Uku Vainik

Mapping the behavioural causes of weight change variability with genetic lottery

Obesity is a heritable chronic condition, costing 2% of GDP worldwide and affecting nearly one billion people. A wide range of treatments, from behavioural recommendations and medications to gastric bypass surgery, have been developed to combat overweight and obesity. However, the interventions have very different effects: some people can easily overcome their weight problem, while others lose just a little weight and quickly regain it despite their best efforts. 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. To advance the field, the project proposes a novel three-step OBECAUSE pipeline consisting of consolidation, genomic causation, and validation. In their research, the project team will use machine learning, health data collected in gene banks, and a mobile app that supports health behaviour.

Results

The project began in summer 2024, so it is too soon to speak of any meaningful results. However, our first effort to find genes associated with weight change in three biobanks across the world resulted in just one gene predicting amount of weight lost across time. This is a lot less than the hundreds of genes that are known for current weight. So either weight change may be a more random process, or perhaps the weight measures were too noisy in the biobanks. My dream is to study the genetics of clinical weight loss and drug trials, where the processes are less chaotic.

Impact

The project team will create a questionnaire that includes a wide range of psychometrically well-measured weight change-related factors in order to identify the bottlenecks in weight loss. Genetic information from the Estonian Biobank will allow the team to identify which factors mentioned in the questionnaire actually influence weight loss. Weight loss programmes created in partnership with the private sector will be used to verify the results, and the users will help confirm whether genetically screened behavioural guidelines for weight loss actually work.

The results may validate new behavioural weight loss techniques that may make weight loss more effective. The project will test whether we can use genetics to find the most potent behavioural change mechanisms. Currently, such genetic approach is used to choose potential drug targets, but a genetic causal inference approach is largely untested for selecting behavioural interventions.