Dr. Philip Wood is immersed in fat: fat metabolism, fatty acids, fat signaling, fatty liver disease. Dr. Wood, a professor in the Metabolic Signaling and Disease program at Sanford-Burnham’s Lake Nona campus, is trying to unravel the consequences of too much fat.
“I’m interested in how the body reacts to excess fat and how fat metabolism and the genetics of fat metabolism play a role in insulin resistance and fatty liver disease,” says Dr. Wood.
Given that recent statistics show a third of Americans are obese, the research being done by Dr. Wood and others could have a profound impact on the nation’s health. One key focus is the underlying genetics that make certain people susceptible to disease.
“We’re not going to find specific genes that cause type 2 diabetes,” says Dr. Wood. “Perhaps they exist in rare cases, but not enough for a genetic risk assessment. We’re not looking for the cause of the disease; we’re looking at the body’s response to this burden of excess fat. We’re asking: Why do some people have a predisposition towards insulin resistance in the face of obesity? So we’re looking at the genetics of response, not the genetics of cause.”
Dr. Wood is developing mouse models to evaluate insulin sensitivity and its correlation with enzyme deficiency. He notes that many researchers have used the leptin-deficient mouse to study diabetes. Leptin is a hormone that regulates appetite and metabolism, and mice with reduced leptin are prone to obesity. But they are not the model Dr. Wood is looking for.
“Leptin-deficient mice weigh three times as much as a normal mouse,” says Dr. Wood. “So if you translate that into humans, we’re talking 400 or 500 pounds. That’s fine, if you’re studying morbid obesity, but we’re interested in a model that’s more moderate, like what we would commonly find in people.”
By developing more precise models, Dr. Wood hopes to tease out the various genetic traits that make up the complex trait of type 2 diabetes. For example, a particular gene might contribute 20 percent to insulin resistance. How does that gene combine with other genes to create the larger trait?
“We want to use models where fat metabolism has been disrupted to characterize these traits,” says Dr. Wood. “We want to see if we can put a genetic print to that. That’s the only way we’ll be able to use genetic biomarkers to predict risk.”