The Development of Individual Human Fatty Acid Reference Ranges

Cody A. C. Lust

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Master of Science in Human Health and Nutritional Sciences

Guelph, Ontario, Canada

THE DEVELOPMENT OF INDIVIDUAL FATTY ACID REFERENCE RANGES

Cody A.C. Lust Advisor(s): University of Guelph, 2020 Dr. David WL Ma

Consumption of dietary fatty acids (FA) are essential for the development and maintenance of human health. Currently, individual FA reference ranges have yet to be established in the field of dietary lipid research. Clinical reference ranges for lipids, such as cholesterol, are routinely utilized in clinical practices for efficient and precise assessments.

However, a number of factors such as dietary consumption, age, sex, and metabolism have been shown to modify FA status. In this study, the effect of aging on FA status in Australians, and the correlation between FA concentrations and lipid biomarkers in Singaporean adults was examined. FA status was modestly correlated to aging across multiple decades and lipid biomarkers strongly correlated to FA concentrations. Continuing to profile multiple cohorts in different countries, using consistent methodology, will allow for FA cut-off values to be developed (i.e. sufficiency vs. deficiency) and provide target values for reducing chronic disease risk.

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ACKNOWLEDGEMENTS

Only two years ago I was working as a bartender in Vancouver B.C., and after receiving an offer in late-August to pursue a master’s degree, decided to drop everything on ten days’ notice and move to

Guelph. I will forever owe my gratitude to my advisor Dr. David Ma for plucking me from obscurity and providing me every opportunity to grow as a researcher and open doors for me which I thought had closed for good. I look forward to the next step in our journey as I begin my PhD and cannot thank you enough for your continued support and patience as I wind my way through the crazy world of academia.

To the other member of my committee, Dr. Lindsay Robinson, thank you for being so supportive from the moment I arrived in Guelph and providing valuable insight on not only this project but, my first publication earlier this year. To all the members of the Ma Lab and friends in HHNS, thank you for welcoming me with open arms and showing me the ropes. Working alongside so many intelligent, driven individuals with such a diverse scope of interests pushes me to work harder myself each day. Most importantly, I would still be in the GC room processing samples for this project if it wasn’t for Lyn

Hilyer. You are the glue which keeps our lab together, and I cannot thank you enough for having the answer to every question even before I ask.

To my family and friends back home on the west coast, there is only so much more I can say here which I haven’t told you before. Each one of you has taught me something that I keep with me each day so, even though we are apart, you are always close by. Finally, Georgia, what is left to say after surviving undergrad, some awkward gap years, our master’s degree, a soon to be PhD, and a global pandemic locked-up together for 6 months. You have made me a kinder, smarter, and all-around better person – I would not have wanted anyone else by my side during this process.

TABLE OF CONTENTS

Abstract ...... ii

Acknowledgements ...... iii

Table of Contents ...... iv

List of Tables/Figures ...... viii

List of Symbols, Abbreviations or Nomenclature ...... ix

1 INTRODUCTION ...... 1

2 LITERATURE REVIEW | LIPID METHODOLOGY AND REPORTING OF DIETARY FATTY ACID STATUS IN HUMANS ...... 4

2.1 Assessments of Fatty Acid Status ...... 4

2.1.1 Food Frequency Questionnaires ...... 4

2.1.2 24-Hour Recall ...... 6

2.1.3 Dietary Records ...... 7

2.1.4 Adipose Tissue ...... 8

2.1.5 Red Blood Cells ...... 10

2.1.6 Blood Plasma and Serum ...... 12

2.2 Lipid Methodology and Reporting of Fatty Acid Status ...... 16

2.2.1 Gas Chromatography ...... 17

2.2.2 Gas Chromatography Mass-Spectrometry ...... 20

2.2.3 Gas Chromatography with Flame Ionization Detector ...... 21

2.2.4 Units of Measure ...... 23

2.3 Developing Individual Fatty Acid Reference Ranges ...... 25

2.3.1 Disease States ...... 28

2.3.2 Genetic Differences ...... 28

2.3.3 Geographical Differences ...... 30

2.4 Conclusion ...... 31

3 RATIONALE, OBJECTIVES, HYPOTHESIS, AND EXPERIMENTAL DESIGN ...... 33

3.1 Rationale ...... 33

3.2 Objectives ...... 33

3.3 Experimental Design ...... 34

4 AGING IS ASSOCIATED WITH DIFFERENTIAL CHANGES OF CIRCULATING SERUM FATTY ACID LEVELS IN AUSTRALIAN MALES ...... 37

4.1 Abstract ...... 37

4.2 Introduction ...... 38

4.3 Materials and methods ...... 40

4.3.1 Participants ...... 40

4.3.2 Gas Chromatography Protocol ...... 41

4.3.3 Fatty Acid Analysis ...... 42

4.3.4 Statistical Analysis ...... 42

4.4 Results ...... 44

4.4.1 General characteristics and lipid biomarkers ...... 45

4.4.2 Correlation between fatty acids and age ...... 48

4.4.3 Absolute concentration when stratified by decade ...... 50

4.4.4 Percent composition when stratified by decade ...... 51

4.4.5 Comparison between absolute concentration and percent composition ...... 52

4.5 Discussion ...... 53

4.5.1 Saturated Fatty Acids ...... 54

4.5.2 Monounsaturated Fatty Acids ...... 55

4.5.3 Omega-6 Polyunsaturated Fatty Acids ...... 56

4.5.4 Omega-3 Polyunsaturated Fatty Acids ...... 58

4.5.5 Statistical and Biological Relevance ...... 62

4.5.6 Absolute vs. Relative Measures ...... 63

4.5.7 Limitations ...... 64

4.6 Conclusion ...... 65

5 FATTY ACID REFERENCE RANGES AND CORRELATIONS TO LIPID BIOMARKERS OF HEALTHY SINGAPOREAN MEN AND WOMEN ...... 66

5.1 Abstract ...... 66

5.2 Introduction ...... 67

5.3 Materials and methods ...... 68

5.3.1 Participants ...... 68

5.3.2 Anthropometric Measures ...... 69

5.3.3 Blood Samples ...... 69

5.3.4 Gas Chromatography Protocol ...... 70

5.3.5 Fatty Acid Analysis ...... 71

5.3.6 Statistical Analysis ...... 71

5.4 Results ...... 72

5.5 Discussion ...... 82

5.5.1 Individual Fatty Acid Reference Ranges ...... 83

5.5.2 Anthropometric Measures ...... 85

5.5.3 Correlations with Lipid Biomarkers ...... 87

5.5.4 Sex Differences ...... 91

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5.5.5 Limitations ...... 93

5.6 Conclusion ...... 93

6 GENERAL DISCUSSION, LIMITATIONS and FUTURE DIRECTIONS ...... 95

6.1 General Discussion ...... 95

6.2 Limitations ...... 96

6.3 Future Directions ...... 97

7 REFERENCES ...... 99

8 APPENDIX A ...... 118

8.1 Appendix B ...... 123

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LIST OF TABLES/FIGURES

Table 2.1 Studies profiling ranges of fatty acids in different cohorts ...... 27

Table 4.1 General characteristics of study population ...... 44

Table 4.2 Correlation between serum fatty acids and age ...... 46

Table 4.3 Correlation and multiple regression of serum fatty acids and age when stratified by decade ...... 50

Table 5.1 General characteristics of study population ...... 72

Table 5.2 Range, mean and percentiles of Fatty Acid concentrations (μmol/L) of serum total lipids ...... 74

Table 5.3 Concentration (μmol/L) of select fatty acids in males and females ...... 78

Table 5.4 Reference ranges and clinical cut-offs of lipid biomarkers ...... 79

Table 5.5 Linear regression model assessing the correlation between individual fatty acids and lipid biomarkers ...... 79

Table 5.6 Upper and lower percentile fatty acid concentrations in participants who exceed risk cut-offs for lipid biomarkers ...... 81

Figure 2.1 Screenshot of a chromatogram in OpenLab CDS EZChrom Edition 3.2.1 ...... viii

APPENDIX A

Table 8.1 Absolute measures of major fatty acids in plasma and serum ...... 118

Table 8.2 Relative percent composition of major fatty acids in plasma and serum ...... 119

Figure 8.1 Bland-Altman plot of the difference of plasma and serum fatty acids ...... 121

APPENDIX B

Table 8.3 Mean concentration of select fatty acids across multiple decades ...... 123

LIST OF SYMBOLS, ABBREVIATIONS OR NOMENCLATURE

24HDR: 24-Hour dietary record

A*STAR: Agency for Science, Technology and Research

ALA: Alpha-linolenic acid

ARA: Arachidonic Acid

AT: Adipose Tissue

BMI: Body Mass Index

CE: Cholesterol esters

CHD: Coronary heart disease

CV: Coefficient variance

CVD: Cardiovascular disease

DHA: Docosahexaenoic acid

DPA: Docosapentaenoic acid

DR: Dietary records

EFA: Essential fatty acids

EPA: Eicosapentaenoic acid

FA: Fatty acids

FAME: Fatty acid methyl esters

FFQ: Food Frequency Questionnaires

GC: Gas chromatography

GC-FID: Gas chromatography with flame ionization detector

GC-MS: Gas chromatography with mass spectrometry

LA: Linoleic acid

LOD: Limit of detection

MAILES: The Men Androgen Inflammation Lifestyle Environment and Stress

MUFA: Monounsaturated fatty acids

NA: North America

NEFA: Non-esterified fatty acids

NHANES: National Health and Nutrition Examination Study

OA: Oleic acid

PA: Palmitic acid

PAO: Palmitoleic acid

PC: Phosphatidylcholine

PL: Phospholipids

PUFA: Polyunsaturated fatty acids

RBC: Red blood cells

RT: Retention time

SA: Stearic Acid

SD: Standard deviation

SFA: Saturated fatty acids t: Trace

TAG: Triacylglycerols

TG: Triglycerides

WC: Waist circumference

1 INTRODUCTION

The consumption of fats, oils, and lipids is essential to the development and maintenance of overall human health and well-being (1). Due to their wide variance in structure and properties, lipids have been classified into eight distinct categories each containing their own classes and subclasses (2). Of these categories, fatty acids (FA) are a diverse group of molecules that are most commonly associated with dietary status/intake. Dietary FA are commonly divided into four classes: saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and trans fats (3). Numerous studies have been published outlining the necessity of obtaining adequate dietary FA to promote optimal health (4). Of these four classes, MUFA and PUFA are commonly seen as the “healthier” fats, in particular, omega-3

PUFA which has been found to exhibit a number of beneficial health effects (1,4). Omega-6

PUFA are quite prevalent in the North American diet but, are found to negatively impact health compared to their omega-3 counterparts (1). Increased consumption of SFA and trans fats have also been found to be harmful to human health and a number of public health initiatives have attempted to reduce levels in the global food supply (4). Therefore, monitoring FA status can provide useful insight into changes which may be related to health status.

In epidemiological studies, dietary FA status is most commonly assessed via the use of a dietary record such as a 24-hour recall, 3 or 7-day food intake record, or a food frequency questionnaire (5). Although technology has helped improve the capabilities of subjective dietary measures, inherent bias of self-reporting and the extrapolation of individual FA quantities from various food sources are significant limitations. FA status can also be assessed via blood lipid analysis, which can determine individual levels of FA and is reflective of dietary consumption 1

and endogenous lipid synthesis (6). FA status, however, is impacted by a number of biological and environmental factors. For example, all SFA and MUFA have the capability to be endogenously produced which is driven via the enzymatic reaction of Δ9-desaturase (3). Only the PUFA alpha-linolenic acid (ALA) and linoleic acid (LA) are considered to be essential fatty acids (EFA), which means they must be consumed via the diet and cannot be produced endogenously (7). The long-chain omega-3 PUFA, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), can also be produced endogenously, but have very limited conversion rates of 1% or less (8). Therefore, PUFA more accurately reflect dietary intake based on our capability to endogenously produce SFA and MUFA (3). However, certain disease-states are known to alter FA metabolism, impacting circulating FA concentrations independent of natural endogenous formation (9). An individual’s geographic location also plays a large role in

FA status due to cultural differences in diet and, accessibility to food sources. For example, countries in South-East Asia and Oceania have far higher reported SFA, and omega-3 PUFA intakes than North America (NA) whereas, NA has greater omega-6 intake (10). Thus, FA status can be modified not only by dietary intake but, specific disease-states and individual metabolic properties.

A current gap in the field of dietary lipid research is that individual FA reference ranges have yet to be established. Objective measures with established clinical ranges for lipids categories such as cholesterol, triglycerides, and total free fatty acids are routinely utilized in clinical practices for efficient and precise assessments (11). Additionally, the reporting and methodology of such FA data is often inconsistent throughout literature (12). In order to address these gaps, one must first comprehensively profile baseline FA concentrations in healthy,

disease-free populations. The use of consistent methodological approaches will improve reproducibility and comparisons of results between studies. Furthermore, taking into consideration differences in the population being assessed such as geographic location, genetics, age, sex and health status, will help to establish more valid baseline measures. Establishing reference ranges would allow for meaningful assessments of FA status in individuals (i.e. sufficiency vs. deficiency) and provide target ranges for health, similar to that of other clinical measures widely used today.

2 LITERATURE REVIEW | LIPID METHODOLOGY AND REPORTING OF DIETARY FATTY ACID STATUS IN HUMANS 2.1 Assessments of Fatty Acid Status

There are a number of subjective and objectives measures currently available to researchers to assess dietary FA status in humans (13). Food Frequency Questionnaires (FFQ) or, diet records are subjective measures used to estimate dietary FA status and intake, whereas measuring blood FA status is an objective measure utilized in certain studies. From a research design perspective, having access to multiple assessments allows researchers to make appropriate choices based on their specific study design and desired outcomes. With the goal of developing dietary FA reference ranges, using accurate and consistent methods which are able to be conducted effectively in large-scale epidemiological studies is paramount. Three key steps take place during this process: the collection of samples/data which reflect dietary FA intake, the analysis of said samples, and the reporting of results. The inconsistency of assessments across different studies when reporting a singular outcome (values representative of FA status) however, limits the ability to accurately compare results between studies. Consistent use of objective measures coupled with validated subjective measures can improve compatibility between studies and provide a richer understanding of FA status in humans.

2.1.1 Food Frequency Questionnaires

FFQ are a self-reported, indirect measure of assessing dietary FA status in individuals

(14). FFQ consist of three main components: The list of individual foods/group of foods, frequency of consumption indicated by number of times per day/week/month, and an estimated portion size of specific items consumed (15). When FFQ include questions regarding the specific 4

quantity of food consumed they are further classified as either semi-quantitative, quantitative, or non-quantitative (14). The use of FFQ in epidemiological studies is quite high due since it is relatively simple to fill-out, only taking 20-30 minutes to complete, can be conducted at home or online, are non-invasive, and cost-effective (14,15). Of 189 studies examined which utilized self- reported dietary assessment tools in Canada, 64% used some type of FFQ or screener (16).

However, FFQ are not a standardized instrument as they can be designed or adapted for use in- specific populations or to answer specific dietary questions (15). To determine if a FFQ accurately reports dietary intake of the population being examined a validity study is almost always performed (14). A number of studies globally have examined the validity of using FFQ to estimate the intake of dietary lipids, most commonly omega-3 PUFA, with often moderate-to- strong correlations (17–25).

As FFQ are an in-direct measure of dietary status, they lack the specificity to accurately assess individual FA status. In order to validate a FFQ, comparisons must be drawn to other more specific dietary assessment tools such as 24-hour dietary recalls (24HDR) or blood lipid analysis (14,15). This is due to FFQ containing a large degree of measurement error and being limited by the ‘select’ list of foods presented, classifying complex mixtures, bias of participants to under/over report consumption, limits in memory, ability to estimate portion sizes, and others

(14,15). Studies have attempted to develop mathematical formulas to determine specific FA status based on FFQ results but, correlating FA with low-levels to FFQ have proven to be ineffective (26). Additionally, FA metabolism can be modified by disease and age resulting in the FA quantity consumed not being an accurate reflection of biological status (27). Overall, although FFQ lack specificity, they provide a simple and informative method for monitoring

dietary trends that can inform whether changes in FA status are indicative of metabolic or dietary changes.

2.1.2 24-Hour Recall

The 24HDR is an indirect, retrospective method of precisely recalling and quantifying all food and beverages consumed over a 24-hour period, which is commonly completed 2-3 times

(28). Typically, a 24HDR is done in-person or over the phone each time with trained interviewers and has been systematically utilized by the National Health and Nutrition

Examination Study (NHANES) in the USA (28). The use of a trained interviewer helps improve precision of the results, especially with low literacy populations (28). Although there are prevailing 24HDR commonly used in literature, similar to FFQ, they must be validated when one is created or modified (29). With the rapid growth of technology, online 24HDR have been developed which participants can fill-out on their own and do not require the supervision of trained professionals (29,30). The Automated Self-Administered 24-hour Recall has been modelled based on the same method utilized in the NHANES and has been found to be valid and perform similarly for assessing true dietary intakes compared to the traditional interviewing method (29,31). Using an online system can significantly reduce costs of a study and scale-up research efforts but, was prone to more inconsistencies compared to a standard interview due to some participants having difficulties with the program (29). When compared to FFQ, 24HDR have been found to a more accurate assessment of true dietary intake due to the exact quantification and specificity of the food products consumed (14,32–34).

24HDR have a greater specificity of measuring dietary consumption compared to FFQ but, are still quite limited at assessing individual FA status. Due to 24HDR also being an in- 6

direct measure of FA status, similar translational limitations to that of FFQ exist. Studies have found 24HDR can be valid for assessing dietary intake in multiple races but, stronger correlations were noted in PUFA than the moderate correlations seen in MUFA and SFA (35).

However, other studies have found relatively low correlations with 24HDR and individual FA levels when compared to blood biomarkers (36). Overall, 24HDR provides an effective tool with greater specificity than FFQ for reporting dietary FA consumption but, lacks consistency for reporting individual FA status.

2.1.3 Dietary Records

Dietary records (DR) are an in-direct, prospective method of assessing an individual’s consumption of food and beverages recorded over a specified period of time, typically 3, 4, or 7 days (37). When assessing dietary consumption, DR are typically the ‘gold standard’ of which

FFQ and 24HDR are often validated against (37). Recording dietary intake as it is happening, opposed to the retroactive methods used in FFQ and 24HDR, eliminates recall bias allowing for more accurate and detailed information to be recorded (5,37). As DR are an open-ended approach, participants still require a training session to ensure they record adequate details of their diet consistently over the 3-7 day timeframe (5).

Due to the human-bias involved in FFQ, 24HDR, and DR, all three methods of assessment typically suffer from underreporting of dietary consumption as participants attempt to exhibit healthier eating habits (38). Furthermore, due to the assessment taking place over multiple days, participants may begin to change dietary habits or lose motivation to fill out detailed reports (5,37). Recent advances in technology are attempting to improve upon DR by using online databases with photos which are easier, less time-consuming, and have been found 7

to reduce underreporting of dietary intake (39). In order to assess the validity of DR, studies often utilize biomarkers such as blood lipids which provide an integrated perspective of dietary consumption as it reflects both absorption and metabolism (40). When DR are validated against biomarkers with respect to FA status they have shown moderate-to-strong correlations

(36,40,41). Comparatively, DR have shown the highest validity of overall and individual FA intake compared to both FFQ and 24HDR (36,41). Taken together, DR are the most accurate subjective estimate of dietary status. Although biomarkers are valid objective estimates of dietary intake, the confounding effect of disease and individual genetics can impact the measured values (5,27). Thus, the use of DR to assess dietary consumption and provide context to objective values obtained from the analysis of biomarkers can provide a richer understanding to changes of individual FA.

2.1.4 Adipose Tissue

Methods of dietary assessment which participants recall/record their food and beverage consumption over a fixed duration of time are highly common in epidemiological studies but, lack biological precision. Dietary fat is not only the largest single source of energy; interest regarding the bioactive capabilities of dietary fat consumption and its effect on chronic disease has grown in recent years (42). Given this increased interest, the need for more precise assessments of dietary fat intake has also grown resulting in a number of studies utilizing tissue and blood samples for strong biomarkers of FA status (3). Adipose tissue (AT) is the largest storage compartment of FA and is often considered the “gold standard” representation of dietary

FA status (3). AT samples are most commonly obtained from either the gluteal or abdominal region, which maintain relatively stable FA content over time (42). However, variability between

quantities of FA has been seen in adipose tissue found at different sites and should be kept consistent throughout sampling (43). Due to the half-life of FA in AT lasting up to 600+ days,

AT is often seen as the strongest predictor of long-term FA intake (44,45). Significant positive correlations, as high as r=0.70, have been seen between dietary intake and AT content of PUFA

(3,35,46) and trans FA (46). Correlations between SFA and MUFA have been more variable with ranges of r=0.03-65 compared to AT content (3,44) yet strong associations with the dairy fats SFA pentadecanoic acid (C15:0) and margaric acid (C17:0) have commonly been reported

(47).

The use of AT as a biomarker strongly correlates to dietary intake, and FA content maintains stable under homeostatic conditions but, it has notable limitations which reduces its practical use in human research. First, obtaining AT samples is a fairly invasive technique which proves to be costly and time-consuming when conducted at a large scale (48). Different tissue sampling sites have also seen notable FA differences, and improper handling of samples or, not being stored at the correct temperature can oxidize PUFA (42). Drastic changes in weight loss have also modified AT PUFA content as much as 15% in both abdominal and gluteal samples

(49). Finally, due to the lengthy turn-over of AT FA in weight stable individuals, assessing when this compartment reaches saturation can take 2-3 years of observation (50). Following supplementation, one study found that after twenty weeks changes in AT PUFA content was

“almost imperceptible” (51). From a logistical perspective, conducting a longitudinal nutritional intervention study of >2 years is useful in some contexts but, incredibly difficult and expensive to be conducted routinely. Furthermore, AT FA concentrations may not be an accurate

“snapshot” of an individual’s current dietary status due to the prolonged time it takes to see

changes. Overall, AT may be considered the “gold standard” for representing long-term dietary

FA but, the prolonged half-life and costly invasive procedure render it impractical for use in large-scale epidemiological studies.

2.1.5 Red Blood Cells

Biomarkers which are best suited for dietary intake are sensitive to small changes, easily obtained, and are reflective of the period of intake being assessed (short vs. long-term) (44).

Asides from obtaining AT samples to assess individual FA status, multiple blood fractions are commonly analyzed in epidemiological studies which meet these criteria. The three blood fractions which are most commonly assessed for individual FA status are erythrocytes/red blood cells (RBC), whole blood, and plasma/serum (3). RBC, plasma and serum can be further divided into phospholipids (PL), triacylglycerols (TAG), non-esterified fatty acids (NEFA), cholesterol esters (CE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), or the total of all fractions (48). A 2016 paper found that ~90% of dietary FA research assessed at least one of: total RBC, total plasma/serum, or plasma PL (52). Each of these fractions are each uniquely modified by dietary and metabolic changes resulting in inherent strengths and limitations of subsequent assessments (3).

RBC contain a more complete spectrum of membrane PL classes resulting in FA concentrations which strongly reflects other biological tissues and consumption (3). Consisting of a half-life of ~120 days, RBC are seen as an effective long-term biomarker of dietary FA status (53). Compared to the long half-life of AT samples, upwards to two years, the 120 days seen in RBC is a more accurate reflection of current dietary status/trends (48). The relatively long half-life results in RBC measures being less sensitive to fasting status, reducing artificial 10

FA spikes via short-term dietary modifications (54). Supplementation studies have seen omega-3

PUFA composition of RBC change in as little as three days but, take beyond twenty-eight days to become fully saturated (55). As PC and PE classes in the RBC membrane saturate at different rates, this could account for the relatively quick changes when assessing total RBC composition

(12). Typically, changes to RBC composition show smaller effects with dietary interventions but, are less variable when compared to other fractions over similar time frames (56). Numerous studies have validated RBC composition as an accurate indicator of dietary FA intake with the strongest correlations being found in PUFA, and more moderate correlations to MUFA and certain trans fats (3,22,44,48,52,53,57). As RBC composition of EPA and DHA have consistently exhibited strong correlations to dietary consumption, the Omega-3 Index (O3I) was developed based on the calculation: (%EPA+%DHA)/% Total FA in RBC (58). This index has been widely cited in literature as a general surrogate to assess overall omega-3 status, with values of >8% correlating to reductions in cardiovascular disease (CVD) and coronary heart disease

(CHD) (58).

Although assessment of FA status via RBC is common in epidemiological studies, they do exhibit some well-known limitations. Correlations of RBC values to SFA and specific MUFA intake have been found to be relatively weak (22,44,53,57). The SFA palmitic acid (PA;C16:0), stearic acid (SA;C18:0), and the MUFA oleic acid (OA;C18:1 c9) have not only been found to weakly correlate to dietary intake but, differ between RBC and other fractions (3,12). Similar discrepancies between fractions have been found with omega-6 PUFA Linoleic acid (LA;C18:2 n6) and Arachidonic acid (ARA;C20:4 n6) (3,12). Our bodies ability to endogenously produce

SFA and MUFA reduces the strength of correlation between dietary consumption and all blood

biomarkers, as this trend is seen in RBC and other fractions (3). Furthermore, the preparation and storage of RBC samples have proven to be troublesome in large-scale studies (56). Standard collection method of blood samples for scientific research typically utilizes plasma and serum samples, not RBC (59). The procedures for separating RBC samples from whole-blood and for lipid extraction differs entirely from plasma or serum and is more time-consuming and labour intensive to conduct (60). RBC samples display intermediate stability and require very particular storage conditions immediately following collection to limit the degradation of FA which can occur in as little as two days (42,56). The additional labour and expertise required to prepare, and store RBC samples is a significant limitation when conducting large-scale experiments. Overall, the use of RBC as a biomarker of long-term dietary intake has shown strong correlation to a number of FA but, weakly correlates to a few key FA of interest and requires very specific and costly procedures in order to process and store samples effectively.

2.1.6 Blood Plasma and Serum

The two most common biomarkers used to assess dietary FA intake, blood plasma and serum, are the final biomarkers to be reviewed (52). Both blood plasma and serum also contain multiple lipid fractions between them such as PL, CE, NEFA, etc. (48). For simplicity, any discussion of plasma or serum can be assumed to be the total analysis of all individual lipid fractions, unless otherwise stated.

The process of preparing blood plasma and serum for analysis from whole-blood differs between each of the fractions. In a whole-blood sample, the component which blood cells are suspended in is the blood plasma, which comprises ~55% of the total blood volume (61). To prepare plasma, once a blood sample is collected an anti-coagulant is added which prevents 12

clotting and is then centrifuged to separate the plasma from the remaining blood cells (61).

Serum preparation differs as the collected sample is allowed to clot and is then centrifuged followed by removal of the separated serum layer (62). Upon collection plasma samples typically require refrigeration, making serum samples slightly easier to prepare and store (62). One notable compositional difference between the fractions is the lack of anticoagulants in serum results in fibrinogen being present in the sample (63). Additionally, the use of anticoagulants in plasma has sometimes interfered with samples in proteomic research (61). With proper collection and subsequent storage, plasma and serum samples have the best long-term FA stability of any biomarker assessed with some studies reporting samples remain stable for up to 10 years (56).

Having samples stored in a freezer at -80°C is the general consensus amongst researchers to be most effective at reducing FA degradation over long periods of time (12,56). If samples are unable to be kept at -80°C they must be treated with additives such as butylated hydoxytoluene to maintain the sample integrity and reduce FA loss but, this process may further modify FA status during processing (56).

Blood plasma and serum have long been thought to be interchangeable for FA analysis but, little research has examined this phenomenon. A 2011 study was able to determine subgroups of PL between plasma and serum samples were strongly correlated to each other but, they did not examine individual FA (64). In an attempt to directly answer this question, our lab conducted a study comparing fifty matched plasma and serum samples of middle-aged males which can be found in Table 8-1 and Table 8-2. No significant differences were found between plasma and serum samples of the nine select FA analyzed, using both absolute and relative measures. We concluded plasma and serum samples can be confidently used interchangeably,

and results from studies using either fraction to assess individual FA levels are able to be accurately compared. Thus, the use of plasma and serum as biomarkers of dietary FA intake will be discussed in tandem.

Plasma and serum are biomarkers which best reflect short-term dietary intake, with notable FA changes being seen within less than 24 hours (42). The time it takes to fully incorporate FA via diet/supplementation into plasma or serum varies on the quantity and the class of FA consumed, which can range from three days to two weeks (3,65–67). Certain lipid fractions, such as PL, NEFA, and CE, can become fully saturated at a faster rate and achieve greater relative compositions compared to total plasma and serum analysis (65,67). Additionally, certain plasma fractions have seen weak correlations to dietary FA intake when compared to AT

(68). A recent study found that total plasma correlates to dietary intake of FA equally, if not better, than individual fractions (65). Assessment of total plasma and serum reduces the need to fractionate samples and conduct additional analysis, saving time and resources during large-scale epidemiological studies (65). The decreased timeframe of which changes to dietary FA levels are seen in plasma and serum result in a more appropriate biomarker for large-scale cross-sectional studies as it reflects current dietary FA status more accurately than AT or RBC. Furthermore, a number of studies have validated plasma and serum as accurate biomarkers for assessing dietary

FA (3,42,44,53,65,66,69–71). Similar to RBC, correlations of omega-3 and omega-6 PUFA to dietary intake are strong in plasma and serum (3,44,53,65,66,69,72) but, results with ALA have been inconsistent (6). Correlation of MUFA intakes to plasma and serum tend to be relatively low, with the exception of OA which has seen moderate correlations in some studies (53,65,73).

SFA correlations seen in plasma and serum are similarly inconsistent to that of RBC but, some

studies have found pentadecanoic (C15:0) and margaric acid (C17:0) to have a moderate correlation to dietary intake (65,66,69,74). Compared to RBC, a majority of the FA classes had a similar strength of correlation to the dietary intake in plasma and serum samples. When directly compared, some studies have found plasma to be the more reliable biomarker for assessing dietary FA status (72). The most notable difference was seen in the moderate correlation of two odd-chained SFA which are markers of dairy intake and have limited capacity to be endogenously synthesized in humans, unlike other SFA previously discussed (74).

Large fluctuations which can occur with short-term modifications to dietary intake are a notable limitation of plasma and serum samples (54). Large spikes in FA status have been seen in plasma and serum samples mere hours following a meal (42). Due to this rapid change, participants must fast before a plasma or serum sample is collected or, an artificial increase in

FA may be mistaken as regular intake (54). RBC are less resistant to short-term dietary intakes due to their ~120 day half-life and certain PL fractions of RBC have actually been found to reflect intakes of SFA and omega-6 PUFA similar to that of plasma (66). Additionally, when compared to RBC, plasma exhibits a larger intra-individual variability of PUFA values (53,69).

Although this may reduce the strength of correlations in certain instances, the larger dynamic range of plasma values could benefit statistical analysis when establishing appropriate reference ranges. Overall, as plasma and serum samples are easily and commonly collected, exhibit long- term stability, and strong correlations to dietary FA intake, they are an appropriate biomarker for use in large-scale epidemiological studies. Establishing consistency amongst the dietary assessments used in studies for evaluating dietary FA trends will help improve reliability and the ability to compare results moving forward. This will not only aid in establishing reference ranges

of healthy individuals but, be directly applicable for clinical use and monitoring the risk of chronic disease (75). Thus, the use of plasma and/or serum samples as the primary method of monitoring dietary FA status in studies which aim to establish FA reference ranges are encouraged moving forward.

2.2 Lipid Methodology and Reporting of Fatty Acid Status

Once a biological sample has been collected, three steps must occur before individual FA can be quantified. First, lipids must be extracted from the collected plasma and serum samples in preparation to be analyzed (76). The gold-standard of lipid extraction protocols are the Folch and

Bligh-Dyer methods which are both routinely performed in scientific research (77–79). Second, extracted lipids must undergo further derivatization into fatty acid methyl esters (FAME) in order to analyze individual FA (80). Based on the lipid fraction being analyzed (eg. plasma,

RBC, PL, NEFA) specific techniques to derive FAME must be used which require proper validation studies (12). For plasma and serum samples, boron trifluoride and hydrochloric acid are commonly employed for derivatization due their strong validity and time-efficient process

(81). Third, once converted to their FAME derivates, a number of different chromatography methods can be performed to separate and quantify individual FA from the sample (80). Gas chromatography (GC) is more commonly utilized than liquid chromatography for FA analysis

(82). Analysis of FA via GC can be further specialized by the use of different detectors, the most common being: GC with flame ionization detector (GC-FID) and GC with mass spectrometry

(GC-MS) (80,82). These techniques have increased throughput of samples in large epidemiological studies, with a recent study including 160,000 samples analyzed (12). GC

techniques and methods of reporting FA values will be discussed in respect of analysis of total plasma and serum samples.

2.2.1 Gas Chromatography

GC is a common analysis conducted in analytical chemistry in which compounds are vaporized, separated and subsequently quantified (83). It is termed ‘gas’ chromatography due to the mobile phase, containing the compound(s) of interest, consisting of a steady flow of an inert/unreactive carrier gas (83). In brief, a robotic, automated sampler uses a microsyringe to introduce the sample automatically to a heat controlled inlet/injector (83). Inside the injector, the sample is heated and vaporized where it is then injected into the mobile phase and passes into the

GC machine (83). All GC machines contain a long, coiled column that is temperature controlled by an internal oven which the mobile phase passes through (83). An inner stationary phase that lines the interior of the column functions to separate individual components contained within the mobile phase which passes through the column (83). The time it takes the compound, or in this case individual FA, to travel through the column is known as the retention time (RT) (84). The size, polarity, and molecular mass of individual FA allows them to separate while passing through the column, with a longer RT in the mobile phase allowing for a greater separation and quantification of FA (85). A number of different combinations of columns and stationary phases can be used which impacts the RT of a sample and the subsequent number of individual FA included in the profile (42). Increasing the column length (eg. 30m vs 100m) will increase the

RT allowing for a greater separation of individual FA and a larger subsequent pool of FA to be quantified (84,86). However, this will also increase the run time of each sample, reducing throughput for the sake of a more comprehensive FA profile (42). Upon leaving the column, the

sample reaches a digital detector which is able to quantify the compound and is then exported digitally as a chromatogram, as seen in Figure 2.1 (83). Internal standards are often added to samples prior to the lipid extraction phase in order to determine accurate quantification of results once values are detected (85). Specialized software is used by researchers to check and analyze the resulting chromatogram and determine the quantity of substance detected by the GC (83). A calibration factor is able to be derived if internal standards are added quantitatively, allowing researchers to determine the percent composition and/or absolute concentration of each individual FA with respect to the sample analyzed (12). The use of odd-chain SFA, which only trace amounts are produced endogenously and consumed via diet such as: C17:0, C19:0, and

C21:0, are most commonly used (12,85–87).

Figure 2.1 Screenshot of a chromatogram in OpenLab CDS EZChrom Edition 3.2.1

2.2.2 Gas Chromatography Mass-Spectrometry

Where GC machines primarily differ in their overall function is the detector used for quantification of the substance being measured. As the name would suggest, GC-MS utilizes mass spectrometry to measure the mass-to-charge ratio of different ions and produces a signal on a per mole basis of numerous analytes at once (83,87). This technique coupled with GC allows for specific structural information, along with quantification, of the FA present to be determined via the location of double-bonds (87). Mass spectrometry is a powerful tool but, method optimization such as specific derivation of lipids and optimal instrumental settings are required to quantify results (82,85). The response of GC-MS to certain FAME can be quite varied if the machine has not been calibrated to the specified structure of the analytes being analyzed (87).

Parameters specifying a range of mass-to-charge values expected of the analytes must be set and the total ion count based on integrating peaks in the chromatogram must be completed in order to accurately quantify values (80). Once this is established the sensitivity and specificity can be extended to the selective ion monitoring (80). When methods of GC-MS are optimized, it is very effective at quantifying and identifying geometric isomers of PUFA and conjugate linoleic acids

(CLA) (82). The sensitivity and selectivity of GC-MS allows it to more effectively determine structural information and using selective ion monitoring, separate peaks from ‘noisy’ samples

(80). Although GC-MS yields similar quantifications compared to GC-FID, the specificity of methods and the resulting cost of solvents reduces throughput and hinders the use of GC-MS in large-scale studies (42,82). Current studies are working on procedures to create higher

throughput of GC-MS by establishing less specific parameters to accurately quantify individual

FA (87,88).

2.2.3 Gas Chromatography with Flame Ionization Detector

GC-FID is the most well-established, robust, and commonly used detector for quantifying individual FA status used in scientific literature today (81,82,87,89). Once the mobile phase containing the analyte passes through the column, it is mixed with hydrogen and undergoes combustion inside the flame ionization detector where the ions released are detected by an electrometer (87,90). Each FA is quantified on a chromatogram as a peak on a horizontal baseline (see Figure 2.1) which represents the amount of ions released during combustion on a per milligram basis (12,87). Compared to GC-MS, GC-FID requires far less specific parameters to conduct and produces a nearly identical response factor to all FAME derivatives when analyzed (87). The response however is far less specific, as no structural information is able to be determined during GC-FID (80). Individual FA are determined by their RT when passing through the column which is proportional to the chain length and number of double-bonds present in the FA (82,87). Flame ionization detectors are able to detect even trace amounts of FA but, trans-isomers and CLA are quite challenging to precisely determine (42,90). The RT of FA in both GC-MS and GC-FID have been found to be nearly identical, resulting in unidentified, mislabeled, or overlapping peaks following GC-FID able to be validated against GC-MS (89).

This process of total combustion results in quantitative measures of GC-FID being nearly universal amongst organic compounds, exhibit long-term stability, low cost, excellent precision and strong linearity (80,82,87,90).

Other methods of chromatography are also commonly used, such as thin-layer chromatography which can further fractionate plasma and serum prior to GC analysis (82).

Compared to total plasma or serum analysis alone, this process is time-consuming, and based on the results of Furtado et al. (65) does not provide stronger correlations to dietary FA intake. The use of GC-FID is widely applicable for the quantification of individual FA. Although this detector is limited by a nonspecific response lacking structural information of the analyzed compound and an inability to detect specific impurities, it is the most stable and accurate method widely available (80,91). For all GC quantifications, coefficient variance (CV) is used as a guideline to determine the amount of error present in a sample (92). Using CV as a guideline when repeating sample measures can be valuable to determine the amount of error which may be present due to instrumentation, procedure, or inter-tester differences. However, acceptable CV ranges have yet to be formally established for FA values obtained from GC-FID (65). It has been previously suggested a CV range of 15-20% is considered to be a reasonable (92), while Furtado et al. (65) reports less than 20% and upwards to 30% is common. A study examining different labs from across the world using GC to analyze individual FA levels of plasma and serum samples found a majority agreed within 20% CV but, there was variability between individual

FA (93). Quantifying FA values of total plasma and serum using GC-FID results in a simplified procedure, higher throughput, and reduced cost, which is more effective for large-scale studies.

Furthermore, adequately detailing both extraction and derivatization techniques are important for establishing more consistent practices in lipid research. Recent studies have already begun utilizing this methodology to comprehensively profile individual FA in global cohorts with the goal of developing FA reference ranges (94,95).

2.2.4 Units of Measure

The final component involved in assessing dietary FA intake is the method in which results are reported. As mentioned previously, results of GC analysis are exported as a chromatogram which indicates the quantity of individual FA that was detected as a ‘peak’ on a horizontal baseline (see Figure 2.1) (83). Internal standards are commonly added to samples prior to GC analysis and are used to mathematically derive quantitative results (85). Currently, the method in which dietary FA results are reported is not standardized in scientific literature

(12). The two most common methods of reporting results are as a relative percent composition, or absolute concentration (12). The manner in which FA status is reported has been found to significantly impact the correlation and interpretation of dietary status (95). Relative percent composition and absolute concentration will be reviewed in respect to establishing dietary FA reference ranges.

2.2.4.1 Percent Composition

Individual FA levels representative of dietary intake are most commonly reported as a relative percent composition (12,95). Percent composition reflects the total value of an individual

FA divided by the total of the entire FA pool analyzed represented in terms of percentage (12).

This method is very simple to calculate, the units are universal, and is already commonly used to reflect FA status, such as the Omega-3 Index which correlates to CVD risk (54). Intra-individual

CV when calculated using relative percentages have also been found to be lower compared to absolute measures (69). This is likely due to values reported as a percentage being limited to a 0-

100 scale, reducing the dynamic range compared to absolute values. Additionally, the total pool of FA can have large implications on the relative percentage reported. Currently, there is no

standardized list for which FA must be included in the pool analyzed, with some studies reporting <5 and others >60 individual FA (12). The number of individual FA able to be analyzed at one time is largely dependent on the column used during GC; a longer column allows for more separation and a larger number of individual FA to be detected (86). As relative measures are based on the percentage of the total pool of FA included in analysis, the smaller the pool analyzed equates to a larger apparent percentage of individual FA (12). Although a larger number of FA assessed has useful implications for establishing reference ranges, CV of difficult to detect FA, such as a trans-fats and CLA, are more varied (69). Percent composition is also limited by the direct influence independent FA can have on subsequent calculations of the remaining pool (69). For example, if large increases in one FA are detected from timepoint A to timepoint B, the value of the total pool is also increased. When other FA in the same pool are analyzed, even if no changes are detected from timepoint A to B, their overall percentage would be decreased as the value of the total pool used in the calculation would have increased. Previous studies have found the correlation of relative and absolute values of some FA to be poor, which may be the result of the total FA pool influencing individual FA values (69,95,96). Overall, although the simplicity of the calculation results in an easily replicable process, the influence of independent FA on final values limits the accuracy of reporting FA data via this method.

2.2.4.2 Concentration

The other common method of reporting FA values is in terms of an absolute concentration which reflects the average mass of FA per unit of fluid or tissue (12). Absolute concentrations are calculated via a calibration factor derived from the use of an internal standard applied to the detected value of an individual FA (12,85). Concentrations are typically reported

as either μg/mL or μmol/L depending on the calibration factor but, are easily converted between each other if the molecular mass of the FA is known (12). Of note, relative percentages are able to be derived from absolute concentrations if they are the only method reported but, the same is not possible for the reverse. Comparatively, absolute values have a larger dynamic range than relative values as they are not limited to a 0-100 scale, and have values often in the 1000’s. For statistical analysis of concentrations, data sets may result in non-normal distributions but, for establishing reference ranges, can be more precise as no variance is being reduced. Additionally, absolute concentrations are not directly influenced by the status of other FA as the calculation does not utilize relative values in its computation (12). When both methods of FA reporting have been used in the same study, trends in the status of certain FA have found to differ depending on whether concentration or percent composition is reported (69,94,95). Studies reporting both methods can provide a more in-depth understanding, and greater context to the values being assessed in cross-sectional studies. For the purposes of developing reference ranges of individual

FA, reporting results as absolute concentrations to eliminate the influence of other FA and improve generalizability across studies is recommended (94,95).

2.3 Developing Individual Fatty Acid Reference Ranges

Due to their diverse biological function, maintaining adequate dietary FA status is vital for promoting optimal health and has been implicated in reducing the risk of a number of chronic diseases (4). However, there are currently no established reference ranges for individual FA which denotes “healthy” values. This fundamental gap in FA research hinders the translation of scientific results into clinical practice. Baseline FA values would provide medical practitioners context regarding the direction and magnitude of change necessary to achieve optimal health in

patients with deficient, suboptimal, or even excess amounts of FA. Without context of what individual FA levels equate to adequate status, understanding the relationship between the values reported in scientific studies and human health is currently limited. Values which are reported in scientific literature are often inconsistent for that matter, using different units or methods of reporting altogether (12). Establishing universal practices in dietary FA assessment and reporting are necessary steps to create consistent and generalizable results between individual FA status of different cohorts. A majority of the studies that have profiled FA values have done so in participants with chronic disease which can result in modified levels of individual FA (9). Other studies have completed large-scale assessments of FA status in healthy individuals but, results are reported as a relative percentage which reduces the generalizability of results as individual concentrations are unable to be derived from percent values alone (97,98). To date, only a handful of studies have comprehensively profiled plasma/serum FA status in healthy adults with the primary objective of developing reference ranges, which are highlighted in Table 2.1

(94,95,99,100). Of these studies, only one took place outside of NA, and another contained participants with diabetes and/or metabolic syndrome (95,100). This section will highlight the effect FA status can be modified by certain disease-states, along with genetic and regional differences, and how they are important to consider in the process of developing FA reference ranges.

Table 2.1 Studies profiling ranges of fatty acids in different cohorts

# of Collection FA Authors Population Subjects Year(s) Biomarker Analysis Assessed Units Abdelmagid et al. 2015 Canadian Mixed-Sex 826 2004-2009 Total Plasma GC-FID 61 μmol/L Sergeant et al. 2016 United States Mixed-Sex 152* 2003-2007 Total Serum GC-FID 29 μmol/L Serum PL, CE, Bradbury et al. 2011 New Zealand Mixed-Sex 2793 1996-1997 TAG GC-FID 20 % mg/L & Sera et al. 1994 United States Mixed-Sex 130 N/A Total Serum GC-FID 11 μmol/L *59 samples obtained from a diabetes/metabolic syndrome cohort

2.3.1 Disease States

All things being equal, changes to dietary consumption plays the largest role in modifying plasma/serum FA status. For developing reference ranges, there a number of additional factors which can further impact FA status that need to be considered when establishing baseline values and assessing health-status. One such factor is the impact chronic disease may have on FA status. When compared to healthy individuals, participants with a wide range of health issues such as nonalcoholic fatty liver disease, pancreatitis, type I and II diabetes, cancer, etc. have displayed modified plasma and serum levels of individual FA (101–109).

Plasma FA values have also been found to strongly correlate with increases in the body mass index (BMI), as obese individuals, with or without metabolic syndrome, have modified FA status compared to healthy controls (110–112). These differences in FA status provide strong rationale for the evaluation of “healthy” individuals to establish reference ranges as an effective biomarker to assess disease risk. However, further research will be necessary to profile the FA status reflective of specific disease states in order to establish cut-off FA values which equate to disease risk. For example, long-chain SFA were found to be increased in colorectal cancer whereas increased MUFA were seen in patients with pancreatitis (102,106). Whether these changes in FA status are brought on by the disease itself or genetic alterations which may persist following remission is important to consider (106). For future research objectives, correlating FA status to a wide range of biomarkers reflective of chronic disease risk are necessary. (106).

2.3.2 Genetic Differences

Assessing individual genetic differences independent of complications derived from chronic disease is also important to consider when developing FA reference ranges. Inter-

individual differences in metabolism, dietary absorption, or endogenous lipid production can modify individual FA status (3). Whether healthy ranges require further stratification into groups such as BMI, age, or sex, have yet to be fully elucidated. As discussed previously, plasma FA values exhibit a strong correlation to increases in BMI and have found to be increased in most obese individuals (112,113). In obese adolescents, plasma FA were modified when undergoing a weight loss program which correlated to improvements in biomarkers related to metabolic disorders (114). However, one study found obese individuals and those with metabolic syndrome exhibited very minor differences in plasma FA status compared to healthy controls (111).

Further, a subgroup of obese individuals classified as “metabolically healthy” and disease-free when compared to “metabolically unhealthy” individuals, exhibited FA levels consistent with those at decreased risk of CVD (115). Kang et al. (110) found that only overweight individuals with an increased area of visceral fat had modified plasma FA profiles. Taken together, the use of obese but, otherwise healthy individuals for developing FA reference ranges may still impact interpretations of results and stratification may be necessary.

Sex-specific differences in individual FA status have been shown on a number of occasions (66,111,116,117). Independent of dietary intake, females have a greater genetic capacity to endogenously produce omega-3 PUFA due to increased Δ6-desaturase activity (118).

The use of oral contraceptives in some studies have found to further impact PUFA metabolism but, more recent studies found no significant effect (119,120). Additionally, pregnant and breastfeeding females have obvious metabolic differences and subsequent nutritional needs compared to males which has also been found to modify FA status (121). Whether these differences in FA status result in biologically significant relationships to disease biomarkers

requires further exploration. Based on these distinct sex-related differences in FA status, comparing the results of males and females to establish separate baseline values may be necessary.

Numerous studies have also found significant age-related changes in FA status (122). As our global population continues to grow older, FA metabolism has been found to be further modified by adaptations brought on by old age (27,123). Not only is dietary intake of FA decreased in the elderly, they can also suffer from deficient absorption of essential dietary nutrients even if intake quantities meet dietary standards (124,125). In particular, MUFA values have seen significant age-related decreases in dietary consumption via reduced Δ9-desaturase activity (126). Additionally, previous studies have seen age-related increases in plasma omega-3

PUFA across a number of populations globally (122,127–129). When compared to young individuals, elderly individuals have a greater capacity of incorporating omega-3 PUFA into blood plasma (129). As studies in elderly individuals showcase distinct dietary and genetic modifications which impacts FA status, considering the effect of age as modifying factor for developing reference ranges is necessary.

2.3.3 Geographical Differences

Dietary intake of FA is the strongest modifier of plasma FA status in humans (130). With advancements in technology and urbanization, we currently live in a time with the greatest access to largest variety of food products in human history (131). However, based on national recommendations, cultural preferences, and accessibility of certain products, the intake of specific nutrients and macronutrients can differ based on ones geographic location (132). When examined on a global scale, consumption of FA exhibit dramatic variance between, and even 30

within countries (10). Intakes of SFA in NA are similar to that of Australia, Russia, and many of the countries in Europe but, are consistently higher than that of China, and all countries in

Central and South America (10). Although global trends dictate trans-fats intake is decreasing,

NA still exhibits higher levels than a majority of the countries in the world (10,133).

Interestingly, some countries may exhibit similar trends in FA intake but, as the result of consuming different food sources (132). For example, omega-3 PUFA are most commonly consumed via seafood and to a lesser extent, plant-based foods such as flax (52). In NA, omega-

3 intake via seafood is quite low compared to Southeast Asia but, trends are reversed when examining plant-based consumption of omega-3 PUFA (10). As our global population has become more diverse and less homogenous, ethnic/cultural variances in diet are likely to exist within sampled populations on a national or even regional scale (132). In the context of developing FA reference ranges, these variances in dietary fat intake can have a significant effect on the generalizability of results between different cohorts. Whether a global value for individual

FA is possible or, region specific values must be used remains to be seen. It is necessary when profiling cohorts of different nationalities/ethnicities to assess what regional or cultural influences on diet may account for the variance in FA levels examined.

2.4 Conclusion

In conclusion, the development of individual FA reference ranges would help to address a fundamental gap within lipid research. Further unifying best practices for assessing and quantifying individual FA will help to improve reproducibility and the scale of which studies can be conducted. The use of blood plasma/serum samples as biomarkers of FA status quantified by

GC-FID have exhibited high throughput with cost-effective and consistent results which have

been validated in numerous studies. Reporting FA as absolute concentrations reduces the influence large fluctuations of individual FA may have on the trends of the pool analyzed and still allows for relative percentages to be calculated. Differences in both dietary intake and genetics can modify plasma/serum FA status. In healthy individuals, evaluating the relationship

BMI, sex, age, and variations in dietary status reflective of cultural/ethnic trends should be considered when conducting studies and comparing results. Overall, developing FA reference ranges would provide context to values reported throughout scientific literature and give clinical practitioners a simple and effective tool for evaluating chronic disease risk.

3 RATIONALE, OBJECTIVES, HYPOTHESIS, AND EXPERIMENTAL DESIGN

3.1 Rationale

Due to their structural and functional properties, lipids are vital to human health (2). To date, a number of studies have been published outlining the necessity of obtaining adequate dietary consumption of specific FA to promote optimal health and reduce chronic disease risk

(4). The use of blood biomarkers to assess dietary FA status is a commonly used, validated scientific procedure (52). However, interpretation of FA status for assessing chronic disease risk is currently limited due to the lack of established reference ranges. Established clinical ranges for other lipids such as cholesterol, triglycerides, and total free fatty acids are routinely utilized in clinical practices for assessing the risk of chronic disease (11). This gap in literature can be addressed by utilizing the current best practices in FA assessment and reporting to comprehensively profile the FA status of large cohorts. To our knowledge, very few studies have assessed FA with the intent of reporting reference ranges, and no studies have examined

Singaporean or Australian cohorts. Additionally, the reporting and methodology of such FA data is often inconsistent throughout literature (12). Thus, we will focus on using consistent methodological approaches that align with current best practices to improve reproducibility and generalizability of FA values between studies.

3.2 Objectives

The overall objective of this thesis is to assess the relationship between individual FA and chronic disease biomarkers to add valuable knowledge to the growing work towards establishing validated FA reference ranges. Secondly, by evaluating different international cohorts it would

give us an opportunity to profile FA status in that region and evaluate what impact region specific diet and cultural factors may affect FA values. Specific objectives and hypotheses include:

1. Objective 1. To examine the relationship between serum FA values and aging in

Australian men and how reporting results as an absolute concentration or percent

composition may impact the interpretation of results.

i. Hypothesis 1. Overall FA status in Australian males will exhibit negative

correlations as a function of age but, omega-3 PUFA will be positively

correlated with age.

2. Objective 2. To comprehensively profile serum FA concentrations of Singaporean adults

and investigate their association with established lipid biomarkers and health outcomes.

ii. Hypothesis 2. Saturated and unsaturated fatty acids will be positively and

negatively associated with the lipid biomarkers, respectively. It is anticipated

that a comprehensive analysis will reveal that novel, minor fatty acids, may also

differentially influence lipid biomarkers and health outcomes.

3.3 Experimental Design

International Cohorts:

Blood plasma and serum samples were obtained via the Men Androgen Inflammation

Lifestyle Environment and Stress (MAILES) from the University of Adelaide in Australia, and via the Agency for Science, Technology and Research (A*STAR) in Singapore (134,135). The

MAILES cohort was established in 2009 by combining the population of two studies: the Florey

Adelaide Male Ageing Study and North West Adelaide Health Study (134). The entire cohort consisted of ~2600 males with data collected from 2002-2010 to investigate associations such as sex steroids, and psychosocial factors with cardio-metabolic disease risk (134). The Singaporean cohort consisted of ~450 participants with data collected from 2014-2017 to evaluate various measures of body composition in an Asian population (135).

Gas Chromatography:

A modified Folch method was used for lipid extraction and derivation of plasma/serum samples which has been described in-detail previously (77). In brief, samples were first thawed on ice, where 50µl of plasma/serum was pipetted into a 15ml tube. Stock solution of 2:1 chloroform:methanol (v:v) with a 3.33 µg/mL of internal standard (C19:0 free fatty acid) was prepared and added to the tube. Samples were spun and the lower chloroform layer was transferred into a clean 15 ml glass tube and dried down under a gentle stream of nitrogen. 300

o µL of hexane and 1 ml 14% BF3-MeOH was added and followed by methylation at 100 C for 60 minutes in oven. 1 mL of double-distilled water and 1 mL hexane was added to stop the methylation process. Sample was once again spun down to separate phases and top hexane layer was then extracted and then reconstituted in 400µL of hexane. Total FA were quantified on an

Agilent 7890-A GC-FID and separated on a SP-2560 fused silica capillary column (100m x

0.25mm x 0.2um) (Sigma, Cat # 24056). Total run time was ~49.78 minutes per sample. Peaks were identified and chromatographs were analyzed via OpenLab CDS EZChrom Edition 3.2.1 software. C19:0 internal standard was used to derive absolute and relative FA concentrations from the results obtained from the exported chromatographs. 35

Statistical Analysis:

Statistical analysis was conducted primarily using SAS University Edition (SAS Institute

Inc.; Cary, North Carolina). In Chapter 4, a Tukey’s HSD test was used to determine significant differences between biomarkers and FA concentrations of participants stratified by age.

Additional analysis was conducted using R version 3.6.12 (R Foundation for Statistical

Computing, Vienna, Austria). A multiple regression analysis was conducted to determine the effect of ageing on each FA including multiple covariates for all participants. Correlations between individual FA concentrations and composition with age were expressed as R. In Chapter

5, a Student t-test was used to determine statistical differences between male and female participants. A linear regression model was conducted to assess the relationship between FA concentrations and lipid biomarkers. Correlations were reported as R2 and standardized parameter estimates/beta-weight (β) of age. Sixty-seven total FA were included in the bulk of the analysis with ten FA of interest selected for more detailed analysis. A p-value of <0.05 was considered statistically significant.

4 AGING IS ASSOCIATED WITH DIFFERENTIAL CHANGES OF CIRCULATING SERUM FATTY ACID LEVELS IN AUSTRALIAN MALES 4.1 Abstract

Advanced aging is often implicated in a number of detrimental metabolic changes and modified dietary habits that can lead to fluctuations of individual FA status as one ages. With the number of individuals >65 years old continuing to increase globally, effective monitoring of changes in FA status can prove to be a useful tool for assessing adverse health status. Sixty- seven individual FA were analyzed using GC-FID from serum samples collected from 870

Australian males ranging in age from 40-85 years old. Individual FA status was reported as both percent composition (%) and absolute concentration (μmol/L). In terms of percent composition, twenty-two FA exhibited a significant correlation with age (<0.05), with eleven FA exhibiting negative trends. In terms of absolute concentration, forty-two FA exhibited a significant correlation with age (p<0.05), with only the omega-3 PUFA EPA and DHA exhibiting significant positive trends in both measures. Ten biologically relevant FA were further stratified by decade (40-49 y, 50-59 y, 60-69 y, 70+ y) where a distinct negative correlation with age beginning at the 50-59 y decade was seen. Overall, metabolic and/or dietary factors associated with aging negatively impacts individual serum FA status, excluding positive trends seen with

EPA and DHA, and is most accurately assessed using absolute concentration rather than percent composition.

4.2 Introduction

As our global population continues to age, substantial declines in cognitive status coupled with increased rates of chronic disease such as CVD, rheumatoid arthritis, diabetes and many other ailments are commonly seen (136–139). In Australia, the 65 year-old+ cohort has increased to over 15% of the total population and the ≤19 year-old cohort has been in decline over the last decade (140). CHD was the leading cause of death in Australia in 2012, and there is an estimated

1.2 million Australians currently diagnosed with a CVD-related ailment (136,141). A number of studies have found significant age-related changes in circulating FA status which from both a structural and functional standpoint, play a vital role in the maintenance of human health. (27).

Throughout the body, cell membranes are comprised of FA that are an essential component for proper functioning and aid in processes such as inflammation, cell signaling, growth, and development (1,142). An immense body of work has been published detailing the importance of maintaining healthy FA status in chronic disease states such as obesity, cancer, Alzheimer’s disease, and CVD (143–146). Increasing omega-3 PUFA status has shown to decrease levels of serum triglycerides and overall risk of CHD mortality and cardiovascular events (147). Increased omega-3 PUFA consumption has also been found, in certain instances, to improve declines in cognitive function with old-age and may also have a beneficial effect in the early stages of

Alzheimer’s disease (148,149). As the elderly are quickly becoming the fastest-growing segment of the population globally, adequate consumption and monitoring of FA status is a valuable clinical tool for assessing health risks associated with advanced aging (123).

Assessing whether adequate quantities of FA are being consumed poses a unique set of challenges however, as estimates from dietary assessments such as diet records are imprecise

compared to biological measurements (13). Modifying dietary intake is only one of many factors which impact FA status, as altered metabolism brought on by chronic disease states and genetics also play a role in altering FA levels (3,94,122). For example, our bodies are able to be endogenously produce certain SFA and MUFA via the use of Δ9-desaturase, whereas certain long-chain PUFA are essential and must be consumed via the diet (3). EPA and DHA can also be produced endogenously but, have very limited conversion rates of 1% or less (8). Long-chain

PUFA therefore exhibit a stronger correlation to dietary intake compared to SFA and MUFA based on our individual capacity to produce Δ9-desaturase (3). Additionally, genetic carriers of the apoE e4 allele are not only at an increased risk of Alzheimer’s but, exhibit reduced levels of

EPA and DHA following PUFA supplementation (150). Whole blood and its components: plasma, serum, erythrocytes, etc. are commonly analyzed biomarkers to determine FA levels which exhibit strong correlations to dietary intake of FA (42). Blood samples act as an aggregate reflecting both dietary and genetic/metabolic factors, providing a more specific assessment of FA levels than diet records alone. Consistent monitoring of FA status could provide valuable information for assessing trends in elderly adults which may be the result of age-related changes if dietary status remains unchanged.

Reporting of individual FA status is inconsistent throughout scientific literature. Gas chromatography is a common method where fasted blood is analyzed to determine individual FA levels (77). Individual FA levels representative of whole-body status in a fasted state are either reported as a relative percent composition (%), the proportion of one FA out of the entire pool analyzed, or as an absolute concentration (eg. μmol/L, μg/mL) (12). Currently, there is no standardized list for which FA must be included in the pool analyzed, with some studies

reporting <5 and others >60 FA (12). The number of individual FA able to be analyzed at one time is dependent on the column utilized during gas chromatography; a longer column allows for more separation in the gas phase and a greater amount of FA to be detected compared to a shorter column (86). As relative measures are based on the percentage of the total pool of FA included in analysis, the smaller the pool analyzed results in a larger apparent percentage of individual FA (12). Large fluctuations in one FA could also alter the subsequent percent composition seen in other FA included in the same pool when no biological changes were actually seen. Absolute concentrations, however, are not directly influenced by the status of other

FA and thus, may be more indicative of true trends seen in individual FA. This study aims to examine age-related effects on the status of sixty-seven FA in Australian males. FA status will be evaluated as both absolute concentration and percent composition to assess the similarities and differences between each measure during the aging process.

4.3 Materials and methods

4.3.1 Participants

Participants have been previously described by (134). In brief, the MAILES cohort consisted of Australian males who were 40-85 years of age (n=870) and had completed a 10 to

12 hour fasting procedure prior to blood collection. Participants were later sorted by decade of life: 40-49 y: n=197, 50-59 y: n=246, 60-69 y: n=237, 70+ y: n=191. Each participant had blood drawn that was treated by Grant et al. (134) to separate blood serum. Informed consent forms have been previously completed during the MAILES cohort data collection period (134).

Samples were stored at -80°C as previously discussed by (94). Eight years after initial blood

sample collection serum samples were analyzed at the University of Guelph where they underwent total serum FA analysis via GC-FID.

4.3.2 Gas Chromatography Protocol

CHCl3: MeOH was added to the tube and capped tightly with a phenolic PTFE liner. After a one minute of vortex, 550µL of 0.1M KCl was added to each tube and briefly vortexed again for another 5-10 seconds. Next, samples were spun at 1460 rpm for 10 min (at 21°C) to separate phases. The lower chloroform layer was transferred into a clean 15 ml glass tube and dried down under a gentle stream of nitrogen. After, 300 µL of hexane and 1 ml 14% BF3-MeOH was added and followed with a vortex of 5-10 seconds. Methylation was then completed at 100oC for 60 minutes in oven, where it was then cooled for 10 minutes at room temperature. Next, 1 mL of double-distilled water and 1 mL hexane was added to stop the methylation process. After a one- minute vortex, the sample was once again spun down at 1460 rpm for 10 min (at 21°C) to separate phases. The top hexane layer was then extracted into a clean 2 ml GC vial, dried down under a gentle stream of N2 and then reconstituted in 400µL of hexane.

Total FA were quantified on an Agilent 7890-A gas chromatograph equipped with flame ionization detection and separated on a SP-2560 fused silica capillary column (100m x 0.25mm x

0.2um) (Sigma, Cat # 24056). The splitless inlet (pressure of 19.5 psi and a hydrogen flow of

13.7 mL/min) and detector was set at 250°C (hydrogen flow of 30mL/min, air flow of 41

450mL/min and nitrogen flow of 10mL/min), where 1uL of sample was injected onto a splitless inlet set. The oven was initially set at 60°C and increased by 13°C/min to 170°C, where it was held for 4 minutes, and then further increased by 6.5°C/min to 175°C (with no hold), by

2.6°C/min to 185°C (with no hold), 1.3°C/min to 190°C (with no hold), and13.0°C/min to 240°C where it was held for 13 minutes. Total run time was 49.78 min per sample.

4.3.3 Fatty Acid Analysis

Peaks were identified and chromatographs were analyzed via OpenLab CDS EZChrom

Edition 3.2.1 software. An internal standard (C19:0) was used to calculate FA concentrations

(μmol/L) and percent composition (%). In total, sixty-seven total FA were included in the analysis.

4.3.4 Statistical Analysis

Statistical analysis for Table 4.1 was conducted using SAS University Edition (SAS

Institute Inc.; Cary, North Carolina). A Tukey’s HSD test was used to determine significant differences between age when participants were stratified by decade of biomarkers and FA concentrations. Results are expressed as mean ± standard deviation (SD). All remaining analyses was conducted using R version 3.6.12 (R Foundation for Statistical Computing, Vienna,

Austria). Correlations between individual FA concentrations and composition with age was expressed as a correlation coefficient (R). Of the sixty-seven analyzed, ten major FA to further examine correlations between FA status and specific decades of aging. These FA include: palmitic acid (C16:0), palmitoleic acid (PAO;C16:1c9), stearic acid (C18:0), oleic acid

(C18:1c9), LA (C18:2n6), ALA (C18:3n3), EPA (C20:5n3), docosapentaenoic acid

(DPA;C22:5n3) DHA (C22:6n-3) and ARA (C20:4n6). A multiple regression analysis was 42

conducted with the following covariates included in the model: smoking, blood pressure medication, depression, anxiety, asthma, BMI and osteoarthritis. A p-value of <0.05 was considered statistically significant for all analysis.

4.4 Results

Table 4.1 General characteristics of study population

Total Population 40-49 50-59 60-69 70+

Population (#) 870 197 246 237 191

Age (yrs) 60 ± 11.16 / / / /

BMI (kg/m2) 28.76 ± 4.52 28.53 ± 4.43 29.07 ± 4.59 29.39 ± 4.93 27.84 ± 3.82c

Glucose (mmol/L) 5.00 ± 1.56 5.14 ± 1.03 5.45 ± 1.73 5.65 ± 1.63a 5.61 ± 1.63a

Insulin (pmol/L) 10.00 ± 8.88 8.86 ± 8.82 9.20 ± 7.42 10.85 ± 10.82 9.45 ± 7.78

HDL Cholesterol (mmol/L) 1.30 ± 0.31 1.28 ± 0.27 1.32 ± 0.32 1.30 ± 0.30 1.37 ± 0.34a

LDL- Cholesterol (mmol/L) 3.10 ± 0.98 3.39 ± 0.90 3.31 ± 0.91 2.97 ± 0.96b 2.72 ± 1.00d

Total Cholesterol (mmol/L) 5.20 ± 1.10 5.45 ± 0.99 5.41 ± 1.06 5.02 ± 1.12b 4.76 ± 1.10b

Triglycerides (mmol/L) 1.72 ± 1.02 1.86 ± 1.12 1.81 ± 1.20 1.69 ± 0.85 1.51 ± 0.78a

Total Serum Fatty Acids 13524.71 ± 14409.61 ± 13885.52 ± 13350.5 ± 12363.46 ± (μmol/L) 4120.15 4509.71 4058.98 4072.61a 3539.84b

Data represented as Mean±SD A p-value of <0.05, determined by Tukey's HSD for differences between groups, was considered statistically significant aSignificant compared to 40-49 bSignificant compared to 40-49 and 50-59 cSignificant compared to 50-59 and 60-69 dSignificant compared to 40-49, 50-59 and 60-69

4.4.1 General characteristics and lipid biomarkers

In Table 4.1, the anthropometric measures, biomarkers, and FA concentrations of 870

Australian males are described. To examine age-related effects, participants were stratified by age per decade. As participants aged, lipid biomarkers, such as triglycerides (TG), low-density lipoprotein (LDL), and total cholesterol significantly declined. High-density lipoprotein (HDL) cholesterol increased significantly in those aged 70+ y compared to those aged 40-49 y. Fasting glucose increased from 40-49 y to 60-69 y before significant decreases occurred at decade 70+ y.

Mean FA concentrations decreased significantly as participants aged, from 14409.61μmol/L at

40-49y to 12363.46 μmol/L at 70+ y. BMI increased from 40-49 y (28.53kg/m2) to 60-69 y

(29.39 kg/m2) before significantly decreasing at 70+ y (27.84 kg/m2).

Table 4.2 Correlation between serum fatty acids and age Concentration Composition (%) (μmol/L) Fatty Common Name Correlation p-value Correlation p-value Acid (R) (R) -0.064 0.058 -0.032 0.348 12:0 Lauric acid 12:1c11 t t t t 14:0 Myristic Acid -0.149 <0.0001* -0.092 0.006* 14:1c9 Myristoleic acid -0.073 0.0301* -0.034 0.311 -0.103 0.0023* 0.070 0.0378* 15:0 Pentadecanoic acid 15:1c10 0.047 0.166 0.047 0.166 16:0 Palmitic acid -0.170 <0.0001* -0.010 0.773 16:1t9 Palmitoleic acid -0.077 0.0222* -0.036 0.285 16:1c9 Palmitoleic acid -0.101 0.0028* -0.035 0.307 -0.103 0.0024* 0.067 0.0486* 17:0 Margaric acid 17:1c10 t t t t -0.205 <0.0001* -0.018 0.599 18:0 Stearic acid 18:1t4 -0.002 0.958 0.038 0.266 18:1t5 Thalictric acid -0.009 0.788 0.005 0.885 18:1t6-8 Petroselinic acid -0.147 <0.0001* -0.102 0.0025* -0.177 <0.0001* -0.119 0.0004* 18:1t9 Elaidic acid 18:1t10 -0.071 0.0367* -0.048 0.156 -0.120 <0.0001* -0.061 0.071 18:1t11 trans-Vaccenic acid 18:1t12 -0.161 <0.0001* -0.100 0.0030*

18:1t13 -0.144 <0.0001* -0.061 0.074 18:1c9 Oleic acid -0.177 <0.0001* -0.066 0.0505* 18:1c11 cis-Vaccenic acid -0.029 0.390 0.078 0.0209* Octadecenoic acid -0.092 0.0064* -0.035 0.297 18:1c12 (cis- 12) 18:1c13 -0.026 0.443 -0.026 0.443

18:1t16 -0.100 0.0030* -0.057 0.090

18:1c14 0.050 0.140 0.057 0.091

18:2tt -0.089 0.0090* -0.043 0.204 -0.143 0.0057* -0.035 0.498 18:2t9t12 Linoelaidic acid 18:2c9t13 -0.056 0.100 -0.048 0.161

18:2ct -0.165 <0.0001* -0.075 0.0273*

18:2c9t12 -0.107 0.00155* -0.012 0.732

18:2t9c12 -0.127 0.0002* -0.066 0.051

19:1c10 -0.027 0.429 -0.027 0.428 -0.241 <0.0001* -0.079 0.0201* 18:2n6 Linoleic acid 18:2c9c14 -0.032 0.341 0.012 0.722 18:2c9c15 Mangiferic acid -0.023 0.489 -0.002 0.956 20:0 Arachidic acid -0.126 0.0002* 0.058 0.086 -0.229 <0.0001* -0.157 <0.0001* 18:3n6 Gamma-linolenic acid 20:1c5 -0.117 0.0005* -0.059 0.081

20:1c8 -0.039 0.251 0.017 0.620 20:1c11 Gondoic acid -0.097 0.0040* 0.034 0.319 18:3n3 Alpha-linolenic acid -0.059 0.083 0.080 0.0180* 18:2c9t11 -0.126 0.0002* -0.008 0.821 Rumenic Acid CLA Conjugated linoleic -0.016 0.632 0.035 0.305 18:2c11t13 acid 18:2t10c12 Conjugated linoleic -0.049 0.150 -0.018 0.605 CLA acid 18:2c/c Conjugated linoleic -0.003 0.938 0.030 0.376 CLA1 acid 18:2c/c Conjugated linoleic -0.052 0.123 -0.011 0.755 CLA2 acid 18:2tt Conjugated linoleic -0.091 0.0071* -0.005 0.876 CLA acid 18:4n3 Stearidonic acid -0.051 0.129 -0.047 0.161 21:0 Heneicosylic acid -0.146 0.0045* -0.045 0.387 20:2n6 Eicosadienoic acid -0.051 0.135 0.081 0.0171* 22:0 Behenic acid -0.142 <0.0001* -0.010 0.775 20:3n9 Mead acid -0.106 0.0017* -0.028 0.407 Dihomo-gamma- -0.234 <0.0001* -0.089 0.0086* 20:3n6 linolenic acid 47

22:1n9 Erucic acid -0.070 0.0391* -0.045 0.180 20:3n3 Eicosatrienoic acid -0.070 0.0394* -0.041 0.226 23:0 Tricosylic acid 0.043 0.207 0.092 0.0066* 20:4n6 Arachidonic acid -0.185 <0.0001* 0.016 0.640 22:2n6 Docosadienoic acid -0.066 0.051 0.073 0.0308* 24:0 Lignoceric acid -0.129 0.0001* 0.006 0.864 20:5n3 Eicosapentaenoic acid 0.187 <0.0001* 0.248 <0.0001* 24:1n9 Nervonic acid 0.011 0.746 0.141 <0.0001* 22:3n3 Docosatrienoic acid t t t t 22:4n6 Adrenic acid -0.238 <0.0001* -0.117 0.0006* Docosapentaenoic -0.194 <0.0001* -0.069 0.0417* 22:5n6 acid (Osbond acid) Docosapentaenoic -0.013 0.701 0.227 <0.0001* 22:5n3 acid (Clupanodonic acid) 22:6n3 Docosahexaenoic acid 0.190 <0.0001* 0.336 <0.0001*

t= trace amount

*Significant at the level p<0.05

4.4.2 Correlation between fatty acids and age

The relationship between aging and sixty-seven individual FA is described in Table 4.2. In terms of concentration, forty-two FA reached significance with respect to age, with only EPA and DHA exhibiting positive correlations. In terms of relative percentage, twenty-two FA reached significance with respect to age, with eleven FA exhibiting positive correlations. Fifteen

FA reached statistical significance in both absolute and relative measures. Only two SFA, pentadecanoic acid (C15:0) and margaric acid (C17:0), were statistically significant in both measures but, exhibited the opposite direction correlations to age when compared between measures. Sixteen total FA did not reach statistical significance when reported as either measure, namely: lauric acid (C12:0), thalictric acid (C18:1t5), mangiferic acid (C18:2c9c15), stearidonic

acid (C18:4n3), and a majority of the conjugated linoleic acids. Trace (t) values were either zero or levels below the limit of detection (LOD) (0.05 µg/ml) of the Agilent 7890-A GC-FID as determined by our internal assessments.

Table 4.3 Correlation and multiple regression of serum fatty acids and age when stratified by decade Absolute Concentration 40-49 50-59 60-69 70+ (n=197) (n=246) (n=237) (n=191) Fatty Correlation p- Correlation p- Correlation p-value Correlation p- Acid (R) value (R) value (R) (R) value 16:0 0.076 0.542 -0.112 0.109 -0.169 0.017* -0.169 0.045* 18:0 -0.003 0.666 -0.09 0.115 -0.152 0.038* -0.164 0.043* 16:1c9 0.160 0.107 -0.085 0.306 -0.074 0.214 -0.136 0.095 18:1c9 0.016 0.891 -0.175 0.012* -0.191 0.009* -0.145 0.125 18:2n6 -0.001 0.929 -0.028 0.735 -0.172 0.019* -0.094 0.258 20:4n6 -0.001 0.758 -0.105 0.095 -0.148 0.034* -0.030 0.899 18:3n3 0.010 0.970 -0.068 0.439 -0.031 0.418 -0.086 0.333 20:5n3 0.062 0.301 0.069 0.430 0.101 0.077 -0.062 0.113 22:5n3 0.023 0.831 -0.038 0.719 -0.017 0.811 -0.124 0.016* 22:6n3 0.051 0.416 0.053 0.802 0.109 0.084 -0.058 0.112 Percent Composition 16:0 0.143 0.136 -0.097 0.222 -0.04 0.483 -0.093 0.315 18:0 -0.155 0.032* 0.043 0.986 0.078 0.282 0.028 0.907 16:1c9 0.200 0.031* -0.059 0.698 0.006 0.889 -0.116 0.224 18:1c9 -0.020 0.563 -0.157 0.038* -0.109 0.132 -0.073 0.816 18:2n6 -0.072 0.602 0.137 0.056 -0.001 0.884 0.073 0.388 20:4n6 -0.069 0.348 0.019 0.866 0.026 0.865 0.127 0.096 18:3n3 -0.018 0.975 0.014 0.612 0.077 0.545 0.019 0.761 20:5n3 0.018 0.579 0.115 0.181 0.180 0.004* -0.018 0.313 22:5n3 -0.032 0.885 0.100 0.106 0.214 0.004* 0.024 0.389 22:6n3 0.004 0.685 0.149 0.137 0.254 0.000* 0.052 0.801 Multiple regression model included the covariates: BMI, smoking, blood pressure medication, depression, anxiety, asthma, and osteoarthritis. *Significant at the level p<0.05, values indicate significance to multiple regression model

4.4.3 Absolute concentration when stratified by decade

Of the 67 FA analyzed, due to their implications to health and prominence in the diet, ten were chosen to undergo further analysis stratified by decade of age starting at age 40 y, as described in Table 4.3. Stratification by decade was conducted to assess whether distinct

“breakpoints” in FA exist as one ages.

The SFA, PA and SA, were the only FA to exhibit negative correlations between age and

FA status the latter two decades, 60-69 y and 70+ y, and reach significance. The two MUFA, oleic acid (18:1c9, OA) and palmitoleic (16:1c9, PAO) were negatively correlated in the decades starting from age 50 y, but only OA reached statistical significance in the decades 50-59 y and

60-69 y (p=.012; p=.009). Both omega-6 PUFA, LA and ARA, were negatively correlated in each decade starting at age 40 y and only was found to have a significant p-value in the decade,

60-69 y. The omega-3 PUFA, ALA and DPA, were negatively correlated with age but, only

DPA at decade 70+ exhibited a significant p-value (p=.016). In the decades from 40-49 y to 60-

69 y, EPA and DHA exhibited a position correlation to age (R=0.062-.101; R=0.051-0.109) before becoming negatively correlated at age 70+ y. In summary, EPA and DHA were the only two FA to be positively correlated with age while all other FA exhibited negative correlations between age and FA concentration.

4.4.4 Percent composition when stratified by decade

The two SFA, PA and SA, exhibited opposite trends. PA was initially negatively correlated with age in the 40-49 y decade but, was then seen to be positively correlated in all subsequent decades. In contrast, SA was negatively correlated to age at decade 40-49 y before becoming positively correlated with age in all subsequent decades. SA was found to have a significant p-value in the 40-49 y decade, (R=-0.155; p=.032). The MUFA, PAO exhibited 51

inconsistent direction of correlation, exhibiting a positive correlation at decade 40-49 y (R=0.2; p=.031) before exhibiting a negative correlation at decade 70+ y (R=-0.116; p=.224). In contrast,

OA, exhibited consistent negative correlations between FA status and age. Only the decade 50-

59 y for OA exhibited a statistically significant p-value (R=-0.157; p=.038). Trends in the omega-6 PUFA, LA and ARA varied with age. LA alternated between negative and positive correlations between decades, while ARA was positively correlated with age starting in the decade starting at age 50 y. The omega-3 PUFA, ALA was positively but, weakly correlated by decade (R=-0.018-0.077). The omega-3 PUFA, EPA, DPA, and DHA, all exhibited increasingly positive correlations from decade 50-59 y to 60-69 y and the strength of the correlation weakened at decade 70+ y. EPA, DPA, and DHA were the only FA to exhibit a statistically significant p-value between age and FA status at decade 60-69 y or 70+ y.

4.4.5 Comparison between absolute concentration and percent composition

Only the SFA, PA, exhibited the same direction of correlation when expressed as concentration and percent composition. Whereas OA from the decade 50-59 y onwards exhibited the conflicting directions of correlation: a negative correlation as an absolute concentration and positive correlation in terms of percent composition. Both MUFA exhibited nearly identical directions of correlation between each measure. Statistically significant p-values were seen more often when evaluated as an absolute concentration. Both omega-6 PUFA, LA and ARA, from decade 50-59 y onwards exhibited opposite directions of correlations by decade when examined in both measures. Among the four omega-3 PUFA, ALA and DPA exhibited opposite directions of correlations between measures at each decade, whereas EPA and DHA, exhibited nearly identical directions of correlations in both measures. Similar to the other FA at decade 50-59 y, a

distinct negative shift was seen for both EPA and DHA in both measures at decade 70+ y.

Overall, directions of correlation between age and FA status were more consistent when measured as an absolute concentration compared to percent composition.

Additional sensitivity analysis was also conducted but, not reported. Removal of the top and bottom 5th percentile of participants based on mean FA status yielded statistically similar results. In a separate analysis when individuals with a BMI of >25kg/mg2 were excluded, statistically similar results were seen compared to the use of the entire cohort. Thus, individuals with a BMI of >25kg/m2 were included in our analysis.

4.5 Discussion

Among the sixty-seven FA analyzed in this study, forty-two FA reached statistical significance when assessed in terms of absolute concentration, and twenty-two FA when assessed as percent composition. Fifteen FA exhibited statistically significant results in both absolute concentration and percent composition (Table 4.2). Stratification by decade revealed that negative correlations to age often started by the 50-59 y decade. This negative correlation persists once entering the 60-69 y decade, where R becomes larger and the greatest number of significant values of any decade was seen. This is not surprising as elderly adults typically eat less and often see a shift in their dietary choices compared to younger individuals (151,152).

Mean FA concentrations across each decade can be seen in Table 8.3 of Appendix B and support this notion of declining FA status. EPA and DHA were the only FA that exhibited a positive correlation to aging in both measures unlike all other FA which exhibited negative correlations at the 50-59 y decade. A number of FA exhibited conflicting correlations between measures and were consistently shown to be negatively correlated to age when expressed in absolute 53

concentration but, positively as percent composition. When FA status was assessed across decades, values expressed as percent composition exhibited far more variable direction of correlation compared to that of absolute concentrations (Table 4.3). Overall, the strength of the correlations seen across this study were relatively low but, still provide relevant information regarding FA status with aging.

4.5.1 Saturated Fatty Acids

SFA as a whole exhibited a large number of negative correlations with age in both absolute and relative measures. Our results indicate a shift in dietary and/or metabolic factors begins to occur at ~50 y in Australian males which SFA status was impacted by, as seen in Table

8.3. Although we do not have dietary data to report, a study by Waern et al. (151) found 2/3 of elderly Australian men (75+ y) examined consumed a higher percentage of SFA than recommended. Compared to the rest of the world, few countries consume more SFA on average than Australian adults (10). Palm oil, which contains high-levels of PA, is the second most consumed oil in Australia behind only canola oil, which contains high-levels of OA (153).

Decreases in LDL and total cholesterol were seen in the older decades which is commonly found with decreased SFA intake (Table 4.1) (154). As the elderly typically consume less calories on average (124), the actual quantity of SFA consumed, even if the relative percentage is high, could still likely be lower than that of a middle-aged male. Unfortunately, based on the current results we are unable to distinguish the specific contributions of either diet or metabolic factors on SFA levels.

When correlations of SFA to age as an absolute concentration and percent composition were compared (Table 4.2), a number of conflicting results were seen. When SA was further 54

examined by decade, a negative correlation to age from 40-49 y was seen followed by consistent positive correlations from 50-59 y to 70+ y when measured as percent composition. However, when measured as an absolute concentration, PA and SA were the only FA to maintain negative correlations to age from decade 60-69 y onwards, matching the results seen in Table 4.2. Due to the prevalence of SFA in the Australian diet, relative percentage of PA and SA compared to the rest of the diet could see modest increases with a decline in overall calorie/FA consumption as one ages (124). Thus, the use of absolute concentration or percent composition plays a significant role in the biological interpretation of dietary FA status, as trends of SFA relative to the rest of the FA pool are not indicative of actual concentration. Based on our results we can conclude serum SFA modestly correlates to aging when assessed as an absolute concentration.

4.5.2 Monounsaturated Fatty Acids

Of the large number of MUFA examined in this study, almost all MUFA exhibited negative correlation with age (Table 4.2). cis-Vaccenic acid (18:1 c11) is a minor FA and was the only MUFA to exhibit a positive correlation with age (R=0.078; p=.02) in either measure. trans-Vaccenic acid is commonly found in dairy products which is often consumed by elderly individuals, however, cis-vaccenic acid is not found in the same sources (155). Mangos are the most common food source containing cis-vaccenic acid with small amounts also being detected in vegetable seed oils (156). PAO can be converted to cis-vaccenic acid but, without dietary records or markers of endogenous lipid production we are unable to say for certain the cause of the positive correlation seen (157).

Similar to the SFA previously discussed, a distinct negative shift occurs beginning at the

50-59 y decade, where OA exhibited negative correlations with age in both measures. These 55

results once again indicate a shift in dietary and/or metabolic factors may begin to occur at ~50 y in select SFA and MUFA. The similarity between SFA and MUFA could be attributable in part through metabolism, as MUFA can be synthesized via substrates derived from the interaction of the Δ9-desaturase enzyme and SFA (3). Previous experimental models (158) and surrogate measures of Δ9-desaturase in humans (126) have shown significant age-related decreases in enzymatic activity, reducing the capability of SFA to be converted to MUFA and subsequent FA levels. Due to the close biological relationship of the four SFA and MUFA evaluated, seeing negative correlations in FA status between both FA classes during aging is not surprising. Unlike

SFA, a majority of the MUFA exhibited similar directions of correlation in both measures of absolute concentration and percent composition. This may be due to the overall smaller concentrations and percent composition of circulating MUFA, resulting in a smaller dynamic range of values that are less impacted by the relative abundance of other fatty acids. Based on the results presented we conclude a consistent and modest negative correlation of MUFA status to age is seen in Australian males.

4.5.3 Omega-6 Polyunsaturated Fatty Acids

A majority of the omega-6 PUFA assessed in our study exhibited a negative correlation to aging in at least one measure and displayed the strongest correlations to aging (Table 4.2).

However, only docosadienoic acid (22:2n6) and eicosadienoic acid (20:2n6) were positively correlated with age. Eicosadienoic acid has been found to increase with consumption of LA but,

LA exhibited a negative correlation to age (159). Due to the relatively small values of these two omega-6 PUFA found in the diet, the positive correlations could be due to the decrease of other

FA in the pool, resulting in relative increases of the percent composition of other FA.

At the 70+ y decade, correlations between LA and ARA status and age in both measures were slightly higher compared to the preceding decades. When we compared the mean concentrations of ARA in the 40-49 y decade to the 70+ y decade, the values were found to be significantly lower in the 70+ y decade (calculation not shown). A decrease in ARA serum status with aging is similar to results seen in past studies (152,160). Saga et al. (161) also found in

Scandanavian adults those who had higher omega-3 PUFA status exhibited a decrease in ARA status, which matches the correlations with age and results of Table 8.3 seen between the two classes in our study (see Table 4.3). A recent shift in the North American-style diet which has been largely adopted by Australians is associated with a significant influx of omega-6 PUFA consumption and a decrease in omega-3 PUFA consumption yet, the opposite is seen with aging in this cohort (162). A survey conducted on older Australians found as participants aged, they consumed fewer overall calories which typically came from reducing meat and dairy consumption (124). Common food sources of omega-6 PUFA, primarily ARA, almost exclusively derive from animal products such as meat, dairy, and eggs (163). These changes in dietary habits could likely be the cause of the consistent negative correlation with age seen in the absolute concentration of both LA and ARA.

Omega-6 PUFA are often described as pro-inflammatory and detrimental to human health but, LA and ARA can both exert their own additional independent properties, suggesting this could be an overly simplistic view (162). ARA is second only to DHA in terms of concentration in the brain and increased dietary consumption has found to increase risk of neurogenerative disease and cognitive dysfunction in elderly participants (164). In older adults, dietary ARA consumption has actually been found to reduce the risk of hip fractures in men

(165). However, the minor benefits of ARA are out-classed by the capabilities of its omega-3

PUFA counterparts. Thus, the negative correlations seen in concentrations of both LA and ARA with aging are similar to previous studies and is indicative of modifications to the overall lipid profile which supports the reduction of specific health-risks commonly seen in the aging population.

When compared to all other FA classes the omega-6 PUFA exhibited the largest number of discrepancies between direction of correlation when compared between absolute concentrations and percent composition These contradictions highlight the need to better understand whether absolute concentration or percent values are more relevant for assessing the health of older adults. When the mean concentrations of ARA at the 40-49 y and 70+ y decade were compared, concentrations were lower at 70+ y (Table 8.3). This supports the negative correlation of omega-6 PUFA to age seen when measured as an absolute concentration. The positive correlation of ARA when assessed as percent composition could be due the decrease of the relative mean FA pool as seen in Table 4.1. Although concentrations of ARA were shown to decrease, the relative decrease of the entirety of the FA pool was so large it produced a false positive when expressed as percent composition, which other studies have seen previously with omega-6 PUFA (166). As our results conclude omega-6 PUFA status decline with age, this provides further reasoning for the use of absolute concentrations over percent composition for the accurate interpretation of FA status related to health status.

4.5.4 Omega-3 Polyunsaturated Fatty Acids

The long-chain omega-3 PUFA EPA and DHA were the only two FA to exhibit positive correlations with age within both measures, (Table 4.2). When further examined by decade, 58

correlations of EPA and DHA became increasingly positive from decade 40-49 to 60-69 (Table

4.3). Our study is one of the largest to-date to examining the relationship of FA status and aging, and one of few to further examine the correlations seen by individual decade in this level of detail. To our knowledge, no other study has examined the relationship of plasma/serum omega-

3 PUFA concentrations and aging, without a supplement, in in a cohort of this size. Correlations of omega-3 PUFA status to age seen in our study support the results of previous studies which have seen a similar age-related increases in blood levels of EPA and DHA across a number of populations globally (122,127–129,161,167). It is speculated that this data reflects an adaptive mechanism to utilize blood and/or adipose stores of omega-3 PUFA that could be occurring with advanced aging. Omega-3 PUFA are mobilized from adipose/blood stores of the mother during in-utero development of the fetus, and to breast milk postnatally, to support the growth of the child’s brain (168). In the latter years of life, a similar phenomenon could be occurring where older adults are mobilizing EPA and DHA from adipose/blood to the brain to support cognition.

However, at some point in the 70+ y decade the mechanism of recruiting omega-3 PUFA from adipose stores could be starting to fail. We saw a negative correlation of EPA and DHA to age in the 70+ y decade in Table 4.3 but, mean FA concentrations were greater than the 60-69 y decade in Table 8.3 of Appendix B. Thus, at some point during the 70+ y decade EPA and DHA concentration must be beginning to decline to account for the negative correlation to age seen despite having a higher mean concentration than the previous decade. Based on the age stratification used in this study we are unfortunately unable to say with confidence what age this change in omega-3 serum status may be occurring. Alternatively, even if dietary status remained the same the decline may be due to deficient absorption of essential nutrients (125). Future

studies could examine this phenomenon by giving older adults omega-3 PUFA supplements and monitoring multiple stores of FA to see if any adaptive behaviours occur/change over time.

Consuming fish is the most effective means of improving omega-3 PUFA levels via regular dietary intake with foods such as eggs, flax, selfish, and some vegetable oils also providing small quantities (169). Without dietary data we cannot specify whether dietary changes via an increase in fish occurred with age resulting in the increase seen in this study.

Prior research which describes trends in the dietary habits of older Australians found participants viewed fish as a healthier alternative to red meat and that it was important to include more fish into the diet (124). Studies examining the dietary intakes of older Australians however were mixed, with Flood et al. (170) finding a significant increase in fish consumption from 62-72 y but, Hill et al. (171) found decreased quantities, and Meyer (172) reported no change in fish consumption with age. A common dietary supplement that is also used to improve omega-3

PUFA status are fish-oil pills, which are a simple and cost-effective method of obtaining dietary

EPA and DHA compared to consuming fish (173). A majority of older Australians not consuming any type of fish-oil supplement, even if consuming more than 2 servings of fish per week, were still found unable to meet the dietary recommendations put forth by the National

Heart Foundation of Australia (174). A similar study found only 20% of all Australians, not only the elderly, were able to reach the recommended omega-3 PUFA consumption set forth by

Australian Ministry of Health (172). Some studies have reported values as high as 32.6% of

Australians 45+ y consumed an omega-3 supplement in the past month (175). However, the

Adams et al. (175) study did not take into account the frequency of consumption and this value could be inflated. The trend of increased omega-3 PUFA levels in older vs. younger adults seems

paradoxical however, as increased omega-3 PUFA status has found to decrease the rates of CVD and cognitive impairments of which elderly individuals are at an increased risk for (148,176).

Measures of FA concentrations from blood plasma/serum reflect not only dietary status but, also serve as an aggregate of genetic and metabolic factors. Independent of fish consumption, these genetic and metabolic factors can specifically modify omega-3 status. Elderly individuals when compared to younger individuals, are found to have a greater metabolic capacity for incorporating dietary EPA and DHA into blood plasma, which aligns with the trends seen in

Table 4.2 (129,177). Although positive correlations with omega-3 PUFA are seen as one ages, the strength of this association may not be enough to equate to cardioprotective status. The O3I is a widely utilized tool to assess overall omega-3 PUFA status but, only values of >8% correlate to reductions CVD and CHD (58). The quantity of omega-3 PUFA needed to be consumed to reach this threshold can drastically vary between individuals. Genetic carriers of the apoE e4 allele have shown to exhibit a reduced efficacy of fish oil supplementation for increasing plasma levels of EPA and DHA compared to those who are not a carrier (150). Thus, modified dietary behaviours or, genetic differences independent of dietary consumption, are both implicated in the positive correlations between omega-3 PUFA status and age observed but, we are unable to determine the extent which either factor influenced the trends seen.

When stratified by decade, both ALA and DPA status exhibited opposite directions of correlation to aging in all four decades when compared between both measures in Table 4.3. For

DPA, the results seen in Table 4.2 are primarily driven by only a single decade in each measure, as the remaining correlations to age are weaker in comparison. This implies DPA exhibits relatively consistent correlations to age until decade 70+ y, when correlations of EPA and DHA

to age also become negative. As DPA is a direct intermediate in the conversion pathway between

EPA and DHA and found in common food sources, seeing similar results at 70+ y is not surprising (178). The discrepancies between measures are likely due to the same phenomenon seen in the omega-6 PUFA which resulted in a false positive when assessed as percent composition. Based on the number of discrepancies seen between measures in all FA classes, the use of only one measure can drastically impact the interpretations of results and must be carefully considered when designing future studies examining the relationships of FA status.

4.5.5 Statistical and Biological Relevance

As a whole, the strength of the correlations between FA status and aging in this study were relatively low. In terms of absolute concentration, LA had the strongest correlation coefficient of -0.241 (Table 4.2). When expressed as a coefficient of determination (R2), this predicts only ~5% of the variance in FA status is attributable to age in our current model.

Whether a 5% variance in FA status is biologically relevant is difficult to determine without standardized FA reference ranges, which have yet to be established. However, unlike being prescribed a drug, dietary status is chronically changing and fluctuating. Small dietary adaptations can accumulate to become greater health benefits over long periods of time. For example, SFA concentrations decreased across each decade as shown in Table 8.3 of Appendix

B. Decreased SFA levels are consistent with the observed decrease in LDL and total cholesterol seen in the older decades in this study (Table 4.1) which benefits cardiovascular health (154).

Although modest correlations are seen in Table 4.3, peaking at R= -0.191, small changes over time could amount to a biologically relevant result. Thus, despite having relatively weak correlations, we cannot definitively conclude that aging has a negligible biological impact on

individual serum FA status. More research is needed in diverse cohorts to determine if a similar strength of relationship is seen and the role dietary status and endogenous lipid synthesis play.

4.5.6 Absolute vs. Relative Measures

A unique aspect of our study is that we have reported trends of both absolute concentrations and percent composition for assessing serum FA status. Currently, there is no established protocol for the consistent reporting of FA data, which makes the interpretations of the results often difficult to compare between studies. Brenna et al. (12) recently published twenty-two recommendations for establishing best practices in FA research, specifically stating sufficient information should be included in reports to convert absolute values to relative values, and vice-versa. Of the sixty-seven FA assessed in our study, a large majority saw consistent results between absolute concentrations and percent composition. However, sixteen FA in Table

4.2 exhibited opposing directions of correlations between absolute and relative measures, most notably: ARA, ALA, and DPA. All sixteen FA exhibited a negative correlation when measured as an absolute concentration but, a positive correlation was seen when assessed in terms of percent composition, with respect to age. Our results are similar to that of previous studies which have also evaluated absolute vs. relative measures (95,166,179,180). When the health outcomes of Australian women correlated to plasma FA status was examined, absolute and relative measures were generally similar when predicting all-cause mortality but, did note omega-6

PUFA exhibited the largest disagreement between measures (166). Miura et al. (166) recommended the use of absolute concentrations to be utilized in future studies examining FA status and health outcome measures to improve generalizability between studies, as relative measures are able to be derived from absolute values. A majority of the studies evaluating

absolute vs. relative measures echo a similar sentiment advocating the use of concentration values. This is exemplified by the stronger correlation of absolute concentrations of FA as compared to percent composition values in a study by Sergeant et al. (95) examining circulating

FA and lipid biomarkers. This difference was also readily apparent in the present study. The limitation of percent composition is apparent given the limited dynamic range as compared to concentration values that can differ by several orders of magnitude. Also, percent composition can be influenced by independent fluctuations of other FA. In our study, as the outcome being assessed was individual FA status with respect to aging, in terms of the biological question being asked, we believe FA concentrations are the more accurate measure for basing our interpretations of healthy individuals compared to percent composition. Percent composition still remains a useful tool for assessing FA status in certain instances, such as individuals with lipemia who have high concentrations of all FA where assessing relative trends may be more appropriate (12).

Inclusion of both measures may help to provide a more robust understanding of the relationship between FA status and different disease biomarkers. We believe all future FA research should report individual concentrations for the entire pool of FA analyzed to maintain transparency and improve generalizability which still allows percent composition to be calculated.

4.5.7 Limitations

We performed secondary-analysis on our blood plasma samples and data which was obtained from the MAILES cohort study (134). We were unable to obtain data regarding dietary intake which would have provided a more informed view of FA status and dietary trends during aging. Secondly, as this was a cross-sectional design, we could not assess the effect of aging within each participant. Finally, our study did not include female participants as the original

investigators excluded female participants from their original study. However, studies have found differences in FA metabolism between men and women, with women typically showing greater blood FA levels (117). Thus, the age-related effects seen may differ if females are included in future studies.

4.6 Conclusion

In conclusion, aging has a modest effect on individual serum FA values. Although modest, the biological implications of aging on individual FA status cannot be overlooked. Of the sixty- seven FA included in our analysis, only EPA and DHA were found to exhibit positive correlations with age in this cohort of Australian men. Ten FA were further stratified by decade, where FA status began to exhibit negative correlations with age in the 50-59 decade that became stronger at age 60+ y. When FA status was assessed as an absolute concentration, the direction of the correlations was more consistent and a greater number of significant values were found, compared to percent composition. Due to the influence of individual FA on the whole pool of FA assessed when reported as percent composition, it is recommended that absolute concentrations be the primary reporting method to improve comparability of results.

5 FATTY ACID REFERENCE RANGES AND CORRELATIONS TO LIPID BIOMARKERS OF HEALTHY SINGAPOREAN MEN AND WOMEN 5.1 Abstract

Dietary FA are essential for overall human health and well-being yet, individual FA reference ranges have yet to be established for use in a scientific or clinical setting. Without risk classifications similar to other lipid biomarkers, reported FA concentrations lack context regarding the direction and magnitude of change necessary to achieve optimal health in individuals with deficient, or, excess amounts of FA. Reference ranges of sixty-seven individual

FA (μmol/L) were profiled and analyzed using GC-FID from serum samples collected from 476 middle-aged Singaporean males (BMI: 23.33±2.92) and females (BMI: 21.80±3.56). Measures of TG, HDL, LDL, and TC (mmol/L) were also collected. The mean FA concentration seen in this cohort (11458.09±2477.50) was similar to that of overweight North American cohorts assessed in past studies, and far greater than that of the limited healthy cohorts assessed. A

Student t-test further compared ten biologically relevant FA between sexes, with females exhibiting higher concentrations in four FA (p<.05; SA, PAO, ALA, DHA). A linear regression model including age, sex, and BMI as covariates found significant correlations between all lipid biomarkers and FA concentrations (p<0.05). A majority of the participants who recorded FA concentrations in the >95th percentile also exhibited TG, HDL, LDL, and TC levels in the “high” risk classification of developing CVD. Future work examining the relationship between

individual FA and lipid biomarkers in multiple global cohorts will help to establish an accurate measure for cut-off values of individual FA concentrations highly correlated to disease-risk

5.2 Introduction

Adequate consumption of dietary FA is essential for the development and maintenance of overall human health and well-being (1). Due to their wide array of structural and functional capabilities, FA status has been implicated in reducing the risk of a number of chronic diseases

(2,4). There is currently a gap in the field of dietary lipid research as individual FA reference ranges have yet to be established. This fundamental gap hinders the capability of translating reported values from scientific reports for clinical use. Objective measures with established clinical ranges for other lipids such as cholesterol, and triglycerides are commonly used in clinical practices (11). Developing baseline FA values would provide medical practitioners context regarding the direction and magnitude of change necessary to achieve optimal health in patients with deficient, suboptimal, or even excess amounts of FA. Additionally, FA values which are reported in scientific literature are often inconsistent, using either different units or methods of reporting altogether (12). In order to address this gap, comprehensive profiles of baseline FA concentrations must be collected and analyzed. Developing validated FA reference ranges while using consistent methods of assessment and reporting are necessary steps in creating generalizable results between individual FA status of different cohorts.

To date, only a small number studies have comprehensively profiled plasma or serum FA levels in healthy adults with the primary objective of developing reference ranges

(94,95,99,100). No studies to our knowledge have comprehensively examined FA status of individuals in the Singapore-Southeast Asia region of the world. Due to its unique geographical 67

location as an island country located between Malaysia and Indonesia, Singapore has a unique population consisting of three predominant ethnic groups: Chinese, Malays, and Indian (181).

Independent of ethnicity, rates of obesity in Singapore are steadily increasing as their typical diets are very energy-dense (182). A rise in western-style fast food and high rates of

Singaporeans typically going out to eat, has been positively correlated with these dietary changes

(182,183). Additionally, the rise in large-scale production of palm oil, high in the SFA PA and the MUFA OA, from Indonesia and Malaysia have contributed to high levels of consumption in the region (184). A number of studies have found significant correlations between FA status and the risk of chronic disease specifically in Singapore Chinese adults (185–194). On a global scale, countries in Southeast Asia have reported far higher omega-3 PUFA and SFA dietary consumption than North American countries (10). This study sought to assess and determine the distribution of sixty-seven individual FA concentrations in healthy, adult Singaporean men and women. Correlations between FA status and lipid biomarkers associated with overall health status and differences between males and females were examined. Given that certain disease- states are known to alter FA status independent of dietary intake (9), using only healthy participants will reduce potential confounding between FA modifications associated with a disease-state or natural fluctuation due to daily living.

5.3 Materials and methods

5.3.1 Participants

Participants were originally recruited as described in Bi et al. (135). In brief, 476 healthy male and female adult Singaporeans were recruited from the general public through advertisements placed around the National University of Singapore and the Clinical Research

Centre website from June 2014 to January 2017. All participants lived in Singapore for at least the past five years and were excluded if they had been diagnosed with any major disease or were pregnant. 91% of the participants were ethnically Singaporean Chinese. All participants were instructed to restrict caffeine and alcohol for 24 hours prior and to fast prior to data collection.

All procedures involving human participants were approved by the National Healthcare Group

Domain Specific Review Board (NHG DSRB, Reference Number: 2013/00783), Singapore.

5.3.2 Anthropometric Measures

Anthropometric measurements were obtained in a fasted state. Weight (kg) in light clothing without footwear was measured to the nearest 0.1 kg by using an electrical scale and height (cm) was measured using a stadiometer to the nearest millimeter (Seca 763 digital scale,

Birmingham, UK). BMI (kg/m2) was calculated using weight divided by the height squared.

Waist circumference (WC) was taken at the smallest WC above the umbilicus or navel and below the xiphoid process. DEXA (QDR 4500A, fan-beam densitometer, Hologic, Waltham,

MA, USA) was used for the measurement of fat mass (%). To ensure accuracy of the measurement, all metal items were removed from the participants.

5.3.3 Blood Samples

Blood samples were collected as previously described in Bi et al. (195). In brief, 10 mL of venous blood was collected into Vacutainers (Becton Dickinson Diagnostics, Franklin Lakes,

NJ USA). Blood samples were separated by centrifugation at 1500 xg for 10 min at 4°C within 2 h of being drawn and aliquots were stored at - 80°C until analysis. Fasting lipid parameters including total cholesterol, HDL, and LDL were measured using the COBAS c311

(Roche)chemistry analyzer. Measurements were done in duplicate and readings were averaged. 69

5.3.4 Gas Chromatography Protocol

A modified Folch method was used for lipid extraction for serum samples which has been described in-detail previously (77). In brief, samples were first thawed on ice, where 50 µl of plasma or serum was pipetted into a 15ml tube. Stock solution of 2:1 chloroform:methanol (v:v) with a 3.33 µg/mL of internal standard (C19:0) was prepared in which 3 mL of CHCl3: MeOH was added to the tube and capped tightly with a phenolic PTFE liner. After a one minute of vortex, 550 µL of 0.1 M KCl was added to each tube and briefly vortexed again for another 5-10 seconds. Next, samples were spun at 1460 rpm for 10 min (at 21°C) to separate phases. The lower chloroform layer was transferred into a clean 15 ml glass tube and dried down under a gentle stream of nitrogen. After, 300 µL of hexane and 1 ml 14% BF3-MeOH was added and followed with a vortex of 5-10 seconds. Methylation was then completed at 100oC for 60 minutes in oven, where it was then cooled for 10 min at room temperature. Next, 1 mL of double-distilled water and 1 mL hexane was added to stop the methylation process. After a one- minute vortex, the sample was once again spun down at 1460 rpm for 10 min (at 21°C) to separate phases. The top hexane layer was then extracted into a clean 2 ml GC vial, dried down under a gentle stream of N2 and then reconstituted in 400 µL of hexane.

Total FA were quantified on an Agilent 7890-A GC-FID and separated on a SP-2560 fused silica capillary column (100m x 0.25mm x 0.2um) (Sigma, Cat # 24056). The splitless inlet

(pressure of 19.5 psi and a hydrogen flow of 13.7 mL/min) and detector was set at 250°C

(hydrogen flow of 30 mL/min, air flow of 450mL/min and nitrogen flow of 10mL/min), where

1uL of sample was injected onto a splitless inlet set. The oven was initially set at 60°C, where it was increased to 13°C/min to 170°C and was held for 4 min, then followed 6.5°C/min to 175°C

with no hold to 2.6°C/min to 185°C with no hold, to 1.3°C/min to 190°C with no hold, to

13.0°C/min to 240°C and was held for 13 min. Total run time was 49.78 min per sample.

5.3.5 Fatty Acid Analysis

Peaks were identified and chromatographs were analyzed via OpenLab CDS EZChrom

Edition 3.2.1 software. Sixty-seven total FA were included in the pool analyzed and a C19:0 internal standard was used to calculate FA concentrations (μmol/L) and percent composition (%).

Trace (t) values were either zero or levels below the detectable limit (0.05 µg/ml) of the Agilent

7890-A GC-FID.

5.3.6 Statistical Analysis

Statistical analysis was conducted using SAS University Edition (SAS Institute Inc.;

Cary, North Carolina). The range, mean, and percentiles of the concentration of all sixty-seven

FA and TG, LDL, HDL, and total cholesterol were profiled. Of the sixty-seven FA analyzed, ten were chosen for further analysis due to their implications to health and prominence in the diet. A

Student t-test was used to determine statistical differences between male and female participants with results expressed as mean ± SD. A linear regression model adjusted by age, gender, and

BMI was conducted to assess the relationship between FA concentrations and TG, LDL, HDL, and total cholesterol. Results were reported as coefficient of determination (R2) of the entire and standardized parameter estimates/beta-weight (β) of age. A p-value of <0.05 was considered statistically significant.

5.4 Results

Table 5.1 General characteristics of study population Total Population Males Females p-value Population (#) 476 186 290 / Age (yrs) 38.9±14.62 38.41±14.57 39.21±14.68 0.5601 BMI (kg/m2) 22.4±3.41 23.33±2.92 21.80±3.56 <0.0001* Waist Circumference (cm) 73.93±9.22 79.28±8.09 70.49±8.21 <0.0001* DEXA Fat (%) 30.97±7.68 24.66±5.66 35.05±5.84 <0.0001* Glucose (mmol/L) 4.55±0.49 4.63±0.51 4.50±0.46 0.0032* HDL Cholesterol (mmol/L) 1.68±0.43 1.51±0.35 1.79±0.44 <0.0001* LDL Cholesterol (mmol/L) 3.35±0.92 3.41±0.93 3.32±0.91 0.2771 Total Cholesterol (mmol/L) 5.27±1.03 5.21±0.99 5.31±1.05 0.2868 Triglycerides (mmol/L) 0.97±0.45 1.01±0.44 0.95±0.46 0.1328 Total Serum Fatty Acids (μmol/L) 11458.09±2477.50 11379.0±2421.9 11508.8±2515.4 0.5775 Data represented as Mean±SD * Considered statistically significant at p<0.05 following Student t-test DEXA; Dual-energy X-ray absorptiometry scan, HDL; High-density lipoprotein, LDL; Low-density lipoprotein

The general characteristics of the entire study population, including differences between males and females, are presented in Table 5.1. Participants ethnicity was not included in our calculations as a large majority reported to be Singaporean Chinese (>90%), as previously described (135). All participants self-reported to be healthy, disease-free and on average recorded normal BMI values (22.4 kg/m2). Significant differences were noted between sexes in measures that are commonly seen in other populations such as: BMI, WC, and fat %. Females had significantly greater levels of HDL cholesterol (1.79 to 1.51 mmol/L) but, both groups averaged relatively high concentrations compared to normal values. Both sexes exhibited

moderately high concentrations of LDL-cholesterol, total cholesterol and fat % but, TG were

well below reported normal levels of 1.7 mmol/L.

Table 5. 1 Concentration (μmol/L) of select fatty acids in males and females

Table 5.2 Range, mean and percentiles of Fatty Acid concentrations (μmol/L) of serum total lipids Range Percentile Fatty Common Min. Mean SD Max. 5 10 25 50 75 90 95 Acid Name 12:0 Lauric acid t 10.86 12.38 159.9 t 3.45 5.22 7.93 13.19 20.94 28.97 14:0 Myristic Acid 25.39 82.75 46.99 342.7 33.60 39.52 48.89 69.79 100.00 146.08 180.10 15:0 Pentadyclic acid t 39.17 28.18 207.7 9.59 11.85 16.72 26.38 61.92 77.39 87.82 16:0 Palmitic acid 1400.4 2667.5 625.1 6161 1809.78 1991.27 2256.22 2562.95 2967.94 3529.16 3908.25 17:0 Margaric acid t 37.79 15.42 161.96 17.59 22.22 28.13 35.62 45.04 56.15 66.34 18:0 Stearic acid 415.16 745.21 155.5 1550.6 522.53 568.83 641.51 720.94 828.47 946.03 1021.83 19:0* Nonadecylic acid 20:0 Arachidic acid 1.77 20.06 5.27 50.27 12.82 13.98 16.60 19.49 22.88 26.38 29.83 21:0 Heneicosylic acid 0.72 8.65 7.22 120.05 3.51 4.30 5.70 7.38 9.82 12.98 16.90 22:0 Behenic acid 11.04 45.17 11.63 91.93 28.58 31.11 37.47 44.48 52.07 59.05 65.52 23:0 Tricosylic acid t 6.96 9.2 36.27 t t t t 15.46 20.69 23.02 24:0 Lignoceric acid 4.99 42.66 11.21 94.13 26.16 29.36 34.87 41.73 50.23 55.98 61.68 12:1c11 t t / t t t t t t t t 14:1c9 Myristoleic acid t 10.38 9.15 46.63 t t 2.76 9.09 17.13 22.33 25.94 15:1c10 t t / t t t t t t t t 16:1c9 Palmitoleic acid 6.52 195.97 84.2 526.43 88.79 105.94 135.08 180.22 238.06 309.04 359.35 17:1c10 t t / t t t t t t t 18:1c9 Oleic acid 263.11 2095.7 633.05 5070.1 1311.13 1431.03 1683.16 1975.62 2389.16 2969.18 3381.20 18:1c11 cis-Vaccenic acid 13.1 163.91 43.21 355.91 106.54 115.73 134.73 159.52 186.73 218.78 240.30 18:1c12 t 4.2 3.34 17.34 t t 2.13 3.86 5.85 8.06 10.77 18:1c13 t t / t t t t t t t t 18:1c14 t t / t t t t t t t t 74

19:1c10 t t / t t t t t t t t 20:1c5 t 6.31 2.94 18.81 2.95 3.34 4.33 5.60 7.44 10.73 12.17 20:1c8 t 3.01 1.61 18.01 1.18 1.49 2.04 2.73 3.73 4.81 5.86 20:1c11 Gondoic acid 2.61 16.23 5.48 52.52 9.77 10.81 12.68 15.44 18.52 23.02 26.45 22:1n9 Erucic acid t 4.23 4.77 74.29 t 1.87 2.40 3.29 5.23 7.86 8.92 24:1n9 Nervonic acid 2.59 63.85 17.67 187.13 41.14 44.12 52.68 61.63 72.69 84.22 92.30 16:1t9 Palmitoleic acid t 7.92 5.67 27.13 2.01 2.22 2.89 6.84 10.92 16.29 19.93 18:1t4 t 0.18 1.35 26.78 t t t t t t 1.05 18:1t5 Thalictric acid t 6.57 8.78 62.18 t t t 3.62 9.07 17.87 25.01 18:1t6-8 Petroselaidic acid t 3.33 2.45 16.54 0.73 0.93 1.54 2.66 4.57 6.45 8.42 18:1t9 Elaidic acid t 6.68 3.33 46.96 2.74 3.52 4.71 6.25 8.13 10.01 11.91 18:1t10 t 4.37 3.13 29.71 1.13 1.75 2.56 3.64 5.36 7.50 9.36 trans-Vaccenic 18:1t11 t 7.06 5.04 59.38 acid 1.67 2.41 3.88 6.04 8.87 12.06 15.91 18:1t12 t 4.43 3.76 59.85 1.47 1.94 2.66 3.61 5.14 7.84 9.78 18:1t13 t 11 7.04 59.34 4.25 5.13 6.86 9.39 13.16 17.98 21.92 18:1t16 t 3.62 2.4 26 t t 2.43 3.44 4.59 6.36 7.45 18:2tt t 4.06 4.39 25.93 t t 0.91 2.71 5.53 10.40 13.64 18:2t9t12 Linoelaidic acid t 1.17 3.03 25.63 t t t t t 4.56 6.68 18:2c9t13 t 1.05 2.64 17.83 t t t t t 4.91 7.01 18:2ct t 5.17 3.2 24.07 1.97 2.47 3.27 4.44 5.97 8.83 11.72 18:2c9t12 0.98 13.68 4.58 38.48 8.12 9.38 10.82 12.63 15.59 20.02 23.23 18:2t9c12 t 7.85 3.16 21 3.24 4.19 5.77 7.67 9.46 11.93 13.41 18:2c9c14 t 1.28 2.39 18.22 t t t t 2.14 3.90 6.30 18:2c9c15 Mangiferic acid t 2.49 3.58 21.31 t t t t 4.54 7.20 9.22 18:2c9t11 Conjugated t 8.68 2.59 23.37 CLA linoleic acid 4.80 6.20 7.44 8.54 9.68 11.48 13.11 75

Conjugated 18:2c11t13 t 0.96 2.04 10.73 t t t t t linoleic acid 4.33 5.89 18:2t10c12 Conjugated t 0.58 1.75 13 t t t t t CLA linoleic acid 2.55 5.09 18:2c/c Conjugated t 0.26 1.2 12.7 t t t t t t 1.66 CLA1 linoleic acid 18:2c/c Conjugated t 2.13 2.4 16.2 t t t CLA2 linoleic acid 1.71 2.97 4.68 6.56 18:2tt Conjugated t 8.88 4.42 38.36 CLA linoleic acid 0.55 4.71 6.77 8.59 10.74 13.07 15.23 18:2n6 Linoleic acid 1652.5 3700.2 777.5 7244.2 2559.24 2766.63 3181.02 3640.64 4090.33 4771.69 5081.19 Gamma-linolenic 18:3n6 2.94 27.04 19.71 124.53 acid 7.50 8.67 12.68 22.47 35.00 51.62 63.23 Eicosadienoic 20:2n6 2.52 23.95 7.82 52.69 acid 12.63 15.80 18.84 22.84 28.41 33.59 38.79 Dihomo-gamma- 20:3n6 2.03 115.34 46.26 273.92 linolenic acid 56.59 63.95 80.09 106.35 141.41 181.09 206.44 20:4n6 Arachidonic acid 104.28 685.78 177.42 1538.7 433.66 480.04 572.97 673.92 784.63 908.26 973.53 Docosadienoic 22:2n6 t 7.88 4.77 33.49 acid 2.49 3.22 4.56 6.48 10.33 14.34 16.74 22:4n6 Ardenic acid 3.6 17.72 5.88 50.39 9.25 11.06 14.14 17.21 20.92 25.16 27.35 Docosapentaenoic 22:5n6 acid (Osbond 3.72 19.67 6.24 44.73 acid) 10.99 12.59 15.39 18.99 23.40 27.45 31.08 Alpha-linolenic 18:3n3 7.28 57.63 24.65 175.42 acid 28.66 32.95 41.08 52.37 69.74 91.59 103.29 18:4n3 Stearidonic acid t t / t t t t t t t t Eicosatrienoic 20:3n3 t 16.58 11.32 47.14 t acid 2.28 4.54 19.22 26.17 30.87 33.76 Eicosapentaenoic 20:5n3 4.22 92.97 80.94 596.25 acid 20.06 29.04 39.96 68.19 115.16 193.42 285.06 Docosatrienoic 22:3n3 t t / t t t t t t t t acid Docosapentaenoic 22:5n3 14.7 43.5 15.53 118.69 acid 23.68 26.98 32.49 40.66 50.54 65.28 71.54 76

(Clupanodonic acid)

Docosahexaenoic 22:6n3 44.88 255.52 94.44 663.35 acid 121.88 150.71 193.56 240.45 303.95 391.38 430.12 20:3n9 Mead acid 3.03 8.89 4.06 29.36 4.34 4.90 6.21 8.05 10.24 13.92 17.05 *Internal Standard t; Trace amount

Table 5.3 Concentration (μmol/L) of select fatty acids in males and females

Fatty Males Females Common Name p-value Acid (n=186) (n=290) 16:0 Palmitic acid 2683 ± 630.6 2658 ± 622.4 0.66 18:0 Stearic acid 716 ± 145.2 764 ± 159.2 0.001*

16:1 c9 Palmitoleic acid 185 ± 82.5 203 ± 84.7 0.027* 18:1 c9 Oleic acid 2117 ± 645.0 2082 ± 625.9 0.55 18:2 n6 Linoleic acid 3652 ± 737.4 3731 ± 801.9 0.28

20:4 n6 Arachidonic acid 687 ± 176.2 685 ± 178.5 0.86 18:3 n3 Alpha-linolenic acid 53.9 ± 23.0 60.0 ± 25.4 0.0084* 20:5 n3 Eicosapentaenoic acid 84.4 ± 74.2 98.5 ± 84.7 0.06 22:5 n3 Docosapentaenoic acid 42.6 ± 14.8 44.1 ± 0.9 0.3 22:6 n3 Docosahexaenoic acid 240 ± 86.8 266 ± 97.8 0.0033*

Represented as Mean ± SD.

*Statistically significant at the level p<0.05

Table 5.2 details the mean, range, and percentiles of sixty-seven individual FA concentrations, and the C19:0 internal standard, expressed as μmol/L for the entire study population of 476 participants. Of this large pool, ten common FA which consisted of more than

90% of the total FA concentration, were selected to be analyzed further due to their health implications and prominence in the diet. Significant differences in the concentrations of SA,

PAO, ALA, and DHA (p<0.05) were found between males and females. Females demonstrated higher mean FA concentrations in all four FA which reached significance, with males demonstrating slightly higher means in only PA, OA, and ARA (Table 5.3).

Table 5.4 Reference ranges and clinical cut-offs of lipid biomarkers Range Percentile Class Min Mean ± SD Max 5 10 25 50 75 90 95 TG 0.36 0.97 ± 0.45 3.06 0.5 0.55 0.66 0.87 1.125 1.64 1.91 HDL 0.8 1.68 ± 0.43 3.44 1.07 1.16 1.38 1.65 1.91 2.29 2.48 LDL 0.99 3.35 ± 0.92 7.64 2.06 2.29 2.74 3.23 3.87 4.48 4.99 TC 2.7 5.27 ± 1.03 9.44 3.74 4.08 4.58 5.17 5.85 6.67 7.05 Class Desirablea Borderline/High Very High TG 1.7 - 2.2 mmol/L 2.3 - 4.4 mmol/L ≥ 4.5 mmol/L HDL 1.0 - 1.5 mmol/L >1.6 mmol/L / LDL 2.6 - 3.3 mmol/L 3.4 - 4.8 mmol/L ≥ 4.9 mmol/L TC < 5.2 mmol/L 5.2 - 6.1 mmol/L ≥ 6.2 mmol/L HDL; High-density lipoprotein cholesterol, LDL; Low-density lipoprotein cholesterol, TC; Total cholesterol, TG; Triglycerides aTai et al. (196)

Table 5.5 Linear regression model assessing the correlation between individual fatty acids and lipid biomarkers

Fatty Total Common Name Triglycerides HDL LDL Acid Cholesterol R2 β R2 β R2 β R2 β 16:0 Palmitic acid 0.70 0.78** 0.23 0.02 0.45 0.55** 0.49 0.64** 18:0 Stearic acid 0.50 0.62** 0.25 0.18** 0.49 0.60** 0.57 0.73** 16:1 C9 Palmitoleic acid 0.59 0.70** 0.23 -0.05 0.28 0.31** 0.26 0.38** 18:1 C9 Oleic acid 0.75 0.81** 0.24 -0.1* 0.37 0.46** 0.36 0.50** 18:2N6 Linoleic acid 0.36 0.45** 0.28 0.24** 0.54 0.62** 0.60 0.72** 20:4N6 Arachidonic acid 0.20 0.17** 0.29 0.25** 0.38 0.43** 0.40 0.52** Alpha-linolenic 18:3N3 acid 0.50 0.61** 0.23 -0.06 0.27 0.29** 0.24 0.34** Eicosapentaenoic 20:5N3 acid 0.18 0.00 0.26 0.20** 0.24 0.22** 0.22 0.29**

Docosapentaenoic 22:5N3 acid 0.27 0.33** 0.26 0.19** 0.33 0.40** 0.35 0.49** Docosahexaenoic 22:6N3 acid 0.24 0.27** 0.27 0.21** 0.36 0.44** 0.37 0.52** TOTAL 0.56 0.67** 0.24 0.12* 0.49 0.59** 0.55 0.69** *p<0.05 ** p<0.0001 R2 represents the entire model including covariates: BMI, age, and sex

The mean, range, and percentiles of TG, HDL, LDL, and total cholesterol for the entire study population of 476 participants is profiled in Table 5.4. The cut-off values for lipid biomarkers used by the Singapore Ministry of Health which denotes a health risk are included in the bottom of the table (196). A linear regression model was conducted to determine the coefficient of determination (R2) and standardized parameter estimates (β) which correlated FA values to cholesterol and TG, as seen in Table 5.5. Correlations between FA concentrations and lipid biomarkers was significant for almost all cholesterol and TG values. Only EPA did not exhibit a significant correlation with TG (β =.00). All ten FA correlated significantly with LDL

(β =.22-.62) and total cholesterol (β =.29-.73). A majority of the FA exhibited significant xs=-

0.06-0.02). When the total mean FA concentrations were evaluated, significant correlations were found within all four measures but, HDL was only significant to the degree of p<.05 compared to the other measures which were p<.001.

Table 5.6 Upper and lower percentile fatty acid concentrations in participants who exceed risk cut-offs for lipid biomarkers

16:0 18:0 16:1c9 18:1c9 18:2n6 20:4n6 18:3n3 20:5n3 22:5n3 22:6n3 Exceeded Lipid a >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% >95% <5% Class Cut-off (#) TG 11 10 0 8 0 7 0 11 0 3 0 0 0 6 0 0 0 1 0 1 0 HDL - Low 11 1 1 0 3 0 0 1 0 0 2 0 2 0 3 1 2 1 2 0 2 HDL - High 262 5 11 9 5 5 13 3 15 14 9 14 11 10 6 16 11 16 8 15 8 LDL 92 13 0 16 0 8 0 11 0 15 0 15 1 8 0 9 0 14 0 15 1 TC 82 13 0 15 0 11 0 13 1 17 0 17 1 11 1 10 0 15 0 17 0 TOTALb 137 20 12 21 8 16 13 19 15 21 11 20 13 19 4 21 13 22 10 22 11 Values represented as number of participants. Each FA has 24 participants in the upper and lower 5th percentile. aTG ≥ 2.3 mmol/L; HDL < 1.0 mmol/L; HDL ≥ 1.6 mmol/L; LDL ≥ 4.1 mmol/L; TC ≥ 6.2 mmol/L bTotal number of individual participants who exceeded cut-off

Based on the cut-off values described in Table 5.4, the number of participants who exhibited levels of TG, HDL, LDL, or TC which exceeded recommended values are tallied in

Table 5.6. Using the reference ranges of this cohort described in Table 5.2, FA concentrations in the ≥ 95th percentile and ≤ 5th percentile, outside of the normal range, of each at risk participant was evaluated. Each FA in Table 5.6 had a possible 24 participants in the top and bottom 5% of their recorded reference range. In participants with high TG values, SFA and MUFA concentrations in the ≥ 95th percentile was found in a majority of participants, compared to only a small number of omega-3 and omega-6 PUFA. Participants with low HDL cholesterol values exhibited the fewest scores in the ≥ 95th percentile and only a modest number of concentrations in the ≤ 5th percentile of omega-3 and omega-6 PUFA. Comparatively, participants with high

HDL cholesterol saw a large number of omega-3 and omega-6 PUFA concentrations in the ≥ 95th percentile and simultaneously, the most participants with FA concentrations in the ≤ 5th percentile. LDL and TC exhibited nearly identical trends, with a large number of participants with FA concentrations in the ≥ 95th percentile across all ten FA. Omega-6 PUFA and SFA concentrations in the ≥ 95th percentile was the most commonly seen in LDL and TC, with the omega-3 PUFA, ALA and DPA, exhibiting the lowest frequency. Overall, FA concentrations in the ≥95th percentile were far more prevalent in participants exceeding lipid cut-off values than concentrations in the ≤ 5th percentile.

5.5 Discussion

In this study we determined the percentiles, mean, and ranges of sixty-seven individual FA collected from 476 healthy, adult Singaporean males and females with ten FA highlighted for further correlation analysis with TG and cholesterol. Significant correlations between FA 82

concentrations and TG, LDL, HDL, and total cholesterol were seen in almost all of the ten FA highlighted. Mean total FA concentrations significantly correlated with all four lipid biomarkers but, exhibited the weakest relationship with HDL cholesterol. Individual FA concentrations significantly correlate with all four lipid biomarkers and high concentrations of FA were more often seen in individuals exceeding established cut-off values. Female participants exhibited significantly higher concentrations SA, PAO, ALA and DHA when compared to males (Table

5.3), but total FA concentrations did not statistically differ between sexes (Table 5.1). To our knowledge, the inclusion of sixty-seven individual FA concentrations is the largest pool of FA to be examined to date for the purposes of profiling FA reference ranges in any cohort.

5.5.1 Individual Fatty Acid Reference Ranges

A very small number of studies have, in detail, profiled FA concentrations in healthy adults for the purposes of developing reference ranges (94,95,99,100). Glew et al. (197) have also reported mean percent composition of twenty-six FA from serum samples collected in healthy adult males and females from Northern Nigeria. Of these studies, Bradbury et al. (100) and Glew et al. (197) reported their values in percent composition, whereas absolute concentrations were reported in our study. Sergeant et al. (95) (n=93; obese American women) and Abdelmagid (94) (n=826; healthy, multiethnic Canadians) also reported their results as absolute concentrations (μmol/L) which allows for the most direct and reliable comparisons to our results. Percent composition is determined by the percentage of one FA from the total pool of

FA included in the analysis; a smaller pool analyzed results in a larger apparent percentage of individual FA (12). Large fluctuations in a single FA could significantly modify the total value of the pool and subsequent percent composition seen in other FA when no changes in

concentration actually occurred. Absolute concentrations of an individual FA are independent of changes in the pool analyzed and have a larger dynamic range of values, as percent is limited to a

0-100 scale. Reporting results as absolute concentrations has been found to reduce the loss of statistical significance between FA compared to when values were assessed as weight percentages (198). Furthermore, relative percent can be derived from absolute concentrations but, the same is not possible in reverse as the values of the internal standard used in the study are needed to calculate absolute concentration (12). As our study included over sixty individual FA, whereas Bradbury et al. (100) and Glew et al. (197) only included up to twenty-six, it is difficult to accurately compare our reported values even in terms of percent composition. Thus, the use of absolute concentrations is recommended for future studies profiling FA reference ranges to improve comparability and generalizability of results.

Three of the five studies mentioned previously have examined adult, North American cohorts and the remaining studies examined cohorts from New Zealand and Nigeria

(94,95,99,100,197). To our understanding, ours is the first study to examine individual FA concentrations of a large cohort of Southeast Asian, specifically Singaporean, adults in this level of detail. Previous work done in a multiethnic Singaporean cohort found plasma FA composition is a strong, reliable biomarker reflecting dietary consumption in epidemiological studies (199).

The mean FA concentration in our study (11,458 μmol/L) more closely resembled that of

Sergeant et al. (95), as Abdelmagid (94) reported a mean concentration which was ~40% lower

(6,948 μmol/L) . Of the ten FA highlighted in Table 5.3; SA, PAO, AA, and ALA concentrations were noticeably lower in our study whereas EPA and DHA were higher compared to Sergeant et al. (95). Nearly all ten FA concentrations were greater in our study than Abdelmagid (94), with

notable higher concentrations in PA, SA, OA, LA, ARA, EPA, DPA, and DHA. A study conducted in NA by Asselin et al. (200) found a cohort of overweight, older participants

(BMI=25.9; age=59) reported mean plasma FA concentrations (15,169 μmol/L) similar to that of

Sergeant et al. (95) and over double that of the cohort in Abdelmagid et al. (94). The primary difference that may account for the high concentrations reported in Sergeant et al. (95) is their use of obese middle-aged women (BMI=33.3; age=56.9) compared to healthy, young university students (BMI=22.8; age=22.6) in Abdelmagid et al. (94). FA concentrations have been found to strongly correlate with increases in BMI, as obese individuals exhibited modified FA status compared to healthy controls (110–112). Furthermore, as one ages, genetic adaptations occur which can modify FA status, primarily omega-3 PUFA (177). Taken together, the FA concentrations reported in Sergeant et al. (95) may be more reflective of the average FA status of

North Americans due to the high prevalence of obesity in recent years. The use of healthy university-aged students in Abdelmagid et al. (94) could reflect FA concentrations lower than what is expected of the average NA and is representative of a cohort that has reduced risk factors of chronic disease.

5.5.2 Anthropometric Measures

The high total FA concentrations seen in the present study are in contrast to the lower total FA reported in Abdelmagid et al. (94) but, the BMI are nearly identical to that of the

Singaporean cohort. Our cohort, although reporting a standard healthy BMI, WC, and fasting blood glucose, had mean FA concentrations similar to that of the obese group profiled in

Sergeant et al. (95). The Singaporean participants were nearly twice as old as the participants reported in Abdelmagid et al. (94), which may account for some of the differences. In addition,

cultural and dietary differences are important modifying factors affecting FA concentrations.

First, SFA intake is greater in Singapore than that of NA (10). The countries surrounding

Singapore are the largest producers of palm oil on the planet, which is high in PA, OA, and LA

(184). When examining the mean difference between PA, OA, and LA concentrations between our Singaporean cohort and the Canadian cohort of Abdelmagid et al. (94), those three FA alone account for ~75% of the difference in mean total FA concentrations (calculation not shown). Due to the abundance of palm oil produced in Southeast Asia, dietary intakes have increased which have also been associated with a rise in CVD-related deaths (201). Palm oil has also been found to strongly correlate with increased LDL cholesterol levels (201), which were reported to be borderline high in our study (94). Similarly, frequency of omega-3 PUFA consumption via fish is low in NA but, consumption in Southeast Asia are some of the highest in the world (10,52). The mean difference between EPA, DPA, and DHA between studies accounts for ~5% of the total

FA concentrations (calculation not shown). Nearly 34.3% of adults in Singapore are overweight or obese and 33.1% are not meeting daily physical activity requirements as of 2014 (202,203).

Measures of body fat % in both males and females in the present study were also quite high compared to other Asian cohorts (204). In Caucasian men and women, an obese BMI classification of 30kg/m2 corresponds to approximately 25% and 35% body fat (205), equating to roughly the same body fat % reported of both males and females in this study, seen in Table 5.1.

This phenomenon of a low BMI but, high body fat % in Singaporean adults has also been described in previous studies (206). Widely reported differences between Caucasian populations and Asian populations have resulted in the WHO to make amendments to the current BMI cut- offs for Asian cohorts which was adopted by Singapore in 2005 (207,208). Current WHO BMI

cut points for being considered overweight is 23-27.5 kg/m2 and obese is ≥ 27.5 kg/m2 for Asian populations (208). Although the participants included in our study exhibited healthy levels of TG and HDL cholesterol, and were screened for any chronic diseases, their BMI and body fat % should not be considered to be optimal. Due to this unique paradox found in the Singaporean population, interpretations between FA concentrations and health outcomes related to BMI in other reports should be compared cautiously. Thus, the high FA concentrations seen in our study can be interpreted as a risk factor to chronic disease, similar to that seen in other populations with higher reported body fat % and BMI.

5.5.3 Correlations with Lipid Biomarkers

Unlike FA concentrations, established clinical reference ranges for TG and cholesterol have been established and are routinely utilized in clinical practices to assess health outcomes

(11). By determining the correlation between FA concentrations and lipid biomarkers, individual

FA can be targeted to determine levels which may be deemed adequate or in excess. Mean LDL and total cholesterol levels in this study were found to be borderline high, which is associated with CVD disease risk (3.35 ± 0.92; 5.27 ± 1.03) (196). HDL levels were classified as being high

(1.68 ± 0.43), which has shown to promote cardioprotective effects (196). Due to age, increased

BMI, and gender exhibiting abilities to modify FA and lipid concentrations independently, they were included as covariates in our linear regression model, seen in Table 5.5. Unfortunately, dietary data was not available for this study. Nearly all FA exhibited a significant positive correlation to HDL, LDL, and total cholesterol levels in our study. Only PA, PAO, and ALA did not reach statistical significance when correlated to HDL cholesterol, which has also been in previous studies (209–211). EPA and DHA were found to be significantly positively correlated

with HDL cholesterol, which is similar to many previous studies citing strong positive correlations between omega-3 PUFA consumption and the antiatherogenic properties of HDL cholesterol levels (176). Interestingly, the omega-6 PUFA LA and ARA, exhibited significant positive correlations to HDL cholesterol and the strongest positive correlations to LDL cholesterol in our study. Strong positive correlations between PA and SA to LDL and total cholesterol levels have been reported a number of times previously; SFA intakes positively correlate to total cholesterol levels and the subsequent risk of developing CHD (212). Results of our study support these findings with both PA, SA, and LA exhibiting the strongest positive correlations to LDL and TC in Table 5.5. Compared to other common vegetable cooking oils low in SFA, palm oil, which is widely consumed in Singapore, has been found to increase LDL cholesterol levels significantly and subsequently increasing the risk of developing type II diabetes (201). Thus, the correlations of SFA reported could be disproportionately high based on the dietary habits of Singaporean adults. When the R2 values in Table 5.5 are compared to those of Abdelmagid et al. (94), our study exhibited stronger positive correlations overall. As the cohort described in Abdelmagid et al. (94) was healthy Canadians and the Singaporeans in this study exhibit characteristics of the low BMI and high body fat % paradox, it is not surprising that stronger correlations with lipid biomarkers were observed in the current cohort.

The cohort of Singaporeans in the current study had generally very healthy TG levels, with only participants in the 95th percentile and up reaching borderline high values (based on

Asian cutoffs) and none of our participants were classified as having very high TG levels (Table

5.4) (196,213). All SFA and MUFA exhibited significant positive correlations with TG (Table

5.5). These results are similar to correlations between PA, SA, PAO, OA and TG concentrations

as reported in previous studies (94,214,215). High levels of TG have been found to increase the risk of developing CVD, atherosclerosis, and are often seen in obese individuals and those with type-2 diabetes (216,217). Numerous studies, including work published by the European Food

Safety Authority, have reported omega-3 PUFA consumption reduces disease risk and benefits heart health via reductions in TG levels (218). In our study ARA, EPA, and DHA, exhibited the weakest correlations to TG concentrations, with EPA being the only FA not to reach statistical significance. Southeast-Asians on average consume some of the highest levels of omega-3 PUFA via fish in the world, which is reflected by the concentrations of EPA and DHA reported in Table

5.2 (10). Greater consumption of omega-3 PUFA is positively correlated with HDL cholesterol, and negatively correlated with TG, resulting in a reduction of developing CVD and CHD

(176,218,219). Results in Table 5.5 however, indicate omega-3 PUFA exhibited significant positive correlations with TG, LDL, and total cholesterol levels. High levels of these three lipid biomarkers are considered to put an individual at an increased risk of developing CVD

(176,219). Our findings of long-chain omega-3 PUFA exhibiting positive correlations to TG levels differs to the negative correlations commonly seen in other studies (214). As we reported larger mean concentrations for nearly the entire FA pool profiled compared to that of a very healthy cohort seen in Abdelmagid et al. (94), correlations between high concentrations of health promoting FA and lipid biomarkers may be influenced by increased concentrations of all other

FA. We posit the increased omega-3 PUFA concentrations seen are still resulting in beneficial health effects for disease reduction despite the positive correlations to TG and cholesterol reported. This would provide reasoning for the relatively healthy values of TG and cholesterol seen in Table 5.1, although mean total FA concentrations were similar to that of an obese cohort

(95). By establishing correlations between validated biomarkers and individual FA concentrations more precise clinical recommendations can be made to reduce chronic disease risk.

Further work is necessary to establish cut-off values of individual FA concentrations that are deemed to be deficient or in excess. A large body of evidence from multiple cohorts across multiple countries is necessary to establish baseline or “normal” values, and whether region, sex, and/or, age specific ranges are required (220). Cohorts from the same country have exhibited significantly different mean FA concentrations, showcasing that large variances are possible, and values from all studies should not be compared equally until a better understanding of what ranges are considered normal. Correlations between FA status and validated biomarkers in cohorts consisting of low or high FA concentrations, contrasted with healthy populations, will provide a more accurate measure as to where cut-off values highly correlated to disease-risk could be established. For example, all participants between the 5th and 95th percentile in our study exhibited optimal TG levels but, this does not equate to all participants exhibiting optimal

OA concentrations in that same range although strong correlations were seen between the two. In

Table 5.6, we observed that participants outside of the normal FA range also commonly exceeded current lipid biomarker cut-offs, increasing chronic disease risk. Specifically, individual FA concentrations in the ≥ 95th percentile were more commonly seen in participants who exceeded TG or cholesterol cut-off values compared to concentrations in the ≤ 5th percentile.

In certain instances, 100% of participants who were above a lipid biomarker cut-off value was also found to have a FA concentration in the ≥ 95th percentile. A number of our high concentration FA exhibited normally distributed curves, similar to those displayed Abdelmagid

et al. (94), but, some smaller FA exhibited a skewed distribution (data now shown). Due to the sample size of our study, the ≥ 95th percentile and ≤ 5th percentile contained only 24 participants each, not enough to account for the total participants who exceeded LDL, high HDL, or total cholesterol cut-offs. If we were to broaden our criteria to include the ≥ 75th percentile for both

PA and SA, 119 participants exhibit concentrations in that range of both FA. Of the 92 participants in Table 5.6 who exceeded the cut-off for LDL cholesterol, 67% and 70% exhibit

FA concentrations in the ≥ 75th percentile of PA and SA, respectively (calculation not shown).

Thus, specific FA may pose a greater health risk at values outside of the normal range and others could be detrimental at lower relative concentrations. FA reference ranges will need to be further profiled in multiple cohorts to assess whether our results are generalizable to the population at large or, specifically Singaporean adults. Overall, we saw a higher proportion of participants who exceeded a lipid biomarker cut-off also having a FA concentration in the ≥ 95th percentile compared to those in the ≤ 5th percentile. However, at this point in time we cannot confidently make any conclusions for cut-off values designating adequate/inadequate status of individual FA.

The findings in Table 5.6 provides evidence for individual FA and percentiles to target in future projects for assessing FA status and chronic disease risk. Regression analysis to specifically determine cut-off values will be required in the future once a larger database of cohorts has been compiled and stronger correlations between biomarkers and specific percentiles of FA concentrations have been established.

5.5.4 Sex Differences

Along with anthropometric measures, we examined concentrations of circulating FA between sexes, (Table 5.1 and 5.3). Although Singapore consists of a unique ethnic make-up,

where cultural differences can influence dietary habits (181), our population was >90%

Singaporean Chinese and thus, ethnic differences were not evaluated. Commonly reported differences in anthropometric measures between males and females were seen, such as higher

BMI and WC in males and body fat % in females. Based on the World Health Organization

Global Status Report (202) the BMI and fasting blood glucose of both males and females in this cohort were below national averages but, males were considered to be overweight (23.3 kg/m2) based on the BMI cut-offs for Asian populations (208).

We observed mean values for BMI and WC to be below the risk cut-off of an Asian population for developing CVD, diabetes, and dyslipidemia in both sexes but, a number of participants in the higher percentiles fell into a risk category (221). Of the ten FA concentrations which were compared between males and females, significant differences were found between

SA, PAO, ALA, and DHA with females reporting the greater concentration in all four. Sex- specific differences in individual FA status have been shown on a number of occasions

(66,111,116,117). In Abdelmagid et al. (94) when males and females were compared PAO,

ALA, and DHA were also all significantly greater except, males had greater concentrations of

ALA. In terms of percent composition, Glew et al. (197) found no significant difference between males and females in any of the ten FA highlighted in our study. The larger dynamic range when reporting FA concentrations compared to percent composition can result in greater statistical significance but, may not always equate to biological significance. Independent of dietary intake, females have exhibited a greater genetic capacity to endogenously produce omega-3 PUFA

(118). The use of oral contraceptives in some studies have found to further impact PUFA metabolism but, more recent studies found no significant effect (119,120). Additionally,

pregnant and breastfeeding females have obvious metabolic differences and subsequent nutritional needs compared to males which has also been found to modify FA status (121). Our study excluded female participants who were taking oral contraceptives and who were pregnant or breastfeeding during data collection. Although omega-3 PUFA were significantly greater in females in this study, the mean FA concentration between sexes was not significantly different

(see Table 5.1). As participants were nearly all the same ethnicity and from the same geographical area, dietary habits were likely similar. Genetic differences between sexes may account for some of the variability shown but, dietary consumption still remains the strongest individual modifier of FA concentrations.

5.5.5 Limitations

This study is limited by a lack of dietary data and measures of physical activity recorded.

Both physical activity and dietary consumption, particularly of fat, are modifiers of serum FA concentrations. Inclusion of these two variables in the linear regression model would have provided a more sensitive analysis for assessing the correlation of FA concentrations to lipid biomarkers. Including all ten FA in the linear regression model violated the collinearity tolerance and VIF which, dietary data may have provided a better understanding as to why specific FA were related due to which foods were consumed.

5.6 Conclusion

In conclusion, our study provides valuable knowledge for the growing desire to develop individual FA reference ranges for assessing chronic disease risk. We are one of the few studies to examine a cohort outside of NA which showcases the effect of geographical and cultural differences on individual FA concentrations. Inclusion of more cohorts with very high or low 93

concentrations of FA will help establish a greater understanding of FA levels that are reflective of chronic disease states. Future research should be conducted in multiple global cohorts while maintaining generalizability of the results to allow further correlations used to established FA cut-offs to be easily compared across studies.

6 GENERAL DISCUSSION, LIMITATIONS and FUTURE DIRECTIONS

6.1 General Discussion

The current lack of guidelines surrounding plasma/serum FA concentrations is a major gap for translating scientific results into clinical practice for promotion of optimal health and reducing chronic disease risk. Validated FA reference ranges would allow for meaningful assessments of FA status in individuals (i.e. sufficiency vs. deficiency), and provide target ranges for health, similar to that of other lipid biomarkers commonly used in clinical practice. This study is one of the few that have aimed to specifically address this gap in scientific literature.

Strong correlations between FA concentrations and health-related biomarkers such as age, sex,

TG, and cholesterol were found in Australian and Singaporean adults. In Chapter 4, age significantly correlated to the status of a number of individual FA, notably EPA and DHA, in

Australian males. In Chapter 5, while Singaporean females and males exhibited a small number of significant differences in FA status, mean total FA concentrations were similar between both groups. When compared to similar studies conducted in NA, vastly different profiles of individual FA were seen in this thesis. Cultural and geographical differences that impact dietary

FA consumption vary from region-to-region and are major modifying factors of FA status that likely played a large role in the differences seen in this thesis. Whether separate reference ranges for sex, the elderly, or geographic location are necessary will require additional studies building upon the foundational knowledge described in this thesis. Further assessments of cohorts with broad ranges of FA concentrations (low vs high) will provide specific targets for establishing cut-offs that correlate to disease risk. Overall, the findings from this thesis support correlations between FA status and health-related biomarkers in previous studies (94) and the use of large- 95

powered, healthy, international cohorts contributes foundational knowledge for developing baseline FA reference ranges to assess chronic disease risk across various groups.

6.2 Limitations

The entirety of the data sets and biological samples analyzed in this study was secondary data received from our international collaborators. This influenced the decision-making process of developing our research questions based on what variables were available to be analyzed.

Primarily, the lack of dietary data available in both cohorts limits the strength of our correlation analysis. Highlighting trends in specific food sources would have strengthened inferences made based on blood concentrations and provided more in-depth comparison to region-specific food consumption. Similar limitations due to the lack of physical activity data also persists. Further, as the MAILES cohort was developed to assess prostate cancer risk, the reasoning to exclude female participants is justified based on the original research question developed. However, the inclusion of female participants in our study would have provided results which are generalizable to the entire population and further expanded on sex-related differences in FA status.

Serum FA concentration is an aggregate of both dietary intake and endogenous lipid synthesis. As SFA and MUFA have the capacity to be produced endogenously, metabolic biomarkers of desaturase activity would help to distinguish the relative effect which dietary or genetic factors modify FA concentrations. Endogenous lipid production could limit the correlations seen for disease risk and certain SFA and MUFA as abnormal metabolic activity may be the largest contributor to FA concentration, not dietary intake.

Finally, the processing of blood serum and subsequent analysis of chromatograms conducted using OpenLab CDS EZChrom Edition 3.2.1 is limited by potential human error. For the Australian cohort, two different investigators performed lipid extraction and chromatogram analysis. A comparison analysis was conducted between the resulting FA concentrations of each investigator which yielded a CV <10% (results not shown). The lipid extraction technique used in this study remained consistent for all serum samples but, day-to-day intra and inter-individual variation was still possible. The internal standard (C19:0) was added to the sample manually and may exhibit small variabilities in quantity between samples. Similarly, chromatogram analysis requires training to manually identify individual peaks based on the RT of the sample using a standard reference. Peaks could also be mislabeled which is the responsibility of the investigator to manually check intra and inter-individual variation is possible to influence the values of smaller peaks between samples. All samples from the Singapore cohort were extracted and analyzed by the same investigator.

6.3 Future Directions

Future studies should continue to profile large-scale, global cohorts and follow the current best standards of practice in FA research to maintain comparability and generalizability of results. Reporting full profiles of individual FA as absolute concentrations is recommended over percent composition. When possible, the collection of dietary data coupled with blood plasma/serum samples should be conducted to provide a more robust portrait of what factors may be modifying individual FA status, and differences in global populations. Correlations between

FA concentrations and established biomarkers related to chronic disease should continue to be expanded past TG, cholesterol, age, BMI, and sex. Continuing to assess FA reference ranges in

various populations of healthy participants is necessary to establish what values are considered

“normal”, and whether region/sex/age specific recommendations should be developed. Profiling

“unhealthy” cohorts which exhibit low or high FA concentrations will begin to provide targets as to where cut-offs could be developed for use as a clinical tool for assessing chronic disease risk.

The methodology described in this study was chosen for its efficiency, generalizability, and high-reproducibly which may be used as a guide for future work.

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8 APPENDIX A

Table 8.1 Absolute measures of major fatty acids in plasma and serum

Fatty Acid Plasma (μg/mL) Serum (μg/mL) Difference (μg/mL) p value *

16:0 805.75 ± 25.90 853.61 ± 29.07 -47.86 ± 13.00 0.22

(PA)

16:1c9 101.04 ± 7.45 111.12 ± 10.66 -10.08 ± 4.89 0.44

(POA)

18:0 231.67 ± 5.66 245.74 ± 6.13 -14.06 ± 3.49 0.09

(SA)

18:1c9 805.54 ± 29.94 855.85 ± 37.60 -48.31 ± 18.45 0.32

(OA)

18:2n6 822.23 ± 21.37 863.39 ± 23.62 -41.16 ± 11.39 0.20

(LNA)

18:3n3 22.23 ± 1.12 23.07 ± 1.21 -0.83. ± 0.34 0.47

(ALA)

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20:5n3 48.26 ± 3.83 51.45 ± 4.41 -3.19 ± 0.90 0.59

(EPA)

20:4n6 223.63 ± 6.18 235.11 ± 6.45 -11.47 ± 3.25 0.20

(ARA)

22:6n3 71.88 ± 2.80 73.70 ± 3.24 -1.83 ± 1.49 0.67

(DHA)

Data are mean ± Standard error of means. Absolute (μg/mL) concentration measures of individual fatty acid plasma and serum mean based on 98 samples from the MAILES cohort. Difference mean was composed of the difference between each participants plasma and serum (plasma minus serum) levels. *Significant difference compared participant levels of plasma and serum, p<0.05.

Table 8.2 Relative percent composition of major fatty acids in plasma and serum

Fatty Acid Plasma (%) Serum (%) Difference (%) p value*

16:0 22.53 ± 0.18 22.75 ± 0.19 -0.22 ± 0.11 0.41

(PA)

16:1c9 2.71 ± 0.13 2.74 ± 0.14 -0.04 ± 0.03 0.85

(POA)

18:0 6.60 ± 0.06 6.69 ± 0.09 -0.09 ± 0.07 0.40

(SA)

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18:1c9 22.48 ± 0.34 22.40 ± 0.38 0.08 ± 0.17 0.88

(OA)

18:2n6 22.58 ± 0.45 23.66 ± 0.49 -0.08 ± 0.18 0.90

(LNA)

18:3n3 0.61 ± 0.02 0.60 ± 0.02 0.01 ± 0.00 0.99

(ALA)

20:5n3 1.41 ± 0.12 1.42 ± 0.12 0.00 ± 0.01 0.99

(EPA)

20:4n6 6.48 ± 0.16 6.51 ± 0.16 -0.03 ± 0.05 0.88

(ARA)

22:6n3 2.09 ± 0.08 2.04 ± 0.09 0.05 ± 0.04 0.66

(DHA)

Data are mean ± standard error of means. Relative percentage (%) composition measures of individual fatty acids in plasma and serum mean based on 98 samples from the MAILES cohort. Difference mean was composed of the difference between each participants plasma and serum (plasma minus serum) levels. *Significant difference compared participant levels of plasma and serum, p<0.05.

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Figure 8.1 Bland-Altman plot of the difference of plasma and serum fatty acids

A D ) ) l %

( 2 m

g 200 m u u ( r

m 0

100 s

u - r 5 10 15

e a s

m - 0 -2 s a

100 200 300 400 500 a l m p s -100 a e l

c -4 p

n e

-200 e c r n e f e f r i -6

e -300 d f average plasma + serum (%) f

i average plasma + serum (ug/ml) d E B ) % ) l

( 15

m / m g

500 u r u ( e 10 s

m -

u r

0 a e

500 1000 1500 2000 2500 m s

5 s -

a l a

-500 p

m e s c

a 0 l n p

e 10 20 30 40 50 -1000 r e e c f f n

i -5 e d r average plasma + serum (%)

e -1500 f f

i average plasma + serum (ug/ml) d

C ) l F m /

g 400 u (

) m % u

( 15

r 200 e m s u

- r

10 e a

0 s

- m

s 500 1000 1500 a 5 a l m p s -200 a e l 0 c p

n 10 20 30 40 e e c r n e -400 -5 f e f r i average plasma + serum (ug/ml) e d f f i -10 d average plasma + serum (%)

121

Differences in major fatty acids between 98 matched plasma and serum samples in absolute (A-C) and relative (D-F) terms. Major fatty acid Bland-Altman Plot of: 18:0 (A, D), 18:1 c9 (B, E), and 18:2 n6 (C, F) is displayed. For each participant, the difference was calculated subtracting the amount of plasma to serum and was compared to the average between the amount of plasma and serum. The dashed line The dashed line ( - - - ) represents the mean difference between plasma and serum, with each dotted line ( . . . . . ) reflecting the lower limit (LL) and upper limit (UL) with 95% confidence below and above the mean. In 2A the LL is – 81.71 µg/mL, mean is -14.06 µg/mL, and UL is 53.59 µg/mL. In 2B, the LL is - 406.4 µg/mL, mean is - 48.31 µg/mL, UL is 309.70 µg/mL. For 2C, LL is – 105.00 µg/mL, mean is -10.08 µg/mL, UL is 84.84 µg/mL. For 2D, LL is -1.40 %, mean -0.09 %, and UL is 1.22 %. For 2E, LL is -3.31 %, mean 0.08 %, and UL is 3.50 %. For 2F, LL is – 3.53 %, mean -0.08 %, and UL is 3.36 %.

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8.1 Appendix B

Table 8.3 Mean concentration of select fatty acids across multiple decades Total Decade Fatty Acid Common Name Mean SD 40-49 50-59 60-69 70+ 3584.67 ± 3490.30 ± 3338.58 ± 3064.66 ± 16:0 Palmitic acid 3377.02 1229.73 1302.78 1336.35 1174.77 997.68 947.86 ± 940.09 ± 905.53 ± 802.70 ± 18:0 Stearic acid 902.32 275.66 284.72 294.50 274.19 212.39 441.18 ± 419.65 ± 411.05 ± 366.17 ± 16:1c9 Palmitoleic acid 410.45 276.39 272.27 256.32 262.23 316.43 3326.91 ± 3090.00 ± 2965.07 ± 2794.29 ± 18:1c9 Oleic acid 3044.74 1221.38 1396.73 1164.05 1146.44 1132.38 3372.08 ± 3198.70 ± 3013.36 ± 2783.13 ± 18:2n6 Linoleic acid 3096.36 909.12 924.59 872.85 912.55 828.40 797.51 ± 799.23 ± 755.50 ± 695.26 ± 20:4n6 Arachidonic acid 764.14 222.03 212.24 215.93 243.19 191.72 90.60 ± 88.80 ± 90.55 ± 82.38 ± 18:3n3 Alpha-linolenic acid 88.28 50.15 51.54 45.97 60.36 38.47 121.95 ± 151.46 ± 173.24 ± 187.35 ± 20:5n3 Eicosapentaenoic acid 158.58 121.37 67.27 99.22 109.71 180.49 69.12 ± 71.70 ± 72.97 ± 68.83 ± 22:5n3 Docosapentaenoic acid 70.83 23.47 22.07 24.40 24.35 22.39 190.29 ± 208.59 ± 233.47 ± 238.19 ± 22:6n3 Docosahexaenoic acid 217.71 94.8 81.60 94.04 95.56 99.60 Values expressed as μmol/L ± SD

SD, standard deviation

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