Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study

Abstract

Background: Sleep-disordered breathing is associated with major morbidity and mortality. However, its prevalence has mainly been selectively studied in populations at risk for sleep-disordered breathing or cardiovascular diseases. Taking into account improvements in recording techniques and new criteria used to define respiratory events, we aimed to assess the prevalence of sleep-disordered breathing and associated clinical features in a large population-based sample.

Methods: Between Sept 1, 2009, and June 30, 2013, we did a population-based study (HypnoLaus) in Lausanne, Switzerland. We invited a cohort of 3043 consecutive participants of the CoLaus/PsyCoLaus study to take part. Polysomnography data from 2121 people were included in the final analysis. 1024 (48%) participants were men, with a median age of 57 years (IQR 49-68, range 40-85) and mean body-mass index (BMI) of 25·6 kg/m(2) (SD 4·1). Participants underwent complete polysomnographic recordings at home and had extensive phenotyping for diabetes, hypertension, metabolic syndrome, and depression. The primary outcome was prevalence of sleep-disordered breathing, assessed by the apnoea-hypopnoea index.

Findings: The median apnoea-hypopnoea index was 6·9 events per h (IQR 2·7-14·1) in women and 14·9 per h (7·2-27·1) in men. The prevalence of moderate-to-severe sleep-disordered breathing (≥15 events per h) was 23·4% (95% CI 20·9-26·0) in women and 49·7% (46·6-52·8) in men. After multivariable adjustment, the upper quartile for the apnoea-hypopnoea index (>20·6 events per h) was associated independently with the presence of hypertension (odds ratio 1·60, 95% CI 1·14-2·26; p=0·0292 for trend across severity quartiles), diabetes (2·00, 1·05-3·99; p=0·0467), metabolic syndrome (2·80, 1·86-4·29; p<0·0001), and depression (1·92, 1·01-3·64; p=0·0292).

Interpretation: The high prevalence of sleep-disordered breathing recorded in our population-based sample might be attributable to the increased sensitivity of current recording techniques and scoring criteria. These results suggest that sleep-disordered breathing is highly prevalent, with important public health outcomes, and that the definition of the disorder should be revised.

Funding: Faculty of Biology and Medicine of Lausanne, Lausanne University Hospital, Swiss National Science Foundation, Leenaards Foundation, GlaxoSmithKline, Ligue Pulmonaire Vaudoise.

Figure 1. Prevalence of sleep-disordered breathing and sleep apnoea syndrome, according to age and sex

(A) Mild sleep-disordered breathing was defined as ≥5 to <15 events per h, moderate was ≥15 to <30 events per h, and severe was ≥30 events per h. Sleep-disordered breathing categories differed by age; p<0·0001 in men and p<0·0001 in women. (B) Mild sleep apnoea syndrome was defined as ≥5 to <15 events per h and an Epworth score >10, moderate was ≥15 to <30 events per h and an Epworth score >10, and severe was ≥30 events per h and an Epworth score >10. Categories of sleep apnoea syndrome differed by age; p<0·0001 in men and p<0·0001 in women.

Figure 2. Estimated risk for diabetes, metabolic syndrome, hypertension, and depression associated with severity of sleep-disordered breathing

Circles represent the odds ratio and bars the 95% CI. If bars cross the dotted line at 1·0, risk is not significant. Apnoea-hypopnoea index severity quartiles are defined as: Q1, 0–4·2 events per h; Q2, 4·3–9·9 events per h; Q3, 10·0–20·6 events per h; and Q4, >20·6 events per h. p values are for trend across severity quartiles. For diabetes, metabolic syndrome, and hypertension: model 1 was adjusted for age and sex; model 2 was adjusted for age, sex, and alcohol and tobacco consumption; model 3 was adjusted for age, sex, alcohol and tobacco consumption, and BMI; and model 4 was adjusted for age, sex, alcohol and tobacco consumption, BMI, neck circumference, and waist-to-hip ratio (except for metabolic syndrome because this ratio is part of its definition). For depression: model 1 was raw data; model 2 was adjusted for age and sex; model 3 was adjusted for age, sex, and use of benzodiazepines; and model 4 was adjusted for age, sex, use of benzodiazepines, and use of antidepressant drugs.