Depression gets personal

Evidence from studies of the different pathophysiological dysregulations in depression show that there is huge heterogeneity across this condition. This may produce inconsistent findings in studies, and limit the efficacy of treatments in heterogeneous groups of patients. We therefore need to find ways to cluster more heterogenous groups of patients on the basis of their symptoms and neurobiology – which is the focus of precision psychiatry, leading to personalized medicine. This fascinating approach was discussed by Prof Brenda Pennix, The Netherlands in this plenary lecture.

Deep phenotyping approach

In The Netherlands Study of Depression and Anxiety,1 research has concentrated on a deep-phenotyping approach – looking at patients’ neurobiology and detailed symptom patterns. Latent class analysis of symptoms in 818 patients with MDD revealed three symptom clusters, these were categorized by the researchers as: 29% of patients with moderate symptoms, 46% with severe ‘typical’ (melancholic) symptoms (including weight loss, suicidality and early waking) and 25% of patients with severe ‘atypical’ symptoms (weight gain and increased appetite, hypersomnia and energy loss).2

Patients with different symptom clusters also showed different physiological responses and gene-expression profiles

The two severe-symptom groups not only differed in their symptomatology, but also in their pathophysiological features. Only the ‘typical’ group showed an increased cortisol response on waking, whereas the ‘atypical’ group showed clear increases in inflammatory markers.3 These differences were also reflected in differences in gene-expression profiles and in proteomics in the two groups. The atypical group – now also termed ‘immuno-metabolic depression’ (IMD) showed a pattern of gene expression and protein production indicating higher leptin and insulin resistance and up-regulation of inflammatory markers.4,5

Genome-wide data on genetic risk factors have also demonstrated the differentiation of these two groups of patients. Those in the ‘typical’ group have a high genetic vulnerability for other psychiatric conditions such as schizophrenia. On the other hand, those categorized as in the group with IMD show a genetic vulnerability for metabolic dysregulation, such as a high BMI.6


Stratified approaches to treatment

Recent studies indicate that features of IMD are predictive of a worse treatment response to antidepressants. In the STAR*D trial, antidepressants were effective in treating the core symptoms of MDD, but were less effective against these atypical symptoms.7

Anti-inflammatory treatments show some efficacy in the treatment of some symptoms of depression.8 Other interventions such as exercise can also help reduce inflammation, and were most effective in those patients with features of IMD.9 Similarly, food-related behavioural activation was effective in managing the atypical energy-related symptoms of MDD, but not the core symptoms.10

Immunometabolic depression may be more responsive to anti-inflammatory and lifestyle interventions than conventional antidepressants

This opens up the possibility that more homogenous subgroups of patients may be identified who would respond better to anti-inflammatory and lifestyle interventions than to conventional antidepressant therapies. This possibility is currently under active research. Moreover, IMD will not be the only relevant subtype of MDD, so we need to extend the scale of this research in deep phenotyping, to identify other groups of patients to guide and optimise the treatment of this complex disorder.

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.

  2. Lamers F et al. J Clin Psychiatry 2010;71(12):1582–9.
  3. Lamers F et al. Mol Psychiatry 2013;18(6):692–9.
  4. de Kluiver H et al. Transl Psychiatry 2019;9, 193
  5. Lamers F et al. Transl Psychiatry. 2016 Jul; 6(7): e851.
  6. Milaneschi Y et al. Mol Psychiatry 2016;21(4):516–22.
  7. Chekroud AM et al. JAMA Psychiatry 2017;74(4):370–78.
  8. Wittenberg GM et al. Mol Psychiatry 2020;25(6):1275–85.
  9. Rethorst CD et al. Mol Psychiatry 2013;18(10):1119–24.
  10. Bot M et al. JAMA 2019;321(9):858–68.
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