Colorado State University Researcher Discovers Oxygen Starvation Causes Shift in Metabolism that Leads to Diabetes

Research at Colorado State University has found that stress on the metabolic system forces genes to change how the body converts energy over time, leading individuals toward a progression to type II diabetes. The discovery counters current beliefs that a defective gene or genes predispose some people to developing diabetes and instead points to changes in the role of oxygen in metabolism.

Using a little-known computer algorithm developed in Russia decades ago, a biomedical and computer expert has unlocked the path to diabetes as genes of healthy individuals change the way they function – or are expressed. Over time, they begin to resist insulin because cells don’t receive enough oxygen during metabolism-related functions.  

The discovery shows that people progress toward the disease as a group of genes switch metabolism – the way a body processes energy – away from aerobic to anaerobic pathways in response to a lack of oxygen. Eventually, cells function much as they would under a state of hypoxia, such as altitude hypoxia, induced when cells are starved for oxygen. The continued state of hypoxia triggers a metabolic syndrome that eventually leads to type II diabetes.

What does this mean for the average person? It means exercise, which develops capillaries that carry oxygen to cells, becomes critical in the fight against diabetes not just for weight control, but because it can dictate how a body uses blood sugar for energy. It also helps scientists understand on the molecular level how individuals develop diabetes and changes earlier beliefs that a defect in genes controlling oxidation may be the major cause of type II diabetes. Instead, it is more likely that this group of genes fall into disuse altogether with reduction of oxidation in overall energy balance of a cell.

Andre Ptitsyn, the lead researcher on the project, is at Colorado State University’s Bioinformatics Center in the College of Veterinary Medicine and Biomedical Sciences. He reexamined previously published data on gene expression, body mass index, cellular oxygen use and insulin resistance of 60-something year old males, some with diabetes, some who were pre-diabetic and some who did not show signs of diabetes. He then repeated the study with two similar data sets and reached the same results. The research will be published this month in the BMC Genomics Journal, a professional biomedical publication.

How aerobic vs. anaerobic works: Healthy bodies typically turn sugar in the blood to energy through aerobic means, often called cellular respiration, and oxygen is used within cells for this process. When oxygen is in short supply, cells make "quick energy" by splitting sugar in an anaerobic – without oxygen – process called glycolysis. While aerobic energy production requires oxygen, it is more efficient in the body and provides lasting energy. Anaerobic energy is quick but is not as efficient and could not be used for sustained physical activity.

"Researchers have known for a long time that an individual’s oxygen capacity has something to do with diabetes, but until now we didn’t know how the two were related. We knew that people who do not have diabetes consume more oxygen compared to people who are pre-diabetic or diabetic," said Ptitsyn.

"Even muscle cells of a diabetic don’t seem to be absolutely deaf to insulin. We observe elevated activity in genes known to be stimulated by insulin," Ptitsyn said. "The cells try to comply with the insulin stimulation, but without a proper oxygen supply, they are confined to a less efficient glycolysis. High expression of certain genes indicates craving for oxygen, but to get oxygen supplied to cells, a person must stimulate capillary growth in the muscles for more blood. Cells continue to be stimulated to expend energy by insulin, but a body can’t spend the energy optimally because it lacks oxygen."

When excess sugar is not removed from the blood quickly enough, the pancreas increases insulin production to stimulate the process. Unable to comply, the body gradually begins to ignore the message, which causes still more insulin production. Over the years, this vicious cycle may lead to an exhaustion of insulin-producing cells in pancreas, Ptitsyn notes.

"Our modern, sedentary lifestyle predisposes us for such a loophole in metabolism regulation," Ptitsyn said. "While physical exercise is a natural way to shift the energy balance to aerobic pathway and improve insulin sensitivity, understanding the molecular mechanisms will allow us to access individual risks and develop more personalized and effectively aimed drugs to help people. Our study brings us a step close to this goal."

The study shows that genes of healthy individuals are expressed much the same. Individuals in the study who were shifting from aerobic to anaerobic metabolism showed a change in the way their genes were expressed that begins a progression toward diabetes.

Ptitsyn’s discovery steams from MIT, Harvard and University of East Carolina research projects. This data was published and available in the Internet. This data contains clinical information about dozens of patients in relation to insulin resistance and diabetes and relative levels of activity for thousands of genes in each sample.

Ptitsyn first heard of the study when it was presented at a conference in 2003. While listening to a presentation by those researchers about possible genetic defects that cause diabetes, Ptitsyn, a computer expert, began to wonder if there were more ways to analyze the data and extract more knowledge. He obtained the data and began his own analysis.

Ptitsyn acquired his first experience in classification algorithms in Siberia in late 1980s. As a student, he had a part-time job in a computer lab of Novosibirsk State University, where local scientists taught computational pattern analysis. He became familiar with the inventor of the algorithm that he applied in his current study. This computer algorithm, called FOREL, is little-known among researchers, primarily because it was published only in Russian decades ago and was previously applied in image processing and limited data classification in geology and economics.

FOREL is computationally demanding and, until recently, its application was severely restricted by computer power. However, modifications made by Ptitsyn significantly improved the performance of the old algorithm, while its ability to operate on high-dimensional data made it particularly suitable for analysis of microarray experiments such as this one, which produce a snapshot of activity for thousands of genes at a time.

In collaboration with a team of researchers from the Pennington Biomedical Research Center in Louisiana, Ptitsyn proposed an alternative analysis and interpretation of the data accumulated in previous experiments.

The original studies that provided the data have made a significant impact on the field of diabetes research.

Ptitsyn’s discovery confirms the previously reported findings and reveals new, important details about the data.

"Characteristic patterns of gene expression overlooked in previous studies suggest a different interpretation of the results and point at a different mechanism behind the metabolic syndrome and type II diabetes," Ptitsyn said. "It is also remarkable that the data acquired in a few independent experiments and made publicly available by the authors can be matched and made to produce new results years after the original publication."