Causal mediation analysis with machine learning to understand the increased risks of depression in overweight and obese population
Approved Research ID: 99946
Approval date: March 29th 2023
Worldwide obesity rates have almost tripled since 1975. At the same time, rates of depression have steadily risen. Previous studies have established a link between these two conditions, showing that the prevalence of depression in people with obesity is twice as high as in people of a healthy weight. In terms of treatment, some medications used to reduce appetite for people with obesity have been implicated in the etiology of depressive symptoms. On the other hand, weight gain is a side effect of almost every antidepressant. You take the medication to reduce depression, but it causes you to gain weight which worsens your depression. The vicious cycle of depression and obesity remains largely unknown.
In this project, we aim at unveiling the causal mechanisms of increased risks of depression in overweight and obese population through causal mediation analysis with machine learning. The specific research questions to be answered include:
(1) Is the marriage status (Single, married, divorced, or widowed) associated with the increased risks of depression in overweight or obese people?
(2) Is there a potential association between the usage of diet pills for weight loss and the increased risks of depression in the population?
(3) What are the biochemical, metabolic, environmental, or genetic factors that mediate the increased risks of depression in overweight or obese people?