GENETIC ASSOCIATION OF SOLUTE
CARRIER TRANSPORTER GENE VARIANTS
WITH METFORMIN RESPONSE
Abrahams-October Z1, Xhakaza L1, Pearce B1,*, Mandisa Masilela C1,
Benjeddou M1, Vincent Adeniyi O2, Johnson R3,4, Jebio Ongole J5
*Corresponding Author: Brendon Pearce, Ph.D., Department of Biotechnology, University of the
Western Cape, Private Bag X17, Bellville 7535, South Africa. Tel.: +2721-959-2080. Fax: +2721-959-
2648. E-mail: brendon.biff@gmail.com
page: 47
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Abstract
Type 2 diabetes mellitus (T2DM) is a metabolic
disorder characterized by elevated blood glucose levels
and is influenced by both genetic and environmental factors.
It is treated with various classes of oral antidiabetic
drugs, however, response to treatment is highly variable
with patients failing to achieve adequate glycemic control.
Treatment response variability has been associated with
single nucleotide polymorphisms (SNPs) which influence
the pharma-cokinetics and pharmacodynamics of drug(s).
The aim of this study was to evaluate the genetic association
of 17 SNPs and the response to metformin therapy
in patients diagnosed with diabetes from the indigenous
Nguni population of South Africa. One hundred and forty
indigenous African patients diagnosed with T2DM were
recruited and genotyped using the MassARRAY® system.
Therapeutic response of patients was ascertained by a
change in Hb A1c. Two SNPs (rs1801282 and rs6265) were
monomorphic. All other variants were within the Hardy-
Weinberg equilibrium (HWE). The T allele of the SLC
variant rs316009 [odds ratio (OR) = 0.25, 95% confidence
interval (95% CI) = 0.01-0.09, p value = 0.044] and the
CT genotype of the PCK1 variant rs4810083 (OR = 2.80,
95% CI = 1.01-7.79, p value = 0.049) were associated with
an improved response to treatment after adjustment. No
association was observed with post Bonferroni correction.
Moreover, this study provides important additional data
regarding possible associations between genetic variants
and metformin therapy outcomes. In addition, this is one
of the first studies providing genetic data from the understudied
indigenous sub-Saharan African populations.
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