Abstract
Background
Cardiovascular diseases (CVDs), resulting from complex interactions among socioeconomic, behavioral, and dietary factors, remain a major global health challenge. This study utilized structural equation modeling (SEM) to assess the direct and indirect effects of these factors on CVD risk, with dietary indices—the Dietary Insulin Index (DII) and Dietary Glycemic Index (DGI)—acting as mediators, in a population from western Iran.
Methods
A cross-sectional analysis was conducted on 3,452 adults aged 35–70 years from the Dehgolan Prospective Cohort Study (DehPCS). Data collected included anthropometric measurements, socioeconomic status, lifestyle behaviors, chronic diseases, and dietary indices (DII and DGI). SEM was employed to examine pathways linking latent variables (socioeconomic status, personal habits, and chronic diseases) and mediators (DII, DGI) to CVD risk. Model fit was evaluated using the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and χ² statistics.
Results
The model accounted for 11% of the variance in CVD risk (R² = 0.11). Higher socioeconomic status directly reduced CVD risk (β = −0.28, p < 0.001) and chronic disease burden (β = −0.21, p = 0.002). Favorable personal habits, including physical activity and adequate sleep, were associated with reduced CVD risk (β = −0.27, p < 0.001). Conversely, higher DII increased CVD risk (β = 0.15, p < 0.001), while higher DGI was associated with a modest reduction in risk (β = −0.03, p < 0.001). Indirectly, socioeconomic status (β = 0.086, p = 0.001), personal habits (β = 0.035, p = 0.001), and chronic diseases (β = 0.047, p = 0.001) influenced CVD risk through dietary patterns.
Conclusion
Dietary quality mediates the relationship between lifestyle, chronic conditions, and CVD risk, with socioeconomic disparities and chronic diseases amplifying this risk. Public health interventions should focus on promoting healthy dietary habits and ensuring equitable access to nutritious foods to mitigate cardiovascular risk.
Keywords: Dietary insulin index, Dietary glycemic index, Cardiovascular disease
Background
Cardiovascular diseases (CVDs) represent a significant global health challenge, contributing substantially to morbidity and mortality rates worldwide [1].
They comprise a spectrum of disorders affecting the heart and blood vessels, including coronary heart disease (CHD), coronary artery disease (CAD), and acute coronary syndrome (ACS), among others [2].
Cardiovascular disease and type 2 diabetes, collectively referred to as cardiometabolic disorders, remain the leading causes of mortality both in China and worldwide [3]. According to the 2019 American Heart Association update on heart disease and stroke statistics, the prevalence of cardiovascular disease was approximately 35–40% among individuals aged 40–60 years, 75–78% among those aged 60–80 years, and exceeded 85% in individuals over 80 years of age [4].
It is estimated that approximately half of cardiovascular disease cases are attributable to genetic factors, while the remaining proportion arises from acquired factors, including dietary habits. Notably, even a genetic predisposition to cardiovascular disease can be attenuated through adherence to a healthy lifestyle [5].
Extensive observational studies have demonstrated that diets rich in foods with a high glycemic index (GI) and glycemic load (GL) are associated with an elevated risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease [6]. The glycemic index (GI) and glycemic load (GL) are commonly employed to evaluate the insulin response elicited by foods and to quantify the postprandial rise in blood glucose levels following the consumption of carbohydrate-rich foods [1].
Among the modifiable risk factors for cardiovascular disease—including metabolic, environmental, and behavioral factors—dietary factors are considered the most significant. In 2017, it was estimated that dietary factors accounted for 53% of cardiovascular disease-related deaths and 58% of associated disabilities. Dietary risk factors refer to suboptimal eating patterns, including excessive consumption of foods associated with increased disease risk or inadequate intake of foods linked to a lower risk [7].
Unhealthy dietary habits and insufficient physical activity are associated with obesity and other lifestyle-related diseases, which in turn elevate the risk of cardiovascular disease and may ultimately result in mortality [8]. Structural equation modeling (SEM) is a robust analytical technique that provides several advantages for the analysis of clinical and survey data. It enables the modeling of latent (unobservable) constructs, simultaneously estimates multiple relationships—including mediating and feedback effects—and facilitates the development of comprehensive theoretical models by capturing complex and interrelated associations within a unified framework [9].
To date, no study has investigated the relationship between various dietary indicators and their direct and indirect associations with cardiovascular disease and demographic variables. This study employs structural equation modeling (SEM) to evaluate the direct and indirect effects of different risk factors on cardiovascular disease among 3,452 individuals aged 35 to 70, using baseline data from the DehPCS cohort.
Methods
Study design and participants
This cross-sectional study utilized data from the DehPCS, conducted in western Iran in 2019. Data were collected during the registration phase of the study, coordinated by the Ministry of Health and Medical Education, and included 4,000 adult participants. The Dehgolan Cohort Study focuses on the Kurdish population of Dehgolan County, located in the southern part of Kurdistan Province, and remains ongoing. Dehgolan County has an approximate population of 68,000, with Dehgolan City accounting for around 26,000 residents, nearly all of whom are Kurdish. Participants aged 35 to 70 years, residing in the urban area of Dehgolan and holding Iranian nationality, were selected for this study. The city is served by one hospital, two urban comprehensive health service centers, four rural comprehensive service centers, and 41 health homes. Eligible participants provided both oral and written informed consent. Exclusion criteria included individuals who were blind, deaf, or mute, as well as those with mental disorders (e.g., untreated psychosis) that impeded their ability to participate in interviews. Furthermore, individuals with pre-existing cardiovascular disease at baseline were excluded from the study.
The design and implementation of this study were approved by the Ethics Committee of Ilam University of Medical Sciences, which assigned the ethical approval code IR.MEDILAM.REC.1403.006.
Measurement
Educational attainment was categorized as follows: illiterate, 1–12 years (basic education), and 12 years or more (university education). Employment status, defined as working at least 8 h per week—including seasonal work—was classified as employed, retired, or unemployed. Socio-economic status (SES) was treated as a latent variable, incorporating indicators of wealth, education, and occupation. The Wealth Score Index (WSI) was separately estimated using multiple correspondence analysis (MCA) based on the following variables: access to a freezer, washing machine, dishwasher, computer, internet, motorcycle, car (none or car valued at ≥ 50 million Tomans), vacuum cleaner, type of color TV (none/regular vs. plasma), ownership of a mobile phone, PC or laptop, and international travel history (never, pilgrimage only, or other trips).
Participants were classified according to cigarette use—defined as having smoked at least 100 cigarettes in their lifetime—into smokers, former smokers, and non-smokers. Depression was diagnosed by a physician. Cardiovascular diseases (CVDs), including ischemic heart disease, heart failure, and stroke, were identified based on medication use and physician diagnosis. Joint pain was defined as experiencing pain in the joints over the past 30 days. Sleep duration was measured in hours per day, and circadian eating patterns were assessed based on the number of meals consumed daily.
Marital status was categorized as unmarried (widow, divorced, or single) or married. Alcohol consumption was defined as drinking approximately 200 mL of beer or 45 mL of liquor at least once per week for six months. Physical activity was measured using the Metabolic Equivalent of Task (MET) index over 24 h and categorized as low, moderate, or high. Body mass index (BMI) was classified as obese (BMI ≥ 30) or overweight (BMI 25–29.9).
To assess cardiovascular risk, the WHO/ISH Cardiovascular Risk Assessment Chart for the Eastern Mediterranean Region (EMRO), including Iran, was utilized. This chart estimates the 10-year risk of fatal and non-fatal cardiovascular events in individuals without a prior history of CVD and categorizes risk into five levels: low (≤ 10%), moderate (10–20%), high (20–30%), very high (30–40%), and extremely high (≥ 40%). Risk calculations were performed using the whoishrisk package in R.
Food indicators
Dietary insulin index
The food insulin index (FII) is defined as the ratio of the 2-hour postprandial insulin response (area under the curve, AUC) elicited by a 1,000-kJ portion of the test food to the AUC following a 1,000-kJ portion of the reference food. Reference values for each food item were obtained from previously published studies [10]. For food items not included in the published reference list, the FII of similar foods was applied. For the remaining items in the food frequency questionnaire (FFQ), insulin index values were imputed and calculated based on their respective recipes.
Dietary glycemic index
The term ‘glycemic index’ (GI) was first introduced by David Jenkins and colleagues in 1981, following observations of substantial changes in blood glucose levels after the consumption of carbohydrate-rich foods. In 1995, the first international tables of GI were published with the aim of compiling all available GI data from various studies and providing a convenient reference for both research and clinical practice. The GI values of consumed foods in this study were referenced using the International Tables of GI and glycemic load (GL), based on standard oral glucose [11]. The following formula was used to obtain the total glycemic index:
 |
In this formula, the carbohydrate content of a given food was calculated by subtracting its fiber content from the total carbohydrate content. The USDA Food Composition Database was used to determine the total carbohydrate and fiber contents of foods. When a food item was not listed in the database, the GI values of physically and chemically similar foods were used as substitutes. For instance, the GI of certain traditional Iranian sweets and desserts, such as Gaz—which is primarily composed of sugar with a small proportion of nuts (almonds or pistachios)—was considered equivalent to that of sugar. Foods such as legumes and non-starchy vegetables generally exhibit a low glycemic index, whereas foods like rice, white bread, and honey elicit a greater postprandial rise in blood glucose and are therefore classified as high-GI foods. Additionally, some food items, including various types of bread, were evaluated based on the Iranian GI Table [12]. The GI values for rice and dates were estimated as the average across different brands available in Iran. All obtained glycemic index values were referenced to glucose as the standard. In the Iranian glycemic index table, white bread served as the reference food; therefore, to convert these values relative to glucose, the GI of each food was multiplied by a factor of 0.7.
Patient and public involvement
No patients or members of the public were involved in the design, implementation, or dissemination of this study.
Statistical analysis
AMOS, IBM SPSS version 24, R, and STATA version 17 were used for data management and statistical analysis. Descriptive statistics, including means, maximums, minimums, and standard deviations for nutritional indices, as well as frequencies (%) for categorical variables, were reported for the participants. The normality of the data was assessed using the Kolmogorov-Smirnov test. Sample adequacy was evaluated using the Kaiser-Meyer-Olkin (KMO) statistic, which yielded a value of 0.572, and Bartlett’s sphericity test, which assesses correlations among variables, indicated a significant result (P < 0.001) with 91 degrees of freedom [13]. These results indicate that the data are suitable for factor analysis and support the construction of latent variables. Structural Equation Modeling (SEM) was employed to examine both the direct and indirect effects on the risk of cardiovascular disease (CVD). SEM is a primary method for analyzing complex data structures and exploring relationships among a set of variables, enabling the simultaneous assessment of these variables within a theory-based framework.
A two-step SEM approach was used: [1] Confirmatory Factor Analysis (CFA) to validate latent constructs, such as socioeconomic status and personal habits, and [2] path analysis to quantify the direct and indirect effects of predictors, including the Dietary Insulin Index (DII) and Dietary Glycemic Index (DGI), on CVD risk. Model fit was assessed using criteria including Comparative Fit Index (CFI) >0.90 and Root Mean Square Error of Approximation (RMSEA) < 0.07.
The conceptual model of this study is illustrated in Fig. 1. It comprises three independent latent variables, including socioeconomic status (SES), each represented by three indicators: wealth, education level, and employment status. Variables related to chronic diseases include depression, joint pain, and overweight/obesity. Individual habits are represented by physical activity, sleep duration, and the number of daily meals. Additional indicators, such as the DII and DGI, serve as mediators.
Fig. 1.
Confirmatory factor analysis. Measurement model of the latent construct of chornic diease, personal habit, socioeconomic status(ses)
Model fit was further evaluated using the Comparative Fit Index (CFI), Incremental Fit Index (IFI), and Normed Fit Index (NFI), all set at 0.90 or higher, alongside an RMSEA of 0.07 or less. Model parameters were estimated using Maximum Likelihood estimation. Statistical significance was defined as a P value less than 0.05 for all analyses.
Results
Study participants
After applying the exclusion criteria, a total of 3,452 individuals with a mean age of 48.4 ± 8.9 years were included in the study. Among the participants, 56.1% were women, and 90% were married; 51.8% were employed. The average physical activity level was 39.4 ± 8.4 Metabolic Equivalent of Task (MET) over 24 h, the mean sleep duration was 6.9 ± 1.4 h per night, and the mean Body Mass Index (BMI) was 27.9 ± 4.5.
Regarding educational attainment, 29.1% of participants were illiterate, 53.3% had a diploma or lower, and 14.1% held a bachelor’s degree or higher. A history of smoking was reported by 17.1% of participants, while 13.4% reported alcohol consumption. Socioeconomic status was distributed as follows: 25.1% in level one, 14.4% in level two, 19.9% in level three, 20.5% in level four, and 20.2% in level five.
The prevalence of CVD risk within the study sample, stratified by various variables, is presented in Table 1.
Table 1.
Characteristics of study participants according to risk of CVD
| Variables | < 10 | 10–20 | 20–30 | 30–40 | > 40 | P-value |
|---|---|---|---|---|---|---|
| n(%) | n(%) | n(%) | n(%) | n(%) | ||
| The whole sample | 3284(95. 1) | 105(3) | 37(1.1) | 8(0.2) | 18(0.5) | |
| Gender | < 0.001 | |||||
| Female | 1845(95.34) | 49(2.53) | 24(1.24) | 5(0.25) | 12(0.62) | |
| Male | 1439(94.85) | 56(3.69) | 13(0.85) | 3(0.19) | 6(0.39) | |
| Age | < 0.001 | |||||
| 35–45 | 1629(99.81) | 3(0.18) | 0 | 0 | 0 | |
| 46–60 | 1410(97.17) | 21(1.44) | 14(0.96) | 1(0.06) | 5(0.34) | |
| 60< | 245(66.39) | 81(21.9) | 23(6.2) | 7(1.89) | 23(3.52) | |
| Educationlevel | < 0.001 | |||||
| illiterate | 902(89.39) | 62(6.14) | 28(2.77) | 5(0.49) | 12(1.18) | |
| 1–12 years | 1923(97.12) | 41(2.07) | 7(0.35) | 3(0.15) | 6(0.3) | |
| University | 459(99.13) | 2(0.43) | 2(0.43) | 0 | 0 | |
| Wealth status | < 0.001 | |||||
| Q 1(poorest) | 749(91.79) | 40(4.62) | 19(2.19) | 4(0.46) | 8(0.92) | |
| Q 2 | 467(93.96) | 20(4.02) | 7(1.4) | 1(0.2) | 2(0.4) | |
| Q 3 | 655(95.48) | 23(3.35) | 5(0.72) | 0 | 3(0.42) | |
| Q 4 | 680(96.18) | 23(3.35) | 5(0.72) | 2(0.28) | 3(0.42) | |
| Q 5(wealthiest) | 688(98.7) | 5 (0.71) | 1(0.14) | 1(0.14) | 2(0.028) | |
| Employmentstatus | < 0.001 | |||||
| unemployed | 1658(92.72) | 77(4.3) | 31(1.73) | 6(0.33) | 16(0.89) | |
| employed | 1626(97.71) | 28(1.68) | 6(0.36) | 2(0.12) | 2(0.12) | |
| physicalactivity | < 0.001 | |||||
| low | 1270(92.56) | 64(4.6) | 19(1.38) | 4(0.2) | 15(1.09) | |
| Moderate | 1548(96.68) | 35(2.18) | 13(0.8) | 2(0.12) | 3(0.18) | |
| high | 466(97.28) | 6(1.25) | 5(1.04) | 2(0.41) | 0 | |
| smoking | < 0.001 | |||||
| non smoker | 2579(96.37) | 53(1.98) | 23(0.85) | 7(0.26) | 14(0.52) | |
| Former smoker | 244(90.37) | 21(7.77) | 3(1.1) | 1(0.37) | 1(0.37) | |
| a smoker | 461(90.92) | 31(6.11) | 11(2.16) | 1(0.19) | 3(0.59) | |
| marital status | 0.405 | |||||
| married | 3050(95.76) | 86(2.69) | 31(0.97) | 7(0.21) | 14(0.43) | |
| unmarried | 234(88.63) | 19(7.19) | 6(2.27) | 1(0.37) | 4(1.51) | |
| alcoholconsumption | < 0.001 | |||||
| Yes | 393(96.33) | 11(2.8) | 1(0.24) | 1(0.24) | 1(0.24) | |
| No | 2891(94.97) | 94(3.08) | 36(1.18) | 6(0.19) | 17(0.55) | |
| Depression | 0.002 | |||||
| Yes | 201(97.1) | 4(1.93) | 1(0.48) | 1(0.48) | 0 | |
| No | 3083(95) | 101(3.11) | 36(1.1) | 7(0.21) | 18(0.55) | |
| Joint pain | < 0.001 | |||||
| Yes | 2169(94.75) | 70(3.05) | 31(1.35) | 5(0.21) | 14(0.61) | |
| No | 1115(95.87) | 35(3) | 6(0.51) | 3(0.25) | 4(0.34) | |
| overweight and Obesity | 0.013 | |||||
| Yes | 2436(95.34) | 72(2.81) | 24(0.93) | 8(0.31) | 15(0.58) | |
| No | 848(94.53) | 33(3.6) | 13(1.44) | 0 | 3(0.33) | |
The Dietary Insulin Index (DII) had a mean ± standard deviation of 131.87 ± 60.08, ranging from 15.29 to 403.2. In comparison, the Dietary Glycemic Index (DGI) had a mean ± standard deviation of 63.38 ± 3.7, with values ranging from 41.37 to 73.69.
Confirmatory factor analysis
In the Confirmatory Factor Analysis (CFA) of the model’s latent variables, both the correlation and fit indices were considered satisfactory (IFI = 0.909, NFI = 0.901, CFI = 0.908, RMSEA = 0.058). The correlation between socioeconomic status (SES) and chronic diseases was − 0.58, whereas the correlation between SES and personal habits was 0.13. Additionally, the correlation between chronic diseases and personal habits was − 0.20 (Fig. 1).
Structural equation model
In the final model, which demonstrated good fit (see Table 2), the R² value for the dependent variable, risk of CVD, was 0.11, indicating the proportion of variance in CVD risk explained by the variables included in the model. The Dietary Insulin Index (DII) and Dietary Glycemic Index (DGI) functioned as mediating variables, influenced by socioeconomic status, chronic diseases, and personal habits, which in turn contribute to CVD risk. The R² values for DII and DGI were 0.34 and 0.04, respectively (Fig. 2).
Table 2.
Model fit
| fit index | Abbreviation | Desired amount | estimate | interpretation |
|---|---|---|---|---|
| Chi-square |
|
- | 203.263 | Good fit |
| degree of freedom | df | - | 40 | Good fit |
| Probability value for chi-square | P-value | P-value > 0.05 | < 0.001 | Good fit |
| Comparative Fit Index | CFI | CFI > 0.09 | 0.917 | Good fit |
| Normed Fit Index | NFI | NFI > 0.9 | 0.901 | Good fit |
| Root Mean Square Error of Approximation | RMSEA | RMSEA < 0.1 | 0.036 | Good fit |
| Incremental Fit Index | IFI | IFI > 0.9 | 0.919 | Good fit |
Fig. 2.
Final structural model. The path standardized coefficients of variables are presented on pathways
The direct effects of socioeconomic status, personal habits, and chronic diseases on the Dietary Insulin Index (DII) were positive, whereas the direct effect of socioeconomic status on the Dietary Glycemic Index (DGI) was also positive. In contrast, personal habits and chronic diseases exhibited negative direct effects on the DGI. Furthermore, the direct effects of socioeconomic status, personal habits, and chronic diseases on the risk of cardiovascular disease (CVD) were negative.
Regarding indirect effects, the DGI and DII functioned as mediators, through which personal habits, socioeconomic status, and chronic diseases indirectly exerted a positive influence on CVD risk. Overall, socioeconomic status, personal habits, and chronic diseases had a net negative impact on the risk of CVD (see Table 3).
Table 3.
Direct, indirect, and total effect between predictors and responses in Fig. 2
| Predictor | Response | Direct effect | Indirect effect | Total effect |
|---|---|---|---|---|
| Socioeconomic status | Dietary Insulin Index | 0.611 | 0.611 | |
| Dietary Glycemic Index | 0.129 | 0.129 | ||
| risk of CVD | −0.281 | 0.086 | −0.195 | |
| Personal habits | Dietary Insulin Index | 0.227 | 0.227 | |
| Dietary Glycemic Index | −0.05 | −0.05 | ||
| risk of CVD | −0.272 | 0.035 | −0.237 | |
| Chronic disease | Dietary Insulin Index | 0.301 | 0.301 | |
| Dietary Glycemic Index | −0.1 | −0.1 | ||
| risk of CVD | −0.216 | 0.047 | −0.169 | |
| Dietary Insulin Index | risk of CVD | 0.15 | 0.15 | |
| Dietary Glycemic Index | risk of CVD | −0.03 | −0.03 |
Discussion
This study demonstrated that socioeconomic status, personal habits, and chronic diseases exert distinct direct and indirect effects on the risk of cardiovascular disease (CVD). The Dietary Insulin Index (DII) and Dietary Glycemic Index (DGI) acted as mediators, transmitting the influence of socioeconomic status, personal habits, and chronic conditions on CVD risk. Additionally, higher DII and DGI values were inversely associated with CVD risk (B = 0.15 and B = −0.03, respectively). Overall, higher socioeconomic status was associated with a reduced risk of CVD. Consistent with previous research, lower education levels, unemployment, income insecurity, and lower overall socioeconomic status have been directly linked to increased cardiovascular risk [14–16]. However, socioeconomic status appears to exert an indirect positive effect, potentially because higher socioeconomic levels may be associated with less healthy dietary habits. It also has a direct positive effect on both the Dietary Insulin Index (DII) and Dietary Glycemic Index (DGI). While some studies support this association, others have reported no significant relationship [17–19]. The study findings indicated that chronic conditions, including depression, overweight/obesity, and joint pain, exert a negative direct effect on cardiovascular disease (CVD) risk; however, indirectly, they are associated with an increased risk. A direct relationship with the Dietary Insulin Index (DII) was also observed, although previous studies have reported mixed findings. It is important to note that these relationships were assessed using a cross-sectional design, limiting the ability to infer the temporal sequence between dietary patterns and these variables [20–22]. Individual habits, including longer sleep duration, a higher number of daily meals, and increased physical activity, were directly associated with a reduced risk of cardiovascular disease (CVD), consistent with prior studies. Furthermore, the Dietary Glycemic Index (DGI) was inversely related to these habits, aligning with previous findings [23–27]. Our study found that an increase in the Dietary Insulin Index (DII) is associated with a higher risk of cardiovascular disease (CVD), a finding supported by some previous research, although other studies have reported no such association. These discrepancies may result from differences in methods used to calculate the Dietary Insulin Load (DIL) across studies. In the present study, DIL was calculated per gram of food, whereas previous studies used per serving, which may contribute to the variation in results. Additionally, the higher consumption of carbohydrate-rich foods among the Iranian population compared to other populations may further explain this discrepancy [1, 15, 28–30]. An increased Dietary Glycemic Index (DGI) was associated with a reduced risk of cardiovascular disease (CVD), although some small studies have reported contrary findings, and most studies have found no significant association. This result may reflect the cross-sectional design of the analyzed studies. Further investigation in prospective longitudinal studies is warranted to clarify this relationship [1, 29, 31–36]. Therefore, it is important to consider the impact of harmful dietary components on health. Physical activity alone, without accompanying dietary modifications, may not effectively reduce cardiovascular disease (CVD) risk. Moreover, increasing socioeconomic status may paradoxically elevate CVD risk if it is associated with unhealthy eating habits. Public education on healthy dietary choices and their effects on cardiovascular health may thus play a critical role in promoting overall well-being.
This study has several limitations. First, CVD is a multifactorial condition influenced by numerous factors, with genetics recognized as an important contributor in previous research [37]. Psychosocial stress, inflammatory markers, and food insecurity also play important roles in cardiovascular disease (CVD), but these factors were not included in the present study [38–40]. While the structural equation model (SEM) explained 11% of the variance in CVD risk, unmeasured confounders—such as genetic factors and food insecurity—and the cross-sectional design limit the ability to infer causality. Given the cross-sectional nature of the study, future longitudinal research is warranted to further validate these findings, and the current study should be considered a preliminary investigation. Since this survey was conducted in the small city of Dehgolan in western Iran, the generalizability of the findings to the broader Iranian population is limited. Additionally, due to nonlinear differences in variables between men and women, the model did not achieve adequate fit when stratified by gender. Strengths of this study include the utilization of advanced predictive modeling techniques, such as confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM), combined with a large sample size. The study evaluated multiple risk factors that have been directly associated with cardiovascular disease (CVD) in previous research. Moreover, this is the first study of its kind conducted among Kurdish ethnic groups in Iran, providing a valuable reference for future investigations in other ethnic populations and enabling cross-ethnic comparisons.
It is essential to consider the detrimental effects of dietary habits on health. Even among individuals with high socioeconomic status, failure to adhere to a healthy diet increases the risk of CVD. Furthermore, physical activity alone, without accompanying dietary modifications, exerts minimal influence on reducing this risk. Therefore, public education regarding the impact of diet on CVD risk may ultimately contribute to the improvement of overall health outcomes.
Conclusion
This study demonstrated that factors associated with cardiovascular disease (CVD) risk can influence outcomes both directly and indirectly through mediating variables. Specifically, the insulin index and glycemic index were identified as two mediators that, under the influence of variables such as socioeconomic status (SES), chronic conditions, and personal habits, contribute to an elevated risk of developing CVD.
Acknowledgements
The authors express their gratitude to all participants of the study, as well as to the staff of the Dehgolan Cohort Center in Dehgolan, Iran.
Authors’ contributions
Abstract wrote by khairollah asadollahi and farhad moradpourBackground wrote by reza pakzad and asma salari-moghaddamMethods wrote by Faramarz Amini and kourosh SayehmiriResult wrote by Faramarz Amini and khairollah asadollahiDiscussion and Conclusion wrote by Faramarz Amini and khairollah asadollahiKhairollah Asadollahi is the Corresponding author.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Biomedical Research at Ilam University of Medical Sciences granted ethical approval for this study (IR.MEDILAM.REC.1403.006).
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.
Contributor Information
Khairollah Asadollahi, Email: masoud_1241@yahoo.co.uk.
Farhad Moradpour, Email: Farhadmepid@yahoo.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.