by Martina Svensson,Lund University

Workflow of ProtAIDe-Dx on GNPC. Credit:Nature Medicine(2026). DOI: 10.1038/s41591-026-04303-y

The symptom profiles of different neurodegenerative diseases often overlap, and diagnosing age-related cognitive symptoms is complex. A patient may have multiple overlapping disease processes in the brain at the same time, for example, Alzheimer's disease and Lewy body disease, especially in the early stages of cognitive decline. Now, researchers at Lund University have developed an AI model showing that it is possible to detect several neurodegenerative diseases from a single blood sample. Their paper ispublishedin the journalNature Medicine.

Researchers Jacob Vogel and Lijun An, together with colleagues from the Swedish BioFINDER study and theGlobal Neurodegenerative Proteomics Consortium(GNPC, an international research consortium that has created the world's largest proteomics database for neurodegenerative diseases) have developed the AI model based on protein measurements from more than 17,000 patients and control participants, collected from several datasets within GNPC's proteomics database, the largest in the world for proteins related to neurodegenerative diseases.

"Our hope is to be able to accurately diagnose several diseases at once with a single blood test in the future," says Vogel, who led the study. He is an assistant professor, head of a research group, and part of the strategic research area MultiPark at Lund University.

Usingadvanced statistical learningmethods and a process known as "joint learning," the researchers' AI model was able to identify a specific set of proteins that form a general pattern for diseases involving brain degeneration. This learned pattern was then used to diagnose different neurodegenerative diseases. Vogel confirms that theirAI modeloutperforms previous models, while also being able to diagnose five different dementia-related conditions: Alzheimer's disease, Parkinson's disease, ALS, frontotemporal dementia, and previous stroke.

The study stands out compared to similar research because the model's results were validated across multiple independent datasets, according to the researchers.

"We also found that theprotein profilepredicted cognitive decline better than the clinical diagnosis did, and it seems like individuals with the same clinical diagnosis may have different underlying biological subtypes," says An, the study's first author.

Many individuals diagnosed with Alzheimer's disease showed a protein pattern more similar to other brain disorders. "This could mean they have more than one underlying disease, that Alzheimer's can develop in multiple ways, or that the clinical diagnosis is incorrect. However, I don't think current protein measurements from blood samples will be sufficient on their own to diagnose multiple diseases. We need to refine the method and combine it with other clinical diagnostic tools," says Vogel.

At the same time, he emphasizes that diagnostics is not the only application of their model. Many of the proteins that contributed to the AI model point to areas where follow-up studies could lead to a better understanding of the disease-driving processes behind these neurodegenerative conditions.

The next step is to include moreproteomic markersusing advanced methods such as mass spectrometry to identify patterns unique to each disease. "We hope to inch closer toward a blood test that can make a reliable diagnosis across disorders without aid from other clinical instruments," says Vogel.

Publication details Lijun An et al, A deep joint-learning proteomics model for diagnosis of six conditions associated with dementia, Nature Medicine (2026). DOI: 10.1038/s41591-026-04303-y Journal information: Nature Medicine