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Nose Picking Could Increase Risk for Alzheimer’s and Dementia

Summary: The Chlamydia pneumoniae bacteria can travel directly from olfactory nerve in the nose and into the brain, forcing brain cells to deposit amyloid beta and inducing Alzheimer’s pathologies. Researchers say protecting the lining of the nose by not picking or plucking nasal hairs can help lower Alzheimer’s risks.

Source: Griffith University

Griffith University researchers have demonstrated that a bacteria can travel through the olfactory nerve in the nose and into the brain in mice, where it creates markers that are a tell-tale sign of Alzheimer’s disease.

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

Professor James St John, Head of the Clem Jones Center for Neurobiology and Stem Cell Research, is a co-author of the world first research.

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

The olfactory nerve in the nose is directly exposed to air and offers a short pathway to the brain, one which bypasses the blood-brain barrier. It’s a route that viruses and bacteria have sniffed out as an easy one into the brain.

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

“Picking your nose and plucking the hairs from your nose are not a good idea,” he said. Image is in the public domain

“We need to do this study in humans and confirm whether the same pathway operates in the same way. It’s research that has been proposed by many people, but not yet completed. What we do know is that these same bacteria are present in humans, but we haven’t worked out how they get there.”

There are some simple steps to look after the lining of your nose that Professor St John suggests people can take now if they want to lower their risk of potentially developing late-onset Alzheimer’s disease.

“Picking your nose and plucking the hairs from your nose are not a good idea,” he said.

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

Smell tests may also have potential as detectors for Alzheimer’s and dementia says Professor St John, as loss of sense of smell is an early indicator of Alzheimer’s disease. He suggests smell tests from when a person turns 60 years old could be beneficial as an early detector.

“Once you get over 65 years old, your risk factor goes right up, but we’re looking at other causes as well, because it’s not just age—it is environmental exposure as well. And we think that bacteria and viruses are critical.”

About this Alzheimer’s disease research news

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

Original Research: Open access.
“Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs” by Sheng Liu et al. Scientific Reports

See also

This shows a woman looking at a piece of food as though it smells bad

Abstract

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

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

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

We validate both models on an internal held-out cohort from The Alzheimer’s Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer’s Coordinating Center (NACC).

The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand.

The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An 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 that are predictive of Alzheimer’s disease, and leverage them to achieve accurate early detection of the disease.

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