Recent studies have hinted at links between viruses and bacteria and the onset of IR-NCDs. For instance, SARS-CoV-2causes PCC, and Epstein-Barr virus is strongly linked to MS. But for many diseases such as ME/CFS or RA, only indirect associations exist, and mechanistic insight is still missing. The complexity arises from the vast diversity of potential microbial triggers and the reality that only a subset of infected individuals develops chronic disease. Genetic predisposition and environmental exposures are also thought to influence who transitions from infection to chronic illness, but the specifics remain elusive. This uncharted territory is what we call the "dark matter" of IR-NCDs.

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Our project combines two powerful datasources:

Clinical cohorts (patients diagnosed and followed over time) and population-scale cohorts like LifeLines, which include pre-diagnostic samples from individuals who later developed IR-NCDs. This allows us to identify biomarkers that precede disease onset and can inform on causality beyond just correlation. Our toolbox includes high-throughput antibody repertoire profiling (PhIP-Seq), microbiome sequencing, metabolomics, whole genome sequencing, and novel genotyping methods for immune receptor loci. We integrate these layers with machine learning to predict risk, progression, and treatment response.

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Our consortium is intentionally cross-disciplinary and comparative. We do not just study one disease in isolation—we compare immune, genetic, and environmental features across six IR-NCDs. We match this with a powerful infrastructure: multiple large, longitudinal cohorts, pre-diagnostic biobanked samples, high-throughput immunological screening, and pioneering technologies for genetic profiling of B-cell and T-cell receptors.

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In addition to identifying risk factors, we validate them. Findings from omics data are tested in well-established disease models, including gnotobiotic mice, to establish causality. For example, we can introduce suspected pathogens into mice engineered to reflect human immune genetics and observe whether disease symptoms emerge. This approach gives our findings clinical and biological relevance. We also collaborate with patient organisations and public health experts to translate our findings into diagnostics, treatment guidance, and policy recommendations.

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Ultimately, ID-DarkMatter-NCD represents a transformational leap in how we investigate chronic diseases. We move beyond correlation, beyond speculation. We aim to clarify cause and consequence and unlock new strategies for prevention and treatment—not only for the diseases we study directly, but for a wider category of immune-related conditions impacted by infectious triggers.

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We aim to

1. Identify IDs triggeringIR-NCDs by screening for antibody responses against 600,000 ID antigens
2. To disentangle environmental and genetic factors affecting the transitionfrom IDs to IR-NCDs.

We will combine novel multi-omics approaches and technologies for personalized genotyping of HLA and adaptive immune receptor genes to deeply profile 6,000 patients of six IR-NCDs (PCC, multiple sclerosis,ME/CFS, rheumatoid arthritis, lupus, IBD) to identify novel biomarkers and disease mechanisms.

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This project will represent the largest and most deeply profiled systematic study of multiple IR-NCDs with layered datasets allowing for comparative analyses yielding insights into shared mechanisms and potential differences in the role of IDs between IR-NCDs.Building on associations identified from population scale and clinical cohorts, we will demonstrate causality in gnotobiotic mouse models, and leverage machine learning (ML) algorithms to predict disease progression and response to treatment. The combination of novel assays with ML represents a broadly applicable pipeline that can be used for studying the interplay of any otherIDs/ IR-NCDs.

We are developing a generalizable pipeline for linking infectious agents to chronic diseases. Outputs will include:

• Immunological and genetic biomarkers
• Predictive algorithms for disease onset and treatment response
• Open-access datasets and protocols
• A whitepaper detailing our methodology and use cases
• Proof-of-concept ELISA and PCR test kits for diagnostics
• These deliverables will empower healthcare systems to detect and treat IR-NCDs earlier and more effectively.

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IR-NCDs currently cost Europe around €2.4 trillion annually. A 10% reduction in incidence or severity through better diagnostics and prevention could save hundreds of millions of euros each year.Beyond economics, our work addresses critical gaps in patient care, enabling precision medicine for diseases often misdiagnosed or poorly understood. By developing a platform approach, we also equip Europe for future pandemics and chronic disease challenges.