Viscoelastic and Viscoplastic Glucose Theory (VGT #22): Applying the Concepts of Viscoelasticity, Viscoplasticity and Perturbation Theories to Predict Cancer Risk Percentages using the overall Lifestyle Scores and Calculated Cancer Risk Change Rates with Corresponding Lifestyle Scores as the Respective Viscosity Factorsalong with Studying the Relationship between Cancer Risks and Diabetic Glucoses Based on the GH-Method: Math-Physical Medicine (No. 601)

Abstract

Recently, the author applied theories of viscoelasticity and viscoplasticity from engineering and perturbation theory from quantum mechanics to conduct his biomedical research on output biomarkers of cancer risk probability percentage (symptoms or behaviors) resulting from suspected or identified input biomarkers of lifestyle scores (causes or stressors). The lifestyle scores are calculated as a combination of his individual performance level for food, diet, exercise, sleep, stress, water intake, and daily life routines. His developed lifestyle model includes key environmental factors such as air and water pollution, nuclear radiation, toxic chemical exposure, food poisoning, and hormone therapy along with life-long bad habits, such as drinking alcohol, smoking cigarettes, and illicit drug use. For the author himself, he has no bad habits and has limited environmental exposure.

In this particular article, he studies the correlation between his cancer Risk % versus his lifestyle scores. He then applies the viscoelastic or viscoplastic glucose theory (VGT) to construct a stress-strain diagram to verify the existing time-dependency characters of both input biomarker (lifestyle score) and output biomarker (cancer Risk %). Finally, he utilizes the visco-perturbation model and lifestyle score alone to predict his cancer risk probability % to compare against his calculated cancer risk probability % using his developed metabolism index (MI) model. Out of curiosity, he further investigates the relationship between cancer risk % and diabetic glucose level to explore the strength of inter-relationship between symptoms for two diseases, cancer versus diabetes, not between root-cause and symptom. The timeframe of this study covers a long period of 10+ years from Y2012 to Y2022.

The following two defined equations from viscoelasticity or viscoplasticity are utilized to study the stress-strain relationship in his cases. Here, he wants to use the strain rate or his annual cancer risk change rate multiplied with the viscosity factor, lifestyle score, as the stress:

strain = ε
= individual output biomarker value (cancer risk %) at present time

Stress = σ
= η * (dε/dt)
= η * (d-strain/d-time)
= (viscosity factor η, i.e. lifestyle score) * ((output biomarker of cancer risk at present time - output biomarker of cancer risk at previous time) / (time
duration = 1))

Where the time duration of 1 was chosen due to his annual cancer risk probability % taken at 1-year intervals. After completing the steps from above, he generated the following four useful information:

(1) An organized data table which contains the input biomarker (viscosity factor η, lifestyle score) and output biomarker (cancer risk %) to construct a
time-domain (TD) waveform diagram.
(2) A constructed stress-strain diagram in space-domain (SD) using the strain rate (dε/dt), annual cancer risk changing rate, multiplied with the viscosity
factor η (annual lifestyle score i.e., input biomarker), as the stress. This calculated annual cancer risk probability % is the strain.
(3) A calculation of prediction accuracy and waveform similarity through the correlation coefficient between calculated cancer risk based on MI and predicted cancer risk based on the visco-perturbation model.
(4) An extra correlation study between cancer risk and diabetic glucose (estimated daily glucose or eAG).