Towards Personalising Depression Treatment

Posted on:February 25, 2020
Last Updated: March 16, 2020
Time to read: 4 minutes

This article is based on the talk by Prof Brenda Penninx at RCPsychIC 2019.

Depression involves dysregulation of biological stress systems – the HPA axis, the immune system, and the autonomic nervous system – with close interaction between the brain and the periphery [Otte et al. 2016]. The central thought is that depression is related to chronic over-activation of these systems.

  • Depression involves dysregulation of energy-regulating biological systems and evidence shows an association with insulin and leptin resistance and obesity[Milaneschi et al. 2019]. Large genome-wide studies show that the genetic basis for depression is scattered across the genome and provide evidence of a shared genetic basis between depression and some immuno-metabolic dysregulations.[Wray et al. 2018]
  • The heterogeneity of depression creates inconsistent findings across studies and hinders treatment through small effect sizes. The next step in improving depression treatment must be an investment in precision psychiatry, and to look at variation within individuals as opposed to comparison across groups.  (Learn more about the heterogeneity of depression from a digital perspective)
  • Data from the Netherlands Study of Depression and Anxiety (NESDA) were used to identify depressive subtypes within a large cohort. The severity of depression (moderate vs severe) and the nature of symptoms (melancholic vs atypical) was identified as important differentiators. Higher rates of somatic symptoms and more metabolic syndrome in the atypical class suggest the involvement of a metabolic component. [Lamers et al. 2010]
  • Many studies have examined the impact of symptom profile on immuno-metabolic dysregulations, and investigations of atypical depression reveal that cardiometabolic risk factors, cardiovascular risk, fat mass, and inflammatory markers among others, are all upregulated mainly in people with atypical depression.[Lasserre et al. 2017, Glaus et al. 2013, Lasserre et al. 2014, Glaus et al. 2018]
  • The genetic basis appears to be quite different between typical and atypical depression subgroups,[Milaneschi et al. 2016, Milaneschi et al. 2017] and when using polygenic risk scores to investigate specific traits in typical vs atypical depressed people, the former had the highest genetic vulnerability of schizophrenia with the latter for obesity and high BMI.
  • Patient-reported data from STAR*D and CO-MED trials on major depressive disorder suggest that atypical symptoms seem less responsive to antidepressants [Chekroud et al. 2017].
  • The CO-MED study compared the effect of SSRI as monotherapy with combination therapy of SSRI + bupropion [Jha et al. 2017]. People with high inflammatory markers and high BMI at baseline appear to do better with the combination therapy.  The key findings from the CO-MED trial were:

Pre-treatment C-reactive protein (CRP) levels predict differential response to currently available antidepressant treatments.

Depressed patients with low CRP level (<1 mg/L) respond better to SSRI monotherapy whereas those with higher levels respond better to combination of bupropion and SSRI.

A CRP threshold (< or ≥1 mg/L) based treatment assignment, as compared to random treatment allocation, will require treatment of 8.6 depressed patients for 1 additional remission.