OBECAUSE
Financiado
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...
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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Duración del proyecto: 59 meses
Fecha Inicio: 2024-06-01
Fecha Fin: 2029-05-31
Fecha Fin: 2029-05-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-06-01
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2023-STG:
ERC STARTING GRANTS
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
TARTU ULIKOOL
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5