Archive | August 2016

A high-protein Ovo-Ento Vegetarian diet for body and planet health.

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Are you concerned with the environment? Are you concerned with your health? Like most people, you are most probably concerned with both. The two are probably connected anyway, but in much more complex ways than some people want us to believe. In this blog post I am going to challenge the ethics of both the vegan community and of a group of doctors with strong ties to militant animal rights groups who want you to believe that a whole food plant based diet is supposed to be both good for your health and the environment when objectively it is an ill defined diet that in many forms introduces major health risks, while doing relatively little for the environment. I am also going to challenge, and I know this is going to be a hard one to crack, the cultural aversion of many of you with respect to a certain food sources that you may not only not think of as food, but might even be completely repulsing to you at this point in time.

But first things first, lets look at decomposing the problem with our diets that we are trying to solve. Our primary goal is health. Our own health and that of all other humans. In order to be healthy, we strive for a healthy body AND a healthy environment for us and all others to live in.

Thus the first part of the equation:

Health = Healthy body + Healthy environment.

Lets start of with the first part of the equation. I’ve been looking at many studies, and more importantly, many data-sets for many years now that all make the wildest claims about nutrition and health. When looking at the data though through the eyes of a data-engineer, very few of these claims seem to be very solid. In fact, in most other fields of science the results of most nutritional science studies would be simply considered as inconclusive. Not surprising thus that we get so many conflicting messages about different foods and supplements being super foods one day and being causal in disease pathways the next. For many things, the environment (that we will look at next) might be an actual bigger factor for overall health than something like eating fibrous foods or eating meat. One piece of data that does however stand out above all is a cluster of variables that are directly or indirectly related to obesity. If we zoom into the problem of obesity, the first thing we notice however is that the main measure of obesity as used today, the Body Mass index, is actually one of the weakest variables from that cluster with respect to health impact. If we zoom into this cluster, we get variables like waist to height ration, conicity index, waist circumference, etc as having massive correlations with a wide range of health issues. On the other side, then looking at things like body weight and BMI that are linked much weaker to these health issues, we see that body muscle mass and lean body mass not only mitigate the effects of our harmful cluster in a way that would be expected if the size of the belly was all that mattered and muscle mass was irrelevant. There seems to be quite convincing data showing there is independent risk reduction tied to higher lean body mass levels.  When we look at some excellent studies with respect to the variables in our main cluster, we can see convincing facts pointing to Visceral Adipose Tissue (VAT), the fat that is in between our organs to be the main thriving risk factor within the cluster, and indeed if we apply the hypothesis that VAT is our primary risk driver and add to that the concept that high LBM reduces VAT driven health risk, we get at the second part of the equation:

Healthy body = Low VAT + High LBM

So we want our VAT levels to be low, but we don’t actually care about subcutaneous fat. Have massive love handles? Don’t mind those, they are NOT a health risk. Have a BMI of 35 or even 40? Don’t mind that, it’s not an independent risk factor. We care about the visceral fat time-bomb. If you aren’t a power lifter and have a 150 cm waist and a BMI of 40, chances are your VAT will be high though, but remember, when we are going to try and fix that, we won’t be aiming at lowering your body weight, in fact, your body weight might actually increase quite a bit first while working on the low LBM problem.

It is important to truly grasp the implications of this part of the equation. We want VAT to go down, but NOT at the expense of LBM. We want LBM to go up, but not at the expense of VAT. But trying to fix both can be hard. If there is an imbalance, as there often is, between the two components of good health, it is good to focus on the most urgent one first and aim for balancing the problem first. This may mean that if your LBM is quite decent and your VAT is quite high, focusing on weight loss first might be a good idea, even if it means losing some of your precious LBM. Surprisingly though, and contrary to the way most nutrition experts approach it, for many so called over-weight people, especially those who went through the dieting circle several times around, a low LBM is actually the dominant problem and gaining lbm should take precedence, even at the expense of possibly gaining some more VAT initially. This part can be a big mental issue for many who are conditioned to think of themselves as being over-weight. But now, lets look at how to address the VAT issue at any given caloric level. Its about both what you eat and when you eat it. The next part of the equation:

Low VAT = Low bad carbs + Timed good carbs

So what are bad carbs? In short, its added sugar and refined grains. There are sufficient sources available showing why these two should be avoided and how they are linked to obesity, so no need to rehash that here.

Low bad carbs = low added sugar + low refined grains

So how about timed carbs? We will get to the resistance training part in the section below about LBM, but basically you will want to time most of your good carbs around your workout, dead-center inside of a fat-free time window. So before your carb window start, you should not eat any fat for a period of time. After it closes, you should not eat any fat for an other period of time. Your workout should probably fall inside of your carb window. The reason for this window is that carbs and fat together truly spell growing body fat deposits.

Now for the tricky part, and the part leading to the dietary advise that contradicts that of much of the vegan experts on the subject, increasing our LBM. In order to increase our Lean Body Mass, we want to grow our muscles, most importantly the bigger muscles. Just as with our love handles, this is not about getting a beach body, it’s about health. Do resistance training. If you have time for it do things like shoulders and biceps, but for the bigest part of it focus on the bigger muscles, these are the ones where you can gain the most weight the quickest. Legs, core, triceps maybe. As for this part of the equation, its quite simple really:

High LBM = RT + Protein

You need to really hit those weights, and you really need to eat proteins and not at the levels that your vegan doctors will have you believe are sufficient, well maybe at first if your LBM is really low, but as your LBM goes up, so should your protein intake. Normally I use the metric system, but this one is one where pounds are easier to reason with:

Total Protein = LBM g/lb

Taking your current lean body mass in pounds as a measure of how much total protein in grams you should eat as an active person who does resistance training should be a reasonable level for most of us. You need protein as source of calories, but more importantly as building blocks to repair and grow your muscle mass in the recovery phase of your workout schedule. You can tweak it a bit to see if you get better results, but more than marginally more or less than the 1 gram per lb of LBM is something that is testable sub-optimal for most of us. As for what protein to eat. There is the issue of amino-acid completeness. You don’t want to get a one sided amino acid profile for multiple reasons. There is however also the environmental concerns with many of the broad spectrum protein sources. Given that the total amount of protein we take is relatively high and plant based proteins from nuts, legumes and mixed source protein powder has quite a diverse profile already, we can get most of our protein from plant based sources. Looking at epi for protein source split all cause mortality, especially when looking on epi data from countries/times with low processing levels of common sources of protein such as meat, we see there is a real concern that the lower extreme of animal based protein is not a place without potential health concerns. To steer clear of this extreme, yet also consider environmental concerns, and as we will later see, concerns with respect to cultural acceptability of certain foods,  a safe split of protein sources could be:

Total Protein = ⅔ Plant protein + ⅓ Animal protein.

Lets zoom in a bit. Let us first look at look at the animal sourced part.

Animal protein = ⅓ x LBM g/lb

So for example, if you are a 200 pound man with a body fat percentage of 25%, who has started going to the gym at least 4x/wk. Your lean body mass will be 150 pounds. That means for your protein, 100 grams of plant based protein and 50 grams of animal based protein would be close to your ideal intake. What do you need to eat to get 50 grams of animal protein? Some single source examples:

  • A 200 gram steak
  • Four large eggs
  • 200 grams of Gouda cheese
  • 250 gram of chicken wings
  • 300 gram escargot
  • 175 grams of tuna
  • 250 gram meal-worms
  • 250 gram shrimp
  • 400 gram calamari
  • 75 gram whey protein powder.

It is relatively easy to consume 50 grams of protein a day from these sources if we don’t consider the environmental part of things. A subject we look at later. .But then we need to look at the 100 grams of plant based. We see that getting 100 grams of plant protein without restoring to supplements might actually be quite challenging to many people:

  • 5 kg potatoes
  • 3.7 kg rice
  • 3.5 kg brocoli
  • 2.5 kg kale
  • 1.1 kg of lentils
  • 1.1 kg black beans
  • 800 grams of brown bread (about 30 slices)
  • 700 gram walnuts
  • 500 gram almonds
  • 400 gram peanuts
  • 275 grams hemp seed
  • 175 gram spirulina (sea weed)
  • 125 gram vegan blend protein powder.

If we now look at this list from an ecological footprint point of view,  there are some rather surprising facts. One is: some of our plant based protein sources actually have a worse ecological footprint than some of our animal based protein sources. For fun, as an exercise to the reader, look at the amount water needed to produce a single gram of protein for each of the sources listed here. How much land is needed? How much water? How much pesticides? herbicides? How much does production contribute to eutrophication?  And last but not least, mow much fossil fuel is burned for production and transport? If we add these all up and look at the total ecological footprint PER GRAM OF PROTEIN, we get the outlines of an image where the idea that a plant based protein would implicitly when compared to an animal sourced diet would be implicitly good for the environment can be quite easily dismissed.   The baseline here is, looking at these sources, both plant and animal based, we should look for those sources of protein that are:

  • Relatively low in eccological footprint per gram of protein.
  • Relatively low in number of grams needed to achieve daily targets.
  • If possible provide us with valuable micro nutrients
  • If possible don’t mess with our carb/fat split.
  • Combines a wide spectrum of sources.

Well, for our top list, the steak is a no-brainer. It needs to go. So does the chicken and the fish if we look at the ecological impact. Broccoli, other than what some sources may want you to believe simply isn’t viable as a source of sufficient protein, so eat it, sure but don’t expect too much from it in terms of protein. So what sources should we add to our pallet that allow for a realistic daily protein intake program that fit with our concerns for health and our ecological concerns. Well, everybody will need to weigh these concerns themselves, but for me, weighing them, the following list came to be my list of protein sources:

  • Eggs
  • Mollusks
  • Insects
  • Crustaceans
  • Legumes
  • Nuts
  • Spirulina
  • Vegan blend protein powder

At the moment, Eggs and mollusks make up a major part of my animal based protein intake, due mostly to financial concerns and the fact that my wife doesn’t like me breeding my own insects in our home with little kids running around. These I think, at this moment in time are concerns everyone must look at locally: How ecological sound are your local sources of eggs? Can you breed your own insects ? Can you step over the cultural bias against eating insects that may or may not be strong in your community and may or may not have social aspects that might make eating or breeding insects socially awkward. If however you are truly concerned with the ethical aspects of food, than in many ways eating insects should be a massive part of your considerations. Not only are insects a source of protein that have an ecological footprint lower than that of many plant based sources of protein, considering insects as a source of food, rather than just a pest that needs pest control opens up possibilities for agriculture that might even make many plant based foods more ecologically friendly. While introducing foreign species is never a good idea, there are possibilities in many regions of edible natural pest control. If you look closely at the local ecosystem, chances are you can pair plant based food production with insect based food production in a way that reduces or eliminates the need for pest control. Thus, eating insects as part of a healthy high protein ovo-entho vegetarian diet or an entho vegetarian diet, should not only be superior to a vegan diet from a health perspective, and this is something my vegan friends are likely to want to contest, it is also potentially more ecologically sustainable if you can match up pest control with insects as protein source in your region.

And remember, our ancestors ate insects. Many cultures in the world eat insects. Even our so called vegan cousins, the great apes all eat insects. Insects are one of our natural food sources. A food source that is to our ancestors would commonly have been more abundantly and more effortlessly have been available than things like meat or fruit. If you can’t stand the idea of eating insects, ask yourself why. If you want to start with small steps in looking if an ovo-entho vegetarian diet is something for you, start of with a buffalo-worm omelet. Buffalo worms are not as crunchy as some other insects and their taste is really neutral. Once you are used to the idea, you can move on to meal-worms and crickets and maybe to whatever pest-control insects might be local to your region if you happen to be growing your own food.

This entry was posted on 3rd August 2016.

data-science versus system-engineering for nutrition.

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Let me start this blog post with a big warning. On a scale from zero to one hundred, zero being vulkan style emotional detachment and 100 being a full out rant, this post is a fat ninety five.

Since I got interested in nutrition 6 years ago, after the disastrous effects that dietary advice and medication, given to me by medical and nutritional professionals, had on my personal health, and since I’ve been trying to apply my knowledge and experience with regards to data engineering in a forensics setting to my growing knowledge about nutrition, there have been some aspects of nutritional science that have led to a growing discontent with the whole field of science. To start of with I thought, well, I’m new to the field, so maybe I don’t get why things are done like this, but as my knowledge of nutrition, biochemistry and epidemiology has grown over the years, so has the seed grown that started of as the simple thought that there was something fundamentally wrong with the way nutritional science applies the tools handed to it by the field of data science. There are some great studies out there by people who clearly have a good grasp of data-sci, but many of the studies that come out, and many of the pop-science books about nutritional science, including those that are widely lauded as game changing seem to be using definitions of what constitutes solid proof that, as far as I can judge, align what the rest of science would agree only constitutes a possibly interesting link that may warrant further research. I will not be getting too technical as to not alienate the non technical readers, but I still need to touch on a few technical and data-science concepts.

Obsession with linearity

For some strange unexplained reason, nutritional science seems to have an obsession with linear associations. When I knew little about the subject and read nutritional science papers, after a while I was starting to assume that something fundamental about biochemistry made linear associations a fundamental truth of the field. After getting deeper into the subject however, there appears to be absolutely nothing to justify the obsession of large groups of nutritional scientists with the idea that associations need to somehow be linear. So what is a lineair relationship? Lets say there is a suspected association between the intake of for example a specific amino acid and mortality from a specific ailment, a study could look at intake of this amino acid in a number of regions and then look at mortality numbers and recorded cause of death in these regions. If you plot the per region mortality from our ailment against the average intake of our amino acid in given region, you get a scatterplot representing the relationship within our data set between the two variables. Well its a bit more involved than that as we will later see, but it is the base idea. Now if, as nutritional scientists seem to always do we define a straight line defined by:

Y=aX + b

In such a way as to minimize a the average of a simple function of the error between the line and the points on our scatter plot, this line and the errors as defined by our scatter plot define the hypothesis of  a linear relationship between X and Y. You can use other functions to fit against that are not linear, and you can, as other fields of science tend to do, fit Y against multiple variables at once using a random subset of the data and validate that fitting using the remaining data points, but for some reason these common techniques seem quite rare in both nutritional science and epidemiological science papers. Why is this? I really couldn’t tell. All I can guess is that there is some strange schism between these fields and the rest of science.

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It isn’t just observational data where many scientists assume linearity, it is randomized controlled trails as well. This “golden standard” of research trails in nutritional science often leads to trail design that implicitly assumes linearity.

 

 

 

 

p-values

There are two problems with nutritional studies that aggravate each other. The first is the assumption that everything is lineair. The second are truly low standards and a wide spread misconception of the concept of p-values. The p-values issues seem to allow scatter plots that to visual inspection are clearly not best fit for ‘linear’ regression, to not only be falsely assumed to be linear, but even pass the arbitrary p-value threshold used to signify significance of that linear association.   So what are p-values anyway?

“The p-value is defined as the probability, under the assumption of the null hypothesis H, of obtaining a result equal to or more extreme than what was actually observed.”

While this definition is correct it is also the source of a whole lot of confusion that I wont go into for the sake of not going to technical.  The most important thing we shall need to focus on is the true p value versus the sample calculated p value. I am not a data scientist in the strict sense of the word. I’m not doing raw math most of the time. Instead most of my  data work revolves around writing and running simulations, many times over most of the time, from a data engineering rather than a data-science perspective. One of the things anyone who has run such simulations will know is that sample p-values can have rather erratic behaviour across runs. It came as no surprise thus when  Nassim N Taleb came with his meta distribution for p-values. I know a whole lot of people will probably stop reading here. Sure Taleb has a bit of an inflated ego and lacks the social sense to not refer to people below his intellectual level as imbeciles, but his work on the p-value meta distribution brings to data science what data-engineers have known through experience from simulations. The arbitrary threshold for the p-value as used by nutritional scientists is way to high if we account for the properties of the p-value meta distribution.  I’m probably already too technical here for most readers. The most important thing to take from this is that with a sample p-value marginally below the nut-science p-value threshold will have a very sizeable probability of having a true p-value well above the threshold value below what significance is denoted.  This apart from the fact that there is a general over reliance on p-values that isn’t just a nut-science issue though, and the widespread malpractice of p-hacking that appears to be unusually commonplace in nut-science.

missing variables

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When looking into biochemistry and when studying actual data sets, there is one big chemistry variable that is omitted in most nutritional science data set: heat exposure. You don’t need more than a few months worth of chemistry lessons to know that exposure to heat can make a whole lot of difference. Studies looking at things like macronutrient consumption though hardly seem to record heat exposure or even differentiate between something as crucial as consumption of unheated PUFA versus consumption of PUFA in cooking oil used for baking stuff. Another often omitted variable is lean body mass. While loss of body fat, especially of the visceral type,  is beneficial to most people in western countries, loss of lean body mass is really bad news. Too many studies that record weight change however fail to also record body compositional values.

sub-population variance and skew

skewness-and-kurtosisFor epi studies there is quite a different type of missing variable problem. If we accept that an association is often nonlinear, and knowing that the average for a sub-population is just that, a sample average, we really need to pull some more advanced techniques out of the hat than just work with sample averages. The point is, we can’t just assume all sub populations to only differ in sample mean while sharing the exact same variance. Next, we can’t assume the sub-populations distribution to be skew-less.  Again these are a bit technical terms so lets look at them a bit. If a true association between a nutrient or food and mortality from a specific ailment is shaped like a bathtub, then many of the deaths in a population might arise from one of the extremes of the sub-population distributions. The volume of people contained in these extremes depends on distribution properties like variance, skew and possibly kurtosis that together define the shape of the distribution. If these moments differ between sub-populations, then any conclusion drawn from looking at the population through just the sample mean becomes basically something that is bound to yield misleading results. When recorded, differing variance across sub populations, or sub distributions with significant skew or kurtosis should be reason to call in the big guns.

Static versus differential markers.

Now let’s assume nutritional scientists found a solid association and got the shape of the association right. Let’s say higher levels of marker X are strongly associated with higher levels of mortality number Y. Does that imply that a negative ΔX for an individual will lead to a negative Δ in P(Y) ? Well, maybe, but then there might even be a possibility that it will lead to a positive P(Y). Point is: a marker, even a good marker isn’t necessarily a suitable variable to use as controlling variable for Y. For an engineer these things are blindingly obvious. For many nutritional scientists though, the assumption that all that is needed for a good controlling variable is a  strong correlation seems to be the default assumption.

surrogate endpoints and other die-hard misleading variables

In nutritional science there is one step beyond the wrongful use of  markers as control variable: surrogate endpoints. Nutritional science has an abundance of questionable surrogate endpoints that are stand ins for real endpoints (mortality) for different diseases. Things like LDL-c, BMI and high blood pressure. I’ ve talked about LDL-C and BMI in earlier blog posts and won’t rehash them here now. The big problem with surrogate endpoints is that they persist and contribute to poor follow-up studies. For example, using BMI as surrogate, one might conclude that an increase in lean body mass (LBM) would increase the risk of CVD-death. This while all data shows the actual reverse to be a very viable hypothesis.

Adding it all up

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If we look at all the issues discussed above, we could ask if nutritional science is fixable. Looking at the actual data sets from many studies with a pair of data-engineering spectacles, we must come to the conclusion that from a data-sci perspective, most data is very much inconclusive with respect to the claims being made. If we fix nut-science, the field should become quite boring with very few claims being made either by observational or by RCTs. It seems the influence of individual components of nutritional profiles on mortality numbers is either very small or non existent. Possibly when missing variables such as heat exposure are added there might be interesting claims that could stand up to modern data-sci scrutiny and that is something that should definitely be looked at. But who is going to fund studies when nine out of ten studies results in inconclusiveness. So what other options are there?  While scientists tent to look down at the idea of N=1 stuff, engineering thrives on it. Using engineering practices, especially multivariate control-feedback theory, an individual might find his/her personal ideal nutrition and exercise program all by him/her-self. Do we still need nut-science then? Yes we do, but only at the extremes basically. An N-1 control-feedback loop can’t obviously factor in mortality numbers into the feedback loop, yet individual progress might lead to extremes (100% meat diet, 100% bananas diet) that in turn might result in sudden death of superficially healthy individuals.  If we accept that nutritional science hasn’t got that much useful + conclusive to say about the mid-range of most foods and nutrients, and if we accept the non-lineair nature of many things, including nutrition/mortality associations, the field could focus on the extremes as an important way of supplementing what should be a primarily N=1 engineering effort aimed at individuals.

So that rounds up what I already warned was a bit of a rant. I hope I’m being way to pessimistic with all this, but the more nut-sci papers I read and the more data-sets I actually end up being allowed to look at, the more pessimistic I’m growing about this field of science that as it seems can only excist and can only make funding-worthy claims as long as it keeps ignoring what data-science has to offer. So as it is my conviction that we truly need to develop the field of nutritional engineering to fill in the gaps that nutritional science is failing to fill, sadly just keeps growing with (almost) every paper I read.

This entry was posted on 1st August 2016.