Pharmacogenetic approach to type 2 diabetes
Commonly occurring genetic variations no doubt help explain the failure of many type 2 diabetes patients to reach target fasting glucose levels even though they are being treated with the oral anti-hyperglycemic drug metformin. One example of the known variants is the mutated OCT1 gene that is linked to reduced metformin activity in an estimated 20% of people with European ancestry.
Genetic variants also may help explain the diarrhea, nausea and other side-effects that affect patients treated with metformin, a frequently used first-line therapy for type 2 diabetes. About 30% of patients treated with metformin experience side effects, the most serious of which is metformin-induced lactic acidosis.
Scripps Translational Science Institute (STSI) researchers plan to map individuals with type 2 diabetes into disease sub-types, each of which will be based on genetic susceptibility allele profiles. Such information will help scientists to design clinical trials that evaluate experimental compounds against these risk profiles. The results also may help physicians to predict a patient’s likelihood of benefitting – or not benefitting – from metformin or other medications for type 2 diabetes. (For patients who fail to reach target fasting glucose levels with metformin treatment, physicians often prescribe other medications in addition to metformin.) In addition to predicting response to therapy, genetic profiles may enable clinicians to determine in advance a patient’s risk for developing side effects.
STSI researchers will create the type 2 diabetes susceptibility risk profile from 38 highly associated SNPs in a set of patients with the disease. In the initial phase, the scientists will determine the extent of overlap among individuals with underlying known genetic defects contributing to type 2 diabetes risk. If clear subsets with common gene pathways exist, therapies may be paired to the underlying genetic defects.
These 38 statistically significant single nucleotide polymorphisms (SNPS) were linked to type 2 diabetes in genome-wide association studies that used a case/control study design to search for statistical associations in thousands of samples. Large genome-wide association studies and subsequent meta-analyses indicate that the majority – if not all – of these common genetic variations confer a small to moderate risk for developing type 2 disease.
Because these newly found risk genetic factors confer modest risk, they generally are not effective predictors of an individual’s risk for developing type 2 diabetes above and beyond traditional risk factors such as family history, body mass index and age. However, these variants have generated insights about the biology of disease, including the different pathways, processes and cell types that contribute to the type 2 diabetes phenotype.