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Article

Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data

by
Tanvir Ahmed
1,
Akhi Nath
2,
Nusrat Jahan
3,
Aakanksha Khadka
1,
Jaimala Kishore
1,
Ashley Farokhrouz
4 and
Rodney G. Bowden
5,*
1
Department of Public Health, Baylor University, Waco, TX 76706, USA
2
School of Information Technology Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
3
Department of Public Health, North South University, Dhaka 1212, Bangladesh
4
School of Business, Baylor University, Waco, TX 76706, USA
5
Department of Public Health and Program of Medical Sciences, Baylor University, Waco, TX 76798, USA
*
Author to whom correspondence should be addressed.
Int. J. Transl. Med. 2025, 5(2), 18; https://doi.org/10.3390/ijtm5020018
Submission received: 5 March 2025 / Revised: 14 May 2025 / Accepted: 19 May 2025 / Published: 22 May 2025
(This article belongs to the Collection Feature Papers in International Journal of Translational Medicine)
Review Reports Versions Notes

Abstract

Introduction: Dyslipidemia, characterized by abnormal blood lipid levels, is a key risk factor for cardiovascular disease. Socioeconomic status can play a role in the development of chronic disease, including as an influence on risk factors for chronic diseases such as cardiovascular disease. Methods: This study analyzes the relationship between socioeconomic status and dyslipidemia using a population-based cross-sectional survey (NHANES 2017–2020 data). A cohort of 5862 adults was examined, focusing on socioeconomic factors (income, education, occupation) and their association with lipid profiles while controlling for sociodemographic, lifestyle, and medical variables, contributing to understanding how health disparities may affect chronic disease outcomes. Results: Low socioeconomic status was consistently associated with higher dyslipidemia risk, while high socioeconomic status demonstrated a modest protective effect. Age, BMI, hypertension, and diabetes were key predictors, highlighting the need for targeted interventions. Conclusions: This study underscores the critical role of socioeconomic status in dyslipidemia risk. Low socioeconomic status consistently increased the odds of dyslipidemia. While high socioeconomic status demonstrated some protective effects, these were diminished when accounting for lifestyle and clinical factors, highlighting the complex interplay of socioeconomic status and health behaviors.

1. Introduction

Dyslipidemia refers to abnormal levels of blood lipids, characterized by elevated concentrations of low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) or triglycerides (TG) or decreased levels of high-density lipoprotein cholesterol (HDL-C) [1]. Dyslipidemia is a key risk factor for cardiovascular disease, which is the leading cause of morbidity as well as mortality worldwide [2]. The causal relationship between dyslipidemia and cardiovascular disease is well-established, as evidenced by the Framingham Heart Study and supported by numerous subsequent studies [3,4,5,6]. Most standard guidelines emphasize effective management of lipid levels for treating and preventing cardiovascular disease [7,8]. Globally, the prevalence of dyslipidemia in adults is estimated to range from 20% to 80%, depending on the definitions and diagnostic criteria used [9]. The increased use of statins—rising from 17.9% to 27.8% among U.S. adults aged 40 and older between 2002 and 2013—highlights growing awareness and management efforts.
Because of the significant role of dyslipidemia as a risk factor in cardiovascular diseases, numerous studies have explored its causes and associations. Dyslipidemia arises from a combination of genetic, environmental, and lifestyle factors and is categorized into two primary types: primary dyslipidemia, caused by genetic mutations affecting lipid metabolism, and secondary dyslipidemia, attributed to lifestyle factors or underlying medical conditions [10]. Some of the risk factors of secondary dyslipidemia are physical inactivity, obesity, smoking, excessive alcohol consumption, poor diet, certain medications, medical conditions such as diabetes and hypothyroidism, genetic factors, age, and lifestyle factors. Lifestyle factors are greatly associated with socioeconomic status [11]. Along with these multifactorial etiologies, epidemiological studies have reported that some social predictors, such as socioeconomic status (SES), are also associated with changes in the lipid profile [12]. Socioeconomic status, defined by income, education, and occupation, influences access to healthcare, health behaviors, and lipid metabolism, with lifestyle factors mediating its association with dyslipidemia [13]. SES appears to be an important factor in shaping the overall health status of individuals [14].
The literature indicates a clear association between dyslipidemia and SES, but findings regarding the direction of this relationship—whether inverse or direct—are mixed. Evidence suggests that individuals from lower-SES backgrounds are more likely to exhibit poor dietary habits and physical inactivity and have limited access to healthcare, all of which can contribute to dyslipidemia [15,16]. For example, a study conducted in China identified low SES as significantly associated with an increased risk of dyslipidemia, partly because of these contributing factors [13]. Furthermore, SES disparities often correlate with a higher prevalence of comorbidities such as obesity and diabetes, which are themselves established risk factors for dyslipidemia. Conversely, a study from Brazil reported that individuals in higher SES categories exhibited a greater risk of dyslipidemia, despite having lower rates of obesity and diabetes [17]. This finding highlights the complexity of the relationship between SES and dyslipidemia, which may vary based on regional, cultural, and healthcare-related factors.
The equivocal findings underscore the need to further investigate the relationship between SES and dyslipidemia, particularly in developed nations such as the United States. Understanding this association is essential for addressing health disparities and designing targeted public health interventions to reduce the burden of cardiovascular disease and associated risk factors [12]. While much has been understood about the individual contributions of lifestyle factors, genetics, and medical conditions and dyslipidemia, the influence of SES remains an area of growing interest. The impact of SES on lipid levels can be multifaceted, influencing not only individual health behaviors but the broader environmental and systemic factors that affect health outcomes. A few studies have explored the association between dyslipidemia and SES using the National Health and Nutrition Examination Survey (NHANES) dataset, which provides a weighted, representative sample of the U.S. population.
This study aims to explore the association between SES and dyslipidemia using data from the NHANES 2017–2020 cohort. The analysis provides a comprehensive understanding of how income, education, and occupation may influence lipid profiles across different demographic groups. By investigating these relationships, this research seeks to contribute valuable insights into the role of SES factors regarding cardiovascular health outcomes and to inform more equitable public health strategies and interventions.

2. Methods

2.1. Study Design and Data Source

This study utilized publicly available data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional survey conducted by the National Center for Health Statistics (NCHS), which provides a representative sample of the civilian, noninstitutionalized U.S. population. The NHANES 2017–2020 cohort was used in this study to examine the relationship between socioeconomic status (SES) and dyslipidemia. NHANES data includes demographic, health, and nutrition information, with detailed laboratory data on lipid profiles, including low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). Ethical approval for NHANES was obtained by the NCHS Institutional Review Board, and informed consent was obtained from all participants. Detailed information about the survey design methods and data collection procedures of NHANES 2017–2020 can be found at https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?Cycle=2017-2020 and was (accessed on 1 November 2024).

2.2. Study Population

The initial dataset consisted of 15,560 study participants. Participants who were less than 18 years old and those were pregnant during the survey were excluded. Only participants with complete data on lipid profiles and relevant SES variables (income, education, and occupation) were included in the analysis. As ‘Education’ is one of the important variables for determining socioeconomic status and the survey had an education variable for participants 20 years of age and above, participants less than 20 years old were excluded from further analysis. Missing data for covariates were excluded. The final analysis included 5862 respondents, who are estimated to represent 160,376,524 US citizens.

3. Variable Definitions

3.1. Dyslipidemia

Dyslipidemia was defined based on the guidelines of the Centers for Disease Control and Prevention. Participants were classified as having dyslipidemia if they met any of the following criteria:
Elevated LDL-C levels (≥130 mg/dL);
Elevated total cholesterol (≥240 mg/dL);
Elevated triglycerides (≥150 mg/dL);
Low HDL-C levels (<40 mg/dL for men, <50 mg/dL for women);
Participants reported using cholesterol-lowering medications.

3.2. Socioeconomic Status (SES)

SES was assessed using three key variables.
Income: Based on the ratio of annual family income to poverty (according to the Department of Health and Human Services (HHS) poverty guidelines, ranging from 0 to 5), participants were categorized into five income groups: Below Poverty (<1), Near Poverty (1–2), Low Income (2–3), Middle Income (3–4), and High Income (>4).
Education: Categorized as Low Education (less than high school), Medium Education (high school graduate and some college), and High Education (college graduate or higher).
Occupation: Participants were classified into Employed and Unemployed based on their reported work type.
With these three variables, we created a new SES variable using latent class analysis (LCA) where the income category, level of education, and occupation had five, three, and two levels, respectively. The latent class analysis used multiple observed categorical variables to generate an unmeasured variable (i.e., latent variable, in this case, ‘SES_class’) with a set of mutually exclusive latent classes. We performed LCA using the R library “poLCA”, a software package for the estimation of latent class models. Using the three observed variables (income, education, and occupation), we ran three models with two, three, and four latent classes. The model with three latent classes had better fit indices (lower AIC—34,338.22 and BIC—34,490.85; lower G2—16.76 and X2—16.77); and positive degrees of freedom) than the two-class (higher AIC—34,416.74 and BIC—34,516.29; higher G2—111.28 and X2—115.65) and four-class models (higher AIC and BIC and negative degrees of freedom). Based on the model fits and model interpretability, we chose the model with three latent classes, representing a middle, high, and low SES class according to the item-response probabilities.
To account for potential confounding factors, in this study, we selected the following covariates that may influence the risk of having dyslipidemia.

3.3. Sociodemographic Factors

Among sociodemographic variables, we included age, gender, race, and marital status. As this study was aimed at examining the association between socioeconomic status and dyslipidemia among adults, we took a subset of adults (20 years and above, up to 80 years). Gender was categorized as “Male” and “Female”. Marital status had three categories—“Married/Married/Living with Partner”, “Widowed/Divorced/Separated”, and “Never Married”. Race was classified as Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, and Other Race (including Multiracial).

3.4. Examination/Measurement Variable

BMI was presented in the dataset as weight in kilograms divided by height in meters squared and then rounded to one decimal place.

3.5. Lifestyle Variables

Alcohol consumption was determined with the variable “ALQ121 with the corresponding question: During the past 12 months, how often did you drink any type of alcoholic beverage?” Based on the responses, we categorized participants into four categories: “Nonalcoholic”—those who responded with “never in the last year”; “Rare Drinker”—those who had alcohol “1–2 times in the last year” up to “less than once a month”; “Occasional Drinker”—those who had alcohol “2–3 times a month” up to “2 times a week”; and “Regular Drinker”: those who had alcohol “3 to 4 times a week” up to “every day”. Smoking status was determined by the question “Smoked at least 100 cigarettes in life” and was classified into two categories: Nonsmokers and Smokers. For determining physical activity status, we calculated the total weekly MET score by multiplying the duration and frequency of each type of physical activity (vigorous work/recreational activity, moderate work/recreational activity, walking/bicycling) by its corresponding MET value.

3.6. Medical Conditions

Participants were grouped as having hypertension if they had an average blood pressure of 140/90 mmHg and above, were told on two or more occasions by healthcare providers that they had hypertension, or were currently taking medication for hypertension. Participants were grouped as having diabetes mellitus if they were taking antidiabetic pills or insulin or if they had a serum glucose level of more than 6.99 mmol/L or a serum glycohemoglobin (HbA1c) level ≥ 6.5%.

4. Statistical Analysis

Descriptive statistics were used to summarize the participant characteristics concerning the presence or absence of dyslipidemia, including means and standard deviations for continuous variables and frequencies and percentages for categorical variables. To examine the association between SES and dyslipidemia, logistic regression models were employed. The primary outcome, dyslipidemia, was treated as a binary variable (presence or absence of dyslipidemia).
Univariate analyses were first performed to assess the crude association between SES and dyslipidemia. Multivariate logistic regression models were then used to adjust for potential confounders with the covariates described above. In Model 1, no covariate was adjusted. In Model 2, adjustments were made for sociodemographic variables (gender, age, race, and marital status). Model 3 included covariates from Model 2 plus BMI. Model 4 had additional covariates for the lifestyle factors (alcohol status, smoking status, and physical activity status). The final model, Model 5 included the rest of the covariates for the medical conditions (hypertension and diabetes). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the strength of the associations.
To account for the complex survey design of NHANES, all analyses were performed using appropriate sampling weights, stratification, and clustering to obtain nationally representative estimates. Statistical analyses were conducted using the SAS software (version 9.4) and R software (version 4.0). Statistical significance was set at a p-value of <0.05.

5. Ethical Considerations

This study used deidentified, publicly available data from NHANES, which were approved by the National Center for Health Statistics (NCHS) Institutional Review Board. Since the data were anonymized, informed consent for this analysis was not required.

6. Results

6.1. Demographic and Clinical Characteristics

The study analyzed 5862 participants from the NHANES 2017–2020 dataset, with 4177 (71.3%) having dyslipidemia and 1685 (28.7%) without. Participants with dyslipidemia were significantly older (53.94 ± 16.26 years vs. 42.73 ± 16.97 years, p < 0.0001) and had higher systolic (126.06 ± 18.97 mmHg) and diastolic (75.44 ± 11.54 mmHg) blood pressures than those without dyslipidemia (120.04 ± 17.75 mmHg and 73.08 ± 11.28 mmHg, respectively).
Obesity was more prevalent in the dyslipidemia group (48.75%) than in the nondyslipidemia group (31.89%), as was an elevated waist circumference (67.86% vs. 42.93%, p < 0.0001). Smoking, physical inactivity, and hypertension were also more common in the dyslipidemia group (47.98%, 30.38%, and 50.75%, respectively, p < 0.0001).
Non-Hispanic Whites made up a larger proportion of those with dyslipidemia (40.47%) than Non-Hispanic Blacks (23.11%), who were more prevalent among those without dyslipidemia (31.64%, p < 0.0001). Education and marital status were also significantly associated, with lower education levels and being married/partnered more common among those with dyslipidemia. Results can be found in Table 1.

6.2. Socioeconomic Status and Dyslipidemia

The central aim of this study was to examine the influence of SES on dyslipidemia, with a focus on how SES impacts the likelihood of dyslipidemia after accounting for clinical, lifestyle, and demographic factors.
High SES was associated with a reduced risk of dyslipidemia in both unweighted and weighted models. In unweighted models, where only SES was considered, high SES was linked to a 19.5% reduction in the odds of dyslipidemia (OR = 0.805, p = 0.013, Model 2). However, the protective effect of high SES weakened when adjusting for clinical, lifestyle, and medical conditions in the weighted models. In these models, high SES remained protective (OR = 0.740, p = 0.037 in Model 2), but the effect became nonsignificant after further adjustment in Model 5 (OR = 0.870, p = 0.364). This attenuation suggests that the influence of high SES on dyslipidemia is partly mediated by factors such as BMI, hypertension, and diabetes. Conversely, low SES consistently increased the odds of dyslipidemia in both unweighted and weighted models, even after adjusting for confounding factors. In the unweighted models, low SES was associated with a 17.5% increase in the odds of dyslipidemia (OR = 1.175, p = 0.019, Model 1). This effect remained significant in the weighted models, with a slight attenuation but still significant in Model 5 (OR = 1.237, p = 0.048). These findings indicate that low SES is a consistent and significant risk factor for dyslipidemia, underscoring the importance of addressing socioeconomic disparities in dyslipidemia risk.

6.3. Age and BMI as Predictors

Age and BMI were consistently significant predictors of dyslipidemia in both un-weighted and weighted models. In Model 5 of the weighted models, each additional year of age was associated with an increased likelihood of dyslipidemia (OR = 1.473, p < 0.001), while higher BMI was also a robust predictor (OR = 1.075, p < 0.001). These findings emphasize the importance of age and BMI as key determinants of dyslipidemia risk, regardless of SES.

6.4. Medical Conditions and SES

Hypertension and diabetes were strongly associated with dyslipidemia in both un-weighted and weighted models. In unweighted models, hypertension showed a stronger association with dyslipidemia (OR = 1.789, p < 0.001) than in weighted models (OR = 1.515, p = 0.004). Diabetes exhibited a similar pattern, with the odds ratio for diabetes being 1.537 (p = 0.044) in the weighted models. These findings suggest that comorbid conditions such as hypertension and diabetes play a significant role in the relationship between SES and dyslipidemia.

6.5. Subpopulation Analysis by SES

Subgroup analyses revealed important interactions between race and SES in influencing dyslipidemia risk. Among low SES participants, Non-Hispanic Black individuals had significantly lower odds of dyslipidemia (OR = 0.381, p = 0.006). Similarly, in high SES groups, Non-Hispanic Blacks exhibited a strong protective effect (OR = 0.264, p = 0.042). BMI remained a strong predictor of dyslipidemia across all SES groups, with slightly higher odds in the high SES group (OR = 1.080, p = 0.005). Additionally, marital status showed SES-specific trends, with never-married individuals in the middle SES group having lower odds of dyslipidemia (OR = 0.655, p = 0.048). Results can be found in Table 2.

6.6. Gender-Specific Subgroup Analysis

Gender differences in the associations between SES and dyslipidemia were also evident. High SES showed a nonsignificant protective effect in males (OR = 0.780, p = 0.253), while it had no such effect in females (OR = 0.968, p = 0.784). In contrast, low SES was significantly associated with higher odds of dyslipidemia in females (OR = 1.543, p = 0.004), but not in males. Hypertension and diabetes also exhibited gender differences, with males showing stronger associations between these conditions and dyslipidemia (hypertension OR = 1.591, p = 0.008; diabetes OR = 1.703, p = 0.025). Results can be found in Table 3.
This study underscores the significant impact of socioeconomic status (SES) on dyslipidemia risk. Low SES consistently increased the odds of dyslipidemia compared with the middle SES group, even after adjusting for demographic, clinical, and lifestyle factors. On the other hand, high SES showed a modest protective effect, which diminished after controlling for BMI, lifestyle, and comorbid conditions. The analysis also highlighted important racial disparities, with Non-Hispanic Black individuals having lower odds of dyslipidemia, particularly in higher SES groups. Age, BMI, and medical conditions such as hypertension and diabetes were also key predictors, but SES remained the most consistent and influential factor across all models. These findings emphasize the need for targeted public health interventions that address socioeconomic disparities in the prevention and management of dyslipidemia. Unweighted and weighted results can be found in Table 4 and Table 5.

7. Discussion

Our study demonstrated that SES was consistently associated with dyslipidemia even when controlling for known risk factors for cardiovascular diseases. High and low SES were both predictors of cardiovascular disease risk.
SES emerged as a key determinant of dyslipidemia in this study. Low SES was associated with a significantly higher risk of dyslipidemia compared with middle SES (AOR: 1.237; p = 0.048), while higher SES was protective against dyslipidemia. These findings are consistent with previous research by Minhas et al., which demonstrated that lower income was independently associated with higher mortality rates from cardiovascular diseases in American adults [18]. Similarly, Zhang et al. found that individuals with low SES had more than double the risk of cardiovascular-related mortality [19]. The protective effect of high SES may be attributed to better access to healthcare, healthier dietary habits, and greater opportunities for physical activity. SES disparities in health outcomes are well-documented and underscore the need for targeted interventions to address the social determinants of health.
The prevalence of dyslipidemia in this study was 71.3%, indicating an increase of nearly 18% over the past decade compared with the 53% of U.S. adults with lipid abnormalities estimated by Toth et al. using data from the NHANES 2003–2006 [20]. However, our findings are consistent with the findings of Reese et al., who reported a prevalence of 70.8% among American Indian adolescents [21]. Furthermore, this prevalence is lower than the 80.3% observed in a hospital-based cross-sectional study in Ethiopia among adult cardiac patients by Abera et al. [4]. Conversely, it is notably higher than the prevalence of dyslipidemia reported in studies conducted in Cameroon (26%) and South Africa (67.3%). These discrepancies in prevalence rates can be attributed to variations in lifestyle factors, including dietary habits, physical activity levels, and psychological stress related to the economic, social, and cultural contexts of the respective populations. Such differences highlight the importance of contextual factors in influencing the burden of dyslipidemia and cardiovascular risk across diverse populations.
A significant finding in our study was the higher mean age of participants with dyslipidemia (53.94 ± 16.26 years) compared with those without (42.73 ± 16.97 years, p < 0.0001). This age-related difference in dyslipidemia prevalence is consistent with the results of the MASHAD study in Iran [1], which also reported a higher mean age among individuals with dyslipidemia. Age is a well-established risk factor for dyslipidemia, as changes in lipid metabolism occur with aging, leading to alterations in lipid profiles that increase cardiovascular risk.
Racial disparities in dyslipidemia were also evident in this study. Non-Hispanic Whites constituted a significantly larger proportion of individuals with dyslipidemia (40.47%) than Non-Hispanic Blacks (23.11%). Moreover, Non-Hispanic Black participants demonstrated lower odds of dyslipidemia than Mexican-American participants, with an adjusted odds ratio (AOR) of 0.513 (p < 0.001). These findings contrast with previous studies, such as that by An et al. conducted within Kaiser Permanente Southern California, which reported that Non-Hispanic Blacks were at higher risk of developing atherosclerotic cardiovascular disease compared with Non-Hispanic Whites [21]. The observed lower odds of dyslipidemia among Non-Hispanic Black individuals in our study may reflect underlying biological differences, particularly in lipid metabolism. Recent analyses of NHANES data (2007–2018) have reported that Non-Hispanic Black adults generally exhibit lower triglyceride levels compared with other racial groups [22]. Lower triglyceride concentrations among Non-Hispanic Black individuals may partly explain their lower prevalence of dyslipidemia, as hypertriglyceridemia is a key component of dyslipidemia classification. Additionally, disparities in healthcare access, screening practices, and treatment uptake are likely to influence the diagnosis and management of lipid abnormalities across racial groups. These findings underscore the need for culturally sensitive and equitable cardiovascular prevention strategies.
BMI was identified as a significant predictor of dyslipidemia in this study (OR = 1.075, p < 0.001). This is consistent with findings from the INDEPTH community study in South Africa, where being overweight was a strong predictor of dyslipidemia (OR = 1.76; 95% CI: 1.51–2.05, p < 0.001) [23]. Excess body weight is known to disrupt lipid metabolism, leading to higher levels of triglycerides and low-density lipoprotein cholesterol, while reducing high-density lipoprotein cholesterol levels. These physiological changes contribute to the development of dyslipidemia and its associated cardiovascular risks. As such, our findings underscore the importance of weight management in preventing and managing dyslipidemia.
Physical inactivity was also found to be a significant risk factor for dyslipidemia, consistently with numerous studies that have linked sedentary behavior to an increased risk of cardiovascular diseases and metabolic disorders [24,25]. A study in the United States reported that insufficient physical activity was associated with a substantial increase in healthcare costs, highlighting the broader public health implications of sedentary lifestyles [26]. Our findings emphasize the need for interventions aimed at increasing physical activity to reduce the risk of dyslipidemia and its associated comorbidities.
Educational attainment demonstrated a significant association with dyslipidemia in our study. The prevalence of dyslipidemia was notably lower among individuals with a college education or higher (24.24%) than among those with lower educational attainment, including high school or less (75.76%). Further analysis using weighted multivariable models (Table 5) indicated that individuals with low socioeconomic status (SES) remained at a significantly higher risk of dyslipidemia, even after adjusting for sociodemographic, lifestyle, and medical factors. These findings are consistent with the results of the Prospective Urban Rural Epidemiologic (PURE) study, which reported that lower educational levels were associated with higher cardiovascular risk in high-income countries [27].
Although higher SES initially exhibited a protective effect against dyslipidemia, this association became nonsignificant after adjusting for lifestyle behaviors and medical comorbidities. This attenuation supports a mediation model, wherein higher SES promotes healthier behaviors, better access to preventive healthcare, and improved management of chronic conditions, which collectively reduce dyslipidemia risk. Rather than representing simple confounding, our findings suggest that SES influences dyslipidemia indirectly through its impact on modifiable risk factors, highlighting the critical role of health behaviors in mediating socioeconomic disparities in lipid outcomes. This observation is consistent with findings from large prospective cohort studies, which demonstrated that a healthy lifestyle substantially mediates the association between SES and cardiovascular outcomes, including dyslipidemia and mortality [21].
This study underscores the significant impact of SES on the prevalence and risk of dyslipidemia. Low SES was consistently associated with a higher risk of dyslipidemia, even after adjusting for clinical, demographic, and lifestyle factors. The association between low SES and dyslipidemia is likely mediated by multiple factors, including limited access to healthcare, poorer dietary habits, physical inactivity, and higher rates of comorbid conditions such as obesity, hypertension, and diabetes. These findings are consistent with previous research demonstrating that individuals from lower socioeconomic backgrounds are more likely to experience disparities in health outcomes, particularly in the context of cardiovascular diseases. High SES, on the other hand, was associated with a reduced risk of dyslipidemia, although this protective effect was diminished when accounting for lifestyle factors such as BMI, physical activity, and comorbidities. The inverse relationship between SES and dyslipidemia highlights the need for targeted public health strategies that address the structural determinants of health, such as income, education, and occupation, which shape health behaviors and access to resources. In particular, interventions aimed at reducing SES disparities in healthcare access, improving health literacy, and promoting healthier lifestyles could significantly reduce the burden of dyslipidemia in vulnerable populations. Ultimately, addressing the social determinants of health is crucial for the effective prevention and management of dyslipidemia, and for achieving more equitable cardiovascular health outcomes.

Strengths and Limitations

We utilized the NHANES dataset, a representative resource for the U.S. noninstitutionalized civilian population, as it includes all the necessary variables and blood lipid markers required for our study. This dataset ensures the inclusion of participants from all racial and ethnic groups, enhancing its generalizability. The blood markers were obtained by collecting, transporting, and analyzing blood samples under strict protocols, ensuring high levels of data accuracy and reliability. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation, a widely accepted method with certain limitations. The equation becomes less accurate when triglyceride levels exceed 400 mg/dL, potentially underestimating LDL-C levels. It may also produce unreliable results for individuals with very low LDL-C levels and is unsuitable for those with conditions such as hyperlipoproteinemia or those undergoing lipid-lowering therapy. These limitations could lead to the misclassification of dyslipidemia status. Additionally, because of the cross-sectional nature of the study, we were unable to account for changes in socioeconomic status over time, which could significantly influence the results. Furthermore, as most of the lifestyle variables (e.g., alcohol consumption, smoking, physical activity) were mainly self-reported, the possibility of recall bias can not be eliminated. These considerations highlight both the strengths and limitations of our study.

8. Conclusions

This study underscores the critical role of SES in dyslipidemia risk. Low SES consistently increased the odds of dyslipidemia, emphasizing the need to address health disparities. While high SES demonstrated some protective effects, these were diminished when accounting for lifestyle and clinical factors, highlighting the complex interplay of SES and health behaviors. Racial and gender-specific trends further revealed the importance of tailored public health strategies. Non-Hispanic Black individuals in certain SES groups had lower dyslipidemia risks, while women with low SES faced heightened risks. To reduce the burden of dyslipidemia, policies must address socioeconomic disparities by improving healthcare access, promoting health literacy, and encouraging healthier lifestyles. Tackling these structural determinants is essential for advancing health equity and improving cardiovascular outcomes.

Author Contributions

T.A., R.G.B. created the idea, wrote portions of the manuscript, approved the final draft and interpreted the findings. T.A., A.N. and N.J. conducted the data analyses, interpreted the findings and approved the final draft. A.K., J.K. and A.F., wrote portions of the manuscript, proofed versions of the manuscript and approved the final draft. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. Internal funding from Baylor University was provided through the Brown Foundation Endowed Chair.

Institutional Review Board Statement

This study was conducted in accordance with Decleratio of Helsinki. Ethical approval for NHANES was obtained by the NCHS Institutional Review Board.

Informed Consent Statement

Informed consent was obtained from all participants by the NCHS Institutional Review Board.

Data Availability Statement

Data used in this study can be found at https://wwwn.cdc.gov/nchs/nhanes/ (accessed on 1 November 2024).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hedayatnia, M.; Asadi, Z.; Zare-Feyzabadi, R.; Yaghooti-Khorasani, M.; Ghazizadeh, H.; Ghaffarian-Zirak, R.; Nosrati-Tirkani, A.; Mohammadi-Bajgiran, M.; Rohban, M.; Sadabadi, F.; et al. Dyslipidemia and cardiovascular disease risk among the MASHAD study population. Lipids Health Dis. 2020, 19, 42. [Google Scholar] [CrossRef] [PubMed]
  2. Du, Z.; Qin, Y. Dyslipidemia and Cardiovascular Disease: Current Knowledge, Existing Challenges, and New Opportunities for Management Strategies. J. Clin. Med. 2023, 12, 363. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  3. Pencina, M.J.; Pencina, K.M.; Lloyd-Jones, D.; Catapano, A.L.; Thanassoulis, G.; Sniderman, A.D. The Expected 30-Year Benefits of Early Versus Delayed Primary Prevention of Cardiovascular Disease by Lipid Lowering. Circulation 2020, 142, 827–837. [Google Scholar] [CrossRef] [PubMed]
  4. Abera, A.; Worede, A.; Hirigo, A.T.; Alemayehu, R.; Ambachew, S. Dyslipidemia and associated factors among adult cardiac patients: A hospital-based comparative cross-sectional study. Eur. J. Med. Res. 2024, 29, 237. [Google Scholar] [CrossRef] [PubMed]
  5. Mortensen, M.B.; Nordestgaard, B.G. Elevated LDL cholesterol and increased risk of myocardial infarction and atherosclerotic cardiovascular disease in individuals aged 70–100 years: A contemporary primary prevention cohort. Lancet 2020, 396, 1644–1652. [Google Scholar] [CrossRef]
  6. Klempfner, R.; Erez, A.; Sagit, B.-Z.; Goldenberg, I.; Fisman, E.; Kopel, E.; Shlomo, N.; Israel, A.; Tenenbaum, A. Elevated Triglyceride Level Is Independently Associated with Increased All-Cause Mortality in Patients with Established Coronary Heart Disease: Twenty-Two-Year Follow-Up of the Bezafibrate Infarction Prevention Study and Registry. Circ. Cardiovasc. Qual. Outcomes 2016, 9, 100–108. [Google Scholar] [CrossRef]
  7. Visseren, F.L.J.; Mach, F.; Smulders, Y.M.; Carballo, D.; Koskinas, K.C.; Bäck, M.; Benetos, A.; Biffi, A.; Boavida, J.-M.; Capodanno, D.; et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur. Heart J. 2021, 42, 3227–3337. [Google Scholar] [CrossRef]
  8. Grundy, S.M.; Stone, N.J.; Bailey, A.L.; Beam, C.; Birtcher, K.K.; Blumenthal, R.S.; Braun, L.T.; de Ferranti, S.; Faiella-Tommasino, J.; Forman, D.E.; et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 2019, 73, 3168–3209, Erratum in J. Am. Coll. Cardiol. 2019, 73, 3234–3237. [Google Scholar] [CrossRef]
  9. Mohamed-Yassin, M.S.; Baharudin, N.; Abdul-Razak, S.; Ramli, A.S.; Lai, N.M. Global prevalence of dyslipidaemia in adult populations: A systematic review protocol. BMJ Open 2021, 11, 9–12. [Google Scholar] [CrossRef]
  10. Pappan, N.; Awosika, A.O.; Rehman, A. Dyslipidemia. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024; pp. 1–16. [Google Scholar]
  11. Méjean, C.; Droomers, M.; van der Schouw, Y.T.; Sluijs, I.; Czernichow, S.; Grobbee, D.E.; Bueno-De-Mesquita, H.B.; Beulens, J.W. The contribution of diet and lifestyle to socioeconomic inequalities in cardiovascular morbidity and mortality. Int. J. Cardiol. 2013, 168, 5190–5195. [Google Scholar] [CrossRef]
  12. Santo, L.R.E.; Faria, T.O.; Silva, C.S.O.; Xavier, L.A.; Reis, V.C.; Mota, G.A.; Silveira, M.F.; Mill, J.G.; Baldo, M.P. Socioeconomic status and education level are associated with dyslipidemia in adults not taking lipid-lowering medication: A population-based study. Int. Health 2022, 14, 346–353. [Google Scholar] [CrossRef] [PubMed]
  13. Li, L.; Ouyang, F.; He, J.; Qiu, D.; Luo, D.; Xiao, S. Associations of Socioeconomic Status and Healthy Lifestyle With Incidence of Dyslipidemia: A Prospective Chinese Governmental Employee Cohort Study. Front. Public Health 2022, 10, 878126. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  14. Shaban, E.E.; Shaban, A. Effect of Socioeconomic Status on the Occurrence and In-Hospital Outcome Among Patients with Acute Myocardial Infarction: An Observational Analytic Case-Control Study. Front. Cardiovasc. Med. 2024. [Google Scholar] [CrossRef]
  15. Na, X.; Chen, Y.; Ma, X.; Wang, D.; Wang, H.; Song, Y.; Hua, Y.; Wang, P.; Liu, A. Relations of lifestyle behavior clusters to dyslipidemia in china: A compositional data analysis. Int. J. Environ. Res. Public Health 2021, 18, 7763. [Google Scholar] [CrossRef]
  16. Nam, G.E.; Cho, K.H.; Park, Y.G.; Han, K.D.; Choi, Y.S.; Kim, S.M.; Lee, K.S.; Ko, B.J.; Kim, Y.H.; Han, B.D.; et al. Socioeconomic status and dyslipidemia in Korean adults: The 2008-2010 Korea National Health and Nutrition Examination Survey. Prev. Med. 2013, 57, 304–309. [Google Scholar] [CrossRef] [PubMed]
  17. Konindala, N.; PhyoAung, P.; Park, K. Socioeconomic Disparities in Dyslipidemia and Associated Complications: From the Latest National Database. J. Clin. Lipidol. 2022, 16, e61. [Google Scholar] [CrossRef]
  18. Minhas, A.M.K.; Jain, V.; Li, M.; Ariss, R.W.; Fudim, M.; Michos, E.D.; Virani, S.S.; Sperling, L.; Mehta, A. Family income and cardiovascular disease risk in American adults. Sci. Rep. 2023, 13, 279. [Google Scholar] [CrossRef]
  19. Zhang, Y.-B.; Chen, C.; Pan, X.-F.; Guo, J.; Li, Y.; Franco, O.H.; Liu, G.; Pan, A. Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: Two prospective cohort studies. BMJ 2021, 373, n604. [Google Scholar] [CrossRef]
  20. An, J.; Zhang, Y.; Muntner, P.; Moran, A.E.; Hsu, J.W.; Reynolds, K. Recurrent atherosclerotic cardiovascular event rates differ among patients meeting the very high risk definition according to age, sex, race/ethnicity, and socioeconomic status. J. Am. Heart Assoc. 2020, 9, e017310. [Google Scholar] [CrossRef]
  21. Reese, J.A.; Roman, M.J.; Deen, J.F.; Ali, T.; Cole, S.A.; Devereux, R.B.; Fretts, A.M.; Howard, W.J.; Lee, E.T.; Malloy, K.; et al. Dyslipidemia in American Indian Adolescents and Young Adults: Strong Heart Family Study. J. Am. Heart Assoc. 2024, 13, e031741. [Google Scholar] [CrossRef]
  22. Li, Z.; Zhu, G.; Chen, G.; Luo, M.; Liu, X.; Chen, Z.; Qian, J. Distribution of lipid levels and prevalence of hyperlipidemia: Data from the NHANES 2007–2018. Lipids Health Dis. 2022, 21, 111. [Google Scholar] [CrossRef]
  23. Reiger, S.; Jardim, T.V.; Abrahams-Gessel, S.; Crowther, N.J.; Wade, A.; Gomez-Olive, F.X.; Salomon, J.; Tollman, S.; Gaziano, T.A. Awareness, treatment, and control of dyslipidemia in rural South Africa: The HAALSI (Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa) study. PLoS ONE 2017, 12, e0187347. [Google Scholar] [CrossRef] [PubMed]
  24. Cleland, C.L.; Hunter, R.F.; Kee, F.; Cupples, M.E.; Sallis, J.F.; Tully, M.A. Validity of the Global Physical Activity Questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health 2014, 14, 1255. [Google Scholar] [CrossRef] [PubMed]
  25. Biswas, A.; Oh, P.I.; Faulkner, G.E.; Bajaj, R.R.; Silver, M.A.; Mitchell, M.S.; Alter, D.A. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults a systematic review and meta-analysis. Ann. Intern. Med. 2015, 162, 123–132. [Google Scholar] [CrossRef] [PubMed]
  26. Carlson, S.A.; Fulton, J.E.; Michael, P.; Yang, Z. HHS Public Access. Physiol. Behav. 2017, 176, 100–106. [Google Scholar] [CrossRef]
  27. Rosengren, A.; Smyth, A.; Rangarajan, S.; Ramasundarahettige, C.; Bangdiwala, S.I.; Alhabib, K.F.; Avezum, A.; Boström, K.B.; Chifamba, J.; Gulec, S.; et al. Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: The Prospective Urban Rural Epidemiologic (PURE) study. Lancet Glob. Health 2019, 7, e748–e760. [Google Scholar] [CrossRef]
Table 1. Demographic and other features of participants based on the presence of dyslipidemia from the NHANES 2017–2020.
Table 1. Demographic and other features of participants based on the presence of dyslipidemia from the NHANES 2017–2020.
VariablesOverallNo DyslipidemiaDyslipidemiap Value
(n  =  5862)(n  =  1685)(n  =  4177)
Age50.85 (±17.20)42.73 (±16.97)53.94 (±16.26)<0.0001
Systolic BP124.43 (±18.84)120.04 (±17.75)126.06 (±18.97)
Diastolic BP74.79 (±11.52)73.08 (±11.28)75.44 (±11.54)
Gender 0.49
Male 2899 (51.27)809 (50.19)2175 (51.18)
Female2755 (48.73)803 (49.81)2075 (48.82)
Race <0.0001
Mexi-American679 (11.58)173 (10.73)506 (11.91)
Other Hispanic567 (9.67)142 (8.81)425 (10.00)
NH-White2285 (38.98)565 (35.05)1720 (40.47)
NH-Black1492 (25.45)510 (31.64)982 (23.11)
Multiracial839 (14.31)222 (13.77)617 (14.52)
Marital Status <0.0001
Married/Partner3431 (58.53)870 (53.97)2561 (60.26)
Wid/Div/Sep1337 (22.81)278 (17.25)1059 (24.92)
Never Married1092 (18.63)464 (28.78)628 (14.78)
Education Level <0.0001
High (≥College)1487 (25.37)457 (28.35)1030 (24.24)
Medium (High School < College)3465 (59.11)952 (59.06)2513 (59.13)
Low (<High School)910 (15.52)203 (12.59)707 (16.64)
Income Category 0.20
Below Poverty1041 (17.76)311 (19.29)730 (17.18)
Near Poverty1519 (25.91)400 (24.81)1119 (26.33)
Low Income975 (16.63)265 (16.44)710 (16.71)
Middle Income661 (11.28)194 (12.03)467 (10.99)
High Income1666 (28.42)442 (27.42)1224 (28.80)
Adult Food Security 0.73
Full Food Security3742 (63.83)1015 (62.97)2727 (64.16)
Marginal Food Security844 (14.40)243 (15.07)601 (14.14)
Low Food Security693 (11.82)188 (11.66)505 (11.88)
Very Low Food Security583 (9.95)166 (10.30)417 (9.81)
Covered by Health Insurance <0.0001
Yes4959 (84.60)1305 (80.96)3654 (85.98)
No895 (15.27)303 (18.80)592 (13.93)
Hypertension <0.0001
Yes2573 (43.89)416 (25.81)2157 (50.75)
No3093 (52.76)1111 (68.92)1982 (46.64)
Diabetes <0.0001
Yes 1130 (19.28)125 (7.75)1005 (23.65)
No4732 (80.72)1487 (92.25)3245 (76.35)
Obesity Category <0.0001
Underweight71 (1.21)45 (2.79)26 (0.61)
Normal Weight1363 (23.25)604 (37.47)759 (17.86)
Overweight1842 (31.42)449 (27.85)1393 (32.78)
Obesity2586 (44.11)514 (31.89)2072 (48.75)
Waist Circumference <0.0001
Normal WC2286 (39)920 (57.07)1366 (32.14)
Elevated WC3576 (61)692 (42.93)2884 (67.86)
Alcohol Status <0.0001
Nonalcoholic1258 (21.46)259 (16.07)999 (23.51)
Rare Drinker2021 (34.48)547 (33.93)1474 (34.68)
Occasional Drinker1730 (29.51)530 (32.88)1200 (28.24)
Regular Drinker853 (14.55)276 (17.12)577 (13.58)
Smoking Status <0.0001
Nonsmoker3177 (54.20)967 (59.99)2210 (52.00)
Smoker2683 (45.77)644 (39.95)2039 (47.98)
Physical Activity Status <0.0001
Active4229 (72.14)1270 (78.78)2959 (69.62)
Inactive1633 (27.86)342 (21.22)1291 (30.38)
Age Group <0.0001
20–29859 (14.65)474 (29.40)385 (9.06)
30–39897 (15.30)343 (21.28)554 (13.04)
40–49959 (16.36)261 (16.19)698 (16.42)
50–591024 (17.47)221 (13.71)803 (18.89)
60–691165 (19.87)163 (10.11)1002 (23.58)
70 and above958 (16.34)150 (9.31)808 (19.01)
Data are presented as mean (SD) for continuous variables and n (%) for categorical variables.
Table 2. Subpopulation analysis for each socioeconomic class.
Table 2. Subpopulation analysis for each socioeconomic class.
Dyslipidemia
SES Class: MiddleSES Class: HighSES Class: Low
Sociodemographic Factors
Sex
Male RefRefRef
Female0.8301.1311.331
0.1800.5610.137
Race
Mexican-AmericanRefRefRef
Non-Hispanic White0.9440.3991.066
0.6720.1260.803
Non-Hispanic Black0.624 **0.264 *0.381 **
0.0070.0420.006
Other Hispanic 0.7950.3071.512
0.2920.1400.274
Multiracial1.6500.8160.879
0.0610.7080.721
Marital Status
Married/PartnerRefRefRef
Widowed/Divorced/Separated0.7790.8600.789
0.2910.7210.286
Never Married/Single0.655 *1.0040.892
0.0480.9880.621
Age1.035 ***1.060 ***1.028 ***
<0.001<0.001<0.001
BMI1.076 ***1.080 ***1.069 ***
<0.0010.0050.002
Lifestyle Variables
Alcohol Status
NonalcoholicRefRefRef
Rare Drinker1.1960.9161.171
0.4620.8480.494
Occasional Drinker1.1601.4881.131
0.5400.3830.539
Regular Drinker0.9331.2490.870
0.8230.6210.511
Smoking Status
NonsmokerRefRefRef
Smoker1.1711.3011.105
0.3310.2850.428
Total MET Score1.0001.0001.000 *
0.2110.8220.031
Medical Conditions
Diabetes
NoRefRefRef
Yes1.806 *1.6091.109
0.0460.1710.673
Hypertension
NoRefRefRef
Yes1.440 *1.4621.942 *
0.0310.2030.016
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table 3. Subpopulation analysis based on gender.
Table 3. Subpopulation analysis based on gender.
Dyslipidemia
Gender: FemaleGender: Male
Socioeconomic Class
Middle RefRef
High0.9680.780
0.7840.253
Low1.543 **0.981
0.0040.924
Other Sociodemographic Factors
Race
Mexican-AmericanRefRef
Non-Hispanic White0.9580.822
0.8190.168
Non-Hispanic Black0.609 *0.442 ***
0.037<0.001
Other Hispanic 0.8740.881
0.7010.515
Multiracial1.5171.243
0.1110.397
Marital Status
Married/PartnerRefRef
Widowed/Divorced/Separated0.9540.590
0.7650.051
Never Married/Single0.7620.820
0.2450.313
Age1.045 ***1.034 ***
<0.001<0.001
BMI1.063 ***1.092 ***
<0.001<0.001
Lifestyle Variables
Alcohol Status
NonalcoholicRefRef
Rare Drinker1.0431.201
0.8260.499
Occasional Drinker1.0231.411
0.9040.149
Regular Drinker1.1480.931
0.4930.791
Smoking Status
NonsmokerRefRef
Smoker1.398 *1.042
0.0370.810
Total MET Score1.0001.000
0.5360.180
Medical Conditions
Diabetes
NoRefRef
Yes1.4031.703 *
0.3240.025
Hypertension
NoRefRef
Yes1.3281.591 **
0.1160.008
* p < 0.05, ** p < 0.01, *** p < 0.001. The middle socioeconomic class was considered as the reference group.
Table 4. Adjusted odds ratios (unweighted models).
Table 4. Adjusted odds ratios (unweighted models).
Model 1
SES Class		Model 2
SES + Sociodemographic		Model 3
SES + SD + BMI		Model 4
SES + SD + BMI+ Lifestyle		Model 5
SES + SD + BMI + Lifestyle + Medical Condition
Socioeconomic Class
Middle RefRefRefRefRef
High0.8970.805 *0.8620.8720.895
0.1850.0130.0940.1340.225
Low1.175 *1.1041.1271.1031.058
0.0190.1770.1120.2010.463
Sociodemographic Factors
Sex
Male --RefRefRefRef
Female--1.0370.9690.9420.987
0.5690.6290.3810.845
Race
Mexican-American--RefRefRefRef
Non-Hispanic White 0.8190.8750.8600.872
0.0700.2320.1820.231
Non-Hispanic Black--0.575 ***0.553 ***0.552 ***0.513 ***
<0.001<0.001<0.001<0.001
Other Hispanic --0.9170.9790.9750.976
0.5360.8840.8620.869
Multiracial--1.0681.321 *1.286 *1.237
0.6120.0360.0590.113
Marital Status
Married/Partner--RefRefRefRef
Widowed/Divorced/Separated--0.8740.8920.8890.880
0.1240.1600.1830.153
Never Married/Single--0.838 *0.8580.8570.844
0.0350.0760.0770.052
Age--1.042 ***1.042 ***1.041 ***1.032 ***
<0.001<0.001<0.001<0.001
BMI----1.068 ***1.067 ***1.057 ***
<0.001<0.001<0.001
Lifestyle Variables
Alcohol Status
Nonalcoholic------RefRef
Rare Drinker------1.0681.084
0.4990.412
Occasional Drinker------1.0411.077
0.6870.463
Regular Drinker------0.8630.896
0.1940.339
Smoking Status
Nonsmoker------RefRef
Smoker 1.188 *1.175 *
0.0130.021
Total MET Score------1.000 **1.000 *
0.0070.022
Medical Conditions
Diabetes
No--------Ref
Yes 1.515 ***
<0.001
Hypertension
No--------Ref
Yes--------1.789 ***
-- <0.001
AIC6555.096024.095825.785819.295759.91
* p < 0.05, ** p < 0.01, *** p < 0.001. The middle socioeconomic class was considered as reference group.
Table 5. Adjusted odds ratios (weighted models).
Table 5. Adjusted odds ratios (weighted models).
Model 1
SES Class		Model 2
SES + Sociodemographic		Model 3
SES + SD + BMI		Model 4
SES + SD + BMI+ Lifestyle		Model 5
SES + SD + BMI + Lifestyle + Medical Condition
Socioeconomic Class
Middle RefRefRefRefRef
High0.778 *0.740 *0.8300.8230.870
0.0410.0370.2010.2150.364
Low1.1601.218 *1.281 *1.259 *1.237 *
0.0820.0490.0310.0380.048
Sociodemographic Factors
Sex
Male --RefRefRefRef
Female--0.9910.9770.9510.995
0.9310.8280.6450.966
Race
Mexican-American--RefRefRefRef
Non-Hispanic White 0.816 *0.9050.8890.883
0.0470.2830.2440.233
Non-Hispanic Black--0.558 ***0.535 ***0.538 ***0.501 ***
<0.001<0.001<0.001<0.001
Other Hispanic --0.7820.8850.8810.860
0.2310.5210.5140.431
Multiracial--1.1421.4091.3841.319
0.4400.0760.0950.151
Marital Status
Married/Partner--RefRefRefRef
Widowed/Divorced/Separated--0.7590.7980.7940.788
0.0830.1600.1650.175
Never Married/Single--0.745 *0.7810.7820.754
0.0350.0780.0940.068
Age--1.046 ***1.046 ***1.045 ***1.473 ***
<0.001<0.001<0.001<0.001
BMI----1.085 ***1.083 ***1.075 ***
<0.001<0.001<0.001
Lifestyle Variables
Alcohol Status
Nonalcoholic------RefRef
Rare Drinker------1.1021.116
0.5770.530
Occasional Drinker------1.1611.205
0.3810.285
Regular Drinker------0.9540.973
0.8080.890
Smoking Status
Nonsmoker------RefRef
Smoker 1.1881.178
0.1580.176
Total MET Score------1.0001.000
0.0960.155
Medical Conditions
Diabetes
No--------Ref
Yes 1.537 **
0.044
Hypertension
No--------Ref
Yes--------1.515 ***
-- 0.004
* p < 0.05, ** p < 0.01, *** p < 0.001. The middle socioeconomic class was considered as reference group.
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Ahmed, T.; Nath, A.; Jahan, N.; Khadka, A.; Kishore, J.; Farokhrouz, A.; Bowden, R.G. Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data. Int. J. Transl. Med. 2025, 5, 18. https://doi.org/10.3390/ijtm5020018

AMA Style

Ahmed T, Nath A, Jahan N, Khadka A, Kishore J, Farokhrouz A, Bowden RG. Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data. International Journal of Translational Medicine. 2025; 5(2):18. https://doi.org/10.3390/ijtm5020018

Chicago/Turabian Style

Ahmed, Tanvir, Akhi Nath, Nusrat Jahan, Aakanksha Khadka, Jaimala Kishore, Ashley Farokhrouz, and Rodney G. Bowden. 2025. "Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data" International Journal of Translational Medicine 5, no. 2: 18. https://doi.org/10.3390/ijtm5020018

APA Style

Ahmed, T., Nath, A., Jahan, N., Khadka, A., Kishore, J., Farokhrouz, A., & Bowden, R. G. (2025). Socioeconomic Disparities and Other Factors in Dyslipidemia: Insights from NHANES 2017–2020 Data. International Journal of Translational Medicine, 5(2), 18. https://doi.org/10.3390/ijtm5020018

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