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    Home > Biochemistry News > Biotechnology News > Genetic analysis predicts congenital obesity risk, and new findings stop obesity from the gene MC4R.

    Genetic analysis predicts congenital obesity risk, and new findings stop obesity from the gene MC4R.

    • Last Update: 2020-08-05
    • Source: Internet
    • Author: User
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    Sure enough, some people are "dry eat not fat" physique, can be in the eyes of envy and hate in others said a "I am that magic 6%."
    , however, also not too difficult, after all, there are 94% of people, are need to fight with fat ordinary people.
    don't know what percentage of Chinese have the "slim gene" or whether the "slim genes" of different races are the same.
    these are subject to further study.
    but at least, the discovery offers new ideas for the development of diet pills.
    however, to have a healthy body, although you can expect more targeted weight-loss drugs, but "shut your mouth open legs", but also we ordinary people can not be obese efforts.
    Scientists at the University of Cambridge have discovered the gene MC4R, which prevents four million people in the UK from getting fat, in a major study that opens up new avenues for the development of diet pills, the Daily Telegraph reported.
    medical practitioners have known for years that genes can affect a person's weight, but new research details which genetic variants suppress or enhance appetite.
    the team analysed the genetic profiles of more than half a million volunteers in the UK's biomedical pool.
    they found that about 6 per cent of Britons have a special combination of genetic variants, meaning they are less likely to gain weight regardless of their lifestyle.
    researchers focused on a gene called MC4R, which they previously found controls the melanin receptor 4 in the brain, which regulates appetite.
    new study shows that people with certain MC4R gene variants that interfere with the receptor tend to gain weight, while people in the body who have different combinations of genes that cause the receptor to remain "on" are less likely to gain weight, and that these people eat less, which may be why they are slim.
    team found that people with two copies of these specific genetic variants weighed an average of 2.5 kilograms less than those without these genetic variants, and had a 50 percent lower risk of developing type 2 diabetes and heart disease. Professor Sadaf Faruki, head of the new study at the
    , said: "This study clearly shows that genetics plays an important role in obesity in some people, while others are fortunate to have genes that prevent getting fat.
    new research opens the way to developing new drugs.
    " About a third of adults in the UK are currently obese, and that will rise to 48 per cent over the next 30 years."
    University College London reports that the figure is more than double the global forecast of 22 per cent, while the US obese population will reach 55 per cent.
    about 10 per cent of adults in the UK develop type 2 diabetes, which is closely linked to overweight. Figures presented at the European Congress on Obesity in Vienna last year
    show that by 2045 the figure will be close to 13 per cent.
    genetic analysis to predict obesity risk Researchers recently proposed a genetic marker-based scoring system that predicts an individual's risk of congenital obesity.
    they used the largest genome-wide obesity study data available, using new algorithms to integrate more than 2 million genetic variations that affect body mass index (BMI), and obtained ratings that accurately predicted BMI and obesity in more than 300,000 people from birth to middle-aged.
    's paper was published april 18 in Cell.
    study also showed that some people were more likely to be obese than others: the top 10 percent, on average, weighed 29 pounds and were 25 times more likely to be severely obese than the bottom 10 percent.
    the effects of this score begin to show around the age of 3. "Although this score is associated with only a small difference in birth weight, it predicts significant differences in early childhood weight, as well as subsequent weight changes and significant differences in risk of severe obesity,"
    . Sekar Kathiresan,
    " paper author and director of the Center for Genomic Medicine at Massachusetts General Hospital and director of the Center for Cardiovascular Disease Research at the Broad Institute, said.
    previous studies have shown that a healthy lifestyle can cut the risk of heart attack in half, even in the people with the highest genetic risk. As
    obesity, healthy eating and exercise can counteract some of the effects of genetic predisposition.
    however, unhealthy diets and sedentary lifestyles have the most significant effects on BMI in people with genetic predispositions to obesity. "If high-risk individuals can be identified from birth, it may help develop targeted obesity prevention strategies that can improve effectiveness or cost-effectiveness," said Amit Khera, a
    author and postdoctoral fellow at Kathiresan Labs.
    " researchers used a $50 microarray gene test to detect mutations and mutations in millions of genetic markers, and they predicted that their scoring method could one day predict the genetic risk of a range of health problems, such as heart disease, breast cancer and atrial fibrillation.
    interventions may include prescribing preventive cholesterol-lowering drugs, lifestyle counseling, or using wearable devices to detect arrhythmia.
    While the score is not a perfect predictor, the researchers believe genetic traits can help identify high-risk individuals and help doctors recommend ways to avoid potential health risks from high BMI.
    "If disease can be predicted through genomic analysis, this will present important opportunities and potential challenges for clinical medicine."
    "Of course, we're still in the early stages of deciding when and how it is best to disclose genetic information and how best to help patients overcome any genetic risks they find," Khera said.
    " (Tang Dust) Source: Science Daily
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