The Present and Future of the Clinical Use of Physiological Traits for the Treatment of Patients with OSA: A Narrative Review.

People with obstructive sleep apnea (OSA) are a heterogeneous group. While many succeed in the treatment of their OSA, many others struggle with therapy. Herein, we discuss how anatomical and physiological factors that cause sleep apnea (OSA traits) impact treatment response and may offer an avenue for more precise care. These OSA traits, including anatomical (upper-airway collapsibility) and physiological (loop gain, airway muscle responsiveness, and arousal threshold) factors, may help determine who can succeed with continuous positive airway pressure, oral appliances, hypoglossal nerve stimulation, or pharmacotherapy. In the future, identifying OSA traits before initiating treatment may help guide the selection of the most effective and tolerable therapy modalities for each individual.

Association of Periodic Limb Movements and Obstructive Sleep Apnea With Risk of Cardiovascular Disease and Mortality.

BACKGROUND: Obstructive sleep apnea is a well-established risk factor for cardiovascular disease (CVD). Recent studies have also linked periodic limb movements during sleep to CVD. We aimed to determine whether periodic limb movements during sleep and obstructive sleep apnea are independent or synergistic factors for CVD events or death. METHODS AND RESULTS: We examined data from 1049 US veterans with an apnea-hypopnea index (AHI) <30 events/hour. The primary outcome was incident CVD or death. Cox proportional hazards regression assessed the relationships between the AHI, periodic limb movement index (PLMI), and the AHI×PLMI interaction with the primary outcome. We then examined whether AHI and PLMI were associated with primary outcome after adjustment for age, sex, race and ethnicity, obesity, baseline risk of mortality, and Charlson Comorbidity Index. During a median follow-up of 5.1 years, 237 of 1049 participants developed incident CVD or died. Unadjusted analyses showed an increased risk of the primary outcome with every 10-event/hour increase in PLMI (hazard ratio [HR], 1.08 [95% CI, 1.05-1.13]) and AHI (HR, 1.17 [95% CI, 1.01- 1.37]). Assessment associations of AHI and PLMI and their interaction with the primary outcome revealed no significant interaction between PLMI and AHI. In fully adjusted analyses, PLMI, but not AHI, was associated with an increased risk of primary outcome: HR of 1.05 (95% CI, 1.00-1.09) per every 10 events/hour. Results were similar after adjusting with Framingham risk score. CONCLUSIONS: Our study revealed periodic limb movements during sleep as a risk factor for incident CVD or death among those who had AHI <30 events/hour, without synergistic association between periodic limb movements during sleep and obstructive sleep apnea.

Nicotine, alcohol, and caffeine use among individuals with untreated obstructive sleep apnea.

BACKGROUND: Psychoactive substance use (i.e., nicotine, alcohol, and caffeine) has substantial effects on sleep architecture in healthy individuals, but their effects in those with obstructive sleep apnea (OSA) have not been well described. We aimed to describe the association between psychoactive substance use and sleep characteristics and daytime symptoms in individuals with untreated OSA. METHODS: We performed a secondary, cross-sectional analysis of The Apnea Positive Pressure Long-term Efficacy Study (APPLES). Exposures included current smoking, alcohol and caffeine use in individuals with untreated OSA. Outcome domains included subjective and objective sleep characteristics, daytime symptoms, and comorbid conditions. Linear or logistic regression assessed the association between substance use and each domain (e.g., self-reported sleep duration, total polysomnographic sleep time, sleepiness, and anxiety). RESULTS: Of the 919 individuals with untreated OSA, 116 (12.6%) were current cigarette smokers, 585 (63.7%) were moderate or heavy alcohol users, and 769 (83.7%) were moderate or heavy caffeine users. Participants were on average 52.2±11.9 years old, 65.2% were male with a median BMI of 30.6 (IQR: 27.2, 35.9, kg/m2). Current smokers exhibited lower sleep duration (0.3 h), longer sleep latency (5 min) compared with non-smokers (all p-values < 0.05). People with heavy or moderate alcohol use exhibited more REM sleep (2.5 and 5% of total sleep time respectively), as did those with moderate caffeine use (2%, p-values < 0.05). The combined smoker plus caffeine group exhibited shorter sleep duration (0.4 h, p-value < 0.05) and higher risk for chronic pain [Odds Ratio (95%CI) = 4.83 (1.57, 14.9) compared with non-users. CONCLUSIONS: Psychoactive substance use is associated with sleep characteristics and clinically relevant correlates in people with untreated OSA. Further investigation into the effects that various substances have on this population may present opportunities to understand disease mechanisms more fully and increase the effectiveness of treatment in OSA.

Morning Chronotype Is Associated with Improved Adherence to Continuous Positive Airway Pressure among Individuals with Obstructive Sleep Apnea.

Rationale: Poor adherence limits the effectiveness of continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA). A better understanding of CPAP adherence is needed to develop novel strategies to improve it. Objectives: To determine if the chronotype (morning, evening, or intermediate) of patients with OSA is associated with differences in CPAP adherence. If such an association exists, determine the mechanisms underlying this association. Methods: We performed a secondary analysis of the APPLES (Apnea Positive Pressure Long-term Efficacy Study) clinical trial. We assessed chronotype using the Morningness-Eveningness Questionnaire (MEQ) among participants randomized to the CPAP arm with daily adherence data (n = 469). Evening (MEQ ⩽ 41), intermediate (41 < MEQ < 59), and morning type (MEQ ⩾ 59) categories were the exposures. We modeled daily CPAP use (hours per night) over a 6-month period, using a linear mixed model, adjusted for covariates (e.g., age, sex, marital status). To assess mechanisms of the association, we performed mediation analyses using sleep duration, weekend catch-up sleep, depression, and other factors. Results: Most participants were obese men with severe OSA (body mass index of 32.3 ± 7.3 kg/m2, 65% male, and apnea-hypopnea index 39.8 ± 24.6/h). Participants were 44% morning, 47% intermediate, and 8% evening chronotype. Participants with the morning chronotype reported the shortest sleep duration on weekends (7.3 vs. 7.6 and 7.9 h/night) compared with the intermediate and evening types. Participants with the morning chronotype exhibited a 40-min/night higher CPAP use (P = 0.001) than persons with the intermediate chronotype. This relationship was mildly attenuated (32.8 min/night; P = 0.011) after adjustment for covariates. None of the selected factors (e.g., sleep duration, weekend catch-up sleep) exhibited a significant mediation effect. Conclusions: Morning chronotype is associated with a clinically meaningful increase in CPAP adherence compared with other chronotypes. Mechanisms of this association require further study. Chronotype may be a novel predictor of CPAP adherence. Clinical trial registered with www.clinicaltrials.gov (NCT00051363).

Periodic limb movements during sleep and risk of hypertension: A systematic review.

BACKGROUND: Several studies suggest an association between periodic limb movements during sleep (PLMS) and hypertension; however, a systematic evaluation of this relationship is lacking. METHODS: We conducted a systematic review and meta-analysis of observational studies that reported odds ratio, relative risk, hazard ratio, or standardized incidence ratio, comparing the risk of hypertension in persons with PLMS (defined by the level of periodic limb movements per hour of sleep depended on individual studies) versus those without PLMS. After assessing heterogeneity and bias, the pooled risk ratio and 95% confidence intervals (CIs) were determined using a random-effect, generic inverse variance method of DerSimonian and Laird. RESULTS: Out of 572 potentially relevant articles, six eligible studies were included in the data analysis. Studies (6 cross-sectional) included 8949 participants. The statistical heterogeneity of this study was insignificant, with an I2 of 0%. A funnel plot and Egger's regression asymmetry test showed no publication bias with P-value ≥0.05. The pooled risk ratio of hypertension in patients with PLMS was 1.26 (95% CI, 1.12-1.41). CONCLUSIONS: Our analysis demonstrates an increased hypertension risk among patients with PLMS. Prospective or interventional studies are needed to confirm this association.

Pathophysiology Underlying Demographic and Obesity Determinants of Sleep Apnea Severity.

Rationale: Sleep apnea is the manifestation of key endotypic traits, including greater pharyngeal collapsibility, reduced dilator muscle compensation, and elevated chemoreflex loop gain. Objectives: We investigated how endotypic traits vary with obesity, age, sex, and race/ethnicity to influence sleep apnea disease severity (apnea-hypopnea index [AHI]). Methods: Endotypic traits were estimated from polysomnography in a diverse community-based cohort study (Multi-Ethnic Study of Atherosclerosis, N = 1,971; age range, 54-93 yr). Regression models assessed associations between each exposure (continuous variables per 2 standard deviations [SDs]) and endotypic traits (per SD) or AHI (events/h), independent of other exposures. Generalizability was assessed in two independent cohorts. Results: Greater AHI was associated with obesity (+19 events/h per 11 kg/m2 [2 SD]), male sex (+13 events/h vs. female), older age (+7 events/h per 20 yr), and Chinese ancestry (+5 events/h vs. White, obesity adjusted). Obesity-related increase in AHI was best explained by elevated collapsibility (+0.40 SD) and greater loop gain (+0.38 SD; percentage mediated, 26% [95% confidence interval (CI), 20-32%]). Male-related increase in AHI was explained by elevated collapsibility (+0.86 SD) and reduced compensation (-0.40 SD; percentage mediated, 57% [95% CI, 50-66%]). Age-related AHI increase was explained by elevated collapsibility (+0.37 SD) and greater loop gain (+0.15 SD; percentage mediated, 48% [95% CI, 34-63%]). Increased AHI with Chinese ancestry was explained by collapsibility (+0.57 SD; percentage mediated, 87% [95% CI, 57-100]). Black race was associated with reduced collapsibility (-0.30 SD) and elevated loop gain (+0.29 SD). Similar patterns were observed in the other cohorts. Conclusions: Different subgroups exhibit different underlying pathophysiological pathways to sleep apnea, highlighting the variability in mechanisms that could be targeted for intervention.

Sleep Deficiency in Obstructive Sleep Apnea.

Sleep deficiency in patients with obstructive sleep apnea includes abnormal quality, timing, and duration of sleep, and the presence of other comorbid conditions. These include insomnia, circadian misalignment disorders, and periodic limb movements of sleep. The co-occurrence of these conditions with obstructive sleep apnea likely plays a role in the pathogenesis, clinical presentation, and management of obstructive sleep apnea. Considering these conditions and their treatment in evaluating sleep deficiency in obstructive sleep apnea may help to improve patient outcomes. However, future research is needed to understand the intersection between obstructive sleep apnea and these disorders.

Cardiovascular Benefit of Continuous Positive Airway Pressure in Adults with Coronary Artery Disease and Obstructive Sleep Apnea without Excessive Sleepiness.

Rationale: Randomized controlled trials of continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea (OSA) have not demonstrated protection against adverse cardiovascular outcomes. Recently, observational studies revealed that OSA-related cardiovascular risk is concentrated in patients with an elevated pulse rate response to respiratory events (ΔHR). Objectives: Here, in this post hoc analysis of a prospective clinical trial, we test the hypothesis that a greater pretreatment ΔHR is associated with greater CPAP-related protection against adverse cardiovascular outcomes. Methods: ΔHR was measured from baseline polysomnography of the RICCADSA (Randomized Intervention with CPAP in CAD and OSA) randomized controlled trial (patients with coronary artery disease [CAD] and OSA [apnea-hypopnea index ⩾ 15 events/h] with Epworth Sleepiness Scale score < 10; nCPAP:ncontrol = 113:113; male, 85%; age, 66 ± 8 [mean ± SD] yr). The primary outcome was a composite of repeat revascularization, myocardial infarction, stroke, and cardiovascular mortality. Multivariable Cox regression assessed whether the effect of CPAP was moderated by ΔHR (treatment-by-ΔHR interaction). Measurements and Main Results: The CPAP-related reduction in risk increased progressively with increasing pretreatment ΔHR (interaction hazard ratio [95% confidence interval], 0.49 [0.27 to 0.90] per SD increase in ΔHR; P < 0.05). This means that in patients with a ΔHR of 1 SD above the mean (i.e., 10 beats/min), CPAP was estimated to reduce cardiovascular risk by 59% (6% to 82%) (P < 0.05), but no significant risk reduction was estimated in patients with a mean ΔHR (6 beats/min; CPAP risk reduction, 16% [-53% to 54%]; P = 0.6). Conclusions: The protective effect of CPAP in patients with CAD and OSA without excessive sleepiness was modified by the ΔHR. Specifically, patients with higher ΔHR exhibit greater cardiovascular benefit from CPAP therapy.

Physiological Traits and Adherence to Sleep Apnea Therapy in Individuals with Coronary Artery Disease.

Rationale: Untreated obstructive sleep apnea (OSA) is associated with adverse outcomes in patients with coronary artery disease (CAD). Continuous positive airway pressure (CPAP) is the most common treatment, but despite interventions addressing established adherence determinants, CPAP use remains poor. Objectives: To determine whether physiological traits that cause OSA are associated with long-term CPAP adherence in patients with CAD. Methods: Participants in the RICCADSA (Randomized Intervention with CPAP in CAD and OSA) trial with objective CPAP adherence (h/night) over 2 years and analyzable raw polysomnography data were included (N = 249). The physiological traits-loop gain, arousal threshold (ArTH), pharyngeal collapsibility (Vpassive), and pharyngeal muscle compensation (Vcomp)-were measured by using polysomnography. Linear mixed models were used to assess the relationship between the traits and adherence. We also compared actual CPAP adherence between those with physiologically predicted "poor" adherence (lowest quartile of predicted adherence) and those with physiologically predicted "good" adherence (all others). Measurements and Main Results: The median (interquartile range) CPAP use declined from 3.2 (1.0-5.8) h/night to 3.0 (0.0-5.6) h/night over 24 months (P < 0.001). In analyses adjusted for demographics, anthropometrics, OSA characteristics, and clinical comorbidities, a lower ArTH was associated with worse CPAP adherence (0.7 h/SD of the ArTH; P = 0.021). Both high and low Vcomp were associated with lower adherence (P = 0.008). Those with predicted poor adherence exhibited markedly lower CPAP use than those with predicted good adherence for up to 2 years of follow-up (group differences of 2.0-3.2 h/night; P < 0.003 for all). Conclusions: A low ArTH, as well as a very low and high Vcomp, are associated with worse long-term CPAP adherence in patients with CAD and OSA. Physiological traits-alongside established determinants-may help predict and improve CPAP adherence. Clinical trial registered with www.clinicaltrials.gov (NCT00519597).

Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study.

Rationale: Obstructive sleep apnea (OSA) is associated with cardiovascular disease and incident type 2 diabetes (T2DM). Seven OSA phenotypes, labeled on the basis of their most distinguishing polysomnographic features, have been shown to be differentially associated with incident cardiovascular disease. However, little is known about the relevance of polysomnographic phenotypes for the risk of T2DM. Objectives: To assess whether polysomnographic phenotypes are associated with incident T2DM and to compare the predictive value of baseline polysomnographic phenotypes with the Apnea-Hypopnea Index (AHI) for T2DM. Methods: The study included 840 individuals without baseline diabetes from a multisite observational U.S. veteran cohort who underwent OSA evaluation between 2000 and 2004, with follow-up through 2012. The primary outcome was incident T2DM, defined as no diagnosis at baseline and a new physician diagnosis confirmed by fasting blood glucose >126 mg/dL during follow-up. Relationships between the seven polysomnographic phenotypes (1. mild, 2. periodic limb movements of sleep [PLMS], 3. non-rapid eye movement and poor sleep, 4. rapid eye movement and hypoxia, 5. hypopnea and hypoxia, 6. arousal and poor sleep, and 7. combined severe) and incident T2DM were investigated using Cox proportional hazards regression and competing risk regression models with and without adjustment for baseline covariates. Likelihood ratio tests were conducted to compare the predictive value of the phenotypes with the AHI. Results: During a median follow-up period of 61 months, 122 (14.5%) patients developed incident T2DM. After adjustment for baseline sociodemographics, fasting blood glucose, body mass index, comorbidities, and behavioral risk factors, hazard ratios among persons with "hypopnea and hypoxia" and "PLMS" phenotypes as compared with persons with "mild" phenotype were 3.18 (95% confidence interval [CI], 1.53-6.61] and 2.26 (95% CI, 1.06-4.83) for incident T2DM, respectively. Mild OSA (5 ⩽ AHI < 15) (vs. no OSA) was directly associated with incident T2DM in both unadjusted and multivariable-adjusted regression models. The addition of polysomnographic phenotypes, but not AHI, to known T2DM risk factors greatly improved the predictive value of the computed prediction model. Conclusions: Polysomnographic phenotypes "hypopnea and hypoxia" and "PLMS" independently predict risk of T2DM among a predominantly male veteran population. Polysomnographic phenotypes improved T2DM risk prediction comared with the use of AHI.

Correlates of sleep quality and excessive daytime sleepiness in people with opioid use disorder receiving methadone treatment.

PURPOSE: The aim of this study was to evaluate the prevalence and clinical correlates of impaired sleep quality and excessive daytime sleepiness among patients receiving methadone for opioid use disorder (OUD). METHODS: Patients receiving methadone (n = 164) completed surveys assessing sleep quality (Pittsburgh Sleep Quality Index [PSQI]), daytime sleepiness (Epworth Sleepiness Scale [ESS]), and related comorbidities. We used bivariate and multivariable linear regression models to evaluate correlates of sleep quality and daytime sleepiness. RESULTS: Ninety percent of patients had poor sleep quality (PSQI >5), and the mean PSQI was high (11.0 ±4). Forty-six percent reported excessive daytime sleepiness (ESS > 10). In multivariable analyses, higher PSQI (worse sleep quality) was significantly associated with pain interference (coefficient = 0.40; 95% CI = 0.18-0.62; ß = 0.31), somatization (coefficient = 2.2; 95% CI = 0.75-3.6; ß = 0.26), and negatively associated with employment (coefficient = - 2.6; 95% CI = - 4.9 to - 0.19; ß = - 0.17). Greater sleepiness was significantly associated with body mass index (coefficient = 0.32; 95% CI = 0.18-0.46; ß = 0.33), and there was a non-significant association between sleepiness and current chronic pain (coefficient = 1.6; 95% CI = 0.26-3.5; ß = 0.13; p value = 0.09). CONCLUSIONS: Poor sleep quality and excessive daytime sleepiness are common in patients receiving methadone for OUD. Chronic pain, somatization, employment status, and obesity are potentially modifiable risk factors for sleep problems for individuals maintained on methadone. People with OUD receiving methadone should be routinely and promptly evaluated and treated for sleep disorders.

Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine.

Current strategies for the management of OSA reflect a one-size-fits-all approach. Diagnosis and severity of OSA are based on the apnea-hypopnea index and treatment initiated with CPAP, followed by trials of alternatives (eg, oral appliances) if CPAP "fails." This approach does not consider the heterogeneity of individuals with OSA, reflected by varying risk factors, pathophysiological causes, clinical manifestations, and consequences. Recently, studies using analytic approaches such as cluster analysis have taken advantage of this heterogeneity to identify OSA phenotypes, or subtypes of patients with unique characteristics, that may enable more personalized approaches to prognostication and treatment. Examples include symptom-based subtypes such as "excessively sleepy" and "disturbed sleep" with differing impact of CPAP on symptoms and health-related quality of life. Polysomnographic subtypes, distinguished by respiratory event association with hypoxemia, arousals, or both, exhibit varying risks of cardiovascular disease and response to therapy. This review summarizes the findings from recent cluster analysis studies in sleep apnea and synthesizes common themes to describe the potential role (and limitations) of phenotypic subtypes in precision medicine for OSA. It also highlights future directions, including linking of phenotypes to clinically relevant outcomes, rigorous and transparent assessment of phenotype reproducibility, and need for tools that categorize patients into subtypes, to prospectively validate phenotype-based prognostication and treatment approaches. Finally, we highlight the critical need to include women and more racially/ethnically diverse populations in this area of research if we are to leverage the heterogeneity of OSA to improve patient lives.

Prevalence, Associated Clinical Features, and Impact on Continuous Positive Airway Pressure Use of a Low Respiratory Arousal Threshold Among Male United States Veterans With Obstructive Sleep Apnea.

STUDY OBJECTIVES: Determine the prevalence of, and clinical features associated with, a low respiratory arousal threshold (ArTH) among patients with obstructive sleep apnea (OSA), and to assess whether a low ArTH is associated with reduced long-term CPAP use. METHODS: Cross-sectional and longitudinal analyses were performed in an observational study conducted among 940 male Veterans with OSA. Data for clinical characteristics, polysomnography characteristics, and long-term (5 ± 2 years) CPAP use were obtained from clinical records. Logistic regression was used to assess the associations between low ArTH and clinical features, including regular CPAP use. RESULTS: A low ArTH was observed in 38% of participants overall, and was more common among nonobese (body mass index < 30 kg/m2) patients (55%). In adjusted analyses, increasing body mass index (per 5 kg/m2) and antihypertensive medication use were negatively associated with low ArTH, with odds ratio (OR) (95% confidence interval [CI]) of 0.77 (0.69, 0.87) and 0.69 (0.49, 0.98), respectively. Conversely, increasing age (per 10 years) and antidepressant use-OR (95% CI) 1.15 (1.01,1.31) and 1.54 (1.14,1.98), respectively-were positively associated with low ArTH. Nonobese patients with low ArTH were less likely to be regular CPAP users-OR (95% CI) 0.38 (0.20, 0.72)-in an adjusted model. CONCLUSIONS: Low ArTH is a common trait among Veterans with OSA and is more frequent among those who are older and nonobese and those taking antidepressants, but is less frequent among patients taking antihypertensive medications. A marked reduction of long-term CPAP use in nonobese patients with low ArTH highlights the importance of understanding a patient's physiologic phenotype for OSA management, and suggests potential targets to improve CPAP adherence. COMMENTARY: A commentary on this article appears in this issue on page 713.

A Young Man With Hemoptysis and Cavitary Lung Lesions.

CASE PRESENTATION: A man in his 20s presented with 2 months of mild fatigue and intermittent hemoptysis of less than a tablespoon per episode. He was previously healthy and was on no medications. He denied fevers, night sweats, weight loss, wheezing, dyspnea, musculoskeletal symptoms, and rashes. He had emigrated from a South American country to the United States 3 years earlier. He worked as a groundskeeper but had no exposures to animals, mold, or dusts. He reported rare prior cigarette smoking with no history of alcohol or drug use. He was unsure whether he had received the Bacillus Calmette-Guérin vaccine.

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.

BACKGROUND: Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes. METHODS: Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death. RESULTS: Seven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk. CONCLUSIONS: Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.