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Sleep Med Res > Volume 15(2); 2024 > Article
Song, Oh, Kim, Um, Kim, Seo, Jeong, and Hong: Obstructive Sleep Apnea and α-Synucleinopathies: Nationwide Analysis From South Korea’s Healthcare Database

Abstract

Background and Objective

The complex relationship between obstructive sleep apnea (OSA) and neurodegenerative diseases is increasingly being recognized. This study aims to elucidate the association between OSA and α-synucleinopathies, with a focus on Parkinson’s disease (PD), Parkinson’s disease dementia (PDD), and dementia with Lewy bodies (DLB), highlighting the impact of demographic variables.

Methods

Employing a retrospective cohort approach, we analyzed 103785 patients diagnosed with OSA and their matched controls. The data were sourced from South Koreas National Health Insurance claims database spanning 2010 to 2019. Our investigation centered on the incidence of PD, PDD, and DLB among patients with OSA, utilizing chi-square tests, t-tests, and multivariable logistic regression to compute adjusted odds ratios (AOR). Additionally, we conducted a subgroup analysis focusing on variations by sex and age.

Results

The findings indicate that individuals suffering from OSA had a significantly elevated risk for PD (AOR = 2.166, 95% confidence interval [CI]: 1.840–2.549, p < 0.0001) and DLB (AOR = 4.001, 95% CI: 1.501–10.660, p = 0.0056). Subgroup analyses further highlighted that the association between PD, DLB, and OSA was markedly stronger in men and escalated in those above 60 years of age.

Conclusions

Our analysis establishes a substantial link between OSA and an increased likelihood of developing PD and DLB, emphasizing the vital role demographic factors play. These findings suggest the need for intensified surveillance and customized management strategies for OSA, particularly among individuals at heightened risk for neurodegenerative disorders. Additionally, this research lays the foundation for further studies into the progression and therapeutic interventions for these conditions.

INTRODUCTION

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is characterized by repetitive episodes of upper airway obstruction during sleep. This condition disrupts normal breathing patterns and leads to reduced oxygen saturation [1]. Its prevalence is on the rise, affecting between 9% and 38% of the adult population, with some global estimates suggesting rates as high as 54% [2,3]. Beyond respiratory complications, there is an increasing body of evidence linking OSA to a range of systemic health issues, including cardiovascular and metabolic disorders, and importantly, neurodegenerative conditions [4-6].
Focusing on neurodegenerative consequences, emerging research highlights the neurological implications of OSA, particularly its association with α-synucleinopathies [5,7,8]. These conditions, which include Parkinson’s disease (PD), Parkinson’s disease dementia (PDD), and dementia with Lewy bodies (DLB), are characterized by the abnormal accumulation of α-synuclein protein within neurons [9], affecting cognitive and motor functions as well as emotional well-being [10]. The increasing prevalence of OSA, influenced by factors such as obesity, age, and gender, underscores its significance as a public health issue [11]. Gaining insight into these demographic disparities is crucial for understanding the broad impact of OSA, especially concerning neurodegenerative diseases.
While the connection between OSA’s chronic intermittent hypoxia and the onset and progression of α-synucleinopathies is acknowledged, the specifics of this relationship, especially across different demographic groups, remain inadequately explored [12]. Recent studies have broadened our understanding of the connection between OSA and α-synucleinopathies yet underscore the necessity for more refined investigations. For example, studies examining OSA’s clinical features in PD patients have revealed significant prevalence and associated risk factors, highlighting the need for customized management strategies [13]. Conversely, other research, including Mendelian randomization analyses, suggests that the causal links between OSA and neurodegenerative diseases such as PD and Alzheimer’s might involve complex, multifactorial interactions, necessitating cautious interpretation [14,15]. Furthermore, up-to-date research emphasizes the clinical importance of OSA in PD, promoting proactive screening and intervention strategies to mitigate its effects [16]. Limitations identified in these studies range from a lack of demographic-specific relationship exploration to a general dearth of comprehensive clinical data. Although certain studies have pinpointed the prevalence and risk factors of OSA in PD, contrasting perspectives regarding the causal connections with neurodegenerative diseases call for prudent examination.
To bridge the research gaps previously identified, this study leverages a comprehensive dataset from South Korea’s National Health Insurance system to examine the association between OSA and α-synucleinopathies, with a particular focus on sex and age variations. This approach enables an in-depth exploration of demographic-specific relationships and incorporates a wide spectrum of clinical data, facilitating a nuanced analysis of the complex interplay between OSA and neurodegenerative diseases. Against this backdrop, our study seeks to investigate the relationships between OSA and various α-synucleinopathies. We hypothesize that OSA is associated with an elevated risk of α-synucleinopathies and that this risk is modulated by both sex and age.

METHODS

Study Design and Data Collection

A retrospective cohort study was conducted, leveraging data extracted from South Korea’s National Health Insurance (NHI) claims database spanning 2010 to 2019. Encompassing coverage for 98% of the country’s 50 million populace, the NHI system presents a vast and dependable data source for deducing the nationwide prevalence of various conditions. The extraction of data from the NHI database adhered to rigorously defined protocols, ensuring the collected information’s accuracy and consistency.
Patients diagnosed with OSA were pinpointed within the NHI database, which employs the International Classification of Diseases, 10th revision (ICD-10) for diagnostic categorization. Criteria for including OSA patients comprised encounters with a physician on three or more occasions, listing OSA as the principal diagnosis within the stated timeframe (ICD-10 code G473). Initial diagnosis records provided demographic details such as age and sex. Exclusion criteria encompassed patients with incomplete documentation or diagnosed with major neurodegenerative disorders either prior to or concurrent with the OSA diagnosis.

The α-Synucleinopathies

The focus on α-synucleinopathies was due to their definition as a spectrum of neurodegenerative disorders marked by the aberrant accumulation of the α-synuclein protein [10]. The conditions of interest, namely PD, PDD, and DLB, were identified through specific ICD-10 diagnosis codes. Classification of patients as suffering from these ailments was based on at least three physician consultations specifying these diagnoses within a period exceeding 30 days; PD (ICD-10 code G20.000), PDD (ICD-10 code G20.007), and DLB (ICD-10 code G3182).

Control Group Selection

Control patients, selected for not having an OSA diagnosis, had undergone an appendectomy within the same 2010 to 2019 timeframe. Matching between OSA patients and control subjects was orchestrated on a one-to-one basis, taking into account age and sex, utilizing a standardized methodology to diminish bias and bolster comparability between groups.

Statistical Analyses

Descriptive statistics were employed to depict the demographic profiles of the study population. The incidence rates of α-synucleinopathy within OSA subjects and controls were contrasted utilizing chi-square tests for categorical variables and t-tests for continuous measurements, with a particular focus on age disparities. The exploration of the linkage between OSA and α-synucleinopathies, inclusive of PD, PDD, and DLB, was facilitated through a multivariable logistic regression model. This model enabled the calculation of both unadjusted and adjusted odds ratios, elucidating the strength of the association between OSA and the α-synucleinopathies. Adjustments were executed for confounding variables, enhancing the accuracy of the associations discovered. Further, stratified analysis delved into the influence of sex and age on these relationships. All statistical evaluations were conducted with SAS Enterprise Guide version 7.1 (SAS Institute, Cary, NC, USA). The threshold for statistical significance was set at a p-value less than 0.05, adhering to conventional criteria for significance.

Ethical Considerations

In alignment with the Declaration of Helsinki, this study was duly accredited by the Institutional Review Board of St. Vincent’s Hospital (VC24ZISI0011; Suwon, South Korea). Given its retrospective nature and the anonymity of patient data, the requirement for informed consent was duly waived. Upholding ethical standards, rigorous data security measures were employed to protect patient information, consistent with national privacy laws and norms.

RESULTS

Characteristics of the Study Population

Descriptive characteristics of both groups are presented in Table 1. In our study, we identified 103785 patients with OSA and obtained an equal number of age- and sex-matched individuals from a pool of 2384863 patients who had undergone an appendectomy to create a control group. The average age in both groups was 47.45 ± 14.85 years, with a predominance of male participants (83.06%). Significantly, the group diagnosed with OSA exhibited a higher prevalence of comorbidities such as diabetes (9316 vs. 6414), hypertension (23850 vs. 15356), and hyperlipidemia (4918 vs. 3203), indicating a greater burden of these conditions among OSA sufferers.

Association of α-Synucleinopathies with OSA

Our findings revealed a pronounced association between OSA and PD, as demonstrated by an adjusted odds ratio (AOR) of 2.166 (95% CI: 1.840–2.549, p < 0.0001) (Table 2). This signifies a considerably heightened risk of developing PD in individuals diagnosed with OSA. Additionally, our analyses on DLB also indicated a noteworthy association, with an AOR of 4.001 (95% CI: 1.501–10.660, p = 0.0056), underscoring a robust link between OSA and DLB.

Subgroup Analysis: Sex and Age

To delve deeper into the role demographic factors, play in the risk of developing α-synucleinopathies, our study conducted a comprehensive subgroup analysis focusing on variances across sex and age groups. This analysis, detailed in Tables 3 and 4, uncovered a notable difference in susceptibility to PD among male and female patients with OSA. Specifically, males with OSA showed a substantially higher adjusted odds ratio (AOR) of 2.162 (95% CI: 1.765–2.660), suggesting a more pronounced association with PD in comparison to females, for whom the AOR was 0.98 (95% CI: 0.781–1.229) (Table 3).
Concurrently, the age-stratified analysis offered additional perspective on the correlation between OSA and PD across various stages of life. Remarkably, the risk associated with PD increased with age among OSA patients. For those aged between 40 and 60 years, the AOR was established at 2.144 (95% CI: 1.369–3.428, p = 0.0005). This trend of escalating risk persisted into older age brackets, with AORs of 2.021 (95% CI: 1.667–2.458, p < 0.0001) for individuals aged 60 to 80 years, and an even more significant AOR of 2.562 (95% CI: 1.868–3.491, p < 0.0001) for those over 80 years of age (Table 4).
Conversely, for PDD and DLB, the trend differed. For PDD, neither sex showed a significant association, with males presenting an elevated but statistically insignificant AOR of 1.27 (95% CI: 0.215–8.668) and females a lower AOR of 0.493 (95% CI: 0.047–3.015). Nevertheless, DLB demonstrated a strong association for both sexes, particularly in males (AOR, 2.143; 95% CI: 0.598–9.523 for males and AOR, 1.508; 95% CI: 0.568–4.119 for females) (Table 3), with the 60–80-year age group showcasing a significantly increased risk (AOR, 4.141; 95% CI: 1.336–17.024, p = 0.0095) (Table 4).

DISCUSSION

In the context of this investigation, we delved into the nexus between OSA and α-synucleinopathies, with a particular focus on variations influenced by sex and age demographics. Our findings revealed a heightened risk for PD and DLB among individuals diagnosed with OSA, pinpointing males and those aged above 80 years as notably susceptible to PD. Contrarily, PDD manifested no significant correlation with OSA across varied demographic groups. The prevalence of DLB was strikingly more pronounced in men, especially within the age bracket of 60–80 years. Drawing upon data from 103785 OSA patients and matched controls from the National Health Insurance system of South Korea, our study substantiates the link between OSA and α-synucleinopathies, thereb enriching the comprehension of their potential association.
This inquiry underscores the substantial relationship between OSA and an escalated risk of α-synucleinopathies, resonating with hypotheses on the pathophysiological ramifications of OSA such as oxidative stress and systemic inflammation triggered by intermittent hypoxia [17]. Such factors contribute to neuronal impairment and the accrual of α-synuclein, underscoring OSA’s potential role in hastening these pathologies [18,19]. The disarray in sleep architecture attributed to OSA encumbers intrinsic sleep cycles and homeostatic controls, instigating physiological changes that could exacerbate or initiate the pathogenesis of neurodegenerative diseases [20]. Earlier studies vetted a consistent uptick in the risk of α-synucleinopathies among individuals with OSA, bolstering the premise that OSA serves not merely as a risk factor but also as an accelerator for the progression of neurodegenerative diseases [21,22]. With a substantial cohort from South Korea, our analysis furnishes robust evidence corroborating these linkages and heralds the necessity for more granular research into the intricate interplay between sleep disruptions and neurodegeneration.
The delineated sex disparities in PD risk amongst OSA patients starkly contrast the uniform risk distribution observed in PDD and DLB, necessitating a deeper probe into sex-specific factors influencing disease pathogenesis. The conjectured neuroprotective role of estrogen against neurodegeneration, which might elucidate the diminished incidence of PD in female OSA patients, warrants mention [23]. Estrogen’s neuroprotective capabilities could ameliorate the oxidative stress and systemic inflammation instigated by OSA [24,25]. Additionally, the variable genetic susceptibilities related to sex could modulate the susceptibility to PD [26]. The discernible lifestyle and environmental disparities between genders might also influence the risk and evolution of neurodegenerative ailments [27]. Moreover, the predominance of PD in males per our findings may reflect OSA’s more severe manifestation in men, thereby amplifying the risk of neurodegenerative outcomes [28]. Elucidating these sex-specific mechanisms is pivotal for forging targeted therapeutic and preventive strategies for PD within the OSA context.
The discrepancy in age-specific risk for α-synucleinopathies unveils patterns meriting further investigation. Notably, our findings position age as a critical determinant, with PD risk starting to climb at 40 years, suggesting OSA’s far-reaching and prolonged impact [29]. Conversely, DLB risk heightens exclusively within the 60–80 years bracket, hinting at [30] this age range as a pivotal period for potentially exacerbating DLB progression. Meanwhile, the ambivalence in significant age-related divergences in PDD and OSA’s role indicates PDD’s pathogenesis being more intimately tied to the progression and severity of underlying parkinsonism [31,32]. These insights advocate that the interplay between OSA and the risk of neurodegenerative diseases is modulated by a multifaceted amalgamation of factors, delineating differential susceptibility windows across the α-synucleinopathy spectrum.
Although this study heralds important revelations, its retrospective nature, dependence on ICD-10 codes for diagnostic clarity, and the absence of granular clinical data entail inherent limitations, possibly impacting the preciseness of our discoveries. The exclusivity of findings within the South Korean ambit cautions against their unbridled extrapolation to disparate populations, considering potential variances in health profiles and healthcare frameworks globally. Future inquiries should embrace comprehensive clinical data and assess the generalizability of these findings across diverse cohorts, to amplify their universal applicability.
Nonetheless, the extensive sample size and the longitudinal vista spanning South Korea endow this study with substantial strength, affirming the validity of our conclusions. The meticulous data curation and analytical rigor significantly elevate the credibility of our outcomes. The ties elucidated between OSA and α-synucleinopathies not only enrich the existing corpus of knowledge but also ignite avenues for future scholarly and translational endeavors. Bridging the intricate relationship between sleep disorders and neurodegenerative diseases serves as a beacon for subsequent examinations, potentially revolutionizing clinical protocols and augmenting care for individuals grappling with these complex conditions.
In conclusion, our investigation has revealed a pronounced link between OSA and an elevated risk of α-synucleinopathies, particularly PD and DLB, with noticeable differences across sex and age groups. This analysis, which leverages a comprehensive dataset from the South Korean population, underscores the imperative need to acknowledge OSA’s contribution to the advancement of neurodegenerative disorders. It also emphasizes the urgency for tailored interventions and expanded research in this area.

NOTES

Availability of Data and Material
The datasets generated or analyzed during the study are not publicly available due to personal data protection but are available from the corresponding author on reasonable request.
Author Contributions
Conceptualization: Chaeyoung Song, Jihye Oh, Yoo-Hyun Um, Seung-Chul Hong. Data curation: Jihye Oh. Formal analysis: Jihye Oh. Funding acquisition: Seung-Chul Hong. Investigation: Chaeyoung Song, Jihye Oh, Yoo-Hyun Um, Seung-Chul Hong. Methodology: Chaeyoung Song, Jihye Oh, Seung-Chul Hong. Project administration: Seung-Chul Hong. Resources: Jihye Oh, Seung-Chul Hong. Software: Jihye Oh. Supervision: Young-Chan Kim, Yoo-Hyun Um, Tae-Won Kim, Ho-Jun Seo, Jong-Hyun Jeong, Seung-Chul Hong. Validation: Seung-Chul Hong. Visualization: Chaeyoung Song. Writing—original draft: Chaeyoung Song. Writing—review & editing: Chaeyoung Song, Yoo-Hyun Um.
Conflicts of Interest
Seung-Chul Hong, a contributing editor of the Sleep Medicine Research, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.
Funding Statement
None

ACKNOWLEDGEMENTS

None

REFERENCES

1. Arnaud C, Bochaton T, Pépin JL, Belaidi E. Obstructive sleep apnoea and cardiovascular consequences: pathophysiological mechanisms. Arch Cardiovasc Dis 2020;113:350-8.
crossref pmid
2. de Araujo Dantas AB, Gonçalves FM, Martins AA, Alves GÂ, Stechman-Neto J, Corrêa CC, et al. Worldwide prevalence and associated risk factors of obstructive sleep apnea: a meta-analysis and meta-regression. Sleep Breath 2023;27:2083-109.
crossref pmid
3. Senaratna CV, Perret JL, Lodge CJ, Lowe AJ, Campbell BE, Matheson MC, et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med Rev 2017;34:70-81.
crossref pmid
4. Drager LF, Togeiro SM, Polotsky VY, Lorenzi-Filho G. Obstructive sleep apnea: a cardiometabolic risk in obesity and the metabolic syndrome. J Am Coll Cardiol 2013;62:569-76.
pmid pmc
5. Guay-Gagnon M, Vat S, Forget MF, Tremblay-Gravel M, Ducharme S, Nguyen QD, et al. Sleep apnea and the risk of dementia: a systematic review and meta-analysis. J Sleep Res 2022;31:e13589.
pmid
6. Zhu X, Zhao Y. Sleep-disordered breathing and the risk of cognitive decline: a meta-analysis of 19,940 participants. Sleep Breath 2018;22:165-73.
crossref pmid
7. Anghel L, Ciubară A, Nechita A, Nechita L, Manole C, Baroiu L, et al. Sleep disorders associated with neurodegenerative diseases. Diagnostics (Basel) 2023;13:2898.
crossref pmid pmc
8. Dong J, Yu X, Wang Y, Zhang H, Guo R. Obstructive sleep apnea and cognition: insights gleaned from bibliometric analysis. Front Psychiatry 2023;14:1259251.
crossref pmid pmc
9. McCann H, Stevens CH, Cartwright H, Halliday GM. α-synucleinopathy phenotypes. Parkinsonism Relat Disord 2014;20(Suppl 1):S62-7.
crossref pmid
10. Henderson MX, Trojanowski JQ, Lee VM. α-synuclein pathology in Parkinson’s disease and related α-synucleinopathies. Neurosci Lett 2019;709:134316.
crossref pmid pmc
11. Daulatzai MA. Pathogenesis of cognitive dysfunction in patients with obstructive sleep apnea: a hypothesis with emphasis on the nucleus tractus solitarius. Sleep Disord 2012;2012:251096.
crossref pmid pmc
12. Bahia CMCDS, Pereira JS. Obstructive sleep apnea and neurodegenerative diseases: a bidirectional relation. Dement Neuropsychol 2015;9:9-15.
crossref pmid pmc
13. Shen Y, Shen Y, Dong ZF, Pan PL, Shi HC, Liu CF. Obstructive sleep apnea in Parkinson’s disease: a study in 239 Chinese patients. Sleep Med 2020;67:237-43.
crossref pmid
14. Li J, Zhao L, Ding X, Cui X, Qi L, Chen Y. Obstructive sleep apnea and the risk of Alzheimer’s disease and Parkinson disease: a Mendelian randomization study OSA, Alzheimer’s disease and Parkinson disease. Sleep Med 2022;97:55-63.
crossref pmid
15. Sobreira-Neto MA, Pena-Pereira MA, Sobreira EST, Chagas MHN, Almeida CMO, Fernandes RMF, et al. Obstructive sleep apnea and Parkinson’s disease: characteristics and associated factors. Arq Neuropsiquiatr 2019;77:609-16.
crossref pmid
16. Yu Q, Hu X, Zheng T, Liu L, Kuang G, Liu H, et al. Obstructive sleep apnea in Parkinson’s disease: a prevalent, clinically relevant and treatable feature. Parkinsonism Relat Disord 2023;115:105790.
crossref pmid
17. Gnoni V, Ilic K, Drakatos P, Petrinovic MM, Cash D, Steier J, et al. Obstructive sleep apnea and multiple facets of a neuroinflammatory response: a narrative review. J Thorac Dis 2022;14:564-74.
crossref pmid pmc
18. Trist BG, Hare DJ, Double KL. Oxidative stress in the aging substantia nigra and the etiology of Parkinson’s disease. Aging Cell 2019;18:e13031.
crossref pmid pmc
19. Yang C, Zhou Y, Liu H, Xu P. The role of inflammation in cognitive impairment of obstructive sleep apnea syndrome. Brain Sci 2022;12:1303.
crossref pmid pmc
20. Ju YE, McLeland JS, Toedebusch CD, Xiong C, Fagan AM, Duntley SP, et al. Sleep quality and preclinical Alzheimer disease. JAMA Neurol 2013;70:587-93.
crossref pmid pmc
21. Iranzo A, Tolosa E, Gelpi E, Molinuevo JL, Valldeoriola F, Serradell M, et al. Neurodegenerative disease status and post-mortem pathology in idiopathic rapid-eye-movement sleep behaviour disorder: an observational cohort study. Lancet Neurol 2013;12:443-53.
crossref pmid
22. Chen JC, Tsai TY, Li CY, Hwang JH. Obstructive sleep apnea and risk of Parkinson’s disease: a population-based cohort study. J Sleep Res 2015;24:432-7.
crossref pmid
23. Morale MC, Serra PA, L’episcopo F, Tirolo C, Caniglia S, Testa N, et al. Estrogen, neuroinflammation and neuroprotection in Parkinson’s disease: glia dictates resistance versus vulnerability to neurodegeneration. Neuroscience 2006;138:869-78.
crossref pmid
24. Snyder B, Cunningham RL. Sex differences in sleep apnea and comorbid neurodegenerative diseases. Steroids 2018;133:28-33.
crossref pmid pmc
25. Jurado-Coronel JC, Cabezas R, Ávila Rodríguez MF, Echeverria V, García-Segura LM, Barreto GE. Sex differences in Parkinson’s disease: features on clinical symptoms, treatment outcome, sexual hormones and genetics. Front Neuroendocrinol 2018;50:18-30.
crossref pmid
26. Nordengen K, Cappelletti C, Bahrami S, Frei O, Pihlstrøm L, Henriksen SP, et al. Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson’s disease. Brain 2024;147:858-70.
crossref pmid pmc
27. Boulos C, Yaghi N, El Hayeck R, Heraoui GN, Fakhoury-Sayegh N. Nutritional risk factors, microbiota and Parkinson’s disease: what is the current evidence? Nutrients 2019;11:1896.
crossref pmid pmc
28. Basoglu OK, Tasbakan MS. Gender differences in clinical and polysomnographic features of obstructive sleep apnea: a clinical study of 2827 patients. Sleep Breath 2018;22:241-9.
crossref pmid
29. Ben-Shlomo Y, Darweesh S, Llibre-Guerra J, Marras C, San Luciano M, Tanner C. The epidemiology of Parkinson’s disease. Lancet 2024;403:283-92.
crossref pmid
30. Hogan DB, Fiest KM, Roberts JI, Maxwell CJ, Dykeman J, Pringsheim T, et al. The prevalence and incidence of dementia with Lewy bodies: a systematic review. Can J Neurol Sci 2016;43(Suppl 1):S83-95.
crossref pmid
31. Severiano E Sousa C, Alarcão J, Pavão Martins I, Ferreira JJ. Frequency of dementia in Parkinson’s disease: a systematic review and meta-analysis. J Neurol Sci 2022;432:120077.
crossref pmid
32. Savica R, Grossardt BR, Rocca WA, Bower JH. Parkinson disease with and without dementia: a prevalence study and future projections. Mov Disord 2018;33:537-43.
crossref pmid pmc

Table 1.
Characteristics of the study population
Patients with OSA (n = 103785) Controls without OSA (n = 103785)
Age group
 <40 yrs 31672 (30.52) 31672 (30.52)
 40–<60 yrs 49331 (47.53) 49331 (47.53)
 60–<80 yrs 21740 (20.95) 21740 (20.95)
 ≥80 yrs 1042 (1.0) 1042 (1.0)
Age (yr) 47.45 ± 14.85 47.45 ± 14.85
Sex
 Male 86200 (83.06) 86200 (83.06)
 Female 17585 (16.94) 17585 (16.94)
Insurance type
 Health insurance 103220 (96.89) 102579 (94.99)
 Medical aid 3312 (3.11) 5406 (5.01)
Medical comorbidities
 Diabetes 9316 (8.98) 6414 (6.18)
 Hypertension 23850 (22.98) 15356 (14.80)
 Hyperlipidemia 4918 (4.74) 3203 (3.09)

Values are presented as n (%) or mean ± standard deviation.

OSA, obstructive sleep apnea.

Table 2.
The α-synucleinopathies in patients with OSA
Diseases Frequency (n)
Univariate analysis
Multivariate analysis
OSA Controls Crude odds ratio 95% CI Pr > ChiSq Adjusted odds ratio 95% CI Pr > ChiSq
Parkinson’s disease 458 212 2.582 2.536–2.628 <0.0001 2.166 1.840–2.549 <0.0001
Parkinson’s disease dementia 6 5 1.438 1.246–1.659 <0.0001 1.200 0.366–3.932 0.7633
Dementia with Lewy bodies 20 5 1.538 0.765–3.092 0.2267 4.001 1.501–10.660 0.0056

OSA, obstructive sleep apnea; CI, confidence interval.

Table 3.
Subgroup analysis by sex
Sex Univariate analysis
Multivariate analysis
Exact parameter estimate-crude Exact odds ratios-crude 95% CI Two-sided p-values Exact parameter estimate-adjusted Exact odds ratios-adjusted 95% CI Two-sided p-values
Parkinson’s disease M 0.8201 2.271 1.854–2.793 <0.0001 0.7712 2.162 1.765–2.660 <0.0001
F 0.6764 1.967 1.472–2.647 <0.0001 -0.0202 0.980 0.781–1.220 0.9033
Parkinson’s disease dementia M 0.2877 1.333 0.226–9.103 >0.9999 0.2388 1.270 0.215–8.668 >0.9999
F -2.73E-15 1 0.072–13.797 >0.9999 -0.7064 0.493 0.047–3.015 0.6419
Dementia with Lewy bodies M 0.8110 2.250 0.628–10.000 0.2668 0.7621 2.143 0.598–9.523 0.3071
F 2.3984 11.006 1.599–473.771 0.0063 0.4110 1.508 0.568–4.119 0.4851

CI, confidence interval.

Table 4.
Subgroup analysis by age group
Age group (yr) Univariate analysis
Multivariate analysis
Exact parameter estimate-crude Exact odds ratios-crude 95% CI Two-sided p-values Exact parameter estimate-adjusted Exact odds ratios-adjusted 95% CI Two-sided p-values
Parkinson’s disease <40 0.5109 1.667 0.324–10.734 0.7265 0.3643 1.439 0.28–9.27 0.8916
40–<60 0.6938 2.001 0.0324–10.734 0.0014 0.7626 2.144 1.369–3.428 0.0005
60–<80 0.7393 2.094 1.72–2.559 <0.0001 0.7037 2.021 1.667–2.458 <0.0001
≥80 1.1314 3.1 1.909–5.195 <0.0001 0.9407 2.562 1.868–3.491 <0.0001
Parkinson’s disease dementia <40 Degenerate Degenerate
40–<60 2.44E-16 1 0–19 >0.9999 0.004228 0 19.08–1 1.004
60–<80 0.2232 1.25 0.269–6.301 >0.9999 0.2573 1.293 0.278–6.519 0.958
≥80 -1.65E-15 1 0.053–infinity* >0.9999 0.3414 1.407 0.027–17.542 >0.9999
Dementia with Lewy bodies <40 Degenerate Degenerate
40–<60 -0.6932 0.5 0.008–9.605 >0.9999 -0.6889 0.502 0.009–9.645 >0.9999
60–<80 1.6745 5.336 1.527–28.583 0.0044 1.42 4.141 1.336–17.024 0.0095
≥80 1.3489 3.853 0.584–infinity* 0.2496 0.5936 1.811 0.302–7.947 0.5937

* Infinity indicates a median unbiased estimate;

Indicates that the conditional distribution is degenerate.

CI, confidence interval.

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