Common painkillers linked with heart failure in people with type 2 diabetes
A recent study published in the Journal of the American College of Cardiology suggests that the use of common pain medications such as Advil or Motrin (ibuprofen) may increase the risk of heart failure in people with type 2 diabetes. The study found that NSAIDs (nonsteroidal anti-inflammatory drugs) may lead to first-time hospitalisation for heart failure in patients with type 2 diabetes. This is noteworthy, as these individuals are already known to face an elevated risk of heart failure.
The Danish study included more than 330,000 individuals with type 2 diabetes, of whom 1 in 6 filled at least one NSAID prescription within a year. During a follow-up of almost six years, more than 23,000 subjects were hospitalised with heart failure for the first time, and NSAID use was associated with a 40 percent higher relative risk of first-time heart failure hospitalisation. Ibuprofen and diclofenac were found to increase the risk of heart failure hospitalisation, but not celecoxib and naproxen. There was no association of NSAID use and increased risk in people with well-controlled diabetes. Strong associations were found in people aged 65 and older, while no association was found in those younger than 65. The strongest association was found in very infrequent or new users of NSAIDs. The study highlights the need for education in patients with cardiac risk factors, such as diabetes, on the dangers of over-the-counter medications.
Read MoreHealth Education England publishes roadmap for use of AI in the NHS
Health Education England (HEE) has released the first-ever roadmap outlining the use of artificial intelligence (AI) in the UK’s National Health Service (NHS) and its impact on the healthcare workforce.
The report analyses the implementation of AI and data-driven technologies in the NHS, their adoption rate, and the impact on staff. The roadmap aims to provide insight to healthcare leaders into AI policy, education, regulation, innovation, digital transformation, and workforce strategy. The report examines the timeframe for AI projects’ implementation, the distribution of AI technology in clinical areas and the workforce, the different uses of AI in healthcare, and the effects on staff and patients.
Dr Hatim Abdulhussein, clinical lead for the Digital, Artificial Intelligence and Robotics Technologies in Education (DART-Ed) programme at HEE, states that the AI roadmap is a valuable asset in understanding the AI and data-driven healthcare landscape and its implications on staff and learners. The report is a collaborative effort between Unity Insights, NICE, NHS AI Lab, and the NHS Accelerated Access Collaborative (AAC). The report also builds on the Topol review, providing a framework to identify and classify AI technology in healthcare.
The report finds that diagnostic technology, such as those used in imaging, pathology, and endoscopy, accounts for the most significant proportion of AI use in healthcare (34% share), followed by automation/service efficiency, P4 medicine, remote monitoring, and therapeutic. Of the 56 technologies estimated for large-scale deployment within a year, 77% are for use in secondary care, 23% for use in primary care, and 7% for use in community care. A total of 155 workforce groups, across 67 clinical areas, use AI tech identified by HEE, with medics in clinical radiology and general practice being the most affected, as well as non-clinical admin staff.
Abdulhussein highlights the importance of achieving transformation through emerging technology to improve patient care and scalability throughout the country, and the roadmap’s insights will focus efforts on education and training. The UK government recently unveiled its ten-year national plan to tackle cancer, including an increased use of AI and machine learning in NHS healthcare. England is also trialling a new approach to the ethical adoption of AI in healthcare.
Read MoreThe Potential Role of Digital Health in Obesity Care
The journal Advances in Therapy has recently published a paper exploring the use of digital health technologies in improving obesity care.
Obesity is a complex and chronic condition that increases the risk of developing several diseases, including type 2 diabetes mellitus, cardiovascular disease, and certain cancers. The prevalence of obesity continues to rise and poses a significant economic burden on healthcare systems worldwide. Current obesity treatment approaches tend to focus on individual responsibility, diet, and exercise, but they fail to acknowledge the complexity of the condition and the need for a whole-system approach.
A new approach is necessary that recognises the complexity of obesity and offers patient-centred, multidisciplinary care tailored to the needs of each individual. Digital transformation can significantly benefit obesity treatment, particularly through telehealth and mobile health, which can provide improved support and monitoring of behaviour change. Furthermore, artificial intelligence (AI) can revolutionise obesity care by enabling real-time patient monitoring and personalised interventions.
Digital health technologies offer a range of potential benefits for people with obesity, including improved quality, efficiency, and cost-effectiveness of care at all stages, from patient assessment to treatment and ongoing monitoring and support. Telehealth and mobile health are already widely used in healthcare and can reduce barriers to effective obesity care, improve access to care, and ultimately improve long-term weight management and obesity-related health outcomes. However, equitable access to telehealth and mobile health services must be ensured for patients from the most deprived communities.
Machine learning and AI can play an increasingly important role in healthcare and provide several opportunities for obesity care. By analysing large datasets from electronic health records, healthcare professionals can enhance their understanding of obesity’s complexity, leading to improved patient assessment and personalised treatment. Additionally, AI can be applied to mobile health technologies, connected via the Internet of Medical Things, to provide real-time patient monitoring and personalised weight management interventions.
In the immediate future, the most significant digital advancement in obesity care is likely to be the increased use of telehealth support, allowing greater access to care, more frequent consultations, and longer-term support. Over time, this will increasingly be supported by mobile health apps and devices. Ultimately, interventions and ongoing support are likely to be delivered using AI technology through a chatbot or avatar.
Please read the full paper here: The Potential Role of Digital Health in Obesity Care
Read MoreSmart data and population health can build better, healthier lives
In recent years, there has been growing interest in how population health management can help improve the lives of citizens. Here, we explore how machine learning can provide the data intelligence we need to deliver better healthcare for everyone.
Preventative healthcare is known to be more effective than reactive healthcare. By intervening early and preventing health issues from escalating, individuals, communities, and healthcare providers can all benefit from improved outcomes. With the UK healthcare system under immense pressure, there is a pressing need to shift towards a proactive approach. However, in order to achieve this, we need to understand what factors influence health outcomes. For example, why do people in one area have fewer healthy life years than those in another part of the country?
Population health intelligence is the discipline of finding answers to questions like this. By making the right interventions at the right time, we can improve health outcomes. As Integrated Care Systems (ICSs) work to tackle some of the NHS’s most pressing problems, such as health and care inequalities and financial sustainability, population health intelligence can help make the connections between health outcomes and the factors that influence them.
ICSs bring together NHS, local authority, and third sector bodies, providing access to all the data needed to gain a deeper understanding of population health. This includes everything from NHS records to data on education, housing, and crime. However, the challenge is how to extract the relevant insights that can point to new and better ways of doing things.
Data on life expectancy is a good starting point. It is also a sound measure of health inequality, which is currently in the spotlight. Unlike health data or patient reported outcome measures (PROMS), life expectancy data doesn’t depend on people having accessed healthcare, making it a more inclusive and accurate proxy for overall population health. The variations in the data are stark, with men and women born in Glasgow City today expected to live around 10 years less than those born in Westminster or Kensington & Chelsea.
To understand what’s behind these stats, machine learning can make meaningful analysis of disparate datasets. It can rapidly work across huge volumes and multiple sources of data to identify patterns that can guide decision-making. Population health intelligence can help us analyse the causes of death at different ages in different demographics and the wide range of influences on them.
Factors such as education and housing can affect health outcomes. For example, data analysis may connect high levels of poor housing stock with respiratory illness. This could ultimately show that making improvements to living conditions today could prevent people from developing chronic conditions that lead them to depend on multiple health and care services in the future.
In a similar way, information on dental health, such as the number of people in a single area having teeth removed at a young age, could also be a predictor of chronic conditions such as diabetes and heart disease in future life. This information could direct healthcare interventions towards support for diet and lifestyle changes.
By connecting health data with environmental information, such as air quality or the amount of available green space, population health intelligence techniques could show local authorities where to focus their investment, where people’s physical and mental wellbeing will benefit the most.
Health-related wearables also support the shift towards more personalised and proactive healthcare. From simple step counters and heart rate monitors to sophisticated continuous glucose monitors, people are increasingly willing and motivated to track their own wellbeing. When connected to healthcare systems and analysed by machine learning algorithms, wearable devices and apps could support preventive healthcare by alerting professionals to potential issues, for example, an individual showing pre-diabetic symptoms.
The UK’s move towards integrated care systems presents a huge opportunity to build a proactive approach to healthcare based on insights gleaned from many different data sources. Machine learning is vital for unlocking this potential, helping to build more innovative, impactful, and cost-effective healthcare.
Read MoreAI could help predict medical conditions earlier thanks to unique health data sets
Nightingale Open Science, a project launched by Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, offers a unique and free medical dataset aimed at solving medical mysteries through the use of artificial intelligence (AI).
The project, which was funded with $2 million from former Google CEO Eric Schmidt, consists of 40 terabytes of medical imagery such as X-rays, electrocardiogram waveforms, and pathology specimens from patients with various conditions, including Covid-19, sudden cardiac arrest, fractures, and high-risk breast cancer. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer, whether it resulted in death, or whether a Covid-19 patient required a ventilator.
The datasets were curated with the help of hospitals in the US and Taiwan over a period of two years, and Obermeyer plans to expand this to include more medical diversity in Kenya and Lebanon in the coming months. The key differentiating factor between these datasets and those that are available online is that they are labelled with “ground truth,” meaning that they are based on what actually happened to the patient rather than a doctor’s opinion.
The AI community has recently shifted its focus from collecting “big data” to more meaningful data, which is relevant to a specific problem and can be used to address issues such as inherent biases in healthcare, image recognition, or natural language processing. In healthcare, algorithms have been shown to amplify existing health disparities. For example, an AI system used by hospitals to allocate additional medical support for patients with chronic illnesses was found to be prioritising healthier white patients over sicker black patients who needed help, due to the use of healthcare costs as a proxy for healthcare needs.
The Nightingale Open Science project is aimed at producing high-quality datasets that are more representative of the population and can be used to root out underlying biases that are discriminatory towards underserved and underrepresented groups in healthcare systems, such as women and minorities. These diverse datasets can also help to study the spread of diseases in different cultures and locations, where people may react differently to illnesses.
The availability of high-quality, diverse medical datasets can enable AI to predict medical conditions earlier, triage better, and ultimately save lives. The Nightingale Open Science project, which provides unique and curated datasets based on actual patient outcomes, has the potential to drive progress in healthcare and reduce the impact of biases on healthcare systems.
Read MoreAI could save the healthcare industry up to $360B a year
The ChatGPT AI model is generating a buzz in healthcare as it has passed the U.S. medical licensing exam, authored scientific papers, and is being used to appeal insurance denials. However, despite research indicating the benefits of AI in healthcare, actual adoption of AI-based tools remains low.
In a new paper, researchers estimate that broader adoption of AI in healthcare could result in savings of between 5% and 10%, or roughly $200 billion to $360 billion a year. These savings could be achieved by using AI for use cases that employ current technologies attainable within the next five years, without sacrificing quality or access.
For hospitals, AI could bring cost savings by improving clinical operations, quality, and safety, such as optimising operating rooms or detecting adverse events. Physician groups could use AI for continuity of care, like referral management. Health insurers would see savings from use cases that improve claims management, like automating prior authorisation, along with healthcare and provider relationship management, including preventing readmissions and provider directory management.
Based on AI-driven use cases, private payers could save around 7% to 9% of their total costs, amounting to $80 billion to $110 billion in annual savings within the next five years. Physician groups could save 3% to 8% of costs, amounting to between $20 billion and $60 billion in savings. Hospitals could see savings between 4% to 11%, or between $60 billion and $120 billion each year, according to the report’s estimates.
While the potential benefits of AI in healthcare are significant, and the Food and Drug Administration has accelerated approvals of medical AI tools, actual adoption remains hit-or-miss. A recent study published in JAMA found a “paucity of robust evidence” to support claims that AI could enhance clinical outcomes. However, experts believe 2023 could be a turning point for adoption as more evidence around AI’s efficacy in real-world settings emerges.
Read MoreObesity can lead to frailty in old age, study finds
A new study has found that adults with obesity are at greater risk of experiencing frailty in later life than adults with an average body mass index (BMI).
Researchers conducted a long-term study on adult men and women in Norway and discovered that obesity puts individuals at risk of becoming frail as they age. Frailty is characterised by physical deterioration and increased vulnerability, and while it has been associated with underweight older adults, the study reveals a positive association between obesity and the risk of frailty among older adults.
The study, published in the BMJ Open journal, analysed the body mass index and waist circumference of 2,340 women and 2,169 men over the age of 45 between 1994 and 1995. The participants were followed up for a period of 21 years to determine their risk of frailty. The researchers defined physical frailty as having three or more symptoms including poor grip strength, slow walking speed, exhaustion, unintentional weight loss, and low physical activity.
The findings show that participants with “baseline obesity” were more likely to be frail or pre-frail compared to those with an average BMI. Additionally, those with high waist circumference throughout follow-up were also more likely to be pre-frail or frail compared to participants with a stable normal waist circumference trajectory. Excess weight exacerbating the decline in muscle strength and physical capacity that occurs with age, along with metabolic disorders, inflammaging, and oxidative stress associated with obesity, could contribute to the risk of frailty.
The study’s lead author, Dr. Anju Jain, emphasised the importance of early intervention to prevent and treat obesity. She noted that while weight loss can be challenging, it is possible and can have significant health benefits. She also called for more public health efforts to promote healthy eating and physical activity throughout life.
The researchers caution against viewing frailty as solely a wasting disorder and highlight the importance of routinely assessing and maintaining optimal BMI and waist circumference throughout adulthood to reduce the risk of frailty in later life.
Read MoreExcess weight, obesity more deadly than previously believed
A new study from the University of Colorado Boulder, and published in the journal Population Studies, has found that the risk of death from excess weight or obesity is much higher than previously believed, with mortality rates increased by between 22% to 91%.
The research challenges the “obesity paradox,” which suggests that only extremely high levels of excess weight are associated with increased mortality risk. The study analysed data from nearly 18,000 people and found that using body mass index (BMI) to study health outcomes can bias findings, potentially leading to underestimates of the consequences of living in an environment where unhealthy food is cheap and sedentary lifestyles are the norm. The study estimates that about one in six U.S. deaths are related to excess weight or obesity.
The research found that a full 20% of people classified as having a “healthy” weight had previously been in the overweight or having obesity category, and that these individuals had a substantially worse health profile than those in the “healthy” category whose weight had been stable. The study also found that the health and mortality consequences of high BMI are duration-dependent, meaning that people who have spent most of their lives at a low BMI but have recently gained weight may have better health profiles than those who have had overweight or obesity for most of their lives.
The study’s author, Ryan Masters, hopes that the research will alert scientists to be “extremely cautious” when making conclusions based on BMI, and will draw attention to the public health crisis of an “obesogenic” environment in the U.S. Masters noted that the prospects of healthy ageing into older adulthood do not look good for groups born in the 1970s or 1980s who have lived their whole lives in this obesogenic environment. The study estimates that about 16% of U.S. deaths are related to excess weight or obesity, a figure that is eight times higher than previous research had suggested.
Read MoreScientists find link between obesity and dementia
According to a new study published in the journal Alzhiemers & Dementia journal , obesity could be a major factor in the development of dementia. Researchers found that having overweight or obesity in mid-life could increase the risk of developing Alzheimer’s disease and other forms of dementia later in life.
The study analysed data from over 1.3 million adults in the United States, Europe, and Asia. It found that people who had overweight or obesity in mid-life had a 31% higher risk of developing dementia than those who were of a normal weight. The risk increased to 82% for those who had severe obesity.
The researchers also found that having type 2 diabetes further increased the risk of developing dementia in individuals with overweight or obesity. This is because obesity and diabetes are both associated with inflammation, insulin resistance, and other metabolic abnormalities that can damage the brain and increase the risk of dementia.
The study’s lead author, Dr. Elina Hyppönen, emphasised that the findings highlight the importance of maintaining a healthy weight throughout life to reduce the risk of dementia. She suggested that lifestyle interventions, such as exercise and a healthy diet, could help prevent obesity and diabetes and lower the risk of dementia.
The study’s findings add to the growing body of evidence linking obesity and dementia. Previous research has suggested that obesity can increase the risk of cognitive decline and reduce brain volume, particularly in the hippocampus, which is critical for memory and learning.
The World Health Organization estimates that around 50 million people worldwide have dementia, and that number is expected to triple by 2050. The study’s authors suggest that preventing obesity and diabetes could be an important strategy for reducing the global burden of dementia.
Read MoreMore than half of the world will have overweight or obesity by 2035 – report
A new report by the World Obesity Federation predicts that more than half of the global population will have overweight or obesity by 2035. The report estimates that by 2035, 2.7 billion adults worldwide will have overweight, and 1.1 billion will have obesity.
The report points out that having overweight or obesity is a significant risk factor for many chronic diseases, including type 2 diabetes, cardiovascular disease, and some types of cancer. The cost of treating these diseases puts a significant burden on healthcare systems worldwide, with the federation estimating it will cost more than $4 trillion annually by 2035, or 3% of global GDP.
The report warns that the COVID-19 pandemic has exacerbated the problem by limiting access to healthy food and exercise opportunities, increasing stress and anxiety, and disrupting healthcare services. People who have overweight or obesity are also more likely to experience severe symptoms or complications from COVID-19.
The report calls for urgent action to address the growing obesity crisis, including measures to promote healthy eating, physical activity, and access to healthcare. It also calls for better regulation of the food industry to ensure that healthy options are available and affordable to all.
The authors of the report note that the obesity crisis is a complex issue that requires a multifaceted response from governments, healthcare providers, and individuals. They stress the importance of a coordinated and sustained effort to prevent and treat obesity, which they believe is essential for the health and well-being of individuals and societies worldwide.
The authors of the report have said they are not blaming individuals, but instead calling for a focus on the societal, environmental, and biological factors involved in the conditions. The report’s findings will be presented to United Nations policymakers and member states next week.
Read MoreA good night’s sleep may make it easier to stick to exercise and diet goals, study finds
Preliminary research presented at the American Heart Association’s Epidemiology, Prevention, Lifestyle & Cardiometabolic Health Scientific Sessions 2023 suggests that people who get regular and uninterrupted sleep are more successful at sticking to their exercise and diet plans while trying to lose weight.
The researchers found that good sleep health was associated with higher rates of attendance at group interval sessions, adherence to caloric intake goals and improvement in time spent performing moderate-vigorous physical activity. The researchers examined 125 adults with overweight or obesity over 12 months in a weight loss program that included measurements of sleep habits through patient questionnaires, sleep diaries and wrist-worn devices.
They measured adherence to the program by percentage of group intervention sessions attended, percentage of days that participants ate between 85-115% of their recommended daily calories, and change in daily duration of moderate or vigorous physical activity.
The study’s limitations include that it did not incorporate any intervention to help participants improve their sleep, that the study sample was not recruited based upon participants’ sleep health characteristics, and that the overall sample population had relatively good sleep health at baseline. Additionally, the sample was primarily white and female, so it is unclear whether these results are generalisable to different population groups.
Overall, the preliminary research suggests that sleep may play a key role in promoting self-control and making healthy choices, highlighting the need for public health efforts to promote healthy sleep habits and the importance of getting enough sleep for maintaining a healthy lifestyle.
Read MoreObesity in pregnancy increases the risk of cardiovascular disease in offspring, animal study shows
New research reveals that maternal obesity during pregnancy can have a negative impact on the lifelong health and function of a foetus’s heart.
Researchers from the University of Colorado, US, have found a connection between maternal obesity and a higher risk of cardiac problems in offspring later in life due to the nutrients received while in the womb. The study involved feeding female mice a high-fat diet equivalent to a human eating a burger, chips, and a fizzy drink daily until they had obesity. Researchers then studied the foetuses of the mice while in the womb and up to 24 months after birth. They analysed their genes, proteins, and mitochondria and found that the heart is affected by the nutrients it receives while growing in the uterus. This leads to alterations in how the organ metabolises carbohydrates and fats. Due to the nutrients found in the high-fat diet, the hearts developed a preference for fats and moved away from sugar.
The study found that the hearts of both female and male offspring grew larger than average, impairing the organ’s ability to function normally due to an increase in weight. Male offspring showed signs of cardiovascular impairment from the start, while females’ health became worse over time. This may be due to increased oestrogen levels present in female mice that offer initial protection against cardiovascular dysfunction. However, this resistance weakens as oestrogen levels decline with age.
Having obesity during pregnancy can lead to the development of cardiovascular and metabolic conditions in humans, such as type 2 diabetes, heart disease, and cancer. The study’s lead author, Dr. Owen Vaughan, suggests that by improving our understanding of the mechanisms involved, this research can pave the way for treatments that could be used in early life to prevent later-life cardiometabolic illnesses. This could include offering more tailored advice on nutrition to mothers or children based on their body mass index or sex or developing new drugs that target metabolism in the heart of the foetus. Therefore, keeping active through regular exercise and maintaining a healthy diet during pregnancy are essential for reducing the risk of developing health complications.
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