Prevalence of dyslipidemia and its association with blood pressure control in Iranian hypertensive patients: insights from STEPS 2021

Abstract

Background

Given the association between dyslipidemia and hypertension and their significant impact on cardiovascular diseases (CVDs) in Middle Eastern countries, coordinated monitoring of these risk factors across the region is essential. This study aimed to assess the prevalence of dyslipidemia among hypertensive Iranians and explore its association with hypertension control.

Methods

Data from the Iranian STEPwise Approach to Non-communicable Disease (NCD) Risk Factor Surveillance (STEPS) 2021 survey, including questionnaires, physical measurements, laboratory tests, from a total of 5,997 participants, were analyzed. Hypertension was defined as the current use of antihypertensive medication or blood pressure ≥ 140/90 mmHg. Lipid markers, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Non-HDL-C, and triglycerides (TG), were evaluated using the National Cholesterol Education Program Adult Treatment Panel III 2004 criteria. Associations between lipid markers and uncontrolled hypertension were assessed using a multivariable modified Poisson regression model, with results presented as adjusted prevalence ratio (APR) and 95% confidence interval (CI). All analyses were conducted utilizing STATA software version 14.2.

Results

The prevalence of dyslipidemia among hypertensive Iranian adults was 86.4% [95% CI: 85.0, 87.6], with the most common abnormalities being low HDL-C 71.3% [95% CI: 69.6, 72.9], followed by hypertriglyceridemia 48.4% [95% CI: 46.5, 50.2], hypercholesterolemia 24.9% [95% CI: 23.3, 26.6], and high LDL-C 18.8% [95% CI: 17.3, 20.3]. In the multivariable model, TC, LDL-C, Non-HDL-C, TC/HDL-C, and LDL-C/HDL-C were significantly associated with uncontrolled hypertension in both males and females. Each 1 mmol/L increase in LDL-C was associated with a higher prevalence of uncontrolled hypertension by 1.07 [95% CI: 1.04, 1.09] in males and 1.05 [95% CI: 1.03, 1.07] in females; Non-HDL-C by 1.06 [95% CI: 1.04, 1.08] in males and 1.05 [95% CI: 1.03, 1.07] in females; TC by 1.06 [95% CI: 1.04, 1.08] in males and 1.05 [95% CI: 1.03, 1.07] in females; and TG by 1.02 [95% CI: 1.00, 1.03] in males.

Conclusions

Dyslipidemia is highly prevalent among hypertensive Iranian adults and may be associated with poor blood pressure control. In particular, low HDL-C emerged as the most frequent lipid abnormality and should be carefully assessed and managed as part of integrated, preventive hypertension care.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-025-02660-0.

Background

Dyslipidemia, defined as abnormal levels of blood lipids—including elevated total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), or decreased high-density lipoprotein cholesterol (HDL-C)—affects millions of individuals worldwide, with prevalence estimates ranging from 20% to 80% [1, 2]. Given its high prevalence and serious complications, including atherosclerosis, stroke, coronary artery disease, and increased mortality, monitoring dyslipidemia and its underlying factors is crucial for reducing associated health risks and improving overall patient outcomes [1].

Hypertension, defined as persistently elevated blood pressure (BP) and recognized as the leading risk factor for death worldwide [3], frequently coexists with dyslipidemia, which has been reported in 30–90% of hypertensive patients [4, 5]. Dyslipidemia contributes to hypertension by promoting endothelial dysfunction, increasing arterial stiffness, and impairing nitric oxide production, all of which elevate BP [6]. The simultaneous presence of these conditions is shown to exert an additive detrimental effect on quality of life [7], and increases the risk of mortality beyond the sum of their individual effects, emphasizing the need for concurrent management of both conditions [8, 9].

Studies suggest that addressing dyslipidemia may have a positive association with improved hypertension. For instance, statin therapy has demonstrated significant reductions in BP, particularly systolic blood pressure (SBP) [10, 11]. Moreover, studies indicated that individuals on statin therapy had higher odds of achieving BP control compared to non-users [12]. Therefore, effective management of dyslipidemia in individuals with high BP can lead to considerable reductions in BP and, consequently, lower the risk of heart and vascular complications in the future.

Due to the high burden of cardiovascular disease (CVDs) in the Middle East region [13], it is essential to simultaneously monitor their modifiable risk factors such as hypertension and dyslipidemia in countries across the region, including Iran [14]. The prevalence of hypertension in the Iranian adult population is reported to be about 26% [15]. However, few subnational studies have reported the overall prevalence of dyslipidemia among individuals with hypertension, and comprehensive national data remain limited [4, 16]; they do not provide a complete definition of dyslipidemia and lack detailed data on abnormal lipid markers in the Iranian hypertensive population. Therefore, nationwide studies assessing the prevalence of dyslipidemia and the association of lipid markers with hypertension control are still needed.

Following the World Health Organization’s (WHO) STEP-wise approach to non-communicable disease (NCD) risk factor surveillance (STEPS), Iran has implemented a national survey program to provide comprehensive and reliable data for health policymakers [17, 18]. Building on these data, the present study aims to investigate the prevalence of dyslipidemia among hypertensive individuals and assess the association of lipid abnormality with hypertension control, thereby providing insights to inform health policies and intervention strategies.

Methods

Study population

This cross-sectional study used data from the 2021 STEPS national survey. The complete protocol for STEPS 2021 has been published elsewhere [19]. Briefly, a systematic cluster random sampling method was employed, utilizing various probability sampling techniques. This led to the selection of 3,176 clusters and 28,821 participants aged 18 or older across rural and urban areas in 31 provinces of Iran. After excluding those who could not be located or declined to participate, data were initially collected from 27,874 participants through questionnaires. In the second step, 27,745 individuals underwent anthropometric measurements, and in the third step, 18,119 adults aged 25 or older completed laboratory evaluations. Quality control was conducted at multiple stages, including interview recordings for validation, calibration of instruments, and supervision via an online management system. Randomized checks ensured data accuracy, and a comparison of data confirmed consistency. Individuals were excluded if they had mental disorders that could interfere with questionnaire completion, physical limitations preventing anthropometric measurements, inability to provide laboratory samples, or were pregnant.

The present study included adults aged 25 years or older with available data on SBP, diastolic blood pressure (DBP), and lipid markers. After applying these criteria, 6,481 participants remained eligible. After excluding individuals with missing data on study variables and indices (n = 484), 5,997 participants were included in the final analysis (Fig. 1).

Fig. 1.

Flow diagram of the study participants from the STEPS-2021 survey

Data collection and variable definitions

Baseline characteristics, including sex, age, marital status, occupation status, wealth index, smoking, alcohol consumption, diet, and physical activity, were gathered through comprehensive questionnaires in the 2021 STEPS survey [19]. The questionnaires were rigorously evaluated for face and content validity. They were translated using forward and backward translation, revised with input from diverse participants, and further validated by experts using the content validity index and content validity ratio to ensure relevance, clarity, and necessity. All the above processes provide external validity and support the generalizability of the findings [19].

Individuals who consumed a minimum of five daily servings of vegetables and fruits were classified as having appropriate fruit and vegetable consumption [20]. Physical activity was evaluated utilizing the WHO Global Physical Activity Questionnaire (GPAQ, version 2), and insufficient physical activity was labeled as less than 150 min of moderate-intensity weekly activity [21]. The wealth index, derived from responses to a 36-item household assets questionnaire, was calculated using principal component analysis (PCA), with the first component representing the index, which was then divided into five quintiles ranging from the richest to the poorest [19]. Occupation status was categorized as unemployed, retired, unpaid, or employed. Education was measured by completed years of schooling and classified into three groups: Illiterate/Primary (0–6 years), Secondary (7–11 years), and Tertiary (12 years and more). Current smoking was defined as the smoking of any occasional or daily use of tobacco products, including cigarettes, electronic cigarettes, pipes, hookah, and smokeless tobacco in the past 12 months. Current alcohol consumption was defined as any use of alcoholic beverages during the past 12 months. A history of CVDs and cerebrovascular accidents (CVAs) was evaluated with the following questions: “Has a doctor or healthcare provider ever informed you that you have experienced a heart attack or angina? Or that you have undergone medical procedures such as bypass surgery, angioplasty, or stent and balloon placement?” and “Has a doctor or healthcare provider ever informed you that you have had a stroke?”

All measurements were taken by expert and trained healthcare workers using instruments calibrated before the investigation. Only instruments that passed calibration and standardization were provided to investigators. Weight was measured using a standard digital scale (Inofit), and participants wore light clothing. It was calibrated with a 5 kg reference weight before each use after relocation. Height was recorded using a standard meter stick with participants standing shoeless against a wall. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Normal BMI was classified as 18.5 up to 24.9 kg/m², overweight as 25 up to 29.9 kg/m², obesity as 30 kg/m² or higher, and underweight as less than 18.5 kg/m² [22]. Waist circumference (WC) was measured midway between the hip and the lowest rib, nearly passing the umbilicus in a straight line, with a measurement of ≥ 95 cm classified as high for males and females [23]. After 15 min of rest in a sitting position, SBP and DBP were measured three times at 3-minute intervals using standard Beurer sphygmomanometers, with the average of the second and third readings taken as the final BP value. Based on national and international guidelines, hypertension was defined as the use of antihypertensive medications, SBP ≥ 140 mmHg, or DBP ≥ 90 mmHg [24, 25]. BP staging classified the severity of hypertension: Stage 1, defined as SBP of 140–159 mmHg and/or DBP of 90–99 mmHg, indicates moderate hypertension. Stage 2 hypertension, defined as SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg, represents the more severe elevation of BP and a higher risk of complications [25]. Hypertension control was defined as a binomial variable, with participants categorized as having controlled hypertension if their total BP was below 140/90 mmHg while on antihypertensive medication.

Laboratory measurements

Blood and urine samples were collected, stored, and transferred at 4 °C using vaccine transfer boxes, with transfer times not exceeding 18 h for laboratory measurements. Chronic kidney disease (CKD) was identified based on an estimated glomerular filtration rate (eGFR) of less than 60 ml/min/1.73 m² or a urinary albumin-to-creatinine ratio equal to or greater than 30 mcg/mg [26]. Fasting blood sugar (FBS), serum TG, TC, and HDL-C were measured by an autoanalyzer (Cobas C311 Hitachi High–Technologies Corporation, Japan). Diabetes was defined as any use of antihyperglycemic medication or FBS ≥ 126 mg/dl (7 mmol/L) [27]. Healthy individuals had FBS below 100 mg/dL, while prediabetes was defined as FBS between 100 and 125 mg/dL, both without antihyperglycemic medication. The controlled diabetes group included individuals with FBS of less than 126 mg/dL by using antihyperglycemic medication, while uncontrolled diabetes was defined as having an FBS of 126 mg/dL or higher.

LDL-C levels were calculated using the Friedewald formula, and Non-HDL-C was derived by subtracting HDL-C from TC. Lipid abnormalities were defined based on the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP ATP III) 2004 guidelines [28]. Hypercholesterolemia was TC ≥ 200 mg/dL (5.2 mmol/L), high LDL-C as LDL-C ≥ 130 mg/dL (3.4 mmol/L), low HDL-C as HDL-C < 50 mg/dL (1.29 mmol/L) in females and < 40 mg/dL (1.03 mmol/L) in males, high Non-HDL-C as Non-HDL-C ≥ 160 mg/dL (4.1 mmol/L), and hypertriglyceridemia as TG ≥ 150 mg/dL (1.7 mmol/L). Dyslipidemia was described as self-reported use of lipid-lowering medications or at least one abnormality in lipid markers, including hypercholesterolemia, hypertriglyceridemia, low HDL-C, or high LDL-C [28].

Statistical analysis

The prevalence of lipid abnormalities in the hypertensive population was calculated after applying sample weights. Weighting was applied to estimate study results in a manner that accurately reflects the actual population under investigation, allowing for more precise and generalizable estimates of the studied indicators at the national level. The survey’s weighting process, implemented following data cleaning, consisted of four stages [19]. First, overall non-response weighting was applied to account for individuals who refused to participate, with targeted adjustments for various age groups, especially considering the effects of the Coronavirus disease 2019 (COVID-19) pandemic. Subsequently, at each stage of the survey, non-response weighting was applied using statistical formulas to adjust for data loss and reduce potential measurement bias arising from incomplete responses. Third, sample data from each province were weighted by residential area, sex, and age to achieve demographic representativeness. Finally, all weightings were incorporated and summarized into the dataset.

Data for categorical variables are presented as percentages with confidence intervals (CI). The association between lipid markers and uncontrolled hypertension was evaluated using modified Poisson regression models. Given the statistical significance of the interaction between sex and lipid markers, regression analyses were conducted separately for males and females. A multivariable modified Poisson regression model employing a backward selection approach, with a p-value threshold of < 0.20 for entry, was used to calculate the adjusted prevalence ratio (APR) and 95% CI to assess the association between lipid markers and uncontrolled hypertension [29]. In the multivariable analysis, the models were adjusted for potential confounders, including age, marital status, wealth index, occupation status, physical activity, fruit and vegetable consumption, alcohol consumption, BMI, CVDs/CVAs, glycemic condition, and CKD. Confounders were identified through a review of the scientific literature and assessed using multivariable modified Poisson regression analysis.

In the initial analysis, lipid markers (e.g., TG, TC, LDL-C, HDL-C) were categorized utilizing established clinical cut-off points, enabling the generation of descriptive tables and assessment of the prevalence of these variables within the study population. For the regression analysis, lipid ratios such as LDL-C/HDL-C and TC/HDL-C, among others, were utilized. Since these ratios lack predefined clinical cut-off points, all lipid markers and ratios were divided into quartiles to examine trends and associations across different levels. Moreover, lipid markers were analyzed per 1 mmol/L increment (equivalent to 38.67 mg/dL for cholesterol and 88.57 mg/dL for triglycerides) to assess their continuous relationship with uncontrolled hypertension.

Sensitivity analysis

Since WC was also identified as a confounder and correlated with BMI, a sensitivity analysis was conducted by substituting WC for BMI to assess the robustness of the results. As the findings remained consistent, BMI was retained in the final model.

Given that approximately 7.5% of the data were missing, the Complete Case method (CCA) was employed for the final analysis. To evaluate the impact of the missing data, a sensitivity analysis was performed using Multiple Imputation by Chained Equations (MICE). The findings from the imputed dataset were compared with those from the complete cases, revealing negligible differences between the two approaches. Given the objective of analyzing the actual data, the final analysis was conducted using the CCA. All statistical analyses were performed using STATA software, version 14.2.

Ethical considerations

This study follows the Declaration of Helsinki. Before participation, all participants provided written informed consent for the 2021 STEPS survey. The final dataset was anonymized, with only the primary investigator and the database manager accessing the survey data. The Research Ethics Committees of the Endocrine & Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Code: IR.TUMS.EMRI.REC.1403.071).

Results

Characteristics of the study participants

Among participants, 44.9% [95% CI: 43.0, 46.7] were males, and 76.1% [95% CI: 74.8, 77.4] lived in urban areas. The mean age was 57.2 years [95% CI: 56.7, 57.8], with a mean BMI of 29.0 kg/m² [95% CI: 28.8, 29.2]. Mean SBP and DBP were 144.9 mmHg [95% CI: 144.3, 145.5] and 86.8 mmHg [95% CI: 86.4, 87.3], respectively. Regarding the lipid markers, the mean TG level was 166.6 mg/dL [95% CI: 163.0, 170.3], TC 174.9 mg/dL [95% CI: 173.3, 176.4], LDL-C 100.0 mg/dL [95% CI: 98.7,101.4], HDL-C 41.5 mg/dL [95% CI: 41.1, 41.8], and Non-HDL-C 133.4 mg/dL [95% CI: 131.8, 134.9] (Table 1). Additionally, sensitivity analyses demonstrated consistent results between complete-case analysis and analysis accounting for missing data(imputed), with no significant differences observed.

Table 1.

Characteristics of study participants

Variables		All participants
Total participants, n		5,997
Sex, %	Male	44.9 [43.0, 46.7]
Residence, %	Urban	76.1 [74.8, 77.4]
Age, year		57.2 [56.7, 57.8]
BMI, kg/m2		29.0 [28.8, 29.2]
SBP, mmHg		144.9 [144.3, 145.5]
DBP, mmHg		86.8 [86.4, 87.3]
FBS, mg/dl		115.3 [113.8, 116.7]
Total cholesterol, mg/dL		174.9 [173.3, 176.4]
Triglycerides, mg/dL		166.6 [163.0, 170.3]
LDL-C, mg/dL,		100.0 [98.7, 101.4]
HDL-C, mg/dL,		41.5 [41.1, 41.8]
Non-HDL-C, mg/dL,		133.4 [131.8, 134.9]

Abbreviations: BMI: Body Mass Index; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; FBS: Fasting Blood Sugar; LDL-C; Low-Density Lipoprotein Cholesterol; HDL-C; High-Density Lipoprotein Cholesterol

Summary statistics are weighted

For categorical variables, the data are reported as percentages along with confidence interval (CI)

Prevalence of dyslipidemia and abnormality of lipid markers according to baseline characteristics

It was estimated that 86.4% [95% CI: 85.0, 87.6] of hypertensive Iranians had dyslipidemia. In addition, 71.3% [95% CI: 69.6, 72.9] had low HDL-C levels, 48.4% [95% CI: 46.5, 50.2] had hypertriglyceridemia, 24.9% [95% CI: 23.3, 26.6] had hypercholesterolemia, and 23.3% [95% CI: 21.7, 25.0] had high Non-HDL-C. Among the lipid abnormalities, high LDL-C was found to have the lowest prevalence (18.8%, [95% CI: 17.3, 20.3]) (Table 2).

Table 2.

Prevalence of dyslipidemia and abnormality of lipid markers according to baseline characteristics

Variables		Hypertriglyceridemia	Hypercholesterolemia	High LDL-C	low HDL-C	High Non-HDL-C	Dyslipidemia
		*Percent. [95% CI]	*Percent. [95% CI]	*Percent. [95% CI]	*Percent. [95% CI]	*Percent. [95% CI]	*Percent. [95% CI]
Total		48.4 [46.5,50.2]	24.9 [23.3,26.6]	18.8 [17.3,20.3]	71.3 [69.6,72.9]	23.3 [21.7,25.0]	86.4 [85.0,87.6]
Sex	Male	48.8 [46.0,51.6]	19.8 [17.8,21.9]	15.6 [13.8,17.6]	66.9 [64.4,69.3]	20.6 [18.6,22.8]	81.6 [79.6,83.6]
	Female	48.0 [45.5,50.5]	29.1 [26.8,31.6]	21.3 [19.1,23.7]	74.8 [72.7,76.9]	25.6 [23.3,28.0]	90.2 [88.5,91.7]
Age, year	25–34	57.4 [47.9,66.4]	16.9 [10.7,25.7]	15.0 [7.58,27.4]	79.7 [73.0,85.1]	23.2 [14.8,34.5]	85.6 [79.9,89.9]
	35–44	56.0 [51.0,61.0]	24.0 [19.7,28.9]	16.0 [12.7,19.9]	79.9 [75.5,83.7]	26.2 [21.8,31.2]	88.5 [84.6,91.5]
	45–54	49.1 [45.6,52.6]	28.0 [25.0,31.2]	21.3 [18.5,24.4]	73.8 [70.6,76.7]	26.4 [23.5,29.6]	86.8 [84.4,89.0]
	55–64	50.3 [47.0,53.7]	26.8 [24.1,29.7]	20.0 [17.6,22.6]	68.6 [65.4,71.7]	23.7 [21.2,26.4]	86.6 [83.7,89.0]
	≥ 65	41.1 [37.5,44.7]	22.7 [19.5,26.1]	17.6 [14.6,21.0]	66.7 [63.6,69.7]	19.5 [16.4,22.9]	85.0 [82.6,87.1]
Residence	Urban	50.0 [47.4,52.0]	24.7 [22.7,26.7]	18.1 [16.3,20.1]	71.8 [69.8,73.7]	23.0 [21.1,25.1]	87.0 [85.4,88.5]
	Rural	44.2 [41.6,46.9]	25.8 [23.4,28.2]	20.7 [18.6,23.1]	69.7 [67.2,72.1]	24.4 [22.1,26.8]	84.2 [82.1,86.0]
Marital status	Single	54.6 [44.3,64.5]	23.6 [16.2,33.1]	18.5 [11.9,27.6]	75.1 [66.7,81.9]	25.2 [17.1,35.5]	81.4 [73.7,87.2]
	Married	48.6 [46.6,50.7]	24.9 [23.1,26.8]	18.8 [17.1,20.7]	71.1 [69.3,72.8]	23.7 [21.9,25.6]	86.0 [84.6,87.3]
	Divorced/ Widowed	45.6 [43.8,48.2]	25.5 [22.1,29.2]	18.3 [15.5,21.5]	71.5 [67.1,75.6]	20.8 [17.8,24.2]	89.3 [85.3,92.4]
Education status	Illiterate /Primary	46.0 [42.8,49.3]	26.8 [24.8,29.1]	20.2 [18.3,22.3]	71.3 [69.4,73.2]	24.3 [22.3,26.5]	87.6 [86.2,88.9]
	Secondary	50.9 [46.2,55.6]	22.7 [19.1,26.7]	16.9 [13.7,20.8]	70.8 [66.3,75.0]	22.1 [18.6,26.1]	83.6 [79.5,87.0]
	Tertiary	51.8 [47.6,56.0]	22.1 [19.0,25.6]	16.8 [13.9,20.2]	71.5 [67.7,74.9]	22.0 [18.7,25.6]	85.4 [82.3,88.1]
Occupation Status	Unemployed	40.2 [33.4,47.0]	21.5 [16.4,27.6]	16.4 [12.2,21.6]	62.6 [55.7,69.1]	23.4 [17.8,30.2]	77.2 [70.3,82.9]
	Unpaid work	48.0 [45.4,50.6]	29.6 [27.1,32.1]	21.8 [19.5,24.4]	75.0 [72.7,77.2]	26.1 [23.6,28.7]	90.7 [88.9,92.2]
	Employed	52.1 [48.6,55.7]	21.9 [19.2,24.8]	16.5 [14.2,19.2]	70.9 [67.9,73.8]	22.7 [20.0,25.7]	82.6 [80.0,84.9]
	Retired	46.1 [41.4,50.8]	17.8 [14.8,21.2]	14.3 [11.6,17.5]	63.9 [59.6,68.1]	16.7 [13.9,19.9]	82.8 [79.2,85.9]
Wealth index	Poorest	45.5 [41.6,49.6]	26.9 [23.0,31.1]	20.3 [16.5,24.6]	71.8 [68.6,74.8]	24.0 [20.2,28.3]	86.9 [84.5,89.1]
	Poorer	48.2 [44.2,52.3]	23.7 [20.7,27.1]	17.1 [14.6,20.0]	73.1 [69.5,76.5]	21.6 [18.7,24.8]	87.2 [84.1,89.8]
	Middle	48.2 [44.8,51.7]	23.7 [20.9,26.7]	16.9 [14.5,19.6]	70.5 [67.4,73.5]	22.9 [20.1,25.8]	84.8 [82.1,87.1]
	Richer	52.2 [48.2,56.2]	26.9 [23.4,30.7]	19.0 [16.1,22.2]	69.4 [65.5,73.0]	24.9 [21.5,28.7]	85.5 [82.0,88.4]
	Richest	47.8 [42.7,53.0]	23.2 [19.3,27.6]	20.5 [16.5,25.2]	71.4 [66.8,75.6]	23.3 [19.2,27.9]	87.2 [83.7,90.0]
BMI, kg/m2	Underweight	7.23[3.20,15.5]	15.3[8.54,25.8]	17.7 [10.2,29.0]	43.5 [31.9,55.9]	10.3 [5.34,19.0]	58.1 [45.5,69.7]
	Normal	35.6[31.7,39.6]	23.7[20.5,27.3]	18.0 [15.3,21.1]	61.0 [57.2,64.5]	21.1 [17.9,24.6]	77.6 [74.5,80.4]
	Overweight	47.5[44.7,50.3]	25.4[23.1,27.9]	19.2 [17.1,21.5]	71.4 [68.7,74.0]	23.7 [21.5,26.0]	87.5 [85.1,89.6]
	Obesity	57.5[54.5,60.4]	25.3[22.5,28.3]	18.7 [16.0,21.8]	77.6 [75.1,79.8]	24.6 [21.7,27.7]	90.7 [89.0,92.2]
High waist circumference, cm	Yes	52.5[50.3,54.8]	24.3[22.4,26.4]	18.3 [16.5,20.4]	74.7 [72.8,76.6]	23.5 [21.5,25.6]	89.5 [88.0,90.8]
	No	39.4[36.2,42.6]	24.0[20.7,27.8]	19.7 [17.3,22.2]	63.9 [60.9,66.8]	23.0 [20.5,25.8]	79.6 [76.9,82.0]
Fruit and vegetable consumption	Appropriate	53.1[48.6,57.7]	23.0[19.5,27.0]	17.5 [14.3,21.3]	74.0 [70.0,77.6]	22.6 [19.0,26.7]	89.6 [87.1,91.7]
	Inappropriate	47.7[45.7,49,7]	25.2[23.4,27.0]	18.9 [17.3,20.7]	70.9 [69.1,72.6]	23.4 [21.7,25.3]	85.9 [84.4,87.2]
Physical activity	Sufficient	46.2[43.6,48.8]	24.0[22.0,26.1]	18.2 [16.4,20.2]	69.8 [67.4,72.1]	22.9 [21.0,25.0]	84.3 [82.3,86.2]
	Insufficient	50.5[47.8,53.1]	25.8[23.4,28.4]	19.3 [17.0,21.8]	72.7 [70.5,74.9]	23.7 [21.3,26.3]	88.3 [86.6,89.8]
Smoking	Yes	51.9[46.1,57.8]	20.2[16.4,24.5]	15.5 [12.1,19.6]	73.3 [68.5,77.6]	21.3 [17.5,25.7]	83.5 [79.3,87.0]
	No	47.9[45.9,49.8]	25.6[23.9,27.4]	19.2 [17.6,20.9]	71.0 [69.2,72.7]	23.6 [21.9,25.4]	86.8 [85.4,88.0]
Alcohol Consumption	Yes	55.1[43.1,66.5]	28.0[17.9,41.0]	20.9 [11.7,34.4]	69.8 [59.2,78.7]	29.4 [19.1,42.3]	78.4 [68.7,85.8]
	No	48.2[46.3,50.1]	24.8[23.2,26.5]	18.7 [17.2,20.3]	71.3 [69.7,72.9]	23.2 [21.6,24.8]	86.6 [85.3,87.8]
Hypertension	Controlled	45.8[42.1,49.5]	19.0[16.5,21.8]	13.3 [11.3,15.6]	70.4 [66.8,73.8]	16.4 [14.2,19.0]	86.4 [83.1,89.2]
	Stage 1	48.0[45.3,50.6]	25.6[23.3,28.1]	19.7 [17.4,2.20]	71.8 [69.6,74.0]	24.9 [22.5,27.5]	85.3 [83.5,87.0]
	Stage 2	51.5[47.9,55.1]	28.7[25.6,32.0]	21.6 [18.9,24.6]	70.9 [67.6,74.0]	26.1 [23.2,29.1]	88.5 [86.3,90.3]
Glycemic condition	Normal	42.7[40.0,45.5]	25.3[23.1,27.6]	20.4 [18.3,22.8]	67.8 [65.4,70.2]	23.3 [21.1,25.6]	82.9 [80.9,84.7]
	Prediabetes	47.1[43.4,50.7]	27.1[23.8,30.1]	21.1 [18.0,24.7]	72.3 [69.3,75.2]	25.6 [22.3,29.1]	86.3 [83.7,88.6]
	Controlled diabetes mellitus	50.4[42.6,58.3]	12.1[8.66,16.7]	4.49 [2.70,7.39]	75.1 [67.5,81.4]	11.8[8.43,16.4]	88.9 [81.7,93.5]
	Uncontrolled diabetes mellitus	62.3[58.3,66.2]	24.5[21.1,28.2]	15.6 [12.9,18.7]	76.4 [72.7,79.8]	23.4 [20.1,27.0]	93.4 [90.6,95.5]
Chronic kidney disease	Yes	53.3[49.4,57.2]	25.2[21.4,29.5]	18.0 [14.5,22.3]	74.4 [71.0,77.5]	23.0 [19.3,27.2]	89.3 [86.8,91.4]
	No	46.9[44.8,49.0]	24.8[23.1,26.6]	19.0 [17.4,20.7]	70.4 [68.5,72.2]	23.4 [21.7,25.2]	85.5 [83.9,86.9]
History of CVDs/CVAs	Yes	44.0[39.6,48.5]	16.2[13.3,19.6]	12.5 [9.87,15.7]	74.9 [71.1,78.3]	14.8 [12.0,18.1]	88.9 [85.7,91.2]
	No	49.2[47.2,51.3]	26.7[24.9,28.6]	20.0 [18.3,21.8]	70.5 [68.7,72.3]	25.0 [23.3,26.9]	85.9 [84.4,87.2]
Use of Antihypertensive medication	Yes	47.9[45.5,50.2]	22.3[20.5,24.3]	15.8 [14.2,17.5]	71.4 [69.2,73.5]	19.7 [18.0,21.5]	88.0 [86.2,89.6]
	No	48.9[46.0,51.9]	28.0[25.3,30.8]	22.2 [19.6,25.0]	71.1 [68.7,73.5]	27.6 [24.9,30.5]	84.4 [82.4,86.2]

Summary statistics are weighted

Abbreviations: BMI: Body mass index; HDL-C: High-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; CVDs/CVAs: Cardiovascular diseases/ Cerebrovascular accidents

* Within-row relative frequencies

According to Table 2, dyslipidemia was more prevalent in females (90.2%, [95% CI: 88.5, 91.7]) compared to males (81.6%, [95% CI: 79.6, 83.6]). The prevalence of dyslipidemia was also estimated to be higher among urban residents (87.0%, [95% CI: 85.4, 88.5]) and those engaged in unpaid work (90.7%, [95% CI: 88.9, 92.2]). Dyslipidemia was more prevalent among participants with BMI ≥ 30 kg/m2 (90.7%, [95% CI: 89.0, 92.2]) and those with high WC (89.5%, [95% CI: 88.0, 90.8]). In terms of glycemic condition, a prevalence of 93.4% [95% CI: 90.6, 95.5] was observed among individuals with uncontrolled diabetes mellitus, which was higher than the other groups. Furthermore, the prevalence of dyslipidemia was higher among anti-hypertensive medication users, with a prevalence of 88.0% [95% CI: 86.2, 89.6]. The detailed prevalence of abnormal lipid markers based on the NCEP ATP III 2004 definition is provided in the Supplementary File (Table S1).

Prevalence of controlled hypertension among lipid markers

As shown in Fig. 2, the prevalence of controlled hypertension was generally lower among males. A downward trend in prevalence was observed in both sexes with increasing quartiles of TC, LDL-C, and Non-HDL-C (Fig. 2A, C, E). Similar patterns were found for the LDL-C/HDL-C and TC/HDL-C ratios across quartiles (Fig. 2F, G). The prevalence of controlled hypertension decreased from the first to the second quartile of HDL-C but then increased from the second to the third quartile in both sexes (Fig. 2B). A similar trend occurred across TG and TG/HDL-C quartiles, but the overall prevalence was lower in the fourth versus first quartile for both (Fig. 2D, H).

Fig. 2.

Prevalence of controlled hypertention among different lipid markers quartiles. Abbreviations: HDL-C: High-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TC: Total Cholesterol; TG: Triglycerides;

Prevalence of lipid markers based on hypertension status

According to Table 3, the prevalence of lipid markers varies based on hypertension status among males and females. In males with uncontrolled hypertension, a higher prevalence of TG/HDL-C, LDL-C/HDL-C, and TC/HDL-C ratios were found in the upper quartiles. In contrast, in females with uncontrolled hypertension, in the upper quartiles higher prevalences of TC, LDL-C, HDL-C, and Non-HDL-C were observed.

Table 3.

Prevalence of lipid markers based on hypertension status

		Males (n = 2,649)		Females (n = 3,348)
		Uncontrolled Hypertension	Controlled Hypertension	Uncontrolled Hypertension	Controlled Hypertension
TC	First quartile	25.5 [23.0,28.3]	49.2 [43.2,55.2]	20.5 [18.3,22.9]	27.9 [24.0,32.1]
	Second quartile	27.3 [24.6,30.1]	24.6 [19.6,30.5]	22.9 [20.8,25.3]	28.1 [23.8,32.9]
	Third quartile	26.4 [23.5,29.4]	14.9 [11.5,19.0]	25.7 [23.3,28.2]	22.5 [18.6,27.1]
	Fourth quartile	20.8 [18.5,23.3]	11.4 [8.5,15.0]	30.9 [28.1,33.9]	21.5 [18.3,25.2]
HDL-C	First quartile	38.1 [35.0,41.4]	44.5 [38.5,50.6]	15.2 [13.4,17.2]	16.9 [13.8,20.7]
	Second quartile	29.8 [27.1,32.6]	22.3 [18.2,27.1]	22.2 [19.7,24.8]	20.9 [17.5,24.7]
	Third quartile	18.9 [16.7,21.2]	22.2 [17.5,27.8]	29.0 [26.4,31.8]	27.9 [23.7,32.5]
	Fourth quartile	13.2 [11.5,15.2]	11.0 [7.9,15.1]	33.6 [31.0,36.4]	34.3 [29.9,39.1]
LDL-C	First quartile	23.8 [21.0,26.9]	47.5 [41.5,53.6]	23.2 [20.8,25.7]	30.1 [26.1,34.4]
	Second quartile	28.1 [25.4,31.0]	21.6 [16.9,27.3]	23.1 [20.8,25.5]	26.0 [21.9,30.5]
	Third quartile	25.1 [22.7,27.8]	17.2 [13.4,21.8]	24.8 [22.5,27.2]	23.3 [19.3,28.0]
	Fourth quartile	22.9 [20.4,25.7]	13.7 [10.4,17.8]	29.0 [26.2,31.9]	20.6 [17.3,24.4]
TG	First quartile	25.3 [22.8,27.9]	30.4 [24.9,36.6]	22.3 [20.2,24.7]	23.2 [19.1,27.8]
	Second quartile	22.2 [19.9,24.7]	24.6 [20.0,29.9]	26.8 [24.4,29.3]	26.6 [22.8,30.9]
	Third quartile	23.9 [21.1,26.9]	24.7 [19.9,30.3]	25.4 [23.1,28.0]	27.2 [23.1,31.6]
	Fourth quartile	28.6 [25.8,31.6]	20.3 [16.2,25.2]	25.5 [22.7,28.5]	23.0 [19.5,27.1]
Non-HDL-C	First quartile	22.0 [19.6,24.6]	45.7 [39.8,51.8]	23.3 [21.0,25.9]	31.6 [27.5,35.9]
	Second quartile	27.5 [24.8,30.3]	25.3 [20.2,31.2]	23.4 [21.2,25.7]	26.0 [21.7,30.7]
	Third quartile	27.3 [24.4,30.4]	14.4 [11.1,18.4]	23.7 [21.5,26.1]	23.1 [19.1,27.7]
	Fourth quartile	23.2 [20.8,25.8]	14.6 [11.2,18.9]	29.6 [26.8,32.6]	19.4 [16.3,22.9]
TC/HDL-C	First quartile	16.8 [14.6,19.1]	31.2 [25.9,37.0]	28.4 [25.9,31.0]	33.3 [29.2,37.7]
	Second quartile	22.5 [20.2,25.0]	28.2 [23.0,34.2]	25.3 [23.0,27.8]	29.2 [24.6,34.3]
	Third quartile	27.1 [24.4,29.9]	17.7 [13.5,22.8]	20.0 [22.7,27.6]	22.3 [18.8,26.2]
	Fourth quartile	33.7 [30.6,36.9]	22.9 [18.5,27.9]	21.3 [18.7,24.2]	15.3 [12.4,18.7]
LDL-C/HDL-C	First quartile	17.3 [15.1,19.7]	37.4 [31.7,43.5]	28.8 [26.2,31.5]	35.6 [31.3,40.1]
	Second quartile	24.7 [22.0,27.6]	24.3 [19.4,29.9]	24.0 [21.8,26.3]	29.3 [24.7,34.4]
	Third quartile	27.3 [24.7,30.0]	17.9 [13.6,23.3]	24.9 [22.5,27.4]	19.2 [16.0,22.8]
	Fourth quartile	30.8 [27.9,33.8]	20.4 [16.4,25.1]	22.4 [19.8,25.3]	16.0 [13.1,19.4]
TG/HDL-C	First quartile	19.7 [17.6,22.0]	24.0 [19.0,29.7]	27.0 [24.7,29.6]	27.5 [23.3,32.2]
	Second quartile	22.1 [19.7,24.8]	23.1 [18.3,28.6]	27.0 [24.6,29.5]	27.2 [23.3,31.4]
	Third quartile	24.1 [21.6,26.7]	25.6 [20.8,31.1]	23.7 [21.4,26.1]	25.9 [21.8,30.4]
	Fourth quartile	34.1 [31.0,37.4]	27.4 [22.4,33.0]	22.3 [19.5,25.3]	19.5 [16.2,23.3]

Abbreviations: HDL-C: High-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TC: Total Cholesterol; TG: Triglycerides;

* Within-column relative frequencies

Association between dyslipidemia and blood pressure control

The association of lipid markers with uncontrolled hypertension was analyzed, separately in males and females (Table 4).

Table 4.

Association between dyslipidemia and blood pressure control in patients with hypertension

		Males (n = 2,649)			Females (n = 3,348)
		Multivariable Model			Multivariable Model
		APR	[95% CI]	p value	APR	[95% CI]	p value
TC	First quartile	Ref			Ref
	Second quartile	1.10	[1.04,1.16]	< 0.001	1.02	[0.96,1.09]	0.491
	Third quartile	1.14	[1.08,1.20]	< 0.001	1.10	[1.04,1.17]	0.002
	Fourth quartile	1.15	[1.10,1.21]	< 0.001	1.14	[1.07,1.20]	< 0.001
	Per 1 mmol/L increase*	1.06	[1.04,1.08]	< 0.001	1.05	[1.03,1.07]	< 0.001
	p value for trend	< 0.0001			< 0.0001
HDL-C	First quartile	Ref			Ref
	Second quartile	1.05	[1.00,1.09]	0.031	1.00	[0.94,1.07]	0.879
	Third quartile	0.99	[0.94,1.04]	0.775	1.01	[0.95,1.07]	0.857
	Fourth quartile	1.06	[1.00,1.12]	0.036	1.01	[0.95,1.07]	0.819
	Per 1 mmol/L increase	1.05	[0.97,1.14]	0.191	1.01	[0.94,1.08]	0.812
	p value for trend	0.183			0.832
LDL-C	First quartile	Ref			Ref
	Second quartile	1.15	[1.09,1.22]	< 0.001	1.01	[0.95,1.07]	0.828
	Third quartile	1.16	[1.10,1.22]	< 0.001	1.07	[1.01,1.13]	0.025
	Fourth quartile	1.17	[1.11,1.24]	< 0.001	1.12	[1.06,1.18]	< 0.001
	Per 1 mmol/L increase	1.07	[1.04,1.09]	< 0.001	1.05	[1.03,1.07]	< 0.001
	p value for trend	< 0.0001			< 0.001
TG	First quartile	Ref			Ref
	Second quartile	0.99	[0.94,1.04]	0.578	1.01	[0.96,1.07]	0.620
	Third quartile	0.98	[0.93,1.03]	0.490	1.00	[0.95,1.07]	0.766
	Fourth quartile	1.04	[0.99,1.09]	0.141	1.03	[0.97,1.09]	0.307
	Per 1 mmol/L increase	1.02	[1.00,1.03]	0.016	1.02	[1.00,1.04]	0.062
	p value for trend	0.179			0.367
Non-HDL-C	First quartile	Ref			Ref
	Second quartile	1.14	[1.08,1.21]	< 0.001	1.05	[0.99,1.12]	0.089
	Third quartile	1.17	[1.11,1.24]	< 0.001	1.08	[1.02,1.15]	0.010
	Fourth quartile	1.17	[1.10,1.23]	< 0.001	1.15	[1.08,1.21]	< 0.001
	Per 1 mmol/L increase	1.06	[1.04,1.08]	< 0.001	1.05	[1.03,1.07]	< 0.001
	p value for trend	< 0.0001			< 0.001
TC/HDL-C	First quartile	Ref			Ref
	Second quartile	1.08	[1.01,1.15]	0.025	1.05	[0.99,1.11]	0.091
	Third quartile	1.15	[1.08,1.22]	< 0.001	1.05	[0.99,1.11]	0.091
	Fourth quartile	1.13	[1.06,1.20]	< 0.001	1.11	[1.05,1.17]	< 0.001
	p value for trend	< 0.001			0.001
LDL-C/HDL-C	First quartile	Ref			Ref
	Second quartile	1.13	[1.06,1.21]	< 0.001	1.04	[0.99,1.10]	0.149
	Third quartile	1.19	1.12,1.26]	< 0.001	1.08	[1.03,1.14]	0.003
	Fourth quartile	1.17	[1.10,1.24]	< 0.001	1.11	[1.05,1.17]	< 0.001
	p value for trend	< 0.001			< 0.001
TG/HDL-C	First quartile	Ref			Ref
	Second quartile	1.01	[0.96,1.07]	0.686	1.01	[0.96,1.06]	0.693
	Third quartile	0.99	[0.94,1.04]	0.633	1.01	[0.95,1.06]	0.834
	Fourth quartile	1.03	[0.98,1.09]	0.211	1.03	[0.98,1.09]	0.253
	p value for trend	0.308			0.326

Abbreviations: APR: Adjusted prevalence ratio; HDL-C: High-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TC: Total Cholesterol; TG: Triglycerides;

Model for males: Adjusted for Age, Marital Status, Wealth Index, Occupation Status, Fruit and vegetable consumption, Body Mass Index, Alcohol Consumption, History of Cardiovascular diseases/ Cerebrovascular accidents, Glycemic condition, chronic kidney disease

Model for females: Adjusted for Age, Occupation Status, Wealth Index, Fruit and vegetable consumption, Physical activity, Body Mass Index, Alcohol Consumption, History of Cardiovascular diseases/ Cerebrovascular accidents, Glycemic condition, chronic kidney disease

^*To convert lipid values from mmol/L to mg/dL, multiply by 38.67 for total cholesterol, LDL-C, and HDL-C, and by 88.57 for triglycerides

In the multivariable-adjusted model, trends of TC, LDL-C, and Non-HDL-C showed a statistically significant association with uncontrolled hypertension in males. In this model, each 1 mmol/L increase in LDL-C, Non-HDL-C, TC, and TG increased the prevalence of uncontrolled hypertension, (APR: 1.07, [95% CI: 1.04, 1.09]), (APR: 1.06, [95% CI: 1.04, 1.08]), (APR: 1.06, [95% CI: 1.04, 1.08]), and (APR:1.02, [95% CI: 1.00, 1.03]), respectively. Among the lipid indices, trends of TC/HDL-C and LDL-C/HDL-C showed significant associations. The prevalence of uncontrolled hypertension were 1.17 times higher for participants in the fourth quartile of LDL-C/HDL-C [95% CI: 1.10, 1.24] and 1.13 times higher for those in the fourth quartile of TC/HDL-C [95% CI: 1.06, 1.20], compared to the first quartile (Table 4).

The analysis revealed that trend of TG and HDL-C did not show statistically significant associations in females’ multivariable analyses. In addition, none of the quartiles of these lipid markers demonstrated significant association when compared to the first quartile as a reference. Conversely, significant trends were observed for TC, Non-HDL-C, and LDL-C. Each 1 mmol/L increase in LDL-C was associated with a 5% increase in the prevalence of uncontrolled hypertension [95% CI: 1.03, 1.07]. Similarly, Non-HDL-C showed a 5% increase [95% CI: 1.03, 1.07], as did TC [95% CI: 1.03, 1.07]. Regarding lipid indices, TG/HDL-C did not show any statistically significant association with uncontrolled hypertension. However, the trends for the other two indices remained associated (Table 4).

Discussion

In the present study, 86.4% of Iranian hypertensive adults were identified as having dyslipidemia. The highest and the lowest prevalence in lipid markers were Low HDL-C and high LDL-C, respectively. Additionally, dyslipidemia was more common among females, obese, uncontrolled diabetic mellitus, CKD, and antihypertensive medication users. Trends of TC, Non-HDL-C, LDL-C, LDL-C/HDL-C, and TC/HDL-C were all associated with a higher prevalence of uncontrolled hypertension. The prevalence of controlled hypertension generally decreased in higher quartiles of all lipid markers.

A higher prevalence of dyslipidemia was observed among Iranian adults diagnosed with hypertension compared to normotensive individuals reported in other studies. In this study, 86.4% of hypertensive adults had dyslipidemia, while normotensive populations in different studies showed rates ranging from about 30–50% [4, 16]. Further research in Iran has consistently demonstrated that individuals with hypertension or prehypertension tend to have higher rates of dyslipidemia compared to those without hypertension [30, 31]. Similar results were reported in research conducted outside the country [32]. The present study’s findings also show a higher prevalence of dyslipidemia among hypertensive individuals compared to the general population, where the reported prevalence ranges from 80 to 82.7% in the general population [33–35].

Compared to some previous Iranian and non-Iranian studies, the findings of the present study indicated a notably higher prevalence of dyslipidemia in hypertensive patients, however, several researchers have reported even higher prevalences [4, 5, 36–40]. One reason for this discrepancy could be the variation in the definitions of hypertension and dyslipidemia. For instance, a recent study in the capital of Iran found a dyslipidemia prevalence of 59.7% among adults with BP over 140/90 mmHg. However, dyslipidemia was defined only by a previous diagnosis or the use of lipid-lowering medications [16]. It’s clear that including individuals identified through laboratory measurements would result in a higher prevalence. Moreover, a cross-sectional study from one of the cities in Iran on about 2000 adults, reported a prevalence of 34.3% [4]. However, this difference in reported prevalence could be explained by the varying definitions of abnormal HDL-C and TC; for instance, the article defined low HDL-C as HDL-C < 35 mg/dL, whereas the present study applied sex-specific cutoffs of HDL-C < 50 mg/dL for females and HDL-C < 40 mg/dL for males, leading to a higher proportion of participants in the present study being categorized as having low HDL-C. Additionally, the fact that this study was conducted at the city level may also contribute to the observed differences.

In terms of Middle East region published studies, a survey of 300 patients older than 25 years old with hypertension reported a dyslipidemia prevalence of 57.7% in Iraq [38]. Another study conducted on 250 patients diagnosed with hypertension in Pakistan reported the prevalence of dyslipidemia as 63% [40]. Additional study in Jordan found a lower prevalence of low HDL-C and hypertriglyceridemia, while in Lebanon, hypertriglyceridemia also had a lower prevalence, but hypercholesterolemia rates were higher among hypertensive adults [41, 42].

A study conducted on over 4,000 adults in China reported that the prevalence of low HDL-C (HDL-C < 40 mg/dL), borderline-high TG (150–199 mg/dL), and high TG (200–499 mg/dL) were lower compared to the present study’s findings. However, the prevalence of very high TG (≥ 500 mg/dL), as well as all TC and LDL-C cutoffs were higher by comparison [43]. In Africa, a cross-sectional study in Nigeria involving 354 hypertensive patients, using similar definitions and cutoffs as those in the present study, found a total dyslipidemia prevalence of 60.0% [36]. In Ethiopia, a study reported that 24.5% of hypertensive individuals had TG > 150 mg/dL, 30.9% had HDL-C < 40 mg/dL, 16.1% had LDL-C > 130 mg/ dL, and 19.6% had TC > 200, all of which are lower than the findings of the present study [44].

The findings of the current study suggest that the prevalence of low HDL-C in the Iranian population is notably high, contributing significantly to the overall rate of dyslipidemia. It is worth noting that disparities in the study population and sample size may account for the variation in findings between the studies. While numerous studies focus on a limited number of hypertensive patients in specific centers [4, 5, 36, 37, 44, 45], this study examines a large, community-based population of individuals with hypertension.

Sex differences in the prevalence of dyslipidemia were observed in the study population, with hypertensive females showing a significantly higher prevalence than males. This observation aligns with the results of some studies [36, 37]. The present study further revealed that dyslipidemia was more prevalent among individuals with uncontrolled diabetes, highlighting an association between diabetes status and lipid abnormalities. Especially in type 2 diabetes, elevated blood glucose levels are associated with increases in TG and LDL-C [46]. Furthermore, individuals using antihypertensive medication in the study population indicated significantly higher prevalence rates of dyslipidemia. Whereas some antihypertensive medications are typically considered neutral in their effects, others such as beta blockers and diuretics are believed to raise certain lipid levels [47].

Numerous studies have investigated the predictors of hypertension control to identify older age, obesity, smoking, alcohol consumption, and CKD as significant contributors [48–51]. A cross-sectional study in Iran found that a WC of ≥ 90 cm, smoking, older age, and some medications such as calcium channel blockers or angiotensin-converting enzyme inhibitors (ACEIs) were associated with uncontrolled hypertension (BP ≥ 140/90 mmHg) [52]. There have been inconsistencies regarding the association between dyslipidemia and uncontrolled hypertension. While some studies have found no association, others have reported a significant association [49, 50, 53].

Several studies have found that TC and LDL-C were significantly higher in individuals with uncontrolled hypertension compared to controlled individuals, although findings regarding HDL-C have been inconsistent [54, 55]. Furthermore, in a study conducted on Iranian individuals with a diagnosis of type 2 diabetes, uncontrolled hypertension was significantly associated with TC and Non-HDL-C, with the exception of TG, which aligns with the results of our study [56]. Although TG levels in diabetics are notably elevated due to insulin resistance compared to the general population, it appears that these changes do not influence the association between TG and uncontrolled hypertension [57].

One investigation has shown that lipid markers differ between males and females. In early adulthood to middle age, females tend to have higher HDL-C and lower LDL-C and TG compared to males, due to the protective effects of estrogen and males higher visceral fat accumulation [58]. This protective effect declines after menopause as estrogen levels drop [59]. Whilst examining lipid markers related to hypertension and its control, TC and Non-HDL-C have shown a stronger association with hypertension incidence in males [60]. One study in Ethiopia found that LDL-C was significantly higher in both males and females with uncontrolled hypertension, while females had higher TG levels and males showed increased TC levels [61]. Likewise, a study from Pakistan observed identical trends in lipid markers among males and females [62]. The present study extends these results by demonstrating stronger associations with lipid markers observed in males. These results align with previous studies, further emphasizing that males generally have higher levels of unfavorable lipid markers, which are more strongly associated with both the incidence of hypertension and uncontrolled hypertension.

The relationship between dyslipidemia and hypertension control may be mediated through various pathophysiological pathways. Although HDL-C plays a protective role in regulating BP, acting as an antioxidant by promoting nitric oxide production, its association with hypertension is complex, as this association is observed more at certain levels [63]. TG and TG-rich lipoproteins have been shown to contribute to endothelial dysfunction, which disrupts nitric oxide production and leads to vascular resistance. Additionally, TC and LDL-C can accumulate along artery walls and reduce arterial compliance by increasing arterial stiffness. These effects contribute to an increase in BP [6]. These mechanistic insights underscore the direct impact of dyslipidemia on BP control by impairing endothelial function and increasing arterial stiffness. However, this relationship should not be attributed solely to the influence of pathophysiological mechanisms. Unhealthy behaviors, including poor adherence to dietary guidelines, alcohol consumption, and being overweight are commonly observed in individuals with dyslipidemia, which may increase the risk of uncontrolled hypertension [64].

These associations should not be interpreted as unidirectional. While many studies report significant associations between lipid markers and hypertension control, these findings do not necessarily imply a direct causal pathway between dyslipidemia and uncontrolled hypertension [49]. Reverse causation may also play a role. For instance, individuals with hypertension may be more likely to be referred to or seek out weight-reduction and lifestyle modification programs, which can subsequently improve their lipid profiles [65]. Furthermore, some studies have found that both SBP and DBP levels are associated with lipid parameters, suggesting an intertwined association rather than a simple cause-effect mechanism [36, 66].

Therefore, due to the association between hypertension and dyslipidemia in increasing the risk of mortality in the future [8], a comprehensive approach is recommended to screen and treat these patients. The treatment plans should be closely monitored, with careful consideration of how each BP medication affects lipid levels and vice versa. Whenever possible, it’s recommended to use medications like statins that positively impact both BP and lipid markers [11]. For patients taking antihypertensive medications like diuretics and beta blockers, using the lowest effective doses, regularly monitoring lipid markers, considering alternative treatments for high-risk individuals, and combination therapy with statins can help mitigate adverse effects on lipid profiles. It is also crucial to assess patient adherence, especially when managing complex regimens that involve multiple medications. Along with proper education, these patients should undergo regular monitoring to manage their conditions and encourage healthy behaviors, ultimately improving their health outcomes.

Strengths and limitations

This study is noteworthy for evaluating hypertensive adults and associated dyslipidemia markers across a diverse, nationwide sample encompassing all provinces of Iran. It combines rigorous data collection methods—spanning interviews, physical assessments, and lab analyses—to ensure accuracy and consistency. Robust quality-control processes underpin every stage, enhancing the credibility and applicability of the findings.

It is important to note that complete-case analysis was prioritized over imputation methods for two key reasons. First, complete-case analysis enhances transparency and reproducibility by eliminating uncertainties associated with imputation models, ensuring findings rely solely on observed data without statistical extrapolation. Second, the robustness of the study’s results was supported by the consistency between complete-case and imputed analyses, indicating that missing data had minimal impact on conclusions—thus justifying the preference for this simpler approach.

However, this study isn’t without limitations. The cross-sectional design of the study limits its ability to establish contributory relationships between hypertension control and its associated factors. A key limitation is the possibility of reverse causation, where uncontrolled hypertension might affect lipid levels, or both conditions could be influenced by shared risk factors that were not measure. As a result, conclusions should be interpreted with caution. Given the cross-sectional nature of the data, constructing a precise Directed Acyclic Graph (DAG) for causal modeling was not feasible. Instead, the adjustment variables were selected based on standard epidemiological criteria and a review of previous studies. While the systematic sampling and rigorous quality control measures aimed to minimize biases, potential limitations such as selection bias (due to non-participation or exclusions), information bias (from self-reported data), and misclassification may still affect the results. Moreover, in some subgroups, small sample sizes may have resulted in less precise estimates and wider confidence intervals, requiring cautious interpretation and the avoidance of broad generalizations. Treatment effects on lipid levels could vary between controlled and uncontrolled hypertension groups, potentially influencing the findings. Furthermore, the analysis did not account for variations in hypertension definitions and control targets across different patient subgroups, which may have impacted the interpretation of the results.

Furthermore, stepwise regression can fail to retain important variables while randomly selecting irrelevant ones, potentially leading to bias. Stepwise regression becomes less effective as the number of potential explanatory variables increases, often leading to biased selection and reduced model stability [67]. To mitigate these issues, variables were carefully preselected based on established scientific literature and epidemiological evidence, ensuring only theoretically justified predictors were included in the analysis. The models for males and females each incorporated ten carefully chosen adjustment variables. Following regression analysis, the results were reviewed to confirm that no critical variables were mistakenly excluded, thereby maintaining model accuracy.

Regarding these shortcomings, future research should employ longitudinal designs with repeated lipid measurements to capture more precise associations and clarify any causal patterns. In addition, exploring definitions of hypertension and control targets tailored to population-specific comorbidities would enhance the applicability of the findings. Investigating specific lipid markers and their impact on hypertension control in diverse populations, along with evaluating combined interventions targeting both dyslipidemia and hypertension, could provide further insight and enhance the generalizability of the findings.

Conclusions

This study highlights the high prevalence of dyslipidemia among hypertensive individuals in Iran, with low HDL-C being the most common abnormality, and demonstrates significant associations between multiple lipid markers and blood pressure control. These findings underscore the importance of addressing both hypertension and dyslipidemia concurrently. We support routine lipid-profile assessment in this population as a standard component of risk management. Integrated public health interventions—such as combined screening for blood pressure and lipids, tailored lifestyle modifications, and individualized pharmacotherapy, supported by regular monitoring—should prioritize both conditions to improve health outcomes and reduce related health risks in this population.

Electronic supplementary material

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Abbreviations

APR

Adjusted Prevalence Ratio

ACEIs

Angiotensin-Converting Enzyme Inhibitors

BMI

Body Mass Index

BP

Blood Pressure

CI

Confidence Interval

CKD

Chronic Kidney Disease

CVAs

Cerebrovascular Accidents

CVDs

Cardiovascular Diseases

DBP

Diastolic Blood Pressure

eGFR

Estimated Glomerular Filtration Rate

FBS

Fasting Blood Sugar

HDL-C

High-Density Lipoprotein Cholesterol

LDL-C

Low-Density Lipoprotein Cholesterol

NCDs

Non-Communicable Diseases

NCEP ATP III

National Cholesterol Education Program Adult Treatment Panel III

PCA

Principal Component Analysis

SBP

Systolic Blood Pressure

STEPS

STEPwise Approach to Non-Communicable Disease Risk Factor Surveillance

TC

Total Cholesterol

TG

Triglycerides

WC

Waist Circumference

WHO

World Health Organization

Author contributions

S.M. data curation, methodology, writing—original draft preparation, reviewing, and editing; Y.A. methodology, formal analysis, validation, preparing figures 1, and 2, reviewing, and editing; A.G. methodology, validation, reviewing, and editing; N.R. data curation, reviewing, and editing; S.KH. data curation, reviewing, and editing; M.M. data curation, reviewing, and editing; P.KH. data curation, reviewing, and editing; O.T.M. supervision, data curation, validation, reviewing, and editing. All authors have reviewed and approved the final version of the manuscript.

Funding

This study was supported by the Non-Communicable Diseases Research Center at the Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran [Grant IDs 1403-2-221-73074]. The supporting organization had no involvement in the study’s design, data gathering, analysis, manuscript drafting, or the decision to publish the results.

Data availability

The datasets supporting the conclusions of this article are included within the article and its supplementary files. Requests for access to further data should be directed to the corresponding authors.

Declarations

Ethics approval and consent to participate

The Research Ethics Committees of the Endocrine & Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Reference Code: IR.TUMS.EMRI.REC.1403.071). Participants provided informed consent in person after receiving written information detailing the study’s objectives and procedures in the 2021 STEPS survey.

Consent for publication

Not required.

Competing interests

The authors declare no competing interests.

Usage of AI

During the preparation of this work, the authors used GPT-4 to check grammar. After that, the author reviewed and edited the content as needed and take full responsibility for the content of the publication.