Tag: 12/5/23

Categories : COVID VaccinesTags :

COVID Vaccination Protection Wanes Faster in People with Obesity

On By ModernMedia
Antibodies by Pikisuperstar on Freepix

According to new research from the Universities of Cambridge and Edinburgh, COVID vaccination protection in people with severe obesity wanes faster than in people of normal weight. The study suggests that people with obesity are likely to need more frequent booster doses to maintain their immunity.

Previous studies on COVID vaccines have suggested that antibody levels may be lower in vaccinated people who have obesity and that they may remain at higher risk of severe disease than vaccinated people with normal weight. The reasons for this have, however, remained unclear.

This study, published in the journal Nature Medicine, shows that the ability of antibodies to neutralise the virus (their ‘neutralisation capacity’) declines faster in vaccinated people who have obesity. The findings have important implications for vaccine prioritisation policies around the world.

During the pandemic, people with obesity were more likely to be hospitalised, require ventilators and to die from COVID. In this study, supported by the NIHR Bioresource and funded by UKRI, the researchers set out to investigate how far two of the most extensively used vaccines protect people with obesity compared to those with a normal weight, over time.

A team from the University of Edinburgh looked at real-time data tracking the health of 3.5 million people in the Scottish population as part of the EAVE II study. They looked at hospitalisation and mortality from COVID in adults who received two doses of COVID vaccine (either Pfizer-BioNTech or AstraZeneca).

They found that people with severe obesity (a BMI > 40kg/m2) had a 76% higher risk of severe COVID outcomes, compared to those with a normal BMI. A modest increase in risk was also seen in people with obesity (30-39.9kg/m2), which affects a quarter of the UK population, and those who were underweight. ‘Break-through infections’ after the second vaccine dose also led to hospitalisation and death sooner (from 10 weeks) among people with severe obesity, and among people with obesity (after 15 weeks), than among individuals with normal weight (after 20 weeks).

University of Edinburg leader Prof Sir Aziz Sheikh said: “Our findings demonstrate that protection gained through COVID vaccination drops off faster for people with severe obesity than those with a normal body mass index. Using large-scale data assets such as the EAVE II Platform in Scotland have enabled us to generate important and timely insights that enable improvements to the delivery of COVID vaccine schedules in a post-pandemic UK.”

The University of Cambridge team studied people with severe obesity attending the Obesity clinic at Addenbrooke’s Hospital in Cambridge, and compared the number and function of immune cells in their blood to those of people of normal weight.

They studied people six months after their second vaccine dose and then looked at the response to a third ‘booster’ vaccine dose over time. The Cambridge researchers found that six months after a second vaccine dose, people with severe obesity had similar levels of antibodies to the COVID virus as those with a normal weight – but those antibodies were less effecctive.

The antibodies’ neutralisation capacity was reduced in 55% of individuals with severe obesity were found to have unquantifiable or undetectable ‘neutralising capacity’ compared to 12% of people with normal BMI.

“This study further emphasises that obesity alters the vaccine response and also impacts on the risk of infection,” said first author Dr Agatha van der Klaauw. “We urgently need to understand how to restore immune function and minimise these health risks.”

The researchers found that antibodies produced by people with severe obesity were less effective at neutralising the SARS-CoV-2 virus, potentially because the antibodies were not able to bind to the virus with the same strength.

When given a third (booster) dose of a COVID vaccine, neutralisation capacity was restored in both the normal weight and severely obese groups. But the researchers found that immunity again declined more rapidly in people with severe obesity, putting them at greater risk of infection with time.

Categories : Diseases, Syndromes and Conditions Environmental EffectsTags :

Strong Link Between Polycyclic Aromatic Hydrocarbons and Rheumatoid Arthritis Risk

On By ModernMedia
Photo by Kouji Tsuru on Pexels

Exposure to polycyclic aromatic hydrocarbons (PAH), formed from burning various substances such as coal, wood or tobacco, or from grilled meat, is strongly linked to a person’s risk of developing rheumatoid arthritis, suggests research published in the open access journal BMJ Open.

These chemicals also seem to account for most of smoking’s impact on risk of the disease, the findings indicate. Growing evidence links several environmental toxicants with various long term conditions. But few studies have looked at their association with inflammatory conditions, such as rheumatoid arthritis, which is thought to arise from an interplay between genes, sex, and age, and environmental factors, including smoking, nutrition, and lifestyle.

To try and shed some light on the potential role of environmental exposure on rheumatoid arthritis risk, the researchers drew on responses to the nationally representative US National Health and Nutrition Examination Survey (NHANES) between 2007 and 2016.

NHANES evaluates a wide variety of toxicants, including PAH; chemicals used in the manufacture of plastics and various consumer products (PHTHTEs); and volatile organic compounds (VOCs), derived from paints, cleaning agents, and pesticides, among other things; along with data related to health, nutrition, behaviours and the environment.

The study included 21 987 adults, 1418 of whom had rheumatoid arthritis and 20 569 of whom didn’t. Blood and urine samples were taken to measure the total amount of PAH (7090 participants), PHTHTEs (7024), and VOCs (7129) in the body.

The odds of rheumatoid arthritis were highest among those in the top 25% of bodily PAH levels, irrespective of whether or not they were former or current smokers.

After accounting for potentially influential factors, including dietary fibre intake, physical activity, smoking, household income, educational attainment, age, sex, and weight (BMI), only one PAH, 1-hydroxynaphthalene, was strongly associated with higher odds (80%) of the disease.

PHTHTE and VOC metabolites weren’t associated with heightened risk after accounting for potentially influential factors.

Somewhat surprisingly, however, smoking wasn’t associated with heightened rheumatoid arthritis risk either, after accounting for PAH levels in the body. 

And further analysis to separate out the influences of PAH and smoking showed that bodily PAH level accounted for 90% of the total effect of smoking on rheumatoid arthritis risk.

This is an observational study, and as such, can’t determine cause. And the researchers acknowledge various limitations to their findings, including that measurements of environmental toxicants in fat (adipose) tissue weren’t available.

Nor did they measure heavy metal levels which have previously been linked to rheumatoid arthritis risk. Cigarettes are a major source of the heavy metal cadmium.

But they write: “To our knowledge, this is the first study to demonstrate that PAH not only underlie the majority of the relationship between smoking and [rheumatoid arthritis], but also independently contribute to [it]. 

“This is important as PAH are ubiquitous in the environment, derived from various sources, and are mechanistically linked by the aryl hydrocarbon receptor to the underlying pathophysiology of [rheumatoid arthritis].”

They add: “While PAH levels tend to be higher in adults who smoke…other sources of PAH exposure include indoor environments, motor vehicle exhaust, natural gas, smoke from wood or coal burning fires, fumes from asphalt roads, and consuming grilled or charred foods.

“This is pertinent as households of lower socioeconomic status generally experience poorer indoor air quality and may reside in urban areas next to major roadways or in high traffic areas.” These people may therefore be particularly vulnerable, they suggest.

Source: The BMJ

Categories : Medical Research & TechnologyTags :

In the ICU, Artificial Intelligence Beats Humans

On By ModernMedia
Image created using an AI art program, Craiyon, with the prompt “An AI monitoring a patient in an ICU ward”.

In the future, artificial intelligence will play an important role in medicine. In diagnostics, successful tests have already been performed with AI, such as accurately categorising images according to whether they show pathological changes or not. But training an AI run in real time to examine the time-varying conditions of patients in an ICU and to calculate treatment suggestions has remained a challenge. Now, University of Vienna Researchers report in the Journal of Clinical Medicine that they have accomplished such a feat.

With the help of extensive data from ICUs of various hospitals, an AI was developed that provides suggestions for the treatment of people who require intensive care due to sepsis. Analyses show that AI already surpasses the quality of human decisions making it important to also discuss the legal aspects of such methods.

Making optimal use of existing data

“In an intensive care unit, a lot of different data is collected around the clock. The patients are constantly monitored medically. We wanted to investigate whether these data could be used even better than before,” says Prof Clemens Heitzinger from the Institute for Analysis and Scientific Computing at TU Wien (Vienna).

Medical staff make their decisions on the basis of well-founded rules. Most of the time, they know very well which parameters they have to take into account in order to provide the best care. But now, a computer can easily take many more parameters than a human into account – sometimes leading to even better decisions.

The computer as planning agent

“In our project, we used a form of machine learning called reinforcement learning,” says Clemens Heitzinger. “This is not just about simple categorisation – for example, separating a large number of images into those that show a tumour and those that do not – but about a temporally changing progression, about the development that a certain patient is likely to go through. Mathematically, this is something quite different. There has been little research in this regard in the medical field.”

The computer becomes an agent that makes its own decisions: if the patient is well, the computer is “rewarded”. If the condition deteriorates or death occurs, the computer is “punished”. The computer programme has the task of maximising its virtual “reward” by taking actions. In this way, extensive medical data can be used to automatically determine a strategy which achieves a particularly high probability of success.

Already better than a human

“Sepsis is one of the most common causes of death in intensive care medicine and poses an enormous challenge for doctors and hospitals, as early detection and treatment is crucial for patient survival,” says Prof Oliver Kimberger from the Medical University of Vienna. “So far, there have been few medical breakthroughs in this field, which makes the search for new treatments and approaches all the more urgent. For this reason, it is particularly interesting to investigate the extent to which artificial intelligence can contribute to improve medical care here. Using machine learning models and other AI technologies are an opportunity to improve the diagnosis and treatment of sepsis, ultimately increasing the chances of patient survival.”

Analysis shows that AI capabilities are already outperforming humans: “Cure rates are now higher with an AI strategy than with purely human decisions. In one of our studies, the cure rate in terms of 90-day mortality was increased by about 3% to about 88%,” says Clemens Heitzinger.

Of course, this does not mean that one should leave medical decisions in an ICU to the computer alone. But the artificial intelligence may run along as an additional device at the bedside – and the medical staff can consult it and compare their own assessment with the AI’s suggestions. Such AIs can also be highly useful in education.

Discussion about legal issues is necessary

“However, this raises important questions, especially legal ones,” says Clemens Heitzinger. “One probably thinks of the question who will be held liable for any mistakes made by the artificial intelligence first. But there is also the converse problem: what if the artificial intelligence had made the right decision, but the human chose a different treatment option and the patient suffered harm as a result?” Does the doctor then face the accusation that it would have been better to trust the artificial intelligence because it comes with a huge wealth of experience? Or should it be the human’s right to ignore the computer’s advice at all times?

“The research project shows: artificial intelligence can already be used successfully in clinical practice with today’s technology – but a discussion about the social framework and clear legal rules are still urgently needed,” Clemens Heitzinger is convinced.

Source: EurekAlert!

Categories : Mental HealthTags :

Frequent YouTube Use Tied to Loneliness and Mental Health Problems

On By ModernMedia
Photo by Steinar Engeland on Unsplash

Frequent users of YouTube have higher levels of loneliness, anxiety, and depression according to researchers from the Australian Institute for Suicide Research and Prevention (AISRAP). Published online in MDPI, their study found that the most severely impacted were those under age 29, or who regularly watched content about other people’s lives.

Lead author Dr Luke Balcombe said the development of parasocial relationships between content creators and followers could be cause for concern, however some neutral or positive instances of creators developing closer relationships with their followers also occurred.

“These online ‘relationships’ can fill a gap for people who, for example, have social anxiety, however it can exacerbate their issues when they don’t engage in face-to-face interactions, which are especially important in developmental years,” he said.

“We recommend individuals limit their time on YouTube and seek out other forms of social interaction to combat loneliness and promote positive mental health.”

Dr Balcombe said the amount of time spent on YouTube was often a concern for parents, who struggled to monitor their children’s use of the platform for educational or other purposes.

In the study, two hours per day of YouTube consumption was classed as high frequency use and over five hours a day as saturated use.

In addition, the study determined more needed to be done to prevent suicide-related content being suggested to users by YouTube algorithms. 

While ideally, people shouldn’t be able to search for these topics and be exposed to methods, the YouTube algorithm does push recommendations or suggestions based on previous searches, which can send users further down a disturbing ‘rabbit hole’. 

Users can report this type of content, but sometimes it may not be reported, or it could be there for a few days or weeks and with the sheer volume of content passing through, it’s almost impossible for YouTube’s algorithms to stop all of it.

If a piece of content is flagged as possibly containing suicide or self-harm topics, YouTube then provides a warning and asks the user if they want to play the video.

“With vulnerable children and adolescents who engage in high frequency use, there could be value in monitoring and intervention through artificial intelligence,” Dr Balcombe said.

“We’ve explored human–computer interaction issues and proposed a concept for an independent-of-YouTube algorithmic recommendation system which will steer users toward verified positive mental health content or promotions.

“YouTube is increasingly used for mental health purposes, mainly for information seeking or sharing and many digital mental health approaches are being tried with varying levels of merit, but with over 10,000 mental health apps currently available, it can be really overwhelming knowing which ones to use, or even which ones to recommend from a practitioner point of view.

“There is a gap for verified mental health or suicide tools based on a mix of AI-based machine learning, risk modelling and suitably qualified human decisions, but by getting mental health and suicide experts together to verify information from AI, digital mental health interventions could be a very promising solution to support increasing unmet mental health needs.”  

Source: EurekAlert!