Nose picking could increase the risk of Alzheimer’s disease and dementia

Nose picking could increase the risk of Alzheimer’s disease and dementia

Abstract: The bacterium Chlamydia pneumoniae can travel directly from the olfactory nerve in the nose to the brain, causing brain cells to deposit amyloid beta and induce Alzheimer’s pathology. Researchers say that protecting the lining of the nose by not plucking or plucking nose hairs may help reduce the risk of Alzheimer’s disease.

Source: Griffith University

Researchers from Griffith University have shown that bacteria can travel through the olfactory nerve in the nose to the brains of mice, where they create markers that are a telltale sign of Alzheimer’s disease.

The study, published in the journal Scientific reports, showed that Chlamydia pneumoniae used the nerve that runs between the nasal cavity and the brain as an invasion route to invade the central nervous system. Brain cells then responded by depositing the amyloid beta protein that is a hallmark of Alzheimer’s disease.

Professor James St John, head of the Clem Jones Center for Neurobiology and Stem Cell Research, is the co-author of the first study in the world.

“We are the first to show that Chlamydia pneumoniae can go directly into the nose and into the brain where it can cause pathologies that look like Alzheimer’s disease,” Professor St John said. “We’ve seen this happen in a mouse model, and the evidence is potentially frightening for humans as well.”

The olfactory nerve in the nose is directly exposed to air and offers a short route to the brain, one that bypasses the blood-brain barrier. It is the way that viruses and bacteria smelled as simple to the brain.

The team at the Center is already planning the next phase of research and trying to prove that the same pathway exists in humans.

This shows a man's nose
“Plucking noses and pulling nose hairs is not a good idea,” he said. The image is in the public domain

“We need to do this study in humans and confirm if the same pathway works in the same way. It is a research that has been suggested by many people, but it has not been completed yet. What we do know is that these same bacteria are present in humans, but we haven’t discovered how they get there.”

There are some simple nasal care steps that Professor St John suggests people can take now if they want to reduce their risk of potentially developing late-onset Alzheimer’s disease.

“Plucking noses and pulling nose hairs is not a good idea,” he said.

“We don’t want to damage the inside of the nose, and picking and plucking can do that. If you damage the lining of your nose, you can increase the number of bacteria that can get into your brain.”

Smell tests may also have potential as detectors of Alzheimer’s disease and dementia, Professor St John says, as loss of the sense of smell is an early indicator of Alzheimer’s disease. He suggests that smell tests after a person turns 60 could be useful as an early detector.

“Once you’re over 65, your risk factor goes up immediately, but we’re also looking at other causes, because it’s not just age – it’s also environmental exposure. And we think bacteria and viruses are critical.”

About this Alzheimer’s research news

Author: Press office
Source: Griffith University
Contact: Press Office – Griffith University
Picture: The image is in the public domain

Original research: Open access.
A generalizing deep learning model for early detection of Alzheimer’s disease from structural MRIs” by Sheng Liu et al. Scientific reports

See also

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A generalizing deep learning model for early detection of Alzheimer’s disease from structural MRIs

Early diagnosis of Alzheimer’s disease plays a key role in patient care and clinical trials. In this study, we developed a novel approach based on 3D deep convolutional neural networks to accurately distinguish mild dementia caused by Alzheimer’s disease from mild cognitive impairment and cognitively normal individuals using structural MRI.

For comparison, we built a reference model based on the volumes and thickness of previously reported brain regions known to be involved in disease progression.

We validate both models on an internal, non-powered cohort from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and on an external, independent cohort from the National Alzheimer’s Disease Coordinating Center (NACC).

The deep learning model is accurate, achieving an area under the curve (AUC) of 85.12 when distinguishing between cognitively normal subjects and subjects with MCI or mild Alzheimer’s dementia. In the more demanding task of MCI detection, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model where volumes and thickness must be extracted in advance.

The model can also be used to predict progression: subjects with mild cognitive impairment who were misclassified by the model as having mild Alzheimer’s disease dementia progressed to dementia faster over time. Analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer’s disease.

These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers predictive of Alzheimer’s disease and use them to achieve accurate early detection of the disease.

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