Thursday January 30, 2020

Eating Blueberries Every Day Improves Heart Health

Eating one cup of blueberries per day results in sustained improvements in vascular function

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Apple watch
The key health feature on Apple Watch is yet to arrive in India. Pixabay

Eating a cup of blueberries daily reduces the risk of cardiovascular disease (CVD) by up to 15 per cent, according to a study.

The findings, published in the American Journal of Clinical Nutrition, suggest that blueberries and other berries should be included in diets to reduce the risk of cardiovascular disease.

“Having metabolic syndrome significantly increases the risk of heart disease, stroke and diabetes and often statins and other medications are prescribed to help control this rise,” said study lead author Aedin Cassidy, Professor at the University of East Anglia in Britain.

The researchers studied whether eating blueberries had any effect on metabolic syndrome – a condition, affecting 1/3 of westernised adults, which comprises at least three of the following risk factors: high blood pressure, high blood sugar, excess body fat around the waist, low levels of ‘good cholesterol’ and high levels of triglycerides.

Blueberries, Everyday, Heart Health
Eating a cup of blueberries daily reduces the risk of cardiovascular disease. Pixabay

For the study, the researchers investigated the effects of eating blueberries daily in 138 overweight and obese people, (aged between 50 and 75), and having metabolic syndrome.

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“We found that eating one cup of blueberries per day resulted in sustained improvements in vascular function and arterial stiffness – making enough of a difference to reduce the risk of cardiovascular disease by between 12 and 15 per cent,” said Peter Curtis, co-author of the study. (IANS)

Next Story

This AI Model may Predict Heart Diseases

AI may predict long-term risks of heart attack, cardiac death

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Heart attack
Researchers have found that Artificial Intelligence can be used to predict heart attacks and cardiac deaths. Pixabay

Researchers have found that machine learning, patterns and inferences computers use to learn to perform tasks, can predict the long-term risk of heart attack and cardiac death.

According to the study, published in the journal Cardiovascular Research, machine learning appears to be better at predicting heart attacks and cardiac deaths than the standard clinical risk assessment used by cardiologists.

“Our study showed that machine learning integration of clinical risk factors and imaging measures can accurately personalise the patient’s risk of suffering an adverse event such as heart attack or cardiac death,” said the study researchers from the Biomedical Imaging Research Institute in US

For the findings, the research team studied subjects from the imaging arm of a prospective, randomised research trial, who underwent coronary artery calcium scoring with available cardiac CT scans and long-term follow-up.

Participants here were asymptomatic, middle-aged subjects, with cardiovascular risk factors, but no known coronary artery disease.

Researchers used machine learning to assess the risk of myocardial infarction and cardiac death in the subjects, and then compared the predictions with the actual experiences of the subjects over fifteen years.

Heart Health
Diet, exercise and marital status are some of the factors that can affect the heart health. Pixabay

Subjects here answered a questionnaire to identify cardiovascular risk factors and to describe their diets, exercise and marital status. The final study consisted of 1,912 subjects, fifteen years after they were first studied.

76 subjects presented an event of myocardial infarction and/or cardiac death during this follow-up time. The subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.

The atherosclerotic cardiovascular disease risk score, the standard clinical risk assessment used by cardiologists, overestimated the risk of events in the higher risk categories. Machine learning did not.

In unadjusted analysis, high predicted machine learning risk was significantly associated with a higher risk of a cardiac event.

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“While machine learning models are sometimes regarded as “black boxes”, we have also tried to demystify machine learning; in this manuscript, we describe individual predictions for two patients as examples,” said researchers

“When applied after the scan, such individualised predictions can help guide recommendations for the patient, to decrease their risk of suffering an adverse cardiac event,” they added. (IANS)