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OBECAUSE

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Mapping the behavioural causes of weight change variability with genetic lottery

Obesity is a heritable chronic condition, costing 2% GDP worldwide. Many obesity treatments - behavioural, pharmacological, and surgical have been developed. Intriguingly, people’s responses to treatments vary widely - some may lo... ver más

31/05/2029

UNIVERSITY OF TART...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TARTU ULIKOOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-06-01

ERC-2023-STG: ERC STARTING GRANTS Scope:Objectives

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UNIVERSITY OF TARTU

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Duración del proyecto: 59 meses Fecha Inicio: 2024-06-01
Fecha Fin: 2029-05-31

Líder del proyecto

TARTU ULIKOOL No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Obesity is a heritable chronic condition, costing 2% GDP worldwide. Many obesity treatments - behavioural, pharmacological, and surgical have been developed. Intriguingly, people’s responses to treatments vary widely - some may lose a lot of weight whereas others may even gain weight! To predict such weight change variability, close to 200 measures have been proposed in the past. The measures can be organised into a PEBBL framework across five domains – Psychosocial, Environment, Behavioural, Biology, and Life quality. Still, there are too many measures to be used as predictors or intervention targets. To move the field forward, we propose a novel 3-step OBECAUSE pipeline consisting of consolidation, genomic causation, and validation. 1) In consolidation, we will use machine learning to find best-predicting PEBBL measures in several large-scale weight loss datasets. The PEBBL measures will be integrated into a new PEBBL short questionnaire with wide coverage and good psychometric properties. The questionnaire will be then distributed to all participants of Estonian Biobank to study the genomics of PEBBL. 2) For genomic causation, we will detect genetic variants behind PEBBL measures and weight change. Knowing these variants enables discovering additions to the PEBBL framework through genetic correlations and functional mapping. Importantly, as genetic variants are randomised through genetic lottery, they enable systematic causal mapping of PEBBL measures that have causal effects on weight change. 3) For validation, these causal measures will be used as inputs to design an OBECAUSE toolbox of weight loss interventions. The value of these interventions will be tested in a commercial weight loss app. In summary, the OBECAUSE pipeline of narrowing scattered associations down to potential causal mechanisms with machine learning and genomic causal inference will set a new standard for the behavioural health sciences allowing for quicker discovery of intervention targets.

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