TGen Analysis Identifies Biomarkers for Diabetic Kidney Failure
News Jan 11, 2010
Researchers using a DNA analysis tool developed by the Translational Genomics Research Institute (TGen) and UCLA have identified genetic markers that could help treat chronic kidney disease among diabetics.
Study results, published in the December edition of Diabetic Medicine, show it is possible to identify biomarkers associated with end-stage renal disease (ESRD) from the pooled DNA of more than 1,000 diabetics. Specifically, TGen researchers identified genes that could potentially contribute to ESRD among those with Type 1 Diabetes.
ESRD almost always follows chronic kidney failure and although treatable with dialysis or transplantation, mortality rates remain high. While diabetic kidney disease is one of the most common complications of diabetes, it is currently not possible to determine who is at risk for ESRD.
“Identification of specific DNA variants may enhance our understanding of genetic risk factors for renal disease and may provide diagnostic value in determining which patients are at greatest risk of developing ESRD,” said Dr. Johanna DiStefano, Director of TGen’s Diabetes, Cardiovascular & Metabolic Diseases Division and the paper’s senior author.
The need to rapidly identify individuals with a predisposition to ESRD and to discover new drugs to prevent and treat this devastating condition is becoming critical, as the average onset of this disease affects ever-younger populations. Although Type 1 diabetes is distinctly different from Type 2 diabetes, the development of kidney disease is similar in individuals with either form. Researchers expect the current findings to impact individuals with both forms of the disease.
Nearly 23 percent of Americans diagnosed with ESRD die within the first year. And the annual cost for the nearly 400,000 people who require blood dialysis or kidney transplants is more than $16 billion.
TGen researchers tested the pooled genomic DNA from more than 500 cases of those with ESRD and compared that to more than 500 patients who had Type I diabetes for at least 20 years with no sign of ESRD. Scientists performed a whole genome association scan to look for single nucleotide polymorphisms (SNPs), which are the individual letters of DNA that vary among individuals. They sought SNPs that indicate a susceptibility to ESRD.
Experiments identified at least eight locations along the nearly 3-billion-base human genome that are ripe for further investigation of their ties to ESRD, according to the paper, Genome-wide SNP genotyping study using pooled DNA to identify candidate markers mediating susceptibility to end-stage renal disease attributed to Type 1 diabetes.
The study found at least six markers that may be associated with Type 1 diabetes, a lifelong disease that occurs when the pancreas does not produce enough insulin to properly control blood-sugar levels. There currently is no way to prevent Type 1, which is caused by autoimmune, genetic or environmental factors.
The study found two markers that may be associated with Type 2 diabetes, the most common form of diabetes. Type 2 develops when the body fails to produce enough insulin, which helps turn glucose from the sugars and starches we eat into energy. The failure to process glucose starves cells of energy and over time can damage the eyes, nerves, heart or kidneys. Type 2 diabetes can be prevented or treated with proper diet and exercise.
Mechanism Controlling Multiple Sclerosis Risk IdentifiedNews
Researchers at Karolinska Institutet have now discovered a new mechanism of a major risk gene for multiple sclerosis (MS) that triggers disease through so-called epigenetic regulation. They also found a protective genetic variant that reduces the risk for MS through the same mechanism.
Synthetic DNA Shuffling Enzyme Outpaces Natural CounterpartNews
A new synthetic enzyme, crafted from DNA rather than protein, flips lipid molecules within the cell membrane, triggering a signal pathway that could be harnessed to induce cell death in cancer cells. Researchers say their lipid-scrambling DNA enzyme is the first in its class to outperform naturally occurring enzymes – and does so by three orders of magnitudeREAD MORE
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.