Highlights from

ECNP 2020

ECNP Congress Virtual

Virtual 12 - 15 September 2020

Personalised medicine in depression: a realistic way forward?

Depression is a heterogeneous disease with a complex underlying pathophysiology. For personalised medicine, it is necessary to identify clusters of more homogeneous patient groups. Based on data from the large NESDA cohort, researchers have identified 2 subpopulations according to symptomatology. Whether the underlying neurobiological basis differs between the 2 groups is yet to be identified in larger follow-up studies.

Depression is a complex disease in which a plethora of brain abnormalities are involved which can be at a structural, connectivity, and functional activation level, as Prof. Brenda Penninx (Amsterdam UMC, the Netherlands) explained [1,2,3]. Depression also involves dysregulated stress systems and disrupted energy regulation signalling, which may explain an association between depression and obesity [4,5]. Recent genome-wide analysis studies have provided insights into the genetic basis of depression [6]. When we combine all this data, there is more and more knowledge about the pathophysiology of depression. Combined evidence for the pathophysiology is now available based on numerous studies on:

  • genome-wide single-nucleotide polymorphism (SNPs);
  • smaller hippocampal volume and thinner cortical grey matter;
  • higher cortisol;
  • higher hsCRP and higher IL-6;
  • insulin resistance;
  • higher leptin;
  • metabolic syndrome; and
  • obesity [1,7].

Nonetheless, the effect sizes are small and the heterogeneity across the studies is large, as a result of methodological differences in trials. The heterogeneity of the disease itself also plays a role.

According to Prof. Penninx, the crucial next step is to gain more evidence on how to cluster more homogeneous patient groups –either based on symptoms or on neurobiology– which is the focus of personalised medicine. This raises the question of where to start; from known subtypes or data-based typing? Ideally, these results should be combined to gain the most convincing evidence.

A good example of a study aiming to better understand the heterogeneity of depression is NESDA, a naturalistic cohort study with 3,348 participants (66% female; age 18-65 years). Participants are recruited from the community, general practice, and mental health care and include healthy individuals, people with depression, people with anxiety disorders, and siblings of those people.

Data for the NESDA study was collected at baseline, and after 1, 2, 4, 6, and 9 years. In a NESDA substudy, 3 classes were characterised based on demographic, clinical psychiatric, psychosocial, and physical health descriptors: those with moderate depression (29.1%), those with severe typical depression (46.3%), and those with severe atypical depression (24.6%) [7]. Although those with severe atypical depression were more often female, they did not differ from the severe typical patients in age, duration, functioning, and comorbid anxieties. Based on symptomatology, patients were divided into 2 groups with “typical” or “atypical” depression (see Table) [7].

Table: Evidence for depression subtypes [7]

Tabel-Pagina9

Interestingly, there were differences in cortisol and inflammatory markers between the groups. Other projects also confirmed these findings. There seems to be a different neurobiological basis for major depression disorder subtypes, and this should be followed up in larger studies (see Table). Moreover, these patients have an overlapping genetic basis with obesity and metabolic dysregulation genes [8]. This has implications for treatment choices and may necessitate anti-inflammatory treatment such as COX2 inhibitors.

Prof. Penninx concluded by stating that precision psychiatry is not easy but is the route of choice. “Large-scale deep-phenotyping research is needed to gain a better understanding of the heterogeneity of depression. In our research line, immunometabolic depression is characterised by atypical neurovegetative symptoms and immunometabolic dysregulation, and we hope to show that differentiating immunometabolic depression may be useful in clinical practice.” However, it is not the only relevant distinction; more research lines can further identify subgroups of patients. To unpack the heterogeneity of depression, it is necessary to extend the scale of research, move beyond one marker, and use deep-phenotyping approaches.

Keywords: Depression, Personalised medicine, NESDA

Top image: @ iStockPhoto: CIPhotos

The content and interpretation of these conference highlights are the views and comments of the speakers/authors.