An impedance bode plot modelling simulation has been conducted with the regression method to determine the RC model. The aim of the research is to determine the RC model and determine the resistance and capacitance of the equivalent circuit. The study uses nine RC models which are modelled into a linear regression equation and the equivalent model is determined based on the smallest error. Gradient and regression constants are used to determine the resistance and capacitance of the equivalent model. The results in the RC series show that the equivalent model is the first model with a resistance 998.580? and a capacitance 9.253 pF with an error 2.4%. While the program implementation of the RC parallel produces a resistance 999.809? and a capacitance 9.995 pF with an error 0.042%.
Introduction:
Approximately 25% of diabetic patients develop diabetic foot ulcers (DFUs), which significantly increase morbidity, mortality, and healthcare costs. Effective control and prevention are crucial to mitigate these impacts.
Objective
This study aims to identify easily measurable parameters for predicting DFU risk in routine consultations by assessing the correlation between Phase Angle (PA) and the Triglyceride-Glucose (TyG) index with DFU risk, based on their known association with diabetes.
Materials and Methods
A comparative observational case-control study was conducted at the General Hospital of Elche from March to June 2023, with 70 participants: 33 with diabetes and 37 without. Inclusion criteria for the case group were a diabetes diagnosis for over five years and a diabetic foot risk grade of 0, 1, or 2 according to the 2019 IWGDF stratification system. Exclusion criteria included inability to walk, prior use of plantar orthoses, and severe complications like edema or wounds. Predictive variables included PA, TyG index, body composition, and biochemical markers. Pearson or Spearman tests were used for correlations, Student's t-test or Mann-Whitney test for group comparisons, and one-way ANOVA or Kruskal-Wallis tests for normally and non-normally distributed variables, respectively.
Results
PA and TyG index were strongly linked to diabetic foot risk, supporting their potential as biomarkers. We confirmed the correlation between PA, TyG index, and diabetic foot risk, along with their significant relationships with other relevant biomarkers, enhancing our understanding of the condition.
Conclusion
PA and TyG index are valuable, easily measurable biomarkers for diabetic foot risk assessment. Their use in clinical practice could improve the management of diabetic complications, especially in resource-limited settings.
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