A big theory in autism research involves what is called prenatal stress, or maternal stress. The idea is that a stressful event, such as a hurricane, experienced during a woman’s pregnancy may lead to an increased risk of her child having autism. Several studies supporting this idea have been published. For example, one of my advisor’s papers, published in 2005, demonstrated that the experience of stressors during a specific time period during pregnancy (weeks 21 to 32) may be related to an increased risk of autism. Stressors were assessed via surveys and included events like the death of a spouse or being fired from a job.
Confusingly, however, these results conflict with those of very recent studies. It seems this idea may not be as simple as originally thought. This past June, a paper was published that did not find an increased risk of autism associated with prenatal stress. This study examined many of the same stressors experienced by pregnant women as those of the above paper, yet the authors did not find the same results.
Results from another study, presented at the 2012 International Meeting for Autism Research, further complicate this picture. The study examined the incidence of autism in children of mothers who experienced psychosocial stressors, such as physical abuse, during pregnancy. The twist is that physical abuse experienced during pregnancy was not associated with an increased risk of autism. Instead, children of women who experienced fear of their partner or physical or emotional abuse in the years just before giving birth had a higher risk of autism.
Why did two very similar studies produce opposite results? Why does it seem that physical abuse during pregnancy is not associated with autism when abuse beforehand is? Many reasons may lie beneath these conflicting and confusing results. Potentially, gene variants associated with autism are playing a role alongside prenatal stress in causing this disorder. Or different techniques used to carry out the above studies may have contributed to the differing findings. Otherwise, the reasons are about as clear as mud. For now, the best we can do is put on our finest analytical goggles, jump into the data, and start sorting through the mud.
Photo source: http://earthobservatory.nasa.gov/IOTD/view.php?id=79008