Relação entre alterações em parâmetros do sono REM e sintomas depressivos em uma amostra populacional (Episono – 4ª edição) - Kishi, Tamires Tiemi [UNIFESP] (2024)
Introduction: Studies indicate that alterations in sleep parameters can be a predictive factor for various psychiatric pathologies such as depression, anxiety, posttraumatic stress disorder and the use of drugs of abuse. Individuals with depressive symptoms exhibit various changes in REM sleep parameters. For this reason, alterations in REM sleep parameters may reflect distinct symptomatology in patients with depression. Few epidemiological studies have analyzed changes in REM sleep density in depressed patients. It is therefore important to investigate the distribution and variation of this parameter in a population sample. Objective: This study analyzed REM sleeprelated changes in people with symptoms of depression in a populationbased sample from the city of São Paulo (EPISONO 4th edition). It explored the characteristics of REM sleep density and the possible differences between the methods used to measure this parameter. It also assessed the use of antidepressants in the city of São Paulo. Methods: This study was carried out in two stages: The first addressed different measures of REM sleep density in the population of the city of São Paulo, while the second stage evaluated the relationship of REM sleep parameters with symptoms of depression. A scoping review was conducted with the aim of mapping the literature to identify and describe the REM sleep density scoring methods applied. Subsequently, generalized linear models (GzLM) were used to analyze possible associations between changes in REM sleep parameters and depressive symptoms in the sample of the Epidemiological Study of Sleep in the City of São Paulo (4th edition). Results: The scoping review showed that the predominant methods of measuring REM sleep density are by means of relative frequencies (events/time, 36 analyses 56.25%, 34 articles 59.65%). The most common measure of REM sleep estimation is the frequency of REM events (32 analyses 50.00%, 29 articles 50.88%) and the most common denominator was total REM sleep time (29 analyses 45.31%, 29 articles 50.88%). In the second stage, population data showed that 23.3% of the population selfreported having a depressive disorder. However, only 17.5% had been assessed by a specialist doctor to obtain a diagnosis. As for depressive symptoms, 24.6% of the general population has mild to moderate depressive symptoms (regardless of diagnosis). Of these, 11.8% had moderate to severe symptoms. Our results corroborate the evidence found in studies associating depressive disorder with increased REM pressure (which includes decreased REM sleep latency, increased REM sleep duration and increased REM sleep density) in patients with depression compared to healthy controls. Conclusion: Anomalies in REM sleep parameters in depressed patients have been replicated and linked to pathophysiology and treatment, mainly decreased REM sleep latency and increased REM sleep density. REM sleep density is currently the most widely investigated measure of REM sleep as a possible sensitive and reliable indicator of the clinical state of depression, pointing to it as a risk factor for the onset of depression and a predictor of the patient's response to treatment. The studies show that there is no consistency in the methods used to calculate REM sleep density in the literature. Methodological inconsistencies and omissions between studies limit the comparability and clinical applicability of REM sleep density. It is important that future guidelines describe, discuss and include specific methodologies for scoring REM sleep density, so that it can be implemented in a consensual way in clinical and research services. By increasing the understanding of the mechanisms of regulation and functioning of the REM sleep stage, we can understand better the neurobiological mechanisms of depressive disorder and how it is related to sleep.