New research offers fresh insights into how a type of immune cell can destabilize the fatty deposits, or plaques, that form in arteries during atherosclerosis. Atherosclerosis is a persistent, inflammatory condition in which plaques build up inside arteries, causing them to narrow and restrict blood flow. When an atherosclerotic plaque bursts or breaks, it can trigger a heart attack or stroke. Neutrophils are an abundant type of leukocyte (white blood cell) that defend against infection by attacking microbes. They also serve "many roles in inflammation." The new international study reveals that neutrophils can aggravate atherosclerosis by triggering a previously unknown type of cell death that destabilizes arterial plaques. A recent Nature paper describes how neutrophils can induce a series of molecular events that also kills the smooth muscle cells that help to retain the plaques in the artery wall. "Every inflammatory reaction," says co-corresponding study author Prof. Oliver Söhnlein, who is the director of the Institute for Cardiovascular Prevention at the Ludwig Maximilian University (LMU) of Munich in Germany, "results in some collateral damage, because neutrophils also attack healthy cells." He and his colleagues have also designed and made a "tailored peptide" that could potentially target and block the cell-death process. Atherosclerosis and its consequences Arteries are vessels that supply the heart and other parts of the body with oxygen- and nutrient-rich blood, which cells need to function and live. Atherosclerosis develops when various materials, such as cholesterol, fat, and cellular waste, deposit in the tissue lining the arteries. The deposits, or plaques, build up slowly over time causing the arteries to narrow and harden. When arteries narrow, they impede blood flow and restrict the supply of oxygen and nutrients to cells. Depending on where it occurs, the restricted blood flow can result in heart disease, angina, carotid artery disease, peripheral artery disease, and chronic kidney disease. The plaques themselves are also a risk. They can rupture, or pieces can break off, causing blockages. In addition, blockages can arise from blood clots that stick to the inner walls of narrowed arteries. If the blockage is in an artery that supplies blood to the brain or the heart, it can result in a stroke or heart attack. Blockages in arteries that supply the legs can lead to tissue death, or gangrene. According to statistics that the American Heart Association publish online, cardiovascular conditions, such as heart attack and stroke, were the primary cause of 840,678 deaths in 2016 in the United States. Neutrophils help plaques become unstable Another feature of atherosclerosis is that it triggers signals that prompt the immune system to send neutrophils and other immune cells through the bloodstream to the plaques. When they reach a plaque site, the immune cells slip between the endothelial tissue cells of the artery lining. At the same time, they release chemicals that signal to the immune system to send even more immune cells. This can set up a cycle that turns the initial inflammation response into persistent, or chronic, inflammation. Once the inflammation becomes chronic, it raises the risk that the plaque will grow, rupture, and cause a blockage. Using mouse models of atherosclerosis to investigate what goes on at cell level, the researchers discovered that neutrophils can play a particularly destructive role in destabilizing plaques. "They bind to the smooth muscle cells that underlie the vessel wall, and are activated," Prof. Söhnlein explains. Once active, the neutrophils release "chromosomal DNA and its associated histones, which are highly charged and [toxic to cells]," he continues, adding: "Free histones kill nearby cells – in the case of atherosclerosis, smooth muscle cells." Histones are proteins that help to package DNA tightly inside chromosomes. Peptide could block toxic histones The histones kill the smooth muscle cells by causing pores to form in their walls. This allows extracellular fluids to seep through the pores into the cells, causing them to burst. Because smooth muscle cells help to retain the plaques in the artery wall, their destruction causes the fatty deposits to become unstable and more likely to rupture and break. In another part of the study, the team used molecular modeling to design a small protein molecule, or peptide, that could block the toxic effect of the free histones. The authors suggest that the "histone-inhibitory peptide" could disrupt the histones by binding to them so that they cannot create pores in the cell membranes. Prof. Söhnlein says that the synthetic peptide could have a similar effect on other conditions that involve chronic inflammation, such as chronic bowel inflammation and arthritis. He and his co-authors conclude: "Our data identify a form of cell death found at the core of chronic vascular disease that is instigated by leukocytes and can be targeted therapeutically." Source
Soy protein lowers cholesterol, study suggests Meta-analysis finds soy protein reduced LDL cholesterol by 3% to 4% Date: May 6, 2019 Source: St. Michael's Hospital Summary: With the US Food and Drug Administration (FDA) planning to remove soy from its list of heart healthy foods, researchers set out to provide a meta-analysis of 46 existing trials that evaluated soy and determine whether the proposed move aligns with existing literature. Share: FULL STORY Soy protein has the ability to lower cholesterol by a small but significant amount, suggests a new study led by St. Michael's Hospital in Toronto. With the U.S. Food and Drug Administration (FDA) planning to remove soy from its list of heart healthy foods, researchers at St. Michael's set out to provide a meta-analysis of 46 existing trials that evaluated soy and determine whether the proposed move aligns with existing literature. Of the 46 trials, 43 provided sufficient data for meta-analysis. Forty-one trials examined the protein's effects on low-density lipoprotein (LDL) cholesterol, which is often referred to as the "bad cholesterol" because a high amount of it leads to a build-up of cholesterol in arteries. All 43 studies provided data about "total cholesterol," which reflects the overall amount of cholesterol in the blood. Researchers found that soy protein reduced LDL cholesterol by three to four percent in adults -- a small but significant amount, noted Dr. David Jenkins, the lead author of the study, who is also the director of the Clinical Nutrition and Risk Factor Modification Centre, and a scientist in the Li Ka Shing Knowledge Institute of St. Michael's Hospital. "When one adds the displacement of high saturated fat and cholesterol-rich meats to a diet that includes soy, the reduction of cholesterol could be greater," Dr. Jenkins said. "The existing data and our analysis of it suggest soy protein contributes to heart health." A limitation of this study was that it exclusively analyzed the 46 trials the FDA had referred to previously, as opposed to casting a wider net. Dr. Jenkins and his team hope that this work is taken into account in the FDA's current evaluation of soy protein as it pertains to heart health. "We hope the public will continue to consider plant-based diets as a healthy option," Dr. Jenkins said. "It is in line with Health Canada's recently released Food Guide, which emphasizes plant protein food consumption by Canadians." Dr. David Jenkins has previously consulted for and received research funding from soy food companies and the United States Soy Institute.
AI identifies risk of cholesterol-raising genetic disease Date: April 11, 2019 Source: Stanford Medicine Summary: A new algorithm can determine whether a patient is likely to have a cholesterol-raising genetic disease that can cause early, and sometimes fatal, heart problems, reports a new study. Share: FULL STORY A new algorithm can determine whether a patient is likely to have a cholesterol-raising genetic disease that can cause early, and sometimes fatal, heart problems, reports a new study conducted by researchers at the Stanford University School of Medicine and their collaborators. The disease, known as familial hypercholesterolemia, is often misdiagnosed as garden-variety high cholesterol. "We think that less than 10 percent of individuals with FH in the United States actually know that they have it," said Joshua Knowles, MD, PhD, assistant professor of cardiovascular medicine at Stanford. It's a serious oversight, he added, because an FH patient with high cholesterol is three times more likely to develop early heart disease than someone who has high cholesterol but not FH. A person with FH faces 10 times the risk of heart disease as someone with normal cholesterol. Knowles and Nigam Shah, MBBS, PhD, associate professor of medicine and of biomedical data science, have come up with a solution to help catch more cases of FH: a computer algorithm that flags patients who are likely to have the disease. In test runs of the algorithm, it correctly identified 88 percent of the cases it screened. Theoretically, if the algorithm were used in a clinic, any patient it flagged as having FH could undergo further genetic testing to verify the algorithm's calculation. Without intervention, around 50 percent of men with FH have a heart attack by age 50 and about 30 percent of women by age 60. But swift, early diagnosis and treatment of the disease can essentially neutralize this threat, Shah said. The trick is to catch it before it's too late, and this is where Knowles and Shah think their algorithm could make an impact. One diagnosis could even help multiple people, Knowles said. Because FH is genetic, if one family member has the disease, it's likely that other relatives have it too. "So screening family members of FH patients is really important, just like it would be with breast cancer or any other genetically linked illness," he said. A paper describing the research will be published online April 11 in npj Digital Medicine. Shah and Knowles, who is the director of the FH clinic at Stanford Health Care's Center for Inherited Cardiovascular Disease, share senior authorship. Juan Banda, PhD, a former research scientist at Stanford, is the lead author. The project is part of a larger initiative called Flag, Identify, Network, Deliver FH, or FIND FH, a collaborative effort involving Stanford Medicine and the nonprofit Familial Hypercholesterolemia Foundation that aims to identify and engage individuals and families affected by the disease by leveraging machine learning and big data. Identifying FH People with FH carry a mutation that hinders their bodies' ability to clear harmful LDL cholesterol that collects in arteries and clogs them. Hypothetically, anyone who walked into a hospital could have genetic testing and know whether they had inherited an FH mutation. Unfortunately, Shah said, hospitals don't have the means to sequence patients on such a large scale, even as prices for genome sequencing drop. "The problem is, the chance that someone seen in the cardiology clinic has this genetic condition is somewhere around 1 in 90, or 1 in 100, so it doesn't make sense to sequence every single person," he said. So Shah and his fellow researchers designed an algorithm that works like a sieve, capturing only those who are likely to have the disease. "Theoretically, when someone comes into the clinic with high cholesterol or heart disease, we would run this algorithm," Shah said. "If they're flagged, it means there's an 80 percent chance that they have FH. Those few individuals could then get sequenced to confirm the diagnosis and could start an LDL-lowering treatment right away." To create the algorithm, the team used data from Stanford's FH clinic to learn what distinguishes an FH patient in an electronic health record. The researchers trained the algorithm to pick up on a combination of family history, current prescriptions, lipid levels, lab tests and more to understand what details signal the disease. Shah compared it to training a spam filter that catches fishy emails. Instead of simply applying rules, such as "must mention money," spam filters learn what to flag by using actual spam emails as examples of what to capture -- just as the FH algorithm learns by looking at information about real FH patients. The scientists built the algorithm's foundation using data from 197 patients who had FH and 6,590 who did not, allowing the computer program to learn the difference between the two. "In the end, you get a ranking that shows who is most likely to have the disease," said Shah. "Those who rank at the top have the highest likelihood and, as you move toward the bottom, the likelihood tapers off." While the software could fill a gap in FH diagnoses, Knowles and Shah acknowledge that it's not a sure-fire solution to catch all cases. "Not everything can be solved by an algorithm," Shah said. "We're also thinking about how we can work with the FH Foundation to implement networks of family screening to reach more patients who might have the disease and not know it." Toward AI in the clinic Once the algorithm was trained, the team moved on to the testing phase, initially running it on a set of roughly 70,000 de-identified patient records it had never encountered. From the patients flagged, the team reviewed 100 patient charts, extrapolating that the algorithm had detected patients who had FH with 88 percent accuracy. Next, the researchers teamed up with the Geisinger Healthcare System to test the algorithm on 466 FH patients and 5,000 non-FH patients. "The predictions came back with 85 percent accuracy, and we knew that many of the Geisinger patients had a confirmed FH diagnosis with genetic sequencing," Shah said. "So that's how we convinced ourselves that yes, this indeed works." Now, Knowles and Shah are working on ways to implement the algorithm in doctors' offices, something they're actively pursuing for Stanford's FH clinic. The work is an example of Stanford Medicine's focus on precision health, the goal of which is to anticipate and prevent disease in the healthy and precisely diagnose and treat disease in the ill.
