Posted on Diabetes

Could a genetic flaw be the key to stopping people craving sugary treats?

Diabetes is an increasingly pervasive disease, currently affecting over 500 million adults worldwide. Since there is as yet no cure for type 1 or type 2 diabetes, patients must regularly monitor their BGLs to keep them in check. Though BGL-measuring devices relying on painful finger pricks have been the gold standard for decades, modern technology is slowly opening doors to better alternatives. Many researchers have proposed noninvasive methods to monitor BGLs using widely available wearable devices, such as smartwatches. For example, by placing the LEDs and photodetectors present in certain smartwatches against the skin, oxyhemoglobin and hemoglobin pulse signals can be measured to calculate a metabolic index, from which BGLs can be estimated. However, given the small size and limited power of smartwatches and similar wearables, the data quality of the measured signals tends to be quite low. Moreover, as these devices are worn on extremities, daily movements introduce measurement errors. These problems limit the accuracy and clinical applicability of such wearables for diabetes management. A team from Hamamatsu Photonics K.K., Japan, has been actively researching this issue, looking for effective solutions. In a recent study led by Research and Development Engineer Tomoya Nakazawa, published in the Journal of Biomedical Optics (JBO), they conducted an in-depth theoretical analysis of the sources of errors in the metabolic-index-based method. Based on this analysis, they implemented a novel signal quality index to filter out low-quality data as a preprocessing step and thereby enhance the accuracy of estimated BGLs. “As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is absolutely essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study. First, the researchers mathematically showed that discrepancy between the two types of phase delays in the oxyhemoglobin and hemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two main sources of phase error, namely, a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index. The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data. To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, the latter of which was used to capture the reference values. Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work. The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the performance of the former, hardware improvements in the photodetector and amplifier circuits could go a long way to make wearable electronics a more attractive and clinically acceptable option to monitor BGLs.

The work provides novel genetic insights into dietary preferences and opens the possibility of selectively targeting SI to reduce sucrose intake at the population level.

The study was led by Dr. Peter Aldiss, “Excess calories from sugar are an established contributor to obesity and type 2 diabetes. In the UK, we consume 9-12% of our dietary intake from free sugars, such as sucrose, with 79% consuming up to three sugary snacks daily. At the same time, genetic defects in sucrose digestion have been associated with irritable bowel syndrome, a common functional disorder affecting up to 10% of the population.

“Our study suggests that genetic variation in our ability to digest dietary sucrose may impact not only how much sucrose we eat, but how much we like sugary foods.”

The experts began by investigating the dietary behaviours in mice lacking the SI gene. Here, mice developed a rapid reduction in sucrose intake and preference. This was confirmed in two large population-based cohorts involving 6,000 individuals in Greenland and 134,766 in the UK BioBank.

The team took a nutrigenetics approach to understanding how genetic variation in the SI gene impacts sucrose intake and preference in humans. Strikingly, individuals with a complete inability to digest dietary sucrose in Greenland consumed significantly less sucrose-rich foods, while individuals with a defective, partially functional SI gene in the UK liked sucrose-rich foods less.

“These findings suggest that genetic variation in our ability to digest dietary sucrose can influence our intake, and preference, for sucrose-rich foods whilst opening up the possibility of targeting SI to reduce sucrose intake at the population level selectively,” says Dr Aldiss.

“In the future, understanding how defects in the SI gene act to reduce the intake, and preference, of dietary sucrose will facilitate the development of novel therapeutics to help curb population-wide sucrose intake to improve digestive and metabolic health.”