Can A Genetic Test Say Why You Are Fat?

With the decoding of the human genome came the hope of getting a lever on the chronic diseases, which kill most of us today: heart disease, stroke, diabetes and many cancers. And since overweight and obesity are a common cause of those diseases, many obese people were, and still are, yearning for that exculpatory headline: "It's all in your genes!" Why and how this headline is unlikely to ever appear in any serious media, was a subject of my earlier post "It's not your genes, stupid!".

Now, a group of researchers have looked at the data of a 30-year investigation of health and behavior, ...
which you might call the New Zealand equivalent of the famous U.S. Framingham study [1]. If you ever wondered whether it would make sense to get your children, or yourself, tested for your genetic risk of obesity, you will be surprised to learn what this study tells you. But one step at a time. Let's first have a look at this outstanding piece of research. [tweet this].

The study population consists of all the 1037 babies born in Dunedin, New Zealand, between 1st April 1972 and 31st March 1973 at the Queen Mary Maternity Hospital. Comprehensive health assessments were done at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32 and 38. These investigations will be extended into the future and into the next generation. This is a massive and admirable effort. With data having been collected about virtually all aspects of health and behavior, this project provides a rare opportunity to match those data with genetic information. While genetic profiling wasn't possible in the seventies, it is possible and feasible now. And since study participants' genetic make-up hasn't changed since the time of their conception, we can retrospectively look at the correlation of biomarkers and genes, in this case those that correlate with obesity. To understand this study let me familiarize you with some facts and terms first.

So-called genome-wide association studies (GWAS) have thrown up more than 30 individual single-nucleotide polymorphisms (SNP, pronounced 'snip'), that's geneticists' speak for a variation of a single building block (nucleotide) of a gene. The draw-back: Those SNPs individually correlate only very weakly with obesity. That is, while there is a statistical correlation with obesity, there are obese people who don't carry the SNP, and there are carriers of the SNP who are not obese. To complicate matters a little further, not all SNPs which show statistical correlations in one population, say the U.S., do so in another, say New Zealand. Which is why the Dunedin researchers developed a risk score from the 32 SNPs known from other studies. Of those 32 they could find 29 in their study cohort, and so they developed their score from those 29 SNPs. Participants were grouped according to their score into either high- or low-risk.

The next step was to look at how the participants' genetic risk score (GRS) correlated with BMI in each decade, starting from 15-18 years of age, followed by 21-26 years, and then from 32-38 years. In the second decade (ages 15-18), people with a high risk score had 2.4 times the risk of being obese than those who scored low on the GRS. Had this been you, having a high risk score would have made you almost two and a half times more likely to be obese as a teenager compared to your buddies of the low-risk persuasion. That sounds like a lot, and you might be tempted to think that screening your child for genetic risk would help you to be more vigilant in watching over his or her BMI while he or she is still under your care.

The authors certainly seem to think so when they say that "These findings have implications for clinical practice..." and that "the results suggest promise for using genetic information in obesity risk assessments." I respectfully disagree, and so might you.

Let's simply take your point of view for a moment, and not the one of public health, where we are interested in one patient only, the population under our care. In contrast, the only patient you are interested in is you, or maybe your child. This value of a relative risk of 2.4 doesn't tell you much. What you rather want to know is, what a high- or low-risk score means to you. And the right question to ask would be along the line of "what are the chances of becoming obese when my risk score is high?". And also, "what are my chances of not becoming obese when my risk score is low?". The answers to these 2 questions come in the shape of values, which we call positive predictive value (PPV) and negative predictive value (NPV). Unfortunately the Dunedin researchers don't report those values. But we can calculate them, which I did.

And here is the surprising answer: if you had a high score, your risk of being obese as an adolescent is just about 10%. In other words, even with a high-risk score, you stand a 90% chance of not being obese as an adolescent. And if your risk score had been low you would have a 95% chance of not becoming obese. Beats me, but I can't see the benefit of genetic testing.

I deliberately talk only about the risk at the age of adolescence. There is a simple reason for this. The researchers found that the relative risk of obesity between the high- and low-risk categories diminished progressively from 2.4 in the second decade to 1.6 in the fourth (ages 32-38). That means, our looking at adolescents affords us a look at a time when study participants' exposure to environmental and behavioral influences had been relatively short. Over the years, environment and behavior further diminish the predictive power of the genetic score. Which is akin to saying: your lifestyle choices give you a greater power over your BMI than your genes. And by extension, the choices you make for your children's lifestyle beats their genes easily, too. In other words, it's not so much the luck of the draw, which determines your body weight, but rather your skill of playing the deck of (genetic) cards, which we have been dealt at the moment of conception. The study's data say the same thing just in other words: At birth the high-risk babies were not any heavier than their low-risk peers. Only once they were exposed to the outside world, did BMI careers begin to divert. For some of them.

This tells us one thing: when it comes to obesity, habits and environment are the key, not a potpourri of SNPs. Of course, if you are in the business of peddling genetic tests, you will disagree. And also when selling guilt-free conscience to obese readers is what pays your bills. Which is why I'm curious to see how the media will portray this study. Let's stay tuned. [tweet this].

1.    Belsky, D.W., et al., Polygenic Risk, Rapid Childhood Growth, and the Development of ObesityEvidence From a 4-Decade Longitudinal StudyPolygenic Risk for Adult Obesity. Archives of Pediatrics and Adolescent Medicine, 2012. 166(6): p. 515-521.

Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA, Evans JP, Harrington H, Sugden K, Williams B, Poulton R, & Caspi A (2012). Polygenic Risk, Rapid Childhood Growth, and the Development of Obesity: Evidence From a 4-Decade Longitudinal StudyPolygenic Risk for Adult Obesity. Archives of pediatrics & adolescent medicine, 166 (6), 515-21 PMID: 22665028

Am I shittin' you? Learn to be a skeptic!

Learn to be a skeptic!

Why you cannot believe what you read about medical studies.

In my last blog post I promised to tell you why you shouldn't trust any study results, particularly when you didn't read the study yourself. It has to do with the methods of biomedical research. To make my point, I'll take the gold standard research method, the double blinded randomized controlled trial, or RCT. 

Let's say we want to test a drug, which is supposed to lower blood pressure in those who suffer from hypertension. The researchers have decided to enroll, say, 100 "subjects". That's what we typically call the people who are kind enough to play guinea pig in our studies.   

The researchers will first do a randomization of subjects into one of two groups (very often it is more than one group, but to keep it simple we will assume just two groups). What we mean with randomization is that we randomly assign each subject to one of the two groups. One group - the intervention group - will receive the drug, the other group - the control group - won't. What they get instead is a sugar pill, a placebo. 

With the randomization we want to make sure that, at the start, or baseline, both groups are indistinguishable from each other with respect to their average vital parameters. For example, if we were to calculate the mean age, blood pressure and any other variable for each group, these mean values would be not different between groups. That's important, because we want to isolate the effect of the drug. We don't want to worry at the end whether the effect, or lack thereof, was maybe due to some significant difference between the groups at baseline. 

Once the randomization is done, we organize the trial in such a way that neither the "subjects" nor their physicians and nurses know whether they get the placebo or the active drug. Both sides are blind to what they get and give, which is why this set-up is called double-blinded. That's an important feature, because a researcher often goes into a study with a certain expectation of its outcome. Either that outcome supports his hypothesis, or it doesn't. To eliminate the risk of, more or less subconsciously, influencing the study towards a desired outcome, double-blinding is very effective tool.

Fast forward to the end of our trial. We have now all the data in hand to compare the two groups. After unblinding, the researchers will compare the two groups with each other. In our example, they will compare the average, or mean, of the blood pressure values of all the individuals for each group. If the intervention group's mean value is lower than that of the control group, then it is plausible to reject the null-hypothesis, that is to REJECT the idea that the drug is NOT as ineffective as the placebo (we are, of course, assuming here that the sugar pill didn't lower the blood pressure of the control group). 

There are statistical tools to determine whether the difference between the groups may just be a chance event, or whether chance is a very unlikely explanation. We can never rule out chance completely. Now, when we are confident that it is the drug and not pure chance, which has lowered the mean blood pressure in the intervention group, we write our paper to present it in one of the medical journals. 

If the subject is a little more sexy, than just lowering blood pressure, there will sure be some journalists who pick it up and report to their readers that, say, eating chocolate makes you slimmer. I'm not kidding. This headline very conveniently went through the media shortly before Easter this year [1]. Good for Hershey who are running it of course on their webpage. And in the media it reads like it did in the Irish Times: "Good news for chocoholics this Easter. Medical Matters: No need for guilt over all those Easter eggs."    

I'm not going to comment on the media geniuses, because it's their job to put an angle on every story, so that YOU find it interesting and read their stuff. But since I'm sure you'll follow these links, just let me warn you: the chocolate study was an observational study, not an RCT. And one thing we MUST NOT do with the results of observational studies is to confuse association with causation. Only when we conduct an RCT, where the intervention group eats chocolate and the control group doesn't, might we be able to determine whether there is a causal link. And for obvious reasons we can't blind the subjects, to whether they eat chocolate or not. But I'm digressing.

Back to our blood pressure study. When we compare the group averages, everything looks very convincing. And sure enough, as researchers we are happy with the results, and we are perfectly correct, when we conclude, that this medicine does its job. 

But will it do it for you? When you are hypertensive? You might be wrong if you say "Yes". And you will be wrong more often than we, as researchers, or your doctors care to admit. For one simple reason: The variability of effect within the group. You give 50 people the same drug, and I bet with you, and I'm not the betting type, that you'll have 50 different results. 

The mean value of the entire group glosses over these inter-individual differences. Let me give you an example from a study performed on 35 overweight men, who were studied in a supervised and carefully calculated 12-weeks exercise program, with the intention of reducing body weight. The mean weight loss was 3.7 kg. That was almost exactly the amount of weight loss which the researchers had expected from the additional energy expenditure of the exercise program. But when they looked at each individual, it became clear that the group mean doesn't tell you anything about how YOU would fare in that program. 

First of all, the standard deviation was 3.6 kg. Now, a standard deviation of 3.6 kg simply tells you that approximately two thirds of the participants experienced a weight loss anywhere between 3,7 kg (the mean) minus 3.6 kg and 3.7kg + 3.6 kg, that is between 0.1 kg and 7.3 kg! That's a lot of kilos. And what about the remaining one third of those participants? They are even further from these values. In this case the greatest loser went down by 14 kg, and the biggest "winner" gained almost 2 kg. A spread of 16 kilos!

Here is the graph which shows you the change on body weight and fat for each individual participant. Which one would you be?

This effect is what you do not see when you don't read the studies. And in most studies, it isn't made obvious either. 

Which is why, you shouldn't be surprised to learn that most major drugs are effective only in 25-60% of their users [2]. The same goes for weight loss drugs and interventions, for almost everything we study in biomedicine. 

That's not a problem for us in public health. Because a drug, which works in 60% of the patients, helps us reduce the burden of disease in our population. Public health is not interested whether you are one of the 60% or not. But you are. And that's why I believe not only medicine, but also prevention must be individualized.

 Which is why the GPS to chronic health, which I currently develop, is all about helping you find your individual path to your health objectives.

Why not have a look at it, and maybe even try it out? 

References

1.           Golomb, B.A., S. Koperski, and H.L. White, Association Between More Frequent Chocolate Consumption and Lower Body Mass Index.Archives of Internal Medicine, 2012. 172(6): p. 519-521.

2.           Wilkinson, G.R., Drug Metabolism and Variability among Patients in Drug Response. N Engl J Med, 2005. 352(21): p. 2211-2221.