An international study, Colive Voice, presented at the European Association for the Study of Diabetes (EASD) 2024 conference, shows that These results “open up possibilities for developing a first-line, noninvasive, and rapid screening tool for T2D, feasible with just a few seconds of voice recording on a smartphone or during consultations,” explained the study’s principal investigator Guy Fagherazzi, PhD, a diabetes epidemiologist at the Luxembourg Institute of Health, in an interview with this news organization.
How did the idea of detecting diabetes through voice come about?
During the COVID-19 pandemic, we began analyzing voice recordings from patients with chronic diseases. We wanted to find solutions to assess people’s health remotely, without physical contact. We quickly realized that this approach could be extended to other diseases. Because my main research focus has always been diabetes, I looked into how voice characteristics might correlate with diabetes. Previous studies had indicated that patients with diabetes have distinct voices compared with the general population, and this insight formed the starting point.
What mechanism could explain why patients with T2D have different voice characteristics?
It’s challenging to pinpoint a single factor that would explain why patients with T2D have different voices from those without diabetes. Several factors are involved.
Some biological mechanisms, especially those affecting the vascular system, influence symptoms in people with metabolic diseases such as diabetes. For example, people with T2D have more frequent cardiorespiratory fatigue. Obesity and overweight are also key factors, as these conditions can slightly alter vocal parameters compared with people of normal weight. Hypertension, common in patients with T2D, adds to the complexity.
Neurologic complications can affect the nerves and muscles involved in voice production, particularly the vocal cords.
Therefore, respiratory fatigue, neuropathies, and other conditions such as dehydration and gastric acid reflux, which are more common in patients with diabetes, can contribute to differences in voice.
These differences might not be noticeable to the human ear. That’s why we often don’t notice the link between voice and diabetes. However, technological advancements in signal processing and artificial intelligence allow us to extract a large amount of information from these subtle variations. By analyzing these small differences, we can detect diabetes with a reasonable degree of accuracy.
In your study, you mention that voice tone can indicate diabetic status. Could you elaborate?
Yes, voice tone can be affected, though it’s a complex, multidimensional phenomenon.
Patients who have had diabetes for 5-10 years, or longer, tend to have a rougher voice than those without diabetes of the same age and gender. In our study, we were able to extract many voice characteristics from the raw audio signal, which is why it’s difficult to isolate one specific factor that stands out.
Is there a difference in voice changes between patients with well-managed diabetes and those whose disease is uncontrolled?
The roughness of the voice tends to increase with the duration of diabetes. It’s more noticeable in people with poorly controlled diabetes. Our hypothesis, based on the results we presented at the EASD conference, is that fluctuations in blood sugar levels, both hypo- and hyperglycemia, may cause short-term changes in the voice. There are also many subtle, rapid changes that could potentially be detected, though we haven’t confirmed this yet. We’re currently conducting additional studies to explore this.