Prevalence and associations of respiratory-related leg movements: the MrOS sleep study

Temporal association between leg movements and respiratory events in patients with obstructive sleep apnea: description and differences between the AASM and WASM scoring criteria

PURPOSE

To describe the temporal association between leg movements (LMs) and respiratory events in patients with obstructive sleep apnea (OSA), and to quantify the difference in scoring respiratory-related leg movement (RRLM) between the American Academy of Sleep Medicine (AASM) criterion and the criterion recommended by the World Association of Sleep Medicine (WASM).

METHODS

Patients with OSA who presented with > 10 LMs of any type per hour of sleep were included in this study. For each participant, RRLMs were scored using both the AASM criterion and the recommended WASM criterion. The occurrence of LMs in relation to respiratory events and the difference in scoring RRLM between the AASM criterion and the criterion recommended by the WASM were quantified.

RESULTS

In 32 patients enrolled, mean age was 48.1 ± 11.0 years and 78% were men. LMs were significantly more frequent after respiratory events, followed by before respiratory events, and were rare during respiratory events (P < 0.01). Compared with the AASM criterion, more LMs were classified as RRLMs based on the recommended WASM criterion (P = 0.01).

CONCLUSION

LMs are more frequent after respiratory events than before and during respiratory events, and more LMs are scored as RRLMs based on the recommended WASM criterion than based on the AASM criterion.

Age estimation from sleep studies using deep learning predicts life expectancy

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

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.

The effect of obstructive sleep apnea on peripheral blood amino acid and biogenic amine metabolome at multiple time points overnight

There are no clinical studies that have investigated the differences in blood serum metabolome between obstructive sleep apnea (OSA) patients and controls. In a single-center prospective observational study, we compared metabolomic profiles in the serum of OSA patients with apnea–hypopnea index (AHI) ≥ 15/h and control individuals. Peripheral blood was obtained at 3 different time points overnight: 9:00 p.m.; 5:00 a.m. and 7:00 a.m. We used a targeted approach for detecting amino acids and biogenic amines and analyzed the data with ranked general linear model for repeated measures. We recruited 31 patients with moderate-to-severe OSA and 32 controls. Significant elevations in median concentrations of alanine, proline and kynurenine in OSA patients compared to controls were detected. Significant changes in the overnight dynamics of serum concentrations occurred in OSA: glutamine, serine, threonine, tryptophan, kynurenine and glycine levels increased, whereas a fall occurred in the same biomarker levels in controls. Phenylalanine and proline levels decreased slightly, compared to a steeper fall in controls. The study indicates that serum profiles of amino acid and biogenic amines are significantly altered in patients with OSA referring to vast pathophysiologic shifts reflected in the systemic metabolism.

Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness

OBJECTIVE

Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.

METHODS

A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.

RESULTS

In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).

CONCLUSIONS

The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL.

SIGNIFICANCE

This study validates a fully automatic method for scoring arousals in PSGs.

Diagnostic Criteria, Differential Diagnosis, and Treatment of Minor Motor Activity and Less Well-Known Movement Disorders of Sleep

Purpose of review

Sleep-related movement disorders (SRMD) include several different motor activities during sleep. Few of them are well known and well classified, whereas others are minor motor disorders of sleep which are neither thoroughly characterized and classified nor have been extensively investigated to clarify their pathogenesis and clinical relevance. This review will focus on those minor sleep-related movement disorders.

Recent findings

Before diagnosing periodic limb movement (PLM) disorder in patients with PLM during polysomnography, other disorders associated with PLM need to be excluded, namely restless legs syndrome (RLS), narcolepsy, REM sleep behavior disorder (RBD), and sleep-related breathing disorder. For the diagnosis of propriospinal myoclonus at sleep-onset, multi-channel surface electromyography recording during polysomnography is required and a possible psychogenic origin of the movement disorder has to be considered. Excessive fragmentary myoclonus (EFM) does not require symptomatic treatment, but further evaluation is suggested as electrophysiological abnormalities are present in 50% of cases. Nine percent of healthy sleepers meet the criteria for EFM, raising the question if current, arbitrarily defined, cutoffs are valid. Hypnagogic foot tremor, rhythmic feet movements, alternating leg muscle activation, and high-frequency leg movements are somewhat overlapping minor motor activities during sleep which may exist on their own or represent stereotyped movements to relieve RLS-like symptoms. Neck myoclonus is probably a physiological phenomenon related to REM twitching. RBD is formally a parasomnia but a relevant differential diagnosis when evaluating sleep-related movement disorders. In particular, prodromal RBD is characterized by electromyographic and behavioral findings on video-polysomnography which needs to be differentiated by minor sleep-related movement disorders.

Summary

Minor SRMD beyond the well-known main motor disorders of sleep should be correctly diagnosed, distinguished from differential diagnosis, and understood in their potential clinical relevance, in order also to start an appropriate treatment if needed.

Respiratory-Related Leg Movements of Sleep Are Associated With Serotonergic Antidepressants But Not Bupropion

STUDY OBJECTIVES

Respiratory-related leg movements (RRLMs) may contribute to the cardiovascular risk associated with obstructive sleep apnea (OSA). Selective serotonin reuptake inhibitors (SSRIs), but not bupropion, increase periodic leg movements in sleep. This study examines whether patients with OSA using SSRIs have more RRLMs than those taking bupropion or no antidepressant.

METHODS

Patients with an apnea-hypopnea index (AHI) of at least 10 events/h during a full-night diagnostic study or split-night study, who were taking bupropion (n = 32), an SSRI (n = 31), or no antidepressant (n = 31), were selected from a database of prestudy questionnaires. RRLMs were scored according to World Association of Sleep Medicine 2016 standards.

RESULTS

Patients using SSRIs had significantly greater overall RRLM% (defined as the percentage of respiratory events associated with a leg movement, including apneas, hypopneas, and respiratory effort-related arousals), RRLM index, and periodic limb movement index relative to patients using bupropion and control patients. The difference between the RRLM% in the SSRI and bupropion groups was limited to patients undergoing split-night studies, and that of the SSRI and control groups was limited to patients undergoing full-night diagnostic studies.

CONCLUSIONS

The greater number of RRLMs and PLMs in the SSRI group may contribute to treatment-emergent insomnia often seen with SSRI use. Fragmented sleep and elevated autonomic nervous system activation associated with increased RRLMs in patients with OSA taking SSRIs might also limit the tolerability of antidepressant treatment, as well as increase the risk for cardiovascular disease.

Sleep-Related Leg Movements in Patients with Transient Ischemic Attack and Controls

Background: Periodic leg movements during sleep (PLMS) have been associated with an increased risk for cardiovascular diseases and there is a high prevalence of PLMS found in patients with obstructive sleep apnea syndrome (OSAS). We evaluated patients with transient ischemic attack (TIA) for PLMS and respiratory related leg movements (RRLM), versus a control group without TIA. Methods: Twenty-five patients with TIA and 34 patients with no vascular diagnosis were referred for polysomnography. Diagnosis of PLMS was made if the periodic leg movement index (PLMI) was ≥5 and clinical significant as PLMI ≥15. Results: There was no significant difference in PLMI ≥5 and ≥15 between patients with and without TIA. In the absence of OSAS, 2 out of 5 TIA patients (40%) had a PLMI ≥15 compared to 1 of the 19 patients without TIA (5%; p = 0.037). There was no increase in RRLMs when OSAS was present. Conclusions: TIA patients did not have higher PLMI compared to controls, and in the presence of OSAS, there was no increase in RRLMs compared to patients without TIA. In selective patients, PLMS could be associated with cardiovascular diseases, since PLMS was clinically more often found in the TIA group without OSAS.

Fatigue and disturbances of sleep in patients with osteogenesis imperfecta - a cross-sectional questionnaire study

Background

Persisting fatigue has been reported to be a common complaint by individuals with connective tissue disorders, including Osteogenesis imperfecta (OI). This controlled study evaluated in an adult OI population the subjective experience of fatigue, affecting daily life. Sleep disturbances and chronic pain were examined as hypothesized underlying factors.

Methods

This cross-sectional study analyzed the answers of 56 OI patients and 56 matched healthy controls to a questionnaire, designed to evaluate levels of experienced fatigue and bodily pain, as well as the presence or absence of symptoms related to sleep disturbances or sleep apnea. The relationships between fatigue, pain, and sleep disturbances were evaluated with correlation analysis and regression analysis.

Results

Fatigue was reported by 96%, and daily pain by 87% of the individuals with OI. Notably, the level of fatigue was similarly experienced by patient respondents and controls. In total, 95% of the patients and 77% of the controls reported one to several sleep disturbance symptoms. These symptoms as well as previously diagnosed sleep apnea were statistically significantly more prevalent in the patient group than in the controls (p < 0.05). Likewise, the experienced bodily pain was statistically highly significantly more severe among the respondents with OI (p < 0.001), and correlated with the reported fatigue.

Conclusions

In comparison with age-matched controls, adults with OI do not differ in experienced fatigue, unlike hypothesized. Therefore, sleep disturbances, which based on the frequency of reported related symptoms and previous sleep apnea diagnoses appear to be common in OI patients, may remain undiagnosed.

Electronic supplementary material

The online version of this article (10.1186/s12891-017-1922-5) contains supplementary material, which is available to authorized users.

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

INTRODUCTION

Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge.

METHODS

We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis.

RESULTS

Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy.

CONCLUSION

Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors.