Periodic Reporting for period 4 - BiomeRiskFactors (Discovering microbiome-based disease risk factors)
Okres sprawozdawczy: 2023-09-01 do 2024-12-31
Identifying risk factors for diseases that can be prevented or delayed by early intervention is of major importance. Much effort was directed to this task and, indeed, numerous genetic, lifestyle, anthropometric and clinical risk factors are routinely used for many different diseases, and screening of the population or of high-risk individuals is implemented in several countries for a wide range of conditions. Ongoing efforts are aimed at finding risk factors in rich sources of genomics and omics data, as in the case of early detection of cancer by deep sequencing of circulating cell-free DNA. However, the predictive power of existing risk factors is limited, and most of them appear when physiological derangements have already occurred.
Another rich source of information with the potential to contain pertinent disease risk factor data is the human microbiome – the collective genome of trillions of microbes, including bacteria, viruses, fungi, and parasites that reside in the human gastrointestinal tract. The microbiome contains 100-fold more genes than the human genome, and is considered a bona-fide ‘second genome’ with fundamental roles in multiple aspects of human physiology and health, including obesity, non-alcoholic fatty liver disease, inflammatory diseases, cancer, metabolic diseases, cardiovascular disease, aging, and neurodegenerative disorders. As such, it should capture different aspects of disease than existing risk factors, and their combination can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems. Thus, discovery of microbiome-based risk factors is a promising yet mostly unexplored research area.
Another rich source of information with the potential to contain pertinent disease risk factor data is the human microbiome – the collective genome of trillions of microbes, including bacteria, viruses, fungi, and parasites that reside in the human gastrointestinal tract. The microbiome contains 100-fold more genes than the human genome, and is considered a bona-fide ‘second genome’ with fundamental roles in multiple aspects of human physiology and health, including obesity, non-alcoholic fatty liver disease, inflammatory diseases, cancer, metabolic diseases, cardiovascular disease, aging, and neurodegenerative disorders. As such, it should capture different aspects of disease than existing risk factors, and their combination can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems. Thus, discovery of microbiome-based risk factors is a promising yet mostly unexplored research area.
Over the course of this project, we sought to identify novel microbiome-derived risk factors that improve upon current methods for predicting an individual’s likelihood of developing specific conditions over the next 5–10 years. To accomplish this, we recruited and profiled over 3,000 new individuals (as well as additional disease cohorts) and combined these with a previously assembled cohort of more than 2,200 participants. In total, we collected clinical data and gut microbiome samples, and banked blood and stool from all these individuals. Leveraging these resources, we developed new experimental and computational techniques to deeply characterize microbial gene functions, microbiome-produced metabolites, and the interplay between gut microbes and the host immune system. These collective efforts have led to 57 publications stemming from this grant.
Notably, we uncovered several microbiome-related risk factors across different health domains. For example, in a large-scale population study integrating continuous glucose monitoring, body composition scans, and liver ultrasound data, we found 145 bacterial pathways—including purine ribonucleosides degradation—that consistently correlated with host metabolic markers such as BMI, liver health, and Type-2 Diabetes risk. In another study, we discovered over 1,300 bacterial SNPs significantly linked to BMI, highlighting previously underexplored species and shedding light on potential mechanisms like inflammatory pathways in Bilophila wadsworthia and energy metabolism in Faecalibacterium prausnitzii. Importantly, many of these associations persisted after controlling for diet, medication use, and physical activity.
Additional analyses also demonstrated that gut microbiome pathways and dietary patterns can predict clinical features of sleep apnea—such as daytime sleepiness—more accurately than traditional predictors like age, BMI, or visceral adipose tissue alone, reinforcing the microbiome’s robust influence on multiple facets of human health. In parallel, we have shown how lifestyle factors shape more than half of various sleep parameters, further underscoring the interplay between the microbiome, host physiology, and everyday behavior. Overall, our work has advanced the understanding of how gut microbial composition and genetics intersect with human metabolic and clinical outcomes. We anticipate that these discoveries will pave the way for microbiome-based biomarkers and targeted interventions, offering new avenues for disease prevention and personalized healthcare.
Notably, we uncovered several microbiome-related risk factors across different health domains. For example, in a large-scale population study integrating continuous glucose monitoring, body composition scans, and liver ultrasound data, we found 145 bacterial pathways—including purine ribonucleosides degradation—that consistently correlated with host metabolic markers such as BMI, liver health, and Type-2 Diabetes risk. In another study, we discovered over 1,300 bacterial SNPs significantly linked to BMI, highlighting previously underexplored species and shedding light on potential mechanisms like inflammatory pathways in Bilophila wadsworthia and energy metabolism in Faecalibacterium prausnitzii. Importantly, many of these associations persisted after controlling for diet, medication use, and physical activity.
Additional analyses also demonstrated that gut microbiome pathways and dietary patterns can predict clinical features of sleep apnea—such as daytime sleepiness—more accurately than traditional predictors like age, BMI, or visceral adipose tissue alone, reinforcing the microbiome’s robust influence on multiple facets of human health. In parallel, we have shown how lifestyle factors shape more than half of various sleep parameters, further underscoring the interplay between the microbiome, host physiology, and everyday behavior. Overall, our work has advanced the understanding of how gut microbial composition and genetics intersect with human metabolic and clinical outcomes. We anticipate that these discoveries will pave the way for microbiome-based biomarkers and targeted interventions, offering new avenues for disease prevention and personalized healthcare.
We have developed novel microbiome analysis tools that operate at the strain level, allowing us to identify fine-grained associations between specific bacterial strains and host phenotypes. This is a substantial improvement over traditional methods that focus only on species-level or functional categories. Additionally, we created an immune assay capable of mapping an individual’s entire infection history, which has already been used to detect both COVID-19 antibodies and interactions between the host immune system and gut bacterial strains. On the metabolomics front, our large-scale cohort studies revealed key factors—encompassing genetics, diet, and microbial composition—that collectively shape the levels of numerous blood metabolites. For example, in a recent study of nearly 9,000 participants, we uncovered 145 bacterial pathways significantly linked to metabolic health markers, including BMI and Type-2 Diabetes risk.
Building on these advancements, we plan to continue recruiting participants, expanding the power of our analyses to discover previously unrecognized microbial and immunological determinants of disease risk. By combining strain-level microbiome profiling with immune and metabolomic readouts, we aim to develop robust predictors of future disease onset that exceed current state-of-the-art risk models.
Building on these advancements, we plan to continue recruiting participants, expanding the power of our analyses to discover previously unrecognized microbial and immunological determinants of disease risk. By combining strain-level microbiome profiling with immune and metabolomic readouts, we aim to develop robust predictors of future disease onset that exceed current state-of-the-art risk models.