different statistical methods are represented. Red represents lower p-values indicating stronger statistical significance, while blue represents higher p-values indicating weaker statistical significance. IVW, inverse variance weighted; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 3.

Forest plots of the pooled OR results between eleven sleep-related characteristics and the risk of DN in the forward MR analysis. The red part means p-value<0.05. NSNP, number of SNP; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 4.

Summary-based Mendelian randomization analysis of sleep efficiency on DN. A: Leave-one-out sensitivity analysis illustrated the results where each SNP is systematically removed to assess its influence on the overall causal estimate. The red line represented the overall causal estimate of sleep efficiency on DN when each individual SNP was excluded in turn. It indicated how the causal estimate changed as each SNP was left out, allowing for the assessment of the robustness of the results. The black line represented the causal effect estimate when all SNPs were included in the analysis. It served as a reference line to show the baseline estimate compared to the results when excluding individual SNPs. B: Forest plot visualized MR effect sizes for each SNP associated with sleep efficiency on DN. The red lines represented the confidence intervals for the individual SNP estimates of the causal effect on DN. Each red line extended from the lower to the upper bound of the 95% confidence interval for that SNP’s effect. The black line represented the pooled estimate of the causal efect from all the SNPs combined using the specific MR method. This line showed the overall summary effect size based on the SNP data. C: Scatter plot showed the relationship between SNP effects on sleep efficiency and DN. D: Funnel plot visualized the distribution of SNPs based on the causal effect size (βIV) and the standard error (SEIV). MR, Mendelian randomization; DN, diabetic nephropathy; SNP, single nucleotide polymorphism; βIV, causal estimate (β) obtained from IV methods; SEIV, standard error of the IV estimate; IV, instrumental variable.

The heterogeneity results of sleep efficiency and DN in the forward MR analysis

The horizontal pleiotropy results of the sleep efficiency and DN in the forward MR analysis

The causative impact of DN on eleven sleep-related characteristics

The functional outcomes of DN are considered as exposures, whereas the eleven sleep-related characteristics are considered as outcomes. The results of the reverse MR analysis are shown in Figure 5. The findings from the IVW analysis suggest that worse functional outcomes after DN may be associated with a higher probability of experiencing a greater number of sleep episodes. The OR is 1.015, with a 95% CI ranging from 1.003 to 1.028, and a p-value of 0.016 (Figures 5 and 6). The findings obtained using the weighted median (OR: 1.015; 95% CI 1.000 to 1.031; p=0.047) were consistent. The heterogeneity test indicated no existence of heterogeneity (Cochran’s Q=8.064, p=0.427 for MR-Egger; Q=8.069, p=0.527 for IVW, as shown in Table 3). The SNPs for the number of sleep episodes did not exhibit any evidence of imbalanced horizontal pleiotropy, as shown by the MR-Egger intercept of 0.00025 (p= 0.944, as shown in Table 4). The funnel plots provided no indication of directional pleiotropy for the outcomes (Figure 6D). The leave-one-out analysis verified that the MR estimates were not affected by any single SNP (Figure 6A). The results for the five methodologies for all DN on eleven sleep-related characteristics are shown in Supplementary Table 6.

Figure 5.

Forest plots of the pooled OR results between DN and eleven sleep-related characteristics in the reverse MR analysis. The red part means p-value<0.05. NSNP, number of SNP; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 6.

MR analysis of DN on No. of sleep episodes. A: Leave-one-out sensitivity analysis illustrated the results where each SNP is systematically removed to assess its influence on the overall causal estimate. The red line represented the overall causal estimate of DN on No. of sleep episodes when each individual SNP was excluded in turn. It indicated how the causal estimate changed as each SNP was left out, allowing for the assessment of the robustness of the results. The black line represented the causal effect estimate when all SNPs were included in the analysis. It served as a reference line to show the baseline estimate compared to the results when excluding individual SNPs. B: Forest plot visualized MR effect sizes for each SNP associated with DN on No. of sleep episodes. The red lines represented the confidence intervals for the individual SNP estimates of the causal effect on No. of sleep episodes. Each red line extended from the lower to the upper bound of the 95% confidence interval for that SNP’s effect. The black line represented the pooled estimate of the causal effect from all the SNPs combined using the specific MR method. This line showed the overall summary effect size based on the SNP data. C: Scatter plot showed the relationship between SNP effects on DN and No. of sleep episodes. D: Funnel plot visualized the distribution of SNPs based on the causal effect size (βIV) and the standard error (SEIV). MR, Mendelian randomization; DN, diabetic nephropathy; SNP, single nucleotide polymorphism; βIV, causal estimate (β) obtained from IV methods; SEIV, standard error of the IV estimate; IV, instrumental variable.

The heterogeneity results of DN and number of sleep episodes in the reverse MR analysis

The horizontal pleiotropy results of the DN and number of sleep episodes in the reverse MR analysis

DISCUSSION

Key findings

This research investigated the possible causal relationship between sleep characteristics and the risk of DN using a bidirectional MR approach. The results indicated that higher sleep efficiency was linked to a reduced chance of developing DN in the European population. Furthermore, we discovered a compelling cause-and-effect link suggesting that genetic predisposition to DN may elevate the likelihood of experiencing a greater number of sleep episodes, as shown by reverse MR. These findings might provide data for the prevention of DN and the screening of individuals with a heightened risk of DN.

Potential biological mechanisms

Sleep has a vital role in preserving human health, as it profoundly affects several essential physiological processes [26,27]. Sleep regulation is an intricate process closely governed by physiological systems [28]. Our investigation revealed a possible causal relationship between sleep efficiency and a reduced risk of DN. The correlation discovered between sleep efficiency and DN risk is consistent with previous research indicating that individuals with diabetes and associated comorbidities, such as DN, often have poor sleep quality and disruptions. Sleep efficiency, a measure derived from actigraphy, combines estimates of awake and sleep [29,30]. The inclusion of wake time following sleep onset and sleep latency in the computation allows for precise assessment of sleep quality. Sleep efficiency is believed to be an indicator of the uninterrupted nature of sleep [31,32]. Research has shown a connection between sleep fragmentation and cognitive impairment, as well as decreased insulin sensitivity in healthy individuals [33,34]. Decreased sleep efficiency has been linked to hypertension, the noninvasive evaluation of brachial artery flow-mediated dilation in the general adult population, and the likelihood of developing cardiovascular disease [35,36]. Inadequate sleep quality has also been associated with other negative health consequences, such as metabolic dysfunction, inflammation, and altered glucose metabolism [37,38]. Poor sleep efficiency can lead to chronic inflammation, which is a known contributor to DN. Inflammatory cytokines such as interleukin 6 and tumor necrosis factor-α can promote renal inflammation and fibrosis, leading to impaired kidney function. Sleep efficiency is closely linked to glucose metabolism and insulin sensitivity. Poor sleep can result in insulin resistance and impaired glucose control, both of which are key factors in the development and progression of DN. Hormonal regulation also plays a critical role. Disrupted sleep patterns can affect the secretion of hormones such as cortisol and melatonin. Elevated cortisol levels can increase blood pressure and glucose levels, exacerbating the risk of DN. Additionally, melatonin has antioxidant properties and its dysregulation can lead to increased oxidative stress and kidney damage. These factors are all significant to the development of DN. This work expands upon these discoveries by presenting evidence of a possible cause-and-effect relationship, indicating that therapies targeting the enhancement of sleep efficiency may have positive effects on individuals at risk of DN. The OR of 0.384 (95% CI 0.205–0.717; p=0.003) suggests a significant decrease in the incidence of DN when sleep efficiency is improved. This highlights the crucial role of sleep health in managing diabetes.

Bidirectional relationship between sleep and DN

On the other hand, the discovery that genetic predisposition to DN may raise the likelihood of experiencing a greater number of sleep episodes (OR: 1.015; 95% CI 1.003–1.028; p=0.016) complicates the connection between sleep and DN. This finding indicates a bidirectional relationship where DN may cause sleep disruption or more frequent sleep episodes. This might be due to symptoms like nocturia, pain, or metabolic imbalances associated with diabetes and DN. Sleep fragmentation, which refers to numerous interruptions and awakenings during sleep, may result in low sleep quality and has been linked to negative metabolic consequences such as insulin resistance and heightened inflammation. These results emphasize the need for a comprehensive strategy to address sleep difficulties in patients with DN in order to enhance overall health outcomes.

Strengths and limitations

The strength and reliability of our results were reinforced by conducting sensitivity analyses, which verified that the observed connections were not influenced by horizontal pleiotropy or outlier SNPs. This enhances the soundness of our causal deductions and diminishes the probability of extraneous variables affecting the outcomes. Employing several sensitivity analyses, such as MR-Egger, weighted median, and leave-one-out analysis, instills confidence in the stability and dependability of our findings.

Although our work has notable features, such as the use of extensive GWAS data and rigorous MR techniques, it is important to highlight several limitations. Our results can only be applied to people of European descent, and more research is necessary to investigate similar connections in other ethnic groups. Genetic differences and environmental variables that affect sleep and DN may vary across different groups, and it is important to comprehend these dynamics in a wider perspective. Furthermore, while MR analyses are effective in reducing the impact of confounding and reverse causation, their reliability depends on the accuracy of instrumental variables, which may not include all genetic differences associated with sleep characteristics and DN. The use of SNPs as instrumental factors presupposes that these genetic variations are highly correlated with the exposure (sleep characteristics) and do not influence the outcome (DN) via other means. Sleep efficiency and other sleep characteristics were derived from self-reported data and actigraphy measures. While these are commonly used and validated methods, they are not as precise as polysomnography. Future studies could benefit from using more accurate and detailed measures of sleep. Deviation from these assumptions has the potential to introduce bias into the findings. Finally, the specific biological processes responsible for the observed connections are still not understood, which justifies the need for more experimental and clinical investigations to clarify the pathways involved.

Future directions

Future studies should focus on investigating the underlying processes by which sleep efficiency affects the likelihood of developing DN. Examining the influence of inflammation, metabolic control, and hormonal pathways in this context might provide a deeper understanding of the fundamental mechanisms involved. Furthermore, it is necessary to conduct longitudinal research and clinical trials to evaluate the effects of sleep therapies on DN outcomes in order to confirm our results and apply them in clinical settings. It is vital to comprehend how including strategies to enhance sleep efficiency in diabetes care regimens might minimize the risk of DN.

To summarize, our research indicates that improving sleep efficiency may lower the likelihood of developing DN, underscoring the need to integrate sleep management into preventative measures for diabetic complications. Moreover, the possible causative impact of DN on the number of sleep episodes highlights the need for comprehensive treatment strategies that address both metabolic and sleep health. Subsequent studies should prioritize investigating the underlying mechanisms of these connections and assessing the efficacy of sleep therapies in decreasing the risk of DN. By simultaneously addressing sleep health and metabolic management, we can enhance the quality of life and clinical outcomes for those with diabetes who are at risk of developing DN.

Conclusions

This research, which examines the relationship between genetics and sleep efficiency, reveals that those with a greater genetic predisposition for excellent sleep efficiency have a lower chance of developing DN. This emphasizes the importance of good sleep efficiency in avoiding DN. Moreover, there is compelling evidence that genetic predisposition to DN may elevate the likelihood of experiencing a greater number of sleep episodes, suggesting a possible two-way interaction. These results emphasize the need to include sleep health in diabetes management strategies. Additional investigation is required to validate these connections and delve into the underlying biological processes.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2024.0192.

Supplementary Table 1.

439 SNPs related to 11 sleep-related characterics used in this study.

pi-2024-0192-Supplementary-Table-1.pdf
Supplementary Table 2.

Detailed information of the GWAS data used in this study.

pi-2024-0192-Supplementary-Table-2.pdf
Supplementary Table 3.

Detailed information on the SNPs used in the results of forward MR analysis.

pi-2024-0192-Supplementary-Table-3.pdf
Supplementary Table 4.

Detailed information on the SNPs used in the results of reverse MR analysis.

pi-2024-0192-Supplementary-Table-4.pdf
Supplementary Table 5.

MR analysis results of 11 sleep-related characterics on DN.

pi-2024-0192-Supplementary-Table-5.pdf
Supplementary Table 6.

MR analysis results of DN on 11 sleep-related characteristics.

pi-2024-0192-Supplementary-Table-6.pdf

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Jie Zheng. Data curation: Yuan Li. Formal analysis: Yuan Li. Funding acquisition: Xinfang Tang. Investigation: Chuyan Wu. Methodology: Jie Zheng. Project administration: Hong Wang. Resources: Hong Wang. Software: Yuan Li. Supervision: Hong Wang. Validation: Feng Jiang. Visualization: Chuyan Wu. Writing—original draft: Jie Zheng. Writing—review & editing: Feng Jiang, Xinfang Tang.

Funding Statement

This work was supported by the Lianyungang Health Science and Technology Project (grant number 202029).

Acknowledgements

None

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Article information Continued

Figure 1.

The study design of MR analysis on the possible causal associations between eleven sleep-related characteristics and the risk of DN. The steps include collecting GWAS data for sleep characteristics and DN, selecting independent SNPs, performing bidirectional MR analysis using IVW, MR-Egger, and weighted median methods, conducting sensitivity analyses to ensure robustness, and interpreting the results in the context of biological mechanisms linking sleep efficiency and DN. SNP, single nucleotide polymorphism; LD, linkage disequilibrium; L5 timing, timing of least activity during a 5-hour period; IEU, Integrative Epidemiology Unit; GWAS, genome-wide association studies; MR, Mendelian randomization; DN, diabetic nephropathy; IVW, inverse variance weighted.

Figure 2.

Possible causal effects of eleven sleep-related characteristics on DN. From the inner circle to the outer circle, four different statistical methods are represented. Red represents lower p-values indicating stronger statistical significance, while blue represents higher p-values indicating weaker statistical significance. IVW, inverse variance weighted; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 3.

Forest plots of the pooled OR results between eleven sleep-related characteristics and the risk of DN in the forward MR analysis. The red part means p-value<0.05. NSNP, number of SNP; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 4.

Summary-based Mendelian randomization analysis of sleep efficiency on DN. A: Leave-one-out sensitivity analysis illustrated the results where each SNP is systematically removed to assess its influence on the overall causal estimate. The red line represented the overall causal estimate of sleep efficiency on DN when each individual SNP was excluded in turn. It indicated how the causal estimate changed as each SNP was left out, allowing for the assessment of the robustness of the results. The black line represented the causal effect estimate when all SNPs were included in the analysis. It served as a reference line to show the baseline estimate compared to the results when excluding individual SNPs. B: Forest plot visualized MR effect sizes for each SNP associated with sleep efficiency on DN. The red lines represented the confidence intervals for the individual SNP estimates of the causal effect on DN. Each red line extended from the lower to the upper bound of the 95% confidence interval for that SNP’s effect. The black line represented the pooled estimate of the causal efect from all the SNPs combined using the specific MR method. This line showed the overall summary effect size based on the SNP data. C: Scatter plot showed the relationship between SNP effects on sleep efficiency and DN. D: Funnel plot visualized the distribution of SNPs based on the causal effect size (βIV) and the standard error (SEIV). MR, Mendelian randomization; DN, diabetic nephropathy; SNP, single nucleotide polymorphism; βIV, causal estimate (β) obtained from IV methods; SEIV, standard error of the IV estimate; IV, instrumental variable.

Figure 5.

Forest plots of the pooled OR results between DN and eleven sleep-related characteristics in the reverse MR analysis. The red part means p-value<0.05. NSNP, number of SNP; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; L5 timing, timing of least activity during a 5-hour period; DN, diabetic nephropathy.

Figure 6.

MR analysis of DN on No. of sleep episodes. A: Leave-one-out sensitivity analysis illustrated the results where each SNP is systematically removed to assess its influence on the overall causal estimate. The red line represented the overall causal estimate of DN on No. of sleep episodes when each individual SNP was excluded in turn. It indicated how the causal estimate changed as each SNP was left out, allowing for the assessment of the robustness of the results. The black line represented the causal effect estimate when all SNPs were included in the analysis. It served as a reference line to show the baseline estimate compared to the results when excluding individual SNPs. B: Forest plot visualized MR effect sizes for each SNP associated with DN on No. of sleep episodes. The red lines represented the confidence intervals for the individual SNP estimates of the causal effect on No. of sleep episodes. Each red line extended from the lower to the upper bound of the 95% confidence interval for that SNP’s effect. The black line represented the pooled estimate of the causal effect from all the SNPs combined using the specific MR method. This line showed the overall summary effect size based on the SNP data. C: Scatter plot showed the relationship between SNP effects on DN and No. of sleep episodes. D: Funnel plot visualized the distribution of SNPs based on the causal effect size (βIV) and the standard error (SEIV). MR, Mendelian randomization; DN, diabetic nephropathy; SNP, single nucleotide polymorphism; βIV, causal estimate (β) obtained from IV methods; SEIV, standard error of the IV estimate; IV, instrumental variable.

Table 1.

The heterogeneity results of sleep efficiency and DN in the forward MR analysis

Methods MR-Egger intercept
Q Q_df Q_p
MR-Egger 0.95 3 0.813
Inverse variance weighted 2.571 4 0.631

DN, diabetic nephropathy; MR, Mendelian randomization; Q_df, degrees of freedom for the Cochran’s Q statistic; Q_p, p-value of Cochran’s Q statistic

Table 2.

The horizontal pleiotropy results of the sleep efficiency and DN in the forward MR analysis

GWAS dataset MR-Egger intercept
Egger_intercept SE p
DN -0.037 0.029 0.292

DN, diabetic nephropathy; MR, Mendelian randomization; GWAS, genome-wide association studies; SE, standard error

Table 3.

The heterogeneity results of DN and number of sleep episodes in the reverse MR analysis

Methods MR-Egger intercept
Q Q_df Q_p
MR-Egger 8.064 8 0.427
Inverse variance weighted 8.069 9 0.527

DN, diabetic nephropathy; MR, Mendelian randomization; Q_df, degrees of freedom for the Cochran’s Q statistic; Q_p, p-value of Cochran’s Q statistic

Table 4.

The horizontal pleiotropy results of the DN and number of sleep episodes in the reverse MR analysis

GWAS dataset MR-Egger intercept
Egger_intercept SE p
No. of sleep episodes 0.00025 0.003 0.944

DN, diabetic nephropathy; MR, Mendelian randomization; GWAS, genome-wide association studies; SE, standard error