New paper in Molecular Psychiatry

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

S. Fleur W. Meddens, et al.

Molecular Psychiatry (2020) Cite this article

Abstract

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10⁻⁸), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10⁻⁵) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r_g ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r_g| ≈ 0.1–0.3) and positive genetic correlations with physical activity (r_g ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r_g ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

The low carb war continues

Last month, a paper in the British Journal of Medicine on the effect of low carb diets on energy expenditure, with senior author David Ludwig, made a big splash in the popular press and also instigated a mini-Twitter war. The study, which cost somewhere in the neighborhood of 12 million dollars, addressed the general question of whether a person will burn more energy on a low carbohydrate diet compared to an average or high carb diet. In particular, the study looked at the time period after weight loss where people are susceptible to regaining weight. The argument is that it will be easier to maintain weight loss on a low carb diet since you will be burning more energy. Recent intensive studies by my colleague Kevin Hall and others have found that low carb diets had little effect if any on energy expenditure, so this paper was somewhat of a surprise and gave hope to low carb aficionados. However, Kevin found some possible flaws, which he points out in an official response to BMJ and a BioRxiv paper, which then prompted a none-too-pleased response from Ludwig, which you can follow on Twitter. The bottom line is that the low carb effect size depends on the baseline point you compare too. In the original study plan, the baseline point was chosen to be energy expenditure prior to the weight loss phase of the study. In the publication, the baseline point was changed to after the weight loss but before the weight loss maintenance phase. If the original baseline was chosen, the low carb effect is no longer significant. The authors claim that they were blinded to the data and changed the baseline for technical reasons so this did not represent a case of p-hacking where one tries multiple combinations until something significant turns up. It seems pretty clear to me that low carbs do not have much of a metabolic effect but that is not to say that low carb diets are not effective. The elephant in the room is still appetite. It is possible that you are simply less hungry on a low carb diet and thus you eat less. Also, when you eliminate a whole category of food, there is just less food to eat. That could be the biggest effect of all.

New Papers

Li, Y., Chow, C. C., Courville, A. B., Sumner, A. E. & Periwal, V. Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test. Theoretical Biology and Medical Modelling 1–20 (2016). doi:10.1186/s12976-016-0036-3

Katan, M. B. et al. Impact of Masked Replacement of Sugar-Sweetened with Sugar-Free Beverages on Body Weight Increases with Initial BMI: Secondary Analysis of Data from an 18 Month Double–Blind Trial in Children. PLoS ONE 11, e0159771 (2016).

These two papers took painfully long times to be published, which was completely perplexing and frustrating given that they both seemed rather straightforward and noncontroversial. The first is a generalization of our previously developed minimal model of the fatty acid and glucose as a function of insulin to a response to an ingested meal, where the rate of appearance of fat and glucose in the blood was modeled by an empirically determined time dependent function. The second was a reanalysis of the effects of substituting sugar-sweetened beverages with non-sugar ones. We applied our childhood growth model to predict what the children ate to account for their growth. Interestingly, what we found is that the model predicted that children with higher BMI are less able to compensate for a reduction of calories than children with lower BMI. This could imply that children with higher BMI have a less sensitive caloric sensing system and thus could be prone to overeating but on the flip side, can also be “tricked” into eating less.

The hazards of being obese

One of my favourite contrarian positions is that being overweight is not so bad. I don’t truly believe this but I like to use it to point out that although most everyone holds that being obese is not healthy, there is actually very little evidence to support this assertion. However, this recent rather impressive paper in the Lancet finally shows that being overweight or obese is really bad. The paper is a meta-analysis of hundreds of studies with a combined study size of over 10 million! The take home message is that the hazard ratio for dying is significantly greater than one but not too bad for overweight and mildly obese people (BMI < 30) but increases sharply after that. It is over two and rapidly increasing for BMI greater than 35. The hazard ratio gives the relative probability of mortality (or any outcome) per unit time (i.e. mortality rate) in a survival analysis, which in this case was a Cox proportional hazards model. The hazard ratio as a function of BMI is well fit by a quadratic function with a minimum around 22 kg/m^2. The chances of dying increase if you are thinner or fatter than this. The study was careful to not include smokers and anyone with a chronic disease and also did not start the analysis until 5 years after the measurement to avoid capturing people who are thin because they are already ill. They also broke the model down into various regions. Surprisingly, the chances of dying when you are obese is worse if you are in Europe or North America compared to Asia. Particularly surprising is the fact that the hazard ratio rises slowest in South Asia for increasing BMI. South Asians have been found to be more susceptible to insulin resistance and Type II diabetes with increased body fat but it seems that they die from it at lower rates. However, the error bars were also very large because the sample size was smaller so this may not hold up with more data. In any case, I can no longer use the lack of health consequences of obesity to rib my colleagues so I’ll have to find a new axe to grind.

Low carb diet study paper finally out

Kevin Hall’s long awaited paper on what I dubbed “the land sub” experiment, where subjects were sequestered for two months, is finally in print (see here). This was the study funded by Gary Taube’s organization Nusi. The idea was to do a fully controlled study comparing low carb to a standard high carb diet to test the hypothesis that high carbs lead to weight gain through increased insulin. See here for a summary of the hypothesis. The experiment showed very little effect and refutes the carbohydrate-insulin model of weight gain. Kevin was so frustrated with dealing with Nusi that he opted out of any follow up study. Taubes did not support the conclusions of the paper and claimed that the diet used (which Nusi approved) wasn’t high enough in carbs. This is essentially positing that the carb effect is purely nonlinear – it only shows up if you are just eating white bread and rice all day. Even if this were true it would still mean that carbs could not explain the increase in average body weight over the past three decades since there is a wide range of carb consumption over the general population. It is not as if only the super carb lovers were getting obese. There were some weird effects that warrant further study. One is that study participants seemed to burn 500 more Calories outside of a metabolic chamber compared to inside. This was why the participants lost weight on the lead-in stabilizing diet. These missing Calories far swamped any effect of macronutrient composition.

Two new papers

Pradhan MA1, Blackford JA Jr1, Devaiah BN2, Thompson PS2, Chow CC3, Singer DS2, Simons SS Jr4.  Kinetically Defined Mechanisms and Positions of Action of Two New Modulators of Glucocorticoid Receptor-regulated Gene Induction.  J Biol Chem. 2016 Jan 1;291(1):342-54. doi: 10.1074/jbc.M115.683722. Epub 2015 Oct 26.

Abstract: Most of the steps in, and many of the factors contributing to, glucocorticoid receptor (GR)-regulated gene induction are currently unknown. A competition assay, based on a validated chemical kinetic model of steroid hormone action, is now used to identify two new factors (BRD4 and negative elongation factor (NELF)-E) and to define their sites and mechanisms of action. BRD4 is a kinase involved in numerous initial steps of gene induction. Consistent with its complicated biochemistry, BRD4 is shown to alter both the maximal activity (Amax) and the steroid concentration required for half-maximal induction (EC50) of GR-mediated gene expression by acting at a minimum of three different kinetically defined steps. The action at two of these steps is dependent on BRD4 concentration, whereas the third step requires the association of BRD4 with P-TEFb. BRD4 is also found to bind to NELF-E, a component of the NELF complex. Unexpectedly, NELF-E modifies GR induction in a manner that is independent of the NELF complex. Several of the kinetically defined steps of BRD4 in this study are proposed to be related to its known biochemical actions. However, novel actions of BRD4 and of NELF-E in GR-controlled gene induction have been uncovered. The model-based competition assay is also unique in being able to order, for the first time, the sites of action of the various reaction components: GR < Cdk9 < BRD4 ≤ induced gene < NELF-E. This ability to order factor actions will assist efforts to reduce the side effects of steroid treatments.

Li Y, Chow CC, Courville AB, Sumner AE, Periwal V. Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test. Theor Biol Med Model. 2016 Mar 2;13:8. doi: 10.1186/s12976-016-0036-3.

Abstract:
BACKGROUND:
Quantitative evaluation of insulin regulation on plasma glucose and free fatty acid (FFA) in response to external glucose challenge is clinically important to assess the development of insulin resistance (World J Diabetes 1:36-47, 2010). Mathematical minimal models (MMs) based on insulin modified frequently-sampled intravenous glucose tolerance tests (IM-FSIGT) are widely applied to ascertain an insulin sensitivity index (IEEE Rev Biomed Eng 2:54-96, 2009). Furthermore, it is important to investigate insulin regulation on glucose and FFA in postprandial state as a normal physiological condition. A simple way to calculate the appearance rate (Ra) of glucose and FFA would be especially helpful to evaluate glucose and FFA kinetics for clinical applications.
METHODS:
A new MM is developed to simulate the insulin modulation of plasma glucose and FFA, combining IM-FSIGT with a mixed meal tolerance test (MT). A novel simple functional form for the appearance rate (Ra) of glucose or FFA in the MT is developed. Model results are compared with two other models for data obtained from 28 non-diabetic women (13 African American, 15 white).
RESULTS:
The new functional form for Ra of glucose is an acceptable empirical approximation to the experimental Ra for a subset of individuals. When both glucose and FFA are included in FSIGT and MT, the new model is preferred using the Bayes Information Criterion (BIC).
CONCLUSIONS:
Model simulations show that the new MM allows consistent application to both IM-FSIGT and MT data, balancing model complexity and data fitting. While the appearance of glucose in the circulation has an important effect on FFA kinetics in MT, the rate of appearance of FFA can be neglected for the time-period modeled.

Kevin Hall in the NY Times

Watch Kevin Hall talking about his research on weight regain in the “Biggest Loser” show participants.  I was kicked out of my office during the shoot (my office is next to Kevin’s) for making too much noise (I was having a heated discussion with my fellows).  Here is the accompanying article.

http://graphics8.nytimes.com/video/players/offsite/index.html?videoId=100000004377076

Talk at Imperial College

I’m currently visiting the MRC Clinical Sciences Centre of Imperial College, London to give a talk on my obesity work. The slides are here.

Paper on the effect of food intake fluctuations on body weight

Chow, C. C. & Hall, K. D. Short and long-term energy intake patterns and their implications for human body weight regulation. Physiology & Behavior 134:60–65 (2014). doi:10.1016/j.physbeh.2014.02.044

Abstract: Adults consume millions of kilocalories over the course of a few years, but the typical weight gain amounts to only a few thousand kilocalories of stored energy. Furthermore, food intake is highly variable from day to day and yet body weight is remarkably stable. These facts have been used as evidence to support the hypothesis that human body weight is regulated by active control of food intake operating on both short and long time scales. Here, we demonstrate that active control of human food intake on short time scales is not required for body weight stability and that the current evidence for long term control of food intake is equivocal. To provide more data on this issue, we emphasize the urgent need for developing new methods for accurately measuring energy intake changes over long time scales. We propose that repeated body weight measurements can be used along with mathematical modeling to calculate long-term changes in energy intake and thereby quantify adherence to a diet intervention and provide dynamic feedback to individuals that seek to control their body weight.

The world of Gary Taubes

Science writer Gary Taubes has a recent New York Times commentary criticizing Kevin Hall’s recent paper on the differential metabolic effects of low fat vs low carbohydrate diets. See here for my recent post on the experiment. Taubes is probably best known for his views on nutrition and as an advocate for low carb diets although he has two earlier books on the sociology of physics. The main premise running through his four books is that science is susceptible to capture by the vanity, ambition, arrogance, and plain stupidity of scientists. He is pro-science but anti-scientist.

His first book on nutrition – Good Calories, Bad Calories, was about how the medical establishment and in particular nutritionists have provided wrong and potentially dangerous advice on diets for decades. He takes direct aim at Ancel Keys as one of the main culprits for pushing the reduction of dietary fat to prevent heart disease. The book is a great read and clearly demonstrates Taubes’s sharp mind and gifts as a story teller. In the course of researching the book, Taubes also discovered the biological mechanisms of insulin and this is what has mostly shaped his thinking about carbohydrates and obesity. He spells it out in more detail in his subsequent book – Why We Get Fat. I think that these two books are a perfect demonstration of why having a little knowledge and a high IQ can be a dangerous thing.

Most people know of insulin as the hormone that goes awry in diabetes. When we fast, our insulin levels are low and our body, except for our brain, burns fat. If we then ingest carbohydrates, our insulin levels rise, which induces our body to utilize glucose (the main source of fuel in carbs) in favour of insulin. Exercise will also cause a switch in fuel choice from fat to glucose. What is less well known is that insulin also suppresses the release of fat from fat cells (adipocytes), which is something I have modeled (see here). This seems to have been a revelation to Taubes – Clearly, if you eat lots of carbs, you will have lots of insulin, which will sequester fat in fat cells. Ergo, eating carbs makes you fat! Nutritionists were so focused on their poorly designed studies that they missed the blatantly obvious. This is just another example of how arrogant scientists get things wrong.

Taubes then proposed a simple experiment – take two groups of people and put one group on a high carb diet and the other on a low carb diet with the same caloric content, and see who loses weight. Well, Kevin Hall anticipated this request with basically the same experiment although for a different purpose. What Kevin noticed in his model was that if you cut carbs and keep everything else the same, insulin goes down and the body responds by burning much more fat. However, if you cut fat, there is nothing in the model that told the body that the fat was missing. Insulin didn’t change and thus the body just burned the same amount of carbs as before. He found this puzzling. Surely there must be a fat detector that we don’t know about so he went about to test it. I remember he and his fellows labouring diligently for what seemed like years writing the protocol and getting the necessary approval and resources to do the experiment. The result was exactly as the model predicted. We really don’t have a fat sensor. However, the subjects lost more fat on the low fat diet then they did on the low carb diet.  This is not exactly the experiment Taubes wanted to do, which was to change the macronutrient composition but keep the calories the same. He then hypothesized that those on the low carb diet would lose weight and those on the low fat, high carb diet would gain weight. Kevin and a consortium of top obesity researchers has since done that experiment and the results will come out shortly.

Now is this surprising? Well not really, for while Taubes is absolutely correct in that insulin suppresses fat utilization the net outcome of insulin reduction is a quantitative and not a qualitative question. You cannot deduce the outcome with formal logic. The reason is that insulin cannot be elevated all the time. Even a continuous grazer must sleep at some point where upon insulin falls. You then must consider the net effect of high and low insulin over a day or longer to assess the outcome. This can only be determined empirically and this is what Taubes fails to see or accept. He also commits a logical fallacy –  Just because a scientist is stupid doesn’t mean he is wrong.

Taubes’s recent commentary criticizes Kevin’s experiment by saying that it 1) is a diet that is impossible to follow and 2) it ignores appetite. The response to the first point is that the experiment was meant to test a metabolic hypothesis and was not meant to test the effect of a diet. My response to his second point is to stare agape. When Taubes visited NIH a few years ago after his Good Calories, Bad Calories book came out I offered the hypothesis that low carb diets could suppress appetite and this could be why they may be effective in reducing weight. However, he had no interest in this idea and Kevin has told me that he has repeatedly shown no interest in it. (I don’t need to give details on how people have been interested in appetite for decades since it is well done in this post.) I came to the conclusion that appetite control was the primary driver of the obesity epidemic shortly after arriving at NIH. In fact my first BSC presentation was on this topic. The recommendation by the committee was that I should do something else and that NIH was a bad fit for me. However, I am still here and I still believe appetite control is the key.

A calorie is a calorie (more or less) after all

Just out in Cell Metabolism is Kevin Hall’s most recent paper that shows that low carb diets have no metabolic advantage over a low fat diet. In the experiment, a group of 19 individuals spent 22 days in total in a metabolic ward where their diet was completely specified and metabolic parameters were carefully measured. The individuals were put on both isocaloric carbohydrate reduced diets and fat reduced diets where the order of the diets was randomized over subjects. The short version of the result was that those on the fat reduced diets had more fat loss than the carbohydrate reduced diet although the cumulative difference was small. The body composition changes and metabolic parameters are also matched by the detailed NIDDK body weight model. You most certainly do not lose more fat on a low carb diet.

The results do show that a calorie is not exactly a calorie meaning that the macronutrient composition of the food you eat can matter although over long time periods the body weight model does show that macronutrient differences will always be small. Ultimately, if you want to lose fat, you should eat less and exercise more (in that order). It’s your choice in how you want to reduce your calories. If you like to go low carb then by all means do that. If you like low fat then do that too. You’ll lose weight and fat on both diets. The key is to stick to your diet.

This experimental result is in direct contradiction to the argument of low carb aficionados like Gary Taubes who claim that reducing carbs are particularly beneficial for losing weight and vice versa. Their reasoning is that carbs induce insulin, which suppresses lypolysis from fat cells. Hence, if you ate carbs all the time, your fat would get locked away in adipocytes forever and you would become very fat. However, the problem with this type of reasoning is that it doesn’t account for the fact that no one eats for 24 hours each day. Even the most ardent grazer must sleep at some point and during that time insulin will fall and fat can be released from fat cells. Thus, what you need to do is to account for the net flux of fat over the entire 24 hour cycle and possibly even longer since your body will also adapt to whatever your diet happens to be. When you do that it turns out that you will lose more fat if you reduce fat.

Now this was only for a diet of 6 days but experiments, funded by Gary Taubes’s organization, for longer time scales comparing the two diets have been completed and will be published in the near future. I’ll summarize the results when they come out. I can’t say what the preliminary results are except to remind you that the model has held up pretty well in past.

What is wrong with obesity research

This paper in Nature Communications 14-3-3ζ Coordinates Adipogenesis of Visceral Fat has garnered some attention in the popular press. It is also a perfect example of what is wrong with the way modern obesity research is conducted and reported. This paper finds a protein that regulates adipogenesis or fat cell production. I haven’t gone into details of the results but let’s just assume that it is correct. The problem is that the authors and the press then make the statement that this provides a possible drug target for obesity. Why is this a problem? Well consider the analogy with a car. The gas tank represents the adipocytes, – it is the store of energy. Now, you find a “gene” that shrinks the gas tank and then publish in Nature Automobiles and the press release states that that you have found a potential treatment for car obesity. If it is really true that the car (mouse) still takes in the same amount of petrol (food) as before, then where did this excess energy go? The laws of thermodynamics must still hold. The only possibilities are that your gas mileage went down (energy expenditure increased) or the energy is being stored in some other auxiliary gas tank (liver?). A confounding problem is that rodents have very high metabolic rates compared to humans. They must eat a significant fraction of their body weight each day just to stay alive. Deprive a mouse or rat of food for a few days and it will expire. The amount of energy going into fat storage per day is a small amount by comparison. It is difficult to measure food intake precisely enough to resolve whether or not two rats are eating the same thing and most molecular biology labs are not equipped to make these precise measurements nor understand that they are necessary. One rat needs to only eat more by a small amount to gain more weight. If two cars (mice) grow at different weights then the only two possible explanations is that they have different energy expenditures or they are eating different amounts. Targeting the gas tank (adipocytes) simply does not make sense as a treatment of obesity. It might be interesting from the point of view of understanding development or even cancer but not weight gain. I have argued in the past that if you find that you have too much gas in the car then the most logical thing to do is to put less gas in the car, not to drive faster so you burn up the gas. If you are really interested in understanding obesity, you should try to understand appetite and satiety because that has the highest leverage for affecting body weight.

Have we crossed peak food?

The New York Times has an article today describing the decrease in food consumption over the past decade.  Here is one primary reference. I used to joke that the obesity epidemic would eventually be curbed by either a huge increase in oil prices or a depression. The great recession of 2008 made be believe that food consumption would come down but the data shows that it may have been dropping earlier and mostly in families with children.  The biggest decrease is in sugar sweetened beverages.

Here’s Kevin’s mention:

The recent calorie reductions appear to be good news, but they, alone, will not be enough to reverse the obesity epidemic. A paper by Kevin Hall, a researcher at the National Institutes of Health, estimated that for Americans to return to the body weights of 1978 by 2020, an average adult would need to reduce calorie consumption by 220 calories a day. The recent reductions represent just a fraction of that change.

The body weight planner

Test the new USDA Body Weight Planner, based on the LBM model.

New paper on global obesity

We have a new paper out in the World Health Organization Bulletin looking at the association between an increase in food supply and average weight gain:

Stefanie Vandevijvere, Carson C Chow, Kevin D Hall, Elaine Umali & Boyd A Swinburn. Increased food energy supply as a major driver of the obesity epidemic: a global analysis, Bulletin of the WHO 2015;93:446–456.

This paper extends the analysis we did in our paper on the US food supply to the rest of the world. In the US paper, we showed that an increase in food supply more than explains the increase in average body weight over the duration of the obesity epidemic, as predicted by our experimentally validated body weight model. I had been hoping to do the analysis on the rest of the world and was very happy that my colleagues in Australia and New Zealand were able to collate the global data, which was not a simple undertaking.

What we found was almost completely consistent with the hypothesis that food is the main driver of obesity everywhere. In more than half of the countries (45/83), the increase in food supply more than explains the increase in weight. In other mostly less developed nations (11/83), an increase in food was associated with an increase in body weight although it was not sufficient to explain all of the weight gain. Five countries had a decrease in both food and body weight. Five countries had decreases in food supply and an increase in body weight and finally three countries (Iran, Rwanda, and South Africa) had an increase in food but a decrease in body weight.

Now by formal logic, only one of these observations is inconsistent with the food push hypothesis. Recall that if A implies B then the only logical conclusion you can draw is that not B implies not A. Hence, if we hypothesize that increased food causes increased obesity then that means if we see no obesity then that implies no increase in food. Thus only three countries defied our hypothesis and they were Iran, Rwanda, and South Africa where obtaining accurate data is difficult.

The five countries that had a decrease in food but an increase in body weight do not dispute our hypothesis. They just show that increased food is not necessary, which we know is true. Decreased activity could also lead to increased weight and it is possible that this played a role in these countries and the 11 others where food was not sufficient to explain all of the weight increase.

I was already pretty convinced that food was the main driver of the obesity epidemic and this result puts it to rest for me. This is the main reason that I don’t believe that the obesity epidemic is a health problem per se. It is a social and economic problem.

Journal Club

Here’s the paper I will be covering in Journal Club tomorrow:

Neurons for hunger and thirst transmit a negative-valence teaching signal

• J. Nicholas Betley, Shengjin Xu, Zhen Fang Huang Cao, Rong Gong, Christopher J. Magnus, Yang Yu, & Scott M. Sternson
• Nature 521, 180–185(14 May 2015)doi:10.1038/nature14416

Abstract

Homeostasis is a biological principle for regulation of essential physiological parameters within a set range. Behavioural responses due to deviation from homeostasis are critical for survival, but motivational processes engaged by physiological need states are incompletely understood. We examined motivational characteristics of two separate neuron populations that regulate energy and fluid homeostasis by using cell-type-specific activity manipulations in mice. We found that starvation-sensitive AGRP neurons exhibit properties consistent with a negative-valence teaching signal. Mice avoided activation of AGRP neurons, indicating that AGRP neuron activity has negative valence. AGRP neuron inhibition conditioned preference for flavours and places. Correspondingly, deep-brain calcium imaging revealed that AGRP neuron activity rapidly reduced in response to food-related cues. Complementary experiments activating thirst-promoting neurons also conditioned avoidance. Therefore, these need-sensing neurons condition preference for environmental cues associated with nutrient or water ingestion, which is learned through reduction of negative-valence signals during restoration of homeostasis.

Optimizing food delivery

This Econtalk podcast with Frito-Lay executive Brendan O’Donohoe from 2011 gives a great account of how optimized the production and marketing system for potato chips and other salty snacks has become. The industry has a lot of very smart people trying to figure out how to ensure that you maximize food consumption from how to peel potatoes to how to stack store shelves with bags of chips. This increased efficiency is our hypothesis (e.g. see here) for the obesity epidemic. However, unlike before where I attributed the increase in food production to changes in agricultural policy, I now believe it is mostly due to the vastly increased efficiency of food production. This podcast shows the extent of the optimization after the produce leaves the farm but the efficiency improvements on the farm are just as dramatic. For example, farmers now use GPS to optimally line up their crops.

New paper on childhood growth and obesity

Kevin D Hall, Nancy F Butte, Boyd A Swinburn, Carson C Chow. Dynamics of childhood growth and obesity: development and validation of a quantitative mathematical model. Lancet Diabetes and Endocrinology 2013 .

You can read the press release here.

In order to curb childhood obesity, we need a good measure of how much food kids should eat. Although people like Claire Wang have proposed quantitative models in the past that are plausible, Kevin Hall and I have insisted that this is a hard problem because we don’t fully understand childhood growth. Unlike adults, who are more or less in steady state, growing children are a moving target. After a few fits and starts we finally came up with a satisfactory model that modifies our two compartment adult body composition model to incorporate growth. That previous model partitioned excess energy intake into fat and lean compartments according to the Forbes rule, which basically says that the ratio of added fat to lean is proportional to how much fat you have so the more fat you have the more excess Calories go to fat. The odd consequence of that model is that the steady state body weight is not unique but falls on a one dimensional curve. Thus there is a whole continuum of possible body weights for a fixed diet and lifestyle. I actually don’t believe this and have a modification to fix it but that is a future story.

What puzzled me about childhood growth was how do we know how much more to eat as we grow? After some thought, I realized that what we could do is to eat enough to maintain the fraction of body fat at some level, using leptin as a signal perhaps, and then tap off the energy stored in fat when we needed to grow. So just like we know how much gasoline (petrol) to add by simply filling the tank when it’s empty, we simply eat to keep our fat reserves at some level. In terms of the model, this is a symmetry breaking term that transfers energy from the fat compartment to the lean compartment. In my original model, I made this term a constant and had food intake increase to maintain the fat to lean ratio and showed using singular perturbation theory that his would yield growth that was qualitatively similar to the real thing. This then sat languishing until Kevin had the brilliant idea to make the growth term time dependent and fit it to actual data that Nancy Butte and Boyd Swinburn had taken. We could then fit the model to normal weight and obese kids to quantify how much more obese kids eat, which is more than previously believed. Another nice thing is that when the child stops growing the model is automatically the adult model!

Body weight simulator iPhone app

The body weight simulator, originally a web based java application, is now also an iPhone app (see here in iTunes).  The simulator is based on the human metabolism model developed by Kevin Hall, myself, and collaborators.  The exact model is given in detail in our Lancet paper, which is listed here along with other related references.  The app predicts the time course of your body weight given your baseline parameters and your new diet and/or new physical activity.  It will also give a suggested daily caloric intake to attain a new weight over a specified period of time along with the diet required to maintain that weight.  The model uses parameters calibrated to the average American so your own mileage will vary.  Also, I basically wrote the app in my spare time over the past year so it is pretty primitive as far as apps go but it does the job.  Please try it out and give me feedback.

New paper on fat

Sex-Associated Differences in Free Fatty Acid Flux of Obese Adolescents.

Diane C Adler-Wailes, Vipul Periwal, Asem H Ali, Sheila M Brady, Jennifer R McDuffie, Gabriel I Uwaifo, Marian Tanofsky-Kraff, Christine G Salaita, Van S Hubbard, James C Reynolds, Carson C Chow, Anne E Sumner, Jack A Yanovski

Section on Growth and Obesity (D.C.A.-W., A.H.A., S.J.R.M., G.I.U., M.T.-K., J.A.Y.), Program in Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development; Mathematical Cell Modeling Section (V.P., C.C.C.), Division of Extramural Activities (C.G.S.), Division of Nutrition Research Coordination (V.S.H.), and Laboratory of Endocrinology and Receptor Biology (A.E.S.), National Institute of Diabetes and Digestive and Kidney Diseases; and Nuclear Medicine Department (J.C.R.), Hatfield Clinical Research Center, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland 20892.

The Journal of clinical endocrinology and metabolism (impact factor: 6.5). 02/2013; DOI:10.1210/jc.2012-3817

ABSTRACT Context: In obesity, increases in free fatty acid (FFA) flux can predict development of insulin resistance. Adult women release more FFA relative to resting energy expenditure (REE) and have greater FFA clearance rates than men. In adolescents, it is unknown whether sex differences in FFA flux occur. Objective: Our objective was to determine the associations of sex, REE, and body composition with FFA kinetics in obese adolescents. Participants: Participants were from a convenience sample of 112 non-Hispanic white and black adolescents (31% male; age range, 12-18 years; body mass index SD score range, 1.6-3.1) studied before initiating obesity treatment. Main Outcome Measures: Glucose, insulin, and FFA were measured during insulin-modified frequently sampled iv glucose tolerance tests. Minimal models for glucose and FFA calculated insulin sensitivity index (SI) and FFA kinetics, including maximum (l0 + l2) and insulin-suppressed (l2) lipolysis rates, clearance rate constant (cf), and insulin concentration for 50% lipolysis suppression (ED50). Relationships of FFA measures to sex, REE, fat mass (FM), lean body mass (LBM) and visceral adipose tissue (VAT) were examined. Results: In models accounting for age, race, pubertal status, height, FM, and LBM, we found sex, pubertal status, age, and REE independently contributed to the prediction of l2 and l0 + l2 (P < .05). Sex and REE independently predicted ED50 (P < .05). Sex, FM/VAT, and LBM were independent predictors of cf. Girls had greater l2, l0 + l2 and ED50 (P < .05, adjusted for REE) and greater cf (P < .05, adjusted for FM or VAT) than boys. Conclusion: Independent of the effects of REE and FM, FFA kinetics differ significantly in obese adolescent girls and boys, suggesting greater FFA flux among girls.