Posted on Diabetes, Obesity

Sustained remission of diabetes and other obesity-related conditions found a decade after weight loss surgery

Study finds that type 2 diabetes patients treated with GLP-1RAs who lowered their BMI also reduced their cardiovascular risk

A study published in the New England Journal of Medicine found that ten years after undergoing bariatric surgery during their teenage years, more than half of the participants maintained significant weight loss. Additionally, many of these individuals showed improvements in obesity-related conditions, including type 2 diabetes, high blood pressure, and high cholesterol.

“Our study demonstrates remarkable results from the longest follow-up of weight loss surgery during adolescence, confirming that bariatric surgery is a safe and effective long-term strategy for managing obesity,” stated lead author Justin Ryder, PhD. He is the Vice Chair of Research in the Department of Surgery at Ann & Robert H. Lurie Children’s Hospital of Chicago and an Associate Professor of Surgery and Pediatrics at Northwestern University Feinberg School of Medicine.

Bariatric surgery is significantly under-utilized in the U.S., with only one out of every 2,500 teens with severe obesity undergoing the procedure. Based on existing recommendations, nearly five million adolescents qualify for effective weight loss interventions, such as bariatric surgery.

Hillary Fisher, now 31 years old, is glad she decided to undergo surgery at the age of 16. She was one of 260 adolescents who participated in the long-term Teen-LABS study.

“I felt overwhelmed by the daily struggles I faced due to my weight, health issues, and bullying in high school,” Ms. Fisher said. “After several unsuccessful attempts to lose weight, I weighed 260 pounds, and we decided that bariatric surgery was the solution. It changed my life; the improved health and self-esteem that came with losing 100 pounds were significant for me, and I would absolutely do it again.”

Notably, the study found that 55 per cent of the participants who had type 2 diabetes as teenagers and underwent surgery were still in remission of their diabetes at 10 years. 

“This is considerably better than the outcomes reported in people who underwent bariatric surgery as adults, a major reason why treating obesity seriously in adolescents is so important,” added Dr. Ryder. 

Indeed, a recent multi-centre randomized controlled trial found diabetes type 2 remission in adults to be 12-18 per cent at seven to 12 years after bariatric surgery.

Posted on Autism

Predicting the future and Autism

Your brain processes what you see and makes continuous predictions based on your experiences. This predictive process may be less refined in autistic people.

When someone throws a ball at you, you instinctively know to catch it—even before you consciously think about it. In the past, people believed that the brain worked like a camera: an image of the flying ball enters through your eyes and is then processed by your brain. After that, the brain programs a suitable action in response. However, doesn’t that process take too long? Would you still be able to catch the ball in time?

Researchers Christian Keysers, Giorgia Silani, and Valeria Gazzola reveal that the brain processes information differently than expected. Christian explains, “Your brain doesn’t simply react to what your eyes see; instead, it predicts what will happen based on your expectations and past experiences. Doing this keeps our actions in sync with the ball, even though it takes the brain several hundred milliseconds to process visual input and coordinate movement. It plans to ensure enough time to execute the action and catch the ball. The image that enters through your eyes is primarily used to verify whether your expectations align with reality. It is only when there’s a discrepancy between your expectations and what you see that your brain relies on visual input to adjust its predictions more accurately.”

Predicting others

Ms Gazzola shared, “What’s interesting is that we use our motor programs and somatosensory cortices to predict the actions of others. For instance, when you perform a physical action, like lifting a carton of milk to pour some into your coffee, you have certain expectations about the carton’s weight and how it should feel in your hand as you lift it. Typically, you don’t consciously notice the weight of the carton because your brain has already predicted it. However, if someone else has finished the milk and the carton is much lighter than you anticipated, the sudden discrepancy between your expectations and the sensory feedback will catch your attention.”

“When you see someone else do so, you don’t directly feel the weight of the carton. Still, you can make predictions using your motor programs and test them against what you see. So, you still feel surprised if the carton flies skywards much faster than you expected. We think this has to do with so-called mirror neurons, cells within your motor cortex that become active when you see someone else act. This acts as a sort of ‘shortcut,’ allowing you to use your motor programs and the predictive machinery necessary for your actions to predict the behaviour of others.”

But what about emotions? Gazzola explains: “We know that regions in our brain that are involved in our own emotions become active while we witness the emotions of others. However, how we predict the emotions of others is not fully understood. Reviewing
the literature revealed that the regions in our brain that are active when we receive a reward or punishment also become active when someone else receives a reward or punishment. Reward and punishment are therefore valuable predictors for the emotions of others.”

Complex system

Christian Keysers: “Imagine I have a button, and every time I press it, an actor starts screaming in pain. If I do this five times, your expectations change: the first time, it’s unexpected, but by the fifth time, you can predict what will happen.”

“According to the traditional theory of perception, where the brain only processes the image you see, you should see the same reaction in the brain each time. But if the outside world primarily serves to test your predictions, you’d expect a strong brain response the first time and a much smaller response the last time because you already know what will happen.”

“So what happens in the brain? Over many studies, we have seen that it’s quite complex. Some brain areas respond relatively consistently across all five times. At the same time, there are also brain regions where activity changes across the five times. In this review paper, we look at the many studies that have emerged on the topic to propose how these different brain systems organize into a coherent predictive brain. For example, if some actions become predictable, your premotor cortex knows how to act as your body. This region will then inhibit visual regions in your brain, leading to less visual input. What you perceive is no longer what you see but expect to see. Only if something unexpected happens will this inhibition become ineffective. The visual areas now show a strong response sent forwards to the premotor cortex to revise the predictions”.

Predictions and autism

It’s believed that the predictive system in people with autism is less well-tuned. This makes the world around them more unpredictable, leading to less suppressed stimuli. Christian Keysers: “Imagine standing in a crowded room with many people. Because our brain makes a lot of predictions, we can ignore most stimuli and focus only on what’s important. But when this predictive system doesn’t work well, such a busy environment can suddenly feel overwhelming.”

“The brain is complex and has the unique ability to adapt. It’s interesting to realize that your brain isn’t just a camera simply processing what comes in. Instead, your brain constantly operates based on predictions. Your brain is always ahead and continuously constructs what the world should be.”

Posted on Rheumatoid arthritis

Rhuematoid arthritis- The couples who cope together, stay together

An Australian-first study has lifted the lid on how couples living with rheumatoid arthritis cope with the debilitating disease finding that those who cope with problems together had less psychological distress and better relationships.
An Australian-first study reveals that couples coping with rheumatoid arthritis together experience less psychological distress and stronger relationships.

The study, published in The Journal of Rheumatology, examined dyadic coping—when a couple engages in joint problem-solving information gathering, sharing feelings, and demonstrating mutual commitment—from both partners’ perspectives. T. “Dyadic coping refers to how couples work together to manage the challenges of one partner’s illness. This process is a key predictor of how well patients adjust to their disease and overall well-being,” says Dr. Manasi Murthy Mittinty from the College of Medicine and Public Health. . The sample consisted of 163 couples.

Dyadic coping fosters a sense of unity, helping couples create strategies together to deal with stressful situations, and serves as a protective factor that reduces the likelihood of divorce.

Collaborating as a couple is essential for navigating the challenges posed by one partner’s illness, especially in cases of rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease that can lead to irreversible tissue damage, progressive deformity, and pain. Approximately 18 million people worldwide are affected by RA, including nearly 456,000 Australians.

Although the management of rheumatoid arthritis (RA) has improved significantly due to biologic treatments, many patients still experience severe physical pain and stiffness. Additionally, around 35% of individuals with RA report mental and behavioural conditions, such as bipolar disorder, mania, and anxiety disorders.

“We found that supportive dyadic coping leads to lower depression, anxiety, and stress for patients, as well as improved relationship quality. In contrast, negative dyadic coping increases psychological distress and reduces relationship quality for both partners,” says Dr Mittinty.

“By examining the interpersonal dynamics of couples grappling with chronic disease, we hope to significantly improve the quality of life for patients with rheumatoid arthritis and their spouse.”

The study is the first in Australia to report dyadic coping from the perspective of both participants with RA and their spouses.

RA patients and their spouses were invited to participate in an online survey study if they were more than 18 years old and had lived together for more than a year. The survey included the Chronic Pain Grade Scale, Dyadic Coping Inventory, Depression Anxiety Stress Scale, and Dyadic Adjustment Scale.

“The results underscore the interconnected nature of dyadic coping, highlighting the need to consider both viewpoints in understanding its impact on couples.

“For decades, the focus has been limited to reducing patients’ illness-related distress and improving patient outcomes. More recently, scientists have adopted a new approach into understanding how illness in a spouse can affect the couple’s relationship and the other spouse’s well-being.

“Our findings demonstrate the reciprocal nature of dyadic coping that transpires between patients with RA and their spouses and showcases that integrating dyadic coping training in disease management may be a valuable resource for enhanced mental health outcomes and relationship quality of couples,” she adds.

Posted on Autism

Are autistic adults more vulnerable to criminal exploitation?

Researchers at Flinders University tested the belief that autistic adults are more likely than non-autistic adults to be criminally exploited due to difficulties in recognizing criminal intent.

“It is not uncommon for defence lawyers, often with the backing of testimony from ‘expert’ witnesses, to claim that autistic adults struggle to interpret the intentions of others or understand their thoughts. This difficulty can make them more susceptible to being lured into criminal activity,” says Professor Neil Brewer, Matthew Flinders Distinguished Emeritus Professor of Psychology in the College of Education, Psychology, and Social Work.

“Such arguments reflect the widely-held perspective that difficulties reading others’ intentions, emotions, and motivations are fundamental features of autism.

However, this perspective may not withstand scrutiny, and we found that, in general, autistic adults are no more vulnerable to being involved in criminal acts than non-autistic adults.

“Furthermore, the difficulties in mindreading often associated with autism are not universally present among autistic adults.”

In a study published in the American Psychological Society’s journal, Law and Human Behavior, former PhD student Zoe Michael and her supervisor, Professor Neil Brewer, developed a new and realistic approach called the Suspicious Activity Paradigm (SAP). This paradigm was designed to evaluate how effectively adults can recognize and respond to cues that indicate social interactions may lead to criminal behaviour.

The study included 197 participants: 102 autistic adults and 95 non-autistic adults, who role-played in scenarios that progressively indicated criminal intent from their interactions.

They were asked about their reactions at different stages as the scenarios developed to evaluate their ability to recognize and respond to suspicious actions from others, thus gauging their susceptibility to being unknowingly drawn into criminal activities.

“We found that, overall, both autistic and non-autistic adults responded in similar ways to suspicious behaviour across various scenarios,” says Professor Brewer.

“Importantly, autistic adults did not show lower rates of suspicion or adaptive responses when compared to their non-autistic counterparts as the scenarios unfolded. Nor did they take longer to recognise the potentially problematic nature of the interaction.”

Building on previous research, the study found that verbal intelligence and Theory of Mind (ToM) – a term used to describe the ability to take the perspective or read the mind of others – predicted someone’s ability to recognise and respond to suspicious activity.

“Our findings indicate that the ability to understand others’ perspectives and intentions – and not the presence of an autism diagnosis – was a critical factor influencing their vulnerability to crime,” he says.

In other words, while autistic individuals who had difficulty discerning others’ intentions were vulnerable, the same was true of their non-autistic peers.

It is important to note, however, that a relatively small proportion of autistic individuals’ performance on the mindreading measure was below that of any of the non-autistic sample, a finding consistently replicated by the Flinders research team that developed the measure.

This indicates that there will be some autistic individuals who will likely be particularly vulnerable because of mindreading difficulties – but such challenges cannot be assumed.

“Thus, rather than defence lawyers and clinicians assuming, and arguing, that a diagnosis of autism automatically signals a particular vulnerability to being lured into crime, it is important to formally assess and demonstrate that a criminal suspect or defendant has significant mindreading difficulties that likely have rendered them vulnerable,” he adds.

Posted on Diabetes

Enhancing the accuracy of wearables that measure blood glucose levels

Diabetes is an increasingly pervasive disease, currently affecting over 500 million adults worldwide. Since there is as yet no cure for type 1 or type 2 diabetes, patients must regularly monitor their BGLs to keep them in check. Though BGL-measuring devices relying on painful finger pricks have been the gold standard for decades, modern technology is slowly opening doors to better alternatives. Many researchers have proposed noninvasive methods to monitor BGLs using widely available wearable devices, such as smartwatches. For example, by placing the LEDs and photodetectors present in certain smartwatches against the skin, oxyhemoglobin and hemoglobin pulse signals can be measured to calculate a metabolic index, from which BGLs can be estimated. However, given the small size and limited power of smartwatches and similar wearables, the data quality of the measured signals tends to be quite low. Moreover, as these devices are worn on extremities, daily movements introduce measurement errors. These problems limit the accuracy and clinical applicability of such wearables for diabetes management. A team from Hamamatsu Photonics K.K., Japan, has been actively researching this issue, looking for effective solutions. In a recent study led by Research and Development Engineer Tomoya Nakazawa, published in the Journal of Biomedical Optics (JBO), they conducted an in-depth theoretical analysis of the sources of errors in the metabolic-index-based method. Based on this analysis, they implemented a novel signal quality index to filter out low-quality data as a preprocessing step and thereby enhance the accuracy of estimated BGLs. “As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is absolutely essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study. First, the researchers mathematically showed that discrepancy between the two types of phase delays in the oxyhemoglobin and hemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two main sources of phase error, namely, a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index. The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data. To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, the latter of which was used to capture the reference values. Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work. The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the performance of the former, hardware improvements in the photodetector and amplifier circuits could go a long way to make wearable electronics a more attractive and clinically acceptable option to monitor BGLs.

Diabetes is a growing global issue, currently affecting over 500 million adults. As there is still no cure for either type 1 or type 2 diabetes, patients need to regularly monitor their blood glucose levels (BGLs) to manage their condition. While traditional BGL-measuring devices that require painful finger pricks have been the standard for many years, modern technology is beginning to offer better alternatives.

Many researchers have proposed noninvasive methods to monitor blood glucose levels (BGLs) using commonly available wearable devices, such as smartwatches. For instance, by positioning the LEDs and photodetectors found in certain smartwatches against the skin, it is possible to measure the pulse signals of oxyhemoglobin and haemoglobin. This data can then be used to calculate a metabolic index, which can help estimate BGLs. However, due to the small size and limited power of these smartwatches and similar wearables, the quality of the measured signals is often low. Additionally, daily movements can introduce measurement errors because these devices are typically worn on the extremities. These issues hinder the accuracy and clinical applicability of wearables for managing diabetes.

A team from Hamamatsu Photonics K.K. in Japan has been actively researching solutions to a pressing issue. In a recent study led by Research and Development Engineer Tomoya Nakazawa and published in the Journal of Biomedical Optics (JBO), they conducted a thorough theoretical analysis of the errors associated with the metabolic index-based method. Based on their findings, they developed a novel signal quality index to filter out low-quality data as a preprocessing step, which enhances the accuracy of estimated blood glucose levels (BGLs).

“As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study.

First, the researchers mathematically showed that the discrepancy between the two types of phase delays in the oxyhemoglobin and haemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two primary sources of phase error: a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index.

The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data.

To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, which was used to capture the reference values.

Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work.

The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the former’s performance, hardware improvements in the photodetector and amplifier circuits could go a long way toward making wearable electronics a more attractive and clinically acceptable option for monitoring BGLs.