Hba1c in Non-Diabetic Adults Using Nhanes 2013-2014 Data: The

HBA1C IN NON-DIABETIC ADULTS USING NHANES 2013-2014 DATA: THE

RELATIONSHIP WITH CAFFEINE, CARBOHYDRATES, AND PHYSICAL

ACTIVITY

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science

Hadia Meashi

December, 2018

HBA1C IN NON-DIABETIC ADULTS USING NHANES 2013-2014 DATA: THE

RELATIONSHIP WITH CAFFEINE, CARBOHYDRATES, AND PHYSICAL

ACTIVITY

Hadia Meashi

Thesis

Approved: Accepted:

______Committee Chair Interim Dean of the College Dr. Ronald Otterstetter Dr. Elizabeth Kennedy

______Committee Member Dean of the Graduate School Mr. Brian Miller Dr. Chand Midha

______Committee Member Interim School Director Dr. Laura A. Richardson Dr. Judith A. Juvancic-Heltzel

______Committee Member Date Dr. Mark Fridline

TABLE OF CONTENTS

Page

LIST OF TABLES ...... v

LIST OF FIGURES ...... vi

ABSTRACT ...... vii

ACKNOWLEDGMENT...... ix

CHAPTER

I. INTRODUCTION ...... 1

II. REVIEW OF LITERATURE ...... 5

Glucose Homeostasis ...... 5

Diabetes Overview ...... 8

Caffeine and Type 2 Diabetes Mellitus ...... 12

Caffeine Consumption Trends in the United States ...... 13

Caffeine (Forms and Basics) ...... 14

Caffeine Function and Metabolism ...... 15

Insulin ...... 17

Carbohydrates ...... 20

Simple Carbohydrate Trends in the U.S...... 21

Carbohydrate Metabolism ...... 22

Physical Activity and type 2 diabetes ...... 26

iii

The Interrelationship of Carbohydrates, Caffeine, and HbA1c ...... 27

III. METHODOLOGY ...... 32

Inclusion/Exclusion criteria ...... 33

Variable Inclusion ...... 34

Statistical Analysis ...... 35

IV. RESULTS ...... 40

V. DISCUSSION ...... 50

Limitations ...... 59

Application and Future Studies ...... 59

REFERENCES ...... 62

LIST OF TABLES

Table Page

1. Demographic characteristics of study participants ...... 40

2. Descriptive characteristics of study participants ...... 41

3. CHAID decision rules for the classification Diabetes or Pre-Diabetes based on HbA1C criteria ...... 49

LIST OF FIGURES

Figure Page

1. The full CHAID decision tree ...... 43

2. CHAID decision tree node 1 split ...... 45

3. CHAID decision tree node 4 split ...... 47

ABSTRACT

According to the Centers for Disease Control (2018), more than 30 million

Americans have diabetes with the majority of cases diagnosed as type 2 diabetes. One in three US adults is at risk for type 2 diabetes, due to the substantial volume of people diagnosed with DM, there is a need for healthcare providers and researchers to help better understand the causes attributed to DM to reduce or prevent the occurrence of future cases. Prediabetes is defined by the American Diabetes Association (2016) as blood glucose levels above the normal average but not high enough to be diagnosed with

Diabetes Mellitus. Chronic high levels of blood glucose may lead to many health complications including diabetes. Understanding variables that may increase the risk of prediabetes or diabetes may potentially reduce future cases and lead to a healthier society. There is consensus in the literature explaining the relationship of elevated glucose with the risk of poor body composition, age, sedentary behavior and high carbohydrate diets however caffeine consumption and risk for diabetes has varied.

Research has demonstrated that caffeine consumption has an effect on glycemic control but according to the ADA (2017) conflicting findings among numerous caffeine research studies exist. Some indicate that drinking coffee may prevent type 2 diabetes while other studies reported that coffee intake could elevate blood glucose. The purpose of this study was to investigate the effect of chronic caffeine ingestion on glycemic mechanics in non-diabetic adults by using Chi-square Automatic Interaction Detection (CHAID)

vii decision tree to classify prediabetes and diabetes based on A1C criteria using a sample derived from the National Health and Nutrition Examination Survey (NHANES) 2013-

2014 data. The study sample was derived from 3928 male and females’ participants between 18 to 59 years of age. The CHAID growth criteria was set as 60 cases parent node and 30 cases for child node with significance for a split and merge set at α = 0.05 and maximum tree depth set at the default of 3 levels. Results from the data showed the risk for diabetes or prediabetes was almost five times (4.57) higher with those with high carbohydrate intake compared to those with low carbohydrate intake. Results showed the risk of prediabetes or diabetes was 1.54 times higher in males compared to females and participant’s age increased the risk with 50.1% risk for people who are older than 51 years of age compared to 6.8% risk for whose less than or equal to 27 years of age. Daily habitual caffeine consumption greater than 92 mg/day combined with low physical activity (less than 30 minutes per day of exercise) were 4.17 times more likely to have diabetes or pre-diabetes based on A1C criteria compared to those who were physically active (more than 30 minutes per day of exercise). Overall, non-diabetic adults consuming habitual caffeine consumption combined with regular physical activity had the lowest risk for elevated A1C and being diagnosed with prediabetes or diabetes. The findings provide a unique understanding to the potential relationship between caffeine and physical activity leading to a protective effect on glycemic mechanics in non- diabetic adults.

viii ACKNOWLEDGEMENTS

I would like to thank the faculty and staff of the School of Sport Science and

Wellness Education at The University of Akron for their support throughout my educational career.

A special thank you to my academic advisor and thesis committee chair Dr.

Ronald Otterstetter, for his advice from the first day he met me at the university. Dr. O always helped me whenever I ran into trouble. Thank you for all you have done to make my Master of Science in Exercise Physiology a positive and pleasant experience. I would also like to acknowledge my advisor, Brain Miller, for his guidance and patience through the thesis process. Thank you also for enriching my knowledge and providing me with opportunities to pursue research using NHANES data. I would also like to express thanks to Dr. Laura A. Richardson, for dedicating her time to be on my committee and sharing her expertise. Dr. Mark Fridline provided his guidance and help with statistical interpretations and I am grateful for his time with data analysis.

Finally, I must express my very profound gratitude to my parents, my husband, my sisters, and my friends for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. Also, I would also like to acknowledge my government that provides me with a full scholarship to continue my studying in the U.S. This accomplishment would not have been possible without them. Thank you.

CHAPTER Ⅰ

INTRODUCTION

The total number of diabetes mellitus (DM) is expected to affect more than 366 million people by 2030 compared to 171 million in 2000 (Wild, Roglic, Green, Sicree, &

King, 2004) It has been documented that the magnitude of DM has been continually rising both in the U.S. and globally. According to American Diabetes Association

[ADA] (2018), 30.3 million Americans had diabetes in 2015. Additionally, Centers for

Disease Control and Prevention [CDC] (2018), estimated that 84 million American adults had prediabetes and approximately 90% were unaware they had diabetes. Due to the substantial volume of people diagnosed with DM, there is a need for healthcare providers and researchers to help better understand the causes attributed to DM to reduce or prevent the occurrence of future cases.

Prediabetes is defined by ADA (2016) as blood glucose levels above the normal average but not high enough to be diagnosed with Diabetes Mellitus. According to the

ADA (2014), Diabetes Mellitus is a condition which characterized by hyperglycemia resulting from the inability of the human body to utilize blood glucose for energy.

Specifically, type 1 diabetes (T1DM) occurs when the pancreas does not produce insulin, and therefore blood glucose cannot gain access into the cells to be used for

energy. While, type 2 diabetes (T2DM) occurs when the body is unable to use insulin correctly. The resulting complication arising from both forms of DM is hyperglycemia, defined as a high level of glucose in the bloodstream and is often associated with diabetes or prediabetes.

The chronic high level of blood glucose can lead to many health complications.

Insufficient insulin response leads to an acute elevated the level of blood glucose.

However, chronic hyperglycemia can produce health complications such as cardiovascular disease, blindness, kidney disease, and nerve damage (Tunnicliffe &

Shearer ,2008). DM is characterized by numerous dimensions including glycated- hemoglobin A1C (HbA1C), fasting plasma glucose (FGP), random glucose, and oral glucose tolerance test (OGTT). The HbA1c test reflects the average of blood glucose level over the past 2 to 3 months. The majority of the literature on the interaction between diet and diabetes has focused on carbohydrate intake and type. However, there is paucity in the literature that explores the effect of caffeine on glycemic mechanics, specifically long-term indicators.

Caffeine is the most vastly psychoactive substance that has been consumed worldwide (Clark & Landolt, 2017). There are different forms of caffeine sources.

However, the main source of caffeine has been coffee followed by soft drinks and tea

(Mahoney et al., 2018). Previous studies have discussed varying results with caffeine and health. A study found that a high coffee consumption for 4 weeks increased the concentration of fasting insulin compared to coffee abstinence (Van Dam, Pasman, &

Verhoef, 2004). Another study found that moderate consumption of caffeinated and decaffeinated coffee may lower risk of type 2 diabetes in women whose age between 26 to 46 years (van, Willett, Manson, & Hu, 2006). Caffeine causes alterations in glucose homeostasis by elevating blood glucose levels in people with type 2 diabetes (Zaharieva

& Riddell, 2013). While habitual consumption of caffeine increases chronic glucose levels, thereby caffeine abstinence in people with type 2 diabetes who drink coffee daily, may lead to improving chronic glucose control (Lane, Lane, Surwit, Kuhn, & Feinglos,

2012).

According to Mahoney et al. (2018), the most common reasons reported for consuming caffeine among US college students included to feel awake (79%); enjoy the taste (68%); the social aspects of consumption (39%); improve concentration (31%); raise physical energy (27%); improve mood (18%); and relieve stress (9%). A study done by Dewar and Heuberger (2017), found that caffeine intake with carbohydrates may raise blood glucose levels and may decrease hypoglycemia. Regular physical activity aids to improve overall health & fitness and to decreases the risk for many chronic diseases (CDC, 2018). Bweir et al. (2009), found that both resistance and aerobic exercise were significantly effective in reducing both blood glucose levels and

HbA1c levels before and after exercise, reducing the risk for long-term diabetic complications in type 2 diabetes.

The current study seeks to investigate glycemic mechanism effects using HbA1c in response to caffeine, carbohydrate, and physical activity. With the increasing number

of people with DM, research is needed to examine potential causes to help regulate and maintain healthy blood glucose levels to reduce occurrence of diabetes. The majority of previously published research has focused on the relationship of carbohydrate intake and

DM. This current study examined caffeine, physical activity, carbohydrates and its relationship with chronic hyperglycemia which measured by A1C. This study seeks to explore the effect of diet and physical activity on glycemic mechanism for non-diabetic subjects. Diet focuses primarily on dietary carbohydrate consumption and caffeine intake and the relationship to glycemic mechanics in non-diabetes adults.

The current investigation purposively addressed the following research questions using sample data of non-diabetic adults from the National Health and Nutrition

Examination Survey 2013-2014:

1. What is the interrelationship between caffeine and carbohydrate intake on

HbA1C?

2. What is the association of carbohydrate intake on HbA1c?

3. What is the association of caffeine intake on HbA1c?

4. What impact does this combination of variables have on HbA1C on the decision

tree pathways?

CHAPTER II

REVIEW OF LITERATURE

Glucose Homeostasis

Glucose is a key source of energy for all the cells in the body (Tappy, 2008).

Most of the body organs and tissues continuously need glucose, as an essential source of energy (Szablewski, 2011). Glucose is one of the most common forms of simple sugars

(Fink & Mikesky, 2015). Most of the food that person eat is broken down into glucose, which is then released into the bloodstream, the pancreas produces the hormone insulin permitting blood glucose to get into the body’s cells and use as an energy source

(Centers for Disease Control and Prevention, 2018). Low concentrations of glucose in the blood can lead to seizures, loss of consciousness, and death, while an elevation of blood glucose concentrations can cause blindness, renal failure, vascular disease, and neuropathy (Szablewski, 2011). However, the normal plasma glucose concentration is between 70 and 130 mg/dl before meals (American Diabetes Association, 2018).

It is important to maintain blood glucose concentration at a normal level in order to avoid problems related to high or low blood glucose concentration. The method of maintaining blood glucose at a steady-state level is known as glucose homeostasis which is the balance of insulin and glucagon (Szablewski, 2011; Rö der, Wu, Liu, & Han,

2016). At low blood glucose levels, the pancreas release glucagon, which increases blood glucose levels through the glycogenolysis process (Rö der et al., 2016).The secretion of glucagon by pancreatic a-cells performs an important role in the regulation of glycemia, due to it prevents hypoglycemia and opposes insulin function by stimulating hepatic glucose synthesis and mobilization, thereby blood glucose concentrations raising (Quesada, Tudurí, Ripoll, & Nadal, 2008). Glycogenolysis is the breakdown of glycogen to yield glucose-1-phosphate, which can be converted to glucose-6-phosphate; thus, glucose-6-Phosphate can be broken down by glycolysis or converted to glucose by gluconeogenesis (Voet, Voet, & Pratt, 2013) On the other hand, at an elevated blood glucose levels, the pancreas secretes insulin (Rö der et al., 2016). In healthy non-diabetic adults, as blood glucose levels increase, such as after a meal, the pancreas secretes insulin. Insulin acts to maintain and regulate blood glucose by insulin- dependent muscle and adipose tissues, promoting glycogensis (Rö der et al., 2016). The pancreas modifies the release of insulin and glucagon in response to changes in plasma glucose and other nutrients. Glucose control occurs during a balance of many factors, including consumption rate, intestinal absorption of dietary carbohydrates, the utilization rate of glucose, and the release rate of glucose by the liver and kidneys (Szablewski,

2011).

Some important tests are used to estimate and monitor blood glucose level, including hemoglobin A1c, oral glucose tolerance test (OGTT), fasting plasma glucose

(FPG), and random glucose test. According to the National Institute of Diabetes and

Digestive and Kidney Diseases (2018), the HbA1c test is a blood test that reflects the average levels of blood glucose over the past 2 to 3 months. When HbA1c is equal to or greater than 6.5%, the person is diagnosed with diabetic, while if this test is 5.7% to 6.4

%, the person is considered pre-diabetic (American Diabetic Association, 2016). In people without diabetes, their normal HbA1C range is below 5.7% (Centers for Disease

Control and Prevention, 2018).

There are different types of diabetes including diabetic type 1, diabetic type 2, gestational diabetic, and other types of diabetic, such as cystic fibrosis-related diabetes

(National Institute of Diabetes and Digestive and Kidney Diseases, 2016). Type 2 diabetes is the more common form of diabetes (American Diabetes Association, 2015).

The risk factors of type 2 diabetes are overweight / obese condition, age 45 or older, a pervious family history of diabetes, low HDL level or a high triglycerides level, lack of physical activity, a history of heart disease or stroke, and depression (National Institute of Diabetes and Digestive and Kidney Diseases, 2016).

The glycemic control of patients with type 2 diabetes has been illustrated by the

“glucose triad,” whose components are: HbA1C, fasting glucose levels, and postprandial glucose levels, which is the best assessment of glycemic control (Louis & Claude, 2009).

According to Lebovitz (1999), clinical intervention studies have shown that decreased chronic microvascular and macrovascular complications of diabetes type 2 has required treatment of hyperglycemia to obtain hemoglobin A1C <7.0%, blood pressure <130/80 mmHg, and plasma LDL-cholesterol <2.6 mmol/L (<100 mg/dL). 7

Many prior studies focus on glycemic impact of the type and amount of carbohydrate. For example, Sheard et al. (2004), demonstrate the importance of monitoring amount and type of carbohydrates to maintain blood glucose levels by carbohydrate counting. The metabolic and epidemiologic evidence indicates that changing high-glycemic-index forms of carbohydrate to low-glycemic-index carbohydrate will reduce the risk of type 2 diabetes (Willett, Manson, & Liu, 2002).

Recently, research has begun to focus on the effect of other dietary components that may alter blood glucose and insulin sensitivity in diabetes (Whitehead & White, 2013), caffeine is one of these dietary components.

Diabetes Overview

More than 100 million U.S. adults suffer from diabetes or prediabetes (CDC,

2017). Diabetes mellitus is known as a chronic, progressive disease that occurs when blood glucose levels are elevated (WHO, 2010). Diabetes mellitus was the seventh major cause of death in the United States by 2015 (Centers for Disease Control and Prevention,

2018) The total estimated cost of diabetes had risen to $327 billion by 2017 (compared to 2012, when it was $245 billion) (American Diabetes Association, 2018). There are many types of diabetes, but the most common are type 1, type 2, and gestational diabetes

(National Institute of Diabetes and Digestive and Kidney Diseases, 2016). Type 2 diabetes mellitus (DM) prevalence has been increasing steadily throughout the world

(Olokoba, A., Obateru, & Olokoba, L., 2012). According to the Centers for Disease

Control and Prevention (2018), type 1 diabetes is commonly diagnosed in children and 8

young adults; around 5% of people with diabetes have type 1 diabetes. Type 1 diabetes occurs when the pancreas does not produce insulin is produces a very little amount

(Centers for Disease Control and Prevention, 2018).

According to the CDC (2018), more than 30 million Americans have diabetes with the majority of cases being type 2 diabetes. One in three US adults is at risk for type

2 diabetes, but the vast majority is unaware of this (CDC, 2018). In type 2 diabetes, individuals have a relative insulin deficiency, as their insulin can be either at elevated, reduced, or normal levels; these individuals have a high level of blood glucose

(hyperglycemia) regardless of their insulin status (Durstine, 2009). There are many synonyms for Type 2 DM: it was previously known as adult-onset diabetes as well as non-insulin dependent diabetes. The American Diabetes Association (2015) states that type 2 diabetes includes people who have insulin resistance and relative insulin deficiency; these people may not need insulin treatment to survive.

According to Durstine & American College of Sports Medicine (2009), the pathophysiology of type 2 diabetes remains unclear, but there are some potential contributing factors: genetics, environment, insulin abnormalities, increased glucose production in the liver, increased fat breakdown, and defective hormonal secretions in the intestine. According to the National Institute of Diabetes and Digestive and Kidney

Diseases (2016), in people with type 2 diabetes, the body does not produce enough insulin or does not sufficiently uptake insulin, and this is known as insulin resistance.

Insulin resistance occurs when glucose does not readily enter the insulin-sensitive tissues

(which is primarily muscle and adipose tissue), and blood glucose levels rises. Impaired insulin secretion and increased insulin resistance are the characteristic pathophysiological features of type 2 diabetes that contribute to the development of the disease (Kaku, 2010). Elevated blood glucose levels cause the beta (β) cells of the pancreas to secrete more insulin to maintain an appropriate blood glucose level. As a result, this additional endogenous insulin is ineffective in lowering of blood glucose levels and may contribute to insulin resistance in some people, while in other people, the beta (β) cells may become exhausted over time, and the insulin secretion will decrease.

HbA1c not only provides a reliable measure of chronic hyperglycemia but also correlates well with the risk of long-term diabetes complications (Sherwani, Khan,

Ekhzaimy, Masood, & Sakharkar, 2016).

In people with or without diabetes, the elevated of hemoglobin A1c level has been considered as an independent risk factor for coronary heart disease and stroke (Sherwani et al., 2016). Glucose control may delay the development or/and progression of diabetes complications (Russell, Chen, Jones, & Peiris, 2014). Over time a high blood glucose due to uncontrolled diabetes may cause a critical damage to the blood vessels, heart, kidneys, eyes, and nerves (National Institute of Diabetes and Digestive and Kidney

Diseases, 2017). According to the National Institute of Diabetes and Digestive and

Kidney Diseases (2018), the A1C test alone or with other diabetes tests can be used to diagnose type 2 diabetes and prediabetes. The high risk of diabetes complications in type

2 diabetes is strongly related to previous hyperglycemia (Stratton et al., 2000). The

chronic hyperglycemia in diabetes has been related to long-term damage, function impairment, and failure of different organs, such as the eyes, kidneys, nerves, heart, and blood vessels (Qaseem et al., 2018).

Poor glycemic control increases the risk for cardiovascular complication; however, prospective studies on type1 & 2 DM found that this complication could reduce by modification the lifestyle or medications that lead to improving blood glucose level

(LeRoith & Rayfield, 2007). A study conducted by Hayward et al. (2015), reported that intensive glycemic control relatively minimizes the complication of diabetes such as cardiovascular disease. Sainsbury et al. (2018) found that carbohydrate-restricted diets

(<26% of total energy) produced a reduction in the HbA1c during at 3 months (with weighted mean difference= 0.47%) and 6 months (with weighted mean difference=

0.36%) in adult with type 2 diabetes. The attempts to minimize blood glucose may decrease risk for diabetes complications, but this strategy is accompanied by harms, patient burden, and costs (Qaseem et al., 2018). Uniform intensive control was compared with health policies and clinical programs that encourage U.S. adults with type 2 diabetes to follow a glycemic control regimen to lower their costs and increase quality of life (Laiteerapong et al., 2018). Tight glycemic control and aggressive management of some conditions such as obesity, hypertension, and other risk factors have been crucial in lowering morbidity and mortality in all diabetic individuals

(Vasudevan, Burns, & Fonseca, 2006). For example, the metabolic control can delay the beginning and development of diabetic retinopathy; in addition to, the controlling of

other risk factors that related with blood glucose can prevent or delay cardiovascular complications (WHO, 2018). Habitual consumption of caffeine elevates blood glucose levels, and caffeine abstinence may lead to positive enhancement in chronic glucose control in people with type 2 diabetes who drink coffee daily (Lane et al., 2012).

Caffeine and Type 2 Diabetes Mellitus

Caffeine, an alkaloid of the methylxanthine family that is naturally found in the leaves, seeds, and fruits of over 63 plants species throughout the world (Wanyika,

Gatebe, Gitu, Ngumba, & Maritim, 2010). The most common sources of caffeine are coffee, cocoa beans, cola nuts and tea leaves (Poroch-Seriţan, Michitiuc, & Jarcău,

2018). High consumption of caffeine has been documented as possibly improving health: however, the majority of studies focus on the adverse effects of caffeine on health especially its impact on blood glucose. Mejia & Ramirez-Mares (2014) discuss the impact of coffee and caffeine on health, for the adults with moderate intake of caffeine (110–345 mg/day) that associated with a neutral to the potentially beneficial impact on health. Caffeine is not an essential nutrient, it is one of the most consumed stimulants around the world, due to it being in many food products and some drugs (U S

Food and Drug Administration, 2018). A high amount of caffeine should be avoided due to a high caffeine consumption has adverse health effects, especially for people who have high blood pressure, they should be avoided the caffeine consumption due to caffeine is known to raise the blood pressure (Wanyika et al., 2010). Furthermore,

caffeine beverages should be avoided for those patients who suffer from coronary heart disease due to the potential that caffeine may disrupt normal heart rhythm (Wanyika et al., 2010).

Caffeine has many benefits for the human body, but also a number of harmful effects

(Poroch-Seriţan et al., 2018). A study carried out by Temple et al. (2017) found caffeine consumption is relatively safe in healthy adults, but caffeine could be harmful in some vulnerable populations such as pregnant women, children, and individuals with mental illness (Temple et al., 2017). Based on the prior studies data reviewed, a study done by

Nawrot et al. (2003) found that a healthy adult who consumes moderate caffeine consumption up to 400 mg/day, show no association with adverse effects on cardiovascular health, calcium balance and bone status, adult behavior, cancer risk, or male fertility (Nawrot et al., 2003).

Caffeine Consumption Trends in the United States

Mitchell et al., (2014), demonstrated that 85% of the U.S. population consumes at least one caffeinated beverage per day. High caffeine consumption in the U.S. is driven by coffee and with a lower average intake of tea and carbonated soft drinks (Mitchell et al.,

2014). Coffee is the major source of caffeine intake among the college students

(Mahoney et al., 2018). Majority of people report consuming caffeine for the feeling of alertness, assist with extended waking periods and to promote mood and performance

(Mahoney et al., 2018). Caffeine can be beneficial in restoring a minimum level of wakefulness, more alert and readiness for daily challenges (Snel & Lorist, 2011).

Caffeine (Forms and Basics)

Caffeine is a bioactive component that stimulates the central nervous system and has a positive effect on long-term memory (Mejia et al., 2014). Coffee is the most common form of caffeine intake in the U.S. diet (Mahoney, 2018), but there are many other sources of caffeine, including tea, energy drinks, carbonated soft drinks, and chocolate. According to University of Iowa Hospitals, caffeine consumption in moderation (up to 300 milligrams (mg) of caffeine daily), is safe for most healthy adults.

The prevalence of consumption a high amount of caffeine makes the researchers more concerned about the relationship between caffeine and diabetes. Eighty-nine percent of

U.S. population consumes caffeine regularly (Fulgoni, Keast, & Lieberman, 2015).

Caffeine (1,3,7-trimethylxanthine) is a popular, biologically-active food component that has potential health implications (Robinson, Spafford, Graham, & Smith

2009). It is a pharmacologically-active ingredient in many foods, dietary supplements, beverages, and drugs (Poroch-Seriţan et al., 2018). Caffeine is water soluble and can be efficiently extracted (Poroch-Seriţan et al., 2018). Caffeine is a natural alkaloid which found in more than 60 plants such as coffee beans, tea leaves, cola nuts, and cocoa pods

(Gonzalez & Ramirez-Mares, 2014). Many products contain caffeine including coffee, tea, chocolate, Soft drinks, and energy drinks. Globally, coffee and tea are beverages often connected to cultural, social and economic importance (Poroch-Seriţan et al., 14

2018). According to Zaharieva & Riddell (2013), caffeine supplementation has become more popular as a safe and effective ergogenic aid, due to caffeine’s effectiveness. A study was performed with college students to identify the reasons behind using caffeine; the students reported that they use caffeine to boost their physical energy and, improve their mood and concentration (Mahoney et al., 2018).

Mitchell et al. (2014) examined caffeine preferences in the US population and found that among all age groups, coffee was the primary choice of caffeine consumption, followed by carbonated soft drinks, energy drinks and chocolate. Caffeine concentration depends on the type of the product, plants and other environmental factors, and processing, for example, coffee has the highest concentration compared with soft drinks, tea, and energy sources (Gonzalez & Ramirez-Mares, 2014). Caffeine is one of many ingredients in foods that can produce a physiological effect (Mitchell et al., 2014).

Caffeine has been shown to have beneficial effects on the human body; however, depending on the concentration, it also may cause a number of harmful effects (Poroch-

Seriţan et al., 2018).

Caffeine Function and Metabolism

Methylxanthines such as caffeine are often consumed due to their stimulating impacts on the central nervous system (Nikolic, Bjelakovic, & Stojanovic, 2003).

Caffeine is a xanthine that plays an important role in several mechanisms of action on the vascular tissue, particularly in endothelial tissue and vascular smooth muscle cells

(Echeverri, Montes, Cabrera, Galan, & Prieto, 2010). Metabolism of caffeine yields 15

paraxanthine as final product, which forms 72 - 80% of caffeine metabolic output

(Echeverri et al., 2010). Caffeine works through various mechanisms, the most essential one being the antagonism of adenosine receptors A1 and A2 (Sawynok, 2011).

Caffeine generates most of its biological effects by antagonizing for all types of adenosine receptors (ARs): A1, A2A, A3, and A2B (Ribeiro & Sebastio, 2010).

Furthermore, when caffeine acts as an AR antagonist, it is doing the opposite of activating adenosine receptors due to the removal of endogenous adenosinergic tonus

(Ribeiro & Sebastio, 2010). A study done by Nikolic et al. (2003) reported that caffeine is able to change the metabolism of L-arginine in the brain, which plays an important role in normal brain function. Caffeine consumption can decrease the lipid peroxidation level in the brain (Nikolic et al., 2003).

To summarize, several mechanisms of action explain the effects of caffeine. The most prominent is its reversible blockage of the action of adenosine on its receptor, consequently preventing the onset of drowsiness induced by adenosine, which is created in the brain and binds to adenosine receptors. This binding causes drowsiness by slowing down nerve cell activity and causing blood vessels to dilate. Caffeine looks like adenosine to nerve cells, so when a person consumes caffeine, it binds to the adenosine receptor but doesn't slow down the cell's activity like adenosine. Instead, the caffeine makes the nerve cells speed up and the brain blood vessels constrict; as a result, the receptors are not available for adenosine molecules to bind to, which increases neuron

firing in the brain. Caffeine can play a role in enhancing alertness during times that a person needs to wake up. This effect is primarily why people use caffeine products.

Insulin

Insulin is an essential polypeptide hormone which regulates carbohydrate metabolism (Ahmad, 2014). Insulin plays an essential role in carbohydrate metabolism regulation in association with glucagon (Ünal, Kara, Aksak, Zuhal Altunkaynak, &

Yıldırım, 2012). Insulin is produced from β-cells and it is a critical regulator of metabolism (Fu, Gilbert, & Liu, 2013). The β cells inside the pancreas produce the insulin hormone, with each meal consumed by the person, the beta cell secrete insulin to help the body use or store blood glucose from the meal (American Diabetes Association,

2015). However, the people with type 2 diabetes make insulin, but their bodies don't respond well to it; in this case, they need to use diabetes pills or insulin shots to help their bodies use glucose for energy (American Diabetes Association, 2015).

According to a study done by Ahmad (2014), insulin participated in the regulation of glucose utilization in the human body; thus, the exogenous supply of insulin needed for those who suffer from type 1 and type 2 diabetes. When blood glucose concentration is high, insulin reduces it by increasing glucose uptake by the liver, muscle, and fat cells. On the other hand, when blood glucose concentration is low, the glycogen is converted to glucose and released in the blood (Ahmad, 2014).

Insulin is produced by the pancreas to regulate the amount of glucose in the blood stream. When the level of insulin in the blood is higher than usual, it is considered

hyperinsulinemia which is associated with diabetes. According to the National Institute of Diabetes and Digestive and Kidney Diseases (2018), during insulin resistance, the body's cells do not typically respond to the insulin hormone and the pancreas is no longer able to compensate by secreting the massive amounts of insulin required to keep the blood sugar regular ; however, the reasons behind the insulin resistance and prediabetes are still not fully understand but excess body weight and lack of physical activity are main contributing factors (National Institute of Diabetes and Digestive and

Kidney Diseases, 2018). The characteristic of diabetes is chronic hyperglycemia when the body is unable to synthesize insulin or the body cells resist insulin (Ahmad, 2014).

Prediabetes is defined as a condition when blood sugar levels are higher than the normal level, but not high enough to diagnosed as diabetes (National Institute of Diabetes and

Digestive and Kidney Diseases, 2018). Prediabetes which characteristic with high blood glucose level usually occurs in people who have some insulin resistance or whose beta cells do not producing enough insulin to make the blood glucose on it is normal level, so, with not enough amount of insulin, the blood glucose stays high in the bloodstream instead of entering body cells, over the time that could develop to type 2 diabetes

(National Institute of Diabetes and Digestive and Kidney Diseases, 2018). The tests that used to indicate the presence of diabetes include hemoglobin A1C (HbA1C), fasting plasma glucose (FGP), random glucose, and oral glucose tolerance test (OGTT). The

HbA1c test is reflects the average of blood glucose level over the past 2 to 3 months.

According to National Institute of Diabetes and Digestive and Kidney Disease

(2018), Hemoglobin A1C (HbA1C) which is also referred to as glycohemoglobin or glycated hemoglobin, is a form of hemoglobin (part of a red blood cell that carries oxygen to the cells) that is bound with glucose in blood cells. Hemoglobin A1c describes a series of stable hemoglobin components which form slowly and nonenzymatically from hemoglobin and glucose (Sherwani et al., 2016). The first step of glycated hemoglobin formation is the reaction between blood glucose and the N-terminal end of the β-chain of hemoglobin which forms aldimine or Schiff base in a reversible reaction

(Makris & Spanou, 2011). Next step which is the irreversible reaction, aldimine or

Schiff base is gradually converted to form ketoamine product "HbA1c" (Acharya, Roy,

& Dorai, 1991). The International Expert Committee (2009), states the HbA1C test is an accurate, precise measurement for glycemic control and now an accepted measurement for the diagnosis of DM if the A1C level is above 6.5%. HbA1c provides an indicator for how well glucose levels are being controlled over time, usually providing an average glucose level over a 2 to 3month period. Hemoglobin A1c measurement is considered a gold criterion for monitoring chronic glycemia in diabetes patients (Makris & Spanou,

2011). Clinically, HbA1c has been used to assess glycemic control in people who suffer from diabetes (Saudek, & Brick, 2009). HbA1c gives a trustworthy measure for chronic glycemia and is related to the risk of long-term diabetes complications (Sherwani et al.,

2016). HbA1c is now considered the test of choice for accurate monitoring and chronic management of diabetes (Sherwani et al., 2016). According to the American Diabetic

Association (2016), testing and diagnostic purposes when the HbA1c level is equal or greater than 6.5% is used for diagnosis and HbA1c from 5.7% to 6.4 % is considered prediabetic. The HbA1c test is commonly used in healthcare with the treatment of established patients diagnosed with diabetes.

Carbohydrates

Carbohydrates have many functions in the human body. Carbohydrates are the main energy source in most diets, and they play an important role in energy metabolism and homeostasis (Mann et al., 2007). Carbohydrates are fundamental substrates for energy metabolism, affect many things including satiety, blood glucose level, insulin, and lipid metabolism (Cummings & Stephen, 2007). In addition, carbohydrates impact overall body health including body weight control, diabetes, cardiovascular disease, bone mineral density, and bowel cancer (Cummings & Stephen, 2007). The mammalian brain depends on glucose as its essential source of energy (Mergenthaler, Lindauer,

Dienel, & Meisel, 2013). According to Fink & Mikesky (2015), carbohydrates prevent protein catabolism in body and can be a spare muscle tissue.

Dietary carbohydrates are one of three macronutrients serving as a major energy source for body functions. Carbohydrates are organic molecules that contain carbon with attached oxygen & hydrogen atoms (Fink & Mikesky, 2015). The energy value of digestible carbohydrates is 4 kcals/gram (Slavin & Carlson, 2014). Generally, certain vegetables, fruits, whole grains, milk, and milk products are the essential food source of carbohydrate (Slavin & Carlson, 2014). Carbohydrates are categorized as simple or

complex, depending on the molecular and chemical structure. According to the Harvard

T.H. Chan School of Public Health (2016), simple carbohydrates can be quickly utilized for energy due to their simple chemical structure, and they quickly elevate the blood glucose in the bloodstream, while complex carbohydrates take longer to digest, which leads to a slow rise of glucose in the bloodstream. A simple carbohydrate contains monosaccharides and disaccharides. Examples of monosaccharides include glucose, fructose, and galactose, while disaccharide examples include sucrose, lactose, and maltose; complex carbohydrates contain oligosaccharides and polysaccharides.

According to National Institute of Diabetes and Digestive and Kidney Diseases (2018), when the glucose level rise post prandial, the pancreas releases insulin into the blood to regulate the blood glucose. In the care of abnormal glucose control, of insulin resistance and prediabetes might occur when the human body does not use insulin well.

Simple Carbohydrate Trends in the U.S.

Trends in the percentage of energy from carbohydrates from the first National

Health and Nutrition Examination Survey (NHANES) from 1971 to 1975 to the second

NHANES from 2005 to 2006 increased in both men and women with a healthy weight

(Austin, Ogden, &Hill, 2011). However, the percentage of energy from carbohydrates in normal weight male increased from 43.1% to 47.8 %, while in normal weight female the percentage increased from 45.2% to 49.9% (Austin et al., 2011). Recent analyses indicate that American adults consume approximately 13% of their total calorie intakes came from added sugars during 2005 to 2010 (Ervin & Ogden, 2013). Furthermore,

according to Centers for Disease Control and Prevention (2016), Americans eating and drinking too much-added sugars leads to several health issues like obesity, type 2 diabetes, and heart disease. Added sugars include all consumed sugars using ingredients processed and prepared foods such as cake, bread, and chocolates (Ervin & Ogden,

2013) however, recent carbohydrate trends in the US need additional research for a better understanding of consumption and prevalence.

Carbohydrate Metabolism

Carbohydrates (CHO) are an essential source of energy in our diet (Jéquier,

1994). Metabolism is defined as all chemical reactions that occur in living cells. There are two types of metabolic reactions: anabolism (build up) and catabolism (break down).

During carbohydrate metabolism, the breakdown of the food begins in the gastrointestinal tract and is followed by the enterocytes absorbing carbohydrate ingredients in the form of simple sugar (monosaccharides) (Dashty, 2013). Glucose is quickly metabolized to produce adenosine triphosphate (ATP), a high-energy end product (Szablewski, 2011). The oxidation of glucose occurs through an extensive series of reactions that extract a large amount of energy (Szablewski, 2011).

Glucose is the essential source of energy for most body cells (Meireles et al.,

2017). It is considered a major substrate for many of biochemical reactions (Navale &

Paranjape, 2016). Because of their polar nature and large size, glucose molecules cannot cross the lipid membrane of cells by simple diffusion (Navale & Paranjape, 2016); instead, the molecules cross into cells via glucose transporters (Navale & Paranjape,

2016). The transport of glucose and other carbohydrates into cells is affected by integral membrane glucose transporter (GLUT) molecules (Meireles et al., 2017). The importance of glucose transporters in biology is obvious, as they are the gateways to for glucose transport across membranes (Navale & Paranjape, 2016).

The glucose transport proteins (GLUT1 and GLUT4) assist in transporting glucose into insulin-sensitive cells (Ebeling, Koistinen, & Koivisto,1998). GLUT1 is insulin-independent and is widely distributed in various tissues, while GLUT4 is insulin- dependent and is responsible for most glucose transport into muscle and adipose cells in an anabolic situation (Ebeling et al.,1998). GLUT1 is thought to be the primary distributor for basal glucose transport (Olson & Pessin,1996). In adipocytes, these cells cross GLUT1, the constitutive transporter for basal glucose transport: however, they also cross GLUT4, the specialized insulin-responsive transporter (Wood & Trayhurn, 2003).

If access of glucose through GLUT1 and the activation of the hexosamine pathway is abundant, can decrease the insulin-mediated glucose transport through

GLUT4 causing insulin resistance (Ebeling et al.,1998). GLUT4 is found in heart, skeletal muscle, adipose tissue, and brain, is responsible for reducing postprandial the elevation of plasma glucose levels (Rayner, Thomas, & Trayhurn,1994). If the access of glucose through GLUT1 and the activation of the hexosamine pathway are abundant, the insulin-mediated glucose transport through GLUT4 can be decreased, causing insulin resistance (Ebeling et al.,1998).

When glucose enters the cell, it is phosphorylated by a hexokinase and then is either stored as glycogen or metabolized through glycolysis (Sprague & Arbeláez, 2011).

In healthy situations, the skeletal muscle and liver cells store monosaccharides in the form of glycogen (Dashty, 2013). Glucose is synthesized into glycogen through the process of glycogenesis (anabolism), where glycogen is stored in the liver and muscles until it is needed when glucose levels become low. A Glucose molecule are too a large in size to pass through a cell membrane via simple diffusion, so the glucose requires assistance. Two type of glucose transporters facilitate transport of glucose into a plasma membrane; sodium–glucose linked transporters (SGLTs) and facilitated diffusion of glucose transporters (GLUTs) (Navale & Paranjape, 2016).

Insulin is an important hormone produced by the pancreas that allows the glucose in the blood to enter muscle, fat, and liver cells and be used for energy (National

Institute of Diabetes and Digestive and Kidney Diseases, 2018). Tappy (2008), shows that insulin is the essential anabolic hormone, with relatively low secretion between meals to regulate hepatic glucose production. Insulin secretion increases after a high- carbohydrate meal. According to the National Institute of Diabetes and Digestive and

Kidney Diseases (2018), after consuming a meal containing carbohydrates, blood glucose levels will rise, and the pancreas will release insulin into the blood, to minimize blood glucose and keep it at normal levels. Insulin and glucagon are strong regulators for metabolism of glucose. (Aronoff, Berkowitz, Shreiner, & Want, 2004). Glucose metabolism is essentially controlled by the balance between anabolic (insulin) and

catabolic (epinephrine, glucagon, cortisol, growth hormone) hormones (Tappy, 2008).

Hyperglycemia in type 2 diabetes results from an absolute or relative deficiency in insulin; with relative insulin deficiency is usually caused by an inability to adequately compensate for insulin resistance (Lebovitz,1999).

Research has demonstrated that caffeine consumption has an effect on glycemic control. Caffeine consumption raises the symptomatic caution signs of hypoglycemia in patients with type 1 diabetes. Patients with type 2 DM who consume caffeine in large amounts may have a sign of hyperglycemic (Zaharieva & Riddell, 2013).

A study done by Zaharieva & Riddell (2013), reported that caffeine alters glucose homeostasis by decreasing glucose uptake into skeletal muscle which leads to an elevation in blood glucose concentration. Additionally, Zaharieva & Riddell (2013) mention that caffeine work as a preventative mechanism against hypoglycemia during exercise in type 1 DM, the same actions would effect on blood glucose concentrations in

T2DM who exercise after the meal ingestion. According to American Diabetes

Association (2017), there are conflicting findings among numerous research studies.

Some indicate that drinking coffee may prevent type 2 diabetes while other studies reported that coffee intake could elevate blood glucose, thus more research on the effects of coffee in individuals with diabetes is needed. James, Christina, Richard, & Mark

(2004) demonstrated that nondiabetic subjects have an exaggeration of glucose and insulin responses when caffeine is ingested with carbohydrates and this finding could provide implications for the future clinical management of type 2 diabetes. In contrast, 25

Richardson, Thomas, Ryder, & Kerr (2005), state that caffeine is related to a significant reduction in nighttime hypoglycemia and caffeine plays a role in overcoming hypoglycemia. Agardh et al., (2004), reported that high consumption of coffee has a reduced risk of type 2 diabetes and impaired glucose tolerance. Thus, it is crucial to investigate the effect of chronic caffeine consumption on glycemic index in non-diabetes adults. Aside from the studies on coffee consumption, another important thing to focus on is physical activity

Physical activity and type 2 diabetes

Physical activity is defined as any physical movement generated by skeletal muscles that result in energy expenditure (Caspersen, Powell, & Christenson,1985).

Regular physical activity aids to improve overall health & fitness and to decreases the risk for many chronic diseases (CDC, 2018). Physical active people tend to live longer and have a lower risk for some diseases such as heart disease, stroke, type 2 diabetes, depression, and some cancers (CDC, 2018). There is a specific suggestion for physical activity among adult people. According to the Office of Disease Prevention and Health

Promotion [ODPHP] (2018), adults between 18 and 64 years of age should engage in at least 150 minutes of moderate-intensity aerobic physical activity per week or do at least

75 minutes of vigorous-intensity aerobic physical activity per week or an equivalent combination of moderate- and vigorous-intensity activity. In particular, there are many ways of accumulating a total of 150 minutes per week such as 30 minutes of moderate- intensity activity 5 times per week (WHO, 2018). The consumption of 1.5 mg/kg of

caffeine during 40 minutes of exercise in type 2 diabetes patients contributed to the maintenance of blood glucose levels at ~140 mg/dL (Garcia et al., 2014). Circuit weight training program demonstrated benefits after only adhering for a short time contributed with enhanced glycemic control in Type 2 diabetes, by lowered self-monitored glucose levels and an improved serum insulin response to a glucose load, in spite of only a slight change in glucose tolerance (Dunstan et al., 1998). Furthermore, physical activity is defined as any physical movement produced by skeletal muscles which results in energy expenditure, while exercise is known as planned, structured, and repetitive body movement to improve or sustain on one or more elements of physical fitness (Caspersen et al., 1985).

The Interrelationship of Carbohydrates, Caffeine, and HbA1c

The current study was conducted to investigate the effect of chronic habitual consumption of caffeine intake on glycemic control in healthy adults who have not been diagnosed with diabetes. The prevalence of prediabetes among healthy individuals is diagnosed using the HbA1c test independently, or paired with fasting plasma glucose

(Blum et al., 2015). Nakagami et al. (2017) reported that when the HbA1c level is 6.5% and fasting plasma glucose is 7.0 mmol/L, it is the appropriate diabetes diagnostic thresholds, indicating a high future risk of diabetes complications (specifically retinopathy). Early treatment to enhance the blood glucose levels and to prevent postprandial hyperglycemia is crucial for accurate glycemic control (Torimoto, Okada,

Sugino, & Tanaka, 2017). The high level of HbA1c variability is related to diabetic peripheral neuropathy in type 2 diabetic patients and could be used as a strong indicator of diabetic peripheral neuropathy in those patients (Su et al., 2018). A study done by Lee et al. (2018) found that, family history of diabetes was not only associated with elevated

HbA1c in diabetic individuals but also in non-diabetic individuals. Since family history is an irreversible risk factor, the assessment of diabetes and family history are important to manage and prevent diabetes in the community (Lee et al., 2018).

Habitual consumption of caffeine increases chronic glucose levels; thus caffeine abstinence may lead to improving chronic glucose control in people with type 2 diabetes who drink coffee daily. When adults with habitual coffee intake along with a history of type 2 diabetes abstain from caffeine for 3 months, HbA1c levels decreased after 3 months (Lane et al., 2012). Dewar & Heuberger, (2017) found that caffeine consumption with carbohydrates, may elevate a blood glucose which could prevent the commonly experienced hypoglycemia during exercise among diabetics. Despite elevated and prolonged proinsulin, C-peptide, and insulin responses after caffeine consumption, blood glucose also increased, and an acute consumption of caffeine impaired the blood glucose management in men with type 2 diabetes (Robinson et al., 2004). Schulze et al. (2004) found that a diet high in glycemic index and low in fiber increases the risk of type 2 diabetes. Caffeine intake impaired the insulin-glucose homeostasis in obese men, whereas nutrition and exercise intervention improved it (Petrie et al., 2004).

Following a diet consisting of carbohydrate-restriction, specifically restricting carbohydrates to less than 26% of total energy, leads to a greater reduction in HbA1c at

3 and 6 months (Sainsbury et al., 2018). Keizo et al. (2012) demonstrated long-term impact of caffeine on glucose metabolism with caffeinated and decaffeinated instant coffee consumption (5 cups/day) for 16 weeks on glucose and insulin concentrations during a 75 g oral glucose tolerance test (OGTT). The study results found that after 16- weeks, the average percent decreased 13.1% for 2-hour glucose and with 7.5% for the area under the curve of glucose; thus, the habitual use of both caffeinated and decaffeinated coffee may be protective against deterioration of glucose tolerance (Keizo et al., 2012).In addition, majority of studies have focused on the effects of chronic coffee consumption on the risk of type 2 diabetes using OGTT, rarely study discussing its effect on healthy people.

Caffeine consumption with carbohydrates may elevate a blood glucose, which may prevent the hypoglycemic during exercise (Dewar & Heuberger, 2017). Potential adverse effects of caffeine on healthy adults were investigated, including cardiovascular, calcium balance, bone status, behavioral effects, general toxicity, increased potential of cancer, and effects on male fertility (Nawrot et al.,2003). The previous research studied the effect of caffeine intake in type 2 diabetes population; however, the current study focus on the effects of caffeine consumption in non-diabetic adults. Longitudinally,

Americans have consumed a high amount of caffeine and simple carbohydrates, often paired together. Dewar & Heuberger (2017) found that caffeine intake with

carbohydrates, may raise a blood glucose level. Thus, it is important to understand the relationship between the chronic habits of caffeine consumption and carbohydrate intake in terms of its effect on HbA1c levels, especially for non-diabetic adults who chronically consume caffeine. According to the Dietary Reference Intakes (DRI), the recommended carbohydrate intake for the average adult is 130 grams per day (RDI, 2005). Dietary carbohydrate intake for US adults 20 years of age and older, with mean equal to 47.4% of kilocalories for men, and 49.6% of kilocalories for women (Centers for Disease

Control and Prevention, 2017).

Jung & Choi (2017) demonstrated effects of high- carbohydrate and low- carbohydrate diets on glucose control, weight reduction, and lipid profiles in patients with type 2 diabetes with conflicting results. However, the study found a high carbohydrate diet did not cause any deterioration in glycemic control and lipid profiles.

Furthermore, habitual consumption of caffeine may detrimentally effect on chronic glucose control in patients with type 2 diabetes (Lane et al., 2012). Caffeine intake is significantly associated with a reduction in the incidence of type 2 diabetes (Jiang,

Zhang, & Jiang, 2014). Consequently, early revelation is important for individuals who do not have diabetes but are at high risk of developing it (Lee, et al., 2018).

It is necessary to investigate the effects of chronic caffeine consumption with carbohydrate intake on glycemic index by using HbA1c for a non-diabetes adult. The relationships between dietary CHO intake, Hemoglobin A1c, and caffeine consumption have been documented, along with their effects on individuals with type 2 diabetes; 30

however, to our knowledge, no study has been published concerning the relationship of these variables in non-diabetes individuals. Therefore, this study seeks to purposively address the following research questions using a sample of non-diabetic adults from the

National Health and Nutrition Examination Survey 2013-2014:

1. What is the interrelationship between caffeine and carbohydrate intake on

HbA1C?

2. What is the association of carbohydrate intake on HbA1c?

3. What is the association of caffeine intake on HbA1c?

4. What impact does this combination of variables have on HbA1C on the decision tree pathways?

CHAPTER III METHODOLOGY

The present study is an observational design, which employed a sample derived from the National Health & Nutrition Examination Survey (NHANES) from 2013-2014.

NHANES data is collected in two-year cycles using a nationally representative sample of the civilian, non-institutionalized, US population selected by using a multistage, stratified sampling design (Ford et al., 2014). The essential goal of NHANES is to determine the prevalence and the risk factor of major disease and sample clinical, laboratory, physical, and dietary data. NHANES has collected data in four major areas as follows:

1. Personal interviews. All participants performed 24-hour dietary recall interviews.

This dietary recall interview collected in-person and by telephone. The

interviews included demographic, socioeconomic, dietary, and health-related

questions.

2. Physical examinations. Each physical examination had a computerized data

collection and after each examination session, the data sent to a central survey

database.

3. Laboratory tests. NHANES use several methods to conduct and monitor the

examinations that perform by different laboratories.

4. Nutritional assessment. The participants recalled all foods and beverages that

they consumed during the day before and day of the interview, including

macronutrients, micronutrient and dietary supplements.

Inclusion/Exclusion Criteria:

The inclusion criteria for this study included participants with ages between 18 to

59 years, both males and females who did not present with a preexisting diagnosis of

DM. This sample was selected with this specific age due to the national diabetes statistics report that the percentage of adults with diabetes increased with age reaching

4% for adult whose age between 18 and 44 years, 17% for adults whose age between 45 and 64 years, and 25.2% among those aged 65 years or older (CDC, 2018). After applying inclusion and exclusion criteria, the final actual sample size for analysis was n

=3,928. Preexisting Diabetes was excluded, due to our study focusing on non-diabetic population.

Variable Inclusion

The response variables included glycated hemoglobin (HbA1C) percentage which was categorized by clinical evaluation criteria into normal (<5.7%), prediabetes

(5.7%-6.4%), and diabetics (≥6.5). According to ADA (2016), HbA1c criteria is classify into diabetes (greater than or equal to 6.5%), prediabetes (between 5.7% to 6.4%), and normal (less than 5.7%). However, the current study will be categorized HbA1c as two 33

groups including (Normal) & (prediabetes & diabetes) due to the total number of diabetes participants based on HbA1c criteria was 262 (7.1%) participants which is low.

Hemoglobin A1c reflects the average of blood glucose over the past 3 months (Makris &

Spanou, 2011). Hemoglobin A1c has been accepted to be an indicator of glycemic control since the mid-1970s (Saudek, & Brick, 2009). The current study excludes the children under age 18 because they mostly tend to have a diabetes type one.

Predictor variables included: age (years), sex (both gender), amount of caffeine consumption (mg/day), amount of carbohydrate intake (gram/day) including the two type of carbohydrate simple sugar (gram/day) and dietary fiber known as complex sugar

(gram/day), total carbohydrates (gram/day), an indicator for physical activity (low active and not low active defined as 30 minutes/ day based on ACSM/AHA guidelines).

American College of Sports Medicine (ACSM) & American Heart Association (AHA) recommend all healthy adults among age 18 and 65 years old, that they need a moderate- intensity aerobic physical activity for a minimum of 30 minutes on five days per week or vigorous-intensity aerobic physical activity for a minimum of 20 minutes on three days per week (Nelson, 2007).

Statistical Analysis

Decision Trees

The current investigation employed the use of a decision tree, Chi-square

Automatic Interaction Detection (CHAID). Chi-square Automatic Interaction Detection

is a tool used to prediction and detect the relationship between different variables.

CHAID employs a series of statistical tests to create mutually exclusive, and exhaustive pathways that build from the root node to the terminal node. In CHAID analysis categories and continuous data can be used. The CHAID algorithm that applied in this analysis finds those differences by using χ2 tests to measure the association between the dependent variable and the independent variables (Agresti, 1990).In CHAID analysis, the categorical data are split into subgroups and their effects on the dependent variable are tested, however, it can be applied to continuous or discrete dependent and independent variables (Ö nder & Uyar, 2017). The CHAID algorithm is an exploratory technique that both reduces bias and allows for the creation of classification logic based on multiple variables of different types. The descriptive statistics included means, standard deviations. maximum, minimum, age, carbohydrate amount, caffeine intake, and glycohemoglobin (%).

The decision tree [DT] has been demonstrated as effective statistical methods used for data mining. The DT is a commonly used data mining method to show a classification based on various covariates or to improve prediction algorithms for a target variable (Song & Lu, 2015). Also, this method is used in classification, investigation, data description, visualization, and magnitude reduction (Milanović &

Stamenković, 2016). Also, the DT is used in predictive ways to build a data mining model to solve a different data mining task (Milanović & Stamenković, 2016).

Decision trees are an effective method to use in several areas due to it being easy to use, free of ambiguity, and robust even if there are missing values (Song & Lu, 2015).

Decision tree methodology has become more common in medical research (Song & Lu,

2015). For example, a study was done by Tanner et al., (2008) on using a decision tree algorithm to predict the diagnosis and the result of Dengue fever in the early phase of illness, as well as a study done by Metting et al., (2016) on using a diagnostic decision tree for obstructive pulmonary diseases. Furthermore, a study done by Shouman, Turner,

& Stocker (2010) found that using decision tree is one of the successful data mining methods that used to diagnose people with heart disease. The primary components of a decision tree are branches, which represent a chance result that emits from root nodes and internal nodes and nodes, which contain on three types of nodes including the root node, internal nodes, and leaf nodes (end nodes) that represent the final outcome of a incorporation of decisions or events (Song & Lu, 2015).

The primary tree growth algorithm used in the current study was CHAID. For this research, the response variable was the presence or absence of diabetes or prediabetes. The CHAID algorithm, primarily proposed by Kass (1980), works by using a series of merging, splitting, and stopping steps based on user-specified criteria as follows in accordance with Miller, Fridline, Liu, & Marino (2014):

(A)The first step is merging. Merging process is performed for each predictor variable and merges nonsignificant categories using the following algorithm.

(1) Implementation of cross-tabulation of the predictor variable through the

binary target variable.

(2) If the predictor variable has two categories, then go to step 6.

(3) The Chi-Square test of independence is applied for each two categorical variables of the predictor variable in association with the binary target variable using the

휒2 distribution (df = 1), significance (훼merge) set at 0.05. With regard to nonsignificant consequences, those paired categories are merged.

(4) Nonsignificant tests determined by 훼merge > 0.05, two categories variables

are merged into a single category. For tests of significance defined by 훼merge ≤

0.05, the pairs are not merged.

(5) If there is any category is smaller than the user-specified minimum segment

size, that pair is merged similarly to other categories.

(6) In the merged categories, the adjusted 푃 value using a Bonferroni adjustment

is calculated to control for the Type I error rate.

(B) The next step is splitting, by determining the best split for each predictor

variable. Selecting the predictor variable to be used as the best split for the node

occurs by using the following algorithm (1) The Chi-Square test for

independence using an adjusted 푃 value for each predictor variable.

(2) The predictor with the least adjusted 푃 value (i.e., most statistically

significant) is split if the 푃 value is less than or equal to the user-specified

significance split level (훼split); if not, the node will not be split and considered to

be a terminal node.

(1) The tree process stops when the maximum tree depth level is reached.

(2) If the node size is less than the user-specified minimum node size, then the

node will not be split.

(3) If the split of a node outcome in a child node whose node size is lower than the

user-specified minimum child node size value, the node will not be split.

(4) Once all the stopping rules are met, the CHAID algorithm will discontinue.

Growth Criteria specific to this study was set as 60 cases parent node, 30 cases child node. Significance for split and merge set at α = 0.05. Maximum tree depth set at the default of 3 levels. Variable inclusion stopping criteria was set at α = 0.05. Tree growth was performed in SPSS v22. The tree was described using a nodal analysis based on splitting and variable inclusion. To make direct comparisons between nodes, a risk indicator was used based on the percentage of diabetic cases.

CHAPTER IV

RESULTS

NHANES 2013-2014 Sample

NHANES 2013-2014 subject demographics are illustrated in Table 1.

Table 1. Demographic characteristics of study participants

Variable Frequency Valid Percent Gender Female 1982 52.1 Male 1821 47.9 Race/Hispanic origin Mexican American 541 14.2 Other Hispanic 342 9.0 Non-Hispanic White 1525 40.1 Non-Hispanic Black 772 20.3 Non-Hispanic Asian 478 12.6 Other Race-including multi-racial 145 3.8 Education level Less than 9th grade 213 5.6 9-11th grade (includes 12th grade 515 13.5 with no diploma) High school graduate/GED or equivalents 833 21.9 Some college or AA degree 1242 32.7 College graduate or above 998 26.2 Refused 1 .0 Don't Know 1 .0 Low Active Not low active 1043 62.5 Low active 625 37.5 A1C Classification Bi Non-diabetic 2703 73.6 Diabetic or pre-diabetic 972 26.4 The study is comprised of 3803 participants, 1982 participants (52.1%) were female while 1821 participants (47.9%) were male. The race/Hispanic origin of 39

participants varied, with the largest portion being 1525 non-Hispanic white participants

(40.1%), the next largest group being non-Hispanic black with 772 participants (20.3%),

then 541 Mexican American participants (14.2%), and the smallest group being other

Hispanic with 342 participants (9%). In addition, 478 participants (12.6%) were non-

Hispanic Asian while the remaining 145 participants identified as “Other Race –

including multi-racial” (3.8%).The total number of participants with a self-reported

activity level of more than 30 min/day (not low active) was 1043 (62.5%), while the total

number of participants with a self-reported activity level at or below 30 min/day (low

active) was 625 (37.5 %). The total number of participants who don’t have pre-diabetes

or diabetes was 2703 (73.6%) while the total number of participants who have pre-

diabetes or diabetes was 972 (26.4%).

Table 2: Descriptive Characteristics of Study Participants

Variable N Minimum Maximum Mean SD Age in years at 3803 20 59 39.39 11.409 screening Energy (kcal) 3480 193 12108 2245.03 1090.52 Carbohydrate (gm) 3480 8.67 1423.87 267.79 137.95 Total sugars (gm) 3480 .13 1115.50 118.74 86.98 Dietary fiber (gm) 3480 .00 136.30 17.22 11.20 Caffeine (mg) 3480 0 2448 145.18 191.01 Glycohemoglobin (%) 3675 3.50 17.50 5.58 1.04 Weight (kg) 3774 32.80 222.60 82.70 23.08525 Body Mass Index (kg/푚2) 3771 14.10 82.90 29.16 7.52 Waist Circumference (cm) 3653 55.50 177.90 97.99 17.17

The average participant was middle age, consuming approximately one-to-two cups of caffeine per day, body mass index classifying in the overweight category and consuming above the recommended daily intake of carbohydrate consumption (Table 2).

The complete summary of the CHAID model is displayed in Figure 1.

Additionally, the full decision tree classification rules by node are displayed in Table 3.

The independent variables used to build the model, included age at the time of screening, sex, carbohydrate intake, caffeine intake, and physical are listed. The dependent variable used was glycated hemoglobin (HbA1C) percentage, which was categorized into normal and diabetic (pre-diabetic or diabetic merged into one). The resulting tree included 17 total nodes with 11 terminal nodes (see Figure 1 for full tree nodal performance).

Figure 1. The full CHAID decision tree

Each terminal node in this figure was mutually exclusive and exhaustive. The full

CHAID decision tree is displayed from top to bottom. The first level was age in years and has a total of 6 splits (p <0.001). As a participant’s age increased, it can be observed that the risk for pre-diabetic or diabetic increases. For people who are less than or equal to 27 years old, the prevalence of pre-diabetes or diabetes is 6.8%. For people between

27 and 35 years old, the prevalence of pre-diabetes or diabetes is 14.1%. For people who are 35 to 39 years old, the percentage of pre-diabetes or diabetes is 21.4%. For people between 39 and 43 years old, the percentage of pre-diabetes or diabetes is 28.5%. For people who are 43 to 51 years old, the percentage of pre-diabetes or diabetes is 36.5%.

Finally, for people older than 51, the prevalence of pre-diabetes or diabetes is 50.1%.

Figure 2. CHAID decision tree node 1 split

Figure 2 displays the split at node 1, containing those whose age is less than or equal to 27 years old. The next spilt from node 1 was determined by carbohydrate intake

(p <0.005). Those who consume less than or equal to 188.61 gm/day, the risk of prediabetes or diabetes is 1.9%, while those who consume more than 188.61 gm/day have an 8.7%. The risk of diabetes was almost five times (4.57) higher with those with high carbohydrate intake compared to those with low carbohydrate intake group.

For those cases with a carbohydrate intake less than or equal to 188.6 gm/day who are less than or equal to 23 years old (p=0.027), the risk of pre-diabetes or diabetes is 3.5% compare to those over 23 years old had a 0%.

Figure 3. CHAID decision tree node 4 split

Figure 3 displays the split at node 4 which contains those whose age is between

39 and 43 years old. The next spilt from node 4 was determined by caffeine intake (p =

0.003), with each of those nodes then split into 2 nodes based on gender and activity level. For those who consumed caffeine less than or equal to 92 mg/day, they were 1.86 times risk to be diabetic or prediabetic compared to consumption greater than 92 mg/day. In the node 9 split, a male whose age was between 39 to 43 years old and consumed caffeine less than or equal to 92 mg/day the risk for prediabetic or diabetic was 46.2 % compared to female who had 30%.

For those whose age was between 39 and 43 years old and who consumed more than 92 mg of caffeine per day, the prevalence of pre-diabetes or diabetes was 19.5%.

For participants reporting with less than 30 minutes per day of exercise (p = 0.006), and a daily caffeine intake greater than 92 mg, the risk of pre-diabetes or diabetes was

24.6%, which is a greater chance than those with more than 30 minutes per day of exercise. In comparison, participants with more than 30 minutes per day of exercise per day showed a lowed risk with only a 5.9% risk of prediabetes or diabetes. It is important to recognize that participant reporting less than 30 minutes per day of physical activity were 4.17 times more likely to be pre-diabetic or diabetic compared with participant engaging in more than 30 minutes per day of physical activity.

Table 3: CHAID decision rules for the classification Diabetes or Pre-Diabetes based on HbA1C criteria

% with Terminal Node Level 1 Level 2 Level 3 P/DM¹ Node² 6 Age (>51 years) - - 0.50 * 13 Age (39 up to 43 years) Caffeine (≤ 92mg/day) Gender (Male) 0.46 * 5 Age (43 up to 51 years) - - 0.37 * 9 Age (39 up to 43 years) Caffeine (≤ 92mg/day) - 0.36 14 Age (39 up to 43 years) Caffeine (≤ 92mg/day) Gender (Female) 0.30 * 4 Age (39 up to 43 years) - - 0.29 15 Age (39 up to 43 years) Caffeine (> 92mg/day) Low Active 0.25 * 3 Age (35 up to 39 years) - - 0.21 * 10 Age (39 up to 43 years) Caffeine (> 92mg/day) - 0.20 2 Age (28 up to 35 years) - - 0.14 * 8 Age (≤ 27 years) CHO (> 188gm/day) - 0.09 1 Age (≤ 27 years) - - 0.07 16 Age (39 up to 43 years) Caffeine (> 92mg/day) Not Low Active 0.06 * 7 Age (≤ 27 years) CHO (≤ 188gm/day) - 0.02 ¹ Represents the percentage of cases that met the A1C criteria for Pre-Diabetes (P) or Diabetes (DM) ² Indicates the node was terminal node algorithm.

CHAPTER V

DISCUSSION

The purpose of this study was to investigate the effect of chronic caffeine ingestion on glycemic mechanics in non-diabetic adults by using CHAID decision tree to classify prediabetes and diabetes based on A1C using a sample derived from the

National Health and Nutrition Examination Survey (NHANES) 2013-2014 data. Results from the four research questions revealed tangible information to help better the understanding of caffeine consumption effect on glycemic index among nondiabetics population. Findings revealed no significant interrelationship between caffeine and carbohydrate intake on HbA1C. The second research question showed a significant association between carbohydrate intake and HbA1c exists. A significant association between caffeine intake and HbA1c also exists. Additionally, both carbohydrate and caffeine variables have significant impact on the decision tree pathways with HbA1C based on age.

The data in the study did not show statistical significance among consumption between caffeine and carbohydrate when examining HbA1c levels. The result that indicating no interrelationship between caffeine and carbohydrate based on the HbA1c proposes that additional variables other may be needed to estimate the interrelationship such as specific caffeine sources. Huxley et al., (2009), considered different sources of

caffeine to find the interrelationship between caffeine and blood glucose levels and reported that a high intake of coffee, decaffeinated coffee, and tea are related with to a reduction in the risk of diabetes. Decaffeinated coffee may be more beneficial due to less caffeine content in commercially available sources combined with differences in individual responses to caffeine (Tunnicliffe, & Shearer, 2008). Caffeine elevates blood glucose level with ~160%, caffeinated coffee elevates ~320%, compared with decaffeinated coffee; so, decaffeinated coffee reduced the elevated in blood glucose of all study participants (Tunnicliffe, & Shearer, 2008).

The association between carbohydrate and HbA1c was clearly visible based on age. Data showed that the risk for diabetes significant increase with participants less than or equal to 27 years of age, reporting high carbohydrate daily intake (>188.61 gm/day) compare to people the same age consuming lower carbohydrate (≤ 189 gram daily). The risk for diabetes is almost five times higher with those with high carbohydrate intake compared to those with low carbohydrate intake among participants in the data. This finding provides insight into the importance of a stronger understanding of high carbohydrate consumption and prevalence of diabetes. The findings align with

Schulze et at. (2004) who found high risk of type 2 diabetes is associated with consumption of rapidly absorbed carbohydrates and low in cereal fiber.

The association between caffeine intake and HbA1c was significant in the decision tree pathway. On node 4, the risk of diabetes or prediabetes between ages 39 to

43 years of age reporting caffeine consumption less than or equal to 92 mg/day was

36.3%, while people in the same age group and consuming greater than 92 mg/day of caffeine had a lower risk for prediabetes or diabetes, with only a 19.5% more likely to be prediabetes or diabetes. However, those who consumed less than 92mg of caffeine were

1.86 times more likely to meet the A1C criteria for pre-diabetes or diabetes. Individuals at risk for metabolic syndrome, glucose intolerance, and Type 2 diabetes it is recommended to consume decaffeinated coffee, due to the protective role coffee has in the development and prevention of type 2 diabetes (Battram, Graham, Richter, & Dela,

2005). Additionally, Yarmolinsky et al., (2015), found habitual consumption of coffee had a protective impact against type 2 diabetes. Findings reported that habitual consumption of coffee had a protective impact against type 2 diabetes. Furthermore, the moderate caffeinated and decaffeinated coffee consumption may minimize the risk of type 2 diabetes in younger and middle-aged women (Van Dam et al., 2006). These findings align with the results in the current study, also revealing a strong relationship between caffeine and physical activity due to the people who consume a high amount of caffeine and they are physically active that will reduce their risk for pre-diabetes and diabetes compare to those who were not physically active based on HbA1c criteria.

For node 10, the data showed that the adults between 39 and 43 years of age consuming more than 92 mg of caffeine with low activity levels (less than 30 minutes per day of exercise), were 4.17 times more likely to have diabetes or pre-diabetes based on A1C criteria compare to those who were physically active (more than 30 minutes per day of exercise). Whereas, adults between 39 and 43 years of age who were physically

active (more than 30 minutes per day of exercise) with daily caffeine intake above 92 mg, the prevalence of pre-diabetes or diabetes was only a 5.9%. Therefore, the present study indicates that the caffeine may be protective mechanism from diabetes in people who were physically active. Caffeine has been studied over the past several decades as a substance for daily use along with performance-enhancing capabilities (Zaharieva, &

Riddell, 2013). Furthermore, there is evidence that caffeine is also an ergogenic aid for various types of athletic sports (Burke, 2013). Exercise plays an essential role in the prevention and monitor of insulin resistance, prediabetes, type 2 diabetes, gestational diabetes, and diabetes-related health complications (Colberg et al., 2010). Preliminary

Pedometer Indices determine the physical activity in healthy adults, which classified into; (1) <5000 steps/day that considered as sedentary lifestyle index, (2) 5000-7499 steps/day is typical of daily activity that considered as low active, (3) 7500–9999 that might be considered ‘somewhat active, and (4) ≥10 000 steps/day which classify individuals as active (Tudor-Locke & Bassett, 2004). Type 2 diabetes can be prevented by changes in the lifestyles pattern in both women and men who at high risk for diabetes

(Tuomilehto, Lindstrom, Eriksson, & Finnish, 2001).

The association between carbohydrates and caffeine variables was visible based on age in the decision tree pathways. Results found that the prevalence of diabetes increased with age. For people greater than 50 years of age, prediabetes or diabetes reported a 50% increase, while the younger people less than or equal to 27 years of age have a 6.8% less likely chance to have prediabetes or diabetes. It noticed from the

current study result that the high carbohydrate consumption would increase the risk of diabetes as well as consumption of caffeine with low physical activity. The high consumption of coffee minimizes the risk for type 2 diabetes and impaired glucose tolerance (Agardh et al., 2004).

An interesting finding in this study was located on node 4 (Figure No. 3), between 39 and 43 years of age surfaced three significant factors (gender, physical activity and caffeine). In the present study participants who consumed less than 92mg of caffeine were 1.86 times more likely to meet the A1C criteria for pre-diabetes or diabetes. Examining gender, there is a difference in risk between genders. Males between 39 to 43 years of age, consuming caffeine less than or equal to 92 mg/day, the risk of prediabetes or diabetes was 46.2 % and females between 39 to 43 years of age, consuming caffeine less than or equal to 92 mg/day risk for prediabetes or diabetes is only a 30%. This finding reinforces the importance of the physical activity in preventing type 2 diabetes for those who consume a high amount of caffeine. A study done by

SoJung, Robert, Katherine, Terry, & Robert (2005) shows the effects of caffeine consumption after exercise which resulted in a significant reduction (p = 0.05) in glucose uptake with 23% in the lean men, 26% in obese non-diabetes men, and 36% in obese type 2 diabetes men.

The average caffeine intake in the current study was 145.18 mg/day ±SD 191 mg/day. A standard cup of coffee has 95mg of caffeine so 92mg is pretty close to one cup. So, the average caffeine intake of this study was 1-2 cups with a SD of +/- 2 cups.

This implied the majority of US respondents consume between 0-4 cups of coffee per day. The daily caffeine intake for all ages combined was with a mean (±SE) = 165 ±1 mg/day (Mitchell et al., 2014). It is important to recognize that caffeine may play a protective role when combined with physical activity to minimize the risk of diabetes.

Data in this study used HbA1c levels based on diagnostic levels were divided into normal, prediabetes, and diabetes. The largest group fell in the normal range, without pre-diabetes or diabetes, 2703 participants, comprised (73.6%) participants in the study. The total number of participants with pre-diabetes was 710 (19.3%), and diabetes was 262 (7.1%.). The major finding in this study was the percentage of the participants who did not report knowing they had diabetes. Based on the HbA1C criteria,7.1 % (n = 262 participants) met the A1C criteria for diabetes and did not responded that they have diabetes. This finding represents 1 in 14 adults have diabetes based on the HbA1C criteria and they do not know their glucose levels were elevated.

This is an important finding and more research, education and awareness are warranted to address this problem. Similarly, Yarmolinsky et al., (2015), found that 1341 (10.7%) of participants were newly-diagnosed diabetes, 3083 (24.5%) with impaired fasting glucose, 3114 (24.7%) of cases with an impaired glucose tolerance, and 2651 (21.1%) of participants with HbA1c 5.7%, <6.5%.

Nutrition guidelines are very important and serve as one of the main modes of treatments for patients diagnoses with T2DM (Sato et al., 2017). So, there is a need to determine daily recommendations of carbohydrates to properly assist and maintain 54

glucose levels. Carbohydrate intake is reflective of dietary reference intakes (DRI). The average of total carbohydrate intake in this study was 268 gm/day and the standard deviation was ±138 grams/day. According to DRI, the Recommended Dietary

Allowances for carbohydrate is 130 gram per day (DRI, 2005).

Investigating NHANES data surfaced numerous important finding that may be helpful for understanding the risk of diabetes. The decision tree analysis permitted each variable to be clearly understood enabling the data to detect relationships between different variables. NHANES is comprised of self-report and the powerful finding revealed that many participants did not report knowing they had elevated HbA1c, placing them in prediabetes or diabetes categories. This finding reinforces the importance for the people to adhere to regular annual checkup. It is also important that the risk of elevated of HBA1c is widely understood to help prevent complications from diabetes.

Result from the study showed that 1 in14 respondents met A1C criteria for diabetes. The study used A1c rather fasting plasma glucose or OGTT. A1c provides long rang picture of glucose rather than a single moment time reading. A1c may be a stronger test to reveal glucose level over a period of time which is helpful to understand lifestyle and behaviors.

It is well understood that sedentary and low physical activity poses a high risk for diabetes, encouraging adherence for increased physical activity has been documented to

be beneficial for lowering the risk of numerous conditions but data from this study showed an interesting protective relationship between caffeine and physical activity.

Respondents who reported more than 30 minutes of activity and consumed more than one cup of caffeine had the lowest risk for diabetes. When comparing the risk against low physical activity respondents there was an alarming difference of more than 4 times the likelihood for elevated glucose. This surprising finding may indicate a protective mechanism that caffeine may play on glucose level when combined with physical activity. Helping to promote awareness to decrease sedentary lifestyles is the important message from the data. Educating people to monitor daily step count, aiming above

5,000 steps per day or accumulating more than 30 minutes of activity when combined with 1-2 cups of caffeine per day may have powerful impact.

Three additional findings provide clear insight into glucose changes from the respondents in the study. There were gender differences with males showing a higher risk in comparison to females with diabetes. For both genders between 39-43 years of age, the risk for prediabetes or diabetes was 1.51 times increased likelihood in males compared to females. Respondents consuming higher amounts of carbohydrates, above

188 (mg/day), show increased likelihood in comparison to low carbohydrate diets.

Understanding that gender, age and diet contribute to potential evolution of glucose levels should be considered when helping to lower the risk for diabetes.

This study reinforces that middle age people need to remain active and reduce sedentary behaviors to stay healthy. Understanding the impact of caffeine combined with 56

physical activity has the potential to change the aging process. Healthy diets, low in carbohydrate intake, may also be beneficial for lowering the risk of elevated glucose.

Simple steps such as educating others the importance of monitoring daily pedometer step count to increase physical activity patterns is a simple mechanism to begin to change the aging process, lowering the likelihood for elevated glucose. Despite the data relying on self-report, the decision tree clearly showed a relationship among variables for prediabetes and diabetes.

Most importantly, the current study found that caffeine may have a protective effect in the presence of physical activity and a negative effect in the lack of physical activity with predicting prediabetes and diabetes. The study reinforces the importance of the physical activity in preventing type 2 diabetes and discussing the potential relationship that caffeine may have with lowering the risk for diabetes. It has been document and understood that continuous exercise can reduce blood glucose levels and improve HbA1c however, much of our population remains sedentary (ADA, 2016). For decades, diabetes management has prescribed a combination of medication, diet and exercise as the therapeutic treatment for diabetes (Sigal, Kenny, Wasserman, Castaneda-

Sceppa, & White, 2006). The findings from this study now presents the added benefit of caffeine to minimize risk. A regular activity is the main key to diabetes management along with proper diet, stress Furthermore, data from Node 13 and 14 showed the alarming difference that men were 1.51 times more likely to have A1C criteria than women indicating that lifestyle education and modifications are imperative.

Limitations

There are numerous limitations in this study. Data collection did not report sources of caffeine, with the wide variety of caffeine levels in different products, more specifics about caffeine may help to estimate the association of caffeine more precisely. Caffeine association with stress and sleep. Also, elimination and incomplete/missing variables in the data collection method is another limitation in this study as some participants did not respond to all questions in the original survey resulting with some incomplete data.

However, this study was not a randomized controlled cross-over trial, so the result of this study should not be generalized. Rather, the study could be used to guide future research.

Application and Future Studies

Recently, published health studies that focus on overall health and well-being is an essential aspect to help prevent disease related to food intake and lifestyle for healthy people. Therefore, it is important to focus on expanding studies and education on caffeine to help spread knowledge about healthy levels of consumption. More awareness would be beneficial for people with high caffeine consumption combined with sedentary lifestyles to help understand the adverse effects and prevalence of diabetes. This would also help to provide knowledge with hopes to encourage people to engage in regular physical activity.

Also, it would be beneficial to have an educational curriculum for awareness days in schools and universities about the importance of physical activity especially for those who consume a high volume of daily caffeine. Caffeine is socially acceptable and

the culture of typical students can often result in high consumption of caffeine for energy, focus, and prolonged periods of study. Educating younger generations about the importance of physical activity and its relationship with caffeine and glycemic control from early ages may help to prevent or minimize the prevalence for diabetes in future.

This topic is crucial for all ages throughout the lifespan, not only students, but especially important for sedentary people who are inactive, have prolonged sit-time and consume diets high in caffeine. Implementing awareness programs such as health and wellness lectures discussing details about glycemic control with younger populations may have a positive outcome in the future. This study could be used as preventive intervention since it explains recommendations of 30 minutes per day of exercise with a daily caffeine intake exceeding 92 mg have a 5.9% chance to be pre-diabetic or diabetic; thus, the physical activity could promote health and prevent the disease. There is evidence that regular physical activity contributes to the primary and secondary prevention of many chronic diseases and is related to minimizing the risk of early death; furthermore, the great improvements in health status show in people who are physically active

(Warburton, Nicol, & Bredin, 2006).

Future studies should elaborate on the correlation relationship between physical activity and chronic caffeine consumption on HbA1C level. This is especially important as technology increases in our society, adherence to physical activity and obesity and diabetes rates have increased. Future research may include evaluating the combination of adding more variables such as sleep, stress, physical activity, body mass index, and

mental health to provide a clearer picture of society’s lifestyle and habits for risk of diabetes. Also, it would be beneficial for future studies to explore how much and what type of physical activity is needed to increase benefits. Finally, to make a clear conclusion regarding the interrelationship between caffeine and carbohydrate based on the HbA1c, it would be beneficial to use the research design using an intervention or experimental design.

REFERENCES

Acharya, A. S., Roy, R. P., & Dorai, B. (1991). Aldimine to ketoamine isomerization (Amadori rearrangement) potential at the individual nonenzymic glycation sites of hemoglobin a: Preferential inhibition of glycation by nucleophiles at sites of low isomerization potential. Journal of Protein Chemistry, 10(3), 345-358.

Agardh, E. E., Carlsson, S., Ahlbom, A., Efendic, S., Grill, V., Hammar, N., Hilding, A., ... Ostenson, C.-G. (2004). Coffee consumption, type 2 diabetes and impaired glucose tolerance in Swedish men and women. Journal of Internal Medicine, 255(6), 645-652.

Ahmad, K. (2014). Insulin sources and types: a review of insulin in terms of its mode on diabetes mellitus. Journal of Traditional Chinese Medicine, 34(2), 234-237.

American Diabetes Association. (2014). Common terms. Retrieved from http://www.diabetes.org/diabetes-basics/common-terms/

American Diabetes Association. (2015). 2.Classification and diagnosis of diabetes. Retrieved from https://doi.org/10.2337/dc15-S005

American Diabetes Association. (2016). Diagnosing Diabetes and Learning About Prediabetes. Retrieved from http://www.diabetes.org/diabetes-basics/diagnosis/

American Diabetes Association. (2018). Statistics about diabetes. Retrieved from http://www.diabetes.org/diabetes-basics/statistics/

American Diabetes Association. (2018). Tight Diabetes Control. Retrieved from http://www.diabetes.org/living-with-diabetes/treatment-and-care/blood-glucose- control/tight-diabetes-control.html

American Diabetes Association. (2016). Physical Activity is Important. Retrieved from http://www.diabetes.org/food-and-fitness/fitness/physical-activity-is- important.html

American Diabetes Association. (2017). What Can I Drink?. Retrieved from http://www.diabetes.org/food-and-fitness/food/what-can-i-eat/making-healthy- food-choices/what-can-i-drink.html

Aronoff, S. L., Berkowitz, K., Shreiner, B., & Want, L. (2004). Glucose metabolism and regulation: Beyond insulin and glucagon. Diabetes Spectrum, 17(3), 183-190.

Austin, G. L., Ogden, L. G., & Hill, J. O. (2011). Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: 1971-2006. The American Journal of Clinical Nutrition, 93(4), 836-843.

Battram, D. S., Graham, T. E., Richter, E. A., & Dela, F. (2005). The effect of caffeine on glucose kinetics in humans-influence of adrenaline. The Journal Of Physiology, 569(Pt 1), 347-355. Retrieved from http://ezproxy.uakron.edu:2048/login?url=http://search.ebscohost.com/login.aspx ?direct=true&db=mnh&AN=16150793&site=ehost-live

Blum, J., Aeschbacher, S., Schoen, T., Bossard, M., Pumpol, K., Brasier, N., Risch, M., ... Conen, D. (2015). Prevalence of prediabetes according to hemoglobin A1c versus fasting plasma glucose criteria in healthy adults. Acta Diabetologica, 52(3), 631-632.

Burke, L. (2013). Caffeine and sports performance. Applied Physiology, Nutrition, and Metabolism, 33(6),1319-1334, https://doi.org/10.1139/H08-130

Bweir, S., Al-Jarrah, M., Almalty, A.-M., Maayah, M., Smirnova, I. V., Novikova, L., & Stehno-Bittel, L. (2009). Resistance exercise training lowers HbA1c more than aerobic training in adults with type 2 diabetes. Diabetology & Metabolic Syndrome, 1(1), 27.

Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public health reports (Washington, DC, 1974), 100(2), 126-131.

Centers for Disease Control and Prevention. (2016). Know your limit for added sugars. Retrieved October 29, 2018, from http://www.cdc.gov/nutrition/data- statistics/know-your-limit-for-added-sugars.html

Centers for Disease Control and Prevention. (2018). Deaths and cost. Retrieved October 29, 2018, from https://www.cdc.gov/diabetes/data/statistics-report/deaths- cost.html

Centers for Disease Control and Prevention. (2018). Diabetes Home. Retrieved from https://www.cdc.gov/diabetes/library/features/a1c.html

Centers for Disease Control and Prevention. (2018). Facts about physical activity. Retrieved November 2, 2018, from https://www.cdc.gov/physicalactivity/data/facts.htm

Centers for Disease Control and Prevention. (2018). Physical activity basics. Retrieved from https://www.cdc.gov/physicalactivity/ basics/index.htm

Centers for Disease Control and Prevention. (2018). Getting tested. Retrieved from https://www.cdc.gov/ diabetes/basics/getting-tested.html

Centers for Disease Control and Prevention. (2017). National Center for Health Statistics. Retrieved from https://www.cdc.gov/nchs/fastats/diet.htm

Clark, I., & Landolt, H. P.(2017). Coffee, caffeine, and sleep: A systematic review of epidemiological studies and randomized controlled trials. Sleep Medicine Reviews, 31, 70-78.

Colberg, S. R., Sigal, R. J., Fernhall, B., Regensteiner, J. G., Blissmer, B. J., Rubin, R. R., Chasan-Taber, L., ... Braun, B. (2010). Exercise and Type 2 Diabetes: The American College of Sports Medicine and the American Diabetes Association: joint position statement. Diabetes Care, 33(12), e147-e167.

Cummings, J. H., & Stephen, A. M. (2007). Carbohydrate terminology and classification. European Journal of Clinical Nutrition, 61, 5-18.

Dashty, M. (2013). A quick look at biochemistry: Carbohydrate metabolism. Clinical Biochemistry, 46(5), 1339-1352.

Dewar, L., & Heuberger, R. (2017). The effect of acute caffeine intake on insulin sensitivity and glycemic control in people with diabetes. Diabetes & Metabolic Syndrome, 11, 631.

Dunstan, D. W., Puddey, I. B., Beilin, L. J., Burke, V., Morton, A. R., & Stanton, K. G. (January 01, 1998). Effects of a short-term circuit weight training program on glycaemic control in NIDDM. Diabetes Research and Clinical Practice, 40(1), 53-61.

Durstine, J. L., & American College of Sports Medicine. (2009). ACSM's exercise management for persons with chronic diseases and disabilities (3rd ed). Champaign, IL: Human Kinetics.

Ebeling, P., Koistinen, H. A., & Koivisto, V. A. (1998). Insulin-independent glucose transport regulates insulin sensitivity. Febs Letters, 436(3), 301-303.

Echeverri, D., Montes, F. R., Cabrera, M., Galán, A., & Prieto, A. (2010). Caffeine’s Vascular Mechanisms of Action. International Journal of Vascular Medicine, 1– 10. https://doi-org.ezproxy.uakron.edu:2443/10.1155/2010/834060

Ervin, R. B., & Ogden, C. L. (2013). U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. NCHS Data Brief, No. 122: Consumption of added sugars among U.S. adults, 2005–2010. Retrieved from http://www.cdc.gov/nchs/data/databriefs/db122.pdf

Fink, H. H., & Mikesky, A. E. (2015). Practical applications in sports nutrition (4th ed). Burlington, MA: Jones & Bartlett Learning.

Ford, E. S., Wheaton, A. G., Chapman, D. P., Perry, G. S., Croft, J. B., & Li, C. (2014). Associations between self-reported sleep duration and sleeping disorder with concentrations of fasting and 2-h glucose, insulin, and glycosylated hemoglobin among adults without diagnosed diabetes. Journal of Diabetes, 6(4), 338-350.

Fu, Z., Gilbert, E. R., & Liu, D. (2013). Regulation of insulin synthesis and secretion and pancreatic beta-cell dysfunction in diabetes. Current Diabetes Reviews, 9(1), 25-53.

Fulgoni, V. L., Keast, D. R., & Lieberman, H. R. (2015). Trends in intake and sources of caffeine in the diets of US adults: 2001-2010. American Journal of Clinical Nutrition, 101(5), 1081-1087

Garcia, M. R., Maneck, M. C. R., Medeiros, T. E., Osiecki, R., Snak, A. L., da, S. L. A., & de, F. L. (2014). Caffeine modifies blood glucose availability during prolonged low-intensity exercise in individuals with type-2 diabetes. Colombia Mé dica, 45(2), 72-76.

Gonzalez, M. E., & Ramirez-Mares, M. V. (2014). Impact of caffeine and coffee on our health. Trends in Endocrinology and Metabolism, 25(10), 489-92.

Guidelines Index - 2008 Physical Activity Guidelines - health.gov. (2018). Retrieved from https://health.gov/paguidelines/2008/

Hayward, R. A., Reaven, P. D., Wiitala, W. L., Bahn, G. D., Reda, D. J., Ge, L., McCarren, M., ... Emanuele, N. V. (2015). Follow-up of glycemic control and cardiovascular outcomes in type 2 diabetes. The New England Journal of Medicine, 372(23), 2197-2206.

Huxley, R., Lee, C. M. Y., Barzi, F., Timniernieister, L., Czernichow, S., Perkovic, V., … Woodward, M. (2009). Coffee, decaffeinated coffee, and tea consumption in relation to incident type 2 diabetes mellitus. Archives of Internal Medicine, 169(22), 2053–2063.

IBM SPSS [Decision Trees]. Version 19, August 2010.

Institute of Medicine. (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: The National Academies Press. https://doi.org/10.17226/10490.

James, D. L., Christina, E. B., Richard, S. S., & Mark, N. F. (2004). Caffeine impairs glucose metabolism in type 2 diabetes. Diabetes Care, 27(8), 2047-2048.

Jéquier, E. (1994). Carbohydrates as a source of energy. The American Journal of Clinical Nutrition, 59(3), 682S–685S. https://doi.org/10.1093/ajcn/59.3.682S

Jiang, X., Zhang, D., & Jiang, W. (2014). Coffee and caffeine intake and incidence of type 2 diabetes mellitus: A meta-analysis of prospective studies. European Journal of Nutrition, 53(1), 25-38. doi:10.1007/s00394-013-0603-x

Jung, C.-H., & Choi, K. M. (2017). Impact of high-carbohydrate diet on metabolic parameters in patients with type 2 diabetes. Nutrients, 9(4), 322. https://doi.org/10.3390/ nu9040322

Kaku, K. (2010). Pathophysiology of type 2 diabetes and its treatment policy. Japan Medical Association Journal, 53(1), 41-46.

Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119-127.

Keizo, O., Mizuko, I., Takako, M., Tomoko, O., Takao, S., Masahiro, A., & ... Suminori, K. (2012). Effects of 16-week consumption of caffeinated and decaffeinated instant coffee on glucose metabolism in a randomized controlled trial. Journal of Nutrition & Metabolism, 2012, 1-9. doi:10.1155/2012/207426

Laiteerapong, N., Cooper, J. M., Skandari, M. R., Clarke, P. M., Winn, A. N., Naylor, R. N., & Huang, E. S. (2018). Individualized glycemic control for U.S. adults with type 2 diabetes. Annals of Internal Medicine, 168(3), 170.

Lane, J. D., Lane, A. J., Surwit, R. S., Kuhn, C. M., & Feinglos, M. N. (2012). Pilot study of caffeine abstinence for control of chronic glucose in type 2 diabetes. Journal of Caffeine Research, 2(1), 45-47.

Lebovitz, H. E. (1999). Type 2 diabetes: An overview. Clinical Chemistry, 45(8), 1339- 1345.

Lee, Y.-H., Shin, M.-H., Nam, H.-S., Park, K.-S., Choi, S.-W., Ryu, S.-Y., & Kweon, S.- S. (2018). Effect of family history of diabetes on hemoglobin A1c levels among individuals with and without diabetes: The Dong-gu study. Yonsei Medical Journal, 59(1), 92.

LeRoith, D., & Rayfield, E. J. (2007). The benefits of tight glycemic control in type 2 diabetes mellitus. Clinical Cornerstone, 8, S19–S29. https://doi.org/10.1016/S1098-3597(07)80018-4

Louis, M., & Claude, C. (2009). Target for glycemic control: Concentrating on glucose. Diabetes Care, 32(2): S199-S204.

Mahoney, C. R., Giles, G. E., Marriott, B. P., Judelson, D. A., Glickman, E. L., Geiselman, P. J., & Lieberman, H. R. (2018). Intake of caffeine from all sources and reasons for use by college students. Clinical Nutrition Journal. https://doi.org/10.1016/j.clnu.2018.04.004

Makris, K., & Spanou, L. (November 01, 2011). Is There a Relationship between Mean Blood Glucose and Glycated Hemoglobin? Journal of Diabetes Science and Technology, 5(6), 1572-1583. Mann, J., Cummings, J. H., Englyst, H. N., Key, T., Liu, S., Riccardi, G., … Wiseman, M. 2007). FAO/WHO Scientific Update on carbohydrates in human nutrition: conclusions. European Journal of Clinical Nutrition, 61(S1), S132–S137. https://doi.org/10.1038/sj.ejcn.1602943

Meireles, P., Sales-Dias, J., Andrade, C. M., Mello-Vieira, J., Mancio-Silva, L., Simas, J. P., Staines, H. M., ... Prudê ncio, M. (2017). GLUT1-mediated glucose uptake plays a crucial role during Plasmodium hepatic infection. Cellular Microbiology, 19(2).

Mejia, E. G., & Ramirez-Mares, M. V. (2014). Impact of caffeine and coffee on our health. Trends in Endocrinology & Metabolism, 25(10), 489-492.

Mergenthaler, P., Lindauer, U., Dienel, G. A., & Meisel, A. (2013). Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends in Neurosciences, 36 (10), 587-597.

Metting, E. I., In, . V. J. C., Dekhuijzen, P. N., van, H. E., Kocks, J. W., Muilwijk- Kroes, J. B., Chavannes, N. H., ... van, . M. T. (2016). Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data. Erj Open Research, 2(1).

Milanović, M., & Stamenković, M. (2016). CHAID Decision Tree: Methodological Frame and Application. Economic Themes, 54(4), 563-586

Miller, B., Fridline, M., Liu, P.-Y., & Marino, D. (2014). Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults. Computational and Mathematical Methods in Medicine, 1-7.

Mitchell, D. C., Knight, C. A., Hockenberry, J., Teplansky, R., & Hartman, T. J. (2014). Beverage caffeine intakes in the U.S. Food and Chemical Toxicology, 63, 136- 142.

Nakagami, T., Takahashi, K., Suto, C., Oya, J., Tanaka, Y., Kurita, M., Isago, C., ... Uchigata, Y. (2017). Diabetes diagnostic thresholds of the glycated hemoglobin A1c and fasting plasma glucose levels considering the 5-year incidence of retinopathy. Diabetes Research and Clinical Practice, 124, 20-29.

National Institute of Diabetes and Digestive and Kidney Diseases. (2018). The A1C test & diabetes. Retrieved from https://www.niddk.nih.gov/health- information/diabetes/overview/tests-diagnosis/a1c-test

National Institute of Diabetes and Digestive and Kidney Diseases. (2017). Diabetes, Heart Disease, and Stroke. Retrieved from https://www.niddk.nih.gov/health- information/diabetes/overview/preventing-problems/heart-disease-stroke

National Institute of Diabetes and Digestive and Kidney Diseases. (2018). Insulin resistance & prediabetes. Retrieved from https://www.niddk.nih.gov/health- information/diabetes/overview/what-is-diabetes/prediabetes-insulin-resistance

National Institute of Diabetes and Digestive and Kidney Diseases. (2016). Risk factors for type 2 diabetes. Retrieved from https://www.niddk.nih.gov/health- information/diabetes/ overview/risk-factors-type-2-diabetes

National Institute of Diabetes and Digestive and Kidney Diseases. (2016). What is Diabetes?. Retrieved from https://www.niddk.nih.gov/health- information/diabetes/overview/what-is-diabetes

Navale, A. M., & Paranjape, A. N. (2016). Glucose transporters: physiological and pathological roles. Biophysical Review, 8(1), 5-9.

Nawrot, P., Jordan, S., Eastwood, J., Rotstein, J., Hugenholtz, A., & Feeley, M. (2003). Effects of caffeine on human health. Food Additives and Contaminants, 20(1), 1- 30.

Nelson, M. E., Rejeski, W. J., Blair, S. N., Duncan, P. W., Judge, J. O., King, A. C., Macera, C. A., ... American Heart Association. (2007). Physical activity and public health in older adults: Recommendation from the American College of Sports Medicine and the American Heart Association. Circulation, 116(9), 1081- 1093.

Nikolic, J., Bjelakovic, G., & Stojanovic, I. (2003). Effect of caffeine on the metabolism of L-arginine in the brain. Molecular and Cellular Biochemistry, 244, 1-2.

Olokoba, A. B., Obateru, O. A., & Olokoba, L. B. (2012). Type 2 diabetes mellitus: A review of current trends. Oman Medical Journal, 27(4), 269-273

Olson, A. L., & Pessin, J. E. (1996). Structure, function, and regulation of the mammalian facilitative glucose transporter gene family. Annual Review of Nutrition, 16, 235-56.

Ö nder, E., & Uyar, S. (2017). CHAID Analysis to determine socioeconomic variables that explain students' academic success. Universal Journal of Educational Research, 5(4), 608-619.

Petrie, H. J., Chown, S. E., Belfie, L. M., Duncan, A. M., McLaren, D. H., Conquer, J. A., & Graham, T. E. (2004). Caffeine ingestion increases the insulin response to an oral-glucose-tolerance test in obese men before and after weight loss. The American Journal of Clinical Nutrition, 80(1), 22-8.

Poroch-Seriţan, M., Michitiuc, C. B., & Jarcău, M. (2018). Studies and research on caffeine content of various products. BRAIN: Broad Research In Artificial Intelligence & Neuroscience, 9(1), 29-35.

Qaseem, A., Wilt, T. J., Barry, M. J., Horwitch, C., Kansagara, D., & Forciea, M. A. (2018). Hemoglobin A1c targets for glycemic control with pharmacologic therapy for nonpregnant adults with type 2 diabetes mellitus: A guidance statement update from the American college of physicians. Annals of Internal Medicine, 168(8), 569-576.

Quesada, I., Tudurí, E., Ripoll, C., & Nadal, Á. (2008). Physiology of the pancreatic α- cell and glucagon secretion: role in glucose homeostasis and diabetes. Journal of Endocrinology, 199(1), 5-19. https://doi.org/10.1677/JOE-08-0290

Rayner, D. V., Thomas, M. E. A., & Trayhurn, P. (1994). Glucose transporters (GLUTs 1-4) and their mRNAs in regions of the rat brain: insulin-sensitive transporter expression in the cerebellum. Canadian Journal of Physiology and Pharmacology, 72(5), 476-479.

Ribeiro, J. A., & Sebastião, A. M. (2010). Caffeine and adenosine. Journal of Alzheimer’s Disease, 20(s1), S3-S15. https://doi.org/10.3233/JAD-2010-1379

Richardson, T., Thomas, P., Ryder, J., & Kerr, D. (2005). Influence of caffeine on frequency of hypoglycemia detected by continuous interstitial glucose monitoring system in patients with long-standing type 1 diabetes. Diabetes Care Alexandria Va-, 28(6), 1316-1320.

Robinson, L. E., Savani, S., Battram, D. S., McLaren, D. H., Sathasivam, P., & Graham, T. E. (2004). Caffeine ingestion before an oral glucose tolerance test impairs blood glucose management in men with type 2 diabetes. The Journal of Nutrition, 134(10), 2528-33.

Robinson, L. E., Spafford, C., Graham, T. E., & Smith, G. N. (2009). Acute caffeine ingestion and glucose tolerance in women with or without gestational diabetes mellitus. Journal of Obstetrics and Gynaecology Canada, 31(4), 304-312.

Röder, P. V., Wu, B., Liu, Y., & Han, W. (2016). Pancreatic regulation of glucose homeostasis. Experimental & Molecular Medicine, 48(3), e219. https://doi.org/10.1038/emm.2016.6

Russell, A. G., Chen, L., Jones, K., & Peiris, A. N. (2014). Glucose monitoring as an impediment to Improving glycemic control: a case report. Tennessee Medicine: Journal of the Tennessee Medical Association, 107(1), 37-38.

Sainsbury, E., Kizirian, N. V., Partridge, S. R., Gill, T., Colagiuri, S., & Gibson, A. A. (2018). Effect of dietary carbohydrate restriction on glycemic control in adults with diabetes: A systematic review and meta-analysis. Diabetes Research and Clinical Practice, 139, 239-252.

Sato, J., Kanazawa, A., Makita, S., Hatae, C., Komiya, K., Shimizu, T., … Watada, H. (2017). A randomized controlled trial of 130 g/day low-carbohydrate diet in type 2 diabetes with poor glycemic control. Clinical Nutrition, 36(4), 992-1000. https://doi.org/10.1016/j.clnu.2016.07.003

Saudek, C. D., & Brick, J. C. (2009). The clinical use of hemoglobin A1c. Journal of Diabetes Science and Technology, 3(4), 629-634.

Sawynok, J. (2011). Methylxanthines and pain. Handbook of Experimental Pharmacology, 200, 311-329.

Schulze, M. B., Liu, S., Rimm, E. B., Manson, J. E., Willett, W. C., & Hu, F. B. (January 01, 2004). Glycemic index, glycemic load, and dietary fiber intake and incidence of type 2 diabetes in younger and middle-aged women. American Journal of Clinical Nutrition, 80, 348-356.

Selvin, E., Coresh, J., & Brancati, F. L. (2006). The burden and treatment of diabetes in elderly individuals in the U.S. Diabetes Care Alexandria Va-, 29(11), 2415-2419.

Sheard, N. F., Clark, N. G., Brand-miller, J. C., Franz, M. J., Pi-Sunyer, F. X., Mayer- Davis, E., … Geil, P. (2004) Dietary carbohydrate (amount and type) in the prevention and management of diabetes. Diabetes Care, 27(9), 2266-2271.

Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K. (2016). Significance of HbA1C test in diagnosis and prognosis of diabetic patients. Biomarker Insights, 11, 95-104.

Shouman, M., Turner, T., & Stocker, R. (December 01, 2010). Using decision tree for diagnosing heart disease patients. Conferences in Research and Practice in Information Technology Series, 121, 23-30.

Sigal, R. J., Kenny, G. P., Wasserman, D. H., Castaneda-Sceppa, C., & White, R. D. (2006). Physical activity/exercise and type 2 diabetes: a consensus statement from the American Diabetes Association. Diabetes Care, 29(6), 1433-1438. https://doi.org/10.2337/dc06-9910

Slavin, J., & Carlson, J. (2014). Carbohydrates. Advances in nutrition (Bethesda, Md.), 5(6), 760-1. doi:10.3945/an.114.006163

Snel, J., & Lorist, M. M. (2011). Effects of caffeine on sleep and cognition. Progress in Brain Research, 190, 105-117.

SoJung, L., Robert, H., Katherine, K., Terry, E. G., & Robert, R. (March 01, 2005). Caffeine ingestion is associated with reductions in glucose uptake independent of obesity and type 2 diabetes before and after exercise training. Diabetes Care, 28(3), 566-572.

Song, Y. Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130-135.

Sprague, J. E., & Arbeláez, A. M. (2011). Glucose counterregulatory responses to hypoglycemia. Pediatric Endocrinology Reviews : Per, 9(1), 463-73. 70

Stratton, I. M, Adler, A. I, Neil, H. A. W, Matthews, D. R, Manley, S. E, Cull, C. A, Hadden, D., ... Holman, R. R. (2000). Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): Prospective observational study. BMJ, 321(7258), 405-412.

Su, J., Zhao, L., Zhang, X., Cai, H., Huang, H., Xu, F., Chen, T., ... Wang, X. (2018). HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients. Cardiovascular Diabetology, 17(1).

Szablewski, L. (2011). Glucose homeostasis – Mechanism and defects. InTech, 227-256.

Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., Ng, L. C., Leo, Y. S., Thi Puong, L., Vasudevan, S. G., Simmons, C. P., Hibberd, M. L., … Ooi, E. E. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. Plos Neglected Tropical Diseases, 2(3), e196. doi:10.1371/journal.pntd.0000196

Tappy, L. (2008). Basics in clinical nutrition: Carbohydrate metabolism. E-spen, the European E-Journal of Clinical Nutrition and Metabolism, 3 (5): e192-e195.

Temple, J. L., Bernard, C., Lipshultz, S. E., Czachor, J. D., Westphal, J. A., & Mestre, M. A. (2017). The safety of ingested caffeine: a comprehensive review. Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00080

The International Expert Committee. (2009). International expert committee report on the role of the a1c assay in the diagnosis of diabetes. Diabetes Care, 32(7), 1327- 1334. https://doi.org/10.2337/dc09-9033

The Nutrition Source. (2016). Carbohydrates and Blood Sugar. Retrieved from https://www.hsph.harvard.edu/nutritionsource/carbohydrates/carbohydrates-and- blood-sugar/

Torimoto, K., Okada, Y., Sugino, S., & Tanaka, Y. (2017). Determinants of hemoglobin A1c level in patients with type 2 diabetes after in-hospital diabetes education: A study based on continuous glucose monitoring. Journal of Diabetes Investigation, 8(3), 314-320.

Tudor-Locke, C., & Bassett, D. R. J. (January 01, 2004). How many steps/day are enough? Preliminary pedometer indices for public health. Sports Medicine (auckland, N.z.), 34, 1, 1-8.

Tunnicliffe, J. M., & Shearer, J. (2008). Coffee, glucose homeostasis, and insulin resistance: physiological mechanisms and mediators. Applied Physiology, Nutrition, and Metabolism, 33(6), 1290–1300. https://doi.org/10.1139/H08-123 71

Tuomilehto, J., Lindstrom, J., Eriksson, J. G., & Finnish, D. P. S. G. (May 03, 2001). Prevention of Type 2 Diabetes Mellitus by Changes in Lifestyle among Subjects with Impaired Glucose Tolerance. New England Journal of Medicine, 344, 1343- 1350.

U S Food and Drug Administration. (2018). Guidance for Industry: Highly Concentrated Caffeine in Dietary Supplements. Retrieved from https://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryI nformation/ucm604318.htm

Ünal, D., Kara, A., Aksak, S., Zuhal Altunkaynak, B., & Yıldırım, S. (2012). Insulin hormone: Mechanism and effects on the body and relationship with central nervous system. Dicle Medical Journal / Dicle Tip Dergisi, 39(2), 310-315. doi:10.5798/diclemedj.0921.2012.02.0149

Urry, E., Jetter, A., & Landolt, H.-P. (2016). Assessment of CYP1A2 enzyme activity in relation to type-2 diabetes and habitual caffeine intake. Nutrition & Metabolism, 13(1). https://doi.org/10.1186/s12986-016-0126-6

Use of glycated haemoglobin (Hba1c) in the diagnosis of diabetes mellitus. (2011). Diabetes Research and Clinical Practice, 93(3), 299-309. https://doi.org/10.1016/ j.diabres.2011.03.012

Van Dam, R. M., Pasman, W. J., & Verhoef, P. (2004). Effects of coffee consumption on fasting blood glucose and insulin concentrations: randomized controlled trials in healthy volunteers. Diabetes care, 27(12), 2990-2992.

Van Dam, R. M., Willett, W. C., Manson, J. E., & Hu, F. B. (2006). Coffee, caffeine, and risk of type 2 diabetes: a prospective cohort study in younger and middle- aged US women. Diabetes care, 29(2), 398-403.

Vasudevan, A., Burns, A., & Fonseca, V. (2006). The effectiveness of intensive glycemic control for the prevention of vascular complications in diabetes mellitus. Treatments in Endocrinology, 5(5), 273-286.

Voet, D., Voet, J. G., & Pratt, C. W. (2013). Principles of biochemistry: International student version. Hoboken, NJ: Wiley.

Wanyika H. N., Gatebe E. G., Gitu L. M., Ngumba E. K., & Maritim C. W., (2010). Determination of caffeine content of tea and instant coffee brands found in the Kenyan market. African Journal of Food Science, 4(6),353-358.

Warburton, D. E., Nicol, C. W., & Bredin, S. S. (2006). Health benefits of physical activity: the evidence. CMAJ : Canadian Medical Association Journal, 174(6), 801-809.

Whitehead, N., & White, H. (2013). Systematic review of randomised controlled trials of the effects of caffeine or caffeinated drinks on blood glucose concentrations and insulin sensitivity in people with diabetes mellitus. Journal of Human Nutrition and Dietetics: The Official Journal of the British Dietetic Association, 26(2), 111-25.

Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global Prevalence of Diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27(5), 1047-1053.

Willett, W., Manson, J., & Liu, S. (2002). Glycemic index, glycemic load, and risk of type 2 diabetes. The American Journal of Clinical Nutrition, 76(1).

Wood, I. S., & Trayhurn, P. (2003). Glucose transporters (GLUT and SGLT): expanded families of sugar transport proteins. British Journal of Nutrition, 89(1), 3-9.

World Health Organization. (2010). Diabetes Mellitus. Retrieved from http://www.who.int/mediacentre/factsheets/fs138/en/

World Health Organization. (2011). Use of glycated haemoglobin (HbA1C) in the diagnosis of diabetes mellitus. Available at: http://www.who.int/diabetes/ publications/report-hba1c_2011.pdf.

World Health Organization. (2018). | About diabetes. Retrieved from http://www.who.int/diabetes/action_online/basics/en/index3.html

World Health Organization. (2018). Physical activity and adults. Retrieved from http://www.who.int/dietphysicalactivity/factsheet_adults/en/

World Health Organization. (2018). Raised fasting blood glucose. Retrieved from http://www.who.int/gho/ncd/risk_factors/blood_glucose_prevalence_text/en/

Yarmolinsky, J., Mueller, N. T., Duncan, B. B., Bisi Molina, M. C., Goulart, A. C., & Schmidt, M. I. (2015). Coffee consumption, newly diagnosed diabetes, and other alterations in glucose homeostasis: A cross-sectional analysis of the longitudinal study of adult health (ELSA-Brasil). Plos ONE, 10(5), 1-15. doi:10.1371/journal.pone.0126469

Zaharieva, D. P., & Riddell, M. C. (2013). Caffeine and glucose homeostasis during rest and exercise in diabetes mellitus. Applied Physiology, Nutrition, and Metabolism, 38(8), 813-822.