Pathophysiological discussions and statistical results between diabetes caused by obesity versus cancers caused by obesity via SD-VMT areas comparison using data collected from 2010 to 2023 and based on space-domain viscoplastic energy model of GH-Method: Math-Physical Medicine (No. 893, VGT #293)

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Abstract

The objective of this methodology article is to assess and compare the risks of developing type 2 diabetes (T2D) and cancers caused by obesity as a single input factor. The study spans a timeframe of approximately 14 years, from 2010 to 2023. The author personally collected and analyzed their input data, although there were limitations in obtaining sufficient lab-tested data between 2010 and 2012. From 2013 to 2018, the data collection methods involved more lab testing and personal data obtained through finger-piercing glucose testing. A more complete and comprehensive dataset was obtained from 2018 to 2023, utilizing laboratory tests, automated glucose sensors, and wearable health devices. Despite these limitations, the annual dataset used in this study is considered adequate for drawing useful and reasonably accurate conclusions. The author has been diagnosed with T2D since 1996 but has not received any cancer diagnoses. The assessment of cancer risk in this study is conducted using a sophisticated metabolism index (MI) model developed by the author in 2014. This model takes into account factors such as lifetime bad habits, environmental factors, genetic concerns, four metabolic disorders, and six lifestyle details. To analyze and compare the contribution of T2D (measured through HbA1C levels) and cancer risks based on the single input cause of obesity (measured through body weight, m1), this study utilizes the space-domain viscoplastic energy model from GH-Method: Math-Physical Medicine. In summary, this research presents two key findings: Firstly, the author conducts two separate analyses to evaluate the impact of obesity on his existing T2D condition and his future risks of developing cancer. The SD-VMT analysis shows that the energy level for T2D and obesity is 110.1, while the energy level for cancers and obesity is only 1.18, representing just 1% of the diabetes energy level. This finding indicates that despite being a T2D veteran of 26 years, the author has not shown any signs of developing cancer. Furthermore, his SD time-zone analysis reveals that 98% of the total energy is concentrated in the Y10-Y13 for T2D and Y10-Y17 for cancers, while only 2% falls in the Y14-Y23 for T2D and Y18-Y23 for cancers. Secondly, as an exploratory medical research methodology, he considers obesity as the output symptom, with diabetes and cancers being two separate input causes. The SD-VMT energy ratios demonstrate that diabetes accounts for 69% of the total energy, while cancers account for 31%. Although diabetes has twice as much energy as cancers, the disparity is not as substantial as the 99-fold difference observed in the first case. This finding emphasizes that the author's existing diabetes condition is significantly more severe than his risks of developing cancer.
利用2010 - 2023年数据,基于gh的空间域粘塑性能量模型,通过SD-VMT区域比较,探讨肥胖致糖尿病与肥胖致癌症的病理生理及统计结果。
这篇方法学文章的目的是评估和比较由肥胖作为单一输入因素引起的2型糖尿病(T2D)和癌症的风险。这项研究跨越了大约14年的时间框架,从2010年到2023年。作者亲自收集并分析了他们的输入数据,尽管在2010年至2012年期间获得足够的实验室测试数据存在局限性。从2013年到2018年,数据收集方法涉及更多的实验室测试和通过穿指血糖测试获得的个人数据。利用实验室测试、自动血糖传感器和可穿戴健康设备,从2018年到2023年获得了更完整、更全面的数据集。尽管存在这些限制,本研究中使用的年度数据集被认为足以得出有用且合理准确的结论。作者自1996年被诊断为T2D,但未接受任何癌症诊断。本研究的癌症风险评估采用了作者于2014年开发的复杂代谢指数(MI)模型。该模型考虑了诸如终生不良习惯、环境因素、遗传因素、四种代谢紊乱和六种生活方式细节等因素。为了分析和比较T2D(通过HbA1C水平测量)和癌症风险的贡献,基于肥胖的单一输入原因(通过体重,m1测量),本研究利用GH-Method: Math-Physical Medicine的空间域粘塑性能量模型。综上所述,本研究有两个主要发现:首先,作者进行了两项独立的分析,评估肥胖对其现有T2D状况和未来患癌风险的影响。SD-VMT分析显示,T2D和肥胖症的能量水平为110.1,而癌症和肥胖症的能量水平仅为1.18,仅占糖尿病能量水平的1%。这一发现表明,尽管作者在T2D领域工作了26年,但他并没有表现出任何患癌症的迹象。此外,他的SD时区分析显示,98%的总能量集中在用于T2D的Y10-Y13和用于癌症的Y10-Y17,而用于T2D的Y14-Y23和用于癌症的Y18-Y23只有2%。其次,作为一种探索性的医学研究方法,他认为肥胖是输出症状,糖尿病和癌症是两个独立的输入原因。SD-VMT能量比表明,糖尿病占总能量的69%,而癌症占31%。虽然糖尿病的能量是癌症的两倍,但这种差异并不像第一个病例中观察到的99倍的差异那么大。这一发现强调了作者现有的糖尿病状况明显比他患癌症的风险更严重。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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