Personalized medicine in Depression: hype or hope?

Munich Psychiatry Lecture Series | MPLS

  • Datum: 06.11.2018
  • Uhrzeit: 15:00 - 16:00
  • Vortragende(r): Dr Brenda Penninx
  • Department of Psychiatry | NESDA | VU University Medical Center Amsterdam NL
  • Ort: Max Planck Institute of Psychiatry
  • Raum: Lecture Hall
  • Gastgeber: International Max Planck Research School for Translational Psychiatry (IMPRS-TP)
  • Kontakt: imprs-tp@psych.mpg.de
Personalized medicine in Depression: hype or hope?
Symptom profiles, etiology and course trajectories of Major Depressive Disorder (MDD) are heterogeneous. This heterogeneity is hampering scientific research and is limiting effectiveness of therapeutic interventions. The goal of precision medicine is to tailor medical treatment to the individual characteristics of each patient. In order to implement precision medicine in depression care, it is essential to better understand what the driving factors are behind MDD’s heterogeneity. However, examining MDD’s heterogeneity is no sinecure, and there are various different ways to start such scientific explorations. One could start with data-driven or hypothesis-driven approaches, one could start with a focus on the heterogeneity at the symptom level, the environment level or the pathophysiology level. Ideally, whatever the starting point is should not matter and results of different starting points should synchronize. Using several different analytical approaches, our research group has illustrated the distinction of immunometabolic depression as a potentially useful subtype of depression.

I will mainly present results from from the large-scale, longitudinal Netherlands Study of Depression and Anxiety (NESDA). This study has information of about 3000 respondents with and without MDD who have been followed for up to nine years. Depressive symptoms were derived from the Composite International Diagnostic Interview and the Inventory of Depressive Symptomatology. Associations between symptom classes and individual symptoms with e.g. body mass index (BMI), metabolic syndrome, inflammation (CRP, TNF-a, IL-6), leptin and insulin were determined, adjusted for sociodemographics and lifestyle. We examined to what extent specific clinical and biological indicators usefully discriminate subgroups of patients and whether these predict having a subsequent unfavorable course trajectory. For genetic analyses, we used data from 26,628 samples (59.1% females) from the Psychiatric Genomics Consortium, including 11,837 cases with lifetime MDD and 14,791 controls.

NESDA’s results show accumulating evidence that approximately a quarter of depressed patients fall within a more homogeneous ‘immunometabolic subtype’ that is characterized by a unique symptom, genetic, and neurobiological profile. At the symptom level, evidence indicates that immunometabolic depression is characterized by behavioral symptoms reflecting altered energy intake and utilization (e.g. appetite increase, leaden paralysis, fatigue), symptoms more often seen as atypical features of depression.[1] Latent analyses yielded three symptom classes: severe melancholic (46.3%), severe atypical (24.6%) and moderate severity (29.1%). As compared to other classes, the atypical class had higher BMI (OR=1.13 [95% CI, 1.09-1.17]), more metabolic syndrome (OR=2.17 [95% CI, 1.38-3.42]) and higher CRP and TNF-a. Of all 19 individual depressive symptoms, increased appetite (25.7%) was the most discriminating symptom: it was associated with higher levels of BMI, metabolic syndrome components, waist circumference, CRP and TNF-a.[2] Psychomotor retardation and insomnia also showed a few associations with more immunometabolic dysregulations.[1]

Exploring the more detailed neurobiological level, evidence indicates that immunometabolic depression is also associated with alterations in homeostatic systems that regulate energy (neuroendocrine regulators of energy metabolism including leptin, ghrelin and insulin)[3] and brain circuitries integrating mood and homeostatic regulatory responses. Also at the genetic level, evidence confers that immunometabolic depression is associated with alterations in immuno-inflammatory activation and neuroendocrine regulators of energy metabolism including leptin.[4] When GWA data were used to polygenic risk scores for BMI, CRP and leptin, it was clear that patients presenting with increased appetite in the context of their depression carried a higher polygenetic risk for BMI (OR=1.18, p=1.6e-10), CRP (OR=1.08,p=7.3e-3) and leptin (OR=1.09,p=1.7e-3). These associations were not present for other MDD patients.

Finally, evidence is accumulating that immunometabolic depression may be less responsive to standard antidepressant treatments. The response to for instance SSRIs seems to be lower when depressed patients present with immunometabolic dysregulations.[5] Consequently, there is a need to further explore whether immunometabolic depression responds better to other interventions such as anti-inflammatory agents or interventions targeting lifestyle behavioral changes (e.g. reducing sedentary behavior or unhealthy food intake). In other words, what the most effective treatment strategy is for this subtype of depression should be further explored.

Discussion: There is accumulating evidence that approximately a quarter of depressed patients fall within a more homogeneous ‘immunometabolic subtype’ that is characterized by a unique symptom, genetic, and neurobiological profile. At the symptom level, evidence indicates that immunometabolic depression is characterized by behavioral symptoms reflecting altered energy intake and utilization (e.g. appetite increase, leaden paralysis, fatigue). At the genetic and neurobiological level, evidence exists that immunometabolic depression may be rooted in alterations in homeostatic systems that regulate energy (immuno-inflammatory activation, neuroendocrine regulators of energy metabolism including leptin, ghrelin, and insulin, and gut microbiota) and brain circuitries integrating mood and homeostatic regulatory responses. As there are some indications that immunometabolic depression may be less responsive to standard antidepressant treatments, it should be examined to what extent more specific targeted interventions are more effective for this subgroup of patients. Future research should enable more fully comprehensive characterization and targeting of immunometabolic depression.

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