Genetics of circadian rhythms and mood spectrum disorders.

Inserm, U955, Créteil 94000, France.
European neuropsychopharmacology: the journal of the European College of Neuropsychopharmacology (Impact Factor: 3.68). 08/2011; 21 Suppl 4:S676-82. DOI:10.1016/j.euroneuro.2011.07.007
Source: PubMed

ABSTRACT Mood spectrum disorders (bipolar disorder, recurrent depressive disorder and seasonal affective disorder) are accompanied by circadian deregulations, which can occur during acute mood episodes as well as during euthymic periods, and are particularly common among bipolar patients in remission. This suggests that altered circadian rhythms may be biological markers of these disorders. Rhythm dysfunctions have been observed in mood disorder patients by using actigraphic measures and by assessing social metric rhythms, diurnal preferences and melatonin secretion. Since many of these markers are heritable and therefore driven by clock genes, these genes may represent susceptibility factors for mood spectrum disorders. Indeed, several genetic association studies have suggested that certain circadian gene variants play a role in susceptibility to these disorders. Such connections to circadian genes such as CLOCK, ARNTL1, NPAS2, PER3 and NR1D1 have been repeatedly demonstrated for bipolar disorders, and to a lesser extent for recurrent depressive disorders and seasonal affective disorders. The study of circadian phenotypes and circadian genes in mood spectrum disorders represents a major field of research that may yet reveal the pathophysiological determinants of these disorders.

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    ABSTRACT: Introduction Findings from actigraphic studies suggesting that sleep and circadian rhythms are disrupted in bipolar disorder (BD) patients have been undermined by methodological heterogeneity and the failure to adequately address potential confounders. Method Twenty-six euthymic BD cases and 29 healthy controls (HC), recruited from University Paris-Est and matched for age and gender, were compared on subjective (Pittsburgh Sleep Questionnaire Inventory; PQSI) and objective (mean scores and variability in actigraphy) measures of sleep as recorded by over 21 consecutive days. Results Multivariate generalized linear modelling (GLM) revealed significant differences between BD cases and HC for five PSQI items (total score and four subscales), four actigraphy variables (mean scores) and five actigraphy variability measures. Backward stepwise linear regression (BSLR) indicated that a combination of four variables (mean sleep duration, mean sleep latency, variability of the fragmentation index over 21 days, and mean score on PSQI daytime dysfunction sub-scale) correctly classified 89% of study participants as cases or controls (Chi-square=39.81; df=6; p=0.001). Limitations The sample size (although larger than most actigraphy studies) and incomplete matching of cases and controls may have influenced our findings. It was not possible to control for potential effects of psychotropic medication or differences in employment status between groups. Conclusions When potential confounders of sleep and circadian profiles are adequately taken into account (particularly age, gender, daytime sleepiness, mood symptoms, body mass index, and risk of sleep apnoea), a selected subset of quantitative (mean scores) and qualitative (variability) features differentiated euthymic BD cases from HC.
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