Associations of genetic variants in TCF7L2, MC4R, AGT and ACE genes with cardiometabolic diseases in Black South Africans
Authors: Ndonwi Elvis Ngwa, Keren de Buys, Don Makwakiwe Matshazi, Naomi S Levitt, Carl Lombard, Glenda Mary Davison, Tandi Edith Matsha, Andre Pascal Kengne, Sian Megan Joanna Hemmings, Nasheeta Peer
Published: 2026

ABSTRACT
AbstractBackgroundOur study investigated the association between transcription factor 7-like 2 (TCF7L2-rs7903146), angiotensin convertase enzyme (ACE-rs4646994), angiotensinogen (AGT-rs699), angiotensin II type 1 receptor (AGT1R-rs5186), fat mass and obesity-associated (FTO-rs17817499) and melanocortin 4 receptor (MC4R-rs17782313, rs12970134 and rs229616) single nucleotide polymorphisms (SNPs) with type 2 diabetes (T2D), obesity and hypertension in a Black South African population.MethodsThis cross-sectional study involved 560 Black South Africans aged 25 to 74 years. Participant demographic and lifestyle characteristics were self-reported; anthropometry and blood pressures (BP) measured; oral glucose tolerance tests used to diagnose T2D; and SNPs genotyped by polymerase chain reaction. The Benjamini-Hochberg method was used to control for multiple hypothesis testing, using a significance threshold of 0.12.ResultsUsing the median test, systolic BP was significantly higher in carriers of the G/G genotype of rs229616 within the MC4R gene compared to carriers of the G/A genotype (p = 0.006, p-FDR = 0.030). In the same gene, logistic regression analysis showed that carriers of the minor C/C genotype of the rs17782313 SNP had a significantly higher risk of hypertension (OR = 1.37, p = 0.023, p-FDR = 0.115) in the unadjusted model. However, the significance was attenuated after adjusting for age, gender and BMI (OR = 1.32, p = 0.084, p-FDR = 0.140). Moreover, carriers of the minor T/T genotype of the TCF7L2 rs7903146 SNP had a lower risk of T2D in the crude model (OR = 0.66, p = 0.043, p-FDR = 0.108) and after adjustment for age, gender and BMI (OR = 0.63, p = 0.041, p-FDR = 0.108). These findings remained significant after correction for multiple comparisons.ConclusionTCF7L2-rs7903146 (C/C genotype) and MC4R-17,782,313 (C/C genotype) SNPs are potential genetic risk factors for T2D and hypertension, respectively, in the Black South African population. Replicating these findings and exploring the role played by these genetic variants in downstream molecular pathways could aid in risk stratification and facilitate personalized approaches to healthcare delivery.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12920-026-02395-6.Keywords: Diabetes, Hypertension, Transcription Factor 7 like 2, Angiotensin Convertase Enzyme, Melanocortin 4 receptor

Introduction

Adoption of unhealthy lifestyles such as physical inactivity, excessive alcohol consumption, cigarette smoking and unhealthy diets has significantly contributed to the increase in the prevalence of cardiometabolic diseases (CMDs), particularly type 2 diabetes (T2D), obesity and hypertension. These behavioural factors do not act in isolation, but rather in the presence of a genetic background that increases the CMD risk. This is evident from family studies which have demonstrated a three-fold higher risk of T2D in individuals with first-degree relatives with the disease when compared to individuals without a positive family history of the disease [1, 2]. Moreover, the lifetime risk for T2D was reported to be 40% in individuals with either parent suffering from T2D, whilst that risk almost doubled (70%) if both parents had T2D [3]. Similarly, the risk of obesity and hypertension in childhood increases with a positive family history of these respective conditions [4, 5]. Genetic factors have been estimated to contribute to about 40–70% of obesity [4, 5] and 30–60% of blood pressure (BP) variation [6, 7].

Single nucleotide polymorphisms (SNPs) occurring in at least 1% of a population, are the most investigated genetic variants in CMDs, and they have been uncovered via genome-wide association studies (GWAS) and next-generation sequencing (NGS) in the last 30 years. GWAS have identified several genes associated with an increased risk of T2D and obesity, with the Transcription Factor 7-Like 2 (TCF7L2) demonstrating the largest effect on T2D susceptibility in Caucasians and Asians [8]. Studies in Africa have reported conflicting results with the TCF7L2 rs7903146 T allele associated with increased risk of T2D in an Algerian and Ghanaian population, whereas the allele was associated with a reduced risk of T2D in a Cameroonian population. Moreover, the TCF7L2 rs7903146 C allele has been found to confer increased obesity risk in European [9] and elderly people from Brazil [10], as does the rs17817449 T-allele of the fat mass and obesity-associated gene (FTO) in the Oceanic population [11] which is also associated with increased body mass index (BMI) in Koreans [12]. Other genetic variants associated with obesity include rs17782313 and rs1297034 located within the melanocortin 4 receptor (MC4R) gene [13–16]. These variants are also associated with increased T2D risk in obese and non-obese individuals in European and Asian populations [15, 17] Interestingly, a meta-analysis involving European participants reported the A allele of the rs229616 SNP within the MC4R gene as being protective against obesity [18].

The angiotensinogen (AGT), the angiotensin convertase enzyme (ACE) and the angiotensin II receptor type 1 (AGT1R) genes play an important role in BP regulation through the renin-angiotensin systems (RAS). Several SNPs within these genes are associated with dysregulation of BP levels, and/or hypertension, including the ATG rs699 and rs4762 in Thailand, Mexico, India and China [19–24]. The ACE rs4646994 SNP is also a risk factor for hypertension, with the deletion polymorphism associated with increased disease risk in Africa [25, 26]. Moreover, an association between the C/C genotype of the AGT1R rs5184 and essential hypertension in Indians [27], and blood pressure elevation in Ukrainian patients with essential hypertension has been reported [28]. Notwithstanding, the ATG rs699 SNP has also been associated with visceral obesity, and together with the rs4646994 SNP of the ACE gene, both have been reported to be risk factors for T2D and obesity in Japanese women [29].

Genetic risk factors of CMDs have been extensively investigated and identified in European, Hispanic and Asian populations, whose genetic makeup differs significantly from that of Africans, and the association between SNPs and diseases has been reported to vary across populations [30–33]. Despite most of the research carried out in African-American populations, findings from these studies cannot be extrapolated to the African population, as African-Americans represent a very small subset of the African population, which itself is very genetically diverse [34]. As such, there is a growing need for studies that are more representative of the African genetic pool. Very few studies on SNPs and CMDs have been conducted in sub-Saharan Africa, including South Africa, despite the growing burden of CMDs in the region. As such, this study aimed to address that gap by investigating the relationship between eight SNPs and T2D, obesity, hypertension and cardiometabolic traits in a Black South African population. These eight SNPs were selected based on their well-established association with CMDs across diverse populations.

Methods

Study design and population

Our cross-sectional study forms part of the previously described Cardiovascular Risk in Black South Africans (CRIBSA) study [35], which included 1099 participants (392 men and 707 women) aged 25–74 years old and residing in Cape Town, recruited between 2008 and 2009 using a three-stage cluster sampling technique. Participants were deemed ineligible to take part in the study if they were bedridden, unable to give consent, pregnant or lactating, residing in Cape Town for less than three months, on anti-retroviral or tuberculosis treatment or had received cancer treatment within the previous year. Viable genetic samples from 560 participants were available and analysed in this study, and there were no differences in gender distribution, anthropometric measures, T2D and hypertension prevalences between these participants relative to the entire CRIBSA cohort.

Power calculation.

G*power version 3.1 software was used to calculate the power of the study with the following input parameters;

Using the input values, we obtain a power of 65%.

Data collection and biochemical measurement

Socio-demographic data and family/medical history were self-reported and recorded on a standard questionnaire previously published administered by trained fieldworkers. Clinical examinations including anthropometry (weight, height, waist circumference (WC) and hip circumference (HC)) and BP were measured using standard techniques [36, 37]. The average of the second and third BP measurements was used for analysis.

Fasting blood samples were drawn and an oral glucose tolerance test (OGTT) conducted using standard techniques [38]. Following blood sample collection, the participants’ biochemical parameters, including plasma glucose was determined with the hexokinase method (Beckman Coulter), serum creatinine (CAYMAN CHEMICAL), gamma glutamyl transferase (Abcam) and highly sensitive c-reactive protein (hs-CRP) were measured by Enzyme Linked Immuno-Sorbent Assay (ELISA) (BIOMATIK) according to the manufacturers protocol. Total cholesterol (TC) and HDL cholesterol (HDL-C) were measured by enzymatic immune-inhibition [39, 40], triglycerides (TG) by glycerol phosphate oxidase-peroxidase [41] and low-density lipoprotein cholesterol (LDL-C) by enzymatic selective protection—End Point [42]. The buffy coat was stored at -80 °C and later used for DNA extraction and SNP genotyping.

From nucleic acid isolation to SNP genotyping

The Gentra PureGene Blood kit (Qiagen) was used to extract deoxyribonucleic acid (DNA) from 500 µl of the buffy coat. Thereafter, the DNA yield and quality were assessed using the Nanodrop 2000c spectrophotometer, and samples with DNA yields above 20 ng/µL and 260/280 optical density readings between 1.8 and 2.2 were deemed of acceptable quality for genotyping of the following SNPs (Table 1), chosen after a thorough review of the literature.

Table 1Genes and the polymorphisms genotypedGenePolymorphismLocationReason for selection ACE rs4646994Chr 17q23(intron 16)Involved the renin angiotensin aldosterone system which regulates blood pressure; deletion polymorphism associated with increased BP, diabetes and obesity. AGT rs699Chr 1q42–43 (exon 2)Involved the renin angiotensin aldosterone system which regulates blood pressure; C/C genotype is associated with increased risk of hypertension [43]. AT1R rs5186Chr 3q21–25 (3′ UTR)Involved in renin angiotensin aldosterone system which regulates blood pressure; C/C genotype associated with increased BP [27]. MC4R rs1782313Chr 18q22Validated by the genome-wide association study for obesity as it regulates energy homeostasis and food intake; rs1782313, rs12970134 and rs2229616 are the most commonly investigated SNPs of this gene.rs12970134Chr 18q22rs2229616Chr 18q22 FTO rs17817499Chr 16q12.2 (intron 1)SNP associated with obesity in a large GWAS, and findings replicated in other studies; SNP also associated with T2D. TCF7L2 rs7903146Chr 10q25.3 (exon 4)Associated with T2D in multiple well-powered studies; rs7903146 is one of its most studied SNPs [8]. SNP also associated with obesity [9]ACE Angiotensin-converting enzyme, AGT Angiotensinogen, AT1R Angiotensin II type I receptor, FTO Fat-mass and obesity-associated, Chr Chromosome, MC4R Melanocortin 4 receptor, TCF7L2 Transcription factor 7-like 2

Genes and the polymorphisms genotyped

Chr 17q23

(intron 16)

ACE Angiotensin-converting enzyme, AGT Angiotensinogen, AT1R Angiotensin II type I receptor, FTO Fat-mass and obesity-associated, Chr Chromosome, MC4R Melanocortin 4 receptor, TCF7L2 Transcription factor 7-like 2

ACE rs4646994 was amplified by conventional polymerase chain reaction (PCR) using a QuantStudio 7 Flex PCR thermocycler with previously described primers [44], and the amplicons resolved by agarose gel electrophoresis on a 1.5% agarose gel. The GeneRuler 100 bp DNA Ladder (Catalog number: SM0241) was used as a molecular weight marker to discriminate between the two alleles, with the deletion allele visualized at 190 bp and the insertion allele visualized at 490 bp (see Supplementary Fig. 1). The other seven SNPs were genotyped by Kompetitive Allele-Specific PCR (KASP) using DNA samples diluted to concentrations between 5–20ng/uL [45]. The KASP genotyping was performed at LGC Genomics (Hertfordshire, UK) using in-house primer sequences designed from DNA sequences flanking the SNP of interest (Table 2). A positive control (sample with known genotype) was used during genotyping. Moreover, for each of the SNP random samples were selected from the different genotypes and re-genotyped to check for genotyping errors. Additional details about the methodology from DNA isolation to PCR product visualization and genotyping are provided as part of Supplementary Methodology 1.

Table 2DNA sequences flanking the SNPs of interestSNPSNP sequence (5’ – 3’)rs4646994 CCCATCCTTTCTCCCATTTCTCTAGACCTGCTGCCTATACAGTCACTTTT[ TTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCCAGGCTGGAGTGCAG/ ]TTTTATGTGGTTTCGCCAATTTTATTCCAG CTCTGAAATTCTCTGAGCTC rs699 TGACAGGTTCATGCAGGCTGTGACAGGATGGAAGACTGGCTGCTCCCTGA C/T GGGAGCCAGTGTGGACAGCACCCTGGCTTTCAACACCTACGTCCACTT rs5186 AAGAAGGAGCAAGAGAACATTCCTCTGCAGCACTTCACTACCAAATGAGC A/C TTAGCTACTTTTCAGAATTGAAGGAGAAAATGCATTATGTGGACTGAACC rs1782313 CTTTAATGACTACAACATTATAGAAGTTTAAAGCAGGAGAGATTGTATCC C/T GATGGAAATGACAAGAAAAGCTTCAGGGGGAAGGTGACATTTAAGTTGG rs12970134 CAGACATTTTTTCGTTAATAATTCATCCTTTCGTACTCATTAATCCTTAC A/G TATATCTTACATAATTTCAGTGTTTCTGGGTTGCCTATTCTGTTCTATGC rs2229616 GCAGCTCTAAATGCACAGTCCAGAGTCATCCTTACCTGCCTTCTGCCACC A/G CCGACCCCAGGCTATATTTTGAGTAGGATGGGACCTGAGAAGGCTTGCC rs17817499 CCACTACTTTACAAATATTACTCAATAAATATCAGTTTAATTAAGTTGGG A/T TTTTTCTTATTATTTTAGTAACTTTGGATTCTAAATGTGCTTCTGGGTAT rs7903146 ACCTAGCACAGCTGTTATTTACTGAACAATTAGAGAGCTAAGCACTTTTTAGATA C/T TATATAATTTAATTGCCGTATGAGGCACCCTTAGTTTTCAGACGAGA DNA sequences in red represent the SNP or polymorphism under investigation

DNA sequences flanking the SNPs of interest

DNA sequences in red represent the SNP or polymorphism under investigation

Calculations and definitions

BMI was calculated as weight (kg)/[height (m)]2 and used to categorize participants as normal weight (18.5≤kg/m2 BMI<25 kg/m2), overweight (25 kg/m2≤ BMI < 30 kg/m2) and obese (BMI ≥ 30 kg/m2) [46]. Waist-to-hip ratio (WHR) was calculated as WC (cm)/HC (cm). Central obesity was determined using WC > 94 cm in males and WC > 80 cm in females. Hypertension was defined as having systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg or known hypertension on anti-hypertensive treatment [47]. T2D was defined as fasting plasma glucose ≥ 7.0 mmol/L and/or 2-h post glucose load ≥ 11.1 mmol/L, previously diagnosed or taking antidiabetic medications [38]. Dyslipidaemia was defined as TC > 5 mmol/L, TG > 1.5 mmol/L, HDL-C < 1.2 mmol/L, LDL-C > 3.0 mmol/L or taking anti-lipid agents [48].

Statistical analysis

Data analyses were conducted using the Statistical Package for Social Sciences (SPSS) version 29 software, and categorical variables are reported as counts and percentages, while continuous variables are reported as medians (25th -75th percentiles). Hardy Weinberg Equilibrium (HWE) analysis was conducted using the chi-squared goodness of fit, and only SNPs that were in HWE were further analysed for associations with diseases and traits using the median test. Linear and logistic regression analyses were performed to assess the association between disease traits (FPG, OGTT, WC, SBP, DBP and hs-CRP), T2D and the genotypes of the SNPs investigated using the additive model in SPSS. The additive model who chosen because the underlying genetic model was unknown and the model provides a robust approach for genetic variant and traits association studies [49]. All analyses were conducted at 95% confidence intervals and the level of significance was considered at p < 0.05. Furthermore, to control for multiple testing, the Benjamini-Hochberg method was used. Using a false discovery rate (FDR) of 0.12, the corresponding Benjamini-Hochberg critical value was calculated for each variable after ranking the p-values in ascending order. Due to the small sample size (560 participants) and a corresponding small statistical power (65%) in this study, a less stricter threshold was selected in order to reduce the probability of committing a type II error. This is also taken into account that this study is a pilot study requiring a less stricter threshold to identify all potential SNPs that could be associated with the diseases investigated. Statistical significance was established when the p-value of a variable was less than its corresponding Benjamini-Hochberg critical value [49]. The multiple hypotheses tested were the association of each SNP with a disease and its traits.

Results

Descriptive statistics for study participants

This study included 560 participants, of whom 64.4% (378) were female. The prevalence of T2D, obesity (BMI ≥ 30 kg/m2) and hypertension was 12.7% (n = 71), 48.4% (n = 271) and 40.9% (n = 229), respectively. There was no significant gender distribution difference in the T2D and hypertension groups. Obesity prevalence was significantly higher in females than males (p < 0.001), as was BMI, WC, HC and hs-CRP, whose levels were significantly higher in females (all p < 0.001). Conversely, WHR (p = 0.008), SBP (p < 0.001), creatinine (p < 0.001) and GGT (p < 0.001) were significantly higher in males relative to females (Table 3).

Table 3General participant characteristics by genderVariableTotal (n = 560)Male (n = 182)Female (n = 378)p-valueAge in years43.0 (32.0–53.0)41.0 (31.0–53.1)43.0 (33.0–53.0)0.189Weight in kg77.3 (64.0–92.5)65.1 (56.8–75.7)84.1 (70.1–99.5)< 0.001Height in cm1.63 (1.57–1.68)1.70 (1.65–1.75)1.60 (1.55–1.64)< 0.001BMI (kg/m2)29.5 (23.2–35.7)22.2 (19.9–26.2)33.0 (27.9–38.7)< 0.001WC in cm93.0 (80.7–105.0)80.5 (75.0–91.5)97.8 (88.0–108.4)< 0.001HC in cm107.5 (95.5–121.0)93.4 (87.7–101.0)116.2 (105.0–125.5)< 0.001WHR0.86 (0.81–0.91)0.87 (0.83–0.93)0.85 (0.80–0.90)0.008SBP (mmHg)122 (109–135)126.0 (114.8–138.3)118.0 (107.0–131.3)< 0.001DBP (mmHg)79 (72–89)80.0 (73.0–90.0)79.0 (72.0–88.3)0.836Heart rate (pulse/min)69.0 (61.5–78.5)62.0 (55.0–71.5)72.5 (65.0–80.6)< 0.001Total cholesterol (mmol/L)4.29 (3.62–5.09)4.25 (3.51–5.04)4.33 (3.67–5.12)0.673Triglyceride (mmol/L)0.97 (0.71–1.29)0.98 (0.70–1.31)0.96 (0.71–1.27)0.812HDL-C (mmol/L)1.05 (0.88–1.30)1.12 (0.93–1.41)1.04 (0.86–1.26)0.092LDL-C (mmol/L)2.97 (2.37–3.65)2.82 (2.18–3.43)3.04 (2.46–3.72)0.074FPG (mmol/L)4.9 (4.4–5.3)4.9 (4.5–5.3)4.8 (4.3–5.4)0.3662 H glucose (mmol/L)5.90 (4.83–7.50)5.90 (4.60–7.30)5.90 (4.90–7.70)0.899Creatinine (µmol/L)57.0 (45.3–70.0)64 (51–80)54 (43–65)< 0.001GGT (IU/L)24.0 (16.3–43.0)31 (21–53)22 (15–37)< 0.001hs-CRP (mg/dL)3.60 (1.28–8.47)2.05 (0.73–5.11)4.44 (1.69–10.62)< 0.001BMI Body Mass Index, DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HC Hip Circumference, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, WC Waist Circumference, WHR Waist-to-Hip Ratio

General participant characteristics by gender

BMI Body Mass Index, DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HC Hip Circumference, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, WC Waist Circumference, WHR Waist-to-Hip Ratio

Genotypic frequencies and Hardy-Weinberg Equilibrium

The genotyping success rate ranged from 95.4% to 98.6%. Amongst the eight SNPs genotyped, all but one (FTO rs17817499) (p < 0.001) were in HWE (all p > 0.05). (Table 4).

Table 4Genotypic frequencies and Hardy Weinberg Equilibrium estimationGeneSNP rs numberGenotypesFrequencies n (%)HWE p-values Angiotensin Convertase Enzyme rs4646994(n = 534)D/D310 (58.0)0.059I/D183 (34.3)I/I41 (7.7) Angiotensinogen rs699(n = 552)C/C476 (86.2)0.912C/T73 (13.2)T/T3 (0.6) Melanocortin 4 receptor rs229616(n = 551)G/G313 (56.8)0.343G/A199 (36.1)A/A39 (7.1)rs12970134(n = 550)G/G447 (81.3)0.477G/A96 (17.4)A/A7 (1.3)rs17782313(n = 554)T/T289 (52.2)0.463T/C227 (41.0)C/C38 (6.8) Fat mass and obesity-associated rs17817499(n = 538)T/T495 (92.0) < 0.001* T/A36 (6.7)A/A7 (1.3) Transcription Factor 7-Like 2 rs7903146(n = 546)C/C234 (42.9)0.697C/T250 (45.8)T/T62 (11.3) Angiotensin II receptor type 1 rs5186(n = 544)A/A541 (99.4)0.948A/C3 (0.6)C/C0 (0.0)D Deletion polymorphism, I Insertion polymorphism, n Number of samples, A, C, T, G (Adenine, Cytosine, Thymine, Guanine, respectively)A p-value of less than 0.05 is indicated in bold with an asterisk to show a SNP that was not in Hardy Weinberg Equilibrium

Genotypic frequencies and Hardy Weinberg Equilibrium estimation

rs4646994

(n = 534)

rs699

(n = 552)

rs229616

(n = 551)

rs12970134

(n = 550)

rs17782313

(n = 554)

rs17817499

(n = 538)

rs7903146

(n = 546)

rs5186

(n = 544)

D Deletion polymorphism, I Insertion polymorphism, n Number of samples, A, C, T, G (Adenine, Cytosine, Thymine, Guanine, respectively)

A p-value of less than 0.05 is indicated in bold with an asterisk to show a SNP that was not in Hardy Weinberg Equilibrium

Association between SNPs and cardiometabolic diseases/traits

Heterozygous MC4R rs229616 carriers possessed lower SBP levels than individuals homozygous for the G and A alleles (p = 0.006, p-FDR = 0.030). Median GGT levels were significantly higher in carriers of the G/G genotype of the rs12970134 SNP (p = 0.021, p-FDR = 0.063). Although carriers of the homozygous deletion polymorphism of rs4646994 within the ACE gene had a significantly higher DBP relative to carriers of the homozygous insertion polymorphism (p = 0.027), the significance was lost after FDR adjustment (p-FDR = 0.135), as shown in Table 5 and 6. The other five SNPs did not have significant associations with any of the cardiometabolic traits or diseases (Supplementary Tables 1 and 2).

Table 5Association between rs4646994 and rs17782313 SNPs with cardiometabolic diseases/traitsVariableACE SNP genotype (rs4646994)p-valueBHp-valueMC4R SNP genotype (rs17782313)p-valueBHp-valueD/DI/DI/IT/TT/CC/C Median (25th – 75th ) percentile Median (25th – 75th ) percentile Waist circumference (cm)92.5 (79.8–105.0)95.5 (83.5–105.5)89.0 (79.8–100.3)0.1400.32792.5 (79.3–103.8)94.5 (83.8–107.3)91.0 (80.3–102.5)0.1880.329Hip circumference (cm)106.5 (94.5–120.0)110.0 (98.0–123.0)105.3 (93.8–117.5)0.0560.327106.5 (94.8–119.8)110.0 (97.7–124.0)103.0 (91.0–118.4)0.1170.273Waist-to-Hip ratio0.86 (0.82–0.91 )0.86 (0.80–0.90)0.83 ( 0.79–0.90)0.5070.5920.86 (0.80–0.90)0.86 (0.81–0.91)0.86 (0.83–0.91)0.5430.760SBP (mmHg)123 (110–135)122 (108–133)114 (101.5–133.5)0.5320.532121 (108–134)122 (110–136)117.0 (106.0–140.3)0.9480.948DBP (mmHg)80 (73–90)79 (72–88)73 (70–84)0.0270.13579 (72–88)80 (73–89)78.0 (71.8–93.3)0.6140.768TC (mmol/L)4.25 (3.58–5.09)4.49 (3.69–5.08)4.61 (3.92–5.53)0.0620.2484.34 (3.61–5.07)4.28 (3.59–5.09)4.33 (3.77–5.17)0.8870.998TG (mmol/L)0.97 (0.71–1.29)0.98 (0.73–1.31)1.07 (0.73–1.23)0.2770.4160.95 (0.69–1.30)0.97 (0.72–1.25)1.04 (0.83–1.34)0.6440.998HDL-C (mmol/L)1.05 (0.89–1.28)1.02 (0.85–1.29)1.11 (0.98–1.47)0.0540.2481.05 (0.88–1.28)1.04 (0.88–1.32)1.08 (0.94–1.38)0.9150.998LDL-C (mmol/L)2.93 (2.37–3.65)2.97 (2.44–3.68)3.14 (2.26–3.72)0.1810.4162.96 (2.35–3.61)2.95 (2.44–3.61)3.02 (2.49–3.74)0.6740.998FPG (mmol/L)4.90 (4.40–5.40)4.80 (4.30–5.30)4.90 (4.55–5.25)0.3450.8084.8 (4.3–5.3)4.9 (4.4–5.5)5.00 (4.69–6.10)0.3260.3262 H glucose (mmol/L)5.90 (4.80–7.40)5.90 (4.80–7.57)6.15 (5.15–8.28)0.7010.8085.80 (4.90–7.28)5.95 (4.80–7.80)6.25 (5.43–7.50)0.0860.326Creatinine (µmol/L)56 (45–69)58 (46–73)58 (43–67.5)0.3540.81956.0 (45.0–71.0)57.0 (46.0–69.0)58.0 (45.8–80.8)0.8440.844GGT (IU/L)24 (16–44)25 (17–40)25 (17.5–43.5)0.8810.88126.0 (16.0–44.0)23.0 (17.0–41.5)24.5 (13.0–44.5)0.4530.679hs-CRP (mg/dL)3.19 (1.20–7.96)3.78 (1.39–7.93)3.99 (1.82–12.81)0.5460.8192.96 (1.18–7.64)4.04 (1.39–9.10)3.92 (1.99–9.73)0.1420.426Prevalence, N (%)Prevalence, N (%)Hypertension133 (42.9)66 (36.1)17 (41.5)0.3240.405105 (36.3)102 (44.9)19 (50.0)0.0700.140T2D40 (12.9)21 (11.5)7 (17.1)0.6170.80831 (10.7)34 (15.0)6 (16.2)0.2930.326Obesity (BMI ≥ 30 kg/m2)138 (44.5)101 (55.2)17 (41.5)0.1230.327138 (47.8)115 (50.7)16 (42.1)0.0450.231Central Obesity174 (56.3)115 (62.8)21 (51.2)0.2350.389158 (54.7)146 (64.3)20 (52.6)0.0660.231TC (> 5 mmol/L)77 (25.0)44 (24.2)15 (36.6)0.2410.41671 (24.7)56 (24.8)9 (24.3)0.9980.998TG (> 1.5 mmol/L)50 (16.2)28 (15.4)5 (12.2)0.7940.88248 (16.7)32 (14.2)7 (18.9)0.6410.998HDL-C (< 1.2 mmol/L)208 (67.5)119 (65.4)27 (65.9)0.8820.882188 (65.3)153 (67.7)25 (67.6)0.8370.998LDL-C (> 3 mmol/L)133 (43.5)80 (44.9)23 (56.1)0.3120.416124 (43.5)99 (44.4)18 (48.6)0.8370.998DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, T2D Type 2 Diabetes, TC Total cholesterol, TG Triglycerides*significant finding - p-value smaller than its corresponding Benjamini-Hochberg critical value at FDR = 0.12

Association between rs4646994 and rs17782313 SNPs with cardiometabolic diseases/traits

DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, T2D Type 2 Diabetes, TC Total cholesterol, TG Triglycerides

*significant finding - p-value smaller than its corresponding Benjamini-Hochberg critical value at FDR = 0.12

Table 6Association between MC4R rs229616 and rs12970134 with cardiometabolic diseases/traitsVariableMC4R SNP (rs229616)p-valueBHp-valueMC4R SNP (rs12970134)p-valueBHp-valueG/GG/AA/AG/GG/AA/A Median (25th – 75th ) percentile Median (25th – 75th ) percentile Waist circumference (cm)94.0 (81.8–105.8)92.5 (80.8–105.0)91.5 (76.5–105.0)0.4120.71893.0 (80.5–105.0)93.0 (81.1–103.4)90.5 (84.3–107.3)0.9340.934Hip circumference (cm)108.2 (96.2–120.5)107.5 (95.5–123.5)104.0 (93.5–122.0)0.5130.718107.8 (95.5–122.0)108.2 (96.0–118.5)106.0 (102.8–127.3)0.9000.934Waist-to-Hip ratio0.86 (0.81–0.92)0.85 (0.81–0.90)0.86 (0.82–0.89)0.4180.7180.86 (0.81–0.91)0.86 (0.79–0.92)0.87 (0.80–0.88)0.9300.934SBP (mmHg)124 (110–137)116 (107–131)122 (109–137) 0.006* 0.030* 121 (109–135)123.0 (110.0–134.8)113 (106–150)0.7330.916DBP (mmHg)80.0 (73.0–90.5)77 (70–86)80 (73–91)0.2600.32580 (72–89)77.0 (71.3–89.0)86 (60–88)0.2460.910TC (mmol/L)4.27 (3.58–5.06)4.30 (3.67–5.12)4.29 (3.52–5.29)0.9630.9634.31 (3.63–5.12)4.28 (3.53–5.08)4.07 (3.68–4.95)0.8800.911TG (mmol/L)0.94 (0.68–1.30)0.98 (0.73–1.28)1.01 (0.74–1.27)0.5460.9630.98 (0.70–1.30)0.85 (0.67–1.16)1.01 (0.89–1.15)0.1140.911HDL-C (mmol/L)1.04 (0.88–1.29)1.05 (0.87–1.32)1.14 (0.85–1.24)0.6370.9631.05 (0.88–1.31)1.07 (0.89–1.27)0.85 (0.84–1.29)0.9110.911LDL-C (mmol/L)2.93 (2.37–3.68)3.00 (2.40–3.59)2.93 (2.32–3.82)0.9060.9632.96 (2.38–3.63)3.06 (2.39–3.69)2.80 (2.13–3.80)0.8400.911FPG (mmol/L)4.9 (4.4–5.4)4.8 (4.3–5.2)4.9 (4.4–6.0)0.4030.4034.9 (4.4–5.4)4.80 (4.38–5.30)4.60 (4.40–5.20)0.7850.7852 H glucose (mmol/L)6.0 (4.9–7.7)5.65 (4.70–7.45)6.20 (5.05–7.15)0.0980.1486.0 (4.9–7.5)5.70 (4.40–7.50)7.40 (5.20–7.60)0.5950.785Creatinine (µmol/L)56 (46–71)60.0 (45.5–71.0)55 (48–66)0.1850.27857 (45–71)58.5 (46.3–71.8)62 (51–69)0.5590.839GGT (IU/L)26 (18–46)22 (15–37)29 (20–40) 0.015* 0.045* 26 (17–44)21.5 (15.0–39.3)18.0 (12.0–32.0) 0.021* 0.063* hs-CRP (mg/dL)3.7 (1.30–9.00)3.19 (1.22–7.75)4.08 (1.88–8.72)0.6980.6983.70 (1.29–8.52)3.40 (1.34–8.21)1.29 (0.44–12.59)0.9030.903Prevalence, N (%)Prevalence, N (%)Hypertension139 (44.4)73 (36.7)14 (35.9)0.1780.297185 (41.4)38 (39.6)2 (28.6)0.7590.759T2D48 (15.4)18 (9.0)4 (10.3)0.0990.14858 (13.0)13 (13.7)0 (0.0)0.5800.785Obesity (BMI ≥ 30 kg/m2)151 (48.2)96 (48.2)18 (46.2)0.2460.718216 (48.3)48 (50.0)4 (57.1)0.3050.934Central Obesity190 (60.7)111 (55.8)20 (51.3)0.3580.718266 (59.5)55 (57.3)3 (42.9)0.6330.934TC (> 5 mmol/L)75 (24.1)50 (25.3)11 (28.2)0.8430.963113 (25.4)23 (24.2)0 (0.0)0.3000.911TG (> 1.5 mmol/L)53 (17.0)25 (12.6)8 (20.5)0.2840.96371 (16.0)15 (15.8)0 (0.0)0.5160.911HDL-C (< 1.2 mmol/L)215 (69.1)126 (63.6)25 (64.1)0.4100.963295 (66.3)65 (68.4)5 (71.4)0.8910.911LDL-C (> 3 mmol/L)133 (43.5)91 (46.2)16 (41.0)0.7630.963191 (43.4)46 (48.9)3 (42.9)0.6170.911DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, T2D Type 2 Diabetes, TC Total cholesterol, TG Triglycerides*significant finding - p-value smaller than its corresponding Benjamini-Hochberg critical value at FDR = 0.12

Association between MC4R rs229616 and rs12970134 with cardiometabolic diseases/traits

DBP Diastolic Blood Pressure, FPG Fasting Plasma Glucose, GGT Gamma Glutamyl Transferase, HDL-C High-Density Lipoprotein Cholesterol, hs-CRP Highly sensitive c-Reactive Protein, LDL-C Low-Density Lipoprotein Cholesterol, SBP Systolic Blood Pressure, T2D Type 2 Diabetes, TC Total cholesterol, TG Triglycerides

*significant finding - p-value smaller than its corresponding Benjamini-Hochberg critical value at FDR = 0.12

Binary logistic regressions with and without adjustment for age, gender and BMI, were conducted to estimate the risk of T2D, obesity and hypertension, using the additive (major genotype vs. heterozygous genotype vs. minor genotype) model. Our findings hinted at reduced T2D risk in the presence of the minor TCF7L2 rs7903146 T-allele, and increased hypertension risk with the minor C-allele of MC4R rs17782313 (Supplementary Table 3). Carriers of the T/T genotype of rs7903146 had a lower T2D risk when compared with the C/T and C/C genotypes (OR = 0.66, 95%CI = [0.44–0.99], p = 0.043, p-FDR = 0.108), even after adjusting for age, gender and BMI (OR = 0.63, 95%CI [0.40–0.98], p = 0.041, p-FDR = 0.108). Moreover, the MC4R rs17782313 C/C genotype was associated with increased risk of hypertension (OR = 1.37, 95%CI = [1.05–1.80], p = 0.023, p-FDR = 0.115), as shown in Supplementary Table 3. However, the association was attenuated after adjusting for age, gender and BMI (Fig. 1). Moreover, linear regression analyses were also performed to determine the association between disease traits including FPG, OGTT, WC, SBP, DBP and hs-CRP and the genotypes of the SNPs investigated with no significant association (results not shown).

Association between SNPs and CMDs in a model adjusted for age, gender and BMI

Discussion

Amongst the eight SNPs genotyped, all but one (FTO rs17817499) were in HWE, and two of these SNPs may be genetic risk factors for T2D and hypertension. In this study, the minor C/C genotype of MC4R rs17782313 was associated with an increased risk of hypertension, even though the risk was attenuated after adjustment for age gender and BMI. This is indicative that these covariates might have confounded the association, and that the SNPs may not independently predict the risk of hypertension. Nonetheless, the borderline p-value of 0.083 may suggest a possible residual association that may require a larger sample size for confirmation. The minor TCF7L2 rs7903146 T/T genotype was associated with a reduced risk of T2D. Moreover, some SNPs were associated with clinical BP measurements, as heterozygous G/A carriers of MC4R rs229616 had lower SBP levels compared to carriers of the major G/G and minor A/A genotypes. Furthermore, carriers of the deletion polymorphism of the rs4646994 ACE SNP had a higher DBP compared to carriers of the insertion polymorphism, albeit before FDR-adjustment.

The TCF7L2 gene plays an important role in the wingless and int-1 signal transduction pathway, where it regulates, amongst others, genes involved in glucose and lipid metabolism [50]. The rs7903146 SNP is one of the most extensively studied variants of the TCF7L2 gene in several ethnicities and populations, because of its association with T2D. While the molecular mechanism through which TCF7L2 gene increases the risk of T2D is being unravelled, experimental studies have reported reduced beta-cell function, increased hepatic glucose output, incretin effects and negative regulation of adipogenesis. In 3T3-L1 cells, inactivation of TCF7L2 proteins increased subcutaneous adipose tissue mass, adipocyte hypertrophy, and inflammation leading to whole-body glucose intolerance and hepatic insulin resistance [50]. Similarly, TCF7L2 mRNAs levels were reduced in rats fed with high fat diet and, impaired glucose tolerance, impaired insulin sensitivity, increased weight gain, and increased adipose tissue mass were observed in adipocytes knock out for TCF7L2 and challenged with high fat diet [51]. Studies conducted in Asian, Caucasian and Black participants in Myanmar, the United Kingdom, Brazil and Ghana have reported an increased risk of T2D in carriers of the TCF7L2 rs7903146 TT genotype [52–55]. This contrasts with findings from our study, where the TT genotype was associated with a reduced risk of T2D. Moreover, a study in a Cameroonian population has found a reduced risk of T2D in TT genotype carrier of the TCF7L2 gene when compared to carriers of the CC genotype. This is to say that whilst the T allele seemed to be protective against T2D in our study population, the C allele of the TCF7L2 SNP seems to confer protection against T2D in other populations [56]. Shifts in the allele that confers risk for disease are not impossible, especially in diverse populations/environments. Additionally, the interaction of genes with environmental factors such as diet may also influence the genetic risk profile in that population [57]. As such, functional studies to identify and shed more light on biological roles of alleles in the development of diseases are needed, as they may improve our understanding of why this shift occurs.

Mutations in the MC4R gene have traditionally been associated with a predisposition to weight gain and obesity [58]. Surprisingly, MC4R rs229616, rs12970134 and rs17782313 SNPs found on chromosome 18q22 were not associated with obesity in our study. They were investigated individually and as such, haplotype analysis was not performed. Notwithstanding, obesity was more prevalent in carriers of the rs17782313 T/C genotype relative to carriers of the C/C genotype. These findings might indicate that these SNPs might not constitute a risk factor for obesity in this Black South African population. Nonetheless, obesity is an important risk factor for the development of hypertension, and unsurprisingly, carrying the C/C genotype of rs17782313 MC4R SNP was associated with an increased risk of hypertension in our study, a finding that was also reported in a study of 282 Arabic individuals, where MC4R rs17782313 C allele was associated with hypertension [59]. For the first time, our study reported an association between the rs229616 MC4R SNP and clinical blood pressure parameters, as lower SBP levels were reported in G/A carriers of the rs229616 MC4R SNP, relative to carriers of the major G/G and minor A/A genotypes. To our knowledge, no other study has yet to report a relationship between this SNP and blood pressure or hypertension. As such, further investigations on this polymorphism in this context may be warranted.

Although the association between ACE rs4646994 deletion polymorphism with increased DBP was lost after FDR-adjustment, the ACE gene plays a crucial role in the RAS pathway which is involved in BP regulation. Polymorphisms of this gene have been reported to alter the ACE enzyme activity, increasing the levels of serum angiotensin convertase enzymes which inactivate bradykinins. This inactivation causes vasoconstriction in veins and arteries as well as a reduction in vascular permeability, thereby increasing BP [60]. However, the lack of an observed association between the ACE rs4646994 SNP and hypertension in our study could be explained by the limited sample size, with a study power of 65% potentially being inadequate to detect statistically significant differences, particularly for small effect sizes. Our study also did not find associations between hypertension or traits including SBP and DBP and the rs699 SNPs of the ATG gene, which is involved in the RAS pathway and has been extensively reported to be associated with hypertension in Asia [20–24, 27, 61] and North America [62]. This may suggest that this SNP might constitute a risk factor for hypertension in other populations, but not in the African population we investigated.

Whilst FTO rs17817499 was not in HWE in our study, the FTO gene itself, located on chromosome 16q12.2, plays key roles in appetite regulation and energy metabolism, and its variants have been associated with obesity in other studies [11, 12]. However, various factors can contribute to a SNP not being in HWE. For example, the migration and introduction of new people and thus genetic pools into a community may introduce new alleles, significantly altering existing allele frequencies. Additionally, random allele frequency changes are also possible, especially in smaller populations, leading to deviations from HWE. Furthermore, populations constantly have to ensure their survival by adapting to the ever-changing environment around them, and in some cases, this involves adoption and persistence of alleles that ensure continued survival. Additionally, whilst stringent methods are applied to ensure accuracy, sometimes genotyping errors may occur and these also contribute to a SNP’s deviation in HWE [63] The aforementioned, solely or in combination, may explain the shift in HWE of the FTO gene in our study population.

A strength of this study is that, to the best of our knowledge, it is the first to describe an association between MC4R rs17782313 and MC4R rs229616 with hypertension and systolic blood pressure, respectively, in the African population. Our study was conducted in an understudied, genetically diverse population, hence findings from this study will contribute to a growing pool of African genetic data. However, the small sample used compared with other studies might constitute the main reasons we did not see significant associations between some of the SNPs investigated and diseases or traits. However, findings from this study may be useful in generating hypotheses that can be further explored in much larger studies of higher power. Furthermore, we did not control for population stratification. However, as the sample comprised Black individuals who originated from more or less the same region in South Africa, we would not expect population stratification to impact the results to any large degree. Nonetheless, we acknowledge the need to conduct multi-centre studies involving participants of various ethnic backgrounds to enhance the generalisability of our results to the rest of the African continent. In addition, the study’s small sample size precluded us from performing interaction analyses to identify other environmental factors such as smoking, alcohol consumption and diet, which could act alongside the SNP to increase or decrease the risk of the disease. In spite of efforts to correct the analysis for multiple comparisons, there is still a residual risk of chance findings, and as such our findings should be confirmed in future and adequately powered samples to mitigate these limitations. When a conventional FDR threshold of 0.05 was used in our analyses, the main associations were no longer significant, but for rs229616 which was associated with SBP (p-FDR = 0.03). As such, caution should be taken when interpreting our findings, as the FDR threshold applied in our analysis was exploratory in nature.

Conclusion

The TCF7L2 rs7903146 C allele is associated with an increased risk of T2D, whilst MC4R rs17782313 C allele and MC4R rs229616 G/A are associated with increased risk of hypertension in Black South Africans. Given the novelty of these findings, it is important to conduct similar studies in larger cohorts to confirm the findings and also investigate the influence of gene-environment interactions on the risk of developing disease. Once confirmed, functional studies to identify the pathways affected by these mutations are necessary to shed more light on the pathophysiology and aid in developing therapeutic targets for these diseases.

Supplementary Information

Supplementary Material 1.

Supplementary Material 2.

Supplementary Material 3.

Supplementary Material 4.

Acknowledgements

The authors would like to acknowledge the participants, fieldworkers, MRC research nurse fieldworkers Debbie Jonathan and Theresa Gogela, fieldwork co-ordinator Erica April, study manager Serena van Haght, statisticians Rebecca Shanmugam and Ria Laubscher, and Dr Kirsty Bobrow. We also thank the City of Cape Town.

Abbreviations

Angiotensin Convertase Enzyme

Angiotensinogen

Angiotensin II Type 1 Receptor

Body Mass Index

Blood Pressure

Cardiometabolic Disease

Cardiovascular Risk in Black South Africans

Deoxyribonucleic Acid

False Discovery Rate

Fasting Plasma Glucose

Fat Mass And Obesity-Associated

Gamma-Glutamyl Transferase

Hip Circumference

High-Density Lipoprotein Cholesterol

Highly Sensitive C-Reactive Protein

Hardy Weinberg Equilibrium

Kompetitive allele-specific PCR

Low-Density Lipoprotein Cholesterol

Melanocortin 4 Receptor

Next-Generation Sequencing

Oral Glucose Tolerance Test

Polymerase Chain Reaction

Single Nucleotide Polymorphisms

Statistical Package for Social Sciences

Type 2 Diabetes

Total Cholesterol

Transcription Factor 7-Like 2

Triglycerides

Waist Circumference

Waist-To-Hip Ratio

Authors’ contributions

A.P.K., C.L., N.L. and N.P. contributed to the study conception and design. Material preparation, data collection and analysis were performed by N.E.N., A.P.K., C.L., N.L. and N.P. The first draft of the manuscript was written by N.E.N. K.B. and D.M.M. and all authors commented on previous versions of the manuscript. G.M.D, T.E.M and S.M.J.H revised the manuscript. All authors contributed significantly, read and approved the final version of the manuscript.

Funding

This work was supported by an unrestricted grant from Servier Laboratories (South Africa), the Medical Research Council of South Africa, the Initiative for Cardiovascular Health Research in Developing Countries (IC Health) Foundation Council, and Brigham Women’s Hospital and Harvard University.

Data availability

The datasets generated and/or analysed during the current study are available from the following link https://medat.samrc.ac.za/index.php/catalog/57.

Declarations

Ethics approval and consent to participate

The research protocol and ethics approval for the study was obtained from the South African Medical Research Council’s Human Research Ethics Committee (EC026-9/2016) and the University of Cape Town’s Research and Ethics Committee (224/2006). The study was performed in accordance with the declaration of Helsinki. All participants signed the Informed consent form before participating in this study.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Associated Data

Supplementary Materials

Supplementary Material 1.

Supplementary Material 2.

Supplementary Material 3.

Supplementary Material 4.

Data Availability Statement

The datasets generated and/or analysed during the current study are available from the following link https://medat.samrc.ac.za/index.php/catalog/57.