Dietary health impact modeling often underpins health impact assessments - valuable decision-support tools for public health nutrition. These models depend on epidemiological evidence to quantify associations between dietary risk factors and health outcomes. However, the review and selection of evidence pose remains challenging due to a lack of systematic approaches for risk-outcome pair selection and difficulties extracting harmonized dose-response data from published literature. To address these challenges, this work aimed to 1) update the Nordic Nutrition Recommendations 2023 (NNR2023) epidemiological evidence base and suggest an approach for comparing evidence grading systems, 2) propose a streamlined method to extract non-linear dose-response curves from meta-analyses, and 3) construct an open-access database of the synthesized evidence. The NNR2023 search methodology was replicated, and risk-outcome pairs were selected using modified NNR2023 criteria. Non-linear relationships were estimated by extracting data points with an open-source graph reader and fitting piecewise constant functions. From the selected updated and original NNR2023 evidence, 159 risk-outcome associations were included, of which 51 were non-linear, and around 40% achieved levels of at least mid-range evidence certainty. The provided database and methodology contribute to increased transparency and provide a standard approach to evidence selection and use in dietary health impact modeling.
Jacob et al. (Fri,) studied this question.