01 November 2018

Vitamin D Levels in the Blood linked to Cardiorespiratory Fitness

Thursday, November 01, 2018 0
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Vitamin D levels in the blood are linked to cardiorespiratory fitness, according to a study published today in the European Journal of Preventive Cardiology, a publication of the European Society of Cardiology (ESC).

"Our study shows that higher levels of vitamin D are associated with better exercise capacity," said Dr Amr Marawan, assistant professor of internal medicine, Virginia Commonwealth University, Virginia, US. "We also know from previous research that vitamin D has positive effects on the heart and bones. Make sure your vitamin D levels are normal to high. You can do this with diet, supplements, and a sensible amount of sun exposure."



It is well established that vitamin D is important for healthy bones, but there is increasing evidence that it plays a role in other areas of the body including the heart and muscles.

Cardiorespiratory fitness, a reliable surrogate for physical fitness, is the ability of the heart and lungs to supply oxygen to the muscles during exercise. It is best measured as the maximal oxygen consumption during exercise, referred to as VO2 max. People with higher cardiorespiratory fitness are healthier and live longer.

This study investigated whether people with higher levels of vitamin D in the blood have improved cardiorespiratory fitness. The study was conducted in a representative sample of the US population aged 20-49 years using the National Health and Nutrition Survey (NHANES) in 2001-2004. Data was collected on serum vitamin D and VO2 max. Participants were divided into quartiles of vitamin D levels.

Of 1,995 participants, 45% were women, 49% were white, 13% had hypertension, and 4% had diabetes. Participants in the top quartile of vitamin D had a 4.3-fold higher cardiorespiratory fitness than those in the bottom quartile. The link remained significant, with a 2.9-fold strength, after adjusting for factors that could influence the association such as age, sex, race, body mass index, smoking, hypertension, and diabetes.

Dr Marawan said: "The relationship between higher vitamin D levels and better exercise capacity holds in men and women, across the young and middle age groups, across ethnicities, regardless of body mass index or smoking status, and whether or not participants have hypertension or diabetes."

Each 10 nmol/L increase in vitamin D was associated with a statistically significant 0.78 mL/kg/min increase in VO2 max. "This suggests that there is a dose response relationship, with each rise in vitamin D associated with a rise in exercise capacity," said Dr Marawan.

Dr Marawan noted that this was an observational study and it cannot be concluded that vitamin D improves exercise capacity. But he added: "The association was strong, incremental, and consistent across groups. This suggests that there is a robust connection and provides further impetus for having adequate vitamin D levels, which is particularly challenging in cold, cloudy places where people are less exposed to the sun."

On the other hand, Vitamin D toxicity can lead to excess calcium in the blood, which can cause nausea, vomiting, and weakness. "It is not the case that the more vitamin D, the better," said Dr Marawan. "Toxicity is caused by megadoses of supplements rather than diet or sun exposure, so caution is needed when taking tablets."

Regarding further research, Dr Marawan said: "We know the optimum vitamin D levels for healthy bones but studies are required to determine how much the heart needs to function at its best. Randomised controlled trials should be conducted to examine the impact of differing amounts of vitamin D supplements on cardiorespiratory fitness. From a public health perspective, research should look into whether supplementing food products with vitamin D provides additional benefits beyond bone health."

Story Source:
Materials provided by European Society of Cardiology. Note: Content may be edited for style and length.

Journal Reference:
Amr Marawan, Nargiza Kurbanova, Rehan Qayyum. Association between serum vitamin D levels and cardiorespiratory fitness in the adult population of the USA. European Journal of Preventive Cardiology, 2018; 204748731880727 DOI: 10.1177/2047487318807279

Cite This Page:
European Society of Cardiology. "Vitamin D levels in the blood linked to cardiorespiratory fitness." ScienceDaily. ScienceDaily, 30 October 2018. <www.sciencedaily.com/releases/2018/10/181030091449.htm>.

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31 October 2018

Intermittent Fasting Works, but Only If You Fast For This Long

Wednesday, October 31, 2018 0
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Intermittent fasting (IF), a way of eating that involves going through periods of deliberately not eating (fasting) interspersed with periods of eating, has become a popular way for people to lose weight, regulate insulin levels, and lower blood sugar. As popular as intermittent fasting has become, there's no one-size-fits-all plan. There are several ways to do intermittent fasting; one of the most popular is the Leangains diet, or 16:8. This is where you fast for 16 hours a day and only eat in an eight-hour window, such as from noon until 8 p.m.



However, you don't have to adhere to 16:8 strictly. There are other methods of fasting people follow, such as 14:10 or even 12:12. Unfortunately, there is a cutoff for how long your fasting window should be if you want to see results from intermittent fasting.

Registered dietitian Susan Dixon, MPH, MS, said that research suggests that limiting your feeding window to between eight and 11 hours and your fasting time to between 13 and 16 hours keeps insulin levels lower for a longer period of time throughout the day.

"However, that doesn't mean the relationship is cause and effect," she told POPSUGAR. "It has been observed in the literature that people who fast for 13 or more hours nightly tend to be less likely to have high blood pressure, high cholesterol, large waist circumference, obesity, and elevated blood lipids." She added that these benefits aren't observed in fasting windows of 12 hours or less.

And while these benefits go beyond weight loss, if you are looking to lose weight with IF, you still need to make sure you're eating in a calorie deficit without going below 1,200 calories a day. To find your exact calorie target for weight loss, use this formula.

If intermittent fasting intrigues you, make sure you find a plan that works for you. But if you are looking to do IF daily, make sure you fast for at least 13 hours.

Story source: www.popsugar.com
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30 October 2018

Fruit fly study challenges theories on evolution and high-carb diets

Tuesday, October 30, 2018 0
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A single mitochondrial DNA mutation common in animals could play a role in obesity and other health problems associated with a diet high in carbohydrates.

This was one of the implications of research led by UNSW scientists who looked at how different diets affected fruit fly populations. The researchers observed a surprising difference between two sets of the Drosophila melanogaster fruit flies when feeding them alternate diets high in protein and high in carbohydrates.



Fruit fly larvae with a noted mitochondrial DNA (mtDNA) mutation showed a pronounced increase in development when eating high carbohydrate diet of banana, but stagnated on a high protein diet of passionfruit.

Conversely, fruit fly larvae without the mtDNA mutation thrived on the high protein diet, but dropped in frequency when put on carbohydrates.

UNSW School of Biotechnology & Biomolecular Sciences Professor Bill Ballard, who led the study, says the research is a rare demonstration of positive selection at work in evolution.

"What is unique about this study is we've identified one mutation in the mitochondrial genome, that when fed a specific diet is advantageous and causes the frequency of flies in a population cage to increase," he says.

"Then when you swap the diet back to a high protein diet, the flies with the mutation go down in numbers and the other flies without the mutation go up."

The study, which was an exhaustive six-year collaboration between authors from research institutions in Australia, the US and Spain, challenges the neutral theory of molecular evolution that says changes in species at the molecular level are random, not caused by natural selection and provide no benefit or disadvantage to the species.

UNSW PhD student Sam Towarnicki, who is equal first author of the paper, explained why this was more than just a random, neutral mutation.

"The selective advantage is this: the larvae possessing the mutation fed on high carbohydrate diet grow up nice and early and become adults before the others on the protein diet [also with a mutation]," he says.

"And we found a 10 per cent difference in the development just in one generation between those two groups, which is huge.

"And because we followed 25 generations, those increases compound over time which delivers much bigger numbers and a huge selective advantage."

Given that humans share 75 per cent of the same genes as fruit flies, and have the same mtDNA genes, it is certainly an intriguing prospect that the same mutation inherited in human mtDNA may metabolise carbohydrates in a similar way.

Professor Ballard says while confirmation of this would be "another NHMRC grant away and years of surveying and testing," the idea is worth exploring.

He says knowledge of a person's 'mitotype' could help explain why a diet high in carbohydrates may induce obesity and diabetes in some but not others.

"But, the news is not all bad for people harbouring the mutation," he says.

"Sure, you would need to manage your carbohydrate intake when you are younger, but if you are unfortunate enough to develop Parkinson's Disease, a high carbohydrate diet will help you maintain weight.

"So a consequence of our study is to open up a new area for the development of specific diets and drugs to treat Parkinson's' Disease."

And far from fighting disease and reducing health problems, the knowledge could help people plan and fulfil life-choices.

"The most obvious implication from our work is that people should start to manage their diets to match their genotypes to fulfil their specific goals. This is the growing field of 'Nutrigenomics'," Professor Ballard says.

He uses the analogy of the different physical requirements of a football team: some players need speed, some need to bulk up while others may need layers of fat.

"Knowing a person's mitotype will help each person optimise their diet to fulfil these goals, and it would also help a person choose which sort of role they may be best suited for."

"A second example is that our energetic goals change over time and so the food we feed our body should also change. A goal for some might be to increase fertility while increasing longevity may be the goal for older folks.

"So knowing our mitotype will help us determine the best diet to fulfil life-choices."

Story Source:
Materials provided by University of New South Wales. Original written by Lachlan Gilbert. Note: Content may be edited for style and length.

Journal Reference:
Wen Chyuan Aw et al. Ad Genotype to phenotype: diet-by-mitochondrial DNA haplotype interactions drive metabolic flexibility and organismal fitness. PLOS Genetics, 2018

Cite This Page:
University of New South Wales. "Fruit fly study challenges theories on evolution and high-carb diets." ScienceDaily. ScienceDaily, 26 October 2018. <www.sciencedaily.com/releases/2018/10/181026143401.htm>.

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Obese Mice Lose a Third of Their Fat Using a Natural Protein

Tuesday, October 30, 2018 0
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To the great surprise of cancer researchers, a protein they investigated for its possible role in cancer turned out to be a powerful regulator of metabolism. The Georgetown University-led study found that forced expression of this protein in a laboratory strain of obese mice showed a remarkable reduction of their fat mass despite a genetic predisposition to eat all the time.

The study, published in Scientific Reports, suggests that the protein FGFBP3 (BP3 for short) might offer novel therapy to reverse disorders associated with metabolic syndrome, such as type 2 diabetes and fatty liver disease.



Because BP3 is a natural protein and not an artificial drug, clinical trials of recombinant human BP3 could begin after a final round of preclinical studies, investigators say.

"We found that eight BP3 treatments over 18 days was enough to reduce the fat in obese mice by over a third," says the study's senior investigator, Anton Wellstein, MD, PhD, a professor of oncology and pharmacology at Georgetown Lombardi Comprehensive Cancer Center.

The treatments also reduced a number of obesity-related disorders in the mice, such as hyperglycemia -- excess blood sugar that is often linked to diabetes -- and eliminated the fat in their once fatty livers. Clinical as well as microscopic examination of the mice showed no side effects, researchers say.

Obesity, which affects more than 650 million people worldwide, is the major driver for metabolic syndromes, which includes disorders such as insulin resistance, glucose intolerance, hypertension and elevated lipids in the blood.

BP3 belongs to the family of fibroblast growth factor (FGF) binding proteins (BP). FGFs are found in organisms ranging from worms to humans and are involved in a wide range of biological processes, such as regulating cell growth, wound healing and response to injury. Some FGFs act like hormones.

BP1, 2, and 3 are "chaperone" proteins that latch on to FGF proteins and enhance their activities in the body. Wellstein has long researched the BP1 gene because its production is elevated in a range of cancers, suggesting that growth of some cancers is linked to the excess delivery of FGFs. Only recently has Wellstein turned his attention, and that of his lab and colleagues, to BP3 to understand its role.

The researchers found that this chaperone binds to three FGF proteins (19, 21, and 23) that are involved in the control of metabolism. FGF19 and FGF 21 signaling regulates the storage and use of carbohydrates (sugars) and lipids (fats). FGF23 controls phosphate metabolism.

"We found that BP3 exerts a striking contribution to metabolic control," Wellstein says. "When you have more BP3 chaperone available, FGF19 and FGF21 effect is increased through the increase of their signaling. That makes BP3 a strong driver of carbohydrate and lipid metabolism. It's like having a lot more taxis available in New York City tolic syndrome, such as type 2 diabetes and fatty liver disease. pick up all the people who need a ride."

"With metabolism revved up, sugar in the blood, and fat processed in the liver are used for energy and is not stored," Wellstein says. "And warehouses of fat are tapped as well. For example, the job of FGF21 is to control break down of fat, whether it is stored or just eaten."

While the study results are exciting, additional research is required before BP3 protein can be investigated as a human therapy for metabolic syndromes, he says.

Story Source:
Materials provided by Georgetown University Medical Center. Note: Content may be edited for style and length.

Journal Reference:
Elena Tassi, Khalid A. Garman, Marcel O. Schmidt, Xiaoting Ma, Khaled W. Kabbara, Aykut Uren, York Tomita, Regina Goetz, Moosa Mohammadi, Christopher S. Wilcox, Anna T. Riegel, Mattias Carlstrom, Anton Wellstein. Fibroblast Growth Factor Binding Protein 3 (FGFBP3) impacts carbohydrate and lipid metabolism. Scientific Reports, 2018; 8 (1) DOI: 10.1038/s41598-018-34238-5

Cite This Page:
Georgetown University Medical Center. "Obese mice lose a third of their fat using a natural protein." ScienceDaily. ScienceDaily, 29 October 2018. <www.sciencedaily.com/releases/2018/10/181029084038.htm>.

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18 October 2018

Combining Genetic and Sun Exposure Data Improves Skin Cancer Risk Estimates

Thursday, October 18, 2018 0
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By combining data on individuals' lifetime sun exposure and their genetics, researchers can generate improved predictions of their risk of skin cancer, according to findings presented at the American Society of Human Genetics (ASHG) 2018 Annual Meeting in San Diego, Calif.

Pierre Fontanillas, PhD, and colleagues at 23andMe, Inc., collected genetic and survey data from over 210,000 consented research participants of European descent. They analyzed the data to identify correlations between previously known and potentially novel skin cancer risk factors and the occurrence of three forms of skin cancer: melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Past studies had found that exposure to ultraviolet (UV) light increases skin cancer risk, as do other environmental factors such as living in a sunnier climate or at a higher altitude, and personal factors such as lighter skin pigmentation, higher numbers of moles on the skin, and family history of skin cancer.

"We aimed to validate previously known skin cancer risk factors in a large cohort, add detail to these and explore potential new ones, and find out whether and how these factors might interact with genetic risk," said Dr. Fontanillas.

They found that while each single factor was not particularly significant on its own, multiple factors could be combined into statistical models that were more informative. The best-performing models incorporated a genetic risk score composed of data on up to 50 genetic variants, along with survey data on family history, skin pigmentation and sensitivity, number of moles, estimated current sun exposure, sunbathing frequency before the age of 30, and body mass index (BMI).

The new models achieved a high predictive accuracy (area under the curve [AUC], between 0.81 and 0.85). Genetic factors alone accounted for 8.3 to 15.2 percent of the variance explained in skin cancer risk. Although the three skin cancers have different physiology, models did not find fundamental differences between the three cancer types, nor did they show strong interaction between genetic and environmental risk factors. While the self-reported nature of the survey data permitted researchers to collect a large dataset, it also presented some challenges, Dr. Fontanillas noted.

"Measuring lifetime exposure is generally challenging. It is particularly hard to capture sun exposure and when in life it happened, and it may be that some of the other correlates we found, like higher BMI, reflect a lack of outdoor activity rather than being directly correlated with risk of skin cancer," he said.

Moving forward, the researchers plan to expand their sample to groups with non-European ancestry and are exploring additional methods of calculating genetic risk score and measuring sun exposure. They hope to eventually obtain risk estimates accurate enough to be used by individuals and clinicians.

Story Source:
Materials provided by American Society of Human Genetics. Note: Content may be edited for style and length.

Cite This Page:
American Society of Human Genetics. "Combining genetic and sun exposure data improves skin cancer risk estimates." ScienceDaily. ScienceDaily, 17 October 2018. <www.sciencedaily.com/releases/2018/10/181017140931.htm>.

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17 October 2018

Automated System Identifies Dense Tissue, a Risk Factor for Breast Cancer, in Mammograms

Wednesday, October 17, 2018 0
Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures

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Researchers from MIT and Massachusetts General Hospital have developed an automated model that assesses dense breast tissue in mammograms -- which is an independent risk factor for breast cancer -- as reliably as expert radiologists.

This marks the first time a deep-learning model of its kind has successfully been used in a clinic on real patients, according to the researchers. With broad implementation, the researchers hope the model can help bring greater reliability to breast density assessments across the nation.

It's estimated that more than 40 percent of U.S. women have dense breast tissue, which alone increases the risk of breast cancer. Moreover, dense tissue can mask cancers on the mammogram, making screening more difficult. As a result, 30 U.S. states mandate that women must be notified if their mammograms indicate they have dense breasts.

But breast density assessments rely on subjective human assessment. Due to many factors, results vary -- sometimes dramatically -- across radiologists. The MIT and MGH researchers trained a deep-learning model on tens of thousands of high-quality digital mammograms to learn to distinguish different types of breast tissue, from fatty to extremely dense, based on expert assessments. Given a new mammogram, the model can then identify a density measurement that closely aligns with expert opinion.

"Breast density is an independent risk factor that drives how we communicate with women about their cancer risk. Our motivation was to create an accurate and consistent tool, that can be shared and used across health care systems," says second author Adam Yala, a PhD student in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

The other co-authors are first author Constance Lehman, professor of radiology at Harvard Medical School and the director of breast imaging at the MGH; and senior author Regina Barzilay, the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT.

Mapping density

The model is built on a convolutional neural network (CNN), which is also used for computer vision tasks. The researchers trained and tested their model on a dataset of more than 58,000 randomly selected mammograms from more than 39,000 women screened between 2009 and 2011. For training, they used around 41,000 mammograms and, for testing, about 8,600 mammograms.

Each mammogram in the dataset has a standard Breast Imaging Reporting and Data System (BI-RADS) breast density rating in four categories: fatty, scattered (scattered density), heterogeneous (mostly dense), and dense. In both training and testing mammograms, about 40 percent were assessed as heterogeneous and dense.

During the training process, the model is given random mammograms to analyze. It learns to map the mammogram with expert radiologist density ratings. Dense breasts, for instance, contain glandular and fibrous connective tissue, which appear as compact networks of thick white lines and solid white patches. Fatty tissue networks appear much thinner, with gray area throughout. In testing, the model observes new mammograms and predicts the most likely density category.

Matching assessments

The model was implemented at the breast imaging division at MGH. In a traditional workflow, when a mammogram is taken, it's sent to a workstation for a radiologist to assess. The researchers' model is installed in a separate machine that intercepts the scans before it reaches the radiologist, and assigns each mammogram a density rating. When radiologists pull up a scan at their workstations, they'll see the model's assigned rating, which they then accept or reject.

"It takes less than a second per image ... [and it can be] easily and cheaply scaled throughout hospitals." Yala says.

On over 10,000 mammograms at MGH from January to May of this year, the model achieved 94 percent agreement among the hospital's radiologists in a binary test -- determining whether breasts were either heterogeneous and dense, or fatty and scattered. Across all four BI-RADS categories, it matched radiologists' assessments at 90 percent. "MGH is a top breast imaging center with high inter-radiologist agreement, and this high quality dataset enabled us to develop a strong model," Yala says.

In general testing using the original dataset, the model matched the original human expert interpretations at 77 percent across four BI-RADS categories and, in binary tests, matched the interpretations at 87 percent.

In comparison with traditional prediction models, the researchers used a metric called a kappa score, where 1 indicates that predictions agree every time, and anything lower indicates fewer instances of agreements. Kappa scores for commercially available automatic density-assessment models score a maximum of about 0.6. In the clinical application, the researchers' model scored 0.85 kappa score and, in testing, scored a 0.67. This means the model makes better predictions than traditional models.

In an additional experiment, the researchers tested the model's agreement with consensus from five MGH radiologists from 500 random test mammograms. The radiologists assigned breast density to the mammograms without knowledge of the original assessment, or their peers' or the model's assessments. In this experiment, the model achieved a kappa score of 0.78 with the radiologist consensus.

Next, the researchers aim to scale the model into other hospitals. "Building on this translational experience, we will explore how to transition machine-learning algorithms developed at MIT into clinic benefiting millions of patients," Barzilay says. "This is a charter of the new center at MIT -- the Abdul Latif Jameel Clinic for Machine Learning in Health at MIT -- that was recently launched. And we are excited about new opportunities opened up by this center."

Story Source:
Materials provided by Massachusetts Institute of Technology. Original written by Rob Matheson. Note: Content may be edited for style and length.

Journal Reference:
Constance D. Lehman, Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 2018; 180694 DOI: 10.1148/radiol.2018180694

Cite This Page:
Massachusetts Institute of Technology. "Automated system identifies dense tissue, a risk factor for breast cancer, in mammograms: Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures." ScienceDaily. ScienceDaily, 16 October 2018. <www.sciencedaily.com/releases/2018/10/181016131933.htm>.

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