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Half-cardio, half-strength training reduces cardiovascular disease risks, study finds

Splitting the recommended amount of physical activity between aerobic and resistance exercise reduces cardiovascular disease risks as much as aerobic-only regimens, new US research has found.
Resistance exercise on its own for the same amount of time did not provide the same heart health benefits when compared to the control group in the new study.
Duck-chul Lee is lead author and professor of kinesiology at Iowa State University.
The researcher said: “If you’re bored with aerobic exercise and want variety or you have joint pain that makes running long distances difficult, our study shows you can replace half of your aerobic workout with strength training to get the same cardiovascular benefits.
“The combined workout also offers some other unique health benefits, like improving your muscles.”
A total of 406 people between 35 and 70 years of age enrolled in the one-year randomised controlled exercise trial.
All participants met the threshold for being overweight or obese and had elevated blood pressure.
The researchers randomly assigned the participants to one of four groups: no exercise, aerobic only, resistance only or aerobic plus resistance.
Participants in one of the three exercise groups worked out under supervision for one hour, three times a week for one year.
Every participant in one of these groups received a tailored workout routine based on their individual fitness levels, health conditions and progression.
Researchers also collected physical activity and diet data outside the lab.
At the start of the year-long clinical trial, six months in and at the end, the research team measured each participant’s systolic blood pressure, low-density lipoprotein cholesterol, fasting glucose and body fat percentage.
They used a composite score to fairly quantify changes across all four factors since each uses a different unit of measurement, with a lower composite score indicating less risk for developing cardiovascular disease.
At the end of the year-long trial, the body fat percentage in all three exercise groups had decreased significantly compared to the no-exercise control group.
And taking cardiovascular disease risk factors into account, the aerobic and combined exercise groups had lower composite scores than the control group.
The results were consistent across genders and ages.
Meanwhile, over the course of the 12-month study, those in the aerobic-only group continued to improve with the VO2max test, which is the maximum rate of oxygen consumption attainable during a maximal treadmill test.
However, the resistance-only group stayed relatively flat.
The inverse was true for the maximal bench and leg press tests for muscular strength – the resistance-only group continued to improve while the aerobic-only group did not.
However, the combination exercise group improved both aerobic fitness and muscular strength, the results showed.
The researchers concluded: “These findings may help develop clinical and public health practices and recommendations for the approximately 2 billion adults with overweight or obesity worldwide who are at increased risk of [cardiovascular 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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