Personalised fish intake recommendations: the effect of background exposure on optimisation

National dietary guidelines are directed at the general population. However, these guidelines may be perceived as unrealistic by a substantial part of the population, as they differ considerably from individual consumption patterns and preferences. Personalised dietary recommendations will probably improve adherence, and it has been shown that these recommendations can be derived by mathematical optimisation methods.

However, to better account for risks and benefits of specific foods, the background exposure to nutrients and contaminants needs to be considered as well. This background exposure may come from other foods and supplements, and also from environmental sources like the air and the sun. The objective of this study was therefore to analyse the effect of including individual variation in background exposure when modelling personalised dietary recommendations for fish.

We used a quadratic programming model to generate recommended fish intake accounting for personal preference by deviating as little as possible from observed individual intake. Model constraints ensure that the modelled intake meets recommendations for EPA, DHA and vitamin D without violating tolerable exposure to methyl mercury, dioxins and dioxin-like polychlorinated biphenyls.

Several background exposures were analysed for 3016 Danish adults, whose food intakes and body weights were reported in a national dietary survey. We found that the lower nutrient constraints were critical for the largest part of the study population, and that a total of 55% should be advised to increase their fish intake.

The modelled fish intake recommendations were particularly sensitive to the vitamin D background exposure.