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Study reveals link between air pollution and Parkinson’s disease

People living in regions with median levels of air pollution have a 56 per cent greater risk of developing Parkinson’s disease compared to those living in regions with the lowest level of air pollution, a new study has found.
The US research was conducted to identify national, geographic patterns of Parkinson’s disease and test for nationwide and region-specific associations with fine particulate matter.
Brittany Krzyzanowski, PhD, a researcher at Barrow Neurological Institute, who led the study, said:
“Previous studies have shown fine particulate matter to cause inflammation in the brain, a known mechanism by which Parkinson’s disease could develop.
“Using state-of-the-art geospatial analytical techniques, we were, for the first time, able to confirm a strong nationwide association between incident Parkinson’s disease and fine particulate matter in the US”
The research also found that the relationship between air pollution and Parkinson’s disease is not the same in every part of the country, and varies in strength by region.
The Mississippi-Ohio River Valley was identified as a Parkinson’s disease hotspot, along with central North Dakota, parts of Texas, Kansas, eastern Michigan and the tip of Florida.
People living in the western half of the US are at a reduced risk of developing Parkinson’s disease compared with the rest of the nation.
Krzyzanowski said:
“Regional differences in Parkinson’s disease might reflect regional differences in the composition of the particulate matter.
“Some areas may have particulate matter containing more toxic components compared to other areas.”
Although the researchers have not yet explored the different sources of air pollution, Krzyzanowski notes there is relatively high road network density in the Mississippi-Ohio River Valley and the rust belt makes up part of this region as well.
Krzyzanowski said: “This means that the pollution in these areas may contain more combustion particles from traffic and heavy metals from manufacturing which have been linked to cell death in the part of the brain involved in Parkinson’s disease.”
The population-based geographic study identified nearly 90,000 people with Parkinson’s disease from a Medicare dataset of nearly 22-million.
Those identified with having Parkinson’s disease were geocoded to the neighbourhood of residence, enabling researchers to calculate the rates of Parkinson’s disease within each region.
The average annual concentrations of fine particulate matter in these specific areas were also calculated.
After adjusting for other risk factors, including age, sex, race, smoking history and utilisation of medical care, Barrow researchers were then able to identify an association between a person’s previous exposure to fine particulate matter and their later risk of developing Parkinson’s disease.
Krzyzanowski said: “Population-based geographic studies like this have the potential to reveal important insight into the role of environmental toxins in the development and progression of Parkinson’s, and these same methods can be applied to explore other neurological health outcomes as well.”
The research teams hopes the data from this novel study will help enforce stricter policies that will lower air pollution levels and decrease the risk for Parkinson’s disease and other associated illnesses.
Krzyzanowski said: “Despite years of research trying to identify the environmental risk factors of Parkinson’s disease, most efforts have focused on exposure to pesticides.
“This study suggests that we should also be looking at air pollution as a contributor in the development of Parkinson’s disease.”
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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