Approved Research

Elucidating host-environment interactions in gastrointestinal diseases

Lay summary

Gastrointestinal and metabolic disease are a leading cause of morbidity in the US, Europe and worldwide. The following aims disentangle the causes, mediators, and environmental moderators from covariates in the pathways leading from health to gastrointestinal disease, as well as those governing their outcomes:

1)           Identify prevention strategies for gastrointestinal diseases using genetic environmental and patient-specific data e.g., the metabolome

2)           Investigate the interaction between genetics, psychosocial and environmental factors (e.g. smoking, alcohol use, medication, dietary intake, physical activity and nutrition), and gastrointestinal diseases their progression, response to therapy, survival, and other health-related outcomes.

3)           Improve disease prediction accuracy by developing scores using classical statistics as well as machine learning.

The proposed research could significantly improve knowledge of the biological mechanisms that lead to metabolic disease, refine disease prevention and detect new types of markers for early detection of gastrointestinal diseases.

This information can assist health professionals in advancing treatment and prevention strategies for treating gastrointestinal and metabolic diseases.

Moreover, we would like to investigate serum markers and serum marker-based scores for organ dysfunction (e.g kidney and brain function).

Diseases researched include also cholestatic liver diseases, gastrointestinal cancers (e.g. CCC, Pankreas CA), but also gastrointestinal infections (like Helicobacter). Moreover, we want to use patients with rheumatoid arthritis as a control group and predict the usefulness of liver-related markers in urological (e.g. kidney and prostate cancer) and gynecological disorders (e.g. breast cancer or endometriosis).

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To unravel how host-environment interaction of metabolic diseases drive cardiovascular/pulmonary disease we will investigate the association and prediction performance  of SNPs, lab values, omics on outcomes of cardiovascular (e.g. ASCVD, HF)/lung disease (e.g. COPD, fibrosis).

Scope extension, April 2024:

Lastly we want to integrate MRI and raw accelerometer data in our multiomics data analyses.