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From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data.
Cichosz, Simon Lebech; Kronborg, Thomas; Laugesen, Esben; Hangaard, Stine; Fleischer, Jesper; Hansen, Troels Krarup; Jensen, Morten Hasselstrøm; Poulsen, Per Løgstrup; Vestergaard, Peter.
Afiliación
  • Cichosz SL; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Kronborg T; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Laugesen E; Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark.
  • Hangaard S; Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
  • Fleischer J; Diagnostic Center, Regional Hospital Silkeborg, Silkeborg, Denmark.
  • Hansen TK; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Jensen MH; Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark.
  • Poulsen PL; Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
  • Vestergaard P; Steno Diabetes Center Zealand, Zealand, Denmark.
Diabetes Technol Ther ; 2024 Aug 26.
Article en En | MEDLINE | ID: mdl-39115921
ABSTRACT

Objective:

This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns.

Methods:

Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group.

Results:

The analysis included 836 participants (healthy n = 282; prediabetes n = 133; T2DM n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI) 0.87-0.95] (prediabetes identification) to 0.97 [CI 0.95-0.98] (dysglycemia identification).

Conclusion:

Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diabetes Technol Ther Asunto de la revista: ENDOCRINOLOGIA / TERAPEUTICA Año: 2024 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diabetes Technol Ther Asunto de la revista: ENDOCRINOLOGIA / TERAPEUTICA Año: 2024 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Estados Unidos