Despite international efforts to identify biomarkers of depression, none has been transferred to clinical practice, neither for diagnosis, evolution, nor therapeutic response. This led us to build a French national cohort (through the clinical and research network named SoPsy within the French biological psychiatry society (AFPBN) and sleep society (SFRMS)), to better identify markers of sleep and biological rhythms and validate more homogeneous subgroups of patients, but also to specify the manifestations and pathogeneses of depressive disorders.
Depressive disorders are a group of frequent and severe disorders that affect up to 20% of the general population. The WHO projects that depression will be the leading cause of disability by 2030. This growing public health problem is marked by a decrease in psychosocial functioning and quality of life, and is associated with a high rate of suicide. In addition, there is a significant economic impact including loss of productivity and a significant increase in the use of health care services. To date, the diagnosis of a depressive episode is based solely on clinical assessment and diagnostic criteria. Despite international efforts to identify biomarkers of depression, none of these identified biomarkers have been transferred to clinical practice, either for diagnosis, outcome or treatment prediction. Some of the difficulties and lack of replication of certain results are directly related to the nature of depressive disorders, which include a large number of very heterogeneous entities. Among the markers of interest in patients with a major depressive episode (MDE), the scientific literature has shown close links between depression and disturbances in sleep and biological rhythms. Thus, more than 90% of patients suffering from MDE have sleep complaints (PMID:28972930). Moreover, it is now well demonstrated, via epidemiological and longitudinal follow-up studies, that sleep disorders, and in particular insomnia, are both risk factors and prodromes of MDE. These sleep and rhythm abnormalities seem to persist during remission phases and appear to be risk factors for depressive recurrence. Objective abnormalities, assessed by actigraphy and polysomnography, have also been demonstrated during episodes and in subjects at risk of depression, and thus appear to be both state and trait markers of the disorder. These sleep and circadian rhythm abnormalities, in addition to being associated with depressive relapse, are associated with poor global functioning, poor quality of life and risk of metabolic syndrome. Moreover, depressive disorders encompass a very heterogeneous set of conditions, and these biomarkers seem to hold great promise for better characterising the different subtypes of disorders and for better characterising patient populations. Finally, these clinical observations make sleep and circadian rhythm abnormalities essential therapeutic targets, making it possible to propose a truly more personalised medicine in psychiatry It is therefore urgent to better characterise the different subtypes of depressive disorders and to better understand the pathogenesis and evolution of these disorders in order to have predictive markers for conversion, recurrence or therapeutic responses. The objective of identifying such markers would also ultimately be to better screen patients and to propose adapted and personalised therapeutic strategies. The constitution of a national cohort, with a fine and homogeneous characterisation between the centres, is intended to meet these objectives by assessing psychiatry, addiction, sleep and chronobiology dimensions of depressive disorders.
Study Type
OBSERVATIONAL
Enrollment
1,000
Hôpital Bichat Claude Bernard
Paris, Paris, France
RECRUITINGamount of REM sleep
during the polysomnography, amount of REM sleep (in minutes)
Time frame: at inclusion
duration of the first stage of REM sleep
Time frame: at inclusion
duration of the first episode of N3
Time frame: at inclusion
latency of the first episode of N3
Time frame: at inclusion
duration of N1 slow-wave sleep
Time frame: at inclusion
duration of N2 slow-wave sleep
Time frame: at inclusion
duration of N3 slow-wave sleep
Time frame: at inclusion
percentage of slow-wave sleep N1
Time frame: at inclusion
percentage of slow-wave sleep N2
Time frame: at inclusion
percentage of slow-wave sleep N3
Time frame: at inclusion
measurement of total sleep time
Time frame: at inclusion
sleep efficiency
Time frame: at inclusion
nocturnal awakenings
Time frame: at inclusion
latency of different sleep stages
Time frame: at inclusion
duration of different sleep stages
Time frame: at inclusion
density of different sleep stages
Time frame: at inclusion
movement during sleep
Time frame: at inclusion
percentage of time total sleep spent under 90% SaO2
Time frame: at inclusion
Mean for iterative latency tests falling asleep
Time frame: at inclusion
urinary dosage 6-sulfatoxymelatonin over 24 hours.
Time frame: at inclusion
urinary dosage cortisol over 24 hours.
Time frame: at inclusion
apnea-hypopnea index
Time frame: at inclusion
index of periodic leg movements
Time frame: at inclusion
characterization of the chronotype
by using questionnaire MCTQ
Time frame: at inclusion
characterization of the patient's psychiatric state
using questionnaires: MADRS, YMRS, QIDS-SR, MATHYS and GAD-7
Time frame: at inclusion
Sleep onset and offset
assessed with activity with actigraphy
Time frame: at inclusion
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.