Archive for category: News

Researchers have found in a recent study that febuxostat carries a significantly greater risk of liver function disturbances compared to benzbromarone in patients with gout. Despite common clinical hesitation to use benzbromarone, the findings suggest it may have lower hepatotoxicity than febuxostat.

A new study published in the journal of Diabetes Research and Clinical Practice showed that following bariatric surgery, patients with long-standing type 2 diabetes mellitus experienced remission. Also, results were closely correlated with β-cell activity and the percentage of total weight lost.

The β-cell function appears to have reached a point of no return in patients with type 2 diabetes that has been present for more than 10 years. Observing the first-phase insulin secretion in long-term T2DM patients with diabetes remission and investigating the major determinants of diabetes remission in patients with a period of T2DM ≥ 10 years who went through bariatric surgery, which includes the outcomes of insulin-treated patients (I-T2DM) and non-insulin-treated patients (NI-T2DM), were the goals of this study.

The patients who had bariatric surgery and have had type 2 diabetes for at least 10 years were included retrospectively and monitored. An HbA1c < 6.5% (48 mmol/mol) at least three months after stopping hypoglycemic medications was considered remission of diabetes. The patients who were in remission from diabetes had an intravenous glucose tolerance test (IVGTT).

Nearly, 59.6% of the 203 individuals with T2DM who had been treated with insulin prior to bariatric surgery had been with the disease for at least 10 years. Remission rates at one, two, and three years after surgery were 65.6%, 53.8%, and 41.9%, respectively.

According to Cox regression analysis, the percentage of total weight loss (%TWL) and β-cell function had the strongest correlations with the probabilities of remission one year after bariatric surgery. In 50 individuals with diabetic remission, the first-phase insulin production peak was around 5–8 times the fasting insulin level.

Overall, this study found that one year following bariatric surgery, the diabetes remission rate for T2DM patients who have had the disease for at least ten years can still exceed two-thirds. But between years 1 and 3, the remission rate dropped by around 10% a year, underscoring the significance of long-term monitoring for these individuals.

Source:

Zhang, N., Zhou, B., Wang, H., Xue, X., Huang, Y., Wang, S., Wang, Z., Niu, W., Liu, B., Nie, Y., Li, Z., Zhang, L., Wang, P., Chou, S., Yao, L., Ran, S., Lv, J., Liu, G., Li, G., … ODHM(Obesity and Diabetes Health Management) Study Group. (2025). Predictors of diabetes remission after bariatric surgery in patients with type 2 diabetes mellitus duration ≥ 10 years: A retrospective cohort study. Diabetes Research and Clinical Practice, 112164, 112164. https://doi.org/10.1016/j.diabres.2025.112164

A new guideline to help health care providers manage obesity in children and adolescents takes a patient-centred approach, emphasizing behavioural and psychological supports that focus on outcomes valued by patients and their families.

The guideline based on the latest evidence, is published in CMAJ (Canadian Medical Association Journal).

It was developed by Obesity Canada through an extensive, 4-year-long collaboration involving adolescents and caregivers with lived obesity experience, methodologists, health care providers, and more than 50 multidisciplinary experts.

“The guideline is designed to support shared and informed clinical decision-making rooted in systematic, interpretable summaries of scientific evidence,” said Dr. Bradley Johnston, associate professor of nutrition and health research methods and co-chair of the guideline development committee. “To achieve that, we devised a transparent process to assess the totality of scientific evidence for relevant interventions to manage pediatric obesity, while prioritizing outcomes that matter the most to children, adolescents, and their families.”

These outcomes include quality of life, mental health, related cardiovascular risk factors, avoiding harms, and more.

Pediatric obesity is a complex, chronic, progressive, and stigmatized condition that increases risk for more than 200 health conditions. In Canada, about 1 in 4 children aged 11 years or younger and 1 in 3 adolescents aged 12–17 years have an elevated body mass index, suggesting overweight or obesity. International data indicate that severe obesity appears to be on the rise globally.

“The health consequences and persistence of pediatric obesity into adulthood highlight the need for available, accessible, family-oriented interventions for effective obesity management,” says Dr. Sanjeev Sockalingam, scientific director, Obesity Canada. “Success in managing pediatric obesity is most likely when children and their families can access support and practical strategies to make and sustain positive behavioural changes, complemented by pharmacologic and surgical interventions, if necessary and available.”

The guideline contains 10 recommendations, including guidance on nutritional, physical activity, psychological, and technology interventions, and medications and surgical options, as well as 9 good practice statements. The authors recommend combining at least 2 options to help manage obesity.

“We know the time to act on pediatric obesity is now. Waiting until children and adolescents become adults before providing meaningful, informed, evidence-based care and support only heightens the risk for developing complications and prolongs the experience of living with a stigmatized chronic disease, which can worsen further physical and mental health outcomes,” said Lisa Schaffer, executive director, Obesity Canada.

Reference:

Geoff D.C. Ball, Roah Merdad, Catherine S. Birken, Tamara R. Cohen, Brenndon Goodman, Stasia Hadjiyannakis, Jill Hamilton, Mélanie Henderson, John Lammey, Katherine M. Morrison, Sarah A. Moore, Aislin R. Mushquash, Ian Patton, Nicole Pearce, Managing obesity in children: a clinical practice guideline, Canadian Medical Association Journal, DOI: https://doi.org/10.1503/cmaj.241456.

Myopia, or nearsightedness, affects two billion people worldwide. Uncorrected myopia can impair vision, disrupting education, career prospects, and quality of life. By 2050, nearly half of the global population is estimated to become myopic. High myopia is often associated with complications that can lead to visual impairment, affecting patients’ quality of life and increasing the global medical and economic burden. Therefore, early diagnosis of myopia is important for the prevention of vision damage in patients with myopia.

Artificial intelligence (AI) has opened up new frontiers in the medical field and may be a solution to this global health concern. The subsets of AI, such as machine learning (ML) and deep learning (DL) can help analyze data to diagnose diseases and predict risk factors, biomarkers, and outcomes.

In a new literature review, Dr. Li Li, Dr. Jifeng Yu, and Dr. Nan Liu, all from the Department of Ophthalmology, Capital Medical University, China, have summarized the applications and challenges of AI in myopia, including detection, risk factor assessment, and prediction models. This study was published in the journal of Pediatric Investigation on 18 March 2025.

Interestingly, AI models can be trained using ML/DL to detect myopia from fundus photos and optical coherence tomography images. By feeding a model with a large quantity of fundus images from myopic patients, the AI can be taught to discern minute changes in color and pattern in the retina that are associated with myopia. This allows the model to diagnose future patients from their fundus photos.

In addition, self-monitoring equipment such as SVOne, a handheld device that uses a wavefront sensor to measure eye defects, can use AI algorithms to detect refractive errors in the eyes. The device could access an online database of images, which the AI can use as a reference to diagnose myopia. Moreover, AI can be trained to detect behavioral changes associated with the onset of myopia. Such detection is especially useful for the early detection of myopia in children, which is often ignored otherwise. For example, the Vivior monitor uses ML algorithms to note changes in visual behaviors, such as time spent on near vision activities, in children aged 6–16 years.

Furthermore, ML methods like support vector machine, logistic regression, and XGBoost can be employed to identify risk factors of myopia. “An XGBoost-based model can be fed large quantities of longitudinal data, allowing it to learn the outcomes and associated risk factors of myopia in numerous patients. This, in turn, allows the model to assess the risk factors of new patients based on their genetics, family history, environment, and physiological parameters,” explains Dr. Li Li.

Predicting the progression and outcome of myopia can help doctors adjust their clinical approach. Taken on a large scale, it can shape clinical practice and policymaking that help in myopia control. By feeding an AI model large quantities of biometric data, refractive data, treatment responses, and ocular images from numerous myopia patients, the AI can be taught to predict outcomes of myopia in new patients.

Despite the great potential of AI in myopia, several challenges need to be overcome. Firstly, it is important to ensure that the dataset used to train an AI model is correct and of high quality. Bias, false negatives/positives, and poor data quality can negatively impact the diagnostic and prediction accuracy of the model. Secondly, most AI models are trained using data from large hospitals, which may not be representative of patients going to smaller clinics. This creates a discrepancy between real-world and training populations. Thirdly, an AI model is not a trained doctor and may not be able to provide a clinical basis for its diagnosis, which can cause the diagnosis to be rejected by medical professionals. Finally, with such vast quantities of patient data used to train AI models, it is important to ensure the privacy of patients’ medical records.

“While our study highlights the remarkable progress made in the clinical application of AI in myopia, further studies are needed to overcome the technological challenges. By building high-quality datasets, improving the model’s capacity to process multimodal image data, and improving human-computer interaction capability, the AI models can be further improved for widespread clinical application,” concludes Dr. Jifeng Yu.

Reference:

Nan Liu, Li Li, Jifeng Yu, Application of artificial intelligence in myopia prevention and control, Pediatric Investigation, https://doi.org/10.1002/ped4.70001