A paper I've been trying to get published for two years will finally appear in the American Journal of Physiology - Regulatory, Integrative, and Comparative Physiology. The goal of this paper was to develop a quantitative model for how insulin suppresses free fatty acid levels in the blood. A little background for those unfamiliar with human metabolism. All of the body's cells burn fuel and for most cells this can be fat, carbohydrate or protein. The brain, however, can only burn glucose, which is a carbohydrate, and ketone bodies, which are made when the body is short of glucose. Why the brain can't burn fat is still a mystery. It is not because fat cannot cross the blood brain barrier as is sometimes claimed. Thus, the body has a reason to regulate glucose levels in the blood. It does this through hormones, the most well known of which is insulin.
Muscle cells cannot uptake glucose unless insulin is present. So when you eat a meal with carbohydrates, insulin is released by the pancreas and your body utilizes the glucose that is present. In between meals, muscle cells mostly burn fat in the form of free fatty acids that are released by fat cells (adipocytes) through a process called lipolysis. The glucose that is circulating is thus saved for the brain. When insulin is released, it also suppresses lipolysis. Basically, insulin flips a switch that causes muscle and other body cells to switch from burning fat to glucose and in addition switches off the fuel supply for fat.
If your pancreas cannot produce enough insulin then your glucose levels will be elevated and this is diabetes mellitus. Fifty years ago, diabetes was usually the result of an auto-immune disorder that destroyed pancreatic beta cells that produce insulin. This is known as Type I diabetes. However, recently the most prevalent form of diabetes, called Type II, arises from a drawn out process attributed to overweight or obesity. In Type II diabetes, people first go through a phase called insulin resistance where more insulin is required for glucose to be taken up by muscle cells. The theory is that after prolonged insulin resistance, the pancrease eventually wears out and this leads to diabetes. Insulin resistance is usually reversible by losing weight.
Thus, a means to measure how insulin resistant or sensitive you are is important. This is usually done through a glucose challenge test, where glucose is either ingested or injected and then the response of the body is measured. I don't want to get into all the methods used to assess insulin sensitivity but one of the methods uses what is known as the minimal model of glucose disposal, which was developed in the late seventies by Richard Bergman, Claudio Cobelli and colleagues. This is a system of 2 ordinary differential equations that model insulin's affect on blood glucose levels. The model is fit to the data and an insulin sensitivity index is one of the parameters. Dave Polidori, who is a research scientist at Johnson and Johnson, claims that this is the most used mathematical model in all of biology. I don't know if that is true but it does have great clinical importance.
The flip side to glucose is the control of free fatty acids (FFAs) in the blood and this aspect has not been as well quantified. Several groups have been trying to develop an analogous minimal model for insulin's action on FFA levels. However, none of these models have been validated or even tested against each other on a single data set. In this paper, we used a data set of 102 subjects and tested 23 different models that included previously proposed models and several new ones. The models have the form of an augmented minimal model with compartments for insulin, glucose and FFA. Using Bayesian model comparison methods and a Markov chain Monte Carlo algorithm, we calculated the Bayes factors for all the models. We found that a class of models distinguished themselves from the rest with one model performing the best. I've been using Bayesian methods quite a bit lately and I'll blog about it sometime in the future. If you're interested in the details of the model, I encourage you to read the paper.