Background

Sleep apnea (SA), the most widespread form of sleep-disordered breathing, is defined by the repeated partial or total closure of the upper airway during sleep [1]. Previous research revealed a strong relationship between SA and socioeconomic level and mental health. In contrast to the general population, patients with mental health disorders had a greater incidence of SA, because patients with severe mental illness are frequently prescribed psychotropic medications, which can result in weight gain and metabolic syndrome, both of which are known risk factors for SA, as well as sedative medications interrupt sleep architecture [2]. And vice versa, patients with SA had a considerably higher prevalence of mental health issues than control individuals [2,3,4,5,6,7,8], including attention-deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder, schizophrenia, and anxiety disorders. In addition, having a higher degree of education was linked to a decreased risk of SA, whereas people with a poor socioeconomic status have a higher risk of SA [9, 10]. Given the promiscuous variables and reverse causation that are inherent in observational studies, the connection between mental health, socioeconomic position, and SA is yet unknown and calls for more research.

Mendelian randomization (MR) is an innovative analysis that uses genetic variations as instrumental variables (IV) to evaluate the causality of an observed association between a modifiable exposure and a clinically relevant outcome [11]. Because genotypes are randomly assigned to offspring, the association between genetic variants and outcome is unaffected by common confounders, and a causal sequence is plausible [12]. Furthermore, because genetic variation occurs before disease and the order of the two cannot be reversed, MR can avoid the interruption of reverse causality [13]. Although there is growing evidence that MR is reliable, there are few studies that focus on the relationship between mental health, socioeconomic status, and SA. As a result, we performed a two-sample bidirectional MR study to examine the causal association between mental health (including ADHD, bipolar disorder, major depressive disorder, schizophrenia, and anxiety disorders), socioeconomic status (including educational attainment, income, and social deprivation), and SA. Although the results’ generalizability may be limited by race, this study may help to reveal the genetic characteristics of mental health, socioeconomic status, and SA through MR analysis, which actively contributes to the further study of SA.

Method

Data sources

The study design is simply described in a flowchart (Fig. 1). Educational attainment is assessed by years of schooling, college or university degree, and intelligence. For summary statistics for mental health and socioeconomic status, we used data from the Social Science Genetic Association Consortium (SSGAC) [14], the UK Biobank (UKB) [15], the Psychiatric Genomics Consortium (PGC), and Savage, et al. (2018) [16], as shown in Table 1. The SSGAC is a collaborative effort between social scientists and medical researchers that conducts genetic association studies for social science outcomes and offers a forum for multidisciplinary cooperation and idea-sharing. The UKB project is a sizable prospective cohort study with around 500,000 participants from the United Kingdom. The PGC, which leverages the power of more than 900,000 individuals, is the biggest biological study in the history of psychiatry.

We used large-scale, publicly accessible genome-wide association studies (GWAS) data from the FinnGen research project to construct a summary statistic for sleep apnea [17], which included 16,761 cases and 201,194 control subjects.

Fig. 1
figure 1

Flowchart of MR analysis in this study. GWAS, genome-wide association studies; IV, instrumental variables; MAF, minor allele frequency; SNPs, single-nucleotide polymorphisms; MR analysis, Mendelian randomization analysis

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Table 1 Summary of genome-wide association studies used in the analyses
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Genetic instrumental variables (IV) selection

Based on the GWAS summary data on socioeconomic status and mental health, we chose the appropriate genetic IVs using several quality control criteria. First, for each exposure, we utilized independent genetic variants that were strongly related (P < 5 × 10− 8). To prevent linkage disequilibrium (LD), we then carried out the clumping method with R2 < 0.001 and a window size > 10,000 kb [18]. Third, SNPs having a minor allele frequency (MAF) of less than 0.01 were disregarded. We looked for every single-nucleotide polymorphism (SNP) included in our research and eliminated SNPs linked to SA. To correct the alleles’ orientation, SNP harmonization was also conducted [19].

Evaluation of the strength of the genetic instruments

To evaluate the strength of genetic instruments for various forms of mental health and socioeconomic status, we calculated the F statistics for each SNP. IVs with F statistics less than 10 were considered weak instruments, and the exposure would not be included in MR analysis [20].

Pleiotropy, heterogeneity, and sensitivity analysis

The probability of horizontal pleiotropy was evaluated using MR-Egger regression, as shown by the intercept [21]. MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) package in R was used to detect pleiotropy (P < 0.05), and the SNP with the smallest pleiotropy P value was eliminated [22]. The heterogeneity was also identified using the inverse variance weighted (IVW) method and MR-Egger regression, and Cochrane’s Q statistics were employed to quantify it. To determine the stability of the results, we also performed the leave-one-out analysis.

MR analysis

The wald estimates of causation for each IV are combined using the IVW technique using a meta-analysis methodology, yielding the best accurate estimates with balanced pleiotropy. To reduce the impact of single SNPs on phenotypes, the pleiotropic and causal effects were detected in this study using the MR-Egger approach, when more than half of the chosen SNPs were legitimate genetic variations. Although MR-Egger can identify and modify pleiotropy, the estimation accuracy produced by this method is extremely low [23]. Weighted median gives a trustworthy impact estimate of the causal influence based on the assumption that at least half of IVs are valid, enhancing precision and robustness [24]. Although simple mode is not as impactful as IVW, it could provide pleiotropy robustness [25]. Weighted mode is susceptible to mode estimation’s difficult bandwidth selection [26]. The simple mode and weighted mode can also be applied to estimate the causal effect [27]. When compared to other MR approaches, the IVW method’s statistical effectiveness was noticeably greater [28], thus, IVW data were prioritized in this investigation to determine the causal relationships between exposures and the risk of SA. In addition, we performed MR-Egger, weighted median, simple mode, and weighted mold as complementary methods. The MR analysis was performed in R software (version 4.2.2) using the TwoSampleMR package.

Results

The causal effect of mental health and socioeconomic status on SA (fig. 2)

Anxiety disorders were excluded in the analysis of causal effects from mental health and socioeconomic status to SA since there were no effective IVs. Overall, mental health exposures including ADHD, bipolar disorder, major depressive disorder, and schizophrenia, and socioeconomic status exposures including years of schooling, college or university degree, intelligence, income, and social deprivation were included in our study. The number of SNPs for each exposure varies from 9 to 305 after a series of quality control steps (Table 2, Supplementary Tables 1–10). The F statistic suggests that the SNP is a strong instrument.

MR estimates of different methods are presented in Table 2. Since the analyses of mental health and socioeconomic status showed significant heterogeneity but no directional pleiotropy, the multiplicative random-effect models were applied in the IVW analysis, and four causal associations from five socioeconomic statuses to sleep apnea were identified, while only one causal association from four mental health exposures was observed for sleep apnea. We found evidence that educational attainment, including longer years of schooling, college or university degree, and higher intelligence was associated with a lower risk of sleep apnea (OR = 0.750, 95%CI = 0.653–0.862; OR = 0.558, 95%CI = 0.423–0.735; OR = 0.871, 95%CI = 0.760–0.999, respectively), while social deprivation was associated with a higher risk of sleep apnea (OR = 1.821, 95%CI = 1.075–3.085). The income was not associated with the risk of sleep apnea (OR = 0.877, 95%CI = 0.682–1.129). In mental health exposure, major depressive disorder was associated with a higher risk of sleep apnea (OR = 1.196, 95%CI = 1.015–1.409), while ADHD, bipolar disorder, and schizophrenia were not associated with the risk of sleep apnea (OR = 1.064, 95%CI = 0.958–1.181; OR = 1.030, 95%CI = 0.942–1.127; OR = 0.990, 95%CI = 0.957–1.025, respectively). The results of the MR-Egger intercept indicated that there was no pleiotropic effect (P > 0.05). However, MR-PRESSO detected outliers, revealing that the IVs of years of schooling, college or university degree, and intelligence and outcome had a substantial horizontal pleiotropy (P < 0.05). Results did not change substantially the after removal of outliers (Table 3). The results from the leave-one-out analyses indicated that the causal effect of mental health and socioeconomic status on SA was stable.

The scatter plots, forest plots, and funnel plots for sleep apnea are displayed in complementary materials (Supplementary Figs. 1–3).

Fig. 2
figure 2

The causal effect of mental health and socioeconomic status based on the IVW method. OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted

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Table 2 Results of the MR study testing causing the association between risk factors and sleep apnea
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Table 3 Results of the MRPRESSO testing
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The causal effect of SA on mental health and socioeconomic status

Significant heterogeneity was found by Cochrane’s Q test (Table 4, P < 0.05), and an IVW approach with the multiplicative random-effect model was applied to the main analyses. Reverse MR analysis failed to obtain a causal effect between genetic liability to SA with mental health and socioeconomic status using the IVW method (Table 4). No directional pleiotropy was found by the ME-Egger regression analysis (P > 0.05). However, MR-PRESSO detected outliers, revealing that the IVs of SA and outcome had a substantial horizontal pleiotropy (P < 0.05). Results did not change substantially after the removal of outliers (Table 3).

Table 4 Results of the MR study testing causing the association between sleep apnea and mental health and socioeconomic status
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Discussion

There is evidence that having a higher level of education protects against SA, whereas people with mental health conditions are more likely to develop SA [8, 9]. In our research, MR analysis was carried out to assess the potential causation between mental health, socioeconomic status, and SA, which employs random allocation of alleles to duplicate the randomized procedure in double-blind clinical trials. Appling large-scale summary statistics from mental health, socioeconomic status GWAS, and SA GWAS, we found that higher education attainment was strongly correlated with a lower risk of SA, and social deprivation as well as major depressive disorder were strongly associated with a higher risk of SA.

Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect, the MR-Egger regression provides a valid effect estimate even when all of the genetic variants and invalid instruments are present [21]. The intercept of the MR-Egger regression was corrected to assess for pleiotropy bias. It is a reasonable assumption in this context because no pleiotropic effect of the variant was observed after a look-up of all SNPs and the MR-Egger intercept. The MR-Egger intercept results in our study indicated that there was no pleiotropic effect that affected the outcome through factors other than exposure. MR-PRESSO is used in multi-instrument MR analysis to detect and correct for horizontal pleiotropic outliers through outlier removal [22]. Some outliers were found in the study; however, after the outliers were removed, the results did not change significantly. A leave-one-out analysis was performed to estimate the potential influence of a single SNP, and the results showed that the association between mental health, socioeconomic status, and SA was not significantly driven by any individual SNP. The aforementioned results evaluate the robustness of the MR results.

There have been a few observational studies on the connection between education and SA [9], but due to factors including limited cohorts, measurement inaccuracy, and methodological restrictions, their findings are not reliable. Our result may provide some of the strongest evidence to evaluate the causal role of educational attainment in SA because the questions described previously can be partially or entirely avoided by a two-sample MR method. Our data improved statistical reliability by using summary statistics from the greatest GWAS studies for educational attainment and SA. Our results were in line with earlier MR research investigating the relationship between education level and SA [29].

A connection between low income and an increased risk for SA was shown by earlier observational research [10]. Our findings did not support a causal relationship between income and SA, which is inconsistent. This could be explained by the lower sensitivity and positive predictive value of the Beilin Questionnaire in detecting SA in the black community [10]. Additionally, observational findings supported the reported link between social deprivation and sleep disturbances [30], and the evidence from our study suggests that social deprivation is a risk factor that causes SA. There is little research on this relationship, thus further research is required.

It is significant to emphasize that our study did not find evidence for a generalized role for mental health problems in the risk of SA, but rather for a particular role for major depressive disorder. According to the prevalence meta-analysis, there may be a higher prevalence of SA in people with major depressive disorder, while there is insufficient data to suggest a higher incidence of SA in people with bipolar disorder and schizophrenia [31], which is consistent with our findings. Furthermore, our study could not support the contribution of SA to psychiatric disorders, including ADHD, bipolar disorder, major depressive disorder, schizophrenia, and anxiety disorders, while the available observational evidence was contradictory [5, 7, 32].

Our study possesses several important strengths, the most important of which is the MR design, which is appropriate for concluding causality. MR research might offer crucial insights into the relationships between mental health, socioeconomic position, and SA given the various difficulties in planning and executing RCTs in SA. Furthermore, the relationship between mental health and SA has never been studied in an MR setting.

However, it is crucial to notice that this MR study has several limitations. Firstly, the limited IV numbers weaken the proportion of phenotypic variance explained. As a result, the null findings for the relationship between anxiety disorders and SA do not necessarily indicate that anxiety disorders have no impact. Secondly, the phenotypes considered in this study rely on the definitions and samples utilized in the original GWASs, which are often highly heterogeneous in terms of the recruited population, the definition of the phenotype, and the assessment. Although the necessity for very large samples to uncover tiny genetic effects is what causes this heterogeneity, it might nonetheless have an impact on our conclusions. Thirdly, this study only includes people of European heritage due to data availability. Our findings may be limited in their applicability to other ethnicities. The uniformity of participants, on the other hand, ensures that there is little risk of confounding by population admixture. Fourthly, the lack of gender or age-specific GWAS summary statistics prevented us from examining potential gender and age variations in the link between mental health, socioeconomic position, and SA. Fifthly, the majority of the relationships that were identified only applied to adults, and they could vary throughout various developmental stages. Further MR analysis, after controlling for age, gender, and other environmental confounding factors, is required to reveal the causal relationship between mental health, socioeconomic status, and SA. Furthermore, results from other races may increase the study’s generalizability.

In conclusion, we adequately estimated the potential causal association between mental health, socioeconomic status, and SA. Education attainment was found to be associated with a lower risk of SA, and social deprivation and major depressive disorder were correlated with higher SA risk. Further study is needed to investigate the exact causal association and mechanism between mental health, socioeconomic status, and SA.