Active, middle-aged men who can complete more than 40 push-ups at a time had a significantly lower risk of cardiovascular disease (CVD) outcomes compared to those who did less than 10 push-ups, says a new study.
The study, published in JAMA Network Open, showed that men who are able to do more than 40 push-ups had a 96 per cent reduced risk of CVD events compared with those who were able to do less than 10 push-ups.
In addition, push-up capacity was more strongly associated with lower incidence of cardiovascular disease events than was aerobic capacity as estimated by a submaximal treadmill exercise test.
For the study, the researchers from Harvard University analysed health data from 1,104 active male firefighters whose mean age was 39.6.
During the 10-year study period, 37 CVD-related outcomes were reported.
“Our findings provide evidence that push-up capacity could be an easy, no-cost method to help assess cardiovascular disease risk in almost any setting,” said lead author Justin Yang at the Harvard T.H. Chan School of Public Health in the US.
The results are not generalisable to women, men of other ages or who are less active, the researchers noted. (IANS)
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.
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.
“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)