The dynamics of blood nutrient and lipid levels after a high-fat meal is critical, reflecting not just current but also future cardiovascular well-being. Circulating substances like these can only traditionally have been measured through invasive blood draws, which are not workable on a day-to-day basis to monitor health.
Researchers have eagerly looked for the measurement of cardiovascular conditions through noninvasive measurements, which will be very effective for monitoring effects after meals and identifies factors contributing to cardiovascular disease. Such a promising approach is non-contact optical imaging known as “spatial frequency domain imaging” or SFDI. SFDI allows quantitative assessment of tissue properties and hemodynamics.
Researchers from Boston University, Harvard Medical School, and Brigham and Women’s Hospital have studied how the nutritional composition of a meal affects the properties of skin tissue shortly after ingestion in a new study.
Biophotonics Discovery suggests that the area of peripheral tissue in the hand was selected, and the researchers targeted assessing direct effects caused by a low-fat or high-fat diet.
The scientists acquired 15 subjects to be imaged by SFDI after consuming both kinds of meals on separate days. Researchers then imaged every hour for five hours after the meal with three wavelengths in pursuit of evaluating hemoglobin, water, and lipid levels.
The tissue responses seemed to differ so distinctly. From the results, tissue oxygen saturation increases with the high-fat meal. However, in the low-fat meal, tissue oxygen saturation lowers down signifying the impact of dietary fats not only on health but also as an immediate physiological reaction.
These peak shifts were observed three hours post-injection along with spiking triglyceride levels. In addition to imagining, blood pressure and heart rate were monitored, and blood draws were conducted to assess triglyceride, cholesterol, and glucose levels. The experiment demonstrated variations in the absorption of optical signals at specific wavelengths corresponding to changes in lipid concentrations.
Based on these understandings, the team then used SFDI data to train a machine learning model to predict levels of triglyceride, from within 40 mg/dL. Such precision may enable monitoring of cardiovascular health noninvasively.Â
According to senior author Darren Roblyer, a professor of biomedical engineering at Boston University, this research suggests that SFDI may promise to be used as a promising alternative, which will monitor how meals affect cardiovascular health much more easily. He concludes, “In summary, these findings demonstrate an interactive interaction of diet with bodily response and, in particular with cardiovascular risk, where further research is needed into non-invasive assessment methods”.
Reference:
SPIE. Team introduces a noninvasive method to monitor postprandial cardiovascular health Medicalxpress.com. Medical Xpress
