Medical Bulletin 10/ September/ 2024 - Video

Here are Top Medical News of the Day

Research Finds Consuming Fruit And Oats Increases Type 1 Diabetes Risk But Berries Offer Protection

New research presented at the annual meeting of the European Association for the Study of Diabetes (EASD) in Madrid, Spain, has found that eating fruit, oats and rye in childhood is associated with a higher risk of developing type 1 diabetes (T1D)(1). Eating berries, however, is linked to lower odds of developing the condition.

T1D is an autoimmune condition in which the immune system attacks and destroys the insulin-producing islet cells in the pancreas(2). This prevents the body from producing enough of the hormone insulin to properly regulate blood sugar levels.

T1D, the most common form of diabetes in children, is increasing worldwide. The number of cases worldwide is projected to double in just 20 years, from 8.4 million in 2021 to 17.4 million by 2040.

“Type 1 diabetes is a serious condition that requires lifelong treatment and so places a considerable burden on the patient and their family,”(3) says Professor Suvi Virtanen, of Finnish Institute for Health and Welfare, Helsinki, Finland, who led the research.

“It can lead to complications including eye, heart, nerve and kidney problems and shorten life expectancy and has substantial health care costs”.

“The rapid increase in type 1 diabetes in children suggests that environmental factors play an important role in the development of the disease. Identifying these factors will offer an opportunity to develop strategies to prevent it and its complications.”(3)

Numerous foodstuffs have been linked to islet autoimmunity – the attack on the insulin-producing cells – and T1D but there is a lack of high-quality evidence from prospective studies and the existence of a link remains controversial.

To address this, Professor Virtanen and colleagues explored whether diet in infancy and early childhood was associated with the development of T1D in thousands of children in Finland(4).

5,674 children (3,010 boys and 2,664 girls) with genetic susceptibility to T1D were followed from birth to the age of six. Food records completed by their parents repeatedly from the age of three months to 6 years provided information on the entire diet.

By the age of six, 94 of the children had developed type 1 diabetes. Another 206 developed islet autoimmunity and so were at substantially increased risk of developing T1D in the next few years(5).

The 34 food groups covered the entire diet and, when they were all factored in, several foods were associated with a higher risk of developing T1D.

The results show that the more fruit, oats or rye children ate, the more their risk of T1D increased.(6)

In contrast, eating strawberries, blueberries, lingonberries, raspberries, blackcurrants and other berries appeared to provide protection against T1D(7). The more berries a child ate, the less likely they were to develop T1D.

Reference: Virtanen, S. M., E. J. Peltonen, L. Hakola, S. Niinistö, H.-M. Takkinen, S. Ahonen, M. Akerlund, U. Uusitalo, M. Mattila, T. E. I. Salo, J. Ilonen, J. Toppari, R. Veijola, M. Knip, & J. Nevalainen. (2024, September 9). Food consumption associated with the risk of islet autoimmunity and type 1 diabetes. EASD Annual Meeting, Madrid, Spain.

Study Finds Patients Receiving Steroids Have 2 Times Higher Risk Of Diabetes

New research presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) in Madrid, Spain, has found that patients who are being treated with systemic glucocorticoids are more than twice as likely to develop diabetes as those not receiving the treatment.(8)

Glucocorticoids (sometimes known as steroids) fight inflammation and are used to treat a wide range of inflammatory and autoimmune conditions, including asthma, rheumatoid arthritis, cancers and other medical problems(9).

While they can be very effective in decreasing inflammation, glucocorticoids have many adverse effects including increasing blood sugar levels and causing diabetes. This is more likely when people use glucocorticoid tablets or injections than when used as inhalers, creams or drops.

A new study by researchers at the Diabetes Trials Unit, University of Oxford’s Radcliffe Department of Medicine, Oxford, UK, investigated how commonly patients being treated with glucocorticoids can develop new-onset diabetes(10). The study found that patients receiving systemic glucocorticoids were more than twice as likely (2.6 times) to develop diabetes as those not receiving the treatment.

Dr Rajna Golubic and colleagues compared the rate of new-onset diabetes in hospital patients who received systemic glucocorticoids (tablets, injections or infusions) to patients not treated with these drugs.

The study involved 451,606 adults (median age 52 years, 55% female, 69% White) who were admitted to the Oxford University Hospitals NHS Foundation Trust between 1 January 2013 and 1 October 2023.(11) All were free from diabetes at the start of the study and none were taking systemic glucocorticoids.

17,258 (3.8%) of the patients were treated with systemic glucocorticoids (some names include prednisolone, hydrocortisone, dexamethasone) while in hospital, most commonly for autoimmune and inflammatory diseases and for infections.

316 of these 17,258 patients (1.8%) developed diabetes while in hospital(12). This compares with 3,430 of the 434,348 patients (0.8%) who didn’t receive systemic glucocorticoids. Patients were typically admitted for less than a week.

Further analysis showed that, when age and sex were factored in, patients receiving systemic glucocorticoids were more than twice as likely (2.6 times) to develop diabetes as those not receiving the treatment.

Dr Golubic says: “These latest results give clinical staff a better estimate of how likely new diabetes is to occur and could prompt doctors to plan clinical care more effectively to detect and manage new diabetes.

“While we studied hospital patients, glucocorticoid tablets can be prescribed by GPs for conditions such as asthma and rheumatoid arthritis and it is important that they, too, are aware of the link.”(13)

Reference: Abstract presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) in Madrid, Spain.

Are You A Night Owl? You May Be Face 50% More Likelihood of Developing Diabetes: EASD 2024

Night owls have a higher BMI, larger waists, more hidden body fat and are almost 50% more likely to develop type 2 diabetes (T2D) than those who go to bed earlier(14), new research presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) in Madrid, Spain, has found.

Lead researcher Dr Jeroen van der Velde, of Leiden University Medical Centre, Leiden, Netherlands, says: “Previous studies have indicated that a late chronotype – preferring to go to bed late and wake up later – is associated with an unhealthy lifestyle. Late chronotypes are more likely to smoke or have an unhealthy diet, for example, and it has been suggested this is why they are higher risk of obesity and metabolic disorders including type 2 diabetes.

“However, we believe that lifestyle cannot fully explain the relationship between a late chronotype and metabolic disorders. In addition, while it is known that a late chronotype is associated with high BMI, it isn’t clear to what extent chronotype affects body fat distribution.”(15)

To find out more, Dr van der Velde and colleagues studied the association between sleep timing, T2D and body fat distribution(16) in more than 5,000 individuals, as part of the Netherlands Epidemiology of Obesity study, an ongoing study into the influence of body fat on disease.

The analysis involved participants (54% female) with a mean age of 56 years and mean BMI of 30 kg/m2.

Participants filled in a questionnaire their typical bed and waking times and from this midpoint of sleep (MPS)(17) was calculated.

The participants were then divided into three groups: early chronotype (the 20% of participants with the earliest MPS), late chronotype (the 20% of participants with the latest MPS) and intermediate chronotype (the remaining 60% of participants).

BMI and waist circumference were measured in all participants(18). Visceral fat and liver fat were measured in 1,526 participants, using MRI scans and MR spectroscopy, respectively.

The participants were followed-up for a median of 6.6 years, during which 225 were diagnosed with T2D.

The results, which were adjusted for age, sex, education, total body fat and a range of lifestyle factors (physical activity, diet quality, alcohol intake, smoking and sleep quality and duration), showed that compared with an intermediate chronotype, participants with a late chronotype had a 46% higher risk of T2D.(19)

This suggests that the increased risk of T2D in late chronotypes can’t be explained by lifestyle alone.

“We believe that other mechanisms are also at play,” says Dr van der Velde. “A likely explanation is that the circadian rhythm or body clock in late chronotypes is out of sync with the work and social schedules followed by society. This can lead to circadian misalignment, which we know can lead to metabolic disturbances and ultimately type 2 diabetes.”(15)

Reference: van der Velde, J. H. P., Rutters, F., Rosendaal, F. R., Lamb, H. J., Kalsbeek, A., & de Mutsert, R. (2024). Associations between chronotype, waist circumference, visceral fat, liver fat, and incidence of type 2 diabetes. Abstract presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD), Madrid, Spain.

ERS Conference Highlights: ChatGPT Surpassed Trainee Doctors in Assessing Complex Respiratory Illness In Children

The chatbot ChatGPT performed better than trainee doctors in assessing complex cases of respiratory disease in areas such as cystic fibrosis, asthma and chest infections in a study presented at the European Respiratory Society (ERS) Congress in Vienna, Austria.(20)

The study also showed that Google’s chatbot Bard performed better than trainees in some aspects and Microsoft’s Bing chatbot performed as well as trainees.

The research suggests that these large language models (LLMs) could be used to support trainee doctors, nurses and general practitioners(21) to triage patients more quickly and ease pressure on health services.

The study was presented by Dr Manjith Narayanan, a consultant in paediatric pulmonology at the Royal Hospital for Children and Young People, Edinburgh and honorary senior clinical lecturer at the University of Edinburgh, UK. He said: “Large language models, like ChatGPT, have come into prominence in the last year and a half with their ability to seemingly understand natural language and provide responses that can adequately simulate a human-like conversation. These tools have several potential applications in medicine. My motivation to carry out this research was to assess how well LLMs are able to assist clinicians in real life.”(22)

To investigate this, Dr Narayanan used clinical scenarios that occur frequently in paediatric respiratory medicine. (23)The scenarios were provided by six other experts in paediatric respiratory medicine and covered topics like cystic fibrosis, asthma, sleep disordered breathing, breathlessness and chest infections. They were all scenarios where there is no obvious diagnosis, and where there is no published evidence, guidelines or expert consensus that point to a specific diagnosis or plan.

Ten trainee doctors who had less than four months of clinical experience in paediatrics were given an hour where they could use the internet, but not any chatbots, to solve each scenario with a descriptive answer of 200 to 400 words(24). Each scenario was also presented to the three chatbots.

All the responses were scored by six paediatric respiratory experts for correctness, comprehensiveness, usefulness, plausibility, and coherence. They were also asked to say whether they thought each response was human- or chatbot-generated and to give each response an overall score out of nine.

Solutions provided by ChatGPT version 3.5 scored an average of seven out of nine overall and were believed to be more human-like than responses from the other chatbots(25). Bard scored an average of six out of nine and was scored as more ‘coherent’ than trainee doctors, but in other respects was no better or worse than trainee doctors. Bing scored an average of four out of nine – the same as trainee doctors overall. Experts reliably identified Bing and Bard responses as non-human.

Dr Narayanan said: “Our study is the first, to our knowledge, to test LLMs against trainee doctors in situations that reflect real-life clinical practice. We did this by allowing the trainee doctors to have full access to resources available on the internet, as they would in real life. This moves the focus away from testing memory, where there is a clear advantage for LLMs. Therefore, this study shows us another way we could be using LLMs and how close we are to regular day-to-day clinical application.

“We have not directly tested how LLMs would work in patient facing roles. However, it could be used by triage nurses, trainee doctors and primary care physicians, who are often the first to review a patient.”(22)

Reference: "Clinical scenarios in paediatric pulmonology: Can large language models fare better than trainee doctors?", by Manjith Narayanan et al; Presented in session, "Respiratory care in the digital age: innovative applications and their evidence" at 09:30-10:45 CEST on Monday 9 September 2024.

Study Finds Obesity A Key Risk Factor For COVID-19 Infection

A new study by investigators from Mass General Brigham has found that obesity may be an important risk factor for infection from the virus that causes COVID-19. Researchers analyzed electronic health record data from Mass General Brigham and found that individuals with obesity were 34% more likely to become COVID positive after reported exposure than individuals without obesity.

Their findings, published in PNAS Nexus, indicate that obesity, a well-known risk factor for more severe symptoms and complications from the virus, may also increase risk of infection.(23)

"We knew that obesity raises risk for severe COVID-19 outcomes, but we were surprised to find that it also increases the likelihood of catching the virus in the first place," said corresponding author Masanori Aikawa, MD, PhD, director of the Center for Interdisciplinary Cardiovascular Sciences (CICS) at Brigham and Women's Hospital. "This suggests that obesity may play a more significant role in COVID-19 transmission than previously thought. Our results add to the growing body of evidence that maintaining a healthy weight is crucial for overall health."(24)

Led by first author Joan T. Matamalas, PhD, a research scientist in the CICS, the researchers conducted a case-control study evaluating data in the Mass General Brigham COVID-19 Data Mart(25), which included COVID-19 results for 687,813 subjects tested for COVID-19 from March 2020 to March 2022 -- a period of rampant infection before vaccines were widely available.

The team examined data from more than 72,000 participants who had reported contact with or suspected exposure to COVID-19.

The authors note that the study relied on self-reported information about potential exposure to the virus, which may not accurately reflect actual exposure, and was conducted in a single health system in Massachusetts(26), so the results may not be generalizable to other populations.

Future studies could examine the biological mechanisms that may explain why individuals with obesity may be more susceptible and could help to identify new drug targets or more personalized vaccination approaches to help protect against infection and complications.

"Although our study was conducted on pre-vaccine data, numerous studies have shown that vaccines are the most effective and safe way to prevent COVID-19 infection and severe outcomes, regardless of weight or other risk factors," said Aikawa(25)

Reference: Joan T Matamalas, Sarvesh Chelvanambi, Julius L Decano, Raony F França, Arda Halu, Diego V Santinelli-Pestana, Elena Aikawa, Rajeev Malhotra, Masanori Aikawa. Obesity and age are transmission risk factors for SARS-CoV-2 infection among exposed individuals. PNAS Nexus, 2024; 3 (8) DOI: 10.1093/pnasnexus/pgae294