T5.1/5.2 Use Cases “Nutritional Epidemiology” and “Epidemiology of chronic diseases”
Data standardisation and harmonisation are central elements in making research data FAIR (findable, accessible, interoperable, reusable) and thus improving the data quality of studies. Especially for joint analyses in collaborative research programmes and projects, data harmonisation is essential for the interoperability of study data, i.e., to ensure content equivalence across studies and minimise heterogeneity, which may introduce imprecision in statistical analyses.
The NFDI4Health consortium aims to increase the quality of health research in Germany by increasing the visibility and accessibility of research data according to the FAIR principles. The focus is on research data from epidemiological studies, clinical trials and public health studies. Based on research questions typical for the respective research area, scientists of the NFDI4Health Use Cases 5.1 “Nutritional Epidemiology” and 5.2 “Epidemiology of chronic diseases” are working on pilot projects to develop data standardisation and harmonisation support services that will be available to the scientific community.
The following 3 pilot studies for data standardisation and harmonisation in epidemiological studies are ongoing:
1. Systematic investigation of methodological limitations in the derivation of dietary patterns
Contact: Dr. Franziska Jannasch
Even though numerous exploratorily derived dietary patterns have been generated in the last two decades, there was no comprehensive investigation of methodological constraints, like the influence of different energy adjustment techniques or different levels of granularity, when condensing food items into food groups, on the resulting pattern composition. Furthermore, for simplified dietary patterns, which can be useful in multi-centre analyses, the systematic investigation of different cut-offs for factor loadings on pattern composition and the comparison with the original dietary patterns are useful analyses to conduct.
2.Association of dietary sugar intake and body composition
Contact: Dr. Ines Perrar
The association of added or free sugar intakes with the development of overweight has been discussed for many years. In 2022, European Food Safety Authority (EFSA) published an update on their "Tolerable upper intake level for dietary sugars", in which they state that it is not possible to set a recommendation due to major research gaps. Pooled analyses examining the impact of sugar intake on the development of obesity and non-communicable diseases are desired. Therefore, in addition to the collection of metadata and standards for data harmonisation, a pooled representative study on the level of sugar intake (e.g., total, added and/or free sugars) as well as its main sources (e.g., SSB, juices, sweets) of children, adolescents and adults in Germany and its association with body composition (e.g., BMI) will be conducted. If available, biomarker data (e.g., urinary sugar excretion) may also be considered.
3. Association of dietary sugar intake and glycaemic load with chronic disease risk
Contact: Dr. Carolina Schwedhelm
In Germany, chronic diseases represent 89% of the total disease burden, half of which come from cardiovascular diseases (CVD), type 2 diabetes (T2D), and cancer. While dietary sugar intake has been hypothesised to affect chronic disease risk, existing studies vary widely in the definitions of sugar or sugar-containing foods and the chronic diseases studied, highlighting the importance of discerning between types of sugar and knowledge gaps for some chronic diseases. Furthermore, most of the evidence related to dietary sugar intake stems from studies in non-German populations. Using federated data analyses, we aim to examine the associations of simple sugars intake, sugar-sweetened beverage intake, and glycaemic load with chronic disease risk jointly in multiple German population-based prospective observational studies. Our findings will provide disease risk estimates for various chronic diseases in a diverse German population.
In NFDI4Health, data standardisation and harmonisation will be performed based on the Maelstrom harmonisation procedures, which were adapted to the current pilot project needs. From this, the following steps emerge for the projects: