Links between objective sleep and sleep variability measures and inflammatory markers in adults with bipolar disorder

Greater sleep variability is associated with higher systemic inflammation in type 2 diabetes

Sleep irregularity and variability have been shown to be detrimental to cardiometabolic health. The present pilot study explored if higher day-to-day sleep irregularity and variability were associated with systemic inflammation, as assessed by high-sensitivity C-reactive protein, in type 2 diabetes. Thirty-five patients with type 2 diabetes (mean age 54.3 years, 54.3% female) who were not shift-workers participated. The presence of diabetic retinopathy was determined. The standard deviation of sleep duration and sleep midpoint across all recorded nights were used to quantify sleep variability and regularity, respectively, assessed by 14-day actigraphy. The presence and severity of sleep apnea were assessed using an overnight home monitor. Low-density lipoprotein, haemoglobin A1C and high-sensitivity C-reactive protein were collected. Multiple regression analysis using natural-log-transformed values was performed to establish an independent association between sleep variability and high-sensitivity C-reactive protein. Twenty-two (62.9%) patients had diabetic retinopathy. The median (interquartile range) of high-sensitivity C-reactive protein was 2.4 (1.4, 4.6) mg L-1. Higher sleep variability was significantly associated with higher high-sensitivity C-reactive protein (r = 0.342, p = 0.044), as was haemoglobin A1C (r = 0.431, p = 0.010) and low-density lipoprotein (r = 0.379, p = 0.025), but not sleep regularity, sleep apnea severity or diabetic retinopathy. Multiple regression analysis showed that higher sleep variability (B = 0.907, p = 0.038) and higher HbA1c (B = 1.519, p = 0.035), but not low-density lipoprotein, contributed to higher high-sensitivity C-reactive protein. In conclusion, higher sleep variability in patients with type 2 diabetes who were not shift-workers was independently associated with higher systemic inflammation, conferring increased cardiovascular risk. Whether sleep interventions to reduce sleep variability can reduce systemic inflammation and improve cardiometabolic health should be investigated.

Relationship between sleep and serum inflammatory factors in patients with major depressive disorder

BACKGROUND

At present, the relationship between sleep and inflammatory factors is not clear. The aim of this study was to investigate the relationship between specific inflammatory factors and sleep in MDD patients.

METHODS

We measured and compared clinical features and 10 peripheral blood inflammatory factors in 40 MDD patients with sleep disorders, 80 MDD patients without sleep disorders, and 80 healthy controls. Correlation analysis and multiple linear regression analysis were used to explore the relationship between sleep and inflammatory factors.

RESULT

The levels of IL-1β, IL-2, IL-6, IL-8, IL-10, CRP, TNF-α, CXCL-1, CXCL-2, and IFN-γ were different among the three groups(all p<0.05).Poor sleep quality was significantly negatively correlated with IL-2 and IL-8 (all p<0.01), and significantly positively correlated with IL-6, IL-10, CRP, TNF-α, CXCL-1, CXCL-2 and IFN-γ (all p<0.01). IL-8 could significantly negatively predict the deterioration of sleep quality (p<0.001), and TNF-a and IFN-γ could significantly positively predict the deterioration of sleep quality (all p<0.05).

LIMITATIONS

The self-rating scale was used in this study.

CONCLUSIONS

Inflammatory factors are disrupted in patients with sleep disorders. The lower the level of IL-8 in peripheral blood of MDD patients, the higher the TNF-a and IFN-γ, and the worse the quality of sleep.

Elevation of C-reactive protein in adolescent bipolar disorder vs. anxiety disorders

Bipolar disorder (BD) largely begins in adolescence, but diagnosis lags for years, causing significant morbidity and mortality, and demonstrating the need for better diagnostic tools. Suggesting an association between BD and immune activity, elevated levels of peripheral inflammatory markers, including C-reactive protein (CRP), have been found in adults with BD. As similar data are extremely limited in adolescents, this study examined CRP levels in adolescents with BD (n = 37) compared to those with anxiety disorders (ADs, n = 157) and healthy controls with no psychiatric diagnoses (HCs, n = 2760). CRP blood levels for patients aged 12-17 years were retrieved from a nationwide repository of deidentified clinical data. After excluding patients with inflammatory conditions, differences in CRP were examined using multivariate and weighted regressions (covariates: demographics and BMI). Mean CRP levels were significantly elevated in adolescents with BD relative to those with ADs and HCs. Mean CRP levels were lower in the ADs cohort versus HCs. Although CRP levels were significantly higher in males and younger patients, the significant between-cohort differences in CRP remained after controlling for multiple confounders. To our knowledge, our study is the first to compare CRP levels between adolescent BD, ADs, and HCs, comprising a novel and essential contribution. Our results suggest the presence of a unique immune process in adolescents with BD and indicate that CRP may represent a biomarker with a crucial role in the diagnostic assessment of adolescent BD.

Temporal relationships of ecological momentary mood and actigraphy-based sleep measures in bipolar disorder

Sleep disturbances are a key feature of bipolar disorder (BD), and poor sleep has been linked to mood symptoms. Recent use of ecological momentary assessment (EMA) has allowed for nuanced exploration of the sleep-mood link; though, the scale and directionality of this relationship is still unclear. Using EMA, actigraphy, and self-reported sleep measures, this study examines the concurrent and predictive relationships between sleep and mood. Participants with BD (n = 56) wore actigraphy devices for up to 14 days and completed validated scales and daily EMA surveys about mood and sleep quality. Linear mixed models were used to examine overall and time-lagged relationships between sleep and mood variables. EMA mood ratings were correlated with validated rating scales for depression, mania, anxiety, and impulsivity. Poor self-reported sleep quality was associated with worse overall ratings of sadness and anger. Worse self-reported sleep quality was associated with greater sadness the following day. Higher daytime impulsivity was associated with worse sleep quality the following night. Exploratory analyses found relationships between worse and more variable mood (sadness, anger, and impulsivity) with worse and more variable sleep that evening (efficiency, WASO, and sleep onset time). The sample size was modest, fairly homogenous, and included mainly euthymic persons with BD. EMA-based assessments of mood and sleep are correlated with validated scale scores and provide novel insight into intra-individual variability. Further work on the complex two-way interactions between sleep and mood is needed to better understand how to improve outcomes in BD.

Toward a Digital Future in Bipolar Disorder Assessment: A Systematic Review of Disruptions in the Rest-Activity Cycle as Measured by Actigraphy

BACKGROUND

Disruptions in rest and activity patterns are core features of bipolar disorder (BD). However, previous methods have been limited in fully characterizing the patterns. There is still a need to capture dysfunction in daily activity as well as rest patterns in order to more holistically understand the nature of 24-h rhythms in BD. Recent developments in the standardization, processing, and analyses of wearable digital actigraphy devices are advancing longitudinal investigation of rest-activity patterns in real time. The current systematic review aimed to summarize the literature on actigraphy measures of rest-activity patterns in BD to inform the future use of this technology.

METHODS

A comprehensive systematic review using PRISMA guidelines was conducted through PubMed, MEDLINE, PsycINFO, and EMBASE databases, for papers published up to February 2021. Relevant articles utilizing actigraphy measures were extracted and summarized. These papers contributed to three research areas addressed, pertaining to the nature of rest-activity patterns in BD, and the effects of therapeutic interventions on these patterns.

RESULTS

Seventy articles were included. BD was associated with longer sleep onset latency and duration, particularly during depressive episodes and with predictive value for worsening of future manic symptoms. Lower overall daily activity was also associated with BD, especially during depressive episodes, while more variable activity patterns within a day were seen in mania. A small number of studies linked these disruptions with differential patterns of brain functioning and cognitive impairments, as well as more adverse outcomes including increased suicide risk. The stabilizing effect of therapeutic options, including pharmacotherapies and chronotherapies, on activity patterns was supported.

CONCLUSION

The use of actigraphy provides valuable information about rest-activity patterns in BD. Although results suggest that variability in rhythms over time may be a specific feature of BD, definitive conclusions are limited by the small number of studies assessing longitudinal changes over days. Thus, there is an urgent need to extend this work to examine patterns of rhythmicity and regularity in BD. Actigraphy research holds great promise to identify a much-needed specific phenotypic marker for BD that will aid in the development of improved detection, treatment, and prevention options.

Predictive Biomarkers for Postmyocardial Infarction Heart Failure Using Machine Learning: A Secondary Analysis of a Cohort Study

Background

There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF.

Methods

This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers.

Results

A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively.

Conclusions

This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.

Changes in sleep duration, timing, and variability during the COVID-19 pandemic: Large-scale Fitbit data from 6 major US cities

The COVID-19 pandemic has resulted in societal-level changes to sleep and other behavioral patterns. Objective data would allow for a greater understanding of sleep-related changes at the population level. About 163,524 active Fitbit users from 6 major US cities contributed data, representing areas particularly hard-hit by the pandemic (Chicago, Houston, Los Angeles, New York, San Francisco, and Miami). Sleep variables extracted include nightly and weekly mean sleep duration and bedtime, and variability (standard deviation) of sleep duration and bedtime. Deviation from similar timeframes in 2018 and 2019 were examined, as were changes in these sleep metrics during the pandemic, relationships to changes in resting heart rate, and changes during re-opening in May and June. Overall, compared to 2019, mean sleep duration in 2020 was higher among nearly all groups, mean sleep phase shifted later for nearly all groups, and mean sleep duration and bedtime variability decreased for nearly all groups (owing to decreased weekday-weekend differences). Over the course of January to April 2020, mean sleep duration increased, mean bedtime shifted later, and mean sleep duration variability decreased. Changes in observed resting heart rate correlated positively with changes in sleep and negatively with activity levels. In later months (May and June), many of these changes started to drift back to historical norms.