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Article

Overlapping Pattern of the Four Individual Components of Dyslipidemia in Adults: Analysis of Nationally Representative Data

by
Wenxiao Zheng
1,
Jiayue Zhang
2,
Ying Jiang
3,
Shuting Wang
1 and
Zuyao Yang
1,*
1
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
2
Faculty of Medicine, Macau University of Science of Technology, Macau SAR, China
3
Chronic Disease Research Institute, The Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou 310052, China
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(12), 3624; https://doi.org/10.3390/jcm13123624
Submission received: 25 April 2024 / Revised: 28 May 2024 / Accepted: 18 June 2024 / Published: 20 June 2024
(This article belongs to the Section Cardiovascular Medicine)
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Abstract

:
Background/Objectives: Dyslipidemia is a well-established risk factor for cardiovascular disease (CVD). However, among available drug treatments, only those targeted at lowering LDL-C and consequently TC have demonstrated efficacy in preventing CVD. This is to say that the benefit for those with isolated high TG or low HDL-C is limited. The objective of this study is to examine the overlapping pattern of the four dyslipidemia components in US adult populations, which is important for quantifying the proportion of those who are less likely to benefit from lipid-lowering drugs and for a more precise use of the drug. Methods: A total of 7822 participants aged over 20 with abnormalities in any of the four lipid parameters, excluding those on lipid-lowering medications, were included from the National Health and Nutrition Examination Survey (NHANES) cycles spanning 1999–2000 through 2017–2018. The proportions of different combinations of them were calculated and presented using area-proportional Euler plots. Results: High TC, high LDL-C, high TG, and low HDL-C were seen in 32.8% (95% CI: 31.3%–34.2%), 28.1% (95% CI: 26.6%–29.6%), 26.7% (95% CI: 25.4%–28.0%), and 65.9% (95% CI: 64.0%–67.7%) of the people with dyslipidemia, respectively. The proportions of dyslipidemia cases attributable to “high LDL-C or high TC” (irrespective of HDL-C and TG levels), “normal LDL-C, normal TC, but high TG” (irrespective of HDL-C level), and “normal LDL-C, normal TC, normal TG, but low HDL-C” (i.e., isolated low HDL-C) accounted for 37.5% (95% CI: 35.9%–39.1%), 18.3% (95% CI: 17.2%–19.4%), and 44.2% (95% CI: 42.5%–46.0%), respectively. Conclusions: Some two-thirds of those with dyslipidemia had low HDL-C or high TG but normal LDL-C and normal TC. As these people are less likely to benefit from currently available drug treatments in terms of CVD prevention, it is important to identify other effective strategies or interventions targeted at them in order to achieve more precise and cost-effective management of dyslipidemia.

1. Introduction

Cardiovascular diseases (CVDs) stand as the leading cause of mortality and the main contributor to disability in the United States and globally [1,2,3]. For example, in 2020, CVDs were responsible for 928,741 deaths in the United States, representing approximately one-third of the total number of deaths [2]. Dyslipidemia, defined as the presence of “abnormality” in at least one of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG), is a recognized risk factor for CVDs. Specifically, elevated levels of TC, LDL-C, and TG and lowered levels of HDL-C are linked to increased risk for CVDs [4,5,6,7]. The increase in cardiovascular risk associated with a 10 mg/dL increase in TC, LDL-C, and TG ranges from 1% to 5% [8,9], 3% to 14% [10,11,12], and 1% to 7% [13,14,15], respectively. Each 10 mg/dL decrease in HDL-C levels is associated with a 18% to 34% increase in cardiovascular risk [10,12,16].
Among the currently available drug treatments for dyslipidemia, only those targeting LDL-C (which can also lower TC because the main component of TC is LDL-C) have shown consistent efficacy in reducing CVDs and mortality [17,18]. The effectiveness of TG-lowering drugs in terms of CVD prevention is inconclusive, and drug treatments aimed at increasing HDL-C levels do not improve cardiovascular outcomes [19,20,21,22]. Except in the case of very high TG levels that should be addressed promptly to prevent pancreatitis, the 2018 AHA/ACC guidelines make no particular recommendation regarding the treatment for isolated low HDL-C or isolated high TG, but acknowledge these two as important risk-enhancing factors that may favor statin therapy [23]. Although some individuals with isolated low HDL-C or isolated high TG (and normal LDL-C level) may benefit from treatment to further reduce their LDL-C levels, the 2019 ESC/EAS guidelines (page 126) suggest that “Class I recommendation, Level A evidence” for such treatment is only available for those with a 10-year CVD risk > 10% [24], which represents a small fraction of all these individuals [25,26]. In other words, the majority of individuals with isolated low HDL-C or isolated high TG are less likely to benefit from lipid-lowering drug treatment. Therefore, understanding the overlapping pattern of the four individual components of dyslipidemia in adults is important for identifying those who may potentially benefit from lipid-lowering medications, as well as those for whom LDL-C-lowering treatments are less or even not effective. This knowledge can help refine the management strategy and improve the cost-effectiveness of drug treatment for dyslipidemia.
Previous studies have investigated the prevalence of dyslipidemia in the general population of the United States, its variations across different subgroups, and its temporal trends [27,28,29]. However, the overlapping pattern of the four components of dyslipidemia is largely unclear. We thus conducted this study to address this important question by analyzing 20-year data from the National Health and Nutrition Examination Survey (NHANES), a comprehensive cross-sectional survey representing the general adult population of the United States.

2. Materials and Methods

2.1. Study Population

The data used in this study were obtained from NHANES, which is a series of cross-sectional surveys conducted by the National Center for Health Statistics every two years since 1999. It aims to evaluate the health and nutritional status of the civilian noninstitutionalized population in the United States [30]. The NHANES protocols were approved by the National Center for Health Statistics Research Ethics Review Board. All participants provided informed consent for the utilization of their data. The datasets are publicly available for downloading at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx, accessed on 1 July 2023. The datasets whose names begin with “DEMO”, “LAB13”, “L13”, LAB13AM”, “L13AM”, “TCHOL”, “HDL”, “TRIGLY”, “RXQ_RX”, “RXQ_DRUG”, and “BPQ” were used in this study. The participants who (1) were aged 20 and above, (2) completed a morning fasting blood test during the 10 NHANES cycles from 1999–2000 to 2017–2018, (3) had data on all the four lipid parameters, (4) were abnormal on at least one of the four lipid parameters (see below for definition), and (5) were not taking lipid-lowering drugs, were eligible for this study (N = 7822).

2.2. Measurements and Definitions

Demographic information, including sex, age, and race, was collected through self-reporting of participants during the interview. Race was categorized as non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, and Other/multiracial. The “other Hispanic” group consists of participants who self-reported as Hispanic but not Mexican American. The utilization of lipid-lowering drugs was determined based on participants’ responses to the question BPQ100D in the Blood Pressure/Cholesterol section and Prescription Medications section in the survey. Participants who reported having their blood cholesterol checked (question BPQ060) and having been advised to take a prescription for cholesterol (question BPQ090D) were subsequently asked question BPQ100D, “(Are you/Is SP) now following this advice to take prescribed medicine?”. This question served as a reference for their medication-taking status. In the Prescription Medications section, participants were asked to report all prescription medications they had been taking within the past 30 days. Only participants who answered “no” to BPQ100D and did not mention any lipid-lowering medication in the Prescription Medication section were considered as non-medication users and thus eligible for this study.
Serum lipid profile was measured at mobile examination centers. TC and TG were directly measured by enzymatic reactions. HDL-C was directly measured by the heparin–manganese precipitation method. LDL-C was determined through calculation using the measured values of TC, HDL-C, and TG according to the Friedewald formula [31]. The calculation was valid only when TG < 400 mg/dL. When TG was ≥400 mg/dL, the LDL-C value was considered missing, as the Friedewald formula became invalid in such cases. Dyslipidemia was defined as having at least one lipid parameter in the abnormal range, i.e., TC ≥ 240 mg/dL, LDL-C ≥ 160 mg/dL, HDL-C < 40 mg/dL in males or <50 mg/dL in females, or TG ≥ 200 mg/dL, according to the Guideline on the Management of Blood Cholesterol developed by American Heart Association and other organizations in 2018 [23].

2.3. Statistical Analysis

The demographic characteristics of participants were described with counts and weighted percentages (for categorical variables) or weighted means with standard deviations (for continuous variables). The primary results were the proportions of different combinations of the four lipid abnormalities among individuals with dyslipidemia. Statistical analysis was performed utilizing the survey package (version 4.2-1) in R environment (version 4.2.1). The “survey” package is widely used in the analysis of weighted data. NHANES has also recommended using the “survey” package for data analysis and provided sample codes to account for the complex sampling design, oversampling, and non-response. Fasting sample weights were used in the study as LDL-C and TG levels were measured only in individuals who completed fasting. A 20-year weight was constructed with 4-year weights for the 1999–2002 data and 2-year weights for the 2003–2018 data. The differences in prevalence of lipid abnormalities across demographic characteristics (i.e., age, sex, race) and time period were assessed by χ2 test. Trend tests for age group and time period were conducted by a weighted logistic regression. Area-proportional Euler diagrams were generated to show the overlapping pattern using the “Eulerr” package (version 7.0.0).

3. Results

The flow of the inclusion and exclusion of participants is shown in Figure 1. Among the participants aged 20 years or above in the 10 NHANES cycles from 1999–2000 to 2017–2018, 22,796 had complete data on all four lipid parameters, and 52.1% of them (N = 12,411) had dyslipidemia. After excluding those on lipid-lowering drug treatments, 7822 participants (66.1% of all people with dyslipidemia) were eventually included in this study. Table 1 shows the basic characteristics of the study participants. Of the participants, those with high TC, high LDL-C, high TG, and low HDL-C accounted for 32.8% (95% confidence interval [CI]: 31.3%–34.2%), 28.1% (95% CI: 26.6%–29.6%), 26.7% (95% CI: 25.4%–28.0%), and 65.9% (95% CI: 64.0%–67.7%), respectively (Figure 2A). Notably, the most prevalent lipid abnormality across all subgroups was low HDL-C (Figure 2B–D).
Individuals categorized as “high LDL-C or high TC” (irrespective of HDL-C and TC levels), “normal LDL-C, normal TC, but high TG” (irrespective of HDL-C level), and “normal LDL, normal TC, normal TG, but low HDL-C” (i.e., isolated HDL-C) accounted for 37.5% (95% CI: 35.9%–39.1%), 18.3% (95% CI: 17.2%–19.4%), and 44.2% (95% CI: 42.5%–46.0%) of the total, respectively (Table 2). The total proportion of those who had “normal LDC-C, normal TC, but high TG and/or low HDL-C” out of all people with dyslipidemia was similar (61.1%–63.2%) across different periods (Table 2). The proportion of “high LDL-C or high TC” increased significantly with age and accounted for 54.0% (95% CI: 50.9%–57.1%) of all dyslipidemia in people aged ≥60 years (Table 2). Supplementary analysis revealed that those with TG levels exceeding 400 mg/dL, 500 mg/dL, and 1000 mg/dL, which are believed to be associated with a heightened risk of acute pancreatitis, constituted 2.0% (95% CI: 1.8%–2.2%), 1.1% (95% CI: 0.9%–1.3%), and 0.2% (95% CI: 0.1%–0.3%) of the adult population, respectively.

4. Discussion

Statin therapies for lowering LDL-C levels have proven effective in reducing the risks of CVDs and mortality [32,33], and are currently recommended as a first-line treatment for a broad spectrum of individuals for both primary and secondary prevention of CVD [23]. Since LDL-C level is the main determinant of TC level, the two lipid components are typically highly correlated with each other. For example, in this study, among those with high LDL-C, 83% also had high TC. Among the other 17% who had elevated LDL-C levels but normal TC levels, 7.6% had low HDL-C levels. Therefore, individuals with high LDL-C and those with high TC are expected to derive similar cardiovascular benefits from lipid-lowering treatments, irrespective of their HDL-C and TG levels. For this reason, LDL-C and TC are commonly evaluated together in clinical practice. Our findings indicated that 37.5% of all individuals with dyslipidemia belong to this subset and could potentially benefit from taking LDL-lowering drugs in terms of CVD prevention. Evidence from randomized trials showed that such medications can prevent 25 (95% CI, 19–31) major cardiovascular events among every 1000 treated individuals in the primary prevention setting and 48 (95% CI, 39–57) events in the secondary prevention setting [33]. Assuming (1) a total adult population aged ≥ 20 years in the United States of 217,270,203, according to the sample weight adopted by NHANES [34], (2) a dyslipidemia prevalence of 52.1%, as estimated in this study, and (3) all adults with high LDL-C or TC (37.5% of all dyslipidemia) receiving lipid-lowering medications had high adherence rates, the number of major cardiovascular events that could be prevented by drug treatments of dyslipidemia in the United States over a 5-year period would be between 2,462,052 and 3,735,527. While recognizing the benefit of lipid-lowering statin treatment, it should be noted that the treatment can cause adverse effects in skeletal muscle, liver function, and the nervous system [35,36]. Statin-associated muscle symptoms are the most common, affecting up to 25% of the treated patients [37]. Furthermore, regular treatment with statins represents a huge financial burden to the healthcare system, costing $10 billion in total and $3.1 billion out of pocket every year among the patients who reported taking statin therapy in the United States [38]. The potential adverse effects, cost, and perceived benefit of treatment, individually or jointly, may lead to a low adherence rate or even discontinuation of treatment. A previous study showed that only 36.8% of the patients without coronary heart disease or diabetes mellitus strongly adhered to the treatment, constituting a big challenge to the primary prevention of CVDs [39].
Another finding of this study was that low HDL-C contributed most (65.9%) to the prevalence of dyslipidemia, with isolated low HDL-C accounting for 44.2% of all dyslipidemia cases. Observational studies have consistently demonstrated that low HDL-C was associated with a higher risk of CVDs after adjusting for traditional non-lipid risk factors [40,41]. However, randomized trials found that drug treatment aiming to increase HDL-C levels did not reduce CVDs [21,22]. One possible explanation for this discrepancy is that HDL-C may not fully represent HDL functionality. It is widely believed that HDL-C can lower the risk of atherosclerosis by promoting cholesterol efflux, which reduces cholesterol accumulation in the macrophages and thereby can lower the risk of atherosclerosis by promoting cholesterol efflux, facilitating lipid oxidation, enhancing endothelial function, anti-inflammation, and anti-apoptosis [42]. While HDL-C is indeed correlated with HDL function and can be used to estimate the cholesterol levels within HDL particles, recent studies suggest that the number of HDL particles (HDL-P) per se is a better indicator of HDL functionality. HDL-P demonstrates strong associations with HDL-C function, including cholesterol transport and antioxidation [43,44]. Moreover, the association of HDL-C with CVD becomes statistically insignificant after adjusting for HDL-P, whereas the association of HDL-P with CVD persists even after adjusting for HDL-C [44]. As the HDL-enhancing drugs evaluated in previous randomized trials primarily aimed to boost HDL-C levels rather than HDL-P levels, it was not surprising that they were not associated with the reduction in CVD. Given this evidence, the current AHA/ACC guidelines do not recommend initiating statin therapies based on isolated low HDL-C levels, but acknowledge that low HDL-C level is a risk-enhancing factor that should be considered during the clinician–patient discussion on whether to initiate statin therapy [23]. The US Preventive Services Task Force recommends initiating statin therapies in adults aged 40 to 75 years without a history of CVD and with one or more cardiovascular risk factors, if their 10-year CVD risk exceeds 7.5% [25].
The effects of TG on cardiovascular risk are also ambiguous. Despite numerous epidemiological studies identifying high TG as a risk factor for CVD [45,46,47,48], randomized trials evaluating TG-lowering drug treatments for CVD prevention yielded conflicting results, with only one trial, i.e., the REDUCE-IT study, reporting a statistically significant reduction in CVD [20]. Although individuals with very high TG may benefit from TG-lowering treatment to prevent acute pancreatitis [49,50,51], this study revealed that only a small proportion of individuals with dyslipidemia had a higher TG level (≥400 mg/dL, 3.3%; ≥500 mg/dL, 1.8%; 1000 mg/dL, 0.4%), which was associated with an increased risk of acute pancreatitis. This suggests that the majority of individuals with high TG levels are less likely to derive a significant benefit from such treatments. The 2018 ACC/AHA guidelines recommend initiation of statin therapy only when elevated TG (≥170 mg/dL) persists after tackling lifestyle factors, secondary factors, and potential TG-raising medications [23]. Overall, individuals with isolated low HDL-C or isolated high TG (and normal LDL-C level) are less likely to benefit from lipid-lowering drug treatment to further reduce their LDL-C levels. The 2019 ESC/EAS guidelines (page 126) suggest that “Class I recommendation, Level A evidence” for such treatment is only available for those with a 10-year CVD risk > 10%, which represents a small fraction of all individuals [25,26]. This highlights the need for further research and the development of new drugs that are targeted at isolated high TG and/or low HDL-C and can effectively reduce CVD risk.
In addition to the four lipid measures examined in this study, non-high-density lipoprotein (non-HDL-C), which is calculated by subtracting HDL-C levels from TC levels, is another risk factor for CVD and gaining increasing attention. Previous studies showed that it was associated with increased CVD risk even after adjusting for LDL-C level in the model [52,53]. In the present study, the subjects had abnormalities in at least one of the four routinely assessed lipid components, and therefore none of them had isolated elevated non-HDL-C. Our exploratory analysis showed that, among NHANES participants with measurements of all the four lipid components (normal or abnormal) and not on lipid-lowering medications, 41.5% demonstrated isolated elevated non-HDL-C levels, 18.5% isolated low HDL-C levels, and 10.6% both. Again, this calls for more research on individuals with isolated low HDL-C as their CVD risk may be increased due to their non-HDL-C levels rather than the low HDL-C alone and there are currently no particular recommendations regarding treatment for this subgroup.
The strengths of this study include its utilization of NHANES data—which makes the results generalizable to the general adult population of the United States—and the comprehensive analyses on the overlapping patterns of dyslipidemia components across different subgroups and periods. However, it is important to interpret the findings of this study with some caution. Firstly, only the participants who were not on lipid-lowering medications were included, who may not represent the overall population with dyslipidemia. A comparison of the NHANES participants with dyslipidemia on medications versus those not on medications (Table A1) showed that the former were more likely to be male, older, and non-Hispanic. However, this issue is unavoidable because the lipid levels of participants on medications had been altered and would introduce bias if they were included in the analysis. Secondly, LDL-C in this study was not an independently measured parameter but estimated by using the Friedewald formula. The “TG/5” in the formula reflects the cholesterol in TG-rich ApoB lipoproteins, very-low-density lipoprotein, and intermediate-density lipoproteins. The error in estimation may determine whether the LDL-C values end up just below or just above the limit of the normal range and thus cause the misclassification of some individuals. Furthermore, the participants with a TG level > 400 mg/dL were excluded from the estimation. However, as shown by our supplementary analysis, these participants represented a very small fraction of all people with dyslipidemia and were unlikely to have a large impact on the main results of this study, e.g., the overlapping pattern of individual lipid abnormalities. Thirdly, the use of lipid-lowering medication was self-reported, which in theory was subject to recall bias. However, a previous report from the Atherosclerosis Risk in Communities Study demonstrated a high level of accuracy of self-reported lipid-lowering medication use, with 89% to 99% of the people who self-reported not taking medications truly not on medications, as verified by the information on inventoried lipid medication containers [54]. Thus, the bias arising from self-report, if any, was likely small.

5. Conclusions

Among the individuals with dyslipidemia, only 37.5% would gain proven benefits from currently available medications in terms of LDL-C or TC reduction. Individuals with isolated low HDL-C or isolated high TG, who are less likely to benefit from existing drug treatments in terms of CVD prevention, account for around 60% of the dyslipidemia population. Although some of them could benefit from drug treatment to further reduce their LDL-C levels, they represent a small fraction of individuals. These findings have implications for the precise management of dyslipidemia, resource allocation for primary prevention of CVD, and drug discovery and development in the future.

Author Contributions

Conceptualization, Z.Y.; Acquisition and analysis of data, W.Z., Z.Y.; writing—original draft preparation, W.Z.; writing—review and editing, Z.Y., J.Z., Y.J. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by The Chinese University of Hong Kong (project codes: 4730352, 7106452, 7105959).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Survey and Behavioral Research Ethics Committee of the Chinese University of Hong Kong (Reference No. SBRE-23-0527, approved on 22 January 2024).

Informed Consent Statement

All participants provided written informed consent, as obtained by the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/irba98.htm, accessed on 17 April 2024).

Data Availability Statement

The datasets are publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/, accessed on 17 April 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Basic characteristics of NHANES participants with dyslipidemia by medication use status.
Table A1. Basic characteristics of NHANES participants with dyslipidemia by medication use status.
CharacteristicsNo. (%)p-Value
On Medication
(N = 4589)		Not on Medication
(N = 7822)
Gender, n (%) <0.001
Male2400 (51.9)3405 (45.0)
Female2189 (48.1)4417 (55.0)
Age, n (%) <0.001
20–39 years96 (2.9)3062 (42.2)
40–59 years1165 (35.5)2762 (40.3)
≥60 years3328 (61.6)1998 (17.4)
Race, n (%) <0.001
Non-Hispanic White2368 (76.3)3409 (66.9)
Non-Hispanic Black877 (9.2)1307 (9.9)
Mexican American571 (4.3)1707 (10.0)
Other Hispanic375 (3.4)744 (6.8)
Other/multiracial398 (6.8)655 (6.5)

References

  1. Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update from the GBD 2019 Study. J. Am. Coll. Cardiol. 2020, 76, 2982–3021. [Google Scholar] [CrossRef]
  2. Tsao, C.W.; Aday, A.W.; Almarzooq, Z.I.; Anderson, C.A.M.; Arora, P.; Avery, C.L.; Baker-Smith, C.M.; Beaton, A.Z.; Boehme, A.K.; Buxton, A.E.; et al. Heart Disease and Stroke Statistics-2023 Update: A Report from the American Heart Association. Circulation 2023, 147, e93–e621. [Google Scholar] [CrossRef] [PubMed]
  3. Masaebi, F.; Salehi, M.; Kazemi, M.; Vahabi, N.; Azizmohammad Looha, M.; Zayeri, F. Trend analysis of disability adjusted life years due to cardiovascular diseases: Results from the global burden of disease study 2019. BMC Public Health 2021, 21, 1268. [Google Scholar] [CrossRef] [PubMed]
  4. Andersson, C.; Lyass, A.; Vasan, R.S.; Massaro, J.M.; D’Agostino, R.B., Sr.; Robins, S.J. Long-term risk of cardiovascular events across a spectrum of adverse major plasma lipid combinations in the Framingham Heart Study. Am. Heart J. 2014, 168, 878–883.e871. [Google Scholar] [CrossRef]
  5. Holmes, M.V.; Asselbergs, F.W.; Palmer, T.M.; Drenos, F.; Lanktree, M.B.; Nelson, C.P.; Dale, C.E.; Padmanabhan, S.; Finan, C.; Swerdlow, D.I.; et al. Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J. 2015, 36, 539–550. [Google Scholar] [CrossRef]
  6. Wilson, P.W.; D’Agostino, R.B.; Levy, D.; Belanger, A.M.; Silbershatz, H.; Kannel, W.B. Prediction of coronary heart disease using risk factor categories. Circulation 1998, 97, 1837–1847. [Google Scholar] [CrossRef]
  7. Jeong, S.M.; Choi, S.; Kim, K.; Kim, S.M.; Lee, G.; Park, S.Y.; Kim, Y.Y.; Son, J.S.; Yun, J.M.; Park, S.M. Effect of Change in Total Cholesterol Levels on Cardiovascular Disease Among Young Adults. J. Am. Heart Assoc. 2018, 7, e008819. [Google Scholar] [CrossRef]
  8. Peters, S.A.; Singhateh, Y.; Mackay, D.; Huxley, R.R.; Woodward, M. Total cholesterol as a risk factor for coronary heart disease and stroke in women compared with men: A systematic review and meta-analysis. Atherosclerosis 2016, 248, 123–131. [Google Scholar] [CrossRef] [PubMed]
  9. Kwon, D.; Yi, J.J.; Ohrr, H.; Yi, S.W. Total cholesterol and mortality from ischemic heart disease and overall cardiovascular disease in Korean adults. Medicine 2019, 98, e17013. [Google Scholar] [CrossRef]
  10. Satoh, H.; Tomita, K.; Fujii, S.; Kishi, R.; Tsutsui, H. Lower high-density lipoprotein cholesterol is a significant and independent risk for coronary artery disease in Japanese men. J. Atheroscler. Thromb. 2009, 16, 792–798. [Google Scholar] [CrossRef]
  11. 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] [PubMed]
  12. Sharrett, A.R.; Ballantyne, C.M.; Coady, S.A.; Heiss, G.; Sorlie, P.D.; Catellier, D.; Patsch, W. Coronary heart disease prediction from lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B, and HDL density subfractions: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation 2001, 104, 1108–1113. [Google Scholar] [CrossRef] [PubMed]
  13. Austin, M.A. Plasma triglyceride as a risk factor for cardiovascular disease. Can. J. Cardiol. 1998, 14 (Suppl. SB), 14B–17B. [Google Scholar] [PubMed]
  14. Iso, H.; Naito, Y.; Sato, S.; Kitamura, A.; Okamura, T.; Sankai, T.; Shimamoto, T.; Iida, M.; Komachi, Y. Serum triglycerides and risk of coronary heart disease among Japanese men and women. Am. J. Epidemiol. 2001, 153, 490–499. [Google Scholar] [CrossRef] [PubMed]
  15. Hokanson, J.E.; Austin, M.A. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: A meta-analysis of population-based prospective studies. J. Cardiovasc. Risk 1996, 3, 213–219. [Google Scholar] [CrossRef] [PubMed]
  16. Voight, B.F.; Peloso, G.M.; Orho-Melander, M.; Frikke-Schmidt, R.; Barbalic, M.; Jensen, M.K.; Hindy, G.; Hólm, H.; Ding, E.L.; Johnson, T.; et al. Plasma HDL cholesterol and risk of myocardial infarction: A mendelian randomisation study. Lancet 2012, 380, 572–580. [Google Scholar] [CrossRef]
  17. Navarese, E.P.; Robinson, J.G.; Kowalewski, M.; Kołodziejczak, M.; Andreotti, F.; Bliden, K.; Tantry, U.; Kubica, J.; Raggi, P.; Gurbel, P.A. Association Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering: A Systematic Review and Meta-analysis. JAMA 2018, 319, 1566–1579. [Google Scholar] [CrossRef] [PubMed]
  18. Koskinas, K.C.; Siontis, G.C.M.; Piccolo, R.; Mavridis, D.; Räber, L.; Mach, F.; Windecker, S. Effect of statins and non-statin LDL-lowering medications on cardiovascular outcomes in secondary prevention: A meta-analysis of randomized trials. Eur. Heart J. 2017, 39, 1172–1180. [Google Scholar] [CrossRef]
  19. Das Pradhan, A.; Glynn, R.J.; Fruchart, J.C.; MacFadyen, J.G.; Zaharris, E.S.; Everett, B.M.; Campbell, S.E.; Oshima, R.; Amarenco, P.; Blom, D.J.; et al. Triglyceride Lowering with Pemafibrate to Reduce Cardiovascular Risk. N. Engl. J. Med. 2022, 387, 1923–1934. [Google Scholar] [CrossRef]
  20. Marston, N.A.; Giugliano, R.P.; Im, K.; Silverman, M.G.; O’Donoghue, M.L.; Wiviott, S.D.; Ference, B.A.; Sabatine, M.S. Association Between Triglyceride Lowering and Reduction of Cardiovascular Risk Across Multiple Lipid-Lowering Therapeutic Classes: A Systematic Review and Meta-Regression Analysis of Randomized Controlled Trials. Circulation 2019, 140, 1308–1317. [Google Scholar] [CrossRef]
  21. Lincoff, A.M.; Nicholls, S.J.; Riesmeyer, J.S.; Barter, P.J.; Brewer, H.B.; Fox, K.A.A.; Gibson, C.M.; Granger, C.; Menon, V.; Montalescot, G.; et al. Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease. N. Engl. J. Med. 2017, 376, 1933–1942. [Google Scholar] [CrossRef] [PubMed]
  22. D’Andrea, E.; Hey, S.P.; Ramirez, C.L.; Kesselheim, A.S. Assessment of the Role of Niacin in Managing Cardiovascular Disease Outcomes: A Systematic Review and Meta-analysis. JAMA Netw. Open 2019, 2, e192224. [Google Scholar] [CrossRef] [PubMed]
  23. 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. [Google Scholar] [CrossRef] [PubMed]
  24. Mach, F.; Baigent, C.; Catapano, A.L.; Koskinas, K.C.; Casula, M.; Badimon, L.; Chapman, M.J.; De Backer, G.G.; Delgado, V.; Ference, B.A.; et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk: The Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). Eur. Heart J. 2019, 41, 111–188. [Google Scholar] [CrossRef] [PubMed]
  25. US Preventive Services Task Force; Mangione, C.M.; Barry, M.J.; Nicholson, W.K.; Cabana, M.; Chelmow, D.; Coker, T.R.; Davis, E.M.; Donahue, K.E.; Jaén, C.R.; et al. Statin Use for the Primary Prevention of Cardiovascular Disease in Adults: US Preventive Services Task Force Recommendation Statement. JAMA 2022, 328, 746–753. [Google Scholar] [CrossRef]
  26. Liu, X.; Yang, Y.; Kang, F.; Li, J.; Zhou, M.; Ma, X.; Yu, T.; Zhang, T.; Xue, F. Cardiovascular Disease Risk Across a Spectrum of Adverse Plasma Lipid Combinations by Gender and Glycemic Status. Am. J. Cardiol. 2019, 124, 702–708. [Google Scholar] [CrossRef] [PubMed]
  27. Kit, B.K.; Kuklina, E.; Carroll, M.D.; Ostchega, Y.; Freedman, D.S.; Ogden, C.L. Prevalence of and trends in dyslipidemia and blood pressure among US children and adolescents, 1999–2012. JAMA Pediatr. 2015, 169, 272–279. [Google Scholar] [CrossRef] [PubMed]
  28. 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] [PubMed]
  29. Pu, J.; Romanelli, R.; Zhao, B.; Azar, K.M.; Hastings, K.G.; Nimbal, V.; Fortmann, S.P.; Palaniappan, L.P. Dyslipidemia in Special Ethnic Populations. Endocrinol. Metab. Clin. N. Am. 2016, 45, 205–216. [Google Scholar] [CrossRef] [PubMed]
  30. Centers for Disease Control and Prevention. About the National Health and Nutrition Examination Survey. Available online: https://www.cdc.gov/nchs/nhanes/about_nhanes.htm (accessed on 20 September 2023).
  31. Friedewald, W.T.; Levy, R.I.; Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 1972, 18, 499–502. [Google Scholar] [CrossRef]
  32. Baigent, C.; Blackwell, L.; Emberson, J.; Holland, L.E.; Reith, C.; Bhala, N.; Peto, R.; Barnes, E.H.; Keech, A.; Simes, J.; et al. Efficacy and safety of more intensive lowering of LDL cholesterol: A meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010, 376, 1670–1681. [Google Scholar] [CrossRef] [PubMed]
  33. Baigent, C.; Keech, A.; Kearney, P.M.; Blackwell, L.; Buck, G.; Pollicino, C.; Kirby, A.; Sourjina, T.; Peto, R.; Collins, R.; et al. Efficacy and safety of cholesterol-lowering treatment: Prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 2005, 366, 1267–1278. [Google Scholar] [CrossRef] [PubMed]
  34. Centers for Disease Control and Prevention. Weighting. Available online: https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx (accessed on 12 October 2023).
  35. Attardo, S.; Musumeci, O.; Velardo, D.; Toscano, A. Statins Neuromuscular Adverse Effects. Int. J. Mol. Sci. 2022, 23, 8364. [Google Scholar] [CrossRef] [PubMed]
  36. Ward, N.C.; Watts, G.F.; Eckel, R.H. Statin Toxicity. Circ. Res. 2019, 124, 328–350. [Google Scholar] [CrossRef] [PubMed]
  37. Cohen, J.D.; Brinton, E.A.; Ito, M.K.; Jacobson, T.A. Understanding Statin Use in America and Gaps in Patient Education (USAGE): An internet-based survey of 10,138 current and former statin users. J. Clin. Lipidol. 2012, 6, 208–215. [Google Scholar] [CrossRef]
  38. Matyori, A.; Brown, C.P.; Ali, A.; Sherbeny, F. Statins utilization trends and expenditures in the U.S. before and after the implementation of the 2013 ACC/AHA guidelines. Saudi Pharm. J. 2023, 31, 795–800. [Google Scholar] [CrossRef] [PubMed]
  39. Colantonio, L.D.; Rosenson, R.S.; Deng, L.; Monda, K.L.; Dai, Y.; Farkouh, M.E.; Safford, M.M.; Philip, K.; Mues, K.E.; Muntner, P. Adherence to Statin Therapy Among US Adults Between 2007 and 2014. J. Am. Heart Assoc. 2019, 8, e010376. [Google Scholar] [CrossRef] [PubMed]
  40. Di Angelantonio, E.; Sarwar, N.; Perry, P.; Kaptoge, S.; Ray, K.K.; Thompson, A.; Wood, A.M.; Lewington, S.; Sattar, N.; Packard, C.J.; et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA 2009, 302, 1993–2000. [Google Scholar] [CrossRef] [PubMed]
  41. Huxley, R.R.; Barzi, F.; Lam, T.H.; Czernichow, S.; Fang, X.; Welborn, T.; Shaw, J.; Ueshima, H.; Zimmet, P.; Jee, S.H.; et al. Isolated low levels of high-density lipoprotein cholesterol are associated with an increased risk of coronary heart disease: An individual participant data meta-analysis of 23 studies in the Asia-Pacific region. Circulation 2011, 124, 2056–2064. [Google Scholar] [CrossRef]
  42. Rosenson, R.S.; Brewer, H.B., Jr.; Ansell, B.J.; Barter, P.; Chapman, M.J.; Heinecke, J.W.; Kontush, A.; Tall, A.R.; Webb, N.R. Dysfunctional HDL and atherosclerotic cardiovascular disease. Nat. Rev. Cardiol. 2016, 13, 48–60. [Google Scholar] [CrossRef]
  43. Tan, H.C.; Tai, E.S.; Sviridov, D.; Nestel, P.J.; Ng, C.; Chan, E.; Teo, Y.; Wai, D.C. Relationships between cholesterol efflux and high-density lipoprotein particles in patients with type 2 diabetes mellitus. J. Clin. Lipidol. 2011, 5, 467–473. [Google Scholar] [CrossRef] [PubMed]
  44. El Harchaoui, K.; Arsenault, B.J.; Franssen, R.; Després, J.P.; Hovingh, G.K.; Stroes, E.S.; Otvos, J.D.; Wareham, N.J.; Kastelein, J.J.; Khaw, K.T.; et al. High-density lipoprotein particle size and concentration and coronary risk. Ann. Intern. Med. 2009, 150, 84–93. [Google Scholar] [CrossRef] [PubMed]
  45. Ye, X.; Kong, W.; Zafar, M.I.; Chen, L.-L. Serum triglycerides as a risk factor for cardiovascular diseases in type 2 diabetes mellitus: A systematic review and meta-analysis of prospective studies. Cardiovasc. Diabetol. 2019, 18, 48. [Google Scholar] [CrossRef] [PubMed]
  46. Toth, P.P.; Philip, S.; Hull, M.; Granowitz, C. Association of Elevated Triglycerides with Increased Cardiovascular Risk and Direct Costs in Statin-Treated Patients. Mayo Clin. Proc. 2019, 94, 1670–1680. [Google Scholar] [CrossRef] [PubMed]
  47. Nordestgaard, B.G.; Varbo, A. Triglycerides and cardiovascular disease. Lancet 2014, 384, 626–635. [Google Scholar] [CrossRef] [PubMed]
  48. Miller, M.; Stone, N.J.; Ballantyne, C.; Bittner, V.; Criqui, M.H.; Ginsberg, H.N.; Goldberg, A.C.; Howard, W.J.; Jacobson, M.S.; Kris-Etherton, P.M.; et al. Triglycerides and cardiovascular disease: A scientific statement from the American Heart Association. Circulation 2011, 123, 2292–2333. [Google Scholar] [CrossRef] [PubMed]
  49. Lindkvist, B.; Appelros, S.; Regnér, S.; Manjer, J. A prospective cohort study on risk of acute pancreatitis related to serum triglycerides, cholesterol and fasting glucose. Pancreatology 2012, 12, 317–324. [Google Scholar] [CrossRef] [PubMed]
  50. Murphy, M.J.; Sheng, X.; MacDonald, T.M.; Wei, L. Hypertriglyceridemia and acute pancreatitis. JAMA Intern. Med. 2013, 173, 162–164. [Google Scholar] [CrossRef]
  51. Preiss, D.; Tikkanen, M.J.; Welsh, P.; Ford, I.; Lovato, L.C.; Elam, M.B.; LaRosa, J.C.; DeMicco, D.A.; Colhoun, H.M.; Goldenberg, I.; et al. Lipid-Modifying Therapies and Risk of Pancreatitis: A Meta-analysis. JAMA 2012, 308, 804–811. [Google Scholar] [CrossRef]
  52. Hansen, M.K.; Mortensen, M.B.; Warnakula Olesen, K.K.; Thrane, P.G.; Maeng, M. Non-HDL cholesterol and residual risk of cardiovascular events in patients with ischemic heart disease and well-controlled LDL cholesterol: A cohort study. Lancet Reg. Health Eur. 2024, 36, 100774. [Google Scholar] [CrossRef]
  53. Wen, J.; Huang, Y.; Lu, Y.; Yuan, H. Associations of non-high-density lipoprotein cholesterol, triglycerides and the total cholesterol/HDL-c ratio with arterial stiffness independent of low-density lipoprotein cholesterol in a Chinese population. Hypertens. Res. 2019, 42, 1223–1230. [Google Scholar] [CrossRef] [PubMed]
  54. Bhaskara, S.; Whitsel, E.A.; Ballantyne, C.M.; Folsom, A.R. Validity of self-report of lipid medication use: The Atherosclerosis Risk in Communities (ARIC) Study. Atherosclerosis 2015, 242, 625–629. [Google Scholar] [CrossRef] [PubMed]
Jcm 13 03624 g001
Figure 1. Flow diagram of inclusion and exclusion of participants.
Figure 1. Flow diagram of inclusion and exclusion of participants.
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Figure 2. Weighted percentage of people with different combinations of high LDL-C, high TC, high TG, and low HDL-C among participants with dyslipidemia but not on medications in the overall population (A); stratified by sex (B); stratified by age group (C); and stratified by race (D). The percentages may not add up to 100% because of rounding.
Figure 2. Weighted percentage of people with different combinations of high LDL-C, high TC, high TG, and low HDL-C among participants with dyslipidemia but not on medications in the overall population (A); stratified by sex (B); stratified by age group (C); and stratified by race (D). The percentages may not add up to 100% because of rounding.
Jcm 13 03624 g002
Table 1. Basic characteristics of the participants included in the study (n = 7822).
Table 1. Basic characteristics of the participants included in the study (n = 7822).
CharacteristicsNo. (Weighted %) or Mean (Standard Deviation)
Sex, n (%)
Male3405 (45.0)
Female4417 (55.0)
Age, n (%) *
20–39 years3062 (42.2)
40–59 years2762 (40.3)
≥60 years1998 (17.4)
Race, n (%) *
Non-Hispanic White3409 (66.9)
Non-Hispanic Black1307 (9.9)
Mexican American1707 (10.0)
Other Hispanic744 (6.8)
Other/multiracial655 (6.5)
Lipid, mean (standard deviation), mg/dL
TC212.01 (47.9)
LDL-C133.28 (39.8)
HDL-C46.95 (15.7)
TG158.91 (75.5)
Abbreviations: TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides. * The percentages do not add up to 100% because of rounding.
Table 2. Weighted percentage and 95% CI of different combinations of high LDL-C, high TC, high TG, and low HDL-C among the participants with dyslipidemia but not on medications.
Table 2. Weighted percentage and 95% CI of different combinations of high LDL-C, high TC, high TG, and low HDL-C among the participants with dyslipidemia but not on medications.
High LDL-C or High TCNormal LDL-C, Normal TC, but High TGNormal LDL, Normal TC, Normal TG, but Low HDL-CNormal LDC-C, Normal TC, but High TG and/or Low HDL-C
Overall37.5 (35.9–39.1)18.3 (17.2–19.4)44.2 (42.5–46.0)62.5 (60.9–64.1)
Sex
Male37.4 (35.1–39.7)25.7 (23.9–27.6)36.9 (34.5–39.4)62.6 (60.3–64.9)
Female37.6 (35.5–39.7)12.2 (11.1–13.5)50.2 (48.2–52.2)62.4 (60.3–64.5)
p-Value0.873<0.001<0.0010.873
Age
20–3924.3 (22.3–26.4)18.4 (16.7–20.3)57.2 (54.8–59.7)75.7 (73.6–77.7)
40–5944.1 (41.7–46.6)18.5 (16.7–20.5)37.3 (35.0–39.8)55.9 (53.4–58.3)
≥6054.0 (50.9–57.1)17.4 (15.4–19.7)28.5 (25.9–31.3)46.0 (42.9–49.1)
p for trend <0.0010.494<0.001<0.001
Race
Non-Hispanic White39.6 (37.5–41.8)18.7 (17.3–20.2)41.7 (39.4–44.0)60.4 (58.2–62.5)
Non-Hispanic Black37.2 (34.4–40.0)10.7 (8.9–12.8)52.1 (49.2–55.0)62.8 (60.0–65.6)
Mexican American28.8 (25.8–32.0)22.7 (20.1–25.4)48.5 (45.3–51.8)71.2 (68.0–74.2)
Other Hispanic30.5 (26.6–34.7)17.4 (14.6–20.6)52.1 (47.8–56.4)69.5 (65.3–73.4)
Other/multiracial36.6 (31.3–42.4)19.9 (15.7–25.0)43.4 (37.4–49.6)63.4 (57.6–68.7)
p-Value<0.001<0.001<0.001<0.001
Time period
1999–2004 36.8 (34.0–39.6)20.5 (18.5–22.6)42.7 (39.6–45.9)63.2 (60.4–66.0)
2005–2010 38.9 (36.1–41.7)18.9 (17.2–20.8)42.2 (39.1–45.4)61.1 (58.3–63.9)
2011–2018 37.1 (34.2–40.1)15.9 (14.1–17.8)47.1 (44.1–50.0)62.9 (59.9–65.8)
p for trend 0.8870.0010.0470.887
Abbreviations: TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides.
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Zheng, W.; Zhang, J.; Jiang, Y.; Wang, S.; Yang, Z. Overlapping Pattern of the Four Individual Components of Dyslipidemia in Adults: Analysis of Nationally Representative Data. J. Clin. Med. 2024, 13, 3624. https://doi.org/10.3390/jcm13123624

AMA Style

Zheng W, Zhang J, Jiang Y, Wang S, Yang Z. Overlapping Pattern of the Four Individual Components of Dyslipidemia in Adults: Analysis of Nationally Representative Data. Journal of Clinical Medicine. 2024; 13(12):3624. https://doi.org/10.3390/jcm13123624

Chicago/Turabian Style

Zheng, Wenxiao, Jiayue Zhang, Ying Jiang, Shuting Wang, and Zuyao Yang. 2024. "Overlapping Pattern of the Four Individual Components of Dyslipidemia in Adults: Analysis of Nationally Representative Data" Journal of Clinical Medicine 13, no. 12: 3624. https://doi.org/10.3390/jcm13123624

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