Improved personalized medicine through machine learning in mental disorders (IMPLEMENT)

Psykoselidelser er alvorlige psykiske sykdommer med tidlig debut og kan ha et kronisk, livslangt forløp. Disse lidelsene fører til et stort trykk på helsevesenet og koster samfunnet omtrent 100 milliarder årlig i Europa alene. Årsakene til psykiske lidelser er ikke fullt ut forstått, og det eksisterer foreløpig ingen objektive verktøy som kan hjelpe til med diagnostisering eller valg av behandlingsmetoder. Hos noen pasienter kan dette føre til perioder med mangelfull og ineffektiv behandling. IMPLEMENT vil utvikle et internasjonalt forskningsnettverk for å identifisere biomarkører for schizofreni. Ved hjelp av store datasett vil IMPLEMENT se nærmere på genetisk risiko, og nevrokognitive utviklingsprosesser. Disse dataene skal sammenlignes med kliniske studier, både konvensjonell psykologisk behandling og innovative behandlingsmetoder. Samlet sett vil IMPLEMENT gi grunnlag for biologisk tilpasset behandling for schizofreni, og imøtekomme et stort og umøtt behov i et område der det foreløpig ikke finnes noen robuste kliniske stratifiseringsverktøy på individnivå.

Psychotic disorders are severe mental illnesses with early onset, frequently chronic course and often lifelong impairment. As a consequence, they cause an enormous healthcare burden, costing close to €100 billion annually in Europe alone. The biology of these illnesses is insufficiently understood and no objective tools exist to aid in diagnosis or treatment selection. This leads to long periods of inadequate and ineffective treatment, significantly limiting the opportunity for achieving more optimal clinical outcomes. To address this, IMPLEMENT will develop a translational research framework that identifies biomarkers for treatment-relevant stratification of the most severe psychotic disorder, schizophrenia. Building on known candidates, IMPLEMENT will use advanced machine learning on high-dimensional multi-OMICS and brain scans to identify illness-associated profiles indexing patient subgroups. Using big data approaches (n > 60,000), IMPLEMENT will explore the impact of genetic risk and neurodevelopmental processes on the formation of biological subgroups and use clinical studies of conventional antipsychotic treatment and innovative treatment approaches to tune subgroup profiles towards clinical utility. The IMPLEMENT framework will incorporate preclinical validation to leverage neurobiological understanding and optimize biological subgroup profiles. The clinical utility of these profiles will be validated in independent clinical samples and prospectively recruited subjects. IMPLEMENT will integrate these efforts with ICT development, to optimize the use of high-dimensional datasets across diverse repositories, to optimally harmonize data for personalized medicine investigations and safeguard patient privacy. Overall, IMPLEMENT will provide the basis for biologically-informed personalized medicine approaches in schizophrenia, addressing an enormous unmet medical need in an area of medicine in which currently no robust clinical stratification tools exist.

Publikasjoner hentet fra Cristin

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

The oxytocin signalling gene pathway contributes to the association between loneliness and cardiometabolic health

The age of violence: Mapping brain age in psychosis and psychopathy

Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition

Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness

White Matter Matters: Unraveling Violence in Psychosis and Psychopathy

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