Self-reported insomnia as a marker for anxiety and depression among migraineurs: a population-based cross-sectional study

Right-to-left shunt-associated brain functional changes in migraine: evidences from a resting-state FMRI study

Background

Migraine, a neurological condition perpetually under investigation, remains shrouded in mystery regarding its underlying causes. While a potential link to Right-to-Left Shunt (RLS) has been postulated, the exact nature of this association remains elusive, necessitating further exploration.

Methods

The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and functional connectivity (FC) were employed to investigate functional segregation and functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics.

Results

Compared with migraine patients without RLS, patients with RLS exhibited a significant increase in the ALFF within left middle occipital and superior occipital gyrus; In migraine patients with RLS, significantly reduced brain functional connectivity was found, including the connectivity between default mode network and visual network, ventral attention network, as well as the intra-functional connectivity of somatomotor network and its connection with the limbic network, and also the connectivity between the left rolandic operculum and the right middle cingulate gyrus. Notably, a significantly enhanced functional connectivity between the frontoparietal network and the ventral attention network was found in migraine with RLS; Patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Additionally, these patients displayed a diminished nodal degree in the occipital lobe and reduced nodal efficiency within the fusiform gyrus; Further, the study found positive correlations between ALFF in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity.

Conclusion

RLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.

Exploring the Causal Relationship Between Migraine and Insomnia Through Bidirectional Two-Sample Mendelian Randomization: A Bidirectional Causal Relationship

Introduction

The intricate relationship between migraine and insomnia has been a subject of great interest due to its complex mechanisms. Despite extensive research, understanding the causal link between these conditions remains a challenge.

Material and Methods

This study employs a bidirectional Mendelian randomization approach to investigate the causal relationship between migraine and insomnia. Risk loci for both conditions were derived from large-scale Genome-Wide Association Studies (GWAS). The primary method of Mendelian Randomization utilized in this study is the Inverse Variance Weighted (IVW) method.

Results

Our findings indicate a bidirectional causal relationship between migraine and insomnia. In the discovery set, migraine had a significant effect on insomnia (OR=1.02, 95% CI=1.02 (1.01-1.03), PIVW=5.30E-04). However, this effect was not confirmed in the validation set (OR=1.03, 95% CI=1.03 (0.87-1.21), PIVW=0.77). Insomnia also had a significant effect on migraine (OR=1.02, 95% CI=1.02 (0.01-1.03), PIVW=2.67E-08), and this effect was validated in the validation set (OR=2.30, 95% CI=2.30 (1.60-3.30), PIVW=5.78E-06).

Conclusion

This study provides meaningful insights into the bidirectional causality between migraine and insomnia, highlighting a complex interplay between these conditions. While our findings advance the understanding of the relationship between migraine and insomnia, they also open up new avenues for further research. The results underscore the need for considering both conditions in clinical and therapeutic strategies.

Shared comorbidity of depression, migraine, insomnia, and fibromyalgia in a population-based sample

BACKGROUND

Depression, migraine, insomnia, and fibromyalgia are reportedly comorbidities. Nevertheless, no study has evaluated the comorbidity of all four of these disorders. This study aimed to investigate the comorbidity of these four disorders.

METHODS

Cross-sectional analyses were performed using data of the Circannual Change in Headache and Sleep study, an online nationwide population-based survey. Validated questionnaires were used to diagnose the disorders and measure quality of life. The change of clinical characteristics by addition of any comorbidity was analyzed using the Jonckheere-Terpstra trend test.

RESULTS

The prevalence rates of depression, migraine, insomnia, and fibromyalgia were 7.2 %, 5.6 %, 13.3 %, and 5.8 %, respectively. Among the 3030 included participants, 494 (16.3 %), 164 (5.4 %), 40 (1.3 %), and 6 (0.2 %) had one, two, three, and four of these conditions, respectively. The number of headache days per 30 days (Jonckheere-Terpstra trend test, p = 0.011) and migraine-related disability (migraine disability assessment score, p = 0.021) increased with an increase in the number of comorbidities but not with the intensity of headache (visual analog scale, p = 0.225) among participants with migraine. The severity of insomnia (Insomnia Severity Index, p < 0.001) and fibromyalgia (fibromyalgia severity score, p = 0.002) increased with additional comorbidities; however, depression (Patient Health Questionnaire-9, p = 0.384) did not show such an increase.

LIMITATIONS

The diagnoses of conditions were based on self-reported questionnaires.

CONCLUSIONS

The findings confirmed significant comorbidity between depression, migraine, insomnia, and fibromyalgia. Health professionals should be aware of the probable comorbidity of depression, migraine, insomnia, and fibromyalgia when caring for individuals with any of these four disorders.

Artificial neural network-based model for sleep quality prediction for frontline medical staff during major medical assistance

Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set (n = 1061, 80%) and an independent validation set (n = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. Results: A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% (n = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. Conclusions: In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.

Investigation on sleep-related cognition of Chinese health care workers during the first wave of COVID-19 pandemic

Background

The COVID pandemic has brought tremendous negative effects on the mental health of health care workers, such as anxiety, depression, and sleep disorders. We conducted this study to evaluate the sleep-related cognition of Chinese health care workers (HCWs) during the first wave of COVID-19 pandemic and analyze its association with sleep quality, so as to provide scientific reference for improving sleep of HCWs.

Patients and methods

A total of 404 HCWs from Yijishan Hospital of Wuhu City, China were enrolled in the study, selected by randomized cluster sampling in May 2020. We made a questionnaire to collect the general demographic information of the participants. The Pittsburgh Sleep Quality Index (PSQI) and a brief version of Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) were used to measure sleep quality and sleep-related cognition, respectively.

Results

The results showed that 312 HCWs (77.2%) had false beliefs and attitudes about sleep, while only 92 HCWs (22.8%) had correct beliefs about sleep. In addition, we found that those HCWs who were older, married, with a bachelor’s degree or higher, nurses, more daily working hours (&gt; 8 h) and monthly night shifts (≥ 5 times), had higher DBAS-16 scores (all p &lt; 0.05). However, we did not find significant differences between men and women in DBAS-16 scores. According to the definition of PSQI, a total of 1/4 of the HCWs are poor sleepers and their DBAS-16 score was higher than good sleepers (t = 7.622, p &lt; 0.001). In the end, we confirmed a positive correlation between sleep cognition and sleep quality (r = 0.392, p &lt; 0.01).

Conclusion

Our study revealed false beliefs and attitudes about sleep were prevalent among HCWs during the first wave of COVID-19 pandemic, and these false beliefs about sleep were closely correlated to sleep quality. We recommend fighting against these false beliefs about sleep.

A mental health survey among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China

BACKGROUND

The sporadic coronavirus disease (COVID-19) epidemic has placed enormous psychological stress on people, especially clinicians. The objective of this study was to examine depression, anxiety, quality of life (QOL), and related social psychological factors among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China and to provide a reference for formulating reasonable countermeasures.

METHODS

In this cross-sectional study, demographic information, COVID-19-related questions, anxiety (Generalized Anxiety Disorder-7, GAD-7), depression (Patient Health Questionnaire-9, PHQ-9), insomnia (Insomnia Severity Index, ISI), stress (Perceived Stress Scale-10, PSS-10), and QOL (World Health Organization Quality of Life-brief version, WHOQOL-BREF) were collected. Binary logistic regression analysis was used to test the relationships between anxiety and/or depression and other related problems. Multiple linear regression analysis was used to test the relationships among factors influencing QOL.

RESULTS

A total of 146 young front-line clinicians were included. The prevalence rates of depression, anxiety, and anxiety-depression comorbidity were 37.7% (95% CI = 29.7-45.6%), 26.0% (95% CI = 18.8-33.2%), and 24.0% (95% CI = 17.0-31.0%), respectively. Severe stress (OR = 1.258, 95% CI = 1.098-1.442, P < 0.01) and insomnia (OR = 1.282, 95% CI = 1.135-1.447, P < 0.01) were positively correlated with depression. Severe stress (OR = 1.487, 95% CI = 1.213-1.823, P < 0.01) and insomnia (OR = 1.131, 95% CI = 1.003-1.274, P < 0.05) were positively correlated with anxiety. Severe stress (OR = 1.532, 95% CI = 1.228-1.912, P < 0.01) was positively correlated with anxiety-depression comorbidity. However, insomnia (OR = 1.081, 95% CI = 0.963-1.214, P > 0.05) was not correlated with anxiety-depression comorbidity. The belief that the vaccine will stop the COVID-19 pandemic (OR = 0.099, 95% CI = 0.014-0.715, P < 0.05) was negatively correlated with anxiety and anxiety-depression comorbidity (OR = 0.101, 95% CI = 0.014-0.744, P < 0.05). Severe stress (B = -0.068, 95% CI = -0.129 to -0.007, P < 0.05) and insomnia (B = -0.127, 95% CI = -0.188 to -0.067, P < 0.01) were negatively correlated with QOL. The belief that the vaccine could provide protection (B = 1.442, 95% CI = 0.253-2.631, P < 0.05) was positively correlated with QOL.

CONCLUSIONS

The prevalence of depression, anxiety, and even anxiety-depression comorbidity was high among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China. Various biological and psychological factors as well as COVID-19-related factors were associated with mental health issues and QOL. Psychological intervention should evaluate these related factors and formulate measures for these high-risk groups.

A multicenter survey of perioperative anxiety in China: Pre- and postoperative associations

OBJECTIVES

To describe patient characteristics associated with preoperative anxiety and subsequently assess the relationship between preoperative anxiety and postoperative anxiety, pain, sleep quality, nausea and vomiting.

METHODS

The study collected data from patients undergoing elective operation from 12 hospitals in China. The State-Trait Anxiety Inventory (STAI) and the Athens Insomnia Scale (AIS) were used to assess anxiety and sleep quality before surgery. Evaluations of anxiety, pain, sleep quality, nausea and vomiting were quantified using the Visual Analogue Scale on postoperative days 1 and 2.

RESULTS

Data from 997 patients were analyzed. Preoperatively, 258 (25.9%) patients had high anxiety (STAI-State>44). Multivariate analyses showed a significant relationship between high anxiety and female gender (OR: 1.66, 95% CI: 1.08-2.57, p = 0.02), highly invasive surgery (OR: 2.29, 95% CI: 1.29-4.06, p = 0.005), higher trait anxiety (OR: 1.24, 95% CI: 1.20-1.28, p < 0.001) and insomnia (AIS ≥ 6, OR: 1.79, 95% CI: 1.17-2.76, p = 0.008). Preoperative anxiety demonstrated a negative correlation with postoperative anxiety following highly invasive surgery; this became a positive relationship following less invasive surgery. Preoperative anxiety was also positively related to postoperative pain and poor sleep quality. The correlation between preoperative anxiety and postoperative nausea and vomiting was not statistically significant.

CONCLUSION

Female gender, highly invasive surgery, higher trait anxiety and insomnia are independent risk factors for high preoperative anxiety. Surgical invasiveness influences association between pre- and postoperative anxiety. Higher preoperative anxiety is related to poorer sleep quality and more severe pain postoperatively.

Comorbidities of primary headache disorders: a literature review with meta-analysis

Background

Primary headache disorders are common and burdensome conditions. They are associated to several comorbidities, such as cardiovascular or psychiatric ones, which, in turn, contribute to the global burden of headache. The aim of this study is to provide a comprehensive description of the pooled prevalence of comorbidities of primary headache disorders using a meta-analytical approach based on studies published between 2000 and 2020.

Methods

Scopus was searched for primary research (clinical and population studies) in which medical comorbidities were described in adults with primary headache disorders. Comorbidities were extracted using a taxonomy derived from the Global Burden of Disease (GBD) study. We compared prevalence of comorbidities among headache sufferers against general population using GBD-2019 estimates, and compared comorbidities’ proportions in clinical vs. population studies, and by age and gender.

Results

A total of 139 studies reporting information on 4.19 million subjects with primary headaches were included: in total 2.75 million comorbidities were reported (median per subject 0.64, interquartile range 0.32–1.07). The most frequently addressed comorbidities were: depressive disorders, addressed in 51 studies (pooled proportion 23 %, 95 % CI 20–26 %); hypertension, addressed in 48 studies (pooled proportion 24 %, 95 % CI 22–26 %); anxiety disorders addressed in 40 studies (pooled proportion 25 %, 95 % CI 22–28 %). For conditions such as anxiety, depression and back pain, prevalence among headache sufferers was higher than in GBD-2109 estimates. Associations with average age and female prevalence within studies showed that hypertension was more frequent in studies with higher age and less females, whereas fibromyalgia, restless leg syndrome, and depressive disorders were more frequent in studies with younger age and more female.

Conclusions

Some of the most relevant comorbidities of primary headache disorders – back pain, anxiety and depression, diabetes, ischemic heart disease and stroke – are among the most burdensome conditions, together with headache themselves, according to the GBD study. A joint treatment of headaches and of these comorbidities may positively impact on headache sufferers’ health status and contribute to reduce the impact of a group of highly burdensome diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01281-z.

Cognitive behavioral therapy for insomnia in patients with chronic pain - A systematic review and meta-analysis of randomized controlled trials

Several randomized controlled trials have implemented cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid insomnia and chronic pain. This systematic review and meta-analysis investigated the effectiveness of CBT-I on patient-reported sleep, pain, and other health outcomes (depressive symptoms, anxiety symptoms, and fatigue) in patients with comorbid insomnia and chronic non-cancer pain. A systematic literature search was conducted using eight electronic databases. Upon duplicate removal, 6374 records were screened against the inclusion criteria. Fourteen randomized controlled trials were selected for the review, with twelve (N = 762 participants) included in the meta-analysis. At post-treatment, significant treatment effects were found on global measures of sleep (standardized mean difference = 0.89), pain (0.20), and depressive symptoms (0.44). At follow-up (up to 12 mo), CBT-I significantly improved sleep (0.56). Using global measures of sleep, we found a probability of 81% and 71% for having better sleep after CBT-I at post-treatment and final follow-up, respectively. The probability of having less pain after CBT-I at post-treatment and final follow-up was 58% and 57%, respectively. There were no statistically significant effects on anxiety symptoms and fatigue at either assessment point. Future trials with sufficient power, longer follow-up periods, and inclusion of CBT for pain components are warranted.