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duminică, 5 august 2012

Study Identifies Discrepancies Between National Surveys Tracking Obesity

Despite the increasing awareness of the problem of obesity in the United States, most Americans don't know whether they are gaining or losing weight, according to new research from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.

Obesity increased in the US between 2008 and 2009, but in response to the questions about year-to-year changes in weight that were included in the most widespread public health survey in the country, on average, people said that they lost weight. Men did a worse job estimating their own weight changes than women. And older adults were less attuned to their weight changes than young adults. The findings are being published in the article "In denial: misperceptions of weight change among adults in the United States" * in the August edition of Preventive Medicine.

"If people aren't in touch with their weight and changes in their weight over time, they might not be motivated to lose weight," said Dr. Catherine Wetmore, the lead author on the paper. "Misreporting of weight gains and losses also has policy implications. If we had relied on the reported data about weight change between 2008 and 2009, we would have undercounted approximately 4.4 million obese adults in the US."

A range of public health campaigns in recent years have urged Americans to lose weight to lower their chances of developing heart disease, diabetes, and other chronic conditions. To understand whether people in the US are heeding this advice, Dr. Wetmore, a former Post-Graduate Fellow at IHME and now a biostatistician at Children's National Medical Center, and IHME Professor Dr. Ali Mokdad compared self-reported changes in body weight between 2008 and 2009.

They used data from the Behavioral Risk Factor Surveillance System (BRFSS), a yearly cross-sectional survey of adults in the US designed to monitor leading risk factors for morbidity and mortality nationwide. More than 775,000 people were surveyed in the years analyzed, and they were asked multiple questions about their weight, including how much they weighed on the day of their interview and how much they weighed one year prior to their interview.

The researchers found that, on average, American adults gained weight over the study period - because the reported weights increased between the 2008 and 2009 surveys - but the 2009 study participants told surveyors that they had lost weight during the previous year. Based on the weights they reported, the prevalence of obesity in the US would have declined from 2008 to 2009. Instead, the prevalence of obesity inched upward from 26% to 26.5%, and average weight increased by about one pound per person between 2008 and 2009.

"We all know on some level that people can be dishonest about their weight," Dr. Mokdad said. "But now we know that they can be misreporting annual changes in their weight, to the extent of more than two pounds per year among adults over the age of 50, or more than four pounds per year among those with diabetes. On average, American adults were off by about a pound, which, over time, can really add up and have a significant health impact."

Not everyone reported losing weight. The researchers found that reports of unintentional weight gain were more common in specific groups: men and women under the age of 40 those identifying as black, Native American, or Hispanic current and former smokers those consuming less than five servings of fruits and vegetables per day those reporting no physical activity those with diagnosed chronic diseases, frequent poor mental health, and insufficient sleep those lacking health care coverage "It's very popular right now to talk about the underlying environmental causes of obesity, whether it's too much fast food or not enough parks," Dr. Wetmore said. "While we know that the environment definitely plays a role, these results show that we need to do a better job helping people to be aware of what's going on with their own bodies." Article adapted by Medical News Today from original press release. Click 'references' tab above for source.
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vineri, 9 decembrie 2011

Missing Link Between DNA And Protein Shape

Main Category: Genetics
Also Included In: IT / Internet / E-mail
Article Date: 09 Dec 2011 - 1:00 PST

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Fifty years after the pioneering discovery that a protein's three-dimensional structure is determined solely by the sequence of its amino acids, an international team of researchers has taken a major step toward fulfilling the tantalizing promise: predicting the structure of a protein from its DNA alone.

The team at Harvard Medical School (HMS), Politecnico di Torino / Human Genetics Foundation Torino (HuGeF) and Memorial Sloan-Kettering Cancer Center in New York (MSKCC) has reported substantial progress toward solving a classical problem of molecular biology: the computational protein folding problem.

The results were published in the journal PLoS ONE.

In molecular biology and biomedical engineering, knowing the shape of protein molecules is key to understanding how they perform the work of life, the mechanisms of disease and drug design. Normally the shape of protein molecules is determined by expensive and complicated experiments, and for most proteins these experiments have not yet been done. Computing the shape from genetic information alone is possible in principle. But despite limited success for some smaller proteins, this challenge has remained essentially unsolved. The difficulty lies in the enormous complexity of the search space, an astronomically large number of possible shapes. Without any shortcuts, it would take a supercomputer many years to explore all possible shapes of even a small protein.

"Experimental structure determination has a hard time keeping up with the explosion in genetic sequence information," said Debora Marks, a mathematical biologist in the Department of Systems Biology at HMS, who worked closely with Lucy Colwell, a mathematician, who recently moved from Harvard to Cambridge University. They collaborated with physicists Riccardo Zecchina and Andrea Pagnani in Torino in a team effort initiated by Marks and computational biologist Chris Sander of the Computational Biology Program at MSKCC, who had earlier attempted a similar solution to the problem, when substantially fewer sequences were available.

"Collaboration was key," Sander said. "As with many important discoveries in science, no one could provide the answer in isolation."

The international team tested a bold premise: That evolution can provide a roadmap to how the protein folds. Their approach combined three key elements: evolutionary information accumulated for many millions of years; data from high-throughput genetic sequencing; and a key method from statistical physics, co-developed in the Torino group with Martin Weigt, who recently moved to the University of Paris.

Using the accumulated evolutionary information in the form of the sequences of thousands of proteins, grouped in protein families that are likely to have similar shapes, the team found a way to solve the problem: an algorithm to infer which parts of a protein interact to determine its shape. They used a principle from statistical physics called "maximum entropy" in a method that extracts information about microscopic interactions from measurement of system properties.

"The protein folding problem has been a huge combinatorial challenge for decades," said Zecchina, "but our statistical methods turned out to be surprisingly effective in extracting essential information from the evolutionary record."

With these internal protein interactions in hand, widely used molecular simulation software developed by Axel Brunger at Stanford University generated the atomic details of the protein shape. The team was for the first time able to compute remarkably accurate shapes from sequence information alone for a test set of 15 diverse proteins, with no protein size limit in sight, with unprecedented accuracy.

"Alone, none of the individual pieces are completely novel, but apparently nobody had put all of them together to predict 3D protein structure," Colwell said.

To test their method, the researchers initially focused on the Ras family of signaling proteins, which has been extensively studied because of its known link to cancer. The structure of several Ras-type proteins has already been solved experimentally, but the proteins in the family are larger--with about 160 amino acid residues--than any proteins modeled computationally from sequence alone.

"When we saw the first computationally folded Ras protein, we nearly went through the roof," Marks said. To the researchers' amazement, their model folded within about 3.5 angstroms of the known structure with all the structural elements in the right place. And there is no reason, the authors say, that the method couldn't work with even larger proteins.

The researchers caution that there are other limits, however: Experimental structures, when available, generally are more accurate in atomic detail. And, the method works only when researchers have genetic data for large protein families. But advances in DNA sequencing have yielded a torrent of such data that is forecast to continue growing exponentially in the foreseeable future.

The next step, the researchers say, is to predict the structures of unsolved proteins currently being investigated by structural biologists, before exploring the large uncharted territory of currently unknown protein structures.

"Synergy between computational prediction and experimental determination of structures is likely to yield increasingly valuable insight into the large universe of protein shapes that crucially determine their function and evolutionary dynamics," Sander said.

Article adapted by Medical News Today from original press release. Click 'references' tab above for source.
Visit our genetics section for the latest news on this subject. This research was funded by the National Cancer Institute and the Engineering and Physical Sciences Research Council of the United Kingdom.
Written by R. Alan Leo.
Citation: PLoS ONE, December 6, 2011 "Protein 3D structure computed from evolutionary sequence variation," Marks et al.
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