Glucose and Weight
Relationship Between Weight and Glucose Using Math-Physics Medicine
Background & Aim:
This paper investigates the relationship between weight and glucose, based on 13,480 data covering 2,245 days (1/1/2012 – 2/24/ 2018) from a diabetes patient’s 1.5 million statistics.
Material & Method:
Health conditions prior to 2012 vs. after 2012:
Weight – 210 lbs. vs. 166.9-193.8 lbs.
BMI – 31 vs. 24.65-28.65
Max. PPG – 380 mg/dL vs. 52-280 mg/dL
Average PPG – 280 mg/dL vs. 126.5 mg/dL
A1C – above 10.0% vs. 6.5%
This 4-year research project utilized advanced mathematics, finite element modeling, signal processing, big data analytics, statistics, and artificial intelligence (AI).
Figure 1: Weight & PPG Data Results
Figure 2: Period of 1/1/12 to 2/21/18 Results
Among the five influential factors of fasting plasma glucose (FPG), weight is the most dominant one, contributing ~90%. Weight and FPG have a high correlation of 84%. In spatial analysis, 93% of the total collected data is covered by a +/- 20% band around a “skewed line”. This “relationship band” stretched from point A (24.5, 95) to point B (27.2, 150) on a map with coordinates of x=BMI and y=glucose.
However, among the 15 influential factors of postprandial glucose (PPG), weight is not the dominating factor. Instead, the combined effect of carbs/sugar intake and post-meal exercise contributes 81% of PPG formation. Weather and measurement time delay count for 14% and the other factors impact 5%. Weight and PPG have a low correlation (from 9% to 36%). In spatial analysis, 86% of the total collected data covers by a +/- 20% band centering around a “horizontal” PPG line of 127 mg/dL.
The results show that 93% of FPG data are directly related to weight according to a “fixed” slope. However, 86% of PPG data are kept within a horizontal range from 102 mg/dL to 152 mg/dL because of carbs/sugar intake and post-meal exercise, but not from weight.