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New standards for biomarkers of ageing

A paper has put forward a new framework for standardising the development and validation of biomarkers of ageing to better predict longevity and quality of life.
Led by Harvard researchers, the team has zeroed in on biomarkers of ageing using omic data from population-based studies.
The team included ageing and longevity expert Alex Zhavoronkov, PhD, founder and CEO of AI-driven drug discovery company Insilico Medicine, and the findings appeared in Nature Medicine.
Ageing is associated with a number of biological changes including increased molecular and cellular damage, however, researchers do not yet have a standardised means to evaluate and validate biomarkers related to ageing.
In order to create those standards as well as actionable clinical tools, the team analysed population-based cohort studies built on omic data (data related to biological molecules which can include proteomics, transcriptomics, genomics, and epigenomics) of blood-based biomarkers of ageing. The researchers then compared the predictive strength of different biomarkers, including study design and data collection approaches, and looked at how these biomarkers presented in different populations.
In order to better assess the impact of ageing using biomarkers, the researchers found that clinicians needed to expand their focus to consider not only mortality as an outcome, but also how biomarkers of aging are associated with numerous other health outcomes, including functional decline, frailty, chronic disease, and disability. They also call for the standardisation of omic data to improve reliability.
“Omics and biomarkers harmonisation efforts, such as the Biolearn project, are instrumental in validation of biomarkers of aging” said co-first author Mahdi Moqri, PhD, of the Division of Genetics.
Biolearn is an open-source project for biomarkers of aging and is helping to harmonise existing ageing biomarkers, unify public datasets, and provide computational methodologies.
The team also emphasised the importance of continued collaborations among research groups on “large-scale, longitudinal studies that can track long-term physiological changes and responses to therapeutics in diverse populations”, and that further work is required to understand how implementation of biomarker evaluation in clinical trials might improve patient quality of life and survival.
“If we hope to have clinical trials for interventions that extend healthy lifespan in humans, we need reliable, validated biomarkers of ageing,” said co-first author Jesse Poganik, PhD, of the Division of Genetics.
“We hope that our framework will help prioritise the most promising biomarkers and provide health care providers with clinically valuable and actionable tools.”
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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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