Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

Abstract

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.

Data quality in the context of longitudinal research studies

“…For instance, setting up a causal frame according to observational data from the field was always a challenge. Consequently, researchers started to consider other data sources and integrating them into their survey-based analyses in order to work with innovative research questions (Spjuth et al, 2016). This entails challenges such as data linkage, i.e.…”

“…merging survey data with other sources. Along with data linkage techniques, we can see the rise of harmonizing research data that refers to linking multiple different studies into one unified data warehouse (Spjuth et al, 2016). Finally, one cannot ignore the importance of FAIR guiding principles for research data management and stewardship which emphasises the capacity of computational systems to find, access, interoperate, and reuse data (European Commission, 2016).…”

Finnish Innovations in Medicine and Social Sphere

“…Исследователи разрабатывают новые методы диагностики с помощью визуализации тканей и клеток, а также индивидуализированные подходы к лекарственной терапии рака. Помимо онкологической патологии приоритетными направлениями для исследовательских групп являются сердечнососудистые заболевания, неврологические болезни и вирусные инфекции [2,3,8].…”

“…Доноры образцов (пациенты-владельцы) в любой момент могут узнать о результатах исследования своих материалов. В случае обнаружения при исследованиях значимых для конкретного пациента клинических обстоятельств специалисты обязаны сообщить ему об этом [8,9].…”

La gestión de los datos, un proceso esencial para el desarrollo del Biobanco

“…Sin embargo, a nivel de la Unión Europea, se estableció el conjunto de datos MIABIS (Minimum Information About Biobank data Sharing) con 52 atributos, que pretenden facilitar la armonización de datos para biobancos y que es utilizado por BBMRI (Biobanking and Biomolecular Resources Research Infrastructure) 17 . y el SUMMIT consortium, proporcionando buenos resultados, y continúa en la promoción de la construcción de ontologías con contribución de toda la comunidad de biobancos y el uso de mejores prácticas en pro de la interoperabilidad de los biobancos 22 .…”