![\"This](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/04\/ai-blood-dementia-neuroscience.jpg\")
Researchers developed an AI model that uses the world\u2019s largest proteomics database to identify a general pattern for brain degeneration. Credit: Neuroscience News<\/p>\nSummary: Diagnosing neurodegenerative diseases is notoriously difficult because symptoms often overlap\u2014a patient might have Alzheimer\u2019s, Lewy body disease, and the effects of a minor stroke all at once. Researchers have developed a breakthrough AI model that can detect five different conditions from a single blood sample.<\/p>\n
By analyzing protein patterns across a massive database of 17,000 individuals, the AI identified biological \u201csignatures\u201d for Alzheimer\u2019s, Parkinson\u2019s, ALS, frontotemporal dementia, and stroke. The study suggests that a protein profile is actually a better predictor of cognitive decline than a traditional clinical diagnosis.<\/p>\n
Key Facts<\/p>\n
The \u201cJoint Learning\u201d Advantage: Instead of looking for one disease at a time, the AI used \u201cjoint learning\u201d to identify a general pattern of brain degeneration across multiple disorders.Five-in-One Diagnosis: The model successfully distinguished between Alzheimer\u2019s, Parkinson\u2019s, ALS, frontotemporal dementia, and vascular damage from previous strokes.Biological Subtypes: The AI revealed that people with the same clinical diagnosis (e.g., Alzheimer\u2019s) often have different biological protein profiles, suggesting that \u201cone-size-fits-all\u201d treatments may not work.World\u2019s Largest Database: The model was trained on the GNPC database, the largest proteomics repository in the world for neurodegenerative diseases.<\/p>\n
Source: Lund University<\/p>\n
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. <\/p>\n
Now, researchers at Lund University in Sweden have developed an AI model showing that it is possible to detect several neurodegenerative diseases from a single blood sample.<\/p>\n
The study is published in\u00a0Nature Medicine.<\/p>\n
Researchers developed an AI model that uses the world\u2019s largest proteomics database to identify a general pattern for brain degeneration. Credit: Neuroscience News<\/p>\n
Different neurodegenerative conditions can present with similar symptoms, making it difficult to distinguish between them, for example, Alzheimer\u2019s disease and Lewy body disease, especially in the early stages of cognitive decline.<\/p>\n
Now, researchers Jacob Vogel and Lijun An, together with colleagues from the Swedish BioFINDER study and the Global Neurodegenerative Proteomics Consortium (GNPC, an international research consortium that has created the world\u2019s largest proteomics database for neurodegenerative diseases) have developed an AI model capable of detecting multiple diseases at once.<\/p>\n
The model is based on protein measurements from more than 17,000 patients and control participants, collected from several datasets within GNPC\u2019s proteomics database, the largest in the world for proteins related to neurodegenerative diseases.<\/p>\n
\u201cOur hope is to be able to accurately diagnose several diseases at once with a single blood test in the future,\u201d says Jacob 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, Sweden.\u00a0<\/p>\n
Using advanced statistical learning methods and a process known as \u201cjoint learning,\u201d the researchers\u2019 AI model was able to identify a specific set of proteins that form a general pattern for diseases involving brain degeneration.<\/p>\n
This learned pattern was then used to diagnose different neurodegenerative diseases. Vogel confirms that their AI model outperforms previous models, while also being able to diagnose five different dementia-related conditions: Alzheimer\u2019s disease, Parkinson\u2019s disease, ALS, frontotemporal dementia, and previous stroke.<\/p>\n
The study stands out compared to similar research because the model\u2019s results were validated across multiple independent datasets, according to the researchers.<\/p>\n
\u201cWe also found that the protein profile predicted cognitive decline better than the clinical diagnosis did, and it seems like individuals with the same clinical diagnosis may have different underlying biological subtypes,\u201d says Lijun An, the study\u2019s first author.<\/p>\n
Many individuals diagnosed with Alzheimer\u2019s disease showed a protein pattern more similar to other brain disorders.<\/p>\n
\u201cThis could mean they have more than one underlying disease, that Alzheimer\u2019s can develop in multiple ways, or that the clinical diagnosis is incorrect. However, I don\u2019t 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,\u201d says Jacob Vogel.<\/p>\n
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.<\/p>\n
The next step is to include more proteomic markers using advanced methods such as mass spectrometry to identify patterns unique to each disease.<\/p>\n
\u201cWe hope to inch closer toward a blood test that can make reliable diagnosis across disorders without aid from other clinical instruments,\u201d says Jacob Vogel<\/p>\n
Facts<\/p>\n
GNPC
The Global Neurodegeneration Proteomics Consortium (GNPC) is an international research collaboration and a large-scale database focused on studying proteins linked to neurodegenerative diseases, such as Alzheimer\u2019s disease and frontotemporal dementia. GNPC has created one of the world\u2019s largest databases of proteins associated with neurodegenerative diseases, enabling the systematic analysis of large datasets to accelerate the discovery of biomarkers and advance research on brain disorders.<\/p>\n
Proteomics
Proteomics involves studying large datasets on how all proteins are expressed in a collected biological sample. It reveals the unique pattern of protein levels \u2013 how much of each protein is present \u2013 in, for example, a blood sample, which can provide researchers with important clues about biological functions and how diseases develop.<\/p>\n
Key Questions Answered:Q: Why is a protein test better than a doctor\u2019s diagnosis?<\/p>\n
A: Clinical diagnoses rely on symptoms like memory loss or tremors, which can be caused by many different things. This AI looks at proteomics\u2014the actual molecular \u201ctrash\u201d and signaling proteins the brain leaks into the blood. The study found these biological markers often catch disease processes that doctors miss or misidentify.<\/p>\n
Q: Can this test tell if I have two diseases at once?<\/p>\n
A: Yes. The researchers found many patients diagnosed with Alzheimer\u2019s actually had protein patterns belonging to other disorders. This suggests many people suffer from \u201cmixed dementia,\u201d and this AI is one of the first tools capable of unmasking those overlapping layers.<\/p>\n
Q: When can I get this blood test at my local clinic?<\/p>\n
A: While the results are a \u201cworld first,\u201d lead researcher Jacob Vogel notes that the method still needs refinement. The next step involves using mass spectrometry to find even more specific markers. It\u2019s currently a powerful research and drug-trial tool, with the goal of becoming a standard clinical test in the near future.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this AI and dementia research news<\/p>\n
Author:\u00a0Anna Elizabeth Hellgren<\/a>
Source:\u00a0Lund University<\/a>
Contact:\u00a0Anna Elizabeth Hellgren \u2013 Lund University
Image:\u00a0The image is credited to Neuroscience News<\/p>\n