Body mass index and obstructive sleep apnoea in the UK: a cross-sectional study of the over-50s

Wall, Hannah; Smith, Chris; Hubbard, Richard

doi:10.4104/pcrj.2012.00053

Cited by 50 publications

(47 citation statements)

References 16 publications

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“…We obtained a proportion of normal results in 66 patients (34%), revealing a large number of unnecessary exams that are performed every day in Vila Nova de Gaia/Espinho hospital center, with the possibility of this result being generalized to the different hospital centers in the country. Male gender was more prevalent (63%), agreeing with the literature [1,10,16,[20][21][22]27,30,36,[42][43][44][45]. Possible explanations are the higher prevalence of craniofacial and upperairway abnormalities (21%), and also snoring (90%).…”

Section: Discussionsupporting

confidence: 83%

“…Reported risk factors for developing OSA include different groups of variables, such as demographic, clinical history, comorbidities and even factors collected during the consultation, for example, male gender [1,10,16,20-22, 27,30,36,42-45], aging [1,16,[20][21][22]27,30,36,[42][43][44][45], obesity [1,10,12,16,[20][21][22]30,36,42,43,45], history of smoking [1,10,12,22,30,45], increased neck [1,10,16,[20][21][22]27,30,36,43,45] and abdominal [30,45] circumferences, arterial [1,12,16,20,22,…”

Section: Introductionmentioning

confidence: 99%

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A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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“…This study by Wall and colleagues 9 gives pointers to future research on OSA, as well as illustrating the research potential of large databases derived from electronic primary care records. 13 Given the restricted age-range included in this study's target population, the strongly predictive factor of BMI, and the rapidly rising obesity rates amongst younger age-groups in the UK, additional studies should be carried out to evaluate the contribution of obesity to OSA in people aged under 50.…”

Section: Editorialsmentioning

confidence: 87%

“…9 The reasons for this may include coding variability on primary care computer systems, under-diagnosis by clinicians, or people with OSA not presenting to primary healthcare services. The possibility of selection bias is also discussed, with regard to general practitioners (GPs) being more likely to suspect and diagnose OSA in a patient with a high BMI.…”

Section: -5mentioning

confidence: 99%

“…The authors report that the evident reduction in OSA prevalence with increasing age could be secondary to discrimination or a focus on other co-morbidities in the elderly. 9 This is a possibility, and given the impact on quality of life and the potential effects of conditions associated with OSA, investigation and diagnosis should not be delayed or ignored in primary care. Another explanation is that these OSA-associated diseases -and the mortality linked with them -may be causing the fall in OSA prevalence as age increases, as observed in this study.…”

Section: Editorialsmentioning

confidence: 99%

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Obstructive sleep apnoea: quantifying its association with obesity and snoring

Parekh

Green

Majeed

2012

Primary Care Respiratory Journal

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Obstructive sleep apnoea (OSA) is a chronic condition with an estimated prevalence of between 2-3% in the UK, although a large proportion of individuals with OSA remain undiagnosed. 1,2 It is caused by repetitive pharyngeal collapse during sleep, which is restored with recurrent arousals and activation of the sympathetic nervous system. Interest around the condition has increased following evidence showing an association of OSA with hypertension, type 2 diabetes mellitus and coronary artery disease, together with adverse health outcomes. 3-5The burden of obesity remains one of the largest public health issues facing the UK. Studies suggest that 67% of men and 69% of women in England are obese or overweight.6 Such levels of obesity lead to wide ranging public health consequences, including significant medical, social, and financial burdens on individuals and society. Estimates suggest that the annual direct cost of obesity to the UK National Health Service (NHS) stands at £3.2 billion. 6 The relationship between OSA and obesity remains complex and multi-factorial; a recent review by Carter & Watenpaugh demonstrates the bidirectional causality between OSA and obesity, which can lead to a cycle of worsening symptoms of both conditions. 7 This relationship is well documented in the literature. 8 However, there has been no previous study to quantify this association within the general population in the UK.The study by Wall and colleagues 9 in this issue of the PCRJ is the first time the strength of association between OSA, snoring and obesity has been investigated in the UK using a large primary care database. In a cross-sectional study of records from The Health Improvement Network (THIN) database, and controlling for confounding factors such as gender, age, region, and socioeconomic status, they used logistic regression techniques to explore the relationship between body mass index (BMI), a history of snoring, and a diagnosis of OSA in the over-50s in the UK. They report that 88% of patients aged 50 and over had a recorded BMI, and that 55% of patients with a recorded BMI were overweight or obese, with the prevalence of OSA increasing progressively with increasing BMI. 96% of patients with a diagnosis of OSA had a recorded BMI. Perhaps the most important result from the study is that individuals with a BMI over 40 kg/m 2 were 27 times more likely to have a diagnosis of OSA when compared to those with a normal BMI. 27% of people with a diagnosis of OSA are recorded as being snorers, an equivalent multivariate analysis odds ratio for OSA in snorers of 26. Interestingly, the levels of OSA decreased with increasing age and deprivation after controlling for confounding factors.When comparing these results to those from other studies, the initial striking factor is the low prevalence of a diagnosis of OSA (0.6%) in those with a recorded BMI. 9 The reasons for this may include coding variability on primary care computer systems, under-diagnosis by clinicians, or people with OSA not presenting to primary healthcare service...

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Sleep Apnea and the Risk of Incident Gout: A Population‐Based, Body Mass Index–Matched Cohort Study

Peloquin

Dubreuil

Roddy

et al. 2015

Arthritis & Rheumatology

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Objective Sleep apnea is associated with hyperuricemia owing to hypoxia-induced nucleotide turnover. We undertook this study to assess the relationship between incident sleep apnea and the risk of incident gout. Methods Using data from The Health Improvement Network in the UK, we identified individuals with a first-ever physician diagnosis of sleep apnea. For each patient with sleep apnea, up to 5 individuals without sleep apnea were matched by sex, age, birth year, and body mass index (within ±0.5 kg/m2). We estimated the incidence rates of gout and examined the relationship between sleep apnea and the risk of incident gout using a Cox proportional hazards model, adjusting for potential confounders. In addition, we assessed the rate difference in gout due to sleep apnea using an additive hazard model. Results Among 9,865 patients with newly diagnosed sleep apnea and 43,598 matched individuals without sleep apnea, we identified 270 incident cases of gout over 1 year of followup, resulting in incidence rates of 8.4 per 1,000 person-years and 4.8 per 1,000 person-years, respectively. The crude and multivariable rate ratios of incident gout in patients with sleep apnea were 1.7 (95% confidence interval [95% CI] 1.3, 2.2) and 1.5 (95% CI 1.1, 2.1), respectively. The corresponding rate differences between patients with sleep apnea and the comparison cohort were 3.6 (95% CI 1.6, 5.6) and 2.8 (95% CI 0.7, 4.9) per 1,000 person-years. The effect of sleep apnea persisted across subgroups. Conclusion This general population–based study indicates that sleep apnea is independently associated with an increased risk of incident gout. Future research should examine the potential benefits of correcting sleep apnea–induced hypoxia on the risk of hyperuricemia and gout flares.

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Body mass index and obstructive sleep apnoea in the UK: a cross-sectional study of the over-50s

Abstract: Background: An increasing prevalence of obesity in the UK has also seen a rise in the diagnosis of co-morbidities. Obstructive sleep apnoea (OSA) has previously been associated with body mass index (BMI) but has not been fully explored in a UK population.

Cited by 50 publications

References 16 publications

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Obstructive sleep apnoea: quantifying its association with obesity and snoring

Sleep Apnea and the Risk of Incident Gout: A Population‐Based, Body Mass Index–Matched Cohort Study

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