In a world first study, published in BMJ Open Sport & Exercise Medicine and funded by the Health Research Council, participants completed two four-hour evening intervention sessions of prolonged sitting, and sitting interrupted with three-minute activity breaks every half hour.

The researchers found that after the participants completed the activity breaks intervention they slept for 30 minutes longer.

Lead author Jennifer Gale, PhD candidate in the Department of Human Nutrition, says sitting for long periods is associated with an increased risk of diabetes, cardiovascular disease and death.

“We know that for many of us, our longest period of uninterrupted sitting happens at home in the evening. In our previous studies we have found that getting up and doing 2-3 minutes of exercise every 30 minutes reduces the amount of sugar and fat in your blood stream after a meal.

“However, many sleep guidelines tell us we shouldn’t do longer bouts or higher intensity exercise in the hours before sleep, so we wanted to know what would happen if you did very short bouts of light intensity activity repeatedly throughout the evening,” she says.

Primary investigator Dr Meredith Peddie, Senior Lecturer in the Department of Human Nutrition, says the exercise intervention involved three exercises — chair squats, calf raises, and standing knee raises with straight leg hip extensions.

“These simple, bodyweight exercises were chosen because they don’t require equipment, or a lot of space and you can do them without interrupting the TV show you are watching.

“From what we know from other studies, you could probably get a similar effect if you walked around your house, marched on the spot, or even danced in your living room — the most important thing is that you get out of your chair regularly and move your body,” she says.

The fact this exercise resulted in longer sleep is important because insufficient sleep can negatively affect diet and has been associated with heart disease and type 2 diabetes.

“We know higher levels of physical activity during the day promotes better sleep, but current sleep recommendations discourage high-intensity exercise before bed because it can increase body temperature and heart rate resulting in poor sleep quality.

“It might be time to review these guidelines as our study has shown regularly interrupting long periods of sitting is a promising health intervention,” Dr Peddie says.

The study, appearing today in the Journal of Clinical Investigation, focused on two proteins called melanocortin 3 and melanocortin 4 found primarily on the surface of neurons in the brain that play a central role in regulating feeding behavior and maintaining the body’s energy balance.

Melanocortin 3 and melanocortin 4 impact everything from sensing long-term energy stores to processing signals from the gut regarding short-term fullness, or satiety, said U-M physiologist Roger Cone, who led the study.

The class of drugs known as GLP-1 agonists, which includes semaglutides (e.g., Ozempic) and tirzepatides (e.g., Mounjaro), have received substantial attention recently for their effectiveness in treating not only type 2 diabetes, but also obesity, heart disease and potentially addiction. They work by mimicking a natural hormone that the gut produces when it is full, triggering the brain to reduce feeding behavior.

“So the obvious question for us was: How do these GLP-1 drugs, which work by manipulating satiety signals, function when we prime the melanocortin system?” said Cone, professor of molecular and integrative physiology at the U-M Medical School and director of the U-M Life Sciences Institute where his lab is located.

Working in mouse models, Cone and his colleagues tested the effects of several hormones that reduce food intake. They compared the results in normal mice with mice that genetically lacked the MC3R protein, in mice that were given chemicals to block the activity of MC3R, and in mice that were given a drug to increase the activity of MC4R. (Because MC3R is a natural negative regulator of MC4R, meaning it decreases the activity of MC4R, blocking MC3R and increasing MC4R activity has similar effects.)

In all cases, Naima Dahir, first author of the study and a postdoctoral research fellow in Cone’s lab, and colleagues found that adjusting the melanocortin system — either by inhibiting MC3R or increasing MC4R activity — made the mice more sensitive to GLP-1 drugs and other hormones that affect feeding behavior. The mice that were given a GLP-1 drug in combination with an MC4R agonist or MC3R antagonist showed up to five times more weight loss and reduced feeding than mice receiving only the GLP-1 drugs.

“We found that activating the central melanocortin system hypersensitizes animals to the effects of not just GLP-1s, but to every anti-feeding hormone we tested,” Cone said.

The researchers also measured activity in parts of the brain thought to trigger nausea in response to GLP-1 drugs and observed no increased activation when GLP-1 drugs were combined with alterations to the melanocortin system. In contrast, priming of the melanocortin neurons significantly increased GLP-1 drug activation of neurons in hypothalamic feeding centers in the brain.

The findings indicate that pairing the existing GLP-1 drugs with an MC4R agonist could increase sensitivity to the desired effects of the drugs by up to fivefold, without increasing unwanted side effects.Ultimately, this approach could enable patients who are sensitive to the side effects to take a lower dose, or could improve the results in patients who have not responded to the existing drug dosages. Further drug development and clinical testing are needed before this can occur.

While this research has been conducted only in mouse models, Cone is optimistic that the results will translate well to humans.

“The melanocortin system is highly conserved in humans,” he said. “Everything we’ve observed in the mouse over the past decades studying these proteins has also been found in humans, so I suspect that these results would also be translatable to patients.”

This research was funded by the National Institutes of Health and Courage Therapeutics.

The study — published in PLOS ONE — found that as states responded to the pandemic with school closures and other lockdown measures, citizens’ diet quality improved by up to 8.5% and food diversity improved by up to 2.6%.

Co-author Edward Jaenicke, professor of agricultural economics in the College of Agricultural Sciences, said the findings provide a snapshot of what Americans’ diet and eating habits might look like in the nearly complete absence of restaurant and cafeteria eating.

“When dine-in restaurants closed, our diets got a little more diverse and a little healthier,” Jaenicke said. “One post-pandemic lesson is that we now have some evidence that any future shifts away from restaurant expenditures, even those not caused by the pandemic, could improve Americans’ food diversity and healthfulness.”

Prior to the pandemic, the researchers said, the average U.S. diet was considered generally unhealthy. According to the Dietary Guidelines for Americans, eating patterns in the U.S. have remained far below the guidelines’ recommendations, with only slight improvements in the population’s average Healthy Eating Index score between 2005 and 2016.

Also, before the pandemic, the research team was in the midst of a grant-funded project that asked how people would feed themselves after a giant global catastrophe, such as an asteroid strike or nuclear war. In particular, Jaenicke’s team was tasked with investigating how consumers and food retailers might behave during such a disaster.

“At first, the most impactful events we could study using actual, real-world data were hurricanes and other natural disasters,” Jaenicke said. “But then, along came the COVID-19 pandemic, and we realized that this event was an opportunity to study the closest thing we had to a true global catastrophe.”

For the study, the researchers analyzed data from the NielsenIQ Homescan Consumer Panel on grocery purchases, which includes 41,570 nationally representative U.S. households. Data consisted of the quantity and price paid for every universal product code each family purchased during the study period.

Data was gathered from both before the pandemic hit and after the pandemic led to schools, restaurants and other establishments temporarily closing. Because states did not respond to the pandemic simultaneously, the researchers designated each household’s post-pandemic period as the weeks following the date that their county of residence closed schools in 2020.

Jaenicke noted that this allowed the team to show a true causal effect of the pandemic school closures, which generally occurred around the same time that restaurants and other eateries also closed.

“To establish causality, an individual household’s pre- and post-pandemic food purchases were first compared to the same household’s food purchases from one year earlier,” Jaenicke said. “This way, we controlled for the food-purchasing habits, preferences and idiosyncrasies of individual households.”

The researchers found that in the two to three months following pandemic-based school closures — spanning March to June 2020, depending on the specific U.S. state — there were modest increases in Americans’ food diversity, defined as how many different categories of food a person eats over a period of time.

They also found larger, temporary increases in diet quality, meaning the foods purchased were healthier. This was measured by how closely a household’s purchases adhered to the U.S. Department of Agriculture’s (USDA) Thrifty Food Plan, which was designed to meet the requirements of the recommended healthy diet according to the Dietary Guidelines for Americans.

These patterns were found across households with many different demographics; however, those households with young children, lower incomes and without a car exhibited smaller increases in these measures.

“During the COVID-19 pandemic, dine-in restaurants closed, schools and school cafeterias closed, and many supermarket shelves were empty,” Jaenicke said. “Since about 50% of Americans’ food dollars are spent on ‘away from home’ food from restaurants and cafeterias, the pandemic was a major shock to the food system.”

The researchers said there are several possible explanations for these findings. First, because other studies have found that food from restaurants is often less healthy than food made at home, the dramatic decrease of meals eaten at and purchased from restaurants during the pandemic could have contributed to an increase of food diversity and healthfulness at home.

Second, they said it was possible that a global pandemic triggered some consumers to become more health conscious and contributed to them buying healthier, more diverse groceries. Third, because the pandemic caused widespread disruptions to the supply chain, it’s possible that when familiar products were sold out, consumers shifted to newer ones that led to increased diversity and healthfulness.

Finally, school and business closures may have led to many households having more time to cook and prepare foods than they had before, while others — like those with small children — may have had less free time than pre-pandemic.

Jaenicke said that in the future, additional studies could continue to explore how different disasters affect purchasing and eating habits.

Douglas Wrenn, associate professor of environmental and resource economics at Penn State, and Daniel Simandjuntak, research associate at Newcastle University, were also co-authors on the study.

Open Philanthropy helped support this research.

Using Webb’s NIRSpec (Near-Infrared Spectrograph), astronomers confirmed a temperature difference between the eternal morning and eternal evening on WASP-39 b, with the evening appearing hotter by roughly 300 Fahrenheit degrees (about 200 Celsius degrees). They also found evidence for different cloud cover, with the forever morning portion of the planet being likely cloudier than the evening.

Astronomers analyzed the 2- to 5-micron transmission spectrum of WASP-39 b, a technique that studies the exoplanet’s terminator, the boundary that separates the planet’s dayside and nightside. A transmission spectrum is made by comparing starlight filtered through a planet’s atmosphere as it moves in front of the star, to the unfiltered starlight detected when the planet is beside the star. When making that comparison, researchers can get information about the temperature, composition, and other properties of the planet’s atmosphere.

“WASP-39 b has become a sort of benchmark planet in studying the atmosphere of exoplanets with Webb,” said Néstor Espinoza, an exoplanet researcher at the Space Telescope Science Institute and lead author on the study. “It has an inflated, puffy atmosphere, so the signal coming from starlight filtered through the planet’s atmosphere is quite strong.”

Previously published Webb spectra of WASP-39b’s atmosphere, which revealed the presence of carbon dioxide, sulfur dioxide, water vapor, and sodium, represent the entire day/night boundary — there was no detailed attempt to differentiate between one side and the other.

Now, the new analysis builds two different spectra from the terminator region, essentially splitting the day/night boundary into two semicircles, one from the evening, and the other from the morning. Data reveals the evening as significantly hotter, a searing 1,450 degrees Fahrenheit (800 degrees Celsius), and the morning a relatively cooler 1,150 degrees Fahrenheit (600 degrees Celsius).

“It’s really stunning that we are able to parse this small difference out, and it’s only possible due Webb’s sensitivity across near-infrared wavelengths and its extremely stable photometric sensors,” said Espinoza. “Any tiny movement in the instrument or with the observatory while collecting data would have severely limited our ability to make this detection. It must be extraordinarily precise, and Webb is just that.”

Extensive modeling of the data obtained also allows researchers to investigate the structure of WASP-39 b’s atmosphere, the cloud cover, and why the evening is hotter. While future work by the team will study how the cloud cover may affect temperature, and vice versa, astronomers confirmed gas circulation around the planet as the main culprit of the temperature difference on WASP-39 b.

On a highly irradiated exoplanet like WASP-39 b that orbits relatively close to its star, researchers generally expect the gas to be moving as the planet rotates around its star: Hotter gas from the dayside should move through the evening to the nightside via a powerful equatorial jet stream. Since the temperature difference is so extreme, the air pressure difference would also be significant, which in turn would cause high wind speeds.

Using General Circulation Models, 3-dimensional models similar to the ones used to predict weather patterns on Earth, researchers found that on WASP-39 b the prevailing winds are likely moving from the night side across the morning terminator, around the dayside, across the evening terminator and then around the nightside. As a result, the morning side of the terminator is cooler than the evening side. In other words, the morning side gets slammed with winds of air that have been cooled on the nightside, while the evening is hit by winds of air heated on the dayside. Research suggests the wind speeds on WASP-39 b can reach thousands of miles an hour!

“This analysis is also particularly interesting because you’re getting 3D information on the planet that you weren’t getting before,” added Espinoza. “Because we can tell that the evening edge is hotter, that means it’s a little puffier. So, theoretically, there is a small swell at the terminator approaching the nightside of the planet.”

The team’s results have been published in Nature.

The researchers will now look to use the same method of analysis to study atmospheric differences of other tidally locked hot Jupiters, as part of Webb Cycle 2 General Observers Program 3969.

WASP-39 b was among the first targets analyzed by Webb as it began regular science operations in 2022. The data in this study was collected under Early Release Science program 1366, designed to help scientists quickly learn how to use the telescope’s instruments and realize its full science potential.

The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best.

Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion. The number of cases is expected to almost treble over the next 50 years.

The main cause of dementia is Alzheimer’s disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not available in all memory clinics. As a result, up to a third of patients may be misdiagnosed and others diagnosed too late for treatment to be effective.

A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model able to predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer’s disease. In research published today in eClinical Medicine, they show that it is more accurate than current clinical diagnostic tools.

To build their model, the researchers used routinely-collected, non-invasive, and low-cost patient data — cognitive tests and structural MRI scans showing grey matter atrophy — from over 400 individuals who were part of a research cohort in the USA.

They then tested the model using real-world patient data from a further 600 participants from the US cohort and — importantly — longitudinal data from 900 people from memory clinics in the UK and Singapore.

The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer’s disease within a three-year period. It was able to correctly identify individuals who went on to develop Alzheimer’s in 82% of cases and correctly identify those who didn’t in 81% of cases from cognitive tests and an MRI scan alone.

The algorithm was around three times more accurate at predicting the progression to Alzheimer’s than the current standard of care; that is, standard clinical markers (such as grey matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnosis.

The model also allowed the researchers to stratify people with Alzheimer’s disease using data from each person’s first visit at the memory clinic into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer’s slowly (around 35%) and those who would progress more rapidly (the remaining 15%). These predictions were validated when looking at follow-up data over 6 years. This is important as it could help identify those people at an early enough stage that they may benefit from new treatments, while also identifying those people who need close monitoring as their condition is likely to deteriorate rapidly.

Importantly, those 50% of people who have symptoms such as memory loss but remain stable, would be better directed to a different clinical pathway as their symptoms may be due to other causes rather than dementia, such as anxiety or depression.

Senior author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said: “We’ve created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer’s — and if so, whether this progress will be fast or slow.

“This has the potential to significantly improve patient wellbeing, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable. At a time of intense pressure on healthcare resources, this will also help remove the need for unnecessary invasive and costly diagnostic tests.”

While the researchers tested the algorithm on data from a research cohort, it was validated using independent data that included almost 900 individuals who attended memory clinics in the UK and Singapore. In the UK, patients were recruited through the Quantiative MRI in NHS Memory Clinics Study (QMIN-MC) led by study co-author Dr Timothy Rittman at Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT).

The researchers say this shows it should be applicable in a real-world patient, clinical setting.

Dr Ben Underwood, Honorary Consultant Psychiatrist at CPFT and assistant professor at the Department of Psychiatry, University of Cambridge, said: “Memory problems are common as we get older. In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers. The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge.”

Professor Kourtzi said: “AI models are only as good as the data they are trained on. To make sure ours has the potential to be adopted in a healthcare setting, we trained and tested it on routinely-collected data not just from research cohorts, but from patients in actual memory clinics. This shows it will be generalisable to a real-world setting.”

The team now hope to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests.

Professor Kourtzi added: “If we’re going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage. Our vision is to scale up our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating new drug discovery for disease modifying treatments.”

The study was funded by Wellcome, the Royal Society, Alzheimer’s Research UK, the Alzheimer’s Drug Discovery Foundation Diagnostics Accelerator, the Alan Turing Institute, and the National Institute for Health Research Cambridge Biomedical Research Centre.