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 Factors along with Studying the Relationship between Cancer Risks and Diabetic Glucoses Based on the GH-Method: Math-Physical Medicine (No. 601)
Author(s):
Gerald C Hsu
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).
To offer a simple explanation to readers who do not have a physics or engineering background, the author includes a brief excerpt from Wikipedia regarding the description of basic concepts for elasticity and plasticity theories, viscoelasticity and viscoplasticity theories from the disciplines of engineering and physics, along with perturbation theory of quantum mechanics in the Method section.
In summary, the following five observations outline the findings from this research work.
(1) From the TD analysis, the waveforms of his calculated overall cancer risk probability and his measured lifestyle score are high with a 96% correlation. It should be noted that the lifestyle score is the input biomarker while the cancer risk is the output biomarker.(2) In contrast, from another TD analysis, the waveforms of his calculated overall cancer risk probability % and his measured glucose value (eAG) are also high with an 81% correlation but not as high as 96% of the much closer relationship between the cancer risk as to the symptom and the lifestyle score as the root cause. It should be noted that both glucose (eAG) values of diabetes and cancer risk % are the output symptoms.
(3)“Overall, only 8%-18% of cancer patients have diabetes. In the context of epidemiology, the burden of both diseases, and the small association between them will be clinically relevant and should translate into significant consequences for future healthcare solutions. People with diabetes are at increased risk for cancer. The risk is highest for liver, pancreatic, colorectal, endometrial, breast, and bladder cancer. People with type 2 diabetes (T2D) are twice as likely to develop liver or pancreatic cancer.” The above excerpts are quoted from Hindawi and MD Anderson.
(4) The stress-strain diagram of cancer risk versus cancer change rate multiplied by the lifestyle score demonstrates the viscoplastic characteristics, or time-dependency, of the two biomarkers.
(5) Regarding the comparison between the predicted cancer risk utilizing the visco-perturbation model and the calculated cancer risk using the MI model, they depict an extremely high correlation of 98% along with the prediction accuracy of 98% as well.
In conclusion, utilizing the VGT in combination with the perturbation model can provide a practical and accurate predicted cancer risk probability % using lifestyle score as the influential factor. In addition, this study has shown a very high correlation between cancer risk as to the symptom and lifestyle score as the root cause. However, when he compares the symptoms of these two diseases, cancer and diabetes, the correlation between these two symptoms is not as high as the correlation between the symptom and the root cause of cancer.
