Description du projet
Nouvelle approche thérapeutique du trouble dépressif majeur
Le trouble dépressif majeur (TDM) est un problème de santé publique croissant en Europe qui entraîne un lourd fardeau économique. Les rechutes sont fréquentes car les réponses individuelles aux traitements varient considérablement. Le manque de compréhension des facteurs à l’origine des résultats variables des traitements rend impossible toute amélioration d’un régime de traitement approprié. Le TDM est généralement considéré comme un trouble hétérogène. Le projet SUBTREAT, financé par le CER, considère le sous-type comme la clé pour relier l’hétérogénéité étiologique et de l’effet du traitement. Il décomposera les résultats hétérogènes du traitement du TDM en sous‑types plus étroitement définis ayant des étiologies divergentes. SUBTREAT utilisera des approches avancées de science des données pour identifier de nouveaux sous‑types, déterminer les causes divergentes et développer un nouvel algorithme de prédiction des résultats du traitement.
Objectif
Major depressive disorder (MDD) is a leading contributor to disability and suicide. It is the most costly brain disorder in Europe. Although multiple treatments are of proven efficacy, individual responses to treatments vary considerably and MDD recurrence is common. There is considerable motivation to improve treatment regimen for individuals with MDD. However, it has been challenging because of the fundamental lack of understanding about the causes of variable treatment outcomes. MDD is widely accepted as a heterogeneous disorder; yet, most research strategies effectively consider MDD as a single disorder. Progress in understanding the variable treatment response will depend on “patient stratification”, i.e. identifying and accounting for patient heterogeneity when evaluating treatment efficacy.
SUBTREAT proposes a unique direction which considers subtype as the key to link aetiological and treatment effect heterogeneity. Our approach is to break down the heterogeneous treatment outcomes of MDD into more narrowly defined subtypes with divergent aetiologies. Specially, I propose three work packages: 1) dissect treatment heterogeneity across subtypes; a particularly innovative aspect of SUBTREAT is that we will use advanced data science approaches to identify novel subtypes which correlate with differential treatment outcomes; 2) determine divergent causes underlying MDD subtypes; we will comprehensively investigate causes at three levels including genetic and causal epidemiological risk factors, and brain cell types; 3) develop a novel prediction algorithm for treatment outcomes stratified by patient subgroups. SUBTREAT will illuminate the causes of MDD subtypes and the principal patterns of how subtypes contribute to differential long-term treatment outcomes. SUBTREAT findings will promote targeted drug development and treatment optimization for patient subgroups to achieve precision psychiatry.
Champ scientifique
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
17177 Stockholm
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