The study employed a descriptive observational cross-sectional design. Quantitative data were collected at one point in time to describe dietary patterns and cardiometabolic risk factors among teachers and to assess associations between dietary pattern scores and selected cardiometabolic risk categories. The design was appropriate for estimating the burden of cardiometabolic risk factors and exploring associations, but it does not permit causal inference.

The study was conducted in rural public schools within Makhado Municipality, located in the Vhembe District of Limpopo province, South Africa. Makhado is one of the largest municipalities in the district and includes a mixture of rural villages, small towns and peri-urban settlements. The area is served mainly by public-sector schools and health services. Many communities in the municipality are affected by socioeconomic constraints, transport limitations and variable access to diverse foods, which may influence dietary practices and cardiometabolic health. The school environment was selected because teachers spend a substantial part of their working day at school and represent a feasible occupational group for workplace health promotion.

The target population comprised teachers employed in public schools in Makhado Municipality. The sampling frame consisted of 297 teachers obtained from the Department of Education. Eligible participants were full-time teachers employed at the selected schools who were available during the data collection period and who provided written informed consent. Teachers who were absent during data collection, declined participation, were pregnant or were unable to complete the required measurements were excluded.

The sample size was calculated using Slovin’s formula with a 4% margin of error, which produced a minimum sample size of 201 participants. Stratified random sampling was used to promote representation across the selected schools. The number of teachers selected from each stratum was proportionate to the number of eligible teachers in that stratum. Within each stratum, participants were selected randomly from the staff list until the required sample was reached.

Data were collected by trained research personnel using minimised procedures. Participants completed demographic and dietary questionnaires, after which anthropometric, blood pressure and biochemical measurements were taken. Data collection was scheduled at schools at times agreed with school management to minimise disruption to teaching activities.

Blood pressure was measured using an automated sphygmomanometer (Omron Healthcare Inc., United States [US]) after participants had rested in a seated position for at least 5 min. Two readings were obtained, and the average was used for analysis. Blood pressure categories were defined according to the American College of Sports Medicine guidelines: normal (< 120/80 mmHg), prehypertension (120 mmHg – 139/80–89 mmHg), stage 1 hypertension (140/90 mmHg – 159/99 mmHg) and stage 2 hypertension (≥ 160/100 mmHg).⁹

Fasting total cholesterol (TC), triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and glucose levels were measured after a 10-h fast using capillary blood obtained by finger prick. Samples were applied to Polymer Technology Systems (PTS) Panel glucose and lipid test strips and analysed using a CardioChek Plus PA Analyser (Polymer Technology Systems, Inc., US). The analyser was calculated regularly according to the manufacturer’s instructions.

Standing height was measured to the nearest 0.1 cm using a stadiometer, with participants barefoot and positioned according to standard procedures. Body mass was measured to the nearest 0.1 kg using a calibrated portable digital scale (seca, Germany). Body mass index (BMI) was calculated as weight in kilograms divided by height in metres squared. Waist circumference was measured to the nearest 0.1 cm using a non-elastic measuring tape in accordance with standard procedures.⁹

Habitual dietary intake was assessed using a validated quantitative food-frequency questionnaire (FFQ) adapted to include commonly consumed foods in the study area. Participants reported how frequently they consumed each listed food item during the reference period and provided information on quantities consumed where applicable. Additional questions captured types of cooking oil, added salt use, eating away from home, meal frequency and usual dietary practices. Food items were grouped and used to derive dietary pattern scores.

Questionnaires and measurement forms were checked on the day of data collection for completeness and consistency. Data were captured into an electronic database and checked for missing values, implausible entries and out-of-range measurements. A trained research assistant, qualified as a Biokineticist, assisted with measurement procedures and data checking. Any discrepancies were resolved by referring to the original data collection forms. Instrument calibration, repeated measurements and standardised protocols were used to improve reliability.

A trained research assistant, qualified as a Biokineticist, was recruited and oriented in standardised measurement procedures and testing protocols to ensure validity. All instruments were calibrated before data collection. To enhance reliability, blood pressure and anthropometric assessments were repeated, and test–retest correlation coefficients were calculated to evaluate consistency across measurements. A pilot study was conducted in similar schools within Makhado Municipality not included in the main study. Dietary patterns were assessed using standardised FFQs, and the internal consistency of the questionnaire was evaluated using Cronbach’s alpha.

Descriptive statistics were used to summarise the data. Normality of continuous variables was assessed using Shapiro–Wilk tests and visual inspection of histograms. For comparisons between two groups (male vs female), we used independent samples t-tests when variables were approximately normally distributed, and variances were equal (Levene’s test); where normality or equal-variance assumptions were violated, we used the Mann–Whitney U test. For understanding of dietary patterns, that is food groupings, exploratory factor analysis (EFA) was undertaken through the principal component analysis. Factors with eigenvalues 1 or above were retained, while the matrix was rotated using the Varimax orthogonal method. All factors with factor loadings of 0.4 or above were considered in the factor to which they belong. For each food group, depending on the individual consumption of that specific week was added to obtain the sum of scores to indicate how often an individual consumes a specific food group in a week. Composite reliability was undertaken to measure the internal consistency using Cronbach alpha. All the dietary patterns with an alpha of 0.7 or above were considered for analysis. To further understand if there are any differences between the dietary group and the cardio metabolic factors, the Kruskal–Wallis test was utilised. To compare the post-estimation differences, Bonferroni pairwise test was used to establish if the differences lie within or between the groups. Effect sizes are reported as Cohen’s d for t-tests and eta-squared (η²) for Kruskal–Wallis tests. All analyses were conducted in STATA version 17 (StataCorp LLC, College Station, TX, US), and statistical significance was set at p < 0.05 (adjusted p-values reported for multiple comparisons).

Ethical clearance to conduct this study was obtained from the Tshwane University of Technology Research Ethics Committee (No. REC2020/09/002) and Limpopo Provincial Research Ethics Committee (No. REC-111513-038). Informed written consent forms and information sheets were provided to teachers before data collection. To maintain anonymity, participants were assigned unique identification codes instead of personal identifiers on all data collection forms. No names or identifying details appeared on the data collection forms. Completed data collection forms were stored in a locked cabinet accessible only to the principal investigator.

Table 1 presents the demographic characteristics of the 201 participants. More than half of the participants were female (n = 112; 55.7%), while 89 (44.3%) were male. Participants ranged in age from 23 years to 64 years, with an average age of 40.1 years. Nearly half of the sample was aged 30–44 years (49.3%), followed by those aged 45–65 years (33.3%) and 24–29 years (17.4%).

Table 2 summarises cardiometabolic risk factors by gender. There were no significant differences in age between male and female participants (p = 0.288). However, significant gender differences were observed in several anthropometric measures. Female teachers had significantly higher body weight (p < 0.001), BMI (p < 0.001) and waist circumference (p < 0.001) compared to their male counterparts, indicating a greater burden of adiposity among women. In contrast, males were significantly taller than females (p = 0.002). With regard to biochemical markers, female teachers exhibited significantly higher TC (p = 0.006) and triglyceride levels (p = 0.005), suggesting a less favourable lipid profile. No significant gender differences were found for systolic or diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol or fasting glucose (FG) levels (p > 0.05). Overall, these findings indicate that female teachers in this sample may be at greater risk for cardiometabolic disorders, particularly those associated with obesity and dyslipidaemia.

Table 3 presents the EFA results identifying distinct dietary patterns among teachers, along with measures of internal consistency and item correlations. Seven dietary patterns were extracted, each defined by food items with factor loadings greater than 0.40, indicating meaningful contributions to the respective patterns.

The Mixed Diet pattern demonstrated high internal consistency (Cronbach’s α = 0.8759) and included oats, all bran and boiled eggs, all with moderate-to-strong factor loadings (0.51–0.74) and acceptable item–rest correlations. This suggests a relatively balanced dietary pattern incorporating both plant- and animal-based foods. Similarly, the Western Diet pattern (α = 0.8050) showed strong internal reliability, with high loadings for Morvite and plain yoghurt, particularly yoghurt, which exhibited a very strong item–rest correlation (0.9283), indicating a strong contribution to this pattern.

The Starch Animal Product pattern (α = 0.8068) was characterised by mabele and fried beef, although mabele showed a comparatively lower factor loading (0.41) and weaker internal consistency contribution, suggesting some heterogeneity within this pattern. The Meat-Free Diet pattern (α = 0.7200) included boiled green cabbage and fresh thanga, with moderate factor loadings and acceptable correlations, reflecting a plant-based dietary pattern with reasonable internal consistency.

The Low Carbohydrate and Mediterranean Diet pattern (α = 0.7281) included boiled chicken and salad carrots, indicating a combination of lean protein and vegetables, although variability in item–rest correlations suggests moderate coherence. In contrast, the Mediterranean Fish-Free Diet pattern (α = 0.6689) and the Animal Protein Fruit Diet pattern (α = 0.7538) showed lower proportions of explained variance and moderate reliability, indicating that these patterns are less dominant within the sample.

Overall, the proportion of variance explained by each factor was relatively low (ranging from 3.18% to 9%), suggesting that dietary behaviours among teachers are diverse and not strongly dominated by a single pattern. Nevertheless, the generally acceptable Cronbach’s alpha coefficients (≥ 0.70 for most patterns) indicate that the identified dietary patterns demonstrate reasonable internal consistency and can be considered reliable representations of habitual dietary intake in this population.

Table 4 shows the differences between dietary pattern scores and cardiometabolic risk factors using nonparametric comparisons, the Kruskal–Wallis. There was no statistically significant difference in mixed diet scores across BMI categories (χ² = 2.79, p = 0.42), nor for the starch animal product pattern (χ² = 4.11, p = 0.24), indicating that adherence to these dietary patterns did not vary by weight status.

However, significant differences were observed for severaldietary patterns in relation to specific cardiometabolic variables. Mixed diet scores differed significantly across SBP categories (χ² = 8.70, p = 0.03). Post hoc pairwise comparisons indicated that participants with stage 1 hypertension had significantly higher mean ranks compared to those with prehypertension (mean difference = 2.9, p < 0.001).

Western diet scores differed significantly across TC categories (χ² = 6.11, p = 0.04), with significantly lower scores observed in participants with high TC compared to those with desirable levels (mean difference = −2.16, p = 0.04). Similarly, meat-free diet scores varied significantly across HDL categories (χ² = 6.96, p = 0.03), with participants classified as ‘at risk’ demonstrating significantly lower scores than those with optimal HDL levels (mean difference = −2.34, p = 0.02).

For the Mediterranean fish-free diet pattern, there was a significant difference across HDL categories (χ² = 6.51, p = 0.03). Pairwise comparisons showed that participants in the at-risk group had significantly lower scores than those in both the optimal (mean difference = −2.02, p = 0.03) and borderline categories (mean difference = −2.29, p = 0.03).

The animal protein fruit diet pattern demonstrated significant differences across multiple outcomes. Scores differed across FG categories (χ² = 7.68, p = 0.02), with significantly lower scores observed in participants with diabetes compared to those with prediabetes (mean differences = −2.44, p = 0.02; −2.24, p = 0.03). Significant differences were also observed across HDL categories (χ² = 6.96, p = 0.03), where at-risk participants had lower scores compared to optimal and borderline groups. In addition, scores differed significantly across SBP categories (χ² = 10.36, p = 0.01), with higher scores observed among participants with stage 1 hypertension compared to those with normal blood pressure (mean difference = 3.14, p < 0.001).

Overall, these results indicate that dietary pattern scores were significantly associated with selected cardiometabolic risk factors, particularly lipid profiles, FG and SBP, while no significant associations were observed with BMI.

This study should be interpreted considering several limitations. Firstly, the cross-sectional design permits identification of associations but does not establish temporal direction or causality between dietary patterns and cardiometabolic risk factors. Secondly, dietary intake was assessed using an FFQ, which is subject to recall bias, reporting errors and social desirability bias. Thirdly, the dietary patterns were empirically derived from the current sample, and the labels assigned to factors were based on the food items with the highest loadings; therefore, the patterns may not be directly comparable with standard dietary indices used in other studies. Fourthly, some factors included only a small number of food items and explained a relatively low proportion of variance, which may limit interpretability. Fifthly, residual confounding by physical activity, medication use, socioeconomic status, stress, smoking, alcohol use and total energy intake may have influenced the observed associations. Finally, the study was conducted among teachers in one municipality, which may limit generalisability to teachers in other regions or to the general adult population.

Schools and local health authorities should implement nutrition education programmes targeting teachers to encourage healthier dietary choices, particularly reducing reliance on ‘Western’ diets high in saturated fats and processed foods. Stakeholders in the education and health sectors should collaborate to promote access to healthier meals within school settings, potentially introducing workplace wellness programmes tailored to teachers. Given the limited literature on ‘animal starch product’, ‘meat-free’ and ‘Mediterranean fish-free’ dietary patterns, additional studies are recommended to explore their long-term impact on lipid profiles and cardiometabolic risk. Regular screening of blood pressure, cholesterol and BMI among teachers should be integrated into occupational health programmes to identify and manage early signs of cardiometabolic risk. Teachers, as role models within communities, should be empowered to adopt and promote healthy lifestyle practices, potentially influencing students and families towards healthier dietary behaviours.