Olink Explore 384 Cardiometabolic II Panel

What is the Olink Explore 384 Cardiometabolic II Panel 

Customized panel for human

The Olink Explore 384 CardiometabolicⅡPanel quantifies 368 protein biomarkers in parallel using only 1 μL of biological sample per measurement across 88 samples. Leveraging normalized protein expression (NPX) units, the platform ensures precise relative quantification while combining eight curated assay panels with minimal analyte overlap. Its modular design enables tailored experimental configurations, allowing researchers to expand proteomic coverage and generate customized protein profiles aligned with specific study goals.

Features of the pane

• Species: Optimized for human proteome analysis (cross-species applications untested).
• Proteins: Parallel quantification of 384 cardiometabolic protein biomarkers.
• Sample: Requires only 1 µL of plasma, serum, or equivalent biofluids.
• Readout: NPX-normalized outputs for reproducible relative quantification.
• Platform: Exclusively compatible with Olink Signature Q100 instrumentation.

List of 384 human derived biomarkers

Protein category

The Olink Explore 384 Cardiometabolic II Panel includes 367 proteins categorized into nine main groups: Cytokines & Growth Factors, Receptors, the enzymes, Extracellular Matrix Proteins, Immune-related Proteins, Transport & Binding Proteins, Enzyme Inhibitors, Signaling Molecules, and other functional proteins. (see Table. List of Olink Explore 384 Cardiometabolic II Panle. The selection of these protein biomarkers takes into account their dynamic range in the sample and their proximity to cardiovascular disease. Explore 384 Cardiometabolic II Panel contains known human cardiovascular and inflammatory markers, as well as candidate proteins with great potential as markers of cardiovascular disease. Each protein is carefully selected by our experts in the field. Each low-abundance protein analyte was evaluated for sample material, specificity, precision, sensitivity, dynamic range, matrix effects, and interferences.

Protein Functions

Biological process

Mainly related to immune system diseases, signal transduction, cytokine signal transduction, etc.

Disease area

Mainly associated with metabolic, cardiovascular, cancer, immunology, and diabetes.

The Application of Olink Explore 384 Cardiometabolic II Panel.

The Olink Explore 384 Cardiometabolic II Panel enables high-throughput quantification of 384 protein biomarkers associated with cardiovascular and metabolic pathways, providing a multi-dimensional risk assessment for conditions like atherosclerosis, heart failure, and metabolic syndrome. By integrating proteomic data, researchers can:

• Pathway analysis of insulin signaling, lipid metabolism, and mitochondrial function in vitro.
• Multi-omics integration with transcriptomic/metabolomic datasets to map molecular networks.
• Biomarker discovery for metabolic dysfunction in preclinical studies.

Workflow of Olink Proteomics

Why CPR

Multi-Omics Integration Ready

Seamlessly correlates with genomic, transcriptomic, and metabolomic datasets via advanced bioinformatics pipelines, enabling systems-level cardiometabolic pathway analysis.

Customizable Panel Design

Offers 42 flexible assay slots for study-specific biomarkers while retaining core cardiometabolic coverage (342 fixed targets).

Ultra-Sensitive Detection

Quantifies low-abundance proteins at sub-pg/mL sensitivity using PEA technology, even in complex matrices (e.g., plasma, serum).

High-Throughput Scalability

Processes 88 samples/run with minimal hands-on time, ideal for large cohorts.

Demo Results of Olink Data

Transthyretin levels determined by V142I carrier status (Naman S. Shetty, et al. 2024)

Sample Requirements

Case Study

Using proteomics to identify the mechanisms underlying the benefits of statins on ischemic heart disease

Journal: npj Cardiovascular Health
Year: 2024

• Background
• Methods
• Results

Ischemic heart disease (IHD) is the single leading cause of death worldwide. Statins are the mainstay of treatment for IHD. However, the specific mechanism of statin therapy for IHD has not been elucidated. To study the mechanism by protein, we used a two-step Mendelian randomization (MR) approach. First, we used protein genome-wide associations to examine the association of genetically mimicked statins with 2923 proteins to identify statin-affected proteins and validated these findings using deCODE.

We then obtained associations of this genetic instrument with 2923 proteins assayed in UKB-PPP. The UK Biobank is a large population-based cohort in the UK that recruited about 500,000 participants aged 40~69 years old between 2006 and 201026. The UKB-PPP is a pre-competitive consortium of 13 biopharmaceutical companies that funded multiplex proteomics data generation for 54,219 UKB participants. Details of the UKB-PPP can be found in previous publication. In the antibody-based Olink Explore 3072 platform, the researchers tested 2923 unique proteins in 8 protein panels (cardiometabolic, cardiometabolic Ⅱ., inflammation, inflammation ii., neurology, neurology Ⅱ., oncology, and oncology Ⅱ) and performed quality control. 1 protein (GLIPR1) was excluded due to poor quality control, so 2922 proteins were included in the analysis.

In the protein analysis of genetically mimetic statins (Figure 1), 9 proteins were identified after FDR, of which 4 proteins (PLA2G7, FGFBP1, ANGPTL1, PTPRZ1) were downregulated by statins and 5 proteins (EFNA4, COL6A3, ASGR1, PRSS8, PCOLCE) were up-regulated by statins. In the replication analysis, there was one protein (PTPRZ1) that could not be replicated due to lack of data. For the 8 replicable proteins, we replicated the association of statins with 4 proteins, including PLA2G7, FGFBP1, PRSS8, and PCOLCE.

Figure 1. Volcano map for MR analysis to investigate associations between genetically mimicked statins and 2923 proteins to identify proteins affected by statins. (Jie V. Zhao, et al. 2024)

FAQs

References

1. Shetty, N. S., Gaonkar, M., Patel, N., et al. (2024). Determinants of transthyretin levels and their association with adverse clinical outcomes among UK Biobank participants. Nature communications, 15(1), 6221. https://doi.org/10.1038/s41467-024-50231-1 
2. Jie V. Zhao, Junmeng Zhang. (2024). Using proteomics to identify the mechanisms underlying the benefits of statins on ischemic heart disease. npj Cardiovascular Health, (2024)1:15. Advance online publication. https://doi.org/10.1038/s44325-024-00018-6