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ISSN: 2641-6816
Frequency: Continuous
Format: PDF and HTML
Versions: Online (Open Access)
Year first Published: 2018
Language: English

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Quantitative Analysis of Relationship between Postprandial Plasma Glucose and Food/Meal (Math-Physical Medicine)

Gerald C Hsu^*
eclaireMD Foundation, USA

Received Date: April 03, 2020; Accepted Date: April 15, 2020; Published Date: April 24, 2020
^*Corresponding Author: Gerald C Hsu, eclaireMD Foundation, USA. Tel: +15103315000; E-mail: g.hsu@eclairemd.com

Citation: Hsu GC (2020) Quantitative Analysis of Relationship between Postprandial Plasma Glucose and Food/Meal (Math-Physical Medicine). Adv Nutri and Food Sci: ANAFS-179.

Abstract
       The author has been diagnosed with three chronic diseases including type 2 diabetes (T2D), hypertension, and hyperlipemia. Since 2010, he focused on T2D research to save his life. He collected and processed approximately 1.5 million data regarding his health and life details. In 2014, he developed a mathematical model of the metabolic system known as the math-physical medicine (MPM) approach by applying mathematics, physics, engineering modeling, and computer science such as big data analytics and artificial intelligence. This paper focuses on the quantitative relationship between postprandial plasma glucose and food/meal.

Keywords: Artificial Intelligence; Chronic Diseases; Food; Lifestyle Data; Math-Physical Medicine; Meals; Metabolic Conditions; Metabolism; Nutrition; Postprandial Plasma Glucose; Type 2 Diabetes

Introduction: The author used math-physical medicine to research and identify the quantitative relationship between postprandial plasma glucose (PPG) and food/meal.

Methods
      Food is the most important factor of PPG, but it is also difficult to regulate eating habits. He created an artificial intelligent (AI) based software to collect his meal data by utilizing optical physics, signal processing, mathematics, statistics, and machine learning. He then developed a PPG prediction model by combining 6M food nutrition data from the United States Department of Agriculture (USDA) and his ~4,000 meal photos as his food database. Each meal picture links with data, including nation, meal location, food type, menu/dish name, and nutritional ingredients. The system can estimate consumed carbs/sugar amount and then predict PPG value prior to eating.

Results
       He selected a period of 1,194 days (6/1/2015-9/7/2018) with 3,721 meals (including snacks) and ~100,000 data for his analysis. There were 86 airline meals consumed during his 94 trips during this period. The summary results are listed by both nation and meal location; then, they were sorted by PPG value with the format of PPG (mg/dL) & carbs/sugar (gram).

By Nation (Table 1):

USA: (117.6, 13.0g)
Taiwan: (123.0, 14.9g)
Japan: (117.4, 15.6g)
Canada: (115.1, 14.3g)
Other Nations: (123.7, 19.8g)
Airlines - Cross nations: (137.3, 26.0g)

In summary, he had 58% of meals within the USA and 42% in other nations.

By Location (Table 2):

Home Cooking: (113.8, 11.5g)
Chain Restaurant: (121.2, 11.7g)
Individual Restaurant: (127.7, 20.6g)
Supermarket: (130.3, 25.7g)
Airlines: (137.3, 26.0g)

In summary, he had 59% of meals at home and 41% outside.

Conclusion: The analysis (Table 3 and Figures 1, 2) and predicted PPG model (99.9% accuracy) assisted the author to lower his PPG from 279mg/dL to 119mg/dL.

Figure 1: Using AI Glucometer to Predict Glucose Value via Meal Photos.

Figure 2: Accuracy Comparison between Nutritional Intelligence (NI) and Artificial Intelligence (AI).