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Linking Cardiometabolic Disease and Cancer in the Level of Genetics, Circulating Biomarkers, Microbiota and Environmental Risk Factors

Periodic Reporting for period 4 - BIOMENDELIAN (Linking Cardiometabolic Disease and Cancer in the Level of Genetics, Circulating Biomarkers, Microbiota and Environmental Risk Factors)

Reporting period: 2020-03-01 to 2020-08-31

Project name: Linking Cardiometabolic Disease and Cancer in the Level of Genetics, Circulating Biomarkers, Microbiota and Environmental Risk Factors ( BIOMENDELIAN)

The problem being addressed in BIOMENDELIAN:

The purpose of this proposal is to provide novel understanding of causal connections between cardiometabolic traits and incidence of type 2 diabetes (T2D), cardiovascular disease (CVD) and cancer, and of interactions between genetic and dietary risk factors for cardiometabolic disease, and to clarify their connection to gut and oral microbiota and cancer. Investigating the complex interactions between dietary factors, genetic risk factors, circulating biomarkers and gut and oral microbiota constitution in a comprehensive prospective cohort study design is a crucial first step to allow for subsequent intervention studies. The purpose of this proposal is to provide novel intervention strategies aiming to more effective prevention of cardiometabolic disease and cancer.

Why is BIOMENDELIAN important for society?

The prevalence of obesity and type 2 diabetes increase severely in the World, including in European countries. Obesity and type 2 diabetes increase the risk for cardiometabolic complications such as coronary heart disease and stroke, and cardiovascular mortality, and obesity is also a risk factor for amny common cancer forms.
As these conditions severely decrease the quality of life of a person, but also severely increase the economical burden for the society (loss of working power and high medical costs), it is important to find novel effective ways to prevent obesity and the asociated diseases that increase the risk of mortality.

What are the overall objectives of BIOMENDELIAN?

1. To investigate causality between genetic risk factors for cardiometabolic traits and future incidence of type 2 diabetes (T2D), cardiovascular disease (CVD), cancer (total and subtypes of common forms) and mortality (total, CVD- and cancer mortality), searching for connecting and disconnecting causal factors
2. To investigate how gut and oral microbiome are regulated by dietary factors, gut satiety peptides and host genetics, and how such connections relate to the risk of cardiometabolic diseases and cancer, in a large population
3. To understand the role of diet and gene-diet interactions of importance for cardiometabolic disease and cancer aiming to better nutrition recommendations
4. To perform genotype, biomarker and gut microbiota based diet intervention studies. Individuals for lipid- and carbohydrate challenges and to longer diet interventions are selected based on extreme genotypes, gut hormone levels and gut microbiome.
NOVEL BIOMARKERS
- We have in the Malmö Diet and Cancer Study (MDCS) identified several novel biomarkers of kidney function.
- We have linked neurotensin (NT) with increased fat absorption and obesity and suggest that NT may provide a prognostic marker of future obesity and a potential target for prevention and treatment.

CONNECTION BETWEEN CARDIOMETABOLIC DISEASE AND CANCER.
We have described that comorbid type 2 diabetes and adiposity associate with a marked increased risk of obesity-related cancers and cancer mortality.
We have by MR provided evidence for causal, inverse association between serum triglycerides and overall cancer risk and that LDLC-lowering effect of statins may increase prostate cancer risk.
We have performed a first MR study for stroke subtypes and found that LDL cholesterol lowering is likely to prevent stroke due to large artery atherosclerosis but may not prevent small artery occlusion- nor cardioembolic strokes.

GUT AND ORAL MICROBIOME IN A LARGE POPULATION
A major effort of BIOMENDELIAN was the metagenomic sequencing of microbial DNA from fecal samples of 2200 MOS and 5007 SCAPIS-Malmö participants at Clinical Microbiomics (CM, Copenhagen). The data processing by bioinformatics pipelines for delivery of bacterial and virome taxonomy, gene annotations and functional pathways was finished in June 2020.

In our first publication with human gut microbiota data and metabolomics data in the MOS study our results highlight a metabolic plasma profile that is dominated by high levels of glutamate and branched chain amino acids, which associated with obesity status and specific gut microbiota features. Further, our results indicate that the obesity related metabolite profile may be mediated by gut microbiota. We also identified a dietary pattern that associated with lower occurrence of prediabetes, as well as with higher abundance of the bacterial genera Roseburia in the gut.

ROLE OF DIET AND GENE-DIET INTERACTIONS OF IMPORTANCE FOR CARDIOMETABOLIC DISEASE AND CANCER
We observed a significant interaction between a genetic variation in AMY1 gene and starch intake on BMI and body fat percentage.
In another study we show that all individuals, whether at high or low genetic risk, would benefit from favorable food choices.
Our prospective studies of 12,200 Swedish participants have more power and will cover a much larger proportion of the inter-individual variability of the gut microbiome than earlier studies. Unique to our metagenomic approach is the possibility for strain level resolution and tracking of phages. Novelty lies additionally in the metagenomic approach, which is beyond the state of the art in precision and coverage and allows a) strain level resolution, b) mapping of interrelationships between bacteria and phages, c) characterization of the individualized variability of gut microbiome in large population, and d) identification of compositional and functional elements and bacterial metabolites predisposing to risk of obesity, T2D and CVD. Further originality lies in the combination of clinical unique cardiometabolic phenotyping, plasma metabolome data, environmental (diet, medication etc.) data, and usage of novel data integration and causal interference approaches utilizing MR approach of genetic data.