DOI: 10.25934/pr00009373
title: Cognitive factors as predictors of treatment outcomes in patients with Schizophrenia

description: Schizophrenia is a disorder that affects approximately 24 million people worldwide (approximately 0.30% of the population), with age of onset typically in late adolescence and early adulthood. It is associated with considerable functional impairment, such as ability to work and maintain relationships, and early mortality compared to the rest of the population.

Negative symptoms, such as social withdrawal, decreased motivation and reduction in pleasure, are a core component of schizophrenia and are prominent in up to 60% of those with the disorder. Negative symptoms are consistently linked to worse functional outcomes and higher burden of illness (i.e. the burden on society and the health service as well as the burden on the individual in terms of quality of life etc.), and, unlike positive symptoms, such as hyperactivity, hallucinations and delusions, are inadequately treated by current pharmacological approaches (i.e., drug treatments). Similarly, cognitive impairments (e.g., problems with processing speed, attention, working memory, learning, and problem solving, are present in approximately 80% of individuals with schizophrenia) are associated with approximately 20-60% of functional outcomes in schizophrenia. All approved pharmacologic interventions for schizophrenia are efficacious in treating positive symptoms, but have little impact on treating negative symptoms or cognitive dysfunction. Although negative symptoms and cognitive dysfunction are considered separate domains in schizophrenia, evidence supports a bidirectional relationship between these domains and suggests that improving one domain may improve the other. Lurasidone is one atypical antipsychotic that has demonstrated pro-cognitive effects in schizophrenia and some efficacy in reducing negative symptoms compared to placebo, although no more than quetiapine (another atypical antipsychotic drug). Other compounds have been developed to specifically target Cognitive Impairment Associated with Schizophrenia (CIAS) or negative symptoms, such as phosphodiesterase (PDE) inhibitors and histamine H3 receptor antagonists; however, placebo-controlled trials thus far do not indicate that they improve cognition or negative symptoms.

Pharmacotherapy for the treatment of mental illness is often a trial and error process; this is particularly true in the case of schizophrenia. In the case of negative symptoms or cognitive dysfunction, understanding the relationship between pretreatment cognitive profiles, and cognitive and symptom-related outcomes across different treatments for (as well as placebo and traditional antipsychotics) may provide greater insight that can pave the way for identifying cognitive or clinical profiles that show a greater (or lesser) benefit from these novel interventions. The aim of the proposed study is to address these questions by employing a robust statistical analysis of cognitive and clinical data from a large scale placebo-controlled trial. Our analytical approach will blend conventional methodologies with select elements of machine learning.

Considering the profound and often debilitating nature of negative symptoms and cognitive dysfunction on quality of life and functional outcomes in schizophrenia, the implications of these proposed analyses are vast. A refined understanding of treatment responsiveness can markedly improve treatment outcomes among individuals with schizophrenia. Given the prominence of negative symptoms and cognitive dysfunction in schizophrenia, precision approaches such as the one outlined in this proposal hold considerable potential for positively influencing large numbers of individuals living with this disorder.