Genetics linked to BMI differences across socio-economic groups

Exploring the Link Between Genetics, SEP, and BMI

In a recent study published in the International Journal of Obesity, researchers investigated the effect of genetic factors on the differences in the body mass index (BMI) observed in populations with varied socio-economic position (SEP). They found significant BMI differences across educational levels, social classes, and incomes, with genetic factors contributing to these differences, particularly in lower SEP groups.

Brief Communication: Socio-economic differences in body mass index: the contribution of genetic factors. Image Credit: New Africa / Shutterstock

Inverse Relationship Between SEP and BMI

Evidence suggests that SEP and BMI are inversely associated, as observed in Western societies. Genome-wide association and large-scale twin studies show that genetic factors also influence BMI. Two mechanisms have been proposed in literature to explain how genetics may affect SEP differences in BMI. First, genetic variants may simultaneously affect BMI and SEP, enriching polymorphisms in brain regions that control appetite and cognition. Secondly, the impact of genetic factors on BMI may be influenced by SEP-related factors such as income and education. Further, polygenic scores for BMI (PGS-BMI) are shown to interact with SEP in some studies.

However, there is a dearth of studies parallelly investigating the effect of genetic factors on the SEP differences in BMI. Given that previous studies have only explored a single SEP indicator, the present large, population-based cohort study aimed to overcome this limitation by using three SEP indicators. Additionally, researchers explored the role of PGS-BMI in the complex interplay between genetic factors, SEP, and BMI.

Study Design and Data Analysis

In the present investigation, data from Finnish health surveys (1992–2017) with response rates from 65% to 93% were utilized, incorporating three measures of SEP: education, occupational social class, and income quintiles. The study focused on individuals aged 25–70 during the survey. A total of 33,523 participants were included, 53% female. Participants with missing information were excluded. PGS-BMI were derived from a genome-wide association study and adjusted for linkage disequilibrium. About 14% of BMI variance was observed in men and 15% in women. Linear regression models, adjusting for various factors, including age, residence region, and population structure, were employed to analyze associations between SEP indicators and BMI.

Key Findings: Genetic Factors and Socio-Economic Position Impact BMI

Results show that more advantaged SEP (across all indicators) was associated with a lower BMI, with higher gradients evident in women than in men (p<0.00001). BMI difference was highest among individuals with basic and higher tertiary education. For all SEP indicators, gradients were observed in BMI predicted by PGS, with the most significant difference seen for education. Between basic and tertiary education, the difference in PGS-predicted BMI was 0.57 in men and 0.72 in women. Unlike BMI, the associations between PGS-predicted BMI and SEP indicators were comparable in men and women.

In high SEP participants, a lower association was consistently observed in BMI and PGS-predicted BMI. An increase of one unit in PGS-predicted BMI was linked to a greater BMI of 0.85 kg/m² for men and 0.75 kg/m² for women with higher tertiary education. In contrast, the corresponding associations for individuals with basic education were 0.98 kg/m² for men and 1.05 kg/m² for women. The SEP gradients of association between BMI and PGS-predicted BMI were found to be comparable in men and women.

The findings of the present study are consistent with previous studies and are strengthened by the inclusion of a large, population-representative sample with high response rates. Additional strengths include the use of three SEP indicators across different life phases as well as the use of measured BMI and register-based SEP indicators, which help to minimize reporting bias. However, the BMI-PGS employed accounts for 20% of the total genetic BMI variation, and environmental factors may influence the link between SEP and genetic factors. These findings are particularly relevant for regions with comparable BMI levels, such as other European countries. The associations may exhibit greater strength in regions with higher BMI, like the USA, and comparatively weaker connections in regions with lower BMI, such as Japan. Comparative studies in the future could further investigate and validate this hypothesis.

Conclusions and Implications for Future Research and Policy

In summary, genetic factors are found to contribute to SEP-related differences in BMI, with lower SEP categories potentially accumulating genetic variants associated with higher BMI. Additionally, the impact of these genetic factors on BMI is reinforced by low SEP. Studying the impact of genetic factors on SEP-related differences in BMI is crucial for several reasons. Firstly, it helps to reveal the complex link between genetic and environmental influences on body weight, contributing to our understanding of the underlying mechanisms. Secondly, such research can shed light on disparities in health outcomes related to SEP, providing insights into potential avenues for intervention and health policy. Additionally, identifying the role of genetics in SEP-related variations in BMI may guide the development of more personalized strategies for obesity prevention and management in the future.