Treatment of schizophrenia and bipolar disorder is hallmarked by a low response rate of approximately 50% to current treatment regimes. To compound matters, response is typically observed for only a subset of symptoms. In schizophrenia, negative and cognitive symptoms are particularly difficult to treat and frequently persist leading to chronic and often life-long impairments. Currently, there are no means to identify which of the 50% of patients do not respond to antipsychotic medication and available clinical algorithms offer little guidance for personalized treatment beyond sequential tryouts of drugs that are switched when response is insufficient, a procedure that is time-consuming, expensive, and often frustrating for client and therapist alike, reducing adherence. Further contributors to limited adherence in patients are the often serious side effect profile of many psychotropic drugs and the stigma associated with mental illness, which reduces all classes of help-seeking behavior but perhaps especially prominently drug compliance.
Second generation antipsychotics show particularly strong metabolic side-effects including weight gain, diabetes mellitus and an atherogenic lipid profile, which have been linked to increased patient morbidity and mortality, besides the resulting lack of medication compliance. To complicate matters, several lines of evidence support that mechanisms underlying the development of side-effects and treatment response are shared, since it has been observed clinically that an increase in weight often coincides with an improvement of symptoms. In the same vein, molecular studies suggest that early response of the metabolic system to antipsychotic treatment is linked to the likelihood of subsequent relapse (Schwarz et al. 2012), providing further evidence for the link between metabolic processes and clinical course. This demonstrates the potential of biological approaches to untangle a given patients response/side-effect balance for different antipsychotic medications and obtain more personalized treatment algorithms with improved clinical outcomes.
A meta-analysis found that if persistence of ADHD was defined as the maintenance of full diagnostic status, then the rate of persistence by age 25 years was ~15%. When cases were included that were consistent with the DSM-IV definition of ADHD in partial remission, the rate of persistence was much higher at ~65%. It is unclear as to why some children will show full remission whilst others will continue to present with full or partial symptoms of ADHD in adulthood (Faraone, Biederman, and Mick 2006).
Since ADHD that persists into adult age is the more severe chronic form of the disorder, neuroimaging and genetic markers of persistence would enable to develop targeted diagnostic procedures and treatment programs in adolescence.