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]

Cancer: linking metabolism to genetics

Cancer is considered a genetic disease based on the currently accepted Somatic Mutation Theory (SMT). It states that the disease arises when “an individual mutant clone of cells begins by prospering at the expense of its neighbors through a microevolutionary process of clonal selection. The abnormal metabolism seen in cancer cells is said to result from these cancerous mutations. A competing theory of cancer effectively reverses the arrow of causality, saying that these mutations in fact result from dysfunctional metabolism rather than cause it. This Metabolic Theory (MT) of cancer deems the disease to have a metabolic rather than genetic origin. It postulates that faulty oxidative phosophorylation (OxPhos) – the end stage of cellular respiration – is the root cause. This causes the continued expression of glycolysis in the presence of oxygen (known as the Warburg effect) which is a or possibly the defining feature of cancer cells. Whether mutations cause cancer or simply emerge in the cancerous cell as a result of defective OxPhos, it remains important to investigate links between genetic, gene regulatory and epigenetic profiles characterizing cancers as well as their influence on the 6 hallmarks of the disease: unlimited replication, evading death, avoiding growth suppressors, sustaining proliferative signaling, inducing angiogenesis, invasion and metastasis. The initially limited genetic features of cancers included abnormal karyotypes (such as chromosome number, called aneuploidy) and somatic mutations. The discovery of epigenetic and broader gene regulatory phenomena provide additional avenues via which to distinguish cancer cells from normal ones. This can be done regardless of which theory of cancer one operates under. The question herein explores Which gene regulatory mechanisms are linked to dysfunctional OxPhos in cancer? The relevant laboratory and software techniques are discussed first, followed by the particular mechanisms underlying the disease, hopefully helping to further elucidate the merits and demerits of both theories.

Before delving into data gathering techniques, a clear and parsimonious exploration of the question necessitates defining genetic, epigenetic and broader gene regulatory phenomena. Genetic changes refer to alterations in the genetic code, whether that be different nucleotides, their order in a sequence, the orientation of a sequence, the translocation of a sequence or the number of times a sequence repeats. As for epigenetics, in 1952 Waddington used the term quite broadly “to refer in general to what we now know to be changing patterns of gene expression that underlie development and that are oſten triggered by signals sent from other cells. According to the 2014 6th edition of Molecular Biology of the Cell, the covalent modification of histones in nucleosomes falls under an epigenetic “form of inheritance that is superimposed on the genetic inheritance based on DNA. Mark Ptashne argues that the covalent modifications of histones is not an epigenetic mechanism given that “the enzymes that impose such modifications lack requisite specificity; the modified states are not self-perpetuating; and the roles played by the modifications remain for the most part obscure. Experiments tell us that “histone modifications are not maintained as cells divide. These histone modifying enzymes are part of a response to epigenetic regulation and not a cause thereof. According to his criteria, histone modifications fall under the more general category of gene regulation, leading to a more narrow definition of epigenetics whereby such phenomena must have memory (“continual activities of the specific regulators to maintain that state of expression”) and specificity (“activating one gene or set of genes and not another”). One epigenetic mechanism uses a positive feedback loop (such as the auto-activation of the cl gene) and another, a negative feedback loop (like the auto-repression of the cl gene). Examples of epigenetic phenomena include viral immunity passed down to the next generation or transient inflammatory stimuli causing chronic inflammation via feedback-loops mediated by sequence-specific microRNAs (or other regulatory proteins).

An early cancer diagnosis, or better yet anticipating the transition of a benign tumor to a malignant one, is of utmost importance in reducing cancer associated deaths. For this reason, assays detecting DNA methylation patterns regulating gene expression and activities of their upstream enzymatic effectors have become of great interest. They are the subject of Shinjo and Kondo’s 2015 paper upon which most of the following analysis is based. Unlike Mark Ptashne, the authors classify DNA methylation as epigenetic.

Assays of DNA methylation patterns first require differentiating methylated DNA from unmethylated DNA, which can be done physically, using a methyl-binding column, or by inducing sequence changes through bisulfite treatment for example. Other techniques based upon sequence changes include the use of restriction enzymes of varying sensitivities to DNA methylation sites, as well as methylated DNA enrichment with anti-cytosine or methyl-binding anti-bodies. DNA methylation appears to be stable in cancers and can thus be relied upon to establish methylation patters related to gene expression.

Assays of DNA methylation patterns can be conducted on samples of cell-free DNA (cfDNA) blood, stool, urine, tissue and other bodily fluids. Although cancer cells resist apoptosis, the disease process results in overall higher rates of apoptosis and tissue necrosis, leading to increased circulating cfDNA blood levels that can indicate loss of heterozygosity and the presence of mutations. For example, blood samples of cfDNA have shown RASSF1A DNA demethylation is associated with increased sensitivity to cancer treatment and the converse, decreased sensitivity to treatment, is associated with increased methylation.

Methylation tests on fecal, urine and sputum samples each use different biomarkers and do not all perform equally well. For stool samples, Cologuard™ uses NDRG4, BMP3 and KRAS mutation methylation biomarkers showing 92.3% sensitivity and 86.6% specificity. For urine samples, the GSTP1 methylation biomarker shows 88% sensitivity and 60% specificity. For sputum samples, the Epi prolung test uses the SHOX2 methylation biomarker showing 60% sensitivity and 90% specificity.

Cancer treatments directly targeting gene regulatory mechanisms broadly cover 3 areas; histone modifications, DNA methylation associated pathways and readers of gene regulation. Regarding the first area, there are 18 known histone deacetylase inhibitors (HDIs) divided into classes 1 through 4 with which to work with. They stop the deacetylation of lysine (K) and are associated with the restoration of silenced genes, the induction of growth arrest, the interruption of differentiation and the prompting of tumor cell apoptosis. HDI based cancer treatments are still quite new and currently there are only 3 FDA approved HDIs in the United States of America: Vorinostate, Romidespine and Belinostat.

Regarding the second area, DNA methyltransferase inhibitors (DNMT1 inhibitors) are equally novel and have only one approved therapy for myelodysplastic syndromes (MDS) – bone marrow cancers. The mechanism of action of another molecule operating in this area is visible in Figure 1 below, taken from Shinjo and Kondo’s paper. IDH1 usually oxidatively decarboxylates isocitrate to α-ketoglutarate (α-KG) which can then go on to replenish the TCA cycle. IDH-1 mutants divert α-KG towards the catalysis of oncometabolite 2-HG. As it accumulates it inhibits α-KG-dependent dioxygenases, histone demethylases and TET family proteins. IDH-1 inhibitors interfere with 2-HG accumulation.

The third area is concerned with cancer treatments exploiting molecules capable of interfering with gene regulatory ‘reading’. Bromo and Extra Terminal proteins (BETs) read the code of histone modification, particularly acetylated histones. BET inhibitors bind particular residues of histone modification, effectively out-competing bromodomain-competing proteins (BRDs) of the BET family. For example, fusion oncoprotein BRD4 is displaced from chromatin by BET inhibitor I-BET151, thereby decreasing the oncogenic transcription potential of BCL2, C-MYC (cytoplasmic myelocytomatosis oncogene) and CDK6.


Figure 1. IDH1-mutant

Chromatin Immunoprecipitation Sequencing (ChIP-seq) assays are used to map the genome-wide binding of gene regulatory proteins. For example, this technique reveals the different binding patterns of the Sirtuin1 protein (Silent Information Regulator 1) in the nucleus accumbens of rats before and after the administration of cocaine over 7 days. ChIP-seq in conjunction with RNA and immunoblotting assays can link posttranslational modifications made to newly synthesized histones and the subsequent patterns of gene expression found at specific genomic loci affected by this chromatin assembly. Yang et al. applied this combination of techniques to show ”how one modification that occurs on newly synthesized histone H3, acetylation of K56, influences gene expression at epigenetically regulated loci in Saccharomyces cerevisiae. The above methods allow for the gathering of essential information about cellular behavior, but they are only part of the scientific process; information then needs to be appropriately triaged and visualized for fruitful interpretation. For example, RNA-seq visualization tool Cascade does just that for copy number variants (CNVs) and the MAP2K4 pathway of ovarian cancers, visible in Figure 2 below.

RNA-Seq Cascade

Figure 2. RNA-seq visualization tool Cascade for ovarian cancer CNVs & MAP2K4 pathway

The first mechanism explored here involves sirtuin proteins 1 to 7 which according to Mark Ptashne’s criteria falls under the category of gene regulation. Amongst the 7 sirtuin proteins those of particular note are sirtuins 3, 4, 6 (and 7 to a lesser degree) because of their pronounced ability to suppress the Warburg effect as a function of their gene regulatory capacities. Sirtuins are NAD+-dependent class III histone deacetylases (HDACs) catalyzing ADP-ribosylation reactions which can loosen chromatin – making DNA more or less accessible – as well as facilitate or hinder the activities of histone effector proteins. They are most directly involved in cancer via the Warburg effect since their activities depend on the NAD+/NADH ratio, an indicator of metabolism, cellular redox state and energy status. NAD (the reduced form of NAD+) and NADH are physiological sirtuin inhibitors whilst NAD+ and resveratrol are sirtuin-activating compounds (STACs). The following discussion of sirtuins 1 to 7 is based on Kleszcz et al.’s 2015 paper as well as Douglas Wallace’s 2014 NIH presentation “A Mitochondrial Etiology of Metabolic and Degenerative Diseases, Cancer and Aging.

SIRT6 is found only in the nucleus and is particularly interesting amongst sirtuins, in large part because of its co-repression of transcription factor MYC (myelocytomatosis oncogene). This repression is associated with both the inhibitions of glycolysis and ribosomal activity. In cancer cells, the nucleoli containing ribosomes have the striking morphological feature of being engorged due to increased biosynthetic demand. Non-transformed cells lacking SIRT6 become tumors when glycolysis is intact but fail to do so when it is abolished, a pattern which strongly argues that the Warburg effect drives tumorigenesis. Like for most sirtuins, SIRT6’s tumor-suppressing effect depends on the cell type and disease stage. Nevertheless, SIRT6 overexpression affects chromatin structure such that cancer cells will apoptose unlike normal ones. One way this is thought to occur is through the H3K9 deacetylation of the Hypoxia-Inducible Factor-1α (HIF-1α) gene promoter. HIF-1α is stable in cancer cells, enabling pseudorespiration which occurs when ATP is produced through mitochondrial fermentation involving substrate-level phosphorylation. The cell is less sensitive to apoptotic signals when the HIF-1α complex is stabilized but fortunately, SIRT6 overexpression can destabilize it. Furthermore, mouse models of liver cancer have shown that the ‘genome guardian tumor suppressor p53 can induce SIRT6 expression so as to inhibit hepatic gluconeogenesis, thus underscoring a mechanism for p53 in maintaining glucose homeostasis.

Other sirtuins affect HIF-1α stability such as SIRT3 which is found both within the mitochondrial matrix and nucleus under normal growth conditions. Downregulation of its activity effectively reprograms metabolism such that an increased level of reactive oxygen species (ROSs) signals HIF-1α stabilization which in turn upregulates glycolytic enzymes. SIRT3 impedes aerobic glycolysis and thus the production of lactate through an additional pathway involving pyruvate dehydrogenase E1α (PDHA1). The deacetylation of lysine 321 on PDHA1 will increase its activity, diverting and increasing glucose utilization through the Citric Acid Cycle (TCA). Like SIRT6, SIRT3 appears to suppress or promote tumorigenesis depending on the cell type as well as the confluence of stress and apoptotic stimuli. Inflammation is one such stressor typically present in tumors and can be attenuated by fasting in human subjects. SIRT3 is NAD-dependent (or ‘nutrient sensing’) and when fasting, its activity diminishes, consequently blunting the NLRP3 (NOD-Like Receptor family Protein 3) inflammasome response.

Metabolism is inextricably interwoven with biosynthetic demand given their functional interdependency. This is exemplified in the interaction of the mammalian target of rapamycin complex 1 (mTORC1) and SIRT4. The latter is only located in the mitochondrial matrix and is involved in balancing the oxidation and synthesis of fatty acids whilst the former is a general growth promoter. mTORC1 activation will upregulate glutamate dehydrogenase (GDH) via SIRT4 repression and in so doing, enable cancer progression. This enhanced glutamine metabolism both replenishes TCA cycle intermediates required for biosynthesis and serves as fermentable substrate contributing to filling the energy gap that results from a cancer cell’s defective oxidative phosphorylation. Interestingly, sufficiently extensive DNA damage can induce SIRT4 expression and shut down glutamine metabolism, a requisite process for cellular repair. This sirtuin appears to suppress the formation of tumors by inhibiting excessive glutamine metabolism and maintaining genomic stability.

SIRT1 is the most highly conserved sirtuin in mammals and is found both in the nucleus and cytoplasm. It modulates the metabolism of lipids and glucose in the liver, insulin secretion in the pancreas and engenders fat mobilization in adipose tissue. All of these features together point to SIRT1 as a key metabolic sensor. SIRT1 negatively regulates phosphoglycerate mutase 1 (PGAM1) which is upregulated in many cancers and occupies an important point in the glycolytic pathway where many glycolytic intermediates upstream of it serve as biosynthetic precursors. SIRT1-mediated deacetylation of PGAM1 will thus reduce glycolysis, flux in the pentose phosphate pathway (PPP) and consequently the aforementioned biosynthetic intermediates too. This all serves to inhibit tumor growth given their reliance on these factors. Like other sirtuins, SIRT1 also demonstrates tumor suppressive action mediated by HIF-1α deacetylation. This sirtuin, like others in its family, has its tumor suppressing capacities contrasted by context-dependent tumor promoting ones. Through its deacetylating activities it can promote tumorigenesis by inhibiting p53, p73 and HIC1 (Hypermethylated In Cancer 1). Given how common it is for genes and gene regulating molecules to both suppress and promote tumorigenesis, they should not be viewed as solely doing one or the other, but rather as sitting at the intersections in cell pathways leading towards or away from growth. The same analogy can be applied to the gene themselves, implying that terming them ‘oncogenic’ may have been premature and now thoroughly confusing.

Along with SIRT1, SIRT2 is also located in both the cytoplasm and mitochondria. It is yet another sirtuin that, when overexpressed, can destabilize HIF-1α under hypoxic conditions. Experiments with cells that do not express SIRT2 corroborate this fact given that they fail to affect HIF-1α levels. SIRT2 positively regulates PGAM as opposed to SIRT1’s negative regulation. SIRT2 deacetylates K100 (lysine100) on PGAM, thereby upregulating its activity and thus glycolysis. This sirtuin is mainly found in the cytoplasm and shuttles back and forth to the nucleus. The significance of sirtuin localization is not yet known. However it is interesting to consider the possibility that the direction in which they carry out their activities (either from nucleus to cytoplasm or cytoplasm to nucleus) might explain their context-dependent pro- or anti-tumorigenic effects. During the study of breast tumors, SIRT2 was shown to have both tumor suppressive and promoting capabilities. Here however, tumor grade appeared decisive rather than cell type.

Both SIRT5 and SIRT7 are lesser studied sirtuins. The former is only found in mitochondria where it acts as a global succinylation regulator. Through its dessucinylation of superoxide dismutase (SOD1) it can lower the level of ROSs that cancer cells produce as a consequence of elevated glycolysis and mitochondrial fermentation concommitant with defective oxidative phosphorylation in their electron transport chain (ETC). Found only in the nucleus, lysine deacetylase SIRT7 targets H3K18 found in the vicinity of multiple gene promoters involved in tumor suppression. Similar to the SIRT6, it attenuates engorged nucleoli containing ribosomes in a MYC-dependent manner by deacetylating MYC transcript targets of ribosomal proteins. SIRT7’s maintenance of H3K18 acetylation patterns appears important in keeping this morphological cancer phenotype. Enhanced rRNA production is such a conspicuous feature of cancer that it has been proposed as the 7th hallmark. Returning to the idea of sirtuin localization as potentially explaining opposing actions in different contexts, SIRT7 is present and highly active in the ribosome-containing engorged nucleoli, suggesting its activity may promote rRNA synthesis and thus ribosome biogenesis. Finding a SIRT7 inhibitory compound is of great interest. Nevertheless, STACS may be generally preferable to to sirtuin inhibitors for 3 reasons; activators need not be as potent as inhibitors due to downstream signaling amplification, they are more selective due to their ability to bind non-catalytic sites of target proteins and their mimicry of natural activators is liable to induce fewer side-effects. Unfortunately, Kleszcz et al. make clear that until now (2015) “it has been a conundrum that such a diverse set of small molecules can activate SIRT1, but so far no activators of SIRT2-7 have been described”. This difficulty should motivate research aimed at exploiting endogenous sirtuin activators in order to improve prevention as well as palliative and adjunctive cancer care by understanding the influence of lifestyle factors. Diet is always front and center as a lifestyle factor and interestingly, it has been shown that multiple long-chain fatty acids at physiological concentrations can make SIRT6 35 times more active. Futhermore, mice given nicotinamide riboside to increases endogenous sirtuin activator NAD+ levels benefitted from increased insulin sensitivity. This correlated with SIRT1 and SIRT3 upregulation.

When cancer is viewed through the genetic paradigm, a parrallel between the inconsistent tumorigenic effects of sirtuins and oncogenes emerges. A particular sirtuin might flip from being anti-tumorigenic to tumorigenic depending on the tissue type or cancer grade. An oncogene can be both pro-tumorigenic and suppress tumors without a satisfying explanation as to why. Supporting this latter point is Soto and Sonneschein’s observation that “a mutation that should have produced uncontrolled cell proliferation resulted in cell death or arrest of cell proliferation. The significance of this parallel is that the opposing actions of both (onco)genes and sirtuins in the genesis and progression of cancer highlight SMT’s poor predictive power, calling for serious reconsideration of the theory. Case in point, genetic analyses of BRCA variants only explain about 10% of breast cancer susceptibility and amount to our best current predictions. However, appreciating BRCA1’s metabolic interaction with enzyme acetyl coenzyme A carboxylase alpha (ACCA) goes further to explain oncogenesis than purely genetic analyses of BRCA1 do. SMT’s poor predictive power has led some to wonder how such a specific process (cancer) can stem from a number of unspecific mutation-inducing events (radition, viruses, inflammation etc.). Albert Szent-Györgi termed the latter phenomenon the “oncogenic paradox. Fortunately, this paradox and oncogenic inconsistency can be stress-tested by exploring a prediction of SMT: transferring transformed (cancerous) nuclei into non-transformed (normal) cytoplasm will induce tumorigenesis in the latter. Such nuclear-cytoplasm transfer experiments have been carried out and show that nuclei from cancerous cells transferred into normal ones do not reliably transform them. The converse of that experiment should yield similar results: cells with tumor nuclei should revert back to a normal state when cytoplasmic contents of normal cells, specifically their respiration competent mitochondria, are transferred to them. In 1988 Israel and Schaeffer showed exactly that using cytoplasmic hybrid (cybrids) cell preparations of rat liver epithelial cells. Both kind of transfer experiments are depicted in Figure 3 below.

transfer experiments

Figure 3. Nuclear/cytoplasm transfer experiments

Kulesa et al. demonstrated “the ability of adult human metastatic melanoma cells to respond to chick embryonic environmental cues, a subset of which may undergo a reprogramming of their metastatic phenotype. Thomas Seyfried of Boston College summarizes results from multiple nuclear/cytoplasm experiments saying that “nuclei from cancer cells can be reprogrammed to form normal tissues when transplanted into normal cytoplasm despite the continued presence of the tumor-associated genomic defects in the cells of the derived tissues. This suggests that something in the cytoplasm rather than the nucleus is driving tumorigenesis. It is this – together with Warburg’s nearly century old observations that cancers display some degree of defective OxPhos – that begs the question as to what role gene regulatory elements may play in linking respiration defective mitochondria with a highly disordered and mutated nuclear genome.

One manifestation of impaired respiration is excessive reactive oxygen species (ROSs). The electron transport chain (ETC) in the inner mitochondrial membrane (IMM) uses electron carrying molecules to synthesize ATP and some of these electrons also (fully) reduce water. This imperfect process also partially reduces water: electrons become unpaired in oxygen’s outer orbital and so ROSs such as superoxide anions, hydrogen peroxides and hydroxyl radicals form. For instance, hydroxides (OH) may ‘steal’ electrons from membrane lipids, thus initiating a chain-reaction event whereby neighboring lipids steal electrons from one another and thus change properties of membranes in an uncontrolled manner. ROSs are physiologically unavoidable and participate in cell signaling, yet because they are inherently damaging to cellular components they must be managed to avoid disease and accelerated ageing. Additional manifestations of impaired respiration include abnormalities in mtDNA, the proton motive gradient (ΔΨm) and the TCA cycle. Yeast and animal cells have ‘surveillance systems’ to react to these signs of impaired respiration, one of which is called the retrograde (RTG) response.  It “responds to mitochondrial dysfunction by adapting cell metabolism to the loss of tricarboxylic acid (TCA) cycle activity. Thomas Seyfried correctly categorizes the RTG response as an epigenetic mechanism because it fulfills the criteria of specificity and memory: the basic helix-loop-helix-leucine zippers Rtg1/Rtg2 transcriptional factors complex demonstrate specificity when binding to the DNA R Box binding site for the transcription of a particular set of genes, as well as memory given that yeast maintains  those changes during cellular division to extend their lifespan. The RTG response is a signaling pathway relaying information about the organelle’s status to the nucleus so that, amongst other processes, the major quality control mechanisms of mitophagy and autophagy may be induced or held at bay.

The RTG response is a central mechanism underlying the MT of cancer because it links the hypothesized metabolic origin to the characteristic abnormally unstable and mutated nuclear genome. Essentially, the RTG response upregulates non-oxidative metabolic networks (or oncogenes from the gene-centric perspective) which shifts the cell’s energy yielding pathways to a predominant mix of glycolysis and glutamine fermentation so as to maintain ATP homeostsis (stable ΔG’ATP) despite insufficient respiration. Figure 4 below is Thomas Seyfried’s illustration (adapted from Michal Jazwinski’s work in yeast) of how the mammalian RTG response occurs.

RTG Response

Figure 4. RTG Response

Normally respiring cells have a dormant RTG response located in the cytoplasm. It takes the form of a complex composed of Rtg1 dimerized to a strongly phosphorylated Rtg3. When a cell experiences defective or insufficient respiration, cytoplasmic Rtg2 partially dephosphorylates the Rtg1/Rtg3 complex, prompting both proteins to enter the nucleus so that Rtg3 may bind the R Box, followed by Rtg1 re-engaging Rtg3, effectively awakening the RTG response. This leads to the targeted transcription of multiple metabolic and antiapoptotic genes including CHOP, MYC, TOR, NF-𝜅B, Ras and CREB. This non-exhaustive set of genes has multiple associations with  cancer initation, maintenance and metastasis. For example, MYC has been associated with increased levels of ROS and the inhibition of tumor suppressor p53. An example pertaining to metastatic behavior links the RTG response to increased levels of Matrix Metalloproteinase 2 (MMP2) seen for activated macrophages hybridizing to neoplastic epithelia. It is important to note that RTG response mediated destabilization of the nuclear genome and induction of aneuploidy and somatic mutations is strong evidence favoring the MT of cancer over the SMT. Furthermore, the RTG response is also associated with multi-drug cancer resistant phenotypes, increased cytoplasmic calcium, increased ROSs, iron-sulfur complex abnormalities, decreased mitochondrial ATP production as well as a lowered ΔΨm. Although there is no direct equivalent in higher eukaryotes of the yeast Rtg2 mitochondrial sensor transducing mitochondrial signals, authors Srinivasan et al. explain that “strong homologies between inhibitors and pathways of both [RTG genes and NF-κB] leads one to believe that the retrograde response is a potential predecessor of the now-central stress-regulator, NF-κB. In that same paper by Srinivasan et al., reproduced here in Figure 5, one can see the nearly identical RTG and NF-κB activity levels as they relate to 6 major metrics of cellular behavior.


Figure 5. RTG vs NF-kB

Jazwinski also comments on the many similarities between yeast and human RTG responses, adding that “mammalian cells not only display many of the molecular features of yeast retrograde signaling, but they also present the cellular outcome of extended life span that characterizes the retrograde response. Lifespan extension mechanisms are inextricably tied to tumorigenic control ones. In figure 6 below Klement and Champ illustrate the mechanistic overlap between ketogenic diets (KDs), which can suppress tumors, and calorie restriction (CR), which has been shown to extend lifespan in multiple animal models.

cr kd

Figure 6. CR & a KD target the same molecular pathways

Figure 7 below illustrates the integrated circuits of a cell. A virtually identical 2011 version of this one from the year 2000 exists but was not used here due to a lack of detailed annotation. What is interesting is that cellular circuitry is obviously drawn through the lens of the SMT; pathways (arrows) leaving mitochondria towards ‘DNA Damage Sensor’ are nowhere to be seen. There seems to be little acknowledgement of how defective OxPhos leads to direct or indirect upregulation of glycolytic and fermentative networks. Rather, this shift in energy metabolism is deemed to originate from nuclear mutations. Re-imagining Figure 7 through the MT lens, the RTG response would constitute a ‘Mitochondrial Damage Sensor’ with an arrow leading to NF-κB and integrating back into the web of arrows already present. Furthermore, there would be an increase in the proportion of arrows going from the mitochondria to the nuclear genome (and back), highlighting the metabolic origin as well as the bi-directional cross-talk enabling nuclear genomes to upregulate aforementioned metabolic networks. This is not a baseless hypothetical. For instance, the classic view of p53 as solely regulating tumorigenesis (and thus genomic stability) by inducing mitochondrial apoptosis or via transcriptional factor response elements must be updated, because it is now known to also regulate transcriptional target SCO2 (Synthesis of Cytochrome c Oxidase 2) through which mitochondrial energy production is influenced. Genomic stability, by way of p53, is thus highly dependent upon OxPhos status. Integrating this into Figure 7 would look like an arrow leaving the mitochondria to SCO2 and then on to p53.

cellular circuitry

Figure 7. Cellular Circuitry (as of 2000)

Returning to Jazwinski’s paper on the RTG response, another gene regulatory (not epigenetic) mechanism lending support to MT of cancer can be inferred from exploring how ceramide synthase activity found in the endoplasmic reticulum (ER) relates to mitochondrial dysfuncton. The hydrolysis of sphingomyelin by sphingomyelinase (Smase) is a catalyzing reaction generating ceramide, a structural and signaling cellular component of mammalian cells. Ceramide appears to be stimulated by a variety of stressors such as TNF-α/matrix metalloproteinases/ROSs, cannabinoids and ionizing radiation. Ceramide is linked to retrograde signaling by way of it prompting Isc1 (Inositol phosphosphingolipase C), leading to autophagy. Jazwinski explains that “sphingosine and ceramide are precursors of complex sphingolipids [which] suggests that the balance in sphingolipid biosynthetic activity can tip the scale in autophagy from quality control to wholesale degradation and remodeling”.

In fact, phytocannabinoid THC ((−)-trans-Δ9-tetrahydrocannabinol) was shown to induce apoptosis in vitro whereby its “first step in the apoptotic pathway was ceramide production, and that this led to loss of membrane potential, and caspase activation, respectively. Lesser known phytocannabinoid CBD (cannabidiol) was shown by McAllister et al. to reduce the aggressiveness of breast cancer cell proliferation, invasion, and metastasis by affecting mitochondrial ROS production in vitro. This CBD-mediated effect appears to occur down 2 pathways: ERK (extracellular signal-regulated kinase) activation and increasing ROSs further. Both of these pathways can decrease Id-1 (Inhibitor of DNA Binding 1) gene expression, leading to reduced cell proliferation and invasion. The nature of action of cannabinoids lends further evidence to the MT of cancer due to their effects on energy metabolism, like for example, when “ceramide generated upon CB1 cannabinoid receptor activation may enhance ketone body production by [rat] astrocytes independently of MAPK. Interestingly, this line of investigating leads to promising in vitro and in vivo experiments highlighting the emerging role of ketone bodies as alluring metabolic fuels capable of simultaneously stressing cancer cells and supporting normal ones. The link between the MT of cancer and gene regulatory effects of ketones is apparent when considering some of the properties of endogenous ones like d-β-hydroxybutyrate (βOHB). The latter specifically inhibits class I HDACs. This βOHB-induced HDAC action “correlated with global changes in transcription, including that of the genes encoding oxidative stress resistance factors FOXO3A and MT2 […] consistent with increased FOXO3A and MT2 activity, treatment of mice with βOHB conferred substantial protection against oxidative stress. The shared mechanisms via which both cannabinoids and ketones place metabolic stress on cancer cells whilst protecting normal ones strongly support a MT of cancer.

In conclusion, it is worth pausing for a moment and reflecting on the fact that ketones and cannabinoids, 2 highly non-toxic compounds endogenous to humans, have been unfairly maligned; the former because of its conflation with diabetic ketoacidosis and the latter becaue of harms associated with illegal drug abuse. Yet, both are now re-surfacing in medical research as highly promising compounds, especially for treating the emperor of all maladies. Furthermore, it is also worth pondering how the range of new cancer therapies like HBOT (hyperbaric oxygen therapy), ketogenic diets and deuterium (2H) depleted water (DDW) share overlapping mechanisms directly impacting metabolism by influencing mitochondrial status – especially OxPhos. The latter therapy, DDW, delayed prostate cancer progression in a 4-month phase 2 clinical trial that was double-blind and randomized. The mechanism of action of DDW as explained by Boros et al. de facto serves as further evidence of a metabolic origin of cancer:

The excessive appearance, i.e. accumulation of ‘‘metabolically dry” oncometabolites [such as 2-HG] is consistent with our hypothesis that cancer is formed on the basis of mitochondrial defects that lack hydration of TCA cycle intermediates with low deuterium matrix water as the result of such defects. Such claim is supported by the fact that restoring hydratase function of mitochondria reverses tumor cells back to their genetically stable non-proliferating normal phenotype [37] with normal matrix water content, composition and morphology

It is time for SMT-centric mainstream cancer researchers to reconsider the fundamental mechanisms of action of the few marginally successful gene-based therapies on the market. Rather than viewing genetic, epigenetic and gene regulatory phenomena as epicentres of action of ‘gene-based therapies’, they may simply mediate primary changes occuring at the metabolic level where oncogenesis first emerged.


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”>>.

Basophil Activation Test

CD34+ pluripotent progenitor stems cells can give rise to a type of leukocyte termed granulocyte, specifically of the basophil kind. The latter represents less than 1% of peripheral blood leukocytes. It has a characteristically segmented nucleus so that it may maintain the integrity of its genome under an increased load of reactive oxygen species (ROS) originating from its immunological activities. The potential for basophils to be used as markers of inflammation and allergy was first explored in 1991 whilst the technological basis for doing so using flow cytometry preceded it by 26 years. The prominence of basophil activation tests (BAT) has grow in research settings but is only in the introductory phases of clinical settings for the most part as of 2015. In 2011 it was still at the pilot stage as a diagnostic method for allergies to certain venoms and immediate drug hypersensitivity reactions (IDHR). According to the traditional Gell & Coombs hypersensitivity classification system, BAT is quantifying a Type 1 reaction based on the involvement of IgE for releasing histamine. However this is not helpful given that the classification system cannot adequately reflect a continually refined appreciation of the immune system. Understanding the principle underlying BAT first requires comprehending which relevant characteristics of basophils are changing and secondly, how flow cytometry quantifies them. Broadly speaking, the isolation and incubation of cells is the first step of BAT and the second one is the quantification of target cells by flow cytometry.

Figure 1 below depicts the steps by which masts cells release histamine, an immune and inflammatory mediator, in an IgE-FcεRI high-affinity receptor dependent manner. For all intents and purposes the mast cell can be substituted for a basophil in this example.

Mast or basophil IgE-dependent degranulation

Figure 1

IgE antibodies presents the antigen to its IgE-specific FcεRI high-affinity receptor. When enough multivalent antigen cross-linking with adjacent IgE molecules occurs, the cytoplasmic granules fuse with the cell surface membrane and degranulate. This process involves the signal transducing transmembrane tetraspanin CD63 protein coming to the surface of the basophil. This is how it and similar protein CD203c serve as markers of basophil activation. Non-IgE dependent (otherwise named ‘direct’) basophil activation can be triggered by opiates, iodinated radio-contrast dye and antibiotics such vancomycin, β-lactams and quinolones. The mechanism via which this happens is not yet understood. Non-IgE dependent degranulation may partially explain the specificity shortcomings of the assay. Nevertheless, whether activation is mediated by IgE or otherwise, the histamines and other immune mediators released by basophils are involved in early and late-phase allergic reactions.

Inventor M. J. Fulwyler describes his early flow cytometry apparatus in below as essentially, a cell separator, saying “cell volume is measured in a Coulter aperture, and the cells are subsequently isolated in droplets of the medium which are charged according to the sensed volume. The charged droplets then enter an electrostatic field and are deflected into a collection vessel.” Figure 3 is the modern iteration of Fulwyler’s invention with an added laser-based component. Cell size is correlated to how the laser beams reflecting off of them scatters forwards and cell granularity correlates to the side scatter. Different lymphocytes have different granular contents. Separating cells according to their charge can still be used after the laser for further cell sorting.

Early flow cytometer

Figure 2

Modern flow cytometer

Figure 3


Figure 4 shows different populations of cells according to their forward and side scatter characteristics. Basophils are identified in the larger upper central cluster in the figure. The basic principle of how intensely light is reflected and scatters forwards and sideways enables flow cytometry to very precisely identify basophils amongst other molecules present in fluid samples. Furthermore, Subsets of basophils or ‘gates’ are identified using their surface markers like CD123/HLA-DR, CCR3/CD3 and IgE/CD203c.

Figure 4

Figure 4

The experiment use specific monoclonal antibodies complexed to fluorochromes to bind them. Fluorochromes reflect the laser beam which the flow cytometer records for use in subsequent data analysis. Using the same fluorescent signal for cell surface basophil activation receptors CD63 and CD203c, the number of activated basophils within those ‘gated’ subsets can be quantified on a single-cell basis. Figure 5 below depicts how IgE positive (IgE+) basophils are identified. First, Panel A plots the cells’ forward scatter by area and height (FSC-A and FSC-H, respectively). This produces aggregates (or distinct bands) from which basophils can be picked out from. Panel B then plots side scatter height (SSC_H) of the target aggregate against the fluorescent signal reflected by the complexed fluorochromes bound to IgEs. Both the amount of IgE basophils and of IgE on the cell surface is quantified.

Figure 5

Figure 5

Other BAT variations utilize markers of basophil degranulation such as inhibitory receptor Cd300a or “phosphorylation of intracellular signaling molecules such as p38 mitogen-activated protein kinase and signal transducer and activator of transcription (STAT)”. Alternatively, the diamine oxidase enzyme affinity HistaFlow technique can quantify intracellular histamine content. These techniques are not included in this assessment of BAT’s clinical utility. The technique focused on here uses forward and side light scattering as well as fluorochromes for IgE-dependent activation. Food allergen testing will be primarily applied to assessing BAT here.

In 2015, BAT’s in vitro ability to predict the severity of an allergic reaction to a variety of common food allergens was tested against the gold-standard double-blind placebo controlled food challenge (DBPCFC) in 67 patients by Song et al.. Skin prick testing (SPT), secretory IgE (sIgE), total IgE (tIgE), allergen-specific IgG4 (sIgG4) and peanut component-specific IgE (when applicable) were also measured and correlated to BAT. Patients were identified as ‘allergic’ based on an initial 3mm wheal SPT threshold and then administered DBPCFCs consisting of 2 grams of proteins cumulatively dosed from peanut, tree nut, sesame, fish, or shrimp. Positive reactions were graded on a 1 to 5 scale with 5 being the most severe. For BAT, the flow cytometer identified basophils if cells were CCR3+ with a low side scatter and amongst these, activated basophils if they were CD63+. BAT’s 3-dose response curve consisted of 200, 20 and 0.2ng/mL doses of crude peanut, pecan, hazelnut, cashew, sesame, catfish, cod, salmon and shrimp extracts. The negative control was interleukin-3 (IL3). One positive control consisted of an isolated allergen combined with a highly specific monoclonal antibody recognizing the high-affinity binding receptor Fc3RI and the other was the chemotactic peptide N-formyl-methionyl-leucyl-phenylalanine (FMLP). Each allergen has an optimal dose for eliciting maximal basophil activation and determining it is crucial for establishing a robust correlation between the severity of a reaction with a level of basophil activation.

Figure 6 below plots the receiver operating characteristics (ROC) curve of BAT, sIgE and SPT using a binary classification system of ‘sensitivity’ and ‘specificity’. Sensitivity refers to the number of true positives identified as positive and specificity the number of true negatives identified as negative. The BAT results were dose-dependent which is an absolute requirement for starting to validate its use clinically. The highest BAT dose of 200ng/mL produced the greatest area under the curve (AUC) compared to sIgE and SPT, with a value 0.904. This dose was where the most sensitive and specific measures were seen amongst all 3 techniques. The 95% confidence interval for this result is 0.821-0.98 and is statistically significant with a P-value of less than 0.0001. Such a p-value means there is a 99.9999% chance that this result did happen by happenstance.

Figure 6

Figure 6

This is an excellent result for BAT. It adds support to the hypothesis that integrating it into clinical practice will reduce the need for DBPCFCs which are invasive and carry considerable risks. However, important caveats accompany the results. It should be kept in mind that this more traditional CD63 detection method is very specific to the IgE-mediated activation of basophils. This study did not utilize CD203c which “among hematopoietic cells, expression of CD203c is restricted to basophils, mast cells and their precursors, and has been described as specific for this lineage”. Hoffmann et al. have further argued that CD203c could be a more sensitive marker than CD63 for BAT. One on hand, results obtained by Song et al. using the traditional CD63 BAT marker are good because they covers a large segment of reactions in the population. On the other hand, they do not encompass non-IgE basophil activation reactions which are prominent in IHDRs. Despite using the less sensitive activation marker which potentially minimizes the results, this outcome may be partially skewed the other way (that is, positively) because tree nuts and peanuts activate basophils more aggressively than other allergens. It is worth visualizing the correlations in Figure 7 between the BAT dosing curve and DBPCFC scores in order to distinguish the statistical significance of a correlation with the strength of the correlation itself.

Figure 7

Figure 7

Going from left to right, the boxes in Figure 7 are 200, 2 and 0.2ng/mL doses of BAT allergens. For reference, a perfect correlation holds an ‘r’ value of 1. Despite the correlations all being statistically significant, they are quite weak, ranging from 0.32 to 0.5. Looking at the left-most box for example, multiple individuals had a basophil activation rate of 50% yet are found amongst 0 to 5 DBPCFC scorers. Authors Song et al. are more enthusiastic, saying there is in fact a “moderate correlation with clinical reactivity observed in this study [and this] is in line with results from prior studies demonstrating the sensitivity of the BAT”. They bolster this line of thinking by adding that it is a reasonable expectation stemming from the elucidated mechanism discussed above, whereby IgE is involved in basophil degranulation via FcεRIa receptor expression. Ultimately, BAT looks at one kind of allergic response mediated by this non-exclusive mechanism. Luengo and Cardona emphasize the complexity involved in predicting the degrees of allergic reactions by stating how “the risk of developing anaphylaxis depends not only on the allergen sensitization pattern, but also on the avidity and affinity of immunoglobulins to bind the allergen, the route of application, characteristics of the allergen and the presence of cofactors”. In fact, Song et al. pay tribute to this notion when discussing methods for predicting the severity of reactions to peanuts, saying “recent data have suggested that component-resolved diagnostics could be a better predictor of clinical reactivity [to peanuts] and could have some utility in predicting severity of symptoms”. This testifies to the stage of infancy immunology current finds itself in. Which markers best identify acute reactions, allergies or simple exposures are just starting to get teased out. For example, it is only recently that IgG4 has been deemed a better indicator of recent allergen exposure rather one of reaction severity.

BAT’s specific clinical utility regarding peanut allergies is worth exploring via a 2014 controlled study by Santos et al.. 1 primary study population (N=104) was divided into children allergic to peanuts (PA), children sensitive (PS) or not allergic to peanuts at all (NA). An independent population (N=65) was used to assess BAT’s performance in making diagnoses using flow cytometry. The BAT flow cytometry methods for assessing both the primary study population and the independent one were the same. Activated basophils were first identified using the SSClow/CD203c+ and CD123+/HLA-DR gates. Secondly, the activation levels of gated basophils were evaluated as a percentage of CD63+ ones and by the stimulation index of mean fluorescence intensity (MFI) of CD203c+ ones. Finally, the BAT dose curve for peanut allergens was established with a 10-point serial dilution covering 10µg/ml to 0.1ng/mL concentrations. The performance of BAT was assessed against oral food challenges (OFCs) followed by a DBPCFCs and other measures like SPT, peanut-sIgE and Ara h2-sIgE. Ara h2 is not a whole peanut allergen extract (peanut-sIgE) but a particularly allergenic protein in peanuts. The clinical implications the authors extrapolated from their study were that “the basophil activation test to peanut can be performed in cases in which standard allergy tests have failed to diagnose peanut allergy before considering oral food challenges”.

Indeed, within the cut-off values established in the independent study population, BAT’s ROC (green line) was superior to that of the other 3 with a value of 0.97, a sensitivity of 97.6% and specificity of 96.0%. This is represented in Figure 8 below. Panel A shows BAT’s ROC for children with an ambiguous SPT and sIgE history for peanuts and its components. Panel B highlights its ROC according to it performance at the 10 and 100ng/mL concentration points along the 10-point serial dilution.

Figure 8

Figure 8

BAT correctly diagnosed 96.7% of PA children identified via OFCs confirmed with DBPCFCs. Interestingly, BAT achieved the 2 largest clinically useful reductions in the amount of OFCs necessary for identifying PA children. When used as a secondary diagnostic tool following SPT 35 less OFCs are need. When used as a third one following SPT and Ara h2-sIgE 36 less OFCs are need. So far, the authors clinical recommendations appear warranted. However, the BAT assay for PS children cannot detect any significant changes in basophil activation. Furthermore, 11.5% of non-responders were found in the primary study population and 6.2% in the independent (external) one. This means BAT is incapable of identifying between 6 and 11 PAs in a room of 100 people. It is doubtful that BAT can be improved to identify many more PS or non-responders. It is more likely that the test is inherently limited, in the sense that it simply does not cover the entire range of immunological responses capable of inducing a peanut sensitivity or allergy. DBPCFCs remain the gold-standard.

The 2014 paper by Uyttebroek et al. titled “Basophil activation tests: time for a reconsideration” summarizes BAT’s limitations and in so doing carves out the best current and prospective uses for it in clinical settings. There has been wide clinical usage of BAT for IHDR despite ambiguous testing issues regarding the antibiotic fluoroquinolone. The latter has been reported to mediate non-IgE basophil degranulation and when using activation marker CD63, fails in terms of sensitivity and specificity or both when tested across 3 studies in a total of 56 patients. However, when activation marker CD203c is used in only 5 patients in 1 study, there is 100% specificity and sensitivity. Until further study, BAT is in fact potentially confounded by fluoroquinolones. BAT’s sensitivity to NSAIDs – a globally and widely prescribed class of anti-inflammatory agents – is too low for clinical use. However, the authors predict than within 5 years BAT and HistaFlow will receive mainstream implementation for IgE and non-IgE mediated IDHR, respectively – assuming those IDHRs are underpinned by basophil or mast cell (effector cells) degranulation. All considered as of 2014, BAT’s sensitivity and specificity varies between 75% and 95%. Despite its niche use for peanut testing in a second or third diagnosis-reinforcing option, Uyttebroek et al.’s 5 year review of the literature has not uncovered any novel diagnostic applications for BAT in the field of food allergies.


During the upper-middle Neolithic era, about 1500 years B.C, a first description of diabetes as a “too great emptying of the urine” appeared in Egyptian manuscripts. Since then it has suffered from further flimsy nomenclature, attesting to the medical and scientific challenges posed in characterizing its murky & complex etiology. This is somewhat justified by the fact that diabetes carries a disagreeable trio of origins; genetics play a non-trivial role in its incidence (about 10% of cases), it has an autoimmune presentation (Type I Diabetes) and most importantly, lifestyle factors contribute the largest chunk on a population basis (i.e. it is a disease of civilization for the most part). Science however is about making the complex simpler and in the 1960s Peter Cleave did just that by describing diabetes as ”the saccharine disease because he believed sugar and other refined carbohydrates were responsible”. Although scientifically incomplete, it remains a fair and useful heuristic-like description.

If you are diabetic, you will either be a Type I (autoimmune), a Type II or if you also happen to be pregnant you might fit into the (transient) Gestational Diabetes category. A diagnosis is made on the basis of:

  • elevated fasting blood sugars (before at >140mg/dL and currently lower at >125mg/dL)
  • ongoing hyper and/or hypoglycaemic episodes as well as elevated 3 month blood sugar averages (HbA1c ≥ 6.5%, calculated as a % of non-enzymatically glycated hemoglobin). Note that insulin measures are not necessarily part of the diagnosis, one important point which we will get back to.

Most diseases are traditionally viewed as discontinuous qualitative entities but diabetes is not so. The term pre-diabetes implies that the diabetic state is in fact continuous and thus quantifiable. To be considered pre-diabetic one only needs to have fasting blood sugars at or above 100mg/dL.


To get a sense of what proportion of a population this can include nowadays, the 2014 age-adjusted World Health Organization (WHO) estimate considers 22.4% of people in Bangladesh to be pre-diabetics. Before the biochemistry of diabetes is explored further, it is helpful to note that most physicians now consider oral glucose tolerance tests (OGTTs) the gold standard for assessing a patient’s location on the diabetic spectrum rather than fasting blood glucose measures. How one handles a 75g liquid bolus of glucose over the following 2 hours holds better predictive power than fasting glucose measures do because this post-prandial mimic better accounts for the interplay of glucose management as a function of insulin dynamics. However this test is also far from perfect. It fails to distinguish between generalized pathological insulin resistance characteristic of diabetes and non-pathological tissue-specific insulin resistance. For example, it can produce false-negatives by failing some individuals on low-carbohydrate or ketogenic diets. This is thought to happen because of non-pathological peripheral insulin resistance which prioritises glucose produced from the liver or coming in from the diet to serve the human brains’ minimal ~40% need for glucose-derived energy; the musculoskeletal system does equally well (if not better) on fatty acids and ketones and can thus become somewhat insulin resistant to manage tissue-specific and whole-body energy homeostasis.

A simple descriptive model of diabetes provides context for grasping its finer intricacies and contentious mechanistic points. When carbohydrates from a meal are absorbed through the intestines they go on to circulate in the blood. They reach various organs like the heart, muscle, liver, brain and non-organ cells like adipocytes. Insulin is (mainly) secreted in response to carbohydrates and performs its fundamental job of getting them (as well as proteins and fats) into cells. Diabetes results when insulin’s action is insufficient for managing the carbohydrate load by getting most of it into various cells and thus out of the blood stream. This results in higher levels of circulating glucose in the blood, namely hyperglycemia. Depending on whether the diabetic is a type I (T1D) or II (T2D), hypoinsulinemia and hyperinsulinemia develop over the long term, respectively.

The resolution of this simplified insulinocentric model can be sharpened in both depth and breadth by integrating 3 more angles. First and most importantly, is to integrate the endocrinological dynamics of insulin with the metabolism of cellular energy management. This should help relate the interplay right down to the flux of energy currency molecules between the cytoplasm and mitochondria as well as those molecules that handle them. Secondly, some thought will be dedicated to insulin’s ‘opposing hormone’ glucagon which will refine the diabetic model, the corollary of which is to render it less insulinocentric. Lastly, the quite mysterious influence of the microbiome upon glucose and whole body energy homeostasis is gaining undeniable scientific prominence due to the alluring correlations it has so far presented. An attempt is made to show how these 3 angles are strongly dependent upon lifestyle factors which includes but is not limited to; nutrition, sleep and movement (or exercise). Each ‘angle’ is differentially understood and does not contribute to the etiology and phenotype of T2D equally. Quantifying each ones apportioning to it is the ball of yarn that has yet to be untangled.

For glucose to get into myocytes, insulin must be present within the blood in sufficient concentrations as to bind its tyrosine kinase insulin receptors (isoform IR-B mainly) that are found within plasma membranes and stick out of the cell surface. Insulin acts as a ligand here. The insulin-binding domain of the receptor is formed by 2 α chains which are the extruding part of the receptor. Its intracellular domain is composed of 2 transmembrane β chains subunits which have carboxyl termini protruding into the cytosol. When the α chains are activated, autophosphorylation of both of the β chain subunits takes place. 2 αβ monomers form upon α chain activation where 3 tyrosine residues on each β chain become phosphorylated by the other β chain. This has for effect of displacing the activation loop 30Å away from the substrate-binding site it was previously occupying, now enabling target proteins to access the site. In turn, insulin-receptor substrate 1 (IRS-1) gets phosphorylated by having a phosphoryl group from adenosine triphosphate (ATP) transferred to the hydroxyl group on its Tyrosine residues (since the initial stimulus comes from insulin in this scenario). Phosphorylated IRS-1 is a nucleation site – somewhat acting akin to a sorting junction – from which protein complexes go on to carry out messages down into the cytosol and nucleus, affecting GLUT4 translocation and gene expression, respectively. GLUT4 translocation happens through a variety of interweaving and still incompletely understood pathways. GLUT4 translocation to the plasma membrane can happen in a phosphatidylinositide 3-kinase (PI3K)-dependent or independent manner. PI3K is a signal transducing enzyme activated by G-protein coupled receptors or tyrosine kinase receptors. Muscle contractions can stimulate GLUT4 translocation in a PI3K-independent manner affecting whole body glucose homeostasis, making it relevant to diabetes and general health. Here however, we consider and continue on from phosphorylated IRS-1 in an insulin stimulated GLUT4 translocation scenario engaging the requisite PI3K. The diagram below depicts phosphorylated IRS-1 forming a complex with PI3K.

Phosphorylated IRS-1 - PI3K complex

PI3K is composed of 2 subunits bound together; regulatory and stabilizing subunit P85 and catalytic subunit p110. p85’s inhibitory activity on p110 is disabled via phosphorylation of its tyrosine residue located on the amino-terminal of its SH2 domain. This activates p110’s catalytic activity and translocates PI3K to the cell membrane where its substrates reside (see ‘Fig.2 The key molecular signals that are turned on and off by insulin regulating GLUT4 traffic’). PI3K phosphorylates it residue phosphatidylinositol-4,5-bisphosphate (PI[4,5]P2), producing phosphatidylinositol-3,4,5-trisphosphate (PI[3,4,5]P3). A key point worth stressing is that the amplitude and time component of the stimuli acting to produce PI(3,4,5)P3 can determine the extent to which and whether or not GLUT4 translocates to the plasma membrane. This point may be an important determinant in the development of insulin resistance since the pulsatility of macronutrient intake (frequency and amplitude) provides important energy availability signals to the cellular milieu in addition to satiety and satiation cues to neuronal circuitry.

Insulin GLUT4 pathway(s)

Returning to the pathway at hand, the phosphorylated PI3K substrate PI(3,4,5)P3 in turn “recruits Akt1/2 [Protein kinase B] to the PM [plasma membrane] and possibly to endomembranes, where the enzyme [Akt1/2] is phosphorylated on the Thr308/309 residue of its activation loop by 3-phosphoinositide-dependent kinase (PDK1)”. It appears Akt2 rather than Akt1 is predominantly involved in this process. Isoforms λ and ζ of atypical Protein Kinase C (aPKC) are only stimulated by insulin and participate in GLUT4 translocation by being recruited to the PDK1/Akt complex, effectively forming a phosphorylated trio. It should be noted that the downstream effectors of PI3K (Akt and aPKC) control GLUT4 translocation both from within intracellular compartments and at the plasma membrane. This is evidenced by the arrow leaving PKC-λ/ζ leading to GLUT4 translocation and by it being complexed to PDKI/Akt visible in ‘Fig.2 The key molecular signals that are turned on and off by insulin regulating GLUT4 traffic’. The IRS-1/PI3K and Akt/aPKC-λ/ζ complexes form by co-purifying (meaning they attract each other) to cellular subfractions enriched in GLUT4 in order to enact its translocation to the plasma membrane. It is still uncertain whether aPKC is necessary for GLUT4 translocation or if Akt alone is sufficient for insulin-stimulated glucose uptake. In any case, a 160kDa protein substrate target of Akt (AS160) acts as a figurative ‘brake’ on insulin-stimulated GLUT4 translocation when its GTPase-activity protein (GAP) domain is in its active form and maintaining its yet unidentified target Rab protein in GDP form. Rab proteins are a branch of the Ras GTPase superfamily of proteins steering intracellular membrane traffic. When 5 of 6 of AS160’s target sites for Akt are phosphorylated, its GAP domain is now inactive. This means its target Rab is phosphorylated and thus changed to GTP form, now rendering it capable of partially inducing GLUT4 translocation from both ERC/TGN (endosomal recycling compartments/trans Golgi network) and SC (specialized compartments) compartments. In vivo data from mice suggest Rab10 is the likely Rab target of AS160. GLUT4 buds off in endomembranes (erroneously termed vesicles), making its way to the plasma membrane where it can catch glucose molecules floating around the cell surface. GLUT4 then mediates the entry of glucose molecules within the cell’s cytoplasm. When insulin levels decrease sufficiently, GLUT4 transporters are removed from the plasma membrane by endocytosis. In so doing, an endomembrane is formed so that the GLUT4 transporter can make its way back inside the cell and fuse with other ones, forming a larger endosome which effectively completes its cycle.

The importance of insulin signaling amplitude and frequency (previously referred to as pulsatility) is reiterated, in so many words, by authors Grant and Donaldson, saying:

“The balance between endocytic uptake and recycling controls the composition of the plasma membrane and contributes to diverse cellular processes including nutrient uptake, cell adhesion and junction formation, cell migration, cytokinesis, cell polarity and signal transduction. Since it is estimated that cells internalize their cell surface equivalent one to five times per hour 1 [my emphasis], endocytic recycling pathways must be robust and coordinately regulated.

At this point it is clear that what and when one eats is front and center in the development and reversal of Type II Diabetes (T2D). The etiology of Type I Diabetes (T1D) however, is immune mediated, so food plays more of a direct role in its management rather than development, as attested to by T1D doctors and athletes. Circadian rhythms compete with food in this hierarchy of factors since they are biological metronomes orchestrating membrane traffic and receptor sensitivity, amongst other tasks. Simply put, food and sleep (1,2) are at the root of prevention, management and reversal of the metabolic syndrome and diabetic phenotype. At last, the discovery of AS160 in the last 10 years provides an exciting new candidate that may possibly lend mechanistic power to another factor in diabetes, namely movement. It presents as a ‘missing link’ or common pathway for both insulin and exercise-stimulated GLUT4 translocation in myocytes capable of affecting whole body glucose homeostasis. This would go some way in explaining the effectiveness of lifestyle interventions over pharmacological ones for diabetes management because the same pathways are exploited from multiple stimuli. Nevertheless, it is important to realize that despite exercise being a tool for improving insulin resistance, it has never been shown that one can ‘out-exercise’ a poor diet or sleep debt.

Getting back to the sequence of molecular events described above, these are specific but not necessarily exclusive to muscle cells. It is but a snapshot of an incredibly more complex overarching process. The point is driven home by Table 11-4 which lists tissue-specific glucose transporters.

Table 11-4 Glucose Transporters

The kind of insulin receptors and isoforms directly affect glucose metabolism and thus diabetes, in addition to also being implicated in certain cancer phenotypes. The genetic and non-genetic (herein referred to as epigenetic or exposomic) influences on insulin are presently innumerable. Insulin resistance characterizes pathological insulin dynamics and may well be the single biggest lever determining human health and disease. This notion is reinforced in Table 15-3, where insulin’s wide influence on genes and pathways is listed.

Table 15-3

Cynthia Kenyon’s work with C. elegans adds support to the importance of insulin and insulin response elements by identifying the influence of orthologous genes daf-2 and daf-16 (IGF-1 and FOXO respectively in humans) which strongly impacting ageing and the development of diabetes, respectively. One pathway via which ageing and diseases manifestation are thought to be influenced is simplified in her mouse-human diagram.

Ageing cynthia kenyon insulin

It delineates a cascade of interactions between growth hormone (GH) and its receptor (GHr), insulin-like growth factor 1 (IGF-1), insulin receptor substrate protein (IRS), Akt and the forkhead family of transcription factors (FOXO). Together they play a role in communicating messages of apoptosis or new growth and repair at the cellular level which not only has obvious implications in cancers but also in diseases of accelerated ageing like diabetes (which will be explored further). Our understanding of how this is mediated is still unclear though. For example, although FOXO is associated with centenarians, the strongest FOXO single nucleotide polymorphism (SNP) association is weak statistically speaking, with an odds ratio (OR) of 1.42 (CI 1.18-1.70), to which the authors caution, saying “…the large confidence intervals for the ORs in both studies imply some uncertainty”. There are stronger longevity measures assessing the impact of diabetes such as the 2010 British report, which estimates that T1D’s live 20 years less and T2D’s 10 years less on average. As evidenced in Table 2, the role of growth hormone and related components of the insulin pathway offer contradictory evidence as it pertains to longevity.

GH & IGF-1 ageing humans

It does however give every indication that the insulin pathway and its components are levers for both and longevity and health.It does however give every indication that the insulin pathway and its components are levers for both and longevity and health. It has yet to be understood how particular actioning of these levers goes on to manifest a diabetic phenotype.

Amino acids and pyruvate, the latter resulting from cytosolic glycolysis (catabolism of glucose), are transported from the cytosol into mitochondria where they feed into the Citric Acid Cycle (TCA cycle). It takes place on the outer mitochondrial membrane where two-thirds of carbon compounds are oxidized in human eukaryotic cells. Fatty acids and ketone bodies circulating in the blood stream are also imported into the mitochondria where they undergo oxidation on the inner mitochondrial membrane along with products from the TCA cycle. These 3 reactions effectively feed into each other, starting with cytosolic glycolysis, leading into the TCA cycle and ending with oxidative phosphorylation. Most of the daily energy derived in humans is derived from fatty acid oxidation. The TCA cycle produces 3 kinds activated carrier molecules NAD+-NADH (oxidized and reduced nicotinamide adenine dinucleotide), FADH2 (reduced flavin adenine dinucleotide) and ribonucleotide GTP (guanosine triphosphate) which all participate in generating ATP that cells and their tissues need at all times for proper function. NADH is also used directly by the cell for energy. Glycolysis and the TCA cycle produce intermediates used in cell building (biosynthesis) in addition to providing raw metabolic energy materials. Of particular interest in diabetes is the balance of  ATP and activated carrier molecule ‘pools’ cells need to continuously maintain, as well as the array of molecules handling these. How diabetic neuropathies manifest can serve to characterize their roles.

Table 2 by Chowdhury et al. lists some of the aberrant mitochondrial physiology found in certain diabetic tissue/culture models.

Table 2 Tissue mitochondrial alterations human rats T2D

In many cases, respiration is compromised from a variety of points. This feature is eerily similar to respiratory deficiencies characteristic of cancer cells. Thiazolidenodione diabetic drugs aim to increase deficient peroxisome proliferator-activated receptor [PPAR] γ co-activator 1α (PGC1-α) activity since PGC1-α stimulates mitochondrial biogenesis. PGC1-α is highly expressed in energy intensive tissues such as skeletal muscle, brain, heart, liver and brown adipose in addition to during energy demanding activities like being cold, exercising and fasting. PGC1-α is a potential molecular link for explaining why, for example, fasting was such a popular intervention for non-insulin dependent diabetics in the pre-insulin era as well as its present day re-implementation.

Elliott P. Joslin explains the clinical significance of fasting and macronutrient restrictions:

“Such individuals [diabetics] should be taught to regulate the quantity of food eaten by the body weight, and never to indulge in unusual quantities of carbohydrate. […] That temporary periods of under-nutrition are helpful in the treatment of diabetes will probably be acknowledged by all after these two years of experience with fasting. In no other way can one so readily keep the urine free from sugar and this is the foundation of all diabetic treatment.”

Chowdhury et al. make the point that “apoptosis-dependent loss of sensory sympathetic neuron perikarya has not [emphasis mine] been found in diabetic humans or animals” but that “mutation or loss of proteins associated with mitochondrial function can lead to neuropathy that mimics that seen in diabetes, for example, loss of function of mitofusin-2 or bcl-w results in a length-dependent sensory fiber degeneration.” As shown in the screenshot of an NIH video presentation, Bcl-w has an anti-apoptotic role, regulating mitochondrial turnover.

Anti & pro-apoptotic genes NIH

Multiple proteins involved in the latter present as strong candidates for pin-pointing molecular disturbances in people with diabetes and metabolic syndromes. It appears that a causal link for mitochondrial dysfunction runs from 5′ adenosine monophosphate-activated protein kinase (AMPK), NAD-dependent deacetylase sirtuins (SIRT1 and 3) and PGC1-α. AMPKinase senses the ATP/ADP ratio and in a state of excess glycolysis that ratio is increased such that enough AMP turns into ADP via substrate-level phosphorylation, causing PGC1-α to downregulate. PGC1-α can no longer prompt adequate mitochondrial biogenesis (as seen in Fig.5).

Axonal degeneration in Diabetic phenoty

Furthermore, cytoplasmic enzyme SIRT1 can sense a hyperglycemia-induced depressed NAD+/NADH ratio. This is often secondary to insulin failing to maintain intracellular glucose homeostasis. This reduces SIRT1’s activity, thereby lowering that of PGC1-α’s, which decreases the rate of mitochondrial biogenesis. The corollary of which is that faulty mitochondria are not adequately repaired or repurposed by autophagosomes. SIRT1 has far reaching impacts on FOXO, PPARγ, PGC1-α and tumor protein 53 (p53).

Fig.4 is edited by Royal Veterinary College trained veterinarian Peter Dobromylskyj and it schematizes the development of insulin resistance in T2D patients by linking faulty mitochondrial action and impaired lipid oxidation.

mitochondria ROS diabetes

For all intents and purposes, the type and manner of fuel use resulting from these molecular cascades resembles the Crabtree effect. In effect, increased glycolysis produces abnormally high levels of ATP through substrate-level phosphorylation which essentially ‘tells’ the cell that TCA cycle oxidative phosphorylation run through the electron transport chain (ETC) is no longer as necessary. Hyperglycemia begets further hyperglycemia. Oxygen consumption is thus reduced. Interestingly, less superoxide (O2) was shown to be associated with T2D in rats. In contrast, more ROSs overall are usually implicated as a hallmark of mitochondrial dysfunction. Which ROSs and the reason for them not being handled by anti-oxidants may provide important clues as to the precise etiological route in one diabetic versus another. This logic may apply to the development of  other metabolic syndromes.

The complex picture interweaving molecular biology and endocrinological mechanisms can easily overshadow the necessity of establishing a sufficiently simple and solid clinical picture for practitioners to guide them in patient care. Thankfully but also tragically, Dr.Joseph Kraft appears to have succeeded in the latter to a great extent, all the while having his findings mostly ignored by the wider medical profession. From 1972 to 1989, Dr.Kraft gathered empirical clinical data on his patients (aged 3 to 91 years old) by performing a glucose tolerance test in conjunction with an insulin assay. Done together, the earliest signs of diabetes and insulin resistance can be identified. This level of diligence and rigor is rare, even by modern standards. During this time period he performed an astonishing 14,383 tests. 95% (2,635) of his patients out of the 2,775 identified as glucose intolerant had T2D, 1.98% (55) had T1D and 2.99% (83) did not have diabetes. 74% (7,102) of his patients out of the 9,598 identified as normally glucose tolerant had T2D hyperinsulinemia, 22% (2,112) did not have diabetes and 4% (384) had pre-hyperglycemic T1D hypoinsulinemia. Dr.Kraft’s several thousand autopsies suggested to him that diabetic pathology is vascular. This observation was also espoused earlier on by Dr.Stout’s from the 1970s onwards due to his experiments strongly implicating the need of insulin action upon the endothelium in order for it to become ‘injured’.

Table 1 lists selective glucagon receptor antagonists that are in phase 1 and 2 clinical trials.

glucagon targets

In large part, the main resurgence of interest in interventions for diabetes targeting glucagon stems from the ugly fact that T1D and far-progressed insulin-dependent T2D is poorly managed by insulin injections. Granted, low carbohydrate-high fat and ketogenic diets greatly reduce the need for insulin injections and broaden this interventions’ margin of error. Nevertheless, this balancing act remains difficult and perilous. The bihormonal (insulin and glucagon) rationale for the management of diabetes is partly based on 6 observations recounted by Robert Unger and Alan Cherrington:

  • insulin deficiency is associated with catabolic events that are partially mediated by glucagon, such as hepatic glucose and ketone production
  • whichever form of poorly controlled diabetes, it is always characterized by hyperglucagonemia
  • leptin and somatostatin are glucagon suppressors and they manage to suppress the catabolic manifestations occurring during insulin deficiency
  • glucagon suppressors leptin and somatostatin suppress all catabolic manifestations of diabetes during total insulin deficiency
  • total destruction of β-cells in -/- glucagon receptor mice does not cause diabetes (a truly inconvenient and reproducible result which the insulinocentric model of diabetes cannot explain)
  • when the pancreas of rats are perfused with anti-insulin serum this engenders severe hyperglucagonemia

In addition to these observations, a known but poorly addressed issue in insulin-dependent diabetes protocols is plasma insulin ‘concentration disparity’. Subcutaneous insulin injections go into adipose and skeletal tissues as well as the liver and pancreatic α and β islet cells. These all require different concentrations of the hormone. This blanket approach “results either in underinsulinization of α cells or in over-insulinization of peripheral tissues [as depicted in panels A, B and C]”.

insulin concentration disparity

These lines of evidence supporting glucagon’s bihormonal importance in the management of diabetes are partially reflected in a 1975 New England Journal of Medicine study of 7 human patients with juvenile-type diabetes (T1D). It would most likely be impossible to replicate nowadays. It showed that these patients did not develop diabetic ketoacidosis for 18 hours after acute withdrawal off of insulin as long as glucagon was suppressed from pancreatic α cells using somatostatin-14. The latter form of somatostatin has higher affinity with pancreatic α cell receptors while somatostatin-28, which is released upon intake of dietary fat, has higher affinity with pancreatic β cell receptors. When β cells are non-functional, as was essentially the case in this study, a high-fat diet will partially suppress glucagon, albeit in a relatively weak manner due to somatostatin-28 release acting on α cells.

The authors schematize the mechanism in Figure 6 whereby insulin’s action is secondary in importance to that of glucagon’s and they describe the implication of their result, explaining how “insulin per se does not lead to diabetic ketoacidosis in man and that glucagon, by means of if its gluconeogenic, ketogenic, and lipolytic actions, is a prerequisite to development of this condition.

Bihormonal diabetic ketoacidosis

Beyond the hormones insulin and glucagon, the human microbiome presents itself as potentially novel player of note impinging upon whole body glucose and energy homeostasis. The last 10 years have heralded the emergence and importance of the human microbiome which now also adds an astounding layer of complexity to diabetes. A 2012 study by Vrieze et al. showed that within 6 weeks of a fecal microbiota transplantation (FMT) from lean donors to obese recipients with metabolic syndrome, the obese recipients displayed improved insulin sensitivity as measured by “a median rate of glucose disappearance […] from 26.2 to 45.3µmol/kg/min; P < 0.05”. The mechanisms mediating this are uncertain. The statistically significant (P < 0.05) changes in butyrate-producing bacterial species in the proximal and distal intestines suggest to the authors “that butyrate produced by certain bacteria prevents translocation of endotoxic compounds derived from the gut microbiota which has been shown to drive insulin resistance”.

The diagram of Udayappan et al. depicts 2 ways in which “altered [i.e. less Short Chain Fatty Acid-producing] gut microbiota composition may affect the host metabolism via impaired intestinal barrier function resulting in low-grade endotoxaemia”.

SCFAs endotoxemia

It is important to note that the statistically significant changes in bacterial populations in the Vrieze et al.-FMT study should not be assumed to biologically significant long-term – not until further replication and supporting data is seen. Nevertheless, the changes in glucose metabolism seen after FMT interventions are striking and so far do not suggest they would bring with them the brutal and numerous side affects of popular glucose lowering agents.

There is an irony whereby conventional medicine both espouses insulinocentrism in many respects yet concomitantly fails to appreciate its veritable actions and wider relevance. The insulinocentric models of diabetes does need broadening and refining. The bihormonal approach to diabetes sharpens the classic endocrinological picture. Renewed appreciation for the powerful effects of food, sleep and exercise (otherwise known as lifestyle factors) capable of altering the diabetic phenotype revives effective late 19th and early 20th century interventions. The microbiome is posed to help fill-in knowledge gaps about diabetes and metabolic syndromes by characterizing the nature of the relationship between our organism and microbiome. It can be concluded that the preponderance of evidence points to insulin as being the biggest single lever in the etiology and reversal of diabetes. However, insulin can be acted upon by many inputs, reflecting its central endocrinological and metabolic role in regulating whole-body energy homeostasis. There is more than mere hope for culling the impact of diabetes worldwide.

Gluten and FODMAPs: The lady doth protest too much, methinks

This 2013 study in the journal of Gastroenterology

No Effects of Gluten in Patients With Self-Reported Non-Celiac Gluten Sensitivity After Dietary Reduction of Fermentable, Poorly Absorbed, Short-Chain Carbohydrates

has been heralded as definitive proof that avoiding gluten does not confer any health benefits, save for non-Celiacs (maybe). This is a tenuous claim at best and most likely results from:

  • a lack of attention to study design details
  • discounting past in vitro studies & in vivo studies in mice and humans
  • an annoyance towards those behaviors associated with ‘going gluten-free’

Before exploring why this study reframes the gluten question rather than dismisses it, delineating the specific hypothesis being tested is necessary. The authors ask:

How do people respond to different whey and gluten challenges in the context of a diet lowered in FODMAPs?

Uh, what are FODMAPs? They are fibers of the short chain Fermentable Oligo-Di-MonosAccharides & Polyols sort.

So who are we talking about? Entry criteria:

To find data pertaining to this question, they enrolled 37 non-Celiac subjects diagnosed with irritable bowel syndrome (IBS) and reporting stable improvements in their symptoms for at least 6 weeks when going on a gluten-free diet (GFD) prior to the study.

What did they feed to who and when? Study design:

The study was double-blind, randomized and with cross-over. This is reasonably rigorous. The authors settled on a sample size of 37 subjects as to attain 80% statistical power, which is “the probability that the test will give the right result when there is a real effect […] and [it] depends on the sample size, and on the size of the effect we hope to detect”. As far as nutritional studies go, this is good. Yet, in the words of David Colquhoun, “The minimum false discovery rate for p=0.05 is seen to be 0.289 [using Berger’s approach]. In other words, if you claim you have discovered something when you observe a p∼0.05, you will make a fool of yourself in about 30% of cases”. Let that settle in for a moment…Statistics aside, the study proceeded as follows.

  • 2 week run-in period: all subjects went on a gluten-free (GF) low FODMAP diet.
  • Randomization to 3 different diets for 7-days: High-gluten (16g/day), Low-gluten (2g/day) or a whey-control diet (16g/day).
  • 2 week wash-out period.
  • 3-day rechallenge on 1 of 3 different diets: High-gluten (16g/day), whey (16g/day) or a control diet (0g/day of whey or gluten).

Why diet for this length of time? Does it matter? Rationale for trial lengths:

The authors speak of the 7-day trial and the 3-day rechallenge as 2 separate trials. They give 2 reasons explaining why the rechallenge lasts 3 days. First of all, the original plan was for the participants to stay on each diet for 6 weeks (not 7 days) because “symptoms were uniformly induced within the first week of the original study”. Secondly, the 3 putative gluten responders in the 7-day trial had apparent symptoms within 3 days, thus negating the need for a longer rechallenge period. When discussing possible mechanisms surrounding negative and reproducible effects of gluten, this last point may act as a potential confounder when considering longer-term autoimmune reactions involving molecular mimicry with the thyroid gland or with the nervous system (e.g. cerebellar ataxia).

How do we tell if things got better or worse? Endpoints:

Their primary endpoint was “the change in overall symptom score” on the Visual Analog Scale (VAS) from the 2 week run-in period on GF low FODMAP diet to the treatment-period on 1 of 3 diets. Their secondary endpoints were that slice of participants with a change in overall and individual VAS symptom scores of >20mm as well as markers of protein metabolism byproducts, magnitude of gluten-specific T-cell receptors response, fatigue, activity levels and specifically reproducible GI symptom between the 7-day trial and the 3-day trial.

The easiest person to fool is yourself, so how did they mitigate that? Controls:

They tried to minimize the influence of added food chemicals, they (apparently) successfully reproduced the texture of gluten in gluten-free products and the whey-isolate product was lactose & FODMAP free.

How can we make sure the participants did what they were told to? Adherence:

How was the participants adherence monitored? Daily symptom cards were filled out, significant (>20mm) changes in VAS scores were recorded, notes were taken on fatigue using a daily fatigue scale (D-FIS) and an accelerometer checked activity levels. Furthermore, IgA and IgG specific T-cell responses to gliadin and deaminated gliadin were assayed along with IgE wheat antibodies. Lastly, poop was collected between days 5-7 so to monitor ammonia, β-defensin and calprotectin levels.

So what actually happened? Will gluten shoot your dicks off? Results:

A general trend emerged where overall VAS symptoms improved from baseline to week 2 of the low FODMAP GF run-in period. Only 8 participants (a mere 22% of the total IBS cohort) saw significant improvements of >20mm in abdominal symptoms. The 63% reduction in FODMAPs – from 19g at baseline to 12g during the run-in – may have been insufficient  to uncover subtler effects.

Baseline to run-in

Despite this general improvement, symptoms generally worsened on all 7-day trial diets compared to baseline assessments – irrespective of the quantity or absence of whey, gluten or FODMAPs. Considering that FODAMPs were lowered by 86.4-80.5% compared to baseline (down to 2.6-3.7g) across all 3 diets for 7 days, one could reasonably expects commensurate or bigger improvements showing up. Since this did not happen, could the nocebo effect be to blame? The authors suggest it might. This would be because diet content was not associated with degrees of symptomatic responses but diet order was – in both the 7-day trial (A) and 3-day rechallenge (B).

It was about diet order, not content

It was about diet order, not content

The possibility is reinforced when considering how, in both trials, 1st interventions had mean VAS score changes of 15.5mm whilst 2nd and 3rd ones only changed by 5.3mm and 4.0mm, respectively. In other words, transitioning from the run-in period to the diet-week has an effect that is independent of the kind of diet one transitions to. The fact that only 3 subjects showed gluten specific effects and 7 showed whey-specific ones also argues against an independent diet-content effect. Furthermore, fatigue D-FIS scores presented similarly, showing no noteworthy changes across the 7-Day diets but again, with worse fatigue symptoms when transitioning from the run-in period, irrespective of the diet transitioned to. The authors astutely raise the possibility of “more focused attention to anxiety and depression rather than fatigue might provide additional clues to why patients who follow a GFD feel better”, implying that the D-FIS scale is in fact less appropriate than measures of anxiety and depression. This is strongly supported by the enormity of clinical and anecdotal feedback.

Returning, again, to the order effect and baseline to run-in VAS improvements, these kind of results point to a relatively common sort of response, where gluten may be ‘necessary but not sufficient’ for inducing clinically observable negative effects that have also been reproduced elsewhere, by the very same authors and in other studies.

With the exception of 1 participant with a 3-fold Celiac-like T-cell specific response, that of all other participants was not noteworthy. Neither were the changes in biomarkers obtained from fecal samples. To their credit the authors recognized the discrepancy between their data set and that of others, explaining how “the serological pattern was mostly negative, but there were a lower proportion of cases with positive IgG AGA [emphasis mine] compared with recent data on gluten sensitivity[24]”.

Reference 24 is taken from the results of Umberto Volta et al.’s 2012 paper in the journal of Clinical Gastroenterology, informing us that “[…] IgG AGA were positive in 56.4% of GS [gluten sensitive] patients” out of a cohort of 78.

IgG antibody and Fc receptor guiding macrophage phagocytosis

IgG antibody and Fc receptor guiding macrophage phagocytosis

Interestingly, HLA-D status did not correlate to biomarker changes. I do not have a good enough explanation for this. It is all the more puzzling when one considers that 21 patients on the High-gluten diet, 13 on the Low and another 13 on the whey-control diet were all borderline positive for Whole gliadin IgA values, hovering around 19±3.5U/mol. A negative assay for Celiacs is at <20U/mol. So there is no gradient or dose-response relationship here. Yet, the double-blind placebo-controlled, larger sample study in journal of the American College of Gastroenterology by Carroccio et al. in 2012 demonstrated such a gradient amongst IBS sufferers, wheat sensitive patients and Celiacs. 10%, 40% and 72% respectively tested positive for serum Gliadin IgA. A longer time component, as previously alluded to and exemplified here by Carroccio et al., seems more appropriate for the type of effects being teased out: 4 weeks for the elimination diet, 1 week for between-diet wash-outs and 2 weeks for the single-item reintroduction diets (also with cross-over design).

We’re left with gluten showing no dose-dependent relationship with the severity of symptoms in IBS patients, yet symptoms get noticeably better across the board when FODMAPs are more than halved and gluten withdrawn. What’s more, although the 3-Day Rechallenge gluten & whey-free control diet ‘provoked’ lots of symptoms, all of the gluten containing diets also scored poorly in terms of symptoms.

Ultimately, this study in IBS patients adds to the notion that when gluten is eliminated, symptoms improve – but not just because of gluten. It’s as if it always hangs out in the wrong foods (neighbourhoods) where, when combined with FODMAPs and possibly other components, wreaks havoc. Here FODMAPs seem to have played the role of its context-dependent partner in crime. Knowing the laundry list of other possible offenders in industrialized processed diets, it would be foolish to assume other combinations with gluten won’t produce a vast and continuous (rather than discrete) presentation of symptoms.

All in all, gluten can both stimulate “zonulin, the only known physiologic modulator of intercellular TJs [Tight Junctions] described so far” and cause reproducible immunological reactions (here, here & here). It has both the key to the house and a weapon, its immunoreactive amino acid sequence. What’s more, a genetic model (Celiac disease) gets us lots of supporting information and a ‘most sensitive’ model to help disentangle other symptom presentations. This literal ‘textbook’ explanation of basic immunological mechanisms should help clarify why dismissing gluten as a fad is, actually quite brazen or down right silly.

“The clonal selection theory provides a useful conceptual framework for understanding the  cellular basis of immunological memory. In an adult animal, the peripheral lymphoid organs contain a mixture of lymphocytes in at least three stages of maturation: naïve cells, effector cells, and memory cells. When naïve cells encounter their antigen for the first time, the antigen stimulates some of them to proliferate and differentiate into effector cells, which then carry out an immune response (effector B cells secrete antibody, while effector T cells either kill infected cells or influence the response of other cells). Some of the antigen-stimulated naïve cells multiply and differentiate into memory cells, which do not themselves carry out immune responses but are more easily and more quickly induced to become effector cells by a later encounter with the same antigen. When they encounter their antigen, memory cells (like naïve cells), give rise to either effector cells or more memory cells (Figure 25–11).

Naive, Effector & Memory Cells

Naive, Effector & Memory Cells

Thus, the primary response generates immunological memory because of clonal expansion, whereby the proliferation of antigen-stimulated naïve cells creates many memory cells, as well as because these memory cells are able to respond more sensitively, rapidly, and effectively to the same antigen than do naïve cells. And, unlike most effector cells, which die within days or weeks, memory cells can persist for the lifetime of the animal, even in the absence of their specific antigen, thereby providing lifelong immunological memory “(p.1546, Chapter 25: The Adaptive Immune System, Molecular Biology of the Cell, 5th Edition, Alberts et al., 2008).

IgG belongs to the cell-surface proteins Ig superfamily. Amongst other tasks, IgG activates the complement system – “complement activation can also greatly increase the immune response to an antigen: the binding of an activated complement component to an antibody–antigen complex, for example, can increase the ability of the antigen to stimulate a B cell response more than a thousand fold” (Figure 25–74, p.1599, Chapter 25: The Adaptive Immune System, Molecular Biology of the Cell, 5th Edition, Alberts et al., 2008).

Some Ig superfamily cell-surface proteins

Some Ig superfamily cell-surface proteins

The Figure ‘Kinetics of Antibody Response’ depicts their 2-humped response pattern and the characteristically long time component of the mechanism within which they operate.

Kinetics of Antibody Response

Kinetics of Antibody Response

Lastly, consider the Carroccio et al. 2012 study highlights and points from “Non-celiac wheat sensitivity diagnosed by double-blind placebo-controlled challenge: exploring a new clinical entity

Study highlights: Now & Then

Study highlights: Now & Then

How gluten affects the brain is not clear. It doesn’t seem to need to cross the BBB itself for many of its neuropathological symptoms.

  • “In the present study, none of the patients had hypovitaminosis or malabsorption, and more than half of the patients did not even show any duodenal abnormalities. Neuropathologically, there is loss of Purkinje cells and/or degeneration of the dorsal columns (Bhatia et al., 1995; Hadjivassiliou et al., 1998) with facultative lymphocytic infiltration of the cerebellum, dorsal columns and peripheral nerves (Hadjivassiliou et al., 1998)”

Reproducing empirically observable effects with gluten cannot be ignored and is confirmed elsewhere.

  • ”Our results clearly showed that a relevant percentage — more than one-fourth — of the patients who underwent DBPC [double-blind placebo-controlled] wheat challenge were really suffering from WS [wheat sensitivity]”

The ‘necessary but not sufficient’ notion stated another way.

  • “[…] there was evidence that coexistent triggers, e.g., intestine-damaging drugs or dysbacteriosis, can lead to a more severe intestinal impairment (28 [mouse study – gasp!]). Clearly, wheat antigens may also act in a similar manner”

Whether it is gluten alone or gluten + whatever else is also in gluten-containing products that is not healthy for humans, the gluten-free recommendation stands. This does not include advice to sport a gas mask when walking past bakeries.