Newly identified type-2 diabetes gene’s odds of being a false finding equal one in 1 followed by 19 zeroes
Although diet, lack of exercise, and consequent obesity get the bulk (as it were) of the blame for type-2 diabetes, genes play a huge role, too. But which genes are really doing the heavy lifting? Penny-ante candidates abound, but nobody has fingered any smoking-gun culprit.
Now, in a new study published in Proceedings of the National Academy of Sciences, database-search king Atul Butte, MD, PhD, and his colleagues have collared a gene that appears to be not just statistically but causally linked to type-2 diabetes. This gene, called CD44, which is especially active in fat tissue of insulin-resistant people (and mice and rats), is interesting in itself. As I wrote in my release about the study:
CD44 codes for a cell-surface receptor not found on fat cells, although those cells do have surface molecules that bind to it. Rather, this receptor sits on the surface of scavenger cells called macrophages (from the Greek words for “big eater”) that can cause inflammation. In obese individuals, macrophages migrate to and take up positions in fat tissue. (Indeed, as many as half the cells in a big potbelly can be macrophages.) Recent medical research has strongly implicated inflammation in initiating type-2 diabetes.
As interesting, in some respects, as the gene Butte & Co. identified was the way they went about finding it: They combed through freely available public repositories filled with dust-gathering data from hundreds of previous studies comparing gene-activity levels in various tissues of insulin-resistant versus insulin-sensitive mice, rats, and humans.
This on-the-cheap integrated search led to the exposure of CD44, which had never before been tied to diabetes. But sophisticated statistical methods the team used indicate that the chance of this gene’s being falsely associated with the disease came to less than one in 10 million-trillion. That’s the number 1 followed by 19 zeroes, folks.
Previously: Women report feeling more pain than men, huge EMR analysis shows, Cheap data! Stanford scientists’ “opposites attract” algorithm plunders public databases, scores surprising drug-disease hook-ups
Photo by Jacrews7