Kotagal, Suresh

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.

Abstract This study was aimed to evaluate the yearly incidence of pediatric narcolepsy prior to and following the 2009 H1N1 pandemic and to evaluate seasonal patterns of narcolepsy onset and associations with H1N1 influenza infection in the United States. This was a multicenter retrospective study with prospective follow-up. Participants were recruited from members of the Pediatric Working Group of the Sleep Research Network including 22 sites across the United States. The main outcomes were monthly and yearly incident cases of childhood narcolepsy in the United States, and its relationship to historical H1N1 influenza data. A total of 950 participants were included in the analysis; 487 participants were male (51.3%). The mean age at onset of excessive daytime sleepiness (EDS) was 9.6 ± 3.9 years. Significant trend changes in pediatric narcolepsy incidence based on EDS onset (p &lt; .0001) occurred over the 1998–2016 period, peaking in 2010, reflecting a 1.6-fold increase in narcolepsy incidence. In addition, there was significant seasonal variation in narcolepsy incident cases, with increased cases in spring (p &lt; .05). Cross-correlation analysis demonstrated a significant correlation between monthly H1N1 infection and monthly narcolepsy incident cases (p = .397, p &lt; .0001) with a lag time of 8 months. We conclude that there is a significant increase in pediatric narcolepsy incidence after the 2009 H1N1 pandemic in the United States. However, the magnitude of increase is lower than reported in European countries and in China. The temporal correlation between monthly H1N1 infection and monthly narcolepsy incidence, suggests that H1N1 infection may be a contributing factor to the increased pediatric narcolepsy incidence after the 2009 H1N1 pandemics.

Abstract Introduction Obstructive sleep apnea (OSA) is characterized by recurrent, partial or complete obstructive respiratory events accompanied by interruptions in sleep with frequent arousals, deoxygenation, and/or sleep disturbance. The present study profiled the largest array of plasma proteins, to date, to contribute to the identification of proteomic biomarkers associated with the presence and severity of OSA. Methods The SomaScan highly multiplexed aptamer assay was used to profile 5,000 proteins in 24–48-hour old EDTA plasma samples from the Stanford Technology Analytics and Genomics in Sleep (STAGES) study. The apnea-hypopnea index (AHI) was derived from overnight polysomnography and all participants provided a blood sample. OSA severity was classified as moderate-to-severe (AHI&gt;15) and controls/mild OSA (AHI&lt;15). Univariate linear regression analyses included log2-normalized relative protein expression as the dependent variable, AHI/OSA as the independent variable, and important covariates such as age, gender, BMI, BMI2, age x gender x BMI, sample storage time, and blood draw period. False discovery rate (FDR) to control for multiple testing was applied with an a-priori p-value of 0.05 for identifying significance. Results Univariate analyses identified 101 (65 upregulated, 36 downregulated) differentially expressed proteins (DEPs) between moderate-to-severe OSA and controls/mild OSA and 120 proteins (69 positive, 51 negative) associated with AHI as a continuous outcome with 70 proteins consistent in both models. Upregulated proteins involved pathways related to complement and coagulation cascades and metabolic processes and downregulated proteins involved pathways related to regulation of insulin-like growth factor, fibrin clot formation, and MAPK signaling. An OSA machine learning classifier (AHI&gt;15 vs AHI&lt;15) trained on relative protein expression performed robustly, achieving 72% accuracy in a validation dataset. Significant contributing features of the classifier included age, BMI, and 134 proteins, including 22 DEPs identified in univariate analyses for OSA categories. Conclusion The present study identified differential protein expression patters associated with OSA and AHI, thereby supporting the potential of proteomic biomarkers in OSA and providing new insight into the mechanisms underlying OSA. Support (If Any) This work was supported, in part, by the National Heart, lung, and Blood institute [T32HL110952] and the Klarman Family Foundation.

Abstract Introduction Periodic limb movements (PLMs) are episodes of involuntary, repetitive muscle movements that are highly associated with restless legs syndrome (RLS). Although PLMs and RLS are reportedly two separate phenomena, both are tightly correlated and may result from a similar pathology. The present study profiled plasma protein biomarkers of PLMs and RLS to contribute to the identification of mechanisms associated with each disorder/trait. Methods The SomaScan highly multiplexed aptamer assay was used to profile 5,000 proteins in 24–48-hour old EDTA plasma samples from the Stanford Technology Analytics and Genomics in Sleep (STAGES) study. PLMs per hour (PLMI) were derived from overnight polysomnography and RLS was classified based on affirmative responses to questions of the Alliance Sleep Questionnaire. Three linear regression models were conducted to examine significant protein markers of 1) PLMI in the sample as a whole; 2) PLMI controlling for the presence of RLS; and 3) RLS without PLMs (i.e.,PLMI&lt;5). All models included log2-normalized relative protein expression as the dependent variable and important covariates such as age, gender, BMI, sample storage time, and blood draw period. False discovery rate (FDR) to control for multiple testing was applied with an a-priori p-value of 0.05 for identifying significant associations. Results PLMI was significantly associated with 253 proteins (219 positive,34 negative). The inclusion of RLS in the model mitigated the significance of most proteins, and only 8 proteins remained significant. Negatively associated proteins (LEAP-1, Ferritin, SELH, Caspase-8) included functions related to iron storage, absorption, and delivery and negative regulation of inflammatory/immune responses. Positively associated proteins (SFRP4, RANTES, CathepsinA, DKK1) included proteins with functions related to immune response, inflammatory response, bone formation, and protein stability. RLS without PLMs was associated with 7 upregulated proteins (megalin, RUFY1, TADBP, ANGL7, LRTM2, SNAPN, STOM) with functions related to vitamin D metabolism, calcium and zinc binding, circadian rhythm regulation, and calcium-dependent neurotransmitter secretion. Conclusion These large proteomic analyses identified independent differential protein expressions for PLMs and RLS that suggest different pathophysiological contributions. Support (If Any) This work was supported, in part, by the National Heart, lung, and Blood institute [T32HL110952] and the Klarman Family Foundation.