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Article

Distribution of ApoE Gene Polymorphism and Its Association with the Lipid Profile Among Type 2 Diabetes Mellitus Black South Africans

by
Siphesihle Mkhwanazi
1,*,
Tumelo Jessica Mapheto
2,3,
Honey Bridget Mkhondo
4,
Olebogeng Harold Majane
5,
Sechene Stanley Gololo
1 and
Mashudu Nemukula
1,*
1
Department of Biochemistry and Biotechnology, Sefako Makgatho Health Sciences University, Pretoria 0208, South Africa
2
Department of Chemical Pathology, School of Medicine, Sefako Makgatho Health Sciences University, Pretoria 0208, South Africa
3
National Health Laboratory Service, Dr George Mukhari Academic Hospital, Ga-Rankuwa 0208, South Africa
4
Department of Preclinical Sciences, University of Limpopo, Polokwane 0727, South Africa
5
Department of Human Physiology, Sefako Makgatho Health Sciences University, Pretoria 0208, South Africa
*
Authors to whom correspondence should be addressed.
Diabetology 2026, 7(1), 8; https://doi.org/10.3390/diabetology7010008 (registering DOI)
Submission received: 25 November 2025 / Revised: 25 December 2025 / Accepted: 31 December 2025 / Published: 4 January 2026
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Abstract

Background: ApoE is a major regulator of lipid metabolism and glycaemic control. The aim of the current study is to investigate the ApoE gene polymorphisms among Black South Africans with and without type 2 diabetes mellitus (T2DM) and associate them with their lipid profile. Methods: A cross-sectional case–control study was conducted among 107 participants, divided into two groups: patients with T2DM (n = 65) and non-diabetic controls (n = 42). Blood samples were collected for analysis of glycated haemoglobin, lipid profile, nitric oxide, high-sensitivity C-reactive protein and DNA genotyping using the MALDI-TOF. Continuous variables were analysed using Student’s t-test or one-way analysis of variance (ANOVA). Genotype and allele frequencies were compared using Fisher’s exact tests. Results: The ApoE3 allele was the most prevalent among both groups, observed in 55.47% in T2DM patients and 52.38% in the non-diabetic group, followed by E4 and E2. HWE analysis revealed a deviation from equilibrium [χ2 (3) = 9.137, p = 0.0275]. TG levels differed significantly across ApoE alleles (F = 3.68, p = 0.03), with higher TG concentrations observed among E3 allele carriers and E4 allele carriers. Poor glycaemic control (HbA1c ≥ 7.0%) predominated across all ApoE alleles. Among E3 allele carriers, 75.0% of participants exhibited poor glycaemic control, whereas only 25.0% achieved good glycaemic control (p = 0.002). Conclusions: ApoE polymorphisms are associated with allele-specific heterogeneity in lipid metabolism and glycaemic control among individuals with T2DM, underscoring the complex, context-dependent role of genetic variation in metabolic dysregulation within African populations.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder with an increasing prevalence among the African descendants, particularly Black South Africans [1,2]. T2DM is characterised by insulin resistance and chronic hyperglycaemia as well as oxidative stress, leading to vascular endothelial dysfunction and stimulating atherosclerosis [3]. Elevated glycated haemoglobin (HbA1c), a marker of glycaemic control, is associated with macrovascular complications such as coronary artery disease, cerebrovascular accident, and peripheral arterial disease [4].
The apolipoprotein E (ApoE) gene is the most studied as a potential candidate to elucidate the genetic basis of cardiovascular disease in individuals with T2DM among Caucasian, African American, Asian and Indian descendants [5,6,7]. The ApoE gene is located at 19q13.32 with 4 exons separated by 3 introns [8]. The gene encodes a protein that is involved in lipid metabolism [9]. ApoE is a polymorphic gene with single nucleotide polymorphisms (SNPs) at positions 112 and 158, which results in three common isoforms, namely, ApoE2, ApoE3 and ApoE4, which results in three alleles, namely, E2, E3, and E4, having six genotypes, three of which are homozygous (E2/E2, E3/E3, and E4/E4) and three of which are heterozygous (E2/E3, E2/E4 and E3/E4), each with distinctive structural and functional properties [10].
Evidence-based studies have shown that these variants are associated with the risks of various diseases [11,12], including ACVD in individuals with T2DM [4]. Moreover, studies have indicated that ApoE variants may influence insulin sensitivity, including pancreatic β-cell function, contributing to the progression of T2DM [13]. In addition, ApoE variants have been implicated in modulating inflammation, oxidative stress, and lipid homeostasis, which are significant factors in both T2DM and ACVD [14].
However, little is known about the ApoE gene polymorphism in the context of Black South Africans with T2DM. Thus, the aim of the current study is to investigate the ApoE genetic polymorphisms among Black South African patients with and without T2DM and to associate them with their lipid profile. In the current study, a novel approach to characterising ApoE gene polymorphism was carried out using matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) analysis.

2. Materials and Methods

2.1. Study Design and Participant

A cross-sectional case–control study was conducted to investigate the distribution of ApoE gene rs429358 and rs7412 polymorphisms and their association with lipid and inflammatory biomarkers in Black South African patients with T2DM. The study participants included in the present genetic analysis were recruited as part of a broader study conducted at an academic hospital located in Ga-Rankuwa, Pretoria, Gauteng, South Africa. A subset of the clinical and biochemical data from this cohort has been previously reported in a related publication focusing on the magnitude of dyslipidaemia and factors associated with low low-density lipoprotein cholesterol (LDL-C) among T2DM Black South Africans [15]. The present study included 65 participants with T2DM attending the diabetic clinic and 42 non-diabetic individuals recruited from staff members of the Sefako Makgatho Health Sciences University (SMU). Written informed consent was obtained from the study participants after the purpose of the study and their rights were clearly explained to them. The study was approved by the Research and Ethics Committee of SMU (SMUREC/S/350/2022: PG). Inclusion criteria: Black South African patients, both male and female, aged 18 years or older, who have consented to participate in the study. Patients diagnosed with T2DM and receiving diabetic treatment at an academic hospital in Ga-Rankuwa, Pretoria, South Africa, who have complete medical records. Exclusion criteria: Study participants with missing information in their medical records and those with an active SARS-CoV-2 and HIV infection. Subjects who were on antiretroviral treatment. Dyslipidaemia was defined as elevated levels of TC ≥ 4.5 mmol/L, elevated levels of LDL-C ≥ 1.8 mmol/L, elevated levels of TG ≥ 1.7 mmol/L, and low HDL-C < 1 mmol/L for men and <1.2 mmol/L for women.

2.2. Blood Sample Collection

Venus blood samples were collected from study participants into EDTA and serum separator tubes (BD Vacutainers®, Franklin Lakes, NJ, USA), for analysis of HbA1c, lipid profile including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and LDL-C; and nitric oxide (NO) and high sensitivity C-reactive protein (hs-CRP), as well as for DNA extraction for ApoE genotyping.

2.3. Biochemical Assessment

HbA1c and lipid parameters (TC, TG, HDL-C and LDL-C) were analysed using the Siemens Atellica™ analyser (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA). Very low-density lipoprotein cholesterol (VLDL-C) was calculated using the following equation: VLDL-C = (TC − LDL-C − HDL-C), and non-HDL-C was calculated by subtracting the HDL-C value from the TC value [16]. NO concentration was measured using a colourimetric NO assay kit according to the manufacturer’s instructions (Sciencell Research Laboratories, Carlsbad, CA, USA). The assay estimated NO concentration by quantifying nitrite (NO2) through the Griess reaction, as described by Nemukula et al. [17].

2.4. ApoE Genotyping

2.4.1. Genomic DNA Extraction

Genomic DNA was extracted from a whole blood sample using the PureDireX protocol DNA purification kit, purchased from BIO-Helix Co., Ltd. (New Taipei, Taiwan, China). DNA concentration and purity were estimated by measuring the optical density at 260 nm and 280 nm using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

2.4.2. ApoE SNP Selection and PCR Amplification

ApoE SNPs (rs429358 and rs7412) were selected for genotyping using the MassARRAY system (Angena Bioscience Dx Analyser 4 system). In brief, primers for PCR and extension were designed using the Agena Biosciences Assay Designer software, version 2.2. Oligonucleotides were ordered unmodified with standard purification. Forward and reverse primers were reconstituted at 100 µM and diluted to a working concentration of 0.5 µM, while the probes (extension) were reconstituted at concentrations of 500 µM (Table 1).
A multiplexed PCR cocktail was prepared, containing PCR buffer, 2 mM MgCl2, 500 µM dNTP mix, 100 nM primer mix, and 1.0 U/reaction PCR enzyme. Four microliters of the cocktail were added to a 96-well microtiter plate along with the DNA samples. PCR cycling was performed to amplify the target loci. Shrimp alkaline phosphatase (SAP) treatment was used to remove unincorporated dNTPs from PCR products. 2 µL of SAP mix was then added to each well containing PCR products and incubated, followed by heat inactivation. Then, 2 µL of the primer extension reaction cocktail (iPLEX-single-Base Extension), containing the extend primer, buffer, enzyme, and mass-modified ddNTPs, was added to the PCR products. The reaction was performed with specific cycling conditions to extend the primers by one mass-modified nucleotide. The PCR operation conditions for amplification of target loci included an initial denaturation phase at 95 °C for 5 min, followed by 45 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s, an extension phase at 72 °C for 60 s, and the final elongation at 72 °C for 5 min. The PCR operation conditions for Shrimp alkaline phosphatase included 37 °C for 40 min, followed by 85 °C for 5 min. Lastly, the PCR operation conditions for the iPLEX single base extension included an initial denaturation phase at 95 °C for 30seconds, followed by 60 cycles of denaturation at 95 °C for 5 s, annealing at 52 °C for 3 s, an extension phase at 80 °C for 60 s, followed by 5 cycles, and the final elongation at 72 °C for 3 min.

2.4.3. MALDI-TOF SNPs Profiling

A resin slurry was added to the primer extension reaction products to effectively remove salts. In brief, nuclease-free water was introduced to each well of the SBE plate. The plate was sealed and centrifuged at 3400 rpm for 1 min. 15 mg of resin was spread onto a dimple plate and allowed to dry for 60 min. The SBE plate was then inverted over the dimple plate to transfer the resin into the wells, followed by sealing and additional centrifugation at 3400 rpm. A 30 min rotation allowed for thorough mixing before a final centrifugation to pellet the resin. The dimple plate was then cleaned with nano-pure water, dried, and stored. The desalted primer extension products were spotted onto Spectro-chips for MALDI-TOF analysis. In brief, a new chip was placed on the nano dispenser, and a calibration run was performed with a calibrant. The target dispensing volume was 12 nL. The SBE plate was sealed and stored at −20 °C. The MALDI-TOF mass spectrometer was set up for analysis of the primer extension products. In brief, the laser ionised the products, which were then accelerated and analysed using a time-of-flight technique. The time taken for the ions to reach the detector provided a measurement of their mass-to-charge ratio (m/z). The spectrum data was exported as an .xml file for further analysis using the TYPER Analyzer software, 4.1. version. Cluster plots for the ApoE rs7412 (E2) and rs429358 (E4) SNPs are provided in Supplementary Figures S1 and S2, showing the distribution of homozygous and heterozygous genotypes.

2.5. Data Analysis

All statistical analyses were conducted using Python (version 3.9.13) with the following scientific computing packages: Pandas (version 1.5.3) for data manipulation, SciPy (version 1.10.1) for statistical testing, and Matplotlib (version 3.7.1) for data visualisation. Categorical variables were summarised as frequencies and percentages, while continuous variables were expressed as mean ± standard deviation (SD). Allele and genotype frequencies were calculated for each polymorphism, and a chi-square test was performed to investigate deviation from the Hardy–Weinberg equilibrium (HWE). Differences between the groups were determined using Fisher’s exact test and Student’s t-test, or one-way analysis of variance (ANOVA). A p-value < 0.05 was considered statistically significant.

3. Results

3.1. Overview of Sociodemographic and Clinical Characteristics of the Study Participants

The sociodemographic and clinical characteristics of the study participants are shown in Table 2. The current study consisted of 107 study participants, divided into two groups: T2DM (n = 65) and a non-diabetic control group (n = 42), which comprised non-diabetic individuals. The average age of the patients with T2DM was higher compared to that of non-diabetic study participants (p < 0.001). The gender distribution across the two groups showed a similar pattern, with females being the majority in both groups. There was a higher frequency of smoking in the non-diabetic group compared to the T2DM group (p < 0.01), and alcohol consumption was higher in the non-diabetic group compared to the T2DM group (p < 0.001). In the current cohort, T2DM study participants had reduced physical activity compared to the non-diabetic participants (p = 0.02). T2DM patients had significantly higher HbA1c levels compared to non-diabetic participants (p < 0.001). Equally important, these variables were considered potential confounders and were interpreted with caution, considering the significant age imbalance among the study groups. The majority of T2DM patients (73.80%) had an HbA1c level of >7%, reflecting poor glycaemic control. The prevalence of dyslipidaemia was high and not significantly different between the groups, occurring at 76.2% in the non-diabetic control group and 80.0% in T2DM patients.
A positive family history of diabetes was reported by 67.7% of T2DM patients and 57.1% of non-diabetic control participants; however, this difference was not statistically significant (p = 0.27). LDL-C levels were slightly higher in the non-diabetic group compared to the T2DM group, with no significant differences (p = 0.23). Significantly higher levels of TC, HDL-C, and ApoB-100 were observed in the non-diabetic group compared to the T2DM group, but the differences were not statistically significant. There was also no significant difference observed for TG, non-HDL-C and VLDL-C among the study participants. The non-diabetic group had significantly higher NO levels compared to the T2DM group (p < 0.001). The T2DM group had significantly higher hs-CRP levels compared to the non-diabetic group.

3.2. Distribution of Genotypes and Allele Frequency Among the Study Participants

The distribution of ApoE genotypes and alleles in non-diabetic control participants (n = 42) and patients with T2DM (n = 64) is summarised in Table 3. No statistically significant differences in genotype frequencies were observed between the two groups. The E3/E3 genotype was the most frequent in both the non-diabetic controls (33.33%) and T2DM patients (35.94%), followed by the E3/E4 genotype (33.33% and 25.00%, respectively). The E2/E2 genotype was the least prevalent in both groups, occurring in 4.76% of the non-diabetic controls and 3.12% of T2DM patients.
Analysis of allele frequencies also showed no significant differences between groups. The E3 allele was the most common allele in the non-diabetic control group (52.38%) and T2DM patients (55.47%), followed by the E4 allele (30.95% and 28.91%, respectively), while the E2 allele was the least frequent in both groups (16.67% in the non-T2DM and 15.62% in T2DM). No statistically significant differences in allele distributions were observed between the non-diabetic control and T2DM groups. HWE analysis performed in the study population revealed a deviation from equilibrium [χ2 (3) = 9.137, p = 0.0275].

3.3. The Relationship Between the ApoE Alleles and Glycated Haemoglobin and Lipid Profiles Amongst Study Participants

Figure 1 illustrates the distribution of HbA1c and lipid parameters across different ApoE allele groups (E2, E3, and E4) in non-diabetic control and T2DM participants. Participants who were ApoE3-bearing E2/E4 genotype were excluded from this analysis due to the opposing biological effects of the E2 and E4 alleles on lipid metabolism E2/E4 genotype were excluded from this analysis due to the opposing biological effects of the E2 and E4 alleles on lipid metabolism [18,19]. HbA1c levels did not differ significantly across ApoE alleles, with an overlap evident in the distribution patterns of E2, E3, and E4 allele carriers among both study groups (F = 0.45, p = 0.64). Although there was not a significant difference, E2 allele carriers showed higher HbA1c levels, with a greater proportion of elevated measurements observed among the T2DM participants. In contrast, E3 and E4 allele carriers reported comparatively lower and more clustered HbA1c distributions within the non-diabetic control group.
TG levels displayed significant differences across ApoE alleles (F = 3.68, p = 0.03). E3 and E4 allele carriers exhibited higher TG concentrations compared with E2 allele carriers, despite the fact that an overlap between study groups remained evident. LDL-C levels across the different ApoE allele carriers did not differ significantly (F = 1.87, p = 0.16). E2 allele carriers exhibited lower LDL-C levels, with a downward shift in the distribution observed in both the study groups. In contrast, carriers of the E3 and E4 alleles exhibited higher and more variable levels of LDL-C, particularly among participants with T2DM. TC levels showed a trend toward variation that did not reach statistical significance (F = 2.67, p = 0.07). Carriers of the E2 allele generally demonstrated lower TC values compared to carriers of the E3 and E4 alleles, with this pattern being consistent across both study groups. In contrast, the E3 allele carriers exhibited the widest variability in TC levels, particularly among participants with T2DM. Similarly, HDL-C levels did not show statistically significant differences across ApoE allele carriers (F = 1.23, p = 0.30). Carriers of the E2 allele exhibited higher HDL-C levels, with a narrower distribution and higher median values compared to carriers of the E3 and E4 alleles. This trend was observed in both study groups, although the variability was greater among participants with T2DM.

3.4. The Distribution of Glycaemic Control Status Among Participants with T2DM Stratified by ApoE Allele (E2, E3, E4)

The distribution of glycaemic control status among participants with T2DM stratified by ApoE allele is shown in Figure 2. Across all ApoE alleles, the majority of T2DM participants exhibited poor glycaemic control (HbA1c ≥ 7.0%). Among ApoE E2 carriers, 81.8% of T2DM participants had poor glycaemic control, compared to 18.2% with good control (p = 0.07). In ApoE E3 carriers, poor glycaemic control was observed in 75.0% of participants, while 25.0% achieved good control (p = 0.002). Among ApoE E4 carriers, 65.2% had poor glycaemic control and 34.8% had good glyceamic control (p = 0.21).

4. Discussion

T2DM is a major contributor to the global economic burden and a leading cause of mortality and morbidity [20]. Recent clinical and epidemiological studies have investigated the relationship between genetic predisposition, gene polymorphisms, and T2DM; however, data from Black South African ancestry are still lacking [21,22]. Understanding genetic predispositions associated with diabetes and its relationship to lipid profiles enables the identification of high-risk groups, early detection and prevention, targeted pharmacotherapy, and gene therapy, and helps elucidate the multifactorial molecular pathogenesis of diabetes, including that of dyslipidaemia. ApoE is a key apolipoprotein in lipoprotein metabolism, and its isoforms are associated with diabetes, which is consequently linked with altered lipid and lipoprotein levels and cardiovascular risk [9,23]. The current study examined clinical, metabolic, and genetic characteristics of individuals with T2DM compared with non-diabetic controls in a Black South African cohort.
Consistent with global evidence, participants with T2DM in our study were significantly older, reinforcing age as a major non-modifiable risk factor linked to metabolic decline, hyperglycaemia, and increased ACVD risk [24,25,26,27]. Notably, the non-diabetic control group was younger than the T2DM group. Although gender distribution was similar across groups, the higher proportion of women, especially in the T2DM group, aligns with reports of increased T2DM prevalence among older females, partly due to postmenopausal loss of oestrogen’s cardioprotective effects. [28,29]. Furthermore, this observation may be related to recruitment bias, suggesting that females are more willing to participate in health studies compared to males [30,31]. Lifestyle differences amongst the study participants were notable; interestingly, smoking and alcohol consumption were more prevalent among the non-diabetic control group. This observation could likely be explained by the fact that patients with T2DM post diagnosis tend to change their behaviour, leading to cessation of smoking and abstinence from alcohol consumption, due to increased awareness of the detrimental effects [32,33]. In addition, the non-diabetic group included a younger age group where unhealthy lifestyles are significantly more common. Smoking and excessive alcohol consumption are associated with an increased risk of T2DM, and they are known to influence the development and progression of the condition [34,35]. Physical inactivity was significantly higher among T2DM participants, supporting the notion that sedentary lifestyle factors are known contributors to insulin resistance and progression of T2DM [36].
The prevalence of dyslipidaemia was similar in both groups. Furthermore, the observed high prevalence of dyslipidaemia among T2DM participants in the current study (80%) is consistent with our previously reported findings in the same cohort, which indicate a significant burden of lipid abnormalities among Black South Africans with T2DM [15]. In our previously published study, we reported on the determinants of dyslipidaemia without considering gene polymorphism. Thus, in the current study, we explored whether specific gene polymorphisms, particularly rs7412 and rs429358, contribute to the observed inter-individual variability in lipid profiles and glycaemic control. In addition, the observed high prevalence of dyslipidaemia among the non-diabetic control group (76.2%) is consistent with the previously published reports of a significant burden of undiagnosed cases in African populations [37,38]. As a result, several factors may explain these findings. For instance, the non-diabetic control group was defined based on HbA1c criteria. Secondly, many individuals part of the non-diabetic group exhibited higher rates of alcohol consumption and smoking, both of which are known to adversely affect lipid metabolism. Thirdly, dyslipidaemia may precede the development of overt hyperglycaemia and T2DM, suggesting early metabolic dysregulations within the continuum of the disease. Biomarker analysis revealed significantly reduced NO levels and elevated hs-CRP in T2DM participants, indicating vascular endothelial dysfunction and heightened systemic inflammation, which are primary drivers of ACVD pathogenesis in diabetes. The findings of the present study agree with the notion that T2DM, like other cardiovascular risk factors, promotes ACVD through inflammation and atherogenesis [39].
In the current study, the ApoE allele and genotype distributions mirrored global patterns, with the E3 allele (E2/E4, E3/E3) being the most prevalent, followed by the E4 allele (E3/E4, E4/E4) and E2 alleles (E2/E3, E2/E2) in both the T2DM and non-diabetic control groups. This distribution aligns with global and African population trends, where E3 is the most common allele, indicating its primary role as the reference allele and its balanced effects on lipid transport and metabolism compared to E2 and E4 [40,41]. The E4 allele, the second most prevalent among the study participants, is clinically relevant due to its well-documented association with an atherogenic lipid profile, characterised by elevated LDL-C and TC [42,43,44]. Though significant differences were not reported among the study participants, its high frequency in both study groups may be associated with the observed high prevalence of dyslipidaemia. The reported distribution of E2 is consistent with findings of previously published studies in African [45,46], Asian [47] and European [48] populations.
In the current study, the distribution of glycated haemoglobin and lipid parameters across ApoE alleles was examined in the non-diabetic control and T2DM groups. TG was reported to be significant, while HbA1c, LDL-C, TC and HDL-C were not significant across the ApoE alleles. A significant difference in TG levels was observed across ApoE alleles, with higher concentrations evident among carriers of the E3 and E4 alleles compared to those carrying the E2 allele. This was unexpected due to the fact that E3 allele carriers usually display a metabolically neutral reference isoform and are associated with normal lipoprotein clearance through efficient LDL-receptor binding [32,34], while E4 allele carriers are characterised by normal to lower TC levels as a result of increased hepatic uptake of TG-rich lipoprotein remnants, including increased hepatic cholesterol flux and LDL production [49,50]. Moreover, carriers of E4 are known to have dyslipidaemia that is mainly driven by elevated levels of LDL-C rather than elevated levels of TG diseases [51,52]. In contrast, E2 allele carriers often exhibit reduced affinity for the LDLR, resulting in delayed clearance of chylomicron and VLDL remnants, thereby predisposing carriers of the E2 allele to elevated circulating TG levels and remnant accumulation [53].
The current study also assessed the glycaemic control status across ApoE alleles using the HbA1c-based cut-off. For this reason, T2DM participants with an HbA1c level of <7.0% were classified as having good glycaemic control, and those with an HbA1c level of ≥7.0% were classified as having poor glycaemic control. Across ApoE alleles, the majority of T2DM participants exhibited poor glycaemic control, highlighting the overall burden of suboptimal glycaemic management within the current cohort. Carriers of E2 alleles exhibited a higher prevalence of poor glycaemic control, followed by carriers of E3 and E4 alleles among participants with T2DM. Consistent with the findings of our study, a meta-analysis of multiple studies has demonstrated that carriers of E2 alleles may present a moderate increase in T2DM susceptibility compared to carriers of other alleles [54]. Furthermore, evidence from an admixed population in West Mexico reported that the E2 allele was significantly associated with insulin resistance and an increased risk of T2DM, including poor glycaemic control, compared to the E3 and E4 alleles [55]. Moreover, it is worth noting that these observations provide a biologically plausible explanation for the higher prevalence of poor glycaemic control among carriers of the E2 allele in T2DM participants. The observed significant difference in glycaemic control among carriers of the E3 allele suggests a potential association with less favourable glycaemic status in the current cohort. These observations were unexpected and should be interpreted with caution, considering that the E3 allele is considered the most prevalent ApoE allele globally in many populations, including those of African ancestry [10,32,34]. The observed high prevalence of poor glycaemic control among E4 allele carriers is biologically plausible, as accumulating evidence suggests that the E4 allele isoform is associated with insulin resistance, dyslipidaemia, and features of metabolic syndrome, including poor glycaemic control, in individuals with T2DM. For instance, a study conducted by El-Lebedy et al. [56] demonstrated that the E4 allele was linked to adverse cardiometabolic profiles characterised by insulin resistance and dyslipidaemia, conditions that are closely associated with poor glycaemic control in T2DM patients. In addition, a recent analysis reported that the E4 allele was associated with metabolic syndrome traits and poorer glycaemic control, further supporting the plausibility that E4 carriers may be predisposed to worse glycaemic control within T2DM populations [57]. Overall, the findings of the current study suggest heterogeneity in glycaemic control across ApoE alleles among T2DM participants.
The present study provides novel insights into the distribution and metabolic implications of ApoE polymorphisms in Black South Africans with T2DM, a population that remains under-represented in genetic research. Although the study cohort partially overlaps with previously reported data in a related publication focusing on the magnitude of dyslipidaemia and factors associated with LDL-C among T2DM Black South Africans, the current work is methodologically and conceptually distinct. Unlike the prior publication, this study uniquely explores genetic variation in ApoE and its relationship with lipid metabolism and glycaemic regulation. The inclusion of high-resolution MALDI-TOF genotyping further strengthens the novelty and scientific contribution of this work.
Several limitations should be considered when interpreting the study’s findings. Firstly, the cross-sectional design limits the ability to establish causal relationships between ApoE genotypes, glycaemic control, and lipid abnormalities; therefore, longitudinal studies are required to assess the predictive value of ApoE polymorphisms for incident T2DM. Secondly, the relatively small sample size may have reduced the statistical power to detect subtle associations; therefore, larger multicentre studies are required to validate these findings in diverse South African populations. Thirdly, potential self-report bias may have influenced some clinical and lifestyle data collected through questionnaires. Fourthly, the age difference between the non-diabetic control and T2DM groups may confound comparisons of lifestyle behaviours and metabolic parameters. Although the non-diabetic control participants met non-diabetic criteria at recruitment, it is possible that some may develop T2DM later in life. Consequently, observed differences cannot be attributed solely to diabetes status, and age-related effects may partly explain these findings. Additionally, genotype distributions in the non-diabetic group should be interpreted as reflecting current metabolic status rather than lifelong disease resistance. Future studies should employ age-matched designs or multivariable adjustment to better isolate disease-specific effects. A further limitation of this study is the absence of direct cardiovascular outcome data. Consequently, conclusions related to cardiovascular risk are based on established surrogate markers and should be interpreted with caution.

5. Conclusions

Our study found that ApoE polymorphisms are associated with heterogeneity in lipid parameters and glycaemic control among individuals with T2DM, with allele-specific patterns observed across E2, E3, and E4 carriers. Although poor glycaemic control was prevalent across all genotypes, its distribution varied by allele, with biologically plausible associations observed particularly among carriers of E2 and E4 alleles. These findings suggest that while ApoE polymorphisms contribute to alterations in lipid metabolism within this cohort, their impact on glycaemic control is complex and context dependent. The observed differential metabolic patterns across ApoE alleles underscore the importance of population-specific genetic and clinical assessments when evaluating diabetes risk and metabolic dysregulation in African populations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diabetology7010008/s1, Figure S1: Cluster plot showing the distribution of data points for the E2 (RS7412) SNP; Figure S2: Cluster plot showing the distribution of data points for the single nucleotide polymorphism E4 (RS429358) and the mass spectrum of the samples.

Author Contributions

Conceptualization, S.S.G., M.N. and O.H.M.; methodology, M.N. and T.J.M.; software, S.M. and M.N.; validation, O.H.M., H.B.M., S.S.G. and M.N.; formal analysis, M.N., H.B.M. and S.M.; investigation, S.M., M.N. and T.J.M.; resources, S.S.G.; data curation, S.M. and M.N.; writing—original draft preparation, S.M.; writing—review and editing, O.H.M., S.S.G. and T.J.M.; visualisation, H.B.M.; supervision, M.N., S.S.G. and T.J.M.; project administration, S.M. and M.N.; funding acquisition, S.S.G. All authors have read and agreed to the published version of the manuscript.

Funding

We are grateful for the research funding received from Sefako Makgatho Health Sciences University.

Institutional Review Board Statement

The study was conducted in accordance with the guidelines of the Research and Ethics Committee of Sefako Makgatho Health Sciences University (SMUREC/S/350/2022: PG, dated 1 September 2022).

Informed Consent Statement

Consent was obtained from all study participants involved in the study. Participants were assured that their responses would remain confidential to ensure compliance with the POPI Act.

Data Availability Statement

The data supporting the findings of this study are not publicly accessible but can be obtained upon reasonable request from the corresponding author.

Acknowledgments

We acknowledge the contributions of the nursing and medical personnel, as well as the phlebotomists at the outpatient diabetes clinic of Dr George Mukhari Academic Hospital.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
T2DMType 2 Diabetes Mellitus
HbA1cGlycated Haemoglobin
ApoEApolipoprotein E
SNPsSingle Nucleotide Polymorphisms
ACVDAtherosclerotic Cardiovascular Disease
HDL-CHigh-Density Lipoprotein Cholesterol
LDL-CLow-Density Lipoprotein Cholesterol
DGMAHDr George Mukhari Academic Hospital
CIConfidence Interval
ARVAntiretroviral
NOPlasma Nitric Oxide
PCRPolymerase Chain Reaction
DNADeoxyribonucleic Acid
ANOVAOne-Way Analysis of Variance
hs-CRPHigh-Sensitivity C-Reactive Protein
MALDI-TOFMatrix-Assisted Laser Desorption/Ionisation Time-Of-Flight
VLDLVery Low-Density Lipoprotein
TGTriglycerides
TCTotal Cholesterol
HWEHardy–Weinberg equilibrium

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Diabetology 07 00008 g001
Figure 1. Association among glycated haemoglobin, serum lipid levels and ApoE alleles in study participants. HbA1c: glycated haemoglobin, TC: total cholesterol, TG: triglyceride, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, T2DM: type 2 diabetic mellitus. Comparisons of HbA1C and lipid parameters across ApoE genotype groups were performed using one-way ANOVA.
Figure 1. Association among glycated haemoglobin, serum lipid levels and ApoE alleles in study participants. HbA1c: glycated haemoglobin, TC: total cholesterol, TG: triglyceride, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, T2DM: type 2 diabetic mellitus. Comparisons of HbA1C and lipid parameters across ApoE genotype groups were performed using one-way ANOVA.
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Figure 2. Proportion of T2DM participants with good and poor glycaemic control stratified by the ApoE alleles (E2, E3, E4). The differences in the proportion among the participants were assessed using Fisher’s exact test.
Figure 2. Proportion of T2DM participants with good and poor glycaemic control stratified by the ApoE alleles (E2, E3, E4). The differences in the proportion among the participants were assessed using Fisher’s exact test.
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Table 1. Primers for each of the ApoE single-nucleotide polymorphisms.
Table 1. Primers for each of the ApoE single-nucleotide polymorphisms.
PrimersRS429358RS7412
ForwardACGTTGGATGGCTGGGCGCGGACATGGGACGTTGGATGTCCTCCGCGATGCCGATGA
ReverseACGTTGGATGGAGCATGGCCTGCACCTCGACGTTGGATGGCCCCGGCCTGGTACACTG
ExtensionGCGGACATGGAGGACGTGCCGATGACCTGCAGAAG
Table 2. Baseline characteristics of the study participants.
Table 2. Baseline characteristics of the study participants.
VariableControls (n = 42)T2DM (n = 65)p-Value
Age (years)36.10 ± 13.6660.17 ± 13.59<0.001
Gender
Male n (%)12 (28.57)17 (26.15)0.78
Female n (%)30 (71.43)48 (73.85)
Smoking (%)18 (42.86)13 (20.00)0.01
Alcohol consumption (%)36 (85.71)12 (18.46)<0.001
Family history (%)24 (57.14)44 (67.69)0.27
Physical inactivity (%)14 (33.33)37 (56.92)0.02
Duration of diabetes-12.42 ± 8.87-
Glycaemic Control
≤7 HbA1c n (%) 17 (26.20)-
≥7 HbA1c n (%) 48 (73.80)
Dyslipidaemia n (%)32 (76.2)52 (80.0)0.64
HbA1c (%)5.08 ± 0.399.03 ± 2.46<0.001
TC (mmol/L)3.76 ± 1.033.81 ± 1.190.82
TG (mmol/L)1.45 ± 0.761.66 ± 0.940.23
HDL-C (mmol/L)1.37 ± 0.271.26 ± 0.380.11
LDL-C (mmol/L)2.64 ± 0.982.41 ± 0.960.23
Non-HDL-C (mmol/L)2.39 ± 1.082.56 ± 1.160.47
ApoB-100 (mmol/L)2.14 ± 0.732.01 ± 0.720.37
VLDL-C (mmol/L)0.66 ± 0.350.75 ± 0.430.23
Nitric Oxide (µmol/L)30.64 ± 8.5221.97 ± 9.25<0.001
hs-CRP0.91 ± 0.211.34 ± 0.51<0.001
Glycated haemoglobin, TC: Total cholesterol, TG: Triglyceride, HDL-C: High-density lipoprotein cholesterol, LDL-C: Low-density lipoprotein cholesterol, Non-HDL-C: Non-high-density lipoprotein, ApoB-100: apolipoprotein B-100, VLDL-C: Very Low-density lipoprotein, NO: Nitric oxide. hs-CRP: high-sensitivity C-reactive protein. Data are presented as mean ± SD or numbers (n) and percentage: Mean differences between the T2DM and nondiabetic control group were assessed using Student’s t-test.
Table 3. Genotype and allele distribution of the ApoE gene in T2DM patients and the non-diabetic control participants.
Table 3. Genotype and allele distribution of the ApoE gene in T2DM patients and the non-diabetic control participants.
VariablesControl (n = 42)T2DM (n = 64)χ2p-Value
Genotype, n (%)
E2/E22 (4.76%)2 (3.12%)0.1870.665
E2/E32 (4.76%)9 (14.06%)2.3590.125
E2/E48 (19.05%)7 (10.94%)1.3730.241
E3/E314 (33.33%)23 (35.94%)0.0760.783
E3/E414 (33.33%)16 (25.00%)0.8680.352
E4/E42 (4.76%)7 (10.94%)1.2450.265
Allele, n (%)
E214 (16.67%)20 (15.62%)0.0410.84
E344 (52.38%)71 (55.47%)0.1950.659
E426 (30.95%)37 (28.91%)0.1020.75
HWEχ2 (3) = 9.137 0.0275
Data are presented as numbers (n) and percentages. T2DM: type 2 diabetic mellitus.
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Mkhwanazi, S.; Mapheto, T.J.; Mkhondo, H.B.; Majane, O.H.; Gololo, S.S.; Nemukula, M. Distribution of ApoE Gene Polymorphism and Its Association with the Lipid Profile Among Type 2 Diabetes Mellitus Black South Africans. Diabetology 2026, 7, 8. https://doi.org/10.3390/diabetology7010008

AMA Style

Mkhwanazi S, Mapheto TJ, Mkhondo HB, Majane OH, Gololo SS, Nemukula M. Distribution of ApoE Gene Polymorphism and Its Association with the Lipid Profile Among Type 2 Diabetes Mellitus Black South Africans. Diabetology. 2026; 7(1):8. https://doi.org/10.3390/diabetology7010008

Chicago/Turabian Style

Mkhwanazi, Siphesihle, Tumelo Jessica Mapheto, Honey Bridget Mkhondo, Olebogeng Harold Majane, Sechene Stanley Gololo, and Mashudu Nemukula. 2026. "Distribution of ApoE Gene Polymorphism and Its Association with the Lipid Profile Among Type 2 Diabetes Mellitus Black South Africans" Diabetology 7, no. 1: 8. https://doi.org/10.3390/diabetology7010008

APA Style

Mkhwanazi, S., Mapheto, T. J., Mkhondo, H. B., Majane, O. H., Gololo, S. S., & Nemukula, M. (2026). Distribution of ApoE Gene Polymorphism and Its Association with the Lipid Profile Among Type 2 Diabetes Mellitus Black South Africans. Diabetology, 7(1), 8. https://doi.org/10.3390/diabetology7010008

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