We knew how to reverse type II diabetes in the 50s

Before the low-fat-diet insanity of the 1970s onward, sanity about diet and obesity was fairly common among the medical associations of the world.

Shown below is the abstract of an article published by George Thorpe, MD in JAMA (Journal of the American Medical Association) in 1957 [1]. This article predates the better-known work of Atkins by sixteen years!

The abstract is as simple as it is clear: to lose body fat, eat meat, have some vegetables, avoid sugar and grains. This was the standard procedure for weight loss, or “slimming”, until the 1960s. It was the standard procedure for a good reason: it worked. Given this, it’s not clear why Atkins took so much heat for repeating what was previously conventional wisdom.

Actually, maybe it is clear. Through the 1980s, low-fat eating became common. Cholesterol and saturated fat were the enemy. People felt like they had no choice but to stop eating fat, otherwise they would surely get heart disease. The choice was clear: either suffer with tasteless and dry food for your whole life, or suffer with chest pains and shortened life in the cardiac ward. In this world, Atkins was a heretic.

The good news is that it wasn’t true. Atkins was right. If we had only held to what we already understood to be true about nutrition in the 1950s, we would have been just fine. Probably better.

Sharp reduction in average daily energy intake while maintaining high carbohydrate consumption leads to hunger and loss of lean tissue (muscle mass). This creates the “yo-yo diet” or “rebound” phenomenon. Due to reduced muscle mass, used up for energy in the presence of insufficient food, the metabolism slows down and it’s harder to burn fat. The dieter either doubles down on the calorie restriction misery, for weaker results, or else gives up and returns to his normal diet. Of course, going back to his old way of eating will replace the lost fat and muscle mass with new fat mass. 

The indigenous Eskimos knew how to do it right. The above paragraph describes a textbook ketogenic diet, with the vast majority (80%) of the calories coming from fat, the rest from protein, and almost no carbohydrate. A healthy, non-restricted energy intake of 2000-3000 kcal facilitates burning body fat when the components of the diet (fat and protein) do not cause hyperinsulinemia. In other words, consistently low blood levels of insulin enable the body to regulate fat storage and consumption in a healthy manner.  

A simple and clear recipe for weight loss without hunger, discomfort, or muscle loss.

I received the reference to this article from P. D. Mangan.

The original PDF article, from which I clipped the excerpts above, may be found here.

By the way, the 50s I refer to in the title are actually the 1850s, not the 1950s. Dr. Thorpe was scooped by around one hundred years! Banting’s famous Letter on Corpulence was written at that time. This information has been known both empirically and clinically for a very long time.

[1] George L. Thorpe, M.D., Treating Overweight Patients, JAMA. 1957;165(11):1361-1365.

 

Taking the red (meat) pill

As so many others have said about low-carb eating, I can’t unsee what I have seen or un-experience what I have experienced.

I can’t pretend that I didn’t drop 40 pounds with minimal effort, discomfort, or hunger.

And I certainly can’t pretend that I didn’t do this while eating foods that the medical and nutritional mainstream deems “unhealthy”, and avoiding foods that they deem “healthy”.

The medical and nutritional establishment has claimed (on extremely weak and speculative evidence) that red meat, dairy, and eggs will make you obese and unhealthy.

They claim that grains are “heart healthy” and that we should replace animal fats with “vegetable” oils. The name “vegetable” suggests healthy foods like broccoli or cabbage, but in reality, so-called vegetable oils are not pressed from fresh green leaves. Instead, these oils come from seeds that must be heat-treated and solvent-extracted to yield their oils, and chemically processed to remove toxic compounds.

The establishment doesn’t really say too much about sugar except that it’s “empty calories” and try not to eat too much of it. (Unless it’s a “healthy” juice or smoothie or fruit and then you should probably have a lot, they say.) Curiously, Coca-Cola, Nabisco, Kraft, and other packaged food companies donate millions to fund nutritional studies to support the hypothesis that eating 10-20% of your diet as sugar is benign. I wonder why they do that (note: sarcasm).

It is clear the the “food pyramid”, MyPlate, or whatever other guidelines the various national governments create are far more about selling agricultural product and sustaining the packaged food industry than they are about human health and wellness.

More and more people are discovering that this dogma is the exact opposite of the clinical and experimental truth. People are taking their health into their own hands, experimenting with low-carb or ketogenic eating, and seeing massive improvement in many different symptoms simultaneously. Thousands of people (perhaps even millions) are:

  • reversing their type II diabetes,
  • returning from obesity and even morbid obesity to normal, healthy weight
  • recovering from metabolic syndrome,
  • reducing their cancer risk factors, and
  • reducing their heart disease risk factors.

People are achieving these results without medication, and in many cases they are even able to reduce or discontinue use of their prescription drugs. 

Does this torrent of good results mean that the “food pyramid” is a lie, and that the guidelines and “official” dietary advice of the past 50+ years has been actively harmful to people’s health? Quite possibly, yes. When breaking most of the conventional “rules” achieves better results than following them, then perhaps it’s time to rewrite the rules according to the clinical, scientific, and anecdotal observations.

Many medical doctors have themselves experienced a similar red pill moment where they made the choice to test out low-carb eating on themselves. Perhaps they entered middle age and put on a few too many pounds above their college athlete weight. So they tried keto, or Atkins, or LCHF, or Whole30. And it worked for them. Then they tried it out on some of their adventurous patients and it worked for those patients too. At that point, they got the lightning bolt and realized that the eternal “eat less and move more” or “calories in equals calories out” advice was misguided. Or, at the very least, profoundly oversimplified.

If someone’s a skeptic, the answer is simple: “try it for yourself, seriously, for a month or two, and see what happens”. More and more former skeptics are doing this, and getting good results. And yet for now the guidelines remain unchanged, and the medical and nutritional mainstream seems immovable.

What does this mean? It’s becoming increasingly clear that true change only comes from individual exploration and community activity. Following average advice from average doctors gets average results, and the average result is pretty terrible: metabolic syndrome, diabetes, cancer, heart disease, statin drugs and their side effects, and apparently unstoppable disease progression. A quarter of Americans already have Type II diabetes, and this fraction is increasing. Many more are prediabetic and don’t know it, because a formal diagnosis of Type II diabetes arrived at very late stage in the disease progression.

In contrast, people get good results quickly from going against the conventional wisdom – i.e. right after “taking the red pill”. After they save themselves, they feel good and have high energy, and they want to help unplug others from the nutritional Matrix.

Change has not come from the top-down. The medical and nutritional community has been too invested in the status quo of the past 50+ years. And too many long careers have been invested in supporting the orthodoxy of the failed diet-heart hypothesis, in opposition to the science, clinical results, and thousands of individual success stories.

No one in the prestigious medical associations will ever awkwardly apologize and say “we made a ‘little’ mistake in the 1960s, and 50 million people died of diabetes and heart disease who might have been saved … uh, sorry, I guess?”

But maybe they should.

Updates after 8 months of LCHF

As shown in the plot above, I’ve been doing this practice and lifestyle for about eight months. During the past few days, I’ve attempted to update and enhance my representation and visualization of body weight data.

In the plot, the red vertical bars represent individual daily measurements. The solid black line is a 15-day centered moving average (15d-CMA), and the dashed black lines show the 15d-CMA +/- 1 standard deviation (SD). The SD is computed over the same 15 day window of the moving average itself, and provides an estimate of measurement error.

During the four month period from Mar 1 – Jun 30, we can see a net reduction in weight of approximately 30 lbs, corresponding to a sustained average decrease of about 2 lbs per week. After this point we reach a fairly stable and sustainable plateau around 182 lbs, which is right where I want to be.

The average uncertainty in the dependent variable (measured weight) over the interval is +/- 1.5 lbs. This describes the average channel width between the dashed lines in the plot. This provides an estimate of how accurate a given daily measurement is expected to be. In other words, a short term trend of +/- a couple of pounds is just as likely to be “noise” as “signal”. Therefore, there’s minimal value in taking short term fluctuations seriously.

Lessons learned:

  • Protein leverage appears to work well in appetite suppression and making daily fasting easier. This means aiming for 25+% of daily calories from protein. Supplementation (whey protein) has helped me in reaching this target.
  • OMAD works well for me as long as I eat enough food at that one meal (e.g. at least 2000+ calories, 100+ g protein).
  • The starting point suggests that my stable weight when I eat an ad-libitum standard American diet is around 215-220 lbs. Sustainable and stable weight for me following a LCHF diet, including intermittent fasting,  looks to be in the range of 180 lbs.

I’ll keep observing the data and seeing what changes in the weeks and months ahead, but I’m pleased with the current status of things.

Lipid measurements after eight months on LCHF

Lipid measurements after 8 months (242 days) on low-carb, high-fat (LCHF) eating.

  • Total Cholesterol: 242 (mg/dL)
  • Direct LDL: 191 (mg/dL)
  • TC:HDL Ratio: 4.75
  • HDL: 51 (mg/dL)
  • Triglycerides: 93 (mg/dL)
  • TG:HDL Ratio: 1.82

These numbers have come in largely where I expected them to. The main surprise was a lower-than-expected (but still good) HDL.

My guess for total cholesterol count was 230, and so the actual measurement landed within about 5% of my guess. This is a relatively useless metric for heart disease risk, but it’s still popular, and so a lot of medical guidance continues to be based on this biomarker.

My HDL was lower than I expected it to be, but I attribute that to a lack of exercise during the past month due to a persistent cough and cold. I plan to retest in several months after resuming regular high-intensity exercise to see if that raises HDL (which would incidentally improve the TC:HDL and TG:HDL ratios as well).

The most important measurements for heart health and/or disease risk are triglycerides and TG:HDL ratio, and both of these are in the optimal (low) range.

A relatively high LDL combined with a low triglyceride measurement suggests (indirectly) the occurrence of LDL Pattern A, which is large, buoyant, non-oxidized LDL. This is more desirable than Pattern B, which refers to a preponderance of small, dense, oxidized LDL.

The relatively high LDL-P number suggests that I am a hyper-responder on a keto/LCHF diet. This result calls for further research and reading on my part.

The goal going forward is to:

  • increase HDL (mainly via exercise)
  • maintain low triglycerides
  • keep an eye on any movements LDL
  • retest in a few months

Subtraction > addition

Several months before my recent weight loss (starting in February of 2019) I began the habit of eating a healthy low-carbohydrate breakfast: an omelet with egg whites, ground sausage, and spinach.

However, this new habit did not cause weight loss. Why? The answer is simple: I didn’t change my foods outside of breakfast. I was still eating high carb food and junk food, and eating it too often. Adding a “healthy breakfast” couldn’t fix things when I was still eating unhealthy lunch, dinner, and snacks.

This further reinforced for me the lesson that you can’t “add” your way to weight loss and body fat reduction. Despite the fondest wishes of dieters and supplement manufacturers everywhere, there exists no dietary supplement that you can take that will burn body fat. Instead, you need to “remove” those influences that cause accumulation of body fat.

Removing two key factors stands far above the rest in terms of their impact:

    • Fasting (not eating) during a significant fraction of the day allows blood insulin levels to fall naturally. When this happens, the body accesses stored fat and metabolizes it for energy.
    • Avoiding carbohydrates during the time you do eat, to reduce the insulin spiking activity associated with eating and metabolizing food.

To summarize, to reduce body fat accumulation (aka “lose weight”):

  1. Spike insulin less often (through fasting).
  2. Spike insulin less strongly (through carbohydrate reduction).

In the words of Professor Miles Spencer Kimball quoting Dr Jason Fung

“obesity is always and everywhere an insulin phenomenon”

When you lower your body’s insulin response, you reduce your storage of body fat and enable use of your existing body fat as energy. Carbohydrates elicit the strongest insulin response, and thus they are the macronutrient that is most responsible for obesity. (Protein causes a weaker insulin response, and fat does not cause any insulin response at all.)

Weight loss through simple data science

I presented a technical talk at the PyOhio conference this year, describing applications of elementary data science techniques to weight loss. (As I described here, I generally prefer the term “body fat reduction”, because it’s more specific, but most people are more familiar with the term “weight loss”. So it goes.)

You can watch the video here:

My presentation slideshow and the script used to generate the data set are available on github.

The so-called “Hawthorne Effect” describes the result of an experiment in industrial engineering and management at the Hawthorne Works factory in Illinois in 1925. The result suggested that observing workers tended to alter their performance and productivity, in a positive direction.

Wikipedia describes the Hawthorne Effect as:

a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed

Suggested explanations varied widely: hypotheses include excitement that management was taking an uncommon interest in their work, and anxiety that the reason for the increased interest was planning for layoffs.

A similar effect seems to operate in the simpler case of “personal observations”, which is what I described in my talk. In this case, the “manager” and the “worker” are the same person, and the “observations” were simple, daily measurements of body weight using a smart scale for easy data logging. The “why” doesn’t matter as much as the fact that the effect seems to work, and you can use it to reach your goals.

Studies suggest that test subjects who log their meals and snacks in a food diary (“observation”) tend to experience greater weight loss (“effect”). This apparently happens even when the doctors running the study do not ask the subjects to change or limit their eating habits. Similarly, measuring weight on a daily tempo seems to generate a similar self-awareness, whether on a conscious or unconscious level. Something about the awareness that you are being observed tends to foster habit change in a desired direction.

Over time, this observation can lead a person to adopt new habits and modify old habits – both consciously and unconsciously – that cause the long-term trend line on the graph to move in the desired direction.

Weight over time (raw data and seven-day moving average)

This is definitely the case in my personal data set that I starting recording on Feb 14, 2019.

Computing the daily weight change values, or “deltas”, delivers some interesting and actionable insights. The “delta” or weight change at day i (today) is defined as delta[i] = w[i] – w[i-1]. That is, today’s weight minus yesterday’s weight. It is the answer to the question “how much did my weight change between yesterday and today?” (For best results, I measure my body weight at approximately the same time every day.)

One of the surprising things I observed is this: approximately half the time, I was gaining weight.

Even though I reduced my body weight by over 30 lbs over the interval, nearly half of the measured daily delta values are greater than zero, indicating weight gain.

As of now (2019-09-04), for N=202 observations, the breakdown is:

  • 95 days increasing weight
  • 95 days decreasing weight
  • 12 days with no change

Daily deltas over time

The graph of delta versus time shows this clearly. With an apparently random mix of increases and decreases, it’s very hard to tell from this plot alone whether it adds up to a net gain or loss. If you add it up, the numbers are clear: the total gain is about 60 lbs and the total reduction is about 90 lbs, adding up to a net reduction of around 30 lbs.

Similarly, the histogram showing the distribution of deltas shows no obvious skew or asymmetry toward weight gain (right side) or or weight loss (left side).

Histogram of daily deltas

What we can learn from this is that:

  • very short-term (daily) weight changes bear little or no relation to the long term trend (monthly)
  • a long-term decrease in body weight contains many days during which a weight gain occurs (and vice versa)

In other words, nobody becomes obese overnight, and nobody drops 50 lbs of body fat overnight either. These changes take place over the long term, in response to changes in food composition and quantity, hormone levels, activity levels, and other inputs.

The practical lesson seems to be that there’s no point in feeling joy over a 2 lb drop in the number on the scale, or misery over a 2 lb rise. As hard as it may be to believe in the moment, a one-day increase or decrease appears to be absolutely meaningless in its implications for long-term weight change.

It’s a real challenge for many people to disconnect their emotions from the random daily fluctuations of the number on the scale. However, seeing today’s “number” in the context of historical numbers is a great way of keeping a broad perspective: does it matter if the body weight went from 181 to 183 lbs today if the starting point was 211 lbs?

Another advantage of collecting long-term data like this is that it enables you to run experiments and to catch and observe trends before they become a problem. Without the data recorded and plotted, it’s unlikely that you would make the connection between (e.g.) experimentally adding a new food, and a slow rise in body weight over three weeks.

Perhaps you think that adding food X or removing habit Y might give good results. By running an experiment, perhaps for two weeks or thirty days, and making a change (“input”), you can observe the result (“output”). Of course, to make this work, you need to keep other input variables as constant as possible. If you change three inputs at the same time, it’s very hard to isolate which one had an influence on the output.

The conclusion is that long-term change in body weight, in one direction, is made up of lots of small daily changes, in both directions. The data is very noisy. Therefore, the weight change on a random day has very little to do with either the long-term trend, or the endpoint. Accumulating a body of data over time is a great way to create an objective and impersonal reference about a body metric like weight. Human memory is ineffective and subject to revision and distortion, whereas recorded data is far less likely to lie. Tracking your body measurements is a powerful way for you to observe change and to drive it.

Recent updates on diet and body composition

 

Body weight measurements from Feb 2019 to April 2019. 5 day moving average with +/- 1 sigma channels to estimate measurement error.

I had a “goal weight” of 190 lbs, and I crossed that milestone recently, as the plot above shows.

My revised goal is to reach 12% body fat. The challenge with a body fat goal is that it’s a much harder measurement to perform at home. Of course, the real goal of most people who want to “lose weight” is to preferentially burn body fat and build (or maintain) muscle.

Body weight is what we can measure, at least cheaply and easily, but body fat is what we want to measure. Yes, technically, we can measure body fat as well, but not as cheaply or easily.

With all that said, I no longer care about the number on the scale. The main goals are to:

  • get stronger;
  • feel stronger, and
  • look stronger in the mirror.

I place the most emphasis on the first point: getting physically stronger as measured objectively in the size of the weights I can lift and move around. If this increases body weight by adding muscle, that’s great.

One of the great things for a person eating a low-carb / high-fat (LCHF) diet is that there’s no subjective experience of deprivation or hunger. Within certain specific constraints, you eat what you want, when you want, without counting calories.

It really feels like eating this way works in alignment with what we know about science and biochemistry, rather than against it. The “traditional” weight-loss diet – at least “traditional” since the 1970s – consists of minimal fat, lots of carbohydrates, and calorie restriction. This kind of weight-loss diet always seems to have people counting “points” or calories permanently, and always feeling hungry. It also seems to keep people coming back for more when the first attempt stops working – first Weight Watchers, then Jenny Craig, then NutriSystem. I have no idea whether these are all still a thing, but I remember that they were popular when I was a kid – TV advertising, etc [1].

Some subjective anecdotes:

  • Still paleo, still low-carb: everything I’ve written before continues to apply.
  • Protein-fat shakes: I’ve observed that a protein shake consisting of two scoops of whey protein (60g) and one liquid ounce of heavy whipping cream is extremely filling. As in, “not thinking about eating for five hours” level of filling. This is surprising for a small “meal” of 350 calories or so.
    • Protein leverage: I’ve been doing some reading about the protein leverage hypothesis of satiety, and it seems to work for me (N=1, anecdotes). Briefly, the protein leverage hypothesis suggests that the subjective feeling of satiety or fullness is driven by protein as a macronutrient [2]. Therefore, by this hypothesis, if you eat high-protein foods early in the day, you will eat fewer calories than if you eat low-protein foods. Why? The hypothesis suggests that people experience hunger until they meet their body’s protein needs. If they never meet those needs, then they may just continue snacking (on high-carb and/or high-fat foods) and never really feel full.
    • Reduced insulin response: Blogger and economist Miles Spencer Kimball argues persuasively that dieting is most effective when it minimizes the insulin response of the foods you eat. This is the rationale behind adding the heavy whipping cream – by adding 10g / 150 cal or so of fat, you further slow down the body’s insulin response to the protein [3]. Mixing with fat probably also converts the protein bolus into a “timed release” dose, further improving satiety and tapering the insulin response over time. That’s a fancy way of saying that the fat slows down the digestion of the protein and makes the insulin response even more gradual.
    • The above representation a lot of hand-waving but ultimately the proof is in the observed and experienced results. I find a couple of things very surprising:
      • (1) The flavor of the shakes is so good with the cream (think, high-end chocolate ice cream) that I worry I will make another, and then another, and then another. This is what Whole 30 people call the “sugar dragon” – the urge to engage in compulsive behavior around sweet junk food. But I never feel like I “have to” eat more after finishing one of these protein shakes.
      • (2) This recipe switches off hunger in a way that I’ve never experienced before – for the better part of a day. This makes the practice of intermittent fasting even easier.

[1] Update: apparently they still exist. At a client site the other day, they have TVs in the cafeteria. I saw both a Jenny Craig and NutriSystem ad during lunch.

[2] Some sources suggest that a relatively small reduction in the protein fraction of the American diet, from 14% to 12%, may have created the obesity epidemic. Hence, protein leverage. References: Obesity: the protein leverage hypothesis; Testing the Protein Leverage Hypothesis in a free-living human population.

[3] Roughly speaking, metabolism of pure dietary fat causes no insulin response, pure protein causes a moderate insulin response, and pure carbohydrate causes a high insulin response. All natural foods are a blend of fat, protein, and carb, and this makes things more complex.

DogFoodCon 2019 CFP is open!

DogFoodCon (DFC) is a popular conference that draws speakers and attendees from the Great Lakes region and across the nation. DFC has experienced sustained growth and popularity since the inaugural conference back in 2008.

Taking place in Columbus, Ohio on the weekend of October 3-4, 2019, DFC is a two-day event that covers a broad range of topics of interest to programmers, technology managers, business analysts, and other technology workers. Presentation topics include: personal and career development, Ruby, Python, Adobe Creative Suite, C#, UX, Office 365, System Administration for Linux and Windows, JavaScript/HTML/CSS, R, Azure, Java, MS Teams, PostrgeSQL, JavaScript, PowerBI, SQL, Security, Blockchain and GitHub.

No matter where you are on or off the tech stack, we know that you’ll meet some great people and learn something new!

I’m delighted to be involved with DFC this year, as the track owner for dynamic languages (Ruby and Python).

If the above sounds interesting to you, I encourage you to submit a talk (or several): DFC Call for Presentations (Sessionize link)