Updated on 29 October 2026
CORE TRACE: Seeking partners for AI-enabled kidney progression and treatment-benefit biomarkers
Computational Epidemiologist | NordicRWE at NordicRWE AS
Oslo, Norway
About
Can a blood protein profile reveal clinically useful kidney risk that routine tests and previous kidney-function measurements still miss—and help inform treatment decisions?
CORE TRACE is an emerging Horizon Europe concept addressing this question in chronic kidney disease. We are developing a collaboration that connects proteomics, longitudinal clinical information, established AI methods and real-world evidence, with a clear focus on clinical usefulness.
THE PROPOSED APPROACH
We aim to evaluate whether circulating proteins improve prediction of kidney deterioration beyond current kidney function, albuminuria and the preceding clinical course. Selected markers would undergo independent validation, with complementary tubular and genetic evidence supporting biological interpretation.
A separately designed treatment study would investigate whether prespecified markers or scores help identify variation in treatment benefit. The eventual design will depend on suitable data, sample timing and statistical feasibility.
WHAT WE BRING
NordicRWE contributes expertise in epidemiology, linked health-data research, longitudinal analysis, comparative effectiveness and causal inference. Scientific development is supported by experience in kidney and cardiovascular research, proteomics and genetic epidemiology.
The concept builds on potential Norwegian cohort and registry resources. Dataset access, participant overlap and study-specific permissions will be established with the relevant custodians.
PARTNERS WE ARE SEEKING
We welcome discussions with teams offering:
• CKD cohorts suitable for independent biomarker and prediction-model validation.
• Existing clinical trials or well-characterised treatment cohorts with suitable pretreatment samples and kidney outcomes.
• Clinical laboratory expertise in protein assays, analytical validation and transfer to practical panels.
• Trustworthy AI and multimodal modelling, with strengths in reproducibility, interpretability and external validation.
• Regulatory strategy, health economics, clinical implementation and patient involvement.
WHY COLLABORATE?
Our ambition is to build a credible evidence pathway from biomarker signals to independently validated, clinically useful information. Partners can help shape the clinical question, study design and their scientific contribution while the consortium is being developed.
Target topic: HORIZON-HLTH-2027-02-TOOL-01-two-stage.
We would welcome a focused brokerage meeting to explore complementary expertise, suitable resources and a concrete role in developing the proposal.
Topic
- Destination 5: HORIZON-HLTH-2027-02-TOOL-01-two-stage: Development of predictive biomarkers of disease progression and treatment response by using AI methodologies for chronic non-communicable diseases
Type
- Consortium/Coordinator seeks Partners
Organisation
Similar opportunities
Project cooperation
AI Predictive Biomarkers – Chronic Kidney Disease
- Partner seeks Consortium/Coordinator
- Destination 5: HORIZON-HLTH-2027-02-TOOL-01-two-stage: Development of predictive biomarkers of disease progression and treatment response by using AI methodologies for chronic non-communicable diseases
AHMET BAS
Prof. Dr. Ahmet Baş at Istanbul University Cerrahpasa
İstanbul, Türkiye
Project cooperation
Research partner with responsibility for a defined experimental task
- Destination 5: HORIZON-HLTH-2027-03-TOOL-02: Advancing bio-printing of living cells for regenerative medicine
- Destination 3: HORIZON-HLTH-2027-02-DISEASE-01-two-stage: Innovative healthcare interventions for non-communicable diseases
- Destination 5: HORIZON-HLTH-2027-03-TOOL-08: Towards the next generation of frontier Artificial Intelligence models for healthcare
- Destination 5: HORIZON-HLTH-2027-03-TOOL-04: Virtual Human Twins (VHTs) for integrated clinical decision support in prevention and diagnosis
- Destination 4: HORIZON-HLTH-2027-01-CARE-02: Personalised approaches to reduce risks from Adverse Drug Reactions due to administration of multiple medications
- Destination 5: HORIZON-HLTH-2027-02-TOOL-01-two-stage: Development of predictive biomarkers of disease progression and treatment response by using AI methodologies for chronic non-communicable diseases
Mirel Adrian Popa
Scientific Researcher at Instititute of Cellular Biology and Pathology Nicoale Simionescu
Bucharest, Romania
Project cooperation
Clinical validation of point-of-care diagnostics for cardiovascular and cardiorenal diseases
- Destination 6: HORIZON-HLTH-2027-02-IND-02-two-stage: Portable and versatile Point-of-care diagnostics
Gian Luca Colussi
Associate Professor of Internal Medicine at Università degli Studi di Ferrara
Ferrara, Italy