Faculty Bibliography

BACKGROUND:Sleep-disordered breathing (SDB) is a known risk factor for hypertension. Despite the well-established link between HIV infection and hypertension, it remains to be determined whether HIV infection modifies the association between SDB and hypertension. SETTING:The Multicenter AIDS Cohort Study. METHODS:SDB was assessed using in-home polysomnography in 779 men (436 with and 343 without HIV). The apnea-hypopnea index (AHI) based on oxyhemoglobin desaturation threshold of ≥3% or arousal (AHI 3a ) and ≥4% (AHI 4 ) along with oxygen desaturation index (ODI) were used to quantify SDB severity. Hypertension was defined as a blood pressure ≥140/90 mm Hg, use of antihypertensive medication, or self-report of a clinical diagnosis. The associations between HIV, SDB, and hypertension were characterized using multivariable logistic regression. RESULTS:The prevalence of hypertension and SDB (AHI 3a ≥ 5 events/hr) was high, with estimates of 53.8% and 82.8%, respectively. Among men without SDB, HIV was independently associated with hypertension, with an adjusted odds ratio (OR) of 3.05 [95% confidence interval (CI): 1.33 to 7.01]. In men without HIV, SDB was associated with hypertension (OR: 2.93; 95% CI: 1.46 to 5.86). No significant increase in the odds of hypertension was noted in men with both HIV and SDB compared with men with either factor alone, with an OR of 3.24 (95% CI: 1.62 to 6.47). These results were consistent across different measures used to define SDB (AHI 3a , AHI 4 , ODI 3 , and ODI 4 ). CONCLUSIONS:Predictors of hypertension differed by HIV status. SDB was associated with hypertension in men without HIV, but not in men with HIV. Among men with HIV, SDB did not affect the odds of hypertension.

Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating study contains CGM data during sleep for 174 study participants with type II diabetes mellitus measured at a 5-min frequency for an average of 10 nights. We aim to quantify the effects of diabetes medications and sleep apnea severity on glucose levels. Statistically, this is an inference question about the association between scalar covariates and functional responses observed at multiple visits (sleep periods). However, many characteristics of the data make analyses difficult, including (1) nonstationary within-period patterns; (2) substantial between-period heterogeneity, non-Gaussianity, and outliers; and (3) large dimensionality due to the number of study participants, sleep periods, and time points. For our analyses, we evaluate and compare two methods: fast univariate inference (FUI) and functional additive mixed models (FAMMs). We extend FUI and introduce a new approach for testing the hypotheses of no effect and time invariance of the covariates. We also highlight areas for further methodological development for FAMM. Our study reveals that (1) biguanide medication and sleep apnea severity significantly affect glucose trajectories during sleep and (2) the estimated effects are time invariant.

BACKGROUND:Glycemic variability is associated with increased risk for cardiovascular disease in patients with type 2 diabetes independent of glycosylated hemoglobin A1c (HbA1c) levels. Given the conflicting evidence on the effect of positive airway pressure (PAP) therapy for OSA on HbA1c, elucidating its effect on glycemic variability has value. RESEARCH QUESTION/OBJECTIVE:Does the use of PAP therapy for OSA improve glycemic variability in patients with type 2 diabetes? STUDY DESIGN AND METHODS/METHODS:A randomized controlled trial was conducted in 184 patients with type 2 diabetes and moderate-to-severe OSA. Participants received either 3 months of PAP therapy with lifestyle counseling or lifestyle counseling alone. End points included the SD of glucose levels along with other metrics derived from continuous glucose monitoring and self-monitoring of blood glucose. RESULTS:No differences were noted in either primary or secondary continuous glucose monitoring end points between the two groups. Average use of PAP therapy was 5.4 h/night (SD, 1.6). Exploratory analyses by sex showed significant differences in the primary and secondary outcomes. In women, PAP therapy was associated with improvement in the SD of glucose levels, with a mean difference in change between intervention and control groups of 3.5 mg/dL (P = .02). PAP therapy was also associated with lower postdinner and bedtime glucose levels: 20.1 mg/dL (P < .01) and 34.6 mg/dL (P < .01), respectively. INTERPRETATION/CONCLUSIONS:PAP therapy did not improve glycemic control or variability in patients with moderate-to-severe OSA and type 2 diabetes. Exploratory analyses suggested that PAP therapy may improve glucose variability in women. Postdinner and bedtime glucose levels were higher in those who did not receive PAP therapy. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov; No.: NCT02454153; URL: www. CLINICALTRIALS/RESULTS:gov.

BACKGROUND:The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized. METHODS:We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor. RESULTS:Median wear time was 27 days (IQR: 23-29) for the Dexcom G4 and 28 days (IQR: 24-29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12-0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68-0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66-0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference >1 percentage points (>11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4. CONCLUSIONS:GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice.Clinicaltrials.gov Registration Number: NCT02454153.

BACKGROUND:Data on the prevalence of sleep-disordered breathing (SDB) in people with HIV are limited. Moreover, whether the associations between SDB and age or BMI differ by HIV status is unknown. RESEARCH QUESTION/OBJECTIVE:Is SDB more prevalent in men with HIV than those without HIV, and do the predictors of SDB differ between the two groups? STUDY DESIGN AND METHODS/METHODS:). RESULTS:definitions (57.9% vs 50.4%; P = .06). Mild and moderate SDB were more common in men with than without HIV. Associations between SDB prevalence and age, race, and BMI were similar in men with and without HIV. Among men with HIV, viral load, CD4 cell count, and use of antiretroviral medications were not associated with SDB prevalence. INTERPRETATION/CONCLUSIONS:threshold definition. Efforts to diagnose SDB are warranted in those with HIV, given that SDB is associated with daytime sleepiness and impaired quality of life.

BACKGROUND:The within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear. METHODS:Secondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit. RESULTS:At baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70-180 mg/dL [3.9-10.0 mmol/L]) was just below 80%. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4% (Abbott) and 14.2% (Dexcom). CVw for percent time in range: 20.1% (Abbott) and 18.6% (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r = 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r = 0.88, RMSE, 8 percentage points. CONCLUSIONS:Substantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions. ClinicalTrials.gov Identifier: NCT02454153.

Associations of mean glucose and time in range (70-180 mg/dL) from continuous glucose monitoring (CGM) with HbA1c in adults with type 2 diabetes are not well characterized. We conducted a secondary analysis of 186 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors up to 4 weeks. Mean HbA1c was 7.7% (SD, 1.3). There were strong negative Pearson's correlations of HbA1c with CGM time in range (-0.79, Abbott; -0.81, Dexcom) and strong positive correlations with CGM mean glucose (Dexcom, 0.84; Abbott, 0.82). However, there were large differences in CGM mean glucose (±20 mg/dL) and time in range (±14%) at any given HbA1c value. Mean glucose and HbA1c are strongly correlated in type 2 diabetes patients not taking insulin but discordance is evident at the individual level. Clinicians should expect discordance and use HbA1c and CGM in a complementary manner. ClinicalTrials.gov Identifier: NCT02454153.

UNLABELLED:Obstructive sleep apnea (OSA) may lead to serious health, safety, and financial implications-including sleepiness-related crashes and incidents-in workers who perform safety-sensitive functions in the transportation industry. Evidence and expert consensus support its identification and treatment in high-risk commercial operators. An Advanced Notice of Proposed Rulemaking regarding the diagnosis and treatment of OSA in commercial truck and rail operators was issued by the Federal Motor Carrier Safety Administration and Federal Railroad Administration, but it was later withdrawn. This reversal has led to questions about whether efforts to identify and treat OSA are warranted. In the absence of clear directives, we urge key stakeholders, including clinicians and patients, to engage in a collaborative approach to address OSA by following, at a minimum, the 2016 guidelines issued by a Medical Review Board of the Federal Motor Carrier Safety Administration, alone or in combination with 2006 guidance by a joint task force. The current standard of care demands action to mitigate the serious health and safety risks of OSA. CITATION:2022;18(10):2467-2470.

STUDY OBJECTIVES:Continuous positive airway pressure (CPAP) improves sleepiness in patients with obstructive sleep apnea, but some patients remain sleepy. The objective of this study was to identify determinants that are associated with improvements in self-reported sleepiness in patients with obstructive sleep apnea on CPAP therapy. METHODS:A retrospective cohort study was performed in a clinic-based population to determine which variables contributed to the improvement in the Epworth Sleepiness Scale (ESS) in patients on CPAP therapy for OSA, stratified by baseline ESS score (< 11 or ≥ 11). Variables associated with ESS scores normalizing with CPAP were also assessed. RESULTS:Patients with a baseline high ESS score showed greater improvements in the ESS with CPAP. When looking at interactions between baseline ESS classification and changes in ESS, we found that a higher apnea-hypopnea index was only associated with improvement in the ESS among patients with a high baseline ESS. Other assessed factors or covariates were not significantly different. When looking at ESS normalization, we found that female sex and lower body mass index were associated with a lower likelihood of ESS normalization. The difference in the rate of ESS normalization between females and males was higher with more days on CPAP. CONCLUSIONS:Of all the assessed factors and covariates, only the apnea-hypopnea index was associated with the change in the ESS differently in patients with a high or normal baseline ESS score. ESS normalization rates were lower in females than in males, and this disparity was amplified by more days on CPAP. CITATION:2022;18(9):2273-2279.

UNLABELLED:This position statement provides guidance for age and weight considerations for using continuous positive airway pressure therapy in pediatric populations. The American Academy of Sleep Medicine commissioned a task force of experts in pediatric sleep medicine to review the medical literature and develop a position statement based on a thorough review of these studies and their clinical expertise. The American Academy of Sleep Medicine Board of Directors approved the final position statement. It is the position of the American Academy of Sleep Medicine that continuous positive airway pressure can be safe and effective for the treatment of obstructive sleep apnea for pediatric patients, even in children of younger ages and lower weights, when managed by a clinician with expertise in evaluating and treating pediatric obstructive sleep apnea. The clinician must make the ultimate judgment regarding any specific care in light of the individual circumstances presented by the patient, accessible treatment options, patient/parental preference, and resources. CITATION:. 2022;18(8):2041-2043.