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.
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”):
Spike insulin less often (through fasting).
Spike insulin less strongly (through carbohydrate reduction).
In the words of Professor Miles Spencer Kimball quoting Dr Jason Fung
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.)
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.
Official conference videos will follow, with live speaker action as well as screen recording. However, I had a glitchy radio microphone, so the audio quality of this presentation is likely to be better.
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:
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 .
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 . 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 . 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.
 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.
 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 (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.
No matter where you are on or off the tech stack, we know that you’ll meet some great peopleand learn something new!
I’m delighted to be involved with DFC this year, as the track owner for dynamic languages (Ruby and Python).
For the first time in quite a while – since April, 2018 – my body weight has dropped below 200 lbs. I’m pleased with this milestone and wanted to make a note about what techniques appear to be working:
Intermittent fasting. A minimum of 16:8 but usually closer to 18:6. That’s 16 hours “fasting” and 8 hours “eating”. In practice, during a typical day this corresponds to eating lunch after 1100 and eating dinner between 1700 and 1800 before stopping eating in the evening (no snacking).
Don’t drink calories. Beverages include black coffee, water, tea (herbal or caffeinated). In practice, most of what I drink is coffee and water.
“Paleo” slash “keto” slash “low carb” focus.
Eat a lot of meat (including poultry and fish), eggs, and non-starchy vegetables like spinach, kale, cabbage, and so forth.
Use natural fats like butter, coconut oil, avocado oil. Avoid industrial seed oils like canola, soybean, cottonseed, and so forth (too high in Omega-6).
Eat some starchy vegetables like carrots, sweet potato, and so forth, but not too much.
A little bit of fun snack food like fresh fruit, nuts, and dates seems to work for me but I aim to avoid going overboard with these kind of foods. They are so calorie-dense, non-filling (“low satiety”), and habit-forming. I also aim to avoid adding excess Omega-6 fats through too many nuts.
For the first time ever, I’ve supplemented with whey protein powder.
Technically, this violates the “don’t drink calories” recommendation, but this is a dietary supplement rather than a beverage, so I’m making a specific exception. (Same exception applies to cod liver oil – yes, you’re “drinking calories” but let’s not be crazy here.)
One rationale is that if a 200 calorie protein shake crowds out 500 calories of snacking, then I’m still ahead. Some science suggests that satiety (subjective feeling of fullness) is guided by protein intake.
Research also suggests that whey protein helps preferentially spare muscle mass during body weight reduction. Subjectively, this practice does seem to reduce feelings of hunger and increase feelings of fullness during the day. I’m running a 30 day experiment for this and will review my observations at the end of the trial period.
This experiment is largely based on new (for me) learnings about whey protein presented in Best Supplements for Men, by P. D. Mangan. I highly recommend the book for its other insights and teachings as well. Mr. Mangan wrote it very well and very clearly.
When I talk and write about this subject, I get pedantic and specific about terminology. In particular, I avoid using the term “weight loss” for two main reasons:
First, the real goal is to reduce body fat or fat mass, not just weight. It’s important to be specific about this. Nobody wants to reduce bone mass or muscle mass or water mass. If you reduce your body mass by 20 pounds, and 19 of those 20 pounds are muscle, you may have seriously damaged your health.
Second, the word loss tends to be universally associated with negative things in people’s minds – “I lost money” or “I’m so sorry for your loss”. Most things that people “lose”, they don’t feel good about. It’s just not a very good word to use in association with a health-oriented goal. Body fat reduction is a win, not a loss.
Therefore, instead of the common, but misguided, term weight loss, I use the preferred and more specific term body fat reduction.