Friday, November 23, 2018

Different types of physical activity offer varying protection against heart disease

While it is well known that physical activity is important for heart health, neither research nor recommendations consistently differentiate between the benefits of different types of physical activity. New research, presented at the ACC Latin America Conference 2018 in Lima, Peru, found that while all physical activity is beneficial, static activities -- such as strength training -- were more strongly associated with reducing heart disease risks than dynamic activities like walking and cycling.
"Both strength training and aerobic activity appeared to be heart healthy, even in small amounts, at the population level," said Maia P. Smith, PhD, MS, statistical epidemiologist and assistant professor in the Department of Public Health and Preventive Medicine at St. George's University in St. George's, Grenada. "Clinicians should counsel patients to exercise regardless -- both activity types were beneficial. However, static activity appeared more beneficial than dynamic, and patients who did both types of physical activity fared better than patients who simply increased the level of one type of activity."
Researchers analyzed cardiovascular risk factors, such as high blood pressure, overweight, diabetes and high cholesterol, as a function of self-reported static and/or dynamic activity (strength training or walking/biking) in 4,086 American adults using data from the 2005-2006 National Health and Nutrition Examination Survey. The researchers then adjusted for age, ethnicity, gender and smoking and stratified by age: 21 to 44 years old or over 45 years old.
In total, 36 percent of younger and 25 percent of older adults engaged in static activity, and 28 percent of younger and 21 percent of older adults engaged in dynamic activity. Researchers found engaging in either type of activity was associated with 30 to 70 percent lower rates of cardiovascular disease risk factors, but associations were strongest for static activity and in youth.
"One interesting takeaway was that both static and dynamic activity were almost as popular in older people as younger," Smith said. "I believe this gives clinicians the opportunity to counsel their older patients that they will fit into the gym or the road race just fine. The important thing is to make sure they are engaging in physical activity."
Smith said future research and data collection should use definitions of physical activity that separate static from dynamic activity to further investigate independent effects.

Story Source:
Materials provided by American College of Cardiology.

Courtesy: ScienceDaily

Wednesday, November 21, 2018

A new approach to detecting cancer earlier from blood tests

Cancer scientists led by principal investigator Dr. Daniel De Carvalho at Princess Margaret Cancer Centre have combined "liquid biopsy," epigenetic alterations and machine learning to develop a blood test to detect and classify cancer at its earliest stages.
The findings, published online today in Nature, describe not only a way to detect cancer, but hold promise of being able to find it earlier when it is more easily treated and long before symptoms ever appear, says Dr. De Carvalho, Senior Scientist at the cancer centre, University Health Network.
"We are very excited at this stage," says Dr. De Carvalho. "A major problem in cancer is how to detect it early. It has been a 'needle in the haystack' problem of how to find that one-in-a-billion cancer-specific mutation in the blood, especially at earlier stages, where the amount of tumour DNA in the blood is minimal."
By profiling epigenetic alterations instead of mutations, the team was able to identify thousands of modifications unique to each cancer type. Then, using a big data approach, they applied machine learning to create classifiers able to identify the presence of cancer-derived DNA within blood samples and to determine what cancer type. This basically turns the 'one needle in the haystack' problem into a more solvable 'thousands of needles in the haystack', where the computer just needs to find a few needles to define which haystack has needles.
The scientists tracked the cancer origin and type by comparing 300 patient tumour samples from seven disease sites (lung, pancreatic, colorectal, breast, leukemia, bladder and kidney) and samples from healthy donors with the analysis of cell-free DNA circulating in the blood plasma. In every sample, the "floating" plasma DNA matched the tumour DNA. The team has since expanded the research and has now profiled and successfully matched more than 700 tumour and blood samples from more cancer types.
Beyond the lab, next steps to further validate this approach include analysing data from large population health research studies already under way in several countries, where blood samples were collected months to years before cancer diagnosis. Then the approach will need to be ultimately validated in prospective studies for cancer screening.
Dr. De Carvalho is a trained immunologist (University of Sao Paulo, Brazil) with postdoctoral training in cancer epigenomics (University of Southern California, USA) whose research focuses on cancer epigenetics. He holds the Canada Research Chair in Cancer Epigenetics and Epigenetic Therapy and is an Associate Professor in Cancer Epigenetics, Department of Medical Biophysics, University of Toronto.
The research was supported by University of Toronto's McLaughlin Centre, Canadian Institutes of Health Research, Canadian Cancer Society, Ontario Institute for Cancer Research through the Province of Ontario, and The Princess Margaret Cancer Foundation.
 
Journal Reference:
  1. Shu Yi Shen, Rajat Singhania, Gordon Fehringer, Ankur Chakravarthy, Michael H. A. Roehrl, Dianne Chadwick, Philip C. Zuzarte, Ayelet Borgida, Ting Ting Wang, Tiantian Li, Olena Kis, Zhen Zhao, Anna Spreafico, Tiago da Silva Medina, Yadon Wang, David Roulois, Ilias Ettayebi, Zhuo Chen, Signy Chow, Tracy Murphy, Andrea Arruda, Grainne M. O’Kane, Jessica Liu, Mark Mansour, John D. McPherson, Catherine O’Brien, Natasha Leighl, Philippe L. Bedard, Neil Fleshner, Geoffrey Liu, Mark D. Minden, Steven Gallinger, Anna Goldenberg, Trevor J. Pugh, Michael M. Hoffman, Scott V. Bratman, Rayjean J. Hung, Daniel D. De Carvalho. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature, 2018; DOI: 10.1038/s41586-018-0703-0 
Courtesy: ScienceDaily