Science Driven Nutrition DRIVEN BY SCIENCE| GUIDED BY EVIDENCE Thu, 18 May 2017 14:33:34 +0000 en-US hourly 1 Science Driven Nutrition 32 32 Is Apple Cider Vinegar a Miracle Food? Thu, 18 May 2017 00:53:55 +0000 Is Apple Cider Vinegar a Miracle Food  – What is the Evidence? By: Peter Fitschen, PhD Read Time: 6.7 oz of coffee Vinegar can be made from nearly any fermentable carbohydrate source, including apples.  To make vinegar, yeast ferment sugars into alcohol which is then converted into acetic acid by bacteria.  The final acetic acid concentration of commercially[...]

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Is Apple Cider Vinegar a Miracle Food  – What is the Evidence?

By: Peter Fitschen, PhD

Read Time: 6.7 oz of coffee

Vinegar can be made from nearly any fermentable carbohydrate source, including apples.  To make vinegar, yeast ferment sugars into alcohol which is then converted into acetic acid by bacteria.  The final acetic acid concentration of commercially available vinegar is 4-7 percent [1].

Recently, mainstream media has marketed apple cider vinegar as a cure for literally everything.  From weight loss to cancer to detoxification and many other claimed uses, it can seem like all you need to do is take some apple cider vinegar and all of your problems will be solved.

Coming from a science background, any time I hear these types of claims I ask, “What is the evidence?” 

To answer this question, I dug into the scientific literature on apple cider vinegar (and vinegar in general) to determine which of these claims have sound scientific backing and which do not.  Below is a summary of the current peer-reviewed scientific evidence for a number of claims made about apple cider vinegar.

Blood Sugar

This is a claim that has been investigated.  Studies in rodents have found that both vinegar [2] and apple cider vinegar [3] have positive effects on blood glucose control.

In humans, consumption of vinegar prior to a carbohydrate containing meal has been shown to reduce blood glucose response by approximately 20-30 percent [4, 5].  However, it should be noted that total area under the curve for blood glucose 2hrs after a meal was not different between a group that consumed vinegar and one that did not [6].  What this means is that while vinegar consumption reduces the initial increase in blood glucose, it does not prevent glucose absorption and instead delays it. 

Indeed, vinegar consumption has not been shown to interfere with carbohydrate absorption [7].  Instead, consumption of apple cider vinegar [8] or regular vinegar (most often made from rice) [9] has been shown to reduce gastric emptying rate. 

Although vinegar has been shown to reduce the initial glycemic response to a meal, it should be noted that the significance of the glycemic index and glycemic response of a meal to overall health in non-diabetic individuals has been debated [10].

In insulin resistant individuals, increases in insulin sensitivity have also been observed following consumption of vinegar with a high carbohydrate meal [11].  In addition, a small, short-term intervention in type 2 diabetics observed a small reduction in fasting blood glucose when apple cider vinegar was consumed prior to bed [12].  Similarly, a small study in diabetics observed a slight decrease in hemoglobin A1C levels after consumption of 2 tablespoons vinegar twice daily for 12 weeks [13].  However, it should be noted that this effect was not observed in a larger controlled trial in non-diabetics [14].

Although more research is needed on this topic, it does appear that consumption of vinegar with high carbohydrate meals may help to blunt the glycemic response and also may help blood glucose control diabetics.

Weight Loss

Many in the health and fitness community promote apple cider vinegar for weight loss.  Fortunately, some scientific investigation has been made into this claim. 

Rodent studies have found that acetic acid administration increases fatty acid oxidation and prevents fat accumulation [15, 16].

In humans, consumption of a vinegar with a high glycemic meal had a non-significant trend to reduce caloric intake throughout the rest of the day by 200 calories [17].  This was thought to be due to a decrease in gastric emptying rate resulting in increased satiety.  However, there is some evidence that the appetite suppressing effect may actually be due to nausea caused by vinegar consumption [18].

To date, only one study has investigated the effects of a vinegar intervention on weight loss in humans.  Kondo et al. [19] recruited 155 obese Japanese individuals and assigned them to either 15ml vinegar, 30ml vinegar or placebo daily for 12 weeks.  Weight loss was observed in a dose-dependent manner.  The 15ml group lost 1.2kg, 30ml group lost 1.9kg and placebo group body weight remained unchanged following the intervention despite no differences reported in caloric intake. 

However, it should be noted that participants self-reported nutrition intake which has been shown to be highly inaccurate in obese individuals [20].  Moreover, it should be noted that since this study was published in 2009, the results have not been replicated.

Therefore, although vinegar consumption may increase satiety to some extent (as a result of either delayed gastric emptying and/or nausea); far more research needs to be done before claims of accelerated weight loss can be made.

Cardiovascular Disease

A number of claims related to cardiovascular disease risk factors and mortality have been made about apple cider vinegar.  However, much of the existing data to support these claims are from rodent studies and very little research has been done in humans.

In rodents, apple cider vinegar consumption improved blood lipids [3, 21] and reduced oxidative stress [22].  Black vinegar has also been shown to reduce oxidative stress in rodents [23].  In addition, high doses of vinegar have been shown to reduce blood pressure in an animal model of hypertension [24].

In humans, an observational study observed a correlation between consumption of oil and vinegar salad dressings and reduction in cardiovascular disease mortality in women [25].  However, it should be noted that these individuals also consumed a greater amount of polyunsaturated fatty acids; therefore, it is not clear if the reduction in cardiovascular disease mortality observed was due to vinegar, polyunsaturated fatty acids or some other factor.

To date, few trials looking at vinegar consumption and cardiovascular risk factors has been conducted.  In addition to weight loss, Kondo et al. [19] observed a reduction in triglycerides; however, as previously discussed after publication of this data in 2009, the findings have not been replicated.  Panetta et al. [14] observed no significant change in blood lipid after 8 weeks of daily apple cider vinegar consumption in a randomized controlled trial of 97 non-diabetics.

Moreover, no studies to date have been performed examining the effects of regular vinegar consumption on outcomes such as cardiovascular events or mortality.  Therefore, much more research is needed before any claims can be made about vinegar and reduction of cardiovascular disease risk.


Recently, there have been an increased number of claims regarding apple cider vinegar consumption and cancer; however, as a whole there is not a lot of evidence to support this claim.  Studies from cell culture [26] and animals [27, 28] have shown anti-cancer effects of vinegar.  However, observational studies in humans have been mixed showing both an increase [29] and decrease [30] in cancer with increased vinegar consumption.  Thus, this claim is not supported at this time.


Numerous skin-related claims have been made about apple cider vinegar including: acne treatment, improvement of wrinkles, wound healing, mole removal, reduction in bruise discoloration and others.  However, to date there is no evidence to support these claims.  Vinegar has not been shown to be effective for wound care [31] or lice treatment [32].  Moreover, chemical burns have been reported from attempted mole removal [33] and prolonged skin exposure [34] with vinegar.  Based upon these findings you would be better served staying away from vinegar for skin treatment.

Oral Health

Apple cider vinegar has been claimed to whiten teeth and improve bad breath.  However, there is not evidence to support these claims.  Vinegar is acidic; therefore, prolonged exposure can erode tooth enamel like any other highly acidic food [35].  With that being said, there is some evidence that vinegar may be an effective denture cleaner [36].


There have been a number of claims made about the ability of apple cider vinegar to cure illness such as the common cold or reduce the duration of a sore throat.  However, the evidence to support these claims is poor.

Much of the research on the antibacterial effects of vinegar has been focused on its ability to kill pathogenic bacteria in food [37].  It has also been investigated as a cleaner, but has been shown to not be as effective as commercially available cleaners [38].

In shrimp, high dosages of apple cider vinegar (1-4% of the diet) enhanced expression of immune related genes [39].  However, the relationship between vinegar and immunity, illness or throat soreness has not been studied in humans.  In fact, esophageal injury has been reported from consumption of apple cider vinegar supplements.  This lead to a follow up study which found wide variability in the composition of apple cider vinegar supplements [40].


Apple cider vinegar has been claimed to reduce a wide array of allergies.  However, the supporting data for this claim is lacking.  To date, only one study on vinegar and allergies has been performed in humans.  In this study 7 subjects with food allergies to egg, chicken and lentils were subjected to skin prick tests in which foods were prepared with or without white wine vinegar.  The foods prepared with vinegar resulted in a reduction in reaction during the skin prick tests [41].  However, it should be noted that vinegar is acidic and likely denatured proteins similar to the denaturation of proteins that occur in the stomach as part of dietary protein digestion.  Therefore, it is unclear if denaturation of proteins by vinegar prior to oral intake would have a different outcome than protein denaturation that occurs in the stomach.  It should also be noted that this is only one study in 7 individuals and far more research is necessary before vinegar can be claimed as effective against allergies.


Many individuals claim apple cider vinegar will reduce inflammation from arthritis or other inflammatory conditions.  However, evidence to support this claim is scarce.  In an animal model of colitis, large doses of vinegar reduced inflammation, improved gut bacterial populations and attenuated body weight loss [42].  To date, this data has not been replicated in humans and there is no human data supporting these claims.


There have been some claims that apple cider vinegar improves fertility.  The only human study along these lines to date was a small study of 7 Japanese women with polycystic ovary syndrome (PCOS) that did not have a normal menstrual cycle.  After consumption of 15g vinegar daily for 90-110 days, 4 of the 7 women re-gained their menstrual cycle and this was thought to be due to vinegar’s effect on normalizing insulin resistance commonly associated with PCOS [43].  However, far more research is necessary before any claims about apple cider vinegar and fertility can be made.


Detox diets are increasingly popular fad diets based around the premise that “toxins” have accumulated in the body and to remove them a dietary intervention is required.  These diets often contain apple cider vinegar due to the claim that it can help detoxify the body.  However, an individual with a healthy liver and kidneys is continuously removing chemicals from the body to prevent them from building up to toxic levels.  Moreover, a recent literature review concluded that there was no evidence that detox diets removed toxins form the body [44].  Therefore, there is no evidence that apple cider vinegar or any other food removes toxins from the body.

pH Balance

Claims have been made that apple cider vinegar can help to make the body’s pH more alkaline.  However, the pH of blood is tightly controlled at 7.35 – 7.45 by the kidneys.  Even slight variations from this range can result in severe illness, hospitalization and death.  Fortunately, there is no evidence that human diets have a significant effect on blood pH in individuals with normal kidney function [45] and there is no evidence to support this claim.

Other Claims

Many other claims about apple cider vinegar and health have been made.  These include reduced acid reflux, osteoporosis prevention, dandruff treatment, energy booster, a reduction in cramps, hiccups and many other claims.  To date, there is no evidence to support these claims.

What’s the verdict?

A majority of claims made about apple cider vinegar and health are not supported by the current body of scientific literature.  There is some evidence that vinegar consumption can reduce the glycemic response to a meal and potentially increase satiety.  Therefore, individuals who are diabetic or who may be trying to lose weight may notice some benefit to consumption.  At 3 calories per tablespoon it won’t eat up all of your daily caloric allotment if you are in one of those situations and want to give it a try [46].  It also likely isn’t going to be detrimental to health or weight loss progress in any way.  However, if you think apple cider vinegar alone will be the solution to your weight loss, you will be greatly disappointed because there is no magic pill.  Ultimately, for weight management and overall health your best bet is to consume an adequate calorie intake for your goals from a nutrient-dense diet, stay active and maintain a healthy body weight. 


1. Johnston, C.S. and C.A. Gaas, Vinegar: medicinal uses and antiglycemic effect. MedGenMed, 2006. 8(2): p. 61.

2. Sakakibara, S., et al., Acetic acid activates hepatic AMPK and reduces hyperglycemia in diabetic KK-A(y) mice. Biochem Biophys Res Commun, 2006. 344(2): p. 597-604.

3. Shishehbor, F., et al., Apple cider vinegar attenuates lipid profile in normal and diabetic rats. Pak J Biol Sci, 2008. 11(23): p. 2634-8.

4. Brighenti, F., et al., Effect of neutralized and native vinegar on blood glucose and acetate responses to a mixed meal in healthy subjects. Eur J Clin Nutr, 1995. 49(4): p. 242-7.

5. Johnston, C.S., et al., Examination of the antiglycemic properties of vinegar in healthy adults. Ann Nutr Metab, 2010. 56(1): p. 74-9.

6. Ostman, E., et al., Vinegar supplementation lowers glucose and insulin responses and increases satiety after a bread meal in healthy subjects. Eur J Clin Nutr, 2005. 59(9): p. 983-8.

7. Salbe, A.D., et al., Vinegar lacks antiglycemic action on enteral carbohydrate absorption in human subjects. Nutr Res, 2009. 29(12): p. 846-9.

8. Hlebowicz, J., et al., Effect of apple cider vinegar on delayed gastric emptying in patients with type 1 diabetes mellitus: a pilot study. BMC Gastroenterol, 2007. 7: p. 46.

9. Liljeberg, H. and I. Bjorck, Delayed gastric emptying rate may explain improved glycaemia in healthy subjects to a starchy meal with added vinegar. Eur J Clin Nutr, 1998. 52(5): p. 368-71.

10. Raben, A., Should obese patients be counselled to follow a low-glycaemic index diet? No. Obes Rev, 2002. 3(4): p. 245-56.

11. Johnston, C.S., C.M. Kim, and A.J. Buller, Vinegar improves insulin sensitivity to a high-carbohydrate meal in subjects with insulin resistance or type 2 diabetes. Diabetes Care, 2004. 27(1): p. 281-2.

12. White, A.M. and C.S. Johnston, Vinegar ingestion at bedtime moderates waking glucose concentrations in adults with well-controlled type 2 diabetes. Diabetes Care, 2007. 30(11): p. 2814-5.

13. Johnston, C.S., A.M. White, and S.M. Kent, Preliminary evidence that regular vinegar ingestion favorably influences hemoglobin A1c values in individuals with type 2 diabetes mellitus. Diabetes Res Clin Pract, 2009. 84(2): p. e15-7.

14. Panetta, C.J., C.J. Yvonne, and A.C. Shapiro, Prospective randomized clinical trial evaluating the impact of vinegar on lipids in non-diabetics. World Journal of Cardiovascular Diseases, 2013. 3(2): p. 191-196.

15. Kondo, T., et al., Acetic acid upregulates the expression of genes for fatty acid oxidation enzymes in liver to suppress body fat accumulation. J Agric Food Chem, 2009. 57(13): p. 5982-6.

16. Fushimi, T. and Y. Sato, Effect of acetic acid feeding on the circadian changes in glycogen and metabolites of glucose and lipid in liver and skeletal muscle of rats. Br J Nutr, 2005. 94(5): p. 714-9.

17. Johnston, C.S. and A.J. Buller, Vinegar and peanut products as complementary foods to reduce postprandial glycemia. J Am Diet Assoc, 2005. 105(12): p. 1939-42.

18. Darzi, J., et al., Influence of the tolerability of vinegar as an oral source of short-chain fatty acids on appetite control and food intake. Int J Obes (Lond), 2014. 38(5): p. 675-81.

19. Kondo, T., et al., Vinegar intake reduces body weight, body fat mass, and serum triglyceride levels in obese Japanese subjects. Biosci Biotechnol Biochem, 2009. 73(8): p. 1837-43.

20. Lichtman, S.W., et al., Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med, 1992. 327(27): p. 1893-8.

21. Budak, N.H., et al., Effects of apple cider vinegars produced with different techniques on blood lipids in high-cholesterol-fed rats. J Agric Food Chem, 2011. 59(12): p. 6638-44.

22. Naziroglu, M., et al., Apple cider vinegar modulates serum lipid profile, erythrocyte, kidney, and liver membrane oxidative stress in ovariectomized mice fed high cholesterol. J Membr Biol, 2014. 247(8): p. 667-73.

23. Chou, C.H., et al., Amino acid, mineral, and polyphenolic profiles of black vinegar, and its lipid lowering and antioxidant effects in vivo. Food Chem, 2015. 168: p. 63-9.

24. Kondo, S., et al., Antihypertensive effects of acetic acid and vinegar on spontaneously hypertensive rats. Biosci Biotechnol Biochem, 2001. 65(12): p. 2690-4.

25. Hu, F.B., et al., Dietary intake of alpha-linolenic acid and risk of fatal ischemic heart disease among women. Am J Clin Nutr, 1999. 69(5): p. 890-7.

26. Mimura, A., et al., Induction of apoptosis in human leukemia cells by naturally fermented sugar cane vinegar (kibizu) of Amami Ohshima Island. Biofactors, 2004. 22(1-4): p. 93-7.

27. Seki, T., et al., Antitumor activity of rice-shochu post-distillation slurry and vinegar produced from the post-distillation slurry via oral administration in a mouse model. Biofactors, 2004. 22(1-4): p. 103-5.

28. Shimoji, Y., et al., Extract of Kurosu, a vinegar from unpolished rice, inhibits azoxymethane-induced colon carcinogenesis in male F344 rats. Nutr Cancer, 2004. 49(2): p. 170-3.

29. Radosavljevic, V., et al., Non-occupational risk factors for bladder cancer: a case-control study. Tumori, 2004. 90(2): p. 175-80.

30. Xibib, S., et al., Risk factors for oesophageal cancer in Linzhou, China: a case-control study. Asian Pac J Cancer Prev, 2003. 4(2): p. 119-24.

31. Rund, C.R., Non-conventional topical therapies for wound care. Ostomy Wound Manage, 1996. 42(5): p. 18-20, 22-4, 26.

32. Takano-Lee, M., et al., Home remedies to control head lice: assessment of home remedies to control the human head louse, Pediculus humanus capitis (Anoplura: Pediculidae). J Pediatr Nurs, 2004. 19(6): p. 393-8.

33. Feldstein, S., M. Afshar, and A.C. Krakowski, Chemical Burn from Vinegar Following an Internet-based Protocol for Self-removal of Nevi. J Clin Aesthet Dermatol, 2015. 8(6): p. 50.

34. Bunick, C.G., et al., Chemical burn from topical apple cider vinegar. J Am Acad Dermatol, 2012. 67(4): p. e143-4.

35. Willershausen, I., et al., In vitro study on dental erosion caused by different vinegar varieties using an electron microprobe. Clin Lab, 2014. 60(5): p. 783-90.

36. Mota, A.C., et al., Antifungal Activity of Apple Cider Vinegar on Candida Species Involved in Denture Stomatitis. J Prosthodont, 2015. 24(4): p. 296-302.

37. Entani, E., et al., Antibacterial action of vinegar against food-borne pathogenic bacteria including Escherichia coli O157:H7. J Food Prot, 1998. 61(8): p. 953-9.

38. Rutala, W.A., et al., Antimicrobial activity of home disinfectants and natural products against potential human pathogens. Infect Control Hosp Epidemiol, 2000. 21(1): p. 33-8.

39. Pourmozaffar, S., A. Hajimoradloo, and H.K. Miandare, Dietary effect of apple cider vinegar and propionic acid on immune related transcriptional responses and growth performance in white shrimp, Litopenaeus vannamei. Fish Shellfish Immunol, 2017. 60: p. 65-71.

40. Hill, L.L., et al., Esophageal injury by apple cider vinegar tablets and subsequent evaluation of products. J Am Diet Assoc, 2005. 105(7): p. 1141-4.

41. Armentia, A., et al., Vinegar decreases allergenic response in lentil and egg food allergy. Allergol Immunopathol (Madr), 2010. 38(2): p. 74-7.

42. Shen, F., et al., Vinegar Treatment Prevents the Development of Murine Experimental Colitis via Inhibition of Inflammation and Apoptosis. J Agric Food Chem, 2016. 64(5): p. 1111-21.

43. Wu, D., et al., Intake of vinegar beverage is associated with restoration of ovulatory function in women with polycystic ovary syndrome. Tohoku J Exp Med, 2013. 230(1): p. 17-23.

44. Klein, A.V. and H. Kiat, Detox diets for toxin elimination and weight management: a critical review of the evidence. J Hum Nutr Diet, 2015. 28(6): p. 675-86.

45. Bonjour, J.P., Nutritional disturbance in acid-base balance and osteoporosis: a hypothesis that disregards the essential homeostatic role of the kidney. Br J Nutr, 2013. 110(7): p. 1168-77.

46. Kohn, J.B., Is vinegar an effective treatment for glycemic control or weight loss? J Acad Nutr Diet, 2015. 115(7): p. 1188.

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The Truth About High Fat Diets Sun, 16 Apr 2017 15:27:05 +0000 The Truth About High Fat Diets For some strange reason there has been a pendulum swing from low fat to high fat diets over the last decade. As high fat diets have become popular, there have been many claims made about the use of high fat diets and why they might be the best tool[...]

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The Truth About High Fat Diets

For some strange reason there has been a pendulum swing from low fat to high fat diets over the last decade. As high fat diets have become popular, there have been many claims made about the use of high fat diets and why they might be the best tool for fat loss. Let me enumerate them here in a short list:

  1. Eating fat makes you burn fat
  2. High fat diets make you burn more calories
  3. A ketogenic state makes you burn the most fat and offers a metabolic advantage
  4. Eating more fat makes your lose more fat since you are using fat for fuel
  5. Carbohydrates are stored more easily than fat.
  6. You eat less when you eat high fat meals
  7. Fat is more satiating than carbohydrates
  8. Eating fat before a meal makes you eat less.

Now as science is a methodology to test hypotheses, and all of these are hypotheses and they have been tested, the data is out there. Let us see what we can learn from the data.

Does Eating Fat Make You Oxidize More Fat for ATP

The short answer is yes, your body adapts quite quickly and robustly to the types of foods you consume. If you eat a low-carb, high-fat diet your body will start using more fat for fuel (1,2,3,4). If you eat a low-fat, high-carb diet, your body will start using more carbohydrates for fuel. This is incredibly well documented in the literature.  Here are just a few data points and studies showing this is true (1).

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RC= Restricted Carbohydrate; RF=Restricted Fat

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RC= Restricted Carbohydrate; RF=Restricted Fat

Here is the other interesting thing, it appears that it isn’t the “high fat” that makes you burn more fat, it is the lack of carbohydrates. When you consume high fat and high carbs you don’t push the RER as much as if you just restrict carbohydrates. You also see the same effect if you eat low carb and low fat or if you are fasting (5,6,7). Carbohydrates really appear to be the main “fuel selector” (more on these details in a later article). This really should fundamentally change how you view high fat diets and their mechanisms.

As you consume less carbohydrates and more fat your body WILL start oxidizing more fat for fuel. That is really incontrovertible. However, we need to be very smart with how we interpret this. Using more fat for fuel does not equal more weight loss. This has also been demonstrated in several studies, including the following.

Clearly, more fat oxidation does not equal more weight/fat loss.  More on this topic to follow.

Does Eating Fat Make You Use More Energy

It has been claimed that eating fat makes your body expend more calories due to something about “efficiency” or some weird perturbation of biochemistry. Honestly, these claims never made any sense to me from a basic physics and chemistry stand point  and the fact if you wanted to lose weight you would want to be less “efficient”, but I will entertain the idea nonetheless and see what the data says.

To test this we simply have to look at studies that compare energy expenditure between high-fat and low-fat diets (1,8,9). These have been done dozens of times and there is plenty of data to pour through. Here is a subset of those data.

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The data are pretty clear at this point that consuming a higher percentage of your overall food intake from dietary fat does not convey any magical metabolic effects that increase overall energy expenditure.   Here are some data to demonstrate the effect of increasing fat intake as a percentage of diet and showing the effect on energy expenditure: nada (10).

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Does Ketosis Offer a Metabolic Advantage?

Once we see the data that a high fat diet does not offer any metabolic advantage the next logical question is, does pushing that to the extreme and entering a state of ketosis offer any advantage? If there were any dietary perturbation that one would think would offer a metabolic advantage it would be a ketogenic state as it essentially requires an additional “step” in human metabolism.

The data exists for this question and the answer is quite compellingly no (8,11). In several different populations studied in very rigorous study designs, there is no metabolic advantage one can observe. Here is some data from Kevin Hall’s study showing a ketogenic state does not offer a metabolic advantage nor a benefit to fat loss in a calorie controlled, protein clamped state, even when ketones are elevated.

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Now it IS possible that this effect is so tiny we can’t see it, but then what would be the point, pragmatically speaking.

Does Eating More Fat Result in A Greater Loss of Fat?

The data above show that eating fat does make you oxidize more fat, but does that actually translate into greater overall fat loss since it doesn’t change total energy expenditure?

Well there is a plethora of data to tease this out. Mechanistic studies show us that high fat diets do not result in more fat loss.

No mechanistic evidence exists to support this idea (See the two studies shown above). The case is pretty much closed on that front.

Now that doesn’t mean in the real world high fat Diets might have some other magical property that results in more weight loss and fat loss. This has also been tested, ad naseum in trials that look at the diets in more real-world settings among a diverse populations. I mean seriously, here are just a few studies, along with their data (12, 13, 14, 15).    

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Here is a meta-analysis that we discussed in an earlier post (15). 

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A roughly 2 pound benefit over 12 months, that is almost meaningless when we are talking about the goal of diets for weight loss, especially when you hold the data juxtapose to data showing adherence effects weight loss at much more drastic scales (pun intended). Below you can see how high adherence results in 10-20 pounds more lost over 12 months, where as diet composition had zero effect on weight loss.

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The data is just crystal clear, there is no meaningful benefit to high fat diets for fat loss. We can probably stop spending our dwindling science dollars funding them**.

Storing Fat From Carbohydrate in Humans

The claim is often made that when consumed in excess carbohydrates convert to fat and are stored through De Novo Lipogenesis. Yes, that is a big, cool, fancy sounding word, but it doesn’t really mean all that much in humans. A lot of people cite rodent studies showing that excessive carbohydrate intake results in the creation of lots of fat. Rodents have VERY different liver metabolism than humans and the human capacity for De Novo Lipogensis is a fraction of rodents.

In fact, one of my favorite papers on the subject is titled, “No common energy currency: de novo lipogenesis as the road less traveled”. In this seminal editorial, Hellerstein opens with the lines, “Bees make wax (lipid) from honey (carbohydrate). Pigs fatten on a grain diet. Indeed, all organisms, from bacteria to mammals, have the enzymes of de novo lipogenesis. The physiologic function of de novo lipogenesis has therefore seemed obvious to biochemists: the de novo lipogenesis pathway links carbohydrates and fats, the 2 most important forms of chemical energy for most organisms.

Because storage of energy as lipid is much more efficient than storage as carbohydrate, the presumption has been that animals use de novo lipogenesis as a metabolic safety valve for storage of carbohydrate energy present in excess of carbohydrate oxidative needs (ie, carbohydrate energy surplus). On the basis of this presumed role, inhibitors of de novo lipogenesis [such as (–)hydroxycitrate, an inhibitor of ATP citrate (pro-S)-lyase] have received attention as potential therapeutic agents for obesity and hyperlipidemia.

Most experimental data in humans, however, contradict this view of the function of de novo lipogenesis. Initial studies in which indirect calorimetry was used showed little or no net de novo lipogenesis after short-term carbohydrate overfeeding (16). Subsequent isotopic studies confirmed the absence of quantitatively significant flux through hepatic de novo lipogenesis under most conditions of carbohydrate energy surplus (17,18).”

Additionally, in that same editorial he contextualizes one of the best carbohydrate overfeeding studies where they examined how much fat is “created” from consuming large amounts of carbohydrate, “Additionally, McDevitt et al  (19) report that, in all settings, the total de novo lipogenesis flux represented a small fraction of both the surplus carbohydrate energy ingested and the total fat stored in the body. The authors calculated that between 3 and 8 g fat/d was produced through de novo lipogenesis compared with 360–390 g carbohydrate ingested/d and 60–75 g body fat stored/d. Thus, the addition of excess carbohydrate energy to a mixed diet so that total energy intake exceeded total energy expenditure (TEE) increased body fat stores, but not by conversion of the carbohydrate to fat. Instead, the oxidation of dietary fat was suppressed and fat storage thereby increased.”

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Do you see those TINY little hashed areas at the very top of the bars? That is the amount that de novo lipogenesis contributes to to fat balance in a hypercaloric state.

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When it comes to de novo lipogenesis in humans the data tells us it is largely much ado about nothing. Here are some data showing de novo lipogenesis under eucaloric and hyper caloric settings in lean and obese people. However even despite the higher rates of conversion seen in both the McDevitt study above, and this data, it is abundantly clear that that fat is then oxidize and only a TINY percent contributes to overall energy balance.

Storage “Efficiency”

Another argument about high fat diets is that fat is burned when consumed so it is not stored as efficiently as carbohydrate. This claim also makes zero sense when you think about it. Dietary fat, when consumed, is either utilized for energy or stored directly in its “native” state as a fatty acid molecule. Dietary carbohydrates are either utilized for fuel, stored as muscle glycogen, or converted into fatty acids and then stored. There are more “checkpoints” and “conversions” that must occur, and each biochemical conversion requires energy to do so. In theory, storing carbohydrates should be less efficient than storing fat.  Here are the relevant biochemistry pathways just to show your the giant cluster that is DNL.*

Image from Jeong et al (

Image from Jeong et al

This is exactly what the data tells us (Table taken from 10, adapted from Blaxter 1989).

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Dietary fat is stored as body fat with roughly 96% efficiency. Dietary carbohydrate is stored as body fat with roughly 80% efficiency, while it is stored as muscle glycogen with roughly 95% efficiency. It is quite clear that dietary fat is stored far more efficiently as body fat than carbohydrate.

We can say with about 95.8% confidence that diets high in fat intake are more efficient at storing food calories as fat than diets high in carbohydrate intake.

Do You Consume Less Food When Eating a High Fat Diet?

In studies that do not strictly clamp calories you see a dose dependent relationship between the percentage of dietary fat intake and overall caloric intake. Essentially, as you increase the percentage of your diet from dietary fat your total calorie intake goes up, not down. This is opposite of the hypothesis that high fat diets are naturally more satiating and you eat less without counting calories (Figures taken from 10, adapted from Lissner et al. 1987 and Stubbs and Prentice, 1993; Stubbs et al., 1995a, b). 

Screen Shot 2017-04-16 at 8.14.30 AMScreen Shot 2017-04-16 at 8.16.09 AM

In some really cool studies where they “covertly” changed the percentage of dietary fat intake in free living situations and in metabolic ward studies this also holds true, meaning it isn’t some purely  “psychological” effect of knowing you have higher fat content in your food. In fact, when people consumed 20 or 40% of their daily energy from fat they actually consumed less calorie per day in free living situations but consumed more calories once they hit the 60% mark. 

Now there is some data to show the opposite, but I think the cumulative data show it isn’t as simple as, “high fat diets make you eat less”.

Does Eating Fat Before a Meal Make You Eat Less?

When I was younger Men’s Health was my bible. Not kidding, that was what got me into the whole nutrition game in the first place. I’ve even emailed their editors trying to convince them to let me write for them, still no luck (MH editors, if you read this and are feeling charitable email me Seriously, one of my life long dreams is to see an article of mine in there. Make a brother’s dream come true!).  I remember very vividly an article, well one of their little side bar articles, stating that eating a serving of fat 15 minutes before a meal can make you feel full and eat less during the meal so you can lose more weight.  You know I tried that little trick. I don’t remember exactly what happened that night but I still have the idea seared into my mind. This is something that has been a cornerstone of nutrition circles for decades, but is it true?

There are several studies where they give people dietary fat or dietary carbohydrates prior to a meal and see if this makes them eat less food at that meal and throughout the rest of the day. When you look at that data (see below) it doesn’t appear that fat intake prior to a meal lowers the intake at that meal and may actual contribute to higher calorie intake the rest of the day (sorry Men’s Health, we might need to update that one).

Screen Shot 2017-04-16 at 8.06.36 AM Screen Shot 2017-04-16 at 8.06.20 AM

Is Fat more Satiating Than Carbohydrate?

I saved the best for last!

If I had a nickel for every time I heard someone say that fat is more satiating that carbohydrate I would be building an Iron Man suit with my billions (what else do you do with a billion dollars?).  Now, to be fair I have also uttered this a lot in my life, for years it was dogma, a known fact. Fortunately there is data we can use to change our minds on topics.

To start, when you look at the satiety index fat intake does not appear to be positively correlated with satiety, it is actually negatively correlated as assessed by Holt et al. in 1992 (20). Opposite of what most people might thing, the humble white potato, which has virtually no fat and is all starch, has the greatest “satiety score”. This also matches well with much of the high carbohydrate vs high fat meal data shown throughout this article. 

Screen Shot 2017-04-08 at 5.23.23 PM

Another study actually examined the individual macronutrients and it appears that fat may actually be the least satiating macronutrient in terms of its impact on satiety over the course of roughly 3 hours.

Screen Shot 2017-04-08 at 5.05.17 PM

The Wrap Up

So let us sum up those initial statements and evaluate their “trueness” and “falseness”

  1. Eating fat makes you burn fat: True
  2. High fat diets make you burn more calories: False
  3. A ketogenic state makes you burn the most fat and offers a metabolic advantage: False
  4. Eating more fat makes your lose more fat since you are using fat for fuel: False
  5. Carbohydrates are stored more easily than fat: False
  6. You eat less when you eat high fat meals: False
  7. Fat is more satiating than carbohydrates: False
  8. Eating fat before a meal makes you eat less: False

So basically, eating high fat diets make you use more fat for fuel …. that is about it.***

*I started to do the Free Energy equations to show the energy costs associated but had nightmares about undergrad chemistry and drank a nice glass of Cabernet Franc while finishing this article instead. Gosh being done with school has its perks, except bills, those suck.

** My lab is taking donations though, kidney disease is kinda a big deal and more money helps us do better science. Seriously, kidney disease is probably one of the worst results of diabetes there is.

*** There are other reasons why one might follow a high fat diet but they aren’t any of the reasons listed above.


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Applying Science and Evidence to Dieting Tue, 28 Mar 2017 15:11:15 +0000 Over the course of my scientific career I have spent over a decade in training (undergrad, masters, doctoral, and now fellowship) and 90% of that training is on how to think and answer questions, the other 10% is content area expertise. For the last several decades I think most of the nutrition world has lost[...]

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Title slide

Over the course of my scientific career I have spent over a decade in training (undergrad, masters, doctoral, and now fellowship) and 90% of that training is on how to think and answer questions, the other 10% is content area expertise.

For the last several decades I think most of the nutrition world has lost the forrest for the trees and spent entirely too much time focusing on content, and not how to think about dieting and what REALLY are the key things we need to know.  I want to try and rectify that today.


My training over the course of my scientific career has given me a unique perspective. I have been trained across the translational spectrum; last week in the span of 24 hours I cultured cells, inject animals, talked to a patient, and crunched clinical trial data. This training has taught me a lot about how to think about large problems and how to tie evidence through three key layers into coherent ideas that we can then translate into application for people.

These three layers of evidence are: observational, mechanistic, and intervention.


 Observational studies are simply those that take a large group of people and collect data and attempt to find things are are associated or correlated with one another. For example, in a population of people, does a higher intake of Food X correlate positively with body weight (Figure 1).


Figure 1

Observational studies are often cross-sectional in nature, only showing data from a “snap shot” in time. They can also be longitudinal in nature, wherein you track several data points overtime and follow someone over longer periods of time (Figure 2). Regardless of whether data is cross-sectional or longitudinal, you still have a very substantial limitation of this study design: correlation does not equal causation. The best this type of study can do is find correlation and generate hypotheses about mechanisms.

Figure 2

Figure 2

Correlations are spurious and random things can be correlated. In the example below, it is quite clear that the correlation is present, but there is no mechanism to even begin to support cause and effect (Figure 3).

Figure 3

Figure 3

They are singular snapshots in time, or they look backward, or sometimes even forward. Regardless of their temporal characteristics, they all suffer from the inability to determine causality.        


Mechanistic studies are highly controlled studies designed to elucidate how a phenomena works. For example, if we observe that a lot of people who are consuming a low-carb diet are losing weight, how exactly is that working? Is it due to lower levels of insulin? Is it due to magical properties of eating higher fat by percentage? Is it a magical creature that lives in carbs that prevent you from losing weight? Or is it simply calories?

These studies often employ models (e.g. animal models) and create somewhat artificial scenarios in order to control every variable possible to drill down on that specific mechanism and reduce the noise as much as possible.

A mechanistic study might be something like genetically removing the gene for the protein leptin in a mouse and then examining what the effect on carbohydrate metabolism in skeletal muscle. From this type of experiment you could learn something very fundamental about the role of leptin in muscle metabolism but it would have substantial limitations (e.g. it is not done in humans and doesn’t tell you anything about gradations in leptin levels and how those effect carbohydrate metabolism. This only examines a binary effect.

Another example would be a study like Dr. Kevin Hall’s recent metabolic ward study wherein they took humans, confined them to a hospital and controlled their food intake, exercise, and monitored the rest of their lives (Figure 4). The study compared low-fat versus low-carb ketogenic diets and asked whether the lower insulin response from a low-carb diet increased fat oxidation, weight loss, and fat loss (we will cover this in detail below). 

Figure 4

Figure 4


 Intervention studies are those that directly test a hypothesis in question through isolating and directly manipulating a variable as much as possible in order to achieve a specific effect. An intervention study looks something like this. Take a large group of people, randomize them to intervention X and intervention Y and see which one lowers the risk of developing a disease or which one is the best diet for weight-loss (Figure 5).

Figure 5

Figure 5

These studies can be very mechanistic in nature but are often not. Additionally, the primary outcome is different  than mechanistic studies. The goal for these studies is often to assess whether the intervention improves (or worsens) a specific outcome (e.g. weight loss, heart disease, cancer risk, etc.), not designed to determine exactly how a treatment/intervention works (e.g the PCSK9i inhibitor works via reducing LDL receptor degradation).

These studies also have limitations in that they often are done in small samples (but not always), lack complete control over variables, and are sometimes limited in the demographic of their population.            

            Bonus Step: Meta-Analysis

 In addition to the three layers of evidence discussed, we also have a tool at our disposal called meta-analysis. A meta-analysis is essentially a computational tool to “synthesize” the literature. More formally,

             “Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis. The examination of variability or heterogeneity in study results is also a critical outcome. The benefits of meta-analysis include a consolidated and quantitative review of a large, and often complex, sometimes apparently conflicting, body of literature. The specification of the outcome and hypotheses that are tested is critical to the conduct of meta-analyses, as is a sensitive literature search. A failure to identify the majority of existing studies can lead to erroneous conclusions; however, there are methods of examining data to identify the potential for studies to be missing; for example, by the use of funnel plots. Rigorously conducted meta-analyses are useful tools in evidence-based medicine. The need to integrate findings from many studies ensures that meta-analytic research is desirable and the large body of research now generated makes the conduct of this research feasible” (1).

Up to this moment, this article has been theory driven and fairly esoteric. Lets use these principles to work through a popular dietary intervention and see what happens when we evaluate it across the levels of evidence.

 Low Carb and Ketogenic Diets as a Use Case

             The Underlying Hypothesis of Low Carb, Ketogenic Diets

 A scientific approach to answer a questions begins with a hypothesis. We have covered the underlying hypothesis of the low-carb or ketogenic diet being a superior diet for fat loss ad-naseum (here, and here). The point of this article is about “how to think about diets and fat loss” so much of this article is simply about how to test hypothesis. Thus I will just lay out the hypothesis and then step through it, each “data level” at a time.

The Hypothesis for Low-Carb, Ketogenic Diets: The accumulation of fat mass is a result of dietary carbohydrates leading to elevated insulin levels which shifts metabolism into fat storage and away from fat oxidation.

Fortunately for us, science is a set of tools designed specifically to test hypotheses.  If the hypothesis were true we would see the following things:

  • Observational Data: High carbohydrate intakes as a percentage of diet would be associated with higher body weight or body fatness.
  • Mechanistic Data: In highly controlled experimental studies, carbohydrate restriction should result in more fat loss than fat restriction
  • Interventional Data: In well-controlled experiments in fairly, free living humans, low-carbohydrate diets should be far more effective than higher-carbohydrate diets.

The evidence at all three levels exists, so let us walk through it.

            The Observational Data

 A simple test of the hypothesis at an observational level would be to sample a large group of people and look at whether higher amounts of dietary carbohydrate intake as a percentage of food intake is associated with higher body weight or body fat. In short, if this hypothesis is correct we should see the red line, if incorrect we should see the blue line.

Figure 6

Figure 6

These type of studies have been done in a wide range of populations and the data is available (2, 3, 4). When we look across the studies done in this area it is the blue line that dominates the results. So the observational data to support the hypothesis is quite scant.

             The Mechanistic Data

 When we test the hypothesis that carbohydrates are a main regulator of fat mass accumulation in a mechanistic manner, we would design highly controlled experimental studies that show that carbohydrate restriction should lower insulin levels, increase fat oxidation, and result in more fat loss than fat restriction. Fortunately for us, this work has already been done.

We have covered these studies previously here on Science Driven Nutrition so I will just reiterate it.


 The first study was essentially a cross-over design where people were fed either a restrictive carbohydrate diet or a restrictive fat diet for 6 days after 5 days on a  baseline diet (5).

To summarize the findings from this study, they found that decreasing carbohydrate intake did lead to increased fatty acid oxidation and decreased carbohydrate oxidation; however, reducing fat intake led to a slightly bigger loss in body fat than the group that restricted carbohydrates (Figure 7).

Figure 7

Figure 7

The other important finding from this study was that an increase in fatty acid oxidation (one of the measurements that is considered a benefit of the ketogenic diet) doesn’t necessarily result in more fat loss, in fact it may be the opposite (this remains to be elucidated) (figure 8).


Figure 8

Figure 8


 This study is one of the most tightly controlled and well-designed dietary studies done to date (6). Metabolic wards, metabolic chambers, DEXA, doubly labeled water, multisite, mutli-investigator, and well done statistics . . . this is the Cadillac of dietary studies.

Briefly, “Seventeen overweight or obese men were admitted to metabolic wards, where they consumed a high-carbohydrate baseline diet (BD) for 4 wk followed by 4 wk of an isocaloric KD with clamped protein”.

In this study the subjects lost about 0.8 kg of body weight (with 0.5 kg from body fat) during the 15 days of the high carbohydrate baseline diet. The ketogenic diet led to a rapid weight loss of 1.6 kg due to water weight loss as only 0.2 kg of body fat were lost during the 15 day ketogenic diet) (Figure 9).

Figure 9

Figure 9

So during this study the participants lost about 1 pound of body fat during the 15 days on the higher carbohydrate diet while they only lost about ½ pound on the ketogenic diet. This all occurred despite lower insulin levels and higher ketone bodies present during the duration of the ketogenic diet phase. This suggests that the ketogenic diet does not offer a metabolic advantage for weight loss during this time frame either.

This study also showed a very rapid “adaptation” wherein insulin secretion dropped, fatty acid and ketone levels rose, and fat oxidation increased substantially.  This piece of data is incredibly important as it CLEARLY demonstrates a swift, and robust “adaptation” to a ketogenic diet.

Figure 10

Figure 10

The mechanistic data to support the hypothesis is not even present.

             The Intervention Data

                         Study 1

 The first study we will cover is, “Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial” (7). Essentially, this study took 160 people and randomize them to either an Atkins Diet (Low-Carb) an Ornish Diet (Low-Fat Vegan Diet), a Weight Watchers Diet (IIFYM of the 90s), and a Zone Diet (Macronutrient Balanced Diet). They followed these people for a year and here is what the data shows (Figure 11).

Figure 10

Figure 11

In the left figure, they show that each diet had very similar weight loss profiles and that there was no difference in weight loss between diets. In the right figure they show that adherence is what determined weight loss more so than any specific diet when it comes to weight loss.

                        Study 2

 The second study, “Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial” is virtually identical in design, but consisted of overweight menopausal women (8).  In this study, they took approximately 76-79 people and randomized them to either an  Atkins Diet (Low-Carb) an Ornish Diet (Low-Fat Vegan Diet), a LEARN Diet (IIFYM of the 90s), and a Zone Diet (Macronutrient Balanced Diet) (Figure 12).

Figure 12

Figure 12

This study also demonstrated that each diet had very similar weight loss profiles and that there was no difference in weight loss between diets. They also demonstrated that adherence was substantially more important for weight loss than which diet people we on.

                         Study 3

 The third study, “Comparative study of the effects of a 1-year dietary intervention of a low-carbohydrate diet versus a low-fat diet on weight and glycemic control in type 2 diabetes”, examined the effects of a low-carb versus and low-fat diet in people with type 2 diabetes (9).

In this study they demonstrated that a low-carb and a low-fat diet had virtually identical results on changes in HbA1c, weight, and blood pressure. This effectively demonstrates that if there is a benefit to low-carb diets, the benefit is likely to be small and that weight loss still determines most of the health benefits of dieting in people with type 2 diabetes (Figure 13).

Figure 13

Figure 13


 We could continue ad nasuem with the data, or we can simply use a powerful tool called a meta-analysis where we pool all the data from these types of studies together. When you compile the intervention based research into a meta-analysis for the comparison of Low-carb ketogenic diets to Low fat diets you see this picture emerge (Figure 14) (10).

Figure 14

Figure 14

This picture tells us three critical pieces of information: 1) There is high heterogeneity between the results, 2) there appears to be a slight benefit of about 2 pounds of a ketogenic diet over a low fat diet over a 12 month time frame, 3) This is regardless of adherence.

This lends us to several critical observations. The clear, repeatable data from the intervention studies that showed the variable that most explained meaningful superiority for weight loss was adherence (e.g. a ~15 pound difference between low and high adherers over 12 months). The superiority of low-carb versus low fat is ~2 pounds over 12 months. The juxtaposition of those two data points should show you what “battle” you should really be fighting.

The cumulative data from myriad intervention trials also do not support the hypothesis.

 What Do We Know?

 What does all this data from low-carb versus low-fat studies tell us? It tells us that calorie  balance is more important than a specific diet, and that adherence matters more than any macronutrient feature of any diet.

 This means that adherence needs to be the most important factor you consider when determining a diet for yourself or a client and that diets are tools, they are not the one size fits all answer to all your problems like your Swiss army knife was when you were 11.


P.S. This article is based on a lecture I gave at the Inland Empire Fitness Conference.

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3 Things to Look for in a Nutrition Coach Wed, 04 Jan 2017 21:08:30 +0000 3 Things to Look for in a Nutrition Coach By: Brad Dieter, PhD Read Time: 136 seconds Hiring a nutrition coach is an investment, financially and emotionally.  In an era where nutrition coaches are popping up left and right and abs and good butt are more likely get you followers on Instagram than solid content and brain[...]

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3 Things to Look for in a Nutrition Coach

By: Brad Dieter, PhD

Read Time: 136 seconds

Hiring a nutrition coach is an investment, financially and emotionally.  In an era where nutrition coaches are popping up left and right and abs and good butt are more likely get you followers on Instagram than solid content and brain power, navigating the waters in picking a coach can be difficult. Here are 3 quick things to consider when you look for a coach.

1) Find someone who cares about you. 

99% of nutrition coaching is connecting with your clients, finding out who they are, and what makes them tick. 1% is dialing in the perfect macros and meal plan.  For this reason it is imperative that you find a coach who actually cares about you are a person and your success.

Meaningful, long term success often requires personal changes. This is rarely achieved by simply handing someone macros, a meal plan, and an invoice. The coach-client/athlete relationship is one of the most important aspect of coaching. Find someone you can connect with on that level.

2) Look for Education AND Experience

Education and letters behind a coaches name are important. They mean that they care enough to go to school and to work hard at the intellectual side of their craft. This should not be taken lightly.  Education and letters behind a name also don’t necessarily mean they are going to be an excellent coach who gets results. Usually advanced degrees are research related degrees. While research is useful it doesn’t guarantee results.

Experience is invaluable. Decades of coaching not only teaches you personal skills and how to relate and understand clients, it also gives you exposure to a wide range of clients and situations. As a beginning coach you have likely only encountered a few types of clients and have to really troubleshoot your way through a lot of things. As an experience coach you can reflect upon those earlier clients and can pinpoint answers faster. The education piece helps coaches temper their experience and continue to question and learn. However, if I had to weigh these things, I would probably weight experience over education.

3) Value a Bricolage Approach

Not every client is a nail, so a coach should not be a hammer. Part of coaching is developing a toolbox of skills you can use to apply to each individual client. If a coach states they are, “a keto coach” or “a paleo coach” you should inquire why they only use one tool to get results. Many different dietary tools can be used to achieve results in a context dependent manner. I prefer to adapt main principles to each client instead of prescribing my own personal biases or proclivities.

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2016: The Year in Review Fri, 30 Dec 2016 18:49:26 +0000 I refuse to let this be the typical, boring, stock “Year in Review” where I tell you how wonderful we are and which articles were our “biggest hits” hoping you click on them. Hopefully I deliver on that refusal. This is raw, true, completely written off the cuff and unedited, as in I am writing[...]

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I refuse to let this be the typical, boring, stock “Year in Review” where I tell you how wonderful we are and which articles were our “biggest hits” hoping you click on them. Hopefully I deliver on that refusal.

This is raw, true, completely written off the cuff and unedited, as in I am writing this in one sitting, working forward and not rereading it (maybe this will be my new style in 2017). Without further ado here is the list of things I have learned in 2017. Warning:this may not even be nutrition related (we don’t know right now since I am writing this unidirectionally).

  1. Writing is hard. Writing a lot shows you how much you suck at writing.

Over the last 12 months we have published 35 blog articles, 8 magazines, written a book, and a few guides just on this website. I am entirely too busy (read: I am too lazy) to count all the words in this content but I think its somewhere around 100,000 words of free content just on this blog. For all the other projects I have worked on that involve writing (included peer-reviewed papers), I estimated my word count to be in the neighborhood of 250,000 words. That makes my fingers hurt just thinking about it.

Each of those pieces took hours of thinking about and hours of writing, deleting, rewriting, and editing.  I have learned a lot about writing, attention to detail, and how horrid my writing sometimes can be. I went back and read some of the old stuff and man, it needs some serious work. Now instead of going back and polishing it all up I am actually going to leave it so I can go back and laugh at how much I sucked. This is my nature. Don’t cover up the blemishes just learn from them and grow.

2) Success isn’t built overnight. It also shouldn’t be. 

To be completely honest I started this website with the hope it would blow up overnight, that people would love what we do and that they would tell all their friends. Then magically,  money would rain from the sky. With said money I would be able to be totally cliche and post pictures of myself working on a beach with my laptop (do these people not worry about sand in their computers?!) and sell you a course on how to build an online business for a convenient price of 2997. Apparently 7s are magical marketing?????????? Oddly enough, none of these things happened.

Image result for work from a beach entrepreneur life

This is not me. My hair has some grey in it (recent development) and I would have a beer in my hand, not a laptop.

Here is what really happened. I worked my face off writing free content and trying to bring value to the world. Growth in readership was slow, about 1/20th of what I hoped, we had about 250,000 visitors this year to the website.  I also haven’t quite figured out how to make any meaningful money from it. So a 40 hour blog post that nets me 0 dollars is probably the worst investment of my time. That being said, that post might benefit the lives of 5-10 people. This is something I need to figure out. I refuse to charge people for crappy products so it may be a bit before I figure this out. I will keep charging ahead.

3) Science is hard, fickle, and teaches you a lot about life. 

Science really is a brutal field, no sugar coating it.

Don’t quit. Experiments fail, a lot. I literally spent 6 months trying to figure out some small detail of an experiment. Tenacity, persistence, and being dumb enough to just keep impaling yourself on the same problem are good traits to have.

Life is about resiliency. This year I spent 6 months writing a grant that was rejected. This rejections mirrors most of my romantic attempts in high school; this is fine, I won the significant other lottery this year. Rejections and failures are inevitable, you just have to bounce back.

Sometimes you just aren’t good enough. In said grant above I finished in the top 10%, but still didn’t get it.  Now I could whine and complain about this and say how it was rigged against me or I could just acknowledge I wasn’t good enough. The cold, hard truth was that I just wasn’t good enough. This is OK. I can handle that. Time to get better. As such, participation trophies are bullshit.

4) That is all I got

The list is long enough to teach you something and give you some insight into what 2016 was, but short enough to keep your ever dwindling attention span. 2017 shall be quite the show. I hope you bought your tickets


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The Gluten Manifesto: Everything You Need to Know About Gluten Thu, 29 Dec 2016 22:21:19 +0000 By: Brad Dieter, PhD and Sergio Fontinhas, CISSN Read Time: 2 cups of Coffee Tl;dr: The rapid rise in people following gluten free diets doesn’t match the data showing prevalence of Celiac Disease. Gluten is metabolized by your gut bacteria and the type and amount of bacteria may play a big role in whether your experience symptoms related[...]

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By: Brad Dieter, PhD and Sergio Fontinhas, CISSN

Read Time: 2 cups of Coffee

Tl;dr: The rapid rise in people following gluten free diets doesn’t match the data showing prevalence of Celiac Disease. Gluten is metabolized by your gut bacteria and the type and amount of bacteria may play a big role in whether your experience symptoms related to gluten intake.



The singular goal of this article is to flesh out the role of gluten in your diet by walking you through the scope of the issue, the biological data and plausible mechanisms, and then help you fully think through the pragmatic aspects of gluten-free diets.

Part 1: About Gluten

The Scope of the Issue

There has been a substantial increase in the interest of gluten and the application of gluten free diets. As you will see below, much of this interest can be attributed to the popularization of diets like the Paleo Diet and popular press books like Wheat Belly than by some meteoric rise in celiac disease (CD).gluten-2

As disease prevalence goes CD is fairly common; it effects between 0.5%-1.0% of the entire human population, with about 1 in 133 Americans suffering from the disease. This accounts for roughly 2-3 million people in the United States. In addition to those suffering directly from CD, there is another class of people who do not have CD react to the ingestion of gluten, this is known as non-celiac gluten sensitivity (NCGS). From most reports in the literature, between 5-9% of people without CD report adverse reactions to gluten*; the symptoms that people with NSGS report to have occur across such a wide spectrum (foggy mind”, depression, ADHD-like behavior, abdominal pain, bloating, diarrhea, constipation, headaches, bone or joint pain, and chronic fatigue). When take the entire American population, estimated at 318 million people, and we break down the numbers of people with and without CD, and those with and without adverse reactions to the gluten protein, it is clearly easy to see the actual numbers of people who may truly require a gluten free diet (Table 1).


Table 1

When you hold these numbers juxtapose to the explosion of the interest in gluten and gluten free diets since 2009 (See Figure 1) it is clear that this rise in interest is better explained by the popularization of diets like the Paleo Diet and popular press books like Wheat Belly than by some meteoric rise in celiac disease (CD). There is clearly a dichotomy between the interest and use of gluten-free diets and the medical necessity of them; this is most likely due to the lack of understanding of exactly what gluten is, how it is involved in weight gain and disease, and a combination of people cashing in on a fad and people honestly not really knowing what they are talking about.  The topic is quite complex and requires sorting through some things in detail. At the end of this article we should be able to answer the following questions: how big of a deal is gluten and should you be on a gluten free diet?

What is Celiac Disease and Non-Celiac Gluten Sensitivity

Celiac disease (CD) is a life-long gluten-sensitive autoimmune disease of the small intestine and CD diagnosis is based on presence of predisposing genetic factor human leukocyte antigen (HLA) DQ2/8, with positive biopsy and serological antibodies upon gluten contained diet (1). CD commonly appears in early childhood, with severe symptoms including chronic diarrhea, abdominal distension, and failure to thrive. In many patients, symptoms may not develop until later in life, when the disease symptoms include fatigue, diarrhea, and weight loss due to malabsorption, anemia, and neurological symptoms (2). Interestingly, CD does not typically manifest at birth and is usually “triggered” by something. The development of CD requires the genetic predisposition (specific HLA haplotype) and an exogenous trigger (sometimes gluten) or even viral infections or other intestinal tissue damage. NCGS presents with similar symptoms to gluten exposure without the presence of the predisposing genetic factor human leukocyte antigen (HLA) DQ2/8 and no serological antibodies upon exposure to a gluten containing diet. A gluten allergy (or wheat allergy) presents differently than both CD and NCGS and is an immune reaction in which the body develops IgE antibodies against gluten or wheat proteins.

What is Gluten?

Gluten is often thought of as a singular protein; however, it is actually a mixture of proteins found wheat and other evolutionarily related grains. It is a composite of prolamins like giadin, glutenin, hordeins, secalins, and avenins. Gluten, as traditionally thought of is a combination of gliadin and glutenin protein. As it is currently understood gliadin appears to the protein most causal in the disease process associated with CD and NCGS.

Part 2: Hard Science

Proposed Mechanisms on non-celiac gluten sensitivity.

CD is relatively straight forward. The body produces an immune response to the ingestion of gluten that increases gut permeability and causes villious atrophy and flattening of the cells that line your intestines. Removal of gluten from the diet usually ameliorates most symptoms.

NCGS is a much more complicated, nuanced issue as there appears to be some merit to the condition. One of the leading and most compelling hypothesis surrounding NCGS is increased intestinal permeability through a gliadin-CXCR3-Zonulin mechanism. Essentially, gliadin, a gluten protein, interacts with the CXCR3 receptor in intestinal cells, releasing a protein called zonulin which increases intestinal permeability. This intestinal permeability is what causes NCGS symptoms.

There are in vitro and animal studies that work out this mechanism in detail (3, 4). In ex-vivo human duodenal biopsies from people with active celiac disease, people with remitted celiac, non-celiac gluten sensitivity, and non-celiac controls, increased intestinal permeability after gliadin exposure occurs in all individuals. Following gliadin exposure, both patients with gluten sensitivity and those with active celiac disease demonstrate a greater increase in intestinal permeability than celiacs in disease remission (5) (Figure 2). This data seems to suggest that when exposed to gliadin, everyone has some increase in intestinal permeability with those with CD and GS showing a greater response than those without CD or in remission from CD. Whether this actually leads to any health outcomes is still largely unknown.


Figure 2

The Culprits

One of the critical aspects of both celiac and NCGS is the antigens/problematic proteins/problematic compounds themselves. There are essentially two major things the research has highlight as culprits: gluten proteins (mainly gliadin) and FODMAPS.

Gluten Proteins

For this article (solely for exploration of an idea) let us grant a major assumption: NCGS is a result of the exposure to undigested gluten proteins to the intestinal tract that results in a pathological response. There is an alternative hypothesis that it is solely FODMAPS, but let us just stick with gluten for now. This assumption is perhaps the most widely accepted hypothesis about NCGS. I would contend that granting this assumption is wildly generous, but for this article I believe we can grant this and still sort through this topic with nuance and highlight key concepts about why I think this may be a piece to the story but perhaps not the whole story.

Gluten proteins, mainly gliadin, are often believe to be “indigestible” due to their proline rich structure. This allows them to be exert their “badness” in the intestinal tract with varying degrees of efficacy based on genetic and other factors. It appears that gluten proteins can be digested in humans; interestingly in both healthy people and those with celiac (6, 7). Now this does not completely exclude the idea that short regions of the protein may not be fully digested and still exert detrimental effects on the human gut. However, it does indicate that gluten proteins are indeed digested.  It may be that the amount to which these proteins are digested and how much makes it to the intestine determines what type of response, if any, people have to gluten. However, as we will see below it may be the actual digestion of gluten that causes a problem.

The Microbiome: The Human-Environment Interface.

            Gluten and the Microbiome

“Approximately 95% of the patients inherit the alleles encoding for the HLA-DQ2 and HLA-DQ8 molecules, but only a small percentage develops CD [8]. Studies of identical twins have also shown that one twin did not develop CD in 25% of the cases studied [9], supporting the role played by environmental factors in the aetiology of this disorder.”

In addition to personal variation of the amount of pepsin and trypsin produced and the digestion of gluten by these enzymes, the microbiome may play a substantial role in the digestion of gluten. Despite the studies cited above, a mixture of undigested proteins and peptides is available for bacterial metabolism in the gastrointestinal tract.  As the microbiome is an integral part of the interface between the inside of our bodies and the environment, aka the human gut, it is likely that the microbiome plays an important role in CD and NCGS. Indeed, There are several studies showing that the human intestine exhibits a large variety of bacteria capable of utilizing gluten proteins and peptides as nutrients, essentially breaking down the proteins to use for their own metabolism (10, 11, 12).

Now it is tempting for one to make the following conclusion: individuals with higher levels of bacteria that fully digest gluten, thus having less gluten based proteins present, will have lower rates of CD and NCGS. This is the natural hypothesis that arises from the data above. Fortunately, science is never straight forward and always more interesting than we initially imagine. In a very interesting experiment a group of scientists examined people with and without CD and showed that bacteria and their enzymes that breakdown gluten were only present in individuals with CD, not in those without CD (13). Thus induction of gliadin proteolysis (gliadin break down) in the human gut might not be the solution, but the origin of CD. One of the proposed mechanisms in this study was that the type of bacteria that feed on gluten attack mucosal linings and degrade the intestinal lumen. As it currently stands both hypothesis seems to present some compelling arguments and we can look at the data from two perspectives:

  • Digestion of gluten by bacteria contributes to celiac disease and NCGS via these bacteria metabolizing gluten and attacking the intestinal mucosa and disrupting the integrity of the gut barrier.
  • Digestion of gluten by bacteria reduces the amount of reactive peptides in the intestinal track and help to ameliorate gliadin induced increases in gut permeability.

Now on the surface these seem mutually exclusive, but they may not be as the type and amount of bacteria in your gut may determine which of these scenarios is occurring. If you are content with this surface level explanation than you can skip to the last section, “How to Think About Gluten”. However, if you want a better, more thorough answer.  A much deeper dive on this topic is needed. While complex, this will definitely square away a lot of the research. So down the rabbit hole we go.

Part 3: Even Harder Science

Down the Digestive Rabbit Hole: A Deep Dive on the Microbiome and Gluten

Mammalian digestive enzymes are only partly capable of cleaving gluten. Fragments of undigested gluten induce toxic responses in celiac patients (14). The major human gastrointestinal proteases, such as pepsin, trypsin, chymotrypsin, carboxypeptidases A and B, elastases and brush-border membrane enzymes of the small intestine are unable to cleave certain immunogenic gluten peptides, due to a lack of post-proline cleavage-site specificity (14). For example, the 33-mer peptide derived from α-gliadins is resistant to human intestinal proteases, and also stimulate CD4+ T-cells in the lamina propria of HLA-DQ2-positive celiac disease (CD) patients (15).

For this reason it is often assumed, and sometimes defended fanatically by certain “groups”, that humans cannot digest gluten, and therefore no single human (with or without CD) should be eating gluten-containing food, such as wheat. For them the story ends here. But there is a lot more to it.

The gastrointestinal microbiome and intestinal dysbiosis in CD

The rapidity of the increase in disease rates could never be solely explained by changes in genetic make-up (16). For example, CD autoimmunity doubled between 1974 thus excluding the genetic component as the cause of this increased prevalence (17). Alterations in gut microbiota can trigger increased immune stimulation, epithelial dysfunction and enhanced mucosal permeability (18,19) and predispose to the development of autoimmune disorders, specially food-related disease (20).

Non-human gluten-degrading proteases are naturally present in the upper human gastro-intestinal tract. These proteases are produced by the wealth of bacteria present in the oral cavity and duodenum (14,21,22). A large variety of bacteria capable of digesting gluten and use it as nutrients has been observed in the human intestine (23). Gluten can also be digested by pepsin and trypsin under physiological condition (24).

Intestinal dysbiosis has been detected in celiac disease patients. This intestinal dysbiosis is characterized by increased Gram-negative bacteria and reduced bifidobacteria (25). Factors such as milk-feeding type, duration of breast-feeding, and gastrointestinal infections, are known to influence the composition of the gut microbiota with possible relevance to celiac disease (26,27,28).

While ingested gluten is a necessary trigger for CD development, gluten reactivity alone cannot explain the absence of disease in most subjects with HLA-DQ2 and −DQ8 phenotypes, suggesting a role for the gastrointestinal microbiome (25). The overgrowth of potentially pathogenic bacteria and infections have been suggested to contribute to CD pathogenesis. In a study in free-germ rats it was apparent that the microbiome composition (potential (E. coli CBL2) and pathogenic (Shigella CBD8) enterobacteria isolated from CD patients) could play a role in the switch from tolerance to an inflammatory immune response to gluten, by altering the permeability of the intestinal mucosa (29). These enterobacteria aggravated the adverse effects of CD triggers and contributed to reducing even more goblet cell numbers and also induced massive mucus secretion.

Another study observed a distinctive ‘microbial signature’ in composition in subjects genetically susceptible to CD (30).The amount and quality of ingested gluten, together with the pattern of infant feeding and the age at which gluten is introduced in the diet might influence the risk of CD occurrence. Infants genetically susceptible to CD who are exposed to gluten early mount an immune response against gluten and develop CD autoimmunity more frequently than at-risk infants in which gluten exposure is delayed until 12 months of age (30). A lack of maturation of the gut microbiota was observed within the first 2 years of life in infants at risk of CD characterized by a relative absence of Bacteroidetes and a parallel high abundance of Firmicutes, a characteristic that remains at 24 months of age (31).

The early introduction of gluten and the lack of maturity in the GI microbiota could trigger or accelerate the development of autoimmunity. Therefore the gut microbiome between these children and children from healthy mothers (32) differ, and the composition is also different than healthy adults (31,33-38). In particular one study observed a significantly higher number of Gram-negative and potentially pro-inflammatory bacteria associated with CD (36), and another study observed increased levels of total SCFA and acetic, valeric, and butyric acids (38). Indeed it was shown that bacterial strains belonging to Bifidobacterium and Bacteroides fragilis are capable of digesting gliadin-derived peptides (39,40).

As mentioned before, emerging evidence indicate that the oral cavity is colonised with microorganisms that produce proteases capable of hydrolysing peptides rich in proline and glutamine residues (14,33,41). Another study described a faecal glutenasic activity related to diet-ingested gluten most likely derived from bacterial metabolism (42). Of 144 bacterial strains that could be involved in the metabolism of gluten proteins in the human gut, 73% belonged to Firmicutes, 15% to Actinobacteria and 12% to Proteobacteria (gram-negative bacteria) (23). Firmicutes were mainly from the genera Lactobacillus, Streptococcus, Staphylococcus and Clostridium. Several Lactobacillus strains have the ability to completely hydrolyse the 33-mer peptide, and it is known that a gluten-free diet consumed by healthy volunteers and patients with coeliac significantly affects lactobacillus populations (43,44).

Four different bacterial strains belonging to Bifidobacterium are able to metabolize gluten (23), and a gluten-free diet also appears to reduce the diversity and amount of Bifidobacterium species (43,44). A specific gliadinase pattern in duodenal samples from patients with CD was once described (45), and Bifidobacterium species, such as B. longum, B. animalis and B. bifidum, of human origin can digest gliadin peptides and inhibit the inflammatory response induced by gliadins (49). B. longum IATA-ES1 is able to hydrolyse the immunogenic peptide 33-mer and modulate an immune response (46).

Generally Lactic acid bacteria (LAB) account for 39% of the strains isolated from human faeces and these bacteria are also able to metabolize gluten (23). Proteases from LAB play an important role in the digestion of not-fully hydrolysed proteins in the human gut and may shorten long- and medium-sized peptides, particularly those peptides derived from dairy proteins (23,47). A higher LAB diversity was found in patients with treated coeliac disease and controls than in patients with active coeliac disease but species that showed significant differences between groups were food-related bacteria (48). Bacteroides diversity was higher in controls than in patients with active and treated coeliac disease, but Bacteroides dorei was more common in patients with active coeliac disease than in those with treated coeliac disease and control children. Conversely Bifidobacterium diversity was higher in patients with coeliac disease than in controls, specificaly Bifidobacterium adolescentis and Bifidobacterium animalis subsp lactis (48). Weissella spp and Lactobacillus fermentum were more frequently in patients with treated coeliac disease than in controls and patients with active coeliac disease.

One important point is that not all the bacteria involved in gluten metabolism are health promoting. Bacterial proteases from Staphylococcus epidermidis, Enterococcus faecalis, Escherichia coli, Clostridium perfringens and C. sordellii, may be related to inflammatory bowel disease (23,49,50).

The bacterial groups related to gluten metabolism that are altered in patients with CD include Bifidobacterium, Lactobacillus, Bacteroides, Staphylococcus, Clostridium and Escherichia coli (25,34,51). Bacteroides (34,52) and Clostridium leptum groups are more abundant in faeces and biopsies of CD patients than in controls regardless of the stage of the disease. E coli and Staphylococcus counts are also higher, but their levels were normalised after treatment with a GFD (33). Bifidobacterium levels are lower in faeces of both groups of CD patients and in biopsies of untreated CD patients, suggesting that either Bifidobacterium could protect against CD, or inherent features of the CD intestine influence Bifidobacterium colonization (3). In another study Clostridium histolyticum, C. lituseburense and Faecalibacterium prausnitzii group proportions were less abundant (51)

Mucosal immune response through IgA secretion constitutes a first line of defence responsible for neutralizing noxious antigens and pathogens. IgA, IgG and IgM-coated faecal bacterial levels were significantly lower in both untreated and treated CD patients than in healthy controls. Gram-positive to Gram-negative bacteria ratio was significantly reduced in both CD patients compared to controls (52). In these patients, reduced IgA-coated bacteria is associated with intestinal dysbiosis suggesting the existence of a barrier defect, which fails to stabilize the gut microbiota and prevent the host from the invasion of harmful antigens and pathogens (51).


Phylum Species Gluten hydrolysis
Firmicutes Enterococcus faecalis ++
Lactobacillus mucosae MCG1,2,3 +
Lactobacillus gasseri MCG3 +/−
Pediococcus acidilactici MCG3 ++
Bacillus licheniformis MCG3 ++
Bacillus subtilis MCG1 ++
Bacillus pumilus MCG1 ++
Paenibacillus jamilae MCG1 ++
Clostridium botulinum/sporogenes MCG2,3 ++
Clostridium perfringens MCG2,3 ++
Clostridium sordellii MCG2 ++
Clostridium butyricum/beijerinckii MCG2 ++
Propionibacterium acnes MCG3 ++
Stenotrophomonas maltophilia MCG1 +

Table 3. Isolated bacteria from human faeces (23).

Gliadin proteolysis

Some authors propose that the induction of gliadin proteolysis in the human gut might not be the solution but the origin of CD, since gliadinases are CD specific and not of leucocyte origin (46). Gliadin-degrading protease (gliadinase) pattern is found in almost all samples from patients with CD regardless of GFD treatment and remains nearly absent in non-CD samples. Gliadinases might have a bacterial origin within the duodenum of patients with CD.

As noted before, Bifidobacterium species can digest gliadin peptides (39). These gliadin-metabolising bacteria may represent one of the environmental missing links in the development of CD, and could be absent, or present to a much lower degree, in the duodenum of all non-predisposed individuals, as compared with those patients who develop CD (45).This possible role of duodenal bacteria in the pathogenesis of CD has been described (36,52,53).

A rod-shaped bacteria were frequently associated with the mucosa of CD patients, with both active and inactive disease, but not with controls (54). Clostridium spp., Prevotella spp., and Actinomyces spp. were identified as the main components, and an important CD risk factor (54). E. coli and Staphylococcus seemed to be associated with the active phase of the disease and their increases could be a secondary consequence of the inflammatory milieu trigger by gluten ingestion (34). Increased levels of Staphylococcus in duodenal biopsies were also detected (34,55). In another study, increased levels of Staphylococcus and Enterobacteriaceae in infants with allergies suggested a relationship between these bacterial groups and immune dysregulation (56).

In CD the mucosal tolerance to the gut microbiota is deregulated possibly due to a higher percentage of IgA-coated Bifidobacterium than IgA-coated Bacteroides-Prevotella. This results in an increased interaction between the gut mucosal immune system and this bacterial group, which contributes to mucosal tolerance towards high gut Bifidobacterium concentrations (52,57).

The reductions in beneficial Gram-positive bacteria could favor the residence and interactions of harmful Gram-negative bacteria within the mucosal surface thereby contributing to loss of gluten tolerance (53). An increased proportion of Bacteroides-Prevotella group coupled with a weaker defensive IgA response could explain the recurrent relationship found between Bacteroides and inflamed gut mucosa in CD (53,34,36).  A study observed increased levels of total SCFA and acetic, valeric, and butyric acids in CD patients (38). Increased butyric acid production could be a common feature of patients and relatives (60), since first degree CD relatives only have higher levels butyric acid but not higher levels of acetic acids and total SCFAs (39).

Small-intestinal bacterial overgrowth or SIBO is also known to affect most CD patients, with persistence of gastrointestinal symptoms after gluten withdrawal (25) and is found in both symptomatic treated or untreated CD (60). While increased numbers of enterobacteria or staphylococci may be are secondary consequences of the disease, increased Bacteroides species numbers and virulent-E. coli clones and decreased Bifidobacterium species numbers are associated with CD, regardless of symptoms and inflammation and, therefore, could play a more prominent role in this disorder (25).

The glycocalyx/mucous layer of the jejunal mucosa has unique carbohydrate structures that could modify the specificity of bacterial adhesion. These structures were found to be are altered in CD patients (25,53,61), suggesting that either a particular glycosylation pattern in predisposed individuals favors harmful bacterial adhesion, which contributes to CD pathogenesis (25,53) or modifications in the composition of the intestinal microbiota lead to alterations in the glycosylation pattern and its defensive role of the mucus layer against infections and CD (25).


Schematic representation of the pathogenic mechanism underlying celiac disease, and key points of possible interaction with the microbiota and bifidobacteria (12).

  1. (1) Expression of mucin-2 (MUC2) is significantly increased, probably in response to high IFN-γ production by intraepithelial lymphocytes and secondarily to the overgrowth of potentially pathogenic bacteria in active CD patients; the glycosylation pattern of themucus layer is also modified in active and nonactive CD, and these aspects can also be modulated by the microbiota and predispose to CD.
  2. (2) Expression of α-defensins HD-5 and HD-6 by Paneth cells is increased in active CD patients, probably in response to high IFN-γ production by intraepithelial lymphocytes and secondarily to the overgrowth of potentially pathogenic bacteria  in active CD patients.
  3. (4) Expression of TLR4 is increased in CD patients and TLR signaling involved in the response to commensals and pathogens.
  4. (5) TLR4 signaling can lead to activate interferon regulatory factor 3 (IRF3) or IRF7 leading to the production of type I IFNs that stimulate IFN-γ, already overproduced in the aberrant response to gluten.
  5. (6) CD patients present increased intestinal permeability due to alterations in distribution and expression of tight junction (TJ) and TJ-associated proteins.
  6. Specific bifidobacterial strains could play a protective role in CD pathogenesis, by increasing TJ expression of intestinal epithelial cells and reducing paracellular permeability and thus preventing/limiting gliadin translocation to the lamina propria and the consequent inflammatory response; by regulating the inflammatory effects of the altered microbiota via production of anti-inflammatory cytokines (IL-10) and  reduction of IFN-γ ; by contributing to hydrolyzing gliadin peptides and thus reducing their toxicity on epithelial cells; and by increasing the number of goblet cells producing mucus and enhancing the production of inhibitors of metalloproteases (TIMP-1), which protect against tissue damage.


Defense are proteins that contribute to host defense. Expression of α-defensins HD-5 and HD-6 in Paneth cells exceptionally high in the human small intestine (62). These defensins have antimicrobial activity and contribute to create a hostile environment that prevents overgrowth of bacteria and infections (63).

In CD patients, increased expression of α-defensins HD-5 and HD-6 would appear to be a secondary consequence of the inflammatory intestinal milieu characteristic of the disease linked to an overgrowth of some potentially pathogenic bacteria (E. coli), since expression returns to normal levels in patients under a gluten-free diet (25). β-Defensins are inducible antimicrobial peptides mainly produced by epithelial cells and they also play a crucial role in the innate immune system (64).

High copy numbers (more than 4) of β-defensins were underrepresented among CD patients, suggesting that increased copy numbers of β-defensins in CD could afford protection from CD, possibly by preventing bacterial infiltration and preserving gut epithelial integrity (25,64,65).


Aside from a gluten-free diet, gluten-degrading enzymes are being pursued as adjunctive therapeutics for celiac disease, proline and glutamine-specific endopeptidases from bacteria, fungi and barley are currently being explored (14,66-72). The gluten-degrading enzymes from the oral bacteria (mostly a harmless residents) could be isolated and further pursued as a pharmaceutical drug, or dietary supplement (14). The gluten-degrading bacteria themselves could be developed as probiotic agents to achieve gluten digestion. B. longum IATA-ES1 is commercially available in food as a probiotic bacterium for patients with CD (14).

            The Microbiome Story Summarized.

It appears there is a large variety of bacteria capable of digesting gluten with gluten-degrading proteases naturally present in the upper human gastro-intestinal tract, indicating humans can and do digest gluten. The oral cavity is colonised with microorganisms that produce proteases capable of hydrolysing peptides rich in proline and glutamine residues such as those found in gluten proteins. It is quite clear that bacterial groups related to gluten metabolism are altered in patients with CD. Intestinal dysbiosis is present in celiac disease patients, characterized by increased Gram-negative bacteria, other potentially pro-inflammatory bacteria and reduced bifidobacteria. Small-intestinal bacterial overgrowth (SIBO) and infections have been suggested to contribute to CD pathogenesis with persistence of gastrointestinal symptoms after gluten withdrawal. Pathogenic enterobacteria could play a role in the switch from tolerance to an inflammatory immune response to gluten, by altering the permeability of the intestinal mucosa.

It appears that a lack of maturation of the gut microbiota is observed within the first 2 years of life in infants at risk of CD and that the early introduction of gluten and the lack of maturity in the GI microbiota could trigger or accelerate the development of autoimmunity. Reduced IgA-coated bacteria is associated with intestinal dysbiosis suggesting the existence of a barrier defect, which fails to stabilize the gut microbiota and prevent the host from the invasion of harmful antigens and pathogens. Lastly, either Bifidobacterium could protect against CD, or inherent features of the CD intestine influence Bifidobacterium colonization.

Part 4: How To Think About Gluten

Out of the rabbit hole and up into the clouds.

If you poll people on how they think about gluten they usually fall somewhere on the spectrum of “Gluten is Satan” to “Eating Gluten Free is for Idiots” (Ironically, a vegetarian protein option made entirely from gluten protein is called seitan and pronounced say-tan, just like satan).

In order to think logically about gluten we need to separate our ideas from who we are, and remove the emotional aspect of this discussion. Remember, it is ok to be wrong and change your stance on an issue. Personally, I waffle over where I fall on this issue probably weekly.  But let’s review what we have covered so far and present some additional data and ways to think about the issue.

Now we need to discuss some additional data. First we can dissect down into who benefits from a gluten free diet. If we stratify people based on celiac diagnosis vs. no celiac diagnosis and based on their response or lack of response to gluten we can then label these groups based on whether the evidence supports a therapeutic benefit of eating gluten free (Table 3) 


Table 3


Now we can overlay these groups with the figures from table one and calculate: 1) the number of people that evidence supports eating gluten free, and 2) the number of people that evidence supports eating gluten free.

Number of People that Evidence Supports the Benefit of Eating Gluten Free: 24,478,050

Number of People that Evidence Does Not Support the Benefit of Eating Gluten Free: 293,521,950

This suggests that approximately 25 million people likely have some legitimate reason to avoid gluten based on the cumulative evidence to date.

Let us then ask the next logical question. Who is likely to be harmed from removing gluten from their diet based on the same categorical stratification and calculate: 1) the number of people that are likely to be harmed eating gluten free.


Table 4

Table 4

Number of People that are Like to be Harmed Eating Gluten Free: 0

Here is currently how I view the “gluten issue”. Celiac disease presents and interesting autoimmune condition in which gluten exposure presents a substantial health detriment both acutely and chronically. Individuals without CD but who consistently and repeatedly report adverse reactions to gluten (anywhere along the spectrum) are likely to benefit from restricting gluten from their diet. While there are some interesting hypothesis about the effect of gluten intake and chronic diseases (e.g. Alzheimer’s), the data is not substantial enough to warrant guidelines recommending the restriction of gluten to people who are not celiac or who do not present with adverse reactions to gluten exposure.

Is this rise of glutensanity the worst thing? Probably not. There are likely a lot of people who would not have known to attribute their GI issues to gluten as it was not a conversation being had a dinner tables, bars (drinking gluten free beer of course), or gym locker rooms until more recently. The abuse of marketing to drive profit is an issue, but then again what do you expect, marketers ruin everything.

*I would conjecture this number is artificially high. The data showing these ranges of percentages are from research studies in which many of the patients are self-selected. It is very likely that the actual rate of NCGS is lower, perhaps 2-4% of the entire population. Regardless, it does not detract from the point made in any appreciate sense.

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Fat Metabolism and Diabetes Tue, 20 Dec 2016 23:30:00 +0000 By: Brad Dieter, PhD Read Time: 5 minutes Tl;dr: It appears that basal fat metabolism is the most “disturbed” aspect of metabolism in diabetes. Fat Metabolism and Diabetes The title of this piece alone should be enough to make you scratch your head. I mean everyone and their dog knows people with a “messed up”[...]

The post Fat Metabolism and Diabetes appeared first on Science Driven Nutrition.

By: Brad Dieter, PhD

Read Time: 5 minutes

Tl;dr: It appears that basal fat metabolism is the most “disturbed” aspect of metabolism in diabetes.

Fat Metabolism and Diabetes

The title of this piece alone should be enough to make you scratch your head. I mean everyone and their dog knows people with a “messed up” metabolism or type 2 diabetes (T2DM) has a carbohydrate/sugar problem right? Well, it turns out it may be the exact opposite. At a fundamental level, individuals with T2DM actually have a fat metabolism problem, not a carbohydrate problem as previously believed.
Now how can this be? I mean blood sugar is elevated in these individuals and they also have “insulin resistance” so clearly it has to be a carbohydrate metabolism problem. Right?! Well let us ask ourselves a basic question that should ferret that out.

What would the baseline metabolism of a person with diabetes be if the underlying cause was in fact, impaired carbohydrate metabolism?

If true, they would have a lower carbohydrate metabolism to fat metabolism ratio than a lean, healthy person.  Thus, their respiratory exchange ratio (carbs-to-fat oxidation ratio aka RER) should be lower.

Here is a brief synopsis of RER.


This figure below should give you some idea of what RER looks like in application.


Essentially, an RER of 1.0 means you are burning virtually 100% carbohydrates and an RER of 0.7 means you are burning virtually 100% fat.

Now that we have an understanding of RER, we can tackle question # 1.  When you look at the data we actually see that individuals who have “impaired metabolism” actually have a higher carb-to-fat oxidation ratio, meaning they actually are burning more carbs at baseline (1,2).  Importantly, it appears that much of the “extra” carbohydrate utilization occurs in nonoxidative metabolism; meaning it never goes through the mitochondrial and the electron transport chain (Table 1).

Importantly, this hints at a reduced mitochondrial function and also likely some issues with the beta-oxidation of fat (a separate process from the primary mechanism of creating energy from fat). Think of it like prepping the ingredients for a meal. . . it is a necessary step, but you still gotta cook the ingredients afterwards!

Not only is their baseline metabolism of fat impaired, their ability to utilize fat during exercise is also impaired. Individuals with T2DM rely more heavily on carbohydrates than fat at virtually all levels of exercise compared to their lean and healthy counterparts.  In the figure below, look at the relative contributions of fat and carbohydrate toward energy production in a “lean”  (top) and an “obese” (bottom) individual at identical, low level relative efforts. The lean person uses about 55% fat and 45% carbohydrate while the obese individual is almost exclusively carbohydrate metabolism. And it is not because there is not enough fat around, they have high levels of free fatty acids in the blood and intramuscular fat; they have a problem in turning that fat into usable energy.



Now there is another important piece of data I think we need to address as it points to a fairly big observation.

It has been clearly demonstrated that the issue with glucose metabolism in individuals with T2DM comes down to transporting glucose into cells, not the actual biochemical utilization of the glucose (3).

I think this is actually a fairly important observation in regards to applied nutrition and exercise for the following reasons:

(1) Glucose disposal (a fancy word for getting sugar into cells) can be increased in individuals with diabetes or  “impaired metabolism’s” immediately through a very simple intervention; exercise. Engaging in exercise results in an acute increase in glucose disposal both independent of insulin action and therefore temporarily improves insulin resistance.  Exercise also improves fat metabolism in those individuals with impaired fat metabolism. This is a critical point. People with impaired fat metabolism can increase their ability to oxidize fat much more rapidly when they exercise!
(2) Basal insulin resistance can be improved through weight loss. Weight loss can be achieved and sustained through a combination of diet and exercise.

(3) It suggests that carbohydrates themselves are actually not the major culprit.

The Wrap Up

The presented data points to the same conclusion: individuals who have T2DM, pre-diabetes, metabolic syndrome, or are somewhere along that spectrum most likely have a fat metabolism problem which likely leads to a glucose disposal problem.  Exercise and weight loss work in conjunction with each other to improve both carbohydrate utilization and overall metabolism acutely.



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Your Diet Does Not Make You Morally Superior Mon, 07 Nov 2016 17:35:36 +0000 By: Brad Dieter, PhD Read Time: Much shorter than Plato’s Republic Tl;dr:  Your diet does not make you morally superior. That sums this up nicely Your Diet Does Not Make You Morally Superior When we map out a person’s nutritional “journey” we can think about it much like a pendulum. You start on one side. Let’s call[...]

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By: Brad Dieter, PhD

Read Time: Much shorter than Plato’s Republic

Tl;dr:  Your diet does not make you morally superior. That sums this up nicely

Your Diet Does Not Make You Morally Superior

When we map out a person’s nutritional “journey” we can think about it much like a pendulum. You start on one side. Let’s call this the IDGAF side. At this point you don’t know anything about nutrition and honestly you don’t care. One day you decide you wanna look good naked and get rid of that excess body fat and become Instagram famous. This starts the pendulum going in your journey.

At some point early in this part of the journey you find some “truth” about nutrition and you decide, “My god, I found the cure for everything and this diet is gonna change the world”. You hunker in, start making superfood smoothies, Instagramming and Snapping allllll your fav meals, and becoming an evangelical about the power of this diet. If it is a MLM marketing company you also begin spamming your old high school friends Facebook messenger with “business opportunities”. This continues for an indeterminate amount of time and you either, a) learn more about nutrition and realize you probably don’t know everything you need to know, relax your dogmatism and return to a more sane, grounded, position, B) you become a radical personality and make a fruit your middle name.


This explains most peoples narrative arc through nutrition.  There is a substantial social and moral issue imbedded in this journey. Let me flesh this out for you, beginning by throwing myself under this literary bus with my own story (much to my own horror).
About 7 years ago I was at a point in my life when I had begun really starting to learn about nutrition and recently discovered the awesome power of nutritional zealotry and diet dogma.  Coincidently, this is typically the point that coincides with mount stupid.


It was during this time that I was sitting on an airplane and watching the person next to me eat a sandwich. As I was sitting there a thought crept into my mind, “OMG this person is eating gluten, processed deli meat, and mayonnaise, I am way better than he is”.  Let this detonate in your brain for a moment. Here I was, sitting next to a complete stranger and the idea that my dietary choices made me a substantially superior human being to him was the first thing that came to my mind when I saw this person.  I had effectively reached mount stupid and the calibration of my morality meter was atrociously tuned.


This is a problem, a gargantuan problem.


I was fortunate enough to have an immediate moment of reflection and spent the next 4 hours of this flight contemplating my own existence, moral fiber, and level of self-aggrandizing douchery.  This 4 hour descent into the recesses of my own epistemological garbage was quite difficult and it cause me to do a lot “soul” searching.  After a few months of thinking on the issue and separating my dietary choices from my moral judgments of others I tabled the issue and continued on with my life. Recently, I have had to grapple with this issue on a larger scale as I have been more and more involved with people through the internet and observe this happening quite frequently and at larger scales (Larger Scales from Monty Python and the Holy Grail feature below).


Your dietary choices do not make your morally superior to people*.  Here are a few things you should consider before taking the moral high ground based on your diet.

1) You are probably wrong

First and foremost, nutrition is a quagmire with a lot of unanswered questions. Many of the positions you use to prop yourself up on the moral high ground are likely going to turn out to be false at some point or in some circumstance.  A little humility in your own beliefs goes a long way.

2) Your values are not their values

6 pack abs, 20 inch biceps, and eating clean may be your definition of the best life possible. For other people solving the clean energy problem, making human beings an interstellar species, and revolutionizing buying things on the internet is their definition of the best life possible.  Elon Musk might not be an Instagram fitness model and casting moral authority upon him because you had a kale smoothie this morning and he didn’t is about the silliest thing you could do.

3) You don’t wear their shoes

When you decide you are superior to someone because you follow a strict diet while they don’t you make a lot of assumptions about their lives. Maybe the just lost their significant other and don’t have the desire to focus on dieting, maybe the are a chef and they eat gluten because it helps them become a better pastry chef by knowing the exact texture and mouth feel of the pastries they make.

4) No one cares about your superiority

An entire movie was devoted to satirizing self-aggrandization and assuming superiority based on trivial things. This movie was called Anchorman. Assuming you are superior and casting a moral judgement on someone because they eat gluten, or aren’t keto, or follow paleo, is equivalent to Ron Burgundy holding himself above other people because of his hair, mustache, and ability to read from a teleprompter.

When you stop and think, it is completely absurd that anyone should case a downward gaze on someone because you count every macro and have single digit body fat and they don’t.

There is a lot of “campiness” and bickering, sneering, and scrambling for the moral high ground based on people’s dietary philosophies and dogmas. This is just silliness.

PhotoELF Edits: 2009:12:09 --- Saved as: 24-Bit 98% JPEG YUV444 --- batch crop --- crop 2009:12:07 --- Batch Resized File written by Adobe Photoshop¨ 5.0

* We can discuss the ethical implications of veganism another time.

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Genetically Modified Organisms Mon, 24 Oct 2016 15:35:37 +0000 By: Brad Dieter, PhD Read Time: .65 cups of coffee Tl;dr: Genetic modification of organisms (GMOs) should be viewed as a technology and blanket statements about how they are bad for human health are not supported by over 30 years of data. GMOs should be viewed on a case-by-case basis and careful thought should be given to[...]

The post Genetically Modified Organisms appeared first on Science Driven Nutrition.


By: Brad Dieter, PhD

Read Time: .65 cups of coffee

Tl;dr: Genetic modification of organisms (GMOs) should be viewed as a technology and blanket statements about how they are bad for human health are not supported by over 30 years of data. GMOs should be viewed on a case-by-case basis and careful thought should be given to their use and application.

Genetically Modified Organisms

Genetically modified organisms (GMOs) have been a hot bed of discussion amongst the blogosphere, internet forums, and the scientific community. For a long time it was a topic one could consider a minor issue and didn’t need that much attention. However, given the recent changes in the trajectory of human population, our climate, and advances in genetic engineering this topic needs to be at the forefront of scientific and moral discussions. Historically the conversations surrounding GMOs has involved scare rhetoric, faulty logic, appeals to nature, and caused a substantial polarization (i.e. GMOs are good or GMOs are evil) of the topic. What we really need is an honest, nuanced conversation about the topic because it is as far from black and white as one can get.

The Empirical Evidence Against GMOs

A thorough and critical examination of the scientific literature reveals zero, let me repeat that ZERO, evidence that GMO foods pose any measurable health risks. There is over 30 years of research regarding GMO foods and health and safety concerns and there is no consistent, coherent body of literature to indicate that there are measurable detrimental health effects in humans. I remain open to evidence so if you have any data in humans please send it my way.  Thus much of what we will cover in this article relates to the philosophical and logical arguments about GMOs as those are what we can actually have a well-founded, well-reasoned, rational discussion about.

The Naturalistic Fallacy

The first order of business is to address one of the most common logical fallacies that occurs during a conversation about GMOs, the naturalistic fallacy. The naturalistic fallacy introduced by British philosopher G. E. Moore in his 1903 book Principia Ethica, essentially states that because something is natural it is good is fallacious. This is the “appeal to nature” argument stating that natural is good and artificial is bad. Easy examples to highlight this fallacy are the bubonic plague, AIDS, and natural toxins.  Additionally, things like polio vaccines, organ transplants, and emergency medicine are also not natural but definitely fall into the “good” category. GMOs are claimed to be inherently bad because they are “not natural”. Clearly, this charge is guilty of committing the naturalistic fallacy and I think we need to move on from such a parochial argument.This is one of primary argument used in the “GMOs are not healthy” argument.

Additionally, there is the topic of transgenic organisms in which genes from one species are inserted into an entirely different species. For example, a gene encoding a fluorescent protein might be inserted into a fish making the fish glow (This is actually super cool and doesn’t appear to have any negative health consequences on the fish). Now this has also been leveraged as an argument against GMOs, stating that genes from a different species ought not to be incorporated into a different one. This is also not a reasonable argument. The human genome contains a massive swath of genes from organisms, mostly viruses. Transgenic manipulations of genomes occur all the time in nature and are a part of evolutionary biology and may even be involved in beneficial adaptations of the human species.

The Status Quo Bias

One of the other main arguments used against GMOs is the status quo bias. The status quo bias is an emotional bias; a preference for the current state of affairs. The current baseline (or status quo) is taken as a reference point, and any change from that baseline is perceived as a loss. I prefer to discuss the status quo bias as stated by Kahneman, “The status quo bias” – individuals’ tendency to prefer to remain at the status-quo – is similarly attributed to loss aversion: It is assumed that the loss of the status-quo option looms larger than the gain of an alternative option”.

Departure from the status quo can be either disastrous or prosperous; often times remaining in the status quo is not in the best interest of the human race. For example, Homo Sapiens Sapiens lived in what can be described as abhorrent conditions for millennia: lack of food, inability to avoid the elements, infection, death in child birth, etc. If substantial changes had not been made (e.g. control over fire, domestication of food sources, building of homes, invention of clothes, the enlightenment, and the germ theory of disease) our species would be in objectively a worse situation than it is now. When we examine the “GMOs are a departure of current food practices and there are unknown future consequences” we fall victim to this bias. This also does not paint GMOs in a correct light, nor is it a useful argument for or against them.

GMOs as Information Technology

The third main issue that arises in discussions about GMOs is that they are often not well understood. There are important aspects of GMOs that we need to flesh out in order to understand them. The first concept is that GMOs are in essence an information technology, specifically gene selection and modification, which we have been applying for millennia with the caveat that we have had an exponential growth in our control and application of this technology in the last 20 years. Let us explore this concept further.

            Historical genetic selection

Humans, as a species, have been genetically modifying organisms since their inception and we need to acknowledge that genetically modifying our food sources (and ourselves) has been an integral part of our evolution as a species. It began when they selected mates based on certain traits and expanded when they domesticated animals and plants and beginning selecting the specific, favorable traits of their livestock and agriculture.

The natural, organic, non-GMO apples, broccoli, lettuce, and corn you buy at the store have gone through millennia of trait selection through selective breeding practices. By definition, those foods are genetically modified from their original form. The genetic modifications of our food sources for 99% of human history occurred by selectively breeding plants or animals with desired traits hoping for specific outcomes. These changes often took several generations to manifest to an appreciable, meaningful degree in an entire species. These modifications also occurred within the same gene pool and relied on the manipulation of either existing mutations or the exploitation of an advantageous de novo mutation. This stands in stark contrast to current approaches to genetic modifications of organisms; specifically the use of transgenes and newly synthesized artificial genes.

            Current genetic manipulation

The recent discovery of DNA, genes, and advances in molecular biology have given us control over the genomes of organisms not previously available to mankind. This has resulted in an exponential increase in our ability to modify the genomes of plants and animals, accelerating the changes of our food sources. Whether the rapid changes are beneficial or maladaptive will be discussed later but for now it is sufficient to say these changes occur on a time scale incongruent with the rate of our own evolution as a species.

One of the most overlooked aspects of current gene manipulation, especially given the recent advances in gene editing technology via the CRISPR-CAS system (I have experience working with this and doing actual gene editing myself), is the degree to which our control over gene manipulation effects the outcomes. It is often stated that traditional gene manipulation (i.e. selective breeding) is somehow safer than direct genome manipulation. This is a false conclusion.  The difference is that selective breeding or mutagenic techniques tend to result in large swaths of genes being swapped or altered. GM technology, in contrast, enables scientists to insert into a plant’s genome a single gene (or a few of them) from another species of plant or even from a bacterium, virus or animal. Supporters argue that this precision makes the technology much less likely to produce surprises. Most plant molecular biologists also say that in the highly unlikely case that an unexpected health threat emerged from a new GM plant, scientists would quickly identify and eliminate it. “We know where the gene goes and can measure the activity of every single gene around it,” says Dr. Robert Goldberg, a molecular biologist at the University of California, Los Angeles “We can show exactly which changes occur and which don’t” (cite). I resonate with the idea that selective breeding is much less in our control than direct genome manipulation and that advances to be made in the future ought to implore direct gene manipulation; however, I don’t feel that we know enough about 2nd and 3rd order effects of gene-to-gene interactions that we can safely claim, “We can show exactly which changes occur and which don’t”.

One of the arguments were hear against GMOs is that the introduction of a novel chemical or protein will proof harmful as we are not “used to metabolizing it”. This is a highly innacurate, ineffective claim. A thought experiment fleshes this out quite easily. Imagine someone of virtually pure African ancestry, their entire genetic lineage is from the African continent. This person, nor his genes, have been been exposed to foods that originate in South America or North America. If said individual moves to the United States and consumes blueberries (a plant touted for its health benefits) is it a valid assumption that the blueberries will cause this person to be stricken with disease? No. The introduction of a novel compound or protein is not an issue per se, it is the specific nature of that compound.
Fair Criticisms of GMOs

When I started writing this article I knew I wanted to be as intellectually honest as possible and I spent more time reviewing the arguments against GMOs than I did for them. As one can imagine I came across a swath of arguments ranging from stupid and dangerous, to cogent and thoughtful. I want to focus on the arguments that were substantive both philosophically and scientifically.

Uncontrolled Propagation (GMOs contaminate forever)

Modifying the genomes of organisms and releasing them into a population without a “genetic kill switch” is the equivalent of firing a gun, you can’t take it back. When you introduce a novel genome into a population and it is bred with current organisms you introduce that genome into the gene pool forever (unless some fluke mutation renders it inactive or inert after the first generation). While we can predict the effect of the gene on the initial phenotype and likely the phenotypes of the F1, F2, and F3 generations we lose our ability to predict how that gene will interact many generations down the road.

Now we need to really assess what this means. It may be that our intervention leads to some catastrophic collapse due to unforeseeable circumstances.  This has been argued by Nassim Taleb in his paper on the Precautionary Principle. I think he makes a salient point about the fact we ought to be cautious in how we use this technology, but I think he misses some key points that render his argument not quite as solid as it is sold in his paper (see paper here and counter arguments here). Conversely, it could produce a phenotype that provides greater crop yield and becomes a staple that saves a large part of the world’s population during a famine. Additionally, we don’t know that following the natural course of the current crops evolution that it dies out due to some fluke phenomena. Based on what we currently know I find it foolish to make a bold, strong decision about what GMOs will do a priori. Speculations at this point ought to be tempered and carefully thought out. Scare rhetoric, doomsday proclamations, nor life saving claims  about GMOS are well supported by any of the current data.  The most honest assessment of this argument is that we need to acknowledge that “contaminating the gene pool” forever is something that we need to think very careful about before we introduce a novel gene and do due diligence in attempting to project as far out as we can.

Monopolies over seeds with IP

Perhaps one of the most compelling issues with GMOs is the misuse and abuse of intellectual property. Genetic manipulation can substantially reduce the cost of farming and increase crop yields (cite), increasing the quality of life of farmers and reducing the cost of goods for the public. However, corporate and individual greed cannot be overlooked and many companies that manufacture sees for GMO plants often raise prices and make plants “sterile” so farmers must repurchase seeds at exorbitant costs year after year. There are cases opposite of this where GMO technology has been designed with noble intentions and government regulations and red tape have prevented its wide spread “gifting” to third world countries. Golden rice is an excellent example of this. Golden rice was created in a lab to be high in beta-carotene to help ameliorate nutrient deficiency in third world countries. It took roughly a decade to get through the government regulations in order to be distributed in places like Africa. Issues like these might be solved through legislation and laws that regulate GMO technology, their uses, and enact statues much like those in drug production where special cases can be fast tracked.

Over confidence in meeting food supply

It has been argued that GMOs will be the only way to meet the growing demand for food supply. Based on current technology and an analysis of all the contributing factors I think it is safe to say that GMO technology alone, as it currently is, will not solve a food shortage problem if the world population continues to grow at the same exponential rate and our usage of fossil fuels, land, water, and other natural resources continue at its current rate. Overconfidence in GMO technology may be a contributing factor to a future food storage. This is pure speculation on my part as there is no good, solid evidence to support either side.

How we really ought to view GMOs

Categorizing GMOs as either bad or good is a rather near sighted perspective. GMO technology ought to be view just as that, a technology. It can be utilized to help buoy the human race forward as we begin to encounter unforeseen obstacles. For example, GMO technology may be critical in adapting to climate change as the climate may change faster than plants and animals can naturally adapt. We may require GMO technology to create plants that thrive on Martian soil if/when we become an interplanetary species. Conversely, engineering plants to display massive herbicide resistance or to profit off of GMO IP at the expense of farmers may be an abuse of the technology. In reality GMOs need to be viewed as a technology and how it is applied on a case by case basis.


This paper also provides an excellent technical read of many of these issues

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Body Weight Regulation: What Controls the Scale? Mon, 03 Oct 2016 23:08:56 +0000 By: Brad Dieter, PhD Read Time: About a full cup of coffee. Tl;dr: We talk about body weight regulation too simplistically. We need to talk about quality and quantity. Body Weight Regulation: What Controls the Scale? There is a famous joke about physicists and it goes something like this, “Milk production at a dairy farm[...]

The post Body Weight Regulation: What Controls the Scale? appeared first on Science Driven Nutrition.


By: Brad Dieter, PhD

Read Time: About a full cup of coffee.

Tl;dr: We talk about body weight regulation too simplistically. We need to talk about quality and quantity.

Body Weight Regulation: What Controls the Scale?

There is a famous joke about physicists and it goes something like this, “Milk production at a dairy farm was low, so the farmer wrote to the local university, asking for help from academia. A multidisciplinary team of professors was assembled, headed by a theoretical physicist, and two weeks of intensive on-site investigation took place. The scholars then returned to the university, notebooks crammed with data, where the task of writing the report was left to the team leader. Shortly thereafter the physicist returned to the farm, saying to the farmer, ‘I have the solution, but it works only in the case of spherical cows in a vacuum’”.

This joke is a humorous way of stating that we use models to simplify complex problems in order to understand them. In nutrition and physiology we do this quite often, especially when it comes to explaining weight loss. While this can get us pretty close to the right answer and our current tools work really well from a pragmatic perspective, there is immense value in fleshing out the deeper, underlying concepts surrounding weight and body fat regulation.

Put on your big boy pants and take your nootropics because this is not going to be the easiest or lightest read of the week. Frankly, the internet suffers from lack of deep thinking, so I refuse to apologize for the length of this one. Let this be an exercise in breaking bad habits of reading “fluff” articles. Time to put that gray matter to work.

Thermodynamics in context

Any discussion about the mass of a system has to first begin with thermodynamics*. We shall being by briefly reviewing the three laws of thermodynamics as a refresher. This also is a refresher for my mind.

  • The law of conservation of energy: Energy cannot be created or destroyed in an isolated system.
  • The Entropy of any isolated system always increases
  • The entropy of a system approaches a constant value as the temperature approaches absolute zero.

As the internet has no peer review to stop bad arguments from being circulated, there are a lot of pedantic arguments stating we can’t treat the body as an isolated system; this is nonsense. You can quite easily model the human body as an isolated system with regard to energy input and output. Anyone who says otherwise has not really done much critical thinking on the subject. I will throw myself under the bus here as I have been sloppy in the past and used that argument. This is life, we learn and get better.

Now, onto context. When we think about thermodynamics and how it applies to the human body we can look at the cellular level and discuss the biochemical reactions and free energy equations, or we can take the pragmatic approach and discuss the notion of overall energy balance. Pragmatism is highly underrated and underutilized, let’s go that route. From the physicists perspective about the spherical cow, we can approximate the changes in the overall mass/weight of by tracking the food that goes in and the energy expended. There have been metabolic chamber studies that measure all of the energy consumed and expended and the change in the energy in versus the energy out and the change in body weight correlate extremely well. There are also tools such as doubly labeled water that can be used to also approximate weight changes to a surprising degree of accuracy.
Now before we proceed any further, let me plant a flag, out in the open for all to see and let you know exactly what my interpretation of the data is on this topic. No matter what your “theory” of weight (or body composition) regulation is, it has to obey the laws of thermodynamics. You simply CANNOT have more energy coming out of the body than going into the body and gain weight, conversely, you CANNOT have more energy going into the body than coming out of the body and lose weight. Thus, any theory one contrives must fall under the laws of thermodynamics.

Calories-in vs Calories-out model (CICO)

One of the current models in nutrition that is equivalent to the physicists sphere is the Calories-in-calories-out (I will take the liberty of calling it CICO) model of body weight regulation. The calories in calories out (CICO) model holds a lot of truth and can explain a lot of the variation in body weight, yet it is incomplete. I like to compare the CICO model with Newtonian Physics… it is accurate and describes gravity for a majority of cases but it is not complete. We need Einstein’s theories of relativity to describe the enormously large* and fast and we need quantum theory for the incredibly small.

The CICO model is an excellent “first approximation”, but it suffers from what I like to call the “black box” approach where it fails to fully address the nuances of parts of the equation that drastically impact the output. The CICO model treats the body as a bomb calorimeter, failing to account for the complexities of the black box that is human physiology. I offer the following figures to illustrate my point. The second figure is my interpretation of the “physicist’s sphere” where the output is just a complex, indeterminate set of equations




In science we make assumptions. You have to. If you want to accomplish anything meaningful you have to make assumptions. For example, if I wanted to ask a question about some chemical property I have to assume that the atomic theory of matter is correct. I can’t spend 20 years conducting all my own experiments to confirm previous work, I have to rely on assumptions.  Good scientists are very forth coming and open about their assumptions. We should do so with regard to the CICO model of weight regulation.  Here are the assumptions we make when we adopt a strict CICO model of weight and fat mass regulation (in no particular order)

  • When 100% accounted for, the energy into a system minus the energy out should equal the change in the mass of that system (remember E=MC2 shows us that energy and mass are equivalent).
  • This model treats the body as a “black box”
  • All energy inputs have the same effect on energy outputs


Another hallmark of scientific integrity and intellectual honesty is to put forth your limitations in clear detail for all to see. There is no hand-waving, nor is there any pulling the wool over the eyes. So let us put forth the two largest the limitations of the CICO model (I am probably missing a few but these hit the major ones)

  1. The CICO model fails to account for the differential effects of macronutrients. For example, protein elicits a thermic effect of food and is “harder” to store as body fat than either carbohydrates or fat.
  2. It completely fails to account for changes in tissue quality. A gain of 5 pounds of lean mass and a loss of 5 pounds of body fat are quantitatively the same in regards to gravity but qualitatively different appearance and health wise (quantitative assessments can also be made of appearance and health)

Hormone Theories of Weight Regulation

On the other side of the weight regulation coin are the hormonal theories. Put simple, these theories indicate that hormones are the primary cause of common obesity and that caloric intake is simple a red herring; it is hormones that are the key. While several hormones have been implicated I think it is safe to say that there are two hormones that deserve the most attention, insulin and leptin, due to their “popularity” and their scientific credentials, respectively (obviously the latter reason holds more weight in a universe that rewards evidence based approaches to problems).


Someone smart once said, “I don’t get into debates over certain topics because even engaging in the debate means that there is something to debate. Some sides of some topics just don’t have enough evidence to even warrant a debate”. From my own interpretation of the VAST amounts of data, this is where I stand on the Carbohydrate – Insulin Hypothesis. The vast amount of evidence indicates that insulin is not a primary regulator of body weight.  We have covered this topic in depth so I won’t rehash it here.  What I will do though is cover the role that insulin DOES play in body weight regulation.

Primarily, insulin functions to regulate blood glucose through hepatic glucose output and peripheral tissue uptake of glucose and coordinate the metabolism of other nutrients (e.g. amino acids and fatty acids). When no glucose is being ingested and insulin levels are low, the liver is “allowed” to produce glucose to keep glucose levels in normal range. When glucose is consumed and insulin is released, the insulin puts the breaks on the liver and then tells the rest of the tissues that glucose and other nutrients are available for uptake and to start using more glucose for fuel. Put simply, insulin is a substrate conductor, it coordinates the metabolism of your different nutrients and regulates levels of these nutrients to sustain life.

Now we need to address the role of insulin in fat storage and nuance the details because they are incredibly important. Insulin does in fact decrease the rate of lipolysis in adipose tissue and does stimulate fatty acid and triacylglycerol synthesis, the science is crystal clear on this. This suggests that insulin is “lipogenic” and promotes overall fat storage and thus obesity. This idea is simple, parsimonious, and honestly quite attractive. This single idea is the impetus behind the carbohydrate-insulin hypothesis of obesity.  Unfortunately it misses the mark and when you add in all the information insulin just doesn’t have enough “power” to really make substantial impacts on body fat in common obesity. Let us walk through a few key pieces of data.

The first is that the lipogenic effect of insulin only occurs when insulin has high “action”. Meaning the lipogenic signal of insulin is being heard loud and clear by the cells. In order for insulin to exert “fat gaining” effects it would need to be constantly signaling at above basal levels for high levels of lipogenesis to occur. This is not seen in common obesity for two main reasons:

  1. In non-diabetic/non-insulin resistant people who become obese have fluctuating periods of insulin signaling with more time during the day with low insulin levels than high insulin levels due to post-prandial insulin rises and low fasting insulin levels (read James Krieger’s series on this phenomena, starting here). When you really look at insulin action over time periods that are congruent with the development of obesity (days, weeks, months, years) you see that the cumulative result of its action doesn’t square with the idea that insulin primarily directs obesity. The next fat storage vs fat burning doesn’t favor the former from this isolated perspective. This becomes even more apparent when we address issue #2

    Image c/o

  2. As goes obesity so goes insulin resistance. . . . put simple, as body fat rises, body fat tissue (adipose tissue) becomes resistant to insulin, meaning insulin action actually decreases.
    1. To quote Dr. Stephan Guyenet on this topic,  “The fat tissue of obese people doesn’t suppress fatty acid release in response to experimentally elevated insulin or mixed meals as effectively as the fat tissue of a lean individual (18, 19).  In fact, obese people release an equal or larger amount of fatty acids from their fat tissue than lean people under basal conditions as well (20, 21).  If this is true, then why do they remain obese?  It’s simple: the long-term rate of fat entering the fat cells is equal to the rate exiting, or higher.  There is no defect in the ability of fat cells to release fat in obesity, the problem is that the fat that is released is not being oxidized (burned) at a rate that exceeds what is coming in from the diet, therefore it all ends up back in the fat tissue.”
    2. If insulin action were increased in obese individuals due to the elevated insulin than we would observe decreased plasma FA as insulin action promotes lipogenesis and decreases lipolysis in adipose tissue; however we see the opposite in obese individuals. There is substantial experimental evidence to show that as fat mass increases there is an increasing amount of free fatty acids, suggesting fat tissue isn’t really better at storing fat than in lean people. It ends up storing it eventually as more energy is present than needed so eventually it is stored. If insulin action were reducing lipolysis and increasing fatty acid synthesis and storage to the degree that it were the key driver of fat accumulation than this data (below) is very unlikely to be true. weight-4

Another key issue point we can make before moving on is that if insulin were indeed a primary driver of common obesity than higher levels of insulin should predict fat gain (remember science is largely about testable predictions). Several studies have looked at whether higher levels of insulin predict future weight gain (see the table below from the review by Hivert et al.). The largely negative nature of this data makes it very difficult to square this data with the notion that insulin, not caloric balance, is a key driver. In fact, I would go as far to say that it plays a very, very, minor role (we will expand on this in later in the post).weight-5


Leptin is a far better “case study” to examine the role of hormones in regulating body weight, specifically fat mass and the history of leptin is also quite interesting, it involves luck and sewing mice together (parabiosis). The role of leptin is actually fairly multifaceted having both central and peripheral functions. Briefly, leptin works in the hypothalamus through a variety of mechanisms to decrease hunger (mainly through peptide based signaling molecules that regulate both hunger and fullness). Briefly, leptin levels increase as fat mass increases and should result in decreased food intake and increased energy expenditure, bringing the fat mass back down. When fat mass drops too low, food intake increases and energy expenditure decreases until the fat mass returns to a “normal” level. In short, if any hormone should be the candidate for regulating fat mass it would be leptin, not insulin.

In humans who are genetically leptin deficient, they are unable to control body weight and are obese. This obese phenotype is corrected through the administration of exogenous leptin. This demonstrates that leptin can play a major role in body weight regulation when the entire “system” is broken.

Now what happens in people with a complete system, yet have some abnormalities in the system? Is that abnormality enough to really make massive differences in their body weight (i.e. enough to go from obese to lean)? It turns out that in common obesity leptin signaling isn’t “broken” but it isn’t really functioning properly and leptin resistance is often seen. One could posit that you could just administer more leptin and that would fix the issue. It turns out that these clinical trials have been done and leptin therapy doesn’t really correct body weight in any meaningful sense. See here for a very nice, thorough review. The leptin resistance appears to be secondary and resultant from similar biological processes that create insulin resistance (e.g. obesity and inflammation).

So we can use the information we have learned from two hormones that are intimately involved in metabolism, fat storage, and fat mass regulation and come to a few conclusions. Clearly hormones play an essential role in maintaining metabolism. Without insulin you die (typically from diabetic ketoacidosis). Too much insulin you die (hypoglycemia). They also play an essential role in maintaining fat mass. Without leptin you are unable to control fat mass resulting in run-away obesity. However, it appears that in normal, functioning human physiology leptin levels self-regulate through feedback loops to control body fat.

Homeostasis: How the body really functions

The human body is a living, dynamic organism that responds to stimuli; it reacts to a wide range of stimuli: chemical, electrical, and physical. It also follows, albeit roughly, a concept known as homeostasis: the tendency toward a relatively stable equilibrium between interdependent elements, especially as maintained by physiological processes. Essentially it means that when perturbed, the system corrects itself into equilibrium. This equilibrium can be a return to a former state, or a new state that balances out the “forces” being put on the system.

We can use two fairly well known examples to show how robust the human body is and how it adapts quite specifically to a given stimulus.

The first example is the body’s adaptation to exercise stimulus. One simple way to look at this is the differential adaptations to muscle tissue that occur muscle tissue in people who engage in body building style training versus endurance exercise. Body building results in increased muscle protein and increases in muscle cell size. Endurance training increases the mitochondrial density and alters cellular metabolism. Conversely, sedentary behavior results in muscle atrophy and loss of mitochondria and decreases muscle metabolism.

The second example is testosterone. We know that physiological and supraphysiological levels of testosterone can have substantial, divergent effects on muscle tissue. The administration of supraphysiological levels of testosterone is well documented to increased muscle mass while simultaneously decreasing fat mass. Now when you add resistance training on top of testosterone you magnify these effects as you have a chemical stimulus (testosterone) and a physical stimulus (the mechanical tension of weight lifting) working synergistically.

We can continue with examples such as these ad naseum, but I think they illustrate the point. The body is highly dynamic and aggregates a lot of signals into adaptations. As such, both the CICO model and the hormone model are incomplete. They only address one signal, neglecting the others. A full “theory”** of weight regulation must consider all signals

The Start of a New “Theory”

A theory is a system of ideas intended to explain something, especially one based on general principles independent of the thing to be explained. Essentially, a theory is a set of ideas that are substantiated by individual hypotheses.  Thus, a new theory of weight regulation must consist of several independent ideas that we can test empirically that all add up to a coherent set of ideas.

Let me explicitly set out my theory of weight regulation in a series of clear, concise ideas.

  • Body weight is regulated by the net change in the energy going into the system and out of the system (the body).
  • The energy going is regulated by the food intake.
  • The energy going out is regulated by a complex interaction of varying stimuli.
  • The “quality” of the tissues in the body is also regulated by the complex interaction of the same stimuli.

When we boil this theory down we can describe it in a relatively simple set of equations.


This new “theory” does not necessarily through the baby out with the bathwater. In fact it utilizes current knowledge and organizes them into a hierarchy.  It is, in essence, a refinement of ideas into a parsimonious model. It does require us to shift our thinking in some areas. First, we need to separate our vague notion that the weight of the system (i.e. the total) mass reflects the “quality” of the system (i.e. the body composition). So from a heuristic standpoint we can begin to think about our model as stratified from the beginning (this is a crude approach but it begins to separate our thinking, we will bring them back together in the end).

When we distill the concept of CICO into our new theory of weight regulation we allow it to govern the “energy balance” part of the equation. In the roughest sense, we can allow CICO to remain relatively intact and acknowledge that if we wish to simply estimate the change in the mass of the system (i.e. the amount of weight gain or lost) we can approximate it quite closely using CICO as a  “module” in our theory.


Let us explore this module a bit more. Within the CICO module we have our input, which is food, and we have our output, which is our energy expenditure. Between the input and the output we have even smaller modules that each contribute to the overall amount of energy that is being expended.

As you notice, we haveweight-8 weighted portions of the module to reflect their relative contribution to the energy output.  This needs some explanation. You will notice that we do not have what most people consider the most important factors (age, weight, height). The reason for this is quite simple, those factors all act upstream of our “micromodules” and can be accounted for in the pieces represented (e.g. hormones, NEAT, BMR).  We have also selected a few key hormones that represent different aspects of human physiology: neuronal regulation of hunger, basal metabolic rate, and gastric regulation of hunger (ends up having central nervous system effects but you get the idea). You will notice that exercise makes up substantial smaller piece of energy expenditure than non-exercise activity thermogenesis aka NEAT; the amount of physical activity you get just moving around during the day. Environment also plays a fairly important role in energy expenditure; for example, living in cold temperatures can substantially increase energy output. Importantly, you will also notice that the food component makes up the smallest piece of the expenditure. While there are minor differences on energy output based on the thermic effect, the hormonal changes, and the timing of your food choices, the relative contribution to your total expenditure is highly insignificant when compared to the rest of the things that control expenditure. Importantly, all of these are modifiable, meaning you can turn these knobs through your lifestyle.


Changes in Quality

Now that we have addressed changes in the quantity of the mass of the system (i.e. the number on the scale) now we need to address the changes in the quality of the mass of the system (i.e. the relative amounts of muscle, fat, bone, and overall metabolic health of these systems and other organs). As you can see in the figure below the overall “modules” in this piece are the same as those in the Change in Mass Quality Modules, they are just weighted a bit different.

The first large difference is the exercise piece. Decades of research have clearly shown that specific modes of training elicit specific physiological adaptations. For example, resistance training elicits a robust hypertrophy response in skeletal muscle while aerobic training elicits more metabolic responses (i.e. increased mitochondria). Secondly, we know that the volume, or quantity, of the work you do is a major driver of physiological adaptation. Thus the quality, quantity, and intensity of the work you do dictates a large portion of the changes in mass quality. We will discuss this in more depth in a future post but for now, this explanation gets us 90% of the way there.


Decoupling Our Ideas

The prevailing idea is that energy balance is at the bottom of all things regarding weight regulation. I find this to be a tad myopic, it neglects the second module of changes in quality of the system. And although the two are inexplicably linked, we can decouple our ideas of Tissue Quality and Tissue Quantity. This actually allows you to make adjustments and adaptations to your body in a much more nimble way than the over simplistic approach about calories-in-calories out.  At bottom, it is about calories when it comes to weight. When it comes to tissue quality it is about calories in, and the work being done and the signals driving tissue adaptations.

Applying the framework

Reasoning by analogy is inherently flawed and narrows the scope of our ability to think about a topic; however, in this situation I think an analogy gets us part of the way there.  Imagine the body is a mixing board. When using a mixing board a signal of energy comes in and you move knobs and levers to control the volume, pitch, and frequency, of the signal that goes out. The body is similar and you have a lot of knobs that determine the amount of energy that leaves the system and what that energy does to the system while it is in there. The levers and knobs consist of our hormones, our activity levels, our sleep, our type of training, and a lot of other things we probably don’t even know about.

Now we get to the hard part, which knobs are most important, and which ones do we turn to augment our fat loss. The answer? No one has a very good idea of the exact contributions in normally functioning human physiology. However, it is safe to say that at given times we are able to use clues and hints as to which knobs to turn and focus on. For example, if a client comes to you and has had substantial trouble losing weight despite a low calorie diet (~1,200 kcal/day) and substantial exercise (assuming this is true and they aren’t misinforming you) and blood work comes back with a T4 level of 0.6 ug/DL then you know that cutting more calories to around 800 kcals/day is probably not the solution to fat loss.

Based on the current state of knowledge we can use systematic inquiry (essentially guess and check) to fine tune nutrition, training, and lifestyle to adequately address each of the components that dictate changes in weight (quantity) and body composition (quality). People who are solely interested in the number on the scale simply have a math problem, albeit a complex one. In those cases you should focus on the modifiable “knobs” or modules that have the biggest impact on the energy balance equation: Food intake, NEAT, exercise, and hormones; however, the exercise piece is really the least important piece to emphasize for energy balance amongst these.

The people who are interested in changes to the quality of the system present a harder challenge. In addition to the balancing of the energy scale you need to really consider training modalities, hormones, and macros to a much bigger extent. These two things can have a substantial effect on the outcome. For example, if someone wants to lose twenty pounds you can really give them any ratio of macronutrients (example: 10% protein, 70% carbs, 20% fat) you want and any type of training program (buy one of those crappy programs from a facebook ad and give it to a client) and if you have the right caloric deficit and hormones aren’t completely disastrous you can get that scale to move. Conversely, if you have someone who wants to add 10 pounds of lean muscle mass and lose 10 pounds of fat mass (net balance on the scale) that same approach is going to be a disaster.


That was over 4,500 words to essentially say that Weight Regulation ought to really be thought about as two “overlapping magisterial”. Weight Regulation of the human body is best thought of in terms of Quantity (weight) and Quality (types of tissue). Quantity is easily understood and explained by thermodynamics and is, in essence, a math problem. Quality, or body composition, is best thought of as a complex mixture of signals that range from food type to hormones to exercise type, quality, and frequency. While this framework is not simple, it gets us much closer to the truth and allows for more nuanced discussion and a better way forward.  The next piece in this series will flesh out the models even further and get down into a little more detail with how we should think about this topic on a “societal and global” scale.


*There are some interesting hypotheses about how the laws of physics may not be homogenous throughout the entire universe, but even if true all experimental evidence indicates that the established laws of thermodynamics hold true here on earth, where this discussion is being had.

**Using the word theory implies a very special meaning. Comments about how gravity is “just a theory” really misses the mark. Don’t be one of those people who assumes a theory is an idea held together tenuously by disparate pieces of data. . . .

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