# Body Mass Index as an Example of a Negative Confounder: Evidence and Solutions

## TL;DR

This paper shows how body mass index (BMI) can hide true health effects due to a type of bias called negative confounding.

## Contribution

The study demonstrates how Mendelian randomization can systematically detect negative confounders like BMI.

## Key findings

- BMI is a potential negative confounder for apolipoprotein B and total testosterone in men.
- BMI may obscure effects of harmful exposures like low-density lipoprotein cholesterol and cholesterol in both sexes.
- Sex-specific differences in BMI's confounding effects were identified using genetic data.

## Abstract

Background: Adequate control for confounding is key to many observational study designs. Confounders are often identified based on subject matter knowledge from empirical investigations. Negative confounders, which typically generate type 2 error, i.e., false nulls, can be elusive. Such confounders can be identified comprehensively by using Mendelian randomization (MR) to search the wealth of publicly available data systematically. Here, to demonstrate the concept, we examined whether a common positive confounder, body mass index (BMI), is also a negative confounder of any common physiological exposures on health outcomes, overall and specifically by sex. Methods: We used an MR study, based on the largest overall and sex-specific genome-wide association studies of BMI (i.e., from the Genetic Investigation of ANthropometric Traits and the UK Biobank) and of relevant exposures likely affected by BMI, to assess, overall and sex-specifically, whether BMI is a negative confounder potentially obscuring effects of harmful physiological exposures. Inverse variance weighting was the main method. We assessed sex differences using a z-test. Results: BMI was a potential negative confounder for apolipoprotein B and total testosterone in men, and for both sexes regarding low-density lipoprotein cholesterol, choline, linoleic acid, polyunsaturated fatty acids, and cholesterol. Conclusions: Using BMI as an illustrative example, we demonstrate that negative confounding is an easily overlooked bias. Given negative confounding is not always obvious or known, using MR systematically to identify potential negative confounders in relevant studies may be helpful.