Psychiatric disorders are complex and give rise to patient groups with diverse clinical features. For example, two patients with similar symptoms can have different underlying pathologies, while identical twins can have different symptoms. To unravel the causes of this heterogeneity, it is important to develop better clinical definitions based on detailed clinical data sets and quantifiable biological information on a given disorder, rather than a diagnosis based on comparison with healthy controls.
New biomarkers of psychiatric disorders will prove important, not only for diagnosis and determining therapy, but also for the identification of disease onset, progression, remission and relapse5. Many of the technologies needed to determine molecular differences between healthy and diseased individuals are complex and thus problematic to use in large patient cohorts. Advances in instrumentation, in addition to strong bioinformatics support, are needed in several disciplines.
The first is genetics. Faster and more accurate gene-sequencing technologies will continue to drive advances in the field. These technologies will eventually allow more affordable, in-depth genome scans.
The second is transcriptomics. It has been challenging to determine differences in gene-expression patterns between diseased and healthy states, because DNA microarrays and other methods lack reproducibility and adequate signal intensity. With more sensitive and accurate technologies, researchers will be better able to identify disease modified regions of the genome.
The third is proteomics. Proteins are key players in physiology and pathophysiology; however, realizing comprehensive analyses of protein biomarkers of psychiatric disorders has been a daunting task. New developments in detection, analysis and mass-spectrometry techniques are likely to resolve many of the current challenges. The fourth is metabolomics. The metabolic profile of a patient reflects the interplay of ongoing gene–environment interactions6. Such profiles can serve as key indicators of possible pathophysiological perturbations or reactions to a drug. The metabolome of an individual is complex and heterogeneous with regard to its molecular composition.
The fifth is brain imaging. Evidence from nuclear magnetic resonance (NMR)-based neuroimaging indicates that morphological changes in specific brain areas can be used as predictors of responses to therapy. Electroencephalography (EEG) readings taken during sleep have already yielded information on biomarker candidates. To realize their benefits, imaging technologies must be made available to more patients, which will require major investments in instrumentation and training.
The sixth is neuroendocrinology. For example, measurements of stress hormones in patients with depression, have important clinical prognostic value. However, progress will depend on the application of new proteomic and metabolomic approaches.
Another major goal in the search for biomarkers is to discover signalling pathways that can be targeted by drugs. For this, scientists need direct access to small-molecule libraries and high-throughput analysis resources so that they can screen their targets under ideal conditions.