Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD

Early-life growth and emotional, behavior and cognitive outcomes in childhood and adolescence in the EU child cohort network: individual participant data meta-analysis of over 109,000 individuals

Background

Fetal and infant development might be critical for cognitive outcomes and psychopathology later in life. We assessed the associations of birth characteristics and early life growth with behavior and cognitive outcomes from childhood to adolescence.

Methods

We used harmonized data of 109,481 children from 8 European birth cohorts. Birth weight, gestational age, and body mass index (BMI) tertiles at the age of 2 years were used as the exposure variables. Outcomes included internalizing and externalizing problems and attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and non-verbal intelligence quotient (Non-verbal IQ) in childhood (4-10 years), early adolescence (11-16 years), and late adolescence (17-20 years). We used 1-stage individual participant data meta-analyses using generalized linear models.

Findings

A one-week older gestational age was associated with lower scores for internalizing problems (difference -0·48 (95% CI: -0·59, -0·37)), externalizing problems (difference -0·34 (95% CI: -0·44, -0·23)), and ADHD symptoms (difference -0·38 (95% CI: -0·49, -0·27)), and with higher scores for non-verbal IQ (difference 0·65 (95% CI: 0·41, 0·89)). As compared to term birth, preterm birth was associated with higher internalizing problems (difference 3·43 (95% CI: 2·52, 4·33)) and externalizing problems (difference 2·31 (95% CI: 1·16, 3·46)), ADHD symptoms (difference 4·15 (95% CI: 3·15, 5·16)), ASD symptoms (difference 3·23 (95% CI: 0·37, 6·08)), and lower non-verbal IQ (difference -5·44 (95% CI: -7·44, -3·44)). Small size for gestational age at birth (SGA) in comparison with appropriate size for gestational age (AGA) was associated with higher ADHD symptoms (difference 4·88 (95% CI: 3·87, 5·90)) and lower Non-verbal IQ (difference -7·02 (95% CI: -8·84, -5·21)). Large size for gestational age at birth was associated with lower ADHD symptoms (difference -1·09 (95% CI: -1·73, 0·45)) and higher non-verbal IQ (difference 2·47 (95% CI: 0·77, 4·18)). Explorative analyses showed that as compared to children with an appropriate size for gestational age at birth and a normal BMI at the age of 2 years, children born SGA who remained small at 2 years had the lowest non-verbal IQ score (difference -8·14 percentiles (95% CI: -11·89, -4·39)).

Interpretation

Both fetal and early childhood growth are associated with emotional, behavioral and cognitive outcomes throughout childhood and adolescence. Compensatory infant growth might partly attenuate the adverse effects of suboptimal fetal growth. Future studies are needed to identify the potential for optimizing mental health outcomes in new generations by improving early-life growth.

Funding

This project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206, 2016; EUCAN-Connect grant agreement No 824989; ATHLETE, grant agreement No 874583).

DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform

Motivation

The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions.

Results

DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the 'Five Safes' framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements.

Availability and implementation

Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.

MOLGENIS Armadillo: a lightweight server for federated analysis using DataSHIELD

SUMMARY

Extensive human health data from cohort studies, national registries, and biobanks can reveal lifecourse risk factors impacting health. Combining these sources offers increased statistical power, rare outcome detection, replication of findings, and extended study periods. Traditionally, this required data transfer to a central location or separate partner analyses with pooled summary statistics, posing ethical, legal, and time constraints. Federated analysis-which involves remote data analysis without sharing individual-level data-is a promising alternative. One promising solution is DataSHIELD (https://datashield.org/), an open-source R based implementation. To enable federated analysis, data owners need a user-friendly way to install the federated infrastructure and manage users and data. Here, we present MOLGENIS Armadillo: a lightweight server for federated analysis solutions such as DataSHIELD.

AVAILABILITY AND IMPLEMENTATION

Armadillo is implemented as a collection of three packages freely available under the open source licence LGPLv3: two R packages downloadable from the Comprehensive R Archive Network (CRAN) ("MolgenisArmadillo" and "DSMolgenisArmdillo") and one Java application ("ArmadilloService") as jar and docker images via Github (https://github.com/molgenis/molgenis-service-armadillo).

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform

The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.

Federated difference-in-differences with multiple time periods in DataSHIELD

Difference-in-differences (DID) is a key tool for causal impact evaluation but faces challenges when applied to sensitive data restricted by privacy regulations. Obtaining consent can shrink sample sizes and reduce statistical power, limiting the analysis's effectiveness. Federated learning addresses these issues by sharing aggregated statistics rather than individual data, though advanced federated DID software is limited. We developed a federated version of the Callaway and Sant'Anna difference-in-differences (CSDID), integrated into the DataSHIELD platform, adhering to stringent privacy protocols. Our approach reproduces key estimates and standard errors while preserving confidentiality. Using simulated and real-world data from a malaria intervention in Mozambique, we demonstrate that federated estimates increase sample sizes, reduce estimation uncertainty, and enable analyses when data owners cannot share treated or untreated group data. Our work contributes to facilitating the evaluation of policy interventions or treatments across centers and borders.

DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification

The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.

A Federated Database for Obesity Research: An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Unlocking the potential of big data and AI in medicine: insights from biobanking

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD

Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.

An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation

Background

The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR).

Methods

The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation.

Results

The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2.

Conclusions

Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.

Impact of domiciliary administration of NSAIDs on COVID-19 hospital outcomes: an unCoVer analysis

Background: Effective domiciliary treatment can be useful in the early phase of COVID-19 to limit disease progression, and pressure on hospitals. There are discrepant data on the use of non-steroidal anti-inflammatory drugs (NSAIDs). Aim of this study is to evaluate whether the clinical outcome of patients who were hospitalized for COVID-19 is influenced by domiciliary treatment with NSAIDs. Secondary objective was to explore the association between other patient characteristics/therapies and outcome. Methods: A large dataset of COVID-19 patients was created in the context of a European Union-funded project (unCoVer). The primary outcome was explored using a study level random effects meta-analysis for binary (multivariate logistic regression models) outcomes adjusted for selected factors, including demographics and other comorbidities. Results: 218 out of 1,144 patients reported use of NSAIDs before admission. No association between NSAIDs use and clinical outcome was found (unadj. OR: 0.96, 95%CI: 0.68-1.38). The model showed an independent upward risk of death with increasing age (OR 1.06; 95% CI 1.05-1.07) and male sex (1.36; 95% CI 1.04-1.76). Conclusion: In our study, the domiciliary use of NSAIDs did not show association with clinical outcome in patients hospitalized with COVID-19. Older ages and male sex were associated to an increased risk of death.

Accessibility of covariance information creates vulnerability in Federated Learning frameworks

MOTIVATION

Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side.

RESULTS

We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.

AVAILABILITY AND IMPLEMENTATION

The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).

Ten Topics to Get Started in Medical Informatics Research

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis

BACKGROUND

The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats.

OBJECTIVES

The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise.

METHODS

We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license.

RESULTS

The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms.

CONCLUSION

The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

Software Application Profile: ShinyDataSHIELD—an R Shiny application to perform federated non-disclosive data analysis in multicohort studies

Motivation

DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD.

Implementation

ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure.

General features

The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration).

Availability

ShinyDataSHIELD is publicly hosted online [https://datashield-demo.obiba.org/ ], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available [https://hub.docker.com/r/brgelab/shiny-data-shield ].

Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response

The COVID-19 pandemic saw a massive investment into collaborative research projects with a focus on producing data to support public health decisions. We relay our direct experience of four projects funded under the Horizon2020 programme, namely ReCoDID, ORCHESTRA, unCoVer and SYNCHROS. The projects provide insight into the complexities of sharing patient level data from observational cohorts. We focus on compliance with the General Data Protection Regulation (GDPR) and ethics approvals when sharing data across national borders. We discuss procedures for data mapping; submission of new international codes to standards organisation; federated approach; and centralised data curation. Finally, we put forward recommendations for the development of guidelines for the application of GDPR in case of major public health threats; mandatory standards for data collection in funding frameworks; training and capacity building for data owners; cataloguing of international use of metadata standards; and dedicated funding for identified critical areas.

An exposomic framework to uncover environmental drivers of aging

The exposome, the environmental complement of the genome, is an omics level characterization of an individual's exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.

A deterministic approach for protecting privacy in sensitive personal data

BACKGROUND

Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge.

METHODS

In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm.

RESULTS

We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort.

CONCLUSIONS

The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Architecture for a feasibility query portal for distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) patient data repositories: Design and Implementation Study (Preprint)

Background

An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing.

Objective

This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data.

Methods

We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service.

Results

We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.

Conclusions

We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review

Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. In the last decade, metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. We applied a rigorous protocol that relied on PRISMA guidelines and filtering criteria to obtain an exhaustive study selection that finally resulted in a group of 10 studies, based on metaproteomics and that aim at investigating obesity and diabetes. This batch of studies was used to discuss specific microbial and human metaproteome alterations and metabolic patterns in subjects affected by diabetes (T1D and T2D) and obesity. We provided the main up- and down-regulated protein patterns in the inspected pathologies. Despite the available results, the evident paucity of metaproteomic data is to be considered as a limiting factor in drawing objective considerations. To date, ad hoc prepared metaproteomic databases collecting pathologic data and related metadata, together with standardized analysis protocols, are required to increase our knowledge on these widespread pathologies.

Gut Microbiome Composition in Obese and Non-Obese Persons: A Systematic Review and Meta-Analysis

Whether the gut microbiome in obesity is characterized by lower diversity and altered composition at the phylum or genus level may be more accurately investigated using high-throughput sequencing technologies. We conducted a systematic review in PubMed and Embase including 32 cross-sectional studies assessing the gut microbiome composition by high-throughput sequencing in obese and non-obese adults. A significantly lower alpha diversity (Shannon index) in obese versus non-obese adults was observed in nine out of 22 studies, and meta-analysis of seven studies revealed a non-significant mean difference (−0.06, 95% CI −0.24, 0.12, I² = 81%). At the phylum level, significantly more Firmicutes and fewer Bacteroidetes in obese versus non-obese adults were observed in six out of seventeen, and in four out of eighteen studies, respectively. Meta-analyses of six studies revealed significantly higher Firmicutes (5.50, 95% 0.27, 10.73, I² = 81%) and non-significantly lower Bacteroidetes (−4.79, 95% CI −10.77, 1.20, I² = 86%). At the genus level, lower relative proportions of Bifidobacterium and Eggerthella and higher Acidaminococcus, Anaerococcus, Catenibacterium, Dialister, Dorea, Escherichia-Shigella, Eubacterium, Fusobacterium, Megasphera, Prevotella, Roseburia, Streptococcus, and Sutterella were found in obese versus non-obese adults. Although a proportion of studies found lower diversity and differences in gut microbiome composition in obese versus non-obese adults, the observed heterogeneity across studies precludes clear answers.

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.

Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis

In any research field, data access and data integration are major challenges that even large, well-established consortia face. Although data sharing initiatives are increasing, joint data analyses on nutrition and microbiomics in health and disease are still scarce. We aimed to identify observational studies with data on nutrition and gut microbiome composition from the Intestinal Microbiomics (INTIMIC) Knowledge Platform following the findable, accessible, interoperable, and reusable (FAIR) principles. An adapted template from the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) consortium was used to collect microbiome-specific information and other related factors. In total, 23 studies (17 longitudinal and 6 cross-sectional) were identified from Italy (7), Germany (6), Netherlands (3), Spain (2), Belgium (1), and France (1) or multiple countries (3). Of these, 21 studies collected information on both dietary intake (24 h dietary recall, food frequency questionnaire (FFQ), or Food Records) and gut microbiome. All studies collected stool samples. The most often used sequencing platform was Illumina MiSeq, and the preferred hypervariable regions of the 16S rRNA gene were V3-V4 or V4. The combination of datasets will allow for sufficiently powered investigations to increase the knowledge and understanding of the relationship between food and gut microbiome in health and disease.