Foodomics Database: a New Tool for Estimating Molecular Profiles in Nutrients Intake During Precision Ketogenic Therapy

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FOODOMICS DATABASE: A NEW TOOL FOR ESTIMATING MOLECULAR PROFILES IN NUTRIENTS INTAKE DURING PRECISION KETOGENIC THERAPY

LUJIA YANG

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2018

To Haiming

ACKNOWLEDGMENTS

I would like to thank my mentor, Dr. Peggy Borum, for her support and guidance which helped me learn a lot through two years. I would like to thank all my committee members, Dr. Anne Mathews, Dr. Amarat Simonne and Dr. Daniel Maxwell, for their support and contribution to my project. I would like to thank Jurate Lukosaityte for her support to my work. I would like to thank undergraduate students, Alexa and Shreya for their contribution to my work. Lastly, I would like to thank my parents and my best friend, Haiming Ye, for their continued love.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 14

ABSTRACT ...... 15

CHAPTER

1 INTRODUCTION ...... 17

Diet Therapy ...... 17 Precision Ketogenic Therapy ...... 18 Data Collections Methods for Dietary Intake ...... 21 Molecular Compounds Associated with Seizure Responses ...... 22 USDA SR 28 Database...... 25 Hypothesis ...... 26

2 CREATION OF FOODOMICS DATABASE ...... 27

Background ...... 27 Methods ...... 27 Nutrition Facts Label Database ...... 27 The structure of NFL database ...... 28 NFL data collection ...... 29 NFL data entering and auditing ...... 29 Create Foodomics Database ...... 30 Observations from USDA SR 28 ...... 30 The assumptions based on USDA SR 28 ...... 31 The purpose of using USDA SR 28 database to create foodomics database ...... 31 Molecular profile of individual USDA foods database ...... 31 Molecular profile of USDA composite generic foods database ...... 32 Foodomics Database ...... 33 Foodomics Database Results ...... 34 Discussion ...... 38 Conclusion ...... 40

3 APPLICATION OF FOODOMCIS DATABASE TO TWO PATIENTS ...... 68

Background ...... 68

Methods ...... 68 Study Design ...... 68 PKT Recipe ...... 70 Daily Intake Records ...... 70 Subjects...... 71 Classification of Macronutrients ...... 71 Classification of amino acids ...... 72 Classification of fatty acids ...... 73 Classification of carbohydrates ...... 75 Statistics ...... 75 Results ...... 75 Can the Foodomics Database Document the PKT Ratio and Macronutrients Intake Over Time on PKT? ...... 75 Can the Foodomics Database Document Intake of Each Amino Acid, Fatty Acid, and Individual Carbohydrate Over Time on PKT? ...... 76 Can the Foodomics Database Document the Intake of EAA Over Time on PKT? ...... 77 Can the Foodomics Database Show the Relationship of Each Amino Acid to Other Individual Amino Acids? ...... 78 Can the Foodomics Database Document Saturated Fatty Acids Profile/ Monounsaturated Fatty Acids Profile/ Polyunsaturated Fatty Acids Profile of Intake Over Time? ...... 78 Can the Foodomics Database Document the Daily Omega-6 to Omega-3 Ratio of Intake Over Time? ...... 81 Can the Foodomics Database Document the Difference Between Using the Net Carbohydrates Calculation Method and PKT Carbohydrate Calculation Method? ...... 82 Discussion ...... 83 Conclusion ...... 85

4 SUMMARY ...... 140

APPENDIX

A DOCUMENTATION FOR CREATING THE MOLECULAR PROFILE OF INDIVIDUAL USDA FOODS DATABASE ...... 142

B DOCUMENTATION FOR CREATING MOLECULAR PROFILE OF USDA COMPOSITE GENERIC FOODS DATABASE ...... 147

Single Composite Generic Food Database ...... 147 Mixed Composite Generic Food Database ...... 150 Molecular Profile of USDA Composite Generic Foods database ...... 151

C DOCUMENTATION FOR CUTTING 30 NUTRIENTS FROM MOLECULAR PROFILE OF INDIVIDUAL USDA FOODS DATABASE ...... 158

D FOODOMICS NUTRIENTS LIST ...... 161

E SUPPLEMENTAL TABLE OF NUMBERS OF COMPOSITE GENERIC FOOD IN DIFFERENT FOOD CATEGORIES ...... 164

F SUPPLEMENTAL TABLE OF FOODOMICS DATABASE DICTIONARY ...... 165

G SUPPLEMENTAL FIGURE OF NUTRITION FACTS LABEL OF “ROSS CARBOHYDRATE FREE” PRODUCT...... 178

LIST OF REFERENCES ...... 179

BIOGRAPHICAL SKETCH ...... 187

LIST OF TABLES

Table page

2-1 Table of summary of five database ...... 41

2-2 Table of summary of molecular profile of USDA composite generic foods database ...... 42

3-1 Data quality number ...... 86

3-2 Day quality number ...... 87

3-3 Demographic of two patients ...... 88

3-4 Classification of amino acids ...... 89

3-5 Classification of fatty acids ...... 90

3-6 Classification of carbohydrates ...... 92

E-1 The numbers of composite generic food in different food categories ...... 164

F-1 Foodomics database dictionary ...... 166

LIST OF FIGURES

Figure page

2-1 Foodomics database used to estimate the amount of molecular compounds in the foods consumed by patients...... 43

2-2 The keto-carbohydrate reflects the higher level of carbohydrate ...... 44

2-3 NFL leader distributes the collection lists, and the collectors go to the grocery stores to take the pictures for brand named products...... 45

2-4 CGFNDID_SOUCE is used to link the brand named product with composite generic food...... 46

2-5 Molecular profile of individual USDA foods database ...... 47

2-6 Amino acids profile of American cheese composite generic food...... 48

2-7 Saturated fatty acids profile of American cheese composite generic food...... 49

2-8 Monounsaturated fatty acids profile of American cheese composite generic food...... 50

2-9 Polyunsaturated fatty acids profile of American cheese composite generic food ...... 51

2-10 Carbohydrate families profile of American cheese composite generic food ...... 52

2-11 Sugar profile of American cheese composite generic food...... 53

2-12 The amount of each individual amino acid of “Kraft Deli Deluxe-Individually Wrapped American cheese (24 slices)”...... 54

2-13 Fatty acid families profile of American cheese composite generic food...... 55

2-14 The amount of each individual fatty acids intake in “Kraft Deli Deluxe- Individual Wrapped American Cheese (24 slices)” ...... 56

2-15 The amount of saturated fatty acids intake in “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”...... 57

2-16 The amount of fatty acid families intake in “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”...... 58

2-17 The amount of carbohydrate families intake of “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)” ...... 59

2-18 The amount of sugar profile of “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”...... 60

2-19 Comparison of the amount of amino acids between “Publix Deluxe Pasteurized Process American cheese” and “Kraft Deli Deluxe Individual Wrapped American Cheese (24 slices)”...... 61

2-20 Comparison of the amount of saturated fatty acid between “Publix Deluxe Pasteurized Process American cheese” and “Kraft Deli Deluxe Individual Wrapped American Cheese (24 slices)”...... 62

2-21 Saturated fatty acids profile of “Nutiva Organic Virgin Coconut Oil” composite generic food...... 63

2-22 The amount of each individual saturated fatty acids of “Nutiva Organic Virgin Coconut Oil”...... 64

2-23 Saturated fatty acids profile of MCT oil composite generic food for “Nestle Healthcare Nutrition MCT Oil”...... 65

2-24 The amount of saturated fatty acids of “Nestle Healthcare Nutrition MCT Oil”. .. 66

2-25 Comparison of saturated fatty acids content between “Nativa Organic Virgin Coconut Oil” and “Nestle Healthcare Nutrition MCT Oil”...... 67

3-1 Example of KG0232’s daily diaries records ...... 93

3-2 An example of PKT recipes ...... 94

3-3 The precision ketogenic therapy (PKT) ratio of KG0232 and his macronutrients intake in percent of calories for two years...... 95

3-4 The PKT ratio of KG0222, and his macronutrients intake in percent of calories for two years...... 96

3-5 KG0232’s each individual amino acid daily intake while on PKT...... 97

3-6 KG0232’s each excitatory amino acid daily intake while on PKT...... 98

3-7 KG0232’s each inhibitory amino acid daily intake while on PKT...... 99

3-8 KG0232’s carbohydrate families intake while on PKT...... 100

3-9 KG0232’s sugar profile intake while on PKT...... 101

3-10 Intake of KG0222’s each individual amino acid while on PKT...... 102

3-11 Intake of KG0222’s excitatory amino acid while on PKT ...... 103

3-12 Intake of KG0222’s inhibitory amino acid while on PKT ...... 104

3-13 KG0222’s carbohydrate families intake while on PKT...... 105

3-14 KG0222 sugar profile intake while on PKT...... 106

3-15 KG0232’s essential amino acids (EAA) intake while on PKT...... 107

3-16 KG0232’s branched chain amino acids (BCAA) intake while on PKT...... 108

3-17 KG0222’s EAA intake while on PKT...... 109

3-18 KG0222 BCAA intake while on PKT...... 110

3-19 KG0232’s gluconeogenic amino acids (GAA) and ketogenic amino acids (KAA) intake while on PKT...... 111

3-20 KG0222’s gluconeogenic amino acids (GAA) and ketogenic amino acids (KAA) intake while on PKT...... 112

3-21 KG0232’s fatty acids families intake while on PKT...... 113

3-22 KG0232’s saturated fatty acids intake while on PKT ...... 114

3-23 KG0232’s medium-chain saturated fatty acids intake while on PKT...... 115

3-24 Comparison medium-chain fatty acids intake of KG0232’s meals with coconut oil and meals with MCT oil...... 116

3-25 KG0232’s monounsaturated fatty acids intake while on PKT...... 117

3-26 KG0232’s polyunsaturated fatty acids intake while on PKT...... 118

3-27 KG0222’s fatty acids families intake while on PKT...... 119

3-28 KG0222’s saturated fatty acids intake while on PKT ...... 120

3-29 An example of KG0222’s intake on day 431 (during hospital stay)...... 121

3-30 Comparison of medium-chain fatty acids intake in KG0222’s meal with MCT oil and meals without MCT oil...... 122

3-31 KG0222 medium-chain saturated fatty acids intake while on PKT...... 123

3-32 KG0222’s monounsaturated fatty acids intake while on PKT...... 124

3-33 KG0222 polyunsaturated fatty acids intake while on PKT...... 125

3-34 KG0232’s omega-6 to omega-3 ratio while on PKT...... 126

3-35 KG0232’s intake of fatty acids with 18 carbon chain numbers in omega-3 fatty acids while on PKT...... 127

3-36 KG0232’s intake of fatty acids with 18 carbon chain numbers in omega-6 fatty acids while on PKT...... 128

3-37 KG0222 intake of fatty acids with 18 carbon chain numbers in omega-3 fatty acids while on PKT...... 129

3-38 KG0222 intake of fatty acids with 18 carbon chain numbers in omega-6 fatty acids while on PKT...... 130

3-39 KG0222’s omega-6 to omega-3 ratio while on PKT...... 131

3-40 An example of KG0222’s intake on day 392 (before being hospitalized)...... 132

3-41 An example of KG0222’s intake on day 462 (after being discharged from hospital) ...... 132

3-42 Comparison of omega-6 and omega-3 fatty acids intake in KG0222’s meals with fish oil and meals without fish oil...... 133

3-43 Foodomics database lists the molecular intake for two patients during PKT. ... 134

3-44 Comparison of carbohydrate families and sugar profile intake of KG0222 when using net carbohydrate and keto-carbohydrate to calculate the total carbohydrate...... 138

3-45 Comparison of carbohydrate families and sugar profile intake of KG0232 when using net carbohydrate and keto-carbohydrate to calculate the total carbohydrate...... 139

A-1 To show the contribution of each molecular compound to the corresponding macronutrients, we first removed 30 nutrients ...... 146

B-1 Create a composite generic food file from molecular profile of individual USDA foods database...... 152

B-2 Give a food name for each brand named product, and search in the same food category of the molecular profile of individual USDA foods database by using that food names...... 153

B-3 Calculate the standard deviation (SD%) of nutrients in each molecular profile tab of the “Composite generic food_Food Name” file...... 154

B-4 Apple composite generic food test file...... 155

B-5 An example of creating the mixed composite generic food ...... 156

B-6 An example of calculating the amount of molecular compounds found in brand named products ...... 157

C-1 Nutrients are not list in the molecular profile of individual USDA foods database...... 160

D-1 Foodomics database nutrients list ...... 161

G-1 Nutrition facts label of “Ross Carbohydrate Free”...... 178

LIST OF ABBREVIATIONS

BCAA Branched Chain Amino Acids

CEAA Conditionally Essential Amino Acids

EAA Essential Amino Acids

EFA Essential Fatty Acids

FDA Food and Drug Administration

IOM Institute of Medicine

KD Ketogenic Diet

LCFA Long-Chain Fatty Acids

MCSAFA Medium-Chain Saturated Fatty Acid

NDSR Nutrition Data System for Research

NEAA Nonessential Amino Acids

NEFA Nonessential Fatty Acids

NFL Nutrition Facts Label

PKT Precision Ketogenic Therapy

PUFA Polyunsaturated Fatty Acid

RDA Recommended Dietary Allowance

SAFA Saturated Fatty Acid

USDA United States Department of Agriculture

USDA SR Agriculture National Nutrient Database for Standard Reference

VLCFA Very Long-Chain Fatty Acids

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

FOODOMICS DATABASE: A NEW TOOL FOR ESTIMATING MOLECULAR PROFILES IN NUTRIENTS INTAKE DURING PRECISION KETOGENIC THERAPY

Lujia Yang

May 2018

Chair: Peggy Borum Major: Food Science and Human Nutrition

Precision Ketogenic Therapy (PKT) is a high fat, low carbohydrate, adequate- protein diet that is prescribed using the macronutrient information on nutrition facts labels (NFL) of brand named products in order to help patients control seizures. Recent studies have shown that molecular compounds (individual amino acids, fatty acids, and carbohydrates) are associated with seizure control. We need to know what kind of molecular compounds our patients are consuming in order to ask research questions about the potential association of molecular compounds in the diet with disease. Using the daily intake records collected from patients and the NFL, we know their macronutrient intake. However, we do not have specific information from the NFL about the intake of individual amino acids, individual fatty acids, and individual carbohydrates.

There is no publicly available database that can be used to estimate the molecular compounds consumed daily by patients. Therefore, we used the NFL and the USDA SR

28 database to create the foodomics database, which lists the amount of molecular compounds of brand named products. Daily dietary intake data during a two-year PKT treatment period for an orally fed patient and a tube fed patient were used to illustrate the use of the foodomics database to document the intake of molecular compounds for

the patients on PKT. The foodomics database was applied to a series of representative research questions illustrating its use with actual data collected for two years from two patients living at home with their families. The foodomics database allows researchers to estimate the difference between molecular compounds in similar types of food and also gives the opportunity for researchers to address questions concerning the association between diet at the molecular compound level and disease.

CHAPTER 1 INTRODUCTION

Diet Therapy

The quote by Hippocrates, “Let food be thy medicine and medicine be thy food.”

[1], is commonly heard by doctors and nutritionists. Humans obtain nutrients and energy from their daily diet intake. Not only does diet provide sufficient energy for maintaining the basic physiology and metabolism of life, but it also protects from disease invasion.

Balanced nutrition is the vital principle to maintain good health. Too much or too little food could cause disease, or even aggravate an already present disease. Long-term improper diet intake could cause chronic diseases, such as obesity, hypertension, and cardiovascular diseases. As of 2012, 117 million people were diagnosed with at least one chronic disease [2], and 86% of the annual healthcare expenditure in the United

States is used for people with chronic and mental health problems [3]. Although medicine is still the primary way to control and treat disease, its unsatisfying effects have prompted more people to search for a way to use foods as medicine.

Diet therapy is a way to use the nutrients in the food to treat diseases, recover from illness and improve health [4]. During such therapy, researchers or dietitians ask people to record their daily intakes. This information helps them monitor the patients’ diets, make the appropriate adjustments, and understand the correlation of food intake with diseases [5]. Ketogenic diet (KD) is considered a diet therapy to treat people with seizures. Current KD uses the ratio between fat and protein plus carbohydrate, protein requirements, and calorie requirements to prescribe the diet. As they learned more about nutrition, dietitians began to emphasize the importance of the quality of nutrient intake rather than only quantity of nutrient intake. The Institute of Medicine (IOM) of the

National Academies establishes reference values for some main nutrients, such as

Recommended Dietary Allowance (RDA), to help the healthy people maintain good health [6]. Unfortunately, there are no clear guidelines to teach people with chronic disease about how many nutrients they need and what kind of nutrients they need [7].

Many researchers have recognized these issues and tried to get the answers from animal studies and clinical studies. However, the reality is that current and past studies usually do research focusing on one or several nutrients. Capozzi et al. mentioned that food contains all kinds of nutrients, and researchers should not just study the target nutrients to know their association with disease [8]. It is very difficult to respond to the issues regarding nutrient quality intake because of limited available data sources [7].

My research interests are focused on establishing a food database listing comprehensive nutrient data and using this database as an effective tool to estimate the amount of nutrient compounds in patients’ daily intakes. My personal research involves creating a database of the molecular compounds making up the macronutrients and illustrating its use in documenting nutrient intake of two patients on Precision Ketogenic

Therapy (PKT) as a proof of principle. The created database is available to all researchers to ask questions about the association of food with disease or other relevant questions.

Precision Ketogenic Therapy

Epilepsy is a chronic brain disorder caused by recurrent and unpredictable interruptions of normal brain function [9, 10]. According to Centers for Disease Control and Prevention (CDC), as of 2017, there are at least 3.4 million people with epilepsy in the US [11]. For those patients who failed to improve with antiepileptic drugs, ketogenic diet is often considered.

Ketogenic diet (KD) is the high fat, low carbohydrate, and adequate protein intake diet [12, 13]. It has been developed as a treatment for children with epilepsy since the 1920s [12]. The classical ketogenic diet uses ratios such as 3:1 or 4:1, that is, three or four grams of fat to one gram of protein plus carbohydrate [14, 15].

KD has been used for a century to treat people with seizures. Gulep and Maries,

Parisian physicians, were the first to report fasting as a treatment for epilepsy [16, 17].

In the early 20th century, American osteopathic physician, Dr. Hugh W. Conkin was the first to document the positive effect of fasting on seizures [18]. In 1921, Dr. Woodyatt found that the acetone and beta-hydroxybutyric acid levels would change during the fasting period or when people had a diet with a high proportion of fat and a low proportion of carbohydrate [19]. In the same year, Dr. Wilder proposed using the diet as an alternate method to achieve ketosis and gave a name to this high-fat diet as the ketogenic diet. He then proposed to use this diet to treat people with epilepsy [20]. In

1924, Dr. Peterman first formulated the principle for calculation of KD ratios, which were similar to today's KD ratio calculations. Peterman also documented care guidelines for the families [21]. Subsequently, these guidelines were followed up by Talbot et al. from

Harvard and McQuarrie and Keith studies [17, 21].

Because consuming the KD was much more difficult than taking the medicines,

KD was gradually replaced with antiepileptic drugs at the end of 20th century. This situation did not change until the story of Charlie, a two-year-old boy with refractory epilepsy, appeared on television [17]. He was initiated on KD by Dr. Freeman at Johns

Hopkins Hospital and quickly became seizure free. His father then established the

Charlie Foundation and directed a movie based on his son’s experience. Charlie’s story

gave hope to the patients who failed with antiepileptic drugs, and also re-attracted scientists’ attention. In the following years, KD has been widely applied to patients with epilepsy [17].

Although the mechanism was still unclear, the efficiency of KD was confirmed by many clinical studies [22-24]. In the study conducted by Neal et al., it was reported that

38% children with epilepsy had more than 50% seizure reduction compared to the control group after three months on KD, and 7% of pediatric children with epilepsy had a seizure reduction of more than 90% [23]. In the study conducted by Thammongkol et al., 48% of patients with epilepsy had responded to the KD with a seizure reduction of more than 50% at three months [24].

Precision Ketogenic Therapy (PKT) is a stricter form of KD. In 1995, the PKT program was established at the University of Florida. Different from traditional KD, PKT uses the macronutrient information on the nutrition facts label (NFL) of brand named products to prescribe the meals [7, 25]. As manufacturers keep updating the nutrient content of labels, we need to collect the latest nutrient data listed on the nutrition facts label in a timely manner to provide the most accurate dietary prescriptions for the patient [7, 25]. PKT requires a precise controlling of the amount of food consumed by a patient. Caregivers are required to weigh food to the nearest tenth of a gram. With PKT, a personalized diet prescription is developed for each patient based on their nutritional needs, food preferences, and household incomes [7, 25].

Caregivers need to be educated before a patient’s initiation of PKT, on topics including what is PKT, how to prepare the meals, how to weigh the food, and how to record the daily data. During PKT, caregivers need to record the patients’ daily intake,

urine specific gravity, urine ketones, seizure frequency, seizure length, medication intake, etc. To monitor the patients’ nutritional status and the diet compliance, patients need to attend the clinic regularly. During the clinic, caregivers are asked to submit all the records since the last clinic visit and a stool sample collected a few days before the clinic visit. Patient’s weight, height, vitals, fasting blood, etc. are collected to help us precisely know the patients’ health status, make appropriate diet adjustments, and continue other studies in the future.

My research focuses on using patient’s daily intake records and nutrition facts label of brand named products to estimate patients’ nutrient intake during the PKT.

Data Collections Methods for Dietary Intake

People consume different foods every day. It is difficult to know exactly what kind of food and how much food they eat based on their memory. To understand the association between disease and food intake, nutritionists suggest using dietary assessments to record the people’s food intake, which includes 24-hour dietary recall, dietary record, dietary history, and food frequency questionnaire [5, 26]. Dietary record is a method to account for food intake during a specific period of time, and it is widely used in the clinical and epidemiologic studies [5, 26]. For individuals and populations with special nutritional needs, dietary record is considered to be an efficient way to estimate their food intake. Depending on the purpose of the study, people are required to record the dietary intake with as much detail as possible, including food name, the amount of food, recipes, even the brand named products, etc. [5, 26]. Based on the records, researchers use the relevant nutrition database to estimate the amount of nutrients that people have consumed over a period of time, make the appropriate diet adjustment, and/or do the study on association between food and disease [5, 26].

Nutrition Data System for Research (NDSR) is a food database that is used by researchers to estimate people’s nutrient intake [27]. However, the database provider requires the users to pay a high cost every year, which results in some low-capital users to stop the usage of the NDSR database. To calculate the KD ratio, many researchers studying KD are using the online database with two different names known as

KetoDietCalculator and MyKetoPlanner. This database was originally developed by

Beth Zupec-Kania who is working at the Charlie Foundation [28]. KetoDietCalculator or

MyKetoPlanner provides a convenient way to calculate the KD ratio free of charge, but does not document every detail as much as the United State Development of

Agriculture National Nutrient Database for Standard Reference (USDA SR) database. In addition, KetoDietCalculator or MyKetoPlanner is not available for the public. It only allows people who have a healthcare license to utilize it. This would block the way for many researchers who want to study more about KD.

KetoDietCalculator uses net carbohydrate to calculate the carbohydrate which subtracts fiber and sugar alcohol from total carbohydrate [29]. Net carbohydrate is a marketing term and not a defined scientific term recognized by the Food and Drug

Administration (FDA), USDA, etc. It is not allowed by law to be used on the Nutrition

Facts label. The use of the term net carbohydrate is a controversial issue in the field.

Thus, there is a need for creating a publicly available database to calculate the KD ratio, estimate the patient’s daily intake and provide a way to perform the calculations using total carbohydrates in KD.

Molecular Compounds Associated with Seizure Responses

The PKT program at the University of Florida is committed to improving patients’ nutritional status and treating epilepsy by regulating the ratio of fat to protein and

carbohydrate as well as asking research questions. PKT provides the optimal diet prescriptions based on each patient’s own situation. However, patients with the same

PKT ratio may have different seizure responses [7, 25]. Although different genetic information and disease severity is specific to each patient, a possible way to explain the variety of seizure responses could be that in spite of having the same ratio the molecular components of the diet consumed by each patient are different. Some recent studies have pointed out that intake of molecular compounds is associated with seizure response. The molecular compounds include the amino acids in the protein, the fatty acids in the fat, and the individual carbohydrates in the carbohydrate.

One study demonstrated that a boy with epilepsy before a diet rich in polyunsaturated fatty acids (PUFAs) had eight seizures per day at the age of six. By consuming the enriched PUFAs diet for three months, his seizures became completely controlled and his cognitive abilities dramatically improved [30]. In the clinical study conducted by Schlanger et al., they reported seizure frequency and seizure length reduction in patients that had been on a six-month diet enriched with omega-3 fatty acids [31]. In an animal study conducted by Lauritzen et al., they pointed out that linolenic acid protected against seizures, but this effect is not true for palmitic acid and arachidonic acid [32].

Besides fatty acids, recent studies [33-36] also show positive seizure responses to amino acids. In a study conducted by Evangeliou et al., they reported that seventeen patients that had been on KD for six to twenty-four months, were given branched-chain amino acids as their supplements for three months [33]. Evangeliou et al. found that in two patients, seizures improved from 70% to 90%. One patient’s seizures improved by

50%, and another patient became seizure free. In addition, four patients had a decrease in medication [33]. In a rat study of Gietzen et al., they showed that deficiency in essential amino acids (EAA) could increase seizures by activating anterior piriform cortex, the seizure stimulation in the limbic brain [34]. Gietzen et al. hypothesized that the insufficiency of EAA intake leads to a decrease in synthesis of nonessential amino acids (NEAA), and some NEAAs have anti-epilepsy effects [34]. Glutamate is both a

NEAA and an excitatory amino acid [37]. Glutamate is released from neuronal presynaptic terminals of the synapse and subsequently taken by transporters into astrocytes where it is then converted to glutamine and ultimately converted to GABA

[22, 34]. GABA is an inhibitory amino acid and is known to control seizures [38]. Thus, insufficient EAA intake could influence the GABA synthesis, which influences seizure responses. Aspartate is the inhibitor of glutamate decarboxylase, which is the enzyme that converts the glutamine to GABA [35]. Theoretically, increasing the aspartate intake would affect the synthesis of GABA and thus influence the results of epilepsy [39].

These studies [30-36] indicated that molecular compound intake could influence seizure responses. This information has led us to ask what kind of nutrients our patients have taken in. According to the patients’ daily intake records, what we do know is patients’ macronutrient intake by using NFL. However, we do not have specific information of other nutrients intake (amino acids, fatty acids, etc.) from NFL. Direct analytical analysis of food via mass spectrometry is technically feasible, but the data are not complete. Without any research or reliable database listing all nutrients in the brand named products (as we use brand named products to prescribe diet), our PKT team cannot have any other nutrient information beyond the nutrition facts label. Therefore,

we need to create a comprehensive food database before we can continue to ask relevant questions.

USDA SR 28 Database

The United States Department of Agriculture National Nutrient Database for

Standard Reference (USDA SR) is the primary food composition source data in the US

[40]. The purpose of USDA SR database is to provide the nutrient contents for food. It is the basic infrastructure for food policy, research, diet prescription practice and nutrition monitoring [40]. The first version was released in 1963. The current version, USDA SR

28, was released in September 2015 and slightly revised in May 2016 [40, 41].

USDA SR 28 database reports up to 150 nutrients for 8987 foods. The data in

USDA SR 28 are collected from manufacturers’ reports, literature, government agencies and research projects conducted by USDA [40]. The nutrient value is reported as the content in 100 grams of food based on laboratory analysis or calculated by appropriate algorithms, factors or recipes [40].

Most of the foods in the USDA SR 28 database are generic foods, and thus, cannot be used to estimate the nutrient compounds in brand named products directly.

However, this database could tell us the relationships of amino acids to each other/ relationships of fatty acids to each other/ and relationships of individual carbohydrates to each other. In this study, we defined molecular profiles as the profile of amino acids in the protein, fatty acids in fat, and individual carbohydrates in total carbohydrate. By using molecular profile of the corresponding macronutrient from the USDA SR 28 database, along with the macronutrients data listed on NFL, we can estimate the quantity of molecular compounds in the brand named product. Only when we know the amount of molecular compounds in the brand named products that patients have eaten,

can we know what nutrients and what amounts of the nutrients patients have consumed during a time period. Subsequently, we could ask questions about PKT such as mechanisms of PKT, the association of seizure responses with molecular compounds intake etc. Therefore, the primary purpose of this study is to establish a comprehensive food database so that we can know the exact kinds of nutrients in the brand named products that the patient consumed and use this database to estimate the amount of molecular compounds in the patient daily intake while on PKT.

Hypothesis

A comprehensive and reliable food database, foodomics database-could estimate the amount of molecular compounds in patients’ daily dietary intake.

Objective 1: To create foodomics database listing molecular compounds for brand named products by using the nutrition facts label and the molecular profile data in

USDA SR 28.

Objective 2：Apply the foodomics database to the daily intake of two pediatric patients on precision ketogenic therapy to examine whether this database could be used as a tool to estimate the amount of each amino acid, fatty acid, and carbohydrate consumed by the patients as well as estimate the amount of molecular compounds grouped by different criteria.

CHAPTER 2 CREATION OF FOODOMICS DATABASE

Background

The previous chapter mentioned that studies have shown that the molecular compound intake is associated with seizure control [30-34]. PKT uses the macronutrient information on the NFL to prescribe the diet prescription, but the NFL does not provide information on molecular compounds, which forces us to stop and create a comprehensive food database before we continue to ask relevant questions.

To know the daily intake of molecular compounds in patients with PKT, we first need to know the amount of molecular compounds in the food consumed by the PKT patients. The objective of this chapter is to create a database, Foodomics database, by using the amounts of macronutrients on the nutrient facts label (NFL) and molecular profiles from USDA SR 28 database (Figure 2-1).

Methods

Nutrition Facts Label Database

NFL database was created and has been maintained by our lab since 1996. It contains brand named products and fresh products that our patients consumed. One of the purposes of NFL database is to develop PKT diet prescription for patients by using the nutrients information on the NFL. As manufacturers frequently update nutritional information on labels, we update the NFL database once or twice a year to provide the most accurate nutrient prescription for our patients. Another purpose of NFL database is to provide the amount of macronutrients as the determinant factors for the amount of molecular compounds in the foodomics database.

The structure of NFL database

The NFL database was created as an Excel file, which provides information about the products consumed by patients including nutrient content information, product prices, package sizes, manufacturers, grocery stores, collectors, etc. In the NFL database, each unique ID number indicates the specific brand named product. For example, the NDID00056 refers to “Gerber Second Food All Nature Baby Food-Apple

Blueberry (jar)”. All the products in the NFL database are grouped into different food categories according to the food categories in the USDA SR database. To facilitate the data collection and management, we added a column to distinguish the food types. The term “Label” refers to the brand named products while the term “USDA” refers to the fresh products. To facilitate PKT formulation and household measurement, we also added three additional columns for each product to indicate the percentage of macronutrients (keto-carbohydrate, protein, and fat).

To ensure our PKT team is not underestimating the carbohydrate intake by patients, we use the keto-carbohydrate to represent the grams of carbohydrate per 100 g of food. Keto-carbohydrate reflects the higher level of carbohydrate that is determined by calculating carbohydrate content by grams of weight or by calories. Carbohydrate content calculated by grams of weight is using the numbers reported on the NFL (Figure

2-2). Calories from protein, fat and carbohydrate are 4 calories per gram, 9 calories per gram, and 4 calories per gram, respectively. The way to calculate the amount of carbohydrate by calories is to use the total calories subtracted the calories from the protein and fat, and then divided by 4 (carbohydrate (g) = [Total Calories (kcal) – Protein

(g)*4-Fat (g)*9]/4). The yield higher number of carbohydrate is used to represent the percentage of carbohydrates in the product (Figure 2-2).

In general, brand named products list the information for macronutrients and a few key vitamins and minerals. However, some manufacturers may report more nutrients information on the NFL. We created 147 columns to cover the different nutrients that could be obtained from the market. Other information in the NFL database includes manufacturers, data collection time, collectors, etc.

NFL data collection

The NFL collection leader distributes food collection lists to collectors and asks them to collect the food from Gainesville, Florida. After receiving the lists, the collectors are asked to visit different grocery stores and take pictures of products, including the nutrition facts labels, package sizes, prices, control numbers, manufacturer names, store names, and collection dates (Figure 2-3). For brand named products that could not be found at the local grocery stores, we search through the official websites of those products to find their availabilities at local stores. If the products are not available at the local grocery stores, we use the nutrition facts labels found on the official websites. If the products are discontinued during the time of the product collection, “RNDID” would be added in the front of its ID number.

The fresh products, such as avocado and beef, usually not given nutrition facts label. Collectors need to find the generic equivalent the USDA product from USDA SR database, and put nutrition data into the NFL database.

NFL data entering and auditing

The lab member then needs to upload the collected pictures to the designated file in the lab computers. One lab member enters the data for the corresponding product in the NFL database based on the information from the collected pictures. Another lab

member then audits the entered data. The NFL collection leader is responsible for maintaining the data quality and managing food lists and collections (Figure 2-3).

Lab members use the data in the NFL database to create the PKT recipes for patients, and caregivers shop at the grocery stores based on the ingredients lists on the recipes.

Create Foodomics Database

Observations from USDA SR 28

As mentioned earlier, the USDA SR 28 database was released in 2015 and it provides 150 nutrients for 8987 foods [40]. Since most products in the USDA SR 28 are generic foods, it cannot be used to directly estimate the amount of molecular compounds in brand named product. The nutrient values in the USDA SR 28 were obtained from literature, manufacturer report, government agencies, and research projects conducted by the USDA [40].

The USDA SR 28 manuscript mentions that some nutrients are measured directly by different labs, and other nutrients are calculated using appropriate algorithms, factors, etc. [40]. In addition, they reported that one product may not contain all 150 nutrients, thus, empty cells could be found in the USDA SR 28 database. In total,

3672 foods in the USDA SR 28 are directly analyzed.

It is also notable that analyzes of amino acids could be used to calculate the amino acids data for the foods with similar names since they share a similar pattern of amino acids [40]. This is applicable to the fatty acids and individual carbohydrates as well, based on the table named “Data Derivation Code Description” in the USDA SR manuscript [40]. Therefore, the pattern of molecular compounds in corresponding macronutrients are similar for the foods with similar names.

The assumptions based on USDA SR 28

Based on the observations mentioned above, we made the two following assumptions: 1) the relationships of molecular compounds for similar foods can be combined to create a molecular profile for a composite generic food; 2) the molecular profile of a composite generic food can be used to calculate the molecular compounds for each brand named food in the foodomics database.

The purpose of using USDA SR 28 database to create foodomics database

Although the data in the USDA SR 28 database cannot be directly used to estimate the amount of molecular compounds in the brand named product, sufficient data allows us to understand the contribution of each amino acids/ fatty acid/ individual carbohydrate to the corresponding macronutrients in each USDA products. This provides the foundation to create the composite generic food used to estimate the amount of brand named product. Another purpose is using the data in the USDA SR 28 database to provide the nutrients information for the fresh products.

Molecular profile of individual USDA foods database

In order to illustrate the contribution of the amino acid in the protein, the fatty acid in the fat, and each individual carbohydrate in the carbohydrate to each USDA product, we convert the unit of molecular compounds from g per 100 g of food to the g per 1 g of macronutrient. Because the macronutrients and the molecular compounds are analyzed by different assays and different labs, the sum of the molecular compounds is not always equal to the macronutrients (e.g. the sum of amino acids is not equal to the protein) [40].To show the pattern of molecular compounds in the corresponding macronutrients, we first calculate the sum of each molecular compound in protein, fat, or carbohydrate, and then rescale each molecular compound in the corresponding

macronutrients (e.g. (amino acids *protein) / the sum of amino acids)). Next, the unit of molecular compounds is changed from g of 100 g of food to g of 1 g corresponding macronutrients to show the relationship of each molecular compounds with their macronutrient. All the data are stored in the molecular profile of individual USDA foods database (Figure 2-5).

Molecular profile of USDA composite generic foods database

The documented main difference between various brand named products of similar food types is the macronutrient contents (e.g., different types of American cheese). This difference directly relates to the difference in the amount of molecular compounds of various brand named products since they share the similar pattern of molecular compounds. As mentioned earlier, the food products with similar names in the USDA SR 28 database came from different origins. In addition, one food product may be missing an amino acid profile, while others (with similar food names) may be missing a fatty acid profile. It is biased to select only a specific row of data to estimate the contents of molecular compounds in the brand named product because it is hard to know which data are better than the others. Therefore, we selected all the data with similar food names to create the composite generic food that defined the generic equivalent of brand named product with relatively complete molecular profile. The composite generic food illustrates the percentage of each amino acid in the protein, the percentage of fatty acid in the fat, and the percentage of each individual carbohydrate in the carbohydrate. After pulling out the data of the food with similar names, the average and SD% of each nutrient in each molecular profile tab is calculated. Next, the outlier(s) with extremely high or extremely low values are deleted. The SD% is defined as the percentage of the standard deviation divided by the mean [42], which helps us decide

how many composite generic foods needs to be created. If the SD% is less than 15%

(amino acids profile, fatty acid families profile and fatty acid profile) or 25%

(carbohydrate families profile or sugar profile), this means all the data are very similar, and thus, only one composite generic food is needed for a brand named product.

However, when SD% is higher than 15% or 25%, more than one composite generic food is needed. The data is sorted beginning with the nutrient that has the highest SD% value, and is then separated the data into different groups based on that nutrient. The average and SD% needs to be re-calculated in each group. Then, review the USDA food names in each group. When each group has the different USDA food names, give each group a potential composite generic food named. However, when the products’ names of each group overlap with the name of another group, we combine them into one group to create a composite generic food and assign a potential name to that composite generic food.

After finishing testing in all molecular profile tabs, a summary tab is created and the final result of each tab is copied into the summary tab. Then, count the number of composite generic foods that need to be created. The final averages of each molecular profile would be used for the molecular profile of composite generic food and all the

USDA products contributing to molecular profiles would be used for mineral, vitamin and others (e.g., cholesterol) data of composite generic food. Last, assign a reasonable name, four unique digital numbers to indicate the composite generic food. More details are documented in the APPENDIX A and APPENDIX B.

Foodomics Database

One of the purposes of the foodomics database is to estimate the amount of molecular compounds in brand named products. In order to create the foodomics

database, we created "CGFNDID_SOURCE" as a bridge linking each product with the appropriate composite generic food to do the calculation between macronutrients and molecular compounds (Figure 2-4).

In the foodomics database, the estimated amount of molecular compounds can be obtained by using the content of macronutrients on NFL and the percent of each amino acid in protein, percent of each fatty acid in fat, and percent of each carbohydrate in the keto carbohydrate of the composite generic food product (Figure 2-5).

Foodomics Database Results

We utilized 2371 products for 7980 entries collected in the NFL database from

1997-2016. The majority of products that we collected are within the following food categories: Sausages and Luncheon Meat (N=382), Dairy and Egg (N=357), Baby

Foods (N=341), Fats and Oil (N=201), and Vegetables and Vegetable Products

(N=180).

Most of the products list only about 7 nutrients (macronutrients and a few vitamins and minerals) on the NFL, but some companies could report more. We created

147 nutrient columns in the NFL database to cover the available nutrients in the market as much as possible (Table2-1).

The molecular profile of individual USDA foods database has 585 composite generic foods with 115 nutrients’ information (Table 2-1 and Table 2-2). The majority of composite generic foods are in Vegetable and Vegetable Products category (N=43), following by Fruits and Fruit Juices (N=35) and Dairy and Egg Products section (N=34)

(APPENDIX E).

The lower SD% shows the similarity of the data in each molecular profile from

USDA products with similar food names. At least 81.5 % of composite generic foods

have the SD% lower than 15% or 25% for each of the molecular profile. The profile of each molecular compounds for most of composite generic food are with the good SD%

(Table 2-2).

The foodomics database retains the same number of nutrients as the NFL database, but provides relatively complete molecular compound’s information for 2731 different products including 7980 entries. This database can be created using the content of macronutrients on NFL of brand named product, along with composite generic food that provides the pattern of molecular compounds in corresponding macronutrients. For example, “American Cheese” composite generic food has the molecular profile for different brand named American cheese products. In “American

Cheese” composite generic food, glutamate is the primary amino acid that contribute

20.5% to the amino acids profile (Figure 2-6). Palmitic acid (F16D0), oleic acid (F18D1) and linoleic acid (F18D2) are major contributors to the composite generic food of saturated, monounsaturated and polyunsaturated fatty acids, respectively (Figure 2-7,

Figure 2-8 and Figure 2-9). In addition, sugar and lactose are the main contributors to the carbohydrate families profile and sugar profile, respectively (Figure 2-10 and Figure

2-11). Kraft Deli Deluxe Individually Wrapped American Cheese (24 slices) is an

American brand named cheese product that provides only limited nutritional information.

The protein is listed as 15.79 g per 100 g of food for this brand named American cheese. Therefore, the estimated amount of glutamate in this brand named product is

3.231 g per 100 g of food by using the proportion of glutamate (20.5% of protein) in composite generic food multiplied the total amount of protein (Figure 2-12). Other

molecular compounds can be estimated in the same way (Figure 2-13, Figure 2-14,

Figure 2-15, Figure 2-16, Figure 2-17 and Figure 2-18).

As mentioned in the first chapter, increasing the intake of some amino acids could help control seizures. The foodomics database can show the difference in the amount of amino acids of similar brand named products and provides convenience for dietitians to choose the best product when they make the diet prescription. The same composite generic foods are shared by similar types of brand named products, but the different amounts of macronutrients cause a difference in the amount of molecular compounds.

Publix Deluxe Pasteurized Process American Cheese (24 slices) shares same

“American Cheese” composite generic food with Kraft Deli Deluxe Individually Wrapped

American Cheese (24 slices). The protein reported as 21.05 g per 100 g of food in

Publix Deluxe Pasteurized Process American Cheese (24 slices), but it is reported as

15.79 g per 100 g of food in Kraft Deli Deluxe Individually Wrapped American Cheese

(24 slices). Using the same amino acid profile shown above as an example, we can see that glutamate contributes 20.5% to the protein of American cheese composite generic food. Thus, by using the proportion of glutamate with amount of protein in two products, we can estimate that the differences in the amount of glutamate between the two products is 1.07 g per 100 g food (Figure 2-19).

Foodomics database can also show the difference in the amount of fatty acids in the similar brand named products. Palmitic acid (F16D0) and steric acid (F18D0) contribute 54.8% and 23.5% respectively to the saturated fatty acids profile in American cheese composite generic food (Figure 2-7).The saturated fatty acids reported as

20.126 g per 100 g of food in Publix Deluxe Pasteurized Process American Cheese (24 slices), but it is reported as 14.826 g per 100 g of food in Kraft Deli Deluxe Individually

Wrapped American Cheese (24 slices). Using the proportion of palmitic acids with amount of saturated fatty acids in two products, we can estimate the amount of palmitic acid is 11.019 g and 8.117 g respectively for Publix American cheese and Kraft

American cheese (Figure 2-20). The same method also can be applied to estimating the amount of stearic acid in two products (Figure 2-20).

Another example is Nutiva Organic Coconut oil which is often used for making

PKT meals and is rich in saturated fatty acid (SAFA). The only information about fatty acids on NFL for this product is the amount of total fat and saturated fatty acids. Figure

2-21 shows the saturated fatty acids pattern in the coconut composite generic food, and the contribution of lauric acid (F12D0) and myristic acid (F14D0) contribute to the total saturated fatty acids the most, then followed by palmitic acid (F16D0), caprylic acid

(F8D0) and caproic acid (F6D0). The amount of individual saturated fatty acids could be obtained by using the content of SAFA on NFL (83.678 g per 100 g of food) with the portion of corresponding fatty acid in coconut composite generic food (Figure 2-22).

Some studies mention that medium-chain saturated fatty acids could quickly break down into short chain fatty acids and raise the ketosis level in a short period of time, which may contribute to the seizure control [43, 44]. Nutiva Organic Coconut oil and Nestle MCT oil are rich in medium chain fatty acids, and they are often consumed by our patients. However, the limited nutrients information on NFL could not permit us to know which kind of medium-chain fatty acids and the content of each medium-chain fatty acid contained in these two brand named products. Thus, it is difficult to make the

right decision when crafting the patient’s recipe. The emergence of the foodomics database gives very clear answers to the questions above. Each individual saturated fatty acid information of these two products can be obtained from the foodomics database by using the amount of saturated fatty acids on the NFL, along with their own composite generic food (Figure 2-21, Figure 2-22, Figure 2-23 and Figure 2-24). Figure

2-25 shows that two products are abundant with medium-chain saturated fatty acids, but coconut oil product has more fatty acids with 12 carbon chain numbers than MCT oil.

On the contrary, the major contributors to the medium chain fatty acids of MCT oils are fatty acids with 8 and 10 carbon chain numbers.

Foodomics database allows lab members to compare the amount of molecular compounds between similar brand named products.

Discussion

The molecular compounds’ data of brand named products in the foodomics database allows lab members or dietitians to choose the optimal product for the patient through a full understanding of the differences in molecular compounds between similar products. In addition, foodomics database also permits lab members to make recipes with molecular compound information which responds to the concept referred to by dietitians regarding nutrient quality intake rather than nutrient quantity intake.

Since there is no publicly available brand named products molecular compound database that can be used to evaluate the accuracy of database, the reliability of the data source became very important. Data from the USDA SR database and NFL data are considered reliable as the data are supervised by the USDA and the FDA, respectively.

To ensure the accuracy of the data in NFL database, well-trained NFL collection teams, efficient collection methods, and accurate data input are needed. Instead of handwriting the numbers from the NFL, NFL team members are using phone cameras to take the pictures for the products that need to be collected. They bring all the pictures to the lab. The NFL team leader checks each collected picture to ensure the collected picture is the correct product. The correctly entered data plays a crucial role on the reliability of the NFL database. Thus, before entering every available data into the NFL database, the lab member needs to first recheck if the input photo corresponds to the product that needs to be entered. Next, the auditor needs to recheck each datum that is entered by the previous person to ensure the accuracy of data. As a result, it greatly reduces the human error, making the entire NFL database more reliable.

Besides ensuring the accuracy of the data, it is also necessary to update the NFL database in a timely manner. As manufacturers routinely update NFL, this can result in different molecular content in the same brand named product since the determinant is macronutrients’ content on NFL. Therefore, if the NFL database is not updated in a timely manner, this may have a direct impact on the accuracy of diet prescriptions and affect the patients’ seizure response and nutritional status. Therefore, to provide latest and accurate information, the lab has kept collecting NFL data and updating the database once or twice a year.

Although the foodomics database successfully documents the molecular compounds for brand named products, it is still cannot avoid some limitations. The molecular data for brand named products in the foodomics database is based on estimates, so it is impossible to achieve the same accuracy as using chemical analysis.

The 15% or 25% of SD% and review of the USDA names are used to determine the numbers of the composite generic food. These principles may also affect the accuracy of the database due to the human errors. We recognize that the values used for SD% were arbitrary, but this is the best way we can do at the moment. In the subsequent update database, we will propose more comprehensive methods to improve the accuracy of the database.

Conclusion

The foodomics database listing the molecular compounds for brand named products has been created by using the nutrient information on the NFL and the publicly available generic equivalent molecular database that is USDA SR 28 database.

Table 2-1. Table of summary of five database USDA Molecular profile of Molecular Profile of USDA NFL Foodomics individual Composite Generic Foods Database Database USDA Foods database Database # of Nutrients 150 121 115 147 147 # of Products 8789 8987 585 2731 2731 Macronutrients g/ 100 g of g/ 100 g of food - g/ serving g/ 100 g of food size food Amino acids profile g/ 100 g of g/ 1 g of protein g/ 1 g of protein g/ serving g/ 100 g of food size food Saturated fatty acids g/ 100 g of g/ 1 g of fat g/ 1 g of fat g/ serving g/ 100 g of food size food Monounsaturated g/ 100 g of g/ 1 g of fat g/ 1 g of fat g/ serving g/ 100 g of fatty acids food size food Polyunsaturated g/ 100 g of g/ 1 g of fat g/ 1 g of fat g/ serving g/ 100 g of fatty acids food size food Trans-fatty acids g/ 100 g of g/ 1 g of fat - g/ serving g/ 100 g of food size food Saturated fatty acids g/ 100 g of g/1 g of saturated fatty acid g/1 g of saturated fatty acid g/ serving g/ 100 g of profile food size food Monounsaturated g/ 100 g of g/1 g of monounsaturated g/1 g of monounsaturated fatty g/ serving g/ 100 g of fatty acids profile food fatty acid acid size food Polyunsaturated g/ 100 g of g/1 g of polyunsaturated g/1 g of polyunsaturated fatty g/ serving g/ 100 g of fatty acids profile food fatty acid acid size food Carbohydrate g/ 100 g of g/1 g of keto-carbohydrate g/1 g of keto-carbohydrate g/ serving g/ 100 g of families food size food Sugar profile g/ 100 g of g/1 g of sugar g/1 g of sugar g/ serving g/ 100 g of food size food Mineral, Vitamin, variable /100 g/100 g of food variable /100 g of food variable/ variable /100 and others g of food serving g of food size

Table 2-2. Table of summary of molecular profile of USDA composite generic foods database # Composite generic % of Composite generic foods foods Total composite generic food 585 - SD% of amino acids profile < 15 % 556 95.0% SD% of fatty acid families profile < 15 % 548 93.7 % SD% of fatty acids profile < 15 % 520 88.9% SD% of carbohydrate families profile 557 95.2% <25 % SD% of sugar profile < 25 % 566 95.8%

Figure 2-1. Foodomics database used to estimate the amount of molecular compounds in the foods consumed by patients. It is created by using the amounts of macronutrients on nutrition facts label (NFL) and molecular profiles from USDA SR 28 database.

Figure 2-2. The keto-carbohydrate reflects the higher level of carbohydrate, which is determined by calculating the carbohydrate content by grams of weight or by calories

Figure 2-3. NFL leader distributes the collection lists, and the collectors go to the grocery stores to take the pictures for brand named products. Then, the collectors need to upload all the pictures into the lab computer, enter and audit the information of brand named products into NFL database. Lab members use the NFL database to make the recipe for patients, and caregivers shop at the grocery stores based on the ingredient lists on the recipes. “Hute’s Tomtato Sauce” image credits: https://www.walmart.com/ip/Hunt-s-Tomato-Sauce-105-oz/26832098.

Figure 2-4. CGFNDID_SOUCE is used to link the brand named product with composite generic food. It is used by R script to create the foodomics database by doing the scaling between the amount of macronutrients of brand named product and the molecular profile in composite generic food.

Figure 2-5. Molecular profile of individual USDA foods database is the database illustrating the pattern of molecular profiles in their corresponding macronutrients for 8789 foods from USDA SR 28 database by converting the unit of g per 100 g of food to g per 1 g of macronutrient. Then, select all related equivalent generic products from molecular profile of individual USDA foods database to create the composite generic food that is the average of nutrients of all equivalent products, and register for each brand named product. By using information on NFL along with composite generic food, a relatively complete foodomics molecular profile database can be accomplished.

Figure 2-6. Amino acids profile of American cheese composite generic food; GLU: glutamate; PRO: proline; LEU: leucine; LYS: lysine; ASP: aspartate; VAL: valine; SER: serine; ILE: isoleucine; PHE: phenylalanine; TYR: tyrosine; ALA: alanine; ARG: arginine; HISTN: histidine; MET: methionine; GLY: glycine; TRP: tryptophan; CYS: cysteine. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american- chee-396.aspx.

Figure 2-7. Saturated fatty acids profile of American cheese composite generic food; F- number indicates the carbon bonds; D-number indicates the double bonds in the fatty acid. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-8. Monounsaturated fatty acids profile of American cheese composite generic food. F-number indicates the carbon bonds, D-number indicates the double bonds in the fatty acid; C indicates cis-fatty acid. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli- deluxe-american-chee-396.aspx.

Figure 2-9. Polyunsaturated fatty acids profile of American cheese composite generic food; F-number indicates the carbon bonds, D-number indicates the double bonds in the fatty acid; CN3 indicates the fatty acids is the omega-3 fatty acids; CN6 indicates the fatty acids is the omega-6 fatty acids. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-10. Carbohydrate families profile of American cheese composite generic food; SUG: sugar. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-11. Sugar profile of American cheese composite generic food; LAC: lactose; GALS: galactose; SUCS: sucrose; MALS: maltose; FRUS: fructose; GLUS: glucose. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-12. The amount of each individual amino acid of “Kraft Deli Deluxe-Individually Wrapped American cheese (24 slices)”. GLU: glutamate; PRO: proline; LEU: leucine; LYS: lysine; ASP: aspartate; VAL: valine; SER: serine; ILE: isoleucine; PHE: phenylalanine; TYR: tyrosine; ALA: alanine; ARG: arginine; HISTN: histidine; MET: methionine; GLY: glycine; TRP: tryptophan; CYS: cysteine. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-13. Fatty acid families profile of American cheese composite generic food. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-14. The amount of each individual fatty acids intake in “Kraft Deli Deluxe- Individual Wrapped American Cheese (24 slices)”. F-number indicates the carbon bonds; D-number indicates the double bonds in the fatty acid; CN3 indicates the fatty acids is the omega-3 fatty acids; CN6 indicates the fatty acids is the omega-6 fatty acids. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american- chee-396.aspx.

Figure 2-15. The amount of saturated fatty acids intake in “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-16. The amount of fatty acid families intake in “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”. PUFA: polyunsaturated fatty acid; MUFA: monounsaturated fatty acid; FATRN: trans-fatty acid; SAFA: saturated fatty acid. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-17. The amount of carbohydrate families intake of “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”. SUG_ALC: sugar alcohol. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-18. The amount of sugar profile of “Kraft Deli Deluxe-Individual Wrapped American Cheese (24 slices)”. GLUS: glucose; GLAS: galactose; FRUS: fructose; MALS: maltose; LACS: lactose; DEX: dextrose; SUCS: sucrose. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

Figure 2-19. Comparison of the amount of amino acids between “Publix Deluxe Pasteurized Process American cheese” and “Kraft Deli Deluxe Individual Wrapped American Cheese (24 slices)”. GLU: glutamate; PRO: proline; LEU: leucine; LYS: lysine; ASP: aspartate; VAL: valine; SER: serine; ILE: isoleucine; PHE: phenylalanine; TYR: tyrosine; ALA: alanine; ARG: arginine; HISTN: histidine; MET: methionine; GLY: glycine; TRP: tryptophan; CYS: cysteine. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx. “Publix Deluxe American Cheese” image credits: http://www.publix.com/pd/publix-deluxe-american-cheese-slices/RIO-PCI- 114370.

Figure 2-20. Comparison of the amount of saturated fatty acid between “Publix Deluxe Pasteurized Process American cheese” and “Kraft Deli Deluxe Individual Wrapped American Cheese (24 slices)”. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx. “Publix Deluxe American Cheese” image credits: http://www.publix.com/pd/publix-deluxe-american-cheese-slices/RIO-PCI- 114370.

Figure 2-21. Saturated fatty acids profile of “Nutiva Organic Virgin Coconut Oil” composite generic food. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Nutiva Organic Virgin Coconut Oil” image credits: https://www.amazon.com/Nutiva-Cold-Pressed-Unrefined- Sustainably-Coconuts/dp/B004NTCE1M?th=1.

Figure 2-22. The amount of each individual saturated fatty acids of “Nutiva Organic Virgin Coconut Oil”. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Nutiva Organic Virgin Coconut Oil” image credits: https://www.amazon.com/Nutiva-Cold-Pressed-Unrefined- Sustainably-Coconuts/dp/B004NTCE1M?th=1.

Figure 2-23. Saturated fatty acids profile of MCT oil composite generic food for “Nestle Healthcare Nutrition MCT Oil”. F-number indicates the carbon bonds, and D- number indicates the double bonds in the fatty acid. “Nestle Healthcare Nutrition MCT Oil” image credits: https://www.nestlehealthscience.us/brands/mct-oil/mct-oil.

Figure 2-24. The amount of saturated fatty acids of “Nestle Healthcare Nutrition MCT Oil”. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Nestle Healthcare Nutrition MCT Oil” image credits: https://www.nestlehealthscience.us/brands/mct-oil/mct-oil.

Figure 2-25. Comparison of saturated fatty acids content between “Nativa Organic Virgin Coconut Oil” and “Nestle Healthcare Nutrition MCT Oil”. F-number indicates the carbon bonds, and D-number indicates the double bonds in the fatty acid. “Nutiva Organic Virgin Coconut Oil” image credits: https://www.amazon.com/Nutiva-Cold-Pressed-Unrefined-Sustainably- Coconuts/dp/B004NTCE1M?th=1. “Nestle Healthcare Nutrition MCT Oil” image credits: https://www.nestlehealthscience.us/brands/mct-oil/mct-oil.

CHAPTER 3 APPLICATION OF FOODOMCIS DATABASE TO TWO PATIENTS

Background

Foodomics database provides the molecular compound information for the food consumed by patients, which permits researchers to further study the association of

PKT and seizure response. In this chapter, we use daily diaries data of two patients who live at the home as examples to show foodomics database can be used as an effective tool to help researcher address the following questions:

 Can the foodomics database document the PKT ratio and macronutrients intake over time on PKT?

 Can the foodomics database document intake of each amino acid, fatty acid, and individual carbohydrate over time on PKT?

 Can the foodomics database document the intake of EAA over time on PKT?

 Can the foodomics database show the relationship of each amino acid to other individual amino acids?

 Can the foodomics database document saturated fatty acids profile/ monounsaturated fatty acids profile/ polyunsaturated fatty acids profile of intake over time?

 Can the foodomics database document the daily omega-6 to omega-3 ratio of intake over time?

 Can the foodomics database document the difference between using the net carbohydrates calculation method and PKT carbohydrate calculation method? Do the two methods provide different results for a patient?

Methods

Study Design

Patients on PKT attend an outpatient pre-initiation visit, initiation in hospital, and outpatient clinic visits at 1, 3, 6, 9, and 12 months in the first year after initiation. After

patients are on PKT for 12 months, they attend outpatient clinic visits every 4 months.

After 24 months on PKT, patients attend outpatient clinic visits every 4 or 6 months.

During pre-initiation, our PKT team educates caregivers on the following topics: how to weigh food to the nearest tenth of a gram, how to prepare the meals, how to estimate the percent of the meals consumed by patients, and how to record the daily diaries data. Two to eight weeks after pre-initiation, the patient enters the University of

Florida Shands Hospital for three days for PKT initiation. Our team guides the caregivers in preparation of food for PKT and teaches the caregiver how to record the following patient daily data: recipe numbers of meals eaten, percentage of meal consumed, the dosage and time of medicines and supplements, urine ketones, urine specific gravity, and bowel movements. When patients consume the whole meal, caregivers write down “100%” in the “% eaten” column of the therapy daily diaries

(Figure 3-1). However, if the patient does not consume the whole meal, caregivers need to estimate a percentage of the meal that consumed by the patient. Any other comments, such as illnesses, should be recorded by the caregivers on the daily records.

After discharge from the hospital, caregivers at home maintain the same daily data records as in the hospital. In order to improve the accuracy of the record and to understand the patient's status, our team calls the families once or twice a month. In the next clinic visit, caregivers submit all the records since last clinic visit. During the clinic visit, our team asks caregivers about patient’s dietary intake, seizure response, cognitive ability, etc. Team members scan or take pictures of all the records and bring the data to the lab to update patient’s daily intake data, seizure data, etc.

PKT Recipe

A lab member makes a personalized recipe for each patient based on their nutritional needs and food preferences. Although the specific content of each recipe is different, the recipes are generally divided into six sections: the recipe names, unique recipe number that is used to indicate the specific recipe, creation date, amount of ingredients, meal prescription and cooking directions. Meal prescription includes calorie needs per day of the patient, calories per meal, and calories per snack, PKT ratio, protein needs per day and meal numbers per day. The meal numbers, meal names, list of brand named products, and amount of brand named products are stored in

“MENUS_SOURCE” excel file in each patient’s folder (Figure 3-2).

Daily Intake Records

Lab members enter the patients’ daily intake data into the

“DAILY_INTAKE_SOURCE” excel sheets according to the patient’s daily diaries. There are nine columns in the “DAILY_INTAKE_SOURCE” sheet. These include the medical number, date, day type, PKT recipe numbers, data quality, day quality, entered person, audited person and comments. If a patient consumed all the meals, the recipe numbers are entered in the PKT recipe numbers column, and the appropriate digital numbers are used to indicate the data quality (Table 3-1) and day quality (Table 3-2). However, when the patient does not consume all the meals based on records, the lab member needs to create a new recipe with the same products but with the amount of food actually eaten.

The information is then saved into the “MENUS_SOURCE” excel file. The recipe number of the new recipe that was just created is then entered into the PKT recipe numbers column of the “DAILY_INTAKE_SOURCE” sheet. When caregivers do not record the patient’s daily intake, our team calls the caregivers to request information or

assume that the patient consumed the same meals as the previous day. When patients have the food(s) that is not on the PKT recipes or caregivers remove the food(s) from the PKT recipe, caregivers required to record all the details including the food names and amount of food that patients consumed. In addition, we also ask caregivers during the clinic visit about the patient’s intake to help us accurately know the patient’s intake status. Then, lab members create a new recipe according to caregivers’ information and save it into the “MENUS_SOURCE” excel file. Appropriate digital numbers are entered into the data quality (Table 3-1) and day quality (Table 3-2) of

“DAILY_INTAKE_SOURCE” sheet.

Subjects

Data from two pediatric patients who received PKT at the University of Florida as outpatients were used to illustrate the use of the foodomics database with an orally fed actual patient and a tube fed actual patient receiving PKT while living at home with family.

Patient KG0222 received PKT via gastrostomy tube since 2011. Patient KG0232 received PKT orally since 2014 (Table 3-3). The foodomics database was applied to both patients’ daily dietary intake data from 2015 to 2016. The caregivers recorded 98% of daily intake data of KG0232 and 100% of daily intake data of KG0222. The dietary guideline published by the Institute of Medicine (IOM) was used as the basis for recommended intake. The study has been approved by the University of Florida

Institutional Review Board (IRB # 201500942).

Classification of Macronutrients

To show the results in several ways, each amino acid/ fatty acid/ individual carbohydrate is examined separately for each patient’s intake within two years on PKT.

Molecular compounds are classified into categories such as EAA, essential fatty acids

(EFA), etc.

Classification of amino acids

Amino acids could be grouped in various ways based on their functions and structures. The most common way to classify amino acids is by using the nutritional categories, which are essential amino acids (EAA), nonessential amino acids (NEAA) and conditionally essential amino acids (CEAA) for humans [45]. EAA must be supplied from the diet because their carbon skeletons are not synthesized at all by human cells or insufficiently synthesized de novo to meet requirements [46, 47]. In contrast to EAA,

NEAA can be synthesized sufficiently by humans to meet their requirement for maximal growth and optimal health [47]. For some NEAA, their rates of de novo synthesis may fall below the rate of physiological demands [48], and these NEAA are defined as conditionally essential amino acids. EAA for children are histidine, methionine, valine, leucine, isoleucine, lysine, tryptophan, phenylalanine, and threonine [49-51] (Table 3-4).

Arginine should be considered as CEAA for children under the following conditions: trauma, burn injury, massive small-bowel resection and renal failure [50, 52]. Cysteine can be depleted with chronic stress of metal burden. Thus, it should also be considered as CEAA as well as glutamine [49, 53-55]. For patients who require long-term PN, taurine should be considered as CEAA [56]. Serine and proline should be considered as

CEAA for patients with schizophrenia [51, 57] (Table 3-4).

Ketogenic amino acids are defined as amino acids that can be degraded into acetyl-CoA, which is the precursor of ketone bodies [58, 59]. Gluconeogenic amino acids can convert to glucose through gluconeogenesis [60]. For some amino acids, their degradation products can be both ketone bodies and glucose. These amino acids are

tyrosine, isoleucine, phenylalanine, tryptophan and threonine [58, 61, 62]. Only two amino acids, leucine, and lysine are truly ketogenic amino acids [61, 63]. The other thirteen amino acids are gluconeogenic amino acids, which are alanine, asparagine, aspartate, cysteine, glutamate, glutamine, glycine, proline, serine, arginine, histidine, methionine and valine [61, 64-66] (Table 3-4).

Amino acids also can be classified into the excitatory amino acid neurotransmitters, and inhibitory amino acid neurotransmitters. Excitatory amino acids neurotransmitters, which include glutamate, aspartate, homocysteine, and cysteine sulfinic acid, lead to depolarization and increased neuronal firing rate [67]. Inhibitory amino acid neurotransmitters, glycine, taurine, GABA, and β-alanine, can open Cl- channels allowing chloride to enter into the postsynaptic membrane, which can cause hyperpolarization and decreased neuronal firing rate [67]. Homocysteine synthesized from methionine, at the same time, is related to cysteine synthesis [68]. In addition, amounts of cysteine daily intake can determine cysteine sulfinic acid level because it is synthesized from cysteine [69, 70]. Taurine, which is an inhibitory amino acid neurotransmitter, is synthesized from cysteine in the brain [69, 70]. Glutamate, an excitatory amino acid neurotransmitter, synthesized from glutamine, is the biological precursor for GABA [38] (Table 3-4).

Classification of fatty acids

Fatty acids could be classified based on nutritional needs, chain length, and saturation. Essential fatty acids (EFA) cannot be synthesized by the body and must be supplied from the diet. They play an important role in the cell membrane, body growth, and disease prevention. Linolenic is considered as EFA [71-74]. It is a substrate for many other chemical components in the body, such as DHA, and EPA which are

important in fetal development, cardiovascular function, and inflammatory prevention

[75] (Table 3-5).

The classification of fatty acids based on the length of the chain is also a common classification method. The fatty acids (FA) with carbon chain of less than 6 carbons are considered as short-chain fatty acids (SCFA) [76-79]. Most of the SCFA are synthesized in the gastrointestinal tract of mammals by microbial fermentation of the carbohydrates [76]. Medium-chain saturated fatty acids (MCSAFA) have carbon numbers with 6, 8, 10 and 12 [79-82], which are caproic acid, caprylic acid, capric acid and lauric acid [83]. Long-chain fatty acids have carbon chains with 13-18 carbons.

Fatty acids (FA) with 16 or 18 carbons are most abundant FAs in mammalian animals

[84]. When FA has a carbon chain over 18 carbons, it is considered as very long-chain fatty acid [79, 85-87] (Table 3-5).

Another classification of FAs could be based on the number of double bonds.

Saturated fatty acids (SAFA) do not have double bonds between carbon atoms [88], while monounsaturated fatty acids (MUFA) contain one carbon double bond [85, 88].

Polyunsaturated fatty acids (PUFA) contain at least two carbon double bonds, and the linoleic acid and linolenic acid are two abundant fatty acids in the PUFA profile [89]

(Table 3-5).

Fatty acids also can be classified by the first double bond from the methyl end of the carbon chains. The double bonds that start at the third position from the methyl end of the carbon chain, are omega-3 fatty acids [40]. These are linolenic acid, EPA, docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA). The omega-6 fatty acids have the first double bond at the sixth position from the methyl end of the carbon

chain. These are linoleic acid, gamma-linolenic acid, eicosadienoic acid, eicosatrienoic acid, arachidonic acid, and F22D4 (22 carbons and 4 double bonds) [40] (Table 3-5).

Classification of carbohydrates

Carbohydrates can be classified into monosaccharides, disaccharides, and polysaccharides [90-92]. Monosaccharides have only one fundamental carbohydrate unit that could not be further hydrolyzed. Glucose, fructose, and galactose are monosaccharides [90-92]. Sucrose, lactose, and maltose are disaccharides because they can be hydrolyzed into two fundamental carbohydrate units [90-92].

Polysaccharides include fiber and starch because they can be hydrolyzed into more than two fundamental carbohydrate units [90-92] (Table 3-6).

Statistics

All data are analyzed by using R studio (version 3.3.1). Shapiro–Wilk test is used to determine if the data followed a normal distribution.

Results

The molecular compounds daily intake from 2015 to 2016 was determined for

PKT patient KG0232 and PKT patient KG0222 using their daily diaries. The following questions were addressed.

Can the Foodomics Database Document the PKT Ratio and Macronutrients Intake Over Time on PKT?

Yes. Figure 3-3 shows KG0232’s daily PKT ratio and the percentage of three macronutrients in calories over time on PKT. His daily PKT ratio stays within the prescribed PKT ratio of 4 to 1 for most of the time. However, in the early 2015, he did not consume all the prescribed fat intake, which led him to maintain the daily PKT ratio around 3.5 without meeting the prescribed PKT ratio. For two years his daily calorie

intake was about 90% from fat, and the rest from protein and carbohydrates, consistent with his dietary prescription (Figure 3-3).

Since KG0222 is G-tube fed patient, his daily molecular compounds intake is consistent over two years (Figure 3-4). KG0222 had a prescribed PKT ratio of 4 to 1 prior to day 464 and then, his ratio was adjusted to 3 to 1 due to his heavy illness in the hospital. Overall, he was on the prescribed PKT ratio excluding the sick days (Day 418 to day 465). Before his illness, 90% of calories are obtained from fat intake, and 10% of calories are from protein and carbohydrate. After changing the PKT ratio, around 85% of calories are from fat intake, and the rest of calories are from protein and carbohydrate.

Can the Foodomics Database Document Intake of Each Amino Acid, Fatty Acid, and Individual Carbohydrate Over Time on PKT?

Yes. The daily intake of each amino acid, fatty acid, and individual carbohydrate for two patients was documented using the foodomics database. Figure 3-5 shows the results of KG0232 for each amino acid daily intake. Glutamic acid is the highest amino acid intake among 21 amino acids, followed by aspartic acids, and lysine. Foodomics database also shows the result of his excitatory amino acids and inhibitory amino acids intake for two years (Figure 3-6 and Figure 3-7).

Before Day 113, KG0232’s daily intake of sugar was greater than that of fiber, but the amount of fiber increased gradually with increased intake of fruits and vegetables and reduced amount of applesauce (Figure 3-8). Sucrose, fructose and galactose are the main three contributors to the sugar profile within two years, related to many of the dairy products he consumes (Figure 3-9).

For KG0222, each individual amino acid present in percent of total protein as well as excitatory amino acids and inhibitory amino acids respectively shown in Figure 3-10,

Figure 3-11, and Figure 3-12. For his carbohydrate intake, 90% of carbohydrate families are from sugar intake, while fiber intake provides 5-8% of carbohydrate families. With the diet changed due to illness, the fiber intake decreased to around 2% of total carbohydrates (Figure 3-13). This is because the carbohydrate intake is from the “Sol carb” products in his meals while carbohydrates in this product are almost all from sugar

(Figure 3-14). The ingredients of “Sol carb” are from corn syrup (mainly contain maltose and glucose), and the foodomics database is a good reflection of the changes in maltose and glucose.

Foodomics database also documented two patients’ fatty acids intake, and more details will be discussed later.

Can the Foodomics Database Document the Intake of EAA Over Time on PKT?

Yes. Overall, KG0232’s EAA intake is higher than the EAA intake of IOM recommendation (Figure 3-15). Although in the early 2015, his daily PKT ratio did not reach the prescribed ratio, the intake of EAA was not affected and was still above the recommendation of EAA. The overall trend of EAA intake has decreased over time because we changed his daily protein intake from 19 g per day to 17.5 g per day.

However, the pattern of EAA remains the same in two years (Figure 3-15). Since

BCAAs are also EAA, they have a similar pattern with EAA (Figure 3-16). Isoleucine is the highest amino acid intake of all three BCAA, followed by valine and leucine. The extremely low point on the graph indicates that he was sick at that time. The reason for the lower EAA or BCAA intake below recommendation during day 308 to 315 is

because he had a deviation for his meal. Unfortunately, we do not know the cause of the dietary deviation according to his daily records.

The KG0222’s intake of EAA within two years is above the IOM recommendation

(Figure 3-17). Tryptophan intake is the highest, followed by isoleucine, valine and leucine. Due to changes in diet after illness, the percentage recommendation of histidine intake increased from 116% to 165% and was maintained to the end of 2016

(Figure 3-17). Although intake of BCAAs decreases after illness, the pattern of each

BCAA to the others are still similar (Figure 3-18).

Can the Foodomics Database Show the Relationship of Each Amino Acid to Other Individual Amino Acids?

Yes. As mentioned in the chapter one, studies shows that the intake of BCAA or ketogenic amino acid is associated with seizure response. The intake of BCAA is above recommendation for both patients. Both patients’ intake of gluconeogenic amino acids is maintained at a high level compared to the ketogenic amino acids and the intake of amino acids that are both gluconeogenic and ketogenic (Figure 3-19 and Figure 3-20).

Foodomics database does not give an answer about which BCAA or ketogenic amino acid is related to seizure response, but here we just point out that the foodomics database can document the relationship of each amino acid to each other and give a foundation for researchers doing relevant studies.

Can the Foodomics Database Document Saturated Fatty Acids Profile/ Monounsaturated Fatty Acids Profile/ Polyunsaturated Fatty Acids Profile of Intake Over Time?

Yes. In daily intake of KG0232’s fatty acid families, saturated fatty acid intake and monounsaturated fatty acids are intersected with each other, while polyunsaturated fatty acids intake is lower than other two fatty acid families (Figure 3-21). Similar to the

EAA pattern at the early time in 2015, fatty acids families pattern did not fluctuate even though he did not reach the prescribed PKT ratio. The pattern of each fatty acid family to the other are maintained until day 614. From day 614, his intake of monounsaturated fatty acid exceeds intake of saturated fatty acids because of his diet with a lot of butter, cheddar cheese and mayonnaise products.

His main contributor to saturated fatty acids is palmitic fatty acid, and maintains this high level over two years (Figure 3-22). At the early time of 2015, lauric acids intake was higher than the fatty acids containing the carbon chain number less than ten.

However, from day 168, lauric acid intake rapidly decreased while caprylic acid and capric acid intake greatly increased (Figure 3-23). This reflects the switched from coconut oil (rich in lauric acid) to MCT oil (rich in caprylic acid and capric acids) in his meals.

Figure 3-24 shows the changes of medium chain saturated fatty acids before and after switching from coconut oil to MCT oil. MCT oil is rich in fatty acids with 8 and 10 carbon chain numbers, while coconut oil contains more fatty acids with 12 carbon chain numbers [93]. In total, he consumed a meal containing coconut oil for 139 days, then compared the median of MCSAFA intake with the first 139 days of the MCT oil meals.

The results showed that when the diet contained MCT oil, fatty acids containing 8 and

10 fatty acids were respectively 5 and 3 times greater compared to the corresponding fatty acids containing coconut oil in the diet. Correspondingly, lauric acid dropped when the MCT oil diet replaced with coconut oil diet. Changes in these data indicate that the foodomics database properly records the daily intake of the patient's molecular compounds.

The pattern of KG0232’s monounsaturated fatty acid maintains a relatively stable level for two years. Forty percent of total fat comes from oleic acid, while other monounsaturated fatty acids contribute less to total fat (Figure 3-25). Among the polyunsaturated fatty acids, linoleic acid and linolenic acid are the main contributors

(Figure 3-26).

KG0222 had more MUFA and PUFA than SAFA during two years (Figure 3-27).

The changed diet prescription after illness contains the “Ross Carbohydrates free” product, with a large amount of linoleic acid (Figure 3-40 and APPENDIX G). This leads to a slight increase in the intake of polyunsaturated fatty acids (Figure 3-27).

Before the illness, KG0222’s meals are rich in MCT oil. As reflected in the foodomics database, the daily intake of medium-chain saturated fatty acids (caprylic and capric) is a major contributor to total saturated fatty acids (Figure 3-28). Due to illness, MCT oil was replaced with “Crisco Canola oil” that is rich in long chain fatty acids and “Ross carbohydrate free” product in the dietary prescription (Figure 3-29).

Therefore, the intake of long chain saturated fatty acids increased while intake of caprylic acid and capric acid almost disappeared (Figure 3-28).

The total number of days that KG0222 did not get sick is 668 days, and his days on the diet without MCT oil is 264 days. Then, we took the first 264 days that he was on meals with MCT oil to review the changes in MCSAFA. Since he did not consume the

MCT oil any more after switching the diet, the fatty acids with carbon chain number of 8 and 10 are rapidly deceased as well as the total MCSAFA when representing the fatty acids in percentage of total saturated fatty acids (Figure 3-30 and Figure 3-31).

The KG0222’s intake of oleic acids was stable within two years except illness days (Figure 3-32). Linolenic acids kept its own level in two years (Figure 3-33). The changed diet prescription is rich in linoleic acids which is mainly coming from “Ross

Carbohydrate Free” product (18.8% of fatty acids are linoleic acids, APPENDIX G).

Can the Foodomics Database Document the Daily Omega-6 to Omega-3 Ratio of Intake Over Time?

The ratio of omega-6 to omega-3 fatty acids is necessary to be reviewed as they play a crucial role for brain function, growth and body development [94]. Overall, except for the few days due to illness (Day 125-129), the lower ratio of KG0232’s omega 6 to omega 3 that was considered beneficial to his health [94] (Figure 3-34). Of the omega 6 and omega 3 fatty acids, the 18 carbon chain fatty acids accounted for around 90%

(Figure 3-35 and Figure 3-36). This indicated that intake of his EPA and DHA are low which also plays an important role in brain function.

The KG0222’s intake of polyunsaturated fatty acids with a carbon chain number of 18 are the largest contributors to omega 3 fatty acids and omega 6 fatty acids (Figure

3-37 and Figure 3-38). As the results show, this means the intakes of EPA and DHA are less.

The lower ratio of omega-6 to omega 3-fatty acids is desirable [94]. Moreover, the ratio of the level is closely related to what type of food consumed. For KG0222, the ratio of omega-6 fatty acids to omega-3 fatty acids increased from around 2 to 2.5 before and after diet changes (Figure 3-39). Before his illness (Day 329), his diet contain 1.5 grams of “NaturaMade Ultra Omega-3 Fish Oil 1400 mg Liquid Softgel”

(Figure 3-40) as a daily supplement, also with “Crisco Canola Oil with Omega-3 DHA” and other different types of oils. From day 462, after being discharged from hospital, his

diet prescription did not contain any fish oil capsules but still contained “Crisco Canola

Oil with Omega-3 DHA”. The ratio of omega 6 to omega 3 fatty acids remains in the lower range as his pre-illness diet (Figure 3-41).

The total days of his diet without fish oil is 264 days, we took the first 264 days to review each individual omega-3 and -6 fatty acid. Initially, we administered a very small amount of fish oil in conjunction with “Crisco Canola Oil with Omega-3 DHA” and many other oils. After the diet change, we eliminated the fish oil and increased the amount of

“Crisco Canola Oil with Omega-3 DHA”. Interestingly, the data shows that for this patient, administering a large amount of canola oil with omega- 3 fatty acid still provides a low omega-6 to omega-3 ratio which ultimately benefits the patient (Figure 3-42). This indicated that choosing the appropriate product could play an important role in maintaining the lower ratio of omega-6 to omega-3 fatty acids.

Can the Foodomics Database Document the Difference Between Using the Net Carbohydrates Calculation Method and PKT Carbohydrate Calculation Method?

The data shown in Figure 3-43 is cut off by every 10th day without counting illness days, and it lists the amount of daily intake of sugar, starch, fiber and sugar alcohol for two patients during two years. Many KD centers only calculate the net carbohydrates (subtracts fiber from carbohydrate) as the total carbohydrate. However, this leads the patients to potentially increase the amount of carbohydrates intake from the diet, which may influence the seizure outcomes. Different from other KD centers, our PKT center calculates all the carbohydrates as the total carbohydrate, and foodomics database shows the difference between two different calculations (Figure 3-

44 and Figure 3-45). Since KG0222 is the tube fed patients, he does not have many opportunities to consume fibers, so only a tiny difference has been shown between the

two calculations in the foodomics database (Figure 3-44). However, KG0232 is the oral fed patient and has more opportunities to consume fiber in his meal. The foodomics database uses two different carbohydrate calculations to clearly show the difference in

KG0232’s daily PKT ratio and the difference in sugar contribution to total carbohydrate.

Without including the fiber (the net carbohydrate calculation method), almost all the net carbohydrate comes from sugar (97.45%) (Figure 3-45). However, 59.39% of total carbohydrate comes from sugar (Figure 3-45). The difference in amount of carbohydrate is reflected on the PKT ratio. The less carbohydrate calculated, the higher ratio could be obtained. The median of KG0232’s daily ratio is 3.97 that is very close to the prescribed PKT ratio by using calculation of carbohydrates in PKT methods, while the median of daily PKT ratio is higher than the prescribed ratio when using the net carbohydrate calculation method (Figure 3-45). Therefore, foodomics database allows researchers to understand the difference between different calculation methods and allows them to choose the best way to prevent the patients from over-consuming carbohydrates.

Discussion

The prescribed PKT ratio is used for helping patients control their seizures as well as improve or maintain their nutritional status. According to patient’s daily dietary records, researchers could calculate the estimates of daily PKT ratio and use it to evaluate patient’s dietary intake. However, the ratio does not tell researchers whether molecular compound intake meets the Reference Daily Intake. In addition, the patients with similar PKT ratio have differences in nutrition status and seizure responses. This means, although the amount of macronutrients is similar, each individual molecular compound could be different, and the relationship of one molecular compound to the

others may also differ. Therefore, only knowing the PKT ratio does not show you exactly what nutrients or molecular compounds are consumed. Foodomics database can be used to not only list the patient’s daily PKT ratio but also list the daily intake of 147 nutrients consumed by patients. These listed nutrients’ information permits researchers to address many clinical or research questions, such as the questions mentioned in this chapter.

The traditional thought for the mechanism of KT is that ketone bodies, are the key contributors to the seizure control [12, 39]. Subsequently, many food companies began to develop new products (e.g., ketone ester) that could increase the ketone bodies after consumed. The ketogenic amino acids have a similar mechanism to the ketone ester, which could convert to the ketone bodies. However, recently studies have shown that ketone bodies do not play vital role on seizure controls [95, 96]. Thus, it is hard to give clear answers as to whether or not the amount of ketone bodies does affect on seizure controls. Foodomics database can provide the ketogenic amino acids information (Figure 3-43) and permit the researchers to design the experimental diet for studies about the role of ketogenic amino acids.

The omega-6 to omega-3 fatty acids ratio plays a crucial role in people’s health, cardiovascular health, brain function, growth, etc. [94]. Use of the foodomics database shows that the lower ratio of omega-6 to omega-3 fatty acids in both patients indicates that the omega-fatty acids in the patients’ diets are balanced and healthy. In addition, foodomics database also shows which omga-3 fatty acid or omega-6 fatty acid is the largest contributor the total omega-3 or omega 6 fatty acids. Moreover, it also permits

researchers to know whether the patient’s daily intake of EPA or DHA meets the recommendation.

As mentioned in chapter one, it is necessary to study a profile rather than do the study on a particular compound. Foodomics database allows us to list the intake of two patients’ fatty acids families profile and shows the difference of SAFA, MUFA and PUFA between an oral patient and a tube fed patient (Figure 3-43). We understand that only two patients cannot represent all the cases, but the foodomics database definitely could allow researchers to do studies on populations with different characteristics such as oral fed and tube fed.

The classification of saturated fatty acids by chain length is one of the ways to evaluate the saturated fatty acids intake in patients’ diet. Studies have shown that myristic acids (14:0) and palmitic acids (16:0) are positively associated with cardiovascular disease by raising LDL level [97, 98]. Foodomics database shows that both patients have higher level of palmitic acids than steric acid (18:0) that is considered as the healthy fatty acid (Figure 3-43) [97, 98]. Therefore, foodomics database provides a convenient tool for researchers and dietitians to quickly know what the fatty acids profile is and the amount of individual fatty acids in the daily intake, which allows researchers or dietitians to make an appropriate diet prescription in a timely manner.

Conclusion

In conclusion, the foodomics database can be used as a tool to document the daily intake of patient’s molecular compounds and give an opportunity for the researchers to address many questions and help understand more about the associations with PKT and seizures responses.

Table 3-1. Data quality number Data quality Explanations number 1  If the menus came from the PKT program or we have a hard copy of what was given to the child and the data was provided on record templates by the caregiver.  If we know the child received no food this date.

2  If the menus cannot be located or we do not have a hard copy of what was given to the child (which means we have to estimate the menus the child was getting).

3  If the menus came from the PKT program or we have a hard copy of what was given to the child but the data was not provided on record templates by the caregiver, which means we have to estimate what the child was getting since we have no record.

4  If the menus cannot be located or we do not have a hard copy of what was given to the child (which means we have to estimate the menus the child was getting).  If the data was not provided on record templates by the caregiver, which means we have to estimate what the child was getting since we have no record.

5  If the patient is off diet with no records, recipe number column will be blank.

6  If on diet but has no record.

7  If the date is a repeat of a date immediately before, needs to be input from most immediate previous date.

Table 3-2. Day quality number Day quality Explanations number 1  If the patient received what was prescribed to them (i.e. nothing abnormal happened this day).  If we send a constituent that is still considered prescribed.

2  If there was a deviation from prescribed due to illness (includes % or whole meal/snack not consumed due to illness).  If the child was vomiting due to sickness.  If you know a date the child was sick and a date they were healthy then enter 2 until the healthy date.  If you only know the date a child was sick then assume sickness lasted for 14 days.  If deviations lasted that long and then if deviations persist after 14 days enter unknown reason for deviation.

3  If unauthorized deviation from prescribed due to improper administration of KT (includes cheating and giving or removing meal or snack).

4  If prescribed change.

5  If missing data.

6  If on diet no record, needs to be input.

7  If off protocol.

8  If unknown reason for deviation (includes % of meal consumed if unknown why).

9  If the patient had zero food for the whole date.

Table 3-3. Demographic table of two patients KG0232 KG0222 Gender Male Male Age (years) 3 11 Types of feeding Oral Tube Year on PKT 2014 2011

Table 3-4. Classification of amino acids

Children Gluconeogenic Amino Ketogenic Inhibitory Excitatory AA Amino Acid EAA NEAA CEAA Acids Amino Acids AA Alanine X X X Arginine X X Aspartate X X X Cysteine X X CSA Glutamate X X X Glycine X X X Glutamine X X GABA Histidine X X Serine X X Proline X X Methionine X X HC Valine X X Leucine X X Isoleucine X X X Lysine X X Tyrosine X X X Tryptophan X X X Phenylalanine X X X Threonine X X X Taurine X X X X CSA: Related with cysteine sulfinic acid (excitatory AA) GAB: Related with GABA (inhibitory amino acids) HC: Related with homocysteine (excitatory amino acids)

Table 3-5. Classification of fatty acids Fatty Acids EFA NEFA SCFA MCSAFA LCFA VLCFA SAFA MUFA PUFA ω−3 ω−6 Butyric acid(F4D0) X X X Caproic acid(F6D0) X X X Caprylic acid(F8D0) X X X Capric acid(F10D0) X X X Lauric acid(F12D0) X X X Tridecylic acid(F13D0) X X X Myristic acid(F14D0) X X X Pentadecylic acid(F15D0) X X X Palmitic acid(F16D0) X X X Margaric acid(F17D0) X X X Stearic acid(F18D0) X X X Arachidic acid(F20D0) X X X Behenic acid(F22D0) X X X Lignoceric acid(F24D0) X X X Tetradecenoic acid(F14D1) X X X X Pentadecenoic X X X acid(F15D1)

Palmitoleic acid(F16D1) X X X Heptadecenoic X X X acid(F17D1) Oleic acid (F18D1) X X X Linoleic acid(F18D2) X X X X Linolenic acid(F18D3) X X X X Gamma-Linolenic acid X X X X (F18D3N6) Parinaric acid (F18D4) X X X Eicosadienoic X X X X acid(F20D2CN6) Eicosatrienoic acid (F20D3) X X X X Arachidonic acid(F20D4) X X X X EPA (F20D5CN3) X X X X F21D5 X X X

Table 3-5. Continued Fatty Acids EFA NEFA SCFA MCSAFA LCFA VLCFA SAFA MUFA PUFA ω−3 ω−6 F22D4, ω−6 X X X X Docosapentaenoic acid X X X X (DPA) ( F22D5CN3) Docosahexaenoic acid X X X X (DHA) (F22D6CN3) The classification of fatty acids is based on nutritional need, chain length and saturation. F-number indicates the number of carbons, D-number indicates the number of double bonds, and CN-number indicates the first double bond from the methyl end of carbon chain. EFA: essential fatty acids; NEFA: nonessential fatty acids; SCFA: short-chain fatty acid; MCSAFA: medium-chain saturated fatty acids; LCFA: long-chain fatty acids; VLCFA: very long-chain fatty acids; SAFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids; ω−3: omega-3 fatty acids; ω−6: omega-6 fatty acids.

Table 3-6. Classification of carbohydrates Monosaccharide Disaccharide Polysaccharide Glucose Sucrose Fiber Fructose Lactose Starch Galactose maltose

Figure 3-1. Example of KG0232’s daily diaries records

Figure 3-2. An example of PKT recipes

Figure 3-3. The precision ketogenic therapy (PKT) ratio of KG0232 and his macronutrients intake in percent of calories for two years.

Figure 3-4. The PKT ratio of KG0222, and his macronutrients intake in percent of calories for two years.

Figure 3-5. KG0232’s each individual amino acid daily intake while on PKT.

Figure 3-6. KG0232’s each excitatory amino acid daily intake while on PKT.

Figure 3-7. KG0232’s each inhibitory amino acid daily intake while on PKT.

Figure 3-8. KG0232’s carbohydrate families intake while on PKT.

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Figure 3-9. KG0232’s sugar profile intake while on PKT.

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Figure 3-10. Intake of KG0222’s each individual amino acid while on PKT.

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Figure 3-11. Intake of KG0222’s excitatory amino acid while on PKT.

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Figure 3-12. Intake of KG0222’s inhibitory amino acid while on PKT.

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Figure 3-13. KG0222’s carbohydrate families intake while on PKT.

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Figure 3-14. KG0222 sugar profile intake while on PKT.

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Figure 3-15. KG0232’s essential amino acids (EAA) intake while on PKT.

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Figure 3-16. KG0232’s branched chain amino acids (BCAA) intake while on PKT.

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Figure 3-17. KG0222’s EAA intake while on PKT.

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Figure 3-18. KG0222 BCAA intake while on PKT.

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Figure 3-19. KG0232’s gluconeogenic amino acids (GAA) and ketogenic amino acids (KAA) intake while on PKT.

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Figure 3-20. KG0222’s gluconeogenic amino acids (GAA) and ketogenic amino acids (KAA) intake while on PKT.

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Figure 3-21. KG0232’s fatty acids families intake while on PKT.

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Figure 3-22. KG0232’s saturated fatty acids intake while on PKT.

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Figure 3-23. KG0232’s medium-chain saturated fatty acids intake while on PKT. On day 168, we switch the meal containing coconut oil (rich in lauric acid) to the MCT oil (caprylic acid and capric acid). Foodomics database reflects a decrease in lauric acid intake while an increase in the intake of caprylic acid and capric acid.

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Figure 3-24. Comparison medium-chain fatty acids intake of KG0232’s meals with coconut oil and meals with MCT oil.

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Figure 3-25. KG0232’s monounsaturated fatty acids intake while on PKT.

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Figure 3-26. KG0232’s polyunsaturated fatty acids intake while on PKT.

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Figure 3-27. KG0222’s fatty acids families intake while on PKT.

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Figure 3-28. KG0222’s saturated fatty acids intake while on PKT.

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Day 461 PKT Recipe Number: 35k

Product Name Ingredient Amount (g)

Ross Carbohydrate Free 396.16

Colavita Premium Blended Oil 35.82 32 Floz. Plastic Bottle Solcarb 5.89

ω−3/6 Fatty Acids Daily Intake (g) Daily ω−3 Fatty Acids 3.88

Daily ω−6 Fatty Acids 24.73

Daily ω−6 to ω−3 6.38 Fatty Acids Ratio

Figure 3-29. An example of KG0222’s intake on day 431 (during hospital stay).

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Figure 3-30. Comparison of medium-chain fatty acids intake in KG0222’s meal with MCT oil and meals without MCT oil.

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Figure 3-31. KG0222 medium-chain saturated fatty acids intake while on PKT.

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Figure 3-32. KG0222’s monounsaturated fatty acids intake while on PKT.

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Figure 3-33. KG0222 polyunsaturated fatty acids intake while on PKT.

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Figure 3-34. KG0232’s omega-6 to omega-3 ratio while on PKT.

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Figure 3-35. KG0232’s intake of fatty acids with 18 carbon chain numbers in omega-3 fatty acids while on PKT.

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Figure 3-36. KG0232’s intake of fatty acids with 18 carbon chain numbers in omega-6 fatty acids while on PKT.

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Figure 3-37. KG0222 intake of fatty acids with 18 carbon chain numbers in omega-3 fatty acids while on PKT.

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Figure 3-38. KG0222 intake of fatty acids with 18 carbon chain numbers in omega-6 fatty acids while on PKT.

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Figure 3-39. KG0222’s omega-6 to omega-3 ratio while on PKT.

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Figure 3-40. An example of KG0222’s intake on day 392 (before being hospitalized).

Figure 3-41. An example of KG0222’s intake on day 462 (after being discharged from hospital).

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Figure 3-42. Comparison of omega-6 and omega-3 fatty acids intake in KG0222’s meals with fish oil and meals without fish oil.

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Figure 3-43. Foodomics database lists the molecular intake for two patients during PKT.

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Figure 3-43. Continued

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Figure 3-43. Continued

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Figure 3-43. Continued

F-number indicates the carbon bonds, D-number indicates the double bonds in the fatty acid, and N- number indicates the first double bond from the methyl end of carbon chain. The color highlight green means people can obtain the information from nutrition facts label. KAA: Ketogenic Amino Acids; GAA: Gluconeogenic Amino Acid.

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Figure 3-44. Comparison of carbohydrate families and sugar profile intake of KG0222 when using net carbohydrate and keto-carbohydrate to calculate the total carbohydrate.

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Figure 3-45. Comparison of carbohydrate families and sugar profile intake of KG0232 when using net carbohydrate and keto-carbohydrate to calculate the total carbohydrate.

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CHAPTER 4 SUMMARY

In conclusion, we created the foodomics database that lists the molecular compounds in brand named products using the NFL and USDA SR database. This database can be used as an effective tool to document patients’ daily intake of nutrients and allows researchers to ask many relevant questions.

The molecular compound data of brand named products in the foodomics database provides an opportunity for researchers/ dietitians to choose the optimal products to prescribe diet prescriptions for their patients. Although the estimated molecular data cannot get the same accuracy as using chemical assays, this is the best way at the moment. In addition, demonstration of the usefulness of the foodomics database in clinical studies will be used to propose that food be analyzed via mass spectrometry for the molecular compounds. These chemical analyses would be helpful for making the database more accurate and improve providers’ ability to help patients achieve the desired outcomes.

In this study, we do not answer any questions about the association of PKT with seizures. We want to share the idea that the emergence of the foodomics database, a publicly available database, provides the foundation for researchers to address and answer questions. We hope the idea of the foodomics database can assist researchers who want to study the association of diet and disease, help patients recover from disease, and ensure they maintain good health.

Future Work. The foodomics database will be used in patient’s clinical care to prescribe the diet prescription with molecular compounds as well as document their daily intake while patients are on PKT. The laboratory will also apply this database to

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the clinical research populations to address questions about the association between seizure response and the molecular profile in the diet.

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APPENDIX A DOCUMENTATION FOR CREATING THE MOLECULAR PROFILE OF INDIVIDUAL USDA FOODS DATABASE

Step 1. Cut off 30 nutrients from the USDA SR 28 database, and add keto- carbohydrate into the molecular profile of individual USDA foods database:

In order to illustrate the contribution of the amino acid in the protein, the fatty acid in the fat, and each individual carbohydrate in the carbohydrate to each USDA product, we first cut off 30 nutrients from the USDA SR 28 database because: 1) those nutrients did not influence the pattern of molecular profiles in macronutrients, such as water and ash, and 2) USDA SR manuscript mentioned that undifferentiated fatty acids, defined as the sum of individual fatty acids coming from same class of undifferentiated fatty acids, have already been included these individual fatty acids (e.g., oleic acid undifferentiated is the sum of cis, oleic acids and trans, oleic acid) [40]. In addition, most of the individual fatty acids (e.g., linoleic trans, trans fatty acid) are reported as blanks by the USDA database and do not show the relationship between molecular profile and macronutrients. Therefore, to avoid double calculating fatty acids, only undifferentiated fatty acids were used in the molecular profile of individual USDA foods database

(APPENDIX C).

Keto-carbohydrate was intentionally added into the individual USDA foods database to ensure we are not underestimating the carbohydrate intake by patients.

Keto-carbohydrate reflected the higher level of carbohydrate that is determined by calculating carbohydrate content by grams of weight or by calories. Instead of directly measuring the content of the carbohydrate, USDA estimated the carbohydrates in 100 g of food by using the following equation: carbohydrate (g) =100g of food- water content

(g/100g of food)-protein (g/100g of food)-fat (g/100g of food)-ash (g/100g of food)-

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alcohol (g/100g of food) [40]. Another way is to use calories to calculate the amount of carbohydrates by using the same equation used in the NFL database (Figure 2-2). The higher value of carbohydrate is used to represent the content of carbohydrate for the product in the molecular profile of individual USDA foods database (Figure 2-2).

Step 2. Calculating the sum of molecular compounds and rescaling the amount of each individual molecular compound:

USDA SR28 manuscript mentioned that the sum of molecular compounds does not equal the number of macronutrients because the nutrients were measured by a different lab and a different assay [40]. For example, the protein of blue cheese (USDA

ID 1004) in the USDA SR 28 database is reported to be 21.4 g per 100 g of food, while the sum of total amino acids is 22.975 g per 100 g of food. To show the contribution of each individual molecular compound to the corresponding macronutrients, we first calculate the sum of molecular profile and then rescale each molecular compound for the corresponding macronutrient (Figure A-1). For example, tryptophan in “Cheese

Blue” (USDA ID 1004) was reported as 0.312 g in 22.975 total amino acids per 100 g of food. By doing the calculation, the new number of tryptophan was 0.2906 g in 21.4 g of protein per 100 g of food.

Step 3. Convert the units of molecular compounds from g per 100 g of food to g per 1 g of macronutrients:

To show the pattern of molecular compounds in corresponding macronutrients, the units of molecular compounds are converted from g per 100 g of food to g per 1 g of macronutrients. For example, the amount of tryptophan for blue cheese (USDA ID

1004) is calculated in step 2 as 0.2906 g per 100 g of food. Then, the content of

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tryptophan divided by the total protein, that is 21.4 g per 100 g of food, and the yield number, 0.01358 g per 1 g of protein, shows the pattern of tryptophan in 1 g of protein.

The units for vitamins, minerals and others are variable per 100 g of food in the molecular profile of individual USDA foods database (Figure A-1).

In USDA SR 28 database, many fatty acids are reported as zeros, and USDA SR

28 manuscript states that when the fatty acid value is less than 0.0005 g per 100 g of food, the value is rounded to zero [40]. Therefore, it is hard to distinguish between the true zero and the assumed/ rounded zero in the USDA SR 28 database. In addition, 0 g of the molecular compound per 1 g of the corresponding macronutrient reflects that the molecular compound contributes by 0% to its macronutrient. This is not helpful for recommendation of food or nutrients for patients when prescribing PKT diets. Therefore, zero was left blank in the molecular profile of individual USDA foods database.

To better manage the data, we documented how we deal with each nutrient’s value in the molecular profile of individual USDA foods. We add XXX_DERIVE (XXX means nutrient, e.g., VALINE_DERIVE) next to each nutrient. If the sum of the molecular profile is not equal to the macronutrient, each molecular compound needs to be scaled by the corresponding macronutrient, and “SUSDA” will be filled in the

XXX_DERIVE column. If the sum of the molecular profile is equal to the macronutrient,

“USDA” will be filled in the XXX_DERIVE column. If the molecular compound is zero, it is left blank and “BUSDA” will be filled in the XXX_DERIVE column. The nutrients that do not have a scaling factor (e.g., Vitamin and Mineral) are changed to g per 100 g of food, and then “UTUSDA” will be filled in the XXX_DERIVE column. We signed

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“CKUSDA” in the CHO_KETO_DERIVE column to indicate the value of keto- carbohydrate.

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Figure A-1. To show the contribution of each molecular compound to the corresponding macronutrients, we first removed 30 nutrients and then intentionally added the keto-carbohydrate to ensure we are not underestimating the carbohydrate intake by patients. Then, we calculate the sum of molecular profiles, and rescale each molecular compound. Lastly, we change the units of each molecular compound from grams per 100 g of food to grams per 1 g of macronutrients.

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APPENDIX B DOCUMENTATION FOR CREATING MOLECULAR PROFILE OF USDA COMPOSITE GENERIC FOODS DATABASE

Single Composite Generic Food Database

A product is defined as a single brand named product when the majority of the product is made from one ingredient. The foods consumed by patients are classified into different categories based on USDA food groups. Each brand named product is given a broad food name, for example, for Kraft Singles- American Cheese (24 slices), the broad food name is “American Cheese”. Then, pull out all data related to that food name from the same food category of molecular profiles of individual USDA foods database and create a excel file called “Composite generic food_Food name” (Figure B-

1 and Figure B-2). The composite generic food excel file has the following five tabs for testing the molecular profile: amino acids profile, fatty acid families profile (saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids), fatty acids profile (e.g., oleic acids, palmitic acids, etc.), carbohydrate families profile (sugar, fiber and starch), and sugar profiles (e.g., sucrose, fructose, etc.) (APPENDIX D).

Some products may be missing a whole molecular profile, which does not contribute to the composite generic food, so it is necessary to exclude these products first in each molecular profile tab. Each molecular profile tab needs to be sorted by the following nutrients: tryptophan (amino acids profile), saturated fatty acids (fatty acid families profile), palmitic acid (fatty acids profile), sugar (carbohydrate families profile), and sucrose (sugar profile). Next, the average and SD% of each nutrient in each molecular profile tab is calculated. Next, the outlier(s) with extremely high or extremely low values is deleted. The SD% is defined as the percent of the standard deviation divided by the mean [42], which helps us decide how many composite generic foods

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need to be created. If the SD% is less than 15% (amino acids profile, fatty acid families profile and fatty acid profile) or 25% (carbohydrate families profile or sugar profile), this means all the data are very similar, and thus, only one composite generic food is needed for a brand named product.

If the SD% is higher than 15% or 25%, this means more than one composite generic food is needed. The data are sorted beginning with the nutrient that has the highest SD% value, and are then separated into different groups based on that nutrient.

Then, delete the outliers and unrelated USDA products, and re-calculate the average and SD% in each group. If the SD% of each group is lower than 15% or 25%, each group is given a reasonable potential composite generic food name (Figure B-1 and

Figure B-3). For example, to create a composite generic food for brand named product

“Crunch Pak Sweet Apple Slice” (Figure B-4), we first give “Apple” as the food name for that product. Then, we select all the data with the word “Apple” from the fruits and fruit juice categories in the molecular profile of individual USDA foods database and create the apple composite generic food excel file for testing. Next, the nutrient’s mean and

SD% is calculated in each molecular tab. Because the SD% of all the data with the word

“Apple” is higher than 15% in the amino acids profile tab, the data is then separated into two groups based on the tyrosine that has the highest SD%. Next, the average and

SD% is re-calculated for each group. The best result is the group with the lower SD%.

By reviewing the USDA names in each group of amino acids profile, we find that similar food names are aggregated into the same group. One group has the word “Apple” and the other group has the word “Apple Sauce”.

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When the SD% of each group is above 15% or 25% after grouping, the name of

USDA products in each group still needs to be reviewed. If the foods with similar names aggregate into one group, a potential reasonable name is given for each group. The review of USDA's name in each group also applies to the case where one group had the

SD% higher than 15% (or 25%) and the other group less than 15% (or 25%). For example, the carbohydrates profile of the apple composite generic food is separated into two groups based on fibers that have the highest SD%. One group aggregates all apple juice products with SD% above 25% and the other group is apple sauce with

SD% below 25%. In this case, two basic products are created because the product names of the two groups did not overlap. However, when the products’ names of each group overlap with the name of another group, we combine them into one group to create a composite generic food even though the SD% is higher than 15% or 25%

(Figure B-1, Figure B-2, and Figure B-3).

After finishing testing in all molecular profile tabs, a summary tab is created and the final result of each tab is copied into the summary tab. Then, count the number of composite generic food that need to be created. For example, the amino acids profile showed that it needs two composite generic foods in apple composite generic food testing file, which are “Apple Sauce” and “Apple”. In fatty acid families’ profile and fatty acid profile, they have the lower SD%, and thus, all the data can contribute to the corresponding profile of the composite generic food. However, in the carbohydrate families tab, the data is separated into two groups with the lower SD%, one group with the word “Apple”, and the other group with the word “Apple Juice”. In this case, we need to create three composite generic foods, which are “Apple composite generic food”,

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“Apple sauce composite generic food” and “Apple juice composite generic food” (Figure

B-4).

The final averages of each molecular profile would be used for the composite generic food molecular profile and all the USDA products contributing to molecular profiles would be used for mineral, vitamin and others (e.g., cholesterol) data of composite generic food. Last, assign a reasonable name, four unique digital numbers to indicate the composite generic food and copy the final result to the file called “Single composite generic food database".

When molecular profile(s) of the composite generic food is missing all values, it is necessary to review the NFL products macronutrients. In the case of the macronutrient reported as zero, missing molecular profiles are allowed. However, when macronutrient is reported as some value greater than zero but molecular profiles are missing, ingredients of brand named products will be reviewed to identify a similar food and the needed data used from that food. The average and SD% are calculated by using the same principles listed above.

Mixed Composite Generic Food Database

The mixed brand named product is defined as the product made from more than one ingredient and created by multiple composite generic foods (i.e. Gerber Vegetable

Beef dinner includes carrots and beef) which must be processed as two different single composite generic foods. First, the main ingredients are found according to the order of ingredients on the brand named product. Then, based on these ingredients, the appropriate composite generic foods are selected and the contribution of each composite generic food to the brand named product is estimated based on the order of ingredients. Next, assign a reasonable name and a unique ID number for the mixed

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composite generic food, and all information then stored in the excel file called “Mixed composite generic food database”. For example, the majority ingredients for Gerber

Second Foods All Natural Baby Food-Apple Blueberry (jar) are apple and blueberry.

Then, apple composite generic food and blueberry composite generic food are selected from the single composite generic food database, and are then combined into a new composite generic food, “Apple-blueberry composite generic food”. Because two ingredients are contributing equally to the composite generic food, each ingredient contributes to 50% of the new composite generic food. An ID number is assigned to the new composite generic food and the data is stored into the mixed composite generic food database (Figure B-5).

Molecular Profile of USDA Composite Generic Foods database

The molecular profile of USDA composite generic foods database is created by using “Molecular profile of USDA composite generic foods database” R script to combine the information in “Single composite generic food database” and “Mixed composite generic food database”.

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Figure B-1. Create a composite generic food file from molecular profile of individual USDA foods database by using the food name that assigns for each brand named product. Calculate the SD% and average in each molecular tab. After removing outlier, mixed USDA products, etc., the SD% of each nutrient needs to be reviewed. When SD% is lower than 15% or 25%, only one composite generic food needs to be created. However, when SD% is higher than 15% or 25%, separate the data into different groups, and recalculate the SD% and average. Then, the USDA products’ names in each group need to be reviewed for deciding the numbers of composite generic food s that need to be created. Last, assign the reasonable name for the composite generic food and a unique ID number. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli- deluxe-american-chee-396.aspx.

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Figure B-2. Give a food name for each brand named product, and search in the same food category of the molecular profile of individual USDA foods database by using that food names. Pulling out all related USDA products into the file called “Composite generic food _Food name” for testing the composite generic food. “Kraft Deli Deluxe American Cheese” image credits: http://www.kraftrecipes.com/products/kraft-deli-deluxe-american-chee- 396.aspx.

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Figure B-3. Calculate the standard deviation (SD%) of nutrients in each molecular profile tab of the “Composite generic food_Food Name” file. If the SD% is lower than 15% or 25%, only one composite generic food needs to be created. If the SD% is higher than 15% or 25%, separate the data into groups and re-calculate the SD% and average in each group. After removing the outliers, blanks, etc., the USDA food names in each group need to be reviewed. If the names overlap between groups, combine them into one group. If the name in each group is different, give the potential composite generic food names for each group and count the number of composite generic foods that need to be created.

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Figure B-4. Apple composite generic food test file is created by selecting the data from molecular profile of individual USDA foods database. Average and SD% are calculated for each nutrients in each molecular profile tab. When the SD% is lower than 15% or 25%, only one composite generic food is required. However, when SD% is higher than 15% or 25%, the data needs to be grouped using the nutrient with highest SD%, and then average and SD% in each group are recalculated. Next, review USDA food names in each group, assigning potential composite generic food names to each group. Finally, count the total number of composite generic foods that need to be created and assign appropriate composite generic food name(s) and ID number(s) in the summary tab. “Crunch Pak Sweet Apple slice” image credits: https://www.hy-vee.com/grocery/PD6190039/Crunch-Pak-Sweet-Apple- Slices.

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Figure B-5. An example of creating the mixed composite generic food. “Gerber Second Foods All Natural Baby Food- Apple Blueberry (jar)” image credits: https://www.hy-vee.com/grocery/PD22578470/Gerber-2nd-Foods-Apple- Blueberry-Baby-Food-2-Pack.

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Figure B-6. An example of calculating the amount of molecular compounds found in brand named products (NFL product) based on the composite generic food and the amount of macronutrients specified on the NFL. “Cheese” image credits: https://boards.na.leagueoflegends.com/en/c/general- discussion/cE21Wrd0-cheese-thread. “Sargento (R) Classic Cheese, Mild Cheese” image credits: https://www.fooducate.com/app#!page=product&id=5455FC4C-0CF6-11E0- BF92-FEFD45A4D471.

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APPENDIX C DOCUMENTATION FOR CUTTING 30 NUTRIENTS FROM MOLECULAR PROFILE OF INDIVIDUAL USDA FOODS DATABASE

Adjusted protein: adjusted protein determined by non-protein nitrogenous material [40], and it was not calculated from the amount of total nitrogen. Also, only four items existed in USDA database, so adjusted protein is not included in the adjusted nutrient database.

Undifferentiated fatty acids: Molecular profile of individual USDA foods database listed fatty acids undifferentiated rather than individual fatty acids. Fatty acids undifferentiated defined as the sum of individual fatty acids coming from the same class of undifferentiated fatty acids (e.g., oleic acid undifferentiated is the sum of cis, oleic acids and trans, oleic acid). If all fatty acids were included in the database, it would double calculate some individual fatty acids making it inaccurate to estimate the NFL product. Furthermore, some individual fatty acids are reported as blanks, which do not help to estimate the fatty acids molecular profile. Thus, only undifferentiated fatty acids are used in molecular profile of individual USDA foods database.

Trans-fatty acids: before the 1990s, very little knowledge about trans-fatty acids, and in 1990s, many types of research showed that trans-fatty acid are related with cardiovascular disease [83, 84]. Thus, the FDA required manufacturers to report the quantity of trans-fatty acids on NFL and encouraged people to reduce trans-fatty acids intake [99]. Natural foods that contain trans-fatty acids are mainly produced by cattle and sheep, such as butter and cheese. Since many foods do not have trans-fatty acids, and many individual trans-fatty acids were not reported in USDA SR28, total trans-fatty acids rather than every individual trans-fatty acid was be used in the molecular profile of individual USDA foods database.

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Water and ash: When prescribing PKT for patients, we do not consider water or ash content in meals. These two nutrients would not influence the relation of the molecular profile in macronutrients.

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Nutrients Nutrients Cis, Palmitoleic Cis, Cis, Linoleic Trans, Palmitoleic Trans, Trans, Linoleic Cis, Oleic Conjugated Linoleic Acid Trans, Oleic Eicosatrienoic,N-3 Cis, Erucic Eicosatrienoic,N-6 Trans,Erucic Arachidonic Trans Not Further Defined Linoleic Fatty Acids, Total Trans-Polyenoic Mixed Isomers, Linoleic Lycopene Fatty Acids, Phytosterol Total Trans-Monoenoic Energy Kilojoule Stigmasterol Alcohol Campesterol Caffeine Beta-Sitosterol Theobromine Water Adjusted Protein Theobromine Ash Lycopene

Figure C-1. Nutrients are not list in the molecular profile of individual USDA foods database.

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APPENDIX D FOODOMICS NUTRIENTS LIST

Figure D-1. Foodomics database nutrients list

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Figure D-1. Continued

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Figure D-1. Continued

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APPENDIX E SUPPLEMENTAL TABLE OF NUMBERS OF COMPOSITE GENERIC FOOD IN DIFFERENT FOOD CATEGORIES

Table E-1. The numbers of composite generic food in different food categories # Single composite # Single composite Category Category generic food generic food Vegetables and 43 Spices and Herbs 13 Vegetable Products Fruits and Fruit Juices 35 Beverages 13 Finfish and Dairy and Egg Products 34 11 Shellfish Products Fats and Oils 25 Poultry Products 9 Soups, Sauces, and 22 Pork Products 8 Gravies Sweets 19 Baked Products 8 Nut and Seed Products 18 Fast Foods 6 Legumes and Legume 18 Beef Products 5 Products Cereal Grains and 16 Snacks 4 Pasta Sausages and 14 Breakfast Cereals 2 Luncheon Meats

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APPENDIX F SUPPLEMENTAL TABLE OF FOODOMICS DATABASE DICTIONARY

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Table F-1. Foodomics database dictionary Column Long Name Short Name Unit Definition Number 1 COLUMN_INTENTIALLY_BLAN N/A N/A N/A K 2 BRAND-NAME NDID_FDB N/A nutrition facts database identification _PRODUCT_ID_NUMBER_FOO number DOMICS_DATABASE 3 YEAR_FOODOMICS_DATABAS YEAR_FDB N/A calendar year the food was collected E 4 INDEX_FOODMICS_DATABAS INDEX_FDB N/A row number with the first row equal to 1 E 5 PHOTO_FILE_PATH_FOODOMI PHOTO_FILE_PATH_F N/A food meta data: file path of source data CS_DATABASE DB used to input information from label or manufacturer 6 PRODUCT_NAME_FOODOMIC PROD_NAME_FDB N/A brand or generic name of product S_DATABASE 7 SOURCE_FOODOMICS_DATA SOURCE_FDB N/A write label or USDA to signify if brand BASE named food (label) or USDA food 8 USDA_FOOD_GROUP_FOODO USDA_FOOD_GROUP_ N/A food meta data: high-level grouping MICS_DATABASE FDB assigned to product using USDA grouping categories 9 SUB_FOOD_GROUP_FOODOM SUB_FOOD_GROUP_F N/A food meta data: lower-level grouping ICS_DATABASE DB assigned to product 10 SUB_SUB_FOOD_GROUP_FO SUB_SUB_FOOD_GRO N/A food meta data: lowest-level grouping ODOMICS_DATABASE UP_FDB assigned to product 11 RAW_COOKED_FOODOMICS_ RAW_COOKED_FDB N/A food meta data: form of product weighed DATABASE before administration 12 PACKAGE_TYPE_FOODOMICS PKG_TYPE_FDB N/A food meta data: form of package product is _DATABASE sold in

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 13 WEIGH_INSTRUCTION_FOOD WEIGH_INSTRUCTION N/A food meta data: how food was weighed OMICS_DATABASE _FDB prior to administration 14 CONTROL_NUMBER_FOODO CONTROL_NUMBER_F N/A control number located on the food MICS_DATABASE DB product found in the grocery store 15 MANUFACTURER_FOODOMIC MANUFACTURER_FDB N/A manufacturer S_DATABASE 16 STORE_FOODOMICS_DATABA STORE_FDB N/A name of store, abbreviated, the food was SE_FOODOMICS_DATABASE found. see for list of store abbreviations 17 PACKAGE_AMOUNT_FOODOM PKG_AMOUNT_FDB N/A weight of package ICS_DATABASE 18 PACKAGE_UNIT_FOODOMICS PKG_UNIT_FDB N/A unit of package weight in grams or ml _DATABASE 19 PRICE_PER_PACKAGE_FOOD PRICE_PER_PKG_FDB $/kg price of package in American dollars OMICS_DATABASE 20 PRICE_PER_UNIT_FOODOMIC PRICE_PER_UNIT_FDB $ price of package in American dollars per S_DATABASE unit weight either in g or mg 21 SERVING_SIZE_FOODOMICS_ SERVING_SIZE_FDB N/A serving size from the nutrition facts label DATABASE of the food product 22 WEIGHT_PER_SERVING_G_F WT_FDB g or ml weight of food in grams or ml OODOMICS_DATABASE 23 CALORIES_PER_KCAL_FOOD ENERC_CAL_FDB Cal/100 g amount of calories per 100 g of food OMICS_DATABASE of food 24 TOTAL_FAT_FOODOMICS_DA FAT_FDB g/100 g of amount of fat per 100 g of food TABASE food 25 SATURATED_FAT_FOODOMIC FASAT_FDB g/100 g of amount of saturated fatty acids per 100 g S_DATABASE food of food 26 TRANS_FAT_FOODOMICS_DA FATRN_FDB g/100 g of amount of trans- fatty acids per 100 g of TABASE food food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 27 MONOUNSATURATED_FAT_F FAMS_FDB g/100 g of amount of monounsaturated fatty acids OODOMICS_DATABASE food per 100 g of food 28 POLYUNSATURATED_FAT_FO FAPU_FDB g/100 g of amount of polyunsaturated fatty acids per ODOMICS_DATABASE food 100 g of food 29 LA_FOODOMICS_DATABASE F18D2_FDB g/100 g of amount of 18:2 per 100 g of food food 30 ALA_FOODOMICS_DATABASE F18D3CN3_FDB g/100 g of amount of 18:3 n 3 per 100 g of food food 31 EPA_FOODOMICS_DATABASE F20D5_FDB g/100 g of amount of 20:5 per 100 g of food food 32 DHA_FOODOMICS_DATABASE F22D6_FDB g/100 g of amount of 22:6 per 100 g of food food 33 CHOLESTEROL_FOODOMICS_ CHOLE_FDB mg/100 g amount of cholesterol per 100 g of food DATABASE of food 34 KETO_CARBOHYDRATE_FOO CHO_KETO_FDB g/100 g of amount of keto-carbohydrate per 100 g of DOMICS_DATABASE food food 35 CARBOHYDRATE_FOODOIMC CHOCDF_FDB g/100 g of amount of carbohydrate per 100 g of food S_DATABASE food 36 FIBER_FOODOMICS_DATABA FIBTG_FDB g/100 g of amount of fiber per 100 g of food SE food 37 SUGAR_FOODOMICS_DATAB SUG_FDB g/100 g of amount of sugar per 100 g of food ASE food 38 PROTEIN_FOODOMICS_DATA PROCNT_FDB g/100 g of amount of protein per 100 g of food BASE food 39 SODIUM_FOODOMICS_DATAB NA_FDB mg/100g of amount of sodium per 100g of food ASE food 40 POTASSIUM_FOODOMICS_DA K_FDB mg/100g of amount of potassium per 100g of food TABASE food 41 INSOLUBLE_FIBER_FOODOMI FINSOL_FDB g/100 g of amount of insoluble fiber per 100 g of CS_DATABASE food food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 42 SOLUBLE_FIBER_FOODOMCI FSOL_FDB g/100 g of amount of soluble fiber per 100 g of food S_DATABASE food 43 SUGAR_ALCOHOLS_FOODOM SUG_ALC_FDB g/100 g of amount of sugar alcohols per 100 g of ICS_DATABASE food food 44 STARCH_FOODOMICS_DATAB STARCH_FDB g/100 g of amount of starch per 100 g of food ASE food 45 SUCROSE_FOODOMICS_DAT SUCS_FDB g/100 g of amount of sucrose per 100 g of food ABASE food 46 DEXTROSE_FOODOMICS_DAT DEX_FDB g/100 g of amount of dextrose per 100 g of food ABASE food 47 GLUTAMINE_FOODOMICS_DA GLN_FDB g/100g of amount of glutamine per 100 g of food TABASE food 48 LACTOSE_FOODOMICS_DATA LACS_FDB g/100 g of amount of lactose per 100 g of food BASE food 49 MALTOSE_FOODOMICS_DATA MALS_FDB g/100 g of amount of maltose per 100 g of food BASE food 50 FRUCTOSE_FOODOMICS_DAT FRUS_FDB g/100 g of amount of fructose per 100 g of food ABASE food 51 GALACTOSE_FOODOMICS_DA GALS_FDB g/100 g of amount of galactose per 100 g of food TABASE food 52 GLUCOSE_FOODOMCIS_DAT GLUS_FDB g/100 g of amount of glucose per 100 g of food ABASE food 53 VITAMIN_A_FOODOMICS_DAT VITA_FDB IU/100 g of amount of vitamin A per 100 g of food ABASE food 54 VITAMIN_C_FOODOMICS_DAT VITC_FDB mg/100g of amount of vitamin C per 100g of food ABASE food 55 VITAMIN_E_ALPHA_TOCOPHE TOCPHA_FDB mg/100g of amount of vitamin E (alpha-tocopherol) ROL_FOODOMICS_DATABASE food per 100g of food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 56 THIAMIN_FOODOMICS_DATAB THIA_FDB mg/100g of amount of thiamin per 100g of food ASE food 57 RIBOFLAVIN_FOODOMICS_DA RIBF_FDB mg/100g of amount of riboflavin per 100g of food TABASE food 58 NIACIN_FOODOMICS_DATABA NIA_FDB mg/100g of amount of niacin per 100g of food SE food 59 VITAMIN_B6_FOODOMICS_DA VITB6_FDB mg/100g of amount of vitamin B6 per 100g of food TABASE food 60 VITAMIN_B12_FOODOMICS_D VITB12_FDB µg/100 g of amount of vitamin B12 per 100 g of food ATABASE food 61 VITAMIN_D_D2_D3_FOODOMI VITD_D2_D3_FDB µg/100 g of amount of vitamin D2+D3 per 100 g of CS_DATABASE food food 62 VITAMIN_K_FOODOMICS_DAT VITK_FDB µg/100 g of amount of vitamin K per 100 g of food ABASE food 63 PANTOTHENIC_FOODOMICS_ PANTAC_FDB mg/100g of amount of pantothenic per 100g of food DATABASE food 64 BIOTIN_FOODOMICS_DATABA BIO_FDB mcg/100 g amount of biotin per 100 g of food SE of food 65 CAROTENE_ALPHA_FOODOMI CARTA_FDB µg/100 g of amount of carotene alpha per 100 g of CS_DATABASE food food 66 CAROTENE_BETA_FOODOMIC CARTB_FDB µg/100 g of amount of carotene beta per 100 g of S_DATABASE food food 67 FOLIC_ACID_FOODOMICS_DA FOLAC_FDB µg/100 g of amount of folic acid per 100 g of food TABASE food 68 MENAQUINONE_4_FOODOMIC MK4_FDB µg/100 g of amount of menaquinone per 100 g of food S_DATABASE food 69 RETINOL_FOODOMICS_DATA RETOL_FDB µg/100 g of amount of retinol per 100 g of food BASE food 70 TOCOPHEROL_BETA_FOODO TOCPHB_FDB mg/100g of amount of tocopherol beta per 100g of MICS_DATABASE food food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 71 TOCOPHEROL_DELTA_FOOD TOCPHD_FDB mg/100g of amount of tocopherol delta per 100g of OMICS_DATABASE food food 72 TOCOPHEROL_GAMMA_FOOD TOCPHG_FDB mg/100g of amount of tocopherol gamma per 100g of OMICS_DATABASE food food 73 TOCOTRIENOL_ALPHA_FOOD TOCTRA_FDB mg/100g of amount of tocotrienol alpha per 100g of OMICS_DATABASE food food 74 TOCOTRIENOL_BETA_FOODO TOCTRB_FDB mg/100g of amount of tocotrienol beta per 100g of MICS_DATABASE food food 75 TOCOTRIENOL_DELTA_FOOD TOCTRD_FDB mg/100g of amount of tocotrienol delta per 100g of OMICS_DATABASE food food 76 TOCOTRIENOL_GAMMA_FOO TOCTRG_FDB mg/100g of amount of tocotrienol gama per 100g of DOMICS_DATABASE food food 77 VITAMIN_A_RAE_PER_FOODO VITA_RAE_FDB µg/100 g of amount of vitamin A (retinol activity MICS_DATABASE food equivalents) per 100 g of food 78 VITAMIN_B_12_ADDED_FOOD VITB12_ADDED_FDB µg/100 g of amount of vitamin per B12 added 100 g of OMICS_DATABASE food food 79 VITAMIN_D_FOODOMICS_DAT VITD_IU_FDB IU/100 g of amount of vitamin D per 100 g of food ABASE food 80 VITAMIN_D2_ERGOCALCIFER ERGCAL_FDB µg/100 g of amount of vitamin D2 per 100 g of food OL_FOODOMICS_DATABASE food 81 CHOLECALCIFERAL_FOODOM CHOLAC_FDB µg/100 g of amount of cholecalciferal per 100 g of ICS_DATABASE food food 82 VITAMIN_E_FOODOMICS_DAT VITE_ADDED_FDB mg/100g of amount of vitamin E per 100g of food ABASE food 83 FOLATE_TOTAL_FOODOMICS FOL_FDB µg/100 g of amount of total folate per 100 g of food _DATABASE food 84 FOLATE_DFE_FOODOMICS_D FOLDFE_FDB µg/100 g of amount of dietary folate per 100 g of food ATABASE food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 85 FOLATE_FROM_FOOD_FOOD FOLFD_FDB µg/100 g of amount of food folate per 100 g of food OMICS_DATABASE food 86 CALCIUM_FOODOMICS_DATA CA_FDB mg/100g of amount of calcium per 100g of food BASE food 87 IRON_FOODOMICS_DATABAS FE_FDB mg/100g of amount of iron per 100g of food E food 88 ZINC_FOODOMICS_DATABAS ZN_FDB mg/100g of amount of zinc per 100g of food E food 89 PHOSPHORUS_FOODOMICS_ P_FDB mg/100g of amount of phosphorus per 100g of food DATABASE food 90 MAGNESIUM_FOODOMICS_D MAG_FDB mg/100g of amount of magnesium per 100g of food ATABASE4 food 91 COPPER_FOODOMICS_DATA CU_FDB mg/100g of amount of copper per 100g of food BASE food 92 SELENIUM_FOODOMICS_DAT SE_FDB µg/100 g of amount of selenium per 100 g of food ABASE food 93 MANGANESE_FOODOMICS_D MN_FDB mg/100g of amount of manganese per 100g of food ATABASE food 94 IODINE_FOODOMICS_DATABA IOD_FDB mcg/100 g amount of iodine per 100 g of food SE of food 95 BORON_FOODOMICS_DATAB BORON_FDB mg/100g of amount of boron per 100g of food ASE food 96 CHROMIUM_FOODOMICS_DA CHR_FDB mcg/100 g amount of chromium per 100 g of food TABASE of food 97 FLUORIDE_FOODOMICS_DAT FLD_FDB mg/100g of amount of fluoride per 100g of food ABASE food 98 MOLYBDENUM_FOODOMICS_ MLY_FDB mcg/100 g amount of molybdenum per 100 g of food DATABASE of food 99 NICKEL_FOODOMICS_DATAB NCK_FDB mg/100g of amount of nickel per 100g of food ASE food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 100 SILICON_FOODOMICS_DATAB SIL_FDB mcg/100 g amount of silicon per 100 g of food ASE of food 101 CHLORIDE_FOODOMICS_DAT CHL_FDB mg/100g of amount of chloride per 100g of food ABASE food 102 TIN_FOODOMICS_DATABASE TIN_FDB mg/100g of amount of tin per 100g of food food 103 TAURINE_FOODOMICS_DATA TAUR_FDB g/100 g of amount of taurine per 100 g of food BASE food 104 TRYPTOPHAN_FOODOMICS_ TRP_FDB g/100 g of amount of tryptophan per 100 g of food DATABASE food 105 THREONINE_FOODOMICS_DA THR_FDB g/100 g of amount of threonine per 100 g of food TABASE food 106 ISOLEUCINE_FOODOMICS_DA ILE_FDB g/100 g of amount of isoleucine per 100 g of food TABASE food 107 LEUCINE_FOODOMCIS_DATA LEU_FDB g/100 g of amount of leucine per 100 g of food BASE food 108 LYSINE_FOODOMICS_DATAB LYS_FDB g/100 g of amount of lysine per 100 g of food ASE food 109 METHIONINE_FOODOMICS_D MET_FDB g/100 g of amount of methionine per 100 g of food ATABASE food 110 CYSTINE_FOODOMICS_DATA CYS_FDB g/100 g of amount of cystine per 100 g of food BASE food 111 PHENYLALANINE_FOODOMIC PHE_FDB g/100 g of amount of phenylalanine per 100 g of S_DATABASE food food 112 TYROSINE_FOODOMICS_DAT TYR_FDB g/100 g of amount of tyrosine per 100 g of food ABASE food 113 VALINE_FOODOMICS_DATAB VAL_FDB g/100 g of amount of valine per 100 g of food ASE food 114 ARGININE_FOODOMICS_DATA ARG_FDB g/100 g of amount of arginine per 100 g of food BASE food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 115 HISTIDINE_FOODOMICS_DAT HISTN_FDB g/100 g of amount of histidine per 100 g of food ABASE food 116 ALANINE_FOODOMICS_DATA ALA_FDB g/100 g of amount of alanine per 100 g of food BASE food 117 ASPARTIC_ACID_FOODOMICS ASP_FDB g/100 g of amount of aspartic per 100 g of food _DATABASE food 118 GLUTAMIC_ACID_FOODOMIC GLU_FDB g/100 g of amount of glutamic per 100 g of food S_DATABASE food 119 GLYCINE_FOODOMICS_DATA GLY_FDB g/100 g of amount of glycine per 100 g of food BASE food 120 PROLINE_FOODOMIC_DATAB PRO_FDB g/100 g of amount of proline per 100 g of food ASE food 121 SERINE_FOODOMICS_DATAB SER_FDB g/100 g of amount of serine per 100 g of food ASE food 122 HYDROXYPROLINE_FOODOMI HYP_FDB g/100 g of amount of hydroxyproline per 100 g of CS_DATABASE food food 123 GAMMA_LINOLENIC_FOODOM F18D3CN6_FDB g/100 g of amount of 18:3cn6 per 100 g of food ICS_DATABASE food 124 BUTYRIC_FOODOMICS_DATA F4D0_FDB g/100 g of amount of 4:0 per 100 g of food BASE food 125 CARPROIC_FOODOMICS_DAT F6D0_FDB g/100 g of amount of 6:0 per 100 g of food ABASE food 126 CAPRYLIC_FOODOMICS_DAT F8D0_FDB g/100 g of amount of 8:0 per 100 g of food ABASE food 127 CAPRIC_FOODOMICS_DATAB F10D0_FDB g/100 g of amount of 10:0 per 100 g of food ASE food 128 LAURIC_FOODOMICS_DATAB F12D0_FDB g/100 g of amount of 12:0 per 100 g of food ASE food 129 MYRISTIC_FOODOMICS_DATA F14D0_FDB g/100 g of amount of 14:0 per 100 g of food BASE food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 130 PALMITIC_FOODOMICS_DATA F16D0_FDB g/100 g of amount of 16:0 per 100 g of food BASE food 131 STERIC_FOODOMICS_DATAB F18D0_FDB g/100 g of amount of 18:0 per 100 g of food ASE food 132 EICOSANOIC_FOODOMICS_D F20D0_FDB g/100 g of amount of 20:0 per 100 g of food ATABASE food 133 OLEIC_UNDIFFERENTIATED_F F18D1_FDB g/100 g of amount of 18:1 per 100 g of food OODOMICS_DATABASE food 134 20:4_FOODOMICS_DATABASE F20D4_FDB g/100 g of amount of 20:4 per 100 g of food food 135 DOCOSANOIC_FOODOMICS_ F22D0_FDB g/100 g of amount of 22:0 per 100 g of food DATABASE food 136 MYRISTOLEIC_FOODOMICS_D F14D1_FDB g/100 g of amount of 14:1 per 100 g of food ATABASE food 137 PALMITOLEIC_FOODOMICS_D F16D1_FDB g/100 g of amount of 16:1 per 100 g of food ATABASE food 138 PARINARIC_FOODOMICS_DAT F18D4_FDB g/100 g of amount of 18:4 per 100 g of food ABASE food 139 EICISENOIC_FOODOMICS_DA F20D1_FDB g/100 g of amount of 20:1 per 100 g of food TABASE food 140 DOCOSENOIC_UNDIFFERENTI F22D1_FDB g/100 g of amount of 22:1 per 100 g of food ATED_FOODOMICS_DATABAS food E 141 DOCOSAPENTAENOIC_FOOD F22D5_FDB g/100 g of amount of 22:5 per 100 g of food OMICS_DATABASE food 142 PENTADECANOIC_FOODOMIC F15D0_FDB g/100 g of amount of 15:0 per 100 g of food S_DATABASE food 143 HEPTADECANOIC_FOODOMIC F17D0_FDB g/100 g of amount of 17:0 per 100 g of food S_DATABASE food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 144 TETRACOSANOIC_FOODOMIC F24D0_FDB g/100 g of amount of 24:0 per 100 g of food S_DATABASE food 145 NERVONIC_FOODOMICS_DAT F24D1C_FDB g/100 g of amount of 24:1c per 100 g of food ABASE food 146 EICOSADIENOIC_FOODOMICS F20D2CN6_FDB g/100 g of amount of 20:2cn6 per 100 g of food _DATABASE food 147 HEPTADECENOIC_FOODOMIC F17D1_FDB g/100 g of amount of 17:1 per 100 g of food S_DATABASE food 148 EICOSATRIENOIC_UNDIFFER F20D3_FDB g/100 g of amount of 20:3 per 100 g of food ENTIATED_FOODOMICS_DAT food ABASE 149 TRIDECANOIC_FOODOMICS_ F13D0_FDB g/100 g of amount of 13:0 per 100 g of food DATABASE food 150 PENTADECENOIC_FOODOMIC F15D1_FDB g/100 g of amount of 15:1 per 100 g of food S_DATABASE food 151 21:5_FOODOMICS_DATABASE F21D5_FDB g/100 g of amount of 21:5 per 100 g of food food 152 22:4_FOODOMICS_DATABASE F22D4_FDB g/100 g of amount of 22:4 per 100 g of food food 153 18:3_FOODOMICS_DATABASE F18D3_FDB g/100 g of amount of 18:3 per 100 g of food food 154 CHOLINE_FOODOMICS_DATA CHOLN_FDB mg/100g of amount of choline per 100g of food BASE food 155 N_ACETYL_CARNITINE_FOOD NAC_FDB mg/100g of amount of acetyl carnitine per 100g of OMICS_DATABASEW food food 156 L_CARNITINE_FOODOMICS_D LCRN_FDB mcg/100 g amount of l carnitine per 100 g of food ATABASE of food 157 LUTEIN_ZEAXANTHIN_FOODO LUTZEA_FDB mcg/100 g amount of lutein per 100 g of food MICS_DATABASE of food

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Table F-1. Continued Column Long Name Short Name Unit Definition Number 158 LYCOPENE_FOODOMICS_DAT LYCPN_FDB µg/100 g of amount of lycopene per 100 g of food ABASE food 159 ALCOHOL_FOODOMICS_DATA ALC_FDB g/100 g of amount of alcohol per 100 g of food BASE food 160 BSITOSTEROL_FOODOMICS_ BSITOSTEROL_FDB mg/100g of amount of bsitosterol per 100g of food DATABASE food 161 BETAINE_FOODOMICS_DATA BETN_FDB g/100 g of amount of betaine per 100 g of food BASE food 162 CAFFEINE_FOODOMICS_DAT CAFFN_FDB g/100 g of amount of caffeine per 100 g of food ABASE food 163 CAMPESTEROL_FOODOMICS CAMPESTEROL_FDB mg/100g of amount of campesterol per 100g of food _DATABASE food 164 CRYPTOXANTHIN_BETA_FOO CRYPX_FDB mcg/100 g amount of cryptoxanthin per 100 g of food DOMICS_DATABASE of food 165 DIHYDROPHYLLOQUINONE_F VITK1D_FDB mcg/100 g amount of dihydrophylloquinone per 100 OODOMICS_DATABASE of food g of food 166 ENERGY_KILOJOULE_FOODO ENERC_KJ_FDB KJ/100g of amount of energy per 100g of food MICS_DATABASE food 167 PHYTOSTEROLS_FOODOMIC PHYTOSTEROLS_FDB mg/100g of amount of phytosterols per 100g of food S_DATABASE food 168 THEOBROMINE_FOODOMICS_ THEBRN_FDB g/100 g of amount of theobromine per 100 g of food DATABASE food 169 STIGMASTEROL_FOODOMICS STIGMASTEROL_FDB mg/100g of amount of stigmasterol per 100g of food _DATABASE food 170 PEND_FOOD_FOODOMICS_D PEND_FOOD_FDB N/A needs to be audited. Not ready to be used ATABASE 171 STAMP_CREATED_FOODOMI STAMP_CREATED_FDB N/A date the product was added to the CS_DATABASE foodomics database. 172 STAMP_UPDATED_FOODOMI STAMP_UPDATED_FDB N/A date the product was updated by a CS_DATABASE person 173 ACTIVE_FOODOMICS_DATAB ACTIVE_FDB N/A will be true or false to be able to use in ASE meals

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APPENDIX G SUPPLEMENTAL FIGURE OF NUTRITION FACTS LABEL OF “ROSS CARBOHYDRATE FREE” PRODUCT

Figure G-1. Nutrition facts label of “Ross Carbohydrate Free”. Image credits: https://nutrition.abbott/au/product/ross-carbohydrate-free/nutrition.

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BIOGRAPHICAL SKETCH

Lujia Yang was born in Guangyuan, Sichuan Province, China and raised in

Mianyang, Sichuan Province, China. She received her high school diploma in June,

2009 from Mianyang Nanshan High School and started her undergraduate studies at the Pusan National University, South Korea in the spring of 2011. Lujia graduated from

Pusan National University in the spring of 2015 with a Bachelor of Food Science and

Human Nutrition. She started her master’s program in food science and human nutrition at the University of Florida in 2016.

Lujia received the Pusan National University Full Scholarship for overseas students (2011-2015), the Korea-China Friendship Association Chinese Students

Studying Abroad Scholarship (2012), the Foreigner Students Scholarship Provided by

Pusan Education Bureau (2013), the South Korean Government Invited Foreigner

Scholarship (2013-2014) and graduated on President’s list of Pusan National University

(2015).

Lujia graduated with her Master of Science in food science and human nutrition at the University of Florida in the spring of 2018.

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