Scientists Develop a Patch That Can Continuously Monitor Cholesterol Through Sweat

Engineers at the California Institute of Technology have developed a wearable sensor in the form of a small patch that can estimate cholesterol and triglyceride levels without a blood test. The device analyzes the composition of sweat and uses machine learning to calculate blood lipid levels.

The study was published in the journal Nature Sensors.

Unlike conventional lipid testing, which requires periodic blood samples to be analyzed in a laboratory, the new sensor could potentially provide continuous monitoring of changes in lipid levels.

The main challenge was that lipid concentrations in sweat and blood are related in a nonlinear way. To address this, the researchers used a causal machine-learning model that takes into account additional physiological factors, including body mass index, sex, and sweating rate. This allows the system to more accurately estimate blood lipid levels based on sensor readings.

The developers believe the technology could provide early warning of cardiovascular and metabolic disease risks. According to the researchers, many people do not have their cholesterol and triglyceride levels tested regularly, making continuous, noninvasive monitoring potentially particularly useful.

Developing the sensor required the researchers to overcome several challenges related to the chemical composition of sweat. A significant proportion of cholesterol in sweat is present in the form of esterified cholesterol, which cannot be measured directly. The researchers therefore developed a two-step enzymatic reaction: first, the esterified form is converted into free cholesterol, which is then oxidized to produce hydrogen peroxide that can be detected by the sensor.

To measure triglycerides, the researchers incorporated a special polymer into the patch that gradually releases ATP, a molecule required for the system to function. This allows the sensor to maintain measurements for more than 20 hours.

The device was tested on 24 participants. The authors note that the results are promising, but the technology is still at the research stage and requires further validation in larger groups of people.

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