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Montmayeur JP, le Coutre J, editors. Fat Detection: Taste, Texture, and Post Ingestive Effects. Boca Raton (FL): CRC Press/Taylor & Francis; 2010.

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Fat Detection: Taste, Texture, and Post Ingestive Effects.

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Chapter 22Food Intake and Obesity: The Case of Fat

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22.1. INTRODUCTION

The prevalence of overweight and obesity today is unprecedented and is steadily and globally rising (Balkau et al., 2007). Why are so many apparently healthy individuals consuming more calories than they need? Variations in the development and consequences of obesity have been proposed to depend on genetic predisposition combined with various environmental factors that lead to a chronically unbalanced energy intake relative to its expenditure. The variables that could contribute to fluctuations in body weight and composition have been proposed to include genetics (Corella and Ordovas, 2005; Warensjo et al., 2007; Benzinou et al., 2008; Masuo et al., 2008; Pichler et al., 2008); activity (Hill and Melanson, 1999), including nonexercise activity thermogenesis (Levine et al., 1999); diet composition (Labayen et al., 2003; Layman et al., 2003; Meckling et al., 2004; Cornier et al., 2005; Capel et al., 2008) and structure (Brand-Miller et al., 2002; McMillan-Price et al., 2006); and metabolic phenotype (Cornier et al., 2005). There is also evidence that today’s “obesogenic environment,” which includes easy, 24 h access to high-energy foods, large portion sizes, and a social environment that promotes a sedentary lifestyle, is contributing to obesity (Poston and Foreyt, 1999). Yet, not all individuals become obese in response to this obesogenic environment, indicating that there is a strong genetic component involved in the development of obesity (Wardle et al., 2008). Individuals are different, and the causes and solutions for their overweight condition are therefore necessarily also different.

Because of the inherent biological differences between people the solution to the obesity epidemic will not be easy. In fact, it is unlikely that there will be “one” solution. The failure of dietary modification approaches for obesity treatment and prevention has been illustrated clearly in weight loss studies time after time, leading to the frustrating conclusion that diet-based weight loss approaches are largely unsuccessful in the long-term. Even more frustrating is the fact that the failure of dietary approaches to weight loss is consistent even across different types of diets, from national guidelines to popular or fad diets. Researchers found that after 1 year of self-help vs. a commercial program (in this case weight watchers), study participants’ weight change ranged from a loss of 28 kg to a “gain” of 12 kg on the commercial program, and a loss of 26 kg to a “gain” of 15 kg on the self-help program (Heshka et al., 2003). After 2 years, the ranges were from −23 to +21 kg, and −26 to +30 kg respectively. Over the 2 year period as many as 27% of participants dropped out of the study. Another recent study showed even lower adherence rates, with up to 42% of participants dropping out of their weight loss program by year 1 (Dansinger et al., 2005). Furthermore, regardless of diet type (Atkins, Ornish, weight watchers, and zone diets) as early as month 1 of the study, participants’ adherence levels based on 3-day food records and telephone interviews were at 50%, meaning that they adhered to the diet recommendations only about half the time. By the end of year 1, adherence levels were as low as 30% with an average weight loss of just 2–4 kg. Only about 25% of the 160 participants were able to sustain the recommended minimum 5% weight loss over the year-long period, which is associated with improved metabolic markers of disease risk. Even fewer participants, just over 10%, maintained the recommended 10% weight loss.

The evidence clearly indicates that a one-size-fits-all approach is inconsistent with a condition as refractory to dietary intervention as obesity. Even with the current array of available diets from low-carbohydrate to low-fat diets, from commercial prepared-meal approaches to Internet-based programs, the successful maintenance of weight loss has been an elusive target.

A logical conclusion in the face of high variability among individuals and in light of recent advancements of “omic” technology is to pursue the identification of genotype as a way to identify biochemical differences in response to dietary intervention. Identifying genes responsive to nutrients and their polymorphisms that relate to varying health outcomes is already being used for drug discovery. These same relationships may lead to the eventual use of dietary prescriptions to specific individuals. Yet genotype has proven to be difficult to assign directly to variations in phenotype. Genes do not change in response to diet and environment, but phenotype does. The span between genes and metabolites involves many biochemical steps, the sensitivities and specificities of which respond to diet and the environment. Metabolites are the functional outcomes of the interactions between genotype and environment. Hence, the clinical practice of assigning individual health status and risk will likely require direct measures of metabolism rather than genetic predispositions for errors.

Studies that simultaneously quantify the lipid metabolites—substrates and products of biochemical pathways—in tissues and biofluids have proven to be extremely valuable in revealing dysregulation in biochemical pathways associated with other metabolic diseases such as atherosclerosis. This chapter describes the use of comprehensive analysis of lipids associated with various biochemical pathways combined with specific dietary challenges to reveal the dynamic nature of an individual’s metabolic phenotype (German et al., 2007). Circulating lipids are derived from both diet and endogenous metabolism. These lipids are highly dynamic, interactive biological molecules that make up most cellular components and signaling molecules, and they dictate energy partitioning and control of food intake.

Remarkably, although food intake is central to the problem of obesity, the vast majority of studies attempting to explain the variations in metabolism that could account for excess intake and for its metabolic consequences have examined individuals and their various physiological, metabolic, and endocrine characteristics in the fasted condition. Furthermore, studies to date have examined only a subset of the metabolites representing the various biochemical pathways that are both responsive to dietary intake and associated with energy metabolism. Both of these decisions—to largely avoid examining the fed state and to constrain metabolic interrogation to a small subset of metabolites—have severely limited the ability of studies of energy metabolism to clarify precisely how diet as a variable impacts weight regulation.

Clinically, lipids are measured in the fasted condition, yet this is the period when most indices of diet and its effects on lipid metabolism are minimal. In this chapter, the principles of metabolomics are extended into two directions—input variables as food metabolite composition and output variables as the subsequent effects on post-prandial metabolism within individual humans. This approach is providing insights into the metabolic regulation associated with energy balance and obesity.

The practical application of a challenge approach that measures the fluxes through specific biochemical pathways is the ability to personalize dietary recommendations based on an individual’s metabolic phenotype. We propose that this approach would have a profound impact on the long-term success of diet and lifestyle-based interventions. Not only would metabolically appropriate diet and lifestyle modification be more effective in producing measurable improvements in health, perhaps even more importantly, it would increase patient acceptance and long-term adherence. Currently, people are wary of dietary recommendations because they seem to be changing every day. One day it is “beneficial” to consume eggs, the next day it is “deleterious” to consume eggs. The truth is that for some individuals eggs are beneficial while for others the cons outweigh the pros and for them egg consumption is a net negative. If we measure with accuracy and specificity the metabolic responses of individuals to specific meals and food items, and provide clear evidence that specific dietary components are causing harm whereas others are beneficial, the acceptance of recommendations will be much higher. Instead of rigidly imposed levels of acceptable intake of foods and food components that are deleterious to the health of “the average person” individuals would be free to choose foods that are palatable and enjoyable to them in doses that are metabolically appropriate for them. The success of long-term dietary and lifestyle approaches that prevent obesity and produce weight loss will ultimately depend on the acceptability of those regimens to individuals living their normal lives.

22.2. FAT METABOLISM

22.2.1. Fat Digestion

The successful digestion of fat is one of the most impressive biological feats of animals. Humans, in particular, are genuinely remarkable in their ability to absorb energy dense but highly insoluble triglycerides (TG) from complex food matrices, to deliver these lipids to adipose tissue for storage, and thence to mobilize from storage and distribute them to peripheral tissues according to varying energy requirements. The processes of digestion, absorption, transport, and distribution are highly involved and utilize a wide spectrum of active molecules. The highly interactive nature of digestion begins almost immediately after commencing to eat. The digestive system itself is complex, with many structural, chemical, and enzymatic systems combining to successfully digest and absorb impressive quantities of lipids.

The importance of lipids to the signaling of metabolic regulation was generally ignored in early research largely because lipids were not considered to stimulate insulin, and it was thought that there were no taste receptors and sensory responses to lipids or their digestion products. However, the importance of fats to food intake regulation from hunger to satiety and metabolic processes from fuel prioritization to energetic efficiency is being increasingly recognized (Feinle-Bisset et al., 2005).

It is well known that the sensory qualities of foods are critical to dietary preference and total food consumption. Taste, among other variables that influence food intake, is an important determinant of what and how much we eat. Until recently, fats were thought to influence taste perception only on the level of texture and consistency, since pure, fresh fatty acids (FA) are flavorless (Mizushige et al., 2007). However, new evidence suggests that a fat-sensing mechanism separate from the detection of viscosity exists in the mouth, and is responsible for “tasting” fat (Garcia-Bailo et al., 2008). The putative fat taste receptor is the integral membrane protein CD36, which by unknown sensory mechanisms detects free FA released by lingual lipase and involves the prolonged depolarization of potassium channels on taste receptor cells (Houdali et al., 2003; Steer et al., 2003; Fukuchi et al., 2004; Gilbertson et al., 2005; Laugerette et al., 2005; Garcia-Bailo et al., 2008). There is also evidence that β-endorphin, an opioid peptide, and dopamine, a neurotransmitter, are released in the brain following fat ingestion, suggesting that fat perception in the mouth may bypass traditional flavor mechanisms and instead stimulate directly the brain’s reward system through signaling networks that have yet to be worked out (Mizushige et al., 2007).

The stomach plays a significant role in the overall digestive process by acting as a strong homogenizer as the fat exits through the muscular pyloric valve into the duodenum. This emulsifying action of the pylorus dramatically increases the surface area of the emulsified oil, affording much greater access to lipolytic enzymes. The emulsified oil exits the stomach into the small intestine, and with the arrival of the bile and pancreatic secretions, the process of fat digestion and absorption ensues.

Bile contains primarily bile acids, cholesterol, and phospholipids in an aqueous micellar mixture produced in the liver and accumulated in the gall bladder. Release of bile into the intestine is stimulated by the intake of food. Bile itself is regulated via a variety of factors that are now emerging as among the most important biochemical steps in human physiological regulation. Bile acids, derived from cholesterol, are now known to constitute one of the central signaling systems in the fasted–fed transition. Their abundance is sensed by multiple nuclear receptors in many tissue types and is controlled by a wide variety of genetic, physiologic, and dietary determinants (Thomas et al., 2008). Bile acid production, release, recirculation, and conjugation are stimulated in response to and regulated by the quantity and composition of dietary lipids (Costarelli and Sanders, 2001).

These various predigestive and digestive events then cascade into a wide range of physiologic, metabolic, and endocrine processes (Feinle et al., 2001) that can influence whole body metabolism. The diversity of metabolism within individuals can be considered their metabolic phenotype.

22.2.2. Metabolic Phenotype

Metabolic phenotype has been described in relation to the sum of an individual’s genetic blueprint and environment and their interaction to manifest physical and biochemical characteristics (German et al., 2003). Metabolic phenotype is influenced in part by developmental plasticity and imprinting early in life, and in part by the interactions of multiple influential factors over time (Figure 22.1). Both intrauterine signaling and early childhood influences determine the full expression of genotype, which in turn establishes the foundation of metabolic phenotype. For example, polymorphisms in FA desaturase genes involved in long-chain polyunsaturated fatty acid (PUFA) synthesis are associated with increased benefit from breastfeeding on IQ, illustrating the crucial effects of early imprinting (Caspi et al., 2007). The epigenetic control of gene expression by dietary and environmental factors in utero determines lifelong health trajectories through DNA methylation and chromatin remodeling (reviewed in Nafee et al., 2008).

FIGURE 22.1. Determinants of metabolic phenotype.

FIGURE 22.1

Determinants of metabolic phenotype. Metabolic phenotype is determined by a wide variety of factors including genotype, environment, developmental plasticity, and imprinting early in life. Both intrauterine signaling and early childhood influences determine (more...)

Over time, the cumulative and immediate effects of lifestyle (diet, exercise, smoking, alcohol intake, micronutrient supplementation, etc.), health state, chronic as well as acute diet, extent of irreversible tissue damage, toxic exposure, mental, and emotional health, fitness level, and doubtless other factors all influence the current metabolic phenotype of an individual at any point in time. Major dietary modulations such as switching from a vegetarian diet to a low-meat diet or a high-meat diet (Stella et al., 2006) and metabolic status such as insulin sensitivity coupled to macronutrient composition (Cornier et al., 2005) produce measurable differences in metabolic phenotype. Even presumably minor dietary preferences such as the consumption of chocolate affect plasma metabolic profiles (Rezzi et al., 2007). Metabolic phenotypes differ and are measurable among cultural and ethnic groups as well as among populations with widely different dietary patterns (Holmes et al., 2008). Gut microbiome differences can also be detected by metabolic phenotyping (Li et al., 2008).

22.2.2.1. Measurement of Metabolic Phenotype

An individual’s current metabolic phenotype can be assessed and evaluated by a variety of complementary approaches, each of which is designed to reveal an important aspect of overall metabolic functioning at a given time (Figure 22.2). The evaluation of symptoms in a clinical setting has been widely used in a variety of diagnoses. Clinical assessments of biochemical functions include the measurement of blood glucose, insulin, lipids and other biomarkers, anthropometric measurements such as body mass index (BMI, kg/m2) and waist to hip ratio, and physiological measurements such as blood pressure. Imaging technology provides the ability to visualize function and includes radiology, magnetic resonance imaging (MRI), computerized axial tomography scans, dual energy x-ray absorptiometry, and others. Functional assessment of metabolic phenotype employs in vivo techniques that reveal the functioning of a system in real time, which include real-time MRI, stress testing, flow-mediated vasodilation of the brachial artery (FMD), and measurement of postprandial response, among others.

FIGURE 22.2. Measurement of metabolic phenotype.

FIGURE 22.2

Measurement of metabolic phenotype. Current metabolic phenotype can be measured in a number of ways. Clinical evaluation includes a variety of scales, assessors, and diagnoses. Clinical measures of biochemical function include the measurement of blood (more...)

22.2.2.2. Functional Assessment of Metabolic Phenotype

Real-time and in vivo measures of metabolic function are a logical next step for informative assessment methodologies to understand the dynamic nature of metabolism. There are theoretical advantages of assessing health as the progressive metabolism of nutrients by measuring their flux dynamics in the blood after a meal. But are they practical? This concept is being applied using “omic” technologies to measure various processes in response to pharmacologic stimuli over time. Plasma FA profiles are associated with disease risk such as the risk of developing insulin resistance (reviewed in Steer et al., 2003). We propose that the multifactorial nature of the long-term and short-term influence of diet on the metabolic phenotypes of different individuals is reflected in the relative and absolute composition of plasma lipids measured comprehensively as FA within separated lipid classes (Figure 22.3).

FIGURE 22.3. Determinants of plasma FA composition.

FIGURE 22.3

Determinants of plasma FA composition. The determinants of plasma FA composition include metabolic phenotype, genetic variation, endogenous metabolism, intestinal microflora or gut microbiome, and diet. Gene expression and enzyme function are influenced (more...)

22.2.3. Determinants of Plasma Lipid Composition

As key regulators in integrative lipid anabolic and catabolic pathways, FA composition of circulating lipid metabolites are influenced by diet (Houdali et al., 2003; Fukuchi et al., 2004; Ntambi and Miyazaki, 2004), genetics (Schaeffer et al., 2006; Baylin et al., 2007; Warensjo et al., 2007), pharmacologic (Kim et al., 2000), hormonal (Ghebremeskel et al., 2004; Thomas et al., 2005; Bitsanis et al., 2006), metabolic status (Sjogren et al., 2008), and environmental triggers (Kis et al., 1998). The determinants of plasma FA composition include metabolic phenotype (Figure 22.1), genetic variation, endogenous metabolism, intestinal microflora or gut microbiome and, of course, diet (Figure 22.3). Gene expression and enzyme function are influenced by both genotype and endogenous metabolic function, including hormonal modulation, feedback regulation, and substrate competition. The influence of chronic and short-term diet includes the effects of macronutrients and micronutrients as well as diet quality. The relative intakes, composition, and structure of protein, carbohydrate, and fat, are important. Saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), trans-fatty acids (trans-FA), and PUFA all modulate plasma lipids, as does the relative intake of ω3 and ω6 FA. The digestibility of carbohydrates, which influences glycemic index, the absolute intakes of fiber—both soluble and insoluble—and other characteristics of starches such as viscosity all have important effects on metabolism and lipid status. The intake of essential micronutrients, the nutrient density of food, and other aspects of diet quality such as meal frequency also play important roles in diet-mediated modulation of plasma FA composition.

22.2.4. Postprandial Response to Challenge

The importance of postprandial lipid metabolism was first reported in 1979 (Zilversmit, 1979), when the hypothesis was proposed that plasma chylomicrons, their remnants, and very low-density lipoprotein (VLDL) remnants, together termed TG-rich lipoproteins (TGRL), are mediators of atherogenesis. The term postprandial lipemia was coined and studies examining the impact of high-fat meals on plasma lipoproteins and TG excursions ensued. Around the same time, the effects of FA composition on the endothelial function of arteries was under investigation (Nordoy, 1979). Today the postprandial literature continues to discuss primarily the effects of varying the relative fat content and FA composition of meals on postprandial lipemia and endothelial function (de Koning and Rabelink, 2002). Postprandial lipemia is associated with an increased risk for myocardial infarction (Stampfer et al., 1996) and is increased in patients with cardiovascular disease (Karpe, 1997).

Research on postprandial response has expanded to include a variety of approaches for using challenge meals to investigate acute metabolic and physiological responsiveness to meals. These include the use of oral fat tolerance tests (OFTT), oral glucose tolerance tests, and tests of response to different types of meals (Rudolph et al., 2007), food components such as polyphenols (Papamichael et al., 2008), diet quality such as the use of oils that had been previously used for deep frying (Williams et al., 2001), medications (Boquist et al., 1998; Wilmink et al., 2001; Boquist et al., 2002; Ceriello et al., 2002), lifestyle factors such as exercise (Silvestre et al., 2008; Weiss et al., 2008), and meal frequency (Murphy et al., 1996). Postprandial responsiveness has been used both in a long-term sense to investigate the effects of modulating chronic diet and lifestyle (Fuentes et al., 2008) and in an acute sense to investigate the immediate effects of varying meal composition on postprandial metabolism (Chong et al., 2007).

22.2.5. Postprandial Assessment

In addition to the effects of varying diet, lifestyle, and meal composition on postprandial lipid metabolism and endothelial function, the challenge approach has been used to investigate physiological effects on satiety and gastric emptying (Hlebowicz et al., 2007), appetite and energy intake (Bowen et al., 2007), substrate oxidation (Roberts et al., 2008; Stiegler et al., 2008), glycemic response (Tuomasjukka et al., 2007; Leeman et al., 2008), insulin response (Milton et al., 2007; Poppitt et al., 2007; Maki et al., 2008), energy expenditure (Smeets et al., 2008), enzyme activity (Chong et al., 2008), nitrogen utilization (Juillet et al., 2008), colonic fermentation (Nilsson et al., 2008), and absorption of lipid peroxidation products (Gorelik et al., 2008) as well as carbohydrates with varying digestibility characteristics (Nilsson et al., 2008).

22.2.5.1. Oral Fat Tolerance Tests

The concept of a nutritional challenge to model metabolism is not new. The oral glucose tolerance test, used since the 1950s (Mosenthal and Barry, 1950), is a standardized test involving the administration of 75 g of glucose in a liquid solution, followed by the measurement of blood glucose and insulin over 120 min. Yet, the OFTT is not standardized as to amount, composition, and structure of the fat used, or even the name used for the challenge. Some studies administer 80 g fat (Murphy et al., 1996; Cortes et al., 2006), others 50 g fat (Gudmundsson et al., 2000; Jackson et al., 2005), still others administer fat as a percentage of energy relative to body size (Shimabukuro et al., 2007). The term “high-fat meal” is used throughout the literature, yet the composition of the high-fat meal varies widely from study to study and spans the spectrum from dairy fat high in SFA to extra-virgin olive oil enriched in MUFA, to safflower oil enriched in PUFA. The OFTT is also administered under various names, including “oral fat load,” “oral fat-loading test,” “high-fat meal,” and “high-fat mixed test meal.”

This lack of standardization in the fat challenge model has made it impossible to compare across studies to conclude consistently any biological discoveries generated from this method. There is an obvious need to standardize the OFTT so that the acute metabolic responsiveness of different groups of individuals on different treatments can be compared. Only by standardizing the challenge can the variable in question be studied without potential confounding effects from the variation in the challenge itself. With such standardized experimental protocols in place, it is possible to take advantage of the substantial knowledge of lipid biochemistry and the analytical tools of metabolomics to study the variation in metabolic responses to a dietary lipid challenge in individual humans.

22.2.6. Challenge Design

The dietary challenge approach, coupled to lipid metabolomics, is an experimental instrument for measuring the dynamic and interactive changes of lipid pathways in the postprandial state. In the near term, designing dietary challenges will depend on the targets of biochemical pathways and the responsiveness of physiological outcomes in question such as blood pressure and insulin sensitivity. The complexity of lipid pathways is mediated by diet in several ways: (1) as substrates and products of these pathways, (2) as modulators of enzymatic activities, (3) as stimulators of hormonal regulation of enzymatic activities, and (4) as effectors of gene expression regulation. Developing a standardized challenge to scrutinize lipid metabolism combines lipid biochemistry and effects from food, points of regulation at various transcriptional and posttranslational levels, cellular lipids and abundance, and interactions between organ systems and the plasma compartment.

22.2.6.1. Fasted vs. Fed

Using a response-to-challenge model adds several dimensions to existing methods for revealing lipid metabolism. The fasted condition is the metabolic state when the influence of diet is least detectable. In the fasted condition of a healthy individual, insulin’s counterregulatory hormones—glucagon and epinephrine—peak while circulating insulin concentrations are low. As a result, free fatty acids (FFA) are liberated from adipose tissue and are oxidized by extra-hepatic tissues: the liver and kidneys are gluconeogenic and hepatic glycogenolysis is active. During this state, energy partitioning shifts to the catabolic, oxidative state. Conversely, in the fed state, insulin peaks, glucagon and epinephrine are suppressed, and energy partitioning shifts to the anabolic state. Circulating lipids change in response to both of these states, yet the fasted condition has received the most attention. To achieve a complete picture of lipid metabolism, both states need to be rigorously studied and compared. Until now the fasted state has been the focus of investigation because the fed state was thought to be a confounding factor for understanding metabolism. However, if an individual is fasted overnight and then subjected to a well-defined meal, their metabolic responses to that specific meal can be quantified. Rather than being a confounding factor, the fed state becomes a window into the functioning of metabolism following a metabolic challenge—the meal.

There are several advantages to measuring lipids in the fed condition to discriminate individual variation as metabolic phenotypes. A diet challenge augments not only substrates, products, and intermediates of existing pathways, but also metabolic, hormonal, intestinal, and stress signals, all of which are points of potential individual discrimination. When designed appropriately, the challenge can be a tool to perturb specific pathways of interest. A challenge in which all lipid pathways are interrogated and measured simultaneously would be ideal, but this feat would require formulation of dietary components that are substrates to all pathways in question, and not abundant in the food supply and circulation. Assessment of pathway activities can be done for isolated pathways with ease. Intermediates of pathways that are not highly regulated such as stearidonic acid (SDA, 18:4n3) or γ-linolenic acid and odd-chain FA may serve as valuable assessors of pathway activity.

22.2.6.2. Inclusion of Time

The concentrations of circulating lipids are dynamic, and actively respond to environmental stimuli and diet. One time point only reveals a snapshot, whereas metabolites measured over a time course provide a short film of the dynamic interactions between lipid pathways. Measuring changes of lipid metabolism provides a valuable assessment of metabolic regulation. For example, postprandial circulating TG is proving to be independent predictor of cardiovascular disease risk when compared with measurements in the fasted condition (Bansal et al., 2007). In the future, as tools develop to control the composition and structure of food coupled to more comprehensive measurements of repeated time samples using lipid metabolomics, investigators and clinicians will gain a powerful diagnostic tool to understand an individuals’ postprandial lipid metabolism. Metabolic outcomes of interest will determine the appropriate time intervals and frequency of blood draws (Cohn et al., 1988).

22.2.7. Crossover vs. Placebo-Controlled Trials

The high interindividual variability in postprandial lipid metabolism as well as the multifactorial and complex dynamic nature of metabolism both require that the study of postprandial response to challenge use an adequate study design capable of detecting signal in an environment that is prone to high noise. In other words, because small variations in the exact composition of meals result in variable responses and because the influence of diet is variable depending on the individual, studies must be designed to capture both variables. Thus, researchers have used the crossover study design in order to increase statistical power. The traditional approach of using a randomized, placebo-controlled design, in which half of the participants are randomized to treatment and the other half are randomized to the control arm, fails when the aim is to understand the phenotypic differences among individuals. The crossover design is powerful when each participant acts as his or her own control, undergoing both the treatment and control arms repeatedly to obtain estimates of intraindividual variation.

22.2.7.1. Interindividual Variation in Postprandial Metabolism

Early postprandial research revealed that the interindividual variation in response to meals and diets is high. Many components contribute to an individual’s postprandial response (Figure 22.1). Differences between people in the various aspects of postprandial response are due to multifactorial interactions involving genetic background, intrauterine and early childhood imprinting (which comprise developmental plasticity), the chronic effects of diet and lifestyle, exposure to toxins and other environmental factors, as well as recent short-term and acute aspects of diet and lifestyle. Gender and BMI influence peak/nadir and time courses of postprandial hormonal responses (Carroll et al., 2007). Metabolic status such as obesity determines circulating postprandial leptin, which in turn depends on the macronutrient composition of the challenge (Romon et al., 2003). Gender also influences lipid metabolism in a variety of ways, including greater VLDL secretion rates and lower postprandial TG concentrations in women compared with men (reviewed in Mittendorfer, 2005). Differences in postprandial lipid metabolism are also caused by normal dynamic fluctuations in metabolism as part of diurnal rhythms and other cyclical events. For example, in women, lipid metabolism is affected by the phase of the menstrual cycle (Gill et al., 2005). Other determinants include health status (e.g., diabetic vs. healthy) (Nappo et al., 2002), age (Lin et al., 2007), and even place of residence, which reflects cultural differences as well as differences in food availability and composition (van Oostrom et al., 2004).

Some aspects of interindividual variation in lipid metabolism and its responses to diet have been assigned to specific genetic variations. For instance, polymorphisms in the FADS1 and FADS2 genes for desaturases, which metabolize essential FA, contribute not only to differences in membrane FA composition, but also the susceptibility to inflammatory disease (Schaeffer et al., 2006). The interaction of genes and diet determines the phenotypic outcome of a genotype (Ordovas et al., 2002). For example, in 6% of 470 healthy women and men, the combination of a variant lipoxygenase genotype and increased dietary intake of the ω6 FA resulted in increased intima-media thickness, a measurement of atherosclerosis progression (Neufeld et al., 2004), whereas, the intake of ω3 FA was negatively associated with intima-media thickness. On the other hand, in people without the lipoxygenase genotype variant, dietary FA intake did not influence the extent of atherosclerosis in the carotid artery.

The developing field of nutrigenomics has not yet had the same success with postprandial variations, yet the data from existing postprandial studies imply that this will be a fertile field of investigation. Strikingly, Burge et al. (2003) recently found coefficients of variation for plasma TG concentrations and areas under the curve over 6 h in response to a lipid challenge as high as 98% (Table 22.1). This study included six similarly healthy, young, white, male subjects, indicating that even within a very narrowly defined group of individuals, variation in postprandial responses is high. Such high signals within healthy individuals provide a significant opportunity for nutrigenomic research, which is typically frustrated by small variations in most metabolic outcomes.

TABLE 22.1

TABLE 22.1

Coefficients of Variation (CV) for Plasma TG Metabolites

22.2.7.2. Intraindividual Variation in Postprandial Lipid Metabolism

Both interindividual variation and intraindividual variation are assumed to contribute to the observed variability of response to challenge. However, this issue has not been adequately addressed. The crossover design in and of itself allows researchers to better account for inter- and intraindividual variation, but it does not distinguish between them. Statistical methods are employed to normalize response to the individual. For example, repeated measures ANOVA takes into account intraindividual variation to estimate responses. However, this essentially eliminates the intraindividual variation rather than addressing it directly. We investigated the contribution of inter- vs. intraindividual variation in postprandial lipid metabolism to resolve whether the variability that is observed in response to a meal results from noise or true biological variation (Zivkovic, 2008).

Three normal individuals were tested for their responses to the same challenge meal by three discrete tests over a period of several months. To capture the full extent of inter- and intraindividual variations, the subjects were explicitly instructed “not” to control diet and lifestyle during that time period. The three individuals tested were all considered metabolically healthy and had normal clinical chemistries (TG < 150 mg/dL, total cholesterol < 200 mg/dL, glucose < 126 mg/dL) and were all normal weight. Despite the lack of dietary and lifestyle control, the three individuals were consistent within their own responses and yet sufficiently different from each other that their lipid responses were all distinguishable, meaning that the interindividual variation among the subjects was significantly greater than the intraindividual variation within each subject over the three test days. The implications of these findings are first, that the high variability in postprandial response is true biological variability among individuals rather than noise or random fluctuation in metabolites in response only to short-term dietary changes. Second, the details of an individual’s metabolic phenotype can be uncovered through the approach of combining “omic” technology with the in vivo assessment of metabolic function (e.g., the response to challenge approach).

22.2.8. Lipid Biochemical Pathways

The complex structures and composition of cellular lipids are determined by diet, endogenous metabolism through the FA synthase (Maier et al., 2008), desaturation, and elongation pathways (Nakamura and Nara, 2004), and mono- and peroxisomal β-oxidation (Wang et al., 2006). There are three endoplasmic reticulum membrane-bound desaturases in humans: stearoyl CoA desaturase (SCD), catalyzing the synthesis of MUFA from SFA; Δ6-desaturase and Δ5-desaturase, involved in synthesizing highly unsaturated FAs from dietary PUFA (Nakamura and Nara, 2004). Seven distinct human elongase subtypes—based on endoplasmic reticulum tissue expression—are involved in FA chain elongation of SFA, MUFA, and PUFA (Wang et al., 2006).

22.2.8.1. MUFA

SCD is a lipogenic enzyme that catalyzes the synthesis of MUFA, integral components of membrane phospholipids, TG, wax esters, and cholesterol esters (Ntambi et al., 2002). Endogenous MUFA are synthesized via the insertion of a cis-double bond by SCD on SFA with 12–19 carbons in length (Nakamura and Nara, 2004). SCD expression and activity are regulated by diet, hormones, pharmacology (reviewed in Nakamura and Nara, 2004), and development (Zhang et al., 2005).

SCD is predominately expressed in liver, adipose (Nakamura and Nara, 2004; Sjogren et al., 2008), pancreas and brain (Zhang et al., 2005), skeletal muscle (Le et al., 2008), testis, and epididymis. This enzyme is regulated dietarily through induction by carbohydrates (Saether et al., 2003b; Wang et al., 2006), fructose (Le et al., 2008), vitamin A (Miller et al., 1997), PUFA (Nakamura and Nara, 2004; Wang et al., 2006), protein type (Tovar et al., 2005), chronic alcohol (Umeki et al., 1984; Pawlosky and Salem, 2004), and plant sterols (Leikin and Brenner, 1989); developmentally (Zhang et al., 2005); pharmacologically through peroxisome proliferator-activated receptor (PPAR)-α agonists (Riserus et al., 2005; Wang et al., 2006) and PPAR-γ agonists (Takasawa et al., 2008); hormonally by insulin (Wang et al., 2006; Flowers et al., 2007), leptin (Wang et al., 2006); glucocorticoids (Marra and Alaniz, 1995; Saether et al., 2003b); estrogen (Lippiello et al., 1979; Marra and Alaniz, 1995; Hermier et al., 1996); mineralcorticoids, testosterone (Marra and Alaniz, 1995; Saether et al., 2003b); follicle-stimulating hormone (Saether et al., 2003b); dehydroepiandrosterone in rats and mice, but not in guinea pigs (Imai et al., 2001); thyroid hormone (Waters et al., 1997); and exercise (Ikeda et al., 2002).

Based on animal data, SCD has received much attention as an important regulator of diet-induced obesity and insulin resistance (Flowers and Ntambi, 2008). Global deletion of SCD1 in mice results in resistance to high-fat diet-induced obesity and glucose intolerance (Ntambi et al., 2002), yet recently, leptin-deficient mice also deficient in SCD were reported to develop severe diabetes (Flowers et al., 2007).

The practical application of metabolite profiling to describe metabolic function of SCD activity in humans has been attempted (Warensjo et al., 2006, 2007). Currently, an index of SCD activity is used as the ratio of its products to precursors in circulation and tissues. A case-cohort study demonstrated a higher ratio of 18:1n9/18:0 in adipose of insulin resistant compared with healthy controls. Yet, the ratio of adipose 16:1n7/16:0 was not different between these two groups (Sjogren et al., 2008). Because 18:1n9 is abundant in the food supply, the application of the SCD index is confounded by habitual and acute diet. Furthermore, the SCD index in plasma does not necessarily accurately reflect the activity among various organ systems that contribute to the net flux of lipids into and out of plasma. In obese individuals, SCD is up-regulated in skeletal muscle associated with low rates of FA oxidation, increased TG synthesis and increased MUFA in muscle lipids. This phenotype of coexisting dysregulation of muscle metabolism was mimicked by overexpressing human SCD in myotubes of lean individuals (Hulver et al., 2005). An SCD activity index in plasma and expression in adipose was increased in insulin-resistant individuals in response to insulin-sensitizing therapy with a PPAR-α agonist (Riserus et al., 2005). The use of animal models and estimates of circulating FA ratios have not clarified the metabolic function and clinical relevance of SCD in humans.

22.2.8.2. PUFA

PUFA are essential for maintaining membrane composition, the various structural properties of membranes and regulation of transcription factors for energy metabolism (Jump et al., 2005), immune function (Brassard et al., 2007), growth and development (Williard et al., 2001), visual and neuronal development (Salem et al., 2001; Kuperstein et al., 2005), and eicosanoid synthesis (Schmitz and Ecker, 2008). The mammalian PUFA pathways consist of a series of desaturation and elongation reactions that convert 18-carbon essential FA in the diet to 24-carbon intermediates, followed by a peroxisomal retroconversion reaction that forms the final 22-carbon product (Williard et al., 2001) (Figure 22.4).

FIGURE 22.4. Regulation of PUFAs using the omega-6 pathway as a model.

FIGURE 22.4

Regulation of PUFAs using the omega-6 pathway as a model. Nutritional, hormonal, pharmacological, and developmental regulation of expression, and activity of Δ5- and Δ6-desaturase and elongase enzymes.

The Δ5- and Δ6-desaturase enzyme systems are most highly expressed in liver, followed by brain, skeletal muscle, lung, heart, placenta, kidney, pancreas (Cho et al., 1999a,b), intestine (Garg et al., 1992; Dias and Parsons, 1995), mammary tissue (Rodriguez-Cruz et al., 2006), testis, and epididymis (Saether et al., 2003b). The varying activities of these pathways are still being recognized and their diversity of functions is remarkable. For example, Δ6-desaturase expressed in sebaceous glands in human skin catalyzes an unexpected “sebaceous type” reaction converting palmitate into the MUFA, sapienate. Alterations in this pathway have been implicated in the pathogenesis of acne (Ge et al., 2003).

The Δ5- and Δ6-desaturases have essential roles in synthesizing PUFA, and the expression of both enzymes is regulated by nutrition (Cho et al., 1999a, b; Matsuzaka et al., 2002), hormones, metabolic status, pharmacology (Wang et al., 2006), and exercise (Zhang et al., 2005). The importance of the elongation reactions in regulating lipid metabolism and function was revealed only recently. Several distinct FA elongase subtypes are regulated by diet, drugs, hormones, postnatal development, and metabolic status, and they are present in human genomes with different tissue distribution (Wang et al., 2006). More specifically, carbohydrates (Wang et al., 2006), amino acid composition (Shimada et al., 2003), vitamin B6 (Bordoni et al., 1998), and even secondary plant metabolites such as curcumin-related compounds (Nakano et al., 2000) affect Δ6-desaturase expression. Drugs such as statins and PPAR-α agonists synergistically alter Δ5-desaturase expression (Rise et al., 2007), as do insulin (Brenner, 2003; Saether et al., 2003b; Wang et al., 2006), dietary lipid composition (Cho et al., 1999a, b), protein type (Tovar et al., 2005), plant sterols (Leikin and Brenner, 1989), zinc (Eder and Kirchgessner, 1996), adrenocorticotropic hormone (Mandon et al., 1987), epinephrine (Mandon et al., 1986), and PPAR-α-regulated (Wang et al., 2006) expression of Δ5- and Δ6-desaturases (Figure 22.5).

FIGURE 22.5. Omega-3 and omega-6 PUFA pathways.

FIGURE 22.5

Omega-3 and omega-6 PUFA pathways. The metabolism of dietary fats and endogenously synthesized FA substrates, intermediates and products of omega-3 and omega-6 PUFA pathways. Linoleic acid, α-linolenic acid, and their metabolic products, arachidonic acid (more...)

22.2.8.3. Omega-3

Omega-3 PUFA are now considered to be essential for the assembly of specific membrane compositions and presumably structures in neurological membranes. Higher intakes of ω3 PUFA than those minimally required have been documented to provide therapeutic benefit in reducing cardiovascular disease (Sun et al., 2008) and inflammation (Schmitz and Ecker, 2008). These therapeutic benefits are derived largely by the long-chain PUFA eicosapentaenoic acid (EPA, 20:5n3) and docosahexaenoic acid (DHA, 22:6n3) (Jump, 2002). α-Linolenic acid (LNA, 18:3n3), largely from plant sources, is a substrate in the desaturation and elongation pathway; however, conversion into highly unsaturated FA is inefficient and varies between the sexes. Twenty-one days after ingestion of [13C]-LNA, interconversion into EPA and DHA, respectively, was 21% and 9% in females (Burdge and Wootton, 2002) and 7.9% and 0% in males (Burdge et al., 2002). Additionally, oxidation rates of [13C]-LNA 24 h after ingestion was 50% greater in males compared with females (Burdge et al., 2002; Burdge and Wootton, 2002). The stark sex differences in rates of interconversion could stem from the difference in measurement outcomes. Labeled FA of plasma cholesterol esters, the predominant carriers of EPA, were measured in females but not in males (Burdge and Wootton, 2002; Burdge et al., 2002). Additionally, the sex difference in body composition, hormones, differential loss of label through feces, and habitual diet were not controlled or adjusted.

Intermediates in the ω3 FA pathway have been used in a double-blinded parallel group design. Males and menopausal females ingested encapsulated EPA, SDA, and LNA in doses of 0.75 g/day for 3 weeks followed by an increase in dose to 1.5 g/day for another 3 weeks. The relative effectiveness in increasing circulating phospho-lipid EPA among the EPA, SDA, and LNA groups was 1 to 0.3 to 0.07, respectively. None of the treatment groups increased circulating phospholipid DHA (James et al., 2003). DHA, a highly labile molecule, is strongly regulated in plasma, suggesting possible synthesis by precursors at targeted tissues such as the brain. Labeled studies in mice found that 85% of an administered dose of labeled DHA was recovered in circulating VLDL and low-density lipoprotein (LDL) fractions compared with 30% of labeled oleic acid 30 min after each injection. These data suggest an important role for highly selective transport processes for delivering FA such as DHA to target tissues (Polozova and Salem, 2007). It is yet unclear the extent to which dietary sources of LNA convert to DHA at targeted tissues in vivo and how this molecule is transported and regulated in the fed state.

22.2.8.4. Omega-6

Animal, mechanistic, and case-cohort studies have attributed variations in the essential polyunsaturated ω6 FA pathway with a host of diseases including inflammation (Obukowicz et al., 1998), atopic eczema, allergic rhinitis (Schaeffer et al., 2006; Rzehak et al., 2008), and cardiovascular disease (Glew et al., 2004). Epidemiological case-cohort studies found associations between a common polymorphism in the promoter of the human Δ6-desaturase gene FADS2 and lower levels of very long-chain ω3 PUFA (Baylin et al., 2007). There were associations between variations in the gene cluster of the human Δ5- and Δ6-desaturase genes FADS1 and FADS2, respectively, and reduced levels of erythrocyte phospholipid arachidonic acid (Rzehak et al., 2008) and reductions in plasma phospholipid arachidonic acid (Schaeffer et al., 2006; Malerba et al., 2008; Rzehak et al., 2008), the direct precursor of inflammatory eicosanoids. Carriers of the rare alleles of several single nucleotide polymorphisms had a lower prevalence of allergic rhinitis and atopic eczema (Schaeffer et al., 2006). These studies reveal variation in nutritional genomics in the population, yet the direct effects of diet on these variations have not been measured.

Nutrient–gene interactions have been explored with the advancements of genomic technology coupled with the response-to-challenge model. Recently, de Vogel-van den Bosch et al. (2008) measured the global expression of intestinal barrier genes in response to challenges of PPAR-α (de Vogel-van den Bosch et al., 2008), EPA, DHA, and oleic acid. Out of 74 barrier genes that were upregulated 6 h after the PPAR-α challenge, 62%, 55%, and 26% of these genes were upregulated by EPA, DHA, and oleic acid treatments, respectively. These data demonstrate the indirect effects of dietary fat as important regulators of gut barrier function. The advances in genomics technologies coupled to the challenge model have led to the identification of interactions between nutrients and specific genes and with polymorphisms of dietary responsive enzymes.

22.3. FAT AND DIET

22.3.1. Lipids in Control of Food Intake

The association between excess energy intake and obesity is partly mediated through insulin sensitivity and leptin action. Control of food intake is a hypothalamic response to circulating concentrations of leptin and insulin that link energy intake and fuel stores (Ahima et al., 2006). Insulin is an important regulator of glucose homeostasis, modulator of energy partitioning (Schenck et al., 2008), PUFA synthesis (Brenner, 2003; Wang et al., 2006), FA transport, (Ghebremeskel et al., 2004; Thomas et al., 2005), and hypothalamic action on the control of food intake (Bruning et al., 2000). In the postprandial state, insulin enhances fuel storage, inhibits lipolysis (Unger, 2003), and is involved in leptin release by adipocytes (Cheng et al., 2000). Leptin mediates food intake through sympathetic outflow to the hypothalamus, but also exerts a direct effect on tissues by enhancing lipid oxidation and reducing lipogenesis (Unger, 2003). A state of chronic positive energy balance can lead to insulin resistance and complex etiologies including increased FA flux, nutrient overload, endoplasmic reticulum stress, chronic tissue inflammation (Schenck et al., 2008), lipotoxicity (Unger, 2008), and hyperleptinemia (Unger, 2003), all with potential consequences on the central nervous system (CNS). Insulin secretion influences the fate of lipids in the postprandial state. High FA turnover induces insulin resistance (Schenk et al., 2008) through lipotoxicity of nonadipose tissues (Unger, 2003). Although insulin regulates lipid metabolism and food intake directly at the CNS, lipids may also directly regulate food intake. Injected intracerebroventricularly, oleic acid exerted anorectic effects similar to those of insulin and leptin in rats 4 h after administration measured as a decrease in expression of neuropeptide Y, inhibition of hepatic glucose production, and concomitant reduction in food intake (Obici et al., 2002). With access to the CNS, circulating FFA (Miller et al., 1987) may exert a regulatory role on energy homeostasis in the brain as a signal of energy surplus. Moreover, FA composition (Feltrin et al., 2008), lipid load (Feltrin et al., 2007), and lipid structure (Little et al., 2007) exert different effects on the control of food intake. FA with ≥12 carbon atoms slowdown gastric emptying, modulate gastrointestinal hormone secretion, and suppress energy intake more than FA with <10 carbon atoms (Feltrin et al., 2008). In a double-blinded placebo-controlled study, intraduodenal administration of lauric acid (12:0), oleic acid, or saline, lauric acid suppressed energy intake; however, oleic acid increased peptide YY to the greatest extent (Feltrin et al., 2008). In a crossover study of men administered intragastrically with either 40 g of oleic acid as a FFA, 40 g of macadamia oil as a TG, or a control gastric emptying of FFA was slower than that of TG; hunger was less and fullness was greater with FFA compared with TG and control; increases of cholecystokinin and peptide YY were greater, and energy intake was less after administration of FFA compared with TG (Little et al., 2007).

22.3.2. Impact of a High-Fat Meal on Endothelial Function

The impact of high-fat meals on endothelial function in humans has been extensively studied and reviewed (O’Keefe and Bell, 2007). Within the literature, there is a lack of consistency in methods used to measure endothelial function; however, the use of the crossover study design has been consistently used. Postprandial hypertriglyceridemia-induced vasoconstriction measured as endothelium-dependent FMD quantifies percentage change in arterial diameter (Bae et al., 2003). Neither endothelium-independent vasodilation, as measured by responsiveness to nitroprus-side (an endothelium-independent vasodilator), nor endothelium-dependent blood flow was impaired by high-fat meals in forearm resistance vessels, the small arteries and arterioles that are involved in the regulation of blood pressure (Gudmundsson et al., 2000). A reduction in endothelial function measured as forearm blood flow 2 and 4 h after consuming a high-fat meal was correlated with increased plasma FFA (Shimabukuro et al., 2007). Ingestion of a high-fat meal with or without 75 g glucose decreased endothelial function, measured as FMD, and increased nitrotyrosine, a marker of pro-oxidant formation (Ceriello et al., 2002).

The addition of dietary antioxidants to a high-fat meal partially attenuated endothelial dysfunction, as measured by change in blood pressure following l-arginine infusion (Nappo et al., 2002). The addition of vitamins E and C at dosages of 800 IU and 1000 mg, respectively, also prevented a high-fat meal-induced increase in the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin-6, as well as adhesion molecules, which are known to mediate endothelial activation, an important component of endothelial dysfunction (Nappo et al., 2002). Timing of antioxidant ingestion had an impact on the adverse effects of high-fat meals, with 800 IU of vitamin E and 1000 mg vitamin C reducing C-reactive protein, a marker of systemic inflammation, to a greater extent when administered pre-supper compared with pre-breakfast, whereas only pre-breakfast antioxidant ingestion prevented the high-fat meal-induced elevation of plasminogen activator inhibitor-1, a prothrombotic molecule (Carroll and Schade, 2003).

22.3.3. Effect of Varying Dietary Fatty Acid Composition on Endothelial Function

22.3.3.1. MUFA

The effects of a large dietary load of SFA on endothelial function in the postprandial state have been reviewed (Botham et al., 2005). The effects of modifying the SFA to MUFA ratios have also been studied and have produced fewer consistent results. A Mediterranean diet style meal, rich in MUFA, increased endothelium-dependent vasodilation, as measured by laser Doppler, and decreased adhesion molecules compared with a SFA-rich meal (Fuentes et al., 2008). A meal enriched in MUFA from extra-virgin olive oil attenuated the increase in procoagulant factors relative to a SFA-rich meal (Delgado-Lista et al., 2008).

A high-fat test meal with a lower SFA:MUFA ratio (55:45) compared with a high SFA:MUFA ratio (70:30) did not attenuate the postprandial increase in IL-6, whereas it increased the level of plasma TNF-α (Poppitt et al., 2008). In type 2 diabetes patients, a meal consisting of skim milk plus 50 g of a MUFA-rich oil (high-oleic safflower oil and canola oil) was associated with impaired endothelial function (Hilpert et al., 2007). The modification of dairy fat to lower the SFA:MUFA ratio by increasing the content of 18:1n9 in the milk of cows on a modified feeding regimen had no effect on thrombogenic factors in the postprandial state after subjects ingested a challenge meal containing 1.2 g of the modified dairy fat/kg body weight (Tholstrup et al., 1999). A high-fat meal enriched in MUFA providing 80 g fat, of which 25 g was olive oil, caused a higher degree of endothelial dysfunction, as measured by FMD, compared with a high-fat meal enriched in walnuts; however, both meals reduced proinflammatory cytokines and adhesion molecules (Cortes et al., 2006). In normotensive and hypertensive patients with hypertriglyceridemia as well as in healthy subjects, a meal enriched in MUFA (refined olive oil) decreased adhesion molecules relative to a meal high in SFA (Pacheco et al., 2008).

22.3.3.2. PUFA

The effects of PUFA consumption on endothelial function may be divergent. A high-fat meal containing walnuts, which are rich in LNA, reduced endothelial dysfunction as measured by FMD compared with a similar meal enriched in MUFA, although both reduced postprandial inflammatory cytokines and adhesion molecules (Cortes et al., 2006). The addition of the long-chain ω3 FA EPA and DHA to a meal enriched in MUFA attenuated the impairment in endothelial function relative to MUFA alone (Hilpert et al., 2007). A meal enriched in walnuts lowered postprandial procoagulant activity relative to a SFA-rich meal (Delgado-Lista et al., 2008). When 9 g fish oil, which is rich in EPA and DHA, was added to a mixed-fat meal providing 40 g fat, endothelium-independent vasodilation was increased as measured by sodium nitroprusside-induced reactivity (Armah et al., 2008). As measured by FMD, high-fat meals containing 3–5 g ω3 FA as either EPA + DHA or LNA increased endothelial function 50%–80% relative to MUFA-enriched meals (West et al., 2005).

22.3.4. Long-Term and Acute Effects of Dietary Fat Content and Composition on Postprandial Lipemia

Postprandial lipemia is an independent risk factor for heart disease (Karpe, 1997). Interestingly, a diet high in simple carbohydrates is generally associated with prolonged lipemia largely due to the production of VLDL in the liver. Studies attempting to assign the role of dietary fat to postprandial lipemia have yielded mixed outcomes. Saturated fat is associated with deleterious effects, whereas MUFA and PUFA are generally associated with beneficial effects on postprandial plasma lipid profiles; however, these associations have been inconsistent. Sixteen days of consuming a SFA-rich diet resulted in the accumulation of postprandial TGRL 7 h after a challenge meal, whereas consuming a PUFA-rich diet for 16 days attenuated this effect (Chung et al., 2004). Relative to SFA-rich meals, a PUFA-rich meal (ω6 FA from safflower oil) reduced postprandial TGRL-cholesterol and apolipoproteins E and C-III content (Jackson et al., 2005). A low-fat diet enriched in linoleic acid (LA, 18:2n6) attenuated the increase in average and maximal increments of TGRL-cholesterol postprandially relative to a low-fat diet enriched in oleic acid (18:1n9) (Higashi et al., 2001). Fish oil provided as TG oil or as plant sterol esters both reduced postprandial TG concentrations (Demonty et al., 2006). Relative to test meals enriched in palmitate (16:0) or LA, meals enriched in oleic acid and EPA + DHA reduced insulin response and nonsignificantly reduced postprandial TG in patients with type 2 diabetes mellitus (Shah et al., 2007). A meal containing MUFA and EPA + DHA attenuated the increase in postprandial TGRL relative to a meal containing MUFA alone (Hilpert et al., 2007).

Supplementation of hypertriglyceridemic men with 6 g/day fish oil providing 3 g/day EPA + DHA decreased postprandial plasma TG and FFA concentrations, and increased ex vivo LDL-oxidation and LDL-cholesterol relative to 6 weeks on 6 g/day olive oil (Leigh-Firbank et al., 2002). A MUFA-rich diet consumed for 3 weeks by patients with type 2 diabetes resulted in decreased small VLDL-TG; however, it also resulted in an earlier chylomicron peak in response to a high-fat meal relative to when a SFA-rich diet was consumed for 3 weeks (Rivellese et al., 2008). One study found no effect of modifying meal FA composition on any postprandial plasma lipid measure, finding that a meal rich in LA, a meal rich in MUFA, and a meal rich in EPA + DHA had no differential effect on areas under the curve for TG and cholesterol, TG and cholesterol peak values, or time to maximum concentration in healthy middle-aged men and women (Burdge et al., 2006).

22.4. CONCLUSIONS

Obesity is the result of chronic imbalance between caloric intake and caloric demands. This balance between intake and expenditure appears simple at first glance: calories in must equal calories out. Yet the metabolic regulation underlying energy expenditure and food intake behavior is anything but simple, as is clearly illustrated by the failure of diet-based interventions to produce and sustain weight loss in most individuals.

The variation of dietary fat intake is one of the central controls on whole body metabolic dynamics. Future mechanistic studies that characterize the signals linking diet to endogenous metabolism are needed as caricatured in Figure 22.6. In such studies, appreciating all of the components of diet as input variables will be necessary for success. Furthermore, the variations between individuals with respect to the responses to dietary fat are a part of the causes in disparity in health among the population. This implies that as the science of metabolic regulation is brought to practice, assessing individual metabolism will need to include individual metabolic responses to standardized dietary challenge.

FIGURE 22.6. Assessment of fasted and fed states.

FIGURE 22.6

Assessment of fasted and fed states. Metabolic regulation is a continuous process varying from fasted to fed states. Levels of metabolites rise and fall in response to the influx of nutrients from foods, as do the signaling systems that stimulate appetite (more...)

In this chapter, we have detailed the biochemical and physiological rationale for a standardized dietary challenge approach as a means of assessing individual metabolism that is capable of assessing the response to diet. Primary in the list of reasons for this approach is to understand the function of metabolism in order to employ a functional assessment that includes diet rather than a static assessment based on the fasted condition. Measuring the flux of metabolites in response to a well-defined challenge meal provides a means to assess in real time the metabolic networks at work and how diet affects metabolism. Whereas measurements in the fasted state can offer only a limited perspective on the long-term effects of diet, measurements in the fed state, if carried out with an appropriate study design, can reveal a more complete picture of how an individual metabolizes a particular meal, as well as the long-term effects of a particular diet. Ultimately, the goal is not simply to produce weight loss at any cost but to improve health.

Establishing the parameters of a standardized fat challenge test will require a consensus of nutrition scientists and, once in place will provide a framework to a new dimension of human health assessment. Many questions remain unanswered and need to be resolved for the challenge approach to become useful and clinically applicable for metabolic assessment. The exact composition of challenge meals intended to uncover specific aspects of metabolism needs to be defined. For example, how much LNA should be included in a challenge meal designed to uncover an individual’s ability to convert LNA to the longer chain EPA and DHA? The implication is that understanding an individual’s capacity to convert LNA to EPA and DHA will guide recommendations for the consumption of plant-based sources of ω3 FA vs. fish oil. Another example is the influence of hormonal status on the effects of diet in women. Among the implications are: (1) that the assessment of metabolic status in menstruating women may need to be adjusted for the phase of the menstrual cycle or estradiol levels, which should be measured simultaneously in order to obtain an accurate representation of metabolism and (2) that different foods may be appropriate at different times of the month for optimal health in menstruating women. Many other questions include the timing of blood draws, the minimum set of phenotypic characteristics that should be accounted for in the analyses, and the number of replicates that is required to ascertain underlying metabolic response. Such issues need to be addressed in order to facilitate the assessment of metabolic phenotypes.

An additional challenge will involve building databases that reflect the boundaries of normal metabolic responses in which abnormal or dysregulated metabolism can be detected. Given the high degree of interindividual variability in responsiveness to diet within the population as discussed extensively in this chapter, how can a “normal” response be defined? The task of building comprehensively annotated databases that can be interrogated for similarities and differences among individuals will be crucial in understanding of how different individuals can be grouped together to derive a normal range of responses and to predict trajectories of varying long-term health outcomes. This, in turn, will have important implications for how we devise nutrition recommendations for individuals.

The current approach to tailor dietary recommendations by age, gender, weight, height, and activity level is the first step toward guiding personal health assessment in practice. Obesity continues to be a major health concern in the United States, and increasingly around the world. Diet and lifestyle-based weight loss and obesity prevention approaches continue to have high failure rates. It is equally important to be able to assess the overall consequences of ongoing attempts and ultimately successful interventions to reduce adiposity. There is an urgent need to devise accurate and effective approaches for assessing the metabolic status of individuals.

The ultimate goal is to comprehensively assess individual responses to different diets and meals. By measuring individuals’ responses it will not only be possible to determine the underlying metabolic mechanisms that are contributing to that individual’s obesity-induced disease burden and therefore tailor dietary intervention appropriately to correct the metabolic dysregulation, but also to tailor diets and meals that will increase adherence because they will be personalized and thus optimized for that individual’s metabolic phenotype.

ABBREVIATIONS

TG

triglycerides

FA

fatty acids

PUFA

polyunsaturated fatty acids

BMI

body mass index

MRI

magnetic resonance imaging

FMD

flow-mediated vasodilation of the brachial artery

SFA

saturated fatty acids

MUFA

monounsaturated fatty acids

trans-FA

trans-fatty acids

VLDL

very low-density lipoprotein

TGRL

triglyceride-rich lipoproteins

OFTT

oral fat tolerance test

FFA

free fatty acids

SDA

stearidonic acid

SCD

stearoyl CoA desaturase

PPAR

peroxisome proliferator-activated receptor

EPA

eicosapentaenoic acid

DHA

docosahexaenoic acid

LNA

α-linolenic acid

LDL

low-density lipoprotein

CNS

central nervous system

TNF

α-tumor necrosis factor alpha

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