Increased Health Risk in Subjects with High Self-Reported Seasonality

Background: Seasonal variations in mood and behaviour, termed seasonality, are commonly reported in the general population. As a part of a large cross-sectional health survey in Hordaland, Norway, we investigated the relationship between seasonality, objective health measurements and health behaviours. Methodology/Principal Findings: A total of 11,545 subjects between 40–44 years old participated, completing the Global Seasonality Score, measuring seasonality. Waist/hip circumference, BMI and blood pressure were measured, and blood samples were analyzed for total cholesterol, HDL cholesterol, triglycerides and glucose. Subjects also completed a questionnaire on miscellaneous health behaviours (exercise, smoking, alcohol consumption). Hierarchical linear regression analyses were used to investigate associations between seasonality and objective health measurements, while binary logistic regression was used for analysing associations between seasonality and health behaviours. Analyses were adjusted for sociodemographic factors, month of questionnaire completion and sleep duration. Seasonality was positively associated with high waist-hip-ratio, BMI, triglyceride levels, and in men high total cholesterol. Seasonality was negatively associated with HDL cholesterol. In women seasonality was negatively associated with prevalence of exercise and positively associated with daily cigarette smoking. Conclusions/Significance: High seasonality was associated with objective health risk factors and in women also with health behaviours associated with an increased risk for cardiovascular disease

Midlife insomnia and subsequent mortality: the Hordaland health study

Background: Previous research suggests a possible link between insomnia and mortality, but findings are mixed and well-controlled studies are lacking. The aim of the current study was to examine the effect of insomnia in middle age on all-cause mortality. Methods: Using a cohort design with 13-15 years follow-up, mortality registry data were linked to health information obtained during 1997-99, as part of the community-based Hordaland Health Study (HUSK), in Western Norway. 6,236 participants aged 40–45 provided baseline information on self- reported insomnia using the Karolinska Sleep Questionnaire Scale (defined according to the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), sociodemographic factors, health behaviors, shift/night-work, obstructive sleep apnea symptoms, sleep duration, sleep medication use, anxiety, depression, as well as a range of somatic diagnoses and symptoms. Height, weight and blood pressure were measured. Information on mortality was obtained from the Norwegian Cause of Death Registry. Results: Insomnia was reported by 5.6% (349/6236) at baseline and a significant predictor of all-cause-mortality (hazard ratio [HR] = 2.74 [95% CI:1.75-4.30]). Adjusting for all confounders did not attenuate the effect (HR = 3.34 [95% CI:1.67-6.69]). Stratifying by gender, the effect was especially strong in men (HR = 4.72 [95% CI:2.48-9.03]); but also significant in women (adjusted HR = 1.96 [95% CI:1.04-3.67]). The mortality risk among participants with both insomnia and short sleep duration (<6.5 hours) was particularly high, whereas insomnia in combination with normal/greater sleep duration was not associated with mortality. Conclusions: Insomnia was associated with a three-fold risk of mortality over 13-15 years follow-up. The risk appeared even higher in males or when insomnia was combined with short sleep duration, although such unadjusted subgroup analyses should be interpreted with caution. Establishing prevention strategies and low-threshold interventions should consequently be a prioritized task for public health policy

Early and Later Life Stress Alter Brain Activity and Sleep in Rats

Exposure to early life stress may profoundly influence the developing brain in lasting ways. Neuropsychiatric disorders associated with early life adversity may involve neural changes reflected in EEG power as a measure of brain activity and disturbed sleep. The main aim of the present study was for the first time to characterize possible changes in adult EEG power after postnatal maternal separation in rats. Furthermore, in the same animals, we investigated how EEG power and sleep architecture were affected after exposure to a chronic mild stress protocol. During postnatal day 2–14 male rats were exposed to either long maternal separation (180 min) or brief maternal separation (10 min). Long maternally separated offspring showed a sleep-wake nonspecific reduction in adult EEG power at the frontal EEG derivation compared to the brief maternally separated group. The quality of slow wave sleep differed as the long maternally separated group showed lower delta power in the frontal-frontal EEG and a slower reduction of the sleep pressure. Exposure to chronic mild stress led to a lower EEG power in both groups. Chronic exposure to mild stressors affected sleep differently in the two groups of maternal separation. Long maternally separated offspring showed more total sleep time, more episodes of rapid eye movement sleep and higher percentage of non-rapid eye movement episodes ending in rapid eye movement sleep compared to brief maternal separation. Chronic stress affected similarly other sleep parameters and flattened the sleep homeostasis curves in all offspring. The results confirm that early environmental conditions modulate the brain functioning in a long-lasting way

The impact of the Global Seasonality Score on objective health risk factors.

<p>Hierarchic linear regression model controlled for month of questionnaire completion, marital status, income, education, living area and sleep duration (n = 11,544).</p><p>β: Standardized regression coefficient; NS: not significant.</p><p>*p<.05;</p><p>***p<.001.</p

Prevalence of objective health risk factors associated with cardiovascular disease in different seasonality groups.

<p>Each chart has prevalence of health risks on the vertical axis and seasonality group on the horizontal axis, and results for men and women are shown separately. GSS = Global Seasonality Score. Vertical lines depict the 95% Confidence intervals.</p

Effect of season on the associations between high seasonality and objective health risk factors.

<p>Analysis of variance using objective health measurements as dependent variables and seasonality group, season and the interaction term seasonality group*season as independent variables. Only significant effects are shown.</p><p><b>GSS = Global Seasonality Score.</b></p