Adam V Benjafield

Najib T Ayas

Peter R Eastwood

Raphael Heinzer

Mary S M Ip

Mary J Morrell

Carlos M Nunez

Sanjay R Patel

Thomas Penzel

Jean-Louis D Pépin

Paul E Peppard

Professor Atul Malhotra
email: amalhotra@ucsd.edu.

Sanjeev Sinha

Sergio Tufik

Kate Valentine

Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis

Introduction

Obstructive sleep apnoea is a common disorder that can present with or without symptoms and is accompanied by major neurocognitive and cardiovascular sequelae. [START_REF] Redline | Epidemiology and natural history of obstructive sleep apnea[END_REF][START_REF] Levy | Obstructive sleep apnoea syndrome[END_REF][START_REF] Malhotra | On the cutting edge of obstructive sleep apnoea: where next?[END_REF] At present, care of patients with obstructive sleep apnoea varies by country and depends on a patient's symptoms. In well resourced settings, considerable efforts are being made to diagnose and treat individuals with obstructive sleep apnoea, but available data suggest that most cases of obstructive sleep apnoea remain undiagnosed and untreated, even in developed countries. In developing countries, there is generally little awareness of obstructive sleep apnoea, and diagnostic and treatment options are often not available or have not been adapted for resource-poor settings. [START_REF] Jaiswal | Raising awareness about sleep disorders[END_REF] Because of the multifactorial and social consequences of obstructive sleep apnoea, the disorder is associated with a high economic and societal burden. In 2015, the cost of diagnosing and treating obstructive sleep apnoea in the USA was approximately US$12•4 billion. [START_REF] Watson | Health Care Savings: The economic value of diagnostic and therapeutic care for obstructive sleep apnea[END_REF] The global cost of diagnosing and treating obstructive sleep apnoea has not been estimated because information about global prevalence is required first.

Evidence suggests that obstructive sleep apnoea is an important contributor to poor health outcomes and that treatment of this condition is generally beneficial in minimising the associated adverse clinical outcomes and improving sleep-related quality of life. [START_REF] Javaheri | Cardiovascular diseases and sleep apnea[END_REF] Thus, focusing on effective treatment of obstructive sleep apnoea might be one approach for reducing associated health-care costs and the negative impact of the condition, such as the cognitive impact of sleepiness. [START_REF] Weaver | CPAP Treatment of sleepy patients with milder OSA: results of the CATNAP randomized clinical trial[END_REF][START_REF] Teran-Santos | The association between sleep apnea and the risk of traffic accidents. Cooperative Group Burgos-Santander[END_REF] Additionally, given the shift in focus from issues around malnutrition and basic hygiene towards chronic health conditions, such as the obesity pandemic and its associated metabolic complications, [START_REF] Mctigue | Cardiovascular risk factors, mortality, and overweight[END_REF][START_REF] Olshansky | A potential decline in life expectancy in the United States in the 21st century[END_REF][START_REF] Hales | Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016[END_REF][START_REF]NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128•9 million children, adolescents, and adults[END_REF] the ageing population demographic, 13 and the association between obstructive sleep apnoea and various noncommunicable diseases, [START_REF] Farrell | Recognition and treatment of sleep-disordered breathing: an important component of chronic disease management[END_REF] obstructive sleep apnoea is likely to be a rising global problem over the coming years. [START_REF] Malhotra | Update in respiratory sleep disorders: epilogue to a modern review series[END_REF][START_REF] Jordan | Adult obstructive sleep apnoea[END_REF] Planning for effective diagnosis and management strategies requires accurate and countryspecific estimates of disease prevalence. In 2007, WHO estimated that more than 100 million individuals were affected by obstructive sleep apnoea worldwide, [START_REF] Who | Global surveillance, prevention and control of chronic respiratory diseases[END_REF] but this estimate was acknowledged to be only an approximation based on data available at the time. In this study we used a new approach and the latest publicly available data to estimate the global prevalence of obstructive sleep apnoea. We aimed to estimate the total number of affected individuals around the world and the proportion of those with moderate or severe obstructive sleep apnoea because positive airway pressure is recommended in all patients with excessive sleepiness, impaired sleep-related quality of life, and comorbid hypertension, which are more likely with an apnoea-hypopnoea index (AHI) of 15 or more events per h. [START_REF] Epstein | Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults[END_REF] The aim of our analysis was to raise awareness of the global burden of obstructive sleep apnoea by providing data to help guide strategies and health policies to address this important health and societal problem, and to highlight substantial gaps in rigorously assessed obstructive sleep apnoea prevalence data that are currently unavailable for much of the global population. These gaps should be of great concern because of the wide-ranging negative sequelae of obstructive sleep apnoea

Methods

Search strategy and selection criteria

We searched PubMed and Embase to identify prevalence studies of obstructive sleep apnoea done in the general or community population, in adults (aged 18 years and older), where obstructive sleep apnoea was measured objectively with a sleep study. We selected general population prevalence studies rather than those done in selected groups on the basis of concomitant diseases or those done in clinical settings where referral bias could be an important limitation. Only studies that used objective testing (rather than questionnaires) to diagnose obstructive sleep apnoea were included in our analysis. This included use of either home sleep testing (including airflow and oximetry) or in-laboratory polysomnography and reporting the AHI. We had no specific requirement for how obstructive sleep apnoea symptoms were evaluated or reported. The timeframe for studies was not limited in the search and the key words used in all combinations were "sleep disordered breathing", "sleep apnoea", "sleep apnoea syndrome", "obstructive", "prevalence", and "population". The original search was done in April, 2017, and then rechecked in February, 2019 (no new papers were identified in the second search). Reference lists of identified papers were manually reviewed. We applied no language exclusions so papers published in other languages were translated into English. Authors of the major studies identified were invited to collaborate. Our estimates focused on individuals aged 30-69 years because data for this group were generally available from the published studies and related to information from the UN for all countries. Therefore, the resulting prevalence estimates relate specifically to a subset of the global population aged 30-69 years.

Data collection and analysis

For studies that did not use the American Academy of Sleep Medicine (AASM) 2012 scoring criteria [START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF] to identify obstructive sleep apnoea, we created a conversion algorithm based on the study by Duce and colleagues [START_REF] Duce | The 2012 AASM respiratory event criteria increase the incidence of hypopneas in an adult sleep center population[END_REF] ; this approach allowed us to determine an equivalent AHI for publications that used different criteria: Thus, we were able to report AHI figures based on the AASM 1999, AASM 2007, and AASM 2012 criteria, even if these criteria were not used in the original studies. [START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF][START_REF]Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in adults[END_REF] The main differences between the criteria relate to scoring of hypopnoeas (table 1).

An algorithm was developed in consultation with all authors to estimate global prevalence of obstructive sleep apnoea in individuals aged 30-69 years from the available published data. Prevalence for men and women was estimated separately and then combined to provide total prevalence. For countries with published prevalence estimates for obstructive sleep apnoea, these data were used. If no country-specific estimates were available, we matched countries without prevalence data to similar countries with prevalence data; determination of country population similarity was based on the population body-mass index (BMI; within 1 kg/m 2 ), race (smallest difference in proportion of white, black, and Asian populations between the country being matched and the reference country or countries), and geographical proximity based on distance (appendix p 1). Age, sex, BMI, and race were used to match countries because these factors are clinically recognised to affect prevalence of obstructive sleep apnoea and these data were available for all countries. We obtained data on global estimates of BMI from the WHO Global Health Observatory data repository, data on race from the Central Intelligence Agency's World Factbook, and data on population-based age and sex from the UN World Population Prospects 2015. We evaluated the performance of our estimation procedure by comparing its performance in countries where prevalence data were available. Goodness of fit for these comparisons is shown in the appendix (pp 1-3). In two studies, the overall number of individuals used for the obstructive sleep apnoea prevalence estimates were reported, but not sex-specific numbers. In these cases, we assumed a 50:50 distribution between the sexes.

Our confidence intervals for the global estimate incorporate the sampling variability in the estimates of obstructive sleep apnoea prevalence from 17 studies, but do not incorporate the modelling error in estimating each country's prevalence estimate (since the algorithm was not a statistical model). A sensitivity analysis based on a regression model is described below, which does incorporate modelling error into the confidence interval. The sampling variability confidence interval assesses how the variability in each study's prevalence estimate affects the global obstructive sleep apnoea estimate. Specifically, the global estimate can be defined as follows:

X = ∑ i = 1 K N i p i
where N i is the population size of the i th subgroup (each subgroup is a sex in a specific country), K=386 (193 countries multiplied by 2 for both sexes) and p i is the obstructive sleep apnoea prevalence estimate applied to the i th subgroup. Since M=32 prevalence estimates were applied to the 386 population sizes (M is the number of reference prevalence estimates; 15 studies produced estimates for men and women, and two studies produced a single-sex estimate), the estimate can be rewritten by summing the population sizes of all subgroups to which each estimate was applied:

X = ∑ k = 1 M M k p k
where M k is the total population size to which the k th obstructive sleep apnoea prevalence estimate was applied. The obstructive sleep apnoea prevalence estimate p k has variance p k (1 -p k )/n k , where n k is the number of individuals used to estimate p k . The individuals used in each prevalence estimate are independent; thus, the variance of the global estimate is Var(X), and the 95% CI is X ± 1 ⋅ 96 Var(X):

Var(X) = ∑ k = 1 M M k 2 Var p k = ∑ k = 1 M M k 2 p k 1 -p k n k
As a sensitivity analysis, an over-dispersed logistic regression model was fit to data from the prevalence estimate studies (each sex was modelled separately). The prevalence estimate of obstructive sleep apnoea was treated as the dependent variable; BMI and proportion of the white population were treated as continuous independent variables, continent was treated as a categorical independent variable, and an over-dispersion factor was added to the model to account for the wide variability across countries. This model then allowed for construction of a 95% CI that incorporated sampling variability in each study as well as the modelling errors in predicting each country's prevalence estimate. Since we found no studies of obstructive sleep apnoea prevalence in Africa, Africa was grouped with Oceania (similar estimates were obtained if Africa was grouped with Europe, North America, or South America). Statistical analyses were done with R software, version 3.3.

Role of the funding source

This work represents an academic and industry partnership. The study sponsor was involved in study design, data collection, data analysis, data interpretation, and writing of the report.

All authors had full access to all the data and collectively made the decision to submit for publication.

Results

17 articles were included in the analyses (table 2). [START_REF] Heinzer | Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study[END_REF][START_REF] Marshall | Sleep apnea as an independent risk factor for all-cause mortality: the Busselton Health Study[END_REF][START_REF] Tufik | Obstructive sleep apnea syndrome in the Sao Paulo Epidemiologic Sleep Study[END_REF][START_REF] Peppard | Increased prevalence of sleepdisordered breathing in adults[END_REF][START_REF] Ip | A community study of sleep-disordered breathing in middle-aged Chinese men in Hong Kong[END_REF][START_REF] Ip | A community study of sleep-disordered breathing in middle-aged Chinese women in Hong Kong: prevalence and gender differences[END_REF][START_REF] Arnardottir | Obstructive sleep apnoea in the general population: highly prevalent but minimal symptoms[END_REF][START_REF] Reddy | Prevalence and risk factors of obstructive sleep apnea among middle-aged urban Indians: a community-based study[END_REF][START_REF] Mihaere | Obstructive sleep apnea in New Zealand adults: prevalence and risk factors among Maori and non-Maori[END_REF][START_REF] Nakayama-Ashida | Sleep-disordered breathing in the usual lifestyle setting as detected with home monitoring in a population of working men in Japan[END_REF][START_REF] Hrubos-Strom | A Norwegian population-based study on the risk and prevalence of obstructive sleep apnea. The Akershus Sleep Apnea Project (ASAP)[END_REF][START_REF] Plywaczewski | Sleep-disordered breathing in a middle-aged and older Polish urban population[END_REF][START_REF] Kim | Prevalence of sleep-disordered breathing in middle-aged Korean men and women[END_REF][START_REF] Tan | Prevalence of sleep-disordered breathing in a multiethnic Asian population in Singapore: a community-based study[END_REF][START_REF] Duran | Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr[END_REF][START_REF] Li | The prevalence of obstructive sleep apnea-hypopnea syndrome in adults aged over 20 years in Changchun city[END_REF][START_REF] Fietze | Prevalence and association analysis of obstructive sleep apnea with gender and age differences-Results of SHIP-Trend[END_REF] Reliable prevalence estimates were obtained on the basis of available data for 16 of 193 countries in the world; prevalence estimates for the remaining countries were extrapolated as described above.

Using an AHI criterion of five or more events per h and the AASM 2012 criteria, an estimated 936 million (95% CI 903-970) individuals aged 30-69 years (men and women) worldwide were found to have obstructive sleep apnoea; the corresponding figure for an AHI of 15 or more events per h was 425 million (399-450; figure 1). The logistic regression model yielded a global obstructive sleep apnoea (AHI of five or more events per h) estimate of 730 million (618-842). Numbers of affected individuals were lower when the AASM 2007 criteria were applied and highest with the 1999 AASM criteria (figure 1). The estimated number of individuals with obstructive sleep apnoea was highest in China, followed by the USA, Brazil, and India; other countries in the top ten for the number of individuals with obstructive sleep apnoea were Pakistan, Russia, Nigeria, Germany, France, and Japan (figure 2), which predominantly reflects the overall size of the population of these countries. When expressed as a prevalence, the obstructive sleep apnoea burden for each country is shown as a heat map for an AHI of five or more events per h and an AHI of 15 or more events per h (figures 3, 4). A summary of the number of affected individuals and overall obstructive sleep apnoea prevalence for each country, region, or territory is provided in table 3.

Discussion

Our estimates suggest that nearly 1 billion adults aged 30-69 years worldwide could have obstructive sleep apnoea, and the number of people with moderate to severe obstructive sleep apnoea, for which treatment is generally recommended, is estimated to be almost 425 million. To the best of our knowledge, this analysis represents the first set of global estimates of the prevalence of obstructive sleep apnoea.

Based on the published literature and by matching countries based on population demographics where specific data were not available, we estimated that almost 1 billion people worldwide aged between 30-69 years have an AHI of five or more events per h. The overall prevalence of obstructive sleep apnoea is likely to be higher given that the analysis focused only on adults aged 30-69 years and we used AASM 2012 criteria rather than the more liberal AASM 1999 criteria. [START_REF]Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in adults[END_REF][START_REF] Aasm | International classification of sleep disorders, 2nd edn Diagnostic and coding manual[END_REF] The prevalence of obstructive sleep apnoea will be lower in adults younger than 30 years, and higher in adults aged 70 years and older, than we have estimated in this analysis, since previous publications show a linear relationship between age and obstructive sleep apnoea prevalence. [START_REF] Tufik | Obstructive sleep apnea syndrome in the Sao Paulo Epidemiologic Sleep Study[END_REF] The number of individuals with moderate to severe obstructive sleep apnoea, based on an AHI of 15 or more events per h, was 425 million. This group could be considered the clinically important obstructive sleep apnoea population for whom treatment would be recommended, even though symptoms were not included in our estimate. We found that obstructive sleep apnoea prevalence estimates were dependent on the respiratory event scoring criteria used, but we focused on AASM 2012 [START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF] because these guidelines are in widespread clinical use, and standardised data from different studies can be generated with a conversion factor. The optimal criteria for scoring is likely to depend on the technology used and the clinical outcomes of interest, but these variations are subtle compared with the large burden of disease that is undiagnosed at present. Based on the magnitude of these results, re-evaluation of the current AHI thresholds might be prudent when the latest sleep study scoring criteria and technologies are used in relation to clinical outcomes to identify the thresholds at which treatment is most appropriate. In 2007, WHO estimated that more than 100 million people worldwide were likely to have obstructive sleep apnoea. [START_REF] Who | Global surveillance, prevention and control of chronic respiratory diseases[END_REF] However, to our knowledge, this value was acknowledged to be a gross approximation based on the data available at the time. We believe our approach of a country-imputation algorithm is a legitimate attempt to quantitatively synthesise the available evidence to estimate the global prevalence of obstructive sleep apnoea. Our approach used the major known factors that influence prevalence of obstructive sleep apnoea by matching on age, sex, BMI, and race, and, if required, geographical proximity, to identify the most suitable reference study to be applied to a country without introducing further assumptions. Both the primary country-imputation algorithm and the secondary logistic modelling approach led to global estimates that were much larger than those previously reported by WHO, although they were similar in magnitude to previously reported estimates. Increasing prevalence of obstructive sleep apnoea globally is consistent with single-country trends. [START_REF] Peppard | Increased prevalence of sleepdisordered breathing in adults[END_REF] Various complex reasons probably underlie the discrepancy between our estimates and those from WHO in 2007, including the more precise methodological approach in our study and advances in detection technology. [START_REF] Hosselet | Detection of flow limitation with a nasal cannula/pressure transducer[END_REF] Various population and population health factors are also likely to have had a role. Obstructive sleep apnoea is common in patients with high BMI [START_REF] Peppard | Increased prevalence of sleepdisordered breathing in adults[END_REF][START_REF] Young | The epidemiology of obstructive sleep apnea: a population health perspective[END_REF] and with increasing age. [START_REF] Peppard | Increased prevalence of sleepdisordered breathing in adults[END_REF][START_REF] Duran | Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr[END_REF][START_REF] Young | The epidemiology of obstructive sleep apnea: a population health perspective[END_REF][START_REF] Ancoli-Israel | Sleep-disordered breathing in community-dwelling elderly[END_REF] Therefore, the worldwide obesity epidemic and the ageing population demographic are likely to contribute to the rising global prevalence of obstructive sleep apnoea. Additionally, the high prevalence seen in countries such as China is likely to be due to racial and genetic differences in common anatomical features that increase the likelihood of obstructive sleep apnoea, such as a narrower airway. [START_REF] Lam | Craniofacial profile in Asian and white subjects with obstructive sleep apnoea[END_REF] The increasing burden of non-communicable diseases, such as diabetes, and more sedentary lifestyles could also have a role in the rising global prevalence of obstructive sleep apnoea. [START_REF] Jehan | Obesity, obstructive sleep apnea and type 2 diabetes mellitus: epidemiology and pathophysiologic insights[END_REF] Our analysis highlights the importance of considering how the burden of obstructive sleep apnoea should be managed. In countries where a diagnosis of obstructive sleep apnoea is less well recognised, education and advocacy are required to inform both patients and health-care providers about this condition and its associated complications. [START_REF] Jaiswal | Raising awareness about sleep disorders[END_REF][START_REF] Iber | The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications[END_REF] In settings where technologies for diagnosing and treating obstructive sleep apnoea are not available, efforts to leverage alternative technologies to facilitate provision of cost-effective care need to be considered, along with strategies to improve accessibility. For example, wearable technologies, validated questionnaires, and connected devices could be used to optimise diagnosis. [START_REF] Malhotra | Patient engagement using new technology to improve adherence to positive airway pressure therapy: a retrospective analysis[END_REF][START_REF] Penzel | New technology to assess sleep apnea: wearables, smartphones, and accessories[END_REF] In this context, efforts are underway to try to diagnose obstructive sleep apnoea with simple devices that can be used anywhere in the world. [START_REF] Goverdovsky | Hearables: multimodal physiological in-ear sensing[END_REF][START_REF] Rodriguez-Villegas | A pilot study of a wearable apnoea detection device[END_REF] Moreover, cloudbased technologies allow centralised reading and scoring of sleep tests so that local expertise is no longer needed to deliver appropriate diagnostic care. [START_REF] Penzel | New technology to assess sleep apnea: wearables, smartphones, and accessories[END_REF][START_REF] Kuna | Agreement in computer-assisted manual scoring of polysomnograms across sleep centers[END_REF] The high estimated global prevalence in this study suggests that the development of new and cheaper alternatives to diagnose and treat obstructive sleep apnoea are required.

For countries with well resourced health-care systems, delivery models whereby many patients can receive high-quality care without the need for multiple office visits with subspecialists need to be considered, along with alternative payment models. [START_REF] Freedman | Doing it better for less: incorporating OSA management into alternative payment models[END_REF] For uncomplicated obstructive sleep apnoea, models of care are being developed that allow excellent clinical outcomes while potentially minimising costs. For example, Antic and colleagues [START_REF] Antic | A randomized controlled trial of nurse-led care for symptomatic moderate-severe obstructive sleep apnea[END_REF] have shown that primary care physicians and nurse practitioners, under appropriate supervision, can achieve good outcomes in management of obstructive sleep apnoea. With the push towards cost effective care across the entire health-care system, efficient care delivery models are being increasingly discussed [START_REF] Freedman | Doing it better for less: incorporating OSA management into alternative payment models[END_REF] and resources reallocated to multimorbid obstructive sleep apnoea. [START_REF] Ayas | Cost-effectiveness of continuous positive airway pressure therapy for moderate to severe obstructive sleep apnea/hypopnea[END_REF] Notably, although the diagnosis and treatment of sleep disorders can be viewed as a large economic burden, some health economic studies have suggested that management of obstructive sleep apnoea is not just cost effective but potentially cost saving as a result of prevention of major complications. [START_REF] Ayas | Cost-effectiveness of continuous positive airway pressure therapy for moderate to severe obstructive sleep apnea/hypopnea[END_REF][START_REF] Frost | Hidden health crisis costing America billions: underdiagnosing and undertreating sleep apnea draining health care system[END_REF][START_REF] Pendharkar | Testing and treatment for obstructive sleep apnea in Canada: funding models must change[END_REF] Nevertheless, the cost of treatment devices might be an important barrier to treatment in low-income and middle-income countries. To improve health equity, it might be necessary for global health bodies, including WHO, the UN, and non-governmental organisations, to lobby for provision of treatment devices for obstructive sleep apnoea at costs substantially lower than current pricing to facilitate access in developing regions.

The findings of our study need to be interpreted in the context of various limitations. First, although the goal of our analysis was to include as much published data from several countries and be as accurate as possible, we found no published prevalence data for obstructive sleep apnoea for most countries, including no countries from Africa. We therefore used techniques designed to provide the best estimates possible using available data from groups with similar demographic characteristics. This led to the reference studies being represented many times because of the nature of our country-imputation algorithm (ie, we used data from 16 countries to make estimates for 177 countries worldwide). Although we believe our global estimates are robust when compared with the country-specific extrapolated estimates, individual country estimates were needed to generate the global estimate, so the country-specific estimates for those countries where no prevalence study was previously done should not be used in any formal way because these estimates might not be as precise as those for countries with available prevalence data. Additionally, we have not provided specific data for men and women or data about disease burden in different age groups. We hope that the data gaps identified in our analysis will help encourage more scrupulous epidemiological studies, particularly from regions where rigorous data are not currently available. Our sensitivity analysis, based on a logistic regression of the available data, yielded a lower estimate of the prevalence of obstructive sleep apnoea than the countryimputation algorithm (730 million vs 936 million) although it still confirmed a high prevalence of obstructive sleep apnoea. The logistic regression is lower because estimates from countries such as Singapore, Germany, and Switzerland are attenuated toward the means of their continent; the country-imputation algorithm does not reduce these estimates. Furthermore, logistic regression weights all predictor variables equally, whereas the countryimputation algorithm weights BMI as the most important factor in matching countries, racial distribution is a secondary factor, and geographical proximity is only used to resolve multiple matches on the first two predictors. This weighting of factors allows Australian men to be matched to Canadian men, for example, which would not be feasible in the logistic regression model. The logistic regression model also estimated that Asian countries similar to Singapore (BMI 24•2 kg/m 2 , 9% white) have an obstructive sleep apnoea prevalence of 16•7%, whereas the country-imputation algorithm estimated these countries' prevalence as being 62•3%, as reported by Tan and colleagues. [START_REF] Tan | Prevalence of sleep-disordered breathing in a multiethnic Asian population in Singapore: a community-based study[END_REF] These were the major drivers for the difference in the global estimate between the country-imputation algorithm and the more generalised logistic regression model.

Second, we recognise that the parent studies from which we derived our estimates also have their own limitations, including first night effect, degree of sleep deprivation, body position, night to night variability, and participation and selection bias. Participation bias is an issue with many epidemiological studies and cannot easily be addressed with changing study design. Selection bias reflects the fact that people who are invited to participate might not be representative of the general population. This issue is amplified by the reliance on convenience samples rather than efforts to make sampling truly representative of the broader population. In acknowledging this limitation, we hope that new technologies will become available that might allow more consistent assessments of the general population.

Third, issues around methodological variability are clearly important, given the improved sensitivity observed with nasal pressure measurements versus with thermistor signals alone. [START_REF] Hosselet | Detection of flow limitation with a nasal cannula/pressure transducer[END_REF][START_REF] Hosselet | Classification of sleep-disordered breathing[END_REF] Furthermore, pulse oximeters have variable time constraints and thus varying sensitivity and specificity for desaturation depending on equipment characteristics. Manual scoring of sleep studies is also likely to result in interscorer variability, especially in scoring arousals, which could affect hypopnea scoring and partly explain the variation seen in the published prevalence data for obstructive sleep apnoea. Re-scoring the raw data from the original studies according to consistent criteria would be challenging and standardisation of equipment from previous studies is not possible. Instead, we attempted to make estimates based on these known sources of variance. As a result, there are wide confidence intervals around our estimates based on strict versus liberal criteria. Nonetheless, we are aware that much higher prevalence figures could be generated depending on the assumptions used. None of the limitations mentioned here were considered in calculation of the 95% CIs; these intervals only reflect the sampling variability due to the study sizes used to estimate obstructive sleep apnoea prevalence. The confidence intervals do not reflect uncertainty in the obstructive sleep apnoea prevalence estimates for each country. The goodness of fit of the country-imputation algorithm was assessed as a cross-validation; this analysis indicates a potential underestimation of the global obstructive sleep apnoea prevalence (of up to -48%).

A sensitivity analysis, based on a logistic regression model, indicated a potential overestimation of up to 28%. However, the logistic regression models severely underestimated prevalence of obstructive sleep apnoea in Singapore, Germany, and Switzerland. Clearly, estimating obstructive sleep apnoea prevalence in 193 countries based on sleep studies from just 16 countries will be subject to some error. These estimates can be refined when further data become available. Despite these limitations, our findings address an important gap in the published literature with regard to reporting comprehensive prevalence data for obstructive sleep apnoea.

In conclusion, this analysis highlights the high worldwide prevalence of obstructive sleep apnoea and variations by country and region. Additional, well designed studies are needed to investigate the prevalence of obstructive sleep apnoea, particularly in countries where published data are not available. This high prevalence and the documented association between obstructive sleep apnoea and numerous adverse clinical outcomes, including cardiovascular disease morbidity and mortality, mean that health-care systems around the world need to consider strategies to raise awareness of obstructive sleep apnoea and to diagnose and treat the condition to have a positive impact on population health and healthcare expenditures.

Research in context

Evidence before this study Despite increasing recognition of obstructive sleep apnoea as a contributor to poor health outcomes, our literature searches identified a scarcity of data about global prevalence of obstructive sleep apnoea. We searched PubMed and Embase using search terms of "adult", "sleep disordered breathing", "sleep apnoea", "sleep apnoea syndrome", "obstructive", "prevalence", and "population", with no limits on timeframe nor any language restrictions. The original search was done in April, 2017, and then rechecked in February, 2019 (no new papers were identified in the second search). Relevant and local prevalence data are important to facilitate implementation of effective and efficient strategies for diagnosis and management of obstructive sleep apnoea.

Added value of this study

To the best of knowledge, this is the first study to report a global estimate of obstructive sleep apnoea prevalence and to estimate the number of individuals affected by this condition worldwide. Nearly 1 billion adults aged 30-69 years worldwide were estimated to have obstructive sleep apnoea, with and without symptoms, based on an apnoea-hypopnoea index (AHI) cutoff value of five or more events per h, with 425 million (>45%) of these individuals having an AHI of 15 or more events per h (defined as a moderate to severe disorder requiring treatment). Wide geographical variation exists in the prevalence of obstructive sleep apnoea, with prevalence exceeding 50% in some countries.

Implications of all the available evidence

Given the high burden of obstructive sleep apnoea worldwide, health-care systems need to adopt effective diagnostic and management strategies so that the negative health impacts of obstructive sleep apnoea can be minimised AHI=apnoea-hypopnoea index. Differences in hypopnea scoring rules between American Academy of Sleep Medicine guidelines [START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF][START_REF]Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in adults[END_REF] Hypopnoea criteria 1999 guideline ≥50% decrease in flow OR a clear reduction in flow that does not reach ≥50% AND is associated with either an oxygen desaturation of ≥3% or an arousal 2007 guideline ≥30% decrease in flow from baseline with an associated oxygen desaturation of ≥4% 2012 guideline ≥30% decrease in flow from baseline with an associated oxygen desaturation of ≥3% OR an associated arousal Lancet Respir Med. Author manuscript; available in PMC 2020 February 08.

Figure 1 :

 1 Figure 1: Estimated prevalence of obstructive sleep apnoea based on different scoring rules AASM=American Academy of Sleep Medicine. AHI=apnoea-hypopnoea index.

Figure 2 :

 2 Figure 2: Top ten countries with the highest estimated number of individuals with obstructive sleep apnoea based on the American Academy of Sleep Medicine 2012 criteria[START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF]

Figure 3 :

 3 Figure 3: Global heat map of estimated prevalence of obstructive sleep apnoea (AHI five or more events per h) for each country AHI=apnoea-hypopnoea index.

Figure 4 :

 4 Figure 4: Global heat map of estimated prevalence of obstructive sleep apnoea (AHI 15 or more events per h) for each country AHI=apnoea-hypopnoea index.

21 Alternative Scoring Rule Conversion Factor = Reference study result × Duce et al result for alternative scoring rule Duce et al result for reference study scoring rule

  GlaxoSmithKline, and the Faculty of Biology and Medicine of Lausanne University, and personal fees for medical advisory board work from NightBalance. MJM was a principal investigator on a multicentre trial funded by ResMed. SRP received grant funding through his institution from the American Sleep Medicine Foundation, the ResMed Foundation, Bayer Pharmaceuticals, and Philips Respironics. TP reports personal funding by ICRC St. Anne Hospital Brno Czech Republic, and institutional funding by Heel, Bioprojet, ResMed, Philips Respironics, and Löwenstein Medical Technology. J-LDP is supported by a research grant from the French National Research Agency (ANR-12-TECS-0010), in the framework of the Investissements d'avenir programme (ANR-15-IDEX-02) and reports participation in medXcloud-an academic and industry partnership sponsored by ResMed. AM relinquished all outside personal income as an Officer of the American Thoracic Society in 2012. ResMed provided a philanthropic donation to UC San Diego and reports participation in medXcloud-an academic and industry partnership sponsored by ResMed. MSMI, PEP, SS, and ST report no competing interests.

Table 1 :

 1

Table 2 :

 2 Studies reporting country-specific data on obstructive sleep apnoea prevalence

Sample size Year Age range, years Men (%) Scoring criteria Nasal pressure AHI ≥5 events per h AHI ≥15 events per h Men Women Men Women

	4•9%	9•6%	9•5%	13•2%	••	1•9%	4•8%	6•1%	••	0•2%	6•0%	7•6%	4•7%	26•1%	30•5%	7•0%	23•4%	6•0%
	4•7%	24•8%	9•5%	29•7%	5•3%	••	10•6%	5•5%	22•3%	3•9%	11•0%	15•8%	10•1%	26•1%	30•5%	14•2%	49•7%	13•0%
	23•5%	30•6%	24•2%	33•2%	••	3•7%	10•8%	6•1%	••	3•4%	13•0%	18•4%	16•8%	62•3%	70•8%	28•0%	60•8%	17•4%
	25•5%	46•5%	24•2%	59•4%	8•8%	••	13•3%	13•5%	59•7%	12•5%	21•0%	36•2%	27•1%	62•3%	70•8%	26•2%	83•8%	33•9%
	Yes	Yes	Unspecified	Yes	No	No	Yes	Yes	Yes	Yes	No	Yes	No	Yes	Yes	No	Yes	Yes
	AASM 2012	AASM 2007	Chicago 1999	AASM 2007	AASM 2007	AASM 2007	AASM 2007	Chicago 1999	AASM 2012	AASM 2007	AASM 2007	AASM 2007	AASM 2007	AASM 2007	AASM 2012	AASM 2007	AASM 2012	AASM 2007
	Australia 24 380 2008 40-65 73%	Brazil 25 1042 2010 20-80 45%	China 38 3648 2005 ≥20 50%	Germany 39 1208 2018 20-81 54%	Hong Kong 27 153 2001 30-60 100%	Hong Kong 28 106 2004 30-60 0%	Iceland 29 415 2016 40-65 ••	India 30 365 2009 30-65 ••	Japan 32 322 2008 23-59 ••	New Zealand 31 364 2009 30-59 ••	Norway 33 518 2011 30-65 55%	Poland 34 676 2008 41-72 54%	South Korea 35 457 2004 40-69 69%	Singapore 36 242 2016 21-79 50%	Singapore 36 242 2016 21-79 50%	Spain 37 2148 2001 30-70 49%	Switzerland 23 2121 2015 40-85 48%	USA 26 1520 2013 30-70 55%	Based on two different apnoea-hypopnoea index (AHI) cutoff values.

Lancet Respir Med. Author manuscript; available in PMC 2020 February 08.

Lancet Respir Med. Author manuscript; available in PMC 2020 February 08.

556 642 (9•6%) 28 831 856 (5•4%) Lancet Respir Med. Author manuscript; available in PMC 2020 February 08.

Acknowledgments

Statistical assistance with the generation of confidence interval values was provided by Colleen Kelly, an independent statistician, from Kelly Statistical Consulting, funded by ResMed. Medical writing assistance for editing was provided by Nicola Ryan, an independent medical writer, funded by ResMed.

Funding ResMed.

Declaration of interests

AVB, KV, and CMN are employees of ResMed. NTA reports being on the advisory board of Bresotec. PRE is supported in part by a Senior Research Fellowship from the NHMRC (grant number 1042341). RH reports grants from the Swiss National Science Foundation (grants 3200B0-105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468, and 33CS30-148401), the Leenaards Foundation, the Ligue Pulmonaire Vaudoise,

All data included in this study are available in the public domain. Specific requests or questions should be submitted to the corresponding

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Author Manuscript

Benjafield et al.

Page 21


PharmD Marie Joyeux-Faure

MD Renaud Tamisier

PhD Jean-Christian Borel

PhD Sandrine Millasseau

MD Louis-Marie Galerneau

MD Marie Destors

PhD Sébastien Bailly Pharmd

MD Jean Louis Pepin
email: jpepin@chu-grenoble.fr

Pr Jean

Louis Pépin

Contribution of obstructive sleep apnoea to arterial stiffness: a meta-analysis using individual patient data

Keywords: Sleep apnoea Arterial stiffness, Pulse wave velocity, Obstructive sleep apnoea, Sleep disordered breathing https://mc.manuscriptcentral.com/thorax Thorax

Methods. Individual data from well-characterized patients referred for suspicion of OSA, included in 9 studies in which carotid-femoral PWV was measured using a Complior TM device, were merged for an individual patient data meta-analysis.

Results. 893 subjects were included (age: 56 ± 11 (mean ± SD), 72% male, 84% with confirmed OSA). BMI varied from 15 to 81 kg/m 2 (30 ± 7 kg/m 2 ). PWV ranged from 5.3 to 20.5 m/s (10.4 ± 2.3 m/s). In univariate analysis, log(PWV) was strongly related to age, gender, systolic blood pressure, presence of type 2 diabetes (all p<0.01) as well as to dyslipidaemia (p=0.03) and an Epworth sleepiness score ≥ 9 (p=0.04), whereas it was not related to obesity (p=0.54), a severe apnoea-hypopnea index (p=0.14), mean nocturnal saturation (p=0.33), or sleep time with oxygen saturation below 90% (p=0.47). In multivariable analysis, PWV was independently associated with age, SBP and diabetes (all p<0.01) whereas severe OSA was not significantly associated with PWV.

Conclusion.

Our individual patient meta-analysis showed that elevated arterial stiffness in OSA patients is driven by conventional cardiovascular risk factors rather than apnoea parameters.

SUMMARY 'BOX'

• What is the key question? What are the relative impacts of obstructive sleep apnoea severity and conventional risk factors on arterial stiffness?

• What is the bottom line? The elevation of arterial stiffness in obstructive sleep apnoea patients is driven more by the conventional cardiovascular risk factors (age, hypertension and the presence of diabetes) than by apnoea severity indices.

• Why read on? This meta-analysis used individual patient data from 9 studies performed by the same centre with 893 well characterized patients and pulse wave velocity measured using the same technique for all studies.

INTRODUCTION

Arteries not only carry oxygen and nutriments but also attenuate cardiac pulsatility due to their elastic properties. With aging and in the presence of cardio-vascular disease, large arteries, in particular the aorta, tend to stiffen. [1] In the European Hypertension and Cardiology Society guidelines elevated arterial stiffness is recognized as a strong predictor of end organ damage and cardiovascular mortality that is independent of other usual cardiovascular risk factors. [2][3][4] The international expert committee recommends carotidfemoral pulse wave velocity (PWV) as the "gold standard" for the assessment of aortic stiffness. [1] Patients with chronic diseases that have high cardiovascular risk such as hypertension or diabetes exhibit higher PWV. [5,6] However, contrary to what may be intuitively expected, in obese subjects conflicting results regarding arterial stiffness are reported. [7] This might be partly explained by heterogeneity of obesity patients including its association, or not, with obstructive sleep apnoea (OSA).

Obstructive sleep apnoea syndrome is strongly associated with cardiovascular morbidity and mortality. [8] Data summarized in a systematic review by Doonan et al. [9] and later in a critical review by Phillips et al. [START_REF] Phillips | Is obstructive sleep apnoea causally related to arterial stiffness? A critical review of the experimental evidence[END_REF] reported that severe OSA patients have elevated PWV compared to healthy volunteers or control groups exhibiting no or mild OSA. [START_REF] Drager | Early signs of atherosclerosis in obstructive sleep apnoea[END_REF][START_REF] Phillips | Diurnal and obstructive sleep apnoea influences on arterial stiffness and central blood pressure in men[END_REF][START_REF] Tsioufis | The incremental effect of obstructive sleep apnoea syndrome on arterial stiffness in newly diagnosed essential hypertensive subjects[END_REF][14][START_REF] Suh | Association between pulse wave velocity and nerve conduction study in diabetic patients[END_REF][START_REF] Chen | Obstructive sleep apnoea is independently associated with arterial stiffness in ischemic stroke patients[END_REF][START_REF] Wang | Impact of Obstructive Sleep Apnoea Syndrome on Endothelial Function, Arterial Stiffening, and Serum Inflammatory Markers: An Updated Meta-analysis and Metaregression of 18 Studies[END_REF] A relationship between PWV and OSA severity [START_REF] Drager | Early signs of atherosclerosis in obstructive sleep apnoea[END_REF][START_REF] Chung | The association of nocturnal hypoxemia with arterial stiffness and endothelial dysfunction in male patients with obstructive sleep apnoea syndrome[END_REF] was suggested but was not systematically confirmed.[14, [START_REF] Baguet | Early cardiovascular abnormalities in newly diagnosed obstructive sleep apnoea[END_REF][START_REF] Kim | Impacts of comorbidities on the association between arterial stiffness and obstructive sleep apnoea in the elderly[END_REF] OSA syndrome is associated with several clusters of co-morbidities [START_REF] Bailly | Obstructive Sleep Apnoea: A Cluster Analysis at Time of Diagnosis[END_REF] such as hypertension,[22] obesity, [23,24] diabetes, coronary heart disease and cardiac failure. These co-existing chronic diseases also each strongly contribute towards elevated PWV. [5] The relationship between OSA severity and arterial stiffness assessed by PWV, needs clarifying particularly as previous meta-analyses and reviews in the field have included heterogeneous studies with relatively limited sample sizes and poorly described comorbidities. [START_REF] Phillips | Is obstructive sleep apnoea causally related to arterial stiffness? A critical review of the experimental evidence[END_REF]25] To determine the relative impacts of OSA severity and conventional risk factors on arterial stiffness we conducted an individual patient data meta-analysis on data from a large group of nearly 900 patients with suspicion of OSA, explored in the same centre, for whom PWV and comorbidities were systematically and carefully assessed.

METHODS

Individual data from participants involved in separate studies performed by our group between 2006 and 2015 were pooled. We excluded studies in which PWV was recorded for less than 80% of patients, or in which there was heterogeneity in the PWV or polysomnography methods used. Only data from baseline measurements from observational studies and randomized clinical trials were used in this individual data meta-analysis.

All studies were conducted in accordance with applicable good clinical practice requirements in Europe, French law, ICH E6 recommendations, and the ethical principles of the Helsinki Declaration (1996 and 2000). They were approved by an independent Ethics Committee (Comité de Protection des Personnes, Sud Est V IRB0006705 France) and registered on the ClinicalTrials.gov site (Table S1 in the online supplementary file). All patients gave their written informed consent.

Clinical evaluation

Hypertension was defined as systolic blood pressure (SBP) greater than 140 mmHg, diastolic blood pressure (DBP) greater than 90 mmHg or need for anti-hypertensive treatment.

Clinical blood pressure (BP) was measured in triplicate using a mercury sphygmomanometer or with validated oscillometric devices. The average of 3 measurements was used. Diabetes At least two measurements of PWV were systematically performed. When the difference between measurements was less than 0.5 m/s, the mean value was keep. When the difference was above 0.5 m/s, a third measurement was done and the median value was taken into account. The same procedure and methodology was conducted across studies.

Statistical analysis

Qualitative variables were described using numbers and proportion and quantitative variables are described using mean and standard deviation. Missing values were imputed using a multiple imputation method when the proportion of missing values was < 20%. Five datasets were generated using logistic regression for qualitative variables or linear regression for quantitative variables, considering that missing values were random. More information about data imputation methods are given in the online supplementary file (Table S3). As PWV distribution was skewed, the analysis was first performed with log[PWV].

However, a sensitivity analysis using untransformed PWV showed that the results did not differ from those using log[PWV]. Hence, we re-analysed and present results from non-log transformed PWV for easier interpretation by clinicians and readers.

A one-step IPD meta-analysis was performed to adjust on patient variables. A univariate linear mixed model with a random effect by study was built to identify factors associated with PWV. Factors significantly associated with PWV and severe OSA (AHI ≥ 30 events/h) were introduced in a multivariate linear mixed model to identify independent factors P-values < 0.05 were considered statistically significant. Data were analysed using SAS v9.4

(SAS Institute Inc., Cary, NC, USA).

RESULTS

The 9 studies included in the meta-analysis are detailed in the online supplement in terms of regulatory approval, design and publications (Figure 1 and Table S1 in the online supplementary file). All these studies concerned patients with OSA or suspected of OSA.

There were 6 randomized and 3 observational studies.

The characteristics of the whole group of 893 patients are shown in Patients with confirmed OSA were older and had higher BP (Table 2). A two-step meta-analysis was also performed. Results are provided in the online supplement. A limited heterogeneity was observed across the 9 studies (Figure S1)

The univariate analysis for the one-step IPD meta-analysis (Figure 2) showed that PWV was strongly related to age (p<0.01), male gender (p<0.01), SBP (p<0.01), presence of diabetes In multivariable analysis, PWV was independently associated with age, SBP and diabetes (all p<0.01) whereas severe OSA was not significantly associated with PWV (Figure 3).

DISCUSSION

To our knowledge, this is the first meta-analysis using individual patient data aiming to disentangle the respective influences of OSA and comorbidities on arterial stiffness. Most reviews and previous meta-analyses used data from different groups with several different methods of arterial stiffness assessment. [9,[START_REF] Phillips | Is obstructive sleep apnoea causally related to arterial stiffness? A critical review of the experimental evidence[END_REF] A strength of our meta-analysis using individual patient data was that all data came from a single centre with standard operating procedures and long expertise in performing PWV and sleep studies. The use of complete individual data sets allowed subgroup analyses to be made, to adjust for co-variates and hence increase power, as recommended by Bratton et al. [31] The systematic review of Doonan et al., [9] published in 2011, found 24 studies investigating the effect of OSA on arterial stiffness, while that of Phillips et al. [START_REF] Phillips | Is obstructive sleep apnoea causally related to arterial stiffness? A critical review of the experimental evidence[END_REF] Careful management of OSA associated comorbidities in combination with CPAP is more likely to have a greater impact on the occurrence of late cardiovascular events and survival.

Limitations

Data were merged from observational and interventional clinical trials. Therefore the population might not be completely homogenous depending on the specific inclusion and exclusion criteria of each trial. Moreover, while polygraphy was used in some studies, polysomnography was used in others, which might also have introduced some heterogeneity. Nevertheless, compared to other meta-analyses that included data from different centres with different techniques for PWV measurements, the impact was certainly small. Moreover, significant hypoxia was present in the control non-OSA group and was explained by documented COPD or morbid obesity and smoking were not included in the analysis. We cannot exclude a bias in patient recruitment and our results might not be generalizable to the general OSA population. However our population exhibited multiple comorbidities, which is clinically relevant. We observed a tendency to higher PWV in OSA patients than in controls, which was not significant after adjustment in multivariable analysis, potentially owing the small size of the control group. Finally, the cross-sectional nature of our meta-analysis does not allow us to explain any causality other than associations between the observed parameters.

Conclusion

Our individual patient meta-analysis showed that elevated arterial stiffness in OSA patients is mainly driven by the conventional cardiovascular risk factors; age, BP and the presence of diabetes and apnoea severity indices had limited influence.

Online supplement

Table S1. Details of the 9 clinical studies included

Statistical complement about the data imputation method used

Missing values were imputed using a multiple imputation method when the proportion of missing values was < 20%. Five datasets were generated using logistic regression for qualitative variables or linear regression for quantitative variables, considering that missing values were random. Two regression models were used to impute missing data: a logistic regression model for qualitative (binary) variables and a linear regression method for quantitative variables. To confirm the imputation strategy, a sensitivity analysis was performed by comparing the results based on complete cases and multiple imputation. This comparison showed no difference. Categorical variables were first imputed using a continuous model and then the imputed values were categorized using the same threshold in each dataset. The initial study was introduced as a fixed effect in all regressions models. After imputation, the results of the five dataset were combined using the MI ANALYZE procedure from SAS, which corresponded to the Rubin's rules. All comparisons between initial and imputed datasets were not significant.

Statistical complement about the two-step meta-analysis performed

A two-step individual patient data (IPD) meta-analysis was first performed to assess the heterogeneity between studies. The first step consisted of aggregating the individual data and the second step used a DerSimonian and Laird random effects meta-analysis model. The heterogeneity between studies was measured using the I² inconsistency index, which provides an estimation of the variability due to the heterogeneity rather than chance. An I² index greater than 60% reflects high heterogeneity. Finally, the robustness of the results was assessed using sensitivity analysis by leaving out one study.

The two-step IPD meta-analysis showed that the test for heterogeneity was not significant (I²=44.7%, p=0.09), which means that only limited heterogeneity was observed across the 9 studies.

Finally, the result of the two-step meta-analysis showed a slight association between severe OSA and PWV (Figure S1). However, this result should be interpreted carefully because there was no adjustment on the different confounders. Moreover, the leave-one-out study we performed showed that the main heterogeneity was mainly due to one study (ADISAS) (Table S4) and by excluding this study the result was not significant. The observed trend was not significant after adjustment in the one-step meta-analysis. The leave-one-out verifies the robustness of the results by leaving out one study at a time.

Figure 1 .

 1 Figure 1. Flowchart of selected studies and patients.

Figure 2 .

 2 Figure 2. Forest plot of the factors associated with PWV (univariate analysis).

Figure 3 .

 3 Figure 3. Forest plot of the independent factors associated with PWV (multivariate analysis).

Figure 2 169x84mmO n l y 1

 21 Figure 2 169x84mm (300 x 300 DPI)

O n l y 7 Figure S1 .

 7S1 Figure S1. Forest plot of the influence of the OSA severity on PWV

  Arterial stiffness, measured by Pulse Wave Velocity (PWV), is a strong independent predictor of late cardiovascular events and mortality. It is recognized that obstructive sleep apnoea (OSA) is associated with cardiovascular co-morbidities and mortality. Although previous meta-analyses concluded that PWV is elevated in OSA, we feel that an individual patient data analysis from 9 relatively homogeneous studies could help answer: to what extent does OSA drive arterial stiffness?

	Page 1 of 35		Thorax Thorax	Page 2 of 35
	Abbreviations list ABSTRACT
	AHI Background. Apnoea-hypopnea index
	BP	Blood pressure
	C o n f i C o n f i BMI Body mass index C o CAVI Cardio-ankle vascular index n f CPAP Continuous positive airway pressure i
	d e d e DBP Diastolic blood pressure d e
	ESS	n n Epworth sleepiness scale n
	t i a l : t i a l : t Individual patient data i a OSA Obstructive sleep apnoea IPD l :
	PAD Peripheral arterial disease PSG Polysomnography	F o r F o r F o r
	PWV Pulse wave velocity SaO 2 Oxygen saturation	R e R e R e
	SBP Systolic blood pressure	v i e v i e v i e
			w w w
			O n O n O n
			l l l
			y y y
			2 3
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https://mc.manuscriptcentral.com/thorax

Arterial stiffness assessed by pulse wave velocity

	Page 7 of 35	Thorax Thorax
	was defined based on current use of antidiabetic medications and dyslipidaemia on current carotid and femoral tracings and the arterial distance between these two sites. At the time
	lipid-lowering medications. Body mass index (BMI) was determined and obesity classes were of the studies, the arterial distance pathway was estimated from surface measurements and
	defined according to World Health Organization (WHO) definitions: normal: BMI < 25 kg/m 2 ; later multiplied by 0.8 as advised by the latest expert consensus on PWV measurements.[30]
	overweight: 25 ≤ BMI < 30 kg/m 2 ; obese: 30 ≤ BMI < 35 kg/m 2; severely obese: 35 ≤ BMI < 40
	C o kg/m 2 , and morbidly obese: ≥ 40 kg/m 2 . C o
	n f i n f Sleep studies i
	d e All patients were diagnosed with OSA using overnight respiratory polygraphy or d e
	n polysomnography (PSG), performed with spontaneous breathing to characterize abnormal n
	t i a l : t respiratory events during sleep. Details the procedure as well as of the definitions of apnoea i a and hypopnea can be found in the e-Appendix 1 of our previous publication.[26] The l :
	F o r apnoea-hypopnoea index (AHI) was calculated as the number of apnoea and hypopnoea events per hour of sleep (full PSG). Patients also completed the Epworth sleepiness scale F o r
	R e (ESS). Subjects were classified as controls if AHI < 5 events/h, or suffering from mild (5 ≤ AHI < 15 events/h), moderate (15 ≤ AHI < 30 events/h) or severe OSA (AHI ≥ 30 events/h). Mean R e
	v i e w v nocturnal oxygen saturation (SaO 2 ) and time spent under 90% of SaO 2 were also collected in i e order to characterize sleep apnoea severity. Overnight Sleep studies were scored according to international guidelines.[27] w
		O n O n
		l l
	y PWV was measured using the same technique for all studies: carotid and femoral waveforms y
	were acquired with the Complior SP device (Alam Medical, France). The validation and
	reproducibility of Complior measurements have been published elsewhere.[28,29] PWV is
	automatically calculated by the Complior from the transit time between simultaneous
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Table 1

 1

	Thorax

and patients' characteristics per study are shown in Table

S2

of the online supplementary file. Most subjects were male (72%) and 84% had confirmed OSA syndrome. We had a large range of PWV: from 5.3 to 20.5 m/s with a mean value of 10.4 ± 2.3 m/s and also a large range of BMI: from 15 to 81 kg/m 2 (mean value: 30 ± 7 kg/m 2 ) with 22% normal, 39% overweight, 18% obese, 12% severely obese and 9% morbidly obese patients.

https://mc.manuscriptcentral.com/thorax

Table 1 .

 1 Patients' characteristics for the whole population

		N = 893
	Male (n (%))	644 (72)
	Age (yrs)	56 ± 11
	BMI (kg/m 2 )	29.9 ± 6.9
	Normal (n (%))	199 (22)
	Overweight (n (%))	345 (39)
	Obese (n (%))	163 (18)
	Severely obese (n (%))	106 (12)
	Morbidly obese (n (%))	80 (9)
	Hypertension (n (%))	694 (78)
	Type 2 diabetes (n (%))	401 (45)
	Dyslipidaemia (n (%))	551 (62)
	OSA (n (%))	683 (84)
	SBP (mmHg)	133 ± 17
	DBP (mmHg)	81 ± 12
	HR (bpm)	69 ± 12
	PWV (m/s)	10.4 ± 2.3
	AHI (events/h)	36.8 ± 24.1
	Mean nocturnal SaO 2 (%)	92.9 ± 2.7
	SaO 2 < 90% (% of recording time)	10.8 ± 18.3
	ESS score	9.5 ± 5.2
	Results are observed data. They are expressed as mean ± SD or n (%). These percentages are
	before imputation of missing data. The imputation of missing data is shown in table S3 in the
	online supplement file. AHI, apnoea-hypopnea index; BMI, body mass index; ESS, DBP,
	diastolic blood pressure; Epworth Sleepiness Scale; SaO2, oxygen saturation; HR, heart rate;
	PWV, Pulse wave velocity; SaO 2 < 90%, percentage of recording time spent at a SaO 2 < 90%;
	SBP, systolic blood pressure.

Table 2 .

 2 Patients' characteristics according to OSA severity

	Page 10 of 35

*

Results are observed data. They are expressed as mean ± SD or n (%). These percentages are before imputation of missing data. AHI, apnoea-hypopnea index (events/h); BMI, body mass index; ESS, Epworth Sleepiness Scale; DBP, diastolic blood pressure; SaO 2 , oxygen saturation; HR, heart rate; PWV, Pulse wave velocity; SaO 2 < 90%, percentage of recording time spent at a SaO 2 < 90%; SBP, systolic blood pressure. * p-value versus controls < 0.01

  published 2 years later included 28 studies. A broad variety of techniques had been used to assess arterial stiffness, some studies used carotid-femoral PWV, other studies used brachial-ankle PWV and some authors only estimated arterial stiffness indirectly by derivatives such as the augmentation index. Although most studies found a significant difference in arterial stiffness between controls and OSA patients, almost a third did not find a difference.[14,19] Recently, Kim et al. reported no association between OSA severity and arterial stiffness assessed by brachial-of measuring arterial stiffness. Indeed ankle-brachial PWV assesses arterial stiffness not only over the aortic segment but also in the lower limbs. The brachial-ankle PWV or the cardio-ankle vascular index (CAVI), another non-standard measure of arterial stiffness, should not be used in patients with peripheral arterial disease (PAD), a very highly prevalent condition in OSA patients.[32] Alberto et al. carefully excluded PAD patients in their study and only observed a non-significant trend between CAVI and nocturnal intermittent hypoxia.[33]In the present study, we systematically used the gold standard carotid-femoral PWV methodology to assess arterial stiffness. In univariate analysis, ESS ≥9 was the only OSA The association between age SBP and PWV is complex, as shown by AlGhatrif et al.[36]. In our study, the interaction between age and SBP was tested in the model and was found to be not significant (p=0.06) showing that the impact of OSA on PWV remained unchanged. In obesity and metabolic syndrome vascular tone is modified. In our analysis, confirming previous results in non-OSA patients,[7] obesity did not play a significant role in the increased arterial stiffness. As stated by Drager et al, while OSA is one of the

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		C o n f i d e n t i a l : F o r R e v i e w O n l y C o n f i d e n t i a l : F o r R e v i e w O n l characteristics.[37] consequence of obesity, it might not have a large additional influence on cardiovascular risk.[38] Chirinos et al showed that continuous positive airway pressure (CPAP) treatment has an incremental effect over weight loss alone in reducing blood pressure but the impact of combined therapy on arterial stiffness was not evaluated.[24] C o n f i d e n t i a l : F o r R e v i e w O n l recognized standard method Obesity is very often associated with OSA syndrome.[24] Seetho et al. observed that y y
		normotensive obese subjects with OSA had a higher augmentation index than normotensive
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ankle PWV in a population of patients over 60 years of age.

[START_REF] Kim | Impacts of comorbidities on the association between arterial stiffness and obstructive sleep apnoea in the elderly[END_REF]

Ankle-brachial PWV, as opposed to carotid-femoral PWV used in our meta-analysis, is not the internationally Page 12 of 35 https://mc.manuscriptcentral.com/thorax parameter statistically associated with PWV. When studying non-selected OSA groups reflecting the diverse populations referred to a sleep clinic, the relationship between OSA and arterial stiffness is modest and only identifiable in univariate analysis in the most severe OSA patients, who also have the highest prevalence of comorbidities.

As confirmed by our data, PWV and OSA syndrome share confounders. Besides age, BP is the strongest confounding factor of PWV.

[5]

The prevalence of hypertension among OSA patients is particularly high

[22]

and OSA and hypertension are also linked in a dose-response fashion.

[34,35]

. obese subjects without OSA.[23] The augmentation index is sometimes used as a surrogate measure of arterial stiffness however it is highly dependent on vascular tone and cardiac Despite the use of brachial-ankle PWV, it is interesting to note that Kim et al observed a relationship between arterial stiffness and OSA severity in a sub-group of patients over the age of 60 with no other comorbidity or medical treatment.

[START_REF] Kim | Impacts of comorbidities on the association between arterial stiffness and obstructive sleep apnoea in the elderly[END_REF]

They hypothesized that chronic diseases (hypertension, diabetes and dyslipidaemia) increased arterial stiffness while the use of anti-hypertensive, anti-diabetic or lipid lowering drugs, might have a positive impact on PWV, blunting the association between arterial stiffness and OSA severity.

[START_REF] Kim | Impacts of comorbidities on the association between arterial stiffness and obstructive sleep apnoea in the elderly[END_REF]

In a meta-analysis Wang et al looked at studies only comparing controls to OSA patients free of other cardiovascular risk factors and also concluded that carotid-femoral PWV is elevated by OSA per se.

[START_REF] Wang | Impact of Obstructive Sleep Apnoea Syndrome on Endothelial Function, Arterial Stiffening, and Serum Inflammatory Markers: An Updated Meta-analysis and Metaregression of 18 Studies[END_REF]

Our meta-analysis showed that arterial stiffness measured by PWV was marginally influenced by OSA in the presence of co-morbidities. As stronger association might be found in the conduit arteries in of younger OSA patients, which are more sensitive to sympathetic activation compared to the aorta. This requires further studies. The impact of OSA syndrome per se on the arteries seems negligible compared to the impact of hypertension, diabetes and dyslipidaemia. This might explain why CPAP treatment has relatively modest impact on BP or PWV.

[25,35,39]

Only the most severe OSA patients with higher baseline values significantly benefit from CPAP in terms of BP reduction.

[40,41]

  -American Thoracic Society congress, 19 th to 24 th May 2016, San Franscisco, CA, USA -European Society of Hypertension congress (26th European Meeting on Hypertension and Cardiovascular Protection), 10 th to 13 th June 2016, Paris, France

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	Prior abstract publication/presentation: 22. Baguet J-P, Lévy P, Barone-Rochette G, et al. Masked hypertension in obstructive sleep 30. Van Bortel LM, Laurent S, Boutouyrie P, et al. Expert consensus document on the 37. Kelly RP, Millasseau SC, Ritter JM, et al. Vasoactive drugs influence aortic augmentation
	apnoea syndrome. J Hypertens 2008;26:885-92. measurement of aortic stiffness in daily practice using carotid-femoral pulse wave index independently of pulse-wave velocity in healthy men. Hypertens Dallas Tex 1979
	velocity. J Hypertens 2012;30:445-8. 2001;37:1429-33.
	23. Seetho IW, Parker RJ, Craig S, et al. Obstructive sleep apnoea is associated with
	C o n f C o n f C o n f C o n f C o n f increased arterial stiffness in severe obesity. J Sleep Res 2014;23:700-8. C o 31. Bratton DJ, Stradling JR, Barbé F, et al. Effect of CPAP on blood pressure in patients with minimally symptomatic obstructive sleep apnoea: a meta-analysis using individual C o 38. Drager LF, Togeiro SM, Polotsky VY, et al. Obstructive sleep apnoea: a cardiometabolic risk in obesity and the metabolic syndrome. J Am Coll Cardiol 2013;62:569-76. C o 24. Chirinos JA, Gurubhagavatula I, Teff K, et al. CPAP, weight loss, or both for obstructive sleep apnoea. N Engl J Med 2014;370:2265-75. n f patient data from four randomised controlled trials. Thorax 2014;69:1128-35. n f 39. Kartali N, Daskalopoulou E, Geleris P, et al. The effect of continuous positive airway n f
	i i i i i i i i
	d e n d e n d e n d e n d e n d e n 32. Schaefer CA, Adam L, Weisser-Thomas J, et al. High prevalence of peripheral arterial disease in patients with obstructive sleep apnoea. Clin Res Cardiol Off J Ger Card Soc d e n pressure therapy on blood pressure and arterial stiffness in hypertensive patients with d e obstructive sleep apnoea. Sleep Breath Schlaf Atm 2014;18:635-40. n
	2015;104:719-26.	t i a t i a t i a t i a t i a t i a t t i a i a
		l : l : l : l : l : l : l : l :
		F o r F o r F o r F o r F o r F o r F o r F o r
		R e R e R e R e R e R e R e R e
		v v v v v v v v
		i e i e i e i e i e i e i e i e
		w w w w w w w w
	O n O n O n O n O n O n 36. AlGhatrif M, Strait JB, Morrell CH, et al. Longitudinal trajectories of arterial stiffness and O n O n
	l y l y l y l y l y l y l l the role of blood pressure: the Baltimore Longitudinal Study of Aging. Hypertension Hypertens Dallas Tex 1979 1995;26:485-90. y y
	2013;62:934-41.
	29. Mathieu S, Pereira B, Couderc M, et al. No significant changes in arterial stiffness in
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https://mc.manuscriptcentral.com/thorax https://mc.manuscriptcentral.com/thorax https://mc.manuscriptcentral.com/thorax https://mc.manuscriptcentral.com/thorax 25. Vlachantoni I-T, Dikaiakou E, Antonopoulos CN, et al. Effects of continuous positive airway pressure (CPAP) treatment for obstructive sleep apnoea in arterial stiffness: a meta-analysis. Sleep Med Rev 2013;17:19-28. 26. Borel J-C, Tamisier R, Gonzalez-Bermejo J, et al. Noninvasive ventilation in mild obesity hypoventilation syndrome: a randomized controlled trial. Chest 2012;141:692-702. 27. Berry RB, Budhiraja R, Gottlieb DJ, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnoea Definitions Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med JCSM Off Publ Am Acad Sleep Med 2012;8:597-619. 28. Asmar R, Benetos A, Topouchian J, et al. Assessment of arterial distensibility by automatic pulse wave velocity measurement. Validation and clinical application studies. patients with ankylosing spondylitis after tumour necrosis factor alpha blockade treatment for 6 and 12 months. Rheumatol Oxf Engl 2013;52:204-9. 33. Alberto EC, Tanigawa T, Maruyama K, et al. Relationships between nocturnal intermittent hypoxia, arterial stiffness and cardiovascular risk factors in a communitybased population: the Toon health study. J Atheroscler Thromb 2014;21:1290-97. 34. Peppard PE, Young T, Palta M, et al. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378-84. 35. Pepin J-L, Borel A-L, Tamisier R, et al. Hypertension and sleep: overview of a tight relationship. Sleep Med Rev 2014;18:509-19. 40. Bratton DJ, Gaisl T, Wons AM, et al. CPAP vs Mandibular Advancement Devices and Blood Pressure in Patients With Obstructive Sleep Apnoea: A Systematic Review and Meta-analysis. JAMA 2015;314:2280-93. 41. Martinez-Garcia MA, Capote F, Campos-Rodriguez F, et al. Effect of CPAP on blood pressure in patients with obstructive sleep apnoea and resistant hypertension: the HIPARCO randomized clinical trial. JAMA 2013;310:2407-15.

Table S2 .

 S2 Patients' characteristics for the 9 clinical studies included

Table S3 .

 S3 Patients' characteristics for the whole population

Table S4 .

 S4 Result of the leave-one-out analysis Figure S1. Forest plot of the influence of the OSA severity on PWV

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Table S1 .

 S1 Details for the 9 clinical studies included

	Short name Clinical trial number Ethics committee* approval date Design Primary outcome Key eligibility criteria Referral base Publications (reference) * Ethics committee: Comité de Protection des Personnes-Sud Est V IRB0006705 ADISAS AGIRSAS ADOM BPCO BPCO-SAS DIAMETASA S Aortic Dissection NCT011968 45 NCT010902 97 NCT004044 30 NCT01195064 NA NCT01068691 07/10/2009 29/07/2009 10/01/2007 16/10/2009 02/08/2006 06/02/2008 RCT RCT Observation al study Observational study RCT RCT Inflammatio n in adipose tissue Clinical blood pressure Peripheral arterial tone Peripheral arterial tone Flow-deviated vasodilatati on Ambulatory blood pressure monitoring OSA patients obese or not who need CPAP treatment OSA patients who need CPAP treatment Patients with exacerbated COPD Patient suspected for OSA or BPCO with planned coronary artery bypass graft surgery or vascular surgery Patients with metabolic syndrome Patients suspected for OSA or BPCO and surgically treated for type A acute aortic syndrome 24 322 102 58 37 156 1 2 to 5 5 6 NA, not applicable since confidential trial; RCT, randomized controlled trial C o n f i d e n t i a l : F o r R e v i e w INFRASAS VALSAS NCT010892 57 NCT004094 87 04/07/2007 07/06/2006 10/05/2006 NIV-OHS NCT006030 96 Observation al study RCT RCT Anatomic and functional vascular study Ambulatory blood pressure monitoring Diurnal PaCO 2 Patients suspected for OSA OSA patients with hyperten -sion Patients with obesity-hypoventi -lation syndrome 95 28 160 7 8 9,10 O n l y
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Table S2 .

 S2 Patients' characteristics for the 9 clinical studies included Results are expressed as mean ± SD or n (%). The percentages correspond to patients with missing data before imputation. *Number of patients from the referral base (TableS1) after exclusion of patients without PWV data. AHI, apnea-hypopnea index; BMI, body mass index; DBP, diastolic blood pressure; ESS, Epworth Sleepiness Scale; SaO 2 , oxygen saturation; HR, heart rate; PWV, Pulse wave velocity; SaO 2 < 90%, percentage of recording time spent at a SaO 2 < 90%; SBP, systolic blood pressure.

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	Short name Number of patients* Male (n (%)) Age (yrs) BMI (kg/m 2 ) Normal (n (%)) Overweight (n (%)) Obese (n (%)) Severely obese (n (%)) Morbidly obese (n (%)) Hypertension (n (%)) Diabetes (n (%)) Dyslipidemia (n (%)) OSA (n (%)) SBP (mmHg) DBP (mmHg) HR (bpm) PWV (m/s) AHI (events/h) Mean nocturnal SaO2 (%) SaO2 < 90% (%)	ADISAS 22 22 (100) 54.4 (8.1) 31.2 (8.2) 5 (22.7) 6 (27.3) 5 (22.7) 4 (18.2) 2 (9.1) 12 (54.5) 6 (27.3) 18 (81.8) 17 (100) 130.1 ± 11.3 79.7 ± 9.4 64.8 ± 13 10.2 ± 1.6 44.9 ± 21.2 92.7 ± 2.4 7 ± 13.7 C o n f i d e n t i a l : F o r R e v i e w AGIRSAS ADOM BPCO BPCO-SAS DIAMETASAS Aortic dissection INFRASAS 313 99 53 34 131 90 221 (70.6) 81 (81.8) 43 (86) 18 (52.9) 113 (86.3) 74 (82.2) 57.4 (11.1) 63.7 (8.5) 62.7 (9.1) 59.4 (6.9) 49.1 (10.3) 52 (10.7) 31 (6.6) 25.4 (4.7) 26.3 (4) 29.5 (3.4) 26.6 (3.3) 25.2 (2.8) 41 (13.1) 60 (60.6) 23 (43.4) 1 (2.9) 40 (30.5) 40 (44.4) 117 (37.4) 44 (44.4) 22 (41.5) 18 (52.9) 77 (58.8) 49 (54.4) 86 (27.5) 40 (40.4) 6 (11.3) 12 (35.3) 12 (9.2) 1 (1.1) 44 (14.1) 12 (12.1) 2 (3.8) 3 (8.8) 1 (0.8) -25 (8) 3 (3) --1 (0.8) -268 (85.6) 45 (45.5) 45 (84.9) 32 (94.1) 111 (84.7) 62 (68.9) 175 (55.9) 37 (37.4) 29 (54.7) 24 (70.6) 24 (18.3) 15 (16.7) 207 (66.1) 56 (.) 48 (90.6) 29 (85.3) 81 (61.8) 42 (46.7) 304 (97.1) 28 (65.1) 27 (56.3) 20 (58.8) 131 (100) 56 (62.2) 134 ± 14.1 136.8 ± 16.8 132.9 ± 16.2 147 ± 17.3 131.6 ± 16.5 121.1 ± 13.8 82.5 ± 9 77 ± 9.8 71.7 ± 11.6 89.6 ± 14 86.2 ± 10.1 74.7 ± 11.3 75.5 ± 10.7 68.8 ± 13.2 65.7 ± 11.2 66.9 ± 12.2 64.4 ± 9.8 58.2 ± 8.3 10.7 ± 2.2 12 ± 2.4 11.6 ± 2.7 10.4 ± 1.4 9.1 ± 1.5 9.6 ± 1.6 42.8 ± 21 30.1 ± 30.4 24.7 ± 23.6 22.2 ± 22.3 40.6 ± 16 23.8 ± 19.6 92.9 ± 2.9 91.7 ± 2.1 93.1 ± 2 92.9 ± 1.8 93.4 ± 2 94.1 ± 1.9 13.2 ± 19.5 14.8 ± 21.7 7.2 ± 14.4 4.3 ± 5.4 6.2 ± 12.8 4.2 ± 10.3	VALSAS 28 22 (78.6) 56.4 (8.5) 28.9 (4.9) 5 (17.9) 14 (50) 5 (17.9) 4 (14.3) -28 (100) 12 (42.9) 15 (53.6) 23 (82.1) 153.2 ± 14.8 100.2 ± 11.4 68.9 ± 12.6 10.5 ± 2.3 30.9 ± 17.8 93.3 ± 2.3 11 ± 22.4 O n l y NIV-OHS 123 50 (40.7) 53.2 (12.7) 39.3 (5.5) -2 (1.6) 24 (19.5) 45 (36.6) 52 (42.3) 76 (61.8) 71 (57.7) 74 (60.2) 77 (68.8) 130.3 ± 16.9) 73.8 ± 12.1) -9.7 ± 2.4 38.3 ± 33.4 92 ± 3.6 16.8 ± 23.3
	ESS score	10.3 ± 5.3	10 ± 5.3	-	5.6 ± 3.7	7.7 ± 5.2	10 ± 5.3	8.9 ± 5.1	9.9 ± 4	10.1 ± 4.8
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  AHI, apnoea-hypopnea index; BMI, body mass index; DBP, diastolic blood pressure; ESS, Epworth Sleepiness Scale; SaO 2 , oxygen saturation; HR, heart rate; PWV, Pulse wave velocity; SaO 2 < 90%, percentage of recording time spent at a SaO 2 < 90%; SBP, systolic blood pressure.

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Table S4 .

 S4 Result of the leave-one-out analysis

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	Study ADISAS AGIRSASADOM BPCO BPCO SAS DIAMETASAS Aortic dissection INFRASAS VALSAS NIV-OHS	Estimate (SE) 0.2761 (0.1443) 0.4794 (0.2266) 0.4097 (0.2130) 0.4739 (0.1993) 0.4405 (0.2053) 0.4380 (0.2285) 0.4827 (0.2213) 0.4923 (0.1939) C o n f i d e n t i a l : F o r R e v i e w P value I² 0.0557 0 0.0344 46.4611 0.0544 50.0736 0.0174 47.689 0.0319 49.7725 0.0553 50.0152 0.0292 47.2912 0.0111 45.4131 0.4838 (0.2129) 0.023 47.9618	O n l y
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ACKNOWLEDGEMENTS

The authors are grateful to Marion Perrin for participating in statistical analyses (INSERM U1042, HP2 Laboratory, Grenoble Alpes University Hospital, France). We thank Alison Foote (Grenoble Alpes University Hospital, France) for critically editing the manuscript.

Funding: This study received unrestricted grants from the endowment fund "Agir pour les maladies chroniques". This work was also supported by the French National Research Agency in the framework of the "Investissements d'avenir" program (ANR-15-IDEX-02).

Role of sponsors:

The sponsors had no role in data analysis and study design.

Author contributions

MJF, SB, RT, JCB, SM and JLP: Researched data, contributed to discussion, wrote the manuscript, reviewed/edited the manuscript.

LMG and MD: Researched data, contributed to discussion, reviewed/edited the manuscript.

Jean-Louis Pépin takes responsibility for the integrity of the work as a whole, including the data and analysis, from inception to revised article.

Summary of Competing Interest Statements:

None of the authors has a competing interest with respect to this study except Sandrine Millasseau. Sandrine Millasseau works as a freelance specialist on pulse wave analysis and receives revenues from several medical device companies including Alam Medical whose device was used in this study. 29.9 ± 6.9 0 (0) 29.9 ± 6.9

Normal (n (%)) 199 ( 22) 995 ( 22)

Overweight (n (%)) 345 ( 39) 1725 ( 39)

Obese (n (%)) 163 ( 18) 815 [START_REF] Chung | The association of nocturnal hypoxemia with arterial stiffness and endothelial dysfunction in male patients with obstructive sleep apnoea syndrome[END_REF] Severely obese (n (%)) 106 ( 12) 530 [START_REF] Phillips | Diurnal and obstructive sleep apnoea influences on arterial stiffness and central blood pressure in men[END_REF] Morbidly obese (n (%)) 80 ( 9) 400 ( 9)

Hypertension (n (%)) 694 ( 78 Results are expressed as mean ± SD or n (%). Percentages of initial data set correspond to patients with missing data before imputation.


Bruno Revol

Ingrid Jullian-Desayes

Sébastien Bailly

Michel Mallaret

Renaud Tamisier

Marie-Sara Agier

Frédéric Lador

Marie Joyeux-Faure

Jean-Louis L Pepin
email: jpepin@chu-grenoble.fr

Baclofen and sleep apnoea syndrome: analysis of VigiBase® the WHO pharmacovigilance database

To the Editor:

Baclofen is a centrally acting gamma aminobutyric acid (GABA)-B agonist, widely used for chronic spasticity in neurological disorders, available in oral and intrathecal formulations. Depending on the severity of spasticity and tolerance, standard treatment includes daily oral administration of 40 to 80 mg [START_REF] Bauman | Low-dose baclofen therapy raised plasma insulin-like growth factor-1 concentrations, but not into the normal range in a predictable and sustained manner in men with chronic spinal cord injury[END_REF]. The main adverse effects reported with baclofen are sedation, sleepiness, weakness, dizziness and psychological disturbances [START_REF] Dario | A benefit-risk assessment of baclofen in severe spinal spasticity[END_REF]. According to the depressant effects of GABA on the central nervous system, baclofen might also induce or aggravate sleep-disordered breathing by depressing central ventilatory drive and/or increasing upper airway obstruction. While a single oral low dose of baclofen did not significantly impair the apnoea-hypopnoea index (AHI) in a population with moderate obstructive sleep apnoea [START_REF] Finnimore | The effects of the GABA agonist, baclofen, on sleep and breathing[END_REF], bolus intrathecal administration of the drug increased central sleep apnoea (CSA) in patients with severe spasticity [START_REF] Bensmail | Pilot study assessing the impact of intrathecal baclofen administration mode on sleep-related respiratory parameters[END_REF].

During the last decade, baclofen has been shown to be clinically effective for the treatment of alcohol addiction. A dose-dependent effect in reducing daily alcohol intake was suggested [5]. Even though randomized controlled trials have yielded conflicting results [START_REF] Brennan | Clinical effectiveness of baclofen for the treatment of alcohol dependence: a review[END_REF], in March 2014 the French health safety agency allowed the prescription of oral baclofen up to 300 mg/day for the treatment of alcohol dependence under a "temporary recommendation for use" license [7].

In this context, baclofen 200 mg/day has been shown as initiating severe CSA with an AHI of 81.6/h [START_REF] Perogamvros | Baclofen-Associated Onset of Central Sleep Apnea in Alcohol Use Disorder: A Case Report[END_REF]. Furthermore, Olivier and colleagues described a case series of CSA attributed to oral baclofen therapy [START_REF] Olivier | Severe Central Sleep Apnea Associated With Chronic Baclofen Therapy: A Case Series[END_REF].

To investigate the safety signal for baclofen-related sleep apnoea syndrome (SAS) in a large pharmacovigilance database, we compared the reporting of SAS among baclofen users with users of other drugs by performing a disproportionality analysis within VigiBase® over a forty-seven-year time window and across different countries.

VigiBase®, the WHO Global Individual Case Safety Reports (ICSRs) database, has been developed and maintained since 1978 by the Uppsala Monitoring Centre (UMC) the WHO Collaborating Center for Pharmacovigilance. In July 2017 it included more than 15 million ICSRs collected from 127 countries. Most reports came from Europe and North America, and were declared by health professionals, pharmaceutical companies or patients. They usually contain reporter information, patient characteristics (age, gender, and medical history), a clinical description of the adverse drug reaction (ADR), its seriousness and evolution, drug exposures with dates, dosages and indications. Drugs are classified according to the Anatomical Therapeutic Chemical (ATC) classification system. ADR coding is carried out using two different vocabularies: WHO-Adverse Reaction Terms (WHO-ART) and the Medical Dictionary for Regulatory Activities (MedDRA) [START_REF] Lindquist | the WHO Global ICSR Database System: Basic Facts[END_REF].

We extracted from VigiBase® all ICSRs with a MedDRA (version 20.0) Preferred Terms "sleep apnoea syndrome" code associated with baclofen, recorded from December 9, 1970 to July 30, 2017. A de-duplicated dataset was used.

The relationship between the use of baclofen and the occurrence of the ADR (i.e., SAS) was assessed by calculating the reporting odds ratio (ROR) associated with its 95% confidence interval (CI) in a case-noncase study compared to other drugs. ROR was calculated using the following formula [START_REF] Wilson | Application of data mining techniques in pharmacovigilance[END_REF]: ROR = (a/c)/(b/d) with (a) the number of SAS cases with baclofen, (b) the number of ADRs other than SAS with baclofen, (c) the number of SAS with all other drugs in VigiBase®, and (d) the number of ADRs with all other drugs in VigiBase® excepting SAS. A ROR value > 2 with a number of cases ≥ 5 was considered to be significant [START_REF] Candore | Comparison of statistical signal detection methods within and across spontaneous reporting databases[END_REF]. Our secondary analyses studied the variation in reporting rate across years and countries with the same approach.

Of the 15 083 681 ICSRs reported to VigiBase® between December 1970 and July 2017, 7459 (0.05 %) were SAS cases. Among the ADRs with exposure to baclofen, there were 50 cases of SAS and 23 507 other adverse reactions. The majority of SAS cases attributed to exposure to baclofen originated from America and Europe (29 and 20 respectively) and was reported after 2013 (86 %). In total, 44 % of the patients with baclofen-related cases of SAS were also exposed to other drugs recognized to induce or aggravate sleep disordered breathing, such as opioids or benzodiazepines [START_REF] Jullian-Desayes | Impact of concomitant medications on obstructive sleep apnoea[END_REF].

Among the countries which reported at least 5 cases, sex ratio and age were not statistically different between the USA (n=29) and France (n=16). On the contrary, indications, administration route and dosages were very different. Alcohol dependence (n=12) and binge eating disorders (n=3) were only reported in France, with a mean daily oral dose of 125 mg, whereas the intrathecal route was mainly reported in the USA (n=23), with a mean daily dose of 1.1 mg for chronic spasticity.

In July 2017 the use of baclofen was associated with a global increased reporting of SAS compared with other drugs (ROR 4.32, n=50, 95 % CI 3.27-5.71). ROR ranged from 1.90 (n=29, 95 % CI 1.32-2.74) in the USA to 110.97 (n=16, 95 % CI 62. 40-197.33) in France. The year-by-year analysis of cumulative ROR of SAS associated with baclofen is presented in figure 1. The pharmacovigilance signal became significant in 2014. If a ROR value > 2 is considered as significant, then the pharmacovigilance signal is questionable in the USA. While the signal is 50 times higher in France, since 2014 when the first French case was reported.

We used a case-noncase design to evaluate the association between the use of baclofen and the reporting of SAS in the WHO drug adverse event database. Reports of SAS as an ADR appear to be rare and probably underestimated [START_REF] Linselle | Can drugs induce or aggravate sleep apneas? A case-noncase study in VigiBase(®) , the WHO pharmacovigilance database[END_REF]. Despite the well-known problem of underreporting in pharmacovigilance, a significant disproportionality signal was found. The year-by-year analysis combined with the geographic analysis confirmed a major French safety signal, associated with oral high doses of baclofen mainly for the treatment of alcohol dependence since 2014, suggesting a dose-dependent effect. Difference in ROR between France and the USA is probably explained by different indications, administration route and dosages of baclofen. Thus we cannot exclude that baclofen impaired a previous unknown condition, as alcohol increase the risk of sleep apnoea itself [START_REF] Choi | Habitual Alcohol Consumption and Metabolic Syndrome in Patients with Sleep Disordered Breathing[END_REF].

Our result has to be considered as a pharmacovigilance signal, confirming previous observations with baclofen for alcoholism [START_REF] Perogamvros | Baclofen-Associated Onset of Central Sleep Apnea in Alcohol Use Disorder: A Case Report[END_REF][START_REF] Olivier | Severe Central Sleep Apnea Associated With Chronic Baclofen Therapy: A Case Series[END_REF]. Interestingly, this signal emerged in 2014, one year before the first case report, avoiding a notoriety bias. However, it is important to note that the information about age, gender, baclofen indication or dose, polysomnographic testing and outcome are not systematically available in pharmacovigilance databases. An important limitation is the large number of cases in which baclofen was associated to other drugs known to induce sleep disordered breathing [START_REF] Jullian-Desayes | Impact of concomitant medications on obstructive sleep apnoea[END_REF]. The lack of a gold standard is another problem in any assessment using signal detection methods. No one was found to be uniformly better than the others and the limits of the current disproportionality statistics are similar [START_REF] Candore | Comparison of statistical signal detection methods within and across spontaneous reporting databases[END_REF].

Despite its authorized usage in the management of alcohol dependence in France, there is a growing caution concerning baclofen use. At high doses, it could be associated with an increased risk of hospitalization and death compared to other treatments for alcohol dependence, according to the results of a recent study conducted by the French health safety agency [START_REF]Résultats de l'étude sur les usages et la sécurité du baclofène en France entre 2009 et 2015 -Communiqué -ANSM[END_REF]. Finally, the 2014 "temporary recommendation for use" was modified in July 2017, to a maximum oral daily dose of 80 mg. There is currently a heated debate between the health authorities, patient groups and physicians regarding this indication. We think our results are of interest in this context.

Acknowledgements

The authors would like to thank the UMC that provided and gave permission to use the data analyzed in the present study. Results and conclusions are those of the authors and not necessarily those of the National Centers, UMC or WHO. We thank Alison Foote (Grenoble Alps University Hospital, France) for critically editing the