Detecting Type 2 Diabetes Through Voice: How Does It Work?

Why did you ask participants to read a passage from the  Universal Declaration of Human Rights?

We used a highly standardized approach. Participants completed several recordings, including holding the sound “Aaaaaa” for as long as possible in one breath. They also read a passage, which helps us better distinguish between patients with and those without diabetes. This method works slightly better than other sounds typically used for analyzing diseases. We chose this particular text in the participant’s native language because it’s neutral and doesn’t trigger emotional fluctuations. Because Colive Voice is an international, multilingual study, we use official translations in various languages.

Your research focuses on T2D. Do you plan to study type 1 diabetes (T1D) as well?

We believe that individuals with T1D also exhibit voice changes over time. However, our current focus is on T2D because our goal is to develop large-scale screening methods. T1D, typically diagnosed in childhood, requires different screening approaches. For now, our research mainly involves adults.

Were there any gender differences in the accuracy of your voice analysis?

Yes, voice studies generally show that women have different vocal signatures from men, partly owing to hormonal fluctuations that affect pitch and tone. Detecting differences between healthy individuals and those with diabetes can sometimes be more challenging in women, depending on the condition. In our study, we achieved about 70% accuracy for women compared with 75% for men.

The EASD results focused on a US-based population. When can we expect data from France?

We started with the US because we could quickly gather a large number of patients. Now, we’re expanding to global and language-specific analyses. French data are certainly a priority, and we’re working on it. We encourage people to participate — it takes only 20 minutes and contributes to innovative research on noninvasive diabetes detection. Participants can sign up at www.colivevoice.org

Dr. Fagherazzi heads the Deep Digital Phenotyping laboratory and the Department of Precision Health at the Luxembourg Institute of Health. His research focuses on integrating new technologies and digital data into diabetes research. He has declared no relevant financial relationships. 

This story was translated from the Medscape French edition using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.