NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

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

Cover of Fat Detection

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

Show details

Chapter 21Dietary Fat and Carbohydrate Composition: Metabolic Disease

, , , , , , and .

21.1. INTRODUCTION

The term “metabolic syndrome” is used to describe a cluster of disease states including blood lipid disorders, hypertension, propensity for thrombus formation, low-grade chronic inflammation, abdominal obesity, and type-2 diabetes. Insulin resistance is the key to the metabolic syndrome, particularly the relative failure of insulin to exert its multiple biological effects on carbohydrate and lipid metabolism.

The current epidemic of obesity and type-2 diabetes in developed and developing countries has focused major attention on the metabolic derangements critical to their etiology. The Banting lecture of 20 years ago by Reaven sparked great interest in the clustering of diseases, which he termed “Syndrome X” and subsequently is more commonly referred to as the metabolic syndrome (Reaven, 1988). The basic symptomatology included dyslipidemias (high cholesterol and triglycerides), insulin resistance, obesity, and hypertension. With continuing research, subsequent iterations have added central (and in particular visceral) adiposity and chronic low-grade inflammation as key elements. Diet plays a powerful role in modulating expression of the metabolic syndrome and it is increasingly clear that both amount and type of both fats and carbohydrates, and the interaction between them, are important variables.

21.2. DIETARY FAT AS A PROPORTION OF CALORIES

Fat is the most calorically dense of macronutrients. A recent review by Mendoza, Drewnowski, and Christakis (Mendoza et al., 2007) has very nicely covered the energy density literature and the conclusion is clear: “Dietary energy density is an independent predictor of obesity, elevated fasting insulin levels, and the metabolic syndrome in U.S. adults.” Fat, while certainly not the only factor, is a strong contributor. It then makes intrinsic sense that increased proportion of calories from fat would be associated with increased obesity and related disorders. Since obesity is the result of a very long-term excess of intake over expenditure and may consist of many life phases of both subtle caloric imbalances and more dramatic episodes, it is difficult to assign a role to the proportion of calories as fat in the development of obesity. Indeed, some studies suggest a reduction in fat calories during the 1990s, a period of explosive growth in obesity (Prentice and Jebb, 1995; Arnett et al., 2000). This overall obesity rise will reflect the interplay with reduced energy expenditure attendant on a society with much less demand for hard physical work both in the home and workplace, but even these data are not consistent with a message that lowering the proportion of fat in the diet will automatically lower body weight.

More useful data have come from intervention studies for weight loss. Studies from 2000 to 2002 were somewhat conflicting. Astrup and colleagues suggested a significant effect of reducing the proportion of fat calories on top of the weight loss of caloric restriction without proportional fat reduction (see Astrup et al., 2000). The effect was very modest but nevertheless represented, over a year, an excess loss of 4–500 g per percentage of fat reduction. In contrast, a Cochrane review (Pirozzo et al., 2002) concluded “that fat-restricted diets are no better than calorie-restricted diets in achieving long-term weight loss in overweight and obese people.” More recently this conclusion has been reinforced by work from the excellent Nugenob Consortium from Europe (Petersen et al., 2006) which showed no difference in weight loss, nor any difference in measures of dyslipidemia, insulin, or glucose, between a 10 week low- or high-fat hypocaloric dietary intervention.

In the United States, the focus has been on comparing the sometimes outrageous claims of the various “wonder” diets (Foster et al., 2003; Gardner et al., 2007). The media hysteria over fad diets has encouraged all sorts of dietary interventions for weight loss ranging from high-fat to very low-fat diets. The studies done scientifically to test these claims are interesting for a few reasons. First, they speak to the issue of percentage of fat and weight loss. Here, we recommend reading the Gardner et al. (2007) paper where weight loss was compared across the so-called Atkins, Zone, Ornish, and LEARN (Lifestyle, Exercise, Attitudes, Relationships, and Nutrition) diets. The latter three were quite similar in weight loss (very little at 1–2 kg in a year or less than 1 BMI point) while the effect of the low carbohydrate, high-protein, and high-fat diet (Atkins) did better at 4.7 kg (still less than 2 BMI points). This can be compared with the study of Foster et al. (2003) who compared the Atkins to a more “conventional” low-fat diet in obese individuals. As later confirmed by the Gardner study, the Atkins diet resulted in greater weight loss by 3 and 6 months. However, if one looks at the weight curves, then the trajectory of weight change is upwards for the low-fat diet such that the differences between diets are no longer significant at 12 months. Indeed, the pattern is very similar to the Gardner study (compare Figure 2 in Gardner with Figure 1 in Foster) but in this case statistical significance is still there at 12 months. One wonders if the significance would still be present at 2 years?

Of course, dietary interventions are aimed at more than just weight loss. Glycemic control, dyslipidemias, and inflammation are also therapeutic targets. These will be covered more in the sections under dietary fat subtypes. However, there is one particular issue of importance in the discussions about Atkins style diets. As noted, chronic low-grade inflammation is now seen as a critical component of the metabolic syndrome. Recent work (Rankin and Turpyn, 2007) has shown a potential danger of the high-fat approach. While weight loss was again somewhat larger with the high- vs low-fat diet, and blood glucose was similarly improved, markers of inflammation increased on the high-fat diet. This was indexed, for example, by a 20% increase in C-reactive protein (CRP) compared to a reduction of 43% in the low fat. These results provide a cautionary note about ensuring that the full metabolic picture is viewed with any dietary intervention.

What is clear in almost all “diet” studies is that weight regain is pervasive after a few months of weight loss. This is despite continued reporting of caloric deficit compared to baseline. A commentary on the Gardner paper by Heymsfield and Blackburn (2007) points out the difficulty this creates for the laws of thermodynamics (or, more properly, the other way around) as noted many years ago by Grande (1968). The only way this could occur would be for metabolic rate to drop substantially in the dieting individuals when they have reached their nadir of weight. Certainly this is possible as early work from Leibel et al. (1995) showed quite substantial drops in energy expenditure on a per kilo of lean mass (i.e., the metabolically active component of body weight) basis with 10%–20% weight loss maintained. Equally, an outstanding study of a 9 month diet and exercise weight loss intervention in adolescents from Lazzer et al. (2004) showed a quite substantial (~10%) decrease in all types of energy expenditure (resting, sleeping, exercise, etc.) beyond that which would have been predicted on the basis of lean tissue loss which, in this case, was minimized by a focus on greatly increased physical activity.

Two-year follow-up data are available from a 1 year intervention study, also in adolescents (Rolland-Cachera et al., 2004), and similar to the Lazzer work. In the initial phase over 30 kg were lost but the same, odious pattern of weight regain was seen as in most work on adults despite reported intake levels substantially below baseline. Is this truth or misreporting? Certainly the reduction in energy expenditure beyond that expected by lean tissue loss would seem to be correct from both the Leibel and Lazzer data. Whether or not misreporting is also a substantial component explaining the effect is unknown but very likely. Recent data have shown that no matter the macronutrient mix in a weight loss diet, compliance is initially quite good and then deteriorates (see in particular Dansinger, 2005). The point with most diets is that there is some success, particularly over 3–6 months, but in the few diet studies that go out beyond 1 year, regain is universal. This is a critical issue in the pursuit of antiobesity therapy. It seems clear that regardless of dietary strategy, calorie intake is the main determinant of success.

21.2.1. Dietary Fat Subtypes

Lipids play multiple roles in metabolism. First, they are a source of energy-dense calories and, given the body’s very limited ability to modulate carbohydrate and protein stores, fat balance over time effectively determines development of, or resistance to, obesity. However, the roles of lipids as major structural components of membranes and as potent metabolic intermediates in cellular signaling are equally important in expression of the metabolic syndrome disease cluster.

21.2.1.1. n − 6/n − 3 Ratio

The ratio of n − 3/n − 6 polyunsaturated fatty acids (PUFAs) is an important composite variable that has received a good deal of attention. Unfortunately, there are few good long-term studies of the effect of increasing n − 3 intake on either insulin action or obesity, and the data from those that exist generally show no effect (see, for example, Vessby et al., 2001). As noted above, the issue that always occurs with altering fatty acid composition is the length of the intervention, and we may not have yet had studies of appropriate duration. In addition, it is still not known if the beneficial effects are due to the n − 6/n − 3 ratio or to the absolute amounts of either, but it seems to be dependent on the pathway(s) of interest.

One aspect of the metabolic syndrome that is receiving increased attention is that of chronic, low-grade inflammation. Here the story around the n − 6/n − 3 ratio is much clearer. We highly recommend a very nice review by Robinson et al. (2007), which comprehensively analyses the available literature in the area of n − 3 fatty acids and inflammation as well as touching on the obesity issue. One can summarize by saying that there is clear evidence for the anti-inflammatory effects of n − 3 fatty acids and certainly the interrelationship between n − 3 and n − 6 polyunsaturated fatty acid (PUFA) metabolism would suggest an increased n − 6/n − 3 would be deleterious. However, again the authors must conclude that the critical studies are lacking. Fortunately, since their review one area has started to be explored. They comment that “… the relationship between low-grade inflammation and acute postprandial response remains largely unknown.” The recent work by Erridge et al. (2007) has shown that, even in healthy men, a high saturated fat meal generated an endotoxemia measurable by increased plasma lipopolysaccharides (LPS). How this postprandial inflammation is modulated, by changing the fatty acid profile is currently unknown, but seems to be a fruitful line of investigation to pursue.

21.2.1.2. Trans Fatty Acids

The role of trans fatty acids in cardiovascular disease is now quite clear from epidemiological studies and has been recently reviewed by Willett and colleagues (Mozaffarian et al., 2006; Mozaffarian and Willett, 2007). Equally the startling data in Figure 21.1 suggests trans fatty acids to be a potent driver of diabetes. To summarize the conclusions of the review articles, there is evidence that, in humans, intake of trans fatty acids results in dyslipidemia (increased triglyceride and LDL cholesterol), inflammation (circulating CRP, IL-6, and TNFa activity), and insulin resistance (at least in overweight, if not healthy, individuals) leading to increased cardiovascular disease. Of course, as noted above in nonhuman primates, we can add increased visceral adiposity. The good thing with trans fatty acids is that the level of their intake over time can be indexed by membrane trans fatty acid levels and correlations with the above markers of dysmetabolism adds good strength to the other cross-sectional data. Pleasingly, this weight of evidence has led to successful public health campaigns to bring legislation to severely limiting trans fatty acids in the food supply. It is a shocking result that a 2004–2005 survey in European countries showed that fried chips from a certain fast food chain had 28 times the amount of trans fats in Hungary than in Denmark where the legislation has led the world.

FIGURE 21.1. Data showing the change in diabetes risk which would theoretically be achieved by only a 2% substitution of one type of fatty acid with another, or by carbohydrate.

FIGURE 21.1

Data showing the change in diabetes risk which would theoretically be achieved by only a 2% substitution of one type of fatty acid with another, or by carbohydrate. These are quite remarkable changes in major disease risk for such a small dietary change. (more...)

21.2.1.3. Conjugated Linoleic Acids

Conjugated linoleic acids (CLAs) are another subgroup of dietary fats largely, but not solely, of ruminant metabolism origin. They have a controversial history in terms of human health, which has been very nicely reviewed by Tricon and Yaqoob (2006) and that relatively brief review is recommended. CLAs were touted as a body fat loss supplement but the vast majority of studies in humans have shown no effect. As noted in that review, very careful studies by Vessby’s group showed reduced insulin sensitivity as well as an increase in markers of inflammation (Smedman et al., 2005), albeit in both cases with differences between isomers. The opposing effects of isomers on cholesterol metabolism is illustrated in the Tricon and Yaqoob review from their own work (Figure 1 from Tricon and Yaqoob, 2006). Overall, the conclusion would seem to be that there are inconsistent effects on metabolic parameters and that specific isomers may have different, and opposing, effects. This is a confusing area in which the power input has created considerable heat but less illumination.

In 1996 some of us wrote a review on dietary fat subtypes and insulin action (Storlien et al., 1996). At that time there was a stunning lack of data, from both animal and human studies, on the effects of fats and fat subtypes on this key variable related to development of the prevalent diseases of the metabolic syndrome. However, since then, there has been an explosion of interest and there are a number of excellent recent reviews, covering both the experimental animal and human literature. Rather than reiterate the content of these we will reference them in the context of their particular focus; summarize the main messages; and then concentrate on potential underlying mechanisms.

21.2.2. Obesity, Dyslipidemia, and Insulin Resistance—Animal Studies

Experimental animals are particularly useful in this area because precise control of both amount and fatty acid profile of the diet can be achieved. The picture from many studies in rats and mice is fairly consistent and has been comprehensively reviewed last year by Buettner et al. (2007). This is really an excellent, succinct review nicely covering the obesity angle and effects in the key tissues. We highly recommend it, and their comprehensive investigation report from a year earlier (Buettner et al., 2006), and will not try to reprise it here. Overall, n − 3 fatty acids are generally good. This has been shown repeatedly in a range of studies now going back almost 20 years and applies to countering the obesogenic, dyslipidemic, and insulin resistance provoking qualities of other types of dietary fat. Saturated fat, in comparison, is deleterious, but not hugely more than either monounsaturated fat or polyunsaturated fat of the n − 6 class, all of which impinge negatively on insulin action, at least in the context of diets with a high proportion of calories as fat. It is striking that n − 3 fats seem to be even protective against high-fat diet induced weight gain (see Pan and Storlien, 1993 and Figure 1 from the Buettner 2007 review). n − 3 fats also tend to reduce blood triglyceride levels. Olive oil is generally seen to be beneficial, particularly in the context of a Mediterranean diet. However, most rodent studies using olive oil as the source of oleic monounsaturated fat show both excess weight gain and insulin resistance, albeit not as severe as with primarily saturated fat diets (Storlien et al., 1991). Most work in rodents show that diets high in dietary n − 6 fatty acids induce both obesity and insulin resistance despite their being very unsaturated.

One further area of dietary lipid subtypes not well covered by rodent studies is that of trans fatty acids. However, here we have an excellent study in primates (Kavanagh et al., 2007). The authors fed cis or trans fatty acids representing 8% of the total calories ingested over a 6 year period as part of a diet intended to maintain stable weight levels. The results were clear and disturbing. Trans fatty acid-fed monkeys gained weight and deposited more central fat even without increased food consumption. They also displayed impaired glucose tolerance as predicted during severe insulin resistance as well as impaired insulin signal transduction at the cellular level. Altogether, the results demonstrated a very negative profile.

Given the excellent reviews of Buettner and colleagues covering this aspect in rodents, we will focus on human studies drawing parallels with rodent work in the context of cellular mechanisms. However, we will also focus on the effects of dietary lipid subtypes on neural circuits controlling energy balance as this aspect has not been comprehensively reviewed yet.

21.2.3. Fats and Effects on Brain Mechanisms of Energy Balance—Fat Subtypes and Satiety Hormones

The classic monoamine (norepinephrine, serotonin, and dopamine) systems of energy balance were described many years ago although their roles are constantly being refined. Since the discovery of leptin over 10 years ago, there has been an explosion of knowledge about novel neuropeptides acting in concert to regulate energy balance. By quick oversimplification, one can note some key elements of two opposing systems, one orexigenic or appetite stimulating and one anorexigenic or appetite suppressing. Of particular importance is the fact that neuropeptide Y (NPY) and agouti-related protein (AgRP) are orexigenic, such that increased activity of either system will augment food intake and support obesity. On the other hand, melanocortin (α-MSH) an anorexigenic nonopioid peptide encoded by the pro-opiomelanocortin (POMC) gene, is distributed throughout the hypothalamus and acts via the melanocortin receptor 4 (MC4-R) and possibly melanocortin receptor 3 (MC3-R) to inhibit food intake. These two systems interact through AgRP a potent and selective antagonist of MC3-R and MC4-R. Since NPY-producing neurons in the arcuate nucleus of the hypothalamus (Arc) project to the paraventricular nucleus where they release NPY thereby stimulating feeding, the co-release of AgRP may act as a modulator of the balance between NPY and α-MSH.

There is comparatively little research on the effects of dietary fat subtypes on the neural systems controlling energy balance. However, in the following section we briefly outline some of this work, concentrating on NPY, AgRP, leptin, and serotonin.

21.2.3.1. Fats and Neuropeptide Y

As noted above, diets particularly high in saturated fat induce obesity in mice, whereas diets emphasizing PUFAs do not. The reduction in Arc NPY mRNA with obesogenic high-fat feeding has been reported from a number of laboratories including our own (Giraudo et al., 1994; Guan et al., 1998; Stricker-Krongrad et al., 1998; Wang et al., 1998; Piggott et al., 2002). Arc NPY protein levels follow mRNA levels down with longer feeding periods. As well, NPY protein in the paraventricular hypothalamic nucleus falls with long-term feeding and development of obesity (Stricker-Krongrad et al., 1998). The argument has then been made around the carbohydrate/fat ratio as a controller of hypothalamic NPY. Compared to high n − 3 fat diet, a high-fat diet with a higher proportion of saturated fat perturbs the NPY (and AgRP) system. This suggests that it is not the level of dietary fat per se which influences NPY expression, but that it is some obesogenic property or properties of saturated fats, which are detected either peripherally or centrally. The NPY/AGRP systems then could be seen to be reacting in a homeostatic manner. In the case of the PUFA diets, there is no evidence for increased body fatness and the lack of change in Arc NPY mRNA is appropriate. The issue is clearly not dietary fat vs carbohydrate.

The NPY and AgRP systems (remember they are orexigenic) are reacting to some signal indexing positive energy balance (driven by the high saturated fat diet) and are reacting appropriately. What is equally apparent is that the reduced Arc NPY and AgRP are insufficient to maintain energy homeostasis. High-fat diets emphasizing PUFAs do not act to impair energy balance and the Arc NPY or AgRP systems are, appropriately, not responding. This strongly argues for a dysregulation, not in the NPY/AGRP system, but in other parts of the energy balance organization induced specifically by saturated fat or at least by the fatty acid profile of that diet.

As a potent regulator of the NPY system, leptin is one such candidate. However, we showed that at week 1 of a high saturated fat diet, Arc NPY mRNA levels were reduced by half, but no increase in circulating leptin and no change in leptin receptor mRNA expression could be detected. That leptin is not responsible for the decline in NPY with fat feeding supports earlier suggestions (Stricker-Krongrad et al., 1998). Other regulatory mechanisms must therefore exist to modulate Arc NPY and AgRP mRNA expression in the mice fed a high saturated fat diet.

A good deal can be learned about the control of energy balance by attempting to ameliorate the obesity of prolonged high saturated fat diets. This dietary “reversal” stage can be achieved in mice by both change in dietary fat profile (diet emphasizing n − 3 fatty acids) and alteration in fat/carbohydrate balance (i.e., low-fat diet). However, it is striking that the effects are very modest with the low-fat diet compared to a major effect to reverse obesity with change only in dietary fat profile (Wang et al., 2002). This is equally reflected in the leptin levels. In contrast, both dietary reversal interventions resulted in “normalization” (to the chronic low-fat control) of NPY and AgRP mRNA expression.

In summary, changing the fatty acid profile of the diet alone can profoundly alter the expression of major hypothalamic neuropeptides of energy balance. The effects would appear to relate to whether a particular dietary fatty acid profile is obesogenic or not, but dysregulation appears independent of leptin, and of the NPY/AgRP system, as mediators.

21.2.3.2. PUFAs and Serotonin Receptor

Serotonin (5-HT) is a monoamine of primary interest for energy balance because of its long history of investigation and many decades of marketed drugs like fenfluramine which is thought to act via the 5-HT receptors. The hypophagic effect of serotonin is believed to be mediated mostly via postsynaptic 5-HT2C receptors. Knockout mice lacking 5-HT2C receptors are hyperphagic and become obese.

Our work has shown that different fat types can increase or decrease serotonin receptor binding, depending on the area being examined in rat brain in chronic feeding conditions (du Bois et al., 2006). For example 5-HT2A receptor binding was increased in the caudate putamen but reduced in the mamillary nucleus by long-term feeding of diets high in saturated fat or n − 6 PUFA fatty acids, but not n − 3 PUFA diets. 5-HT2C receptor binding was similarly reduced in the mamillary nucleus of saturated fat group and n − 6 PUFA, and here also in the n − 3 PUFA group.

n − 6 PUFAs may be the most influential on serotonin receptor binding as this diet has previously been shown to decrease density of rat opioid (Oktem and Apaydin, 1998) and adenosine A1 (Cunha et al., 2001) receptors in several brain regions. Since only major changes in membrane viscosity can modify adenosine 1A receptor density (Casado et al., 1992), the results from Cunha et al. (2001) would suggest that n − 6 PUFA interact directly with adenosine receptors to alter density levels. Consistent with this, we previously found that n − 6 PUFA was the most influential fatty acid on muscarinic receptor binding, which is likely due to direct interactions between arachidonic acid, acetylcholine, and muscarinic receptor downregulation (du Bois et al., 2005).

No effect on 5-HT2A receptor density was observed following n − 3 PUFA diet treatment in most areas of the brain, consistent with Chalon et al. (1998). However, the dentate gyrus, an area not examined by Chalon et al. (1998), was a specific area where 5-HT2A receptor density was solely affected by the n − 3 PUFA treatment. Conversely, an n − 3 PUFA-deficient diet had a number of effects on mono-aminergic receptors including an increase in 5-HT2A receptor binding (Delion et al., 1994) and decrease in D2 receptor binding in the frontal cortex (Delion et al., 1996). Interestingly, suicide victims have been reported to show a similar increase in 5-HT2A receptor density in the cortex as n − 3 PUFA-deficient rats (Stanley and Mann, 1983). The links between depression and obesity are well established.

In summary, while changes in binding density as a result of high-fat diets can be attributable to numerous global factors, including altered membrane fluidity, gene expression of receptors, receptor affinity, or to dynamic interactions between essential fatty acids, our results support that they are direct receptor-mediated interactions as receptors were affected differentially in a regional-specific manner. This area of research may be particularly fruitful in the pursuit of antiobesity agents.

21.2.4. Obesity, Dyslipidemia, and Insulin Resistance—Human Studies

The diets of free range humans are notoriously hard to control or even to monitor. In addressing the issues of amount and type of fats in the diet and effects on obesity and insulin resistance, one relies on cross-sectional studies and the far fewer longer term (3 months or greater) diet intervention studies. In both, one has to make allowance for the high degree of uncertainty around compliance. Equally, when we are discussing insulin resistance and obesity, the whole-body pool of fatty acids is very high. The length of time needed to achieve a change in metabolic outcome may be several months or even years.

One marvelous human longitudinal study is the well-known Nurses Health Study. One graph of an analysis of this study from a publication some years ago provides a remarkable testament to the likelihood that even relatively small changes both in the fatty acid profile of the fat component, and the balance between fat and carbohydrate calories (which we will explore later) will have a major impact on diseases related to insulin secretion and action. These data, shown above, suggest that exchanging as little as 2% of, for example, trans fatty acids with PUFA (not even specifying which class of PUFA) can have the effect to reduce diabetes by 40% (Salmerón et al., 2001). One limitation of this study however, was that the analysis was based on a hypothetical model of shifting patterns of dietary intakes, not on an actual intervention. These results appear consistent with knowledge from both cellular and animal research models, but more translational research is needed at this level to confirm effects with actual food patterns.

There are very few human intervention studies of any duration which compare diets of similar percentage of fat calories but where the fatty acid profile has been manipulated. Cross-sectional work is informative but the measurement uncertainty noted above limits the useful to only those few with very large subject numbers. Two recent papers have reviewed this limited literature from the perspective of insulin sensitivity (Galgani et al., 2008; Riserus, 2008 and see Vessby, 2003). The reader is referred to these for the detail, but in summary, they all conclude that the evidence is tantalizing and support the deleterious effects of saturated fats while more unsaturated fats are better, consistent with the cross-sectional and longitudinal studies. All point out the paucity of data and the need for more studies of appropriate length, rigor, and power.

Data also comes from a particular instance of type-2 diabetes, that occurring during pregnancy (gestational diabetes mellitus, GDM). Occurrence of glucose intolerance and GDM in relation to diet has been studied in a Chinese urban population (Wang et al., 2000). Dietary fat as a percentage of total energy intake was lower in GDM women compared to those with normal pregnancies. This was consistent with earlier work (Major et al., 1998), but not with others (Moses et al., 1997). In addition, glucose intolerance and GDM were associated with low polyunsaturated/saturated (P/S) ratio. In other words, a higher intake of PUFAs appeared to protect against glucose intolerance and GDM. This conclusion was consistent with the Moses et al. work, and supported by Bo et al. (2001) who also found in an Italian cohort that saturated fat was associated with increased incidence of glucose intolerance and diabetes during pregnancy, and PUFA with a decreased incidence.

One final key point that is worth noting here comes from the initial KANWU Study publication (Vessby et al., 2001). When the authors analyzed the effect of dietary fat subtype in relation to total fat intake, a significant improvement in insulin sensitivity was seen in the monounsaturated diet group compared with the more saturated fat group in the half of subjects with the lowest total proportion of calories as fat (in this study those consuming <37% fat calories). This beneficial effect of monounsaturates was lost when the total percentages of fat calories was high, suggesting the differential effect was swamped by total fat overload. This is an important point to remember when evaluating this literature, and in guiding further studies.

In relation to obesity, the evidence for a major effect of changes in dietary fatty acid profile is very limited and inconsistent (Moussavi et al., 2008). However, there are some positive studies where increasing n − 3s in the fat component of a diet did lead to increased fat oxidation (Couet et al., 1997) and increased weight loss (Kunesová et al., 2006) during a VLCD diet. As Moussavi et al. (2008) conclude “there is an urgent need for conducting more studies—particularly case-control studies and analyses of large data sets (cross-sectional or longitudinal).”

21.2.5. Mechanisms for Fatty Acid Effects at the Cellular Level

There are multiple ways in which membrane and membrane-derived lipids can influence cellular metabolism. These range from the simple mechanical to provision of intracellular second messenger and potent signaling molecules.

The fatty acid composition of structural membrane lipids is influenced both by genetic predisposition and by environment, particularly dietary fatty acid profile. Phospholipid is the major membrane structural lipid, and evidence linking obesity and insulin resistance to the fatty acid composition of phospholipids has existed for some time. Most of these studies have been carried out in skeletal muscle, the major tissue for insulin-stimulated glucose disposal (see, for example, Pan et al., 1995). This work shows that an increased proportion of saturated fatty acids in skeletal muscle phospholipid relate directly and positively to impaired insulin action and to various measures of regional and total increased adiposity. Conversely, PUFAs in phospholipid, and particularly the highly unsaturated n − 3 PUFA class, convey protection against both insulin resistance and development of obesity and type-2 diabetes.

Two mechanisms have been proposed to underpin such observations, which have been proposed and which are in the “mechanical” class. First, increased saturation of membrane lipids will be associated with higher density of membrane lipid packing because of the lack of double bonds to provide “shape” to phospholipids. This should decrease membrane fluidity and leakiness to ions and protons through interactions between the lipids and proteins within the membrane. Since proton/ion leak and the necessary pumping to maintain ionic homeostasis contributes heavily to the basal metabolism of the cell, increased saturation of membranes will decrease metabolic rate (Hulbert et al., 2005). This, in turn, will predispose increased accumulation of body fat stores for any given level of nutrient ingestion (positive fat balance). Of course such positive fat balance will result in whole body fat accretion as well as accumulation in the liver and skeletal muscle, both critical for insulin action. There is now a considerable body of convincing literature showing increased intramyocellular and hepatic lipid accumulation in close association with muscle insulin resistance is now large and convincing (van Herpen and Schrauwen-Hinderling, 2008). Conversely, there are data in support of the notions that more unsaturated fats will increase membrane proton/ion leakiness, increase metabolic rate, and impact favorably on fat balance (Hulbert et al., 2005). Interestingly, increased membrane unsaturation provides an environment conducive for the improved intrinsic activity of ion transporters thus providing the conditions, in concert, to allow maintenance of ion homeostasis.

A second mechanical process which would allow for membrane fatty acid composition to influence insulin action by a “mechanical” means is via the alteration of membrane proteins, both in terms of affinities of receptors and in translocation to membrane and intrinsic activity of membrane nutrient transporters. Such changes in affinity of the insulin receptor were demonstrated many years ago. Early in vitro studies showed impaired insulin binding with saturated fatty acids added to the medium (Grunfeld et al., 1981; Field et al., 1988). Conversely there is also some evidence that highly unsaturated n − 3 fatty acids were beneficial (Sohal et al., 1992; Clandinin et al., 1993). It is interesting in this context that insulin sensitizers such as bezafibrate (Matsui et al., 1997) also unsaturate membrane lipids and this effect on the insulin receptor may be a part of their beneficial action.

While one can clearly see the direct implications of changes in insulin receptor binding, other data has shown that beta adrenergic receptor affinity is decreased with dietary treatment emphasizing saturated fat intake (Matsuo et al., 1995), an observation which is also consistent with decreased metabolic rate. That is also true in brain (Matsuo and Suzuki, 1997). Taken together with the results of diets of differing fatty acid profiles on brain neuropeptides/neurotransmitters of energy balance, one could hypothesize that these changes could come about via modulation of membrane receptor binding.

In addition to the more mechanical processes that relate membrane structural lipid composition to key metabolic derangements of the metabolic syndrome, there are a number of ways in which membrane lipid components can influence intracellular lipid and carbohydrate metabolism, either directly by contributing metabolic products which themselves are involved or by generating second messenger molecules which modulate activities of key enzymes. Following is a figure of some elements of intracellular lipid and carbohydrate metabolism to orient the reader during the following discussion of some of these mechanisms.

One prominent mechanism involves diacylglycerols (DAGs), which are lipid second messengers that are produced during signal transduction by hydrolysis of membrane phospholipids. Protein kinase Cs are a family of DAG responsive enzymes that are recruited to cellular membranes as a consequence of DAG production where they phosphorylate specific target proteins responsible for regulating many cellular processes. In the current context, PKC inhibition of glucose transporter translocation will impair insulin-stimulated glucose uptake by making unavailable the specific proteins responsible for its transport. From the perspective of a functional validation of this hypothesis, it has been shown that increased availability of DAG, specifically from palmitate, impairs insulin-stimulated glucose uptake in primary myocyte cultures. In terms of specificity of the fatty acids on the DAGs for PKC translocation, the work of Montell et al. (2001) clearly shows the effect of palmitate on translocation of PKC (indexed by PKC activity) to the membrane where it can have its deleterious effects on glucose transport. An equal exposure of cells to the monounsautrated fatty acid, oleate, had no such effect.

A further mechanism involves the complex interaction of the membrane lipid class, sphingomyelins as a sink, and source, for ceramides. Ceramides, as can be seen diagrammatically in Figure 21.2, are deleterious for glucose transport and storage via, inter alia, inhibition of PKB/Akt activation. This has been shown to impair insulin-stimulated glucose uptake in primary myocyte cultures, and to inhibit insulin stimulation of glycogen synthase kinase-3, thus impairing cellular capacity for glycogen synthesis (Schmitz-Peiffer et al., 1999). Ceramides are on the synthetic pathway for sphingomyelin, and are also formed from its breakdown.

FIGURE 21.2. Some intracellular pathways of fatty acid handling which impinge strongly on insulin-stimulated glucose handling.

FIGURE 21.2

Some intracellular pathways of fatty acid handling which impinge strongly on insulin-stimulated glucose handling. Please see the text for further detail. ACC2, acetyl CoA carboxylase; ADP, adenosine diphosphate; ATP, adenosine triphosphate; CPT, carnitine (more...)

Sphingomyelin concentrations in adipose tissue and plasma have shown to be positively related to obesity and to insulin resistance (Zeghari et al., 2000). While there are excellent mechanistic links between membrane sphingomyelins, ceramides, and deleterious effects on glucose uptake and storage, there are also ceramide effects on cell apoptosis, and on lipid uptake as well, that should not be forgotten in the context of the metabolic syndrome. A review some time ago from Unger and Orci (2001) showed a pathway by which elevated ceramide levels might induce apoptosis. Apoptosis now appears to be the major reason for reduced β-cell mass, and hence impaired insulin secreting capacity—the final stage going from insulin resistance and glucose intolerance to frank diabetes (Butler et al., 2003). Thus, the possible role of membrane sphingomyelin in pancreatic-cell destruction is another key element in the multiple ways in which membrane lipids influence expression of the metabolic syndrome.

Finally, on this point of ceramide metabolism, cytokines such as TNFα are known to have very deleterious effects on a range of metabolic syndrome parameters. It is interesting that they are potent activators of sphingomyelinase and thus play a role in generation of ceramides, yet another way in which lipid membrane components must be considered, and important in the context of our understanding of the importance of chronic low-grade inflammation in the metabolic syndrome.

The relationships between membrane lipid components and fatty acid patterns within them, to obesity and insulin resistance as key elements of the metabolic syndrome are well described. There are many ways to account for these relationships mechanistically. The examples chosen above are illustrative and not exhaustive. However, they should serve to illustrate the importance of understanding the factors, both genetic and lifestyle, which regulate membrane lipid composition in our efforts to combat the current major epidemic of obesity and type-2 diabetes.

21.2.6. Summary of Dietary Lipids

There is an increasing body of knowledge on the role of dietary fat in metabolic syndrome, justifying further research to test the efficacy of food interventions with idealized fat profiles, but also considering the total diet context, including possible synergistic effects from the presence of other macronutrients such as carbohydrate.

21.3. CARBOHYDRATES AND LIPID BALANCE

As mentioned previously, population studies and NHANES data show a reduction in the percentage of dietary calories from fat during the 1990s but an increase in the prevalence of obesity (Prentice and Jebb, 1995 and www.cdc.gov/nchs/nhanes.htm). This reduction in the percentage of the diet from fat calories was despite an overall increase in caloric consumption so the absolute amount of fat in the diet was relatively constant. Also, physical activity levels declined over the same period which may also be contributing to the increasing rate of obesity. Nonetheless, these data, in conjunction with prospective weight loss studies showing that low-fat diets are no more effective for weight loss than high-fat diets, sparked interest in other macronutrients, mainly carbohydrate, as significant factors in the development of obesity and metabolic dysregulation that leads to the metabolic syndrome particularly because by reducing fat in the diet there is a subsequent increase in dietary carbohydrate (CHO). The type of CHO may be important. In particular, carbohydrate quality rather than carbohydrate quantity in the diet has been the focus of intense investigation.

21.3.1. Carbohydrate Classification

Carbohydrates are the most important food energy provider among the macronutrients and accounts for between 40% and 80% of the total energy intake. The FAO/WHO report in 1998 (FAO/WHO, 1998) recommended the consumption of at least 55% of total energy from a variety of carbohydrate sources. Carbohydrates may be classified according to their degree of polymerization and may be divided initially into three principal groups: sugars, oligosaccharides, and polysaccharides (see Table 21.1).

TABLE 21.1

TABLE 21.1

Major Dietary Carbohydrates

Each of these groups can be subdivided by the monosaccharide composition of the individual carbohydrates. Sugars comprise monosaccharides, disaccharides, and polyols; oligosaccharides include malto-oligosaccharides, other oligosaccharides, and fructo-oligosaccharides; the last group are polysaccharides which are divided into starch and nonstarch polysaccharides (Asp, 1996; Cummings et al., 1997; Englyst and Hudson, 1996).

The effects of dietary carbohydrates have been studied for many years with this broad range of compounds showing both beneficial and detrimental physiological actions. This conundrum can be explained by the examination of carbohydrate subtypes. The effects of different carbohydrates are often directly related to (1) the type of carbohydrate, (2) their rate of digestibility and the products produced, (3) their physical function in the gastrointestinal tract, and (4) their ability to act as substrates for fermentation by the microflora present in the colon. The major source of carbohydrate in the diet is starch and materials derived from starch. Starch is a complex energy storage structure found in many of the higher plants species and is composed mainly of glucose. However, the digestibility of starch by humans is affected by a myriad of factors including the type of starch, how food is prepared, and the physiological status of the person consuming the starch (Birkett and Brown, 2007, Table 21.2).

TABLE 21.2

TABLE 21.2

Food and Physiological Factors Affecting the Rate and Extent of Starch Digestion

In recent times, simple sugars such as glucose, which are rapidly digested and absorbed, have been implicated in the initiation and promotion of diseases and conditions associated with issues of global public health concern, such as diabetes and the “obesity epidemic.” Total sugar intake (g/day) has been positively correlated with BMI and total fat mass in overweight children, but negatively correlated with insulin sensitivity (Davis et al., 2007). Another simple sugar, fructose, has been linked to promotion of liver dysregulation, the accumulation of visceral adiposity, and the development of the metabolic syndrome relative to either glucose or a number of complex carbohydrates. This area has been well studied (Gaby, 2005; Kabir et al., 2005).

21.3.2. Absorption of Dietary Carbohydrate—Influence on Insulin Sensitivity

The absorption rate of glucose from starch or other sources may have important ramifications in terms of insulin sensitivity. Postprandial hyperglycemia and concomitant hyperinsulinemia have been implicated in the development of insulin resistance in both humans and rats. In humans, consumption of foods which cause a large rise in postprandial plasma glucose concentrations is associated with an increased concentration of free fatty acids in the plasma (Ritz et al., 1991; Vanamelsvoort and Weststrate, 1992). This increase in plasma free fatty acid concentration causes a decrease in glucose oxidation (Ritz et al., 1991), which may impair insulin sensitivity (Yki-Jarvinen, 1990). Elevated plasma free fatty acid concentrations also act to increase the concentration of mitochondrial acetyl CoA (Belfiore and Iannello, 1998) which inhibits pyruvate dehydrogenase thereby decreasing glucose oxidation. Postprandial hyperinsulinemia and hyperglycemia have also been shown to decrease glucose uptake through a decrease in GLUT4 mRNA and protein abundance (Cusin et al., 1990; Petersen et al., 1991).

In an attempt to define and predict the physiological effects of different carbohydrates, the glycemic index (GI) was developed. GI refers to the postprandial area under the glucose curve for a given food, expressed as a percentage of that for glucose or white bread which are defined as 100%. The GI is a measure for predicting the relative rate of glucose absorption from a food and, therefore, the rate of glucose appearance in the bloodstream. It should be noted however that although all CHO are glycemic, foods containing CHO have various levels of glycemic response depending on portion size and amount of CHO. The GI does not always reflect the true effect because it is based on a standard amount of CHO (50 g of glycemic CHO). Also, glycemic response is dependent on the type of CHO consumed. According to the AOAC method, glycemic carbohydrate (also referred to as readily available) is measured as total carbohydrate minus dietary fiber. Since this method does not include resistant starch (RS) when it is present, it will be mistakenly included as glycemic carbohydrate, leading to an overestimation of available CHO. Further to this, Robertson et al. (2003) showed that prior consumption of RS can modify the GI response to subsequent meals thereby limiting their accuracy. Therefore, when dealing with RS it is more appropriate to refer to excursions in glucose in terms of glycemic response (GR), instead of GI. In rats, a high GR diet has been demonstrated to increase GLUT4 expression in adipocytes and stimulate lipogenesis (Kabir et al., 1998a,b). In addition, high GR diets cause higher postprandial plasma triglyceride levels. These data are paralleled by human studies which show that consumption of high GR foods is associated with an increased concentration of free fatty acids in the plasma (Ritz et al., 1991). A high GR diet also acts to increase serum triglyceride concentrations not only postprandially but also in response to subsequent meals (Liljeberg and Bjorck, 2000). That is, triglyceride concentrations at lunch are significantly higher if a high GR breakfast is consumed relative to a low GR breakfast. Thus, slowly absorbed starches which have been shown to decrease peak glucose and insulin concentrations and total area under the curve for glucose and insulin, such as those from whole grains, may prove beneficial in the prevention of insulin resistance and associated disorders.

Resistant starch (RS) comprises starch, or materials derived from starch, that resist digestion in the small intestine and arrive in the large bowel (Asp et al., 1987). RS has been divided into four subcategories (numbered from 1 to 4) which reflect the mechanism by which the digestibility of the starch has been reduced (Brown et al., 1995). RS has demonstrated a variety of beneficial physiological effects that can be summarized from the observations made as a result of studies of one particular type, specifically the starch derived from high amylose maize or corn (a type of RS2) which is currently the most extensively researched. These effects are listed in Table 21.3.

TABLE 21.3

TABLE 21.3

Potential Health Benefits of RS2 from High Amylose Corn

It has been shown in rats that the long-term consumption of a low RS diet causes insulin resistance whereas consumption of a high RS diet maintains insulin sensitivity (Higgins et al., 1996). In addition, the degree of insulin resistance caused by chronic ingestion of digestible starch was as severe as that caused by feeding a diet in which 60% of the carbohydrate was glucose (Higgins et al., 1996). Kabir et al. (1998a,b) have demonstrated that chronic RS feeding in rats causes a decrease in adipocyte size and an increase in maximal insulin-stimulated glucose oxidation. In healthy adults, Robertson and coworkers have demonstrated that dietary supplementation with RS in the form of high amylose maize starch can improve insulin sensitivity 24 h after a meal (Robertson et al., 2003) and also after 4 weeks of chronic consumption (Robertson et al., 2005).

Here we have demonstrated that there are several mechanisms whereby hyperglycemia and hyperinsulinemia can cause insulin resistance, a recognized feature of many of the diseases comprising the metabolic syndrome. These data, in conjunction with the fact that slowly absorbed or resistant carbohydrates, such as RS, have been associated with decreased BMI and improved insulin sensitivity, suggest that long-term ingestion of slowly absorbed or resistant carbohydrates may aid in the prevention and/or treatment of the metabolic syndrome. Conversely, rapidly absorbed carbohydrates seem detrimental to insulin sensitivity and have been associated with the development of metabolic syndrome.

21.3.3. Fermentation of Dietary Carbohydrates—Influence on Insulin Sensitivity

As mentioned previously, RS, as well as dietary fiber (including fructo-oligosaccharides and inulin), and some lente carbohydrates are resistant to digestion in the small intestine and therefore pass to the large bowel for fermentation. This fermentation in the large bowel gives rise to an increased concentration of short-chain fatty acids (SCFA), a majority of which are utilized by the intestinal mucosa but, systemically, can improve insulin sensitivity (Robertson et al., 2003, 2005). This may be because elevated SCFA concentrations cause a decrease in circulating free fatty acid concentrations (Anderson and Bridges, 1984; Scheppach et al., 1988; Wolever et al., 1989). In addition, RS and dietary lipid can form a strong association in which the absorption of dietary fat will be delayed until the RS molecule is at least partially degraded. Thus, any lipid associated with RS will be absorbed more slowly acting to decrease the postprandial circulating concentration of free fatty acids which are detrimental to insulin sensitivity (as discussed previously).

RS assists in the development and maintenance of a healthy large bowel through both physical effects and the beneficial stimulation of the colonic microflora, in particular by increasing the numbers of beneficial bifidobacteria and lactobacilli and leading to the decreased presence of pathogenic bacteria, and the chemical compounds that they produce (Bird et al., 2000; Brown, 2006; Birkett and Brown, 2007). The composition of the colonic microflora can have important impacts on health including providing protection against the initiation of certain types of cancer (Le Leu et al., 2003), increased absorption of micronutrients such as minerals (Lopez et al., 2001) and improving energy recovery (FAO/WHO, 1998). It has been observed that there is a significant difference in the relative abundance of certain types of colonic bacteria in obese and lean humans (Turnbaugh et al., 2006). In particular obese mice were found to have increased numbers of Firmicutes relative to those of Bacteroidetes when compared to lean mice. How this change in the microbiota contributes to the manipulation of energy metabolism and lipid utilization in the body is unknown but it has now been shown that dietary fermentable carbohydrates, such as RS, can affect the expression of genes associated with peripheral lipid regulation (Keenan et al., 2007).

The colonic microflora can be influenced by the consumption of beneficial bacteria or probiotics. Many probiotics are available commercially, including lactobacilli and bifidobacteria, and are routinely incorporated in supplements and foods such as yoghurt. Many of the fermentable carbohydrates (FOS, inulin, RS resistant maltodextrin galactooligosaccharides, etc.) promote the growth of beneficial bacteria in the colon and are called “prebiotics.” The consumption of a specific probiotic, a Bifidobacteria lactis, in combination with a particular RS (this is referred to as a synbiotic) has shown a protective effect by increasing apoptosis colon cancer model in rats (Le Leu et al., 2005). Using this approach, it may be may be possible to adjust an individual’s microbiota to favor a lean and healthy body composition.

21.3.4. Carbohydrates and Lipid Metabolism

Among the complex carbohydrates, resistant starch (RS), lente carbohydrate and whole grains have demonstrated some ability to positively impact on lipid metabolism and reduce the incidence of conditions such as metabolic syndrome (Deroos et al., 1995). In rats, high RS intake causes a significant decrease in adipocyte cell size and total fat pad weight relative to low RS intake (Dedeckere et al., 1993; Lerer-Metzger et al., 1996; Kabir et al., 1998a,b). This change is associated with decreased expression of fatty acid synthase, the rate limiting step in fat synthesis, and GLUT4 (Yunes et al., 1995; Kabir et al., 1998a,b). It has also been demonstrated that the rate of de novo lipogenesis, as measured by incorporation of 14C-glucose into adipocyte triglyceride, was higher for rats fed a low RS diet than those fed a high RS diet (Kabir et al., 1998a,b). In humans, there is also evidence for an increase in fat oxidation in response to RS intake (Higgins et al., 2004).

At a genetic level RS has been shown to upregulate genes involved in lipid oxidation, synthesis, and storage, as well as production of a variety of peptides, such as glucagon-like peptide 1 (GLP-1) and peptide tyrosine tyrosine (PYY), that are associated with increasing satiety, reducing appetite, and stimulating insulin release (Watford, 2002). This effect appears to be mediated via the colonic production of bacterially produced short-chain fatty acids. The data demonstrate that RS consumption significantly lowers plasma cholesterol and triglyceride concentrations (Dedeckere et al., 1993; Yunes et al., 1995; Fukushima et al., 2001; Lopez et al., 2001; Kishida et al., 2002). This effect is also evident in diabetic rats (Kim et al., 2003). However, data in humans is less clear, with no change in fasting or postprandial plasma triglycerides in response to RS ingestion (Jenkins et al., 1998) but a small decrease later in the day (Liljeberg and Bjorck, 2000). However, in a population-based study, high whole grain intake was associated with lower total cholesterol, LDL cholesterol, and 2 h glucose concentration. This data needs to be verified by controlled clinical trials.

21.3.5. Summary of Dietary Carbohydrates

There are a number of mechanisms whereby dietary carbohydrate quality can influence weight gain, fat oxidation, and insulin sensitivity. These effects are generally mediated either by the rate of absorption or the degree of fermentation of carbohydrates. As a whole, these data show that the type of dietary carbohydrate can be a key factor in the development or prevention of insulin resistance, obesity, and the metabolic syndrome and should be carefully considered in terms of lifestyle recommendations aimed at both the prevention and treatment of this condition.

21.4. INTERACTION BETWEEN DIETARY FAT AND CARBOHYDRATE SUBTYPES

As summarized above, there is an extensive body of evidence supporting the beneficial effects of both fat and carbohydrate subtypes on various mechanisms of energy balance, metabolism, and disease. However, very little has been published on the combined effects that these macronutrients may have when given as an integrated diet. Unfortunately, this is an area that remains poorly investigated.

As mentioned earlier, RS promotes large bowel function through fecal bulking and the production of SCFA through bacterial fermentation, which increases insulin sensitivity. It has also been noted that n − 3 PUFAs have an anti-inflammatory effect and can also increase insulin sensitivity through changes in plasma membrane fatty acid composition and fluidity. Therefore, it would stand to reason, as an example, that a diet combining these two macronutrients would have an additive effect toward improving bowel function and insulin sensitivity.

Regarding bowel function, one study by Patten et al. (2006) investigated the effects that RS and n − 3 PUFA, separately and in combination, had on bowel function, specifically ileal tissue. They concluded that there were significant independent effects in SCFA production (most probably due to the RS), fatty acid composition, and ileal contractility, but there were few interactive effects. Other studies (Coleman et al., 2002; Conlon and Bird, 2003) have looked at the combined effects of these diets on markers for colon cancer and on genetic colon damage, with much the same outcomes. More investigation needs to be done on the mechanisms surrounding this lack of synergy between RS and n − 3 PUFA.

In contrast to these negative findings, unpublished data by Brown et al. showed significant differences in body weight and in glucose excursions between diets of different starch (RS vs amylopectin, a more readily digestible starch) and lipid composition (n − 3 PUFA vs more saturated fat). While the potent variable was RS, the combination RS and n − 3 PUFA diet was superior to the individual effects, resulting in significantly lower body weights and postprandial glucose levels than three other combination diets. It must be mentioned however that most of the difference was due to the independent effect of RS. To explain this effect, one could postulate that RS and n − 3 PUFA bind as they travel through the bowel. Lipid and RS can form a strong bond in which the lipid is sequestered in the core of the RS molecule. In this case, it is likely that the absorption of dietary fat will be delayed until the RS molecule is at least partially degraded. Thus, from a positive perspective, any lipid bound with RS will be absorbed more slowly, acting to decrease the postprandial circulating concentration of free fatty acids which are detrimental to insulin sensitivity. This is of course, at present, pure speculation but the very limited data available point out a strong need for studies at the interface of fat and carbohydrate subtypes where significant health benefits might accrue.

21.5. CONCLUSION

Both fat and CHO subtypes influence outcomes in metabolic syndrome, but in reality people eat foods and whole diets, not just nutrients. Emerging mechanistic theories on how certain types of lipids and CHO interact may begin to provide a basis for a framework in which knowledge of this synergy might be built (Jacobs and Tapsell, 2007). In turn, food product development may draw in this knowledge to create novel foods with particular health benefits.

REFERENCES

  1. Anderson JW, Bridges SR. Short-chain fatty-acid fermentation products of plant fiber affect glucose-metabolism of isolated rat hepatocytes. Proc Soc Exp Biol Med. 1984;177:372–376. [PubMed: 6091151]
  2. Arnett DK, Xiong B, McGovern PG, Blackburn H, Luepker RV. Secular trends in dietary macronutrient intake in Minneapolis-St. Paul, Minnesota, 1980–1992. Am J Epidemiol. 2000;152:868–873. [PubMed: 11085399]
  3. Asp NG. Dietary carbohydrates: Classification by chemistry and physiology. Food Chem. 1996;57:9–14.
  4. Asp NG, Bjorck I, Holm J, Nyman M, Siljestrom M. Enzyme resistant starch fractions and dietary fiber. Scand J Gastroenterol. 1987;22:29–32. [PubMed: 2442809]
  5. Astrup A, Ryan L, Grunwald GK, Storgaard M, Saris W, Melanson E, Hill JO. The role of dietary fat in body fatness: Evidence from a preliminary meta-analysis of ad libitum low-fat dietary intervention studies. Br J Nutr. 2000;83(Suppl 1):S25–S32. [PubMed: 10889789]
  6. Belfiore F, Iannello S. Insulin resistance in obesity: Metabolic mechanisms and measurement methods. Mol Genet Metabol. 1998;65:121–128. [PubMed: 9787104]
  7. Bird AR, Brown IL, Topping DL. Starches, resistant starches, the gut microflora and human health. Curr Iss Intest Microbiol. 2000;1:25–37. [PubMed: 11709851]
  8. Birkett AM, Brown IL. Novel food ingredients for weight control. In: Henry CJK, editor. Resistant Starch. Cambridge, U.K.: Woodhead Publishing Limited; 2007. pp. 174–197.
  9. Birkett AM, Brown IL. Technology of functional cereal products. In: Hamaker B, editor. Resistant Starch and Health. Cambridge, U.K.: Woodhead Publishing Limited; 2007. pp. 63–85.
  10. Bo S, Menato G, Lezo A, Signorile A, Bardelli C, De Michieli F, Massobrio M, Pagano G. Dietary fat and gestational hyperglycemia. Diabetologia. 2001;44:972–978. [PubMed: 11484073]
  11. Brown IL, McNaught KJ, Moloney E. Hi-Maize(Tm)—New directions in starch technology and nutrition. Food Aust. 1995;47:272–275.
  12. Brown IL YM, Birkett A, Henriksson A. Prebiotics, synbiotics and resistant starch. J Jpn Assoc Diet Fibre Res. 2006;10:1–10.
  13. Buettner R, Parhofer KG WM, Wrede CE, Kunz-Schughart LA, Scholmerich J, Bollheimer LC. Defining high-fat-diet rat models: Metabolic and molecular effects of different fat types. J Mol Endocrinol. 2006;36:485–501. [PubMed: 16720718]
  14. Buettner R, Scholmerich J, Bollheimer LC. High-fat diets: Modeling the metabolic disorders of human obesity in rodents. Obesity. 2007;15:798–808. [PubMed: 17426312]
  15. Butler AE, Janson J, Bonner-Weir S, Ritzel R, Rizza RA, Butler PC. β-Cell deficit and increased β-cell apoptosis in humans with type 2 diabetes. Diabetes. 2003;52:102–110. [PubMed: 12502499]
  16. Casado V, Mallol J, Canela EI, Franco R, Lluis C. Modulation of adenosine agonist [3H]N6-(R)-phenylisopropyladenosine binding to pig brain cortical membranes by changes of membrane fluidity and of medium physicochemical characteristics. Eur J Pharmacol. 1992;225:7–14. [PubMed: 1541326]
  17. Chalon S, Delion-Vancassel S, Belzung C, Guilloteau D, Leguisquet A-M, Besnard J-C, Durand G. Dietary fish oil affects monoaminergic neurotransmission and behavior in rats. J Nutr. 1998;128:2512–2519. [PubMed: 9868201]
  18. Clandinin MT, Cheema S, Field CJ, Baracos VE. Dietary lipids influence insulin action. In: Klimes I, Howard BV, Storlien LH, Sebokova E, editors. Dietary Lipids and Insulin Action. New York: New York Academy of Sciences; 1993. pp. 151–163.
  19. Coleman LJ, Landstrom EK, Royle PJ, Bird AR, McIntosh GH. A diet containing alpha-cellulose and fish oil reduces aberrant crypt foci formation and modulates other possible markers for colon cancer risk in azoxymethane-treated rats. J Nutr. 2002;132:2312–2318. [PubMed: 12163681]
  20. Conlon M, Bird A. Interactions of dietary fibre and resistant starch with oil on genetic damage in the rat colon. Asia Pac J Clin Nutr. 2003;12:S54.
  21. Couet C, Delarue J, Ritz P, Antione J-M, Lamisse F. Effect of dietary fish oil on body fat mass and basal fat oxidation in healthy adults. Int J Obes. 1997;21:637–643. [PubMed: 15481762]
  22. Cummings JH, Roberfroid MB, Andersson H, Barth C, Ferroluzzi A, Ghoos Y, Gibney M, et al. A new look of dietary carbohydrate: Chemistry, physiology and health. Eur J Clin Nutr. 1997;51:417–423. [PubMed: 9234022]
  23. Cunha RA, Constantino MD, Fonseca E, Ribeiro JA. Age-dependent decrease in adenosine A1 receptor binding sites in the rat brain. Effect of cis unsaturated free fatty acids. Eur J Biochem. 2001;268:2939–2947. [PubMed: 11358511]
  24. Cusin I, Terrettaz J, Rohnerjeanrenaud F, Zarjevski N, Assimacopoulosjeannet F, Jeanrenaud B. Hyperinsulinemia increases the amount of Glut4 messenger-RNA in white adipose-tissue and decreases that of muscles—A clue for increased fat depot and insulin resistance. Endocrinology. 1990;127:3246–3248. [PubMed: 2249650]
  25. Dansinger ML GJ, Griffith JL, Selker HP, Schaefer EJ. Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: A randomized trial. J Am Med Assoc. 2005;293:43–53. [PubMed: 15632335]
  26. Davis JN, Alexander KE, Ventura EE, Kelly LA, Lane CJ, Byrd-Williams CE, Toledo-Corral CM, et al. Associations of dietary sugar and glycemic index with adiposity and insulin dynamics in overweight Latino youth. Am J Clin Nutr. 2007;86:1331–1338. [PubMed: 17991643]
  27. Dedeckere EAM, Kloots WJ, Vnamelsvoort JMM. Resistant starch decreases serum total cholesterol and triacylglycerol concentrations in rats. J Nutr. 1993;123:2142–2151. [PubMed: 8263609]
  28. Delion S, Chalon S, Herault J, Guilloteau D, Besnard J-C, Durand G. Chronic dietary {alpha}-linolenic acid deficiency alters dopaminergic and serotoninergic neurotransmission in rats. J Nutr. 1994;124:2466–2476. [PubMed: 16856329]
  29. Delion S, Chalon S, Guilloteau D, Besnard JC, Durand G. Alpha-linolenic acid dietary deficiency alters age-related changes of dopaminergic and serotoninergic neurotransmission in the rat frontal cortex. J Neurochem. 1996;66:1582–1591. [PubMed: 8627314]
  30. Deroos N, Heijnen ML, Degraaf C, Woestenenk G, Hobbel E. Resistant starch has little effect on appetite, food-intake and insulin-secretion of healthy-young men. Eur J Clin Nutr. 1995;49:532–541. [PubMed: 7588504]
  31. du Bois TM, Bell W, Deng C, Huang XF. A high n−6 polyunsaturated fatty acid diet reduces muscarinic M2/M4 receptor binding in the rat brain. J Chem Neuroanat. 2005;29:282–288. [PubMed: 15927789]
  32. du Bois TM, Deng C, Bell W, Huang XF. Fatty acids differentially affect serotonin receptor and transporter binding in the rat brain. Neuroscience. 2006;139:1397–1403. [PubMed: 16600514]
  33. Englyst HN, Hudson GJ. The classification and measurement of dietary carbohydrates. Food Chem. 1996;57:15–21.
  34. Erridge C, Attina T, Spickett CM, Webb DJ. A high-fat meal induces low-grade endotoxemia: Evidence of a novel mechanism of postprandial inflammation. Am J Clin Nutr. 2007;86:1286–1292. [PubMed: 17991637]
  35. FAO/WHO. Carbohydrates in Human Nutrition: Report of a Joint FAO/WHO Expert Consultation. FAO Food and Nutrition Paper No. 66.; Rome. 1998.
  36. Field CJ, Ryan EA, Thomson AR, Clandinin MT. Dietary fat and the diabetic state alter insulin binding and the fatty acyl composition of the adipocyte plasma membrane. Biochem J. 1988;253:417–424. [PMC free article: PMC1149315] [PubMed: 3052424]
  37. Foster GD, Wyatt HR, Hill JO, McGuckin BG, Brill C, Mohammed BS, Szapary PO, Rader DJ, Edman JS, Klein S. A randomized trial of a low-carbohydrate diet for obesity. N Engl J Med. 2003;348:2082–2090. [PubMed: 12761365]
  38. Fukushima M, Ohashi T, Kojima M, Ohba K, Shimizu H, Sonoyama K, Nakano M. Low density lipoprotein receptor mRNA in rat liver is affected by resistant starch of beans. Lipids. 2001;36:129–134. [PubMed: 11269692]
  39. Gaby AR. Adverse effects of dietary fructose. Alternat Med Rev. 2005;10:294–306. [PubMed: 16366738]
  40. Galgani JE, Uauy RD, Aguirre CA, Diaz EO. Effect of the dietary fat quality on insulin sensitivity. Br J Nutr. 2008;8:1–9. [PubMed: 18394213]
  41. Gardner CD, Kiazand A, Alhassan S, Kim S, Stafford RS, Balise RR, Kraemer HC, King AC. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women. J Am Med Assoc. 2007;297:969–977. [PubMed: 17341711]
  42. Giraudo SQ, Kotz CM, Grace MK, Levine AS, Billington CJ. Rat hypothalamic NPY mRNA and brown fat uncoupling protein mRNA after high-carbohydrate or high-fat diets. Am J Physiol. 1994;266:R1578–1583. [PubMed: 8203634]
  43. Grande F. Energy balance and body composition changes—A critical study of three recent publications. Ann Intern Med. 1968;68:467–480. [PubMed: 4890510]
  44. Grunfeld C, Baird K, Kahn CR. Maintenance of 3T3-L1 cells in culture media containing saturated fatty acids decreases insulin binding and insulin action. Biochem Biophys Res Commun. 1981;103:219–226. [PubMed: 7032521]
  45. Guan XM, Yu H, Van der Ploeg LH. Evidence of altered hypothalamic pro-opiomelanocortin/neuropeptide Y mRNA expression in tubby mice. Brain Res. Mol Brain Res. 1998;59:273–279. [PubMed: 9729427]
  46. Heymsfield SB, Blackburn GL. Comparison of weight-loss diets. J Am Med Assoc. 2007;298:173–174. [PubMed: 17622592]
  47. Higgins JA, Higbee DR, Donahoo WT, Brown IL, Bell ML, Bessesen DH. Resistant starch consumption promotes lipid oxidation. Nutr Metab. 2004;1:8. [PMC free article: PMC526391] [PubMed: 15507129]
  48. Higgins JA, Miller JCB, Denyer GS. Development of insulin resistance in the rat is dependent on the rate of glucose absorption from the diet. J Nutr. 1996;126:596–602. [PubMed: 8598543]
  49. Hulbert AJ, Turner N, Storlien LH, Else PL. Dietary fats and membrane function: Implications for metabolism and disease. Biol Rev Camb Philos Soc. 2005;80:155–169. [PubMed: 15727042]
  50. Jacobs DR, Tapsell LC. Food, not nutrients; the fundamental unit in nutrition. Nutr Rev. 2007;65:439–450. [PubMed: 17972438]
  51. Jenkins DJA, Vuksan V, Kendall CWC, Wursch P, Jeffcoat R, Waring S, Mehling CC, Vidgen E, Augustin LSA, Wong E. Physiological effects of resistant starches on fecal bulk, short chain fatty acids, blood lipids and glycemic index. J Am Coll Nutr. 1998;17:609–616. [PubMed: 9853541]
  52. Kabir M, Catalano KJ, Ananthnarayan S, Kim SP, Van Citters GW, Dea MK, Bergman RN. Molecular evidence supporting the portal theory: A causative link between visceral adiposity and hepatic insulin resistance. Am J Physiol-Endocrinol Metab. 2005;288:E454–E461. [PubMed: 15522994]
  53. Kabir M, Rizkalla SW, Champ M, Luo J, Boillot J, Bruzzo F, Slama G. Dietary amylose-amylopectin starch content affects glucose and lipid metabolism in adipocytes of normal and diabetic rats. J Nutr. 1998a;128:35–42. [PubMed: 9430599]
  54. Kabir M, Rizkalla SW, Quignard-Boulange A, Guerre-Millo M, Boillot J, Ardouin B, Luo J, Slama G. A high glycemic index starch diet affects lipid storage-related enzymes in normal and to a lesser extent in diabetic rats. J Nutr. 1998b;128:1878–1883. [PubMed: 9808637]
  55. Kavanagh K, Jones KL, Sawyer J, Kelley K, Carr JJ, Wagner JD, Rudel LL. Trans fat diet induces abdominal obesity and changes in insulin sensitivity in monkeys. Obesity. 2007;15:1675–1684. [PubMed: 17636085]
  56. Keenan MJ, Zhou J, Raggio AM, McCutcheon KL, Tulley RT, Hegsted M, Bateman HG, et al. Health benefits of dietary resistant starch, a nondigestible fermentable glucose polymer. Symposium: Polymer Design for Foods and Nutrition. Polymer Preprints. 2007;48:737–738.
  57. Kim WK, Chung MK, Kang NE, Kim MH, Park OJ. Effect of resistant starch from corn or rice on glucose control, colonic events, and blood lipid concentrations in streptozotocin-induced diabetic rats. J Nutr Biochem. 2003;14:166–172. [PubMed: 12742545]
  58. Kishida T, Nogami H, Ogawa H, Ebihara K. The hypocholesterolemic effect of high amylose cornstarch in rats is mediated by an enlarged bile acid pool and increased fecal bile acid excretion, not by cecal fermented products. J Nutr. 2002;132:2519–2524. [PubMed: 12221203]
  59. Kunesová M, Braunerová R, Hlavatý P, Tvrzická E, Stanková B, Skrha J, Hilgertová J, et al. The influence of n−3 polyunsaturated fatty acids and very low calorie diet during a short-term weight reducing regimen on weight loss and serum fatty acid composition in severely obese women. Physiol Res. 2006;55:63–72. [PubMed: 15857162]
  60. Lazzer S, Boirie Y, Montaurier C, Vernet J, Meyer M, Vermorel M. A weight reduction program preserves fat-free mass but not metabolic rate in obese adolescents. Obes Res. 2004;12:233–240. [PubMed: 14981215]
  61. Le Leu RK, Brown IL, Hu Y, Young GP. Effect of resistant starch on genotoxin-induced apoptosis, colonic epithelium, and lumenal contents in rats. Carcinogenesis. 2003;24:1347–1352. [PubMed: 12807738]
  62. Le Leu RK, Brown IL, Hu Y, Bird AR, Jackson M, Esterman A, Young GP. A synbiotic combination of resistant starch and Bifidobacterium lactis facilitates apoptotic deletion of carcinogen-damaged cells in rat colon. J Nutr. 2005;135:996–1001. [PubMed: 15867271]
  63. Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from altered body weight. N Engl J Med. 1995;332:621–628. [PubMed: 7632212]
  64. Lerer-Metzger M, Rizkalla SW, Luo J, Champ M, Kabir M, Bruzzo F, Bornet F, Slama G. Effects of long-term low-glycaemic index starchy food on plasma glucose and lipid concentrations and adipose tissue cellularity in normal and diabetic rats. Br J Nutr. 1996;75:723–732. [PubMed: 8695599]
  65. Liljeberg H, Bjorck I. Effects of a low-glycaemic index spaghetti meal on glucose tolerance and lipaemia at a subsequent meal in healthy subjects. Eur J Clin Nutr. 2000;54:24–28. [PubMed: 10694768]
  66. Lopez HW, Levrat-Verny MA, Coudray C, Besson C, Krespine V, Messager A, Demigne C, Remesy C. Class 2 resistant starches lower plasma and liver lipids and improve mineral retention in rats. J Nutr. 2001;131:1283–1289. [PubMed: 11285339]
  67. Major CA, Henry MJ, De Venciana M, Morgan MA. The effects of carbohydrate restriction in patients with diet-controlled gestational diabetes. Obstetr Gynecol. 1998;91:600–604. [PubMed: 9540949]
  68. Matsui H, Okumura K, Kawakami K, Hibino M, Toki Y, Ito T. Improved insulin sensitivity by bezafibrate in rats: Relationship to fatty acid composition of skeletal-muscle triglycerides. Diabetes. 1997;46:348–353. [PubMed: 9032088]
  69. Matsuo T, Sumida H, Suzuki M. Beef tallow diet decreases β-adrenergic receptor binding and lipolytic activities in different adipose tissues of rat. Metabolism. 1995;44:1271–1277. [PubMed: 7476283]
  70. Matsuo T, Suzuki M. Brain beta-adrenergic receptor binding in rats with obesity induced by a beef tallow diet. Metabolism. 1997;46:18–22. [PubMed: 9005963]
  71. Mendoza JA, Drewnowski A, Christakis DA. Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults. Diabetes Care. 2007;30:974–979. [PubMed: 17229942]
  72. Montell E, Turini M, Marotta M, Roberts M, Noe V, Ciudad CJ, Mace K, Gomez-Foix AM. DAG accumulation from saturated fatty acids desensitizes insulin stimulation of glucose uptake in muscle cells. Am J Physiol. 2001;280:E229–E237. [PubMed: 11158925]
  73. Moses RG, Shand JL, Tapsell LC. The recurrence of gestational diabetes: Could dietary differences in fat intake be an explanation? Diabetes Care. 1997;20:1647–1650. [PubMed: 9353601]
  74. Moussavi N, Gavino V, Receveur O. Could the quality of dietary fat, and not just its quantity, be related to risk of obesity? Obesity. 2008;16:7–15. [PubMed: 18223605]
  75. Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans fatty acids and cardiovascular disease. N Engl J Med. 2006;354:1601–1613. [PubMed: 16611951]
  76. Mozaffarian D, Willett WC. Trans fatty acids and cardiovascular risk: A unique cardiometabolic imprint? Curr Atherosclerosis Rep. 2007;9:486–493. [PubMed: 18377789]
  77. Oktem HA, Apaydin S. Arachidonic acid modulation of [3H]naloxone specific binding to rat brain opioid receptors. Neurobiology (Bp) 1998;6:323–332. [PubMed: 9778651]
  78. Pan DA, Storlien LH. Dietary lipid profile is a determinant of tissue phospholipid fatty acid composition and rate of weight gain in rats. J Nutr. 1993;123:512–519. [PubMed: 8463854]
  79. Pan DA, Lillioja S, Milner MR, Kriketos AD, Baur LA, Bogardus C, Storlien LH. Skeletal muscle membrane lipid composition is related to adiposity and insulin action. J Clin Invest. 1995;96:2802–2808. [PMC free article: PMC185990] [PubMed: 8675650]
  80. Patten GS, Conlon MA, Bird AR, Adams MJ, Topping DL, Abeywardena MY. Interactive effects of dietary resistant starch and fish oil on short-chain fatty acid production and agonist-induced contractility in ileum of young rats. Digest Dis Sci. 2006;51:254–261. [PubMed: 16534666]
  81. Petersen S, Russ M, Reinauer H, Eckel J. Inverse regulation of glucose transporter Glut4 and G-protein Gs messenger-RNA expression in cardiac myocytes from insulin resistant rats. FEBS Lett. 1991;286:1–5. [PubMed: 1907567]
  82. Petersen M, Taylor MA, Saris WH, Verdich C, Toubro S, Macdonald I, Rossner S, et al. Randomized, multi-center trial of two hypo-energetic diets in obese subjects: High-versus low-fat content. Int J Obes. 2006;30:552–560. [PubMed: 16331300]
  83. Piggott M, Owens J, O’Brien J, Paling S, Wyper D, Fenwick J, Johnson M, Perry R, Perry E. Comparative distribution of binding of the muscarinic receptor ligands pirenzepine, AF-DX 384, (R,R)-I-QNB and (R,S)-I-QNB to human brain. J Chem Neuroanat. 2002;24:211–223. [PubMed: 12297267]
  84. Pirozzo S, Summerbell C, Cameron C, Glasziou P. Advice on low-fat diets for obesity. Cochrane Database Syst Rev. 2002:CD003640. [PubMed: 12076496]
  85. Prentice AM, Jebb SA. Obesity in Britain: Gluttony or sloth? Br Med J. 1995;311:437–439. [PMC free article: PMC2550498] [PubMed: 7640595]
  86. Rankin JW, Turpyn AD. Low carbohydrate, high fat diet increases C-reactive protein during weight loss. J Am Coll Nutr. 2007;26:163–169. [PubMed: 17536128]
  87. Reaven GM. Role of insulin resistance in human disease. Diabetes. 1988;37:1595–1607. [PubMed: 3056758]
  88. Riserus U. Fatty acids and insulin sensitivity. Curr Opin Clin Nutr Metabol Care. 2008;11:100–105. [PubMed: 18301083]
  89. Ritz P, Krempf M, Cloarec D, Champ M, Charbonnel B. Comparative continuous-indirect-calorimetry study of 2 carbohydrates with different glycemic indexes. Am J Clin Nutr. 1991;54:855–859. [PubMed: 1951156]
  90. Robertson MD, Currie JM, Morgan LM, Jewell DP, Frayn KN. Prior short-term consumption of resistant starch enhances postprandial insulin sensitivity in healthy subjects. Diabetologia. 2003;46:659–665. [PubMed: 12712245]
  91. Robertson MD, Bickerton AS, Dennis AL, Vidal H, Frayn KN. Insulin-sensitizing effects of dietary resistant starch and effects on skeletal muscle and adipose tissue metabolism. Am J Clin Nutr. 2005;82:559–567. [PubMed: 16155268]
  92. Robinson LE, Buchholz AC, Mazurak VC. Inflammation, obesity, and fatty acid metabolism: Influence of n−3 polyunsaturated fatty acids on factors contributing to metabolic syndrome. Appl Physiol Nutr Metab-Physiologie Appliquee Nutrition Et Metabolisme. 2007;32:1008–1024. [PubMed: 18059573]
  93. Rolland-Cachera MF, Thibault H, Souberbielle JC, Soulié D, Carbonel P, Deheeger M, Roinsol D, Longueville E, Bellisle F, Serog P. Massive obesity in adolescents: Dietary interventions and behaviours associated with weight regain at 2y follow-up. Int J Obes. 2004;28:514–519. [PubMed: 14968129]
  94. Salmerón J, Hu FB, Manson JE, Stampfer MJ, Colditz GA, Rimm EB, Willett WC. Dietary fat intake and risk of type 2 diabetes in women. Am J Clin Nutr. 2001;73:1019–1026. [PubMed: 11382654]
  95. Scheppach W, Cummings JH, Branch WJ, Schrezenmeir J. Effect of gut-derived acetate on oral glucose-tolerance in man. Clin Sci. 1988;75:355–361. [PubMed: 2848652]
  96. Schmitz-Peiffer C, Craig DL, Biden TJ. Ceramide generation is sufficient to account for the inhibition of the insulin-stimulated PKB pathway in C2C12 skeletal muscle cells pretreated with palmitate. J Biol Chem. 1999;274:24202–24210. [PubMed: 10446195]
  97. Smedman A, Basu S, Jovinge S, Fredrikson GN, Vessby B. Conjugated linoleic acid increased C-reactive protein in human subjects. Br J Nutr. 2005;94:791–795. [PubMed: 16277783]
  98. Sohal PS, Baracos VE, Clandinin MT. Dietary ω3 fatty acid alters prostaglandin synthesis, glucose transport and protein turnover in skeletal muscle of healthy and diabetic rats. Biochem J. 1992;286:405–411. [PMC free article: PMC1132913] [PubMed: 1530573]
  99. Stanley M, Mann JJ. Increased serotonin-2 binding sites in frontal cortex of suicide victims. Lancet. 1983;1:214–216. [PubMed: 6130248]
  100. Storlien LH, Jenkins AB, Chisholm DJ, Pascoe WS, Khouri S, Kraegen EW. Influence of dietary fat composition on development of insulin resistance in rats. Relationship to muscle triglyceride and ω-3 fatty acids in muscle phospholipids. Diabetes. 1991;40:280–289. [PubMed: 1991575]
  101. Storlien LH, Baur LA, Kriketos AD, Pan DA, Cooney GJ, Jenkins AB, Calvert GD, Campbell LV. Dietary fats and insulin action. Diabetologia. 1996;39:621–631. [PubMed: 8781757]
  102. Stricker-Krongrad A, Cumin F, Burlet C, Beck B. Hypothalamic neuropeptide Y and plasma leptin after long-term high-fat feeding in the rat. Neurosci Lett. 1998;254:157–160. [PubMed: 10214981]
  103. Tricon S, Yaqoob P. Conjugated linoleic acid and human health: A critical evaluation of the evidence. Curr Opin Clin Nutr Metab Care. 2006;9:105–110. [PubMed: 16477173]
  104. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–1031. [PubMed: 17183312]
  105. Unger RH, Orci L. Diseases of liporegulation: New perspective on obesity and related disorders. FASEB J. 2001;15:312–321. [PubMed: 11156947]
  106. van Herpen NA, Schrauwen-Hinderling VB. Lipid accumulation in non-adipose tissue and lipotoxicity. Physiol Behav. 2008;94:231–241. [PubMed: 18222498]
  107. Vanamelsvoort JMM, Weststrate JA. Amylose–amylopectin ratio in a meal affects postprandial variables in male-volunteers. Am J Clin Nutr. 1992;55:712–718. [PubMed: 1550048]
  108. Vessby B. Dietary fat, fatty acid composition in plasma and the metabolic syndrome. Curr Opin Lipidol. 2003;14:15–19. [PubMed: 12544656]
  109. Vessby B, Uusitupa M, Hermansen K, Riccardi G, Rivellese AA, Tapsell LC, Nälsén C, et al. Substituting dietary saturated for monounsaturated fat impairs insulin sensitivity in healthy men and women: The KANWU study. Diabetologia. 2001;44:312–319. [PubMed: 11317662]
  110. Wang J, Akabayashi A, Dourmashkin J, Yu HJ, Alexander JT, Chae HJ, Leibowitz SF. Neuropeptide Y in relation to carbohydrate intake, corticosterone and dietary obesity. Brain Res. 1998;802:75–88. [PubMed: 9748512]
  111. Wang Y, Storlien LH, Jenkins AB, Tapsell LC, Jin Y, Pan JF, Shao YF, et al. Dietary variables and glucose tolerance in pregnancy. Diabetes Care. 2000;23:460–464. [PubMed: 10857935]
  112. Wang H, Storlien LH, Huang X-F. Effects of dietary fat types on body fatness, leptin, and ARC leptin receptor, NPY, and AgRP mRNA expression. Am J Physiol. Endocrinol Metab. 2002;282:E1352–E1359. [PubMed: 12006366]
  113. Watford M. Small amounts of dietary fructose dramatically increase hepatic glucose uptake through a novel mechanism of glucokinase activation. Nutr Rev. 2002;60:253–257. [PubMed: 12199300]
  114. Wolever TMS, Brighenti F, Royall D, Jenkins AL, Jenkins DJA. Effect of rectal infusion of short chain fatty-acids in human-subjects. Am J Gastroenterol. 1989;84:1027–1033. [PubMed: 2773895]
  115. Yki-Jarvinen H. Acute and chronic effects of hyperglycaemia on glucose metabolism. Diabetologia. 1990;33:579–585. [PubMed: 2257995]
  116. Yunes H, Levrat MA, Demigne C, Remesy C. Resistant starch is more effective than cholestyramine as a lipid-lowering agent in the rat. Lipids. 1995;30:847–853. [PubMed: 8577229]
  117. Zeghari N, Younsi M, Meyer L, Donner M, Drouin P, Ziegler O. Adipocyte and erythrocyte plasma membrane phospholipid composition and hyperinsulinemia: A study in nondiabetic and diabetic obese women. Int J Obesity. 2000;24:1600–1607. [PubMed: 11126212]
Copyright © 2010, Taylor & Francis Group, LLC.
Bookshelf ID: NBK53531PMID: 21452465

Views

  • PubReader
  • Print View
  • Cite this Page

Other titles in this collection

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to PubMed

Similar articles in PubMed

See reviews...See all...

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...