Diet principles I wrote for my gym with a focus on low-carb keto diets by request

Nothing in biology makes sense except in the light of evolution

Theodosius Dobzhansky (1973)

  • All food is composed of 1 or more of the 3 macronutrients
        • protein (made from amino acids; like leucine, tryptophan, glutamine…)
        • carbohydrate (made from sugars; like starch, sucrose, lactose, dextrose…)
        • fat (made from fatty acids; like palmitic acid, oleic acid, cholesterol…)
  • All food contains a variety of micronutrients
        • minerals (atoms like Magnesium, Sodium, Copper…)
        • vitamins (like Vitamin A, Vitamin B, Vitamin E…)
  • All animals have essential and non-essential macronutrients and micronutrients
        • The non-essential ones are those their bodies can make and the essential ones are those their bodies cannot make and so must obtained from their food or water
  • 2 essential macronutrients for humans
        • fat
        • protein
  • ~ 40 essential micronutrients for humans
      • 15 essential minerals
        • Potassium (K), Chloride (Cl), Sodium (Na), Calcium (Ca), Phosphorous (P), Magnesium (Mg), Iron (Fe), Zinc (Zn), Manganese (Mn), Copper (Cu), Iodine (I), Chromium (Cr), Molybdenum (Mo), Selenium (Se), Cobalt (Co)
      • 14 essential vitamins
        • Biotin, Folic acid, Niacin, Pantothenate, Riboflavin, Thiamine, Vitamin A, Vitamin B6, Vitamin B12, Choline, Vitamin C, Vitamin D, Vitamin E, Vitamin K
      • 2 essential fatty acids (from fats)
        • omega 3 (ω-3) fatty acid Docosahexaenoic acid (DHA)
        • omega 6 (ω-6) fatty acid Arachidonic acid (AA)
      • 9 essential amino acids (from protein)
        • Phenylalanine (F), Valine (V), Threonine (T), Tryptophan (W), Methionine (M), Leucine (L), Isoleucine (I), Lysine (K), Histidine (H)
      • 6 conditionally essential amino acids (from protein)
        • Arginine (R), Cysteine (C), Glycine (G), Glutamine (Q), Proline (P), Tyrosine (Y)
      • Humans are obligate carnivores but preferential omnivores
        • all of our essential macro and micronutrients can only be obtained from animals
          • only some of our essential macro and micronutrients can be obtained from plant foods
        • historically, humans have nearly always eaten a mixture of animals and more or less plants
      • It is more correct to say that humans are adapted to certain properties of foods rather than say that humans are adapted to this or that food



Individualizing diet

Understanding that “humans are adapted to certain properties of foods” we can then visualize foods and their hierarchy in a healthy diet for humans, using Dr.Naiman’s (@tednaiman) Food Pyramid.Ted Naiman low-carb food pyramid

Unhealthy foods for all humans

The following 3 kinds of foods are what the vast majority of processed (‘junk’) food is made of. However, they are also found in unprocessed forms and said to be healthy – this is false.

  • Cereal grains (wheat, maize/corn, rye, barley oats, spelt, teff…)
      • this includes their processed flour forms (croissants, cakes, bread, muffins…)
  • Vegetable oils (soybean oil, corn oil, cottonseed oil, canola oil, sunflower soil, rapeseed oil, grapeseed oil, safflower oil, rice brain oil…)
  • Sugar, processed out of their original foods (Agave nectar, Beet sugar, Cane sugar, Caramel, Corn syrup, Dextrose, Fructose, Galactose, Molasses – there are ~56 different names!)

Healthy foods for all humans

*assuming no allergies or rare diseases

Ted Naiman low-carb food

Personalize the Food Pyramid/Guide

  • Grey Area foods
    • foods that can be part of a healthy diet for a minority of people because either (1) the food has not very many of the properties humans are adapted or (2) it has too many properties that humans are not adapted to (like anti-nutrients)
    • Dairy is a ‘grey area’ food
      • if you eat dairy, it should always be full-fat and raw
      • do not eat dairy if, after testing, you notice negative symptoms (allergies, poor digestion, loss of focus, joint pain…)
      • do not eat dairy, according to the precautionary principle, if if you have an autoimmune disease (asthma, celiac, multiple sclerosis…) or cancer
    • Legumes (from Fabaceae family) are a ‘grey area’ food
      • examples:  peas, beans, lentils, soybeans, peanuts…
      • if you eat legumes, (1) they should be a small part of your diet that does not displace essential animal foods because you cannot rely on them alone for adequate micronutrition and (2) they should not lead you to exceeding your level of carbohydrate tolerance
      • do not eat legumes (or eat very little of them only occasionally) if (1) you are on a low-carbohydrate diet or (2) if, after testing, you notice negative symptoms (allergies, poor digestion, low of concentration, joint pain…)
      • do not eat legumes, according to the precautionary principle, if if you have an autoimmune disease (asthma, celiac, multiple sclerosis…) or cancer
    • Rice is a ‘grey area’ food
      • Rice is a cereal grain which humans are poorly adapted to because, generally, they contain high levels of anti-nutrients, many indigestible elements, a lack of sufficient essential amino acids and fatty acids as well as being very high in starch (carbohydrates)
      • White rice is probably the least harmful cereal grain because it is stripped of many of its anti-nutrients and is mostly plain starch
      • do not eat white rice if, after testing, you notice negative symptoms (allergies, poor digestion, low of concentration, joint pain…)
      • do not eat white rice, according to the precautionary principle, if if you have an autoimmune disease (asthma, celiac, multiple sclerosis…) or cancer
    • Potatoes are a ‘grey area’ food
      • if you eat potatoes, (1) they should be a small part of your diet that does not displace essential animal foods because you cannot rely on them alone for adequate micronutrition and (2) they should not lead you to exceeding your level of carbohydrate tolerance
      • do not eat potatoes (or eat very little of them only occasionally) if (1) you are on a low-carbohydrate diet or (2) if, after testing, you notice negative symptoms (allergies, poor digestion, loss of focus, joint pain…)
      • potatoes are not a food you need or should be eating if diabetic or trying to lose weight
    • Sweeteners
      • do not use sweeteners if, after testing, you notice negative symptoms (allergies, poor digestion,loss of focus, joint pain…) and difficulties with appetite or weight loss
      • if you use sweeteners, they should not cause you to crave more sugary/starchy foods
  • Allergies to usually healthy foods
      • for example, if you are allergic to a shellfish or a certain fruit, do not eat it
  • Carbohydrate tolerance
      • historically, humans mostly ate a lower-carbohydrate diet
        • 16%-22% carbohydrate was the most common and 29%-34% carbohydrate was the next
      • in 2016, no modern population is healthy on a high-carbohydrate diet (65%-75% carbohydrate)
      • however, there are a few but important non-industrial examples of healthy groups (Kitava, Tokealau…) eating high-carbohydrate diets
      • if you suffer from diseases (diabetes, obesity, heart disease, cancer…) you should not be on a high-carbohydrate diet and should eat a low-carbohydrate or ketogenic diet



What we are (mainly) trying to avoid

We want to avoid being ‘hangry’ (hungry + angry = hangry). We can understand it better by once again looking at my friend Dr.Naiman’s (@tednaiman) diagram of the simplified physiology.

hangry ted naiman

Carbohydrate tolerance

The best, widely available, clinical test for figuring out your carbohydrate tolerance measures your sensitivity to the hormone insulin. You can ask your doctor for a 2hr Oral Glucose Tolerance Test (OGTT) with insulin assay. Your doctor might not order it for you if you are not diabetic or pregnant so you might have to pay for it directly.

  • The test
    • at time 0 (T0), the nurse takes 2 fasting blood samples, 1 for your glucose & 1 for your insulin
      • you then drink 75g-100g of a (medical) glucose drink
    • 30min later at T1, the nurse takes 2 ‘fed’ blood samples, 1 for your glucose & 1 for your insulin
    • 30min later at T2, the nurse takes 2 ‘fed’ blood samples, 1 for your glucose & 1 for your insulin
    • 30min later at T3, the nurse takes 2 ‘fed’ blood samples, 1 for your glucose & 1 for your insulin
    • 30min later at T4, the nurse takes 2 ‘fed’ blood samples, 1 for your glucose & 1 for your insulin
  • What the test tells you
      • it measures how well your body handles the glucose you drank
      • you want to be a Kraft Pattern I: the other responses are pre-diabetic, type 1 or 2 diabetic

Screen Shot 2016-07-20 at 16.18.55



Ketogenic diets

  • Basics
      • very low in carbohydrates (< 15%-10% carbohydrate; usually < 10%)
      • moderate in protein, not high-protein diets (< 25%-20% protein; usually ~20%)
      • high in fats (>65%)

A ketogenic diet is called “ketogenic” because eating this way turns some of the fat in your diet and in your body into molecules mainly used for energy called ketones: acetoacetate, acetone and β-hydroxybutyrate. Being ‘in ketosis’ is not an exact thing but usually means having more than 0.5mmol/L of β-hydroxybutyrate (BhB) in your blood.

  • Adaptation period
        • getting your body used to (1) using much more fat than carbohydrate for energy and (2) using much more animal fats (from beef and sardines for example) than processed vegetable oils (like canola and sunflower oil) for maintaing your basic biology
        • the time and difficulty in getting fully keto-adapted depends on (1) your general health, (2) your genetics and (3) your compliance to the diet
        • short-term adaptation
          • the 2 to 8 weeks it takes for most people to ‘feel good’ (or normal) after switching from a higher-carbohydrate modern Western diet to a well formulated ketogenic diet
        • long-term adaptation
          • the couple of months or +1year it can take athletes to perform the same or better at their activity or sport
        • Women, physiologically, can adapt to a ketogenic diet as well as men can. However, cultural differences may present women with a few more challenges
          • (1) they are often expected to ‘cut calories’, giving them lower energy levels
          • (2) they are told even more than men that ‘fat makes you fat’ which is false
          • (3) the fluctuations in mood and energy that often come with menstruation can make an already challenging adaptation even more uncomfortable; it will pass!
        • Men tend to be less patient than women, finding it harder to take a short break from their ‘performance goals’ to properly keto-adapt.
        • ketogenic diets are diuretic, meaning they make you pee more, leading to an increase loss of minerals (salt, potassium, magnesium…)
          • (1) this is a normal, healthy response (doctors tell us ‘the less salt the better’, this is false)
          • (3) mineral loss often causes negative symptoms some people have whilst keto-adapting
          • (4) if you feel low in energy, have a headache or heart palpitations, supplement with salt, potassium and/or magnesium
          • (5) add salt to your food using (a) your taste and (b) your sense of well-being afterwards
  • Factors influencing ketosis
      • the more exercise we do, the more grams of protein or carbohydrates we can eat without getting out of ketosis
      • being stressed (for e.g., releasing lots of cortisol) can temporarily lower blood ketone levels
      • the better you sleep, the easier it is to be in ketosis
        • sleep quality depends on (1) duration [how long…], (2) frequency [how many times you sleep in a 24hr cycle…], (3) quality [how ‘deep’ your sleep is, how complete and effectice your sleep cycles are]
      • omega-3 fats from fish (like sardines and mackerels) seem to help people get into ketosis more easily, although it is not known why

Ketogenic-specific Food Pyramid

This is a ketogenic food pyramid by Luis Villasenor’s (@ketogains). It has quantitative recommendations for health and performance goals.

keto gains food pyramid

Questions? raphi.inter[at]

Cholesterol, Pauli Ohukainen & Authority

No. Just that it’s always the totality and magnitude of all risk factors over a lifetime that ultimately determines when CVD hits

So it’s multifactorial? Sure. But you see, saying that explains everything and anything. Or, nothing…A good theory unifies these disparate factors. That’s what we failed to do in medicine so far. And risk factors are just that, statistical associations. A theory does away with those statistical artefacts that are mere associations and identifies only those relevant, causal elements. The 2 hypotheses that feed into one another, the Diet-heart hypothesis (fat => cholesterol => bad) and Cholesterol-CVD hypothesis (cholesterol blocks arteries) have both been falsified. First off, there are multiple associations which falsify the both theories: midle-aged women have lower all-cause mortality the higher the cholesterol, Japanese cohorts don’t see increased risk of death or CVD with higher LDLc or TC, the supposed French Paradoxes etc..It’s important to note that you don’t need to weigh the number of associations supporting & not supporting your hypothesis; (assuming the associational study is well done – granted, a big ‘if’) all that is needed is a single association that doesn’t fit your hypothesis and that is enough to have it thrown out. All the other favorable ones be damned!

Although LDL’s causality is inferred from many independent lines of evidence, it may not always predict a clinical event.

This is where peopole in medicine and nutrition need to wake up: IF YOUR THEORY FAILS IN ITS PREDICTIONS, ITS A SHITTY THEORY. We need to realize that the fields of medicine & nutrition are the alcoholics of the science world: the first step for solving a problem is realizing & admitting we have one. Mainstream advice for weight loss, diabetes, CVD, cancer, alzheimers etc. SUCKS! Why? No good working theory. Neither about what causes these diseases or what revereses them. We fail to admit we cannot predict, to any useful degree, who will get it & why. We rather brandish the Multifactorial Flag – a useless truism & tautology mostly – so that we can hide & excuse our ignorance, pretending there is no general lack of scientific acumen amongst researchers.


So how ’bout dem receptor kinetics? Part of the ‘great work’ or ‘misapplied findings’?

What about receptor kinetics? What are you expecting the Michaelis-Menten constant (Km), in and of itself, to tell you about the causal process of CVD? You can do all the molecular biological work you want, but that will not replace animal & human models with controlled variables and well donce associational studies falsfiying predictions (or anything you might see in in vitro for that matter). I study molecular biology and can honestly say that Brown & Goldstein’s work to elucidate the FH gene is impressive and exemplar of good science for aspiring researchers. Credit where credit is due. But this isn’t politics, so there are no ‘carry over credits’ for the other claims they make using their original discovery. On day 1 of a genetics course, you understand that although there is ‘the gene for X’, this is in NO WAY A GUARANTEE that only X is affected by the gene encoding it. The nuclear DNA library is a vanishingly small fraction of the story of how phenotype emerges from genotype. The way laymen & the majority of doctors I know, really do not understand genetics. I would count myelf in that group until about 2010. Furthermore, my understanding of epigenetics has completely changed in the past 4 months after reading Mark Ptashne’s work. In fact, many *geneticists* talk a lot of nonsense about epigenetics (as I did previously). No one needs to take the word of Nobel Laureates. That’s the beauty of it all, we can and should scrutinize their ideas. The findings of Brown & Goldstein do not support Cholesterol-CVD hypothesis; rather interestingly, it open up a door to the pivotal role cholesterol plays in the maintaing epithelial integrity and how this affects its interactions with solutes in the blood. Cholesterol IS important in CVD, but not as an inherently negative agent.

Thoughts on Cancer

Edit (26/01/2016)

The problem of cancer is not to explain life, but to discover the differences between cancer cells and normal growing cells. Fortunately this can be done without knowing what life really is. Imagine two engines, the one being driven by complete and the other by incomplete combustion of coal. A man who knows nothing at all about engines, their structure, and their purpose, may discover the difference. He may, for example, smell it.” – Otto Warburg 1956

If one could only learn about human health studying a single disease, I would pick cancer hands down. Its study boils down to askying why and how some cells either live or die. The distilled question is simple, yet cancer is everything but. It forces us to confront its behavior which is easily anthropomorphized: why are these cancer cells malcontent with simply being alive? What is this insatiable need for invading other body parts? Stranger yet, cancer is synonymous with death even though it is part and parcel with biological life – even trees get cancer or at least they also manifest uncontrolled cell growth. Stringent definitions are hard and tedious to establish but they really matter, so for the sake of clarity lets use the Hallmarks of Cancer. It embodies the 6 behavioral traits characterizing the disease:

  • Evading death
  • Ignoring anti-growth signals
  • Self-directing growth
  • Literally being blood-thirsty
  • Invading other tissues
  • Unlimited replication

The currently accepted Somatic Mutation Theory of cancer (SMT) is based in genetics. Basically, it asserts that a single cell suffering 1 or more random mutations conferring it with a growth advantage deleterious to its host, can cause cancer. In other words “it is a disease in which an individual mutant clone of cells begins by prospering at the expense of its neighbors” (Molecular Biology of The Cell, 6th Edition, page 1091). Progress towards cures for this still enigmatic disease has been thoroughly underwhelming. In my view, this is mostly because mainstream cancer researchers place too much confidence in SMT. To be fair, cancer cells are 1 big tangle of mutations. But they are not just that. They are also deformed (abnormal morphology). Maybe most importantly, how they make their energy changes. More specifically, their respiration is damaged. Or maybe not – and this is where the bone of contention lies. Some SMT proponents will argue that respiration is not actually broken, that it is just not used as much nor necessarily for the same purpose. Others, that it does not matter either way. The Metabolic Theory of cancer (MT) argues that the functional state of respiration in a cell really matters because broken respiration can cause cancer. The mutations seen in cancers thus result from, rather than cause, damaged respiration. MT is thus based in metabolism rather than genetics. MT proponents say that all cancer cells are characterized by faulty energy production systems. Theys can no longer produce their share of energy (ATP) by respiring and that respiratory function is substantially decoupled from ATP production. By analogy, the cell is stomping on both the accelerator and clutch simultaneously. This is not good for cars or cells. MT argues that such respiratory dysfunction instantiates changes (many of which are genetic) leading to the 6 behavioral traits somehow exhibited by the disease.

Mainstream research has primarily dimissed MT because of 2 observations. 1) A normal cell can be reliably turned cancerous upon suffering certain mutations. This is undeniable. Consequently MT proponents argue that such mutations leads to cancer to the extent that they interfere with normal respiration. 2) Not all cancer cells have a broken respiration system. This is disputed. MT proponents say respiration only appears functional because the cell is in fact ‘pseudo-respiring’. Pseudo-respiration is the generation ATP using the mitochondria’s respiratory apparatus for fermentation, but without consuming oxygen. Mitochondrial fermenatation involves a process called substrate-level phosphorylation.

Given that the first observation supporting SMT over MT suffers from a pesky alternative causal explanation and the accuracy of the second one is disputed, a good clarifying question to pose at this is point is whether it is easier to identify a cancer cell based on its profile of mutations or on the integrity of its respiratory apparatus? I would venture the latter. Consider the thousands of mutated genes strongly associated with cancer (oncogenes) that, when woven together into clinical interventions duly fail to save lives by and large. At this juncture it would be sound to question the entire conceptual premise upon which these interventions were conceived. Instead we do what so many health gurus excel at: polishing turds. We dress up our failure as success and give it a sexy name, ’personalized cancer genomics’. Tilt your head to the left and it is the most exciting, cutting-edge approach that is truly tailored to the special snow-flakes that we all are. Tilt your head to the right and it is an implicit admission that our interventions do not work to any significant extent  on a population level because the SMT underpinning them is plain wrong. Similarly, the notion that cancer is not just 1 disease but many different ones may stem from the same reluctance of recognizing failure. See the fascinating success story of treating Chronic Myeloid Leukemia (CML) with Imatinib (Gleevec). It is all the more interesting given how plausible MT-derived alternative explanations are.

There are many finer theoretical and experimental points that can and should be debated. However, the final point I wish to make is somewhat different. Consider the phenomenon of Epithelial to Mesenchymal Transition (EMT) which proposes a mechanism for metastases within the SMT framework: clonal selection (natural selection) acts upon a series of genomic alterations (mutations) such that a cell acquires invasive (metastatic) behavior. Simple, right? Not quite. Especially not when considering what metastases actually involve. Thomas Seyfried explains this best (bold highlights & numbering are my additions):

“It is difficult to understand how a collection of gene mutations, many of which are random, could produce cells with the capacity to [1] detach from the primary tumor, [2] intravasate into the circulation and lymphatic systems, [3] evade immune attack, [4] extravasate at distant capillary beds, and [5] recapitulate epithelial characteristics following invasion and proliferation in distant organs. This would be quite a feat for a cell with a disorganized genome.” (Metabolic Theory of Cancer, Chapter 13.2.1)

Yes, cancer cells are very clever but their genomes are also very messy. They are successful but not happy. Ultimately the ‘mutations all the way down’ explanation for their malicious behavior is unsatisfying. A better although incomplete explanation is tentatively available: damaged respiration usually forces a cell to (1) repair itself or (2) commit suicide. Things go awry when it ignores options 1 and 2. It is in the adaptation to (3) staying alive without proper repairs that cancer emerges. Adapting to alternative methods of energy production due to damaged respiration forces a cell to survive through different means. These different surival strategies call for a lot of glucose, glutamine, always more vasculature and tissues capable of sustaining the engendered momentum. Does MT do a better job of explaining the seemingly calculated behavior of metastases? Somewhat. The ‘energy seeking tumor’ explanation is more plausible in my mind than the one of ‘random mutations conferring super-powers’.

Regardless, cancer is a bitch.

The Thrifty Genotype/Phenotype Hypothesis is wrong

In 1962 James Neel proposes the Thrifty Genotype hypothesis and then the Thrifty Phenotype hypothesis as an addendum of sorts to the original theory. A thrifty gene uses resources carefully. It does not waste them, so the idea goes, because its predecessors dealt with famine by conserving energy. A thrifty phenotype however develops because of pre-natal (intrauterine) famine conditions which signal energy conservation. Given that modernity doles out virtually famineless environments, there is a consequent mismatch with this thrifty character trait. A life-saving adaptation comes back to bite us in the ass; diseases of civilization such as diabetes and obesity ensue. The simplicity of the mismatch hypothesis (an outgrowth of Darwin’s theory of natural seclection) lends undue credence to the Thrifty Gene and Thrifty Phenotype hypotheses, making them alluring, even palatable. Both are perfectly reasonable hypotheses but are wrong.

The hypothesis stumbles when its foundational assumption is challenged. The assumption is that Paleolithic humans feasted and were frugal with those calories to pre-empt recurring famines. The burden of proof clearly lies with proponents of this claim. Accordingly, Allen and Cheer say that “this is far from generally accepted. Nevertheless, the burden is not typically shouldered, most likely because of how entrenched Hobbes’ solitary, poor, nasty, brutish and short description of pre-agricultural societies has become. So what evidence is there for these recurring Paleolithic famines? Not much aside from flimsy extrapolations derived from observing modern hunter-gatherers struggling to feed themselves within dwindling hunting areas losing flora and fauna. There is strong evidence of famines reliably threatening humans once agriculture took off. Do we entertain such assumptions for other species? Not without pertinent climate or archeological evidence. Evidence suggestive of recurring famines seems entirely lacking for pre-Neolithic humans. Many find it hard to accept that the same innovation (farming) responsible for population explosions (putative progress) also regularly incurs famines (an obvious negative). Too bad.

The thrifty phenotype purports to stem from an increased propensity to store energy. It is only logical to consider how insulin dynamics may participate in this. Non-insulin dependent glucose uptake aside, the less insulin you need to get glucose into a cell, the more insulin sensitive you are. The more insulin sensitive you are, the more thrifty your genotype/phenotype, since you require less (insulin) to store a given amount of calories. Thriftiness is maintained in a famine not by being insulin sensitive but by being more insulin resistant. Specifically, you become more peripherally insulin resistant, prioritizing glucose shunting to your brain which cannot do without it. In other words, more of the limited available energy is used for essential functions (thriftiness). This insulin resistance is not pathological, unlike hyperinsulinemia-associated insulin resistance characterizing metabolic syndrome. Rather, the innate capacity to move along either extremes of the insulin sensitivity spectrum is central to how varying energy availability is handled. This thrifty hypothesis is problematic because it pathologizes a normal adaptation.

Watve and Yajnik raise 5 principle objections to the Thrifty genotype/phenotype hypothesis worth considering:

  1. Neel’s arrow of causality indicates that insulin resistance begets obesity. Watve and Yajnik argue obesity begets insulin resistance by pointing out how insulin sensitive Pima Indians are more likely to be overweight.

Point 1 is still an open question. Just as there are different ways one can suffer an infection, it is plausible that obesity could result from insulin resistance in some and in others engender it. However, obesity is most strongly correlated with insulin resistance. This correlation likely strengthens upon adopting more stringent measures of insulin resistance like Kraft’s criteria.

  1. Neel argues lower birth rates would give rise to thrifty phenotypes but this relationship does not bear out empirically.

Point 2 indicates a correlation between diabetes risk factors (as measured by a 2 hour OGTT with insulin assay) and low birth weights. This correlation is robust. The tissue of these infants born underweight is predicted to have a lower resting metabolic rate, reflecting a thrifty phenotype. However, authors Eriksson et al. found that “the muscle tissue of people who had a lower birth weight is more metabolically active than those with a higher birth weight. Yet another correlation bites the dust in the wake of prediction testing.

  1. Food is intermittently available throughout the year in colder climates, predisposing inhabitants to insulin resistance in order to cope with periods of low-food availability. Contradicting this prediction is the observation that ethnic groups inhabiting more northern latitudes do not appear more insulin resistant than their equatorial counterparts.

The prediction in point 3 is that people in colder climate are more insulin resistant and thus more likely to become obese (or diabetic). This correlation does not bear out. Many counter observations  are available.

  1. Obesity might be more of a neurobehavioral disorder than a metabolic one according to O’Rahilly and Farooqi. This is not a convincing argument in and of itself (for many reasons) and is best summed up as ‘moving the goal posts’.

Point 4 is a weak attempt by Watve and Yajnik to argue against an insulinocentric theory of obesity by invoking leptin as the center piece of a neuro-hormonal approach. By now it is clear to anyone paying attention that obesity cannot be explained by insulin dynamics alone. Yes, we should attempt to understand how our brain integrates insulin and leptin signaling into behavioral outputs. Re-branding the problem as neurohormonal does nothing to advance that.

  1. The Thrifty hypothesis does not attempt to account for insulin signaling aspects including but not limited to longevity, reproduction and immunity.

Point 5 argues non-metabolic aspects of insulin are ignored by Neel’s theory. This is correct. However the point is somewhat facile given how much has been learned about insulin since the theory’s inception.

The cure for a disease is not necessarily the reciprocal of its cause. Keeping this in mind, the thriftiness-insulin axis of obesity is seriously dented by Christopher Gardner’s recent study randomizing insulin sensitive and insulin resistant subjects to a low-fat (~57% carbs) or low-carb (~18% carbs) diet. It failed to detect “a significant interaction between diet assignment and IR-IS status.

The Thrifty hypothesis is doomed to fail considering it does not account for the insulin sensitivity of different tissues. In a similar vein, it also fails to distinguish between pathological and adaptive insulin resistance. Finally, Allen and Cheer nicely summarize how this hypothesis falls prey to taking metaphorical reasoning too far, saying <<Mcgarvey states that the wide acceptance of the concept illustrates the generative role of metaphorical thinking in bioanthropology. Whether the concept is correct or not (in the narrow sense), it “allows for the generation of concrete studies of metabolic processes and their fertility, mortality and morbidity concomitants”>>.

Rebuttal of 14 claims about metabolism, genetics, paleoanthropology & stable isotope analyses in Hardy et al.’s 2015 paper “The Importance of Dietary Carbohydrate in Human Evolution”


The evolutionary selective pressures which drove human encephalization are passionately debated, largely because the brain’s function accounts for much of our ill-defined and ever-changing ‘human uniqueness’. Apportioning individual contributions from the multitude of factors ranging from climate change to food and socialization dynamics requires a truly multidisplinary approach encompassing evolutionary biology, genetics, medicine, archeology, chemistry, physics, climatology and many more scientific fields. It is with this wider perspective that evidence for how food contributed to human encephalization is assessed. Clues from human metabolism, anatomy and food web positioning in addition to stable isotope analyses do not generally support the hypothesis that cooked starches were a major driver of human encephalization. Land and marine life as wells as birds and insects seem to have contributed substantially more to it. 11 points on metabolism, 1 on genetics, 1 on paleoanthropology and 1 on stable isotope analyses are individually rebutted.

In their paper, The importance of dietary carbohydrate in human evolution, Hardy, Brand-Miller, Brown, Thomas and Copeland argue for the importance of dietary carbohydrate (mainly in the form of cooked starches) in human evolution and particularly encephalization. One way they do this is by drawing an association between the supposed advent of controlled fire use (cooking) by hominims with increases in AMY1 copy numbers. Neither the timeline for the emergence of cooking or the AMY1 copy number increase are supported by their citations or by more recent evidence discrediting this purported association. Much of their argument is thus based on genetics, paleoanthropology and both general and specific claims of human metabolism as well as stable isotope anlayses, all of which can be individually rebutted.

Stable istope analysis

Hardy et al. severely misquote a 2009 paper by Richards and Trinkauss – in which stable isotope analyses of Oase 1 humans and Neanderthals were performed – because it is used to support the notion that human diets likely included substantial amounts of starch given the variations in  δ15N and δ13C ratios.

[1]“a wider range of isotopic values have been observed in contemporary Middle Pleistocene H. sapiens (Richards and Trinkaus 2009), indicating that considerable differences in the levels of starch consumption existed between these two species”

The 2009 paper by Richards & Trinkauss actually found that “early modern humans (~40,000 to ~27,000 cal BP) exhibited a wider range of isotopic values, and a number of individuals had evidence for the consumption of aquatic (marine and freshwater) resources […] The other early modern humans all have δ13C values < –18.5‰ (see Fig. 1 and Table S2), which indicate that their protein came from terrestrial C3 (or freshwater) foods, yet many of them have high δ15N values, at or above the highest Neanderthal values”. It is unequivocal that the δ15N and δ13C variations refer to the dietary apportioning of land versus marine protein, not to the ratio of dietary carbohydrate versus fat. In fact, the authors clearly state that “The Oase I δ15N value is also above those of the hyena (11.1‰), and the highest wolf value (11.5‰) from the same site and dating to about the same time”. This also unequivocally contradicts the notion that starches were a significant dietary contributor for these hominins given that this evidence suggests that they were more carnivorous than Neanderthals, hyenas and wolves as evidenced by their Figure 2 represented here.

Isotope Analysis of Oase 1 humans & Neanderthals vs other animals


Hardy et al. cite 2 papers to substantiate the claim that the copy number increase of AMY1 occured less than 1 million years ago despite neither paper supporting it.

[2]“it [multiplication of the AMY1 genes] is thought to be less than 1 million years ago (Samuelson et al. 1996; Lazaridis et al. 2014)”

  • The first paper from 1996 by Samuelson et al. gives no specific date concerning the emergence of AMY1 copy number increase since it was not the objective; their objective was to “infer the structures of common ancestors and trace the evolution of the modem human amylase promoters”. The second is a 2014 paper by Lazaridis et al. which also does not mention a date for AMY1 copy number increase since it is focused on issues of lineage by studying 7-8kya Neolithic skeletons from La Braña (Spain), Motala (Sweden) & Loschbour (Germany). Only once does Lazaradis et al. briefly reference a hypothesized association between AMY1 copy number increase and high starch diets by citing Perry et al. 2007. Interestingly, the latter reference contradicts Hardy et al.’s purported timeline for AMY1 gene copies increase as, in their words, it was mosty likely of “a relatively recent origin that may be within the timeframe of modern human origins (i.e., within the last ∼200,000 years”.
  • Irrespective of when the when copy numbers of AMY1 increased, the significance of this is still unclear. It has been hypothesized that more copies of the AMY genes would improve glucose homeostasis on higher starch diets and protect against obesity (1, 2, 3). Nevertheless, Nature Genetics in June 2015 published a study by Usher et al. where the authors did “not observe even a nominal association between obesity and the copy number of any amylase gene (P = 0.70 for AMY1)” nor did they in diabetic cohorts where “AMY1 copy number did not associate with BMI in any group (P = 0.31 for GoT2D controls, P = 0.24 for GoT2D cases and P = 0.53 for InCHIANTI samples)”. Usher et al. explain why previous studies may have found associations that were not there given the use of ”lower-precision molecular methods, such as RT-PCR and array comparative genomic hybridization (CGH), or lower-precision analyses of whole-genome sequencing data to measure copy number”. It would be prudent to first understand the significance of the AMY genes in humans before using them as a mechanistic foundation in arguments relating to the evolution of humans and their encephalization.
  • In June 2015 Perry et al. avoided using “lower-precision molecular methods […] to measure copy number”, unlike Carpenter et al.’s group in March of that same year. This improved copy number assessment enabled Perry et al. to confidently conclude that “AMY1 gene duplications are likely human-specific and that they occurred following the divergence of our lineage from the Neandertal/Denisovan lineage ~550-590 kya”, contradicting Hardy et al.’s less than 1 million years ago timeline.


Hardy et al. correctly cite the only paper to date supporting their view of hominim cooking emerging less than 800kya. However, the natural event confounding the interpretation provided by this paper and more recent evidence to the contrary are not mentioned as a counterbalance. Furthermore, Hardy et al. do not provide the reader with a representative view of the balance of evidence which currently is heavily biased towards the hypothesis of fire emerging 400-300kya.

[3]”Gesher Benot Ya’aqov, in Israel, which dates to around 780,000 bp, has charcoal, plant remains, and burned microartifacts in concentrations that the excavators believe suggests evidence for hearths (Alperson-Afil 2008)”

  • A 2008 paper by Alperson-Afil is cited as evidence of when hominin cooking emerged 780kya as evidenced by the Gesher Benot Ya’aqov (GBY) site in Israel. It does in fact conclude that “as the scenario of a natural fire is unlikely, we conclude that the concentrations of burned flint microartifacts in the different occupational surfaces of GBY represent phantom hearths, i.e. remnants of hominins’ use of fire”.
  • This conclusion is probelmatic for 2 major reasons. The first is addressed by Shimelmitz et al. in 2014, where it is explained that “consistent evidence for fire is found not just in the Tabun sequence but at every Acheulo-Yabrudian cave site where good information is available. The near-absence of burnt flints in the lower 8m of the sequence at Tabun (composed of 19 layers) also indicates that the scarcity of fire evidence before 350 kya is not just a matter of spotty preservation, or cave sites versus open air-sites (e.g., Gowlett and Wrangham, 2013). Rather, the negative evidence from the early layers is genuine, and there is a significant and permanent increase in the frequency of evidence for burning between 357 and 324 kya […] our best estimate for the onset of regular fire use at Tabun is between 357 and 324 kya”.
  • The second problem is that lava probably invaded the GBY site, as correlated with the Matuyama-Brunhes chron boundary event taking place 781kya. This occurence further substantially confounds Alperson-Afil’s interpretation. Evidence for this event at GBY stems from “artefacts in fluvial conglomerates, organic-rich calcareous muds and coquinas that accumulated along the shorelines of the palaeo-Hula lake (Goren-Ibar et al. 2000)”.


A lot of Hardy et al.’s argument for the important role of dietary carbohydrate in human evolution and the spectacular encephalization is based upon claims about metabolism. These 11 points are at best taken out of context or at worse entirely false.

[4]“There is debate on whether dietary carbohydrates are actually essential for human nutrition”

  • Micronutrients and macronutrients have been defined as essential when their absence causes a deficiency syndrome. No such ‘carbohydrate deficiency’ has been found to date and, to the best of my knowledge, nor has any suggestive evidence surfaced in the last century.
  • The 1999 report by the IDECG Working Group, led by DM Bier, recognizes that “the theoretical minimal level of carbohydrate (CHO) intake is zero”, before following on about its importance in human biological function.
  • Glucose is essential for cells to function but it does not have to originate from dietary sources due to an evolved gluconeogenetic capacity capable of providing 150 grams per day for CNS functions. This lack of reliance on dietary glycose has been validated since at least 1975 by Cahill and Owen’s 2 month starvation experiment. Depicted in their Figure 1, they found that the human brain always requires at least 35% of its energy from glucose (not necessarily of dietary origin).brain usage of glucose & ketones
  • In 1972 Drenick et al. stress-tested human reliance on endogenous glucose showing that “after fasting 2 months, administration of weight-adjusted doses of insulin […] no insulin reactions nor significant rises in catecholamine excretion occurred despite equal extent and rate of glucose fall. Glucose concentrations as low as 0.5 mmoles/liter (9 mg/100 ml) failed to precipitate hypoglycemic reactions.”
  • When dietary carbohydrates are avoided entirely and protein is moderated the human brain will use ketone bodies as its primary energy substrate. In such a metabolic state, Vanitalie and Nufert say that “although these data need confirmation, they suggest an increase in the metabolic efficiency in human brains using ketoacids as their principal energy source in place of glucose”.

[5]“a more realistic recommendation is that at least one-third of dietary energy should be supplied from carbohydrates (Bier et al. 1999)”

  • For this Hardy et al. quote, the 1999 report by the IDECG Working Group, led by DM Bier, does not identify a carbohydrate deficiency syndrome but advises at least 150g per day for “practical reasons”.
  • Dr.Eric Westman summarizes his findings about carbohydrates being non-essential saying “although there is certainly no evidence from which to conclude that extreme restriction of dietary carbohydrate is harmless, I was surprised to find that there is similarly little evidence to conclude that extreme restriction of carbohydrate is harmful.”

[6]“Glucose is the only energy source for sustaining running speeds above 70% of maximal oxygen consumption (Romijn et al. 1993)”

  • Brook et al., who developed the Cross-Over Point hypothesis, did not state that glucose was the only energy source above 70% VO2 max, only that it and glycogen were the main sources and free fatty acids the minor source. Hardy et al. essentially describe a binary change in energy substrate use when in fact the change is quantitative.
  • It is an unproven assumption that carbohydrates of dietary origin are necessary to use glycogen and glucose as the predominant substrate for instances of near maximal VO2. Even if it were true that glucose use always predominates across all individuals at >70% VO2 max, this does not automatically imply that dietary glucose is the necessary fuelling strategy for sustaining such intense efforts. These are separate claims.
  • In 2015 Hetlelid et al. showed that well-trained runners performing high intensity training at 85% VO2 max, nearly one third of the total energy expenditure comes from fat oxidation. Furthermore, the lower intensity, steady state equations of indirect calorimetry used here and elsewhere overestimate carbohydrate oxidation and underestimate fat oxidation.
  • In 2014 Noakes, Volek and Phinney characterize this quantitative change in energy substrates according to effort intensity, saying “some highly adapted runners consuming less than 10% of energy from carbohydrate are able to oxidise fat at greater than 1.5 g/min during progressive intensity exercise and consistently sustain rates of fat oxidation exceeding 1.2 g/min during exercise at ∼65% VO2max, thereby providing 56 kJ/min during prolonged exercise. The remaining energy would comfortably be covered by the oxidation of blood lactate, ketone bodies and glucose derived from gluconeogenesis”
  • Preliminary results from Volek et al.’s soon to be published FASTER study (Fat Adaptated Substrate Oxidation in Trained Elite Runners) have been reported at Maximal fat oxidation rates higher above 1.54g per minute with 1 subject reaching 1.8g were shown. Classic sports physiology literature performed in non-ketogenic dieters with sub-optimal fatty acid oxidation capacities previously found maximal fat oxidation rates of only 1g min. These findings will, quite literally, require re-writing text books.
  • Elite ultra-runner and FASTER study participant Zach Bitter shared his personal data. At 75% VO2 max he used 98% fat and 2% carbohydrate. At 84% VO2 max he used 76% fat and 24% carbohydrate. Finally, at 96% VO2 max he was still using 23% fat and 77% carbohydrate.

[7]“In an evolutionary context, large stores of glycogen must be generated in order to provide sources of glucose for periods of sustained fasting or hardship. To build these reserves, the diet must consistently provide energy surplus to basal metabolic requirements”

  • The human gluconeogenetic capacity mentioned above disqualifies this imperative statement. Red blood cells, immune cells and the brain, as well as the more extended CNS, all function properly with glucose solely of endogenous origin.
  • The average lean male has tens of thousands of stored calories available to him in adipose tissue in contrast to his glycogen storage capacity of approximately 15 g/kg. Furthermore, his adipose tissue contains all the glycerol precursors required for endogenous production of glucose when combined with amino acids.

[8]“The diets of traditional Arctic populations are sometimes given as examples of successful high-protein diets (Lindeberg 2009)”

  • It is a common mistake to assume that the Inuit ate high-protein diets or it may simply be a misnomer arising from its association with the consumption of animal protein. In humans, high-protein diets lead to protein poisoning, otherwise known as ‘rabbit starvation’ (4, 5). The protein ceiling is 35-40% of calories or 200-300g of protein a day. The Inuit ate high-fat diets and their protein intake was moderate. Unlike the livers and kidneys of lions or wolves, human livers have substantially lower functional hepatic nitrogen clearances (FHNC) and urea nitrogen synthesis rates (UNSR) (6, 7).

[9]“15–20% from carbohydrate principally in the form of glycogen from the meat they consume (Ho et al. 1972)”

[10] “Meat frozen soon after slaughter will retain much of its muscle glycogen (Varmin and Sutherland 1995)”

  • Points 9 and 10 can be addressed together. The citation for point 7 should be “Varnam and Sutherland 1995”.
  • Greenberg et al. recognize the Inuit diet as one of “80–85% fat, 15–20% protein, and, apart from a little muscle glycogen, almost no carbohydrate” by citing Phinney’s 2004 paper.
  • Ho et al.’s 1972 study conduced in Point hope, Alaska, does not provide evidence of how this estimate is arrived except that it is based off of a 3,000-4,500kcal diet. Presumably, the reported glycogen levels are those of live animals.
  • Raw meat from a dead seal contains 0 grams of carbohydrate. In fact, in 1995 Varnam and Sutherland themselves explain that “if meat is frozen before ATP and glycogen levels are depleted post-mortem glycolysis is suspended. On thawing, however, the meat undergoes severe contraction with associated toughening and loss of large quantities of drip (thaw rigor)”. Simply stated “in response glycogen, the main energy store in the muscle, is converted to lactic acid by anaerobic, post-mortem glycolysis”. Momentarily and drastically slowing post-mortem glycolysis by flash-freezing meat will not stop glycolysis from depleting glycogen when the meat is finally thawed for consumption. The resulting lacate is a glucose element but is not counted into the percentage of dietary carbohydrate in a diet.
  • Flash-frozen meat does not contain glycogen levels anywhere near those necessary to support Hardey et al.’s statement. In 1976 Hamm explains how “the regulatory enzymes which control ATP metabolism and glycolysis in the living tissue are still active in the muscle postmortem, but these enzymic mechanisms are not able to maintain the ante-mortem levels of ATP and glycogen because the oxygen supply of the cell is stopped as soon as the blood circulation is interrupted by death of the animal. The lack of the aerobic ATP synthesis from ADP in the muscle mitochondria results in an anaerobic depletion of glycogen and consequently in a disappearance of ATP within a few hours p.m.”
  • In 1936 Sharp related glycogen’s conversion to lactic acid over time as a function of temperature (°C) saying that “in fish-muscle in the frozen state the maximum rate of glycogenolysis occurs in the interval -3.2° to -3.7 [and] freezing at -2° and lower temperatures for a period of 4 hours causes injury to the muscle, resulting in very rapid lactic acid formation on thawing. Freezing at 1.6° has no such effect, and on thawing the normal rate of lactic acid formation is resumed.” Figure 2 and 3 from his paper graphically illustrate this relationship.Lactic acid & glycogen in frozen muscle

[11]“the derived A-allele [CPT1A gene] has been shown to associate associate with hypoketotic hypoglycemia and high infant mortality […] suggests that it is an important adaptation to high meat, low- carbohydrate diets”

  • The association between hypoketotic hypoglycemia and high infant mortality with the CPT1A allele is from Clemente et al.’s 2014 paper which references a 2009 paper by Greenberg et al. entitled “The paradox of the carnitine palmitoyltransferase type Ia P479L variant in Canadian Aboriginal populations” in which 3 familes for a total of 7 patients were studied. The conclusion was that “severe clinical effects have been observed in only some, but not all, infants and young children [and] the occurrence of hypoglycemia, the main initial clinical effect of CPT-I deficiency, is dependent upon many environmental factors, including infection, feeding history and long-chain fat content of the diet, glycogen stores in the liver, and perhaps even climate“.
  • Clemente et al. found “strong evidence in favor of selection from a de novo mutation P(SDN) = 0.98, as opposed to selection on standing variation”. In simple terms, this means it was strongly positively selected for in high latitude populations eating very high-fat diets. Clemente et al. characterize these CPT1A mutations as “strong deviations from mutation-drift equilibrium”. Interestingly, Greenberg et al. describe the CPT1A mutations as ‘a paradox’ whilst Clemente et al. as ‘deleterious’. Patient 2 in the study of the former, for example, was most likely on a Westernized, relatively high carbohydrate diet described as a “regular diet and skim milk”. Such a diet underpins many ‘diseases of civilization’ and most likely contributed to the hypoketotic hypoglycemia and high infant mortality that is associated with these particular alleles.
  • Neither Hardy et al. nor Clemente et al. considered the evidence of compensatory mechanisms paralleling the drop in ketogenesis provided in Greenberg et al.’s paper. Veterinarian Petro Dobromylskyj from the Royal Veterinary College explains it best, saying “there is also evidence that the mutation decreases the inhibitory effect of malonyl-CoA on fatty-acid β-oxidation in mitochondria, thereby partially compensating for the drop in ketogenesis associated with reduced CPT1A activity”.
  • Finally, Greenberg et al. mention their “results of detailed β-oxidation studies in family C showed that oxidation was low at 37°C and were further decreased when measurements were conducted at a higher temperature”. These mutations may well be an adaptation to the freezing temperatures of the environments in which they arose.

[12] “high levels ketones in the blood, which can compromise reproductive function (Kim and Felig 1972)”

[13] “larger infants are born to women with higher blood glucose (Butte 2000), while a link has been made between maternal gestational ketonemia and a reduced off-spring IQ (Rizzo et al. 1991)”

  • Points 12 and 13 can be addressed together. Hardey al. quote a 1972 study by Kim and Felig that studied metabolic responses in pregnant women fasting for 84-90hrs. Specifically, their interests lay in the interplay between amino acids, glucose and insulin. They describe their interest saying “the influence of pregnancy on the changes in plasma glycine, threonine, and serine during fasting are of interest in as much as these amino acids are unique in demonstrating a delayed increase during prolonged starvation of obese nonpregnant subjects”. In fact they recognize the importance and adequacy of gluconeogenesis saying “maternal hepatic gluconeogenic mechanisms are capable of responding to increased substrate [amino acids] delivery during starvation in pregnancy”. Nowhere in their paper are ketones suggested to compromise reproductive function. This study looked at the effects of multi-day fasts – not ketogenic diets – in the context of gestation.
  • In another paper from 1972, Kim and Felig also note that in pregnant mothers, “starvation resulted in significant hypoglycemia and hyperketonemia and in an elevation of free fatty acid and glycerol concentrations. In 13 of 18 fasted subjects, blood glucose levels fell below 50 mg/100 ml. No specific symptoms or signs of hypoglycemia were noted.” Furthermore, the evidence was suggestive “that ketones may become an important fetal fuel during maternal caloric deprivation”.
  • In a 2015 retrospective cohort study of 906 pregnant mothers, Deschamps et al. found that “infants from mothers with a FBG [fasting blood glucose] >95 mg/dL were fatter both in relative (18.7 vs. 14.9%; p<0.05) and absolute (803 vs. 543g; p<0.01) terms. Further, over 50% of infants from mothers with FBG >95 mg/dL had a %fat greater than the 90th %fat percentile”. Increased adiposity may partially account account for the higher birthweight infants of mothers with higher blood glucose levels. This casts a less positive light on Butte’s findings from 2000 than Hardy et al. have.
  • In a 1980 study of calorie-restricted diabetic pregnant mothers, Coetzee et al. found that “neonates born to diabetic mothers with ketonuria had no fetal distress or asphyxia neonatorum [and] positive Ketostix tests in urine samples do not indicate toxic levels in the blood”.
  • It appears that Hardy et al. are confusing the pathological state of diabetic ketoacidosis – a simultaneous and excessive rise in the blood of both glucose and ketones – with simple nutritional ketosis. The latter arises naturally as a result of restricting carbohydrates and moderating protein (or simply fasting).  Hardy et al. quote papers by Nancy Butte and by Rizzo et al. who both looked at gestational diabetes and ketoacidosis, not nutritional ketosis. Rizzo et al. concluded that “the associations between gestational ketonemia in the mother and a lower IQ in the child warrant continued efforts to avoid ketoacidosis and accelerated starvation in all pregnant women”. Butte recommend avoiding ketonemia from her study of gestational diabetes mellitus, not ketogenic dieters. In fact, she recognizes how “the ADA states that the percentage of carbohydrate in the diet is dependent on individual eating habits and that the effect on blood glucose and percentage fat depends on assessment and treatment goals”. She also goes on to emphasize how “the lower percentage of carbohydrate blunts the postprandial hyperglycemia”.

[14] “can also be obtained directly from other dietary sources, or it can be synthesized from other fatty acids such as α-linolenic acid (ALA), which is present in oils from ocean fish, eggs, seed oils, and various leafy plant foods”

  • It is suprising that Hardy et al. do not mention how inefficiently humans convert α-linoleic acid (ALA) into the bioavailable long chain forms, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). In 1998 Gerster H. used radioisotopes to show how adults “with a background diet high in saturated fat conversion to long-chain metabolites is approximately 6% for EPA and 3.8% for DHA. With a diet rich in n-6 PUFA, conversion is reduced by 40 to 50%”. Western diets are excessively rich in omega-6 polyunsaturated fatty acids. Consequently, specifically targeting plant foods as a meaningful source of EPA and DHA is not recommended, especially in light of the 2006 estimate by Simopoulos whereby people eating a Westernized diet have a 15:1 to 16.7:1 ratio of omega-6 to omega-3 (EPA and DHA) fatty acids.

Defining terms for Type 2 Diabetes

In the midst of studying Chronic Multifactorial Diseases the distinction between those caused by 1 mutation (monogenic) or multiple ones (polygenic) came up. I was asked by my teacher

to find more examples to fit the following: A polygenic disorder or a disorder that is thought to have an underlying genetic cause (or a range of underlying genetic causes)

Defining terms is important & it can be annoying to have those terms redefined.

So, I answered:

Using the term ‘genetic causes’ when discussing polygenic diseases is problematic for 2 reasons. First, to many researchers and most laymen, it implies that it is only a matter of time before these genes enact diseased phenotypes, as if on a count-down. Second and more importantly, it assumes there is a direct causal path between the emergence of phenotype from genotype. These ‘genetic causes’ are usually derived from GWAS (Genome Wide Association Studies) which make statistical claims based on associations of phenotypes and specific mutations – not biochemical, causal arguments. With these points in mind and in contrast to them, the term ‘genetic predispositions’ is much less problematic. It recognizes statistical associations between genes/mutations and phenotypes, whereby having certain genotypes makes it more or less likely to see a certain phenotype emerge but ignores causal arguments (since it cannot make them).

Type II Diabetes Mellitus (T2D) is a disease of insulin resistance causing glycemic control issues. It is considered a chronic and progressive lifestyle disease by the Australian Diabetes Association and the American Diabetes Association. Its aetiology stems in large part from modern lifestyle factors,  chief amongst which nutritional ones. However, it is incorrect to call it a chronic and progressive disease. It is considered as such because most diabetics manage their disease by covering their dietary carbohydrate load with exogenous insulin. In this scenario, it does become chronic and progressive. For in depth discussions of why this is the case with the accompanying references and clinical case reports, please watch video presentations by Dr. Jason Fung. Currently, the best treatment for slowing, halting or reversing T2D involves a nutritional intervention which lowers ones total (and especially refined) dietary carbohydrate load to reduce insulin resistance and thus achieve better glycemic management. Assuming an isocaloric macronutrient shift, this automatically entails increasing the total fat load. A focus on lifestyle interventions is all the more logical considering 36 genes identified as predisposing people to diabetes can explain 10% of why people get it. Additional lifestyle factors relevant to T2D are near innumerable, although chief amongst them are sleep and exercise.

My course still uses saturated fat as an example of lifestyle factors negatively affecting CVD/CAD. It’s a sad state of affairs and testifies to how slow and conservative the medical profession is when it comes to updating guidelines.

Myoclonic Epilepsy with Ragged-Red Fibers (MERRF)

1973 saw Tsairis et al. report on case studies within a family where myoclonus epilepsy was associated with ragged-red fibres (RRFs) from muscle tissue biopsies in addition to abnormal blood lactate and pyruvate levels(1). In 1980 Fukuhara N. et al. publish a case study(2) of 2 patients who presented in much the same way as described by Tsairis et al. 7 years ago. Fukuhara et al. entitled their study “Myoclonus Epilepsy associated with ragged-red fibres (mitochondrial abnormalities): Disease Entity or a Syndrome?” and in so doing, introduced a question highlighting difficulties that the classical ‘single disease’ medical model encounters when trying to account for overlapping constellations of symptoms. In November of the same year, Wallace et al. come to identify this particular constellation of symptoms by the acronym ‘MERRF’ following “pathophysiological and biochemical characterization of a mitochondrial DNA disease” as their study title reports(3). ‘Fukuhara’s Disease’ is another name for it. Soon thereafter in 1981, the human mitochondrial genomes’ sequence & organization is published in Nature(4), laying much of the ground work for answering the how’s & why’s behind Fukuhara et al.’s question. The molecular clue for MERRF was elucidated in 1990 by Shoffner, Wallace et al. who identified a mitochondrial tRNALys  (mt-tRNALys) point mutation responsible for much of the epilepsy, defective mitochondrial energy production and age-related genotype-phenotype associations observed in MERRF patients(5). The molecular mechanism boils down to defective oxidative phosphorylation (OXPHOS) in the mitochondrial electron transport chain (mETC), caused by faulty protein synthesis originating from a point mutation in the MTTK gene erroneously encoding the highly conserved TѰC loop of its mt-tRNALys gene product. This results in aberrant taurine modification, also known as a “wobble modification defect [which] is primarily responsible for dispossessing the mutant tRNALys of its cognate codon binding affinity, forcing the mutant tRNALysUUU to become translationally inactive”(6). Nowadays, disorders characterized by defective oxidative phosphorylation are termed ‘mitochondrial disorders’(7).

MERRF is diagnosed by meeting the following 4 criteria:

  1. myoclonus (spasmodic muscular contractions)
  2. generalized epilepsy
  3. ataxia (lack of voluntary coordinated muscle movements)
  4. red-stained ragged fibers from muscle biopsies (RRFs)

Shoffner & Wallace called the tRNALys point mutation in the (modifiable) highly conserved TѰC loop producing a CviJI restriction site “a simple molecular diagnostic test for the disease”(8). See a depiction of this in Figure 3A below, from Shoffner & Wallace’s seminal paper on MERRF.

TRNALys point mutation

Nowadays however, it is known that the mt-tRNALys point mutation exchanging an A base for a G base at nucleotide position 8344 in the mt-tRNA gene MTTK(9) is not solely responsible for all MERRF (and MERRF-like) conditions but is present in ≥80% of patients(1).

Other mutations in mitochondrial genes, including but not limited to: m.8356T>C, m.8361G>A and m.8363G>A account for another 10% and m.611G>A and m.15967G>A for the other 5%(11). This expanded range of mutations in addition to rare MERRF case studies presenting unique mutations (e.g. Mancuso et al. 2004(12)) speaks to the pleiotropic nature of mitochondrial diseases generally, in terms of origins & clinical phenotypes. Furthermore, the etiology of many diseases traditionally regarded as solidly Mendelian within the 1-gene-1-enzyme(13) paradigm is strongly challenged considering that a given clinical phenotype may originate from a staggering diversity of genotypes. A powerful visual depiction of this is seen in Figure 3 from DiMauro et Schon’s 2003 paper(14) which maps “Mutations in the Human Mitochondrial Genome That Are Known to Cause Disease”.

Mutations in the Human Mitochondrial Genome That Are Known to Cause Disease

Mutations in the Human Mitochondrial Genome That Are Known to Cause Disease

There are examples of multivariate influences affecting phenotypes of even classical Mendelian disease like cystic fibrosis. Although 70% of cystic fibrosis patients inherit a mutated CFTR gene (del508) in an autosomal recessive Mendelian manner, in 2005 it was found that in a cohort of 69 Italians carrying this mutation “those who also carried the R131 allele of the immunoglobulin Fc-gamma receptor II gene had a 4-fold increased risk of acquiring chronic Pseudomonas aeruginosa infection (p = 0.042) [which] suggested that FCGR2A locus variability contributes to this infection susceptibility in CF patients”(15). This suggests that nowadays, Mendelian labels are better used for qualifying inheritance patterns than meaningfully categorizing diseases.

MERRF reflects classical mitochondrial inheritance patterns where only an affected or unaffected mother can transmit the condition to her offspring. Fathers cannot transmit mitochondria to their children because sperm do not contribute mitochondria to the zygote which will, however, contain the mitochondria from the mother’s ovum. Interestingly, a few isolated cases of paternal mitochondrial inheritance have been observed but not replicated in such a way as to challenge the standard model of inheritance just mentioned”(16,17,18).

Barring a handful of exceptions, every human cell contains hundreds of mitochondrial organelles, each carrying about 2-10 little (~16.5kb) double-stranded closed-circles of mitochondrial DNA (mtDNA) that make up our mitochondrial genomes. We have inherited this genome from our most recent common female ancestor about 180,000(19) years ago (commonly referred to as ‘Mitochondrial Eve’). These polyploid mitochondria are normally mostly homoplasmic (identical or of the same kind). However, they do intrinsically mutate stochastically at higher rates than observed in our nuclear genome. Per this tendency, they become transiently heteroplasmic during cellular division (mitosis) and are then distributed into daughters cells – also in a random manner. Here heteroplasmy simply refers to the mixture of mutated and non-mutated (or mutated and wild-type) mtDNA molecules within a cell. MERRF is heteroplasmic condition. Affected patients demonstrate a ‘threshold’ effect, wherein the proportion of mutant mtDNA molecules within a certain tissue correlates positively with symptom severity and skeletal muscle anaerobic capacity. Older patients accumulate more deleterious mtDNA which also correlates positively with worse clinical phenotypes. Typical MERRF patients do not show overt symptoms during childhood. The age of onset is thus a strong indicator of disease severity and again adds to the evidence implicating the proportion of mutated to non-mutated mtDNA molecules as a powerful determinant of symptom severity.

Defective mitochondrial respiration can have various causes, originating from both inside and outside the mitochondrial organelle. Identifying the 8344A→G mt-tRNALys mutation as the most common cause of MERRF is based on the following lines of evidence proposed by Shoffner & Wallace in 1990:

  1. 2 mutations in the mtDNA of MERRF-positive patients were discovered in this experiment and 1 was in the MTTK gene encoding evolutionarily conserved gene product elements involved in adequate mitochondrial protein synthesis.
  2. There was a perfect correlation between the mutation and the disease: all 3 MERRF patients had the MTTK mutation whilst none of the 75 controls did.
  3. Reduced numbers of larger mitochondrial translation products were observed in MERRF patients which is expected because the gene products supposed to synthesize them are erroneously encoded due to mutated mtDNA tRNAs.
  4. An A (adenine) base is modified in tRNA’s highly conserved TѰC loop responsible for recognizing the ribosome and accommodating the tRNA-ribosome complex for synthesizing proteins.
  5. Biopsies from MERRF patients reveal mitochondrial heteroplasmy which is consistent with loss-of-function (LoF) mtDNA mutations of recent origin.
  6. The proportion of mutant mtDNA molecules correlates well with symptom severity as well as with skeletal muscle anaerobic threshold.

Interestingly, in Shoffner & Wallace’s observations emerged the case of a quasi entirely homoplasmic MERRF patient who started to manifest overt symptoms in her early teens, suggesting that a small amount of normal mtDNA can provide a disproportionately large protective effect on the phenotype for quite some time. This does not contradict the above statements relating the degree of heteroplasmy to the age of onset. Instead, it hints at just how little the proportion of mutated and wild-type mtDNA molecules need be shifted to spur major alterations in phenotype. This adds understanding to speculations regarding the emergence of phenotypic pleiotropy from putative genotypes.

Continuing along the lines of improving the resolution of genotype-phenotype linkage, starting out with a molecular marker of disease like the mt-tRNALys point mutation used for diagnosis is very useful. Beyond base assessment, quantifiable measures of what this disease marker is actually doing to a particular patient are crucial for subsequent prioritization of potential treatment avenues, discerning risk-reward ratios and also objectively assessing the situations’ general urgency. Shoffner & Wallace looked at oxidative phosphorylation activity levels in skeletal muscle as one such measure.

Fig.1 Shoubridge’s 2001 paper “Nuclear genetic defects of oxidative phosphorylation

Fig.1 Shoubridge’s 2001 paper “Nuclear genetic defects of oxidative phosphorylation

Oxidative phosphorylation occurs along 4 respiratory chain complexes and a final ATP synthase complex as depicted in Figure 1 of Shoubridge’s 2001 paper “Nuclear genetic defects of oxidative phosphorylation”. These complexes reside in the inner mitochondrial membrane which has a matrix side (bottom) and a cytosolic side (top). Electrons flow along the respiratory chain from NADH- and FADH-linked substrates to molecular oxygen as indicated by the narrow black  arrows. Protons are pumped to the cytosolic side of the inner membrane at locations indicated by the thick black arrows. This produces an electrochemical gradient for protons across the inner membrane. The gradient-directed route these protons follow along eventually leads them through the final ATP synthase complex (complex V) where they drive ATP synthesis, providing the universal cellular energy currency. Zeviani & Di Donato highlight how “the fundamental reaction of life, i.e. oxygen activation and the conservation of energy in cell respiration, is essentially a function of the integrity of the inner membrane respiratory chain”(20). Shoffner & Wallace found levels of oxidative phosphorylation obtained by skeletal muscle biopsy correlated best with the patients’ current clinical phenotype. For age-related prognoses, their second measure of mtDNA genotype correlated best. mtDNA genotype refers to the degree of hetero/homoplasmy discussed above. Previous case studies and the recognition that certain tissues have distinct energy demands predicts organ-specific energy thresholds affecting the age of onset in affected individuals. This expectation is confirmed experimentally“(21,22).

Many other variables can jumble the order in which symptoms are expected to manifest in tissues: those with the lowest aerobic thresholds succumbing first. “The prominent involvement of the nervous, cardiac and skeletal muscle systems supports the contention that tissues with high aerobic energy demands will be most affected in oxidative phosphorylation disorders, but this simple view cannot explain the selective vulnerability of different organs […]”(23). Part of the ever-growing list of candidate variables contributing to tissue-specific selective vulnerability includes: nuclear genes, environmental influences and especially the somatic replicative segregation of mitochondrial organelles. Our knowledge of these variables is substantially disparate when it comes to understanding how each might affect MERRF.

The influence of nuclear genes on the stability of mtDNA is obvious, yet complicated. This can be clarified by characterizing the 4 major ways they can exert their effect on mitochondrial processes. First of all, defects in nuclear genes can affect the stability of mtDNA. Secondly, they can also affect structural components & assembly factors involved in oxidative phosphorylation. Thirdly, even if the defects arise in nuclear genes encoding proteins only indirectly related to oxidative phosphorylation, pathology may arise nonetheless. Lastly, non-protein components encoded by nuclear genes are also important for maintaining a functional respiratory chain. With this in mind, we also know our nuclear genome encodes 1,700 mitochondrial proteins(24) and 69 of the 82 subunits composing complexes I to IV of the mETC. A host of partially characterized assembly and maintenance protein factors for mtDNA exert an influence that still needs figuring out(25). Despite their putative roles, statistical analyses of modern genetic issues suggest that “nucleotide changes in mtDNA that are not intrinsically pathogenic may predispose to, modulate the effects of, or reflect a propensity for the occurrence of deleterious mutations. In turn, deleterious mutations may promote the accumulation of somatic changes, through the generation of OXPHOS-related mutagens”(26). This lends explanatory power to the observation that MERRF phenotypes worsen with age. Nucleotide changes in nuclear or mtDNA are not necessarily the only level at which disease causing events can occur. Yasukawa et al. provided the “first evidence that a post-transcriptional modification deficiency causes a human disease”(27) by studying the wobble modification imparted by the 8344A→G tRNALys mutation.

Mitochondrial somatic replicative segregation was discussed above in terms of different tissues having different levels of heteroplasmy. Shoffner & Wallace emphasize the other aspect of heteroplasmic variation by making the point that the “same average mtDNA genotype could result in very different organ-specific genotypes and clinical phenotypes”(28). Their contention is that not only are different tissues segregated with different amounts of mutated mtDNA, but that the same amount of mutated mtDNA exerts differential effects on the phenotype of the tissue depending on that particular tissue’s threshold. Lertrit et al. believe different amounts of mutated mtDNA are distributed amongst different tissues during events originating in utero. They accounted for this by looking at 6 different tissues in a MERRF patient with the 8344A→G mt-tRNALys mutation and observed heteroplasmy in the cerebellum, cerebrum, pancreas, liver, muscle & heart. From this they concluded that “the mutated population of mitochondria must have existed before the formation of the 3 primary embryonic layers”(29). This phenomenon is termed according to the analogy it describes — a ‘mitochondrial bottleneck’ — where “a mother with a low degree of heteroplasmy in her mtDNA can transmit a higher level of heteroplasmy to her children”(30).

Environmental influences on MERRF are rife with speculation but are currently hard to formulate in a testable manner. It is worth mentioning, however, that anything with the potential to moderate inevitable effects of aging (such as practicing an evolutionarily concordant lifestyle) is at the very least worth exploring as an adjunct treatment option. Epigenetics is naturally taking on a more prominent role in the scientific study of ‘lifestyle factors’ and mitochondrial disorders. On a basic level, this is simply because it is a fascinating foray into understanding how environmental (external) information comes to sit ‘atop’ (‘epi’) our human databases (genomes) to change us in some way. The field of epigenetics carries a slew of tantalizing ‘known unknowns’ that are best set aside momentarily when disentangling the effects of improper aminoacylation in MERRF patients.

MTTK encodes mt-tRNALys which has a UUU anticodon that typically undergoes a post-transcriptional “τm5s2U modification … at the wobble position in the anticodon region”(31) if the ribosome is to decode it properly. The third position is the wobble one, containing a 2-thiouridine derivative, normally ready to be modified. Theory and experiment agree that MERRF 8344A→G mutant mt-tRNALys does not recognize AAA-programmed ribosomes and so the wobble base remains unmodified. A 1989 in vitro cybrid clone(32) experiment shows the consequence of this lack of anti-codon recognition by measuring the difference in respiration rate between MERRF 8344A→G mutant cybrid clone cells (ME1-4) and their wild-type control counterparts (Ft2-11). The respective respiration rates of ME1-4 and Ft2-11 was 1.7 and 5.3 fmol/min/cell(33). That same experiment also found the overall protein synthesis rate substantially decreased in the mutant cybrid clones. Could faulty aminoacylation be to blame? To answer this, the group then looked to compare charged (aminoacylated) or uncharged (non-aminoacylated) mt-tRNAs and found very similar aminoacylation levels between both mutant and wild-type control cells. Defective aminoacylation is not the problematic mechanism underlying MERRF. Kolesnikova et al. test this further. They find that yeast-derived “imported tRNALys [into human fibroblasts] is correctly amino-acylated and able to participate in mitochondrial translation, partially rescuing mitochondrial function(34). This view is strengthened by the fact that mt-tRNALys does not lose significant affinity for bovine-derived EF-TU elongation factor compared to wild-type cells, thus retaining its aminoacylation efficiency. Crystallographic analysis of the anticodon stem-loop belonging to mt-tRNALys shows it is important for recognizing the ribosome. It also strengthens the currently accepted molecular mechanism whereby decreased mETC respiration in MERRF stems from defective taurine modification in the wobble position of the anti-codon that results in the inhibition of proper base pairing at the mt-tRNALys-ribosome junction.

In vitro experimentation from the same group provides 2 plausible accounts for the observed increases in intracellular lysine. The ribosomes can stall facing an empty A-site and terminate translation prematurely. Alternatively, frameshifting at lysine codons may produce abortive proteins to be shuttled away from the mETC for degradation by ATP-dependent mitochondrial proteases. On principle at least, both explanations are not mutually exclusive and may conceivably occur together; sometimes the amino acid will not form because translation was prematurely terminated and other times the amino acid will form only to be subsequently degraded.

Oxidative phosphorylation is identified as the defective process in MERRF and the molecular interactions between ribosomes and tRNAs translating amino acids involved in the process have been described above. 2 logical questions might follow, such as; ‘which of the 5 complexes along the mETC cease to function normally as to impact respiration in this disorder?’; and ‘do nuclear genes contribute to mtDNA instability in MERRF (if so, how)? Of the 13 mitochondrially encoded polypeptides involved in oxidative phosphorylation, complexes I and IV have the most, with 7 and 3 subunits, respectively. Despite their numbers, in MERRF complex IV is in fact the most prominently affected whilst complex I only occasionally(35). Complex V just has 2 and complex III only 1 and they are not significantly affected. Defects in more than 1 respiratory complex are common in disorders with mtDNA mutations. As in MERRF, tRNA deletions or mutations predominate and tend to give rise to translation defects. MERRF is somewhat of an exception amongst mitochondrial disorders in that its 4 canonical features correlate very tightly to its predominant, heteroplasmic tRNALys 8344A→G point mutation. Until now, only LHON patients with ND gene mutations and those with exercise intolerances linked to the cytochrome b gene reflect this level of correlation(36).

In contrast to MERRF’s most affected complexes, complex II does not contain any subunits from mtDNA. However, its possible role as a cellular oxygen sensor has many interesting potential implications for the other 4 complexes in MERRF and other mitochondrial disorders(37). 3 genes encode subunits SDHB, SDHD & VHL in complex II that function as putative tumor suppressors by responding to hypoxic conditions via still unknown molecular mechanisms. It is curious that SDHA mutations can produce Leigh syndrome (LS) but mutations in the other 3 subunits do not. Instead, they are associated with cancer and not with nervous system disorders like LS which is puzzling. The mitochondrial disorder MNGIE (mitochondrial neuro-gastro-intestinal encephalomyopathy) is another example of non-mtDNA mutations affecting mitochondrial functions. It involves a defective nuclear gene encoding the TP enzyme (thymidine phosphorylase) impacting mtDNA replication rate and fidelity because of imbalanced dNTP pools containing excess dTTP(38). Thus, certain mitochondrial disorders are clearly affected by nuclear gene-encoded components and MERRF appears vulnerable to their exacerbations — at least in principle.

Table 1 Clinical and Genetic Heterogeneity of Disorders Related to Mutations in Mitochondrial DNA below, serves as a map of sorts highlighting where our predictions about which complexes will be affected in which disorders fall short. This is reflected in the fact that 50% of adult and 80-90% of paediatric patients suffering from a mitochondrial disorder remain unlinked to a defective gene. Why do abnormal mitochondria accumulate beneath the sarcolemmal membrane as to produce RRFs typical of the majority of mitochondrial disorders but not in Leigh syndrome? Thankfully the search field for factors capable of filling-in those explanatory gaps can be narrowed down by learning from those failed predictions and consequently also shed some light on how phenotypes emerge from genotypes. A more immediately answerable question is “whether discrete syndromes based on clinical features can be reliably identified or whether these disorders represent a continuum among the mitochondrial myopathies”?(39). Byrne et al. asked this in 1988 and since then, the evidence that has surfaced leans heavily towards a continuum. Nevertheless, because these diseases “include both Mendelian-inherited and cytoplasmic-inherited diseases”(40), some take this as a challenge to the idea of a continuum. However, this position is rapidly becoming less and less tenable with knowledge of epigenetic methylation and histone modification mechanisms. We also know 1 gene can often originate multiple gene products — and isoforms — because it is actually providing a template suitable for a vast repertoire of possible gene-products ready to be moulded by post-transcriptional and post-translation modifications. Lastly, in 1996 Poyton & McEwan successfully demonstrate the existence of a “bidirectional flow of information between the nuclear genome and the mitochondrial genome to adjust energy production in tissues to different energetic demands”(41), solidifying the idea of oxidative phosphorylation disorders sitting on a continuum. In light of this, it is important to note that diseases inherited in a Mendelian manner appear quite more vulnerable to therapeutic interventions if this ‘information stream’ can be successfully manipulated.

Table 1 Clinical & Genetic Heterogeneity of Disorders Related to Mutations in Mitochondrial DNA

Table 1 Clinical & Genetic Heterogeneity of Disorders Related to Mutations in Mitochondrial DNA

The idea of bidirectional genomic information flow underlies much of the discussion to follow where MERRF is compared and contrasted to other oxidative phosphorylation disorders. Consider MELAS (mitochondrial encephalomyopathy, lactic-acidosis & stroke-like episodes), a maternally inherited disease caused by a 3243A→G point mutation in the MTTL1 gene encoding tRNALeu. The point mutation is very similar to that in MERRF and there is significant symptom overlap (see Table 1 above). However, the proclivity of brain tissue to harbour different mutation concentrations is perplexing: in MERRF the small cerebral vessels contain high concentrations and in MELAS it is the dentate nucleus of the cerebellum(42). Complexes I and IV are also affected in MELAS but the situation is reversed: complex I is most affected and complex IV typically remains intact. Both MELAS and MERRF display COX-depleted RRFs via histochemical staining, although in the former it is not a canonical feature, unlike in MERRF. This fact harkens back to the exceptional phenotype-genotype correlation observed in MERRF that is unequaled in MELAS — even as regards COX-deficient tissues. To explore both disorders further, a timely case study was published this year by Liu et al. who described a 19 year old Chinese girl with overlapping MERRF and MELAS syndromes with a confirmed T3291C point mutation in the MTTL1 gene encoding tRNALeu(UUR). Biopsies from her left bicep revealed a mixed findings that may be somewhat expected with symptom overlap: there were about 10% of scattered RRFs, scattered COX-deficient fibers with 35% of them containing RRFs and scattered succinate dehydrogenase-reactive vessels (SSVs), 85% of which retained COX activity. Respiration assessments on fibroblasts revealed a heavy reliance on glycolysis instead of oxygen consumption, confirming defective oxidative phosphorylation. Western blot analysis of viable mitochondrial complexes in her fibroblasts revealed substantial decreases compared to controls: 24.9% for complex I and 14.8% for complex IV. The latter complex is typically nearly untouched in MELAS. In MERRF it is complex I that usually does not show as much dysfunction. The absence of dysfunction in complexes II, III and V is another clue as to the susceptibility of individual complexes to mutations in genes encoding faulty translation components. The authors offer an interesting observation regarding how heteroplasmic tissue segregates. They report that her muscle tissue retained a higher concentration of mutations compared to her lymphocytes and fibroblasts, convincing the authors that “negative selection for defective mitochondria is possible for rapid turnover cells, such as lymphocytes and fibroblasts, but difficult for highly differentiated, postmitotic syncytial muscle cells”(43). The authors hold progressive views regarding the pleiotropic nature of mitochondrial mutations, as they clearly state that there is a “clinical spectrum associated with the m.3291T>C mutation”(44).

NARP (neuropathy, ataxia, and retinitis pigmentosa) patients also evolve along a spectrum, all the way to (maternally inherited) Leigh syndrome when surpassing a 95% threshold of heteroplasmy. Strangely, NARP patients maternally inherit a  T—>G8993 or T—>C8993 point mutation in the complex V ATPase 6 subunit gene, despite Leigh syndrome associated mutations being present in complexes I to IV but not V! Furthemore, RRF-positive muscle biopsies are nearly unheard of in NARP patients. Why would they not have abnormal mitochondria accumulate beneath the sarcolemmal membrane like in other respiratory chain disorders? This mystery persists.

There are no mainstream cures for mitochondrial disorders, only treatments for the symptoms. Drugs are prescribed to affected patients but this unfortunately and mostly exchanges one symptom for another, or at best, abates symptoms without causing too much collateral damage. Much of this limitation stems from the 1-disease-1-drug paradigm under which conventional but not cutting-edge(45) medicine still functions. It simply does not fit with current knowledge of disease pleiotropy and plasticity. A fundamentally misplaced focus on treating symptoms rather than investigating root causes coupled to a failure to distinguish between these is largely to blame. After all, antibiotics treat infections, immune suppressants treat autoimmune flares, serotonin re-uptake inhibitors treat depression and anti-epileptics drugs (AEDs) treat epilepsy and so on and so forth. That being said, it must be recognized that treating disorders at their root is decidedly easier said than done and in the interim, symptoms must be somehow managed. The Hippocratic oath to ‘first do no harm’ is commonly jeopardized to that end. Unfortunately, the standard of care for MERRF patients conforms succumbs to that trend. MERRF patients are prescribed AEDs for seizure control and myoclonus(46) as well as standard pharmaceuticals for managing cardiac symptoms(47). Some L-carnitine and Coenzyme Q10 (CoQ10) is given; they help transport fatty acid derived components into mitochondria for aerobic respiration and aid the mETC to transfer electrons along the respiratory chain, respectively. Although CoQ10 is ineffective at significantly improving symptoms in MERRF patients it could still benefit those individuals also suffering with a primary CoQ10 deficiency. This benefit is apparent with its shorter chain analogue Idebenone that proves “effective in halting or even improving the hypertrophic cardiomyopathy in Friedreich’s ataxia”(48). CoQ10 is also known to be helpful with myopathies resulting from statin side-effects. It was initially supposed to go hand-in-hand with statin prescriptions, lending further support for trying it as an adjunct to AEDs. Standard physical therapy and aerobic exercise are encouraged as adjuncts to the aforementioned MERRF treatments along with minor surgery for ptosis correction (droopy eyelid). Creatine has been extensively studied; it is quite safe, cheap and supplementing with it is helpful in some mitochondrial disorders but not others. Lactic acidosis is very toxic to cells and is poorly managed, even with “the use of lactate-lowering agents including riboflavin, succinate and coefficient Q”(49).

What kind of futuristic treatments are proposed and what are their promises? Cytoplasmic transfer will enable dysfunctional mitochondrial components to be replaced by functional nuclear-encoded ones that exploit protein import machinery. It has been established as a proof of principle, both in vitro and in vivo(50,51)”. Tachibana et al.’s 2009 experiment provides a perspective on its imminence:

A recent noteworthy experiment featured the transfer of the nuclear genome from a primate oocyte to an enucleated oocyte of another primate containing only mitochondria (Tachibana et al., 2009). The oocytes generated contained the nuclear genome from two parents but mitochondria from the donor; when implanted in pseudopregnant mother they were able to successfully produce healthy rhesus macaque offspring(52)

This technique discusses the manipulation of prenatal events with the view of offering a cure or treatment. This begs the question: What about a prenatal MERRF diagnosis? Unfortunately, a prenatal diagnosis is not possible due to the tissue-partitioning of mitochondria that occurs in utero. Looking forward, gene therapy does not yet offer practical implementations of the exciting theories and principles developing under its purview. Stopping mutant mtDNA from being replicated is one theoretical approach on offer. It involves recognizing these mutant mtDNA sequences with sequence-specific antigenomic peptide-nucleic acids which should subsequently inhibit the replication of their target sequence. Although MERRF is a maternally inherited disease, there may be substantial methodological overlap with potential treatments for autosomal recessive oxidative phosphorylation disorders. For example, microcell-mediated chromosome transfer(53) could potentially help with extra-organellar components influencing MERRF’s severity even if the causal mutation is not resolved. Furthermore, direct cloning by complementation with retroviral cDNA expression libraries(54) is a tantalizing prospect with a meaningful track-record in vitro and in vivo. However, it needs to affect multiple tissues with particular heteroplasmic thresholds and precisely titrate expression in a timely manner. There appear to be many obstacles for every promising solution awaiting discovery.

The elimination of dysfunctional mitochondria by modulating mitophagy, apoptosis and other cellular quality control dynamics has been proposed. This approach is endorsed by DiMauro et Schon who argue that the “possibility of mitochondrial dysfunction needs to be taken into account by every medical subspecialty […] progress in this field has been striking enough to amply justify the term ‘mitochondrial medicine’”(55). Current pharmaceutical drugs targeting mitochondria are still imprecise, weak and replete with side-effects that are often times worse than the disorder itself. Fortunately, a lot of the scientific literature from disparate fields has uncovered epigenetic influences — also known as lifestyle factors — affecting numerous quality control mechanisms of the organelle, inside and outside of it. Despite these encouraging avenues, mainstream research remains committed to distilling these hypercomplex effects into drug form before exploring non-drug approaches. Thankfully, there are roads to explore in the mean time. Examples include  — but are not limited to — lifestyle factors guided by an evolutionarily concordant framework revolving around: nutrition, circadian rhythms entrainment, appropriate movement and activity and electrical stimulation of muscles and the brain(56,67). These attempts are both old and novel in many respects. They are often times significantly more reliable, reproducible, impactful and safe compared to conventional pharmaceutical drug treatments. Labelling such approaches as ‘holistic’ seems appropriate as the influences on mitochondria arise from many yet undiscovered pathways and feedback systems. What is being affected is not a single organ system, but organelles that allow the fundamentally life-sustaining process of respiration, carried out in nearly every cell in our body. Mitochondrial medicine is poised to modernize the medical paradigm.

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Will baby brains really be miswired if pregnant mom smokes pot?

Warning: my bias favors recreational & medicinal use of marijuana.

Which field of scientific research is of poorer quality: cannabis or nutrition?

It is honestly hard to say. They are neck and neck.

Why I am drawn to learning and writing about marijuana

At the most basic level it probably has to do with the immense opportunity to make fundamental pharmacological discoveries all the while exploding ancient walking-dead myths. Both opportunities are appealing.

I wanted to write about marijuana and health for some time now. I find it infinitely explorable and quite uncanny in its unremitting relevance to issues plaguing humanity, old and new. Just for a moment, imagine that for some peculiar reason you were to grant yourself a general education by focusing on a single ‘object’ or ‘thing’. The cannabis plant represents a stream of alluring questions splitting off into interminable rabbit holes. My confusing analogies aside, whether you are a history buff, a science nerd or more of a political junkie (you name it) there is something in it for everyone.

Super short introduction to the 3 Cannabis species

The genus cannabis contains 3 species; ruderalis, indica and sativa. Human intervention has mixed them up quite a bit and correct nomenclature would have us use terms like “indica-dominant” rather than just “indica”. I’m not sure if I’ll follow convention. But for simplicity’s sake, let us generalize. Cannabis sativa is more equatorial than not. It is a tall and lanky plant conferring a euphoric kind of high. In contrast, Cannabis indica does best at altitude, where it prefers to be shorter and quite sturdy. It produces more of a sedating high. Cannabis ruderalis is often shorter than your average sativa and is mainly used as a source of hemp because significant amounts of psychoactive substances are absent. The vast majority of cannabis consumed for medical or recreational purposes is an indica-sativa hybrid. This is true whether you are lighting up in Colorado, ingesting some in Amsterdam (The Netherlands) or vaporizing it in Australia. As far as I know, ‘pure’ strains of one or the other do not exist.

The science part

The study:

Miswiring the brain: D9-tetrahydrocannabinol disrupts cortical development by inducing an SCG10/stathmin-2 degradation pathway

Sentence 1 of the abstract:

“Children exposed in utero to cannabis present permanent neurobehavioral and cognitive impairments”

Before I deconstruct the study, I want to explain why I think certain terms and the 1st sentence of the abstract are inappropriate and incorrect, respectively.

First off, I agree that in utero cannabis exposure results in quantifiable changes to brain structures. However, I disagree with the authors’ implication that these changes are qualitatively understood (i.e. that their significance is known). In order to be credible, the terms “miswiring”, “permanent” and “impairments” should be backed-up by an unambiguous mix of epidemiological and randomized-controlled studies justifying their use. This is not the case. To see why, lets look at what the authors use to back-up their foregone conclusion, citing:

  • 1 prospective study “Prenatal marijuana and alcohol exposure and academic achievement at age 10”
  • 5 longitudinal studies “Effects of prenatal tobacco, alcohol and marijuana exposure on processing speed, visual–motor coordination, and interhemispheric transfer” & “The Effects of Prenatal Marijuana Exposure on Delinquent Behaviors are Mediated by Measures of Neurocognitive Functioning” & “Prenatal marijuana exposure contributes to the prediction of marijuana use at age 14” & “Prenatal Substance Exposure: Effects on Attention and Impulsivity of 6-Year-Olds” & “Intrauterine Cannabis Exposure Affects Fetal Growth Trajectories: The Generation R Study
  • 2 retrospective studies “Maternal smoking, drinking or cannabis use during pregnancy and neurobehavioral and cognitive functioning in human offspring” & “Prenatal marijuana exposure: Effect on child depressive symptoms at 10 years of age

Their list is thoroughly unimpressive. It represents a morass of statistical wand-waving, inappropriate sample sizes, inadequately controlled confounders, untested assumptions and blatant exclusions of contrary data. Even more troubling to me, is that many are solely or mainly funded by NIDA (National Institute of Drug ABUSE) whose grant criteria expects researchers to demonstrate drug harms, not drug effects (either good or bad). This is antithetical to sound methodology and a deal-breaker in terms of scientific credibility.

This compilation of literature refuting central claims made in the 7 above epidemiological studies is a good place to start [ is a great general resource for drugs, plants and experiences people have with them].

Alternatively, this 1994 ethnographic study of pregnant Jamaican women (funded by the March of Dimes Foundation) is interesting and well thought out. It concludes “The absence of any differences between the exposed or nonexposed groups in the early neonatal period suggest that the better scores of exposed neonates at 1 month are traceable to the cultural positioning and social and economic characteristics of mothers using marijuana that select for the use of marijuana but also promote neonatal development”. The authors explain 3 main limitations of their study:

  • recruitment “identification by fieldworkers, with assistance from local midwives, represented a contributive alternative to a random sampling strategy”
  • “although the sample size is small, it provided an opportunity to follow up drug-using women through pregnancy with the level of detail that often is lacking in retrospective studies of large numbers of women”
  • confounders “Although this study was successful in controlling for polydrug use and SES [Socio-Economic Status], other variables (financial independence, mothers education, and household child/adult ratio) emerged as meaningful during the course of this study”

Back to THC’s fascinating effect on cortical development in utero. This study used aborted foetuses, mice and in vitro as well as in vivo techniques. First of all, the authors describe an integrated signaling axis whereby (1) THC acts as the trigger binding to the (2) CB1 receptor (CB1R) which starts to transduce the signal by acting on (3) JNK that has SCG10 as its downstream target. SCG10 is a key neuron-specific protein. It is plentiful in the growth cones of developing neurons because it acts as a destabilizing factor. This means it promotes the disassembly of microtubules by binding to them as a dimer. Somewhat metaphorically, this grants neuronal structures “options”: they can branch out, make new connections, dissolve old ones and basically participate in the brains plasticity (i.e. the ability to change). You certainly want a degree of neuronal ‘instability’ if your are to learn or repair your brain.

After ensuring that SCG10’s downstream activity can in fact be mediated through CB1R transduction, the authors went on to argue that upon THC exposure, long-lasting CB1R signalling defects occur which would cause excessive SCG10 degradation, hence reducing destabilizing potential in neuronal structures. They based this on observations of increased & deregulated presynaptic activity. However, their own data (Fig.1F) does not support the view that these changes are long-term or permanent.

Fig.1F Protein & mRNA expression levels

Rather, the ”rewiring and reduced synaptic plasticity in the cortical circuitry were not associated [my emphasis] with long-lasting modifications of synaptic protein expression in the hippocampus of offspring prenatally exposed to THC”. They incorrectly extrapolated an observation of altered and increased presynaptic activity to mean that these changes were permanent and negative, despite their own data on protein and mRNA expression levels suggesting otherwise.

Furthermore, this other study explores the link between synaptic activity and morphological changes in synaptic constructs and cautions its readers about “the relationship between the number of synapses & their combined strength is likely to be highly complex & therefore one would not expect to find a linear relationship between structural plasticity & changes in synaptic transmission”. This is fancy way of saying: at this point in time, we should be cautious about labelling molecular changes we observe as good or bad – especially when a working theory of brain development is still under construction.

Regardless, you may rightfully ask, so what if the changes are not permanent? Maybe they are still bad for the baby! Fair question and fair point. The answer is that we do not know. However, we have a few points to consider in the mean time:

  1. As argued above, epidemiological studies do not currently suggest such an effect when adequately controlling for other factors. God knows they’ve tried.
  2. Cannabinoids are naturally found in a mother’s breast milk, shifting the burden of proof to those claiming their inherent danger.
  3. No clinical studies have reliably demonstrated children with neurocognitive or motor impairments that resulted from marijuana exposure (pre-natal or post-natal). God knows they’ve tried.
  4. There are tons of data demonstrating therapeutic effects of cannabis on neurocognitive markers* sans the unending list of side-effects constituting the rule rather than the exception for most classical pharmaceuticals.

*For a future post.

Interestingly, THC exposure was also shown to result in ectopic (abnormal) filopodia formation and altered axonal morphology (Fig.7E).

THC fuxxing with Filopodia & actin formation

Bad? Well, maybe. Good? Well, potentially. Lets assume it is bad. How do we then go about squaring that with population-wide data showing no link between marijuana exposure and neurocognitive impairments? Do we reconsider what is normal filopodial formation in utero compared to post-natal stages? Or do we question whether normal axonal morphology can in fact manifest in more varied forms that are context dependent (stage of development and access to nutrients for e.g.)?

This study is actually extremely valuable in terms advancing neuroscientific understanding of foetal brain development and the role played by our endocannabinoid system. In fact, the signaling cascade (THC—>CB1R—>JNK—>SCG10) shown to exist by this study is “the first signaling axis directly linking a GPCR to SCG10 as molecular effector”. GPCR = G-protein coupled receptor. It also demonstrated how “phosphorylation inhibits the microtubule destabilizing activity of SCG10 suggesting that this protein may link extracellular signals to the rearrangement of the neuronal cytoskeleton”. This has exciting implications about potential modes of action to explain successful treatment outcomes in patients using marijuana for depression and PTSD (and lots more).

Finally, maybe the gravest of errors made by the authors of this study was their lack of distinction between cannabis and THC. They certainly were not clear nor explicit about it. THC is not cannabis and cannabis is not THC. Cannabis is not just a ‘pharmacy’ but a polypharmacy. It has > 400 compounds amongst which +60 different cannabinoids and loads of terpenes.

It would have been nice to see the authors speculate about both negative AND positive effects (again, DUH!). From the scientific perspective, there is NO advantage to this kind of narrowed thinking. Politically, the opportunities are plentiful.

#EvoMed inspired: No 2 medicines

I contend that there is only medicine, not a variety of flavors of medicine. The medicine you should care for or call by that name is the kind that is testable and works. Keep in mind that clinical testing is one of many forms of testing that can validate medical interventions.

If I told you once, I’ll tell you again: definitions are important in science. Undoubtedly, the definitions below are imperfect and I’d appreciate alternatives making their way into the comments section. To my taste, they are fine for the purposes of this discussion.

Lets start by defining 3 major intersecting elements of the matter at hand:

These 3 concepts can be seen as feeding back and forth on one another.

  1. A belief arises, inexplicably or based on prior knowledge. It then needs to be broken down as succinctly as possible so that its essential principles can morph into a testable hypothesis with the minimal number of variables possible.
  2. This hypothesis does not become accepted because there is evidence to support it. It becomes accepted — progressively — by accumulating failed experimental attempts seeking to tear it down. Evidence supporting the hypothesis is often sought in parallel or may stem from the attempts to disprove it.
  3. If the hypothesis has enough supportive data of sufficient quality outweighing all evidence to the contrary then it can be termed medicine (in the context of beliefs relating health & disease).

How exactly a hypothesis is accepted as valid or invalid (point #3) is often messy business. This is because we are on the blurry edge of the space where ‘knowns’ & ‘unknowns’ collide.

There are a million different reasons why things that putatively ‘work’ cannot and should not be called medicine. This label is reserved for things with evidence stemming from sound scientific testing. Some things are simply unconfirmed medicines in the waiting. I think this is the case with some so-called ‘alternative medicines’ as well as with consistently poo-pooed lifestyle factors — both of which have roles to play in health. This is not an argument for only using tried-&-true treatments since there are many situations where novel experimentation and educated intuition are appropriate. It is a lot of what makes science exciting! However, until data is in for EVERYONE to question OPENLY, a good scientist should abstain from calling his or her intervention(s) medicine. Or simply making unwarranted claims of certainty or misrepresenting the limitations of their knowledge. A safe bet on whether or not a person is well aware of what they’re doing and saying is that most questions should typically be met with a flat “I don’t know”. Yes, N = 1’s are great and I am an avid self-experimenter finding lots of value in it. I am careful to not form too solid a world view based solely on my experiences. This is one of my pet-peeves with the Paleo blogoshpere (or any other health related blogospheres) which extrapolates the “everyone is different” (sound) meme to suggest that their own evaluation of themselves is — by default — more important than other lines of evidence. If only things were so simple…There is no clear road-map: you have to constantly adjust what you appear to experience with other information out there. It is a life-long lesson in humility to be a true scientist (at heart — not by title. Titles are meaningless).

Lets take 1 tangible example of each scenario. 1 scenario is an ‘alternative medicine’ intervention that is worth scrutinizing/testing. The other scenario is of an ‘alternative medicine’ intervention not deserving a 2nd glance.

Example of an ‘out there’ hypothesis fulfilling deserving further scientific scrutiny: acupuncture. More specifically, it is auricular acupuncture. It has been used for an immense variety of conditions, syndromes and symptoms. The only effect it appears to have is on pain management. The effect appears to be quite weak — at best. A recent review article entitled “Efficacy of Auricular Therapy for Pain Management: A Systematic Review and Meta-Analysis” blandly concludes that:

auricular therapy can be used as an adjunct therapy for pain management and, therefore, reduce analgesic use to minimize potential adverse effects and tolerance. Nonetheless, further studies—particularly large scale of RCTs—are needed to further confirm the efficacy of auricular therapy for pain and must take into consideration important features of methodological design, which include point specification, stimulation, treatment duration, placebo effects, and patient expectations of treatment outcomes

Acupuncture is certainly not quackery. It may just work very poorly. In the grand scheme of medical things however, it is not vying for a prominent role in medicine. Understanding the neurophysiology and placebo/nocebo effects relating to such interventions are interesting avenues to follow, maybe one day telling us more about the nature of acupuncture’s effect(s). Moving on…

Example of an ‘out there’ hypothesis undeserving of further scientific scrutiny: homeopathy

It fails simple logic. It fails right out of the gate, on many fronts. For one, it is based on the silly notion that the more diluted the supposed active component is in the solution, the higher its potency.  This contradicts the time-tested concept that has yet to receive a single piece of evidence disproving it: the greater the quantity of something, the larger the dose. U-curves are still dose-dependent U-curves, people. They simply describe non-linear relationships between 2 or more variables. A clear, logic fail (see below).

logic fail

Furthermore, the dilutions indices are so large that they cause uncontrollable laughing and scoffing amongst the scientifically inclined. See this table below for a good laugh:

The laughing table

The laughing table

This is overkill but deserved. Lets hammer the nail into the coffin of homeopathy by quoting the recent Optum “Overview Report on the Effectiveness of Homeopathy for Clinical Conditions: Evaluation of the Evidence”, which concludes:

There is a paucity of good-quality studies of sufficient size that examine the effectiveness of homeopathy as a treatment for any clinical condition in humans. The available evidence is not compelling and fails to demonstrate that homeopathy is an effective treatment for any of the reported clinical conditions in humans.

The fact that homeopathy got this level of scientific scrutiny when lower-school maths and high-school physics is enough to disprove its outrageous claims is a waste of time and energy. Hopefully they can no serve their intended purpose.

Being clear about what can be called medicine and what can’t or shouldn’t, does not mean one is close minded. On the contrary, it demonstrates an understanding of the general limits of knowledge generally and ones own knowledge. It’ll be hard to think outside of the box, so to speak, if you’re not willing to recognize you’re in a box in the first place. That box is an exciting place to be and it is vast, really vast. It should be cherished as a launching pad for expanding it. If done with a little intellectual integrity — the better!

Check-out the #EvoMed summit and make up your own mind.

[Edit/Mea Culpa: 23/11/2014]

I wrote “Acupuncture is certainly not quackery. It may just work very poorly.” I now realize that acupuncture can be  correctly labeled as quackery as per its definition on Wikipedia: “Quackery is the promotion of unproven or fraudulent medical practices”. I changed my mind, mainly due to 2 things:

1) the reassessment of the ‘significance’ of p-values in biomedical literature (see “An investigation of the false discovery rate & the misinterpretation of p-values” by David Coloquhon

2) the readjustment of my expectations regarding the statistical occurrence of actual ‘true’ results in biomedical literature (see “Why most published research findings & false” by John Ioannidis

I realized that I was applying a double-standard of ‘evidence required’ for acupuncture studies compared to my more rigorous approach with genetic, pharmacological or nutritional studies. My use of the word “weak” was somewhat euphemistic for “nothing to see here”.

Lastly, I would like to note that the definition of quackery can be applied problematically to viable medical treatments. For example, “ketogenic diets for epilepsy” or “marijuana as a treatment for pain” are recognized as unproven or ineffective medical treatments and the general consensus amongst medical institutions mirrors that position. This however, is incorrect. The science behind both of these unconventional modern medical approaches is sound.

This is me trying not to throw the baby out with the bath water.