This study investigated the occurrence of dyslipidemia and its associated risk factors in the southwest region of Iran. The overall prevalence of dyslipidemia was 43.5%, and abnormal HDL, LDL, TC, and TG were 17.9%, 21.8%, 36.2%, and 44%, respectively. The most important factors affecting dyslipidemia were male gender, obese participants, low physical activity, smokers, rich people, and Diabetic patients.
The overall prevalence of dyslipidemia was 43.5% (52.7% in men and 37.1% in women). The Tehran lipid and glucose study in Iranian adults reported the prevalence of dyslipidemia for both sexes, male and female were 63.4%, 66.5% and 61.3% respectively(16). In this study, the odds of experiencing dyslipidemia were found to be higher than in men compared to women. The higher prevalence of dyslipidemia in males may be due to a combination of lifestyle factors, hormonal factors, age, and genetics. Men are more likely to engage in unhealthy behaviors such as smoking and drinking alcohol, which can increase the risk of dyslipidemia (2). Furthermore, testosterone levels have been linked to dyslipidemia, and men generally have higher levels of testosterone than women (17). On the other hand, dyslipidemia tends to increase with age, and men have a higher prevalence of dyslipidemia at younger ages than women(2, 18), Some genetic factors may contribute to the higher prevalence of dyslipidemia in males(19).
We found a direct and significant association between BMI and dyslipidemia. Obesity is a major risk factor for dyslipidemia due to abnormalities in lipid metabolism, insulin resistance, inflammation, and unhealthy lifestyle factors. These factors can contribute to the development of dyslipidemia by altering lipid metabolism and increasing the production of pro-inflammatory cytokines. Obesity is associated with abnormalities in lipid metabolism, including increased levels of triglycerides, low-density lipoprotein cholesterol, and total cholesterol, and decreased levels of high-density lipoprotein cholesterol (20, 21). These abnormalities can contribute to the development of dyslipidemia. Obesity is also associated with insulin resistance, which can lead to dyslipidemia by increasing the production of very low-density lipoprotein and decreasing the clearance of triglyceride-rich lipoproteins(22). Furthermore, obesity is characterized by a chronic low-grade inflammatory state, which can contribute to developing dyslipidemia by altering lipid metabolism and increasing the production of pro-inflammatory cytokines(21, 22). Additionally, obesity is often associated with unhealthy lifestyle factors such as a high-fat diet, lack of exercise, and tobacco use, which can contribute to dyslipidemia(21). In addition to, the presence of a significant association between obesity and dyslipidemia, a biological gradient (dose-response relationship) was also seen in this association, so that, as the BMI categories enhanced, the odds of having dyslipidemia enhanced too. This can increase the probability of a causal relationship according to Hill's criteria for causality.
We found a direct association between smoking and dyslipidemia. It can be due to the disruption of lipid metabolism, oxidative stress and inflammation, unhealthy lifestyle factors, and the duration and intensity of smoking. These factors can contribute to the development of dyslipidemia by altering lipid metabolism and promoting the formation of atherosclerotic plaques. Cigarette smoking has been shown to disrupt lipid metabolism, leading to an increase in triglyceride and low-density lipoprotein cholesterol levels and a decrease in high-density lipoprotein cholesterol levels (6, 23). Additionally, smoking is associated with increased oxidative stress and inflammation, which can contribute to the development of dyslipidemia by altering lipid metabolism and promoting the formation of atherosclerotic plaques (23). Also, smokers are more likely to engage in unhealthy lifestyle behaviors such as a high-fat diet, lack of exercise, and excessive alcohol consumption, which can further increase the risk of dyslipidemia (24). The risk of dyslipidemia may be influenced by the duration and intensity of smoking, with long-term and heavy smokers having a higher risk than short-term and light smokers(25).
In this study, there was an inverse and significant association between physical activity level and dyslipidemia. Several studies have shown that physical activity can help reduce the risk of dyslipidemia by promoting weight loss (26), increasing HDL cholesterol levels (27), lowering LDL cholesterol levels (27), and improving insulin sensitivity (28).
Our results showed that a significant relationship was seen between diabetes and dyslipidemia. This finding may be explained based on abnormalities in lipid metabolism, insulin resistance, inflammation, and unhealthy lifestyle factors. These factors can contribute to the development of dyslipidemia by altering lipid metabolism and increasing the production of pro-inflammatory cytokines. Diabetes is associated with abnormalities in lipid metabolism, including increased levels of triglycerides, low-density lipoprotein cholesterol, and total cholesterol, and decreased levels of high-density lipoprotein cholesterol (29–31). Additionally, diabetes is also associated with insulin resistance, which can lead to dyslipidemia by increasing the production of very low-density lipoprotein and decreasing the clearance of triglyceride-rich lipoproteins (30). Also, diabetes is characterized by a chronic low-grade inflammatory state, which can contribute to the development of dyslipidemia by altering lipid metabolism and increasing the production of pro-inflammatory cytokines (30). On the other hand, individuals with diabetes are at a higher risk of adopting unhealthy lifestyle habits, including consuming a diet rich in fats, lack of exercise, and tobacco use, all of which can contribute to the development of dyslipidemia(31).
In this study, there were no significant associations between education levels with dyslipidemia. Several studies have investigated the association between education level and dyslipidemia, and the results have needed to be more consistent. Some studies have found significant associations between education and blood lipid levels (32, 33), while others have not. Education level may have a weaker influence on dyslipidemia than other factors such as lifestyle and genetics. Lifestyle factors such as diet and physical activity may play a more critical role in developing dyslipidemia (33). One study found that the association between education and dyslipidemia differed by sex and income level (34). The studies may have used different definitions of dyslipidemia and different cut-off values for lipid levels, which could affect the results (32, 33).
The results of our study did not show a statistically significant relationship between residence types with dyslipidemia. A study in rural and urban China found that the prevalence of dyslipidemia was similar among rural and urban participants(2). Another study of adult residents of Mekelle City, Northern Ethiopia found that the prevalence of dyslipidemia was unacceptably high among all residents, regardless of their wealth index(35). A study of adults in rural and urban China found that the prevalence of dyslipidemia was similar in participants with low, medium, and high socioeconomic status(36). The reasons for these differences are not entirely clear, but some factors that may contribute include differences in lifestyle, diet, and access to healthcare(2)
In line with our study, studies in Ethiopia and China found no significant association between wealth index and dyslipidemia (2, 37). These findings indicate that while socioeconomic status and wealth index may influence certain health outcomes, the prevalence of dyslipidemia does not consistently show a significant difference based on these factors. Other factors such as lifestyle, diet, and access to healthcare may have a more prominent impact on the prevalence of dyslipidemia in different populations (2, 32).
Our study showed there were no significant associations between hypertension and dyslipidemia. The result of young adults in Poland found that hypertension and dyslipidemia were major risk factors for cardiovascular disease. However, some studies did not find any significant association between the two conditions (38, 39). The prevalence of dyslipidemia and hypertension may show an interplay, but the relationship between the two conditions is complex. Various epidemiological studies have shown the coexistence of dyslipidemia and hypertension in a range of 15 to 31% (39). These findings suggest that while there may not be a direct difference in the prevalence of dyslipidemia and hypertension, there is an interplay between the two conditions (40).
This study had some limitations, including the cross-sectional design, which can only establish associations and not causality. In this study, the family history of hyperlipidemia was not investigated, which should be part of future studies. Furthermore, lifestyle variables, including smoking, physical activity and diet intake, were assessed by self-report, which may have led to response bias. On the other hand, the present study had several strengths. The measurement of dyslipidemia was done with standard equipment and kits based on the valid guidelines of ATPӀӀӀ, which can reduce measurement errors and improve the accuracy of the prevalence estimates. The accuracy of lipid level measurements can be affected by factors such as the timing of the test, fasting status, and the use of different laboratory methods. Therefore, we used a standard laboratory protocol and laboratory quality control to increase the accuracy of laboratory findings. Our study had a large sample size, which can increase the statistical power and precision of the estimates and representative population, which can enhance the generalized of the results to the broader population. This study was conducted on context longitudinal design. Additionally, we used multivariate analysis to adjust for potential confounding factors, which can increase the validity of the prevalence estimates and identify the independent effects of different risk factors.