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

Abstract

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.

Figure 1. Manhattan plot showing body mass index (BMI)-associated variants with loci identified prior to 2015 in blue and novel loci identified by Locke et al. (33) in red.

Novel loci are labelled with the nearest gene, and the y-axis is truncated to allow easier observation of novel associations. This plot is reproduced from Locke et al. (33) with the permission of the authors.

Figure 2. The interplay between increased variance explained and diminishing marginal return as the number of confirmed body mass index (BMI)-associated genetic loci has increased.

The single line represents the cumulative variance explained and the double line the marginal return, calculated as the cumulative variance explained divided by the number of loci (29, 33, 100, 101, 102).

Figure 3. Mendelian randomisation; the use of genetic proxy measures of risk factors to allow causal inference.

(A) In general, a genotype of use to this study is associated with the exposure, is independent of measured or unmeasured confounders and can only influence outcome via the causal effect of the exposure. (B) The presence or absence of association between the BMI associated genotype and disease risk (from existing genomewide association study data sets) give evidence that the BMI is a causal risk factor for disease. (C) Here genotype acts as a proxy measure for an exposure potentially affecting the BMI in a reciprocal analysis. This type of reciprocal analysis allows for a triangulation or network approach to the assessment of the effects of and effects on BMI.

Figure 4. The comparison of observational and Mendelian randomisation derived estimates for blood pressure and ischaemic heart disease.

(A) Linear relationships between body mass index and blood pressure derived from observational and Mendelian randomization analyses. Upper scatter indicates systolic blood pressure and the lower diastolic. Grey areas around the estimated relationships indicate 95%CI for Mendelian randomisation estimates and in black those for observational estimates (plot generated from analysis for (43)). Note that for this analysis the log of body mass index was regressed on sex, age, age squared, log(height), and an age-sex interaction and exponentiated to give an individual’s “relative BMI,” that is, the ratio between his or her actual BMI and that expected for his or her sex, age, and height. (B) Meta-analysis forest plots of observational and instrumental variable estimates of the relationship between ischaemic heart disease and body mass index. Odds ratios are for a 4kg/m² increase in body mass index (plot generated from analysis for (46)).

Figure 5. A two-step Mendelian randomization design applied to intermediate phenotype analysis in body mass index (BMI) and ischaemic heart disease (IHD).

In Step 1 (shown in red), BMI-associated variants are used to estimate the causal effect of BMI on relevant intermediates. In Step 2 (shown in green), variants associated with each of the intermediate traits are used to estimate the causal effect for those traits on IHD.