MeDaX: A Knowledge Graph on FHIR.

In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats.

[FAIR health data in the national and international data space]. / FAIRe Gesundheitsdaten im nationalen und internationalen Datenraum.

Health data are extremely important in today's data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research.To ensure that a resource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, a set of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles.Neighboring European countries have also invested in the development of a national framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets.

Insights into the FAIRness of the German Network University Medicine: A Survey.

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

The MeDaX Knowledge Graph Prototype.

Data sharing is sustainable for several reasons, including minimising economical and human costs or maximising knowledge gain. Still, reuse of biomedical (research) data is often hampered by the diverse technical, juridical, and scientific requirements for biomedical data handling and specifically sharing. We are building a toolbox for automated generation of knowledge graphs (KGs) from diverse sources, for data enrichment, and for data analysis. Into the MeDaX KG prototype, we integrated data from the core data set of the German Medical Informatics Initiative (MII) with ontological and provenance information. This prototype is currently used for internal concept and method testing only. In subsequent versions it will be expanded by including more meta-data and relevant data sources as well as further tools, including a user interface.

Phytyl fatty acid esters in vegetables pose a risk for patients suffering from Refsum's disease.

Patients suffering from Refsum's disease show mutations in the enzyme necessary for the degradation of phytanic acid. Accumulation of this tetramethyl-branched fatty acid in inner organs leads to severe neurological and cardiac dysfunctions which can even result in death. Thus, patients with Refsum's disease have to follow a specific diet resigning foods with high levels of phytanic acid and trans-phytol like products from ruminant animals with a tolerable daily intake (TDI) of ≤ 10 mg/d. We recently reported the occurrence of phytyl fatty acid esters (PFAE, trans-phytol esterified with a fatty acid) in bell pepper with trans-phytol amounts of up to 5.4 mg/100 g fresh weight (FW). In this study we carried out in vitro-digestion experiments of PFAE with artificial digestion fluids. Our results demonstrate that PFAE actually are a source for bioavailable trans-phytol and thus add to the TDI. Eating only one portion of bell pepper (∼150 g) could therefore lead to exploitation of the TDI of up to 81%. Analysis of additional vegetable matrices showed that also rocket salad with up to 4.2 mg/100 g FW trans-phytol bound in PFAE represents a risk-relevant food for patients with Refsum's disease and should therefore be taken into account.