BMP/TGF-ß signaling as a modulator of neurodegeneration in ALS.

This commentary focuses on the emerging intersection between BMP/TGF-ß signaling roles in nervous system function and the amyotrophic lateral sclerosis (ALS) disease state. Future research is critical to elucidate the molecular underpinnings of this intersection of the cellular processes disrupted in ALS and those influenced by BMP/TGF-ß signaling, including synapse structure, neurotransmission, plasticity, and neuroinflammation. Such knowledge promises to inform us of ideal entry points for the targeted modulation of dysfunctional cellular processes in an effort to abrogate ALS pathologies. It is likely that different interventions are required, either at discrete points in disease progression, or across multiple dysfunctional processes which together lead to motor neuron degeneration and death. We discuss the challenging, but intriguing idea that modulation of the pleiotropic nature of BMP/TGF-ß signaling could be advantageous, as a way to simultaneously treat defects in more than one cell process across different forms of ALS.

Meta-analysis of Genetic Modifiers Reveals Candidate Dysregulated Pathways in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that has significant overlap with frontotemporal dementia (FTD). Mutations in specific genes have been identified that can cause and/or predispose patients to ALS. However, the clinical variability seen in ALS patients suggests that additional genes impact pathology, susceptibility, severity, and/or progression of the disease. To identify molecular pathways involved in ALS, we undertook a meta-analysis of published genetic modifiers both in patients and in model organisms, and undertook bioinformatic pathway analysis. From 72 published studies, we generated a list of 946 genes whose perturbation (1) impacted ALS in patient populations, (2) altered defects in laboratory models, or (3) modified defects caused by ALS gene ortholog loss of function. Herein, these are all called modifier genes. We found 727 modifier genes that encode proteins with human orthologs. Of these, 43 modifier genes were identified as modifiers of more than one ALS gene/model, consistent with the hypothesis that shared genes and pathways may underlie ALS. Further, we used a gene ontology-based bioinformatic analysis to identify pathways and associated genes that may be important in ALS. To our knowledge this is the first comprehensive survey of ALS modifier genes. This work suggests that shared molecular mechanisms may underlie pathology caused by different ALS disease genes. Surprisingly, few ALS modifier genes have been tested in more than one disease model. Understanding genes that modify ALS-associated defects will help to elucidate the molecular pathways that underlie ALS and provide additional targets for therapeutic intervention.

Alternative remedies for insomnia: a proposed method for personalized therapeutic trials.

Insomnia is a common symptom, with chronic insomnia being diagnosed in 5-10% of adults. Although many insomnia patients use prescription therapy for insomnia, the health benefits remain uncertain and adverse risks remain a concern. While similar effectiveness and risk concerns exist for herbal remedies, many individuals turn to such alternatives to prescriptions for insomnia. Like prescription hypnotics, herbal remedies that have undergone clinical testing often show subjective sleep improvements that exceed objective measures, which may relate to interindividual heterogeneity and/or placebo effects. Response heterogeneity can undermine traditional randomized trial approaches, which in some fields has prompted a shift toward stratified trials based on genotype or phenotype, or the so-called n-of-1 method of testing placebo versus active drug in within-person alternating blocks. We reviewed six independent compendiums of herbal agents to assemble a group of over 70 reported to benefit sleep. To bridge the gap between the unfeasible expectation of formal evidence in this space and the reality of common self-medication by those with insomnia, we propose a method for guided self-testing that overcomes certain operational barriers related to inter- and intraindividual sources of phenotypic variability. Patient-chosen outcomes drive a general statistical model that allows personalized self-assessment that can augment the open-label nature of routine practice. The potential advantages of this method include flexibility to implement for other (nonherbal) insomnia interventions.

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.

How Reliable Is Self-Reported Body Position during Sleep?

ABSTRACT: Many patients with obstructive sleep apnea (OSA) exhibit worsening event indices while supine. Positional therapy is an option if indices normalize in non-supine sleep. Although several methods are available for patients choosing positional therapy, monitoring adherence remains challenging in part because the reliability of self-reported sleep position is uncertain. We analyzed self-reported sleep position in a sample of 300 patients who underwent clinical polysomnography (PSG) in our center. We found a broad range of discrepancy with objective body position, which was not correlated with demographics, PSG metrics, or confidence in the self-report. The results suggest that objective position monitoring can be an important complement to self-report in the management of patients opting for positional therapy.

Consumer sleep monitors: is there a baby in the bathwater?

The rapid expansion of consumer sleep devices is outpacing the validation data necessary to assess the potential use of these devices in clinical and research settings. Common sleep monitoring devices utilize a variety of sensors to track movement as well as cardiac and respiratory physiology. The variety of sensors and user-specific factors offer the potential, at least theoretically, for clinically relevant information. We describe the current challenges for interpretation of consumer sleep monitoring data, since the devices are mainly used in non-medical contexts (consumer use) although medically-definable sleep disorders may commonly occur in this setting. A framework for addressing questions of how certain devices might be useful is offered. We suggest that multistage validation efforts are crucially needed, from the level of sensor data and algorithm output, to extrapolations beyond healthy adults and into other populations and real-world environments.

Sustained Attention Across the Life Span in a Sample of 10,000: Dissociating Ability and Strategy.

Normal and abnormal differences in sustained visual attention have long been of interest to scientists, educators, and clinicians. Still lacking, however, is a clear understanding of how sustained visual attention varies across the broad sweep of the human life span. In the present study, we filled this gap in two ways. First, using an unprecedentedly large 10,430-person sample, we modeled age-related differences with substantially greater precision than have prior efforts. Second, using the recently developed gradual-onset continuous performance test (gradCPT), we parsed sustained-attention performance over the life span into its ability and strategy components. We found that after the age of 15 years, the strategy and ability trajectories saliently diverge. Strategy becomes monotonically more conservative with age, whereas ability peaks in the early 40s and is followed by a gradual decline in older adults. These observed life-span trajectories for sustained attention are distinct from results of other life-span studies focusing on fluid and crystallized intelligence.