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Drug developed by Eli Lilly decreases presence of mysterious LP(A) particles related to heart attack risk

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One in five people (an estimated 64 million people in the US) has increased levels of small particles in their blood. It can significantly increase the risk of heart attacks and strokes.

But few people knew about it and there was not much to do, so little doctors would have checked it. Dieting is useless. I don’t even exercise. There were no medicines.

But that may change in the near future.

On Sunday, the cardiologist announced that the experimental drug created by Eli Lily of Repodisilan can lower particle levels by 94% with a single injection. The effect lasted for 6 months and there were no serious side effects.

However, it has not yet been confirmed that lowering LP(a) levels reduces the risk of heart attacks and strokes. It awaits a massive clinical trial currently underway.

Lily’s research was presented on Sunday at the American Society of Cardiology’s Annual Meeting and was presented simultaneously Published New England Journal of Medicine. At least four companies are also testing innovative drugs that block the production of the body of LP(A) and the mixing of lipids and proteins.

Dr. David Maron, a preventive cardiologist at Stanford University who is not involved in Lily’s research, said evidence of a severe and long-term reduction in lipoprotein levels by repodisilans is “thrilling.”

Dr. Martha Gulati, a preventive psychologist at Cedars-Sinai Medical Center, was also not involved in the exam, saying the study was “really elegant.”

Eli Lilly is currently conducting large clinical trials asking whether the drug can prevent heart attacks, strokes or cardiovascular death. It will end in 2029. Clinical trials of other drugs targeting LP(a) end more quickly. The first is a study of Novartis drugs that are injected monthly, with results expected in 2026.

However, cardiologists warn that there is no guarantee that medicine will protect people. They remember too well the lessons they learned, assuming that changing risk factors could change risk. Cardiologists were once keen on drugs that raise HDL levels known as “good cholesterol.” People with naturally higher HDL levels had a lower incidence of heart disease. These HDL raming drugs did not help.

Dr. Daniel Rader, a preventive psychologist at the University of Pennsylvania Perelman School of Medicine, says LP(A)-lowering “is a huge new frontier in cardiovascular medicine.” Dr. Radar is a member of Novartis’ Scientific Advisory Committee and has written editorials to accompany new papers.

Treatments targeting LP(a) took a long time.

Lipoprotein was identified as a in 1974 Risk factors for heart disease This is controlled by genes rather than lifestyle or environment.

People with slightly higher than normal LP(a) levels have an approximately 25% increase in their risk of heart attacks and stroke. And very high levels can double the risk, as seen in 10% of the population.

Cardiologists say patients with no obvious reason for heart attacks or stroke (with normal cholesterol levels and blood pressure and not smoking) often know that their LP levels are high. Usually, it is found that they have a family history of heart disease of unknown cause.

The same applies to people who are experiencing heart attacks at a young age, says Dr. Stephen Nissen, a preventive psychologist at Cleveland Clinic, is an academic leader in the Lilly drug trials, and for clinical trials of three other new drugs.

“If you go to the coronary care unit and see a 40-year-old with an acute myocardial infarction, you need to know your LP(a) level,” he said, referring to a heart attack. Often they said their levels were 250 nanomoles or even higher per liter. The normal limit is 75.

Dr. Maron said his clinic is full of people who don’t know why they developed heart disease until they learn that they have high levels of LP.

One is Montewood, a 71-year-old retired firefighter who lives in Reading, California. His LDL cholesterol levels rose to moderately. His blood pressure was normal. He didn’t smoke. However, he had his first heart attack in 2006 while taking cholesterol-lowering statins.

It appeared that almost all of Mr. Kisae’s family had died of heart disease.

His paternal grandmother had her first heart attack when she was in her 40s. She died of a heart attack at the age of 63. His father and his father’s brother died of heart disease. Mr. Kisae’s brother died of a heart attack.

When Dr. Maron tested Wood’s LP level, it was above 400.

Dr. Maron and other preventive psychologists say they regularly test LP(a) levels in all patients, like Dr. Grati, Dr. Nissen and Dr. Radar. Because LP(a) levels are gene-controlled, patients should only test once.

Dr. Nissen is dull with LP(a) patients.

“We say: You have a disability that has serious meaning. I want to take all the risk factors you’ve been off the table,” he said.

But Dr. Grati said that a study found it. 0.3% The US population is receiving insurance-paid LP(a) tests, with only 3% of heart disease patients being tested.

She and other preventive cardiologists say that all adults should take the LP(a) test. If the level is high, your doctor should actively treat all other risk factors.

For Kisei, it meant taking Repatha, a powerful cholesterol-lowering drug that lowered his LDL cholesterol levels to 30.

However, Mr. Kisae’s case did not end there. Dr. Maron led one of the new drugs that lower LP(a) levels to clinical trial testing.

During the exam, Kisae had no symptoms of heart disease. I had no chest pain or shortness of breath. When the exam was finished, his symptoms returned, leading to a square bypass operation.

“It’s anecdotal,” Dr. Maron said. “But these drugs can prevent heart attacks.”

Source: www.nytimes.com

Study finds new weight loss drug decreases appetite without compromising muscle mass

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Researchers have identified a new drug similar to Ozempic that aids in weight loss without causing muscle loss. This drug, known as NK2R, works by suppressing appetite and boosting calorie burning. According to scientists, it has been successful in promoting weight loss while avoiding negative side effects such as nausea. The team of 47 researchers believe that NK2R could be a valuable option for individuals who have not seen results with other weight loss treatments.

Associate Professor Zach Gerhart-Hines, a metabolic researcher at the University of Copenhagen and co-author of the study, noted that their drug, unlike Ozempic, did not trigger nausea and also resulted in muscle relief rather than muscle loss. The drug targets specific neural circuits in the brain and affects blood sugar, weight, and cholesterol levels.

While Ozempic mimics the hormone GLP-1 to reduce hunger, NK2R works differently by targeting a molecule naturally present in the body’s cells called NK2R. When tested on overweight mice, the drug led to weight loss and decreased food intake.

However, some health experts are cautious about the effects of this treatment on humans, as it is currently based on animal studies. Dr. Adam Collins, an associate professor of nutrition at the University of Surrey, expressed skepticism about the research’s applicability to humans.

Clinical trials of NK2R in humans are scheduled to begin within the next two years.

About our experts:

Dr. Zach Gerhart-Hines is an associate professor at the University of Copenhagen, Denmark, focusing on diet, circadian clocks, and metabolism.

Dr. Adam Collins is an Associate Professor at the University of Surrey with expertise in weight loss, metabolism, and nutrition.

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Source: www.sciencefocus.com

Reducing arm fat decreases dementia risk by 18%

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It is widely known that excess body fat can lead to health issues like heart disease and diabetes. However, recent research has found a correlation between fat distribution in the arms and abdomen and the risk of developing dementia.

Dementia, a group of neurodegenerative disorders that includes Alzheimer’s disease, is on the rise globally. By 2050, it is projected that 139 million people worldwide will be affected. In the UK, it is estimated that one in three people born currently may develop dementia.

The causes of dementia are complex and not fully understood. However, a study published in the Journal of Neurology suggests that having high levels of body fat in the arms and abdomen can significantly increase the likelihood of developing neurodegenerative diseases like dementia.

The study involved over 400,000 participants, of whom a subset developed neurodegenerative diseases. After considering other factors like high blood pressure, smoking, and diabetes, the researchers found that individuals with higher levels of abdominal and arm fat had an increased risk of developing these conditions.

The researchers also found that greater muscle strength was associated with a lower risk of disease. They suggest that targeted interventions to reduce abdominal and arm fat may be more effective in preventing neurodegeneration than general weight management.

Further research is needed to fully understand how body composition affects overall health outcomes. The team plans to investigate the impact of body composition on other health issues like heart failure in the future.

About our experts

Xu Shishi Dr. Xu is a clinical physician specializing in endocrinology and metabolism at West China Hospital of Sichuan University, China. With a background in epidemiology and evidence-based research, his research interests include metabolic diseases and large-scale population cohort data analysis.

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Source: www.sciencefocus.com

New Google AI technology significantly decreases computing power required for weather forecasting

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AI could help us predict the weather more accurately

LaniMiro Lotufo Neto/Alamy

Google researchers have developed an artificial intelligence that they say can predict weather and climate patterns as accurately as current physical models, but with less computing power.

Existing forecasts are based on mathematical models run by extremely powerful supercomputers that deterministically predict what will happen in the future. Since they were first used in the 1950s, these models have become increasingly detailed and require more and more computer power.

Several projects aim to replace these computationally intensive tasks with much less demanding AI, including a DeepMind tool that forecasts localized rainfall over short periods of time. But like most AI models, the problem is that they are “black boxes” whose inner workings are mysterious and whose methods can’t be explained or replicated. And meteorologists say that if these models are trained on historical data, they will have a hard time predicting unprecedented events now being caused by climate change.

now, Dmitry Kochkov The researchers, from Google Research in California, and his colleagues created a model called NeuralGCM that balances the two approaches.

Typical climate models divide the Earth's surface into a grid of cells up to 100 kilometers in size. Due to limitations in computing power, simulating at high resolution is impractical. Phenomena such as clouds, turbulence, and convection within these cells are only approximated by computer codes that are continually adjusted to more closely match observed data. This approach, called parameterization, aims to at least partially capture small-scale phenomena that are not captured by broader physical models.

NeuralGCM has been trained to take over this small-scale approximation, making it less computationally intensive and more accurate. In the paper, the researchers say their model can process 70,000 days of simulations in 24 hours using a single chip called a Tensor Processing Unit (TPU). By comparison, competing models, called X-Shield A supercomputer with thousands of processing units is used to process the simulation, which lasts just 19 days.

The paper also claims that NeuralGCM performs predictions at a rate comparable to or better than best-in-class models. Google did not respond to a request for an interview. New Scientist.

Tim Palmer The Oxford researcher says the work is an interesting attempt to find a third way between pure physics and opaque AI approximations: “I'm uncomfortable with the idea of ​​completely abandoning the equations of motion and moving to AI systems that even experts say they don't fully understand,” he says.

This hybrid approach is likely to spur further discussion and research in the modeling community, but time will tell whether it will be adopted by modeling engineers around the world, he says. “It's a good step in the right direction and the type of research we should be doing. It's great to see different alternatives being explored.”

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Source: www.newscientist.com

AI invents new battery design that decreases lithium usage by 70%

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Researchers test batteries using new materials designed by AI

Microsoft's Dan DeLong

Artificial intelligence can accelerate the process of discovering and testing new materials, and researchers have used that ability to develop batteries that are less dependent on the expensive mineral lithium.

Lithium-ion batteries power not only electric cars but also many devices we use every day. They will also become a necessary part of green power grids, as batteries will be needed to store renewable energy from wind turbines and solar panels. However, lithium is expensive and mining it damages the environment. Finding a replacement for this important metal can be expensive and time-consuming, requiring researchers to develop and test millions of candidates over years. Utilizing AI, nathan baker Microsoft and its colleagues accomplished this task in a few months. They designed and manufactured a battery that uses up to 70% less lithium than some competing designs.

The researchers focused on types of batteries that contain only solid parts, looking for new materials for battery components called electrolytes, through which charge is transferred. They started with 23.6 million candidate materials, designed by tweaking the structure of an established electrolyte and replacing some lithium atoms with other elements. The AI ​​algorithm filtered out materials that were calculated to be unstable or have weak chemical reactions that make the battery work. The researchers also considered how each material behaved when the battery was actively operating. After just a few days, their list contained just a few hundred candidates, some of whom had never been studied before.

“But we're not materials scientists,” Baker says. “So I called the experts who have worked on large-scale battery projects at the Department of Energy and said, 'What do you think? Are we crazy?'

vijay murugesan He works at the Pacific Northwest National Laboratory in Washington state and was one of the scientists who answered the phone. He and his colleagues proposed additional screening criteria for AI. After further rounds of elimination, Murugesan's team finally selected one of his AI proposals and synthesized it in the lab. It was noticeable because half of what Murugesan expected to be lithium atoms were replaced with sodium. This is a very novel recipe for an electrolyte, he said, and the combination of the two elements raises questions about the fundamental physics of how the material works in batteries. Masu.

His team built a working battery using this material, albeit with a lower conductivity than similar prototypes that use more lithium. Both Baker and Murugesan said much work remains to optimize the new batteries. However, the manufacturing process took about nine months, from the time Murugesan first talked to his Microsoft team until the battery was functional enough to light a light bulb.

“The methodology here is cutting edge in terms of machine learning tools, but what really elevates this is that things have been created and tested,” he says. Rafael Gomez-Bombarelli from the Massachusetts Institute of Technology was not involved in this project. “It's very easy to make predictions. It's hard to convince someone to invest in an actual experiment.” He said the team will accelerate calculations that physicists have been making for decades, and It is said that AI was used to strengthen it. However, this approach may also encounter obstacles in the future. For this kind of work, he said, the data needed to train the AI ​​is often sparse, and materials other than battery components may require more complex ways of combining elements. he says.

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Source: www.newscientist.com