Proteomic insights into the pathophysiology of periodic limb movements and restless legs syndrome.

OBJECTIVES: We used a high-throughput assay of 5000 plasma proteins to identify biomarkers associated with periodic limb movements (PLM) and restless legs syndrome (RLS) in adults. METHODS: Participants (n = 1410) of the Stanford Technology Analytics and Genomics in Sleep (STAGES) study had blood collected, completed a sleep questionnaire, and underwent overnight polysomnography with the scoring of PLMs. An aptamer-based array (SomaScan) was used to quantify 5000 proteins in plasma. A second cohort (n = 697) that had serum assayed using a previous iteration of SomaScan (1300 proteins) was used for replication and in a combined analysis (n = 2107). A 5% false discovery rate was used to assess significance. RESULTS: Multivariate analyses in STAGES identified 68 proteins associated with the PLM index after correction for multiple testing (ie, base model). Most significantly decreased proteins were iron-related and included Hepcidin (LEAP-1), Ferritin, and Ferritin light chain. Most significantly increased proteins included RANTES, Cathepsin A, and SULT 1A3. Of 68 proteins significant in the base model, 17 were present in the 1300 panel, and 15 of 17 were replicated. The most significant proteins in the combined model were Hepcidin (LEAP-1), Cathepsin A, Ferritin, and RANTES. Exploration of proteins in RLS versus non-RLS identified Cathepsin Z, Heme oxygenase 2 (HO-2), Interleukin-17A (upregulated in the combined cohort), and Megalin (upregulated in STAGES only) although results were less significant than for proteins associated with PLM index. CONCLUSIONS: These results confirm the association of PLM with low iron status and suggest the involvement of catabolic enzymes in PLM/RLS.

Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P es) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative P es. APPROACH: One thousand one hundred and nineteen patients from the Stanford Sleep Clinic with PSGs containing P es served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory neural network was implemented to achieve a context-based mapping between the non-invasive features and the P es values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive P es. MAIN RESULTS: The median difference between the measured and predicted P es was 0.61 cmH2O with an interquartile range (IQR) of 2.99 cmH2O and 5th and 95th percentiles of -5.85 cmH2O and 5.47 cmH2O, respectively. The model performed well when compared to actual esophageal pressure signal (ρ median = 0.581, p  = 0.01; IQR = 0.298; ρ 5% = 0.106; ρ 95% = 0.843). SIGNIFICANCE: A significant difference in predicted P es was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for P es, improving characterization of SDB events as obstructive or not.

Periodic limb movements in sleep: Prevalence and associated sleepiness in the Wisconsin Sleep Cohort.

OBJECTIVES: Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness. METHODS: We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemar's test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders. RESULTS: Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 × 10-14). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM- group. Multiple Sleep Latency Test trended towards increased alertness in the RLS-/PLM+ group compared to RLS-/PLM-. CONCLUSIONS: A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms. SIGNIFICANCE: Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach.

Potential corner case cautions regarding publicly available implementations of the National Cancer Institute's nonwear/wear classification algorithm for accelerometer data.

The National Cancer Institute's (NCI) wear time classification algorithm uses a rule based on the occurrence of physical activity data counts-a cumulative measure of movement, influenced by both magnitude and duration of acceleration-to differentiate between when a physical activity monitoring (PAM) device (ActiGraph accelerometer) is being worn by a participant (wear) from when it is not (nonwear). It was applied to PAM data generated from the 2003-2004 National Health and Nutrition Examination Survey (NHANES 2003-2004). We discuss two corner case conditions that can produce unexpected, and perhaps unintended results when the algorithm is applied. We show, using simulated data of two special cases, how this algorithm classifies a 24-hour period with only 72 total counts as 100% wear in one case, and classifies a 24-hour period with 96,000 counts as 0.1% wear in another. The prevalence of like scenarios in the NHANES 2003-2004 PAM dataset is presented with corresponding summary statistics for varying degrees of the algorithm's nonwear classification threshold (T). The number of participants with valid days, defined as 10 or more hours classified as wear time in a 24-hour day, increased while the mean counts-per-minute (CPM) decreased as the threshold for excluding non-wear was reduced from the allowed 4,000 counts in an hour. The number of participants with four or more valid days increased 2.29% (n = 113) and mean CPM dropped 2.45% (9.5 CPM) when adjusting the nonwear classification threshold to 50 counts an hour. Applying the most liberal criteria, only excluding hours as nonwear which contained 1 count or less, resulted in a 397 more participants (7.83% increase) and 26.5 fewer CPM (6.98% decrease) in NHANES 2003-2004 participants with four or more valid days. The algorithm should be used with caution due to the potential influence of these corner cases.

Increased EEG Theta Spectral Power in Sleep in Myotonic Dystrophy Type 1.

STUDY OBJECTIVES: Myotonic dystrophy type 1 (DM1) is a multisystemic disorder that involves the central nervous system (CNS). Individuals with DM1 commonly present with sleep dysregulation, including excessive daytime sleepiness and sleep-disordered breathing. We aim to characterize electroencephalogram (EEG) power spectra from nocturnal polysomnography (PSG) in patients with DM1 compared to matched controls to better understand the potential CNS sleep dysfunction in DM1. METHODS: A retrospective, case-control (1:2) chart review of patients with DM1 (n = 18) and matched controls (n = 36) referred for clinical PSG at the Stanford Sleep Center was performed. Controls were matched based on age, sex, apnea-hypopnea index (AHI), body mass index (BMI), and Epworth Sleepiness Scale (ESS). Sleep stage and respiratory metrics for the two groups were compared. Power spectral analysis of the EEG C3-M2 signal was performed using the fast Fourier transformation. RESULTS: Patients with DM1 had significantly increased theta percent power in stage N2 sleep compared to matched controls. Theta/beta and theta/alpha percent power spectral ratios were found to be significantly increased in stage N2, N3, all sleep stages combined, and all wake periods combined in patients with DM1 compared to controls. A significantly lower nadir O2 saturation was also found in patients with DM1 versus controls. CONCLUSIONS: Compared to matched controls, patients with DM1 had increased EEG theta spectral power. Increased theta/beta and theta/alpha power spectral ratios in nocturnal PSG may reflect DM1 pathology in the CNS.

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Association of low ferritin with PLM in the Wisconsin Sleep Cohort.

OBJECTIVE: The origins of periodic leg movements (PLMs), a strong correlate of restless legs syndrome (RLS), are uncertain. This study was performed to assess the relationship between PLMs and peripheral iron deficiency, as measured with ferritin levels corrected for inflammation. METHODS: We included a cross-sectional sample of a cohort study of 801 randomly selected people (n = 1008 assays, mean age 58.6 ± 0.3 years) from Wisconsin state employee agencies. A previously validated automatic detector was used to measure PLMs during sleep. The patients were categorized into RLS symptoms-positive and RLS symptoms-negative based on a mailed survey response and prior analysis. Analyses were performed using a linear model with PLM category above and below 15 PLM/h (periodic leg movement index, PLMI) as the dependent variable, and adjusting for known covariates, including previously associated single-nucleotide polymorphisms (SNPs) within BTBD9, TOX3/BC034767, MEIS1, MAP2K5/SKOR1, and PTPRD. Ferritin and C-reactive protein (CRP) levels were measured in serum, and ferritin levels corrected for inflammation using CRP levels. RESULTS: After controlling for cofactors, PLMI ≥ 15 was associated with low (≤50 ng/mL) ferritin levels (OR = 1.55, p = 0.020). The best model was found using quasi-least squares regression of ferritin as a function of PLMI, with an increase of 0.0034 PLM/h predicted by a decrease of 1 ng/mL ferritin (p = 0.00447). CONCLUSIONS: An association was found between low ferritin and greater PLMs in a general population of older adults, independent of genetic polymorphisms, suggesting a role of low iron stores in the expression of these phenotypes. Patients with high PLMI may require to be checked for iron deficiency.

Correction: Design and Validation of a Periodic Leg Movement Detector.

[This corrects the article DOI: 10.1371/journal.pone.0114565.].

Exploring medical diagnostic performance using interactive, multi-parameter sourced receiver operating characteristic scatter plots.

Determining diagnostic criteria for specific disorders is often a tedious task that involves determining optimal diagnostic thresholds for symptoms and biomarkers using receiver-operating characteristic (ROC) statistics. To help this endeavor, we developed softROC, a user-friendly graphic-based tool that lets users visually explore possible ROC tradeoffs. The software requires MATLAB installation and an Excel file containing threshold symptoms/biological measures, with corresponding gold standard diagnoses for a set of patients. The software scans the input file for diagnostic and symptom/biomarkers columns, and populates the graphical-user-interface (GUI). Users select symptoms/biomarkers of interest using Boolean algebra as potential inputs to create diagnostic criteria outputs. The software evaluates subtests across the user-established range of cut-points and compares them to a gold standard in order to generate ROC and quality ROC scatter plots. These plots can be examined interactively to find optimal cut-points of interest for a given application (e.g. sensitivity versus specificity needs). Split-set validation can also be used to set up criteria and validate these in independent samples. Bootstrapping is used to produce confidence intervals. Additional statistics and measures are provided, such as the area under the ROC curve (AUC). As a testing set, softROC is used to investigate nocturnal polysomnogram measures as diagnostic features for narcolepsy. All measures can be outputted to a text file for offline analysis. The softROC toolbox, with clinical training data and tutorial instruction manual, is provided as supplementary material and can be obtained online at http://www.stanford.edu/~hyatt4/software/softroc or from the open source repository at http://www.github.com/informaton/softroc.

Periodic leg movements during sleep are associated with polymorphisms in BTBD9, TOX3/BC034767, MEIS1, MAP2K5/SKOR1, and PTPRD.

STUDY OBJECTIVES: To examine association between periodic leg movements (PLM) and 13 single nucleotide polymorphisms (SNPs) in 6 loci known to increase risk of restless legs syndrome (RLS). SETTING: Stanford Center for Sleep Sciences and Medicine and Clinical Research Unit of University of Wisconsin Institute for Clinical and Translational Research. PATIENTS: Adult participants (n = 1,090, mean age = 59.7 years) from the Wisconsin Sleep Cohort (2,394 observations, 2000-2012). DESIGN AND INTERVENTIONS: A previously validated automatic detector was used to measure PLMI. Thirteen SNPs within BTBD9, TOX3/BC034767, MEIS1 (2 unlinked loci), MAP2K5/SKOR1, and PTPRD were tested. Analyses were performed using a linear model and by PLM category using a 15 PLM/h cutoff. Statistical significance for loci was Bonferroni corrected for 6 loci (P < 8.3 × 10(-3)). RLS symptoms were categorized into four groups: likely, possible, no symptoms, and unknown based on a mailed survey response. MEASUREMENTS AND RESULTS: Prevalence of PLMI ≥ 15 was 33%. Subjects with PLMs were older, more likely to be male, and had more frequent RLS symptoms, a shorter total sleep time, and higher wake after sleep onset. Strong associations were found at all loci except one. Highest associations for PLMI > 15/h were obtained using a multivariate model including age, sex, sleep disturbances, and the best SNPs for each loci, yielding the following odds ratios (OR) and P values: BTBD9 rs3923809(A) OR = 1.65, P = 1.5×10(-8); TOX3/BC034767 rs3104788(T) OR = 1.35, P = 9.0 × 10(-5); MEIS1 rs12469063(G) OR = 1.38, P = 2.0 × 10(-4); MAP2K5/SKOR1 rs6494696(G) OR = 1.24, P = 1.3×10(-2); and PTPRD(A) rs1975197 OR = 1.31, P = 6.3×10(-3). Linear regression models also revealed significant PLM effects for BTBD9, TOX3/BC034767, and MEIS1. Co-varying for RLS symptoms only modestly reduced the genetic associations. CONCLUSIONS: Single nucleotide polymorphisms demonstrated to increase risk of RLS are strongly linked to increased PLM as well, although some loci may have more effects on one versus the other phenotype.

Design and validation of a periodic leg movement detector.

Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual's overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000-2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999-2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r²â€Š= 0.94 in WSC and r²â€Š= 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.

Nocturnal rapid eye movement sleep latency for identifying patients with narcolepsy/hypocretin deficiency.

IMPORTANCE: Narcolepsy, a disorder associated with HLA-DQB1*06:02 and caused by hypocretin (orexin) deficiency, is diagnosed using the Multiple Sleep Latency Test (MSLT) following nocturnal polysomnography (NPSG). In many patients, a short rapid eye movement sleep latency (REML) during the NPSG is also observed but not used diagnostically. OBJECTIVE: To determine diagnostic accuracy and clinical utility of nocturnal REML measures in narcolepsy/hypocretin deficiency. DESIGN, SETTING, AND PARTICIPANTS: Observational study using receiver operating characteristic curves for NPSG REML and MSLT findings (sleep studies performed between May 1976 and September 2011 at university medical centers in the United States, China, Korea, and Europe) to determine optimal diagnostic cutoffs for narcolepsy/hypocretin deficiency compared with different samples: controls, patients with other sleep disorders, patients with other hypersomnias, and patients with narcolepsy with normal hypocretin levels. Increasingly stringent comparisons were made. In a first comparison, 516 age- and sex-matched patients with narcolepsy/hypocretin deficiency were selected from 1749 patients and compared with 516 controls. In a second comparison, 749 successive patients undergoing sleep evaluation for any sleep disorders (low pretest probability for narcolepsy) were compared within groups by final diagnosis of narcolepsy/hypocretin deficiency. In the third comparison, 254 patients with a high pretest probability of having narcolepsy were compared within group by their final diagnosis. Finally, 118 patients with narcolepsy/hypocretin deficiency were compared with 118 age- and sex-matched patients with a diagnosis of narcolepsy but with normal hypocretin levels. MAIN OUTCOME AND MEASURES: Sensitivity and specificity of NPSG REML and MSLT as diagnostic tests for narcolepsy/hypocretin deficiency. This diagnosis was defined as narcolepsy associated with cataplexy plus HLA-DQB1*06:02 positivity (no cerebrospinal fluid hypocretin-1 results available) or narcolepsy with documented low (≤ 110 pg/mL) cerebrospinal fluid hypocretin-1 level. RESULTS: Short REML (≤15 minutes) during NPSG was highly specific (99.2% [95% CI, 98.5%-100.0%] of 516 and 99.6% [95% CI, 99.1%-100.0%] of 735) but not sensitive (50.6% [95% CI, 46.3%-54.9%] of 516 and 35.7% [95% CI, 10.6%-60.8%] of 14) for patients with narcolepsy/hypocretin deficiency vs population-based controls or all patients with sleep disorders undergoing a nocturnal sleep study (area under the curve, 0.799 [95% CI, 0.771-0.826] and 0.704 [95% CI, 0.524-0.907], respectively). In patients with central hypersomnia and thus a high pretest probability for narcolepsy, short REML remained highly specific (95.4% [95% CI, 90.4%-98.3%] of 132) and similarly sensitive (57.4% [95% CI, 48.1%-66.3%] of 122) for narcolepsy/hypocretin deficiency (area under the curve, 0.765 [95% CI, 0.707-0.831]). Positive predictive value in this high pretest probability sample was 92.1% (95% CI, 83.6%-97.0%). CONCLUSIONS AND RELEVANCE: Among patients being evaluated for possible narcolepsy, short REML (≤15 minutes) at NPSG had high specificity and positive predictive value and may be considered diagnostic without the use of an MSLT; absence of short REML, however, requires a subsequent MSLT.

Predictors of hypocretin (orexin) deficiency in narcolepsy without cataplexy.

STUDY OBJECTIVES: To compare clinical, electrophysiologic, and biologic data in narcolepsy without cataplexy with low (≤ 110 pg/ml), intermediate (110-200 pg/ml), and normal (> 200 pg/ml) concentrations of cerebrospinal fluid (CSF) hypocretin-1. SETTING: University-based sleep clinics and laboratories. PATIENTS: Narcolepsy without cataplexy (n = 171) and control patients (n = 170), all with available CSF hypocretin-1. DESIGN AND INTERVENTIONS: Retrospective comparison and receiver operating characteristics curve analysis. Patients were also recontacted to evaluate if they developed cataplexy by survival curve analysis. MEASUREMENTS AND RESULTS: The optimal cutoff of CSF hypocretin-1 for narcolepsy without cataplexy diagnosis was 200 pg/ml rather than 110 pg/ml (sensitivity 33%, specificity 99%). Forty-one patients (24%), all HLA DQB1*06:02 positive, had low concentrations (≤ 110 pg/ml) of CSF hypocretin-1. Patients with low concentrations of hypocretin-1 only differed subjectively from other groups by a higher Epworth Sleepiness Scale score and more frequent sleep paralysis. Compared with patients with normal hypocretin-1 concentration (n = 117, 68%), those with low hypocretin-1 concentration had higher HLA DQB1*06:02 frequencies, were more frequently non-Caucasians (notably African Americans), with lower age of onset, and longer duration of illness. They also had more frequently short rapid-eye movement (REM) sleep latency (≤ 15 min) during polysomnography (64% versus 23%), and shorter sleep latencies (2.7 ± 0.3 versus 4.4 ± 0.2 min) and more sleep-onset REM periods (3.6 ± 0.1 versus 2.9 ± 0.1 min) during the Multiple Sleep Latency Test (MSLT). Patients with intermediate concentrations of CSF hypocretin-1 (n = 13, 8%) had intermediate HLA DQB1*06:02 and polysomnography results, suggesting heterogeneity. Of the 127 patients we were able to recontact, survival analysis showed that almost half (48%) with low concentration of CSF hypocretin-1 had developed typical cataplexy at 26 yr after onset, whereas only 2% had done so when CSF hypocretin-1 concentration was normal. Almost all patients (87%) still complained of daytime sleepiness independent of hypocretin status. CONCLUSION: Objective (HLA typing, MSLT, and sleep studies) more than subjective (sleepiness and sleep paralysis) features predicted low concentration of CSF hypocretin-1 in patients with narcolepsy without cataplexy.