The prevalence of diabetes mellitus (DM) across the world is constantly rising. It is estimated that 642 million cases of DM will be reported by the year 2040 [1]. In the African region alone, it was found that 15.5 million adults were living with DM and, of these, 7.0% originate from South Africa [2]. Diabetes mellitus is defined as a chronic metabolic disease characterized by prolonged hyperglycemia [3].
The prolonged hyperglycemia experienced by diabetic patients can result in macro- and microvascular complications that increases the risk for heart disease, stroke, and damage to the nervous system, retina, kidneys and other organs [4, 5]. Therefore, DM treatment aims to maintain a blood glucose level within the physiological range [5]. Therapies implemented include dietary and lifestyle modification and the administration of oral anti diabetic drugs.
The preferred first line treatment in most clinical guidelines for the management of type 2 diabetes mellitus (T2DM), accounting for ~90.0% of all DM cases, is metformin [6, 7]. However, 38.0% of T2DM patients respond poorly to metformin [8]. In addition to biguanides, several other classes of drugs are being prescribed to treat T2DM; these include: sulfonylureas, meglitinides, thiazolidinediones, α-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonist, sodium glucose cotransporter-2 inhibitors, insulin and its analogues [9, 10, 11].
Type 2 diabetes mellitus has been linked to variability in candidate genes that interfere with the management of glycemic control [9]. These candidate genes are involved in drug absorption, transportation, distribution, metabolism and the signaling cascade of oral anti diabetic drugs [12]. Studies have shown that the T2DM patient’s response to treatment is characterized by inter-individual variability [13, 14]. This variability in response have been linked to genetic and environmental factors [15, 16].
As metformin is the most common drug prescribed for the treatment and management of T2DM, numerous studies have been conducted to determine the therapeutic effects of metformin in the presence of genetic variants. Amongst the variants investigated, the
Pharmacogenomic and pharmacokinetic studies have been conducted on the treatment response to T2DM in various populations across the world [20, 21, 22, 23]. However, even though numerous studies have been conducted, limited data is available for sub-Saharan African populations and other African populations, regardless of the human genomic diversity found on this continent. Genetic diversity presented by indigenous populations across the world, in this instance South Africa, should be explored for improved diagnostic techniques and treatment plans for conditions such as diabetes, cardiovascular disease and cancer. The indigenous Nguni population of South Africa was selected for investigation in this study. The Nguni population is comprised of the Xhosa, Zulu, Ndebele and Swati clades [24, 25, 26].
Loci identified in previously studied populations observed anti diabetic drug efficacy may or may not affect efficacy in South African populations because of ethnic genetic differences. Seventeen single nucleotide polymorphism (SNP) biomarkers selected for investigation in this study, have previously been associated with T2DM in various populations across the world [17, 18, 19, 20, 21, 22, 23, 27, 28]. The aim of this study was to investigate the genetic association of these 17 SNP biomarkers and the response to anti diabetic treatment to determine their suitability for individualized metformin therapy in patients diagnosed with T2DM in the Nguni indigenous population of South Africa.
In this pool of study subjects, 53 patients demonstrated a controlled T2DM (responders to metformin therapy), with the remaining 87 patients demonstrated an uncontrolled T2DM (non-responders to metformin therapy). Patients were included in the study if they were 18 years or older and had been on treatment for at least 1 year prior to the study. All patients were on metformin mono-therapy. Patients with other diseases such as type 1 diabetes mellitus (T1DM), malignancies, hyperlipidemia, chronic kidney and liver diseases, as well as pregnant patients, were excluded from the study. Information about age, family history, medical history, demographic parameters and medication used was obtained
Clinical and biochemical demographics of the study population.
Parameters | Controlled ( |
Uncontrolled ( |
|
---|---|---|---|
Sex (F; M) | F: 36; M: 14 | F: 57; M: 30 | – |
Age (years) | 60.7 ± 11.0 | 58.3 ± 11.4 | 0.470 |
Weight (kg) | 85.8 ± 19.5 | 85.4 ± 19.1 | 0.833 |
Height (cm) | 162.1 ± 7.8 | 163.1 ± 7.7 | 0.487 |
Body mass index | 31.9 ± 8.4 | 30.4 ± 10.7 | 0.304 |
Hb A1c (%): | |||
baseline | 7.6 ± 2.0 | 11.0 ± 2.9 | |
12 months | 6.7 ± 1.2 | 11.5 ± 2.9 | |
Random blood sugar (%) | 9.4 ± 3.9 | 14.5 ± 6.6 | |
Systolic blood pressure (mmHg) | 147.0 ± 24.0 | 153.5 ± 23.9 | 0.130 |
Diastolic blood pressure (mmHg) | 83.9 ± 15.5 | 90.3 ± 13.7 | |
Total cholesterol (mmol/L) | 4.4 ± 1.1 | 5.0 ± 1.1 | |
High-density lipoprotein (mmol/L) | 1.2 ± 0.4 | 1.2 ± 0.4 | 0.672 |
Low-density lipoprotein (mmol/L) | 2.3 ± 0.9 | 2.8 ± 0.9 | |
Triglycerides (mmol/L) | 2.0 ± 1.2 | 2.3 ± 1.1 | 0.215 |
Creatinine (g/mol) | 157.7 ± 383.1 | 79.3 ± 29.5 | 0.317 |
Glomerular filtration rate (mL/min/1.73 m2 | 41.7 ± 21.5 | 52.6 ± 25.0 | 0.313 |
Values are presented as mean ± SD. Significant
Random venous blood was collected to measure serum glycosylated Hb (Hb A1c) levels. Furthermore, lipid profile [which includes: total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL)] was obtained (Table 1). All blood samples were sent to relevant clinical laboratory centers for analysis.
Genomic DNA was isolated from buccal swabs using a standard salt lysis method [32]. Samples were stored at –20 °C. DNA was quantified using a NanoDrop™2000/ 2000c UV/VIS Spectrophotometer (Thermo Scientific, Waltham, MA, USA). The SNPs were genotyped using the MassARRAY®System IPLEX extension reaction (Agena Bioscience, San Diego, CA, USA). Genotypes of the selected SNP variants were determined for all the study participants (Table 2).
Single nucleotide polymorphism information and Hardy-Weinberg p values in the study population.
SNP | Gene/ Closest Gene | Chromosomal Position | Location | Allele Change | Amino Acid Change | HWE |
---|---|---|---|---|---|---|
rs10783050 | 1:96571527 | intergenic | T>C | – | 0.932 | |
rs1143623 | 2:112838252 | intergenic | C>G | – | 0.309 | |
rs13266634 | 8:117172544 | missense | C>A/T | Arg325Trp | 0.532 | |
rs13376631 | 1:171266603 | intron | A>G | – | 0.903 | |
rs1801282 | 3:12351626 | missense | C>G | Pro12Ala | monomorphic | |
rs249429 | 5:40782137 | intron | C>T | – | 0.299 | |
rs2815752 | 1:72346757 | intergenic | G>A | – | 0.636 | |
rs316009 | 6:160254732 | intron | C>T/G | – | 0.595 | |
rs316019 | 6:160249250 | missense | C>A | Ala270Ser | 0.808 | |
rs391300 | 17:2312964 | intron | C>T | – | 0.739 | |
rs461473 | 6:160122530 | intron | G>A | – | 0.898 | |
rs4810083 | 20:57545215 | intergenic | C>T | – | 0.145 | |
rs578427 | – | 6:91702432 | intergenic | T>C | – | 0.909 |
rs622342 | 6:160151834 | intron | A>C | – | 0.218 | |
rs6265 | 11:27658369 | missense | C>T | Val66Met | monomorphic | |
rs819 2675 | 3:171007094 | intron | C>T | – | 0.674 |
Table 1 displays the clinical and biochemical demographics of the study population. All SNPs are within HWE with two SNPs (rs1801282 and rs6265) being monomorphic (Table 2). Hardy-Weinberg equilibrium
Genotype and allele frequencies of 13 single nucleotide polymorphism(s) demonstrating no significant association to type 2 diabetes mellitus treatment response.
SNP | Genotype/ Allele | Control |
Uncontrolled |
OR (95% CI) | |
---|---|---|---|---|---|
rs10783050 | TT | 52 (98.1) | 86 (98.9) | reference | |
TC | 1 (1.9) | 0 (0.0) | 0.20 (0.01-5.06) | 0.331 | |
T | 105 (99.1) | 172 (98.9) | reference | ||
C | 1 (0.9) | 0 (0.0) | 0.20 (0.01-5.05) | 0.332 | |
rs1143623 | CC | 48 (90.6) | 74 (85.1) | reference | |
CG | 5 (9.4) | 11 (12.6) | 1.43 (0.47-4.36) | 0.533 | |
C | 96 (90.6) | 159 (91.4) | reference | ||
G | 10 (9.4) | 11 (6.3) | 0.66 (0.27-1.62) | 0.369 | |
rs13266634 | CC | 50 (94.3) | 78 (89.7) | reference | |
CT | 3 (5.7) | 8 (9.2) | 1.71 (0.43-6.75) | 0.444 | |
100 (94.3) | 164 (94.3) | reference | |||
6 (5.7) | 8 (4.6) | 0.81 (0.27-2.41) | 0.709 | ||
rs13376631 | AA | 16 (30.2) | 22 (25.3) | reference | |
GG | 11 (20.8) | 17 (19.5) | 1.12 (0.42-3.04) | 0.818 | |
AG | 25 (13.3) | 37 (42.5) | 1.08 (0.47-2.44) | 0.860 | |
57 (53.8) | 81 (46.6) | reference | |||
47 (44.3) | 71 (40.8) | 1.06 (0.64-1.75) | 0.811 | ||
rs1801282 | CC | 53 (100.0) | 86 (98.9) | monomorphic | monomorphic |
C | 106 (100.0) | 172 (98.9) | – | ||
rs249429 | TT | 28 (52.8) | 40 (46.0) | reference | |
CC | 3 (5.7) | 6 (6.9) | 1.40 (0.32-6.07) | 0.650 | |
CT | 22 (41.5) | 40 (46.0) | 1.27 (0.6258-2.5885) | 0.510 | |
T | 78 (73.6) | 120 (69.0) | reference | ||
C | 28 (26.4) | 52 (29.9) | 1.21 (0.70-2.07) | 0.500 | |
rs2815752 | GG | 18 34.0) | 22 (25.3) | reference | |
AA | 13 (24.5) | 20 (23.0) | 1.26 (0.49-3.21) | 0.630 | |
GA | 22 (41.5) | 43 (49.4) | 1.60 (0.71-3.59) | 0.254 | |
58 (54.7) | 87 (50.0) | reference | |||
48 (45.2) | 83 (47.7) | 1.15 (0.71-3.59) | 0.567 | ||
rs34834489 | GG | 46 (86.7) | 80 (92.0) | reference | |
GA | 6 (11.3) | 6 (6.9) | 0.58 (0.18-1.89) | 0.361 | |
98 (92.5) | 166 (95.4) | reference | |||
6 (5.7) | 6 (3.4) | 0.59 (0.19-1.88) | 0.373 | ||
rs391300 | CC | 9 (17.0) | 17 (19.5) | reference | |
TT | 18 (34.0) | 24 (27.6) | 0.71 (0.26-1.94) | 0.500 | |
CT | 26 (49.1) | 44 (50.6) | 0.90 (0.35-02.30) | 0.819 | |
44 (41.5) | 78 (44.8) | reference | |||
62 (58.5) | 92 (52.9) | 0.84 (0.51-1.37) | 0.477 | ||
rs461473 | GG | 52 (98.1) | 84 (96.6) | reference | |
GA | 1 (1.9) | 2 (2.3) | 1.24 (0.11-14.00) | 0.863 | |
105 (99.1) | 170 (97.7) | reference | |||
1 (0.9) | 2 (1.1) | 1.24 (0.11-13.99) | 0.864 | ||
rs622342 | AA | 33 (62.3) | 48 (55.2) | reference | |
CC | 3 (5.7) | 12 (13.8) | 4.13 (0.87-19.65) | 0.075 | |
CA | 18 (34.0) | 26 (29.9) | 0.99 (0.472.10) | 0.985 | |
84 (79.2) | 122 (70.1) | reference | |||
22 (20.8) | 50 (28.7) | 1.56 (0.88-2.78) | 0.113 | ||
rs6265 | CC | 53 (100.0) | 86 (98.9) | monomorphic | monomorphic |
C | 106 (100.0) | 172 (98.9) | – | – | |
rs8192675 | CC | 42 (79.2) | 58 (66.7) | reference | |
TT | 1 (1.9) | 3 (3.4) | 2.17 (0.22-21.62) | 0.508 | |
CT | 9 (17.0) | 25 (28.7) | 2.01 (0.85-4.75) | 0.111 | |
93 (87.7) | 141 (81.0) | reference | |||
11 (10.4) | 31 (17.8 | 1.86 (0.89-3.88) | 0.099 |
OR: odds ratio; 95% CI: 95% confidence interval.
Percent does not account for missing allele(s) at specific loci.
Genotype and allele frequencies of four single nucleotide polymorphism(s) demonstrating no significant association to type 2 diabetes mellitus treatment response.
Unadjusted | Adjusted | |||||||
---|---|---|---|---|---|---|---|---|
SNP | Genotype/ Allele | Controlled |
Uncontrolled |
OR (95% CI) | OR (95% CI) | Bonferonni Corrected |
||
rs316009 | CC | 45 (84.9) | 83 (95.4) | reference | reference | |||
CT | 8 (15.1) | 3 (0.03) | 0.20 (0.05-0.81) | 0.31 (0.05-0.190) | 0.204 | – | ||
98 (92.5) | 169 (97.1) | reference | reference | |||||
8 (0.08) | 3 (0.02) | 0.22 (0.06-0.84) | 0.85 (0.01-0.93) | 0.088 | ||||
rs316019 | CC | 41 (77.4) | 77 (88.5) | reference | reference | |||
AA | 1 (0.02) | 0 (0.0) | 0.18 (0.01-4.48) | 0.295 | ||||
CA | 11 (20.8) | 8 (0.09) | 0.39 (0.14-1.04) | 0.059 | ||||
C | 93 (87.7) | 162 (93.1) | reference | reference | ||||
A | 13 (12.3) | 8 (0.05) | 0.35 (0.14-0.8) | 0.72 (0.21-2.44) | 0.593 | – | ||
rs4810083 | CC | 16 (30.2) | 33 (37.9) | reference | reference | |||
TT | 10 (18.9) | 32 (36.9) | 1.55 (0.61-3.92) | 0.353 | 1.24 (0.42-3.60) | 0.698 | – | |
CT | 27 (35.7) | 21 (43.1) | 0.38 (0.17-0.86) | 2.80 (1.01-7.79) | 0.098 | |||
59 (55.7) | 75 (43.1) | reference | reference | |||||
47 (44.3) | 97 (55.7) | 1.62 (1.00-2.64) | 0.051 | 0.81 (0.48-1.36) | 0.423 | – | ||
rs578427 | TT | 10 (18.9) | 15 (17.2) | reference | reference | |||
CC | 4 (7.5) | 28 (32.2) | 4.67 (1.25-17.44) | 1.80 (0.58-5.64) | 0.312 | |||
CT | 39 (73.6) | 43 (49.4) | 0.74 (0.30-1.83) | 0.507 | 1.70 (0.45-2.55) | 0.884 | ||
T | 47 (44.3) | 73 (42.0) | reference | reference | ||||
C | 59 (55.7) | 99 (56.9) | 1.08 (0.66-1.76) | 0.765 | 1.31 (0.78-2.22) | 0.308 | – |
OR: odds ratio; 95% CI: 95% confidence interval.
Percent does not account for missing allele(s) at specific loci. Significant
In this study the genetic association of 17 pharmacogenomic biomarkers and response to metformin treatment in the indigenous Nguni population of South Africa was determined. Previously, the
All SNPs, besides rs1801282 and rs6265 (which were shown to be monomorphic), were within HWE and showed
The
The
Genotype and allele distribution of the 17 SNPs were determined in all the study participants (Tables 3 and 4). Among the SNPs analyzed, 13 of the SNPs selected for this study showed no statistically significant association between treatment response and the SNP variant (Table 3). The remaining four variants however,
The rs316009 variant is located in a transcription factor binding motif and is in linkage disequilibrium with the non synonymous variant rs316019 [21, 40, 41, 42, 43, 44, 45, 46, 47]. In previous studies, the TT genotype of rs316009 showed an increase response to metformin in comparison to the CC and CT genotypes [41]. Unfortunately, the homozygous TT genotype was not observed in this study population. From the data available, the CT genotype demonstrated a better response to treatment in comparison to the CC genotype (Table 4). The rs316019 is the most common variant of
The interaction of metformin and other drugs in the presence of rs316019 was determined
Prior to correction, the A allele was significantly associated with an improved response to treatment. This is in contradiction to studies conducted by Song
The SNP variant rs4810083 T allele is not associated with a response to metformin treatment in T2DM patients [46]. The results obtained in this study, however, may suggest that the T allele is most likely to be associated with a decrease in response to diabetic treatment as more patients in the uncontrolled category carry the T allele in comparison to the controlled category. This study group also shows the CT genotype to be associated with an improved response to treatment (Table 4). To enable further clarity with regard to the significance of this SNP variant, more data is required from other population groups as well as a bigger sample cohort for the current study group.
In the case of the SNP rs578427, the TT genotype has been associated with an increased renal clearance and secretion clearance of metformin in comparison to the CC genotype in a healthy population [47]. As the accumulation of metformin in the body can result in the development of lactic acidosis, the TT genotype can thus be associated with an improved response to treatment. These results are in concordance with the data generated for this study population as the CC genotype was shown to be significantly associated with a decreased response to treatment (Table 4).
Contradictory, as well as inconclusive, results may have arisen for a number of reasons. The most relevant being sample size as well as SNP selection and the approach used to analyze individual SNPs. Because SNPs do not occur in isolation of each other, but rather as combinations forming defined haplotypes, the phenotypic effect of individual SNPs is not always consistent with functional effects. Thus, genotyping single or a few individual SNPs may fail to reflect the true functionality of genetic variants [48]. Therefore, it should be recommended that future studies evaluate haplotypes to establish the functional effects that a collection of SNPs may have on response to treatment.
In this study, two SNP variants (rs316009 and rs4810083) were significantly associated with improved response to diabetic treatment prior to Bonferroni correction. The greatest limitation of this study was the sample size and this has inadvertently affected the relevance of significantly associated SNP variants. Regardless of this, this study provides additional important data regarding possible associations between genetic variants and metformin therapy outcomes. In, addition, this study is one of the first studies providing genetic data from the understudied indigenous sub-Saharan African populations.