Little is known about the factors that influence inter-individual variability in the thermic effect of food (TEF). Factors such as age, physical activities, insulin resistance and body weight and composition have been proposed as potential predictors, but findings remain inconsistent. TEF may also be influenced by hormonal status in females. Therefore, this study aimed to explore potential physiological and dietary predictors of TEF in a well-phenotyped cohort of females approaching menopause. This study is a secondary analysis of the MONET study. Eighty-six females had complete data for all predictors included in the analyses: fat mass (kg), resting energy expenditure (REE), VO2peak, fasting glucose, follicle-stimulating hormone and habitual protein intake. Stepwise multiple linear regression analyses were conducted to identify predictors of TEF. Mean TEF over the 180-min measurement period was 33 (sd 8·7) kcal. Habitual daily protein intake was the only consistent significant predictor of TEF across regression models (B ≈ 0·12–0·14 kcal·g−1, β ≈ 0·23–0·24, P ≤ 0·04), explaining 6 % of the variance (R2 = 0·060). Inclusion of fat mass, REE, VO2peak, fasting glucose or follicle-stimulating hormone did not meaningfully improve model fit. These findings suggest that TEF may represent a relatively stable physiological trait, with modest contributions from habitual dietary protein intake, and that additional, unmeasured factors may contribute to inter-individual variability.

Nutrigenetics and personalised nutrition are components of the concept that in the future genotyping will be used as a means of defining dietary recommendations to suit the individual. Over the last two decades there has been an explosion of research in this area, with often conflicting findings reported in the literature. Reviews of the literature in the area of apoE genotype and cardiovascular health, apoA5 genotype and postprandial lipaemia and perilipin and adiposity are used to demonstrate the complexities of genotype–phenotype associations and the aetiology of apparent between-study inconsistencies in the significance and size of effects. Furthermore, genetic research currently often takes a very reductionist approach, examining the interactions between individual genotypes and individual disease biomarkers and how they are modified by isolated dietary components or foods. Each individual possesses potentially hundreds of ‘at-risk’ gene variants and consumes a highly-complex diet. In order for nutrigenetics to become a useful public health tool, there is a great need to use mathematical and bioinformatic tools to develop strategies to examine the combined impact of multiple gene variants on a range of health outcomes and establish how these associations can be modified using combined dietary strategies.

Food Based Dietary Guidelines (FBDG), in order to be attainable, should take into account the cultural context of the population for which they are developed. They need to focus on foods that actually determine the intake of nutrients for which desirable changes have been identified. Inter-individual variability of nutrient intakes – a measure of the heterogeneity of intakes – is determined by the variability in the consumption of its food sources among the population. The foods that determine a high proportion of inter-individual variability in nutrient intake can be identified on the basis of data banks that describe food and nutrient intakes in the population. The experience accumulated to design Food Frequency Questionnaire through selection of key foods suggests that high quantitative contributors to a specific nutrient may not be important determinants of its intake. On the other hand, the Pearson correlation between the intake of each food source and the total nutrient intake allows quantification of the percentage of variability explained by each item and takes into account the possibility of correlations between different food sources. Once a key food is identified, several strategies are available to modify its intake in the population: through changes in the percentage of consumers/in the mean portion size/in the frequency of intake. The anticipated level of change can be predicted according to the strategy adopted.