One of the wonderful aspects of living in the age of Big Data is the way scientists are able to discover new, previously undiscovered patterns in gigantic datasets. A team at Columbia University has studied the health records of over ten million people across three different countries and discovered some compelling links between a person's lifetime disease risk and the month they were born in.
Numerous researchers have tackled the strangely interesting correlations between birth month and disease risk over the years. The true goal of this research is to fundamentally understand what specific seasonal and environmental factors faced by a mother during pregnancy can affect an offspring's lifelong susceptibility to certain disease.
These links are undeniably tricky to study. A conventional medical study involving groups of subjects would not be especially ethical, after all, we couldn't exactly withhold a certain environmental factor from a child intentionally to see if it increases their risk of diabetes.
Examining health records on the other hand leaves one with the eternal "correlation is not causation" problem. A team at Columbia University has come at this problem by examining giant volumes of data with a greatly targeted precision and 21st century computing power.
Following on from a 2015 study that concentrated on nearly 2 million people in New York City, the new study looked at the health records of 10.5 million people from five different climates in the United States, Taiwan, and South Korea.
Because the initial study only focused on one location's climate patterns, the latest study broadened the focus to other locations so as to solidify conclusions linking certain seasonal environmental exposures to disease risk. The study also concentrated on certain environmental exposures that had strong evidential connections to specific diseases.
"All of our major findings linking birth seasonal patterns with variance in environmental exposures fit into known mechanistic pathways," says Mary Regina Boland, one of the authors on the study. "This is crucial because it demonstrates the utility of our method and further underscores the importance of environmental exposures during development and the impact they may have throughout life."
One of the key conclusions in the study was a relationship between low levels of sunlight in the late stages of pregnancy and an increased lifetime risk of type 2 diabetes. So, for example, babies born between December and March in New York City had an increased risk of developing type 2 diabetes due to the lower light levels in the city across the deep winter months.
Other correlations varied in timeframe from different sites depending on different local environmental factors. A strong link was found between exposure to carbon monoxide in the first-trimester and increased risk of developing a depressive disorder. The peaks in carbon monoxide presence varied across the year depending on each location but the correlation was consistent across all datasets.
"Basically, we're using the data to connect the dots," says lead on the study Nicholas Tatonetti. "And by clarifying these connections, it may be possible to find new ways to prevent disease—such as recommending seasonal dosing for some prenatal supplements."
The study's authors are clear to note that while the methods are mostly generalizable across culture and climate, not all results are always broadly applicable. The diabetes risk conclusion for example is mostly relevant to similar socioeconomic groupings. But, as this study explicitly connects causal factors to established biological risk pathways it is a little more compelling that more casual observational studies.
This study doesn't mean soon-to-be parents need to be freaking out, although it is yet another affirmation that prenatal conditions are radically important for a person's overall lifelong health.
The study was published in the Journal of the American Medical Informatics Association.
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