One disease or many – the complexity of major depressive disorder

Should we consider major depressive disorder as a single disease entity of a collection of disorders, with different prognoses, risk factors and treatments? This was the question posed by Dr Chiara Fabbri, UK and Dr Yuri Milaneschi, The Netherlands, in a brainstorming session. They presented data on some of the atypical symptom patterns that may indicate cardiometabolic co-morbidities, which could therefore guide treatment in these groups of patients.

Heterogeneity of major depressive disorder

Major depressive disorder (MDD) is a highly heterogeneous condition, to the extent that Dr Fabbri suggested that we should talk about it as disorders – plural not singular. It shows heterogeneity in its clinical manifestations,1 in response to treatments2 and in biological measures.3 Clinically defined subtypes can be based on the DSM 5 specifiers – such as melancholic features, anxious distress and atypical features – or on data from clinical trials. This latter identifies a huge variety of symptom profiles that do not align with the DSM specifiers, many of which are experienced by few individuals.4

Biological heterogeneity allows a link to be made between symptomology and genetic findings. Thus, data from the Netherlands Study of Depression and Anxiety5 and the UK Biobank6 show that in the subset of primarily somatic, neurovegetative symptoms, patients with atypical symptoms (e.g hypersomnia, hyperphagia) there is a genetic overlap with immuno-metabolic risk factors such as high BMI, triglycerides and C-reactive protein. On the other hand, those patients with typical neurovegetative symptomology show a genetic overlap with schizophrenia and alcohol consumption. This biological characterisation is useful in forming prognoses, as there is a risk of co-morbidities in patients with atypical symptoms. Moreover, there may be a link between atypical symptoms, metabolic abnormalities and treatment-resistant depression in a subgroup of patients.7 In addition, the genetic links to immune and metabolic signs means that drugs effective in treating metabolic abnormalities or inflammatory conditions are also under examination in these forms of depression.

Atypical neurovegetative symptoms of MDD show a genetic overlap with immuno-metabolic risk factors


Cardiovascular risk in subtypes of depression

Dr Milaneschi also discussed an immuno-metabolic dimension of depression. This dimension is associated with inflammation, and disruption of neuroendocrine regulators (leptin and insulin) and biomolecules (dyslipidaemia) related to energy metabolism. These changes map more consistently to the atypical energy-related symptoms of MDD (e.g. weight gain, fatigue).8 In a study of patients with MDD, those who were identified as having a high level of symptoms on the immune-metabolic dimension also showed an association with well known cardiovascular risk factors (e.g. higher visceral adipose tissue, lower HDL levels), compared with the overall depression dimension, which was associated with lower levels of markers of cardiometabolic risk.

It may be helpful to screen patients with atypical symptoms of MDD for cardiovascular risk factors

Dr Milaneschi proposed that those patients with immuno-metabolic forms of depression may be at higher cardiometabolic risk. Given the mortality and morbidity associated with these risk factors, it may be helpful to screen patients with atypical symptoms of MDD for cardiovascular risk. In addition, such patients may benefit from specific treatments (including lifestyle changes) that target underlying metabolic dysregulation.

Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.

  1. Østergaard SD et al. Acta Psych Scand 2011; 124:495–6
  2. Kessler RC et al. Epidemiol Psychiatr Sci 2017;26(1): 22–36.
  3. Lamers F et al. Transl Psychiatry 2016;6(7):e851
  4. Fried EI and Nesse RM. J Affect Disord 2015;172:96–102.
  7. Fabbri C, et al. Prog Neuropsychopharmacol Biol Psychiat 2020;104:110050
  8. Milaneschi Y, et al. Biol Psychiatry 2020; 88(5):369–80
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