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There's some recent news that trans fats increase the risk of infertility, but when you look at the data this is nothing more than scare tactics.  Scare tactics don't equal science.

For the past week or so there has been a smattering of news reports that a recent study has found a link between the consumption of trans fats and the reduced risk of fertility problems by 70%. 

 The headlines of London’s Daily Mail web site blares  “Fast Foods Threaten Women’s Fertility” and a CBSNews.com headline tells us “Trans Fats May Increase Fertility; Study Shows Latest Nutritional Bad Guy Can More Than Double The Infertility Risk.”

Were told that eating as little as a few potato chips or one donut a day can cause women increase their risk of infertility by 70%.  As little as four grams of trans fats per day can cause women to have problems ovulation.  

However, as is the case with all of these kinds of snap judgment, scare tactic stories when you look at the details of the study there really isn’t all that much there to back up these bold assertions.  Even the lead researcher of the study, Jorge Chavarro, M.D.a researcher at the Harvard School of Public Health, admits as much.

Chavarro has stated that these findings must be confirmed, but the news reports these findings as if they are ironclad and that trans fats can cause fertility problems.

The news stories front load the “proof” that trans fats can increase infertility problems and saves all the substantial qualifying data and objections to this proof for the end of the study.  If these news organizations were really interested in presenting an objective view of this issue these stories would be written completely differently.

This study was not done to specifically look at how trans fats affect fertility, but the data was drawn from the Nurse’s Heath Study II, which is a long-term, questionnaire-base study that involves female nurses aged 25-42 years old.  Chavarro and his researchers found that 438 nurses out of 18,500 had ovulation related infertility.

So the “proof” that that trans fats can increase the risk of infertility is that 2% of the nurses in this study aged 25-42 had ovulation related infertility. This is not proof of anything.

When you consider that about 10% of the women in the U.S. aged 15-42 have an impaired ability to have children these findings mean nothing

Buried in the CBSNews.com item is the substantial, dissenting view presented by Marion Nestle, Ph.D. a professor in the department of nutrition, food studies and public health at New York University in Manhattan.  Dr. Nestle points out that the raw data from the study doesn’t show that there is any increased risk from eating trans fats, and that even after the data was “adjusted” the increased risk was very small.

The fact that the researchers had to adjust this data at all points out that conclusions reached in this study are extremely shaky.

Dr. Nestle goes on to say that the only nutritional factors known to increase infertility are eating too much and eating too little, as fertility is common among obese women and women who starve themselves to lose weight, and that there’s no data that shows individual foods can effect fertility.

As a matter of fact the researchers aren’t even aware of a mechanism that could cause these fats to effect ovulation.

This “study” serves as an example of how journalists are failing us, especially in the case of the Daily Mail article, as the findings of this “study” are presented in such a way to lead the reader towards a faulty conclusion.  There is nothing in these articles – or in this study – to indicate that trans fats can increase the risk of infertility and yet the articles are written in such a way to make readers this that they can.

There is no objectivity here, as burying balancing views at the end of story doesn’t qualify as being objective.

The larger flaw with this conclusion is that there isn’t a mention with regard to the ages of the subject, the overall infertility rates of the population, the potential causes of ovulation issues or any other pertinent issues with the way in which this data was derived. 

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