A Practical Guide Regarding Carbs and Sugar Intake Amount for Controlling Postprandial Plasma Glucose (A Part of GH-Method: Math-Physical Medicine)

Gerald Hsu, C.

EclaireMD Foundation, USA

*Correspondence to: Dr. Gerald Hsu, C., EclaireMD Foundation, USA.

Copyright © 2020 Dr. Gerald Hsu, C. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction
This paper provides simple yet effective guidelines to control postprandial plasma glucose (PPG) for type 2 diabetes (T2D) patients.

Method
Most food nutrition literatures use terminologies for measurements such as serving size, grams, calories that can be difficult for T2D patients to understand and let alone to be used as a guide. The author has decided to use “Hand” (either palm or fist) as the measurement unit since you never leave home without them. A hand’s volume (380 cc) is about 160% size of a cup (237 cc). The author has studied the impact of food nutrition on diabetes since 2012. He has collected ~8 million food nutrition data and applied optical physics and artificial intelligence to investigate and analyze the relationship between food and glucose. In this paper, he only lists two partial results of food and PPG values for high-carb foods (Table 1) and sample vegetables (Table 2).

In his previous publications, he has mentioned the following four key findings:

(1) PPG has direct correlation with food quality, i.e. carbs/sugar intake amount, contributing ~40% of PPG formation. Carbs/sugar intake amount determines PPG’s peak value.
(2) Food quantity contributes to body weight, where weight has direct correlation with FPG (~20% of A1C), but not with PPG (~80% of A1C).
(3) Each gram of carbs/sugar is converted into ~2 to 2.5mg/dL of PPG.
(4) Post-meal exercise contributes another 40% of PPG formation. Each thousand steps of walking may reduce ~5 to 10mg/dL of PPG depending upon weight, severity of diabetes, and overall metabolism.

Results
Table 1 lists high-carb food’s quantity of carbs/sugar (grams per hand) and their impact on PPG. Foods such as bread, potato, rice, noodle has an average of 50 grams per hand and convert into 100-125mg/dL of PPG. Food materials such as flours and starches has an average of 155 grams per hand and convert into 310-388mg/dL of PPG.

Table 2 provides 12 sample vegetables with various colors and carbs/sugar contents (grams per hand) and their impact on PPG. The chopped raw vegetables have an average carbs/sugar of 14.4 grams per hand and convert into 29mg/dL of PPG. The cooked vegetables, with less water and higher carbs/sugar amount, have an average of 19.2 grams per hand and convert into 38mg/dL of PPG. If you eat one hand of mixed raw and cooked vegetables, your average carbs/sugar intake is 16.8 grams per hand and convert into 34mg/dL of PPG.

In summary, the following three conclusions can be drawn:

(1) If you eat salad with uncut and raw vegetables, you can eat two handfuls, approximately one normal plateful, because they have both water and air space. This may still provide ~15 grams of carbs/sugar and ~30mg/dL of PPG. This is close to one handful of cooked vegetables, less water and no air space.
(2) If you are a severe T2D patient (A1C >8.0%), try to avoid eating high-carb foods and replace them with a combination of vegetables and protein-rich food such as cheese, fish, or chicken to reduce your hunger. If you are a non-severe T2D patient (A1C <8.0%), you may eat only half of your hand size for high-carb foods (50%) to avoid excessive carbs amount which builds up PPG’s peak value.
(3) Every one-thousand walking steps post meal would reduce your PPG approximately 5 to 10mg/dL depending upon your body weight, T2D conditions, and MI.

Conclusion
The specific objective of this paper is to provide simple yet practical guidelines to T2D patients regarding the carbs/sugar intake amounts in their meals and the impact on their PPG values.

Bibliography

1. Hsu Gerald, C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
2. Hsu Gerald, C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
3. Hsu Gerald, C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
4. Hsu Gerald, C. (2018). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3^rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
5. Hsu Gerald, C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.