May 29, 2012 (Florence, Italy) — In patients with newly diagnosed type 2 diabetes, genetic differences might influence beta cell functional mass, new research suggests.
Sara Bonetti, MD, from the University of Verona and the Azienda Ospedaliera Universitaria Integrata, in Italy, and colleagues presented the findings here at the Joint 15th International Congress of Endocrinology and 14th European Congress of Endocrinology.
"This cohort of newly diagnosed type 2 diabetic patients was useful for our purposes because of the absence of the potentially confounding effects of long-lasting antidiabetic treatments and because of the limited impact of duration and severity of hyperglycemia on metabolic phenotypes," Dr. Bonetti told Medscape Medical News.
According to Dr. Bonetti and colleagues, common genetic variability is already known to influence beta cell functional mass in type 2 diabetes, but the way in which this occurs is not clear.
To investigate the role of genetics in beta cell mass, the researchers studied 590 drug-naïve, glutamic-acid-decarboxylase-negative patients newly diagnosed with type 2 diabetes. Median age was 60 years and average body mass index was 29.3 kg/m².
Dr. Bonetti and colleagues assessed beta cell functional mass using mathematical modeling of glucose/C-peptide curves during multiple oral glucose tolerance tests. Insulin sensitivity was measured using the insulin clamp technique.
Forty-five single-nucleotide polymorphisms (SNPs) were selected to represent over 90% of the common genetic variability observed in diabetes. The SNPs came from 8 genes involved in maturity onset diabetes of the young (MODY) and 2 genes associated with neonatal diabetes mellitus (NDM).
The researchers measured both derivative and proportional control of beta cell function. Derivative control measures the capability of beta cells to respond to the rate of increase in glucose concentration (i.e., it takes into account the actual and the baseline glucose concentration). Proportional control measures the capability of beta cells to respond to glucose concentration at a given moment in time.
They found that allelic variants of 4 SNPs — rs1303722 and rs882019 of glucokinase, rs7310409 of HNF1A, and rs5219 of KCNJ11 (a known type 2 diabetes risk variant) — were significantly associated with changes in derivative control of beta cell function (P = .007 to .030).
Allelic variants of 5 other SNPs — rs2869084 and rs6031544 of HNF4A, rs10774580 of HNF1A, rs1801262 of NEUROD1, and rs7129639 of ABCC8 — were found to influence proportional control of beta cell function (P = .001 to .040).
Of the 45 SNPs, 1 was found to be associated with insulin sensitivity (P = .047).
"Common variability of MODY and NDM genes is significantly associated to beta cell functional mass in patients with type 2 diabetes, potentially playing a role in the pathophysiology of the disease and in its metabolic prognosis," Dr. Bonetti and colleagues conclude.
According to Dr. Bonetti, it is possible that genetics will be used in the future to infer metabolic phenotypes that cannot be measured with standard clinical tools, and to personalize diagnosis and treatment.
However, she pointed out that many questions remain unanswered, because "type 2 diabetes is a complex disease and all genetic factors identified so far explain only a small part of the disease."
Vincenzo Trischitta, MD, associate professor of endocrinology at University La Sapienza in Rome, Italy, noted that it is well known that the genes studied modulate insulin secretion, so the findings are not surprising.
"Some data have been previously reported on the role of common variants of monogenic diabetes genes on abnormal glucose homeostasis," Dr. Trischitta told Medscape Medical News. "It makes a lot of sense that some of these variants may, in fact, affect insulin secretion, the most potent determinant of glucose homeostasis."
According to Dr. Trischitta, these data are very important becaause they help to address the pathophysiology of insulin secretion. However, he explained, clinicians "have to be aware that...genetic data on common variants have clinical relevance neither for predicting disease onset nor for treatment choices." Currently, clinicians "should be encouraged to use traditional clinical markers, which perform quite well and are not costly."
This study was supported in part by a European Foundation for the Study of Diabetes/Novartis grant. Dr. Bonetti and Dr. Trischitta have disclosed no relevant financial relationships.
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