Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.

Journal: PLOS digital health
Published Date:

Abstract

Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We aimed to evaluate algorithmic fairness in glucose predictions. This study utilized continuous glucose monitoring (CGM) data from 101 White and 104 Black participants with type 1 diabetes collected by the JAEB Center for Health Research, US. Long short-term memory (LSTM) deep learning models were trained on 11 datasets of different proportions of White and Black participants and tailored to each individual using transfer learning to predict glucose 60 minutes ahead based on 60-minute windows. Root mean squared errors (RMSE) were calculated for each participant. Linear mixed-effect models were used to investigate the association between racial composition and RMSE while accounting for age, sex, and training data size. A median of 9 weeks (IQR: 7, 10) of CGM data was available per participant. The divergence in performance (RMSE slope by proportion) was not statistically significant for either group. However, the slope difference (from 0% White and 100% Black to 100% White and 0% Black) between groups was statistically significant (p = 0.02), meaning the RMSE increased 0.04 [0.01, 0.08] mmol/L more for Black participants compared to White participants when the proportion of White participants increased from 0 to 100% in the training data. This difference was attenuated in the transfer learned models (RMSE: 0.02 [-0.01, 0.05] mmol/L, p = 0.20). The racial composition of training data created a small statistically significant difference in the performance of the models, which was not present after using transfer learning. This demonstrates the importance of diversity in datasets and the potential value of transfer learning for developing more fair prediction models.

Authors

  • Helene Bei Thomsen
    Department of Public Health, Aarhus University, Aarhus, Denmark.
  • Livie Yumeng Li
    Department of Public Health, Aarhus University, Aarhus, Denmark.
  • Anders Aasted Isaksen
    Steno Diabetes Center Aarhus, Aarhus, Denmark.
  • Benjamin Lebiecka-Johansen
    Steno Diabetes Center Aarhus, Aarhus, Denmark.
  • Charline Bour
    Department of Precision Health, Deep Digital Phenotyping Research Unit, Luxembourg Institute of Health, Strassen, Luxembourg.
  • Guy Fagherazzi
    Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, 1445, Strassen, Luxembourg. guy.fagherazzi@lih.lu.
  • William P T M van Doorn
    CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Tibor V Varga
    Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Adam Hulman
    Department of Public Health, Aarhus University, Aarhus, Denmark.

Keywords

No keywords available for this article.