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Polymorphisms in taste receptor genes may be associated with taste sensitivity and possibly with food consumption and body weight. Previous studies relating bitter taste sensitivity to body mass index (BMI) had inconsistent findings. This paper aims to investigate the weight and body composition indicators among the TAS2R38 bitter taste receptor genotype groups.

Adults participating in the STEPS study (steps to the healthy development and well-being of children) cohort in Southwest Finland have been investigated. DNA has been extracted from buccal cell samples, and alleles of the gene TAS2R38 have been determined. Measurements at the follow-up visit include weight and height to calculate BMI, waist circumference (WC) and body composition with bioimpedance (women n = 757, men n = 714).

The mean BMI was 25.3 (SD 5.4) kg/m² among women and 26.7 (SD 3.9) kg/m² among men. BMI, WC and body fat percentage did not differ significantly between the TAS2R38 genotype groups in either gender. The proportion of subjects classified as overweight (BMI ≥ 25) did not vary significantly between the genotype groups.

The TAS2R38 genotype is not associated with being overweight in this cohort. Determinants of body weight are complex, and the role of other taste genotypes and phenotypes should be investigated in the future.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.