Dr. Hazen and his team didn’t set out to nail lecithin. They were simply comparing the blood of subjects that were prone to coronary artery disease, with the blood of subjects that were not. But they found that some subjects had a certain type of gut flora (the bacteria that is in all our digestive tracts) that broke lecithin down into substances that formed atherosclerotic plaque in the coronary arteries.
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Lecithin is everywhere. A somewhat necessary dietary fat, lecithin is a natural part of a diet that includes meat, cheese and other animal products. Lecithin is also found in most multi-vitamins, including children’s vitamins. It’s added to many baked and processed foods. People eat a lot of lecithin. But that may soon change.
A recent study led by Stanley Hazen, MD, Co-Section Head of Preventive Cardiology, has discovered that lecithin plays a major role in the complex biology of coronary artery disease – the leading cause of death in the developed world.
This finding explains why Person A and Person B might eat the same meat-heavy diet, but only Person B gets heart disease. It was previously thought that genes alone were sufficient to explain the difference. But Dr. Hazen’s finding shows that gut flora may be just as important.
In fact, as a predictor of heart disease, the substances produced by the breakdown of lecithin (choline, TMAO and betaine) may be a 10-times more powerful predictor of heart disease than cholesterol. Measuring these substances will give immediate indications of coronary artery disease, peripheral artery disease, as well as a history of heart attack.
For patients, it’s a powerful new reason for dropping meat, milk and cheese from the diet. For physicians, it should lead to the creation of a new and accurate means of detecting heart disease and those who are most at risk for it.
Dr. Hazen has had remarkable success in identifying novel markers for heart disease. In 2010, he and his team developed a computational model that enables them to accurately predict one-year risk for heart attack and death for stable cardiac patients, using blood components obtained by a hematology analyzer. The model, called PEROX, in combination with traditional risk factor data, predicted patient risk with 78% accuracy.