New Insights on Cardiometabolic Disease Biomarkers

The number of people suffering from heart disease, stroke, and diabetes is growing quickly around the world. This rise in these types of diseases is putting a lot of strain on healthcare systems everywhere. To improve health outcomes, it is important to find new biomarkers and Drug Targets that are linked to the biological processes involved in these diseases. However, because cardiometabolic diseases have many causes, finding effective biomarkers and drug targets is often challenging and expensive.

#The Role of Circulating Proteins

Proteins that circulate in the blood are being looked at as potential biomarkers and drug targets. These proteins often act as messengers and regulators in the body, playing an important role in both healthy and unhealthy states. Thanks to recent advances in technology, scientists can more easily measure and change the levels of these circulating proteins. However, determining whether these proteins actually cause cardiometabolic diseases in controlled studies is difficult and costly. Traditional studies that observe trends can also be misleading because they can be affected by various biases.

#Mendelian Randomization as a Solution

In the last ten years, Mendelian randomization (MR) has become a valuable tool for testing whether certain factors truly affect health outcomes. This method uses genetic variants to assess the impact of a specific factor, such as the level of a circulating protein, on a health outcome. To effectively use MR, three important conditions need to be met: the genetic variant must reliably predict the factor, it should not be linked to other confounders, and it must affect the outcome through the factor alone, not through any other pathways.

Recent studies have identified certain genetic variants, known as cis-protein quantitative trait loci (pQTLs), which are linked to the levels of circulating proteins. These variants can effectively serve as genetic tools in MR analyses. However, one complication arises from genetic variants that are close to each other and may influence both the factor and the outcome through different mechanisms. This can violate the conditions needed for MR to work properly. To counter this issue, scientists use Colocalization analyses to determine if the factor and the outcome share the same genetic variants.

#Colocalization Analysis Methods

One of the methods used for colocalization analysis is called coloc. This method evaluates five different hypotheses about the relationship between the factor and the outcome using a statistical approach. It can determine if there is a shared genetic relationship, or if the genetic relationships are distinct for each. The probability of shared genetic relationships is known as the colocalization probability. However, this analysis operates under the assumption that there is either one shared causal variant or two different causal variants, which may not always be accurate, especially for complex traits.

Several extended versions of coloc have been proposed to relax these assumptions. For example, coloc+SuSiE integrates additional statistical techniques to enhance analysis, and another method called SharePro identifies groups of related variants to better assess colocalization.

Recent studies have applied MR analyses using pQTLs along with different colocalization methods to identify possible biomarkers or drug targets for complex diseases.

#Study Overview

In this study, researchers evaluated the effectiveness of coloc, coloc+SuSiE, PWCoCo, and SharePro in identifying associations between circulating protein levels and important traits related to cardiometabolic diseases, such as blood pressure, glucose levels, and cholesterol levels. The researchers wanted to see how well these methods supported high-confidence associations and if their findings matched results from other studies.

The study used data from a large cohort to analyze the relationship between circulating proteins and various cardiometabolic traits. They tested a total of 7,675 protein-trait associations to see how many shared strong colocalization evidence, which can indicate reliable links between the proteins and the traits.

#Findings: Colocalization Evidence

Out of the associations tested, a specific number did not show a clear relationship. The researchers found that the colocalization analyses on these pairs did not support meaningful connections, with one exception where SharePro identified a strong connection between two specific factors.

In another part of the analysis, they looked at associations that showed significant relationships. They found that the methods performed reasonably well in supporting associations with strong colocalization evidence. For example, SharePro identified many associations that were considered significant, highlighting its potential strengths.

Interestingly, while coloc and its variants showed some effectiveness, SharePro consistently provided stronger support for the highest number of significant associations.

#Sensitivity Analyses and Prior Probability

The choice of prior probability in colocalization analyses can have a big impact on the results. Sensitivity analyses showed that SharePro was more stable across varying prior probabilities compared to the other methods. It was able to support associations even when the colocalization probabilities were low, while other methods struggled with lower confidence levels.

These analyses also revealed that SharePro uniquely supported certain associations that were not backed by the other methods, suggesting that it might offer additional value in understanding the data.

#Comparison with Exome-Wide Association Studies

The researchers then compared their findings with significant gene-level associations found in other studies, specifically those examining rare genetic variants. They found that many of the associations supported by MR and colocalization analyses agreed with these gene-level findings.

SharePro was particularly effective, supporting a majority of these gene-trait associations. This indicates that using this method might reveal more biologically relevant connections between proteins and traits compared to others.

#Known Drug Targets

The study also looked at whether the identified protein associations were linked to known drug targets. They found several proteins that were already being targeted in existing treatments, suggesting that these findings could help in the development of new therapies for cardiometabolic diseases.

SharePro stood out by identifying additional potential drug targets that might not have been highlighted by other methods, emphasizing its usefulness in drug discovery efforts.

#Unique Associations Supported by SharePro

Finally, the researchers explored associations uniquely backed by SharePro. They identified a handful of associations that were supported without a high risk of bias. Systematic assessments indicated that while some associations had varying degrees of reliability, others showed strong biological relevance based on known functions in previous research.

For example, certain proteins were linked to changes observed in animal models, suggesting that they could be significant in future studies exploring the connection between proteins and metabolic diseases.

#Limitations of the Study

Despite its findings, the study had limitations. The analyses did not cover all available statistical methods for colocalization, and the study was limited to specific populations, which could affect the generalizability of the results.

The researchers acknowledged the need for further exploration using a broader range of methods and a more diverse population sample to improve insights into these complex diseases.

#Conclusion

In conclusion, this study highlighted the effectiveness of SharePro as a method for supporting high-confidence associations identified through Mendelian randomization. By refining evidence through multiple methods and sensitivity analyses, the potential for discovering biomarkers and drug targets for cardiometabolic diseases could be significantly improved.

This work encourages the application of various colocalization methods and stresses the importance of validating findings against known gene-level associations. The results point towards opportunities for future research in precision medicine and the discovery of new treatments for metabolic disorders.