Monday July 22, 2019

Poor Nutrition During Pregnancy can Cause Early Aging of Baby’s Heart

The poor nutrition can cause diabetes and hypertension which are a major risk factor for heart disease and stroke

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Represenatational image. Pixabay

November 7, 2016: Children born to mothers who were undernourished during pregnancy are more likely to suffer early ageing of the heart, a research has showed.

The animal study found that moderately reducing a mother’s food intake can make it more likely that the baby’s organs will show increased disease susceptibility and early ageing.

These changes in the heart could contribute to decreased quality of life, decreased exercise capability, and increased vulnerability to other diseases such as diabetes and hypertension — major risk factor for heart disease and stroke, the study said.

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Understanding the effect of maternal nutritional stress on ageing of the offspring will allow for interventions early in life, to prevent later-life heart problems, said a team of researchers led by Geoffrey Clarke from the University of Texas at San Antonio, US.

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For the study, the team used MRI scanning to analyse the hearts of male and female baboons whose mothers ate 30 per cent less than the normally fed baboons.

They found that the offspring of baboons, which ate less, showed signs of reduced heart function that comes with age.

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By five years of life, equivalent to 20 human years, the structure and function of the heart were already impaired.

“Women’s health during pregnancy is of fundamental importance to the lifetime health of their babies. Society must pay attention to improving women’s nutrition before and during pregnancy to prevent these adverse outcomes in babies,” said Peter Nathanielsz, Director at the University of Wyoming in the US.

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The study was published in The Journal of Physiology. (IANS)

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Study: Friend Circle More Predictive of your Overall Health and Wellness than Fitbit Watches

According to the study published in the journal PLOS ONE, the researchers were interested in what the structure of social networks says about the state of health, happiness and stress

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fitbit, health
The study showed a strong correlation between social network structures, heart rate, number of steps and level of activity. Pixabay

To get a better reading on your overall health and wellness, you should be better off looking at the strength and structure of your circle of friends, says a study. According to the study published in the journal PLOS ONE, the researchers were interested in what the structure of social networks says about the state of health, happiness and stress.

“What we found was the social network structure provides a significant improvement in predictability of wellness states of an individual over just using the data derived from wearables, like the number of steps or heart rate,” said the study lead author Nitesh V. Chawla, a researcher of Indian origin from University of Notre Dame in the US.

fitbit, health
Fitbit watches have sensors that get information from air temperature and humidity, but also from the physiological response of the individual in that environment. Pixabay

For the study, participants wore Fitbit to capture health behaviour data — such as steps, sleep, heart rate and activity level and completed surveys and self-assessments about their feelings of stress, happiness and positivity.

The research team then analysed the data, using machine learning, alongside an individual’s social network characteristics, including degree, centrality, clustering coefficient and number of triangles.

fitbit, health
According to the study published in the journal PLOS ONE, the researchers were interested in what the structure of social networks says about the state of health, happiness and stress. Pixabay

The study showed a strong correlation between social network structures, heart rate, number of steps and level of activity. According to the researchers, social network structure provided significant improvement in predicting one’s health and well-being compared to just looking at health behaviour data from the Fitbit alone.

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For example, when social network structure is combined with the data derived from wearables, the machine learning model achieved a 65 per cent improvement in predicting happiness, 54 per cent improvement in predicting one’s self-assessed health prediction, 55 per cent improvement in predicting positive attitude, and 38 per cent improvement in predicting success.

“This study asserts that without social network information, we only have an incomplete view of an individual’s wellness state, and to be fully predictive or to be able to derive interventions, it is critical to be aware of the social network structural features as well,” Chawla said. (IANS)