Drinking soda, sweetened drinks may up chronic kidney disease: Study

Washington DC, Apr 17: In recent research, scientists have linked the emotional, social and psychiatric problems in children and adolescents with higher levels of genetic vulnerability for adult depression. The study implies that the genetics passed from parents may be linked with psychiatric problems in children and adolescents and may also leading to depression in adults.

University of Queensland scientists made the finding while analysing the genetic data of more than 42,000 children and adolescents from seven cohorts across Finland, the Netherlands, Norway, Sweden and the UK.

Professor Christel Middeldorp said that researchers have also found a link with a higher genetic vulnerability for insomnia, neuroticism and body mass index.

"By contrast, study participants with higher genetic scores for educational attainment and emotional well-being were found to have reduced childhood problems," Professor Middeldorp said.

"We calculated a person's level of genetic vulnerability by adding up the number of risk genes they had for a specific disorder or trait and then made adjustments based on the level of importance of each gene We found the relationship was mostly similar across ages," Middeldorp added.

The results indicate there are shared genetic factors that affect a range of psychiatric and related traits across a person's lifespan.

Middeldorp said that around 50 per cent of children and adolescents with psychiatric problems, such as attention deficit hyperactivity disorder (ADHD), continue to experience mental disorders as adults, and are at risk of disengaging with their school community among other social and emotional problems.

"Our findings are important as they suggest this continuity between childhood and adult traits is partly explained by genetic risk," the Professor said.

"Individuals at risk of being affected should be the focus of attention and targeted treatment," Middeldorp continued.

"Although the genetic vulnerability is not accurate enough at this stage to make individual predictions about how a person's symptoms will develop over time, it may become so in the future, in combination with other risk factors. And, this may support precision medicine by providing targeted treatments to children at the highest risk of persistent emotional and social problems," Middeldorp added.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.