Massive blood study finds 88,000 new links between genes and metabolism

The chemicals flowing through your bloodstream tell a story that your DNA alone cannot. They reflect what you eat, how your body functions, and even the diseases that may be developing long before symptoms appear. Now, the largest study ever conducted on the genetics of human metabolism has revealed just how deeply our genes shape those chemical signals.

Led by researchers at the University of Tartu in Estonia, the study analyzed genetic and metabolic data from 619,372 people. By combining information from the Estonian Biobank and the UK Biobank, scientists created the most detailed map to date of how genetic differences influence hundreds of substances circulating in the blood.

The findings uncover tens of thousands of previously unknown links between genes and metabolism. They also challenge some long-held assumptions about disease risk and could help guide future drug development.

“This dataset gives us a broad foundation for understanding the pathophysiology of various diseases more deeply and for identifying their causal and drug-targetable factors more precisely,” said Priit Palta, Professor of Translational Genomics at the University of Tartu.

Scientists have spent years using genome-wide association studies, often called GWAS, to identify genetic factors linked to disease. Many of those studies now include more than a million participants. However, research focused on metabolic traits has remained much smaller.

That gap has limited scientists’ ability to understand how genes influence the thousands of molecules that help keep the body functioning.

To overcome this challenge, researchers combined data from 185,352 participants in the Estonian Biobank and 434,020 individuals in the UK Biobank. Together, the datasets provided unprecedented statistical power.

The team examined 249 circulating metabolic traits measured in blood samples. These included cholesterol particles, amino acids, glucose-related compounds, fats, and many other molecules that reflect the body’s metabolic health.

“While a standard blood test provides information on a handful of markers, we examined metabolism much more broadly and in far greater detail,” said Ralf Tambets, Junior Research Fellow of Bioinformatics.

The scale of the findings was striking.

Researchers identified 88,604 associations between genetic variants and metabolic traits across the study population. Those associations emerged from 8,398 separate genetic regions.

Compared with earlier investigations, the new analysis dramatically expanded scientific knowledge. The number of detected associations increased roughly tenfold compared with some previous studies.

The researchers also found thousands of previously unknown genetic signals. Depending on the specific metabolic trait, between 27% and 85% of associations had never been reported before.

Importantly, many of these discoveries came from low-frequency genetic variants. These uncommon variants occur in less than 1% of the population and often remain invisible in smaller datasets.

Because the study included more than 600,000 participants, scientists could detect these rare signals with confidence.

Finding a genetic link is only the first step. One of the biggest challenges in genetics is determining whether a discovered relationship actually reflects cause and effect.

“When you find this many associations, the first task is to understand what is cause and what is effect. Not all 88,000 associations mean that a single gene directly affects many metabolites, most reflect very indirect influences,” explained Associate Professor of Bioinformatics Kaur Alasoo.

To untangle those relationships, researchers used advanced computational techniques and statistical fine-mapping methods. These approaches helped pinpoint which genes and biological pathways most likely exert direct effects on metabolism.

The computational effort was enormous. Scientists analyzed millions of genetic variants against hundreds of biomarkers. Bioinformaticians at the University of Tartu’s Institute of Computer Science played a central role in processing and interpreting the data.

The work highlighted how modern biology increasingly depends on sophisticated computing power alongside laboratory science.

One important finding involved the role of rare and low-frequency genetic variants.

The study showed that these uncommon variants were more likely than common variants to alter protein structure or disrupt gene function. That makes them especially useful for identifying biological mechanisms.

Researchers identified variants in several well-known metabolic genes, including PCSK9, ABCA1, and ANGPTL4, which influence cholesterol and lipid metabolism.

They also uncovered variants linked to amino acid metabolism and energy production pathways.

These discoveries provide clearer clues about which genes directly control metabolic processes and may eventually serve as targets for new medicines.

One of the study’s most important findings involved branched-chain amino acids, known as BCAAs.

Previous research had consistently shown that people with elevated BCAA levels face a higher risk of developing type 2 diabetes. Many scientists wondered whether lowering those amino acids might help prevent the disease.

At first glance, the new dataset appeared to support that idea. Researchers found numerous genetic variants affecting BCAA metabolism.

However, deeper analysis told a different story.

Using advanced causal inference methods, scientists discovered that the relationship may not be directly causal after all. In other words, elevated BCAA levels may signal underlying metabolic problems rather than actively causing diabetes.

“Large datasets alone are not enough to uncover causal relationships, you also need a very good understanding of biological mechanisms,” Alasoo noted.

The finding carries practical significance. It suggests that simply lowering BCAA levels with medication would probably not reduce diabetes risk.

The study also explored how metabolic traits connect to cardiovascular disease.

Researchers found that many metabolic markers shared genetic signals with coronary artery disease. They confirmed several well-established relationships, including the role of low-density lipoprotein, or LDL cholesterol, in increasing heart disease risk.

The team also investigated genes already targeted by cholesterol-lowering drugs.

Their analysis supported existing evidence showing that genes such as HMGCR, LDLR, and PCSK9 influence cardiovascular risk. These findings reinforce the biological foundations behind widely used treatments like statins and PCSK9 inhibitors.

By identifying additional pathways involved in lipid metabolism, the research may help scientists discover new therapeutic targets in the future.

The study provides more than a list of genetic discoveries. It creates a powerful resource for scientists worldwide.

Researchers have released their summary statistics and built online tools that allow other investigators to explore the findings.

The dataset will support future studies of cardiovascular disease, type 2 diabetes, metabolic dysfunction-associated steatotic liver disease, and many other conditions.

Palta emphasized that this resource arrives at an important time.

“This is particularly valuable for studying cardiovascular diseases, which remain the leading cause of premature mortality in Estonia and Europe, as well as for investigating health conditions such as metabolic dysfunction-associated steatotic liver disease, which is becoming increasingly common and appears earlier in life.”

This research creates one of the most comprehensive maps ever assembled of how genes influence human metabolism. That knowledge could help scientists better understand why some people develop diseases while others remain healthy despite similar lifestyles.

The findings may improve efforts to build personalized risk assessments that combine genetic information with metabolic measurements. Because blood metabolites reflect both biology and daily habits, they can provide a richer picture of health than genetics alone.

The study also helps researchers distinguish between biological markers that merely accompany disease and those that actively contribute to it. That distinction is critical for developing effective treatments. By identifying pathways that truly drive disease, scientists can focus on drug targets with a higher chance of success.

In the years ahead, the dataset may accelerate research into heart disease, diabetes, liver disorders, and many other metabolic conditions. As biobanks continue to expand and become more diverse, similar approaches could help move medicine closer to truly personalized prevention and treatment strategies.

Research findings are available online in the journal Nature.

The original story "Massive blood study finds 88,000 new links between genes and metabolism" is published in The Brighter Side of News.

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