Sociodemographic Disparities in the Prevalence of Metabolic Syndrome in Rural South Africa: An Analysis of Gender, Age, and Marital, Employment, and Educational Status

Abstract

Sociodemographic factors affect how metabolic syndrome (MetS) manifests and progresses. This study aimed to investigate the prevalence between MetS and sociodemographic factors among adult participants in the Dikgale HDSS. This was a comprehensive retrospective study where the records of 575 participants were meticulously evaluated. MetS was defined using a joint interim statement (JIS). The data were analyzed using the Statistical Package for SPSS, version 25. A chi-square test was used to compare proportions between groups, with Cramer’s V used to assess the strength of association. Logistic regression was used to determine the association between MetS and sociodemographic profiles. A p-value of less than 0.05 was considered statistically significant. The prevalence of MetS was 28.2% (females 33.3% vs. males 15.6%, p ≤ 0.001). In addition, logistic regression showed males to have lower odds of MetS as compared to females (OR = 0.36, 95% CI: 0.2–0.6, and AOR = 0.4, 95% CI: 0.2–0.6). The 55–60 age group had the highest proportion of affected individuals, and MetS was also more common among individuals with low educational attainment. In addition, on regression, the same association was observed. This study found sociodemographic disparities in MetS among rural adults, especially females, who had an increased risk of MetS, and participants with low educational attainment.

1. Introduction

Cardiometabolic risk factors such as central obesity, raised triglycerides, reduced high-density lipoprotein cholesterol (HDL-C), raised blood pressure, and raised fasting plasma glucose have the potential to cause type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVDs) [1]. The clustering of any of the three factors above is referred to as metabolic syndrome (MetS) [1]. Metabolic syndrome represents a major global health concern, significantly increasing mortality risks across various populations [2,3]. Its rising prevalence closely mirrors the global obesity epidemic, particularly in developing countries [2]. Individuals with MetS are at greater risk of developing non-communicable diseases (NCDs), which are now the leading cause of death worldwide [4]. This burden disproportionately affects low- and middle-income countries (LMICs), where the majority of NCD-related deaths occur [2,3].

In rural South Africa, the prevalence of MetS is steadily increasing, with nearly 22% of individuals aged 15 years and older exhibiting MetS [5]. This study also highlighted a notable gender disparity, with women experiencing a higher prevalence of MetS (25.0%) compared to men (10.5%) [5]. Contrary to earlier beliefs that MetS was uncommon in African populations, recent research reveals a significant epidemiological transition in this region [6,7]. Metabolic syndrome is characterized by a combination of metabolic dysregulation and cardiovascular risk factors, with abdominal obesity at its core [1]. Abdominal obesity, marked by the accumulation of visceral fat, is particularly prevalent in African populations [8,9,10,11], underscoring the urgent need for lifestyle interventions to address obesity and related health risks.

The high prevalence of obesity, elevated fasting blood glucose, central obesity, and hypertension in the population covered by the Dikgale Health and Demographic Surveillance System (HDSS) [8,12,13] raises concerns about the growing burden of MetS in this community. Similarly, a study in the Free State Province of South Africa by van Zyl et al. [14] reported a cumulative risk-factor profile of 40.1% in rural areas and 34.4% in urban areas, with a significant proportion of the population exhibiting three or more risk factors for chronic lifestyle diseases, including MetS (52.2% in rural and 39.7% in urban areas). The convergence of these risk factors points to the need for focused interventions to reduce the risk of MetS and its cardiovascular complications. Understanding the relationship between MetS prevalence and sociodemographic factors can provide crucial insights for developing public health strategies to combat CVDs and improve the overall well-being of the Dikgale population.

This study aims to assess the clustering of MetS risk factors and explore their association with sociodemographic characteristics. It builds on previous findings from the Dikgale HDSS, which reported high levels of obesity (27%), elevated fasting blood glucose (12.5%), increased waist circumference (34.6%), and hypertension (38%), all key components of MetS [12]. However, the specific prevalence of MetS and its connection to sociodemographic factors has remained unclear. Understanding these associations is vital for designing interventions that could help mitigate the rising incidence of hypertension, diabetes, and CVD mortality, ultimately improving public health outcomes.

2. Materials and Methods

2.1. Study Design

This was a retrospective cross-sectional study that used phase 1 data from the Africa Wits INDEPTH Partnership for Genomic Research (AWI-Gen). The phase 1 data consisted of a total of 1399 records of individuals aged 40 years and above (428 males and 971 females). The sample size was calculated based on a reported MetS prevalence rate of 22.1% [5]. There was a sampling error of 5%, and a 95% confidence interval with a design effect set at 2. The sample size for the present study was 575 record files of participants. From the 1339 individual records gathered, a statistical software package for social sciences (SPSS) version 25 was used to randomly select a sample size of 575 participants. The study was conducted in the DIMAMO HDSS, which includes Dikgale, Mamabolo, and Mothiba, previously referred to as the Dikgale Health Demographic Surveillance Site (Dikgale HDSS). This site is situated in the Capricorn District of Limpopo Province, South Africa. The Turfloop Research Ethics Committee (TREC) approved the ethical guidelines for the research (TREC/232/2018:PG). Additionally, the Dikgale Tribal Authority permitted the study, and all participants gave their written informed consent.

2.2. Data Collection

Participant information was gathered using the AWI-Gen H3Africa questionnaire, which included sections on sociodemographic factors (such as gender, marital status, education, occupation, age, employment status, and household characteristics) and lifestyle factors. A qualified research nurse, assisted by trained research assistants, collected anthropometric data and blood samples. Waist circumference was measured with a SECA measuring tape (manufactured by SECA, Hamburg, Germany). Blood pressure was taken with an Omron blood pressure monitor (Omron Healthcare Inc., Shanghai, China), with three readings taken and the average of the last two used. Biochemical analysis, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and glucose, was performed using a Randox Plus clinical chemistry analyzer (UK). The Friedewald formula was applied to calculate low-density lipoprotein cholesterol (LDL-C), using the values of TC, HDL-C, and TG in mmol/L: LDL-C = TC − HDL-C − (TG/5). This formula was not used for TG concentrations exceeding 4.5 mmol/L. Further methodological details are provided elsewhere [15]

2.3. Main Outcome and Selected Explanatory Variables

The outcome variable in this study was MetS status grouped into metabolic syndrome and non-metabolic syndrome. The outcome was obtained by dichotomizing the metabolic category. The data were prepared using the harmonized definition for MetS, which was agreed upon in 2009, defined as the coexistence of any 3 of the following: a waist circumference of ≥94 cm in men and ≥82 cm in women, raised triglycerides of ≥150 mg/dL/1.7 mmol/L, reduced HDL-C < 40 mg/dL/1.03 mmol/L in males and <50 mg/dL/1.29 mml/L in females, raised blood pressure (with a systolic pressure of (≥130 mmHg), and/or a diastolic pressure of ≥85 mmHg and high FPG ≥ 100 mg/dL/5.6 mmol/L [16].

The study’s explanatory variables included sociodemographic factors such as age, sex, marital status, educational attainment, and employment status. Sex was defined based on sex at birth (male and female). Age was categorized into 40–44, 45–49, 50–54, and 55–60 years. Marital status was categorized as married/living together, never married, or divorced/widowed. Educational attainment was classified into no formal education, and primary, secondary, and tertiary levels. Employment status was classified as either unemployed or employed.

2.4. Statistical Analysis

SPSS version 25 was used for data analysis. Categorical variables were presented in terms of frequency and percentage. A chi-square test was used to compared proportions between groups; additionally, a chi-square test was conducted to examine relationships and differences in outcome category proportions across explanatory variables, with Cramer’s V used to assess the strength of association. To interpret Cramer’s V, the following values and descriptors were applied: 0.00–0.10 (redundant), 0.10–0.20 (weak), 0.20–0.40 (moderate), 0.40–0.60 (strong), and 0.60 or above (very strong). Furthermore, logistic regression (univariate and multivariate) was performed to determine the association between MetS and sociodemographic profile. In the model, MetS (variable outcome) was the dependent variable, while sociodemographic profiles (explanatory variables) were the independent variables. This test was performed at the 95% confidence level. A p-value of less than 0.05 was considered statistically significant.

3. Results

Table 1. Relationship between selected demographic variables and gender.

	n = 575	Male 167 (29%)	Female 408 (71%)	p-Value for Trend	Cramer’s V
		n (%)	n (%)
Age in years
40–44	122 (21.2)	34 (20.4)	88 (21.6)	0.990	0.014
45–49	135 (23.5)	40 (24.0)	95 (23.3)
50–54	153 (26.6)	45 (26.9)	108 (26.5)
55–60	165 (28.7)	48 (28.7)	117 (28.7)
Level of education
None	52	18 (10.8)	34 (8.3)	0.016	0.134
Primary	182	39 (23.4)	143 (35.0)
Secondary	314	98 (58.7)	216 (52.9)
Tertiary	26	12 (7.2)	14 (3.4)
Marital status
Never married or cohabited	130	37 (22.2)	93 (22.8)	0.844	0.036
Married/living with partner	313	89 (53.3)	224 (54.9)
Divorced/Widowed	132	41 (24.6)	91 (22.3)
Employment status
Unemployed	359	101 (60.5)	258 (63.2)	0.536	0.025
Employed	216	66 (39.5)	150 (36.8)

presents the association between participant’s characteristics and gender. The average age of participants was 50.1 ± 5.8 years. Most participants, both male and female, were aged 55–60, with each gender accounting for 28.7% of the study sample. However, this was not statistically significant (p = 0.990). With regard to educational status, 58.7% of male participants and 52.9% of female participants had completed secondary education, while 23.4% of males and 35% of females had only primary education, with this difference being statistically significant (p = 0.016). The majority of participants were either married or living with a partner, with 53.3% of males and 54.9% of females in this category, although this result was not statistically significant (p = 0.844). Most participants were unemployed, with 60.5% of males and 63.2% of females in this situation, but this difference was also not statistically significant (p = 0.536). The Cramer’s V coefficients indicated that all associations were negligible, except for education, which showed a Cramer’s V coefficient of 0.134, suggesting a weak association.

Table 2. Individual components for metabolic syndrome by gender.

	n	Male n (%)	Female n (%)	p-Value	Cramer’s V
Waist circumference
Low	240	70 (41.9)	170 (41.7)	0.956	0.002
High	335	97 (58.1)	238 (58.3)
Triglycerides
Low	495	141 (84.4)	354 (86.8)	0.463	0.031
High	80	26 (15.6)	54 (13.2)
HDL-C
Low	179	17 (10.2)	162 (39.7)	<0.001	0.289
High	396	150 (89.8)	246 (60.3)
Average SBP
Low	363	137 (82.0)	226 (55.4)	<0.001	0.251
High	212	30 (18.0)	182 (44.6)
Average DBP
Low	367	139 (83.2)	228 (55.9)	<0.001	0.258
High	208	28 (16.8)	180 (44.1)
Fasting Blood Glucose
Low	362	122 (73.1)	240 (58.8)	<0.001	0.134
High	213	45 (26.9)	168 (41.2)

HDL-C: High-density lipoprotein cholesterol, SBP: systolic blood pressure, DBP: diastolic blood pressure.

shows the relationship between gender and individual components of MetS. Participants with high waist circumference were equally distributed between the genders at 58.1% and 58.3% for males and females, respectively. However, the results were not statistically significant (p = 0.956). Elevated triglycerides had a difference of 2.4% between the genders, with males having a higher measurement. However, the results were not statistically significant among the genders (p > 0.463). Low HDL-C was more prevalent in females than in males (39.7% vs. 10.2%, respectively; p < 0.001). Overall, 18% of males presented with high systolic blood pressure and 16.8% with high diastolic blood pressure. Blood pressure measurements above the normal parameters according to JIS definition were high in females, at 44.6%, and 44.1% for systolic and diastolic blood pressure, respectively (p < 0.001). Fasting blood glucose was higher in females as compared to males(41.2% vs. 26.9%, respectively; p ≤ 0.001). Looking further, the Cramer’s V coefficients for HDL-C, SBP and DBP showed a moderate association, with fasting blood glucose illustrating a weak association.

Table 3. Prevalence of metabolic syndrome and sociodemographic factors.

	N	Have Metabolic Syndrome		p-Value for Trend	Cramer’s V
		Yes 162 (28.2%)	No 413 (71.8%)
Age
40–44 n (%)	122	26 (21.3)	96 (78.7)	0.111	0.102
45–49 n (%)	135	35 (21.3)	100 (78.7)
50–54 n (%)	153	45 (29.4)	108 (70.6)
55–60 n (%)	165	56 (33.9)	109 (66.1)
Gender
Female n (%)	408	136 (33.3)	272 (66.7)	<0.001	0.179
Male n (%)	167	26 (15.6)	141 (84.4)
Employment status
Unemployed n (%)	359	102 (28.4)	257 (71.6)	0.924	0.007
Employed n (%)	216	60 (27.8)	156 (72.2)
Marital status
Never married or never cohabited n (%)	130	31 (23.9)	99 (76.2)	0.444	0.082
Married/living with Partner n (%)	313	91 (29.1)	222 (70.9)
Divorced/Widowed n (%)	132	40 (30.3)	92 (69.7)
Level of education
None n (%)	52	19 (36.5)	33 (63.5)	<0.001	0.203
Primary n (%)	182	72 (39.6)	110 (60.4)
Secondary n (%)	314	68 (21.7)	246 (78.3)
Tertiary n (%)	26	3 (11.5)	23 (88.5)

The prevalence of MetS from the study participants’ records was 28.2% (95% CI: 24.4% to 32.0%). As displayed in Table 3, the prevalence of MetS varied according to age, gender, employment status, marital status, and level of education. As the age of the participants increased, so did the prevalence of MetS. However, there was no significant difference in terms of age groups between participants with metabolic syndrome and those without it (p = 0.111). The prevalence of MetS was significantly higher in females than in males (33.3% vs. 15.6%, p ≤ 0.001). The unemployed participants were more likely to experience MetS when compared to the employed participants; however, this finding was not statistically significant (28.4% vs. 27.8%, p = 0.870). In terms of marital status, participants who were divorced/widowed (30.3%) were more likely to have MetS than the unmarried (23.9%) participants; however, the results were not statistically significant (p = 0.444). The prevalence of MetS was found to be lower among participants with a tertiary level of education (11.5%) when compared to the other groups (p ≤ 0.001). In addition, based on Cramer’s V, the association between MetS and educational attainment was moderate., i.e., 0.203.

Table 4. The relationship of MetS and sociodemographic factors stratified by gender.

	n	Have Metabolic Syndrome		p-Value for Trend	Cramer’s V
		Males n (%)	Females n (%)
Age groups
40–44	26	4 (15.38)	22 (16.18)	0.305	0.150
45–49	35	5 (19.23)	30 (22.06)
50–54	45	11 (42.31)	34 (25.00)
55–60	56	6 (23.08)	50 (36.76)
Employment status
Unemployed	102	14 (53.85)	88 (64.71)	0.203	0.083
Employed	60	12 (46.15)	48 (35.29)
Marital status
Never married or never cohabited	31	5 (19.23)	26 (19.12)	0.714	0.032
Married/living with Partner	91	13 (50.00)	78 (57.35)
Divorced/Widowed	40	8 (30.77)	32 (23.53)
Level of education
None	19	6 (23.08)	13 (9.56)	0.052	0.218
Primary	72	6 (23.08)	66 (48.53)
Secondary	68	13 (50.00)	55 (40.44)
Tertiary	3	1 (13.85)	2 (1.47)

shows the association of MetS and sociodemographic factors, stratified by gender. A greater proportion of males aged 50–54 years presented with a high prevalence of MetS (42.31%). With regard to females, the prevalence of MetS increased with age; however, the results were not statistically significant (p = 0.305). Metabolic syndrome was higher in unemployed (male: 53.85% and females: 64.71) when compared to employed participants (male: 64.71% and females: 35.29%); however, this was not statistically significant (p = 0.203). The association between MetS and marital status, particularly among those who were married/living with a partner, was higher in both genders (50.00%in males and 57.35% in females), while the prevalence among those who were never married was 19.23% in males and 19.13% in females, (p = 0.714). Males with no formal education and those with primary education had an equal prevalence of MetS at 23.08%. Males with tertiary education had a low prevalence of metabolic syndrome at 13.85%, and females with tertiary education had a prevalence of metabolic syndrome at 1.47% (p = 0.052). The Cramer’s V coefficients indicated that all associations were negligible, except for education, which showed a Cramer’s V coefficient of 0.218, suggesting a moderate association.

Table 5. Regression between MetS and sociodemographic profiles.

	Univariate Logistic Regression		Multivariate Logistic Regression
Age	OR (95% CI)	p-value	AOR (95% CI)	p-value
40–44	Ref		Ref
45–49	1.3 (0.7–2.3)	0.386	1.4 (0.8–2.6)	0.237
50–54	1.6 (0.9–2.7)	0.113	1.8 (1.0–2.3)	0.039
55–60	1.9 (1.1–3.2)	0.024	2.1 (1.2–3.7)	0.009
Gender
Female	Ref		Ref
Male	0.36 (0.2–0.6)	<0.001	0.4 (0.2–0.6)	<0.001
Level of education
None	Ref		Ref
Primary	1.2 (0.6–2.2)	0.694	1.0 (0.5–2.0)	0.905
Secondary	0.5 (0.3–0.9)	0.021	0.4 (0.2–0.8)	0.013
Tertiary	0.2 (0.1–0.9)	0.029	0.2 (0.1–0.9)	0.037
Marital status
Never married/cohabited	Ref
Married/living with partner	1.3 (0.8–2.1)	0.263
Divorced/Widowed	1.4 (0.8–2.4)	0.241
Employment status
Unemployed	Ref
Employed	0.96 (0.6–1.4)	0.870

shows regression MetS and sociodemographic profiles; the logistic regression analysis highlights several key demographic factors influencing the outcome. Age is shown to be significant, with individuals aged 55–60 having higher odds in both univariate (OR = 1.9, 95% CI: 1.1–3.2) and multivariate models (AOR = 2.1, 95% CI: 1.2–3.7), compared to the other age groups. Gender also plays a critical role, with males exhibiting significantly lower odds than females (OR = 0.36, 95% CI: 0.2–06, and AOR = 0.4, 95% CI: 0.2–0.6). Education level is another important factor, as those with secondary and tertiary education have notably lower odds, especially those with tertiary education (AOR = 0.2, 95% CI: 0.1–0.9). Although marital status and employment were assessed in the model, neither showed statistically significant results.

4. Discussion

The present study aimed to investigate the prevalence between MetS and specific sociodemographic factors amongst adults residing in rural Limpopo. The prevalence of MetS was found to be 28.2%. The present study reported a higher prevalence compared to a study conducted in rural South Africa, where the prevalence of MetS was reported to be at 22.1%, using the JIS definition to define MetS [5]. The difference between the present study and that of Motala et al. [5] may be due to the age of the study participants enrolled in the study as the participant age in the study conducted by Motala et al. [5] was 15–65 years, while in the current study, the age group was 40–60 years. The findings of the present study are similar to the systematic review and meta-analysis work of Bowo-Ngandji concerning the prevalence of MetS in African populations. The authors found the prevalence of MetS to be 31.6% with JIS, and 29.3% when NCEP-ATP and IDF are used [7].

Previous studies have reported the increase in age as a significant risk factor for developing and progressing metabolic diseases in older adults due to various physiological changes that occur with age [2,5,17]. The present study’s findings reveal some insights into the prevalence of MetS across different age groups and genders. The age-specific prevalence rates of MetS were 21.3% for both the 40–44 and 45–49 age groups, 29.4% for the 50–54 age group, and 33.9% for the 55–60 age group. Although a clear trend shows increased MetS prevalence with advancing age, the differences were not statistically significant. This suggests that while age appears to be a factor in the likelihood of developing MetS, the variations within the studied age range do not reach statistical significance. The highest prevalence of MetS was observed in the 55–60 age group, at 33.9%. In addition, on regression individuals aged 55–60 years had higher odds in both univariate and multivariate models compared to the other age groups. The above shows that older individuals have higher rates of having MetS, highlighting the importance of targeted preventive measures and interventions for older populations to manage and mitigate the risk factors associated with MetS.

The findings of the present study showed that women had a notably higher rate, with 33.3% affected compared to 15.6% of men. Furthermore, the regression analysis showed men to have lower odds of MetS when compared to their counterparts’ females. This considerable gender disparity indicates that females are more susceptible to MetS than their male counterparts. These findings align with previous research and highlight the need for gender-specific strategies to prevent and manage MetS [5,18]. In light of the findings given above, Motala et al. [5] observed that the highest prevalence of MetS occurred among women aged 65 years and older (44.2%) and men aged 45–54 years (25.0%) [5]. The findings of this study align with a previous study conducted in rural South Africa thus suggesting that the rural communities have already entered the epidemic phase of MetS [5]. Additionally, this study supports the results of Santos et al., who found a lower prevalence of MetS in the 40–49 age group (11.6% in women and 11.2% in men) and a higher prevalence in the 60–69 age group (38.9% for women and 22.6% for men) [19]. The difference in the prevalence of MetS between males and females could be that females particularly older females often experience significant hormonal fluctuations throughout their lives, particularly during menopause, which influences factors such as lipid metabolism which ultimately can increase the risk of developing MetS [20,21]. A study by Opoku et al. [22] reported that post menopause, women experience a decline in estrogen levels which is linked to an increase in visceral fat and changes in lipid metabolism, both of which are components of MetS.

Relating to employment status and metabolic syndrome, the findings of the present revealed a 37.0% prevalence of MetS in employed females in the 55–60 age compared to 16.7% in males of the same age group. Similarly, unemployed females in the 55–60 age group had a MetS prevalence of 36.4%, while their male counterparts had a prevalence of 28.6%. Overall, the prevalence of MetS was significantly higher in unemployed females (64.7%) compared to employed females (35.3%). Also, the same trends for females occur in males, where the prevalence was higher among the unemployed (53.8%) than the employed participants (46.2%). The findings of the present study are in alignment with the study by Santos et al., who reported that economically active females had a higher MetS prevalence (16.2%) compared to males in the same group (14.7%) [19]. Additionally, the prevalence of MetS among unemployed females was 25.6%, which is higher than the 23.1% observed in unemployed males [19]. Employed females often face unique stressors related to balancing work and family responsibilities, which can contribute to unhealthy eating habits, a lack of physical activity, and stress-related metabolic changes [23].

The findings of the present study indicated that there was no association between marital status and MetS. In contrast, the findings of the present study by Owolabi et al. reported that married participants were 2.3 times more likely to be associated with MetS [24]. The discrepancies between the present study and those by Owolabi may be due to different study populations and different geographical settings. The present study was conducted among the general population and those by Owolabi were conducted among participants undergoing healthcare treatment.

Education is widely recognized as a key indicator of social position in epidemiological studies, and several studies have shown a significant association between education level and the prevalence of MetS [25,26,27]. Education’s importance as a predictor of health outcomes lies in its influence on lifestyle behaviors, psychosocial attitudes, access to health services, and economic opportunities [19,25]. In the present study, education was used to measure social standing, and the prevalence of MetS among participants varied significantly across different education levels. Participants with no education had a MetS prevalence of 36.5%, those with primary education had a prevalence of 39.6%, those with secondary education had a prevalence of 21.7%, and those with tertiary education had a prevalence of 11.5%. Moreover, concerning the regression analysis, higher educational attainment resulted in higher odds of MetS compared to lower educational attainment. Santos et al. [19] also reported similar trends but with some variations. Among male participants with five to eleven years of education, the prevalence of MetS was 18.8%, compared to 14.8% among those with more than twelve years of education. For those with zero to four years of education, the prevalence was 17.2%. In the current study, the MetS prevalence among participants without education was higher at 36.5%, suggesting a more pronounced effect of education on MetS among this population. Educated individuals often have better access to health information and a greater ability to understand it [28]. This higher health literacy enables them to make informed decisions about diet, exercise, and lifestyle, which are crucial in preventing and managing metabolic syndrome [29].

Similarly, the findings of the present study, are consistent with those of Zhan et al. [30] who found that the prevalence of MetS was 39.85% among individuals with less than seven years of education, 23.91% among those with seven to twelve years of education, and 17.31% among those with more than twelve years of education. The aforementioned studies emphasise that higher levels of education are associated with lower MetS prevalence [25,29]. Therefore, the inverse relationship between education level and MetS prevalence suggests that enhancing educational opportunities could be a key strategy in reducing MetS prevalence and improving overall public health outcomes. Although previous studies have reported many factors that can lead to an increase in the prevalence of metabolic syndrome (MetS) among different populations [31,32,33], a study by Nouri-Keshtkar et al. [31] reported that the integration of waist circumference and triglyceride within genders could be useful as a screening criterion for MetS in rural populations. In addition, menopause and HIV are associated with cardiometabolic disease [34]. In sub-Saharan Africa, there is a growing population of midlife women living with HIV and a high prevalence of cardiometabolic disease [34]. Therefore, future research should continue to explore the mechanisms through which education influences health behaviours and outcomes to develop targeted interventions to reduce health disparities associated with lower educational attainment. These interventions should also take into account gender, body composition, and HIV status.

5. Conclusions

This study identified sociodemographic differences in MetS prevalence among rural black populations, revealing a high rate of MetS in the Dikgale HDSS area at 28.2%. Females showed a notably higher prevalence (33.3%) compared to males (15.6%), and those aged 55–60 had the highest rates across age groups. Additionally, MetS prevalence was elevated among individuals with limited or no formal education. Regression analysis confirmed that being female, aged 55–60, and having low educational status were positively associated with MetS. Based on these findings, targeted health interventions should address the high MetS rates among females, older adults, and those with low levels of education in rural communities. Health programs for women could include lifestyle counseling, screenings, and nutrition guidance, while age-specific initiatives could focus on physical activity and diet for older adults. Simplified education and routine screenings in rural areas could make MetS management more accessible, promoting practical lifestyle changes. These community-based strategies can help reduce MetS disparities and support improved health outcomes.