Health and Social Development, Faculty of (Okanagan)
Health and Exercise Sciences, School of (Okanagan)
DSpace
UBCO
Crampton, Kara
Master of Science - MSc
University of British Columbia
Background: Persons living with type 2 diabetes (T2D) may experience accelerated cognitive decline. Increased dietary intake of the carotenoids lutein or zeaxanthin may have positive effects on cognitive function. Eggs are one of the most bioavailable sources of lutein and zeaxanthin in the human diet. Purpose: To determine if increased egg consumption can increase blood lutein and zeaxanthin levels in people with T2D. A secondary aim was to explore if increased egg consumption influences cognitive function. Methods: We conducted a remote, parallel arm randomized controlled trial in people living with T2D. We tested the hypothesis that instructions to consume ~3 eggs per day via a low-carbohydrate breakfast intervention for three months would increase levels of blood lutein and zeaxanthin compared to eating a dietary guidelines-based low-fat breakfast that contained no eggs. We also examined whether the egg-based breakfast intervention could improve cognitive performance compared to the no eggs breakfast. Participants (N=57) were randomized to one of the breakfast diet interventions, with fingertip capillary blood samples collected at baseline and three months using a lancet and dried blood spot cards for analyses of blood lutein and zeaxanthin. Computerized cognitive assessments were completed at the participant’s home at the beginning, middle, and end of the trial. Results: The low-carbohydrate, egg-based breakfast intervention was successful at increasing egg intake to 1.7 (0.9) eggs per day [mean (SD)], which was significantly greater than daily egg intake [0.3 (0.3)] in the no egg dietary guidelines breakfast group (P > 0.05). There was no significant change in blood lutein or zeaxanthin in either breakfast group (P > 0.05) and all measures of cognitive performance were not significantly altered by the breakfast interventions (all P > 0.05). Conclusion: Three months of consuming ~2 eggs per day did not change blood lutein and zeaxanthin levels, or impact cognitive performance, in participants with T2D. Following a relatively straightforward dietary strategy that manipulates one meal during the day may be a feasible intervention to increase egg intake for people with T2D, but more research is required to determine if this impacts carotenoids and cognitive function.
Investigating the impact of a low-carbohydrate, egg-based breakfast versus a low-fat, guideline breakfast on blood carotenoids and cognitive function in type 2 diabetes by Kara Crampton B.H.K, University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Health and Exercise Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2022 © Kara Crampton, 2022 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Investigating the impact of a low-carbohydrate, egg-based breakfast versus a low-fat, guideline breakfast on blood carotenoids and cognitive function in type 2 diabetes submitted by Kara Crampton in partial fulfillment of the requirements of the degree of Master of Science . Dr. Jonathan Little Faculty of Health and Social Development, School of Health and Exercise Sciences Supervisor Dr. Susan Murch Irving K Barber Faculty of Science, Department of Chemistry Supervisory Committee Member Dr. Paul van Donkelaar Faculty of Health and Social Development, School of Health and Exercise Sciences Supervisory Committee Member Dr. Sarah Purcell Faculty of Medicine, Southern Medical Program University Examiner Click or tap here to enter text. iii Abstract Background: Persons living with type 2 diabetes (T2D) may experience accelerated cognitive decline. Increased dietary intake of the carotenoids lutein or zeaxanthin may have positive effects on cognitive function. Eggs are one of the most bioavailable sources of lutein and zeaxanthin in the human diet. Purpose: To determine if increased egg consumption can increase blood lutein and zeaxanthin levels in people with T2D. A secondary aim was to explore if increased egg consumption influences cognitive function. Methods: We conducted a remote, parallel arm randomized controlled trial in people living with T2D. We tested the hypothesis that instructions to consume ~3 eggs per day via a low-carbohydrate breakfast intervention for three months would increase levels of blood lutein and zeaxanthin compared to eating a dietary guidelines-based low-fat breakfast that contained no eggs. We also examined whether the egg-based breakfast intervention could improve cognitive performance compared to the no eggs breakfast. Participants (N=57) were randomized to one of the breakfast diet interventions, with fingertip capillary blood samples collected at baseline and three months using a lancet and dried blood spot cards for analyses of blood lutein and zeaxanthin. Computerized cognitive assessments were completed at the participant’s home at the beginning, middle, and end of the trial. Results: The low-carbohydrate, egg-based breakfast intervention was successful at increasing egg intake to 1.7 (0.9) eggs per day [mean (SD)], which was significantly greater than daily egg intake [0.3 (0.3)] in the no egg dietary guidelines breakfast group (P > 0.05). There was no significant change in blood lutein or zeaxanthin in either breakfast group (P > 0.05) and all measures of cognitive performance were not significantly altered by the breakfast interventions (all P > 0.05). Conclusion: Three months of consuming ~2 eggs per day did not change blood lutein and zeaxanthin levels, or impact cognitive performance, in participants with T2D. Following a relatively straightforward dietary strategy that manipulates one meal during the iv day may be a feasible intervention to increase egg intake for people with T2D, but more research is required to determine if this impacts carotenoids and cognitive function. v Lay Summary People living with type 2 diabetes experience faster cognitive decline than people without. There is evidence that lutein and zeaxanthin, two nutrients that are found naturally in many foods and are commercially available as supplements, might be able to improve cognitive function. Eggs are an excellent dietary source of lutein and zeaxanthin. In this study, we asked people living with type 2 diabetes to eat three eggs per day at breakfast or no eggs per day at breakfast for three months and measured blood lutein and zeaxanthin and cognitive function before and after. After three months, there were no changes in blood lutein or zeaxanthin levels in either breakfast group, and cognitive performance measured by computer memory tasks did not differ between the groups. Our findings suggest that increasing consumption of eggs may not influence blood lutein or zeaxanthin or impact cognitive performance in people with type 2 diabetes. vi Preface The concept of this study was conceived by Dr. Jonathan Little and Dr. Monique Francois to understand the influence that breakfast composition has on glucose control in individuals living with type 2 diabetes. Dr. Jonathan Little was responsible for the overall study design and securing grants through the Egg Nutrition Centre and the Egg Farmers of Canada to fund the 12-week remote trial and served as the Principal Investigator. Kara Crampton assisted postdoctoral fellow Dr. Barbara Oliveira in the coordination of the trial conducted in Canada. Kara Crampton selected the cognitive assessments to be used and located a laboratory that could analyze carotenoids in the dried blood spot cards. Kara Crampton conducted the literature search and review, helped with data acquisition and wrote this thesis with editorial feedback from Kaja Falkenhain (PhD student), Dr. Oliveira, and Dr. Little. It is planned that the data presented in this thesis will be incorporated into a larger manuscript that will be prepared by Dr. Barbara Oliveira and will include the cognitive and carotenoid data described in this thesis along with the glucose control and other metabolic outcomes that are not described in this thesis. The research was approved by the UBC Clinical Research Ethics Board (H19-03179). vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents .................................................................................................................... vii List of Tables ............................................................................................................................ ix List of Figures ........................................................................................................................... x Acknowledgements ................................................................................................................. xi Dedication ............................................................................................................................... xii Chapter 1: Introduction ............................................................................................................ 1 1.1 Type 2 Diabetes and Cognitive Function .................................................................. 1 1.2 The Role of the Carotenoids Lutein and Zeaxanthin in Cognitive Function ............... 5 1.3 Using Eggs as a Source of Lutein and Zeaxanthin.................................................... 9 1.4 Using Eggs to Improve Cognitive Function in People with T2D ................................11 1.5 Purpose and Hypothesis ..........................................................................................12 Chapter 2: Methods................................................................................................................. 14 2.1 Study Design and Procedures .................................................................................15 2.2 Breakfast Conditions and Dietary Adherence ...........................................................18 2.3 Cognitive Assessment .............................................................................................19 2.4 Measuring Blood Lutein and Zeaxanthin Levels .......................................................20 2.5 Statistical Analysis ...................................................................................................21 2.6 Ethical Considerations .............................................................................................22 Chapter 3: Results .................................................................................................................. 23 3.1 Participants ..............................................................................................................23 3.2 Dietary Data .............................................................................................................23 3.3 Cognitive Data .........................................................................................................25 viii 3.3.1 Delayed Match to Sample Task ........................................................................25 3.3.2 Paired Associated Learning Task .....................................................................28 3.4 Carotenoid Data ......................................................................................................30 3.5 Exploratory analyses ...............................................................................................30 Chapter 4: Discussion ............................................................................................................ 32 4.1 Dietary compliance was high in a remote study .......................................................32 4.2 Higher egg consumption did not increase blood lutein and zeaxanthin levels ..........33 4.3 Higher egg consumption did not improve cognitive performance .............................36 Chapter 5: Conclusion ............................................................................................................ 38 5.1 Strengths, implications, and limitations ....................................................................38 5.2 Conclusion ...............................................................................................................39 Bibliography…………………………………………………………………………………………….40 Appendices .............................................................................................................................47 Appendix A – Recipe Books ..............................................................................................47 Appendix B – Cognitive Assessment .................................................................................59 Appendix C – Cognitive Assessment Analysis ...................................................................60 Appendix D – DBS Carotenoid Values ..............................................................................61 Appendix E – Exploratory Sensitivity Analysis ...................................................................62 Appendix F – Exploratory Analysis of Cognitive Function ..................................................63 Appendix G – Dietary Intake across 12-weeks at three separate timepoints......................65 ix List of Tables Table 1. Baseline descriptive characteristics of participants ......................................................23 Table 2. Differences in dietary intake across 12 weeks .............................................................24 Table 3. Estimated marginal means of blood carotenoid levels .................................................30 Table 4. Cognitive assessment analysis....................................................................................60 Table 5. Mean group carotenoid values as measured in the dried blood spot cards ..................61 Table 6. Interaction between blood carotenoids and cognitive performance ..............................62 Table 7. Analysis of complete, non-distracted cognitive data ....................................................63 Table 8. Analysis of complete, non-distracted cognitive data for T1 and T3 ..............................64 Table 9. Average dietary intake at the 3 timepoints as measured in the 3-day food records .....65 x List of Figures Figure 1. Mechanisms and impact of high blood glucose and type 2 diabetes on the brain. . ..... 4 Figure 2. Chemical structure of the xanthophyll carotenoids Lutein (top) and Zeaxanthin (bottom). .................................................................................................................................... 5 Figure 3.Timeline of the entire trial. ...........................................................................................15 Figure 4. Percent correct on the Delayed Match to Sample task ...............................................25 Figure 5. Probability of error given error in the prior trial on the Delayed Match to Sample task .................................................................................................................................................26 Figure 6. Mean latency on the Delayed Match to Sample task ..................................................27 Figure 7. Total errors adjusted on the Paired Associated Learning task ....................................28 Figure 8. First attempt memory score on the Paired Associated Learning task .........................29 xi Acknowledgements First and foremost a big thank you to Dr. Jonathan Little, Dr. Susan Murch and Dr. Paul van Donkelaar for all of your support and input as my degree progressed and this thesis evolved into what it is now. Special thank you to Jon for accepting me as a graduate student and allowing me to explore my research interests. Thank you for being such an understanding supervisor and a leader that is always encouraging learning and growth of your students and yourself. Barbara – Working with you on this study was a pleasure. I value and appreciate all of the input and effort you put into making sure that everything would run smoothly for me. Without you, this thesis and this trial would not exist. Kaja – Thank you a million times over for all your help with editing and especially with stats. Thank you for never making me feel like my questions were stupid (although, I probably asked you the same questions 5 times over and still didn’t quite get the gist of the concept). Your patience, your support and your friendship has been immensely invaluable to me. Thank you to all the other members of EMIL (past and present), you are a wonderful bunch of individuals, and I am so happy I got to know you and work alongside you. Thank you to Eric, for running the stats and patiently answering and clarifying all of my stats questions. Thank you to my parents, Rob and Lisa, my stepdad Wayne, and my sisters Kayla and Karlee for being there for me and supporting me when I needed it most. Thank you to my partner Zach, for keeping me sane when the stress settled in. xii Dedication For Leslie and Sheila Crampton (Grandpa and Grandma), you won’t ever see this, but without your love and support, I wouldn’t be half the person I am today. 1 Chapter 1: Introduction 1.1 Type 2 Diabetes and Cognitive Function In Canada, one in three people are affected by either diabetes or prediabetes with approximately 90% of instances of diabetes being type 2 diabetes (T2D) (1). T2D is a chronic condition characterized by hyperglycemia as a result of insufficient insulin production and/or insulin resistance. Usually, T2D occurs in people with overweight or obesity and coincides with hypertension and/or dyslipidemia (2). Complications of T2D include cardiovascular disease, retinopathies, kidney failure, and neuropathies, both central and peripheral. Cognitive impairment, a form of central neuropathy, is also very common amongst people with T2D (3-5). Cognitive function is a term that encompasses the vast processes of the brain. Cognitive function can refer to many different aspects of the brain, including, but not limited to, memory (short and long term), attention, perception, executive function, language, processing speed, and reasoning (6). As the brain ages, there is a marked decline in many aspects of cognitive function, including memory, processing speed, reaction time, learning, and executive function (6, 7). Also associated with age is an increased risk for developing T2D (8). It is well established in the literature that people with diabetes experience increased cognitive impairment beyond the effect of healthy age-related cognitive decline, and some professionals have concluded that the presence of T2D accelerates the aging process within the brain causing such advanced cognitive impairments (2, 9). In T2D, the most commonly affected areas of cognitive function have been determined to be memory, processing speed, attention, and executive function (3). Typically, people with T2D will perform 0.3-0.4 standard deviations (SDs) below normative data, however the extent of the cognitive impairment does not usually affect an individual’s daily tasks of life or diabetes management (9, 10). Enhanced cognitive impairment in people with T2D is a concept that has been documented for over a century. In 1922, Miles and Root (5) sought to determine whether the 2 concept of cognitive impairment in people with T2D could be objectively confirmed. They performed psychologic tests on individuals with diabetes attending a patients’ class in ‘diabetic hygiene’ and used the friends and family of the patients as their control group. This research was the first to confirm that people with diabetes performed poorer on measures of memory and attention and that most of the detriments were attributed to slower reaction time while measures of accuracy tended to measure similarly to the controls (5). In the 1950s and 1960s, it was commonly acknowledged that diabetes affected the central nervous system, however the concept of impaired cognitive function in T2D remained vastly unexplored until the late 1980s (11). In the late 1980s and early 1990s, there was an increase in the literature focusing on the changes to cognitive function in people with T2D, which can likely be explained by the increasing technological advancements (11, 12). With the further development and more commercial availability of medical imaging instruments, a more complete picture of what is happening to the brain in real-time was able to develop. The 1970s saw the development of magnetic resonance imaging (MRI) which allowed the brain to be scanned in real time. MRI is used to examine the brain structures, the integrity of the soft tissues, and can even measure cerebral blood flow (13-15). It was not until the 1990s, however, that this technology was utilized to compare the brains of individuals with T2D and healthy controls (12, 16, 17); before this, brains were examined post-mortem for any irregularities (12). Transcranial doppler is another technique that was developed that can measure blood velocity in larger vessels in the brain using ultrasound (13). In both animal and human models of T2D there are changes to brain blood flow, altered concentrations of molecules in the brain such as brain-derived neurotrophic factor and carotenoids, as well as atrophies of both grey and white matter(8, 13, 14, 18, 19). Being able to conduct imaging in the brain has also allowed for the opportunity to further corroborate the effects of T2D on cognitive function by showing the areas of the brain most affected, which is often associated with the impaired performance on cognitive assessments. 3 MRI has been able to show many different aspects of the brain including atrophy of structures (4, 8, 13) and lesions in the brain (20), and functional MRI (fMRI) studies have shown a diminished activation level of the brain of a person with T2D while completing cognitive tasks compared to healthy age-matched controls (13). Using MRI, Saczynski et al. compared the white and grey matter of brains of normoglycemic individuals to brains of individuals with T2D and found that people with T2D had significantly lower white and gray matter volumes (15). People with T2D experience accelerated brain atrophy, specifically in the hippocampal area of the brain. The atrophies observed are not atypical for an aged brain, and therefore it is believed that T2D accelerates the ageing process in the brain (2, 9). The hippocampus is important as this structure is associated with learning and memory (21, 22), one of the main aspects of cognition affected in people with T2D. Lesions in the white matter of the brain have also been found in people with T2D (20). Manschot et al. observed that people with T2D performed poorly compared to healthy controls in the cognitive domains of attention, executive function, information processing speed, and memory (20). They also observed that their participants with T2D had more white matter lesions than the controls, which could account for some of the cognitive impairments observed, although they do acknowledge that there could have been other factors such as atrophy that also influence cognitive performance (20). Despite the historical and emerging knowledge of cognitive impairment being associated with diabetes, the exact molecular, cellular, and physiological mechanisms causing the impairment are still not well understood. Studies have found evidence that the oxidative damage, hyperglycemia, inflammation, and poor vascular health experienced in people with T2D can all contribute to cognitive impairment (18). Other research exploring the effects of glucose on the brain found that healthy individuals with higher, but still within the limits of normal, fasting plasma glucose present with cerebral deficits in the form of hippocampal and amygdalar atrophy (14) as well as decreased delayed associative memory (7). It is suspected that the cognitive impairment experienced by people with T2D begins once fasting blood 4 glucose levels begin to rise, which could be many years before the official diagnosis of the condition (18). This is likely because hyperglycemia, both acutely and chronically, can cause vascular damage (2), creating a strong link between episodes of hyperglycemia and cognitive dysfunction (4). Figure 1 summarizes the impact of T2D and hyperglycemia on the brain and illustrates some of the known molecular, cellular, and physiological mechanisms involved. As research linking T2D to cognitive impairment continues to develop, there is a need for interventions that can help with diabetes control as well as the associated cognitive complications. Medications have been formulated to help control hyperglycemia. Metformin, one of the more common medications given to people with T2D to help control hyperglycemia by increasing insulin sensitivity, has been shown to prevent the progression of further cognitive impairment but has not been shown to reverse the cognitive impairment in both human and rodent models (8, 23). Dietary interventions are frontline therapy for people with T2D (24, 25) and could present accessible lifestyle interventions to help prevent, delay, or reverse cognitive deficits in T2D (21-24). There is evidence that carotenoids, which are pigments found in many foods and act as antioxidants in the body, can help prevent the progression of cognitive Figure 1. Mechanisms and impact of high blood glucose and type 2 diabetes on the brain. Created with BioRender.com, adapted from Cherbuin & Walsh (13). 5 impairment, provide a protective mechanism for the brain and eyes, and potentially even reverse cognitive impairment when consumed via supplement or diet (23, 25-32). 1.2 The Role of the Carotenoids Lutein and Zeaxanthin in Cognitive Function As antioxidants, carotenoids can help mitigate oxidation in the body and thereby prevent excessive damage of the microvascular and other cells/tissues in the body (29, 31, 34, 37). There are two types of carotenoids: carotenes which are non-oxygenated, and xanthophylls which are oxygenated. Carotenoids are lipids that usually have 40 carbon molecules (38) and contain polyene chains with anywhere from 3-15 double bonds (39). Although more than 750 carotenoids have been identified, the human diet only has significant amounts of ~40. Of the many carotenoids, two are of specific interest when it comes to cognitive function: lutein and its isomer zeaxanthin (Figure 2). Lutein and zeaxanthin are non-provitamin A carotenoids found in dark green, leafy vegetables, red, yellow, and orange fruits and vegetables, avocados and the yolks of eggs (40). They also fall within the xanthophyll family of carotenoids. Lutein and zeaxanthin are the only Figure 2. Chemical structure of the xanthophyll carotenoids Lutein (top) and Zeaxanthin (bottom). Adapted from Roohbakhsh et al. (37). 6 two carotenoids that can pass through the blood brain barrier and can specifically accumulate in the macula of the eye and in the brain (36). Because of this, lutein and zeaxanthin have been associated with both eye and brain health (34, 41). Studies have examined both the supplementary and dietary effects of lutein and zeaxanthin on cognitive performance (21, 28, 30-33, 35, 42, 43) and have found that if there is an increase in lutein and zeaxanthin levels in either the serum or macula, as measured by macular pigment ocular density, there is a correlated increase in cognitive performance even in young, healthy individuals (35). Initially, lutein and zeaxanthin were mainly studied and recognized in eye health due to the noticeable accumulation of the carotenoids in the macula of the eye (44). Most commercial supplements of lutein are marketed to improve eye health. Studies have found that people with higher levels of lutein and zeaxanthin experience less age-related macular degeneration compared to age-matched individual that have lower levels (33, 42, 43, 45, 46). Studies examining supplementation of lutein have even found that increasing lutein and zeaxanthin levels is protective of the eyes and increases the macular pigment ocular density of the individuals, thus improving eye health (21, 30, 32, 34, 43). Lutein and zeaxanthin have been attributed with protecting the eyes from blue light damage (37) as well as playing a protective role against diabetic retinopathies (19, 34, 37, 42, 47-49). As research on the effects of lutein and zeaxanthin progressed, it became evident that these carotenoids were not just beneficial for the eyes, but also appeared to play a role in brain health (21, 28-32, 35, 41). The most common areas of cognitive function affected by lutein and zeaxanthin consumption relates to visual processing (21). This is not surprising considering the role that lutein and zeaxanthin play in eye health. However, it has been found that aspects of cognitive function outside of visual processing can also be influenced by lutein and zeaxanthin consumption. A study examining daily supplementation of lutein and zeaxanthin (10 mg lutein, 2 mg zeaxanthin) over twelve months in older, community dwelling adults found that complex attention, cognitive flexibility and executive function were all aspects of cognitive function that 7 benefited from increased lutein and zeaxanthin levels over the entire course of the study compared to a placebo (28). Another study looking at supplementation over 6 months in young healthy adults found that daily lutein and zeaxanthin supplementation (10.86 mg lutein, 2.27 mg zeaxanthin) improved processing speed, sustained attention, composite memory, verbal memory, and psychomotor speed (29). Yet another study found that twelve months of daily lutein and zeaxanthin supplementation (10 mg lutein, 2 mg zeaxanthin) in young, healthy adults resulted in improved visual memory, complex attention, and reasoning ability (35). The observed improvements in young, healthy individuals were considered surprising because people in this age range tend to be at the peak of their cognitive health, but these findings are promising as they indicate that regardless of age and health status, there does appear to be ways to improve aspects of cognitive function (35). It is important to note that the improvements in cognitive function and eye health only happened when the individual experienced an increase in lutein and zeaxanthin levels in either serum or macular pigment measures. In the twelve-month trial with young, healthy participants there was only an improvement in cognitive function when there was a significant increase in lutein and zeaxanthin levels (35). Before deeper investigation into the mechanisms that are involved in the effect that lutein and zeaxanthin have on brain health, it was unknown whether it was simply better visual processing and visual acuity that was responsible for the improvement in cognitive performance or if in fact these carotenoids also were able to affect specific structures in the brain to improve cognitive performance. Because vision and cognition are deeply intertwined, it can be difficult to determine whether changes to cognitive function are a result of improvement in brain health or improvement in the eye health, per se. Cognitive assessments that examine numerous aspects of cognitive function assessing diverse areas of the brain are therefore prudent to determine where the improvements may be coming from. Further examination using fMRI revealed that it is not just improved visual processing and acuity that improved cognitive performance in the individuals with increased lutein and zeaxanthin levels. Lindbergh et al. examined brain activity 8 levels during cognitive tasks using fMRI in healthy, older adults with no history of brain injury (21). They found that participants with higher lutein and zeaxanthin levels had less activation when completing tasks than those with lower lutein and zeaxanthin levels indicating greater neural efficiency (21). This study was also able to differentiate brain activation based on serum lutein and zeaxanthin levels (indicative of acute lutein and zeaxanthin consumption) and macular pigment ocular density (indicative of chronic lutein and zeaxanthin consumption). Serum levels predicted changes in areas of the brain that are associated with somatosensory functions while macular pigment ocular density levels predicted changes in activity in areas of the brain associated with language processing (21). Serum and macular pigment ocular density levels were both associated with the primary visual areas of the brain (21), indicating that the increases in cognitive function related to visual processing are attributable to more than just improvements in eye health. It is important to note that most of the studies using fMRI are cross-sectional, which may or may not have confounding factors related to differences in cognitive performance (e.g., lower cognitive performance resulting in, or interacting with, altered fMRI outcomes). Some studies have considered measuring dietary carotenoid intake to determine whether small changes to the dietary carotenoid intake can influence eye and brain health (30, 31). Studies have found that people who consume more plant-based diets, or more eggs in their diets, tend to have higher levels of lutein and zeaxanthin than those who do not (50-52). It has also been observed that consuming dietary carotenoids is just as efficient and beneficial for eye and brain health as supplementation (30, 31). Therefore, supplementation is not the only way for people to effectively ingest lutein and zeaxanthin and consuming these compounds from regular food sources is effective. However, it is important to note that not all dietary sources of lutein and zeaxanthin are created equally (52). 9 1.3 Using Eggs as a Source of Lutein and Zeaxanthin As a source of lutein and zeaxanthin, eggs do not have the highest concentration of lutein and zeaxanthin per 100 g when compared to other foods. For example, raw spinach contains approximately 6600 μg lutein/100 g while broccoli, cooked in a microwave, contains approximately 990 μg/100 g and eggs (boiled, yolk and white) contain approximately 237 μg/100 g (40). For context, a serving of spinach is typically 30 grams and a serving of broccoli is typically 150 grams. However, this is not directly reflective of the amount of lutein and zeaxanthin that can be absorbed by the human body, also known as the bioavailability. Studies have shown that in plants, the different matrices that contain the carotenoids will affect how easily absorbed the carotenoids will be. For example, Van Het Hof et al. found that when comparing the consumption of cooked spinach and cooked broccoli, plasma lutein levels per mg of ingested lutein were higher from broccoli indicating that the lutein in broccoli is more bioavailable (52). The researchers suggest that the storage location of the carotenoids could be a major contributor to this difference as spinach contains its carotenoids within the chloroplasts of the leaves, while broccoli is more likely to contain its carotenoids within the chromoplasts of the flower. The breakdown of chloroplasts appears to require more work than the breakdown of chromoplasts (53), which results in higher bioavailability of lutein and zeaxanthin when contained in easier to break down structures. Eggs, as an animal source of carotenoids, would lack chloroplasts/chromoplasts, and therefore the carotenoids could be even more accessible than in plant sources. Aside from the matrix that the carotenoids are located in, it is also postulated that the food the carotenoids are consumed with might influence absorption (40, 51). Carotenoids, as previously mentioned, are lipids and therefore will be best absorbed when consumed in conjunction with other lipids. Unlu et al. compared the effect of consuming carotenoids in the form of salad (romaine lettuce, carrots, and spinach) or salsa (Old El Paso Brand) with and 10 without a lipid source (avocado or avocado oil). They found that when the salad or salsa was consumed without the addition of fats from avocado, there was a small, almost negligible increase in lutein and zeaxanthin concentration, but when the same amount of salad or salsa was consumed with the added avocado or avocado oil there was a significant increase in lutein and zeaxanthin concentration (51). It is important to note that avocado is a dietary source of lutein and zeaxanthin, however the difference in responses to the meals went above the expected increase from the addition of carotenoids via avocado or avocado oil. Chung et al. compared the levels of lutein in participants that consumed spinach, lutein-enriched eggs, a lutein supplement, or a lutein ester supplement over two weeks (50). This study had only ten participants, all male, and required all participants to complete the four conditions of the study. All four conditions provided the participants with approximately 6 mg of lutein daily (50). This intervention had participants consume a lutein-enriched breakfast, with all four conditions using the same baseline breakfast recipe, an egg-white frittata. In the egg group, the frittata was instead made with whole eggs, and in the spinach group spinach was added to the frittata. The recipe was unaltered in the supplement groups. Participants were also instructed and taught how to eat a low carotenoid diet for the duration of the trial. This study found that the whole egg intervention resulted in the greatest increase in lutein levels (50). This is likely due to the combination of the carotenoids within the yolk and the higher fat content, which increases the absorption of the carotenoids. Because the carotenoids in eggs naturally occur with fats, this ensures that the carotenoids will be able to get transported into the body without reliance on another lipid source. As such, eggs (whole or yolks) are widely regarded as one of the most bioavailable sources of dietary lutein and zeaxanthin (41, 50). 11 1.4 Using Eggs to Improve Cognitive Function in People with T2D There is some research into the effect of egg consumption on cardiometabolic health markers such as cholesterol, triglycerides, and glycemic control (54-56), risk of T2D (57-67), and risk of metabolic syndrome (68, 69). While some of these studies have found negative effects of eggs on the health markers (58, 59), others have found no benefit or detriment to health markers (54, 56, 57, 62, 64, 66, 67), while others have found positive effects on health markers (60, 61, 63, 65, 68-70). Many of these studies were observational or population studies relying on food recall and estimates of average consumption and did not actually involve dietary intervention. Notably, food recalls can be biased and there are a lot of other dietary components that could be influencing health, and therefore, causal effects cannot be determined (71). A meta-analysis looking at egg consumption and the risk of T2D noted a difference between United States (U.S.) egg studies compared to egg studies conducted in Europe or Asia (72). In the studies conducted in the U.S, there tended to be more adverse or negative effects (58, 59) while neutral or positive effects were found in European and Asian studies (60, 65, 67). It is very likely that these differences are a result of different diets, and not necessarily reflective of egg consumption alone (66). There is also the possibility that eggs from different areas in the world may contain different nutrient profiles, which so far does not appear to be accounted for in the literature. In a three-month randomized controlled trial, Fuller et al. found that there were no adverse effects on cardiometabolic outcomes when consuming 12 eggs/week compared to < 2 eggs/week in Australian participants with T2D (54). Of special interest is the effect that high egg consumption could have on cholesterol, as eggs contain approximately 200 mg of cholesterol/egg (61). However, the literature reflects that increasing egg consumption will not increase cholesterol significantly (54, 56, 57, 60, 61, 70). While the literature is supportive of eggs as a source of lutein and zeaxanthin (41, 50), it has been postulated that in people with overweight or obesity, a condition that often coincides 12 with T2D, there may be lutein and zeaxanthin deficiencies (19). There is evidence that adipose tissue, specifically in the abdomen, will compete with other tissues in the body for carotenoid absorption (73, 74). Bovier et al. found that in young, healthy adults, body fat percentage had an inverse relationship to macular pigment ocular density, indicating that more adipose tissue could affect eye and brain health (73). In a 2013 randomized trial conducted in the U.S., Blesso et al. examined the effect of egg intake on carotenoid status in people with metabolic syndrome (68). In this study, participants were randomized to consume 3 eggs/day or the equivalent amount of yolk-free substitute for 12 weeks along with a low-carbohydrate diet (25-30% of caloric intake). This study found increases in high-density lipoprotein (HDL) cholesterol in both the egg and the substitute group with the egg group having a larger response compared to the substitute, and an overall decrease in triglycerides in both groups. An increase in HDL cholesterol, as opposed to low-density lipoprotein (LDL) cholesterol, is considered a beneficial change to blood lipid profile as higher HDL may be associated with reduced risk for atherosclerotic cardiovascular disease (75). After 12-weeks, plasma carotenoids in the egg group increased, while the plasma carotenoids in the substitute group decreased. This finding is important as it supports the idea that eggs can be used as a dietary intervention to increase carotenoids in people with overweight or obesity, which could henceforth support the idea that eggs could be an important carotenoid source for people with T2D. To our knowledge, an intervention examining the effect of eggs on carotenoids and cognitive function in people with T2D has not been completed yet. 1.5 Purpose and Hypothesis The primary purpose of this study was to conduct a randomized controlled trial to investigate the effect of increased egg consumption on lutein and zeaxanthin levels in people with T2D. A secondary purpose was to explore if increased egg consumption influenced 13 cognitive function in people with T2D. I hypothesized that: i) Being randomized to an intervention that aims to increase consumption to 3 eggs/day at breakfast would increase blood lutein and zeaxanthin levels when compared to an intervention with 0 eggs/day at breakfast in people with T2D; and ii) The intervention aiming to increase consumption to 3 eggs/day at breakfast would improve selected aspects of cognitive function compared to 0 eggs/day at breakfast in people with T2D. This research tested whether a relatively straightforward dietary strategy of consuming more eggs at breakfast would help to increase blood carotenoid levels and potentially improve measures of cognition in participants with T2D. 14 Chapter 2: Methods This study was part of a larger remote, randomized controlled trial comparing a low-carbohydrate, egg-based breakfast to a low-fat breakfast based on Diabetes Canada Clinical Practice Guidelines (25) on glucose control in participants with T2D over three months. Secondary outcomes of this study examined measures of satiety, changes in body weight, changes in blood lipid profiles and inflammation biomarkers, along with changes in lutein and zeaxanthin levels and cognitive function (i.e., the specific measures relevant to my thesis). This trial was conducted at both the University of British Columbia (Canada) and the University of Wollongong (Australia). As part of the larger trial, the study assessed lutein and zeaxanthin levels at baseline and at the conclusion of the trial. Participants were provided with dried blood spot (DBS) cards to measure both biomarkers. Cognitive function was assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB; University of Cambridge), a validated web-based assessment platform. The outcomes associated with this thesis, lutein and zeaxanthin levels and cognitive function, were only assessed in the Canadian participants and as such only these selected outcomes from the Canadian site will be presented. Ethics were approved by the UBC Clinical Research Ethics Board, and all data were collected in accordance with the guidelines and regulations of the approved protocol. The study was pre-registered on ClinicalTrials.gov (NCT04550468). Prior to collection of data, participants provided written informed consent. All data were collected using the UBC Research Electronic Data Capture (REDCap), a secure web application that allows for building and distributing online surveys and databases. All surveys and reminders throughout the study were sent to the participants through a mail merge in REDCap. 15 2.1 Study Design and Procedures The trial was designed to last 84 days, or three months (see Figure 3 for a timeline of the study). Sample size calculations were based on the primary outcome of hemoglobin A1c and as such, no formal sample size calculation was performed for the secondary outcome measures examined in this thesis. Data from 57 participants were collected, but only a subgroup of 24 participants had their DBS cards analyzed for blood lutein and zeaxanthin levels due to funding constraints and limited viable samples. Recruited participants had been diagnosed with type 2 diabetes by their physician more than a year ago, were between the ages of 20-79 years, and met the following inclusion criteria: i) recent (within 1 month) glycated hemoglobin (HbA1c) of Figure 3. Timeline of the entire trial. Components specific to the secondary outcomes of carotenoid levels and cognitive function have been bolded. HbA1c – Hemoglobin A1c; TBP – Theory of Planned Behaviour Questionnaire; CGM – 14-day Continuous Glucose Monitoring; GODIN – Physical activity questionnaire; VAS Hunger – Visual analog scale for Hunger. 16 <8.5%; ii) overweight or obesity (BMI 25-40 kg/m2); iii) non-smoking; iv) not on hormone replacement therapy, corticosteroids, or anti-inflammatory medications. Exclusion criteria included: i) the use of exogenous insulin; ii) the use of more than two glucose lowering medications; iii) current treatment for ongoing diseases (such as cancer, auto immune, or inflammatory disease, liver, or kidney disorders); iv) have an intolerance, allergy, or aversion to eggs or other dietary restrictions that could prevent adherence to the study’s dietary intervention; v) unable to reliably access internet to perform the remote aspects of the study. Participants were instructed to wear their glasses/corrective lenses while completing cognitive tasks. Participants were recruited by contacting past participants that have indicated on previous consent forms that they would like to be contacted for future research by our group, through online advertisements on platforms such as Castanet or Craigslist, through posts on social media groups catered to the target population of people with T2D, through posters put up in the community, and by online third-party recruiting programs (HoneyBee and TrialFacts). Interested individuals were screened via phone call for eligibility, where an overview of the study protocol was also provided. Participants received the informed consent form by email through REDCap and signed it electronically. Once the informed consent was signed and sent back to the lab, the participant was randomized into either the treatment (egg-based) breakfast or the control (low-fat) breakfast condition using sealedenvelope.com (Sealed Envelope Ltd.), a website designed for clinical trial randomization. The participant then received an email from the REDCap mail merge with links to additional surveys, including a medical screening questionnaire (which documented health conditions, T2D duration, any medications and/or supplements, as well as some anthropometric measures), a diet habit survey, a menstrual cycle questionnaire (if applicable), and a link to the cognitive assessment. Once these surveys were completed and the baseline cognitive assessment was completed, the participant was mailed a study resource kit. When completing the cognitive assessments, the participants were instructed to find a quiet, 17 distraction-free space. They were given instructions on how to complete the tasks before being assessed on their performance and the same instructions and prompts were presented to participants every time they completed their cognitive assessments. The study resource kit contained all the required materials for the study including: i) blood sampling kit including three lancets, alcohol swabs, band-aids, a dried blood spot card, a desiccant bag (to prevent moisture when the card is mailed), a Ziploc bag (to protect the sample from contamination when shipped back to the lab), a prepaid envelope to send the sample back to the lab, and instructions on how to self-sample and correctly collect the blood samples; ii) a continuous glucose monitor (CGM; Abbott Freestyle Libre Pro) with a reader and instructions on how to insert the CGM along with a prepaid envelope to send the sensor back after 14 days of wear; iii) a measuring tape with instructions on how to measure waist circumference; iv) a welcome book with the study timeline, food logs to be filled out for three random days at the beginning, middle, and end of the trial (a total of 9 food logs), a recipe book for the breakfast condition, and instructions on how to take and upload breakfast photos onto REDCap. Participants put their CGM on while on a video call to ensure that the sensor was properly inserted and synced. The following day was considered Day 1 of the study. On the morning of Day 1, the participant collected a fasted blood sample on the provided DBS cards, then consumed breakfast according to one of the recipes provided. On day 42 of the trial, the mid point, the participants received an email with a link to complete their second cognitive assessment. In the last week of the trial (between days 77 and 84), participants completed their final cognitive assessment. This time frame for the final cognitive task was chosen to help diminish participant burden as the final day of the study required additional measures and surveys to be taken. On day 84, the last day of the trial, participants completed their second fasted DBS blood sample collection. 18 2.2 Breakfast Conditions and Dietary Adherence Participants were randomized into either the egg-based or the dietary guidelines low-fat (no eggs control) breakfast condition. For the purpose of this study, the control condition consisted of a guideline (18) breakfast with higher carbohydrate content and lower fat content compared to the egg-based breakfast. The egg-based condition consisted of consuming an egg-based breakfast with a higher fat content and lower carbohydrate content. Both conditions received a recipe book, created by Dr. Barbara Oliveira, a registered dietician, containing recipes that are isocaloric and contain the same amount of protein (See Appendix A). Participants’ adherence and compliance to the trial was determined by assessing the photos of the breakfast taken by the participant and uploaded onto REDCap, and a prompt that asks the participant how much of the meal they consumed. A conservative approach was followed; if participants deviated from the recipes provided, or if participants failed to upload a photo, they were considered non-compliant for that day. Aside from breakfast, participants were instructed not to change their dietary habits at other meals of the day (confirmed with the three separate 3-day dietary records). Participants were not instructed to include/exclude eggs from other meals. The design of manipulating breakfast only was chosen as a pragmatic balance between internal and external validity for this free-living clinical trial. We reasoned that compliance would be greater given that eggs are commonly consumed at breakfast, and the overall trial rationale was that an egg-based low-carbohydrate breakfast could improve glucose control by preventing the large breakfast-induced glucose spike that is common in people with T2D (76). The quantity of eggs recommended (3/day) was expected to be well above baseline habitual intake and to have an effect on lutein and zeaxanthin levels of participants regardless of their other dietary habits. During the trial, participants were also asked to complete 3-day food records at the beginning, middle and end of the trial, for a total of nine days of dietary intake data. Based on these food records, the average daily energy and macronutrient consumption in 19 each group were calculated using ESHA Cloud Services Genesis R&D Food Processor (ESHA Research; esha.com). 2.3 Cognitive Assessment To assess cognitive function, participants completed a battery of two cognitive tasks three times during the trial using the CANTAB online platform. One of the tasks was the delayed matching to sample (DMS) task, which predominantly measures visual matching ability and short-term visual recognition memory. During this task, an abstract pattern is displayed on the screen and the participant has to select the matching pattern out of four patterns displayed on the lower part of the screen. The delay between the sample and choice patterns varies before the choices appear, with some trials having both the sample and choices appear simultaneously on the screen. This task lasts approximately seven minutes. The other cognitive performance test was the paired associates learning (PAL) task, which measures visual memory and new learning. With this task, there are eight boxes distributed on the screen which are randomly ‘opened’ to reveal a pattern. Once all the boxes have been ‘opened’, a pattern will appear in the centre of the screen and the participant is required select the perimeter box that this pattern originally appeared in. This task starts with only two patterns and progresses to one pattern per box and lasts approximately eight minutes. Appendix B contains screenshots of the tasks along with the description of the task from the Cambridge Cognition Website (77) The order of the tasks changed throughout the trial and CANTAB offers voice-over as well as written instructions on screen to standardize the test. Both tasks had an introduction as well as a practice session to ensure participants understood the task at hand. The CANTAB platform assessments have been validated in remote settings to provide reliable cognitive results outside of a laboratory setting (78). Participants were instructed to complete the tasks in a quiet setting with no distractions and were prompted to indicate if they experienced any 20 distraction during their testing session. The platform was standardized for both Mac and PC, ensuring that regardless of the computer a participant had, they had the same testing experience. 2.4 Measuring Blood Lutein and Zeaxanthin Levels Capillary blood was collected using finger pricks from a provided lancet and Whatman 903 DBS cards (ZRT Laboratories). Due to the remote nature of the study, DBS collection was chosen based on minimal invasiveness, minimal risk to participants, and ease of sampling (79). The target population of this study are individuals that have likely experienced finger-prick blood collections in the past and should be familiar with the process. Instructions were sent with the sample kit and the process is simple, requiring only one drop of blood per spot on the card (for a total of 12 drops of blood). Participants were provided with all the equipment to ensure sterile conditions for collection to minimize contamination and any risk associated with blood sampling. This process has been used in many epidemiological studies and has been deemed to be a reliable and valid way to collect blood samples remotely (79). Dried blood spots do not require immediate refrigeration once the sample is collected, which allows for the samples to be easily mailed from the participant back to the lab. Due to the length of the study, after the cards were returned to the lab via mail they were stored in a -80℃ freezer until the conclusion of the study for batch analyses. The samples were first examined to confirm adequate blood spots and then sent via mail with desiccant packs to an external laboratory for analysis using high-performance liquid chromatography, the gold standard of carotenoid measurement. Values were reported in percent of ng lutein or zeaxanthin per µg of hemoglobin to control for blood spot quality/quantity. 21 2.5 Statistical Analysis All statistical analyses were conducted by an external statistician and performed using R (80). Cognitive function and changes to carotenoid data were analyzed using a constrained longitudinal data analysis in a linear mixed model. Effect estimates with 95% confidence intervals (CI) for the between group differences from the model are reported as the main analysis of interest. Effect estimates for within group changes over time were also reported. Histograms and Q-Q plots of residuals were used to identify any outliers and to test for normality/skewness. Data with a skewed distribution were log-transformed prior to statistical testing. Missing data was not imputed as per contemporary guidelines for linear mixed models. Significant interactions or main effects are accompanied with a comparison between pre-, mid-, and post-within condition using Bonferroni correction. Significance is assessed at P < 0.05. Primary analyses for this research question investigated whether there was an effect of egg consumption on blood carotenoid levels. A sensitivity analysis was also run to determine whether there was an effect of carotenoid level changes on cognitive performance. For this analysis, a linear mixed model was used with time, lutein (or zeaxanthin), and the interaction of lutein (or zeaxanthin) and time as variables in the model. The main outcome of interest in these exploratory models was the interaction between lutein/zeaxanthin and time, which would indicate that the change in cognitive performance was impacted by the change in blood carotenoids. An additional exploratory analysis was run on the cognitive data which only included the complete and non-distracted data to investigate if the reporting of distractions impacted the data collected. This data was input into the same model that was used for the full cognitive data set. As this study was part of a larger trial, sample size was determined for the primary outcome of the trial which was glucose control (i.e., HbA1c). Based on a previous breakfast intervention in T2D (81)and a breakfast trial recently published in our lab (76), a sample size of 22 68 participants was calculated to detect a medium effect size. To preserve power and account for dropouts, which we estimated to be 20%, we aimed to recruit 82 participants for the main trial. 2.6 Ethical Considerations Due to COVID-19, special consideration had to be given to the study design as the target population is an at-risk group of individuals that could experience more adverse effects from contracting the virus. This resulted in the adaptation of the study to be remote, preventing any in-person contact with the participants and minimizing the risk of viral transmission. Accordingly, DBS was used to assess whole blood lutein and zeaxanthin instead of the preferred method of venous blood sampling for measurement of these carotenoids in plasma, and cognitive function testing was performed by participants at their homes instead of the preferred setting of a controlled laboratory. To protect participant’s personal information, only required information was collected for the purpose of the study. Mailing addresses were collected due to the remote adaptation of the study but are stored securely in REDCap. All participants were given unique study identification codes and their information is stored on an internal UBC network drive. All results were identified only by study identification code. Participant information (e.g., cognitive assessments or blood data) may have been stored in data centres outside of Canada, however, any of this information was be coded and did not contain personal identifiers of the participants. Participants were informed on how their data are stored and were reminded in the consent form that they can withdraw from the study if they are not comfortable with the processes in place. 23 Chapter 3: Results 3.1 Participants 57 participants were recruited and randomized in this trial. Selected descriptive characteristics of the participants are provided in Table 1. Table 1. Baseline descriptive characteristics of participants 3.2 Dietary Data Participants were asked to submit a daily photo of their breakfast to assess compliance. During the trial, participants that completed the trial were on average 86.9% compliant with the study parameters, which included both the uploading of breakfast photos as well as the photos containing food that was appropriate for their breakfast group. During the trial, participants were also asked to complete 3-day food records at the beginning, middle and end of the trial, for a total of nine days of dietary intake data. Based on these food records, the average daily energy and macronutrient consumption in each group were calculated and is depicted in Table 2. The study aimed to manipulate the macronutrient content of breakfast with no specific instructions or Low Carbohydrate Low Fat Overall (N=28) (N=29) (N=57) Age (years) 64 (9) 64 (12) 64 (10) Sex (% male) 57.1 51.7 54.4 T2D Duration (years) 8.8 (6.6) 9.5 (7.1) 9.2 (6.8) HbA1c (%) 7.0 (0.73) 6.9 (0.65) 7.0 (0.69) Weight (kg) 92.3 (23.6) 88.2 (16.5) 90.2 (20.2) BMI (kg/m2) 31.6 (5.9) 30.7 (6.3) 31.2 (6.1) Waist Circumference (cm) 112.2 (16.1) 106.4 (13.6) 109.1 (14.9) Values are presented as Mean (SD). T2D – Type 2 Diabetes; HbA1c – glycated hemoglobin A1c; BMI – Body Mass Index 24 control over what participants ate for lunch or dinner. Despite only manipulating the macronutrient content of the breakfast meal, there was a significant difference in average daily carbohydrate consumption between the two groups with the participants in the low carbohydrate group consuming, on average, approximately 79 g less carbohydrates (or ~317 less kcal from carbohydrate) per day (P < 0.001). There was also a significant difference in the number of eggs consumed between groups, with the low carbohydrate group consuming more eggs per day on average (P < 0.001). There was no significant difference in the consumption of protein (P = 0.28) or fat (P = 0.50) between groups. Although not statistically significant, the difference in energy (kcal) (P = 0.06) and carotenoid intake (P = 0.06) was close to significance. Supplemental Table 9 shows the changes in dietary intake at the three timepoints in both groups (Appendix G). Table 2. Differences in dietary intake across 12 weeks Low Carbohydrate (N = 23) Low Fat (N = 23) P Value Energy intake, kcal/day 1612 (531) 1951 (478) 0.06 Carbohydrate intake Grams/day 142 (68) 221 (48) < 0.001 Kcal/day 566 (271) 883 (191) < 0.001 Fat intake Grams/day 78 (25) 85 (29) 0.50 Kcal/day 704 (229) 761 (265) 0.50 Protein intake Grams/day 85 (27) 77 (20) 0.28 Kcal/day 342 (106) 306 (80) 0.28 Carotenoid intake (micrograms) 267 (260) 126 (133) 0.06 Egg intake1 1.7 (0.9) 0.3 (0.3) < 0.001 P values derived from linear model. Data presented as mean (SD). 1 Interpret with caution due to some evidence of non-normality. 25 3.3 Cognitive Data All cognitive results are presented as Figures in the Results section and a supplemental table is provided in Appendix C. At the conclusion of each cognitive assessment, participants were asked to report whether they experienced any distractions (35 of the tests administered were reported as having a distraction). 3.3.1 Delayed Match to Sample Task Figure 4. Percent correct on the Delayed Match to Sample task over time in both the low carbohydrate and low fat conditions (N = 57). Data are presented separately for males and females in both the low-carbohydrate egg-based breakfast and low-fat dietary guidelines breakfast groups. A constrained baseline longitudinal data analysis using a linear mixed model (both sexes included) revealed no significant effect of condition or time (P > 0.05). Values are estimated marginal means with error bars indicating 95% CI. T1 = Baseline, T2 = 42 days, T3 = End of Study 26 Figure 4 shows the average percent correct across all delays (DMSPCAD). There were no significant differences between breakfast groups on this outcome measure at any timepoint (P = 0.31). In the figure, there does appear to be a trend for both groups to improve from baseline at both timepoints, however, the high variability appeared to prevent this from being a significant main effect of time (P = 0.07). Figure 5 shows the probability of making an error given the participant had made an error on the previous trial (DMSPEGE). There were no statistically significant effects of breakfast group Figure 5. Probability of error given error in the prior trial on the Delayed Match to Sample task over time in both the low carbohydrate and low fat conditions (N = 57). Data are presented separately for males and females in both the low-carbohydrate egg-based breakfast and low-fat dietary guidelines breakfast groups. A constrained baseline longitudinal data analysis using a linear mixed model (both sexes included) revealed no significant effect of condition or time (P > 0.05). Values are estimated marginal means with error bars indicating 95% CI. T1 = Baseline, T2 = 42 days, T3 = End of Study 27 on this outcome measure at any timepoint (P = 0.28). Further analysis indicates that there was no significant effect of time on this variable (P = 0.55). Figure 6 shows the mean latency between the presentation of the response stimuli options and the participant selecting the correct box on their first attempt (DMSMLC). There was no statistically significant effect of breakfast group on this outcome measure at any timepoint (P = 0.55). There does appear to be a trend for participants to respond faster in the follow-up assessments compared to the baseline for both groups, but further analysis revealed no significant effect of time (P = 0.47). Figure 6. Mean latency on the Delayed Match to Sample task over time in both the low carbohydrate and low fat conditions (N = 57). Data are presented separately for males and females in both the low-carbohydrate egg-based breakfast and low-fat dietary guidelines breakfast groups. A constrained baseline longitudinal data analysis using a linear mixed model (both sexes included) revealed no significant effect of condition or time (P > 0.05). Values are estimated marginal means with error bars indicating 95% CI. T1 = Baseline, T2 = 42 days, T3 = End of Study 28 3.3.2 Paired Associated Learning Task 0.1 Figure 7 shows the total errors that the participant made and is adjusted for the estimated number of errors the participant would have made on any attempts and recalls they did not reach (PALTEA). It was found that there was no statistically significant difference between breakfast groups on this outcome measure at any time (P = 0.47). In the figure, it does appear that there is a trend for participants of both groups to make less errors in follow-up timepoints compared to baseline, yet further analysis indicated no significant effect of time on this measure (P = 0.08). Figure 7. Total errors adjusted on the Paired Associated Learning task over time in both the low carbohydrate and low fat conditions (N = 57). Data are presented separately for males and females in both the low-carbohydrate egg-based breakfast and low-fat dietary guidelines breakfast groups. A constrained baseline longitudinal data analysis using a linear mixed model (both sexes included) revealed no significant effect of condition or time (P > 0.05). Values are estimated marginal means with error bars indicating 95% CI. T1 = Baseline, T2 = 42 days, T3 = End of Study 29 Figure 8 shows the number of times that a participant chose the correct box on their first attempt when recalling the pattern locations (PALFAMS). The difference between breakfast groups on this outcome measure at any timepoint did not reach statistical significance (P = 0.40). In the figure, it appears that both groups improved by the end of the study (T3) compared to baseline (T1), which was confirmed by a significant main effect of time (P = 0.02). Figure 8. First attempt memory score on the Paired Associated Learning task over time in both the low carbohydrate and low fat conditions (N = 57). Data are presented separately for males and females in both the low-carbohydrate egg-based breakfast and low-fat dietary guidelines breakfast groups. A constrained baseline longitudinal data analysis using a linear mixed model (both sexes included) revealed no significant effect of condition (P > 0.05) but it did reveal a significant effect of time (P = 0.02). Values are estimated marginal means with error bars indicating 95% CI. T1 = Baseline, T2 = 42 days, T3 = End of Study. 30 3.4 Carotenoid Data Table 3 shows the estimated marginal means in blood lutein and zeaxanthin levels as measured in the dried blood spot cards in a sub sample of 24 participants as well as the estimated marginal means in lutein and zeaxanthin levels when the number of eggs consumed was adjusted for in the model (n = 12 participants per group at both pre and post timepoints). Appendix D contains the group means pre and post. There were no statistically significant differences between breakfast groups for lutein (P = 0.55) or zeaxanthin levels (P = 0.98). Adjusting for egg consumption (as determined by the average eggs/day calculated based on the food records) did not change the effect of breakfast group on lutein (P = 0.20) or zeaxanthin levels (P = 0.40), Table 3. Estimated marginal means of blood carotenoid levels Lutein and Zeaxanthin levels were measured in dried blood spots via high-performance liquid chromatography and are presented as % ng per µg hemoglobin. 3.5 Exploratory analyses We ran an exploratory sensitivity analysis with groups collapsed and using only T1 and T3 (pre and post) timepoints (because blood carotenoids were only measured pre and post) to Unadjusted Adjusted for average egg consumption Outcome Group Mean (SD) Mean difference (95% CI) P-value Mean (SD) Mean difference (95% CI) P-value Lutein Low Carbohydrate 0.008 (0.001) (referent) 0.55 0.010 (0.002) (referent) 0.2 Low Fat 0.007 (0.001) -0.001 (-0.004, 0.002) 0.005 (0.002) -0.005 (-0.013, 0.003) Zeaxanthin Low Carbohydrate 0.008 (0.001) (referent) 0.98 0.010 (0.002) (referent) 0.4 Low Fat 0.008 (0.001) 0.000 (-0.003, 0.004) 0.006 (0.002) -0.004 (-0.012, 0.005) 31 examine whether there was an interaction between blood carotenoids and cognitive performance. The full results can be found in Appendix E. This analysis revealed a significant interaction between time and lutein for both the DMS percent correct and the DMS probability of error given error outcome measures (both P < 0.05). These interactions between lutein and time, however, occurred in the opposite direction to our hypothesis, suggesting in this small sample size with groups collapsed that an increase in blood lutein was associated with lower performance on this cognitive task. There was one statistically significant effect with zeaxanthin on the DMS mean correct latency (P < 0.05), but the standard errors are very large and limit our ability to interpret this interaction. All of these exploratory analyses should be interpreted with caution due to the small sub-sample used and the collapsing of groups. Another exploratory analysis was run on the cognitive data which excluded the data from participants that reported they had been distracted or did not complete one of their assessments so that only complete, non-distracted data were used (n=18). The data from all three timepoints were used in one model and another model was run using only timepoints 1 (beginning) and 3 (end) of the trial to allow a greater sample size. This exploration did not change the original interpretation of the cognitive data from the full data set, with no significant between group differences found. The results table can be found in Appendix F. 32 Chapter 4: Discussion People with T2D are at greater risk of cognitive impairment compared to healthy age-matched controls. Most treatments in T2D are aimed at improving glycemic control, which may indirectly affect cognitive function. This study tests the idea that a small dietary change in people with T2D, consisting of eating a low-carbohydrate, egg-based breakfast may help with glycemic control. Additionally, due to the lutein and zeaxanthin content in eggs, this intervention could also directly impact cognitive performance in people with T2D given the importance of these carotenoids in brain function. This is the first study, at the time of writing and to our knowledge, which has combined the literature of lutein and zeaxanthin levels on cognitive function with the literature on lutein and zeaxanthin bioavailability in eggs and applied it to individuals living with T2D. 4.1 Dietary compliance was high in a remote study As part of a three-month trial, participants were asked to change their breakfast consumption habits for 84 days. To measure compliance, participants were prompted, via email, to upload a photo of their breakfast to REDCap. If a participant failed to upload a photo, or the photo indicated that the meal they consumed did not contain the same ingredients as one of the breakfast recipes provided, that day would be considered ‘non-compliant.’ With an overall compliance of approximately 87% in participants that completed the trial this study indicates that it is possible for people with T2D to manipulate their breakfasts for a prolonged period of time, at least in the context of a randomized trial. Although this study was remote, participants were able to contact the research team whenever they needed and were reminded daily, via email, to upload photos of their breakfasts. This likely contributed to the high overall compliance experienced in this trial. Participants were also asked to complete 3-day diet records thrice during the study, for a total of nine days. While the 3-day diet records provide a quick snapshot of the participants eating habits and are considered among the best methods of dietary 33 assessment, self-report diet records are not without limitations (82). Due to the remote nature of the study, we chose this assessment to collect diet data whilst being minimally burdensome on the participants. Based on the diet records, it is interesting to note that participants in the low-carbohydrate, egg-based breakfast group consumed less average daily carbohydrates than the low-fat, non-egg-based group; however, there was no difference in fat or protein intake between the groups. It appears that, in this sample of participants, something as simple as eating a lower carbohydrate, higher fat breakfast in the morning, resulted in an overall decrease in consumption of carbohydrates. This could indicate that, for these participants, breakfast may be the most carbohydrate-rich meal, and replacing it with a low-carbohydrate breakfast could help reduce the overall glycemic load of the diet. When the main trial is completed and the results are analyzed, we will be able to determine more conclusively if this manipulation to the macronutrient profile of breakfast appreciably impacts glycemic control. 4.2 Higher egg consumption did not increase blood lutein and zeaxanthin levels In this study, we sought to investigate the effect of increased egg consumption on blood lutein and zeaxanthin levels in people with T2D. Because eggs are one of the most bioavailable sources of lutein and zeaxanthin in the human diet, we hypothesized that consuming approximately three eggs per day at breakfast would increase blood lutein and zeaxanthin levels compared to zero eggs per day at breakfast in people with T2D. Analysis of the dietary data collected from participants indicated that the participants in the low carbohydrate, egg-based breakfast group did not quite achieve an average of 3 eggs per day, but rather an average of ~1.7 eggs per day. However, this was significantly more eggs than was consumed by participants in the low fat, non-egg-based breakfast group (~0.3 eggs per day; P = 0.001). Analyses of 3-day food records also indicated a tendency for higher carotenoid intake in the egg-based breakfast group (P = 0.06); with a larger sample size it might be possible to more 34 conclusively determine whether such tendencies were statistically significant differences. It is important to note that total dietary intake of carotenoids does not take into consideration bioavailability so it seems very likely that participants in the egg-based breakfast group, by consuming more eggs per day and showing a tendency for greater overall carotenoid intake, would have been consuming a diet with more bioavailable lutein and zeaxanthin. Analysis of dried blood spots was conducted on a sub-sample of our participants (N=24; 12 participants per group with pre and post values) and showed no significant difference in change of blood lutein and zeaxanthin concentrations between breakfast groups. A sensitivity analysis adjusted for egg consumption revealed no significant differences. Because the carotenoid analysis was performed on a sub-sample (primarily due to cost considerations), there is a possibility that this was not representative of the entire study sample. Alternatively, the method of measurement may have influenced our ability to detect true changes in blood lutein and zeaxanthin. Our findings of no increase in blood carotenoids contradict a study conducted by Blesso et al. where participants with metabolic syndrome (a condition similar to T2D) consumed an average of 3 eggs per day for 12 weeks (68). At the end of their trial, the participants in the egg group had an average plasma lutein increase of 21% and an average plasma zeaxanthin increase of 48%. That study, conducted in 2013, was done in person, with more control, venous blood draws and measured plasma carotenoid levels. Our study was remote, used capillary blood, dried blood spot cards and an external lab for analysis, indicating that despite a similar intervention designed to increase consumption by three eggs per day, there are many key differences between the two studies. Furthermore, the analysis of blood lutein and zeaxanthin on dried blood spots presented a challenge as only four international commercial laboratories were identified that indicated that they could measure lutein and zeaxanthin in dried blood spots. Out of the four identified labs, only one indicated that they could complete the analysis using the filter paper collection material we had used. Other labs indicated that more specialized 35 materials were required for their methods to be able to reliably detect the carotenoids. There was high variability in the blood carotenoid concentrations detected based off the eluted dried blood spot analyses and we chose to present the results corrected to total hemoglobin in the eluted sample, which controls for differences in collection/absorption on the filter paper. Additionally, when using dried blood spot cards sent via mail, like in this study, it is unknown the temperature, condition, and even time in transit that the shipping envelope will experience, which could have an effect on the sample quality itself. Dried blood spots are touted as being able to be stored at ambient temperature (79), however, the validity and suitability of using dried blood spots for blood carotenoid analyses remains somewhat undetermined and it would have been ideal to have venous blood samples for plasma lutein and zeaxanthin analyses in this study. Although eggs are one of the most bioavailable sources of lutein and zeaxanthin, these carotenoids are abundant in fruits and vegetables as well (41, 50). Lutein and zeaxanthin, as lipid-based phytonutrients, are best absorbed by the human body when consumed in conjunction with fats. Participants in this study were not required to provide diet habit information prior to the trial starting, so it cannot be confirmed whether we changed the participants habitual consumption of eggs and/or carotenoids; only that the participants in the egg-based breakfast group maintained a greater habitual consumption of eggs over 12 weeks. A confounding factor may be that lutein and zeaxanthin levels can accumulate in areas in the human body other than plasma, most commonly in the macula of the eye and adipose tissue. It is known that adipose tissue competes for lutein/zeaxanthin absorption (19, 83) and considering that the participants we recruited had an average BMI above 30 kg/m2, it is likely that these participants had a larger percentage of adipose tissue. It is possible that the lutein and zeaxanthin that they consumed may have been selectively absorbed in the adipose tissue. To truly determine whether egg consumption increased lutein and zeaxanthin levels, it would have been optimal to measure both adipose and macular carotenoid levels as well, however, 36 due to the limitations of a remote study, it was only feasible to measure carotenoids from the dried blood spot samples. 4.3 Higher egg consumption did not improve cognitive performance In this study, we also investigated whether there would be an effect of increased egg consumption on cognitive function in people with T2D. Assuming that increased egg consumption would increase lutein and zeaxanthin levels based on the literature, it was hypothesized that consuming three eggs per day at breakfast would improve selected measures of cognitive function including visual matching ability, short-term visual recognition memory, and new learning. Both groups appeared to have a general improvement in cognitive performance (likely due to familiarization and a learning effect) but there was only a significant effect of time in one of the five cognitive outcome measures. There were no significant effects of breakfast group on any of the measures of cognitive performance. An exploratory sensitivity analysis was run to examine whether there was an effect of changes to blood carotenoids on cognitive performance. Although there was indication that in three out of the five outcome measures there is a significant reaction between lutein or zeaxanthin on cognitive performance, this analysis had a small sample size, large estimates and should be interpreted with caution. The fact that participants completed their cognitive testing at home in an uncontrolled environment may have affected these outcomes. Although the assessments chosen were validated in remote settings, it is possible that participants were unable to find a quiet space to conduct their assessments, as would be done if cognitive testing were completed in a lab setting. In 35 out of 171 assessments, participants reported they were ‘distracted’ during the assessment, indicating that the assessment environment was less than ideal. We also experienced a number of participants being reported as ‘incomplete’ which was due to human error (early abortion of task), computer error (computer shut down mid-test or internet cut out), and/or failure to complete the assessment when prompted (32 out of 171 assessments were 37 counted as ‘incomplete’). The distracted and incomplete data were left in the model, which may have influenced the results, but due to having a very small sample of fully complete, non-distracted data, it was concluded that we would not be powered to detect differences if only the “cleanest” (per protocol) data were used in the statistical model. 38 Chapter 5: Conclusion 5.1 Strengths, implications, and limitations When first conceived, this study was meant to be conducted in-person with visits to the lab to have blood work drawn, cognitive performance assessed in controlled conditions, and even visits with an ophthalmologist to measure markers of macular degeneration. When the COVID-19 pandemic began, the trial needed to be adapted and reworked to be conducted completely remotely, which led to changes to how data could be collected and analyzed. Even with all of these changes, the study was launched in October 2020 and an initial round of data collection concluded by December 2021. By adapting this study from being in-person to remote, we were able to recruit our participants from a larger population pool and were able to accept volunteer participants from all over British Columbia, Saskatchewan, Ontario and Quebec. This allowed for increased generalizability from people living with T2D in Kelowna to people living with T2D across Canada. Although this trial failed to reject the null hypothesis on the secondary outcomes presented in this thesis, there is a need to have more research to find straightforward dietary interventions to help people living with T2D to control their glucose and improve their cognitive performance. Although our study found no relationship between the number of eggs consumed and dried blood spot carotenoid levels, other studies have seen a change when measuring plasma carotenoids, and future investigations appear warranted. It would be prudent to conduct a trial with a similar premise, but in a more controlled environment with “gold-standard” carotenoid and cognitive function testing, to determine whether there are differences in lutein and zeaxanthin levels, and cognitive function, when egg consumption is increased in people living with T2D. 39 It is important to note that glycemic control is suspected to play a role in cognitive performance, wherein people living with T2D with good glycemic control tend to have better cognitive performance compared to people living with T2D with poor glycemic control. HbA1c, a marker of glycemic control, was the pre-specified primary outcome of this trial, however, because the larger trial is still ongoing in both Canada and Australia, this outcome has not been statistically analyzed. Preliminary exploration of the data does reveal that, numerically, HbA1c appears to be reduced more (by ~0.3%) in the low-carbohydrate breakfast group compared to the low-fat breakfast group (~0.1%) although conclusive effects of the breakfast manipulation on HbA1c will have to wait until the full trial is completed in June 2022. 5.2 Conclusion In this study, we report that a low-carbohydrate breakfast intervention that resulted in consuming ~2 eggs per day for 12 weeks did not have a significant effect on dried blood spot lutein and zeaxanthin levels nor did it have a significant effect on cognitive performance in people with T2D. In a remote study, there are a lot of factors that are hard to control for and outcome measure assessments may not be ideal. The findings of this thesis indicate that conclusive results will likely require a more controlled trial with in-person visits, venous blood samples, and pre-specified primary outcomes related to cognitive performance. 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Macular pigment optical density is lower in type 2 diabetes, compared with type 1 diabetes and normal controls. Retina 2015;35(9):1808-16. doi: 10.1097/iae.0000000000000551. 47 Appendices Appendix A – Recipe Books Appendix A1 – Low Fat Recipe Book 48 49 50 51 52 Appendix A2 – Low Carbohydrate Recipe Book 53 54 55 56 57 58 59 Appendix B – Cognitive Assessment Delayed Matching to Sample (DMS): The participant is shown a complex visual pattern followed by four similar patterns, after a brief delay. The participant must select the pattern which exactly matches the sample. In some trials the sample and the choice patterns are shown simultaneously, in others there is a delay (of 0, 4 or 12 seconds) before the four choices appear. Paired Associates Learning (PAL): Boxes are displayed on the screen and are “opened” in a randomized order. One or more of them will contain a pattern. The patterns are then displayed in the middle of the screen, one at a time and the participant must select the box in which the pattern was originally located. If the participant makes an error, the boxes are opened in sequence again to remind the participant of the locations of the patterns. 60 Appendix C – Cognitive Assessment Analysis Table 4. Cognitive assessment analysis Outcome Group Timepoint T2 Timepoint T3 P-value (any difference in follow-up) Time p-value Mean (SD) Mean difference (95% CI) P-value Mean (SD) Mean difference (95% CI) P-value DMS Percent Correct (All Delays) Low Carbohydrate 82.9 (9.7) (referent) 0.35 87.4 (8.4) (referent) 0.18 0.31 0.07 Low Fat 85.2 (11.5) 3.00 (-3.36, 9.35) 83.0 (10.0) -4.24 (-10.40, 1.93) DMS Probability of Error Given Error* Low Carbohydrate 0.12 (0.18) (referent) 0.74 0.05 (0.13) (referent) 0.12 0.28 0.55 Low Fat 0.14 (0.19) 0.02 (-0.10, 0.14) 0.14 (0.18) 0.09 (-0.02, 0.21) DMS Mean Correct Latency Low Carbohydrate 4291 (1100) (referent) 0.45 4384 (1308) (referent) 0.77 0.55 0.47 Low Fat 4316 (1578) -202.23 (-734.93, 330.48) 4621 (1161) 93.76 (-547.37, 734.89) PAL Total Errors (Adjusted)* Low Carbohydrate 13.3 (14.4) (referent) 0.22 11.7 (11.8) (referent) 0.49 0.47 0.08 Low Fat 13.4 (10.6) -3.77 (-9.80, 2.27) 9.8 (11.5) -1.98 (-7.58, 3.63) PAL First Attempt Memory Score Low Carbohydrate 12.4 (5.0) (referent) 0.31 14.7 (2.9) (referent) 0.63 0.40 0.02 Low Fat 13.0 (3.8) 1.08 (-1.02, 3.19) 14.2 (5.0) -0.59 (-3.00, 1.81) DMS – Delayed Match to Sample; PAL – Paired Associates Learning * interpret with caution, some evidence of non-normality 61 Appendix D – DBS Carotenoid Values Table 5. Mean group carotenoid values as measured in the dried blood spot cards Lutein Mean (SD) Zeaxanthin Mean (SD) Pre Post Pre Post Low Carbohydrate 0.0058 (0.0027) 0.0077 (0.0032) 0.0072 (0.0040) 0.0078 (0.0030) Low Fat 0.0154 (0.0205) 0.0076 (0.0050) 0.0105 (0.0057) 0.0084 (0.0042) 62 Appendix E – Exploratory Sensitivity Analysis Table 6. Interaction between blood carotenoids and cognitive performance Outcome Exposure Variables Estimate (95% CI) P-value DMS Percent Correct (All Delays) Lutein Time (T3 vs T1) 11.51 (3.87, 19.25) 0.01 Lutein 156.6 (-65.68, 378.45) 0.18 Time*Lutein -1048.49 (-1879.55, -226.54) 0.02 Zeaxanthin Time (T3 vs T1) 8.12 (-3.28, 18.88) 0.16 Zeaxanthin 12.17 (-679.96, 704.66) 0.97 Time*Zeaxanthin -621.13 (-1789.75, 605.14) 0.31 DMS Probability of Error Given Error Lutein Time (T3 vs T1) -0.08 (-0.21, 0.06) 0.27 Lutein -1.39 (-4.93, 2.15) 0.46 Time*Lutein 16.09 (1.60, 30.42) 0.03 Zeaxanthin Time (T3 vs T1) -0.07 (-0.26, 0.12) 0.48 Zeaxanthin -3.17 (-14.09, 7.74) 0.58 Time*Zeaxanthin 14.45 (-5.81, 34.71) 0.18 DMS Mean Correct Latency * Lutein Time (T3 vs T1) -517.43 (-1482.73, 440.76) 0.31 Lutein -12778.6 (-45301.89, 18904.76) 0.45 Time*Lutein 22511.82 (-94040.70, 140418.17) 0.72 Zeaxanthin Time (T3 vs T1) -1713.18 (-2911.31, -505.54) 0.01 Zeaxanthin -8052.34 (-88315.26, 72108.08) 0.85 Time*Zeaxanthin 181677.2 (33389.61, 329300.32) 0.03 PAL Total Errors (Adjusted) Lutein Time (T3 vs T1) -3.93 (-10.88, 3.28) 0.30 Lutein -34.38 (-268.98, 198.48) 0.78 Time*Lutein -270.99 (-1116.24, 544.76) 0.53 Zeaxanthin Time (T3 vs T1) -5.49 (-14.31, 3.61) 0.25 Zeaxanthin -183.02 (-935.68, 578.45) 0.64 Time*Zeaxanthin -43.77 (-1059.08, 935.39) 0.93 PAL First Attempt Memory Score Lutein Time (T3 vs T1) 2.24 (-0.90, 5.31) 0.18 Lutein 81.07 (-14.40, 176.33) 0.11 Time*Lutein 117 (-226.56, 465.00) 0.52 Zeaxanthin Time (T3 vs T1) 4.06 (0.29, 7.70) 0.05 Zeaxanthin 329.45 (29.23, 629.20) 0.03 Time*Zeaxanthin -140.97 (-545.60, 278.74) 0.51 DMS – Delayed Match to Sample; PAL – Paired Associates Learning * interpret with caution, some evidence of non-normality 63 Appendix F – Exploratory Analysis of Cognitive Function Table 7. Analysis of complete, non-distracted cognitive data (N = 18; 8 in low fat group) DMS – Delayed Match to Sample; PAL – Paired Associates Learning Outcome Group Timepoint T2 Timepoint T3 P-value (any difference in follow-up) Time p-value Mean (SD) Mean difference (95% CI) P-value Mean (SD) Mean difference (95% CI) P-value DMS Percent Correct (All Delays) Low Carbohydrate 84.4 (9.3) (referent) 0.47 87.9 (9.3) (referent) 0.81 0.76 0.60 Low Fat 87.6 (9.0) 3.22 (-5.75, 12.19) 86.0 (9.6) -1.07 (-10.10, 7.96) DMS Probability of Error Given Error Low Carbohydrate 0.10 (0.18) (referent) 0.05 0.10 (0.20) (referent) 0.99 0.15 0.98 Low Fat 0.28 (0.23) 0.19 (-0.00, 0.39) 0.10 (0.19) 0.00 (-0.21, 0.21) DMS Mean Correct Latency Low Carbohydrate 4543 (1323) (referent) 0.08 4570 (1278) (referent) 0.58 0.20 0.96 Low Fat 3938 (1227) -542.82 (-1147.79, 62.14) 4365 (1072) -185.16 (-856.37, 486.06) PAL Total Errors (Adjusted) Low Carbohydrate 12.4 (15.6) (referent) 0.46 7.9 (6.3) (referent) 0.86 0.72 0.08 Low Fat 15.4 (11.1) -3.87 (-14.23, 6.49) 12.5 (10.7) -0.44 (-5.29, 4.41) PAL First Attempt Memory Score Low Carbohydrate 13.3 (5.4) (referent) 0.71 14.7 (3.6) (referent) 0.71 0.91 0.36 Low Fat 11.6 (3.1) 0.58 (-2.56, 3.73) 13.2 (5.0) 0.57 (-2.53, 3.67) 64 Table 8. Analysis of complete, non-distracted cognitive data for T1 and T3 (N = 21; 9 in low fat group) DMS – Delayed Match to Sample; PAL – Paired Associates Learning Outcome Group Timepoint T3 Time p-value Mean (SD) Mean difference (95% CI) P-value DMS Percent Correct (All Delays) Low Carbohydrate 88.8 (9.1) (referent) 0.88 0.22 Low Fat 87.6 (10.1) -0.65 (-9.14, 7.84) DMS Probability of Error Given Error Low Carbohydrate 0.04 (0.12) (referent) 0.84 0.76 Low Fat 0.03 (0.08) -0.01 (-0.13, 0.11) DMS Mean Correct Latency Low Carbohydrate 4363 (1328) (referent) 0.84 0.22 Low Fat 4365 (1072) 79.22 (-688.79, 847.22) PAL Total Errors (Adjusted) Low Carbohydrate 7.5 (6.2) (referent) 0.84 0.18 Low Fat 11.1 (10.8) -0.50 (-5.64, 4.64) PAL First Attempt Memory Score Low Carbohydrate 14.9 (3.5) (referent) 0.44 0.66 Low Fat 14.0 (5.2) 1.16 (-1.83, 4.15) 65 Appendix G – Dietary Intake across 12-weeks at three separate timepoints Table 9. Average dietary intake at the 3 timepoints as measured in the 3-day food records Low Fat Breakfast Group Low Carbohydrate Breakfast Group T1 T2 T3 T1 T2 T3 Average Energy Intake (kcal/day) 1952 2084 1949 1534 1529 1507 Average Protein (grams/day) 78 81 77 78 82 70 Average Carbohydrates (grams/day) 220 228 219 135 126 137 Average Fat (grams/day) 83 95 85 72 79 75 Carotenoids (micrograms) 106 146 147 200 297 275
Thesis/Dissertation
eng
Health and Exercise Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Little, Jonathan P.
Investigating the impact of a low-carbohydrate, egg-based breakfast versus a low-fat, guideline breakfast on blood carotenoids and cognitive function in type 2 diabetes
Text