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.

Slides for ACP talk

I just gave a talk on obesity at a diabetes course at the American College of Physicians meeting in San Francisco.  My slides are here.

Epipheo video

The narration comes from an interview with me.

 

A meal for a day

The Center for Science in the Public Interest has some examples of meals in restaurants that contain the caloric requirements for a whole day.  And you doubted the push hypothesis for the obesity epidemic.

The Land Sub Experiment

Gary Taubes penned a column in Nature last month arguing for a rigorous test of the energy balance hypothesis versus what he calls the hormonal hypothesis for the cause of obesity.  Taubes writes

Before the Second World War, European investigators believed that obesity was a hormonal or regulatory disorder. Gustav von Bergmann, a German authority on internal medicine, proposed this hypothesis in the early 1900s.

The theory evaporated with the war. After the lingua franca of science switched from German to English, the German-language literature on obesity was rarely cited. (Imagine the world today if physicists had chosen to ignore the thinking that emerged from Germany and Austria before the war.)

Instead, physicians embraced the ideas of the University of Michigan physician Louis Newburgh, who argued that obese individuals had a “perverted appetite” that failed to match the calories that they consumed with their bodies’ metabolic needs. “All obese persons are alike in one fundamental respect,” Newburgh insisted, “they literally overeat.” This paradigm of energy balance/overeating/gluttony/sloth became the conventional, unquestioned explanation for why we get fat. It is, as Bernard would say, the fixed idea.

This history would be no more than an interesting footnote in obesity science if there were not compelling reason to believe that the overeating hypothesis has failed. In the United States, and elsewhere, obesity and diabetes rates have climbed to crisis levels in the time that Newburgh’s energy-balance idea has held sway, despite the ubiquity of the advice based on it: if we want to lose fat, we have to eat less and/or move more. Yet rather than blame the advice, we have taken to blaming individuals for not following it ‘properly’.

The alternative hypothesis — that obesity is a hormonal, regulatory defect — leads to a different prescription. In this paradigm, it is not excess calories that cause obesity, but the quantity and quality of carbohydrates consumed. The carbohydrate content of the diet must be rectified to restore health.

As I have argued before (see here and here), these two hypotheses are not conflicting.  The question of whether or not carbs make you fat is not an either-or issue but a quantitative one.  I also agree that we don’t yet know the answer and a definitive carefully controlled experiment is required.  I call this the “Land Sub Experiment” because what we need to do is to completely sequester individuals from the outside world for up to a year or more so that we can precisely measure everything they eat and how much energy they expend.  We can then compare a group that consumes mostly carbs to one that doesn’t.  The NIH will actually be involved in the NuSi study that Taubes describes and Kevin Hall is directly involved in the planning.  I anxiously await the outcome.  On a side note, a recent meta-analysis (see here) reports that being overweight actually lowers your mortality rate.

Radio Interview

I was interviewed on a Los Angeles public radio show called Good Food awhile ago.  The link is here.  I haven’t listened t it.  We spoke for twenty or so minutes so I don’t know what they edited it down to.  The host Evan Kleiman was quite knowledgeable I thought.

Quantifying the calorie debate

I wanted to clarify that I am not attacking the “carbs are bad” idea per se. Personally, I’m agnostic. What I was trying to do in my previous posts was to point out that the question is a quantitative one and cannot be settled by a qualitative examination of the underlying physiology. Gary Taubes would be the first to agree and he is actively working towards testing his hypothesis experimentally. I also wanted to point out that single experiments are not definitive. The recent JAMA paper suggests that low carb diets speed up metabolism relative to low fat diets but like many clinical experiments, the effect could have been due to a statistical anomaly or systematic error. As I have posted previously in the past (e.g. see here), clinical and epidemiological results are as likely to be wrong (if not more) as correct. Unlike mathematics, where a theorem is either wrong or right, clinical and epidemiological results are more like imperfect snapshots of the truth. It will only be through a long process of accumulating evidence will the answer be revealed.

The false dichotomy of carbs and obesity

The law of the excluded middle is one of the foundations of logic. It says that if a proposition is false then the opposite must be true. There is no room for a middle ground in classical logic. However, one must be extremely careful when applying the law to  biology where hypotheses are generally situational and rest on many assumptions. In order to apply the law of the excluded middle, one must have only two alternatives and this is seldom true in biology and in particular human metabolism. Gary Taubes argued quite successfully in his book Good Calories, Bad Calories that fat probably doesn’t cause heart disease and in some cases may even be beneficial. A major theme of that book was that scientists can become irrationally attached to hypotheses and willfully ignore any evidence to the contrary. He recently penned a New York Times opinion piece arguing that the medical establishment is equally misguided in asserting that salt is unhealthy. One of the hypotheses that Taubes dislikes the most is that “a calorie is a calorie”, which proposes what you eat is not as important as how much you eat when it comes to weight gain and obesity. Taubes thinks that carbs and especially sugar is what makes you fat (and causes heart disease). This is summarized in his Times opinion piece  today, which covers the recent JAMA result that I posted about recently (see here).

It may very well be true that a calorie is not a calorie but that still may not mean carbs are the cause of the US obesity epidemic. I’ve posted on this a few times before (e.g. see here and here) but I thought it was important enough to reiterate and simplify the points here. In short, the carbs are bad argument is that 1) carbs induce insulin and insulin sequesters fat, and 2) carbs are metabolically more efficient so you burn fewer calories when you eat them compared to fat and protein. Even if this is true (and it may not all be) that still doesn’t mean that calories are unimportant. I don’t care how metabolically efficient carbs may be, you would starve to death if you only ate one sugar cube each day. Conversely, no matter how many excess calories you may burn eating fat, you will become obese if you eat two pounds of butter each day. Hence, even if a calorie is not a calorie, calories still matter. It is then a matter of degree. If you manage to burn everything you eat then your body won’t change. This is true if you eat a high carb or a low carb diet. Now it could be true that you could have a different amount of body fat and weight for the same calorie diet depending on diet composition. So a plausible hypothesis for the cause of the obesity epidemic is that we switched from a high fat diet to a low fat diet and everyone became fatter as a result. This is something that I’m planning to test using the same data that we used to show how the increase in food production is sufficient to explain the obesity epidemic. Ultimately though, the brain is what decides how much we eat and one of the biggest things we don’t understand is how diet composition affects food intake. It could be that low carb diets do make you thinner but the reason is that we tend to eat less when we’re on them.

2012-7-2: changed fat to carb in last  sentence.

New fuel for the calorie debate

The big news in obesity this week is the publication of this paper Effects of Dietary Composition on Energy Expenditure During Weight-Loss Maintenance, by Ebbeling et al. in JAMA. The study examines the effects of three types of diets – low fat, low carbs, and low glycemic index – on energy expenditure and weight loss maintenance. It was a cross-over study on 21 obese young adults, where all three diets were given to each subject consecutively. The basic result of the study was that people on the low carb (i.e. Atkins) diet had the highest total energy expenditure (TEE), followed by the low glycemic index, and coming in last place was the low fat diet. The study certainly bolsters the claims of  those on the “calorie is not a calorie” and  ”carbs are bad” side. The implication being that you can eat more on a low carb diet than on a low fat diet. While the study was carefully done, there are some discrepancies that may call into question some of the results. Here is my colleague Kevin Hall’s take:

The resting RQ values in Table 3 seem too high. Generally, resting RQ should be lower than 24hr RQ which should match the daily food quotient (FQ) when the subjects are in macronutrient balance. My rough calculation of the FQ values for the test diets give about 0.9, 0.84, and 0.76 for the LF, LGI, and VLC diets, respectively. The reported resting RQ values are 0.9, 0.86, and 0.83. This would usually suggest a degree of overfeeding during the weight loss maintenance phases since RQ generally exceeds FQ during positive energy balance. However, the reported energy intake during the test diet phase was 2626 +/- 686 kcal/d which is lower than the TEE in all 3 test diets reported in Table 3. Something is odd here.

For those not up on metabolism lingo, the RQ is the respiratory quotient, which is the ratio between carbon dioxide expired and oxygen inhaled and gives a measure of what types of fuel the body is burning. The RQ works because carbs, fat and protein are all oxidized slightly differently.  Carbs have an RQ of 1 meaning every mole of oxygen consumed produces a mole of carbon dioxide.  Protein has an RQ that is between 0.8 and 0.9 and fat has an RQ around 0.7. Different types of proteins and fats will have slightly different RQs. The FQ is the expected RQ given the diet. The resting RQ values were measured by wearing a mask that measures the air you breath for twenty minutes after an overnight fast. Generally, when you fast, your body switches from burning carbs to burning fat. Hence, fasting RQs are usually lower than FQs. However, in this study the opposite is true. Fasting RQs can be higher than FQs if the person is not in energy balance and increasing in weight. However, the study also reports that the energy intake was lower than the TEE. Their estimates for TEEs are based on results from doubly labelled water measurements which uses the reported RQs as one of the input paramters. Hence, the differences in TEE that they report could be explained by experimental error.

Physical activity

Several people have questioned my assertion in the New York Times interview that physical activity has not changed much in the past thirty years. My claim is partially based on work by Klaas Westerterp and John Speakman, who are two highly respected researchers in the field.  Klaas gave a very nice talk on the topic at a metabolism workshop at  NIMBIOS in 2011.  His slides are here.  What they basically did was to compare total daily energy expenditure (DEE) measurements to basal energy expenditure (BEE) over time.  The ratio of DEE to BEE is called the physical activity level (PAL). The higher the PAL the more of  the energy you burn every day is due to physical activity. Klaas and John showed that PAL has not changed significantly since 1980 and if you squint hard enough at the plots in the slides it looks like it may even have increased a little.  While we seem to be very sedentary now, people tend to forget that we were also very sedentary thirty years ago.

Obesity references

I’ve been asked about references to papers on which my New York Times interview is based so I’ve listed them below.  You can find summaries for some of them as well as the slides for my talks and posts related to obesity here.

K.D. Hall, G.Sacks, D. Chandramohan, C.C Chow, C. Wang; S. Gortmaker; B. Swinburn, `Quantifying the effect of energy imbalance on body weight change.’ The Lancet 378:826-37 (2011).

K.D. Hall and C.C. Chow, `Estimating changes of free-living energy intake and its confidence interval,’ Am J Clin Nutr 94:66-74 (2011).

K.D. Hall, M. Dore, J. Guo, and C.C. Chow, ‘The progressive increase of food waste in America’, PLoS ONE 4(11): e7940 (2009).

C.C. Chow and K.D. Hall, `The dynamics of human body weight change’, PLoS Computational Biology 4 , e1000045 (2008).

K.D. Hall, H.L. Bain and C.C. Chow, `How adaptations of substrate utilization regulate body composition’, International Journal of Obesity, 31 , 1378-83 (2007). [PDF]

V. Periwal and C.C. Chow, ‘Patterns in food intake correlate with body mass index’, American Journal of Physiology: Endocrinology and Metabolism, 291 929-936 (2006) [PDF]

Causality and obesity

The standard adage for complex systems as seen in biology and economics is that “correlation does not imply causation.”  The question then is how do you ever prove that something causes something. In the example of obesity, I stated in my New York Times interview that the obesity epidemic was caused by an increase in food availability.  What does that mean? If you strictly follow formal logic then this means that a) an increase in food supply will lead to an increase in obesity (i.e. modus ponens) and b) if there were no obesity epidemic then there would not have been an increase in food availability (i.e. modus tollens). It doesn’t mean that if there were not an increase in food availability then there would be no obesity epidemic.  This is where many people seem to be confused.  The obesity epidemic could have been caused by many things.  Some argue that it was a decline in physical activity. Some say that it is due to some unknown environmental agent. Some believe it is caused by an overconsumption of sugar and high fructose corn syrup. They could all be true and that still doesn’t mean that increased food supply was not a causal factor. Our validated model shows that if you feed the US population the extra food then there will be an  increase in body weight that more than compensates for the observed rise.  We have thus satisfied a) and thus I can claim that the obesity epidemic was caused by an increase in food supply.

Stating that obesity is a complex phenomenon that involves lots of different factors and that there cannot be a simple explanation is not an argument against my assertion. This is what I called hiding behind complexity. Yes, it is true that obesity is complex but that is not an argument for saying that food is not a causal factor. If you want to disprove my assertion then what you need to do is to find a country that does not have an obesity epidemic but did exhibit an increase in food supply that was sufficient to cause it. My plan is to do this by applying our model to other nations as soon as I am able to get ahold of data of body weights over time. This has proved more difficult than I expected. The US should be commended for having good easily accessible data. Another important point to consider is that even if increased food supply caused the obesity epidemic, this does not mean that reducing food supply will reverse it. There could be other effects that maintain it even in the absence of excess food.  As we all know, it’s complicated.

In the Economist

The Economist blog Babbage posted an article on obesity and quoted parts of the New York Times interview.  I also want to point out that that Kevin Hall is the lead investigator for the body weight simulator (bwsimulator.niddk.nih.gov).

The calorie debate

The day after I appeared on the radio show The Takeaway, Scientific American editor Michael Moyer came on to criticize me.  I welcome the debate and my response is below.

Here is a link to The Takeaway’s series on obesity, which has his audio file.  Moyer also writes in his blog:

Unfortunately Chow’s outsider’s perspective on the obesity crisis isn’t really an outsider’s perspective at all: it is the physicist’s perspective. Physicists have a long history of marching into other sciences with grand plans of stripping complex phenomena down to the essentials with the hope of uncovering simple fundamental laws. Occasionally this works. More often, they tend to overlook the very biochemistry at the heart of the process in question.

Chow’s conclusion is not just obvious—it’s a tautology. Because for Chow, a calorie is just a unit of energy. Eat more calories than you burn, and the energy must go somewhere. That somewhere is fat cells. The conclusion is built into the assumptions.

But perhaps a calorie is not just a calorie. Perhaps, as some prominent researchers argue, the body processes calories from sugar in a fundamentally unique and harmful way. According to this hypothesis, we’re not getting fat because we’re eating more. We’re getting fat because of what we’re eating more of. The biochemistry that explains why this would happen is complex—certainly difficult to include in a computer model—but that doesn’t make it wrong.

Ultimately experiments will decide if this hypothesis is true, or if it is not true, or if it is true but just one part of a nuanced understanding of obesity that includes biochemistry, microbiology, neurobiology, politics, economics and much more. The obesity crisis isn’t rocket science. It’s complicated.

Moyer’s criticism of me is ironic in two ways. The first is with regards to his claim that biology is not like physics. I fully agree and have posted on this very topic here. Additionally, while I have been frustrated in the past trying to get biologists to pay attention to my work, this is the one area where I am not a complete outsider and have access to and input from some of the very best clinicians, experimentalists, and public health scientists in the field.

On the radio

I was a guest on two radio shows this morning.  At 7:45 this morning I was on The Takeaway, a nationally syndicated show on NPR (audio file available on website) and then at 10:30 I was on the Kathleen Dunn Show on Wisconsin Public Radio (audio file available here.  I am on halfway into the show). You can hear clearly that I’m not anywhere near as eloquent as Arif and Sebastian.

New York Times Interview

My conversation with Claudia Dreifus of the New Times can be found here.  I have to commend Claudia for putting in a great deal of effort on this piece. I never realized that they were so much work.  The published interview is a very condensed version of our many conversations.  Claudia did her very best to make sure everything was accurate but some nuance had to be sacrificed for space.  For example, I was engaged but not yet married when I moved to NIH.  Also, I want to point out that I did know that a calorie was a unit of energy but I had no idea that the food Calorie is really a kilocalorie nor how many Calories are contained in common food items.

One of the things that got cut from the story was that I heard about the job at NIDDK from John Rinzel.  The Laboratory of Biological Modeling that I’m now part of, used to be called the Mathematical Research Branch and John was its chief for twenty years. Wilfrid Rall was the chief before John. A brief history of the lab can be found here. One could argue that this was where computational neuroscience was established. Bard Ermentrout is among the many computational neuroscientists that passed through the lab. The branch actually predates the NIDDK and was put there for administrative reasons even though it focused on neuroscience.  However, near the end of John’s tenure as chief, the institute had less enthusiasm for the lab and resources were reduced.  John ended up leaving for NYU. Marvin Gershengorn came in as the new scientific director in the early 2000′s and he wanted to rebuild the lab. I have no doubt that I got the job because of the input Marvin received from John. Although Marvin was interested in obesity, he didn’t compel me to work on the topic.  He was very good about giving me and the lab freedom to work on anything interesting. Right now there are four PIs in the lab – Artie Sherman, Kevin Hall, Vipul Periwal and myself, and we work on a variety of biological topics although mostly with some connection to diabetes and metabolism. One thing that worried me about the piece, aside from a backlash from the food industry, was that it would pigeonhole me as an obesity researcher. I’m still very much interested in many topics including neuroscience, genetics and gene induction.

The last thing that doesn’t really make it through is that our argument for excess food causing the obesity epidemic is not just based on correlations between the increase in food supply and average body weight.  What we did was to take the actual USDA reported food availability per person, feed it to our calibrated model and showed that it more than explained the weight increase.  It may be that other factors liked decreases in physical activity are involved but they are not necessary to explain the obesity epidemic.  Those that doubt it was caused by excess food must show that all of it was thrown away.  We are already arguing that most of it was wasted.  Finally, I don’t really know how to stem the obesity epidemic.  I’m not sure that making food more expensive through taxation is the correct solution since it would cause hardship for low-income people.  I do think that curtailing food marketing to children would help but I’m not hopeful that it would ever happen.

 

Correction: Jun 7, 2012.  Will Rall was a member of the MRB but was never the chief.

Errata recap

I want to stress that there is nothing wrong with the results in the paper. The mistakes are typographical in the sense that the formulas in the methods were transcribed incorrectly from our code.  This was just pointed out to me that the errata could be misinterpreted.  What happened was that MS Word kept turning our equations into pictures so we couldn’t edit them so we retyped them over and over again.  Transcription errors then started to creep in and we were so adapted to the equations that we didn’t notice anymore.  Not a good excuse but unfortunately that is what happened.

Heritability and GWAS

Here is the backstory for the paper in the previous post.  Immediately after the  human genome project was completed a decade ago, people set out to discover the genes responsible for diseases. Traditionally, genes had been discovered by tracing the incidence in families that exhibit the disease. This was a painstaking process – a classic example being the discovery of the gene for Huntington’s disease.  The  completion of the  human genome project provided a simpler approach.  The first thing was to create what is known as a haplotype map or HapMap. This is a catalog of all the common genome differences between people.  The genome between humans only differ by about 0.1%. These differences include  Single Nucleotide Variations (SNPs) where a given base (A,C,G,T) is changed and  Copy Number Variations (CNV) where there are differences in the number of copies of segments of DNA.   There are about 10 million common SNPs.

The Genome Wide Association Study (GWAS) usually looks for differences in SNPs between people with and without diseases.  The working hypothesis at that time was that common diseases like Alzheimer’s disease or Type II diabetes should be due to differences in common SNPs (i.e. common disease, common variant). People thought that the genes for many of these diseases would be found within a decade. Towards this end, companies like Affymetrics and Illumina began making microarray chips, which consist of tiny wells with snippets of complement DNA (cDNA) of a small segment of the sequence around each SNP.  SNP variants are found by seeing what types of DNA fragments in genetic samples (e.g. saliva) get bound to the complement strands in the  array. A classic GWAS study then considers the differences in SNP variants observed in disease and control groups. For any finite sample, there will always be fluctuations  so a statistical criterion must be established to evaluate whether a variant is significant. This is done by computing the probability or p-value of the occurrence of a variant due to random chance.  However, in a set of a million SNPS, which is standard for a chip, the probability of one SNP being randomly associated with a disease is a million fold higher if the SNPs are all independent (i.e. it’s just the sum of the probabilities of each event (see Bonferonni correction)). However, since SNPs are not always independent, this is a very conservative criterion.

The first set of results from GWAS  started to be published shortly after I arrived at the NIH in 2004 and they weren’t very promising. A small number of SNPs were found for some common diseases but they only conferred a very small increase in  risk even when the disease were thought to be highly heritable. Heritability is a measure of the proportion of phenotypic variation between people explained by genetic variation.  (I’ll give a primer on heritability in a following post.) The one notable exception was age-related macular degeneration for which five SNPs were found to be associated with a 2 to 3 fold increase in risk. The difference between the heritability of a disease as measured by classical genetic methods and what was found to be explained by SNPs came to be known as the “Missing heritability” problem. I thought this was an interesting puzzle but didn’t think much about it until I saw the results from this paper (summarized on Steve Hsu’s blog), which showed that different European subpopulations could be separated by just projecting onto two principal components of 300,000 SNPS of 6000 people. (Principle components are the eigenvectors of the 6000 by 6000 correlation matrix of the 300,000 dimensional SNP vectors of each person.)  This was a revelation to me because it implied that although single genes did not carry much weight, the collection of all the genes certainly did.  I decided right then that I would show that the missing heritability for obesity was contained in the “pattern” of SNPs rather than in single SNPs. I did this without really knowing anything at all about genetics or heritability.

New paper on heritability from GWAS

Heritability and genetic correlations explained by common SNPS in metabolic syndrome traits

PLoS Genet 8(3): e1002637. doi:10.1371/journal.pgen.1002637

Shashaank Vattikuti, Juen Guo, and Carson C. Chow

Abstract: We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h²) and heritability explained by the common SNPs (h_g²) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h² accounted for by common SNPs to be 58% of h² for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.

Author Summary: The narrow-sense heritability of a trait such as body-mass index is a measure of the variability of the trait between people that is accounted for by their additive genetic differences. Knowledge of these genetic differences provides insight into biological mechanisms and hence treatments for diseases. Genome-wide association studies (GWAS) survey a large set of genetic markers common to the population. They have identified several single markers that are associated with traits and diseases. However, these markers do not seem to account for all of the known narrow-sense heritability. Here we used a recently developed model to quantify the genetic information contained in GWAS for single traits and shared between traits. We specifically investigated metabolic syndrome traits that are associated with type 2 diabetes and heart disease, and we found that for the majority of these traits much of the previously unaccounted for heritability is contained within common markers surveyed in GWAS. We also computed the genetic correlation between traits, which is a measure of the genetic components shared by traits. We found that the genetic correlation between these traits could be predicted from their phenotypic correlation.

I am very happy that this paper is finally out.  It has been a three year long ordeal.  I’ll write about the story and background for this paper later.

Calories revisited

Kevin Hall gave a talk today on some of his recent results that forces me to revise what I wrote yesterday.  I won’t divulge his exact results since it’s not published yet but he has experimental evidence that a calorie is not a calorie.  What I said in my previous post was that if you are in steady state then it doesn’t matter what diet you are eating.  However, that must be qualified in light of the recent data.  To be in steady state, you must be in both energy and macronutrient balance. This means that you need to burn exactly all the food you eat, both in the caloric content and composition.  What your body burns depends on the food you eat as well as your current body composition.  So let’s say you decide to cut the carbs from your diet but eat the same amount of calories everyday.  Your body must now burn all the extra fat you are eating to stay in steady state.   If it can adjust immediately then you will stay in steady state.  However, if it cannot burn all the extra fat you are eating then that excess fat will be stored and the body will burn extra glycogen or protein instead.  Your body composition will then change until you are back in steady state.  If your body over adjusts and you burn too much fat, then you will lose fat and gain lean tissue.  In hindsight, the assumption that you can immediately adjust to whatever fuel you are taking in seems pretty far-fetched and according to Kevin’s data, it is.