Over two years ago this blog discussed the possibility of incorporating a specialized preparation routine before exercise in an attempt to stimulate both brown and beige adipose tissue in order to increase the efficiency and overall calorie and fat burning potential of standard exercise. However, that post did not seek to fully understand or discuss the specific biological mechanisms that govern the behavior of brown or beige adipose tissue. This lack of knowledge limits the efficiency for exercise programs as individuals could either be consuming certain foods or performing certain warm-up tasks to increase exercise potential in addition to those suggested in the past blog post. Increasing exercise efficiency could be an easy means to increase the overall health of society without having to devote more precious time to exercise; therefore it would prove useful to better understand the processes that activate these types of fat.
At the most basic level there are two key elements to the fat burning capacity of brown fat. First, brown fat has multiple mitochondria versus the single mitochondria possessed by white fat; these additional mitochondria allow for greater rates of metabolism along with an increased lipid concentration. Also brown fat releases norepinephrine which reacts with lipases to breakdown fat into triglycerides and later to glycerol and non-esterified fatty acids finally producing CO2 and water, which can lead to a positive feedback mechanism.1,2 Second, brown fat contains significant expression rates of uncoupling protein 1 (UCP-1).1 UCP-1 is responsible for dissipating energy, which leads to the decoupling of ATP production and mitochondrial respiration.1 Basically UCP-1 returns protons after they have been pumped out of the mitochondria by the electron transport chain where these protons are released as heat instead of producing energy (i.e. leaking).
It is important to understand that there are two types of brown fat: natural brown fat and intermediate brown fat commonly known as beige fat. Natural brown is typically exemplified by the fat located in the interscapular region and contains cells from muscle-like myf5+ and pax7+ lineage.3 Natural brown fat is typically isolated from white fat and almost entirely synthesized in the prenatal stage of development as a means to produce heat apart from shivering.4 Beige fat is commonly interspaced within white fat, do not have these muscle-like cells (although Myh11 could be involved),5 and can be activated by thermogenic pathway and the strain of exercise. Beige fat also has the potential to influence the conversion of white fat to beige fat through a process commonly called “browning”.6,7
Natural brown fat is thought to have larger concentrations of UCP1-expression because they constitutively express it after differentiation versus beige, which expresses large amounts of UCP-1 in response to thermogenic or exercise cues.1,5 Therefore, natural brown fat is more effective at energy expenditure. However, it may not be possible to develop more natural brown fat after development; therefore, any positive progression in brown fat development will come from beige fat.
Early understanding of brown fat activation involved non-discriminate increases in the activity of the sympathetic nervous system (SNS). The standard pathway governing brown fat activation uses a thermogenic response involving the release of norepinephrine, which initiates cAMP-dependent protein kinase (PKA) and p38-MAPK signaling leading to the production of free fatty acids (FFA) through lipolysis due to UCP-1 induced proton uncoupling.4 UCP-1 concentrations are further increased through secondary pathways involving the phosphorylation of PPAR-gamma co-activator 1alpha (PGC1alpha), cAMP response element binding protein (CREB) and activating transcription factor 2 (ATF2).8 Among these three elements PGC1alpha appears to be the most important co-activating many transcription factors and playing an important role in linking oxidative metabolism and mitochondrial action.9
However, due to the complicated nature of SNS activation and its other downstream activators the attempt to replicate it in the form of weight loss drugs like Fenfluoramine or Ephedra resulted in severe negative cardiovascular side effects like elevated blood pressure and heart rate.10 While some argue that either increasing the sensitivity or the rate of simulation to the SNS can improve upon these results, the underlying elements associated with downstream activation of the SNS makes facilitating direct influence too complicated. Therefore, from a biological perspective it makes more sense to focus on a downstream element that interacts with brown fat at a more localized level.
Just a side note based on the differing interactivity between brown/beige and white fat from the SNS, white fat appears to represent long-term energy storage and brown fat is shorter-term energy, an unsurprising conclusion. However, frequent energy expenditure, like exercise, may condition the body to produce more beige fat versus white fat viewing short-term energy needs as more valuable than long-term energy needs. Basically if the above point is accurate then it stands to reason that a person would see more benefit from 20 minutes of exercise 6 days a week versus 40 minutes of exercise 3 days a week.
Moving away from direct SNS stimulation perhaps the appropriate method of increasing browning involves increasing transcription and translation of UCP1. Interestingly enough empirical evidence exists to support the idea that reinoic acid could be an effective inducer of UCP-1 gene transcription in mice and operates through a non-adrenergic pathway.11,12 However, a more focused study using loss of function techniques involving retinaldehyde dehydrogenase, which is responsible for converting retinal to retinoic acid, determined that retinal, not retinoic acid is the major inducer of brown fat activity.13 Unfortunately there is no direct understanding regarding the proportional response of brown fat to retinal or retinoic acid. Therefore, the general fat-soluble nature of vitamin A will probably make it difficult to utilize its derivatives as biological stimulants for brown fat activation or browning.
Another possible strategy to stimulate browning is through activated (type 2/M2) macrophages induced by eosinophils which are commonly triggered by IL-4 and IL-13 signaling. When activated this way these macrophages recruit around subcutaneous white fat and secrete catecholamines to facilitate browning in mice.14,15 A secondary means by which both IL-4 and IL-13 may influence fat conversion is their direct interaction with Th2 cytokines.16 Unfortunately while on its face this strategy looks promising, in a similar vein to vitamin A, it might not be effective due to unknown long-term side effects associated with IL-4 and IL-13 activation. Due to this lack of knowledge, if IL-4 or 13 is thought to be a viable biochemical strategy for inducing weight loss, long-term proper time lines for effects and dosages must be explored in humans, not just short-term studies in mice.
A more controversial agent in browning is fibronectin type III domain-containing protein 5 or more frequently known as irisin. Due to its significantly increased rate of secretion from muscle under the strain of exercise, some individuals believe that irisin is a key mediator in browning acting as a myokine;17 if this characterization is accurate then irisin could be a significant player in the biological benefits produced by exercise including weight loss, white fat conversion and reduced levels of inflammation.18,19 However, other parties believe that because human studies with irisin have produced results that do not demonstrate benefits similar to those studies using mice, irisin is another molecule that cannot scale-up its effectiveness when faced with the added biological complexity of humans versus a mouse.20-22
The key element within this controversy could be that irisin expression is augmented by the increased expression of PGC1alpha, but PGC1alpha increases the expression of many different proteins and other molecules, so the expression of irisin may not be relevant to the positive changes associated with exercise. Another factor may be that a key difference between mice and humans is the mutation in the start codon of the human gene involved in the production of irisin, which significantly reduces irisin availability.23 Thus this mutation could be the limiting factor to why despite a very conserved genetic sequence, humans do not see anywhere near the benefit mice do. If this explanation is correct it does potentially still leave the door open to directly inject irisin into the body to increase concentrations in an attempt to aid exercise derived results, but if PGC1alpha is the key, then this increased concentration of irisin could be of minimal consequence.
Another potential element that demonstrates a significant concentration increase in accordance to increased PGC1alpha is a hormone known as meteorin-like (Metrnl).24 The concentration of this hormone increases in both skeletal muscle and adipose tissue during exercise and exposure to cold temperatures in accordance to increases in PGC1alpha concentrations. When Metrnl circulates in the blood it seems to produce a widespread effect that induces browning resulting in a significant increase in energy expenditure.24 The influence of Metrnl on white fat does not appear due to direct interaction with the fat, but instead indirect action on various immune cells most notably M2 macrophages via the eosinophil pathway, which then interact with the fat through activation of various pro-thermogenic actions.24 As discussed above this interaction with eosinophil appears to function through IL-4 and IL-13 signaling indicating a common pathway purpose between IL-4/IL-13 and the original SNS pathway. Not surprisingly blocking Metrnl has a negative effect on the biological thermogenic response.24
Another potential strategy for browning may be targeting appropriate receptors instead of specific molecules; with this strategy in mind one potential target could be transient receptor potential vanilloid-4 (TRPV4). TRPV4 acts as a negative regulator for browning through its negative action against PGC1a and the thermogenic pathway in general.25 In addition TRPV4 appears to activate various pro-inflammatory genes that interact with white adipose tissue making it more difficult to facilitate browning even if the appropriate signals are present. TRPV4 inhibition and genetic ablation in mice significantly increase resistance to obesity and insulin resistance.25 The link between inflammation and thermogenesis is highlighted by the activity of TRPV4, which is one of the early triggers for immune cell chemoattraction.25
Obesity may also produce a positive feedback effect through TRPV4 by increasing cellular swelling and stretching through the ERK1/2 pathway, which increases the rate of TRPV4 activation.26,27 However, the validity of TRPV4 as a therapeutic target remains questionable for TRPV4 expression not only influences fat/energy expenditure, but also osmotic regulation, bone formation and plays some role in brain function.25,28,29 Fortunately a number of the issues with TRPV4 mutations/mis-function appear to be developmental in influence versus post-development, thus TRPV4 therapies could still be valid.
Natriuretic peptides (NPs) are hormones typically produced in the heart on two different operational capacities: atrial and ventricular. Both of these hormones appear to play a role in browning through association with the adrenergic pathway.30 The most compelling evidence for supporting this behavior is that a lack of NP clearance receptors demonstrated significant enhanced thermogenic gene expression in both white and brown adipose tissue.30 Also direct application of ventricular NP in mice increased energy expenditure.30 In addition to the above results, NPs are an inherent attractive therapeutic possibility because appropriate receptors are located in white and brown fat of both rats and humans31,32 and these receptors go through periods of significant decline in expression when exposed to fasting,33 which may account for some of the benefits seen from low calorie diets.
Atrial NPs increase lipolysis in human adipocytes similar to catecholamines (increasing cAMP levels and activation of PKA) although whether or not this increase is induced through interaction with beta-adrenergic receptors is unclear.34 Some believe that NPs activate the guanylyl cyclase containing NPRA producing the second messenger cGMP activating cGMP-dependent protein kinase (PKG).35,36 PKA and PKG have similar mechanisms for substrate phosphorylation including similar targets in adipocytes,36 thus this interaction may explain why atrial NPs act similar to catecholamines.
Recall from above that one of the means of inducing browning, especially for those tissues that are distant from SNS-based neurons, is macrophage recruitment. This recruitment appears to be initiated by CCR2 and IL-4 for when either is eliminated from mice models the conversion no longer occurs.15 Tyrosine hydroxylase (Th) is also important in this process facilitating the biosynthesis of catecholamines and later PKA levels.
With respects to producing a biomedical agent to enhance browning there appear to be three major pathways in play: 1) the SNS pathway producing a direct activation response; 2) macrophage recruitment pathway potentially involving Metrnl, which activates IL-4 and IL-13 eventually leading to PKA activation and an indirect activation response; 3) NPs activation pathway, which eventually leads to PKG activation and an indirect activation response. As mentioned earlier SNS pathway enhancement has already been attempted by at least two drugs and failed miserably, so that method is probably out. In addition the SNS pathway does not appear to have as much browning potential as the PKA or PKG pathways due to the reliance on the location of certain nerve fibers.
Enhancing macrophage recruitment could be a good strategy, but there appears to be little information regarding negative effects associated with short-term high frequency enhancement of IL-4 or IL-13 concentrations. Some reports have suggested an increase in allergic symptoms, but any more severe consequences are unknown. This is not to say that enhancing IL-4 or IL-13 is not a valid therapeutic strategy, but its overall value is unknown. In contrast enhancement of NPs appear to be a more stable choice due to positive results in initial exploration of both the application and the expected negative side effects. First, NPs can be administrated via the nose-brain pathway enabling access to the brain avoiding some potential systemic side effects.37 Second, there appear to be few, if any significant side effects to intranasal NP application, at least in the short-term.38
Overall the above discussion has merely identified some of the more promising candidates to enhance browning white fat. One could argue that resorting to drugs to enhance the overall health of an individual versus simple diet and exercise is a regretful strategy. Unfortunately the reality of modern society is that more and more people seem to have less available time to exercise or eat right. In addition to a mounting negative weight external environment (increased pollution and industrial chemicals like BPAs) this drug enhancement strategy may be the most time and economically efficient means to ensure proper weight control and overall health for the future.
Citations –
1. van Marken Lichtenbelt, W, et Al. “Cold-activated brown adipose tissue in healthy men.” The New England Journal of Medicine. 2009. 360:1500-08.
2. Lowell, B, and Spiegelman, B. “Towards a molecular understanding of adaptive thermogenesis.” Nature. 2000. 404:652-60.
3. Seale, P, et Al. “PRDM16 controls a brown fat/skeletal muscle switch.” Nature. 2008. 454:961–967.
4. Sidossis, L and Kajimura, S. “Brown and beige fat in humans: thermogenic adipocytes that control energy and glucose homeostasis.” J. Clin. Invest. 2015. 125(2):478-486.
5. Long, J, et Al. “A smooth muscle-like origin for beige adipocytes.” Cell Metab. 2014. 19(5):810–820.
6. Kajimura, S, and Saito, M. “A new era in brown adipose tissue biology: molecular control of brown fat development and energy homeostasis.” Annu Rev Physiol. 2014. 76:225–249.
7. Harms, M, and Seale, P. “Brown and beige fat: development, function and therapeutic potential.” Nat Med. 2013. 19(10):1252–1263.
8. Collins, S. “β-Adrenoceptor signaling networks in adipocytes for recruiting stored fat and energy expenditure.” Front Endocrinol (Lausanne). 2011. 2:102.
9. Handschin, C, and Spiegelman, B. “Peroxisome proliferatoractivated receptor gamma coactivator 1 coactivators, energy homeostasis, and metabolism.” Endocr. Rev. 2006. 27:728–735.
10. Yen, M, and Ewald, M. “Toxicity of weight loss agents.” J. Med. Toxicol. 2012. 8:145–152.
11. Alvarez, R, et Al. “A novel regulatory pathway of brown fat themogenesis, retinoic acid is transcriptional activator of the mitochondrial uncoupling protein gene.” J. Biol. Chem. 270:5666-5673.
12. Mercader, J, et Al. “Remodeling of white adipose tissue after retinoic acid administration in mice.” Endocrinology. 2006. 147:5325–5332.
13. Kiefer, F, et Al. “Retinaldehyde dehydrogenase 1 regulates a thermogenic program in white adipose tissue.” Nat. Med. 2012. 18:918–925.
14. Nguyen, K, et Al. “Alternatively activated macrophages produce catecholamines to sustain adaptive thermogenesis.” Nature. 2011. 480(7375):104–108.
15. Qiu, Y, et Al. “Eosinophils and type 2 cytokine signaling in macrophages orchestrate development of functional beige fat.” Cell. 2014. 157(6):1292–1308.
16. Stanya, K, et Al. “Direct control of hepatic glucose production by interleukins-13 in mice.” The Journal of Clinical Investigation. 2013. 123(1):261-271.
17. Pedersen, B, and Febbraio, M “Muscle as an endocrine organ: focus on muscle-derived interleukin-6.” Physiological Reviews. 2008. 88(4):1379–406.
18. Bostrom, P, et Al. “A PGC1-α-dependent myokine that drives brown-fat-like development of white fat and thermogenesis.” Nature. 2012. 481(7382):463–468.
19. Lee, P, et Al. “Irisin and FGF21 are cold-induced endocrine activators of brown fat function in humans.” Cell Metab. 2014. 19(2):302–309.
20. Erickson, H. “Irisin and FNDC5 in retrospect: An exercise hormone or a transmembrane receptor?” Adipocyte. 2013. 2(4):289-293.
21. Timmons, J, et Al. “Is irisin a human exercise gene?” Nature. 2012. 488(7413):E9-11.
22. Albrecht, E, et Al. “Irisin - a myth rather than an exercise-inducible myokine.” Scientific Reports. 2015. 5:8889.
23. Ivanov, I, et Al. “Identification of evolutionarily conserved non-AUG-initiated N-terminal extensions in human coding sequences.” Nucleic Acids Research. 2011. 39(10):4220-4234.
24. Rao, R, et Al. “Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis.” Cell. 2014. 157:1279-1291.
25. Ye, L, et Al. “TRPV4 is a regulator of adipose oxidative metabolism, inflammation, and energy homeostasis.” Cell. 2012. 151:96-110.
26. Gao, X, Wu, L, and O’Neil, R. “Temperature-modulated diversity of TRPV4 channel gating: activation by physical stresses and phorbol ester derivatives through protein kinase C-dependent and -independent pathways.” J. Biol. Chem. 2003. 278:27129–27137.
27. Thodeti, C, et Al. “TRPV4 channels mediate cyclic strain-induced endothelial cell reorientation through integrin-to-integrin signaling.” Circ. Res. 2009. 104:1123–1130.
28. Masuyama, R, et Al. “TRPV4-mediated calcium influx regulates terminal differentiation of osteoclasts.” Cell Metab. 2008. 8:257–265.
29. Phelps, C, et Al. “Differential regulation of TRPV1, TRPV3, and TRPV4 sensitivity through a conserved binding site on the ankyrin repeat domain.” J. Biol. Chem. 2010. 285:731–740.
30. Bordicchia, M, et Al. “Cardiac natriuretic peptides act via p38 MAPK to induce the brown fat thermogenic program in mouse and human adipocytes.” The Journal of Clinical Investigation. 2012. 122(3):1022-1036.
31. Sarzani, R, et Al. “Comparative analysis of atrial natriuretic peptide receptor expression in rat tissues.” J Hypertens Suppl. 1993. 11(5):S214–215.
32. Sarzani, R, et Al. “Expression of natriuretic peptide receptors in human adipose and other tissues.” J Endocrinol Invest. 1996. 19(9):581–585.
33. Sarzani, R, et Al. “Fasting inhibits natriuretic peptides clearance receptor expression in rat adipose tissue.” J Hypertens. 1995. 13(11):1241–1246.
34. Sengenes, C, et Al. “Natriuretic peptides: a new lipolytic pathway in human adipocytes.” FASEB J. 2000. 14(10):1345–1351.
35. Potter, L, and Hunter, T. “Guanylyl cyclase-linked natriuretic peptide receptors: structure and regulation.” J Biol Chem. 2001. 276(9):6057–6060.
36. Sengenes, C, et Al. “Involvement of a cGMP-dependent pathway in the natriuretic peptide-mediated hormone-sensitive lipase phosphorylation in human adipocytes.” J Biol Chem. 2003. 278(49):48617–48626.
37. Illum, L. “Transport of drugs from nasal cavity to the central nervous system.” Eur. J. Pharm. Sci. 11:1-18.
38. Koopmann, A, et Al. “The impact of atrial natriuretic peptide on anxiety, stress and craving in patients with alcohol dependence.” Alcohol and Alcoholism. 2014. 49(3):282-286.
Showing posts with label Nutrition. Show all posts
Showing posts with label Nutrition. Show all posts
Wednesday, June 10, 2015
Tuesday, September 24, 2013
Looking for the Truth Behind the Health Value of Saturated Fat versus Carbohydrates
One of the more dynamic areas of modern research is nutrition and how it relates to biology. While in the past the more popular medium of discussion was through various diet/weight loss books, now the question of saturated fats and their overall influence on health has shifted to areas of more stringent study. Through most of the modern age of nutrition it has been viewed by the majority that saturated fats were largely negative and should be avoided as much as possible. Even the USDA recommends less than 10% of calories be derived from saturated fats1,2 largely based on the premise that increasing saturated fat consumption increases detrimental health outcomes including cardiovascular heart disease (CHD).3-5 However, there are individuals that believe this recommendation is inappropriate for it has not been clearly demonstrated empirically that increased consumption of saturated fat increases CHD or other negative health outcomes.
In fact some cite that while increasing saturated fat does increase total cholesterol, which is a characteristic for an increased rate of CHD, the rate of increase for high density lipoprotein (HDL) exceeds the rate of increase for low density lipoprotein (LDL), thus increasing the HDL/LDL ratio which is thought to be a more important factor to overall health than total cholesterol.6 Therefore, in the eyes of these individuals increasing saturated fatty acid (SFA) consumption in an equal caloric substitution over other types of nutrients like carbohydrates does not increase CHD risk and may even lower it.7-10 Overall the problem with both positions regarding the health effects of saturated fat is that most analysis fails to appreciate the specificity that addressing such an issue demands.
The crux of the question does involve the level at which one consumes saturated fats, but there are four central questions that govern the importance of that change:
First, different individual SFAs do not have the same biological effects despite similar molecular constructs. For example some epidemiological evidence demonstrates that stearic acid is the most detrimental SFA in terms of increasing the probability of CHD.11 Also despite the difficulty distinguishing between the overall effects of different SFAs palmitic acid is thought to be more detrimental to overall health than lauric acid. Not surprisingly in overall cholesterol raising effects stearic acid is neutral while all other long-chain acids increase both HDL and LDL levels versus low quality carbohydrates.12 There are no definitive conclusions regarding the influence of short and medium-chain SFAs due to a lack of study.
One of the reasons stearic and palmitic acid are so bad is that they can provide a negative feedback on acetyl-CoA carboxylase (ACC), the enzyme responsible for the catalysis of acetyl-CoA to malonyl-CoA which is utilized to increase the size of acyl chains reducing palmitic acid processing.13 In some respects there is little difference between trans-fatty acids and these two SFAs.
One would anticipate that it would be difficult to separate different types of SFAs with respect to what foods an individual consumes due to the combination of various SFAs in foods. However, it is feasible to distinguish certain specific foods that have higher percentages of certain SFAs over others and make broad dietary suggestions about those food items. For example palm oil and coconut oil have very high in palmitic and lauric acid concentrations respectively. Therefore, when studying the difference between SFAs and carbohydrates it is important to track what foods are consumed to ensure objectivity regarding certain SFA specific overload foods.
Second, when changing the amount of consumed SFAs some other molecule will replace those calories, thus it is important to consider the nature of that replacement and its specificity. Of the four issues that will be discussed this issue of substitution is the most studied one. There are a wide variety of elements that can replace saturated fat in a diet: protein, poly-unsaturated fat, mono-unsaturated fat, high glycemic index (low-quality/processed) carbohydrates, low glycemic index (high-quality) carbohydrates, “normal”/unsaturated fat and trans fats. Preliminary studies support the belief that rates of CHD decrease when replacing SFAs with poly-unsaturated fat, mono-unsaturated fat and “normal” fat.3,14,15 CHD rates increase when replacing SFAs with trans fats and refined/processed carbohydrates.3,16 Note that the glycemic index is utilized to measure how quickly blood sugar rise after consuming a given food and uses glucose as a upper level (100).
However, with regards to the carbohydrates since a large number of the carbohydrates that are consumed in modern society are of high glycemic variety if one were to guess it stands to reason that most, if not all, carbohydrate studies involved processed carbohydrates instead of quality carbohydrates. For example some proponents of SFAs like to cite studies that conclude a switch from SFAS to carbohydrates increases CHD probability. However, these studies do not typically differentiate between carbohydrate types. There is a large biological difference between low glycemic and high glycemic carbohydrates; without differentiating between carbohydrates any results born from substituting SFAs with a caloric equivalent amount of carbohydrates are inherently suspect. Think of it this way anyone who concludes that there is no biological and nutritional difference between consuming 150 calories of traditional rolled oats oatmeal versus 150 calories of Twinkie should not have their opinion taken into consideration.
In addition substitution studies must take appropriate caloric equivalency into account. For example if SFAs makes up 300 calories of a person’s daily caloric intake suitable comparison would demand that some percentage of that value be replaced with an equal value of the replacement (carbohydrates, poly unsaturated fat, etc.). It is sometimes difficult to track this equivalency feature if individuals are simply trying to recall what they consume over a given period of time versus keeping a food diary and having nutritionists correct for imbalances. Finally there is almost no information pertaining to replacing SFAs with protein or low glycemic index carbohydrates with regards to influence on CHD rates further limiting the value of substitution studies, an important exclusion that must be corrected for a definitive statement can be made regarding substitution. In the current environment the burden of proof is on those that believe SFAs are neutral or even better for health than processed carbohydrates, thus they need to ensure the quality of these substitution studies if they want to make the above contention.
Third, one of the biggest problems with comparing health effects between consumption of different food elements is a lack of comparison between subject microbiota. In short the microbiota is the concentration and type of bacteria population in an individual’s gastro-intestinal system. Numerous studies have demonstrated that the type of bacteria in an individual’s intestinal system have a significant impact on the ability to process and absorb nutrients.17-21 One of the biggest comparisons in microbiota is between Firmicutes and Bacteroidetes, which generally identifies obese individuals and non-obese individuals where Firmicutes is at larger concentrations in the obese and Bacteroidetes is at larger concentrations in the non-obese, usually with a 100%+ change.17,18 Note that this relationship is obviously not perfect, but is generally a good rule of thumb.
Evidence has shown that prolonged high SFAs feeding induced inflammation, impaired barrier function and changed microbiota profiles.22 The change in profiles lead to increased concentrations of Firmicutes and Oscillibacter and reduced concentrations of Bacteroidetes and Lactobacillus, thus high SFA feedings change the microbiota to one more seen in obese individuals. Clearly obesity is known for numerous detrimental health conditions, thus possessing a similar microbiota can be rationally viewed as a detrimental outcome versus a beneficial one.
Gut microbiota is important relative to carbohydrate processing as well in the rates and what types of carbohydrates are digestible.23,24 Fermentation occurring in the gut also leads to an increase in GLP-1 synthesis and insulin metabolism, thus rats fed a high fiber diet have higher plasma GLP-1, insulin and c-peptide levels after an oral glucose load.17 Various bacteria breakdown carbohydrates in fermentation reactions producing short chain fatty acids (SCFAs) like acetate, propionate and butyrate.25 Butyrate largely provides energy for colonic epithelia, propionate is taken up by the liver and is a precursor for gluconeogenesis, protein synthesis and liponeogenesis26,27 and acetate is metabolized by peripheral tissues and used as a substrate for cholesterol synthesis.28
The ratio of which SCFAs are produced is largely dependent on the ratio of bacteria in the gut. Evidence suggests that Firmicutes is able to produce about 25% more SCFAs than Bacteroidetes.19,20 Firmicutes appears to produce more butyrate and propionate with Bacteroidetes producing more acetate.29 There is question whether or not higher amounts of SCFAs are linked to obesity, but the evidence favors the affirmative: that increased SCFAs increase obesity probability.18,21,30 Also Firmicutes may increase the rate of malonyl-CoA activation over Bacteroidetes, which reduces the rate of carnitine palmitoyl transferase-1 reducing the amount of mitochondrial fatty acid oxidation.31 Overall while there is still a lot of information that needs to be deduced from the relationship between microbiota and health in general, especially obesity, the exclusion of any relationship in substitution studies is inappropriate.
Fourth, with regards to carbohydrate substitution, physical and aerobic conditioning of the research subject must be taken into consideration. Consistent exercise results in the increased direct and indirect consumption of carbohydrates as an energy source. Indirect consumption occurs through the increased production of specific enzymes, which favor the conversion of certain carbohydrates to glycogen versus SFAs like stearic or palmitic acid. Basically if one is substituting carbohydrates for SFAs in overweight and/or inactive research subjects the biological reduction of SFA consumption is heavily handicapped because the substituted carbohydrates are less likely to be used as short-term energy, but instead are converted to SFAs for long term energy storage.
One of the important elements when distinguishing between biological effects of SFAs and carbohydrates is the synthesis and consumption of glycogen. Glycogen is a multi-branched polysaccharide consisting of various glucose derivative molecules bound together and is used for long-term energy storage in a more compact form over triglycerides.
Glycogen is principally isolated within muscles, the liver and red blood cells32,33 and its rate of storage is dependant on physical training, basal metabolic rate and eating habits. Storage begins when blood glucose levels rise and insulin concentrations increase stimulating the action of hepatocytes leading to glycogen synthesis. As long as glucose and insulin remain in high relative concentrations glycogen synthesis continues. In periods of low glucose the pancreas secretes glucagons which catalyzes glycogenolysis.
Not surprisingly glycogen synthesis is exogonic, where UTP reacting with glucose-1-phosphate to drive the formation of UDP-glucose that eventually combine to lengthen glycogen through catalyzation by glycogen synthase forming from a base created by glycogenin.33 Glycogen consumption is endergonic where sections are cleaved by glycogen phosphorylase to produce glucose-1-phosphate monomers, which are later converted to glucose-6-phosphate by phosphoglucomutase. Glucose-6-phosphate produced from glycogen can enter the glycolysis pathway, the pentose phosphate pathway or be dephosphorylated back to glucose.33
A history of exercise, especially endurance training, enhances lipid and carbohydrate oxidation and decrease SNS activity during present time exercising in part due to increased muscle glycogenolysis and increased recruitment of skeletal muscle.34-38 Increasing exercise intensity increases the level of energy demand from the muscles and brain resulting in a faster crossover from lipids to carbohydrates as a source of energy. While some believe that there are special exercise-dietary regimens to enhance muscle glycogen storage/consumption rates when appropriate, there is no doubt that individuals who exercise consistently have higher rates of glycogen conversion of glucose and other sugars versus rates of glucose conversion to SFAs opposed to the rates found in overweight or obese individuals.34,39,40
One of the advantages of this difference is in the short period of time after exercise (24 hrs) is that rate of glycogen synthesis is indiscriminate the type of carbohydrate, be it simple or complex.40 Note that there is still an insulin concentration rise for simple carbohydrates over complex, but while the increase for complex is smaller the duration is longer.40 The fate of consumed carbohydrates is also influenced by the time between consumption and exercise.41 Therefore, individuals that are “in shape” are more likely to burn off consumed carbohydrates versus storing them as either glycogen or SFAs. Again comparison studies between SFAs and substitutions, especially carbohydrates, tend to exclude the influence of exercise.
Another side issue that must be considered when making comparison studies between SFAs and substitutes is the concentration of epinephrine due to its lipolytic, glycogenolytic and insulin-suppressive effects.42 Past training through exercise seems to reduce epinephrine concentrations in both at rest and during present exercise.42,43 Therefore, measurement of epinephrine needs to be conducted to act as a control point to make more accurate comparisons in substitution studies.
Overall there are clearly scientific and accuracy concerns regarding claims made by individuals who believe that SFAs are no more harmful to CHD rates than carbohydrates. Unfortunately such claims are typically made by individuals who are trying to justify a certain nutritional lifestyle versus individuals who actually care about proper nutrition. This is not to say that all forms of carbohydrates are guaranteed to be healthier than SFAs; the problem is that SFAs proponents have not produced evidence to demonstrate this conclusion that satisfies the four above concerns. Therefore, until this evidence is produced it is irresponsible of individuals to suggest that substituting SFAs for carbohydrates produces no detrimental change to CHD rates.
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Citations –
1. Hoenselaar, R. “Saturated fat and cardiovascular disease: The discrepancy between the scientific literature and dietary advice.” Nutrition. 2012. 28:118-123.
2. U.S. Department of Agriculture (USDA) Food Guide or the Dietary Approaches to Stop Hypertension (DASH) Eating Plan. http://www.unco.edu/shc/topics/dietaryguidelines.htm
3. Jakobsen, et Al. “Major types of dietary fat and risk of coronary heart disease: a pooled analysis of 11 cohort studies.” Am J Clin Nutr. 2009. 89:1425–32.
4. Dayton, S, et Al. “A controlled clinical trial of a diet high in unsaturated fat in preventing complications of atherosclerosis.” Circulation. 1969. 40(suppl 2):1–63.
5. Turpeinen, O, et Al. “Dietary prevention of coronary heart disease: the Finnish Mental Hospital Study.” Int J Epidemiol. 1979. 8:99–118.
6. Prospective Studies Collaboration. “Blood cholesterol and vascular mortality by age, sex and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths.” Lancet. 2007. 370:1829–39.
7. Astrup, A, et Al. “The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010?” Am. J. Clin. Nutr. 2011. 93:684-688.
8. Frantz, I Jr, et Al. “Test of effect of lipid lowering by diet on cardiovascular risk. The Minnesota Coronary Survey.” Arteriosclerosis. 1989. 9:129–35.
9. Siri-Tarino, P, et Al. “Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease.” Am. J. Clin. Nutr. 2010. 91:535–546.
10. Skeaff, C, and Miller, J. “Dietary fat and coronary heart disease: summary of evidence from prospective cohort and randomised controlled trials.” Ann Nutr Metab. 2009. 55:173–201.
11. Hu, F, et Al. “Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women.” Am J Clin Nutr. 1999. 70:1001–8.
12. Hunter, J, Zhang, J, Kris-Etherton, P. “Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review.” Am J Clin Nutr. 2010. 91:46–63
13. Wikipedia - Palmitic acid Entry.
14. Laaksonen, D, et Al. “Prediction of cardiovascular mortality in middle-aged men by dietary and serum linoleic and polyunsaturated fatty acids.” Arch Intern Med. 2005. 165:193–9.
15. Soinio, M, et Al. “Dietary fat predicts coronary heart disease events in subjects with type 2 diabetes.” Diabetes Care. 2003. 26:619–24.
16. Danaei, G, et Al. “The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors.” PLoS Med. 2009. 6:e1000058.
17. Cani, P, and Delzenne, N. “The role of the gut microbiota in energy metabolism and metabolic disease.” Current Pharmaceutical Design. 2009. 15:1546-1558.
18. Ley, R, et Al. “Microbial ecology: human gut microbes associated with obesity.” Nature. 2006. 444:1022-3.
19. Schwietz, A, et Al. “Microbiota and SCFA in lean and overweight healthy subjects.” Obesity. 2009. 18:190-195.
20. Bajzer, M, and Seeley, R. “Physiology: obesity and gut flora.” Nature. 2006. 444:1009–1010.
21. Turnbaugh, P, et Al. “An obesity-associated gut microbiome with increased capacity for energy harvest.” Nature. 2006. 444:1027–1031.
22. Lam, Y, et Al. “Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice.” PloS ONE. 2012. 7(3):e34233.
23. Goodlad, R, et Al. “Effects of an elemental diet, inert bulk and different types of dietary fibre on the response of the intestinal epithelium to refeeding in the rat and relationship to plasma gastrin, enteroglucagon, and PYY concentrations.” Gut. 1987. 28: 171-80.
24. Goodlad, R, et Al. “Proliferative effects of 'fibre' on the intestinal epithelium: relationship to gastrin, enteroglucagon and PYY.” Gut. 1987. 28(Suppl): 221-6.
25. Macfarlane, G, and Gibson, G. “Carbohydrate fermentation, energy transduction and gas metabolism in the human large intestine.” In: Mackie RI, White BA (eds). Gastrointestinal Microbiology. Chapman & Hall: New York, USA, 1997. 269–317.
26. Wolever, T, Spadafora, P, and Eshuis, H. “Interaction between colonic acetate and propionate in humans.” Am J Clin Nutr. 1991. 53:681–687.
27. Vernay, M. “Origin and utilization of volatile fatty acids and lactate in the rabbit: influence of the faecal excretion pattern.” Br J Nutr. 1987. 57:371–381.
28. Wolever, T, et Al. “Effect of rectal infusion of short chain fatty acids in human subjects.” Am J Gastroenterol. 1989. 84:1027–1033.
29. Duncan, S, et Al. “Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces.” Appl Environ Microbiol. 2007. 73:1073–1078.
30. Ley, R, et Al. “Obesity alters gut microbial ecology.” PNAS. 2005. 102:11070–11075.
31 Pyra, K, et Al. “Prebiotic Fiber Increases Hepatic Acetyl CoA Carboxylase Phosphorylation and Suppresses Glucose-Dependent Insulinotropic Polypeptide Secretion More Effectively When Used with metformin in obese rats.” J. Nutr. 2012. 142:213–220.
32. Moses, S, Bashan, N, and Gutman, A. “Glycogen metabolism in the normal red blood cell.” Blood. 1972. 40(6):836–43. PMID 5083874.
33. Miwa, I, and Suzuki, S. “An improved quantitative assay of glycogen in erythrocytes”. Annals of Clinical Biochemistry. 2002. 39(Pt 6): 612–3. doi:10.1258/000456302760413432.
34. Brooks, G, and Mercier, J. “Balance of carbohydrate and lipid utilization during exercise: the" crossover" concept.” Journal of Applied Physiology. 1994. 76(6):2253-2261.
35. Davies, K, Packer, L, and Brooks, G. “Biochemical adaptation of mitochondria, muscle and whole-animal respiration to endurance training.” Arch. Biochem. Biophys. 1981. 209:539-554.
36. Holloszy, J. “Endurance training decreases plasma glucose turnover and oxidation during moderate-intensity exercise.” J. Appl. Physiol. 1990. 68:990-996.
37. Henriksson, J, and Reitman, J. “Time course of changes in human muscle succinate dehydrogenase and cytochrome oxidase activities and maximal oxygen uptake with physical activity and inactivity.” Acta Physiol. Stand. 1977. 99:91-97.
38. Kirkwood, S. P., L. Packer, and G. A. Brooks. Effects of endurance training on a mitochondrial reticulum in limb skeletal muscle. Arch. Biochem. Biophys. 255: 80-88, 1987.
39. Sumida, K, and Donovan, C. “Enhanced gluconeogenesis from lactate in perfused livers after endurance training.” J. App. Physiol. 1993. 74:782-787.
40. Costill, D, et Al. “The role of dietary carbohydrates in muscle glycogen resynthesis after strenuous running.” Am. J. Clin. Nutr. 1981. 34:1831-1836.
41. Jeukendrup, A. “High-carbohydrate versus high-fat diets in endurance sports.” Sportmedizin und Sporttraumatologie. 2003. 51(1):17–23.
42. Brooks, G, et Al. “Increased dependence on blood glucose after acclimatization to
4,300 m.” J. Appl. Physiol. 1991. 70:919-927.
43. Deuster, P, et Al. “Hormonal and metabolic responses of untrained, moderately trained, and highly trained men to three exercise intensities.” Metabolism. 1989. 38:141-148.
In fact some cite that while increasing saturated fat does increase total cholesterol, which is a characteristic for an increased rate of CHD, the rate of increase for high density lipoprotein (HDL) exceeds the rate of increase for low density lipoprotein (LDL), thus increasing the HDL/LDL ratio which is thought to be a more important factor to overall health than total cholesterol.6 Therefore, in the eyes of these individuals increasing saturated fatty acid (SFA) consumption in an equal caloric substitution over other types of nutrients like carbohydrates does not increase CHD risk and may even lower it.7-10 Overall the problem with both positions regarding the health effects of saturated fat is that most analysis fails to appreciate the specificity that addressing such an issue demands.
The crux of the question does involve the level at which one consumes saturated fats, but there are four central questions that govern the importance of that change:
First, different individual SFAs do not have the same biological effects despite similar molecular constructs. For example some epidemiological evidence demonstrates that stearic acid is the most detrimental SFA in terms of increasing the probability of CHD.11 Also despite the difficulty distinguishing between the overall effects of different SFAs palmitic acid is thought to be more detrimental to overall health than lauric acid. Not surprisingly in overall cholesterol raising effects stearic acid is neutral while all other long-chain acids increase both HDL and LDL levels versus low quality carbohydrates.12 There are no definitive conclusions regarding the influence of short and medium-chain SFAs due to a lack of study.
One of the reasons stearic and palmitic acid are so bad is that they can provide a negative feedback on acetyl-CoA carboxylase (ACC), the enzyme responsible for the catalysis of acetyl-CoA to malonyl-CoA which is utilized to increase the size of acyl chains reducing palmitic acid processing.13 In some respects there is little difference between trans-fatty acids and these two SFAs.
One would anticipate that it would be difficult to separate different types of SFAs with respect to what foods an individual consumes due to the combination of various SFAs in foods. However, it is feasible to distinguish certain specific foods that have higher percentages of certain SFAs over others and make broad dietary suggestions about those food items. For example palm oil and coconut oil have very high in palmitic and lauric acid concentrations respectively. Therefore, when studying the difference between SFAs and carbohydrates it is important to track what foods are consumed to ensure objectivity regarding certain SFA specific overload foods.
Second, when changing the amount of consumed SFAs some other molecule will replace those calories, thus it is important to consider the nature of that replacement and its specificity. Of the four issues that will be discussed this issue of substitution is the most studied one. There are a wide variety of elements that can replace saturated fat in a diet: protein, poly-unsaturated fat, mono-unsaturated fat, high glycemic index (low-quality/processed) carbohydrates, low glycemic index (high-quality) carbohydrates, “normal”/unsaturated fat and trans fats. Preliminary studies support the belief that rates of CHD decrease when replacing SFAs with poly-unsaturated fat, mono-unsaturated fat and “normal” fat.3,14,15 CHD rates increase when replacing SFAs with trans fats and refined/processed carbohydrates.3,16 Note that the glycemic index is utilized to measure how quickly blood sugar rise after consuming a given food and uses glucose as a upper level (100).
However, with regards to the carbohydrates since a large number of the carbohydrates that are consumed in modern society are of high glycemic variety if one were to guess it stands to reason that most, if not all, carbohydrate studies involved processed carbohydrates instead of quality carbohydrates. For example some proponents of SFAs like to cite studies that conclude a switch from SFAS to carbohydrates increases CHD probability. However, these studies do not typically differentiate between carbohydrate types. There is a large biological difference between low glycemic and high glycemic carbohydrates; without differentiating between carbohydrates any results born from substituting SFAs with a caloric equivalent amount of carbohydrates are inherently suspect. Think of it this way anyone who concludes that there is no biological and nutritional difference between consuming 150 calories of traditional rolled oats oatmeal versus 150 calories of Twinkie should not have their opinion taken into consideration.
In addition substitution studies must take appropriate caloric equivalency into account. For example if SFAs makes up 300 calories of a person’s daily caloric intake suitable comparison would demand that some percentage of that value be replaced with an equal value of the replacement (carbohydrates, poly unsaturated fat, etc.). It is sometimes difficult to track this equivalency feature if individuals are simply trying to recall what they consume over a given period of time versus keeping a food diary and having nutritionists correct for imbalances. Finally there is almost no information pertaining to replacing SFAs with protein or low glycemic index carbohydrates with regards to influence on CHD rates further limiting the value of substitution studies, an important exclusion that must be corrected for a definitive statement can be made regarding substitution. In the current environment the burden of proof is on those that believe SFAs are neutral or even better for health than processed carbohydrates, thus they need to ensure the quality of these substitution studies if they want to make the above contention.
Third, one of the biggest problems with comparing health effects between consumption of different food elements is a lack of comparison between subject microbiota. In short the microbiota is the concentration and type of bacteria population in an individual’s gastro-intestinal system. Numerous studies have demonstrated that the type of bacteria in an individual’s intestinal system have a significant impact on the ability to process and absorb nutrients.17-21 One of the biggest comparisons in microbiota is between Firmicutes and Bacteroidetes, which generally identifies obese individuals and non-obese individuals where Firmicutes is at larger concentrations in the obese and Bacteroidetes is at larger concentrations in the non-obese, usually with a 100%+ change.17,18 Note that this relationship is obviously not perfect, but is generally a good rule of thumb.
Evidence has shown that prolonged high SFAs feeding induced inflammation, impaired barrier function and changed microbiota profiles.22 The change in profiles lead to increased concentrations of Firmicutes and Oscillibacter and reduced concentrations of Bacteroidetes and Lactobacillus, thus high SFA feedings change the microbiota to one more seen in obese individuals. Clearly obesity is known for numerous detrimental health conditions, thus possessing a similar microbiota can be rationally viewed as a detrimental outcome versus a beneficial one.
Gut microbiota is important relative to carbohydrate processing as well in the rates and what types of carbohydrates are digestible.23,24 Fermentation occurring in the gut also leads to an increase in GLP-1 synthesis and insulin metabolism, thus rats fed a high fiber diet have higher plasma GLP-1, insulin and c-peptide levels after an oral glucose load.17 Various bacteria breakdown carbohydrates in fermentation reactions producing short chain fatty acids (SCFAs) like acetate, propionate and butyrate.25 Butyrate largely provides energy for colonic epithelia, propionate is taken up by the liver and is a precursor for gluconeogenesis, protein synthesis and liponeogenesis26,27 and acetate is metabolized by peripheral tissues and used as a substrate for cholesterol synthesis.28
The ratio of which SCFAs are produced is largely dependent on the ratio of bacteria in the gut. Evidence suggests that Firmicutes is able to produce about 25% more SCFAs than Bacteroidetes.19,20 Firmicutes appears to produce more butyrate and propionate with Bacteroidetes producing more acetate.29 There is question whether or not higher amounts of SCFAs are linked to obesity, but the evidence favors the affirmative: that increased SCFAs increase obesity probability.18,21,30 Also Firmicutes may increase the rate of malonyl-CoA activation over Bacteroidetes, which reduces the rate of carnitine palmitoyl transferase-1 reducing the amount of mitochondrial fatty acid oxidation.31 Overall while there is still a lot of information that needs to be deduced from the relationship between microbiota and health in general, especially obesity, the exclusion of any relationship in substitution studies is inappropriate.
Fourth, with regards to carbohydrate substitution, physical and aerobic conditioning of the research subject must be taken into consideration. Consistent exercise results in the increased direct and indirect consumption of carbohydrates as an energy source. Indirect consumption occurs through the increased production of specific enzymes, which favor the conversion of certain carbohydrates to glycogen versus SFAs like stearic or palmitic acid. Basically if one is substituting carbohydrates for SFAs in overweight and/or inactive research subjects the biological reduction of SFA consumption is heavily handicapped because the substituted carbohydrates are less likely to be used as short-term energy, but instead are converted to SFAs for long term energy storage.
One of the important elements when distinguishing between biological effects of SFAs and carbohydrates is the synthesis and consumption of glycogen. Glycogen is a multi-branched polysaccharide consisting of various glucose derivative molecules bound together and is used for long-term energy storage in a more compact form over triglycerides.
Glycogen is principally isolated within muscles, the liver and red blood cells32,33 and its rate of storage is dependant on physical training, basal metabolic rate and eating habits. Storage begins when blood glucose levels rise and insulin concentrations increase stimulating the action of hepatocytes leading to glycogen synthesis. As long as glucose and insulin remain in high relative concentrations glycogen synthesis continues. In periods of low glucose the pancreas secretes glucagons which catalyzes glycogenolysis.
Not surprisingly glycogen synthesis is exogonic, where UTP reacting with glucose-1-phosphate to drive the formation of UDP-glucose that eventually combine to lengthen glycogen through catalyzation by glycogen synthase forming from a base created by glycogenin.33 Glycogen consumption is endergonic where sections are cleaved by glycogen phosphorylase to produce glucose-1-phosphate monomers, which are later converted to glucose-6-phosphate by phosphoglucomutase. Glucose-6-phosphate produced from glycogen can enter the glycolysis pathway, the pentose phosphate pathway or be dephosphorylated back to glucose.33
A history of exercise, especially endurance training, enhances lipid and carbohydrate oxidation and decrease SNS activity during present time exercising in part due to increased muscle glycogenolysis and increased recruitment of skeletal muscle.34-38 Increasing exercise intensity increases the level of energy demand from the muscles and brain resulting in a faster crossover from lipids to carbohydrates as a source of energy. While some believe that there are special exercise-dietary regimens to enhance muscle glycogen storage/consumption rates when appropriate, there is no doubt that individuals who exercise consistently have higher rates of glycogen conversion of glucose and other sugars versus rates of glucose conversion to SFAs opposed to the rates found in overweight or obese individuals.34,39,40
One of the advantages of this difference is in the short period of time after exercise (24 hrs) is that rate of glycogen synthesis is indiscriminate the type of carbohydrate, be it simple or complex.40 Note that there is still an insulin concentration rise for simple carbohydrates over complex, but while the increase for complex is smaller the duration is longer.40 The fate of consumed carbohydrates is also influenced by the time between consumption and exercise.41 Therefore, individuals that are “in shape” are more likely to burn off consumed carbohydrates versus storing them as either glycogen or SFAs. Again comparison studies between SFAs and substitutions, especially carbohydrates, tend to exclude the influence of exercise.
Another side issue that must be considered when making comparison studies between SFAs and substitutes is the concentration of epinephrine due to its lipolytic, glycogenolytic and insulin-suppressive effects.42 Past training through exercise seems to reduce epinephrine concentrations in both at rest and during present exercise.42,43 Therefore, measurement of epinephrine needs to be conducted to act as a control point to make more accurate comparisons in substitution studies.
Overall there are clearly scientific and accuracy concerns regarding claims made by individuals who believe that SFAs are no more harmful to CHD rates than carbohydrates. Unfortunately such claims are typically made by individuals who are trying to justify a certain nutritional lifestyle versus individuals who actually care about proper nutrition. This is not to say that all forms of carbohydrates are guaranteed to be healthier than SFAs; the problem is that SFAs proponents have not produced evidence to demonstrate this conclusion that satisfies the four above concerns. Therefore, until this evidence is produced it is irresponsible of individuals to suggest that substituting SFAs for carbohydrates produces no detrimental change to CHD rates.
--
Citations –
1. Hoenselaar, R. “Saturated fat and cardiovascular disease: The discrepancy between the scientific literature and dietary advice.” Nutrition. 2012. 28:118-123.
2. U.S. Department of Agriculture (USDA) Food Guide or the Dietary Approaches to Stop Hypertension (DASH) Eating Plan. http://www.unco.edu/shc/topics/dietaryguidelines.htm
3. Jakobsen, et Al. “Major types of dietary fat and risk of coronary heart disease: a pooled analysis of 11 cohort studies.” Am J Clin Nutr. 2009. 89:1425–32.
4. Dayton, S, et Al. “A controlled clinical trial of a diet high in unsaturated fat in preventing complications of atherosclerosis.” Circulation. 1969. 40(suppl 2):1–63.
5. Turpeinen, O, et Al. “Dietary prevention of coronary heart disease: the Finnish Mental Hospital Study.” Int J Epidemiol. 1979. 8:99–118.
6. Prospective Studies Collaboration. “Blood cholesterol and vascular mortality by age, sex and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths.” Lancet. 2007. 370:1829–39.
7. Astrup, A, et Al. “The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010?” Am. J. Clin. Nutr. 2011. 93:684-688.
8. Frantz, I Jr, et Al. “Test of effect of lipid lowering by diet on cardiovascular risk. The Minnesota Coronary Survey.” Arteriosclerosis. 1989. 9:129–35.
9. Siri-Tarino, P, et Al. “Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease.” Am. J. Clin. Nutr. 2010. 91:535–546.
10. Skeaff, C, and Miller, J. “Dietary fat and coronary heart disease: summary of evidence from prospective cohort and randomised controlled trials.” Ann Nutr Metab. 2009. 55:173–201.
11. Hu, F, et Al. “Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women.” Am J Clin Nutr. 1999. 70:1001–8.
12. Hunter, J, Zhang, J, Kris-Etherton, P. “Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review.” Am J Clin Nutr. 2010. 91:46–63
13. Wikipedia - Palmitic acid Entry.
14. Laaksonen, D, et Al. “Prediction of cardiovascular mortality in middle-aged men by dietary and serum linoleic and polyunsaturated fatty acids.” Arch Intern Med. 2005. 165:193–9.
15. Soinio, M, et Al. “Dietary fat predicts coronary heart disease events in subjects with type 2 diabetes.” Diabetes Care. 2003. 26:619–24.
16. Danaei, G, et Al. “The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors.” PLoS Med. 2009. 6:e1000058.
17. Cani, P, and Delzenne, N. “The role of the gut microbiota in energy metabolism and metabolic disease.” Current Pharmaceutical Design. 2009. 15:1546-1558.
18. Ley, R, et Al. “Microbial ecology: human gut microbes associated with obesity.” Nature. 2006. 444:1022-3.
19. Schwietz, A, et Al. “Microbiota and SCFA in lean and overweight healthy subjects.” Obesity. 2009. 18:190-195.
20. Bajzer, M, and Seeley, R. “Physiology: obesity and gut flora.” Nature. 2006. 444:1009–1010.
21. Turnbaugh, P, et Al. “An obesity-associated gut microbiome with increased capacity for energy harvest.” Nature. 2006. 444:1027–1031.
22. Lam, Y, et Al. “Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice.” PloS ONE. 2012. 7(3):e34233.
23. Goodlad, R, et Al. “Effects of an elemental diet, inert bulk and different types of dietary fibre on the response of the intestinal epithelium to refeeding in the rat and relationship to plasma gastrin, enteroglucagon, and PYY concentrations.” Gut. 1987. 28: 171-80.
24. Goodlad, R, et Al. “Proliferative effects of 'fibre' on the intestinal epithelium: relationship to gastrin, enteroglucagon and PYY.” Gut. 1987. 28(Suppl): 221-6.
25. Macfarlane, G, and Gibson, G. “Carbohydrate fermentation, energy transduction and gas metabolism in the human large intestine.” In: Mackie RI, White BA (eds). Gastrointestinal Microbiology. Chapman & Hall: New York, USA, 1997. 269–317.
26. Wolever, T, Spadafora, P, and Eshuis, H. “Interaction between colonic acetate and propionate in humans.” Am J Clin Nutr. 1991. 53:681–687.
27. Vernay, M. “Origin and utilization of volatile fatty acids and lactate in the rabbit: influence of the faecal excretion pattern.” Br J Nutr. 1987. 57:371–381.
28. Wolever, T, et Al. “Effect of rectal infusion of short chain fatty acids in human subjects.” Am J Gastroenterol. 1989. 84:1027–1033.
29. Duncan, S, et Al. “Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces.” Appl Environ Microbiol. 2007. 73:1073–1078.
30. Ley, R, et Al. “Obesity alters gut microbial ecology.” PNAS. 2005. 102:11070–11075.
31 Pyra, K, et Al. “Prebiotic Fiber Increases Hepatic Acetyl CoA Carboxylase Phosphorylation and Suppresses Glucose-Dependent Insulinotropic Polypeptide Secretion More Effectively When Used with metformin in obese rats.” J. Nutr. 2012. 142:213–220.
32. Moses, S, Bashan, N, and Gutman, A. “Glycogen metabolism in the normal red blood cell.” Blood. 1972. 40(6):836–43. PMID 5083874.
33. Miwa, I, and Suzuki, S. “An improved quantitative assay of glycogen in erythrocytes”. Annals of Clinical Biochemistry. 2002. 39(Pt 6): 612–3. doi:10.1258/000456302760413432.
34. Brooks, G, and Mercier, J. “Balance of carbohydrate and lipid utilization during exercise: the" crossover" concept.” Journal of Applied Physiology. 1994. 76(6):2253-2261.
35. Davies, K, Packer, L, and Brooks, G. “Biochemical adaptation of mitochondria, muscle and whole-animal respiration to endurance training.” Arch. Biochem. Biophys. 1981. 209:539-554.
36. Holloszy, J. “Endurance training decreases plasma glucose turnover and oxidation during moderate-intensity exercise.” J. Appl. Physiol. 1990. 68:990-996.
37. Henriksson, J, and Reitman, J. “Time course of changes in human muscle succinate dehydrogenase and cytochrome oxidase activities and maximal oxygen uptake with physical activity and inactivity.” Acta Physiol. Stand. 1977. 99:91-97.
38. Kirkwood, S. P., L. Packer, and G. A. Brooks. Effects of endurance training on a mitochondrial reticulum in limb skeletal muscle. Arch. Biochem. Biophys. 255: 80-88, 1987.
39. Sumida, K, and Donovan, C. “Enhanced gluconeogenesis from lactate in perfused livers after endurance training.” J. App. Physiol. 1993. 74:782-787.
40. Costill, D, et Al. “The role of dietary carbohydrates in muscle glycogen resynthesis after strenuous running.” Am. J. Clin. Nutr. 1981. 34:1831-1836.
41. Jeukendrup, A. “High-carbohydrate versus high-fat diets in endurance sports.” Sportmedizin und Sporttraumatologie. 2003. 51(1):17–23.
42. Brooks, G, et Al. “Increased dependence on blood glucose after acclimatization to
4,300 m.” J. Appl. Physiol. 1991. 70:919-927.
43. Deuster, P, et Al. “Hormonal and metabolic responses of untrained, moderately trained, and highly trained men to three exercise intensities.” Metabolism. 1989. 38:141-148.
Thursday, July 12, 2012
Sleep Drugs and Cancer
Sleep drugs (commonly referred to as hypnotic drugs in the pharmaceutical industry) are widely prescribed with an estimated 6-12% of U.S. adults using some form of sleep drug in 2010 and even higher estimates of use in Europe.1,2 Unfortunately one reason the overall level of consumption, both in unique use and repeat use of sleep drugs, is so high is because they do not cure insomnia instead simply reduce its effects. Basically they treat the symptoms of a chronic condition instead of treating the underlying cause. Despite not addressing the cause directly, reducing the influence of insomnia at any level is an important issue because chronic insomnia is thought to significantly increase the probability of developing psychiatric ailments and also reduces wakeful efficiency, productivity and health.2-5
Sleep drugs are commonly divided into pharmacological agents and non-pharmacological agents. While non-pharmacological agents, which range from stimulus control strategies to sleep pattern development with relaxation therapy, are viewed as an initial treatment, most research focuses on pharmacological agents, which are further sub-divided into two categories: benzodiazepines and non-benzodiazepines. The most common sleep drugs are zolpidem, temazepam, eszopiclone and zaleplon with zolpidem as the most prescribed sleep drug between 2002 and 2006 with temazepam in second place.1
Unfortunately meta-analysis has revealed some disturbing information regarding the consumption of sleep inducing drugs and overall mortality. When compared against placebo a number of trials involving commonly used sleep drugs demonstrate a significantly higher rate of cancer including pancreatic, non-melanoma skin, lymphoma, lung, colon or prostate cancer.1,6 The rate of death for those who consume these drugs increases more than three times over those who do not consume these drugs even at the smallest dosage (1-18 pills per year).1 Not surprisingly the probability of death increases as individuals increase the dosage. Also there was no significant difference between the different drugs in relation to how they increase the probability of death,1 thus it appears that these drugs operate over the same or at least a similar mechanism.
One of the more concerning issues with this increased rate of death is that the time variance is scatted; there are probability increases in both short-term and long-term rates of death. The reasoning behind the short-term death increases is currently unknown (peptic ulcers and esophageal damage due to regurgitation are leading theories), but most believe that long-term deaths increases are due to increased rates of cancer.1 In fact in one study the top third of sleep drug consumers (> 132 pills per year) had a 35% greater chance of developing cancer versus non-consumers.1 Another study monitoring 13,177 individuals taking zopiclone determined that 42% of the total deaths were due to cancer.7
The rationality behind the increased probability of cancer development has largely revolved around increasing infections and/or inflammation. The prevailing theory is that sleep drugs somehow suppress immune function.1,6 This suppression of immune function leads to reduced abnormal cell and pathogen destruction resulting in greater rates of cancer and other infections. However, this explanation may not be the only one that accurately describes the increased rates of cancer in individuals that consume sleep drugs.
Most sleep drugs are either benzodiazepines or operate with a similar mechanism of influence on GABAA receptors. Benzodiazepines are agnoists for most GABAA receptors, which increase frequency and duration of their activation. The mechanism of action increases the firing of GABAergic neurons,which reduces the firing rate of excitory neurons increasing the probability of initating sleep and its duration. Application of benzodiazepines result in sedative, anxiolytic, anti-convulsant and hypnotic characterization in the user. There are three types of benzodiazepines largely defined through their residance times: short, intermediate or long.8 Short and intermediate mechanisms are used in controlling insomnia and long mechanisms are used to control anxiety. However, because these sleep compounds are only GABA agnoists their effectiveness is still contingent on the total concentrations of GABA.
Gamma-amino butyric acid (GABA) plays three critical roles in the brain as a signaling molecule, neurotransmitter and metabolite. During development GABA guides neurite outgrowth and directionality.9 Once development of the Central Nervous System (CNS) is complete GABA then alters its function becoming the chief inhibitory neurotransmitter for both the CNS and Peripheral Nervous System (PNS). As the chief inhibitory neurotransmitter GABA plays a role in various neurodegenerative diseases most notably Temporal Lobe Epilepsy (TLE), Parkinson’s Disease (PD) and Huntington’s Disease (HD) stemming from a breakdown in critical components that govern GABA regulation.10-13 However, there is also evidence that GABA plays an important role in the development and progression of certain types of cancer.
GABA has three corresponding biological receptors, which are classified as GABAA, GABAB and GABAC (a.k.a. GABAA-rho). The ion largely associated with GABA receptors is chloride (Cl-), which drives the inhibitory action of GABA. GABAA exists in two activator based constructs, nicotinic and muscimol, and are oligomeric comprised of five different subunits from a pool of seven possible (alpha1-6, beta1-3, gamma1- 3, sigma, epsilon, pi and theta).14-16 GABAA receptors are ionotropic meaning that when an appropriate molecular agent binds the receptor forms a channel/pore in the membrane that allows an appropriate ion to pass through the membrane (direction of passage is largely governed by concentration and charge gradients).17
During development GABAA receptors are more commonly excitatory over inhibitory due to the absence of a chloride pump on the membrane that transfers chloride ions from inside the membrane to the extracellular space. Without this pump there is an excess amount of chloride ions in the cell, thus when the GABAA receptor activates chloride ions escape the neuron along the concentration gradient increasing membrane potential increasing probability of depolarization. Upon the incorporation of the chloride pump the chloride concentration gradient reverses so upon GABAA activation chloride ions flow from the extracellular space into the neuron increasing the probability of hyperpolarization. GABAB receptors are metabotropic activating a G-protein pathway. A majority of GABAB receptors are located on pre-synaptic cells and act as feedback mechanisms.
The third class of GABA receptors, GABAC, is somewhat controversial in whether or not it is uniquely different enough from GABAA to be considered a separate class.18 GABAC receptors are typically insensitive to GABAA receptor allosteric modulators like benzodiazepine and barbiturates because they are exclusively composed of a unique subunit (rho.18,19 This composition does not significantly change the functionality of GABAC receptors compared to GABAA receptors with respect to their interaction with GABA.
Each subunit in a GABAA receptor possesses four hydrophobic membrane-spanning domains.17 Studies have shown that functional GABAA receptors contain at least one alpha and one beta with one gammasubunit typically also involved; sigma, epsilon, pi and thetasubunits are thought to be assembled into GABAA receptors in place of subunits or complementary pairings.20 Overall it is rare to have a GABAA receptor comprised of subunits that lack an alpha or a beta and such a conformation will not be functional.
The importance of receptor composition is largely demonstrated in how individuals subunits are able to confer different sensitivities to GABA and its associated agonists and antagonists.21,22 For example pi subunits appear to highly sensitive to excitation by loreclezole (where non-pi subunit receptors are either unaffected or inhibited), inhibited by lanthanum and unaffected by benzodiazepine diazepam.19,23 Specificially the pi subunit has drawn interest with respects to the role of GABA in the development of cancer.24
Using cDNA libraries the pi subunit was isolated to multiple reproductive tissue (uterus, ovaries, etc.), digestive tissue (gall bladder, small intestine) and specific regions of the brain namely the hippocampus and temporal cortex; two of the major expression cell types in the brain are teratocarcinoma NT2 neuronal precursor and terminally differnetiated NT2-N cells.25,26 However, while NT2 neuronal cells express pi subunit mRNA there is some question to whether those pi subunits are actually incorported into NT2 neuronal GABA receptors.19 Unfortunately despite the apparent importance of the pi subunit, both the developmental expression of the epsilon and pi subunits have yet to be isolated, but both have been cloned.27 From cloning analysis the pi subunit has a 37% relation to the beta subunit, a 35% relation to the sigma subunit and a 33% relation to the rho subunit with very little relation on any of the other subunits.11 Most specifically the pi subunit appears to have similarities to alpha-5, beta-3 and gamma-3.19,27
This similarity of the pi subunits to these other subunits is not surprising in that the pi subunits are commonly incorporated into receptors with alpha-5beta-3 or alpha-5beta-3gamma-3 configurations.19 With respect to insomnia the amplification of benzodiazepine sensitivity is governed by the type of gamma subunit which determines extent of benzodiazepine influence with the requirement of a gamma subunit for a GABA receptor to have signiifcant affinity for benzodiazepines.16,19 Most GABA receptors in the brain have gamma-2 subunits, which demonstrate the highest gamma subunit sensivity to benzodiazepines.16 Receptors with pi subunits are thought to interfere with the ability of the gamma subunit to form the benzodiazepine binding site by either replacing the gamma subunit or blocking the interaction between the gamma subunit and alpha subunit.19,25,28 Receptor interaction with zinc is also influenced by the gamma subunit, but incorporation of the pi subunit into different receptors does not appear to interfear with zinc interaction.19
Receptors that incorporate the pi subunit have demonstrated higher GABA EC50 values, less outward rectification and larger single-channel conductance.19 There is also reason to believe that pi subunits flip activity from hyperpolarization to depolarization in cancer cells.19,24 Therefore, these changes imply longer duration firing over receptors without pi subunits. If this information is accurate then a depolarizing GABAA pi subunit receptor has an advantage over normal hyperpolarizing GABAA receptors demanding greater activity from non-pi subunit GABAA receptors for hyperpolarization and cancer limitation. The extent of pi subunit pentration in cancer cells versus non-pi subunit may also explain the somewhat contradicting results with whether or not GABA aids or hinders cancer development.
The differing action between pi subunit and non-pi subunit containing GABA receptors could offer one reason for why taking benzodiazepine based sleep aids increase cancer rates. Increasing benzodiazepine concentrations act on GABAA receptors, which lead to increased GABAA receptor expression. Increased expression rates would increase the number of mutations simply through volume changes alone (subunit mutation rates may not change, but because more subunits would be created there would be more subunit mutations). Among these subunit mutations could be gamma subunits mutating into pisubunits or pi subunits being incorporated over gamma subunits. Increasing the number of pi subunits would increase the number of GABAA receptors that depolarize instead of hyperpolarize, which would aid cancer development instead of hinder it.
Outside very specific subunit interactions like those involving the pi subunit, the relationship between cancer and GABA appears complex for GABA may influence different cancers in different ways. However, there does appear to be a general pattern of operation between GABA, cancer and the two major GABA receptors. To best understand this relationship temporal issues must be acknowledged between immature/developing cancer and mature cancer. Note that developing cancer refers to cells that have become cancerous and are starting to grow, not cells that are going through the initial mutation stages to become cancerous.
Typically the expression of GABA and its corresponding synthesizing enzyme GAD (both isoforms 65 and 67) significantly increase in concentration in the presence of neoplastic cells ((colorectal carcinoma, breast cancer, prostate cancer, glioma, pancreatic and gastric cancer).29-37 However, what does that increase mean relative to cancer growth? Based on existing evidence it appears that a reduction in GABAA receptor functionality leads to accelerated cancer growth.29,38 Such a result implies that GABA activity when binding to GABAA results in reduced cancer growth, which has been supported through reductions in membrane potentials of cancerous cells.39,40 If GABAA binding is detrimental to cancer growth then why do GABA and GAD concentrations increase in the presence of cancer versus non-cancerous cells? One explanation is that this increased expression may be a general ‘safety’ feedback mechanism designed to curtail excess (i.e. cancer) growth, especially if GABAA receptor expression decreases.38
GABAB interaction appears to play a similar role to GABAA with regards to reducing cancer growth. Activation of GABAB in a cancer cell does not kill the cancer cell, but instead arrests its growth between stages G0 and G1.41 In addition GABAB activation also reduces intra-cellular cAMP concentrations through the inhibition of adenylyl cyclase due to the activation of G-protein alpha-2.42,43 cAMP is important in cellular growth (both normal and cancerous) because it activates phosphokinase A (PKA) which among other things (i.e. ERK1/2 cascade) activates phospholamban.44 Phospholamban activation increases the rate of calcium release from the endoplasmic reticulum (ER). While some of this excess calcium is sequestered by the existing cAMP, in typical situations the calcium release from the ER exceeds the rate of sequestration by cAMP resulting in an increased level of cytosolic calcium.44 This increased cytosolic calcium concentration activates calcium dependent secondary messenger systems increasing the rate of cellular growth. Thus, the ability of GABAB activation to reduce cAMP concentrations reduces the rate of cancer growth through this particular mechanism.
While GABA does appear to influence cancer growth in a negative way regardless of which receptor it binds to it could play an even more important role in cancer migration/metastasis, albeit a slightly confusing one. The confusion in the issue of metastasis appears to come from somewhat conflicting evidence, but the confliction is not insurmountable. First, increased expression of GABA67 is seen in patients with higher Gleason scores30 (note Gleason scores are derived from examination of histological samples in an effort to track cancer progression). Initially such a result could be explained, as mentioned earlier, as a feedback mechanism from the body in effort to control cancer growth and as cancer growth increases (leading to a higher Gleason score) the body compensates further. However, what if there is another explanation, what if GABA actually assists in metastasis? This amplification of cancer metastasis seems to be in play at least for some forms of prostate and lymph node cancers where increasing GABA concentration resulted in an increase in matrix metalloproteinase (MMP) expression due to GABAB activation.30
Metastasis is a complex series of interactions leading from tumor development to detachment from its principle location and movement to a different more distant location in the body. The major events involve detachment of from the primary tumor, invasion of the stromal tissue, entrance to the bloodstream, extravasate, invasion of the new target organ and finally the formation of the metastatic colony.17,30 One of the key steps in this process is the proteolytic degradation of the extracellular matrix and in this step MMPs are critical agents.
Initially MMPs were thought to be degenerative proteases that were limited to cleaving matrix components, but that role has expanded to include the release of growth factors and other bioactive peptides localized at cleaved extracellular matrices.45-47 Most MMP action involves MMP-3 cleaving decorin which releases transforming growth factor-b leading to greater levels of angiogenesis in addition to cleaving TGF-alpha activating MAPK inducing cellular proliferation.48,49 MMPs also cleave matrix receptors that may inhibit metastasis inhibitors like E-cadherin and activate a semi-self-regulating pathway with MMP-3 activating MMP-7 and 9.50
However, there appears to be a problem with concluding that GABA increases metastasis probability through GABAB activation in that another study has demonstrated that GABA decreases metastasis probability through GABAB activation.44 In a study using SW480 colon carcinoma cells cellular locomotion (basically metastasis probability) was reduced due to reduction in cAMP concentration. One possible reason for this difference is the differing cell types, but another line of thought produces another possible solution.
First, the SW480 colon study focused on metastasis induced through norepinephrine, not MMPs. In norepinephrine induced metastasis norepinephrine activates beta-arrestin through the beta-2 adrenoceptor which activates Protein Tyrosine Kinase (PTK) which activates the key agent, protein kinase C gamma (PKC).44 Activation of PKC-gamma results in the dual activation of both inositol-1,4,5-trisphosphate and diacylglycerol.44,51 Inositol-1,4,5-triphosphate opens intracellular calcium channels increasing cAMP activation and diacylglycerol activates PKC-alpha, which has been shown to increase metastasis in cancer cells.52,53
Basically norepinephrine increases cancer proliferation by activating inositol-1,4,5-triphosphate and increases cancer metastasis probability through activating diacylglycerol. This dual activation is important because cAMP cannot activate diacylglycerol on its own, yet it does appear to augment diacylglycerol activity in that if cAMP concentration is reduced diacylglycerol does not aid metastasis. Thus GABAB is able to prevent this form of metastasis by reducing cAMP concentration through secondary messenger inhibition of adenylyl cyclase.
However, while GABAB prevents metastasis through PKC-gamma, why doesn’t the ability to increase MMP expression compensate for the PKC-gamma blocking resulting in greater metastasis versus controls? One explanation may be temporal in nature in that the colon cells were not in a high enough state of maturity to express and/or interact with the MMP that should have been generated from the GABAB activation. If this theory is accurate then GABAB may be a beneficial therapeutic agent in the early stages of cancer, but as cancer progresses its usefulness flips and it becomes more detrimental than beneficial.
Unfortunately the role of GABAA in metastasis may not be as simple. While a number of studies suggest that GABAA binding plays no role in metastasis30,41,44 other studies suggest that GABAA binding increases metastasis probability54 or decrease metastasis.55 Despite this contradiction based on currently understood GABAA behaviors and mechanisms it is hard to believe that GABAA positively influences metastasis if configured properly because of its hyperpolarizing nature. The difference between ‘neutrality’ and reducing metastasis for GABAA may also be temporal, similar to GABAB.
Early in cancer development GABAA has little influence, but has MMP concentrations increase, GABAA works to reduce those concentrations despite GABAB augmenting them.55 Another reason for this disparity may be that in some studies the tumors did not mature to the point where metastasis was at a reasonable probability of occurrence because GABAA activity arrested tumor growth, thus GABAA influenced growth, but not metastasis. Overall it appears that the theory to describe GABA receptor behavior with respect to cancer is that GABAA negatively affects cancer growth and has little influence on cancer metastasis whereas GABAB negative affects cancer growth and has a negative influence on early cancer metastasis and a positive influence on late cancer metastasis.
If the above characterization of GABA receptors with respect to cancer is taken as accurate, then it appears that ability of sleep drugs to increase cancer rates largely relies on receptor subunit configuration. Other than pi subunits47 there is reason to suspect that incorporation of theta or rho subunits also increases cancer proliferation probabilities.56,57 These receptor confirmations may be self-augmenting in that if cancer develops due to their depolarizing characteristics over hyperpolarizing the cancer mutates to produce more receptors with similar configurations explaining why rarer pi and theta subunit configurations, with even rho at times, are so prevalent in cancers.24,27,56,57
Another possibility may be that augmenting GABAA activation through agonists like benzodiazepine may cause greater GABAB activity as a result of feedback. While GABAB activation would be viewed as more cancer-preventative than cancer-aiding, recall that as cancer develops the benefit/detriment ratio for GABAB with respect to cancer prevention decreases. Thus one question to ask is if cancer rates increase with sleep drug administration or does cancer metastasis also increase relative to the increase in cancer rates?
Overall one of the chief concerns is that while rates of death are slightly lower with non-benzodiazepine sleep drugs there is little firm scientific evidence that these non-benzodiazepine treatments result in higher rates of increased sleep time or reduction in wake probability after sleep onset compared against placebo.1,58 Benzodiazepine treatments do decrease sleep latency and increase sleep duration8,58 (although there are some questions regarding the statistical significance of the latter effect despite concerns of overestimation of sleep latency and underestimation of total sleep time),58 thus taking non-benzodiazepine treatments which have less cancer promoting tendencies may not be a suitable alternative to addressing a lack of sleep. While GABA seems to prevent cancer more than aid in its development stimulation of the GABA system through artificial means could also increases the rate of mutation in the subunits which comprise GABA receptors and these mutations significantly increase the probability of generating cancer.
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Citations –
1. Kripke, D, Langer, R, Kline, L. “Hypnotics’ association with mortality or cancer: a matched cohort study.” BMJ Open. 2012. 2: e000850. doi:10.1136/bmjopen-2012-000850.
2. Buscemi, N, et Al. “The Efficacy and Safety of Drug Treatments for Chronic Insomnia in Adults: A meta-analysis of RCTs.” Clinical Review. 2007. 22:1335–1350.
3. Ford, D, and Kamerow, D. “Epidemiological study of sleep disturbances and psychiatric disorders: an opportunity for prevention.” JAMA. 1989. 262: 1479–84.
4. Breslau, N, et Al. “Sleep disturbances and psychiatric disorders: a longitudinal epidemiological study of young adults.” Biol. Psychiatry. 1996. 39: 411–18.
5. Wiessman, M, et Al. “The morbidity of insomnia uncomplicated by psychiatric disorders.” Gen Hosp Psychiatry. 1997. 19:245–50.
6. Kripke, D. “Possibility that certain hypnotics might cause cancer in skin.” J. Sleep Res. 2008. 17: 245–250
7. Hajak, G. “A comparative assessment of the risks and benefits of Zopiclone.” Drug Saf. 1999. 21: 457–469.
8. Holbrook, A, et Al. “Meta-analysis of benzodiazepine use in the treatment of insomnia.” Can Med Assoc J. 2000. 162: 225–33.
9. Martin, D. and Barke, K. "Are GAD65 and GAD67 associated with specific pools of GABA in brain?" Perspect Dev Neurobiol. 1998. 5(2-3): 119-129.
10. Corti, O, et Al. "Parkinson's disease: from causes to mechanisms." C.R. Biologies. 2005. 328: 131-142.
11. Sharp, A.H and Ross, C. "Neurobiology of Huntington's disease." Neurobiol. Dis. 1996. 3: 3-15.
12. Albin, R. and Tagle, D "Genetics and molecular biology of Huntington's disease." Trends Neurosci. 1995. 18: 11-14.
13. Engel Jr, J. "Mesial temporal lobe epilepsy: what have we learned?" Neuroscientist 2001. 7: 340-352.
14. Glassmeier, G, et Al. “Expression of functional GABAA receptors in cholecystokinin-secreting gut neuroendocrine murine STC-1 cells.” J. Physiol. (London). 1998. 510 (Pt. 3): 805–814.
15. Jansen, A, et Al. “GABAC receptors in neuroendocrine gut cells: a new GABA-binding site in the gut.” Eur. J. Physiol. 2000. 441: 294–300.
16. Henschel, O, Gipson, K, and Bordey, A. “GABAA Receptors, Anesthetics and Anticonvulsants in Brain Development.” CNS Neurol Disord Drug Targets. 2008. April 7(2): 211–224.
17. Watanabe, M, et Al. “Gamma-aminobutyric acid (GABA) and cell proliferation: focus on cancer cells.” Histol Histopathol. (2006) 21: 1135-1141.
18. Bolmann J. “The ‘ABC’ of GABA receptors.” Trends. Pharmacol. Sci. 2000. 21: 16-19.
19. Neelands, T and Macdonald, R. “Incorporation of the π Subunit into Functional gamma-Aminobutyric AcidA Receptors.” Molecular Pharmachology. 1999. 56: 598-610.
20. McKerman, R and Whiting, P. “Which GABAA-receptor subtypes really occur in the brain?” Trends Neurosci. 1996. 19: 139-143.
21. Pritchett, D, et Al. “Importance of a novel GABAA receptor subunit for benzodiazepine pharmacology.” Nature (London). 1989. 338: 582-585.
22. Wieland, H, Luddens, H, and Seeburg, P. “Molecular determinants in GABAA/BZ receptor subtypes. Adv. Biochem. Psychopharmacol. 1992. 47: 29-40.
23. Neelands, T, et Al. “GABAA receptors expressed in human teratocarcinoma NT2 neuronal precursor cells differ from those expressed by differentiated NT2-N cells.” Soc. Neurosci. Abstr. 1997. 23: 957.
24. Takehara, A, et Al. “gamma-Aminobutyric Acid (GABA) Stimulates Pancreatic Cancer Growth through Overexpressing GABAA Receptor π Subunit.” Cancer Res. 2007. 67(20): 9704-9712.
25. Hedblom, E and Kirkness, E. “A novel class of GABAA receptor subunit in tissues of the reproductive system.” J. Biol. Chem. 1997. 272: 15346-15350.
26. Neelands, T, et Al. “GABAA receptor pharmacology and subtype mRNA expression in human neuronal NT2-N cells.” J. Neurosci. 1998. 18:4993–5007.
27. Johnson, S and Haun, R. “The Gamma-Aminobutyric Acid A Receptor Subunit is Overexpressed in Pancreatic Adenocarcinomas.” J Pancreas (Online). 2005. 6(2): 136-142.
28. Sigel, E. “Mapping of the Benzodiazepine Recognition Site on GABAA Receptors.” Curr. Top Med. Chem. 2002. 2: 833-839.
29. Labrakakis, C, et Al. “Functional GABAA Receptors on Human Glioma Cells.” Eur. J. Neurosci. 1998. 10(1): 231-238.
30. Azuma, H, et Al. “gamma-Aminobutyric Acid as a Promoting Factor of Cancer Metastasis; Induction of Matrix Metalloproteinase Production Is Potentially Its Underlying Mechanism.” Cancer Res. 2003. 63:8090-8096.
31. Watanabe, M, et Al. “GABA and GABA receptors in the central nervous system and other organs.” Int. Rev. Cytol. 2002. 213: 1–47.
32. Maemura, K, et Al. “gamma-Amino-butyric acid immunoreactivity in intramucosal colonic tumors.” J. Gastroenterol. Hepatol. 2003. 18: 1089–1094.
33. Kleinrok, Z, et Al. “GABA content and GAD activity in colon tumors taken from patients with colon cancer or from xenografted human colon cancer cells growing as s.c. tumors in athymic nu/nu mice.” J. Physiol. Pharmacol. 1998. 49: 303–310.
34. Matuszek, M, Jesipowicz, M, and Kleinrok, Z. “GABA content and GAD activity in
gastric cancer.” Int. Med. J. Exp. Clin. Res. 2001. 7: 377–381.
35. Wang, Y, et Al. “Immunohistochemical study on GABAergic system in rat and human large intestine.” Bull. Osaka Med. 2000. 46: 25-34.
36. Hu, Y, et Al. “Molecular characterization of a metastatic neuroendocrine cell cancer arising in the prostates of transgenic mice.” J. Biol. Chem. 2001. 277: 44462-44474.
37. Opolski, A, et Al. “The role of GABA-ergic system in human mammary gland
pathology and in growth of transplantable murine mammary cancer.” J. Exp. Clin. Cancer Res. 2000. 19: 383-390.
38. Minuk, G, et Al. “Decreased hepatocyte membrane potential differences and GABAA-beta3 expression in human hepatocellular carcinoma.” Hepatology. 2007. 45(3): 735–745.
39. Sun, D, et Al. “Increasing cell membrane potential and GABAergic activity inhibits malignant hepatocyte growth.” American Journal of Physiology – Gastrointestinal and Liver Physiology. 2003. 285(1): G12–G19.
40. Zhang, M, et Al. “Increased GABAergic activity inhibits alpha-fetoprotein mRNA expression and the proliferative activity of the HepG2 human hepatocellular carcinoma cell line.” Journal of Hepatology. 2000. 32(1): 85–91.
41. Wang, T, Huang, W, and Chen, F. “Baclofen, a GABAB receptor agonist, inhibits human hepatocellular carcinoma cell growth in vitro and in vivo.” Life Sciences. 2008. 82: 536–541.
42. Schuller, H, Al-Wadei, H, and Majidi, M. “GABAB Receptor is a Novel Drug Target for Pancreatic Cancer.” Cancer. 2008. 112(4): 767-778.
43. Gladkevich, A, et Al. “The peripheral GABAergic system as a target in endocrine disorders.” Auton. Neurosci. 2006. 124: 1–8.
44. Joseph, J, et Al. “The Neurotransmitter gamma-Aminobutyric Acid Is an Inhibitory Regulator for the Migration of SW 480 Colon Carcinoma Cells.” Cancer Research. 2002. 62:6467-6469.
45. McCawley, L and Matrisian, L. “Matrix metalloproteinases: multifunctional contributors to tumor progression.” Mol. Med. Today. 2000. 6: 149–156.
46. Sternlicht, M and Werb, Z. “How matrix metalloproteinases regulate cell behavior.” Annu. Rev. Cell Dev. Biol. 2001. 17: 463–516.
47. Egeblad, M and Werb, Z. “New functions for the matrix metalloproteinases in cancer progression.” Nat. Rev. Cancer. 2002. 2: 161–174.
48. Imai, K, et Al. “Degradation of decorin by matrix metalloproteinases: identification of the cleavage sites, kinetic analyses and transforming growth factor-beta1 release.” Biochem. J. 1997. 322: 809–814.
49. Yan, Y, Shirakabe, K, and Werb, Z. “The metalloprotease Kuzubanian (ADAM10) mediates the transactivation of EGF receptor by G protein-coupled receptors.” J. Cell Biol. 2002. 158: 221-226.
50. Birchmeier, W. “E-cadherin as a tumor (invasion) suppressor gene.” Bioessays. 1995. 17: 97–99.
51. Masur, K, et, Al. “Norepinephrine-induced migration of SW 480 colon carcinoma cells is inhibited by beta-blockers.” Cancer Res. 2001. 61: 2866–2869.
52. Masur, K, et Al. “High PKCgamma and low E-cadherin expression contribute to high migratory activity in colon carcinoma cells.” Mol. Biol. Cell. 2001. 12: 1973–1982.
53. Entschladen, F and Zanker, K. “Locomotion of tumor cells: a molecular comparison to migrating pre- and post-mitotic leukocytes.” J. Cancer Res. Clin. Oncol. 2000. 126: 671–681.
54. Garib, V, et Al. “Influence of non-volatile anesthetics on the migration behavior of the human breast cancer cell line MDA-MB-468.” Acta Anaesthesiol. Scand. 2002. 46: 836-844.
55. Thaker, P, et Al. “Inhibition of Experimental Colon Cancer Metastasis by the GABA-Receptor Agonist Nembutal.” Cancer Biology & Therapy. 2005. 4(7): 753-758.
56. Hackam, A, et Al. “The N-terminal domain of human GABA receptor rho1 subunits contains signals for homooligomeric and heterooligomeric interaction.” J Biol Chem. 1997. 272: 13750–13757.
57. Li, Y, et Al. “GABA stimulates human hepatocellular carcinoma growth through overexpressed GABAA receptor theta subunit.” World J. Gastroenterol. 2012. 18(21): 2704-2711.
58. Sateia, M, et Al. “Evaluation of chronic insomnia. An American Academy of Sleep Medicine Review.” Sleep. 2000. 23: 1–66.
Sleep drugs are commonly divided into pharmacological agents and non-pharmacological agents. While non-pharmacological agents, which range from stimulus control strategies to sleep pattern development with relaxation therapy, are viewed as an initial treatment, most research focuses on pharmacological agents, which are further sub-divided into two categories: benzodiazepines and non-benzodiazepines. The most common sleep drugs are zolpidem, temazepam, eszopiclone and zaleplon with zolpidem as the most prescribed sleep drug between 2002 and 2006 with temazepam in second place.1
Unfortunately meta-analysis has revealed some disturbing information regarding the consumption of sleep inducing drugs and overall mortality. When compared against placebo a number of trials involving commonly used sleep drugs demonstrate a significantly higher rate of cancer including pancreatic, non-melanoma skin, lymphoma, lung, colon or prostate cancer.1,6 The rate of death for those who consume these drugs increases more than three times over those who do not consume these drugs even at the smallest dosage (1-18 pills per year).1 Not surprisingly the probability of death increases as individuals increase the dosage. Also there was no significant difference between the different drugs in relation to how they increase the probability of death,1 thus it appears that these drugs operate over the same or at least a similar mechanism.
One of the more concerning issues with this increased rate of death is that the time variance is scatted; there are probability increases in both short-term and long-term rates of death. The reasoning behind the short-term death increases is currently unknown (peptic ulcers and esophageal damage due to regurgitation are leading theories), but most believe that long-term deaths increases are due to increased rates of cancer.1 In fact in one study the top third of sleep drug consumers (> 132 pills per year) had a 35% greater chance of developing cancer versus non-consumers.1 Another study monitoring 13,177 individuals taking zopiclone determined that 42% of the total deaths were due to cancer.7
The rationality behind the increased probability of cancer development has largely revolved around increasing infections and/or inflammation. The prevailing theory is that sleep drugs somehow suppress immune function.1,6 This suppression of immune function leads to reduced abnormal cell and pathogen destruction resulting in greater rates of cancer and other infections. However, this explanation may not be the only one that accurately describes the increased rates of cancer in individuals that consume sleep drugs.
Most sleep drugs are either benzodiazepines or operate with a similar mechanism of influence on GABAA receptors. Benzodiazepines are agnoists for most GABAA receptors, which increase frequency and duration of their activation. The mechanism of action increases the firing of GABAergic neurons,which reduces the firing rate of excitory neurons increasing the probability of initating sleep and its duration. Application of benzodiazepines result in sedative, anxiolytic, anti-convulsant and hypnotic characterization in the user. There are three types of benzodiazepines largely defined through their residance times: short, intermediate or long.8 Short and intermediate mechanisms are used in controlling insomnia and long mechanisms are used to control anxiety. However, because these sleep compounds are only GABA agnoists their effectiveness is still contingent on the total concentrations of GABA.
Gamma-amino butyric acid (GABA) plays three critical roles in the brain as a signaling molecule, neurotransmitter and metabolite. During development GABA guides neurite outgrowth and directionality.9 Once development of the Central Nervous System (CNS) is complete GABA then alters its function becoming the chief inhibitory neurotransmitter for both the CNS and Peripheral Nervous System (PNS). As the chief inhibitory neurotransmitter GABA plays a role in various neurodegenerative diseases most notably Temporal Lobe Epilepsy (TLE), Parkinson’s Disease (PD) and Huntington’s Disease (HD) stemming from a breakdown in critical components that govern GABA regulation.10-13 However, there is also evidence that GABA plays an important role in the development and progression of certain types of cancer.
GABA has three corresponding biological receptors, which are classified as GABAA, GABAB and GABAC (a.k.a. GABAA-rho). The ion largely associated with GABA receptors is chloride (Cl-), which drives the inhibitory action of GABA. GABAA exists in two activator based constructs, nicotinic and muscimol, and are oligomeric comprised of five different subunits from a pool of seven possible (alpha1-6, beta1-3, gamma1- 3, sigma, epsilon, pi and theta).14-16 GABAA receptors are ionotropic meaning that when an appropriate molecular agent binds the receptor forms a channel/pore in the membrane that allows an appropriate ion to pass through the membrane (direction of passage is largely governed by concentration and charge gradients).17
During development GABAA receptors are more commonly excitatory over inhibitory due to the absence of a chloride pump on the membrane that transfers chloride ions from inside the membrane to the extracellular space. Without this pump there is an excess amount of chloride ions in the cell, thus when the GABAA receptor activates chloride ions escape the neuron along the concentration gradient increasing membrane potential increasing probability of depolarization. Upon the incorporation of the chloride pump the chloride concentration gradient reverses so upon GABAA activation chloride ions flow from the extracellular space into the neuron increasing the probability of hyperpolarization. GABAB receptors are metabotropic activating a G-protein pathway. A majority of GABAB receptors are located on pre-synaptic cells and act as feedback mechanisms.
The third class of GABA receptors, GABAC, is somewhat controversial in whether or not it is uniquely different enough from GABAA to be considered a separate class.18 GABAC receptors are typically insensitive to GABAA receptor allosteric modulators like benzodiazepine and barbiturates because they are exclusively composed of a unique subunit (rho.18,19 This composition does not significantly change the functionality of GABAC receptors compared to GABAA receptors with respect to their interaction with GABA.
Each subunit in a GABAA receptor possesses four hydrophobic membrane-spanning domains.17 Studies have shown that functional GABAA receptors contain at least one alpha and one beta with one gammasubunit typically also involved; sigma, epsilon, pi and thetasubunits are thought to be assembled into GABAA receptors in place of subunits or complementary pairings.20 Overall it is rare to have a GABAA receptor comprised of subunits that lack an alpha or a beta and such a conformation will not be functional.
The importance of receptor composition is largely demonstrated in how individuals subunits are able to confer different sensitivities to GABA and its associated agonists and antagonists.21,22 For example pi subunits appear to highly sensitive to excitation by loreclezole (where non-pi subunit receptors are either unaffected or inhibited), inhibited by lanthanum and unaffected by benzodiazepine diazepam.19,23 Specificially the pi subunit has drawn interest with respects to the role of GABA in the development of cancer.24
Using cDNA libraries the pi subunit was isolated to multiple reproductive tissue (uterus, ovaries, etc.), digestive tissue (gall bladder, small intestine) and specific regions of the brain namely the hippocampus and temporal cortex; two of the major expression cell types in the brain are teratocarcinoma NT2 neuronal precursor and terminally differnetiated NT2-N cells.25,26 However, while NT2 neuronal cells express pi subunit mRNA there is some question to whether those pi subunits are actually incorported into NT2 neuronal GABA receptors.19 Unfortunately despite the apparent importance of the pi subunit, both the developmental expression of the epsilon and pi subunits have yet to be isolated, but both have been cloned.27 From cloning analysis the pi subunit has a 37% relation to the beta subunit, a 35% relation to the sigma subunit and a 33% relation to the rho subunit with very little relation on any of the other subunits.11 Most specifically the pi subunit appears to have similarities to alpha-5, beta-3 and gamma-3.19,27
This similarity of the pi subunits to these other subunits is not surprising in that the pi subunits are commonly incorporated into receptors with alpha-5beta-3 or alpha-5beta-3gamma-3 configurations.19 With respect to insomnia the amplification of benzodiazepine sensitivity is governed by the type of gamma subunit which determines extent of benzodiazepine influence with the requirement of a gamma subunit for a GABA receptor to have signiifcant affinity for benzodiazepines.16,19 Most GABA receptors in the brain have gamma-2 subunits, which demonstrate the highest gamma subunit sensivity to benzodiazepines.16 Receptors with pi subunits are thought to interfere with the ability of the gamma subunit to form the benzodiazepine binding site by either replacing the gamma subunit or blocking the interaction between the gamma subunit and alpha subunit.19,25,28 Receptor interaction with zinc is also influenced by the gamma subunit, but incorporation of the pi subunit into different receptors does not appear to interfear with zinc interaction.19
Receptors that incorporate the pi subunit have demonstrated higher GABA EC50 values, less outward rectification and larger single-channel conductance.19 There is also reason to believe that pi subunits flip activity from hyperpolarization to depolarization in cancer cells.19,24 Therefore, these changes imply longer duration firing over receptors without pi subunits. If this information is accurate then a depolarizing GABAA pi subunit receptor has an advantage over normal hyperpolarizing GABAA receptors demanding greater activity from non-pi subunit GABAA receptors for hyperpolarization and cancer limitation. The extent of pi subunit pentration in cancer cells versus non-pi subunit may also explain the somewhat contradicting results with whether or not GABA aids or hinders cancer development.
The differing action between pi subunit and non-pi subunit containing GABA receptors could offer one reason for why taking benzodiazepine based sleep aids increase cancer rates. Increasing benzodiazepine concentrations act on GABAA receptors, which lead to increased GABAA receptor expression. Increased expression rates would increase the number of mutations simply through volume changes alone (subunit mutation rates may not change, but because more subunits would be created there would be more subunit mutations). Among these subunit mutations could be gamma subunits mutating into pisubunits or pi subunits being incorporated over gamma subunits. Increasing the number of pi subunits would increase the number of GABAA receptors that depolarize instead of hyperpolarize, which would aid cancer development instead of hinder it.
Outside very specific subunit interactions like those involving the pi subunit, the relationship between cancer and GABA appears complex for GABA may influence different cancers in different ways. However, there does appear to be a general pattern of operation between GABA, cancer and the two major GABA receptors. To best understand this relationship temporal issues must be acknowledged between immature/developing cancer and mature cancer. Note that developing cancer refers to cells that have become cancerous and are starting to grow, not cells that are going through the initial mutation stages to become cancerous.
Typically the expression of GABA and its corresponding synthesizing enzyme GAD (both isoforms 65 and 67) significantly increase in concentration in the presence of neoplastic cells ((colorectal carcinoma, breast cancer, prostate cancer, glioma, pancreatic and gastric cancer).29-37 However, what does that increase mean relative to cancer growth? Based on existing evidence it appears that a reduction in GABAA receptor functionality leads to accelerated cancer growth.29,38 Such a result implies that GABA activity when binding to GABAA results in reduced cancer growth, which has been supported through reductions in membrane potentials of cancerous cells.39,40 If GABAA binding is detrimental to cancer growth then why do GABA and GAD concentrations increase in the presence of cancer versus non-cancerous cells? One explanation is that this increased expression may be a general ‘safety’ feedback mechanism designed to curtail excess (i.e. cancer) growth, especially if GABAA receptor expression decreases.38
GABAB interaction appears to play a similar role to GABAA with regards to reducing cancer growth. Activation of GABAB in a cancer cell does not kill the cancer cell, but instead arrests its growth between stages G0 and G1.41 In addition GABAB activation also reduces intra-cellular cAMP concentrations through the inhibition of adenylyl cyclase due to the activation of G-protein alpha-2.42,43 cAMP is important in cellular growth (both normal and cancerous) because it activates phosphokinase A (PKA) which among other things (i.e. ERK1/2 cascade) activates phospholamban.44 Phospholamban activation increases the rate of calcium release from the endoplasmic reticulum (ER). While some of this excess calcium is sequestered by the existing cAMP, in typical situations the calcium release from the ER exceeds the rate of sequestration by cAMP resulting in an increased level of cytosolic calcium.44 This increased cytosolic calcium concentration activates calcium dependent secondary messenger systems increasing the rate of cellular growth. Thus, the ability of GABAB activation to reduce cAMP concentrations reduces the rate of cancer growth through this particular mechanism.
While GABA does appear to influence cancer growth in a negative way regardless of which receptor it binds to it could play an even more important role in cancer migration/metastasis, albeit a slightly confusing one. The confusion in the issue of metastasis appears to come from somewhat conflicting evidence, but the confliction is not insurmountable. First, increased expression of GABA67 is seen in patients with higher Gleason scores30 (note Gleason scores are derived from examination of histological samples in an effort to track cancer progression). Initially such a result could be explained, as mentioned earlier, as a feedback mechanism from the body in effort to control cancer growth and as cancer growth increases (leading to a higher Gleason score) the body compensates further. However, what if there is another explanation, what if GABA actually assists in metastasis? This amplification of cancer metastasis seems to be in play at least for some forms of prostate and lymph node cancers where increasing GABA concentration resulted in an increase in matrix metalloproteinase (MMP) expression due to GABAB activation.30
Metastasis is a complex series of interactions leading from tumor development to detachment from its principle location and movement to a different more distant location in the body. The major events involve detachment of from the primary tumor, invasion of the stromal tissue, entrance to the bloodstream, extravasate, invasion of the new target organ and finally the formation of the metastatic colony.17,30 One of the key steps in this process is the proteolytic degradation of the extracellular matrix and in this step MMPs are critical agents.
Initially MMPs were thought to be degenerative proteases that were limited to cleaving matrix components, but that role has expanded to include the release of growth factors and other bioactive peptides localized at cleaved extracellular matrices.45-47 Most MMP action involves MMP-3 cleaving decorin which releases transforming growth factor-b leading to greater levels of angiogenesis in addition to cleaving TGF-alpha activating MAPK inducing cellular proliferation.48,49 MMPs also cleave matrix receptors that may inhibit metastasis inhibitors like E-cadherin and activate a semi-self-regulating pathway with MMP-3 activating MMP-7 and 9.50
However, there appears to be a problem with concluding that GABA increases metastasis probability through GABAB activation in that another study has demonstrated that GABA decreases metastasis probability through GABAB activation.44 In a study using SW480 colon carcinoma cells cellular locomotion (basically metastasis probability) was reduced due to reduction in cAMP concentration. One possible reason for this difference is the differing cell types, but another line of thought produces another possible solution.
First, the SW480 colon study focused on metastasis induced through norepinephrine, not MMPs. In norepinephrine induced metastasis norepinephrine activates beta-arrestin through the beta-2 adrenoceptor which activates Protein Tyrosine Kinase (PTK) which activates the key agent, protein kinase C gamma (PKC).44 Activation of PKC-gamma results in the dual activation of both inositol-1,4,5-trisphosphate and diacylglycerol.44,51 Inositol-1,4,5-triphosphate opens intracellular calcium channels increasing cAMP activation and diacylglycerol activates PKC-alpha, which has been shown to increase metastasis in cancer cells.52,53
Basically norepinephrine increases cancer proliferation by activating inositol-1,4,5-triphosphate and increases cancer metastasis probability through activating diacylglycerol. This dual activation is important because cAMP cannot activate diacylglycerol on its own, yet it does appear to augment diacylglycerol activity in that if cAMP concentration is reduced diacylglycerol does not aid metastasis. Thus GABAB is able to prevent this form of metastasis by reducing cAMP concentration through secondary messenger inhibition of adenylyl cyclase.
However, while GABAB prevents metastasis through PKC-gamma, why doesn’t the ability to increase MMP expression compensate for the PKC-gamma blocking resulting in greater metastasis versus controls? One explanation may be temporal in nature in that the colon cells were not in a high enough state of maturity to express and/or interact with the MMP that should have been generated from the GABAB activation. If this theory is accurate then GABAB may be a beneficial therapeutic agent in the early stages of cancer, but as cancer progresses its usefulness flips and it becomes more detrimental than beneficial.
Unfortunately the role of GABAA in metastasis may not be as simple. While a number of studies suggest that GABAA binding plays no role in metastasis30,41,44 other studies suggest that GABAA binding increases metastasis probability54 or decrease metastasis.55 Despite this contradiction based on currently understood GABAA behaviors and mechanisms it is hard to believe that GABAA positively influences metastasis if configured properly because of its hyperpolarizing nature. The difference between ‘neutrality’ and reducing metastasis for GABAA may also be temporal, similar to GABAB.
Early in cancer development GABAA has little influence, but has MMP concentrations increase, GABAA works to reduce those concentrations despite GABAB augmenting them.55 Another reason for this disparity may be that in some studies the tumors did not mature to the point where metastasis was at a reasonable probability of occurrence because GABAA activity arrested tumor growth, thus GABAA influenced growth, but not metastasis. Overall it appears that the theory to describe GABA receptor behavior with respect to cancer is that GABAA negatively affects cancer growth and has little influence on cancer metastasis whereas GABAB negative affects cancer growth and has a negative influence on early cancer metastasis and a positive influence on late cancer metastasis.
If the above characterization of GABA receptors with respect to cancer is taken as accurate, then it appears that ability of sleep drugs to increase cancer rates largely relies on receptor subunit configuration. Other than pi subunits47 there is reason to suspect that incorporation of theta or rho subunits also increases cancer proliferation probabilities.56,57 These receptor confirmations may be self-augmenting in that if cancer develops due to their depolarizing characteristics over hyperpolarizing the cancer mutates to produce more receptors with similar configurations explaining why rarer pi and theta subunit configurations, with even rho at times, are so prevalent in cancers.24,27,56,57
Another possibility may be that augmenting GABAA activation through agonists like benzodiazepine may cause greater GABAB activity as a result of feedback. While GABAB activation would be viewed as more cancer-preventative than cancer-aiding, recall that as cancer develops the benefit/detriment ratio for GABAB with respect to cancer prevention decreases. Thus one question to ask is if cancer rates increase with sleep drug administration or does cancer metastasis also increase relative to the increase in cancer rates?
Overall one of the chief concerns is that while rates of death are slightly lower with non-benzodiazepine sleep drugs there is little firm scientific evidence that these non-benzodiazepine treatments result in higher rates of increased sleep time or reduction in wake probability after sleep onset compared against placebo.1,58 Benzodiazepine treatments do decrease sleep latency and increase sleep duration8,58 (although there are some questions regarding the statistical significance of the latter effect despite concerns of overestimation of sleep latency and underestimation of total sleep time),58 thus taking non-benzodiazepine treatments which have less cancer promoting tendencies may not be a suitable alternative to addressing a lack of sleep. While GABA seems to prevent cancer more than aid in its development stimulation of the GABA system through artificial means could also increases the rate of mutation in the subunits which comprise GABA receptors and these mutations significantly increase the probability of generating cancer.
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Citations –
1. Kripke, D, Langer, R, Kline, L. “Hypnotics’ association with mortality or cancer: a matched cohort study.” BMJ Open. 2012. 2: e000850. doi:10.1136/bmjopen-2012-000850.
2. Buscemi, N, et Al. “The Efficacy and Safety of Drug Treatments for Chronic Insomnia in Adults: A meta-analysis of RCTs.” Clinical Review. 2007. 22:1335–1350.
3. Ford, D, and Kamerow, D. “Epidemiological study of sleep disturbances and psychiatric disorders: an opportunity for prevention.” JAMA. 1989. 262: 1479–84.
4. Breslau, N, et Al. “Sleep disturbances and psychiatric disorders: a longitudinal epidemiological study of young adults.” Biol. Psychiatry. 1996. 39: 411–18.
5. Wiessman, M, et Al. “The morbidity of insomnia uncomplicated by psychiatric disorders.” Gen Hosp Psychiatry. 1997. 19:245–50.
6. Kripke, D. “Possibility that certain hypnotics might cause cancer in skin.” J. Sleep Res. 2008. 17: 245–250
7. Hajak, G. “A comparative assessment of the risks and benefits of Zopiclone.” Drug Saf. 1999. 21: 457–469.
8. Holbrook, A, et Al. “Meta-analysis of benzodiazepine use in the treatment of insomnia.” Can Med Assoc J. 2000. 162: 225–33.
9. Martin, D. and Barke, K. "Are GAD65 and GAD67 associated with specific pools of GABA in brain?" Perspect Dev Neurobiol. 1998. 5(2-3): 119-129.
10. Corti, O, et Al. "Parkinson's disease: from causes to mechanisms." C.R. Biologies. 2005. 328: 131-142.
11. Sharp, A.H and Ross, C. "Neurobiology of Huntington's disease." Neurobiol. Dis. 1996. 3: 3-15.
12. Albin, R. and Tagle, D "Genetics and molecular biology of Huntington's disease." Trends Neurosci. 1995. 18: 11-14.
13. Engel Jr, J. "Mesial temporal lobe epilepsy: what have we learned?" Neuroscientist 2001. 7: 340-352.
14. Glassmeier, G, et Al. “Expression of functional GABAA receptors in cholecystokinin-secreting gut neuroendocrine murine STC-1 cells.” J. Physiol. (London). 1998. 510 (Pt. 3): 805–814.
15. Jansen, A, et Al. “GABAC receptors in neuroendocrine gut cells: a new GABA-binding site in the gut.” Eur. J. Physiol. 2000. 441: 294–300.
16. Henschel, O, Gipson, K, and Bordey, A. “GABAA Receptors, Anesthetics and Anticonvulsants in Brain Development.” CNS Neurol Disord Drug Targets. 2008. April 7(2): 211–224.
17. Watanabe, M, et Al. “Gamma-aminobutyric acid (GABA) and cell proliferation: focus on cancer cells.” Histol Histopathol. (2006) 21: 1135-1141.
18. Bolmann J. “The ‘ABC’ of GABA receptors.” Trends. Pharmacol. Sci. 2000. 21: 16-19.
19. Neelands, T and Macdonald, R. “Incorporation of the π Subunit into Functional gamma-Aminobutyric AcidA Receptors.” Molecular Pharmachology. 1999. 56: 598-610.
20. McKerman, R and Whiting, P. “Which GABAA-receptor subtypes really occur in the brain?” Trends Neurosci. 1996. 19: 139-143.
21. Pritchett, D, et Al. “Importance of a novel GABAA receptor subunit for benzodiazepine pharmacology.” Nature (London). 1989. 338: 582-585.
22. Wieland, H, Luddens, H, and Seeburg, P. “Molecular determinants in GABAA/BZ receptor subtypes. Adv. Biochem. Psychopharmacol. 1992. 47: 29-40.
23. Neelands, T, et Al. “GABAA receptors expressed in human teratocarcinoma NT2 neuronal precursor cells differ from those expressed by differentiated NT2-N cells.” Soc. Neurosci. Abstr. 1997. 23: 957.
24. Takehara, A, et Al. “gamma-Aminobutyric Acid (GABA) Stimulates Pancreatic Cancer Growth through Overexpressing GABAA Receptor π Subunit.” Cancer Res. 2007. 67(20): 9704-9712.
25. Hedblom, E and Kirkness, E. “A novel class of GABAA receptor subunit in tissues of the reproductive system.” J. Biol. Chem. 1997. 272: 15346-15350.
26. Neelands, T, et Al. “GABAA receptor pharmacology and subtype mRNA expression in human neuronal NT2-N cells.” J. Neurosci. 1998. 18:4993–5007.
27. Johnson, S and Haun, R. “The Gamma-Aminobutyric Acid A Receptor Subunit is Overexpressed in Pancreatic Adenocarcinomas.” J Pancreas (Online). 2005. 6(2): 136-142.
28. Sigel, E. “Mapping of the Benzodiazepine Recognition Site on GABAA Receptors.” Curr. Top Med. Chem. 2002. 2: 833-839.
29. Labrakakis, C, et Al. “Functional GABAA Receptors on Human Glioma Cells.” Eur. J. Neurosci. 1998. 10(1): 231-238.
30. Azuma, H, et Al. “gamma-Aminobutyric Acid as a Promoting Factor of Cancer Metastasis; Induction of Matrix Metalloproteinase Production Is Potentially Its Underlying Mechanism.” Cancer Res. 2003. 63:8090-8096.
31. Watanabe, M, et Al. “GABA and GABA receptors in the central nervous system and other organs.” Int. Rev. Cytol. 2002. 213: 1–47.
32. Maemura, K, et Al. “gamma-Amino-butyric acid immunoreactivity in intramucosal colonic tumors.” J. Gastroenterol. Hepatol. 2003. 18: 1089–1094.
33. Kleinrok, Z, et Al. “GABA content and GAD activity in colon tumors taken from patients with colon cancer or from xenografted human colon cancer cells growing as s.c. tumors in athymic nu/nu mice.” J. Physiol. Pharmacol. 1998. 49: 303–310.
34. Matuszek, M, Jesipowicz, M, and Kleinrok, Z. “GABA content and GAD activity in
gastric cancer.” Int. Med. J. Exp. Clin. Res. 2001. 7: 377–381.
35. Wang, Y, et Al. “Immunohistochemical study on GABAergic system in rat and human large intestine.” Bull. Osaka Med. 2000. 46: 25-34.
36. Hu, Y, et Al. “Molecular characterization of a metastatic neuroendocrine cell cancer arising in the prostates of transgenic mice.” J. Biol. Chem. 2001. 277: 44462-44474.
37. Opolski, A, et Al. “The role of GABA-ergic system in human mammary gland
pathology and in growth of transplantable murine mammary cancer.” J. Exp. Clin. Cancer Res. 2000. 19: 383-390.
38. Minuk, G, et Al. “Decreased hepatocyte membrane potential differences and GABAA-beta3 expression in human hepatocellular carcinoma.” Hepatology. 2007. 45(3): 735–745.
39. Sun, D, et Al. “Increasing cell membrane potential and GABAergic activity inhibits malignant hepatocyte growth.” American Journal of Physiology – Gastrointestinal and Liver Physiology. 2003. 285(1): G12–G19.
40. Zhang, M, et Al. “Increased GABAergic activity inhibits alpha-fetoprotein mRNA expression and the proliferative activity of the HepG2 human hepatocellular carcinoma cell line.” Journal of Hepatology. 2000. 32(1): 85–91.
41. Wang, T, Huang, W, and Chen, F. “Baclofen, a GABAB receptor agonist, inhibits human hepatocellular carcinoma cell growth in vitro and in vivo.” Life Sciences. 2008. 82: 536–541.
42. Schuller, H, Al-Wadei, H, and Majidi, M. “GABAB Receptor is a Novel Drug Target for Pancreatic Cancer.” Cancer. 2008. 112(4): 767-778.
43. Gladkevich, A, et Al. “The peripheral GABAergic system as a target in endocrine disorders.” Auton. Neurosci. 2006. 124: 1–8.
44. Joseph, J, et Al. “The Neurotransmitter gamma-Aminobutyric Acid Is an Inhibitory Regulator for the Migration of SW 480 Colon Carcinoma Cells.” Cancer Research. 2002. 62:6467-6469.
45. McCawley, L and Matrisian, L. “Matrix metalloproteinases: multifunctional contributors to tumor progression.” Mol. Med. Today. 2000. 6: 149–156.
46. Sternlicht, M and Werb, Z. “How matrix metalloproteinases regulate cell behavior.” Annu. Rev. Cell Dev. Biol. 2001. 17: 463–516.
47. Egeblad, M and Werb, Z. “New functions for the matrix metalloproteinases in cancer progression.” Nat. Rev. Cancer. 2002. 2: 161–174.
48. Imai, K, et Al. “Degradation of decorin by matrix metalloproteinases: identification of the cleavage sites, kinetic analyses and transforming growth factor-beta1 release.” Biochem. J. 1997. 322: 809–814.
49. Yan, Y, Shirakabe, K, and Werb, Z. “The metalloprotease Kuzubanian (ADAM10) mediates the transactivation of EGF receptor by G protein-coupled receptors.” J. Cell Biol. 2002. 158: 221-226.
50. Birchmeier, W. “E-cadherin as a tumor (invasion) suppressor gene.” Bioessays. 1995. 17: 97–99.
51. Masur, K, et, Al. “Norepinephrine-induced migration of SW 480 colon carcinoma cells is inhibited by beta-blockers.” Cancer Res. 2001. 61: 2866–2869.
52. Masur, K, et Al. “High PKCgamma and low E-cadherin expression contribute to high migratory activity in colon carcinoma cells.” Mol. Biol. Cell. 2001. 12: 1973–1982.
53. Entschladen, F and Zanker, K. “Locomotion of tumor cells: a molecular comparison to migrating pre- and post-mitotic leukocytes.” J. Cancer Res. Clin. Oncol. 2000. 126: 671–681.
54. Garib, V, et Al. “Influence of non-volatile anesthetics on the migration behavior of the human breast cancer cell line MDA-MB-468.” Acta Anaesthesiol. Scand. 2002. 46: 836-844.
55. Thaker, P, et Al. “Inhibition of Experimental Colon Cancer Metastasis by the GABA-Receptor Agonist Nembutal.” Cancer Biology & Therapy. 2005. 4(7): 753-758.
56. Hackam, A, et Al. “The N-terminal domain of human GABA receptor rho1 subunits contains signals for homooligomeric and heterooligomeric interaction.” J Biol Chem. 1997. 272: 13750–13757.
57. Li, Y, et Al. “GABA stimulates human hepatocellular carcinoma growth through overexpressed GABAA receptor theta subunit.” World J. Gastroenterol. 2012. 18(21): 2704-2711.
58. Sateia, M, et Al. “Evaluation of chronic insomnia. An American Academy of Sleep Medicine Review.” Sleep. 2000. 23: 1–66.
Wednesday, June 20, 2012
Eating and Counting at the Same Time – Calorie Charts in Restaurants
Regrettably the rate of obesity has grown significantly in the last 20 years in both adults and children. While some individuals lament the simplistic explanation that most have derived for this outcome, over-consumption of food, instead favoring other explanations like environmental toxins or changes in gut bacteria concentrations; the reality is that the primary reason for an individual being overweight is an imbalance in calorie intake vs. biochemical caloric consumption. Outside influencing factors like toxins and gut bacteria simply influence intake and consumption. One of the explanations for the significant increase in this imbalance is the change in food consumption behavior.1,2 For example it is thought that modern Americans consume approximately 1/3 of their total calories along with approximately ½ of the total money spent on food not at home, but at restaurants.3,4
Even when food is not consumed in the home one still must take measure of its nutritional content. Unfortunately most consumers appear generally unaware of the need to measure calorie content, do not care to count or inaccurately estimate the number of calories they consume when eating out. Part of this problem stems from the fact that few people can accurately estimate the number of calories in a meal by simply looking at it; some surveys have identified that most respondents underestimated the calorie count in various take-out items by nearly ½ with some inaccuracies on single items amounting to 650 calories.5,6 In addition various other studies further demonstrated the inconsistency in consumer knowledge regarding calorie counts and using them to improve decision making as some studies identified different and healthier food choices when exposed to visually presented calorie counts and other studies with similar methodologies, but different people, identified no significant difference in food choice.7-12
It must be noted that the concerns with obesity and out of home eating are only one element behind increasing obesity rates. If one does not track how one eats at home or in non-chain restaurants where calorie counts are typically not available then tracking nutrition when eating at chain restaurants loses most of its purpose. Also people must be committed to using the information which means accepting that they cannot eat whatever they want whenever they want otherwise posting calorie counts will not produce the results that some hope for.
The above points notwithstanding clearly the public needs some assistance because despite what some want to believe the health of general society affects everyone in some context solely due to the interconnectivity of the healthcare system and its limited resources. Therefore, after watching some state governments poke around the edges of regulation, the federal government finally demanded some form of concrete information structure for a calorie information program in section 4205 of the Affordable Care Act passed in 2010. This structure was designed so that ‘chain restaurants’ (restaurants with more than 20 locations) had to provide calorie data and additional nutritional information for basically all food items (menu and self-service), so patrons could make more informed food choices.
Unfortunately there was no generally required format to these calorie postings relying instead upon the FDA to accept recommendations for what type and how mandatory information should be presented. The FDA has proposed five general recommendations: 1) Calories for items are displayed in proximity to food items on the menu board; 2) Menu board contains a statement that written nutritional information is available on request; 3) Menu board contains a succinct statement regarding suggested caloric intake; 4) Menu board contains a statement that puts the calories in context of total requirements; 5) Menu board provides nutrient content for standard menu items that come in different flavors, varieties, or combinations but are listed as a single menu item;12 however, of the five recommendations only the first and last ones have been given any type of guidelines for implementation with the remaining three lacking any type of guidelines. Not surprisingly the three recommendations lacking guidelines are rarely implemented. New guidelines are scheduled to be released sometime in July 2012.
Note that it is estimated that at least ½ of U.S. chain restaurants provided nutrition information publicly either apart from the menu at the restaurant itself or on the company’s website;14 however, it stands to reason that such postings, especially on websites, heavily limits the usefulness of the information due to availability issues when that information is most desired by the consumer.
An improved and firm structure for presenting calorie information is required because a vast majority of postings offered by most restaurants do not provide sufficient information to accurately access the caloric intake of most meal combinations even if the patron is committed to counting calories. There are limited problems presenting calorie information for single static items like black coffee or an egg mcmuffin when there is no ability to change the ingredients; the most glaring problems arise with combination or multi-serving items where the exact ingredients utilized are at the whim of the patron.
There are two chief problems: first due to space limitations on the physical menu calorie counts are actually restricted to ranges due to the different choices associated with a given meal option.13 These calorie postings do not differentiate between the different options instead indicating that the options range between say 500 to 1860 calories. It is almost impossible to expect even active patrons to be able to effectively manage their diets with such a large gap and a lack of more specific information. Second, there is some concern that those ranges are inaccurate, accidentally or purposely lowered on the menu versus actual calorie counts or even information on the restaurant’s own website.13,14 Unfortunately the five options currently being considered by the FDA to present calorie information do not appear to alleviate the problems with combination menu items. The five considered options are: 1) a single average value; 2) a minimum to maximum range; 3) means; 4) medians; 5) hybrid models;13
What is almost insulting about this list of options is that clearly the first four options will do almost nothing to help individuals make informed food selections because of the lack of specificity. The hybrid model does little better due to its complexity because the calorie count posted would depend on the overall range of calories in all of the offered food combinations, but still not good enough.13
The almost silly thing about all of these options is that they seem to exist to avoid inconveniencing or ‘over-burdening’ food establishments. What is the point of applying a food calorie regulation to these establishments if one allows them to skirt along the edge of the requirement. It gives the impression that government is saying to patrons, “well those establishments have some form of calorie count it is not our job to require them to simplify it to the point where you don’t need to do your own intensive research or have a degree in biochemistry to track the amount of calories you are consuming for today’s meal.” The FDA needs to either require transparent simplicity or just not bother at all.
What would transparent simplicity look like? First, the requirement of the calorie posting next to the meal item on the menu itself is understandable in its intent, but as mentioned the limited space significantly reduces specific item combination accuracy. Every ‘chain’ restaurant has available wall space near the ordering area. In this wall space the restaurant should post a large nutritional chart with sufficient sized font (at least 12) providing information on each individual item with outline formatting for each additional option for a given item. Individual ingredient listing will be especially important for ‘construction’ food projects like sub sandwiches, fried chicken and pizzas. An example of this formatting is shown in the below figure (note that the associated numbers are fictional calorie counts)
Looking at the above figure note that the numbers are additive, thus if one orders a medium pizza with onions and olives the total calorie count will 970. The placement of this chart should have its ceiling at six and half feet above the floor allowing for easy viewing for most adults and the items should be listed in alphabetical order to reduce searching time. Also for restaurants that have a lot of combination potential it would also help to provide patrons a quick means of summating the total calorie counts of their meals. To this end the restaurants should supply a calculator tethered to the chart similar to how a bank has pens attached to various writing surfaces. Obviously the calculator does not need to be an expensive scientific model just one that can add, subtract and multiply.
Although there are arguments that it has been politicized due to special interests, a copy of the new FDA food chart should be displayed next to the nutritional information to provide an additional element of context with regard to balanced eating. The calorie chart could then show how each of the major areas in the chart (Grain, Protein, Fruit and Vegetable) is represented in each menu item. Some may argue that this is too much to expect from restaurants, but most menu items in restaurants are static, thus food chart/plate analysis will only need to be conducted once.
However, simply providing the calorie information may not be enough. Some researchers identified interesting self-destructive behavior by some individuals that utilized calorie counts when making food purchases. In certain circumstances the net amount of calories consumed in a day did not significantly change between a group that was exposed to calorie information versus a group that was not exposed to any calorie information.8 There are two possible explanations for this behavior: first, individuals purposely ate less when eating in the restaurant leaving them hungrier later in the day to which their response was to eat more food than those who ordered more food in the restaurant. Second, individuals took a psychological ‘reward’ approach in that because they were ‘good’ and ordered a lower calorie meal at the restaurant there was more leeway to eat more later. Interestingly when patrons were ‘reminded’ that the average person should consume approximately 2000 calories per day this ‘catch-up calorie consumption’ tendency is lessened.8 The reason for this change is not overly clear, but the explanation may be in the next paragraph.
What the above study may identify is that consumers still need a context in which to apply the counted calories. Without floors and ceilings the counts are simply just meaningless numbers. In addition to reminding patrons of the generally acknowledged calorie ceiling, they should also be reminded to actually count calories. A sign near each register asking if the patron is satisfied with the calorie count of their meal with the 2000 calorie per day reminder should be sufficient. It must be understood that restaurants are not responsible for whether or not individuals make healthy food selections; it is simply their responsibility to ensure that patrons are properly informed regarding the nutritional content of their food options and not to favor any food choice over another.
If the above recommendations were accurately followed then patrons should have requisite information to make informed decisions regarding what foods they consume outside of the home. However, there are some other issues that need to be addressed. One important element of addressing calorie count charts, especially for the poor, is monetary efficiency. If a patron can see that one food item costs $2 and has 500 calories and another food item costs $2 and has 300 calories the individual may select the first item because he/she receives more calories for money spent. Also there is some evidence to suggest that providing calorie information could reduce the motivation to tax unhealthy foods.15,16
The potential of calorie counts to shift responsibility on consumers over service providers, thus possibly reducing the probability of applying a tax to unhealthy foods is also a notable consequence. One important issue with regards to changing eating habits is that healthy food is typically more expensive than unhealthy food. Clearly from an economic and health perspective it is important to close, if not reverse, that gap. However, some studies have reported that lowering the costs of healthy foods do not translate into increased purchase, but increasing unhealthy food price does.17,18 Thus policy makers must ensure that creating regulation for posting calorie charts is not the end result of regulation if the obesity problem continues.
In addition accurate calorie information could eliminate a psychological tactics used by some dieters in inherently overestimating calorie counts on high calorie items. The psychology goes that a dieter will see a high calorie food item and assume that based on the ingredients the calorie count has to be x large. If the calorie count is available the dieter may realize that he/she had overestimated the calorie count and justify purchase by presuming overestimation of calories on other food items. However, while it is important to point out these concerns so they can be appropriately addressed none of these above concerns are significant enough to warrant terminating the idea of posting accurate calorie count information in restaurants.
Overall the general idea behind posting calorie counts is sound as long as the regulations surrounding it are genuinely applied. However, one cannot simply presume that the application of this strategy will single-handily end the rise in obesity rates; it is just one element in the fight against societal expansion of obesity.
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1. Bowman, S and Vinyard, B. Fast food consumption of US adults: impact on energy and nutrient intakes and overweight status. J. Am. Coll. Nutr. 2004;23:163–168.
2. Kuo, T, et Al. “Menu Labeling as a Potential Strategy for Combating the Obesity Epidemic: A Health Impact Assessment.” Amer. J. Public Health. Sept. 2009. 99(9): 1680-1686.
3. Industry at a glance. National Restaurant Association Web site. Available at: http://www.restaurant.org/pdfs/research/2009Factbook.pdf. 2009.
4. Keystone center backgrounder—Keystone forum on away-from-home foods: opportunities for prevention weight gain and obesity report. 2006.
http://www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data.
5. Burton, S and Creyer, E. “What consumers don’t know can hurt them: consumer evaluations and disease risk perceptions of restaurant menu items.” J. Consum. Aff. 2004. 38: 121–145.
6. Burton, S, et Al. “Attacking the obesity epidemic: the potential health benefits of providing nutrition information in restaurants. Am. J. Public Health. 2006. 96:1669–75.”
7. Finkelstein, E, et Al. “Mandatory menu labeling in one fast food chain in King County, Washington.” Am J Prev Med. 2011. 40(2): 122–127.
8. Pulos, E and Leng, K. “Evaluation of a voluntary menu-labeling program in full-service restaurants.” Am J Public Health. 2010. 100(6): 1035–1039.
9. Roberto, C, et Al. “Evaluating the impact of menu labeling on food choices and intake.” Am J Public Health. 2010. 100(2): 312–318.
10. Elbel, B, et Al. “Calorie labeling and food choices: a first look at the effects on low-income people in New York City.” Health Aff 2009. 28: 1110–21.
11. Downs, J, Loewenstein, G, and Wisdom, J. “Strategies for promoting healthier food choices.” Am Econ Rev 2009. 99:159–64.
12. Harnack, L and French, S. “Effect of point-of-purchase calorie labeling on restaurant and cafeteria food choices: a review of the literature.” Int J Behav Nutr Phys Act 2008. 5:51.
13. Cohn, E, et Al. “Calorie Postings in Chain Restaurants in a Low-Income Urban Neighborhood: Measuring Practical Utility and Policy Compliance.” Journal of Urban Health. 2012. DOI: 10.1007/s11524-012-9671-0
14. Wootan, M and Osborn, M. “Availability of nutrition information from chain restaurants in the United States.” Am J Prev Med. 2006. 30(3):266–268.
15. Giesen, J, et Al. “Exploring how calorie information and taxes on high-calorie foods influence lunch decisions.” Am J Clin Nutr. 2011. 93:689–94.
16. Epstein, L, et Al. “The influence of taxes and subsidies on energy purchased in an experimental purchasing study.” Psychol Sci. 2010. 21:406–14.
17. Epstein, L, et Al. “Purchases of food in youth. Influence of price and income.” Psychol. Sci. 2006. 17:82–9.
18. Epstein, L, et Al. “Price and maternal obesity influence purchasing of low- and high-energy-dense foods.” Am. J. Clin Nutr. 2007. 86:914–22.
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