Abstract
Background
Somnipathy and diabetes are independently associated with an increased risk of cardiovascular disease (CVD). However, whether a combination of both conditions is associated with a higher risk of CVD events remains uncertain. Therefore, the aim of this meta‐analysis was to clarify this association.
Methods and Results
We searched MEDLINE, Web of Science, EMBASE, ClinicalTrials.gov, and the Cochrane Central Register for Controlled Trials. We included randomized controlled trials, nonrandomized trials, and prospective observational cohort studies that assessed the combined effect of diabetes and comorbid somnipathy on CVD risk and mortality for at least 1 year. Outcomes included CVD, coronary heart disease, stroke, and all‐cause mortality. Twelve studies involving 582 267 participants were included in the meta‐analysis. Patients with somnipathy and comorbid diabetes exhibited increased risks of CVD, coronary heart disease, stroke, and all‐cause mortality (risk ratio [RR], 1.27 [95% CI, 1.12–1.45], P<0.0001; RR, 1.40 [95% CI, 1.21–1.62], P<0.0001; RR, 1.28 [95% CI, 1.08–1.52], P=0.004, and RR, 1.56 [95% CI, 1.26–1.94], P<0.0001, respectively).
Conclusions
The coexistence of somnipathy and diabetes is associated with higher risks of CVD, coronary heart disease, stroke, and mortality than somnipathy or diabetes alone. Resolving sleep problems in patients with diabetes may reduce the risks of CVD, stroke, and mortality.
Registration Information
https://www.crd.york.ac.uk/prospero/. Identifier: PROSPERO CRD42021274566.
Keywords: all‐cause mortality, cardiovascular disease (CVD), coronary heart disease (CHD), meta‐analysis, somnipathy, stroke
Subject Categories: Ischemic Stroke, Sudden Cardiac Death
Clinical Perspective
What Is New?
Somnipathy or diabetes alone could lead to high cardiovascular disease (CVD) events. It is unclear whether the combination of them has higher risk of CVD and mortality.
This meta‐analysis indicated that patients with somnipathy and comorbid diabetes exhibited increased risks of CVD, coronary heart disease, stroke, and all‐cause mortality.
What Are the Clinical Implications?
Current available evidence suggests that coexistence of somnipathy and diabetes is associated with higher risks of CVD, coronary heart disease, stroke, and mortality than somnipathy or diabetes alone.
Physicians should attend to sleep problems in patients with diabetes and attempt to solve them to reduce the risks of CVD, stroke, and mortality.
Type 2 diabetes is a major public health concern that may lead to high morbidity and mortality rates. Some studies have observed that type 2 diabetes commonly coexists with objective sleep disorders or subjective sleep disturbances, 1 , 2 , 3 especially those relating to sleep duration and quality, which are significant risk factors for the development of type 2 diabetes. 4 According to the third edition of the International Classification of Sleep Disorders, somnipathy comprises 7 major categories: insomnia and sleep‐related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep–wake disorders, sleep‐related movement disorders, parasomnias, and other sleep disorders. 5 , 6 Both objective and subjective methods are used to measure somnipathy. Although objective assessments of sleep disorders are more accurate, subjective assessments of sleep disturbances are often used. The sleep patterns of participants in large‐population cohort studies are frequently assessed by subjective self‐reports of sleep disturbances and daytime sleepiness. Sleep disorders and disturbances are associated with increased risks of cardiovascular disease (CVD) and all‐cause mortality. 7 , 8 , 9 However, whether the coexistence of diabetes and somnipathy leads to higher morbidity and mortality rates is uncertain. In the UK Biobank, a prospective population‐based study, frequent sleep disturbances were found to be associated with a high risk of all‐cause mortality with a hazard ratio of 1.11 (95% CI, 1.07–1.15) for sleep disturbances alone, 1.67 (95% CI, 1.57–1.76) for diabetes alone, and 1.87 (95% CI, 1.75–2.01) for the coexistence of diabetes and somnipathy. 10 However, in the UK Biobank study, the main concern was the method used to analyze sleep problems. A sleep questionnaire was used to measure subjective sleep disturbances; the patients were not objectively evaluated by a physician or with respiratory sleep monitoring. Therefore, the aim of this meta‐analysis was to elucidate objectively the combined effects of diabetes and somnipathy on the risks of CVD, stroke, and all‐cause mortality.
Methods
The authors declare that all supporting data are available within the article and its online supplemental files. We followed a standard protocol that we developed according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. 11 This study is registered with PROSPERO (International Prospective Register of Systematic Reviews), number CRD42021274566.
Search Strategy and Study Selection
We searched for relevant literature from several databases, including MEDLINE, Web of Science, EMBASE, ClinicalTrials.gov, and the Cochrane Central Register of Controlled Trials, from inception to March 1, 2022 with the language restriction of English; the search strategy for each database is listed in Table S1. The following keywords were used: “somnipathy,” “sleep disorder,” “sleep disturbance,” “sleep apnea,” “insomnia,” or “short duration sleep”; and “diabetes,” “cardiovascular disease,” “coronary heart disease,” “stroke,” “all‐cause death,” or “all‐cause mortality.” We also manually searched for the references cited by the identified original studies and relevant review articles and selected papers were evaluated. The detailed steps taken are shown in Figure 1.
Figure 1.
PRISMA 2020 flow diagram.
Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) 2020 flow diagram of study selection process.
Inclusion and Exclusion Criteria
Studies that met the following criteria were included in our meta‐analysis: (1) duration of more than 1 year; (2) randomized controlled trials, nonrandomized trials, and prospective observational cohort studies; (3) patients with a combination of diabetes and sleep disorders or disturbances; and (4) availability of an outcome (CVD, coronary heart disease [CHD], stroke, and all‐cause mortality).
Studies were excluded if they met any of the following criteria: (1) the study was not in English; (2) a comparison of outcomes was not reported; (3) there was no description of CVD, CHD, stroke, or all‐cause mortality; and (4) the studies analyzed the same population or duplicates.
Data Collection
Three researchers (X.H. Yang, B.L. Zhang, and Y. Cheng) performed the searches and reviewed the results. Data were collected by all authors and independently extracted by the same 3 researchers (X.H. Yang, B.L. Zhang, and Y. Cheng), who reviewed all the study characteristics (ie, first author's surname, year of publication, study design, sample, follow‐up, and outcomes). Any disagreement regarding data extraction was resolved by an interreviewer discussion in consultation with the other authors (H.M. Jin and S.K. Fun).
Summary Measures and Synthesis of Results
The risk ratios (RRs) for CVD, CHD, stroke, and all‐cause mortality were extracted from each study or calculated by 1 of the researchers (X.H. Yang). The following baseline characteristics were also extracted from all included studies: study design, subjects, mean follow‐up time, age, sex, preexisting conditions, outcomes, and quality. We also conducted a sensitivity analysis, in which the effect of each study on the estimate was evaluated. Sensitivity analysis was performed using the metaninf function (1‐study removal approach).
Assessment of Heterogeneity
Heterogeneity was evaluated using Cochran's Q and I 2 statistics. A study was considered heterogeneous if the P value was <0.1 (Cochran's Q). Studies with I 2 values <50% were considered nonheterogeneous; thus, a fixed effects model was used in the analysis of those studies. However, studies with I 2 > 50% were considered heterogeneous; hence, they were analyzed using a random effects model. 12
Quality Assessment and Risk of Bias Assessment
Two of the previously mentioned researchers (X.H. Yang and B.L. Zhang) assessed the quality of each selected study. The Grading of Recommendations, Assessment, Development, and Evaluation system (https://gdt.gradepro.org/app/) was used to evaluate the quality of evidence. The Risk of Bias in Non‐randomized Studies of Interventions tool was also used to assess the quality of the included nonrandomized controlled trials. 13 Studies were ranked as having a low, moderate, serious, or critical risk of bias in 7 domains. Any discrepancies were resolved through discussions with a third author (Jin HM).
Outcome Measures
The primary outcomes were CVD and CHD. We assessed stroke and all‐cause mortality from baseline to the end of follow‐up as secondary outcomes. CVD events were defined, based on the diagnosis codes in the International Classification of Diseases, Ninth Revision, Clinical Modification, as the occurrence of CHD (including myocardial infarction, angina, and other CHDs), heart failure, or cerebrovascular diseases (including stroke, transient cerebral ischemic attack, cerebrovascular accident, and other cerebrovascular diseases).
Statistical Analysis
Data were analyzed using Stata (version 14.0; StataCorp LLC, Texas, USA). The RRs for CVD, CHD, stroke, and all‐cause mortality were calculated or extracted from the individual studies. The log RRs and log standard errors were calculated based on the reported RRs and the associated 95% CIs. We also conducted a sensitivity analysis, in which the effect of each study on the estimate was evaluated. We used a fixed effects model when I 2 values were<50%, and a random effects model when I 2 values were >50%. Publication bias was assessed by visual inspection of the funnel plots. Data relating to CVD, CHD, stroke, and all‐cause mortality from individual trials and all trials combined are described in Forest plots. Statistical significance for all analyses was set at P<0.05.
Results
Study Flow and Characteristics
The decision‐making process for inclusion is shown in Figure 1. Twelve studies involving 582 267 participants were included in this meta‐analysis. 10 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 Table 1 shows the characteristics of patients with diabetes and comorbid sleep disorders or disturbances. Eight studies examined CVD, 6 evaluated CHD, 5 assessed stroke, and 7 investigated all‐cause mortality. Seven studies were excluded (Figure 1) because of the reasons listed in Table S2.
Table 1.
Baseline Characteristics of 12 Studies
| Study | Study design | Participants (n) | Mean follow‐up (y) | Age (y) | Sex (M%) | Preexisting condition | Outcomes |
|---|---|---|---|---|---|---|---|
| Obstructive sleep apnea (OSA) | |||||||
| Labarca G et al. 14 | Prospective cohort | 1447 | 5.0 | ≥18 | 79.27 | Diabetes | All‐cause mortality; CHD; Stroke |
| Adderley et al. 15 | Retrospective population‐based | 14 117 | 3.0 | 60.7±10.56 | 75.04 | Type 2 diabetes | All‐cause mortality; CVD; Stroke |
| Wang et al. 16 | Prospective cohort | 804 | 2.0 | 57.5±10.2 | 82.60 | Type 2 diabetes | CVD; CHD; Stroke |
| Koo et al. 17 | Observational | 1311 | 1.9 | 59.2±10.5 | 85.20 | Diabetes and coronary revascularization | CVD |
| Rice et al. 18 | Cross‐sectional | 305 | 11.5 | 61.5±6.5 | 40.00 | Type 2 diabetes | CVD; CHD; Stroke |
| Su et al. 23 | Multicenter prospective cohort | 1113 | 42 (months) | 66.0 (62.0–71.0) | 60.60 | Type 2 diabetes | All‐cause mortality; CVD |
| Strausz et al. 24 | Longitudinal population‐based | 36 963 | 12.9 | 24–74 | 47.26 | Type 2 diabetes | All‐cause mortality; CHD |
| Other sleep disturbance | |||||||
| Schantz et al. 10 | Prospective population‐based | 487 728 | 8.9 | 56.5±8.1 | 45.60 | Diabetes | All‐cause mortality; |
| Choi et al. 19 | Cohort | 36 058 | 7.0 | 40–79 | 38.91 | Type 2 diabetes | All‐cause mortality; CVD; CHD; Stroke |
| Meng et al. 20 | Observational | 332 | 1.0 | 59.36±9.39 | 56.63 | Type 2 diabetes | CVD |
| Seicean et al. 21 | Observational | 834 | 4.9 | 56.0±11 | 55.76 | Type 2 diabetes | CHD |
| Eguchi et al. 22 | Prospective cohort | 1255 | 41±14 (months) | 70.4±9.9 | 37.93 | Type 2 diabetes and hypertensive | All‐cause mortality; CVD |
CHD indicates coronary heart disease; and CVD, cardiovascular disease.
Association of the Coexistence of Sleep Disorder or Disturbance and Diabetes With the Risk of CVD Events
As shown in Figure 2, an exploratory data analysis of 8 studies indicated that in comparison with patients with diabetes without somnipathy, patients with diabetes and somnipathy had an increased risk of CVD events (RR, 1.27 [95% CI, 1.12–1.45], P<0.0001).
Figure 2.
Risk ratios (RRs) for cardiovascular diseaseevents associated with diabetes with somnipathy vs. diabetes without somnipathy from pooled studies.
The coexistence of sleep disorders or disturbances and diabetes was associated with increased CHD outcomes. The pooled results from 6 studies indicated that diabetic patients with diabetes and somnipathy exhibited increased CHD outcomes compared with patients with diabetes without somnipathy (RR, 1.40 [95% CI, 1.21–1.62], P<0.0001) (Figure 3).
Figure 3.
Risk ratios (RRs) for coronary heart disease events associated with diabetes with somnipathy vs. diabetes without somnipathy from pooled studies.
Association of the Coexistence of Somnipathy and Diabetes With the Risks of Stroke and Mortality
As shown in Figures 4 and 5, the coexistence of somnipathy and diabetes was associated with increased risks of stroke and all‐cause mortality compared with patients with diabetes without somnipathy (RR, 1.28 [95% CI, 1.08–1.52], P=0.004, and RR, 1.56 [95% CI, 1.26–1.94], P<0.0001, respectively).
Figure 4.
Risk ratios (RRs) for stroke associated with diabetes with somnipathy vs. diabetes without somnipathy from pooled studies.
Figure 5.
Risk ratios (RRs) for all‐cause mortality associated with diabetes with somnipathy vs. diabetes without somnipathy from pooled studies.
Sensitivity Analysis and Publication Bias
Considering the importance of potential confounders in observational studies, sensitivity analyses were conducted by excluding studies with serious risk of bias in this domain. No significant association was found after excluding studies with a serious risk of bias. Similarly, after excluding each study, no significant association emerged for the outcomes in the sensitivity analysis. Publication bias was assessed by visual inspection of the funnel plots. The outcome of CVD events is subject to publication bias (Figure 6).
Figure 6. A.
Funnel plots of cardiovascular disease (), coronary heart disease (B), all‐cause mortality (C) and stroke (D).
Subgroup Analysis of Relative Risk for CVD Events
Age, sex, body mass index, and type of somnipathy were potential confounders related to CVD outcomes. As shown in Figure 7, the estimated RR indicated that compared with patients with diabetes without somnipathy, the coexistence of somnipathy and diabetes was associated with an increased risk of CVD events in both the age <70 years and age ≥70 years (RR, 1.28 [95% CI, 1.03–1.61]; RR, 1.99 [95% CI, 1.08–3.65], respectively) groups. Similarly, regardless of whether the patients had sleep disorders or disturbance, the estimated RR indicated that the coexistence of somnipathy and diabetes was also associated with an increased risk of CVD events (RR, 1.39 [95% CI, 1.12–1.71]; RR, 1.22 [95% CI, 1.02–1.46], respectively). Furthermore, in patients with somnipathy and diabetes, those of the female sex (RR, 1.40 [95% CI, 1.12–1.74]) and with a body mass index ≥23 (RR, 1.75 [95% CI, 1.10–2.80]) showed a significantly higher risk of CVD in comparison with patients with diabetes without somnipathy.
Figure 7.
Subgroup analysis of relative risk for cardiovascular disease events.
Risk of Bias Assessment
The detailed risk assessment of the included studies using the Risk of Bias in Non‐randomized Studies of Interventions tool is shown in Table 2.
Table 2.
Risk of Bias Assessment of Nonrandomized Controlled Trial Based on Risk of Bias in Non‐randomized Studies of Interventions Tool
| Study | Bias due to confounding | Bias in selection of participants | Bias of classification of intervention | Bias due to deviations from intended interventions | Bias due to missing data | Bias in measurement of outcomes | Bias in selection of reported results | Overall |
|---|---|---|---|---|---|---|---|---|
| Labarca G et al. 14 | Low | Low | Moderate | Moderate | Low | Low | Moderate | Moderate* |
| Adderley et al. 15 | Low | Moderate | Low | Low | Moderate | Moderate | Moderate | Moderate* |
| Wang et al. 16 | Low | Low | Low | Low | Moderate | Moderate | Low | Moderate* |
| Koo et al. 17 | Moderate | Moderate | Low | Low | Low | Low | Moderate | Moderate* |
| Rice et al. 18 | Low | Moderate | Low | Low | Moderate | Low | Low | Moderate* |
| Su et al. 23 | Low | Moderate | Low | Low | Low | Low | Moderate | Moderate* |
| Schantz et al. 10 | Moderate | Low | Low | Low | Low | Low | Low | Moderate* |
| Choi et al. 19 | Low | Low | Low | Low | Low | Low | Moderate | Moderate* |
| Meng et al. 20 | Serious | Low | Moderate | Moderate | Low | Low | Low | Serious† |
| Seicean et al. 21 | Low | Low | Serious | Low | Low | Low | Low | Serious† |
| Eguchi K et al. 22 | Moderate | Low | Low | Low | Low | Low | Low | Moderate* |
| Strausz et al. 24 | Low | Low | Low | Low | Moderate | Moderate | Low | Moderate* |
Moderate risk: the study is evaluated to be at low or moderate risk of bias for all domains.
Serious risk: the study is evaluated to be at serious risk of bias in at least 1 domain but not at critical risk of bias in any domain.
Quality of Evidence Assessment
The Grading of Recommendations, Assessment, Development, and Evaluation system was used to assess the quality of the evidence. The evaluation results are listed in Table 3. Briefly, the quality of evidence for CVD, CHD, stroke, and all‐cause mortality was rated moderate.
Table 3.
Patients With Diabetes and Somnipathy Compared With Controls for CVD/CHD/Stroke/All‐Cause Mortality
| Outcomes | No. of participants (studies) Follow‐up | Certainty of the evidence (Grading of Recommendations, Assessment, Development, and Evaluation) | Relative effect (95% CI) |
|---|---|---|---|
| CVD events | (8 observational studies) |
⨁⨁⨁◯ Moderate Due to strongly suspected publication bias. Upgraded due to large magnitude of effect. Upgraded because all plausible confounding would reduce demonstrated effect. |
RR 1.27 (1.12–1.45) |
| CHD | (6 observational studies) |
⨁⨁⨁◯ Moderate Upgraded due to large magnitude of effect. |
RR 1.40 (1.21–1.62) |
| Stroke | (5 observational studies) |
⨁⨁⨁◯ Moderate Due to strongly suspected publication bias. Upgraded due to large magnitude of effect. Upgraded because all plausible confounding would reduce demonstrated effect. |
RR 1.28 (1.08–1.52) |
| All‐cause mortality | (7 observational studies) |
⨁⨁⨁◯ Moderate Upgraded due to large magnitude of effect. |
RR 1.56 (1.26–1.94) |
The risk in the intervention group (and its 95% CI) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). CHD indicates coronary heart disease; CVD, cardiovascular disease; and RR, risk ratio.
Intervention: diabetes and somnipathy. Comparison: control (diabetes without somnipathy).
GRADE Working Group grades of evidence; High certainty: We are very confident that the true effect lies close to that of the estimate of the effect; Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different. Low certainty: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect. Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.
Discussion
This meta‐analysis of pooled results demonstrated that the combination of diabetes and somnipathy is strongly associated with increased risks of CVD, CHD, stroke, and all‐cause mortality. Therefore, the coexistence of subjective and objective somnipathy and diabetes should be further assessed in clinical practice.
Vascular endothelial dysfunction is a major cause of coronary artery disease that is predictive of increases in CVD. 25 Diabetes and somnipathy can cause endothelial cell dysfunction, 26 , 27 , 28 , 29 which is important in the pathogenesis of diabetic angiopathy because of increased vascular tone, vascular inflammation, and oxidative stress. 30 Obstructive sleep apnea (OSA) is associated with increased endothelin‐1 levels and inflammation, and repeated hypoxemia‐reoxygenation episodes induce reactive‐oxygen‐species production. 31 , 32 , 33 Population‐based studies have indicated that OSA is associated with serious endothelial dysfunction in both adults and children. 34 , 35 , 36 It is unclear whether the combination of diabetes and sleep disorders or disturbances causes additional endothelial injury. Previous studies have shown that there are additional effects on early markers of atherosclerosis in patients with OSA, hypertension, and metabolic syndrome. 37 , 38 There are few reports on the impact of the combination of diabetes and somnipathy on microvascular diabetic complications. 15 , 39 A previous study of 140 patients with type 2 diabetes demonstrated that 21.4% and 47.6% had moderate and severe OSA, respectively, indicating that OSA was very common; however, the severity of OSA was not correlated with the degree of endothelial dysfunction. 40
Increased oxidative stress, systemic inflammation, and visceral fat accumulation result in dysregulated adipocytokine production, which potentially contributes to the added risks of CVD, CHD, and mortality in patients with comorbid diabetes and somnipathy. 41 , 42 , 43 , 44 However, the mechanism underlying the increased CVD risk requires further investigation, both in vitro and in vivo.
In addition to OSA, insomnia and short sleep duration are common in the general population. Poor sleep efficiency and longer duration of waking after sleep onset, objectively measured by polysomnography, are associated with an increased risk of incident CVD. 45 Similarly, short sleep duration is also associated with incremental cardiometabolic risk in Indigenous Australians. 46 Future studies should investigate the association between the coexistence of insomnia or short sleep duration and diabetes with the risk of CVD and all‐cause mortality.
This meta‐analysis has several potential limitations. First, most of the included studies were prospective cohort, retrospective population‐based, or cross‐sectional studies, which fall in the middle in the hierarchy of evidence. This might have led to the inclusion of evidence of insufficient quality. Future large, high‐quality randomized controlled trials are required to confirm our conclusions. Second, there is some concern regarding the pooling of results from studies that examine objective sleep disorders and subjective sleep disturbances. However, previously published meta‐analyses of sleep problems and the low heterogeneity observed in this study suggest that our analysis of objective sleep disorders and subjective sleep disturbances is valid. Third, few studies have investigated the combined effects of sleep problems and diabetes on CVD outcomes and all‐cause mortality. The populations of the studies included in this meta‐analysis were sizable, involving a total of 582 267 participants; thus, the sample size was large enough to support our conclusions.
Conclusions
In summary, the pooled results analyzed in this study indicate that a combination of somnipathy and diabetes is associated with increased risks of CVD, CHD, stroke, and all‐cause mortality. Therefore, physicians should attend to sleep problems in patients with diabetes and attempt to solve them to reduce the risks of CVD, stroke, and mortality.
Sources of Funding
This study was supported by Discipline Construction Promoting Project of Shanghai Pudong Hospital in Nephrology (Zdxk2020‐10), Key Specialty of Plasma Purification in Shanghai Pudong Hospital (Zdzk2020‐12).
Disclosures
None.
Supporting information
Table S1
Acknowledgments
Author contributions: H.M. Jin and S.K. Fu conceived and designed the study. X.H. Yang, B.L. Zhang, and Y. Chen selected the articles and extracted and analyzed the data. X.H. Yang, B.L. Zhang, and Y. Chen wrote the first draft of the article. X.H. Yang, B.L. Zhang, and H.M. Jin interpreted the data and contributed to the writing of the final version of the article. All authors agreed with the results and conclusions of this article. All authors have approved the final version of the article and have agreed to submit it to this journal.
Supplemental Material is available at <url href="https://www.ahajournals.org/doi/suppl/10.1161/JAHA.121.024783">https://www.ahajournals.org/doi/suppl/10.1161/JAHA.121.024783</url>
For Sources of Funding and Disclosures, see page xxx.
Contributor Information
Shun Kun Fu, Email: fushunkun@126.com.
Hui Min Jin, Email: hmjgli@163.com.
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Supplementary Materials
Table S1