The author celebrated his birthday with a lunch party on 1/16/2022, where he consumed 20% of a large cake slice. His finger-pierced postprandial plasma glucose (PPG) level reached 171 mg/dL after that luncheon. Over several days, he noticed that his overall PPG levels were persistently higher. Out of curiosity regarding the possible extent of the residual effect on his PPG due to a single hyperglycemic meal, he then queried his ~5000 meals database for the high carbs/sugar meals with hyperglycemic PPG (hyper-PPG) levels above 170 mg/dL. He identified 20 independent hyper-PPG meals over 4 years, Y2018 - Y2022. Next, he grouped his extracted PPG and carbs/sugar data into two separate periods: the period containing 10 days of PPG data before the hyper-PPG day and the post-period containing 10 days of PPG data including the hyper-PPG day. He conducted his research work regarding the special hyper-PPG phenomenon and wrote paper No. 586, VGT#7. Its conclusion is that during these 4 years, any single hyper-PPG meal had a residual effect of approximately 10 days. This phenomenon would eventually dissipate with considerable effort. Since 1/1/2021, the author has maintained his body weight (BW) below 170 lbs. (BMI below 25) for ~1.5 years. On 4/5/2022, he traveled to a different city where he consumed his favorite local meals (rich and heavy) for a couple of days. This incident pushed his BW to 171.4 lbs. with a BMI of 25.3 on 4/7/2022. During the following 40 days, he has tried many weight-reduction techniques, including intermittent fasting, low-calorie meals, reduced food portions, increased exercise level, and good quality sleep to decrease his BW. Finally, on 5/16/2022, he successfully reduced his weight to 169.2 lbs. with a BMI of 24.3 (see figure below). This long, arduous process took 40 days which indicates the hyper-weight scenario had a residual effect of around 40 days. Therefore, he decides to conduct another VGT energy analysis of this hyper-weight scenario to make a direct comparison against his previous hyper-PPG scenario study. In this article, he selected BW values as the strain (ε) and the BW change rate as the strain rate (dε/dt), multiplied by his average food quantity amount (m9a) i.e., viscosity factor (η), as his stress (σ), to construct the desired stress-strain diagram. He used the following defined VGT energy equation from the disciplines of engineering and physics to address this unique “time-dependency characteristic” of biomedical symptoms and causes to establish stress-strain diagrams (i.e. cause-symptom diagram) in a space domain (SD):