Large-scale multi-omics study revealed associations between blood biomarkers, genes and diseases

Omics studies are great! They are versatile, multidisciplinary and – most importantly – highly relevant, as they produce a huge amount of new knowledge of disease-related biology.

In our previous blog post, we discussed different types of omics studies in general. In this post, we present key findings of our recently published multi-omics study.

Associations between biomarkers, genes and diseases

In our new large-scale study, we mainly utilized genomics and metabolomics. Combining these omics approaches enabled studying associations between millions of genetic factors, hundreds of blood biomarkers, and various diseases.

What exactly are blood biomarkers? They are circulating molecules that can be used as indicators of disease and health states. LDL cholesterol, for example, is a biomarker for heart disease. In our study, most of the analyzed biomarkers were related to lipid metabolism, but other biomarkers, such as amino acids and glucose, were also analyzed.

As the main findings of our study, we describe more than 400 genomic regions associated with metabolism, many of which have not been described before. For instance, we identified dozens of genomic regions associated with amino acid levels. We describe as an example the KCNK16 gene, associated with circulating glutamine levels in our study and with diabetes risk in previous investigations. The discovery is interesting, as glutamine plays a role in glucose biology. Overall, a significant portion of the genomic regions identified in our study are also associated with the risk of various diseases.

New knowledge of disease biology

Our results help us better understand the biological processes through which blood biomarkers are linked to diseases, and further identify new targets for medications. Among other things, we described the detailed effects of more than a hundred genetic factors on biomarkers related to lipid metabolism, including LDL and HDL cholesterol and triglycerides. With this approach, we identified the signaling pathway related to the TRIM5 gene as a potential drug target for heart disease.

In our study, we also described the metabolic effects of genetic factors predisposing to intrahepatic cholestasis of pregnancy. Intrahepatic cholestasis of pregnancy is a liver disorder that causes severe itching in pregnant women. Our findings shed light on this poorly understood liver disorder diagnosed in about one in a hundred pregnant women.

We also investigated the causal relationships between blood biomarkers and various diseases. Our results suggest that blood acetone levels may be causally related to hypertension.

Big data and modern quantification methods facilitate omics studies

Modern measurement and analysis methods, as well as large population-based cohorts, enable omics studies. Since huge amounts of data are used, high computing power and excellent data management skills are required.

In our study, we combined more than 30 population datasets, which made it possible to examine samples of more than 130,000 people. Most of the datasets were European, and some were Asian. Six Finnish population cohorts were included, including the Northern Finland Birth Cohorts, housed at the University of Oulu. In addition, we used additional data from the UK Biobank, which allowed confirmative analyses of 120,000 participants.

We determined 233 metabolic biomarkers from blood samples and investigated their associations with more than 13 million genetic factors. State-of-the-art metabolic analyses based on nuclear magnetic resonance spectroscopy were applied to measure over 200 biomarkers from a single blood sample. This method has been developed at the University of Oulu.

Our study nicely demonstrates how combining large datasets, modern methods, and different omics approaches yields a large amount of new knowledge on disease-related biology.

Openly accessible research results enable a plethora of follow-up studies

Our study also exemplifies how the growing amount of data enables a different research approach than before. Long predominant, traditional hypothesis-driven research relies on the strong assumption that we understand biology well.

Instead, nowadays, large data enables data-driven research. This approach necessitates large datasets, a strict threshold for statistical significance and independent replication datasets.

Our study involved huge datasets, rigorous replications of our results, and adjustment of statistical significance thresholds to correct for the extensive number of independent tests. This is how modern epidemiology and omics studies draw lessons from genetic epidemiology, which has paved the way for data-driven science during the last two decades.

The most important outcome of our study is the openly available research results. These results create a resource that aids the scientific community to conduct numerous follow-up studies. The data that we have provided can be used, for example, in the development of new medications and in investigating the cause-and-effect relationships between metabolic biomarkers and diseases.

Main authors:

Minna Karjalainen

Johannes Kettunen

Research article: Karjalainen M.K., Karthikeyan S., Oliver-Williams C., Sliz E.,..., Danesh J., Ala-Korpela M., Butterworth A.S., Kettunen J. Genome-wide characterization of circulating metabolic biomarkers. Nature (2024).