Lifestyle and Cardiovascular Risk Factors by Educational Attainment in a Mediterranean Setting: A Cross-Sectional Analysis of a Population-Based Sample

Study design and population

We conducted a cross-sectional analysis using baseline data from a population-based sample recruited for a randomized controlled trial in Girona and its surrounding areas (north-eastern Spain). The original trial aimed to validate a cardiovascular risk self-screening method and included individuals aged 35–74 years without prior cardiovascular disease. Participants were randomly selected from the reference population and provided written informed consent. The study was approved by the Clinical Research Ethics Committee of the Parc de Salut Mar (CEIC-PSMAR, #2014/5815/I) and registered at ClinicalTrials.gov (#NCT02373319).

The recruitment process has been described in detail elsewhere (4), including the full participant flow. In brief, 955 individuals were initially recruited. Participants with missing data on educational attainment (n = 12), the main exposure variable, were excluded from the present analysis. The final analytical sample therefore comprised 943 individuals with complete baseline data. The sample size for the original trial was estimated at 900 participants to achieve a kappa index of 0.8 (95% confidence interval [CI]) when comparing the self-screening method with a supervised (gold standard) cardiovascular risk assessment. As the present study is a secondary cross-sectional analysis of baseline data, no additional sample size calculation was performed (17).

Data collection

Trained nurses collected data using standardized procedures. Sociodemographic characteristics (including educational attainment), lifestyle behaviors (smoking status, physical activity, dietary habits), and clinical variables were obtained through self-administered questionnaires and direct measurements. Educational attainment was classified into three categories: (1) primary education (no formal schooling or completion of primary education, typically corresponding to up to 6–8 years of schooling), (2) secondary education (completion of lower and/or upper secondary education, typically up to 12–16 years of schooling), and (3) higher education (completion of a university degree or equivalent). Anthropometric measurements included weight, height, and waist circumference; body mass index (BMI) was calculated as weight (kg)/height² (m²). Blood pressure was measured twice after a 5-minute rest using an automated sphygmomanometer, and the lower value was recorded. Hypertension was defined as either self-reported diagnosis, current antihypertensive treatment, or systolic/diastolic blood pressure ≥140/90 mmHg.

Adherence to the Mediterranean diet was assessed using the validated 14-item Mediterranean Diet Adherence Score (MEDAS), ranging from 0 (low adherence) to 14 (high adherence) (5, 18). Physical activity was evaluated with the REGICOR questionnaire, which estimates energy expenditure in leisure-time activities based on type, frequency, duration, and intensity. Assigned metabolic equivalent of task (MET) values were walking (4), brisk walking (5), gardening (5), walking trails (6), climbing stairs (8), and sports (10) (5, 19).

Laboratory measurements

Fasting blood samples (10–14 h) were collected in <60 seconds and stored at –80 °C. Total cholesterol and high-density lipoprotein (HDL) cholesterol were measured by enzymatic and direct methods (ABX-Horiba, Montpellier, France). Low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald formula when triglycerides were <300 mg/dL. HbA1c was determined by colorimetry and latex agglutination (ABX-Horiba).

Hypercholesterolemia was defined as self-reported diagnosis, lipid-lowering treatment, total cholesterol ≥190 mg/dL, or LDL cholesterol ≥115 mg/dL. Diabetes was defined as self-reported diagnosis, antidiabetic treatment, HbA1c ≥6.5%, or fasting glycemia ≥126 mg/dL.

Statistical analysis

Analyses were stratified by sex and educational level. Continuous variables were summarized as means (SD) or medians (IQR), and categorical variables as frequencies. Differences across educational groups were assessed using chi-square tests for categorical variables and ANOVA or Kruskal–Wallis tests for continuous variables, as appropriate. Linear trends were evaluated using p for trend.

Associations between education and cardiovascular risk factors or lifestyle behaviors were examined using age-adjusted regression models: linear regression for continuous outcomes reporting β coefficients and 95% CI, generalized linear models for skewed continuous variables reporting risk ratios and logistic regression for binary outcomes reporting odds ratio (OR) and 95% CI. As a sensitivity analysis, we repeated the analyses in a subsample of women with menopause.

All tests were two-sided, with p-value <0.05 considered statistically significant. Analyses were performed using R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria) (20).

Education and Cardiovascular Risk Pathways

Our findings align with the view of educational attainment as a key social determinant of health that shapes cardiovascular risk through multiple, interlinked pathways (22). At the structural level, education influences employment opportunities, income, and material conditions. These, in turn, affect access to healthy foods, stable housing, and healthcare, factors that provide the broader context in which cardiovascular risk accumulates over the life course (12). At the intermediate level, education is associated with increased health literacy, psychosocial resources, and decision-making capabilities that facilitate engagement with preventive care and adoption of healthy behaviors, including diet quality and smoking avoidance (23, 24). Moreover, evidence shows that the cardiovascular benefits of Mediterranean-style eating and healthy lifestyles tend to be patterned by education or related socioeconomic markers (25). At the individual level, these upstream advantages translate into measurable differences in biomedical risk factors such as adiposity, blood pressure, and lipid profile (12). Despite their relevance as mediators of socioeconomic differences, individual-level factors account for only a small proportion of the overall socioeconomic inequity in health (9). Thus, healthy-lifestyle promotion alone is unlikely to substantially reduce socioeconomic disparities in cardiovascular health, underscoring the need for complementary strategies that address the upstream social determinants of health (26).

Sex Differences and Underlying Mechanisms

Building on these pathways, our results suggest that higher educational attainment is associated with more favorable cardiovascular risk profiles among women than among men, indicating that education-related pathways may be particularly relevant in women. One plausible mechanism is health literacy, for which educational level is a key determinant, conferring greater capacity for risk recognition and adoption of preventive behaviors (27). Limited health literacy has been linked to increased cardiovascular risk and remains more prevalent among women from historically marginalized groups and with lower educational attainment (28, 29). This may contribute to the wider differences observed in biomedical risk factors among women across educational levels.

In addition, gender roles and caregiving responsibilities may further amplify the impact of education on cardiometabolic risk in women. Caregiving burden—more common among women—has been associated with poorer physical activity, diet quality, and stress regulation, whereas resources and greater agency linked to higher education may mitigate these effects (30, 31). This provides an additional pathway through which education may buffer caregiving-related risks, contributing to steeper gradients in adiposity and blood pressure among women (32).

Public Health and Policy Implications

Our findings have clear implications for public health practice. The stronger educational gradients observed among women support the need for prevention strategies that integrate both equity-focused and sex-specific approaches. Strengthening primary care prevention and cardiometabolic screening is essential to reduce undiagnosed and undertreated risk factors, particularly in socially disadvantaged groups (33, 34).

Beyond clinical interventions, reducing cardiovascular inequalities requires sustained action on structural determinants, including educational opportunities, employment conditions, and neighborhood environments that shape daily living conditions (12). Given the patterns observed, women with lower educational attainment may face intersecting constraints—such as socioeconomic disadvantage and caregiving responsibilities—that increase cardiometabolic vulnerability (13, 35, 36).

In sum, a dual strategy is warranted: (1) reinforcing equity-informed clinical and community prevention and (2) addressing the structural drivers of educational inequalities. These findings highlight the importance of gender-responsive approaches across public health practice and policy.

Strengths and Limitations

The consistency of our age-adjusted associations suggests that the observed educational gradients do not merely reflect higher educational attainment among younger—and healthier—participants. Nonetheless, residual confounding cannot be ruled out. Income, occupation, neighborhood context, and life-course exposures (e.g., early-life adversity) were not included and may mediate or confound the observed associations. Selective survival and participation may also bias estimates in cross-sectional designs. Although our population-based sampling frame and standardized recruitment procedures help mitigate this concern, it cannot be completely excluded. Moreover, the cross-sectional nature of the study precludes establishing temporality. Educational attainment is typically completed before age 35, which was the minimum recruitment age in our sample, whereas the development of many cardiovascular risk factors tends to occur later in adulthood, which reduces the likelihood of reverse causation. In addition, given the number of outcomes examined across sex and educational groups, some associations may be due to chance and should be interpreted cautiously. Treatment was also included in the definition of cardiovascular risk factors. As a result, observed differences across educational levels should be understood in the context of underlying disease prevalence rather than as independent differences in access to or use of care.

A major strength of this study is the analysis of a relatively large, population-based sample from Southern Europe, using standardized and objective measurements of key cardiovascular risk factors, together with validated instruments to assess diet and physical activity. In addition, stratification of the analyses by sex represents a further strength, as it allowed us to identify sex-specific patterns that would likely have been obscured in pooled analyses.