Nonpharmacologic Management of Excessive Daytime Sleepiness

Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: A machine learning approach

OBJECTIVES

Comorbid depression is a highly prevalent and debilitating condition in middle-aged and elderly adults, particularly when associated with obesity, diabetes, and sleep disturbances. In this context, there is a growing need to develop efficient screening methods for cases based on clinical health markers for these comorbidities and sleep data. Thus, our objective was to detect depressive symptoms in these subjects, considering general biomarkers of obesity and diabetes and variables related to sleep and physical exercise through a machine learning approach.

METHODS

We used the National Health and Nutrition Examination Survey (NHANES) 2015-2016 data. Eighteen variables on self-reported physical activity, self-reported sleep habits, sleep disturbance indicative, anthropometric measurements, sociodemographic characteristics and plasma biomarkers of obesity and diabetes were selected as predictors. A total of 2907 middle-aged and elderly subjects were eligible for the study. Supervised learning algorithms such as Lasso penalized Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were implemented.

RESULTS

XGBoost provided greater accuracy and precision (87%), with a proportion of hits in cases with depressive symptoms above 80%. In addition, daytime sleepiness was the most significant predictor variable for predicting depressive symptoms.

CONCLUSIONS

Sleep and physical activity variables, in addition to obesity and diabetes biomarkers, together assume significant importance to predict, with accuracy and precision of 87%, the occurrence of depressive symptoms in middle-aged and elderly individuals.

Genetic Markers of Differential Vulnerability to Sleep Loss in Adults

In this review, we discuss reports of genotype-dependent interindividual differences in phenotypic neurobehavioral responses to total sleep deprivation or sleep restriction. We highlight the importance of using the candidate gene approach to further elucidate differential resilience and vulnerability to sleep deprivation in humans, although we acknowledge that other omics techniques and genome-wide association studies can also offer insights into biomarkers of such vulnerability. Specifically, we discuss polymorphisms in adenosinergic genes (ADA and ADORA2A), core circadian clock genes (BHLHE41/DEC2 and PER3), genes related to cognitive development and functioning (BDNF and COMT), dopaminergic genes (DRD2 and DAT), and immune and clearance genes (AQP4, DQB1*0602, and TNFα) as potential genetic indicators of differential vulnerability to deficits induced by sleep loss. Additionally, we review the efficacy of several countermeasures for the neurobehavioral impairments induced by sleep loss, including banking sleep, recovery sleep, caffeine, and naps. The discovery of reliable, novel genetic markers of differential vulnerability to sleep loss has critical implications for future research involving predictors, countermeasures, and treatments in the field of sleep and circadian science.

Natural Cannabinoids as Templates for Sleep Disturbances Treatments

The sleep-wake cycle is a complex composition of specific physiological and behavioral characteristics. In addition, neuroanatomical, neurochemical and molecular systems exerts influences in the modulation of the sleep-wake cycle. Moreover, homeostatic and circadian mechanisms interact to control the waking or sleeping states. As many other behaviors, sleep also develops pathological features that include several signs and symptoms corresponding to medical conditions known as sleep disorders.In addition to the neurobiological mechanisms modulating sleep, external elements also influence the sleep-wake cycle, including the use of Cannabis sativa (C. sativa). In this regard, and over the last decades, the interest of studying the pharmacology of Δ⁹-tetrahydrocannabinol (Δ⁹-THC), the principal psychoactive constituent of C. sativa, has been addressed. Moreover, in recent years, the focus of scientific interest has moved on to studying the second plant constituent with non-psychotropic pharmacological properties: Cannabidiol (CBD).The pharmacological and pharmaceutical interest of CBD has been focus of attention due to the accumulating body of evidence regarding the positive outcomes of using CBD for the treatment of several health issues, such as psychiatric and neurodegenerative disorders, epilepsy, etc. Since the most prominent sleep disruptions include excessive daytime sleepiness (EDS), current treatments include the use of drugs such as stimulants of antidepressants. Notwithstanding, side effects are commonly reported among the patients under prescription of these compounds. Thus, the search of novelty therapeutical approaches aimed to treat ESD may consider the use of cannabinoid-derived compounds, such as CBD. In this chapter, we will show experimental evidence regarding the potential role of CBD as a wake-inducing compound aimed to manage EDS.

An Overview of the Clinical Uses, Pharmacology, and Safety of Modafinil

Modafinil (MOD) is a wakefulness-inducing compound prescribed for treatment of excessive daytime sleepiness as a consequence of sleep disturbances such as shift work sleep disorder, obstructive sleep apnea, restless leg syndrome, or narcolepsy. While providing effective results in patients with sleepiness, MOD also produces positive outcomes in the management of fatigue associated with different conditions including depression, cancer, or tiredness in military personnel. Although there is clear evidence of the stimulant effects of MOD, current data also show that administration of this drug apparently induces positive neurobiological effects, such as improvement in memory. However, serious concerns have been raised since some reports have suggested MOD dependence. Taken together, these findings highlight the need to characterize the changes induced by MOD which have been observed in several neurobiological functions. Moreover, further work should follow up on the likely long-term effects of this drug if used for treatment of drowsiness and tiredness. Here, we review and summarize recent findings of the medical uses of MOD in the management of sleepiness and fatigue associated with depression or cancer as well as exhaustion in military personnel. We also discuss the available literature related with the cognitive enhancing properties of this stimulant, as well as what is known and unknown about MOD addiction.