All people have time bombs!

I received an email from a colleague that said..."interesting.....click here."

Ok, so to anyone surfing on the internet probably not a good thing to do right? My friend likely has a virus on his cpu, right? Well, being the avid risk taker I am...I click. Guess what I find.

A blog called the Belligerati...interesting name, but the blog has argument is all wrong.

"Many will see the headline Congress Passes Bill to Bar Bias Based on Genes and be pleased.
The legislation, known as the Genetic Information Nondiscrimination Act, prohibits health insurance companies from using genetic information to deny benefits or raise premiums for individual policies. (It is already illegal to exclude individuals from a group plan because of their genetic profile.)
Employers who use genetic information to make decisions about hiring, firing or compensation could be fined as much as $300,000 for each violation.

Not me."

The blogger then goes on to detail why they think it is a bad thing. The major argument and one I have been seeing a lot of lately is based on pure fallacy and demonstrates how a very smart and educated person can be completely illiterate in genetics and genomics. I would say that about 10% of the population understands genetic risk when explained to them....that's about it.

From the blog:

There are many forms of luck in our society that we allow people to capitalize upon. For example, citizens keep the majority of the returns from wealthier parents, natural talents, being born into a rich and free society, their appearance, and temperament. Yet congress has passed a law that says that people may not capitalize upon their generic luck. This makes us less free, and ultimately less wealthy as well.

Genetics as luck....interesting. Perhaps they don't know that most heritable genetic changes are for the bad. Mistaken concept 1, in addition, Genotype + Environment = Phenome.

What sense of justice legitimizes forcing those who are poor but blessed with rich genetic endowment of healthy genes subsidize those that are middle class and endowed with a genetic propensity for several diseases? As I have said before on this blog, the way (the just way, if any) to address the suffering in our society is to give money to the poor. These back door methods, like this genetic non-discrimination rule, are often more unfair then the ills they prevent

This is super Bass-Ackwards. What sense of justice forces those with no control of their poor genetic make up to be excluded from health insurance....Man...this blogger is crazy.

His argument is that by genetic testing we should benefit from our "protective genetic make-up" and that GINA will prevent us from doing this.....

The second concept I want to clear up for everyone is this. "We can test for healthy genetic variation".....Let's get this straight. We know very little about the genes in our bodies that are protecting us from crappy environment. Eric Topol talks about this. For a cardiologist, this guy has done a great job of becoming a genetics Hacker! He says we should study the healthy, not the ill. I think he is right.

The third concept......we understand what genetic changes cause common disease. Let's get a clue. We know that several genetic changes have been weakly linked to (to be read as associated, not causative) common diseases. We also know of some very strong monogenic links to diseases like cancer and heart disease. But these are not the most common.

Francis Collins says "Everyone has at least 5 or 6 genetic time bombs in their genomes"
From the guy who headed the HGP....I'll take it at face value. We will only know this for sure by doing whole genome scans on everyone. By whole genome, I don't mean these chips that "claim to be whole genome" I mean, base by base, methylation by methylation, histone change by histone change, genome sequencing.

So if that is the case....why should insurers test for something that no one knows is a risk or protective allele. To them, it makes no sense to have meaningless data. But to have accurate tests for actuarial evaluation. That would be most worthwhile. My guess is that if they were to use this unvalidated and suspect data that everyone would be getting higher rates. So this argument from the Belligerati demonstrates the lack of genetic knowledge even in the most sophisticated and educated public.

There are other arguments against GINA. But I say, right now it is the best start we have at protecting our citizenry.

"GINA is the first major new civil rights bill of the new century," said Senator Edward Kennedy (D-MA), who cosponsored GINA in the Senate with Senator Olympia Snowe (R-ME). "Discrimination in health insurance and the fear of potential discrimination threaten both society's ability to use new genetic technologies to improve human health and the ability to conduct the very research we need to understand, treat, and prevent genetic disease," said Kennedy

I agree wholeheartedly. With so many people having at risk alleles.....in these SNP studies at least 1 % of the population has to carry these SNPs....That means 3 million people at a minimum are at risk of one thing or another. Genetic Discirimination is the new racism.....I hope not to see its ugly head rear for a very long time.

The Sherpa Says:
Confusion and misinformation can lead to making poor choices. That is why I advocate for education and guidance in the field of genomic medicine. How can we expect even the most sophisticated population to understand this on their own? It tooks me years of study to be where I am. And I learn something new every day! That's why we are going to launch a brand new wave of education and service shortly......

Algorithms and Validation

A friend of mine asked me "If the framingham risk assessment fails to take family history into account, then why do we use it to guide anti-cholesterol therapy?" My answer was "It is scientifically validated."

In medicine we like to do things based on evidence. It is true that we do many things that do not have solid evidence behind them. But we always try to acquire data and then make a rational conclusion, leading to a treatment. When it comes to risk prognostication, validation studies are extremely helpful. And often keep us from getting sued.

So what did the Framingham do to be validated?

The Framingham Heart Study and the Framingham Offspring Study were the first epidemiological studies that prospectively collected population based data on the association between risk factors and the occurrence of fatal and non-fatal coronary and other cardiovascular events in a systematic and sustained fashion. It has been dissected for it's validity over the years.

So can we use the Framingham for everyone? Well, in Europe they tried. Several articles like this one show that the risk tool must be validated in the population you plan to use it for. The Framingham doesn't work so well on the Dutch. However when modified by the REGICOR, Spain's NHLBI, it seemed to perform well for the Spanish. The same with the Chinese modification.

You may now be asking what the heck does this have to do with Personalized Medicine. My answer....Everything. You see part of personalized medicine is prediction. That's why Helix Health of Connecticut trademarked "Prediction, Prevention, Privacy" These are the pillars of genomic medicine.

How can you predict the likelihood of Alzheimers in 5 years? Well, there are some corporate genomic companies doing it without having ever submitted articles for peer reviewed publication. They have "Trade Secret" algorithms that calculate risk. What the hell? How can you trust the accuracy of an algorithm without validation?

This is why we advise against using the Gail model for breast cancer risk. It can only work for certain populations, absolutely not for African Americans. There are new attempts at this type of risk stratification, and several attempts to defend the Gail model. But what has evolved is even more important. New algorithms...that were put to the test and peer reviewed.

Which brings me to my last point. What good is a tool if you don't know how to use it, or who it works for? A recent post on Wingedpig points this out. Confusion as to the tools. But what I wonder is what tools they used to create the tools. Would they publish their algorithm? Should they have to? Should other companies who will foray into medicine have to? As for SNPedia...a great resource, but the results are just like a wikipedia....not exactly peer reviewed.

The Sherpa Says:

The votes are in. I am surprised of the results. 23 and Me is the big winner. Why? Well, they specifically state that they do not intend their tools to be used for medicine. Yet all the posts I read have authors who mistakenly are using it as a medical tool. (See the genealogists post "when will they learn") I would have thought the readers would have chosen Navigenics. Navigenics WILL use their tool as a medical device! So I have to ask them. Where are your data on algorithms? Where did you publish and validate them? Which algorithms are you using? I guess we will find out soon enough. 2008 is right around the corner.

Why Can't We Be Friends?

A recent study in the New England Journal of medicine implicated a gene called FTO (Fatso) in increased risk of obesity. If you had one copy, then you had a 33% increased risk of being obese. 2 copies? 67% increased risk. On average people with FTO weighed 7 pounds more.

It is true that we know of some increased risk due to genetics. However, a study published this week in the NEJM suggests perhaps your friends may play a larger role than your genes.

In this study, if you listed someone as your friend, and your friend became obese during the time they were studied, then your risk of becoming obese would be 171%. Much greater than the risk of carrying 2 copies of FTO.

What about the 6 degrees of separation effect? Wht if it was a friend of a friend? Well they found that the increased risk was less than that of FTO's effect. However, there still was increased risk.

The Sherpa Says: This study was performed on the famed Framingham Heart Study offspring. They attempted to control environment by evaluating neighbors. It does turn out that there is no relation between neighbors and obesity.....Unless they are friends...This tells us that social networking is an indicator of risk of disease. Intuitively this makes sense. Smokers hang together, as do illicit drug users, and perhaps as this study shows over-eaters. Why can't we be friends? Because you are fat.

Diabetes Risk Model Without Help from deCODE!

A study was brought to my attention by Helix Health of Connecticut's Genomic Counselor Sarah Coombes. This study which was published in the journal Archives of Internal Medicine(a very respected academic journal for primary care physicians) showed that Parental diabetes, obesity, and a low good cholesterol were better predictors of diabetes risk than complicated algorithms and complex clinical models.

The incidence of type 2 diabetes is skyrocketing and predicting onset can help us guide interventions. In the public health schema it can have tremendous effects when anticipation guides development of preventative strategies. This is the case with heart disease and cholesterol lowering modifications.

Parental history of diabetes, obesity, HDL(good cholesterol) less than 40 predict diabetes onset at a greater rate than ANY GENETIC TEST OUT THERE!

Most importantly, your insurance pays for the HDL and glucose tests.

The Sherpa Says: This study which evaluates the offspring of the famed "Framingham Study" is excellent. The point with personalized medicine is not just sending genetic tests. True, this study needs to be validated in all ethnicities not just those who were found in a suburb of Boston (Caucasians). But, I would use this risk stratification tool on all of my Caucasian patients. True personalization comes not only from genetic tests, but also from other traditional labs AND family history!