Study ObjectivesTo examine the association of objective and subjective sleep parameters with cognitive functioning and markers of brain morphology.MethodsThis cross-sectional study included 3360 participants (mean age: 59.5 ± 8.5 years; 51.1% female) from The Maastricht Study. Time in bed (TIB) and sleep breaks were objectively estimated using a thigh-worn accelerometer. Subjective sleep continuity was assessed via a single-item, and excessive daytime sleepiness was evaluated using the Epworth Sleepiness Scale. Cognitive testing was administered across three domains—memory, information processing speed, and executive functioning and attention. Markers of brain morphology (e.g. hippocampal volume, gray matter (GM) volume, and white matter volume) were assessed with 3 Tesla magnetic resonance imaging. Linear and logistic regression analyses modeled the associations.ResultsA longer TIB (h/night) was associated with worse executive functioning and attention (Blinear = −0.052, 95% CI = −0.084 to −0.019). Categorical analyses showed that a longer TIB (≥9 h/night) was associated with worse executive functioning and attention (B = −0.088, 95% CI = −0.166 to −0.010) compared to a mid-range TIB (≥7 to <9 h/night). A curvilinear association was found between TIB and lower GM volume (Bquadratic = −0.013, 95% CI = −0.025 to −0.001). Sleep breaks (≥2/night) were associated with worse overall cognition (B = −0.069, 95% CI = −0.124 to −0.013), information processing speed (B = −0.125, 95% CI = −0.212 to −0.039), and reduced GM volume (B = −0.068, 95% CI = −0.118 to −0.018). No significant associations were found for memory or other markers of brain morphology. Subjective sleep parameters showed no associations with cognitive functioning or markers of brain morphology.ConclusionsAdequate and uninterrupted TIB was associated with better cognitive functioning and brain morphology.Statement of SignificanceWith the growing prevalence of dementia, the importance of reducing dementia risk is increasingly acknowledged. Sleep has been identified as a potential risk factor for cognitive decline and dementia, yet studies using objective measures of sleep remain limited. This study explores subjective and objective sleep parameters in relation to cognitive function and brain morphology in a large cohort. The findings show that a long time in bed (TIB) is negatively associated with executive functioning and attention, alongside a curvilinear relationship between TIB and gray matter volume. Additionally, sleep breaks (≥2/night) are linked to lower brain volume and poorer cognition. Future research should investigate longitudinal associations and other sleep aspects, including regularity and sleep disorders, to further our understanding and guide dementia prevention strategies.

Sleep deprivation impairs sustained attention, as measured on the psychomotor vigilance task. This is manifested in a general slowing of reaction times and an increase in periods of unresponsiveness, increasing the risk of accidents. However, the mechanisms are not fully understood. This study combines experiments and modeling to better explain and quantify the changes of sustained attention under sleep deprivation. A total of 317 male participants (age 22.1 pm 2.7 y) underwent 40 h of sleep deprivation under a constant routine protocol. A 10-minute psychomotor vigilance task was performed at 2-h intervals, and saliva melatonin was sampled every hour to monitor circadian phase. We report a bimodal distribution of reaction speed in the data. An approximately normal primary peak characterizes typical performance (reaction time ≲0.5 s), while periods of unresponsiveness correspond to reaction times ≳1.5 s and are reflected in a secondary peak which emerges after ∼20 h of wakefulness. We developed a minimal, stochastic model that accurately reproduces the data, attributing the bimodality of the distribution to bistability in vigilance state. We find general response slowing to be subject to an ultradian oscillation (∼3 cycles per day), while periods of unresponsiveness are disproportionately affected during the wake maintenance zone. Our results attribute periods of unresponsiveness to the coexistence of two vigilance states in the sleep-deprived brain, enabling new approaches in understanding vulnerability to sleep loss.Statement of SignificanceSleep deprivation is prevalent in modern society, leading to an increased risk of accidents due to lapses in attention. In many scenarios, like shiftwork, simply getting more sleep is not an option, so a better understanding of mechanisms is needed. Our study, for the first time, shows a bimodality of response rates during sleep deprivation. We explain this by the co-existence of two vigilance states in the brain. The first state corresponds to typical reaction times in all individuals, while the second state is linked to unresponsiveness with reaction times ≳1.5 s and is observed in ∼60% of individuals. Our model enables new approaches to predict and prevent accidents and new insights into the physiology of sustained attention.

Pregnancy is a significant risk factor for the development of restless legs syndrome (RLS). Apart from its negative impact on sleep and general quality of life, RLS is also associated with pregnancy complications and negative outcomes. To elucidate the prevalence, trajectory, severity, and risk factors of RLS throughout pregnancy and the postpartum period, we analyzed data from the multicenter Life-ON Cohort Study (NCT02664467), which recruited 439 pregnant women in the 10th-15th gestational week and followed them up until 12months after delivery. About 26.03% of women (n=113, 95% CI = 22.0% to 30.4%) met the diagnostic criteria for RLS in at least one of the three visits during pregnancy. RLS severity was similar during all trimesters and during the postpartum period with however higher values in women who screened positive for RLS in all the three or two trimesters compared to those who screened positive in just one. A previous history of RLS was found in 50.68% of women who reported RLS during the current pregnancy and 18.7% had a positive family history. During the entire follow-up period out of 386 women, 134 were diagnosed with RLS, corresponding to a cumulative incidence of 34.7%. Previous history of depression, positive family history for RLS and previous RLS were statistically significant risk factors for RLS during pregnancy. Around 20% of women experienced severe to very severe symptoms (IRLS >20 pts), representing a possible target to be carefully considered for a pharmacological treatment. A systematic screening approach and treatment of eligible women could greatly improve quality of life and reduce comorbidity during pregnancy. Statement of Significance In this secondary analysis, we aimed to track the natural progression of RLS during pregnancy and after delivery, using data from the Life-ON Cohort Study. The study found that 26.03% of pregnant women experienced restless legs syndrome (RLS) with 20% of those reporting moderate/severe symptom intensity (IRLS >20). Those findings underline the high prevalence of RLS during pregnancy with the new finding of relative symptom-stability independent of the trimester of pregnancy and 1 in 5 patients exhibiting severe to very severe symptoms. The correct identification and treatment of RLS during pregnancy could potentially greatly improve womens' sleep quality and generally quality of life during pregnancy and reduce associated pregnancy complications and negative pregnancy outcomes.

To evaluate the utility of the odds ratio product (ORP) in differentiating comorbid insomnia and sleep apnea (COMISA) from obstructive sleep apnea (OSA) and chronic insomnia (CIN). We retrospectively analyzed 9750 patients in four groups: (1) 1152 controls, (2) 2395 with CIN, (3) 2297 with OSA, and (4) 3906 with COMISA. CIN was defined as difficulty initiating/maintaining sleep with daytime fatigue/sleepiness occurring "often"/"always." OSA was defined as an apnea-hypopnea index ≥5 on polysomnography. ORP, computed every 3s from polysomnography, was analyzed alongside sleep metrics, comorbidities, and sleep habits. Associations were assessed using univariate multinomial logistic regression, followed by stepwise regression to identify independent predictors of COMISA versus OSA or CIN. Machine learning models classified COMISA, OSA, and CIN as distinct clinical groups. ORP-derived features showed stronger associations with COMISA than traditional sleep metrics (except N3 latency). Independent objective predictors of COMISA included male sex (OR = 1.31, 95% CI = [1.16, 1.47]), BMI (1.27, [1.25, 1.29]), N3 latency (1.21, [1.13, 1.29]), age (1.17, [1.16, 1.19]), peak ORP during spontaneous arousals (1.12, [1.01, 1.25]), and time in ORP decile 7 (1.10, [1.07, 1.13]). Subjective predictors included depression, hypertension, allergy, headache, sleep aid/alcohol use, sleepiness, and lower sleep duration. Machine learning achieved overall accuracy of 61.2 per cent (p<.05), with sensitivity of 71 per cent for COMISA, 65 per cent for OSA, and 43 per cent for CIN. ORP is a promising objective marker for COMISA, distinguishing it from OSA more effectively than sleep metrics but separating COMISA from CIN poorly. Statement of Significance This study highlights the potential of the odds ratio product (ORP) as a novel objective marker to complement self-report questionnaires for identifying comorbid insomnia and sleep apnea (COMISA). Our findings demonstrate that ORP, combined with demographics and self-reported comorbidities and sleep habits, can distinguish COMISA from obstructive sleep apnea more effectively than traditional polysomnography metrics. These findings may refine the current understanding of COMISA from a research perspective and, in the future, may have clinical implications for improving the diagnosis and management of the disorder, which is often underrecognized in clinical patients who are evaluated for sleep-disordered breathing. Future studies should explore how ORP can be integrated into clinical sleep assessments and diagnostic strategies.

To develop and validate a Complete Artificial Intelligence Sleep Report system (CAISR), a system for comprehensive automated sleep analysis, including sleep staging, arousal detection, apnea identification, and limb movement analysis. We utilized a large diverse dataset from four cohorts (MGH, MESA, MrOS, SHHS) comprising 25,749 participants to develop CAISR. Following American Academy of Sleep Medicine (AASM) guidelines, CAISR performs four tasks: it stages sleep into five categories (Wake, NREM 1, NREM 2, NREM 3, REM), detects arousals, detects and classifies breathing events (Obstructive Apnea, Central Apnea, Mixed Apnea, Hypopnea, and RERA), and detects limb movements and categorizes them as periodic or isolated. We tested CAISR against multiple datasets independently annotated by multiple experts, including UPenn (69 subjects, 6 experts), BITS (98 subjects, 3 experts), and Stanford (100 subjects, three experts). Sleep staging and arousal detection were accomplished using customized deep neural networks, while breathing event detection and classification and limb movement analysis were accomplished using rule-based signal processing approaches. We quantified CAISR performance with three metrics: Cohen's Kappa, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). To determine whether CAISR performed on par with human experts, we compared expert inter-rater reliability (IRR) with algorithm-expert IRR. The CAISR model showed strong overall performance across the four tasks: sleep staging, arousal detection, apnea detection, and limb movement detection. In sleep staging, the model achieved AUROC values ranging from 0.82 to 0.97 and AUPRC values between 0.63 and 0.90 across the BITS, Stanford, and Penn datasets, indicating high classification accuracy. The Kappa agreement analysis showed that in the BITS and Stanford datasets, CAISR outperformed human experts, with non-overlapping confidence intervals indicating superiority (Kappa values around 0.7 to 0.8 for CAISR vs. experts). In the Penn dataset, the model's performance was comparable to experts, with overlapping confidence intervals suggesting non-inferiority. For arousal detection, the model maintained reliable performance, with AUROC values ranging from 0.83 to 0.94 and AUPRC values from 0.67 to 0.85, and Kappa analysis showing overlapping confidence intervals, indicating comparable performance to experts in both the BITS and Stanford datasets (Kappa values for CAISR around 0.6 to 0.75). In apnea detection, including the detection of obstructive, central, and mixed apnea, the CAISR model achieved competitive results in the BITS dataset with AUROC values between 0.81 and 0.95 and AUPRC values between 0.58 and 0.82, but in the Stanford dataset, it underperformed compared to human experts, as shown by non-overlapping confidence intervals and lower Kappa values (around 0.55 to 0.65). Finally, in limb movement detection, the model demonstrated superior performance in the BITS dataset, with AUROC values of 0.9 to 0.96 and AUPRC values between 0.75 and 0.85, and Kappa analysis indicating significantly higher reliability compared to experts (CAISR Kappa around 0.8, with non-overlapping confidence intervals). In the Stanford dataset, CAISR's performance was comparable to experts, with overlapping confidence intervals suggesting non-inferiority (Kappa values around 0.65 to 0.7). Overall, the CAISR model consistently exhibited high classification performance and reliability across tasks, often matching or surpassing expert-level performance, with particularly strong results in sleep staging and limb detection. The CAISR model demonstrated high classification accuracy and reliability across sleep staging, arousal, apnea, and limb movement detection tasks, matching or surpassing human expert performance. Human errors and systematic biases in the annotation of micro-events during sleep, such as arousal and apnea detection, likely contributed to variability in expert performance, while the CAISR model showed more consistent results, reducing the impact of these biases and increasing overall reliability across task.

Study ObjectivesYouth from socioeconomically disadvantaged backgrounds and minoritized racial/ethnic groups face a greater risk for sleep disparities and poor cognitive/academic outcomes compared to their peers from higher socioeconomic status (SES) and majority racial/ethnic groups. This meta-analysis has two main objectives. First, it examined SES and racial/ethnic variables as moderators of the association between individual sleep parameters (i.e. objective duration, objective quality, subjective duration, subjective quality, schedule/chronobiology, and variability/consistency) and overall (i.e. combined assessment) cognitive/academic outcomes (e.g. memory, GPA). Second, it assessed sleep parameters and outcome domains as moderators of the association between sleep broadly (i.e. no distinction between various sleep parameters) and overall cognitive/academic outcomes to test whether the magnitude of the association varied across specific sleep parameters and outcome domains.MethodsThirty-three studies comprising 410 effect sizes and 52 854 participants were included. The meta-analysis was conducted per best practices.ResultsFor sleep and cognitive functioning, the overall weighted effect was r = 0.11, 95% CI [0.08, 0.14], p < .001. Several significant differential associations between various sleep parameters and cognitive functioning emerged, showing that some parameters were stronger predictors. Academic performance yielded similar results, with an overall weighted effect size of r = 0.14, 95% CI [0.10, 0.17], p < .001. SES and race/ethnicity also modified relations between certain sleep parameters and cognitive/academic functioning.ConclusionsThe findings provide evidence for SES and race/ethnicity-related disparities, highlighting areas requiring further investigation. The discussion addresses methodological limitations in studying SES and race/ethnicity in relation to sleep and offers future research directions.