Development of an Open-Access Dose-Response Database Linking Food Group Consumption to Non-Communicable Disease Risk and Dietary Disease Burden - a Case from Denmark

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Background: Quantitative estimation of the health impact of dietary change requires comprehensive evidence linking food group consumption to the risk of non-communicable diseases (NCDs). Alt-hough numerous dose-response meta-analyses have been published, they are typically reported inde-pendently and cannot readily be integrated to estimate the combined health effects of whole diets. This study describes the development of an open-access evidence database and analytical framework that enables simultaneous assessment of multiple food group–disease associations and their translation into dietary disease burden. Methods: Using Denmark as a case study, thirteen major diet-related NCDs and twenty-one food groups were identified, resulting in 273 potential food group–disease combinations. Systematic re-views with dose-response meta-analyses were identified through structured literature searches in PubMed, Embase, and the Cochrane Library, followed by predefined eligibility criteria and a hierar-chical study selection procedure. Published dose-response relationships were extracted directly or re-constructed from published figures and harmonized using natural cubic spline regression. Relative risk functions were subsequently re-scaled to habitual population intake, combined multiplicatively across food groups for each disease, and linked to disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) to estimate changes in disease burden associated with dietary modifications. Results: The literature search identified 15,184 records, from which 82 systematic reviews with dose-response meta-analyses were ultimately selected. These provided evidence for 193 of the 273 possible food group–disease combinations (71%). The resulting open-access database comprises harmonized mathematical dose-response functions together with information on evidence grading, heterogeneity, study characteristics, and quantitative evidence coverage. The accompanying modelling framework enables transparent estimation of the combined health effects of multiple food groups and provides a reproducible approach for translating dietary intake into changes in disease burden. Conclusions: The presented database and analytical framework establish a transparent and reproduci-ble foundation for quantitative dietary health impact assessment. By integrating harmonized dose-response relationships across multiple food groups and NCDs, the framework supports comparative risk assessment, dietary optimization, and evidence-based nutrition policy. The framework provides a scalable foundation for integrating emerging epidemiological evidence into quantitative dietary health impact assessments across populations.

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