Integrating metabolome and lipidome with microbiome and clinical data to discover diabetes type 2 markers
Gut microbiome diversity and its function are related to a wide range of diseases in humans, such as
gastrointestinal and metabolic diseases, but also mental disorders (Lynch et al., 2016; Liang et al.,
2018). Furthermore, the gut bacteria and its microbial pathways influence host metabolism and vice
versa. Most of these intricate pathways still need to be elucidated, and it still remains unclear to which
extent the microbiome and metabolome are influenced by genetics, environmental factors (diet,
lifestyle, pollution, etc.) and health or disease (gut disorders, food allergies, diabetes, etc.).
In order to explore these microbiome-metabolome relationships, multi-omics data analysis techniques
have been developed, such as DIABLO and MOFA (Argelaguet et al., 2018; Singh et al., 2018) that allow
integration of, for example, microbiome and metabolome data. However, next to these frameworks,
there is a huge diversity in methods to tackle this kind of analysis (use of machine learning and neural
networks, correlation analysis, …). This wide range of options can be overwhelming to the scientist and
there might not be “one size fits all” solution, depending on the research question.
In this project, a proof-of-concept for multi-omics analysis of microbiome and metabolome data will
be tested on a small cohort, with the goal to aid biomarker discovery for diabetes type 2.