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. 2021 Apr 24;26(1):167–188. doi: 10.1007/s11325-021-02384-2

An umbrella review of systematic reviews and meta-analyses of observational investigations of obstructive sleep apnea and health outcomes

Weiwei Chen 1,#, Yuting Li 1,#, Liliangzi Guo 1, Chenxing Zhang 1, Shaohui Tang 1,✉
PMCID: PMC8856999  PMID: 33893906

Abstract

Purpose

The previous analysis of systematic reviews and meta-analyses have illustrated that obstructive sleep apnea (OSA) is correlated with multiple health outcomes. In the present research, our main aim was to execute an umbrella review to assess the available evidence for the associations between OSA and health outcomes.

Methods

Herein, a meta-analysis of previous observational investigations that have reported associations between OSA and health outcomes in all human populations and settings was performed. We used these studies to execute an umbrella review of available meta-analyses and systematic reviews.

Results

Sixty-six articles comprising 136 unique outcomes were enrolled in this analysis. Of the 136 unique outcomes, 111 unique outcomes had significant associations (p < 0.05). Only 7 outcomes (coronary revascularization after PCI, postoperative respiratory failure, steatosis, alaninetrans aminase (ALT) elevation, metabolic syndrome (MS), psoriasis, and Parkinson’s disease) had a high quality of evidence. Twenty-four outcomes had a moderate quality of evidence, and the remaining 80 outcomes had a weak quality of evidence. Sixty-nine outcomes exhibited significant heterogeneity. Twenty-five outcomes exhibited publication bias. Sixty-three (95%) studies showed critically low methodological quality.

Conclusion

Among the 66 meta-analyses exploring 136 unique outcomes, only 7 statistically significant outcomes were rated as high quality of evidence. OSA may correlate with an increased risk of coronary revascularization after PCI, postoperative respiratory failure, steatosis, ALT elevation, MS, psoriasis, and Parkinson’s disease.

Keywords: Obstructive sleep apnea, Health, Umbrella review, Meta-analysis

Introduction

Obstructive sleep apnea (OSA) is a prevalent but treatable chronic sleep disorder that is determined through episodes of sleep apnea and hypopnea during sleep and results in recurrent episodes of hypercapnia and hypoxemia [1–3]. OSA has a prevalence of between 5 and 20% depending on the population surveyed and the definition utilized [4, 5]. The prevalence is also increasing due to an increase in body mass index which is one of its major predisposing factors. Apart from causing uncomfortable symptoms such as headache [6] and attention deficit [7], earlier studies indicated that OSA also contributed to the advancement of several diseases including hypertension [8], cardiovascular disease [9, 10], and diabetes [11]. Recent studies have drawn consistent conclusions [12–14]. Recently, a great number of researches have explored the correlation between OSA and other diseases. Multiple investigations and meta-analyses have illustrated that OSA poses a threat to human health because it increases the risk of various diseases, including cancers [15–17], depression [18], laryngopharyngeal reflux disease [19], metabolic disease [20], Parkinson’s disease [21], and chronickidney disease (CKD) [22].

These studies suggest a possible causal relationship between OSA and different health outcomes, indicating that OSA has a bad influence on human health. However, several factors are known to decrease the validity and strength of reported evidence including publication bias, protocol design flaws, or inconsistencies of studies. Currently, there have been no systematic reviews that have accurately summarized and critically appraised existing studies. In the current study, an umbrella review was executed to comprehensively evaluate published systematic reviews and meta-analyses of observational researches that reported associations between OSA and health information. This work can provide important guidance in the diagnosis and treatment of OSA.

Materials and methods

The protocol of the research was registered with PROSPERO (registration number: CRD42020220015) before the umbrella review began. A systematic exploration of the literature search was accomplished in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols [23].

Literature search

From initiation until November 23, 2020, literature searches were performed using online databases such as Embase, PubMed, the Cochrane Database of Systematic Reviews, and the Web of Science. Literature searches were independently conducted by two researchers (CZ and LG). The search terms applied were (“obstructive sleep apnea” OR “obstructive sleep apnea–hypopnea” OR “OSA” OR “OSAH”) AND (Meta-Analysis[ptyp] OR metaanaly*[tiab] OR meta-analy*[tiab] OR Systematic review [ptyp] OR “systematic review”[tiab]). The references were manually screened to identify eligible articles to be included in the study. The article titles, abstracts, and the complete manuscripts of the identified paper were then further assessed. A discussion was used to resolve potential discrepancies; ST acted as an arbiter to deal with discrepancies that could not be resolved by discussion among the investigators.

Eligibility criteria and exclusion criteria

The eligibility of articles was based on a systematic search by the authors to identify the most pertinent studies. Only systematic reviews or meta-analyses on the basis of the epidemiological studies performed in humans were considered in the analysis. Diagnostic trials and meta-analyses of interventional trials were not performed as part of the current study. Furthermore, the abstracts of the conference on review questions were not included in the final analysis. The final systematic reviews and meta-analyses that were analyzed had to include the data of pooled summary effects(i.e., relative risks (RRs); odds ratios (ORs); hazard ratios (HRs); mean difference (MD); weighted mean difference (WMD); standard mean difference (SMD); and their 95% confidence intervals (CIs)), number of included researches, number of participants and cases, heterogeneity, and publication bias. Whenever more than one meta-analysis was executed using on the basis of the same outcome, the agreement with the main conclusions reported in the study were verified. When the reported conclusions were conflicting, the meta-analysis with the greatest number of investigations was considered.

Data extraction

For investigations to be eligible for inclusion in the meta-analysis, two researchers (WC and YL) independently extracted data from the articles. This included the first author, the number of included investigations, the year of publication, the study design, the whole numbers of cases, and participants. The reported relative summary risk evaluates (ORs, RRs, HRs, SMD, WMD, or MD) and the corresponding 95% CIs were extracted, for each eligible systematic review and meta-analysis. The values of p for the total pooled effects, Cochran Q measurement, Egger’s measurement, and I2 were extracted. Discrepancies in the analyses were resolved by discussion among the investigators.

Assessment of methodological quality

Two investigators (WC and YL) independently assessed the quality of the methods reported in the studies. This was performed using a 16-criteria checklist included in AMSTAR 2 [24]. AMSTAR 2 is a fundamental revision of the original instrument of AMSTAR which was devised to evaluate systematic reviews that included randomized controlled experiments. The AMSTAR 2 score is categorized as high in studies that have no or one noncritical weakness, moderate in surveys with more than one noncritical weakness, low when the study has only one serious flaw without or with noncritical weaknesses, and seriously low when a study has more than one serious flaw without or with nonserious weaknesses. Discrepancies between the AMSTARS 2 scores for the articles were resolved by discussion between the investigators.

Assessment of the evidence quality

Two investigators (WC and YL) independently evaluated the quality of the evidence conforming to the parameters that have previously been applied in various fields [25–28]. Discrepancies were resolved by discussion. First, p value for the estimate < 0.001 [29, 30] and more than 1000 cases of the disease, which indicated fewer false-positive results. Second, I2 < 50% and p value for Cochran Q test > 0.10, which indicated consistency of results. Third, p value for Egger’s test > 0.10, which exhibited no evidence of small-study impacts. When all of the above criteria were satisfied, the strength of the epidemiologic evidence was rated as high. When 1 of the criterion was not satisfied and the p value for the estimate was < 0.001, the strength of the epidemiologic evidence was rated as moderate. Then, the rest was defined as weak (p < 0.05). The value of p for the evaluation can be assessed from the 95% confidence interval of the pooled impact estimate utilizing an established method [31] if it was not directly reported in the article.

Data analysis

From each of the published studies, the outcome data of the available meta-analyses was extracted along with the estimated summary effect at the corresponding 95% CI. The total impacts of the pooled meta-analysis were considered significant when the p-value was < 0.05. Heterogeneity was appraised by the I2 test and Q test, publication bias was estimated by utilizing Egger’s test, and both were considered significant at p < 0.1. Studies that did not have the heterogeneity or publication bias results were reanalyzed if raw data were available.

Results

Characteristics of the meta-analyses

The outcomes of the systematic investigation and the selection of eligible investigations are summarized in Fig. 1. Overall, 1972 articles were searched from which 66 meta-analyses of observational investigations were identified that had 136 unique outcomes [21, 22, 32–95]. The 66 eligible non-overlapping meta-analyses had publication dates ranging from 2009 to 2020 and are summarized in Table 1. The median number of primary investigations per evidence synthesis was 7 (range 2–64). Furthermore, 1 meta-analysis [54] lacked the data of both participants and cases, and 2 meta-analyses [52, 95] lacked the data of cases. Among the meta-analyses identified in this study, the median number of cases was 900 (88–3,117,496) and the median number of participants was 2962 (170–56,746,100). An extensive range of data were reported such as cardiovascular disorders (n = 31), cerebral and cerebrovascular disease (n = 7), mortality (n = 5), postoperative complications (n = 20), pregnancy-related disorders (n = 13), ophthalmic disorders (n = 8), digestive disorders (n = 13), endocrine and metabolic system disorders(n = 17), urological disorders (n = 7), and other data (n = 15) (Fig. 2).

Fig. 1.

Fig. 1

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Flowchart of the selection procedure

Table 1.

Associations between OSA and multiple heath outcomes

Outcomes Publication Number of studies Number of participants Number of cases Type of metric Relative risk (95% CI) P value* P value # I2 (%) P value※ Whether exist publication bias
Cardiovascular disorders
  Aortic dissection Xiushi Zhou (2018) 1 cohort study, 2 case–control studies 55,911 16,019 OR 1.60 (1.01–2.53) 0.04 0.44 0 0.58 No
  Cardiovascular disease(CVD) Xia Wang (2013) 11 cohort studies 25,594 2628 RR 1.79 (1.47–2.18)  < 0.001 0.131 31.5 0.028 Yes
  Stroke Min Li (2014) 10 cohort studies 18,609 678 RR 2.10 (1.50–2.93)  < 0.001 0.04 47.5 0.288& No
  Ischemic heart disease(IHD) Wuxiang Xie (2014) 6 cohort studies 1083 625 RR 1.83 (1.15–2.93) 0.011 0.111 44.2 0.006 Yes
  Coronary heart disease(CHD) Chengjuan Xie (2017) 6 cohort studies 18,022 15,562 RR 1.63 (1.18–2.26) 0.003 0.061& 52.7& 0.145& No
  Major adverse cardiac events (MACEs) Chengjuan Xie (2017) 9 cohort studies 18,022 15,562 RR 2.04 (1.56–2.66)  < 0.001 0.021 55.7 0.132 No
  Atrial fibrillation Irini Youssef (2018) 4 cross-sectional studies, 5 cohort studies 19,837 12,255 OR 2.12 (1.84–2.43)  < 0.001 0.004 64.42 0.097& Yes
  Resistant hypertension Haifeng Hou (2018) 6 case -control studies 1465 925 OR 2.84 (1.70–3.98)  < 0.001 0.816 0 0.187& No
  Essential hypertension Haifeng Hou (2018) 2 case–control studies, 5 cohort studies 7102 4513 OR 1.80 (1.54–2.06)  < 0.001 0.221 26 0.0526& Yes
  Atrial fibrillation recurrence after catheter ablation Chee Yuan Ng (2011) 6 observational studies 3995 958 RR 1.25 (1.08–1.45) 0.003 0.008 49 0.879& No
  major adverse cardiovascular event (MACE) after PCI Xiao Wang (2018) 9 observational studies 2755 1581 RR 1.96 (1.36–2.81)  < 0.001 0.02 54 0.002 Yes
  Stroke after PCI Xiao Wang (2018) 6 observational studies 2110 1254 RR 1.55 (0.90–2.67) 0.11 0.62 0 0.149& No
  Myocardial infarction (MI) after PCI Hua Qu (2018) 6 observational studies 2342 1112 OR 1.59 (1.14–2.23) 0.007 0.32 15 0.655& No
  Coronary revascularization after PCI Hua Qu (2018) 7 observational studies 2415 1163 OR 1.57 (1.23–2.01)  < 0.001 0.7 0 0.483& No
  Re-admission for heart failure after PCI Hua Qu (2018) 4 observational studies 1774 793 OR 1.71 (0.99–2.96) 0.06 0.86 0 0.254& No
  Left ventricular hypertrophy (LVH) Cesare Cuspidi (2020) 9 observational studies 3244 1802 OR 1.70 (1.44–2.00)  < 0.001  < 0.001 60 0.0876& Yes
  Left ventricular diastolic diameter (LVEDD) LeiYu (2019) 13 observational studies 882 563 WMD 1.24 (0.68, 1.80)  < 0.001 0.658 0 0.431 No
  Left ventricular systolic diameter (LVESD) LeiYu (2019) 11 observational studies 630 396 WMD 1.14 (0.47, 1.81) 0.001 0.696 0 0.722 No
  Left ventricular mass(LVM) LeiYu (2019) 6 observational studies 432 304 WMD 35.34 (20.67, 50.00)  < 0.001  < 0.001 79.1 0.914 No
  Leftventricular ejection fraction (LVEF) LeiYu (2019) 15 observational studies 1104 710 WMD  − 3.01 (− 1.90, − 0.79) 0.001  < 0.001 64.7 0.048 Yes
  Left atrial diameter (LAD) LeiYu (2019) 7 observational studies 468 311 WMD 2.13 (1.48, 2.77)  < 0.001 0.408 2.2 0.072 Yes
  Left atrial diameter volume index (LAVI) LeiYu (2019) 3 observational studies 228 159 WMD 3.96 (3.32, 4.61)  < 0.001 0.445 0 0.735 No
  Right ventricular internal diameter (RVID) Abdirashit Maripov (2017) 16 observational studies 1498 902 WMD 2.49 (1.62, 3.37)  < 0.001  < 0.001 96.8 0.001 Yes
  Right ventricular free wall thickness (RVWT) Abdirashit Maripov (2017) 9 observational studies 976 579 WMD 0.82 (0.51, 1.13)  < 0.001  < 0.001 95.6 0.671 No
  Right ventricular myocardial performance index(RV MPI) Abdirashit Maripov (2017) 14 observational studies 1298 864 WMD 0.08 (0.06, 0.10)  < 0.001  < 0.001 84.1 0.15 No
  Tricuspid annular systolic velocity (RV S′) Abdirashit Maripov (2017) 14 observational studies 1030 639 WMD  − 0.95 (− 0.32, − 1.59) 0.003  < 0.001 88.4 0.347 No
  Tricuspid annular plane systolic excursion (TAPSE) Abdirashit Maripov (2017) 11 observational studies 1033 655 WMD  − 1.76 (− 0.78, − 2.73)  < 0.001  < 0.001 89.3 0.462 No
  Right ventricular fractional area change (RA FAC) Abdirashit Maripov (2017) 6 observational studies 661 422 WMD  − 3.16 (− 0.73, − 5.60) 0.011  < 0.001 80.2 0.006 Yes
  Epicardial adipose tissue (EAT) thickness Guang Song (2020) 9 observational studies 1178 898 WMD 0.95 (0.73, 1.16)  < 0.001  < 0.001 64.7 0.549 No
  Coronary flow reserve (CFR) Rui-Heng Zhang (2020) 1 case–control study, 4 cross-sectional studies 1336 829 WMD ’ − 0.78 (− 0.32, − 1.25)  < 0.001  < 0.001 84.4 0.49 No
  Systolic blood pressure (SBP) De-Lei Kong (2016) 2 cross-sectional studies, 3 cohort studies, 1 case–control studies 1046 534 SMD 0.56 (0.40, 0.71)  < 0.001 0.132 41.03 NA NA
Cerebral and cerebrovascular disease
  Cerebral white matter changes Bo-Lin Ho (2018) 10 observational studies 1582 818 OR 2.06 (1.52–2.80)  < 0.001 0.025 48.5 0.338 No
  Cerebrovascular (CV) disease Zesheng Wu (2018) 15 cohort studies 3,120,368 3,117,496 HR 1.94 (1.31–2.89) 0.001  < 0.001 90.3  > 0.05 No
  White matter hyperintensities (WMH) Yuhong Huang (2019) 11 cross-sectional studies, 2 case–control studies 4412 2065 OR 2.23 (1.53–3.25)  < 0.001  < 0.001 80.3  < 0.01 Yes
  Silent brain infarction (SBI) Yuhong Huang (2019) 9 cross-sectional studies, 2 case–control studies, 1 cohort study 3353 1893 OR 1.54 (1.06–2.23) 0.023 0.018 52 0.605 No
  Cerebral microbleeds (CMBs) Yuhong Huang (2019) 3 cross-sectional studies 342 271 OR 2.17 (0.61–7.73) 0.234  < 0.01 60.2 NA Unclear
  Perivascular spaces (PVS) Yuhong Huang (2019) 2 cross-sectional studies 267 152 OR 1.56 (0.28–8.57) 0.623  < 0.01 69.5 NA NA
  Asymptomatic lacunar infarction (ALI) AnthipaChokesuwattanaskul (2019) 6 cross-sectional studies, 1 cohort study 1756 713 OR 1.78 (1.06–3.01) 0.03 0.128& 41 0.43 No
Mortality
  All-cause mortality Lei Pan (2016) 12 cohort studies 34,382 18,139 HR 1.26 (1.09–1.43) 0.001  < 0.001 70.4 0.003 Yes
  Cardiovascular mortality Xiahui Ge (2013) 4 cohort studies 5228 239 RR 2.21 (1.61–3.04)  < 0.001 0.418 0 0.448 No
  All-cause death after PCI Xiao Wang (2018) 4 cohort studies 1919 1154 RR 1.70 (1.05–2.77) 0.03 0.71 0 0.176& No
  Cardiac death after PCI Hua Qu (2018) 7 cohort studies 2465 1187 OR 2.05 (1.15–3.65) 0.01 0.96 0 0.828& No
  Cancer mortality Xiaobin Zhang (2017) 3 cohort studies 7346 179 HR 1.38 (0.79–2.41) 0.257 0.004 66.1 0.205 No
Postoperative complications
  Postoperative respiratory failure Faizi Hai BA (2013) 12 cohort studies 5611 2390 OR 2.42 (1.53–3.84)  < 0.001 0.39 5 0.28 No
  Postoperative cardiac events Faizi Hai BA (2013) 11 cohort studies 3781 2109 OR 1.63 (1.16–2.29) 0.005 0.7 0 0.187& No
  Postoperative desaturation R. Kaw (2012) 11 cohort studies 3645 1764 OR 2.27 (1.20–4.26) 0.01  < 0.001 68 0.04& Yes
  Postoperative ICU transfer R. Kaw (2012) 9 cohort studies 5743 2062 OR 2.81 (1.46–5.43) 0.002 0.02 57 0.033& Yes
  Postoperative composite endpoints of postoperative cardiac or cerebrovascular complications Ka Ting Ng (2020) 12 observational studies 2,003,694 126,027 OR 1.44 (1.17–1.78)  < 0.001 NA 89 NA Unclear
  Postoperative myocardial infarction Ka Ting Ng (2020) 8 observational studies 714,650 NA OR 1.37 (1.19–1.59)  < 0.001 NA 36 NA Unclear
  Postoperative congestive cardiac failure Ka Ting Ng (2020) 3 observational studies 2104 NA OR 3.16 (1.02–9.81) 0.05 NA 0 NA Unclear
  Postoperative atrial fibrillation Ka Ting Ng (2020) 6 observational studies 1,463,449 NA OR 1.50 (1.30–1.73)  < 0.001 NA 87 NA Unclear
  Postoperative cerebrovascular accident Ka Ting Ng (2020) 5 observational studies 1,641,495 NA OR 1.09 (0.75–1.60) 0.65 NA 61 NA Unclear
  Postoperative composite endpoints of pulmonary complications Ka Ting Ng (2020) 8 observational studies 1,983,748 NA OR 2.52 (1.92–3.31)  < 0.001 NA 96 NA Unclear
  Postoperative pneumonia Ka Ting Ng (2020) 10 observational studies 2,675,205 NA OR 1.66 (1.17–2.35) 0.004 NA 96 NA Unclear
  Postoperative reintubation Ka Ting Ng (2020) 9 observational studies 2,061,268 NA OR 2.29 (0.90–5.82) 0.08 NA 99 NA Unclear
  Postoperative in-hospital mortality Ka Ting Ng (2020) 6 observational studies 2,497,794 NA OR 0.86 (0.42–1.76) 0.68 NA 94 NA Unclear
  Postoperative 30-day mortality Ka Ting Ng (2020) 6 observational studies 616,754 NA OR 1.27 (1.03–1.57) 0.02 NA 0 NA Unclear
  Postoperative acute kidney injury Ka Ting Ng (2020) 5 observational studies 1,724,932 NA OR 2.41 (1.93–3.02)  < 0.001 NA 92 NA Unclear
  Postoperative delirium Ka Ting Ng (2020) 6 observational studies 2346 NA OR 2.45 (1.50–4.01)  < 0.001 NA 2 NA Unclear
  Postoperative venoembolism Ka Ting Ng (2020) 10 observational studies 2,100,013 NA OR 1.63 (1.17–2.27) 0.004 NA 94 NA Unclear
  Postoperative surgical site infection Ka Ting Ng (2020) 5 observational studies 2962 NA OR 1.30 (0.93–1.83) 0.13 NA 0 NA Unclear
  Postoperative bleeding Ka Ting Ng (2020) 3 observational studies 18,712 NA OR 1.10 (0.40–3.01) 0.85 NA 63 NA Unclear
  Postoperative length of hospital stay Ka Ting Ng (2020) 15 observational studies 1,569,278 NA MD 0.09 (0.00–0.17) 0.04 NA 96 NA Unclear
Pregnancy-related disorders
  Gestational diabetes mellitus (GDM) Xinge Zhang (2020 6 cohort studies 2,522,547 139,559 RR 1.60 (1.21–2.12) 0.004 0.003 69.2 0.4829 No
  C-section Lina Liu (2019) 6 observational studies NA NA OR 1.42 (1.12–1.79)  < 0.001  < 0.001 86.5 NA Unclear
  Pregnancy-related prolonged hospital stay Lina Liu (2019) 3 observational studies NA NA OR 1.94 (0.88–4.28) 0.1  < 0.001 98.6 NA Unclear
  Pregnancy-related wound complication Lina Liu (2019) 3 observational studies NA NA OR 1.87 (1.56–2.24)  < 0.001 0.883 0 NA Unclear
  Pregnancy-related pulmonary edema Lina Liu (2019) 3 observational studies NA NA OR 6.35 (4.25–9.50)  < 0.001 0.294 18.2 NA Unclear
  Small for gestational age Lina Liu (2019) 4 observational studies NA NA OR 1.26 (0.80–2.01) 0.321 0.01 73.8 NA Unclear
  Stillbirth Lina Liu (2019) 3 observational studies NA NA OR 1.12 (0.85–1.49) 0.413 0.572 0 NA Unclear
  Poor fetal growth Lina Liu (2019) 4 observational studies NA NA OR 1.15 (0.98–1.34) 0.091 0.266 24.3 NA Unclear
  Gestational hypertension Liwen Li (2018) 4 cross-sectional studies, 7 cohort studies 56,731,077 19,047 OR 1.80 (1.28–2.52) 0.001 0.72 0 0.649& No
  Preeclampsia Liwen Li (2018) 2 cross-sectional studies, 7 cohort studies 56,097,993 19,776 OR 2.63 (1.87–3.70)  < 0.001  < 0.01 78 0.797& No
  Preterm birth Liwen Li (2018) 2 cross-sectional studies, 3 cohort studies 56,746,100 18,337 OR 1.75 (1.21–2.55) 0.003  < 0.01 90 0.931& No
  Birth weight Liwen Li (2018) 4 cohort studies 4311 1387 WMD  − 47.46 (− 242.09, 147.16) 0.281  < 0.01 93 NA No$
  Neonatal intensive care unit (NICU) admission Ting Xu (2014) 4 cohort studies 757 177 RR 2.65 (1.86–3.76)  < 0.001 0.235 29.6 0.063& Yes
Ophthalmic disorders
  Diabetic retinopathy (DR) Zhenliu Zhu (2017) 6 case -control studies 1092 608 OR 2.01 (1.49–2.72)  < 0.001 0.062 52.4 0.112& No
  Keratoconus Marco Pellegrini (2020) 4 case–control studies, 1 cohort study 33,844 16,922 OR 1.84 (1.16–2.91) 0.009 0.003 74.6 0.07 Yes
  Glaucoma Xinhua Wu (2015) 12 observational studies 36,909 11,765 OR 1.65 (1.44–1.88)  < 0.001 0.06 43 0.335 No
  Floppy eyelid syndrome (FES) Leh-Kiong Huon (2016) 7 cross-sectional studies 902 337 OR 4.70 (2.98–7.41)  < 0.001 0.129& 39.3& 0.379& No
  Nonarteritic anterior ischemic optic neuropathy (NAION) Yong Wu (2015) 4 cohort studies, 1 case–control study 5916 164 OR 6.18 (2.00–19.11) 0.002 0.002 77 0.35 No
  Central serous chorioretinopathy (CSCR) Chris Y.Wu (2018) 6 case–control studies 7238 1479 OR 1.56 (1.16–2.10) 0.003 0.237 26.3 0.281 No
  retinal nerve fiber layer (RNFL) thickness Cheng-Lin Sun (2016) 8 case–control studies 1237 763 WMD  − 2.92 (− 4.61, − 1.24) 0.001 0.017 59.1 0.929 No
  Choroidal thickness Chris Y.Wu (2018) 9 case–control studies 778 514 WMD 25.52 (− 78.79, − 27.76) 0.824 0.001 98.6 0.137 No
Digestive disorders
  Gastroesophageal reflux disease Zeng-Hong Wu (2019) 1 case–control study, 6 cross-sectional studies 2699 1452 OR 1.75 (1.18–2.59) 0.006 0.04 54 0.052 Yes
  Steatosis Shanshan Jin (2018) 3 cohort studies, 1 cross-sectional study 1635 1375 OR 3.19 (2.34–4.34)  < 0.001 0.677 0 0.89 No
  Lobular inflammation Shanshan Jin (2018) 3 cohort studies 350 205 OR 2.85 (1.8–-4.49)  < 0.001 0.994 0 0.469 No
  Ballooning degeneration Shanshan Jin (2018) 3 cohort studies 350 205 OR 2.29 (1.36–3.84) 0.002 0.774 0 0.888 No
  NAFLD activity score(NAS) Shanshan Jin (2018) 3 cohort studies 350 205 OR 1.63 (0.68–3.86) 0.271 0.259 25.9 0.839 No
  NAFLD defined by liver histology G. Musso (2013) 8 cross-sectional studies 994 537 OR 2.01 (1.36–2.97)  < 0.001 0.4 4 0.303& No
  NAFLD defined by radiology G. Musso (2013) 6 cross-sectional studies 561 269 OR 2.99 (1.79–4.99)  < 0.001 0.33 13 0.433& No
  NAFLD defined by AST elevation G. Musso (2013) 11 cross-sectional studies 746 368 OR 2.36 (1.46–3.82)  < 0.001 0.99 0 0.65& No
  NAFLD defined by ALT elevation G. Musso (2013) 14 cross-sectional studies 1833 938 OR 2.60 (1.88–3.61)  < 0.001 0.74 0 0.179& No
  Nonalcoholic steatohepatitis(NASH) G. Musso (2013) 10 cross-sectional studies 1114 589 OR 2.37 (1.59–3.51)  < 0.001 0.81 0 0.404& No
  Fibrosis G. Musso (2013) 10 cross-sectional studies 1114 589 OR 2.16 (1.45–3.20)  < 0.001 0.67 0 0.778& No
  Alanine transaminase (ALT) Shanshan Jin (2018) 7 cohort studies, 1 cross-sectional study 2059 1684 SMD 0.21 (0.11, 0.31)  < 0.001 0.672 0 0.468 No
  Aspartate transaminase (AST) Shanshan Jin (2018) 7 cohort studies, 1 cross-sectional study 2059 1684 SMD 0.07 (− 0.03, 0.17) 0.152 0.918 0  < 0.05 Yes
Endocrine and metabolic system disorders
  Type 2 diabetes (T2DM) Ranran Qie (2020) 16 cohort studies 338,912 19,355 RR 1.40 (1.32–1.48)  < 0.001 0.045 40.8 0.221& No
  Metabolic syndrome (MS) Shaoyong Xu (2015) 15 cross-sectional studies 4161 2457 OR 2.87 (2.41–3.42)  < 0.001 0.23 20 0.232 No
  Fasting blood glucose (FBG) De-Lei Kong (2016) 3 cross-sectional studies, 5 cohort studies, 2 case–control studies 2053 1296 SMD 0.35 (0.18, 0.53)  < 0.001 0.008 59.69 NA No$
  Total cholesterol (TC) Rashid Nadeem (2014) 63 observational studies 18,111 NA SMD 0.267 (0.146, 0.389) 0.001 NA NA NA No$
  Low-density lipoprotein (LDL) Rashid Nadeem (2014) 50 observational studies 13,894 NA SMD 0.296 (0.156, 0.436) 0.001 NA NA NA No$
  High-density lipoprotein (HDL) Rashid Nadeem (2014) 64 observational studies 18,116 NA SMD  − 0.433 (− 0.604, − 0.262)  < 0.001 NA NA NA No$
  Triglyceride (TG) Rashid Nadeem (2014) 62 observational studies 17,831 NA SMD 0.603 (0.431, 0.775)  < 0.001 NA NA NA No$
  Adiponectin Mi Lu (2019) 20 case–control studies 1356 878 SMD ′ − 0.71 (− 0.92, − 0.49)  < 0.001  < 0.01 73 0.09 Yes
  Oxidized low-density lipoprotein (Ox-LDL) Reza Fadaei (2020) 8 case -control studies 623 391 SMD 0.95 (0.24, 1.67) 0.009  < 0.001 94.1  < 0.161 No
  Fibrinogen Fang Lu (2019) 25 observational studies 3792 1480 WMD 0.38 (0.29, 0.47)  < 0.001  < 0.001 80.3 0.208 No
  Homocysteine Kun Li (2017) 10 observational studies 773 457 MD 2.40 (0.60, 4.20) 0.009  < 0.001 96 0.947 No
  Advanced glycation end products (AGEs) Xingyu Wu (2018) 5 cross-sectional studies 670 323 SMD 0.98 (0.69, 1.27)  < 0.001 0.08 51 NA No$
  Plasma renin activity(PRA) Ze-Ning Jin (2016) 5 case–control studies 300 180 MD 0.17 (− 0.22, 0.55) 0.4  < 0.001 82 NA Unclear
  Plasma renin concentration(PRC) Ze-Ning Jin (2016) 5 case–control studies 170 101 MD 0.95 (− 0.58, 2.48) 0.23 0.001 78 NA Unclear
  Angiotensin II(AngII) Ze-Ning Jin (2016) 7 case–control studies 384 207 MD 3.39 (2.00, 4.79)  < 0.001  < 0.001 95 0.167 No
  Aldosterone Ze-Ning Jin (2016) 9 case–control studies 474 265 MD 0.95 (− 0.16, 2.07) 0.09  < 0.001 78 0.622 No
  Serum vitamin D Xiaoyan Li (2020) 6 case–control studies, 21 cross-sectional studies, 2 cohort studies 6298 4209 SMD ′ − 0.84(− 1.14, − 0.54)  < 0.001  < 0.001 95 NA No$
Urological disorders
  Diabetic kidney disease (DKD) Wen Bun Leong (2016) 7 cross-sectional studies 1877 1159 OR 1.59 (1.16–2.18) 0.004 0.224& 26.8 0.684& No
  Microalbuminuria Tongtong Liu (2020) 4 cross-sectional studies 667 415 RR 2.32 (1.48–3.62)  < 0.001 0.578 0 0.55 No
  Chronic kidney disease (CKD) Der-Wei Hwu (2017) 2 cohort studies, 16 cross-sectional studies 7090 3720 OR 1.77 (1.37–2.29)  < 0.001  < 0.001& 87.2& 0.011& Yes
  Serum uric acid level Tingting Shi (2019) 14 observational studies 5219 2656 WMD 50.25 (36.16,64.33)  < 0.001  < 0.001 91.2 0.001 Yes
  Serum cystatin C Tongtong Liu (2020) 7 cross-sectional studies 1412 274 SMD 0.53 (0.42,0.64)  < 0.001 0.16 33.7 0.111 No
  Estimated glomerular filtration rate (eGFR) Tongtong Liu (2020) 13 cross-sectional studies 3344 657 SMD  − 0.19 (− 0.27, − 0.12) 0.001 0.057 33.1 0.516 No
  Albumin/creatinine ratio(ACR) Tongtong Liu (2020) 3 cross-sectional studies 740 88 WMD 0.71 (0.58, 0.84)  < 0.001 0.003 69.2 0.574 No
Other outcomes
  Diabetic neuropathy Xiandong Gu (2018) 11 case -control studies 1842 840 OR 1.84 (1.18–2.87) 0.007  < 0.01 68.6 0.13 No
  Psoriasis Tzong-Yun Ger (2020) 3 cohort studies 5,544,674 42,656 RR 2.52 (1.89–3.36)  < 0.001 0.95 0 0.545 No
  Nocturia Jiatong Zhou (2019) 3 cohort studies, 8 case–control studies, 2 cross-sectional studies 9924 406 RR 1.41 (1.26–1.59)  < 0.001 0.001 63.3 0.076 Yes
  Allergic rhinitis Yuan Cao (2018) 1 cross-sectional study, 2 case–control studies, 1 cohort study 1283 371 OR 1.73 (0.94–3.20) 0.078 0.023 64.8 0.977 No
  Parkinson’s disease A-Ping Sun (2020) 4 cohort studies, 1 case–control study 83,449 26,070 HR 1.59 (1.36–1.85)  < 0.001 0.17 40 0.186 No
  Erectile dysfunction Luhao Liu (2015) 1 cohort study, 3 case–control studies, 1 cross-sectional study 834 532 RR 1.82 (1.12–2.97) 0.016 0.002 76.5 0.077 Yes
  Female sexual dysfunction Luhao Liu (2015) 2 case–control studies, 2 cohort studies 438 149 RR 2.0 (1.29–3.08) 0.002 0.194 36.4 0.327 No
  Sexual dysfunction Luhao Liu (2015) 3 cohort studies, 5 case–control studies, 1 cross-sectional study 1272 681 RR 1.87 (1.35–2.58)  < 0.001 0.001 70.1 0.692 No
  Osteoporosis Sikarin Upala (2016) 2 cohort studies, 2 cross-sectional studies 113,922 3141 OR 1.13 (0.60–2.14) 0.703  < 0.001 89.1 0.608& No
  Gout Tingting Shi (2019) 3 cohort studies 154,455 30,109 HR 1.25 (0.91–1.70) 0.162  < 0.001 91 0.876 No
  Cancer incidence Ghanshyam Palamaner Subash Shantha (2015) 5 cohort studies 112,226 904 RR 1.40 (1.01–1.95) 0.04 0.04 60 0.069 Yes
  Depression Cass Edwards (2020) 5 cohort studies 45,056 10,983 RR 2.18 (1.47–2.88)  < 0.001 0.005 72.8 0.667& No
  Crash risk Stephen Tregear (2009) 10 observational studies 10,846 2214 RR 2.43 (1.21–4.89) 0.013  < 0.001 89 0.838& No
  Work accidents Sergio Garbarino (2016) 7 cross-sectional studies 8819 2738 OR 2.18 (1.53–3.10)  < 0.001 0.02 61 0.61 No
  Carotid intima-media thickness (CIMT) Min Zhou (2016) 10 case–control studies, 8 case-sectional studies 1896 1247 SMD 0.88 (0.65, 1.12)  < 0.001  < 0.001 81 0.94 No
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*p value of significance level

#p value of Q test

※p value for Egger’s test

$The publication bias was assessed using funnel plot

&The result was reanalyzed

Fig. 2.

Fig. 2

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Map of achievements related to OSA

Summary effect size

A brief explanation of the effects of the included meta-analysis is given in Table 1. Overall, 111 (82%) of the 136 data reported significant summary outcomes (p < 0.05). These associations relate to the outcomes of the following different systems: 29 meta-analyses in cardiovascular disorders, 5 in cerebral and cerebrovascular disease, 4 in mortality, 14 in postoperative complications, 8 in pregnancy-related disorders, 7 in ophthalmic disorders, 11 in digestive disorders, 14 in endocrine and metabolic system, 7 in urological disorders, and 12 in other outcomes. Therefore, it can be concluded that OSA can enhance the risk of disease and have adverse effects on human health.

Heterogeneity and publication bias

For heterogeneity, 5 results in 5 articles were reanalyzed owing to that they did not exhibit the outcomes of heterogeneity [22, 36, 46, 59, 64]. Among the 136 outcomes including the reanalyzed articles, 47 outcomes showed no heterogeneity between researches (p ≥ 0.1 of Q test), whereas 69 indicated significant heterogeneity (p < 0.1 of Q test). However, there were still 20 results in 2 articles that could not be reanalyzed due to the lack of raw data [52, 95], so we could not evaluate their heterogeneity. For publication bias, 76 outcomes demonstrated no statistical evidence on publication bias (p ≥ 0.1 of Egger’s test), whereas 25 outcomes presented publication bias (p < 0.1 of Egger’s test). There were still 35 results in 9 articles that could not be reanalyzed due to the lack of raw data [45, 52, 54, 55, 87, 92–95], so we could not evaluate their publication bias.

AMSTAR 2 and summary of evidence

The results for the evaluation of the methodological qualities of the 66 included articles are shown in Table 2. Only 3 (5%) studies were determined to be low; the remaining 63 (95%) studies were determined to be critically low (Fig. 3). Based on the AMSTAR 2 criteria, none of the investigations were graded as moderate or high quality.

Table 2.

Assessments of AMSTAR 2 scores

Reference AMSTAR 2 checklist Overall assessment quality
No. 1 No. 2 No. 3 No. 4 No. 5 No. 6 No. 7 No. 8 No. 9 No. 10 No. 11 No. 12 No. 13 No. 14 No. 15 No. 16
Xiushi Zhou (2018) Yes No Yes Partial yes No No Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Xia Wang (2013) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Min Li (2014) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes No No No No No Critically low
Wuxiang Xie (2014) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Chengjuan Xie (2017) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes No No Yes Yes Yes Critically low
Irini Youssef (2018) Yes No No Partial yes No No Partial yes No No No Yes No No No No No Critically low
Haifeng Hou (2018) Yes Yes Yes Partial yes Yes Yes Partial yes Yes No No Yes Yes No Yes Yes Yes Critically low
Chee Yuan Ng (2011) Yes No Yes Partial yes Yes Yes Yes Partial yes Yes No Yes Yes Yes Yes Yes No Critically low
Xiao Wang (2018) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Hua Qu (2018) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes No Yes Yes Yes Yes Critically low
Cesare Cuspidi (2020) Yes No No Partial yes Yes Yes Partial yes Partial yes No No Yes No No Yes No Yes Critically low
Bo-Lin Ho (2018) Yes No No Partial yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Zesheng Wu (2018) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes Yes Yes Yes Yes Yes Critically low
Yuhong Huang (2019) Yes No No Partial yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Anthipa Chokesuwattanaskul (2019) Yes No Yes Partial yes No No Partial yes Yes No No Yes No No Yes Yes Yes Critically low
Lei Pan (2016) Yes No Yes Partial yes Yes Yes Yes Yes No No Yes Yes Yes Yes Yes Yes Critically low
Xiahui Ge (2013) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Xiaobin Zhang (2017) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes No No Yes Yes Yes Critically low
Faizi Hai BA (2013) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
R. Kaw (2012) Yes No Yes Partial yes Yes Yes Yes Partial yes Yes No Yes No No Yes Yes Yes Critically low
Ka Ting Ng (2020) Yes Yes Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes No No Yes Yes Yes Critically low
Xinge Zhang (2020) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
Lina Liu (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
Liwen Li (2018) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes Yes No No Yes Yes Critically low
Ting Xu (2014) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes No Yes Critically low
Marco Pellegrini (2020) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Xinhua Wu (2015) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes No No Yes Yes Yes Critically low
Leh-Kiong Huon (2016) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes No No No No Yes Critically low
Yong Wu (2015) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes Yes Yes Yes Yes Yes Critically low
Chris Y.Wu (2018) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes No No Yes Yes Yes Critically low
Ranran Qie (2020) Yes No Yes Partial yes No No Partial yes Yes No No Yes No No Yes Yes Yes Critically low
Xiandong Gu (2018) Yes No Yes Partial yes No No Partial yes Yes No No Yes No No Yes Yes Yes Critically low
Wen Bun Leong (2016) Yes Yes Yes Yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Low
Zhenliu Zhu (2017) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
Zeng-Hong Wu (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Shanshan Jin (2018) Yes No No Partial yes Yes Yes Partial yes Yes No No Yes Yes No Yes Yes Yes Critically low
G. Musso (2013) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes No No Yes Yes Yes Critically low
Tzong-Yun Ger (2020) Yes Yes Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes No No Yes Critically low
Jiatong Zhou (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes No No Yes Yes Yes Critically low
Yuan Cao (2018) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes No No No No Yes Critically low
A-Ping Sun (2020) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes Yes Yes Yes Yes Critically low
Luhao Liu (2015) Yes No Yes Partial yes Yes Yes No Yes No No Yes No No No Yes Yes Critically low
Sikarin Upala (2016) Yes Yes Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Low
Tingting Shi (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No No Yes Yes Critically low
Tongtong Liu (2020) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes No No No Yes Yes Critically low
Der-Wei Hwu (2017) Yes Yes Yes Partial yes Yes Yes Yes Partial yes No No Yes No No No No Yes Critically low
Ghanshyam Palamaner Subash Shantha (2015) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes Yes Yes Yes Yes Critically low
Shaoyong Xu (2015) Yes No Yes Partial yes Yes Yes Partial yes Yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Cass Edwards (2020) Yes No Yes Partial yes Yes Yes Yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Stephen Tregear (2009) Yes No No Yes Yes Yes Partial yes Partial yes Yes No Yes No No Yes Yes Yes Critically low
Sergio Garbarino (2016) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Cheng-Lin Sun (2016) Yes No Yes Partial yes Yes Yes Partial yes Yes No No Yes No Yes Yes Yes Yes Critically low
Min Zhou (2016) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
Guang Song (2020) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
LeiYu (2019) Yes No No Partial yes Yes Yes Partial yes Partial yes No No Yes Yes No Yes Yes Yes Critically low
Abdirashit Maripov (2017) Yes No No Partial yes Yes Yes Partial yes Partial yes No No Yes No No Yes Yes Yes Critically low
Rui-Heng Zhang (2020) Yes No No Partial yes Yes Yes Partial yes Yes Yes No Yes No No Yes Yes Yes Critically low
De-Lei Kong (2016) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes No Yes Yes Yes Critically low
Rashid Nadeem (2014) Yes No Yes Partial yes Yes Yes Partial yes No No No Yes Yes No Yes Yes Yes Critically low
Mi Lu (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes No Yes Yes Yes Critically low
Reza Fadaei (2020) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes No No Yes Yes Yes Critically low
Fang Lu (2019) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes No Yes Yes Yes Critically low
Kun Li (2017) Yes No Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Critically low
Xingyu Wu (2018) Yes No Yes Partial yes Yes Yes Partial yes Partial yes No No Yes No No Yes Yes Yes Critically low
Ze-Ning Jin (2016) Yes No No Partial yes Yes Yes Partial yes Yes Yes No Yes Yes No Yes Yes Yes Critically low
Xiaoyan Li (2020) Yes Yes Yes Partial yes Yes Yes Partial yes Partial yes Yes No Yes Yes Yes Yes Yes Yes Low
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Fig. 3.

Fig. 3

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Map of results of AMSTAR 2

The outcomes of the evidence measurement are shown in Table 3. When a study did not present the result of heterogeneity and publication bias, the corresponding criteria were considered to be not satisfied. Among the 111 statistically significant outcomes, 7 (6%) showed high epidemiologic evidence, 24 (22%) showed moderate epidemiologic evidence, and the remaining 80 (72%) were rated as weak (Fig. 4).

Table 3.

Detail of results for evidence quality assessing

Outcomes Reference Precision of the estimate Consistency of results No evidence of small-study effects Grade
 > 1000 disease cases P < 0.001 (I2 < 50% and Cochran Q test P > 0.10) (P > 0.10)
Cardiovascular disorders
  Aortic dissection Xiushi Zhou (2018) Yes No Yes Yes Weak
  Cardiovascular disease (CVD) Xia Wang (2013) Yes Yes Yes No Moderate
  Stroke Min Li (2014) No Yes No Yes Weak
  Ischemic heart disease (IHD) Wuxiang Xie (2014) No No Yes No Weak
  Coronary heart disease (CHD) Chengjuan Xie (2017) Yes No No Yes Weak
  Major adverse cardiac events (MACEs) Chengjuan Xie (2017) Yes Yes No Yes Moderate
  Atrial fibrillation Irini Youssef (2018) Yes Yes No No Weak
  Resistant hypertension Haifeng Hou (2018) No Yes Yes Yes Moderate
  Essential hypertension Haifeng Hou (2018) Yes Yes Yes No Moderate
  Atrial fibrillation recurrence after catheter ablation Chee Yuan Ng (2011) No No No Yes Weak
  Major adverse cardiovascular event (MACE) after PCI Xiao Wang (2018) Yes Yes No No Weak
  Myocardial infarction(MI) after PCI Hua Qu (2018) Yes No Yes Yes Weak
  Coronary revascularization after PCI Hua Qu (2018) Yes Yes Yes Yes High
  Left ventricular hypertrophy (LVH) Cesare Cuspidi (2020) Yes Yes No No Weak
  Left ventricular diastolic diameter (LVEDD) LeiYu (2019) No Yes Yes Yes Moderate
  Left ventricular systolic diameter (LVESD) LeiYu (2019) No No Yes Yes Weak
  Left ventricular mass (LVM) LeiYu (2019) No Yes No Yes Weak
  Left ventricular ejection fraction (LVEF) LeiYu (2019) No No No No Weak
  Left atrial diameter (LAD) LeiYu (2019) No Yes Yes No Weak
  Left atrial diameter volume index (LAVI) LeiYu (2019) No Yes Yes Yes Moderate
  Right ventricular internal diameter (RVID) Abdirashit Maripov (2017) No Yes No No Weak
  Right ventricular free wall thickness (RVWT) Abdirashit Maripov (2017) No Yes No Yes Weak
  Right ventricular myocardial performance index (RV MPI) Abdirashit Maripov (2017) No Yes No Yes Weak
  Tricuspid annular systolic velocity (RV S′) Abdirashit Maripov (2017) No No No Yes Weak
  Tricuspid annular plane systolic excursion (TAPSE) Abdirashit Maripov (2017) No Yes No Yes Weak
  Right ventricular fractional area change (RA FAC) Abdirashit Maripov (2017) No No No No Weak
  Epicardial adipose tissue (EAT) thickness Guang Song (2020) No Yes No Yes Weak
  Coronary flow reserve (CFR) Rui-Heng Zhang (2020) No Yes No Yes Weak
  Systolic blood pressure (SBP) De-Lei Kong (2016) No Yes Yes NA Weak
Cerebral and cerebrovascular disease
  Cerebral white matter changes Bo-Lin Ho (2018) No Yes No Yes Weak
  Cerebrovascular (CV) disease Zesheng Wu (2018) Yes No No No Weak
  White matter hyperintensities (WMH) Yuhong Huang (2019) Yes Yes No No Weak
  Silent brain infarction (SBI) Yuhong Huang (2019) Yes No No Yes Weak
  Asymptomatic lacunar infarction (ALI) Anthipa Chokesuwattanaskul (2019) No No Yes Yes Weak
 Mortality
  All-cause mortality Lei Pan (2016) Yes No No No Weak
  Cardiovascular mortality Xiahui Ge (2013) No Yes Yes Yes Moderate
  All-cause death after PCI Xiao Wang (2018) Yes No Yes Yes Weak
  Cardiac death after PCI Hua Qu (2018) Yes No Yes Yes Weak
Postoperative complications
  Postoperative respiratory failure Faizi Hai BA (2013) Yes Yes Yes Yes High
  Postoperative cardiac events Faizi Hai BA (2013) Yes No Yes Yes Weak
  Postoperative desaturation R. Kaw (2012) Yes No No No Weak
  Postoperative ICU transfer R. Kaw (2012) Yes No No No Weak
  Postoperative composite endpoints of postoperative cardiac or cerebrovascular complications Ka Ting Ng (2020) Yes Yes No NA Weak
  Postoperative myocardial infarction Ka Ting Ng (2020) NA Yes Yes NA Weak
  Postoperative atrial fibrillation Ka Ting Ng (2020) NA Yes No NA Weak
  Postoperative composite endpoints of pulmonary complications Ka Ting Ng (2020) NA Yes No NA Weak
  Postoperative pneumonia Ka Ting Ng (2020) NA No No NA Weak
  Postoperative 30-day mortality Ka Ting Ng (2020) NA No Yes NA Weak
  Postoperative acute kidney injury Ka Ting Ng (2020) NA Yes No NA Weak
  Postoperative delirium Ka Ting Ng (2020) NA Yes Yes NA Weak
  Postoperative venoembolism Ka Ting Ng (2020) NA No No NA Weak
  Postoperative length of hospital stay (days) Ka Ting Ng (2020) NA No No NA Weak
Pregnancy-related disorders
  Gestational diabetes mellitus (GDM) Xinge Zhang (2020) Yes No No Yes Weak
  C-section Lina Liu (2019) NA Yes No NA Weak
  Pregnancy-related wound complication Lina Liu (2019) NA Yes Yes NA Weak
  Pregnancy-related pulmonary edema Lina Liu (2019) NA Yes Yes NA Weak
  Gestational hypertension Liwen Li (2018) Yes No Yes Yes Weak
  Preeclampsia Liwen Li (2018) Yes Yes No Yes Moderate
  Preterm birth Liwen Li (2018) Yes No No Yes Weak
  Neonatal intensive care unit (NICU) admission Ting Xu (2014) No Yes No No Weak
Ophthalmic disorders
  Diabetic retinopathy (DR) Zhenliu Zhu (2017) No Yes No Yes Weak
  Keratoconus Marco Pellegrini (2020) Yes No Yes No Weak
  Glaucoma Xinhua Wu (2015) Yes Yes No Yes Moderate
  Floppy eyelid syndrome (FES) Leh-Kiong Huon (2016) No Yes Yes Yes Moderate
  Nonarteritic anterior ischemic optic neuropathy (NAION) Yong Wu (2015) No No No Yes Weak
  Central serous chorioretinopathy (CSCR) Chris Y.Wu (2018) Yes No Yes Yes Weak
  Retinal nerve fiber layer (RNFL) thickness Cheng-Lin Sun (2016) No No No Yes Weak
Digestive disorders
  Gastroesophageal reflux disease Zeng-Hong Wu (2019) Yes No No No Weak
  Steatosis Shanshan Jin (2018) Yes Yes Yes Yes High
  Lobular inflammation Shanshan Jin (2018) No Yes Yes Yes Moderate
  Ballooning degeneration Shanshan Jin (2018) No No Yes Yes Weak
  NAFLD defined by liver histology G. Musso (2013) No Yes Yes Yes Moderate
  NAFLD defined by radiology G. Musso (2013) No Yes Yes Yes Moderate
  NAFLD defined by AST elevation G. Musso (2013) No Yes Yes Yes Moderate
  NAFLD defined by ALT elevation G. Musso (2013) No Yes Yes Yes Moderate
  Nonalcoholic steatohepatitis (NASH) G. Musso (2013) No Yes Yes Yes Moderate
  Fibrosis G. Musso (2013) No Yes Yes Yes Moderate
  Alanine transaminase (ALT) Shanshan Jin (2018) Yes Yes Yes Yes High
Endocrine and metabolic system disorders
  Type 2 diabetes (T2DM) Ranran Qie (2020) Yes Yes No Yes Moderate
  Metabolic syndrome (MS) Shaoyong Xu (2015) Yes Yes Yes Yes High
  Fasting blood glucose (FBG) De-Lei Kong (2016) Yes Yes No NA Meak
  Total cholesterol (TC) Rashid Nadeem (2014) NA No NA NA Weak
  Low-density lipoprotein (LDL) Rashid Nadeem (2014) NA No NA NA Weak
  High-density lipoprotein (HDL) Rashid Nadeem (2014) NA Yes NA NA Weak
  Triglyceride (TG) Rashid Nadeem (2014) NA Yes NA NA Weak
  Adiponectin Mi Lu (2019) No Yes No No Weak
  Oxidized low-density lipoprotein (Ox-LDL) Reza Fadaei (2020) No No No Yes Weak
  Fibrinogen Fang Lu (2019) Yes Yes No Yes Moderate
  Homocysteine Kun Li (2017) No No No Yes Weak
  Advanced glycation end products (AGEs) Xingyu Wu (2018) No Yes No NA Weak
  Angiotensin II (AngII) Ze-Ning Jin (2016) No Yes No Yes Weak
  Serum vitamin D Xiaoyan Li (2020) Yes Yes No NA Weak
Urological disorders
  Diabetic kidney disease (DKD) Wen Bun Leong (2016) Yes No Yes Yes Weak
  Microalbuminuria Tongtong Liu (2020) No Yes Yes Yes Moderate
  Chronic kidney disease (CKD) Der-Wei Hwu (2017) Yes Yes No No Weak
  Serum uric acid level Tingting Shi (2019) Yes Yes No No Weak
  Serum cystatin C Tongtong Liu (2020) No Yes Yes Yes Moderate
  Estimated glomerular filtration rate (eGFR) Tongtong Liu (2020) No No No Yes Weak
  Albumin/creatinine ratio (ACR) Tongtong Liu (2020) No Yes No Yes Weak
Other outcomes
  Diabetic neuropathy Xiandong Gu (2018) No No No Yes Weak
  Psoriasis Tzong-Yun Ger (2020) Yes Yes Yes Yes High
  Nocturia Jiatong Zhou (2019) No Yes No No Weak
  Parkinson’s disease A-Ping Sun (2020) Yes Yes Yes Yes High
  Erectile dysfunction Luhao Liu (2015) No No No No Weak
  Female sexual dysfunction Luhao Liu (2015) No No Yes Yes Weak
  Sexual dysfunction Luhao Liu (2015) No Yes No Yes Weak
  Cancer incidence Ghanshyam Palamaner Subash Shantha (2015) No No No No Weak
  Depression Cass Edwards (2020) Yes Yes No Yes Moderate
  Crash risk Stephen Tregear (2009) Yes No No Yes Weak
  Work accidents Sergio Garbarino (2016) Yes Yes No Yes Moderate
  Carotid intima-media thickness (CIMT) Min Zhou (2016) Yes Yes No Yes Moderate
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Fig. 4.

Fig. 4

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Map of results of evidence assessment

Discussion

In the current umbrella review, we identified 66 meta-analyses of observational studies and evaluated the current evidence supporting an association between OSA and various health outcomes. Also, we provide an extensive overview of the available evidence and critically evaluate the methodological quality of the meta-analyses and the quality of evidence for all the reported associations. OSA increased the risk of 111 health outcomes, including cardiovascular disorders, cerebral and cerebrovascular disease, mortality, postoperative complications, pregnancy-related disorders, ophthalmic disorders, digestive disorders, endocrine and metabolic system disorders, urological disorders, and other outcomes. The evidence quality was graded as high only for coronary revascularization after PCI, postoperative respiratory failure, steatosis, ALT elevation, MS, psoriasis, and Parkinson’s disease. The evidence quality was either moderate or low for the other associations. Furthermore, this umbrella review showed there were no considerable associations between OSA and 25 health outcomes.

Among the 111 outcomes, 54 outcomes had serious heterogeneity between studies. These possible confounding parameters (e.g., sex, body mass index, age, method of assessing OSA, OSA severity, smoking, alcohol drinking, the region of study, and follow-up period) may be the cause of heterogeneity. Substantial heterogeneity led to unreliable results. Of the 111 health outcomes, 23 outcomes possessed a remarkable publication bias, demonstrating that some negative achievements were not presented. Several reasons were leading to publication bias. First, when people start a study, they tend to assume that a positive result may ensure their work complies with the hypothesis during publication. Second, positive results have a higher probability of being published compared to negative results. Third, the study population is only a small fraction of the actual population with the disease. According to AMSTAR 2 criteria, 95% of the studies included in this umbrella analysis had “critically low” methodological quality. The critical flaws considered the absence of a registered protocol, the absence of the risk of bias in the considered investigations, and the absence of consideration of the risk of bias in the included investigations when interpreting or discussing the achieved outcomes of each study. Moreover, none of the meta-analyses in this study explained details of the funding source that had supported the work. The majority of the evaluated meta-analyses had considerable heterogeneity and small-study impacts; these were the main reasons for the evidence rating downgrade.

An umbrella review is a more beneficial method compared to a normal systematic review or meta-analysis due to it representing an overall illustration of achievements for phenomena or special questions [96]. To our knowledge, we are the first to use this method to present a comprehensive critical literature appraisal on published associations between OSA and diverse health information. Also, our two authors systematically searched four scientific databases using a strong search strategy with clearly defined eligibility criteria and data extraction parameters. The quality of included systematic reviews was also evaluated through AMSTAR 2. This is a benchmark methodological quality measurement that is utilized to assessing the quality of the methods utilized for meta-analyses. Furthermore, we graded the epidemiologic evidence conforming to established, prespecified criteria. Its criteria included an assessment of heterogeneity, publication bias, and precision of the estimate, which is more objective than the GRADE system criteria.

There are some limitations in our umbrella review. First, in this analysis, we explained associations evaluated through the meta-analyses of observational investigations. In doing so, we may have missed other health outcomes that have not yet been investigated by meta-analyses. Second, this umbrella analysis included systematic reviews and meta-analyses that were only published in English. The potential missing information in other languages could influence the assessment outcomes. Third, the majority of the meta-analyses had heterogeneity; observational researches are susceptible to uncertainty and confounding bias.

Conclusions

The associations between OSA and an extensive range of health information have been broadly reported in many meta-analyses. Based on our umbrella review, 66 meta-analyses explored 136 unique outcomes, only 7 outcomes showed a high level of epidemiologic evidence with statistical significance. OSA could be associated with the enhanced risk of coronary revascularization after PCI, postoperative respiratory failure, steatosis, ALT elevation, MS, psoriasis, and Parkinson’s disease. Overall, OSA is harmful to human health but will need further exploration on this topic with high-quality prospective studies.

Acknowledgements

We would like to thank the researchers and study participants for their contributions.

Author contribution

Idea and design: TSH, CWW. Literature search: ZCX, GLLZ. Data extraction and analysis:CWW, LYT. Manuscript writing: CWW. Manuscript revision: TSH, CWW. All authors read and approved the version of the manuscript to be published. All authors take responsibility for appropriate content.

Data availability

The data used to support the findings of this study are included within the article. The primary data used to support the findings of this study are available from the corresponding author upon request.

Declarations

Ethics approval

All analyses were based on published studies and no ethical approval was required.

Conflict of interest

The authors declare no competing interests.

Footnotes

Weiwei Chen and Yuting Li contributed equally to this work and should be considered co-first authors

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used to support the findings of this study are included within the article. The primary data used to support the findings of this study are available from the corresponding author upon request.

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