HUPO 2024

We are excited to announce that we will be participating in the 23^rd Human Proteome Organization World Congress, held from October 20 to 24 in Dresden, Germany. As pioneers in proteomics, Olink is thrilled to showcase our latest advancements and innovations at this prestigious event.

Visit us at Booth #2 to engage with our team of experts and witness firsthand the capabilities of our Signature Q100 instrument in action. Our unmatched scalability and proven sensitivity support the entire journey from discovery to clinic, ensuring that you stay at the forefront of proteomics research. Olink is a trusted partner with PEA Technology, with over 2000 publications. Come and check out our instrument in action to see what the Q100 can do for you!

At HUPO 2024, we are particularly excited to highlight our luncheon symposium on “New Biological Truths Discovered In The World’s Largest Proteomics Study.” Join us in exploring the evolution of the UK Biobank and its profound impact on the future of healthcare from industry leaders. Learn how Olink’s technologies are uniquely positioned to support large-scale proteomics studies, driving significant advancements in the field. This symposium is a must-attend for academics, core facilities, and leaders looking for the next big breakthrough in proteomics.

New Biological Truths Discovered In The World’s Largest Proteomics Study
DATE:  22 October 2024
TIME:  12:00 – 13:00 CET
ROOM:  Conference room 1+2

Presenting:
New Frontiers for the Circulating Proteome

Abstract: 
The circulating proteome has long been regarded as one of the most useful sources for exploratory and diagnostic research. Recent technological advancements in mass spectrometry and affinity-based assays have enhanced study scales, sample throughput, and consistency in protein quantifications. However, challenges remain in understanding its biological complexity, and there is a need to emphasize best practices that incorporate pre-analytical factors, inter-individual variation, and genetic associations into our investigations. Recognizing sources of bias from preparatory, biological, and pathological factors is essential for meaningful and sustainable data interpretation, particularly alongside other data types. Longitudinal resampling can minimize confounding and provide insights into the proteome connections over time – but these studies pose coordination challenges. Large-scale studies offer opportunities to explore previously unseen physiologies, and their statistical power allows for the integration of proteomics with other data types – but these efforts require significant resources. Never beforehas there been more data about blood proteins than today, and technological advances and new tools in computational science have the potential to enhance our previous knowledge. Therefore, it is time to adopt a broader proteome classification framework, explore new relationships between detection, concentration, and function, and integrate innovative sampling strategies to better understand human health and disease.

Presenting: 
How AI and Protein Patterns Are Changing Disease Prediction: Blood protein assessment of leading incident diseases and mortality in the UK Biobank

Abstract: 
In this presentation, we review a recent breakthrough in predicting risk of age-related diseases. Using trusted machine learning methodologies, we test and summarise relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). Next, we review the development of protein-based scores (ProteinScores) using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c—a clinical marker used to monitor and diagnose type 2 diabetes. This research represents a promising step forward in risk prediction: augmenting traditional risk factors with proteomic, metabolomic and genetic data types may further hone risk stratification. Being able to detect early warning signs for a broad set of conditions may lead to opportunities for early intervention and prevention, potentially marking a significant moment in healthcare research.