Body mass index: Has epidemiology started to break down causal contributions to health and disease?

OBJECTIVES

To review progress in understanding the methods and results concerning the causal contribution of body mass index (BMI) to health and disease.

METHODS

In the context of conventional evidence focused on the relationship between BMI and health, this review considers current literature on the common, population-based, genetic contribution to BMI and how this has fed into the developing field of applied epidemiology.

RESULTS

Technological and analytical developments have driven considerable success in identifying genetic variants relevant to BMI. This has enabled the implementation of Mendelian randomization to address questions of causality. The product of this work has been the implication of BMI as a causal agent in a host of health outcomes. Further breakdown of causal pathways by integration with other "omics" technologies promises to deliver additional benefit.

CONCLUSIONS

Gaps remain in our understanding of BMI as a risk factor for health and disease, and while promising, applied genetic epidemiology should be considered alongside alternative methods for assessing the impact of BMI on health. Potential limitations, relating to inappropriate or nonspecific measures of obesity and the improper use of genetic instruments, will need to be explored and incorporated into future research aiming to dissect BMI as a risk factor.

An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors

The brain age gap (BAG) has been shown to capture accelerated brain aging patterns and might serve as a biomarker for several neurological diseases. Moreover, it was also shown that it captures other biological information related to modifiable cardiovascular risk factors. Previous studies have explored statistical relationships between the BAG and cardiovascular risk factors. However, none of those studies explored causal relationships between the BAG and cardiovascular risk factors. In this work, we employ causal structure discovery techniques and define a Bayesian network to model the assumed causal relationships between the BAG, estimated using morphometric T1-weighted magnetic resonance imaging brain features from 2025 adults, and several cardiovascular risk factors. This setup allows us to not only assess observed conditional probability distributions of the BAG given cardiovascular risk factors, but also to isolate the causal effect of each cardiovascular risk factor on BAG using causal inference. Results demonstrate the feasibility of the proposed causal analysis approach by illustrating intuitive causal relationships between variables. For example, body-mass-index, waist-to-hip ratio, smoking, and alcohol consumption were found to impact the BAG, with the greatest impact for obesity markers resulting in higher chances of developing accelerated brain aging. Moreover, the findings show that causal effects differ from correlational effects, demonstrating the importance of accounting for variable relationships and confounders when evaluating the information captured by a biomarker. Our work demonstrates the feasibility and advantages of using causal analyses instead of purely correlation-based and univariate statistical analyses in the context of brain aging and related problems.