In reality, symptoms of “depression” are not independent; and some are more important than others
A first step is to accept that traditional DSM subtypes are not generally helpful. For example, the psychotic subtype seems to be the only one that has clear implications for therapy. Of greater utility might be an approach involving dimensions such as cognition, sleep and energy. We should also consider the limitations of measures such as the MADRS and HAM-D and delve more deeply into other outcomes. Computerized testing offers opportunities in this regard.
Ideally, we would be able to link clinical phenotypes to biology and biomarkers. There was some interest in C-reactive protein (CRP) as a measure of inflammation, but a general view that we have a long way to go before finding biomarkers that are helpful in MDD. There are clear concerns about the feasibility and interpretation of investigations such as functional MRI, though ambulatory EEG might be more practical.
Passive tracking technology on smartphones has ecological validity and may have a place, but enthusiasm on the part of some is matched by skepticism on the part of others. However, it would certainly help if we could extend our knowledge about how patients are functioning beyond the few minutes we spend with them in the clinic.
Kuhn’s somewhat humbling worldview was adopted by those LINF faculty members asked to consider the heterogeneity of patients diagnosed with schizophrenia. We work with the imperfect understanding we have, and acknowledge that we can and will do better. Several developments may help.
First, a shift in emphasis from categorical clinical subtypes to dimensional assessment – an approach which DSM-5 and ICD-11 seem to have in common -- may aid evaluation of patients and our understanding of the heterogeneity of psychotic disorders. Secondly, the availability of large amounts of longitudinal data derived from passive digital monitoring of activity and circadian rhythms should improve our ability to understand what is happening to our patients in the real world. This gives rise to the concept of digital phenotyping. But it may be that we can live with using dimensional and categorical classifications in parallel, with the long-term aim of integration.
A third positive point is that we have an increasing number of tools – including artificial intelligence that can be applied to “big data”, machine learning and predictive modeling – to complement the more traditional fields of genomics, proteomics and the identification and validation of biomarkers.4
Large longitudinal datasets give rise to the concept of digital phenotyping
Across psychiatry in general, there is recognition that different biological pathways can lead to similar symptoms. Hence the importance of work showing that biomarker-based categories (or biotypes) are better able to identify homogeneous subgroups than traditional classifications such as schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis.5
Such findings lend support to the National Institute of Mental Health’s RDoC (Research Domain Criteria) initiative which aims to encourage research based on classifying patients by behavioral dimensions or brain biology rather than conventional diagnostic categories. For example, if we can take patients with ICD or DSM diagnoses of bipolar disorder and schizophrenia and find common biological underpinnings, that would be a helpful guide to treatment. However, a potential hurdle in the way of such approaches is the need to convince regulators that agents should be used outside traditional indications.
Bzdok and Meyer-Lindenberg4 recently argued that combining machine learning analytic techniques with the wealth of data available from consortia and repositories does indeed have the potential to redefine major disorders on the basis of biology. Subgrouping patients based on such data should promote early detection of disease and the tailoring of agents and doses to individual circumstances.
In diagnosing schizophrenia, we may have to live with imperfection. But we can probably do better than the concepts we use at present.
Another initiative is that of the PRISM group which aims to identify potential causes of individual symptoms, such as social withdrawal, across AD, schizophrenia and depression.6 This would again be with the aim of identifying a shared underlying neurobiology.
A further potential example of biotyping arises if it can be established that inflammatory processes are a major factor in the development of mental disorders in at least a proportion of patients with different traditional diagnoses. The same would be true if oxidative stress proves to be a contributory factor across indications. In both instances, a common etiology would have clear implications for a common approach to treatment.
Thinking of cultural context, it may be that much of the marked difference in psychosis between northern and southern Europe, and between urban and rural areas, is due to the consumption of cannabis with a high TCH content. If this hypothesis proves correct, then the subtype of patients with cannabis-induced psychosis is again a distinct and potentially relatively homogeneous clinical entity.