Sleep Fragmentation

We further examined the genes within genome-wide significant loci using gene-based pathway and tissue enrichment analyses^{45 (link),47 (link),69 (link)}. Gene-based analysis was performed using PASCAL, which estimated a combined association P value from the summary statistics of multiple SNPs in a gene^{45 (link)}. Pathway and ontology enrichment analyses were performed using FUMA^{69 (link)} and EnrichR^{47 (link)}. Tissue enrichment analysis was performed using MAGMA^{46 (link)} in FUMA, which controlled for gene size. Pathway and tissue enrichment analyses were also performed on genes within loci belonging to sleep propensity and sleep fragmentation clusters separately.
We constructed a weighted GRS comprising the 42 significant sleepiness loci and tested for associations with other self-reported sleep traits (sleep duration, long sleep duration, short sleep duration, insomnia, chronotype, and day naps), and 7-day accelerometry traits in the UK Biobank. Weighted GRS analyses were performed by summing the products or risk allele count multiplied by the effect estimate reported in the primary GWAS of self-reported daytime sleepiness using R package gds (https://cran.r-project.org/web/packages/gds/gds.pdf). We also tested the GRSs of reported loci for insomnia, sleep duration, short sleep, long sleep, day naps, chronotype, restless legs syndrome (RLS), narcolepsy, and coffee consumption associated with self-reported daytime sleepiness using the same approach. The SNPs selected for each trait include 57 genome-wide significant loci for frequent insomnia^{49 (link)}; 78, 27, and 8 loci for sleep duration, long sleep, and short sleep, respectively^{59 (link)}; 348 loci for chronotype^{67 (link)}; 125 loci for daytime napping; 20 genome-wide significant loci for RLS^{48 (link)}; 8 non-HLA suggestive significant loci (P < 10⁻⁴) in a narcolepsy case–control study of European Americans⁵¹ , and 8 loci for coffee consumption^{50 (link)}.

Essentially, a sleep detection method is a function that classifies the sleep state of a patient. Most sleep detection methods such as wrist actigraphy or mobile apps consider a binary function, where the state can be classified as Awake/Sleep. More sophisticated methods consider a ternary function: Awake/NREM/REM. And, finally, the most advanced methods, such as polysomnography—often used as the gold standard—consider a quinquenary function: Awake/N1/N2/N3/REM. Hence, any method can produce a two-dimensional chart where the X-axis is Time, and the Y-axis is the State of the Patient. In the particular case of polysomnography, the Y-axis has five possible values; thus, it can determine the sleep stage of the patient at any time, and study the transitions occurring between the states. Of course, a sleep study such as a polysomnography often produces much more complementary information that can be used, e.g., to diagnose sleep diseases. Among the information reported by a polysomnography we find oxygen saturations, limb movements, apneas, respiratory events by body position, etc. The interested reader is referred to Robertson, Marshall & Carno (2014) , Pandi-Perumal, Spence & BaHammam (2014) and Armon et al. (2016) for information about sleep study reports and their interpretation and usage.
The information that is common to the majority of sleep detection methods is the one that refers to a binary state classification (i.e., Awake/Sleep), because this is achieved by the basic methods, and subsumed by the advanced methods. Table 1 defines the basic parameters that can be collected by a binary state classification method. In grey, we show the primary data that should be collected by the sleep detection device and, in white, we show the most important parameters that can be derived from the primary data.
These parameters are particularly useful to determine the kind of sleep of patients, and each single parameter is relevant for a different sleep disorder or disease. For instance, the sleep onset, sleep latency, and total sleep time are essential to diagnose patients with insomnia. Similarly, an excess in the awakening and arousal indices suggests increased sleep fragmentation. In addition to the number of sleep states that they are able to detect, a sleep detection method can be classified according to other functional and operational characteristics, such as their underlying technology, which in turn directly affects their precision and validity.
In Fig. 2, we present a taxonomy of sleep detection methods. They all can be classified into two main groups according to whether they need medical assistance (Medical Assistance) or not (Self-Assessment). In this respect, there are methods that have been classified as not requiring medical assistance, such as Questionnaires and Sleep Diaries, even though their interpretation should be normally done by a professional. However, in the current state of the art there are many systems such as mobile apps that provide custom sleep questionnaires and produce reports without medical assistance. Hence, they are classified as Self-Assessment. They both deserve a deep discussion and will be explained separately in ‘Medical Assistance Methods’ and ‘Self-Assessment Methods’, respectively.
Self-Assessment methods include subjective methods such as questionnaires and sleep diaries (the figure lists some instances), and objective methods based on hardware sensors, which in turn can be classified as Contact devices or Contactless devices, depending on whether they need to be in contact with the patient’s body during sleep. Those devices that are based on the echo produced by signals can be further classified into Sonar, Radar, and Lidar devices. All of them will be explained in a dedicated section.

Starting at 0800 (lights on), mice were exposed to 1, 2, 6, 12, or 24 h (n=110; all groups, n = 10) of ASF, which involves a sweeping bar that moves horizontally across the modified cage every 120 sec, simulating the rate of SF in patients with severe sleep apnea²⁵ (Fig. 1). For the non-sleep fragmentation (NSF) control mice, subjects were housed in SF chambers, but no sweeping bar movements occurred. The NSF groups matched collection times of ASF mice (1, 2, 6, 12, or 24 h; all groups, n = 10). Both ASF and NSF groups were compared to a baseline group (time = 0) of mice collected at 0800 (n = 10).

Male adult C57BL/6J mice between 8–12 weeks of age were used in this study (n= 110; Jackson Laboratory, Bar Harbor, ME). Mice were given food and water ad libitum and housed under standard rodent colony conditions (lights on: 0800–2000 h, 21°C ± 1°C) at Western Kentucky University. Acute sleep fragmentation (ASF) experiments were performed using automated sleep fragmentation chambers (Lafayette Instrument Company; Lafayette, IN; model 80390) with a thin layer of corn bedding as previously described and each chamber contained no more than five mice^{23 (link)}. These chambers ensure that mice are subjected to sleep fragmentation and not absolute sleep deprivation^{16 (link)}. Mice were acclimated to the sleep fragmentation (SF) chambers for 48 h before the commencement of experiments to minimize carryover effects from the different cage environments^{24 (link)}. This study was conducted under the approval of the Institutional Animal Care and Use Committee at Western Kentucky University (#19–11), and procedures followed the National Institutes of Health’s “Guide for the Use and Care of Laboratory Animals” and ARRIVE guidelines.

The electrodes used for recording the electroencephalographic signal were prepared by assembling an insulated ultra-thin stainless-steel wire (0.3 mm diameter, A-M Systems, Inc.) with a stainless-steel miniature screw (diameter 1.2 mm, P1 Technologies), soldered to a connector for the electronic circuitry. The recording electrodes were put in contact with the dura mater in order to obtain an ipsilateral fronto-parietal EEG signal (referential derivation). The frontal screws (one intended to recording and one to anchor the system) were positioned ± 1.2 mm from the interhemispheric fissure and + 1.2 from Bregma. The parietal screws (one recording and one used as common reference) were placed ± 1.2 mm from the interhemispheric fissure and + 1.2 from Lambda. A pair of insulated ultra-thin stainless-steel wire (0.3 mm diameter, A-M Systems, Inc.) was inserted in the posterior nuchal muscle to record the electromyographic (EMG) signal [20 (link)]. During the entire procedure of implantation of the electrodes for EEG and EMG recording, the animal was deeply anesthetized with 3% isoflurane (gaseous anesthetic for veterinary use), mixed with O₂ (2 L/min) and N₂O (1 L/min), and kept on a heated support to avoid hypothermia. The whole device was firmly attached to the skull by covering it with dental cement. At the end of the surgical procedure a subcutaneous dose of ketoprofen 10 mg/kg was administered. The mice underwent the sleep fragmentation protocol 10 days after surgery, in order to allow adequate recovery time and post-surgery adaptation.