ScienceDaily (Oct. 4, 2012) — A research team led by Anna Floegel of the German Institute of Human Nutrition (DIfE) and Tobias Pischon of the Max Delbrueck Center for Molecular Medicine (MDC) has identified 14 novel biomarkers for type 2 diabetes. They can serve as basis for developing new methods of treatment and prevention of this metabolic disease. The biomarkers can also be used to determine diabetes risk at a very early point in time. At the same time the markers enable insight into the complex mechanisms of this disease, which still have not been completely elucidated.
The researchers studied the blood of study participants from three different studies with respect to their metabolites (metabolomics). The study was based on data and blood samples of the prospective EPIC-Potsdam study with more than 27,500 study participants, the Tuebingen family study and the KORA study. The study was conducted in collaboration with the German Center for Diabetes Research (DZD) and funded by the Federal Ministry of Education and Research (BMBF).
Metabolomics is still a young research field and serves the understanding of biological systems. It studies the dynamic network of metabolites of an organism and thus provides insight into ongoing biochemical processes. Metabolites have quite diverse functions. For instance, they play a role in cellular communication and regulation, transport energy or are building material for the cells. Changes in metabolite concentrations may therefore directly reflect alterations in metabolism and thereby, shed light on the pathogenesis or presence of disease.
The aim of the current study was to identify metabolites in blood which provide insight into the pathomechanisms of type 2 diabetes and in addition can be used as biomarkers to determine the disease risk. To this end, the researchers studied a total of 4,000 blood samples. About 3,000 of these samples came from the EPIC-Potsdam study, nearly 900 samples from the KORA study in Augsburg and 76 from the study in Tuebingen. At the time the blood sample was taken, none of the study participants suffered from type 2 diabetes: However, during the average follow-up time of seven years, 800 Potsdam study participants and 91 Augsburg participants were diagnosed with type 2 diabetes. The 76 participants in the Tuebingen study were already classified at the beginning of the study as individuals at high risk for type 2 diabetes. At the time the blood sample was taken, however, they were still healthy.
163 metabolites analyzed per blood sample
Jerzy Adamski and his team at the Institute of Experimental Genetics of Helmholtz Zentrum Muenchen analyzed the concentrations of 163 metabolites per blood sample. Fourteen of these metabolites exhibited a strong association with the development of type 2 diabetes.
"In addition to simple sugars, the 14 identified metabolites include various protein components and choline-containing phospholipids which play a role in the structure of cell membranes and in the transport of blood lipids," said Anna Floegel, lead author of the study. "Our findings particularly indicate a previously unknown role of phospholipids in type 2 diabetes development. This is a first clue which should definitely be pursued."
"At the same time the metabolites can also be used as biomarkers to precisely determine the risk of diabetes at a very early stage, since the study is based on prospective data, that is data that were collected before the onset of the disease," said Tobias Pischon, who led the study. "The results of the new metabolomic analysis thus provide a good basis for developing new treatment and prevention methods."
ScienceDaily (Oct. 4, 2012) — Nearly 100 years after a British neurologist first mapped the blind spots caused by missile wounds to the brains of soldiers, Perelman School of Medicine researchers at the University of Pennsylvania have perfected his map using modern-day technology. Their results create a map of vision in the brain based upon an individual's brain structure, even for people who cannot see. Their result can, among other things, guide efforts to restore vision using a neural prosthesis that stimulates the surface of the brain.
The study appears in the latest issue of Current Biology, a Cell Press journal.
Scientists frequently use a brain imaging technique called functional MRI (fMRI) to measure the seemingly unique activation map of vision on an individual's brain. This fMRI test requires staring at a flashing screen for many minutes while brain activity is measured, which is an impossibility for people blinded by eye disease. The Penn team has solved this problem by finding a common mathematical description across people of the relationship between visual function and brain anatomy.
"By measuring brain anatomy and applying an algorithm, we can now accurately predict how the visual world for an individual should be arranged on the surface of the brain," said senior author Geoffrey Aguirre, MD, PhD, assistant professor of Neurology. "We are already using this advance to study how vision loss changes the organization of the brain."
The researchers combined traditional fMRI measures of brain activity from 25 people with normal vision. They then identified a precise statistical relationship between the structure of the folds of the brain and the representation of the visual world.
"At first, it seems like the visual area of the brain has a different shape and size in every person," said co-lead author Noah Benson, PhD, post-doctoral researcher in Psychology and Neurology. "Building upon prior studies of regularities in brain anatomy, we found that these individual differences go away when examined with our mathematical template."
A World War I neurologist, Gordon Holmes, is generally credited with creating the first schematic of this relationship. "He produced a remarkably accurate map in 1918 with only the crudest of techniques," said co-lead author Omar Butt, MD/PhD candidate in the Perelman School of Medicine at Penn. "We have now locked down the details, but it's taken 100 years and a lot of technology to get it right."
The research was funded by grants from the Pennsylvania State CURE fund and the National Institutes of Health (P30 EY001583, P30 NS045839-08, R01 EY020516-01A1).
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