Is there an association between daytime napping, cognitive function, and brain volume? A Mendelian randomization study in the UK Biobank

Lifetime depression, sleep disruption and brain structure in the UK Biobank cohort

Whether depression and poor sleep interact or have statistically independent associations with brain structure and its change over time is not known. Within a subset of UK Biobank participants with neuroimaging and subjective and/or objective sleep data (n = 28,351), we examined associations between lifetime depression and sleep disruption, and their interaction with structural neuroimaging measures, both cross-sectionally and longitudinally. Sleep variables were: self-reported insomnia and difficulty getting up; actigraphy-derived short sleep (<7 h); sustained inactivity bouts during daytime (SIBD); and sleep efficiency. Imaging measures were white matter microstructure, subcortical volumes, cortical thickness and surface area of 24 cortical regions of interest. Individuals with lifetime depression (self-reported, mental health questionnaire or health records) were contrasted with healthy controls. Interactions between depression and difficulty getting up for i) right nucleus accumbens volume and ii) mean diffusivity of forceps minor, reflected a larger negative association of poor sleep in the presence vs. absence of depression. Depression was associated with widespread reductions in white matter integrity. Depression, higher SIBD and difficulty getting up were individually associated with smaller cortical volumes and surface area, particularly in the frontal and parietal lobes. Many regions showed age-related decline, but this was not exacerbated by either depression or sleep disturbance. Overall, we identified widespread cross-sectional associations of both lifetime depression and sleep measures with brain structure. Findings were more consistent with additive rather than synergistic effects - although in some regions we observed greater magnitude of deleterious associations from poor sleep phenotypes in the presence of depression.

Genetically predicted brain cortical structure mediates the causality between insulin resistance and cognitive impairment

Background

Insulin resistance is tightly related to cognition; however, the causal association between them remains a matter of debate. Our investigation aims to establish the causal relationship and direction between insulin resistance and cognition, while also quantifying the mediating role of brain cortical structure in this association.

Methods

The publicly available data sources for insulin resistance (fasting insulin, homeostasis model assessment beta-cell function and homeostasis model assessment insulin resistance, proinsulin), brain cortical structure, and cognitive phenotypes (visual memory, reaction time) were obtained from the MAGIC, ENIGMA, and UK Biobank datasets, respectively. We first conducted a bidirectional two-sample Mendelian randomization (MR) analysis to examine the susceptibility of insulin resistance on cognitive phenotypes. Additionally, we applied a two-step MR to assess the mediating role of cortical surficial area and thickness in the pathway from insulin resistance to cognitive impairment. The primary Inverse-variance weighted, accompanied by robust sensitivity analysis, was implemented to explore and verify our findings. The reverse MR analysis was also performed to evaluate the causal effect of cognition on insulin resistance and brain cortical structure.

Results

This study identified genetically determined elevated level of proinsulin increased reaction time (beta=0.03, 95% confidence interval [95%CI]=0.01 to 0.05, p=0.005), while decreasing the surface area of rostral middle frontal (beta=-49.28, 95%CI=-86.30 to -12.27, p=0.009). The surface area of the rostral middle frontal mediated 20.97% (95%CI=1.44% to 40.49%) of the total effect of proinsulin on reaction time. No evidence of heterogeneity, pleiotropy, or reverse causality was observed.

Conclusions

Briefly, our study noticed that elevated level of insulin resistance adversely affected cognition, with a partial mediation effect through alterations in brain cortical structure.

To nap or not? Evidence from a meta-analysis of cohort studies of habitual daytime napping and health outcomes

Habitual daytime napping is a common behavioral and lifestyle practice in particular countries and is often considered part of a normal daily routine. However, recent evidence suggests that the health effects of habitual daytime napping are controversial. We systematically searched PubMed, Web of Science, Embase, and Cochrane Library databases from inception to March 9, 2024, to synthesize cohort studies of napping and health outcome risk. A total of 44 cohort studies with 1,864,274 subjects aged 20-86 years (mean age 56.4 years) were included. Overall, habitual napping increased the risk of several adverse health outcomes, including all-cause mortality, cardiovascular disease, metabolic disease, and cancer, and decreased the risk of cognitive impairment and sarcopenia. Individuals with a napping duration of 30 min or longer exhibited a higher risk of all-cause mortality, cardiovascular disease, and metabolic disease, whereas those with napping durations less than 30 min had no significant risks. No significant differences in napping and health risks were observed for napping frequency, percentage of nappers, sample size, sex, age, body mass index, follow-up years, or comorbidity status. These findings indicate that individuals with a long napping duration should consider shortening their daily nap duration to 30 min or less.

Napping and Obesity in Adults - What do we Know?

PURPOSE OF REVIEW

To review the evidence on the relationship between daytime napping and obesity.

RECENT FINDINGS

There is concern that napping may be harmful to metabolic health. Prospective studies have shown long time daytime napping (> 1 h) is associated with increased diabetes risk which may be partly associated with obesity. Evidence from numerous cross-sectional studies and meta-analyses of cross-sectional studies have shown that long time napping (> 1 h) but not short time napping is associated with increased risk of obesity, and this is seen worldwide. Inference regarding the nature of association from cross-sectional studies is limited; it is suggested the association is bidirectional. Prospective studies on the association between daytime napping and obesity are few and results unclear. Large longitudinal studies integrating daytime napping duration and night-time sleep behaviour and detailed information on lifestyle influences is needed to help elucidate further the associations of long time napping with obesity.

Association between self-reported napping and risk of cardiovascular disease and all-cause mortality: A meta-analysis of cohort studies

OBJECTIVES

This meta-analysis aims to assess the association between adult nap duration and risk of all-cause mortality and cardiovascular diseases (CVD).

METHODS

PubMed, Cochrane Library, Embase and Web of Science databases were searched to identify eligible studies. The quality of observational studies was assessed using the Newcastle-Ottawa Scale. We performed all statistical analyses using Stata software version 14.0. For the meta-analysis, we calculated hazard ratio (HR) and their corresponding 95% confidence intervals (CIs). To assess publication bias, we used a funnel plot and Egger's test.

RESULTS

A total of 21 studies involving 371,306 participants revealed varying methodological quality, from moderate to high. Those who indulged in daytime naps faced a significantly higher mortality risk than non-nappers (HR: 1.28; 95% CI: 1.18-1.38; I2 = 38.8%; P<0.001). Napping for less than 1 hour showed no significant association with mortality (HR: 1.00; 95% CI: 0.90-1.11; I2 = 62.6%; P = 0.971). However, napping for 1 hour or more correlated with a 1.22-fold increased risk of mortality (HR: 1.22; 95% CI: 1.12-1.33; I2 = 40.0%; P<0.001). The risk of CVD associated with napping was 1.18 times higher than that of non-nappers (HR: 1.18; 95% CI: 1.02-1.38; I2 = 87.9%; P = 0.031). Napping for less than 1 hour did not significantly impact CVD risk (HR: 1.03; 95% CI: 0.87-1.12; I2 = 86.4%; P = 0.721). However, napping for 1 hour or more was linked to a 1.37-fold increased risk of CVD (HR: 1.37; 95% CI: 1.09-1.71; I2 = 68.3%; P = 0.007).

CONCLUSIONS

Our meta-analysis indicates that taking a nap increases the risk of overall mortality and CVD mortality. It highlights that the long duration time of the nap can serve as a risk factor for evaluating both overall mortality and cardiovascular mortality.

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

BACKGROUND

Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

OBJECTIVE

This study aimed to use data collected from fitness trackers to predict MCI status.

METHODS

In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status.

RESULTS

Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time.

CONCLUSIONS

Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

Genetic and non-genetic factors in prediction of early pubertal development in Chinese girls

Objective

The objective of this study is to develop a combined predictive model for early pubertal development (EPD) in girls based on both non-genetic and genetic factors.

Methods

The case-control study encompassed 147 girls diagnosed with EPD and 256 girls who exhibited normal pubertal development. The non-genetic risk score (NGRS) was calculated based on 6 independent biochemical predictors screened by multivariate logistic regressions, and the genetic risk score (GRS) was constructed using 28 EPD related single-nucleotide polymorphisms (SNPs). Area under receiver operator characteristic curve (AROC), net reclassification optimization index (NRI) and integration differentiation index (IDI) were used to evaluate the improvement of adding genetic variants to the non-genetic risk model.

Results

Overweight (OR=2.74), longer electronic screen time (OR=1.79) and higher ratio of plastic bottled water (OR=1.01) were potential risk factors, and longer exercise time (OR=0.51) and longer day sleeping time (OR=0.97) were protective factors for EPD, and the AROC of NGRS model was 83.6% (79.3-87.9%). The GRS showed a significant association with EPD (OR=1.90), and the AROC of GRS model was 65.3% (59.7-70.8%). After adding GRS to the NGRS model, the AROC significantly increased to 85.7% (81.7-89.6%) (P=0.020), and the reclassification significantly improved, with NRI of 8.19% (P= 0.023) and IDI of 4.22% (P <0.001).

Conclusions

We established a combined prediction model of EPD in girls. Adding genetic variants to the non-genetic risk model brought modest improvement. However, the non-genetic factors such as overweight and living habits have higher predictive utility.

Sleep Characteristics and Risk of Stroke and Dementia: An Observational and Mendelian Randomization Study

BACKGROUND AND OBJECTIVES

Sleep disturbances are implicated as risk factors of both stroke and dementia. However, whether these associations are causal and whether treatment of sleep disorders could reduce stroke and dementia risk remain uncertain. We aimed to evaluate associations and ascertain causal relationships between sleep characteristics and stroke/dementia risk and MRI markers of small vessel disease (SVD).

METHODS

We used data sets from a multicenter population-based study and summary statistics from genome-wide association studies (GWASs) of sleep characteristics and outcomes. We analyzed 502,383 UK Biobank participants with self-reported sleep measurements, including sleep duration, insomnia, chronotype, napping, daytime dozing, and snoring. In observational analyses, the primary outcomes were incident stroke, dementia, and their subtypes, alongside SVD markers. Hazard ratios (HRs) and odds ratios (ORs) were adjusted for age, sex, and ethnicity, and additional vascular risk factors. In Mendelian randomization (MR) analyses, ORs or risk ratios are reported for the association of each genetic score with clinical or MRI end points.

RESULTS

Among 502,383 participants (mean [SD] age, 56.5 [8.1] years; 54.4% female), there were 7,668 cases of all-cause dementia and 10,334 strokes. In longitudinal analyses, after controlling for cardiovascular risk factors, participants with insomnia, daytime napping, and dozing were associated with increased risk of any stroke (HR 1.05, 95% CI 1.01-1.11, p = 8.53 × 10-3; HR 1.09, 95% CI 1.05-1.14, p = 3.20 × 10-5; HR 1.19, 95% CI 1.08-1.32, p = 4.89 × 10-4, respectively). Almost all sleep measures were associated with dementia risk (all p < 0.001, except insomnia). Cross-sectional analyses identified associations between napping, snoring, and MRI markers of SVD (all p < 0.001). MR analyses supported a causal link between genetically predicted insomnia and increased stroke risk (OR 1.31, 95% CI 1.13-1.51, p = 0.00072), but not with dementia or SVD markers.

DISCUSSION

We found that multiple sleep measures predicted future risk of stroke and dementia, but these associations were attenuated after controlling for cardiovascular risk factors and were absent in MR analyses for Alzheimer disease. This suggests possible confounding or reverse causation, implying caution before proposing sleep disorder modifications for dementia treatment.

Sleep and cardiovascular disease

This review centres around the recent evidence in examining the intersection of sleep and cardiovascular disease (CVD). Sleep in this review will be further subdivided to consider both sleep quantity and quality along and will also consider some of the more common sleep disorders, such as insomnia and obstructive sleep apnoea, in the context of CVD. Sleep disorders have been further explored in several specific populations which are both at risk of sleep disorders and CVD. Secondly, the review will present some of the risk factors for CVD that are affected by sleep and sleep disorders which include hypertension, diabetes, and obesity. It will also examine the potential underlying mechanisms including inflammation, appetite control, endocrine, and genetic processes that are affected by sleep and sleep disorders leading to increased risk of CVD development. In addition, we will consider the observed bi-directional relationships between sleep and cardiovascular risk factors. For example, obesity, a risk factor for CVD can be affected by sleep, but in turn can increase the risk of certain sleep disorder development which disrupts sleep, leading to further risk of obesity development and increased CVD risk. Finally, the review will explore emerging evidence around lifestyle interventions that have included a sleep component and how it impacts the management of CVD risk factor. The need for increased awareness of the health effects of poor sleep and sleep disorders will be discussed alongside the need for policy intervention to improve sleep to facilitate better health and well-being.

Selection of genetic instruments in Mendelian randomisation studies of sleep traits

This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.

Causal associations between sleep traits and brain structure: a bidirectional Mendelian randomization study

BACKGROUND

Emerging evidence suggests bidirectional causal relationships between sleep disturbance and psychiatric disorders, but the underlying mechanisms remain unclear. Understanding the bidirectional causality between sleep traits and brain imaging-derived phenotypes (IDPs) will help elucidate the mechanisms. Although previous studies have identified a range of structural differences in the brains of individuals with sleep disorders, it is still uncertain whether grey matter (GM) volume alterations precede or rather follow from the development of sleep disorders.

RESULTS

After Bonferroni correction, the forward MR analysis showed that insomnia complaint remained positively associated with the surface area (SA) of medial orbitofrontal cortex (β, 0.26; 95% CI, 0.15-0.37; P = 5.27 × 10-6). In the inverse MR analysis, higher global cortical SA predisposed individuals less prone to suffering insomnia complaint (OR, 0.89; 95%CI, 0.85-0.94; P = 1.51 × 10-5) and short sleep (≤ 6 h; OR, 0.98; 95%CI, 0.97-0.99; P = 1.51 × 10-5), while higher SA in posterior cingulate cortex resulted in a vulnerability to shorter sleep durations (β, - 0.09; 95%CI, - 0.13 to - 0.05; P = 1.21 × 10-5).

CONCLUSIONS

Sleep habits not only result from but also contribute to alterations in brain structure, which may shed light on the possible mechanisms linking sleep behaviours with neuropsychiatric disorders, and offer new strategies for prevention and intervention in psychiatric disorders and sleep disturbance.