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US$80m backs exceptional longevity study

NIH funding of US$80m will support further research into exceptional longevity, continuing a long-running study of families whose members live far longer than statistical models predict.
The funding renews support for the Long Life Family Study, an international project tracking multiple generations, including people who have lived to 100 and beyond.
Researchers are seeking genetic clues that may explain how some individuals avoid or delay common diseases of ageing.
Launched in 2004, the study has enrolled more than 5,000 participants from over 530 families in the US and Denmark.
When enrolment began in 2006, the oldest generation averaged 90 years of age, with several surviving beyond 110. Their children are now in their 80s and grandchildren in their 50s and 60s.
The research is based at Washington University School of Medicine in St. Louis and is supported by the National Institute on Aging, part of the National Institutes of Health.
Michael A. Province, the study’s principal investigator and a professor in the department of genetics at WashU Medicine, said: “So much of medical research is focused on genetic problems that cause disease, and importantly so — we have learned a tremendous amount from that strategy.
“But I am also fascinated by the opposite question: are there genetic variants that cause good things to happen in the body?
“Our study suggests that there is a wide variety of genetic ways that these long-lived families could be protected from chronic diseases as they age.”
Over the two decades since the study began, researchers have identified features linked to healthy ageing.
Many long-lived families showed better cardiovascular health than the average population, including healthier blood pressure and lower rates of diabetes.
In the past five years, findings suggested that health advantages were not uniform, pointing to multiple biological routes to healthy ageing.
Some families stood out for cognition or blood pressure, while others showed stronger lung function or grip strength.
Overall, the families tended to have lower rates of diabetes. One analysis identified a genetic variant linked to lower haemoglobin A1c, a measure of average blood sugar levels used to diagnose diabetes.
The data also revealed a paradox. Obesity was as common in long-lived families as in comparison populations, yet these families had around half the expected number of diabetes cases.
The unusually long lifespans also enabled researchers to identify a gene associated with late-onset Alzheimer’s disease.
In a separate finding, they uncovered a genetic variant linked to extreme longevity and lower blood pressure, but also a slightly increased risk of head and neck cancer, highlighting the need for caution when targeting rare genetic variants.
The renewed funding will allow re-analysis of whole genomes using long-read DNA sequencing, which can detect genetic variations missed by earlier methods.
This will expand the study to 7,800 participants. Researchers also plan to enrol more families, particularly those of African ancestry, as participants to date have been largely of European descent.
On the diabetes findings, Province said: “Something is protecting them from diseases associated with obesity, and we’d love to find out what that is.”
He added: “We plan to enrol more families and especially families of African ancestry.
“The larger and more diverse our dataset, the better we will be able to identify inherited genetic variants associated with longevity and then distinguish which are causing the protective effects and which are just inherited and ‘along for the ride,’ so to speak.”
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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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