Predictive Effect of Different Blood Lipid Parameters Combined with Carotid Intima-Media Thickness on Coronary Artery Disease

doi:10.21203/rs.3.rs-1454429/v1

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Research Article

Predictive Effect of Different Blood Lipid Parameters Combined with Carotid Intima-Media Thickness on Coronary Artery Disease

https://doi.org/10.21203/rs.3.rs-1454429/v1

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Background: Blood lipids disorder and atherosclerosis are closely related to coronary artery disease (CAD). This study aims to compare different blood lipid parameters combined with carotid intima-media thickness (cIMT) in predicting CAD.

Methods: This was a retrospective study including patients who underwent coronary angiography for highly suspected CAD. Blood samples were taken for lipid profile analysis and cIMT was evaluated by carotid ultrasound. Logistic analysis was used to establish different models of different lipid parameters in predicting CAD. The area under the receiver operating characteristic curve (AUC) was used to examine the predictive value. The optimal lipid parameter was also used to explore the relationship with multi-vessel CAD.

Results: Patients were classified into two groups based on whether CAD existed. There was no difference in low-density lipoprotein cholesterol (LDL-C) between the two groups. Compared with non-CAD patients, the CAD group had higher lipoprotein (a) (Lp (a)), apolipoprotein B/apolipoprotein A, total cholesterol/high-density lipoprotein cholesterol (HDL-C), triglyceride/HDL-C and LDL-C/HDL-C. According to the AUCs, Lp (a) combined with cIMT (AUC: 0.713, P < 0.001) had the best performance in predicting CAD compared to other lipid parameters. High levels of Lp (a) was also associated with multi-vessel CAD (odds ratio: 1.41, 95% confidence interval: 1.02–1.95, P = 0.036).

Conclusion: For patients with highly suspected CAD, Lp (a) better improved the predictive value of CAD rather than most of blood lipid indices, especially in the absence of high levels of LDL-C. Lp (a) also can be used to predict the multi-vessel CAD.

lipoprotein (a)

blood lipid indice

carotid intima-media thickness

coronary artery disease.

Although the treatment of coronary artery disease (CAD) has greatly improved in recent years, the incidence and mortality of CAD remains high [1]. Atherosclerosis, as the main pathophysiological mechanism of CAD, closely related to lipid disorders that is poor-controlled [2]. Early screening of CAD based on atherosclerosis is of great importance, and there is an urgent need to predict CAD using clinically available information.

The role of lipid parameters in CAD is controversial. Previous studies have shown that blood lipid indices, such as triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio, apolipoprotein B/apolipoprotein A-I (Apo B/Apo A-I) ratio, total cholesterol (TC)/HDL-C ratio or low-density lipoprotein cholesterol (LDL-C)/HDL-C have a better predictive value of CAD than a single parameter [3–8]. Recent findings showed that blood lipid indices were significantly correlated with the risk of intracranial atherosclerotic stenosis and metabolic syndrome [9–10]. The combination of lipid parameters represent relative status of blood lipids, which can comprehensively reflect the balance between atherosclerosis and anti-atherosclerosis and accurately assess lipid deposition [11–12]. Lipoprotein (a) (Lp [a]), which has LDL-like moiety comprising Apo B and apolipoprotein (a) (Apo [a]), is independently associated with the diagnosis and prognosis of CAD [13]. As a marker of the residual risk of cardiovascular disease, Lp (a) has a stronger correlation with CAD than LDL-C when LDL-C is in low levels [14–15]. However, it is still unknown whether Lp (a) is superior than the new lipid indices in predicting CAD.

Carotid intima-media thickness (cIMT), a widely-used noninvasive marker of subclinical atherosclerosis, can also be used as a preliminary screening tool for CAD [16–18]. However, a meta-analysis suggested that the sensitivity and specificity of cIMT for the diagnosis of CAD were quite low, indicating that using cIMT alone as CAD screening tool was still insufficient [19]. Since the total load of atherosclerosis is closely related to the concentration of blood lipids, morphological assessment of atherosclerosis based on cIMT has certain limitations in predicting CAD [20–23]. Considering subclinical atherosclerosis status and blood lipid profiles together would effectively improve the accuracy of the diagnosis of CAD.

In clinical screening for CAD, blood lipid parameters and cIMT are considered to be available tools. However, it is still unknown which blood lipid parameters combined with cIMT has the optimal accuracy in predicting CAD. Therefore, this study was aimed to compare the predictive value of different blood lipid parameters combined with cIMT on CAD.

Study design and participants

This retrospective study included 1598 consecutive patients who underwent coronary angiography for highly suspected CAD, including chest pain with typical change in ECG or severe lesion in coronary CT angiography, from September 2014 to July 2015 in Guangdong Provincial People's Hospital. A total of 105 patients with a history of stroke, 24 patients without cIMT measurement, and 74 patients with missing information on blood lipid parameters were excluded.

This study was approved by the Ethics Committee of Guangdong Provincial People's Hospital, and informed verbal consent was obtained from all patients. This study was conducted in accordance with the Declaration of Helsinki.

Data Collection And Measurements

Demographic data, laboratory test results, and carotid ultrasonography and coronary angiography results from the electronic medical records were collected.

The measurement of cIMT by carotid ultrasonography was detailed in our previous study [24]. All patients underwent coronary angiography, and the degree of coronary stenosis was judged by two experienced cardiologists. Hypertension was diagnosed according to the European Society of Cardiology guidelines [25]. Diabetes mellitus (DM) was defined based on the presence of diabetes or was diagnosed during hospitalization following the criteria of the European Society of Cardiology guidelines [26]. Chronic kidney disease (CKD) was defined as previous medical history. Smoking was defined as previous or current smoking. Alcohol consumption was defined as previous drinking habit. Lp (a) was measured by AU5800 spectrophotometer (Beckman Coulter, USA) via immunoturbidimetry, with trihydroxyaminomethane buffer and anti Lp (a) antibody sensitized granules. HDL-C, LDL-C, TC, TG, Apo A-I, and Apo B were also detected using AU5800 spectrophotometer (Beckman Coulter, USA) via colorimetry or immunoturbidimetry.

Definitions

As suggested by the American College of Cardiology in 2016, a ≥ 70% luminal diameter narrowing of an epicardial stenosis or ≥ 50% luminal diameter narrowing of the left main artery observed by visual assessment was considered as severe lesion that used as the diagnostic criteria for CAD [27]. Two or more coronary arteries with severe stenosis were defined as multi-vessel CAD.

Statistical analysis

The total procedure of statistical analysis was divided into four steps. First, Student’s t-test was used for normally distributed data, the Mann–Whitney U test was used for non-normally distributed data, and the chi-square test or Fisher’s exact test was used for categorical variables to identify significant differences between two groups. Second, except for blood lipid parameters, the basic prediction model considered potential confounding factors that were both clinically and statistically significant in a backward stepwise logistic regression model (with 0.1 significance level for removal), and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Third, a receiver operating characteristic (ROC) curve was constructed to evaluate the sensitivity, specificity, and area under the ROC curve (AUC) of different lipid parameters in predicting CAD based on the basic prediction model. Furthermore, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the improvement of the new model when compared with the basic prediction model at low (< 50%)/intermediate (50–80%)/high risk (> 80%) of CAD. The 95% CI of NRI was obtained after bootstrapping 10,000 times. Fourth, a fully adjusted logistic model was used to explore the relationship between the optimal blood lipid parameter and multi-vessel CAD in the subgroup of diagnosed CAD. Comparisons with P < 0.05 (two-sided) were considered to be statistically significant. All of the analyses were performed with Stata 15.0 (StataCorp LLC, College Station, TX, USA), R version 3.4.3 (The R Project for Statistical Computing, Vienna, Austria), and EmpowerStats (X&Y Solutions Inc., Boston, MA, USA).

Study population

A total of 1395 patients were eventually enrolled in this study (Fig. 1). Baseline information of patients with and without CAD is presented in Table 1. There was no significant difference in LDL-C between the two groups. Hypertension, DM, and CKD were more prevalent in the CAD group. As for blood lipid indices, TC/HDL-C, LDL-C/HDL-C, Apo B/Apo A-I, and TG/HDL-C ratios were higher, whereas the LDL-C/Apo B ratio was lower in the CAD group. Lp (a) and mean-cIMT were also significantly higher in patients with CAD than in those without CAD. In addition, for medication history, statins was more commonly used in the CAD group.

Table 1

Baseline information of CAD and non-CAD patients.
	Non-CAD n = 391	CAD n = 1004	P value
Age, year	62 ± 11	63 ± 11	0.009
Male sex	222 (56.78%)	757 (75.40%)	< 0.001
Hypertension	204 (52.17%)	615 (61.25%)	0.002
Diabetes	97 (24.81%)	382 (38.05%)	< 0.001
Smoking	107 (27.37%)	378 (37.65%)	< 0.001
Drinking	26 (6.65%)	56 (5.58%)	0.445
CKD	45 (11.51%)	179 (17.83%)	0.004
TC, mmol/L	4.61 ± 1.18	4.43 ± 1.25	0.019
TG, mmol/L	1.55 ± 1.13	1.65 ± 1.23	0.161
LDL-C, mmol/L	2.68 ± 1.05	2.60 ± 1.07	0.245
HDL-C, mmol/L	1.15 ± 0.28	1.05 ± 0.27	< 0.001
Apo A-I, mmol/L	1.28 ± 0.31	1.17 ± 0.29	< 0.001
Apo B, mmol/L	0.78 ± 0.21	0.79 ± 0.24	0.448
Apo B/Apo A-I	0.61 (0.48–0.77)	0.68 (0.53–0.86)	< 0.001
LDL-C/Apo B	3.35 (2.98–3.75)	3.22 (2.90–3.59)	< 0.001
TC/HDL-C	4.00 (3.34–4.77)	4.15 (3.48–5.04)	0.002
TG/HDL-C	1.15 (0.80–1.76)	1.41 (0.92–2.06)	< 0.001
LDL-C/HDL-C	2.21 (1.74–2.97)	2.42 (1.82–3.16)	0.007
HDL-C/Apo A-I	0.91 (0.82–0.99)	0.90 (0.82–0.98)	0.361
Mean-cIMT, mm	0.90 (0.75–1.00)	1.00 (0.85–1.10)	< 0.001
Lipoprotein(a), mg/L	113.72 (70.48–215.14)	180.80 (93.34–395.25)	< 0.001
Medication history
ACEI/ARB	57 (14.58%)	171 (17.03%)	0.266
β-blocker	56 (14.32%)	185 (18.43%)	0.069
CCB	41 (10.49%)	149 (14.84%)	0.033
Diuretic	17 (4.35%)	36 (3.59%)	0.504
Statin	66 (16.88%)	432 (43.03%)	< 0.001
Notes: Data are expressed as mean ± standard deviation or median (Q1–Q3) for continuous variables and n (%) for categorical variables.
Abbreviations: CAD, coronary artery disease; DM, diabetes mellitus; CKD, chronic kidney disease; TC: total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; Apo A-I, apolipoprotein A-I; Apo B, apolipoprotein B; cIMT, carotid intima-media thickness; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium calcium entry blocker.

Univariate and multivariate analysis

Univariate and stepwise multivariate logistic models were performed to select both clinically and statistically significant risk factors (Table 2). Age, sex, hypertension, smoking, alcohol consumption, DM, and mean-cIMT were associated with CAD and included in the basic prediction model. Among these, mean-cIMT was the strongly associated with the presence of CAD (OR 2.73, 95% CI: 1.64–4.52, P < 0.001).

Table 2

Backward stepwise logistic analysis.
Variable	Univariate		Multivariate
	OR (95% CI)	P value	OR (95% CI)	P value
Age	1.01 (1.00–1.03)	0.009	1.01 (1.00–1.03)	0.024
Male sex	2.33 (1.82–2.98)	< 0.001	2.41 (1.82–3.19)	< 0.001
Hypertension	1.45 (1.15–1.83)	0.002	1.36 (1.05–1.75)	0.018
Smoking	1.60 (1.24–2.07)	0.000	1.35 (1.00–1.82)	0.052
Alcohol consumption	0.83 (0.51–1.34)	0.445	0.57 (0.34–0.96)	0.035
CKD	1.67 (1.18–2.37)	0.004	-	-
DM	1.86 (1.43–2.42)	< 0.001	1.75 (1.33–2.30)	< 0.001
Mean-cIMT	3.63 (2.25–5.88)	< 0.001	2.73 (1.64–4.52)	< 0.001
Abbreviations: CKD: chronic kidney disease; DM: diabetes mellitus; cIMT: carotid intima-media thickness; OR: odds ratio; CI: confidence interval.

Predictive value of different blood lipid parameters on CAD

The ROC curves of the different blood lipid parameters on the basic prediction model for predicting CAD are shown in Fig. 2. The addition of Lp (a) as well as Apo B/Apo A-I ratio improved the prediction effect of the basic prediction model (AUC: 0.7129 for Lp (a), P < 0.001; AUC: 0.6848 for Apo B/Apo A-I ratio, P < 0.05). However, when LDL-C/Apo B, TC/HDL-C, TG/HDL-C, and LDL-C/HDL-C ratios were added into the basic prediction model, the AUC of the new model was not significantly improved.

The new model that included Lp (a) achieved an NRI of 12.8%, as compared with the basic prediction model for predicting CAD (Table 3), which means that 12.8% of patients were correctly reclassified. The IDI is also listed in Table 3 and also showed a significant improvement in accuracy generated with the new model. These results indicated that the new model including Lp (a) had better predictive capability for CAD than the other blood lipid indices.

Table 3

Comparison of different lipid parameters for predicting CAD based on basic prediction model using the NRI and IDI with cut-off point at low (< 50%)/intermediate (50–80%)/high risk (> 80%) of CAD.
	Lp (a)	Apo B/Apo A-I	LDL-C/Apo B	TG/HDL-C	TC/HDL-C	LDL-C/HDL-C
NRI	0.126** (0.051 to 0.224)	0.028 (− 0.014 to 0.113)	0.040* (− 0.014 to 0.105)	0.010 (− 0.019 to 0.094)	0.005 (− 0.024 to 0.086)	0.012 (− 0.023 to 0.071)
IDI	0.032** (0.024 to 0.040)	0.008* (0.003 to 0.012)	0.005* (0.000 to 0.009)	0.004* (0.000 to 0.008)	0.005* (0.00 to 0.008)	0.003* (0.000 to 0.006)
Notes: NRI: net reclassification improvement; IDI: integrated discrimination improvement;
NRI > 0 or IDI > 0 means the parameter improve the predictive value of disease.
*P < 0.05.
**P < 0.001.
Abbreviations: TC: total cholesterol; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; Apo A-I: apolipoprotein A-I; Apo B: apolipoprotein B; Lp (a): lipoprotein(a); CAD: coronary artery disease.

Association between Lp (a) and multi-vessel CAD

As shown in Fig. 3, after adjusting for other risk factors in the CAD group (n = 1004), high levels of Lp (a) was associated with multi-vessel CAD than those with low levels of Lp (a) (OR 1.41, 95% CI 1.02–1.95, P = 0.036).

By comparing the predictive value of different blood lipid parameters combined with cIMT on CAD., the study had three major findings: (1) Lp (a) better improved the prediction accuracy of CAD in comparison with other blood lipid parameters; (2) in the absence of high levels of LDL-C, the predictive value of Lp (a) on CAD was highlighted; (3) Lp (a) can also be used to predict multi-vessel CAD.

Atherosclerosis is a systemic vascular disease caused by a lipid metabolism disorder and most likely involves the carotid and coronary arteries [28]. Although coronary angiography is the gold standard for the diagnosis of CAD, it is an invasive examination that is unsuitable for general screening of CAD. In addition to identifying traditional risk factors such as demographic information and healthy behavior to predict CAD, screening for subclinical atherosclerosis is also important. Carotid ultrasound is a non-invasive examination [17]. cIMT is widely recognized as a marker of early-stage atherosclerosis and the severity of CAD, which can be used to indirectly assess coronary artery conditions [18, 29]. The results of a meta-analysis indicated that the sensitivity and specificity of cIMT for the diagnosis of CAD were 0.68 (95% CI: 0.57–0.77) and 0.70 (95% CI: 0.64–0.75), respectively, which indicated that only cIMT has limited effectiveness as a diagnostic tool for CAD screening [19]. In addition, since lipid metabolism disorders cannot be reflected directly, it is of limited value in the use of single cIMT in actual clinical application [20, 30]. Combining cIMT and lipid parameters could further improve the diagnostic efficiency of CAD. In recent years, research progress regarding lipid abnormalities has focused on the blood lipid indices, which can reflect lipid metabolism disorders better than a single one [31–33]. Wu et al found that multivariate logistic regression analysis showed that Apo B/Apo A-I ratio, a composite index, had a larger OR value than a single index (LDL-C or Apo B) and was better than a single lipid index in the prediction of CAD [30]. Similarly, in a cohort study by Rabizadeh et al, the analysis showed that LDL-C/Apo B ratio ≤ 1.2 can independently predict CAD (OR = 1.841, P = 0.002) [32]. In a cohort study conducted by Kappelle et al, Apo B/Apo A-I ratio and TC/HDL-C ratio were able to predict CAD and the first major adverse event during follow-up [33].Consistent with previous studies, this study found that Lp(a), TC/HDL-C, LDL-C/HDL-C, Apo B/Apo AI, TG/HDL-C and LDL-C/Apo B ratios were significantly associated with CAD. In terms of effectively predicting CAD, the addition of lipid parameters on traditional risk factors model improved the accuracy of the estimates, and Lp (a) was the best.

Many studies have reported that elevated plasma Lp (a) is an independent risk factor for CAD, which mainly participates in the pathophysiological process of CAD via prothrombotic/anti-fibrinolytic effects and promoting the deposition of cholesterol in the vascular intima [34–36]. On an equimolar basis, Lp (a) is more likely to cause atherosclerosis than LDL because it not only contains all proatherogenic components of LDL-C but also those of Apo (a) [13]. Because the structure of Lp (a) has one more Apo (a) than that of LDL-C, its ability to enhance atherosclerotic thrombosis through other mechanisms including inflammation is stronger than that of LDL-C [35]. Elevated levels of LDL-C is strongly associated with the development of CAD [37]. The 2019 ESC guidelines also recommend using LDL-C as the primary indicator of lipid-lowering therapy [38]. In the baseline information of the study patients, the LDL-C of the CAD group was similar to that of the non-CAD group, possibly because the CAD group took more statins. In such condition, the levels of LDL-C were comparable in both groups, and the additional effect of Lp (a) could be investigated. Previous studies have shown that patients still have a certain risk of CAD after the treatment for reducing LDL-C, and this residual risk was related to elevated Lp (a) [39–43]. Among patients whose target blood lipids are normal or below the target, Lp (a) will play an important role in the prediction of CAD. This may explain the results that the AUC value of Lp (a) was greater than that of LDL-C-associated blood lipid indices in the ROC curve.

In addition, elevated Lp (a) levels were correlated with multi-vessel CAD. This result is consistent with a recent finding demonstrating that high Lp (a) was significantly associated with increased CAD severity, evaluated using the SYNTAX score [44].

The findings of the present study highlight the importance of Lp (a) combined with cIMT in assessing the risk of CAD, especially for those with normal level of LDL-C, which may be useful for guiding primary prevention decisions.

Comparisons with other studies and what does the current work add to the existing knowledge

Previous studies have demonstrated the importance of cIMT and lipid parameters in predicting CAD[18, 29, 31–33]. However, this study compared the predictive values of different lipid parameters combined with cIMT in the diagnosis of CAD. Lp (a) combined with cIMT is more effective than other lipid parameters in identifying CAD.

Study strengths and limitations

This study has some strengths. This study compared the effectiveness of different lipid parameters combined with cIMT in predicting CAD in the absence of high LDL-C for the first time, and recognized that Lp (a) was superior to other lipid parameters in CAD discrimination, through NRI and IDI. This study has certain limitations. First, this was a retrospective study and no causal conclusion can be drawn. Second, the patients in study was highly suspected as CAD on admission, so the conclusion cannot be extrapolated to the general population. Third, the severity of CAD was based on the number of coronary artery lesions, and the corresponding degree of stenosis in each branch was not well quantified.

For patients with highly suspected CAD, Lp (a) combined with cIMT better improved the predictive value of CAD compared with other blood lipid parameters, especially in the absence of high levels of LDL-C. High concentration of Lp (a) was also associated with the multi-vessel CAD. In the future, Lp (a) may need more attention and management in the prevention of CAD.

CAD

coronary artery disease

cIMT

carotid intima-media thickness

LDL-C

low-density lipoprotein cholesterol

Apo A-I

apolipoprotein A-I

Apo B

apolipoprotein B

Lp (a)

lipoprotein(a)

total cholesterol

triglyceride

CKD

chronic kidney disease

diabetes mellitus

ORs

odds ratios

CIs

confidence intervals

ROC

receiver operating characteristic

AUC

area under the curve

NRI

net reclassification improvement

IDI

integrated discrimination improvement.

Acknowledgements

The researchers are grateful to Accdon (www.accdon.com) for its linguistic assistance during the preparation of this manuscript.

Authors' contributions

XMH and BYY: manuscript preparation and writing-original draft. WL, LPZ and YL: data collection and collation. XMH and WMW: data analysis. GL: writing-critical revisions. YLZ and HJD: the conceptualization and approval of the final version of the manuscript for submission. All authors read and approved the final manuscript.

Funding

This research was supported by The National Key Research and Development Program of China (No. 2016YFC1301202).

Availability of data and materials

The data analyzed in this study can be obtained on reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

This study was conducted under the guiding principles of the Declaration of Helsinki and was approved by the Clinical Research Ethics Committee of the Guangdong Provincial People’s Hospital. All participants were verbally informed of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Authors' sdetails

¹The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China;

²Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China;

³School of Medicine, South China University of Technology, Guangzhou 510006, Guangdong, China;

⁴Department of Cardiology, Guangdong Provincial People's Hospital Zhuhai Hospital (Zhuhai Golden Bay Center Hospital), Zhuhai 519040, Guangdong, China;

⁵Shantou University Medical College, Shantou 515041, Guangdong, China.

Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020;141(9):e139-e596. https://doi.org/10.1161/CIR.0000000000000757.
Spannella F, Giulietti F, Di Pentima C, Sarzani R. Prevalence and Control of Dyslipidemia in Patients Referred for High Blood Pressure: The Disregarded "Double-Trouble" Lipid Profile in Overweight/Obese. Adv Ther. 2019;36(6):1426–1437. https://doi.org/10.1007/s12325-019-00941-6.
Gardener H, Della Morte D, Elkind MS, Sacco RL, Rundek T. Lipids and carotid plaque in the Northern Manhattan Study (NOMAS). BMC Cardiovasc Disord. 2009;22;9:55. https://doi.org/10.1186/1471-2261-9-55.
Sone H, Tanaka S, Tanaka S, Iimuro S, Ishibashi S, Oikawa S, et al. Comparison of various lipid variables as predictors of coronary heart disease in Japanese men and women with type 2 diabetes: subanalysis of the Japan Diabetes Complications Study. Diabetes Care. 2012;35(5):1150–7. https://doi.org/10.2337/dc11-1412.
Enomoto M, Adachi H, Hirai Y, Fukami A, Satoh A, Otsuka M, et al. LDL-C/HDL-C Ratio Predicts Carotid Intima-Media Thickness Progression Better Than HDL-C or LDL-C Alone. J Lipids. 2011;2011:549137. https://doi.org/10.1155/2011/549137.
Takata K, Kataoka Y, Andrews J, Puri R, Hammadah M, Duggal B, et al. Triglyceride-to-High-Density Lipoprotein Cholesterol Ratio and Vulnerable Plaque Features With Statin Therapy in Diabetic Patients With Coronary Artery Disease: Frequency-Domain Optical Coherence Tomography Analysis. JACC Cardiovasc Imaging. 2018;11(11):1721–1723. https://doi.org/10.1016/j.jcmg.2018.02.017.
Ljungberg J, Holmgren A, Bergdahl IA, Hultdin J, Norberg M, Näslund U, et al. Lipoprotein(a) and the Apolipoprotein B/A1 Ratio Independently Associate With Surgery for Aortic Stenosis Only in Patients With Concomitant Coronary Artery Disease. J Am Heart Assoc. 2017;6(12):e007160. https://doi.org/10.1161/JAHA.117.007160.
Byun AR, Lee SW, Lee HS, Shim KW. What is the most appropriate lipid profile ratio predictor for insulin resistance in each sex? A cross-sectional study in Korean populations (The Fifth Korea National Health and Nutrition Examination Survey). Diabetol Metab Syndr. 2015;7:59. https://doi.org/10.1186/s13098-015-0051-2.
Yang WS, Li R, Shen YQ, Wang XC, Liu QJ, Wang HY, et al. Importance of lipid ratios for predicting intracranial atherosclerotic stenosis. Lipids Health Dis. 2020;19(1):160. https://doi.org/10.1186/s12944-020-01336-1.
Abbasian M, Delvarianzadeh M, Ebrahimi H, Khosravi F. Lipid ratio as a suitable tool to identify individuals with MetS risk: A case- control study. Diabetes Metab Syndr. 2017;11 Suppl 1:S15-S19. https://doi.org/10.1016/j.dsx.2016.08.011.
Marcovina S, Packard CJ. Measurement and meaning of apolipoprotein AI and apolipoprotein B plasma levels. J Intern Med. 2006;259(5):437 – 46. https://doi.org/10.1111/j.1365-2796.2006.01648.x.
Yaseen RI, El-Leboudy MH, El-Deeb HM. The relation between ApoB/ApoA-1 ratio and the severity of coronary artery disease in patients with acute coronary syndrome. Egypt Heart J. 2021;73(1):24. https://doi.org/10.1186/s43044-021-00150-z.
Tsimikas S. A Test in Context: Lipoprotein(a): Diagnosis, Prognosis, Controversies, and Emerging Therapies. J Am Coll Cardiol. 2017;69(6):692–711. https://doi.org/10.1016/j.jacc.2016.11.042.
Shah NP, Pajidipati NJ, McGarrah RW, Navar AM, Vemulapalli S, Blazing MA, et al. Lipoprotein (a): An Update on a Marker of Residual Risk and Associated Clinical Manifestations. Am J Cardiol. 2020;126:94–102. https://doi.org/10.1016/j.amjcard.2020.03.043.
Li C, Chen Q, Zhang M, Liu Y, Chu Y, Meng F, et al. The correlation between lipoprotein(a) and coronary atherosclerotic lesion is stronger than LDL-C, when LDL-C is less than 104 mg/dL. BMC Cardiovasc Disord. 2021;21(1):41. https://doi.org/10.1186/s12872-021-01861-6.
Kablak-Ziembicka A, Tracz W, Przewlocki T, Pieniazek P, Sokolowski A, Konieczynska M. Association of increased carotid intima-media thickness with the extent of coronary artery disease. Heart. 2004;90(11):1286–90. https://doi.org/10.1136/hrt.2003.025080.
Zhang Y, Guallar E, Qiao Y, Wasserman BA. Is carotid intima-media thickness as predictive as other noninvasive techniques for the detection of coronary artery disease? Arterioscler Thromb Vasc Biol. 2014;34(7):1341–5. https://doi.org/10.1161/ATVBAHA.113.302075.
Nambi V, Chambless L, Folsom AR, He M, Hu Y, Mosley T, et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol. 2010;55(15):1600–7. https://doi.org/10.1016/j.jacc.2009.11.075.
Liu D, Du C, Shao W, Ma G. Diagnostic Role of Carotid Intima-Media Thickness for Coronary Artery Disease: A Meta-Analysis. Biomed Res Int. 2020;2020:9879463. https://doi.org/10.1155/2020/9879463.
Vlachopoulos C, Xaplanteris P, Aboyans V, Brodmann M, Cífková R, Cosentino F, et al. The role of vascular biomarkers for primary and secondary prevention. A position paper from the European Society of Cardiology Working Group on peripheral circulation: Endorsed by the Association for Research into Arterial Structure and Physiology (ARTERY) Society. Atherosclerosis. 2015;241(2):507–32. https://doi.org/10.1016/j.atherosclerosis.2015.05.007.
Tabas I, Williams KJ, Borén J. Subendothelial lipoprotein retention as the initiating process in atherosclerosis: update and therapeutic implications. Circulation. 2007;116(16):1832–44. https://doi.org/10.1161/CIRCULATIONAHA.106.676890.
Borén J, Williams KJ. The central role of arterial retention of cholesterol-rich apolipoprotein-B-containing lipoproteins in the pathogenesis of atherosclerosis: a triumph of simplicity. Curr Opin Lipidol. 2016;27(5):473–83. https://doi.org/10.1097/MOL.0000000000000330.
Jellinger PS, Handelsman Y, Rosenblit PD, Bloomgarden ZT, Fonseca VA, Garber AJ, Grunberger G, Guerin CK, Bell DSH, Mechanick JI, Pessah-Pollack R, Wyne K, Smith D, Brinton EA, Fazio S, Davidson M. AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY GUIDELINES FOR MANAGEMENT OF DYSLIPIDEMIA AND PREVENTION OF CARDIOVASCULAR DISEASE. Endocr Pract. 2017;23(Suppl 2):1–87. https://doi.org/10.4158/EP171764.APPGL.
Hu X, Li W, Wang C, Zhang H, Lu H, Li G, et al. Association between the Plasma-Glycosylated Hemoglobin A1c/High-Density Lipoprotein Cholesterol Ratio and Carotid Atherosclerosis: A Retrospective Study. J Diabetes Res. 2021;2021:9238566. https://doi.org/10.1155/2021/9238566.
Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021–3104. https://doi.org/10.1093/eurheartj/ehy339.
Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41(2):255–323. https://doi.org/10.1093/eurheartj/ehz486.
Patel MR, Calhoon JH, Dehmer GJ, Grantham JA, Maddox TM, Maron DJ, et al. ACC/AATS/AHA/ASE/ASNC/SCAI/SCCT/STS 2016 Appropriate Use Criteria for Coronary Revascularization in Patients With Acute Coronary Syndromes: A Report of the American College of Cardiology Appropriate Use Criteria Task Force, American Association for Thoracic Surgery, American Heart Association, American Society of Echocardiography, American Society of Nuclear Cardiology, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and the Society of Thoracic Surgeons. J Nucl Cardiol. 2017;24(2):439–463. https://doi.org/10.1007/s12350-017-0780-8.
Tuzcu EM, Schoenhagen P. Acute coronary syndromes, plaque vulnerability, and carotid artery disease: the changing role of atherosclerosis imaging. J Am Coll Cardiol. 2003;42(6):1033–6. https://doi.org/10.1016/s0735-1097(03)00904-5.
Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness: a systematic review and meta-analysis. Circulation. 2007;115(4):459–67. https://doi.org/10.1161/CIRCULATIONAHA.106.628875.
Cappelletti A, Astore D, Godino C, Bellini B, Magni V, Mazzavillani M, et al. Relationship between Syntax Score and prognostic localization of coronary artery lesions with conventional risk factors, plasma profile markers, and carotid atherosclerosis (CAPP Study 2). Int J Cardiol. 2018;257:306–311. https://doi.org/10.1016/j.ijcard.2017.12.012.
Wu TT, Zheng YY, Yang YN, Li XM, Ma YT, Xie X. Age, Sex, and Cardiovascular Risk Attributable to Lipoprotein Cholesterol Among Chinese Individuals with Coronary Artery Disease: A Case-Control Study. Metab Syndr Relat Disord. 2019;17(4):223–231. https://doi.org/10.1089/met.2018.0067.
Rabizadeh S, Rajab A, Mechanick JI, Moosaie F, Rahimi Y, Nakhjavani M, et al. LDL/apo B ratio predict coronary heart disease in Type 2 diabetes independent of ASCVD risk score: A case-cohort study. Nutr Metab Cardiovasc Dis. 2021;31(5):1477–1485. https://doi.org/10.1016/j.numecd.2021.01.013.
Kappelle PJ, Gansevoort RT, Hillege JL, Wolffenbuttel BH, Dullaart RP; PREVEND study group. Apolipoprotein B/A-I and total cholesterol/high-density lipoprotein cholesterol ratios both predict cardiovascular events in the general population independently of nonlipid risk factors, albuminuria and C-reactive protein. J Intern Med. 2011;269(2):232–42. https://doi.org/10.1111/j.1365-2796.2010.02323.x.
Mehta A, Virani SS, Ayers CR, Sun W, Hoogeveen RC, Rohatgi A, et al. Lipoprotein(a) and Family History Predict Cardiovascular Disease Risk. J Am Coll Cardiol. 2020;76(7):781–793. https://doi.org/10.1016/j.jacc.2020.06.040.
Nordestgaard BG, Chapman MJ, Ray K, Borén J, Andreotti F, Watts GF, et al. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010;31(23):2844–53. https://doi.org/10.1093/eurheartj/ehq386.
Jang AY, Han SH, Sohn IS, Oh PC, Koh KK. Lipoprotein(a) and Cardiovascular Diseases- Revisited. Circ J. 2020 May 25;84(6):867–874. https://doi.org/10.1253/circj.CJ-20-0051.
Klein-Szanto AJP, Bassi DE. Keep recycling going: New approaches to reduce LDL-C. Biochem Pharmacol. 2019 Jun;164:336–341. https://doi.org/10.1016/j.bcp.2019.04.003.
Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111–188. https://doi.org/10.1093/eurheartj/ehz455.
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–2381. https://doi.org/10.1093/eurheartj/ehw106.
Cai A, Li L, Zhang Y, Mo Y, Mai W, Zhou Y. Lipoprotein(a): a promising marker for residual cardiovascular risk assessment. Dis Markers. 2013;35(5):551–9. https://doi.org/10.1155/2013/563717.
Wei WQ, Li X, Feng Q, Kubo M, Kullo IJ, Peissig PL, et al. LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins. Circulation. 2018;138(17):1839–1849. https://doi.org/ 10.1161/CIRCULATIONAHA.117.031356.
Hoogeveen RC, Ballantyne CM. Residual Cardiovascular Risk at Low LDL: Remnants, Lipoprotein(a), and Inflammation. Clin Chem. 2021;67(1):143–153. https://doi.org/10.1093/clinchem/hvaa252.
Kelly E, Hemphill L. Lipoprotein(a): A Lipoprotein Whose Time Has Come. Curr Treat Options Cardiovasc Med. 2017;19(7):48. https://doi.org/10.1007/s11936-017-0549-z.
44.Liu Y, Ma H, Zhu Q, Zhang B, Yan H, Li H, et al. A genome-wide association study on lipoprotein (a) levels and coronary artery disease severity in a Chinese population. J Lipid Res. 2019;60(8):1440–1448. https://doi.org/10.1194/jlr.P091009.

No competing interests reported.

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Predictive Effect of Different Blood Lipid Parameters Combined with Carotid Intima-Media Thickness on Coronary Artery Disease

Archived Versions:

Version 1

Abstract

Figures

Background

Methods

Data Collection And Measurements

Definitions

Statistical analysis

Results

Study population

Univariate and multivariate analysis

Predictive value of different blood lipid parameters on CAD

Association between Lp (a) and multi-vessel CAD

Discussion

Comparisons with other studies and what does the current work add to the existing knowledge

Study strengths and limitations

Conclusion

Abbreviations

Declarations

Declarations

References

Competing Interests

Archived Versions:

Version 1