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Glucose-independent segmental phase angles from multi-frequency bioimpedance analysis to discriminate diabetes mellitus.

Min-Ho JunSoochan KimBoncho KuJungHee ChoKahye KimHo-Ryong YooJaeuk U Kim
Published in: Scientific reports (2018)
We investigated segmental phase angles (PAs) in the four limbs using a multi-frequency bioimpedance analysis (MF-BIA) technique for noninvasively diagnosing diabetes mellitus. We conducted a meal tolerance test (MTT) for 45 diabetic and 45 control subjects stratified by age, sex and body mass index (BMI). HbA1c and the waist-to-hip-circumference ratio (WHR) were measured before meal intake, and we measured the glucose levels and MF-BIA PAs 5 times for 2 hours after meal intake. We employed a t-test to examine the statistical significance and the area under the curve (AUC) of the receiver operating characteristics (ROC) to test the classification accuracy using segmental PAs at 5, 50, and 250 kHz. Segmental PAs were independent of the HbA1c or glucose levels, or their changes caused by the MTT. However, the segmental PAs were good indicators for noninvasively screening diabetes In particular, leg PAs in females and arm PAs in males showed best classification accuracy (AUC = 0.827 for males, AUC = 0.845 for females). Lastly, we introduced the PA at maximum reactance (PAmax), which is independent of measurement frequencies and can be obtained from any MF-BIA device using a Cole-Cole model, thus showing potential as a useful biomarker for diabetes.
Keyphrases
  • body mass index
  • type diabetes
  • weight gain
  • glycemic control
  • blood glucose
  • cardiovascular disease
  • machine learning
  • deep learning
  • body composition
  • metabolic syndrome
  • blood pressure
  • climate change
  • high resolution