Noninvasive imaging techniques are shedding light on how diet influences cardiovascular health. The way blood nutrients and lipid levels change after a high-fat meal is vital for assessing both immediate and long-term cardiovascular health.
Traditionally, tracking these changes required invasive blood tests, which are impractical for regular monitoring.
Researchers are now investigating noninvasive methods that could enhance the understanding of post-meal effects and identify risk factors for cardiovascular disease. One promising method is called “spatial frequency domain imaging” (SFDI), which measures tissue properties and blood flow without direct contact.
A recent study conducted by researchers from Boston University, Harvard Medical School, and Brigham and Women’s Hospital explored how different meal types affect skin tissue properties shortly after eating. Published in Biophotonics Discovery (BIOS), the study focused on the hand’s peripheral tissue to observe the immediate effects of low-fat versus high-fat meals.
Using SFDI, researchers monitored 15 participants who consumed both meal types on separate days. They took images of the backs of the participants’ hands hourly for five hours after eating, analyzing three specific wavelengths to assess hemoglobin, water, and lipid levels.
The findings showed notable differences in tissue responses based on meal composition. After consuming a high-fat meal, tissue oxygen saturation increased, while a low-fat meal resulted in a decrease. This indicates that dietary fat can influence not only overall health but also immediate physiological reactions. The most significant changes were observed three hours post-meal, aligning with spikes in triglyceride levels.
In addition to imaging, researchers measured blood pressure and heart rate and conducted blood tests to check triglycerides, cholesterol, and glucose levels. The results indicated that changes in optical absorption at specific wavelengths accurately reflected variations in lipid concentrations.
Building on these findings, the research team trained a machine learning model using SFDI data to predict triglyceride levels, achieving an accuracy within 40 mg/dL. This level of precision could facilitate noninvasive monitoring of cardiovascular health in the future.
Darren Roblyer, a senior author and professor of biomedical engineering at Boston University, stated, “This research suggests that SFDI could be a valuable tool for easier monitoring of how meals impact cardiovascular health.”
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