Chronobiological parameters as predictors of early treatment response in major depression Journal Articles uri icon

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abstract

  • BACKGROUND: Alterations in circadian system organization have been related to major depressive disorder manifestations. This study aimed to evaluate chronobiological parameters, such as sleep, levels of 6-sulfatoxymelatonin, and others derived from actimetry as potential predictors of adequate treatment response in MDD. METHODS: 98 adult women with confirmed diagnosis of MDD were included. Participants completed standard questionnaires (Hamilton Depression Rating Scale - HAM-D; Munich Chronotype Questionnaire - MCTQ) at baseline and after 4 weeks of treatment. Urinary samples for assessing 6-sulfatoxymelatonin were collected on the day before and immediately after pharmacological treatment administration, and 28 continuous days of actigraphy data were collected during the protocol. Participants were classified into Responder (R) or Non-responder (NR) to antidepressant treatment in 4 weeks (early responder), which was characterized by a ≥50 % decrease in the HAM-D score. RESULTS: The following biological rhythms variables significantly predicted a better treatment response in a model controlling for age, sex, and previous treatments: higher levels of activity (M10 - average activity in the 10 most active hours within the 24 h-day) and an earlier center of the 10 most active hours (M10c), as well as lower intradaily variability (IV) of light exposure. Sleep parameters and 6-sulfatoxymelatonin levels did not associate with treatment response prediction. LIMITATION: Actimetry data were not assessed before changing in the treatment plan. CONCLUSION: Different patterns in activity and light exposure might be linked to early antidepressant response.

authors

  • Xavier, Nicóli B
  • Abreu, Ana Carolina VO
  • Amando, Guilherme Rodriguez
  • Steibel, Eduardo Giordani
  • Pilz, Luísa K
  • Freitas, Juliana Jury
  • da Silveira Cruz-Machado, Sanseray
  • Markus, Regina P
  • Frey, Benicio
  • Hidalgo, Maria Paz

publication date

  • February 2023