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.
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.
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.
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.
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.
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.
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).
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).
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.
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.