Association between non-high-density lipoprotein cholesterol and coronary artery disease in patients with acute coronary syndrome: a single-centre retrospective cross-sectional analysis

doi:10.21203/rs.3.rs-4905958/v1

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Research Article

Association between non-high-density lipoprotein cholesterol and coronary artery disease in patients with acute coronary syndrome: a single-centre retrospective cross-sectional analysis

https://doi.org/10.21203/rs.3.rs-4905958/v1

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Background

A significant relationship exists between atherosclerotic cardiovascular disease (ASCVD) and non-high-density lipoprotein cholesterol (non-HDL-C). However, the relationship between non-HDL cholesterol levels and coronary artery disease in patients with acute coronary syndrome (ACS) remains under-researched and poorly understood. Therefore, this study aimed to explore the association between non-HDL-C and coronary artery disease in individuals with ACS.

Methods

This single-centre retrospective cross-sectional study included 1695 patients with ACS who underwent percutaneous coronary intervention at Zunyi Medical University's Affiliated Hospital from May 2019 to May 2023. The patients were categorised into three groups based on non-HDL cholesterol tertiles. The severity of coronary artery disease was assessed using the Gensini score, and patients were categorised into high and low Gensini score groups. Logistic regression models, including multifactorial adjustments, were used to analyse the association between non-HDL-C levels and high Gensini scores.

Results

After adjusting for potential risk factors, multifactorial logistic regression (odds ratio [OR] = 1.20; 95% confidence interval [CI] = 1.10–1.31; p < 0.001) revealed that non-HDL-C was an independent risk factor for high Gensini scores. After subgrouping based on age, sex, and glucose metabolic status, non-HDL-C remained an independent risk factor for high Gensini scores in these subgroups. Restricted cubic spline analysis revealed that a higher non-HDL-C level was associated with a greater the risk of having a high Gensini score. Receiver operating characteristic curve analysis revealed that non-HDL-C was incrementally predictive of high Gensini scores in patients with ACS.

Conclusions

Non-HDL-C is dose-dependently associated with the severity of coronary artery disease in patients with ACS. These findings suggest the potential of non-HDL-C in predicting its severity, supporting its use as a cost-effective and accessible marker for assessing coronary artery disease risk.

acute coronary syndrome

non-high-density lipoprotein cholesterol

cardiovascular disease

Gensini scores

Cardiovascular disease (CVD) poses a significant burden on global health, with incidence rates almost doubling in the last 30 years and related deaths increasing drastically, particularly in less developed regions, with acute coronary syndromes (ACS) being a major mortality factor [1–3]. Developing countries have a relatively high burden of disease, and health resources are insufficient to meet the population's needs. Thus, exploring methods that are inexpensive and easy to implement may be an effective approach to reduce the cardiovascular burden in these areas. ACS is classified into the following three types: unstable angina (UA), acute ST-segment elevation myocardial infarction (STEMI), and acute non-ST-segment elevation myocardial infarction (NSTEMI) [4]. For example, studies have demonstrated that early control of lipids improves the prognosis of patients with ACS, particularly in controlling low-density lipoprotein cholesterol (LDL-C) levels [5]. Notably, non-HDL cholesterol encompasses the sum of all potentially atherogenic cholesterol. Recent guidelines suggest that non-HDL cholesterol holds greater significance in atherosclerotic cardiovascular disease (ASCVD) and underscore the cardiovascular benefits of lowering non-HDL cholesterol levels [6]. Currently, invasive coronary angiography is considered the "gold standard" for diagnosing coronary atherosclerotic heart disease [7]. However, non-invasive serologic markers that effectively predict coronary artery disease are lacking. Non-high-density lipoprotein cholesterol (non-HDL-C) is a simple and readily available serologic marker that has also shown promising applications for reducing the disease burden of ACS. Based on the available evidence from the appellate level, we hypothesised a strong association between non-HDL-C and coronary artery disease in patients with ACS. However, this hypothesis has not been tested. Therefore, this study aimed to test this hypothesis and further assess the significance of non-HDL-C in predicting coronary artery disease in these patients.

Study design and population

This observational retrospective cross-sectional study was conducted at Zunyi Medical University's Affiliated Hospital in China. This study included patients with ACS who received percutaneous coronary intervention (PCI) at Zunyi Medical University's Affiliated Hospital's Cardiovascular Medicine Department from May 1, 2019, to May 1, 2023. The inclusion criteria were established for patients with ACS who underwent PCI treatment. The exclusion criteria were as follows: 1) history of cancer or 2) absence of data (Fig. 1). Overall, 1,695 patients with ACS were registered. Patients were categorised into the following three groups based on their non-HDL-C tertiles: T1 (non-HDL-C < 3.22, n = 577), T2 (3.22 ≤ non-HDL-C < 4.33, n = 553), and T3 (4.33 ≤ non-HDL-C, n = 565). The intensity of coronary artery damage in patients with ACS was measured quantitatively using the Gensini score, which is based on coronary angiography findings. Higher Gensini scores were indicative of more critical coronary artery conditions, leading to their classification into the following two groups: those with a low score (Gensini score < 50) and those with a high score (Gensini score ≥ 50), determined based on the median. The primary outcome of this study was achieving a higher Gensini score. This study was performed in accordance with the Declaration of Helsinki guidelines and was approved by the Ethics Committee of Zunyi Medical University's Affiliated Hospital. The requirement for informed consent was waived owing to the study's retrospective design.

Data measurement and definitions

The initial demographic and clinical data of the patients were retrospectively collected from the health records of Zunyi Medical University's Affiliated Hospital. The demographic information included additional health conditions, such as diabetes mellitus (DM), history of strokes, dyslipidaemia, hypertension, history of PCI, earlier coronary artery bypass grafts (CABG), history of myocardial infarction, familial background, smoking habits, sex, age, body mass index (BMI), initial diagnosis (NSTEMI, STEMI, or UA), and use of insulin and oral hypoglycaemic drugs. The clinical records included measurements of diastolic blood pressure (DBP), systolic blood pressure (SBP), outcomes of coronary angiography, laboratory analyses, and supplementary data, including left ventricular ejection fraction (LVEF), total cholesterol (TC), LDL-C, triglycerides (TG), HDL-C, glycosylated haemoglobin (HbA1c) at admission, haemoglobin (Hb), fasting blood glucose, uric acid, ultrasensitive C-reactive protein (hs-CRP), and creatinine (Cr), with laboratory test results recorded during fasting. Levels of TC and HDL-C were measured using the AU5800 system (Beckman Coulter, California, USA). The non-HDL-C level was defined as (TC minus HDL-C) [8]. BMI was determined by dividing the weight in kilograms by the square of the height in metres. The MDRD equation was utilised to compute the estimated glomerular filtration rate (eGFR) [9]. Patients aged ≥ 60 years were classified as elderly [10]. DM manifests as having type 2 DM or an HbA1c level exceeding 6.5% [11]. Gensini scores were calculated from the coronary angiography results [12].

Statistical analysis

Continuous variables that follow a normal distribution are presented as average ± standard deviation, and those that do not follow a normal distribution are presented as median (interquartile spacing). Categorical variables are presented as percentages. Variance analysis was performed to evaluate three continuous variable groups with normal distribution and variance alignment, and the Kruskal–Wallis rank-sum test was used to assess three continuous variable sets that either deviated from normal distribution or exhibited variance alignment. Three groups of categorical variables were compared using the chi-square test. The relationship between non-HDL-C and the severity of coronary artery lesions (Gensini score ≥ 50) in patients with ACS was analysed using logistic regression, leading to the calculation of the odds ratio (OR) and 95% confidence interval (CI). Model 1 was subjected to an unmodified single-factor logistic regression evaluation. Adjustments were made to Model 2 to account for various risk elements, including age, BMI, SBP, DBP, TG, eGFR, Cr, glycemia, uric acid, Hb, hs-CRP, HbA1c, LVEF, sex, smoking habits, history of myocardial infarction, history of CABG, PCI, high blood pressure, earlier strokes, familial background, and insulin consumption. Before developing Model 2, the relationship between the non-HDL-C level and other variables was evaluated by computing the generalised variance inflation factor (GVIF). When GVIF^ (1/2Df) exceeded or matched 2 (with Df indicating degrees of freedom), it was deemed to have a notable covariance with non-HDL-C, leading to its exclusion from the model. The inclusion variables were further refined through least absolute shrinkage and selection operator regression, followed by multifactorial logistic regression analysis. Additionally, the relationship between non-HDL-C levels and severe coronary artery disease (indicated by a high Gensini score) was examined using restricted cubic spline analysis. Furthermore, potential linear trends between non-HDL-C and Gensini scores were assessed using the Cochran–Armitage trend test in the overall ACS population. The analysis of subgroups was conducted according to their age categories, sex, and whether they had diabetes or not. To evaluate the precision of the non-HDL-C test in diagnosing coronary artery disease, a receiver operating characteristic (ROC) curve was employed, and the C-statistics area under the curve (AUC) was utilised to determine the predictive significance of non-HDL-C in diagnosing coronary arteries. The DeLong test was used to compare the AUC values among the models. Moreover, the Net Reclassification Index (NRI) and Integrated Discriminant Improvement Index (IDI) were computed to assess the enhanced predictive value of non-HDL-C, exceeding potential risk factors associated with a high Gensini score. P-values were calculated from the Z-statistic to determine whether the NRI and IDI were statistically significant. Finally, internal validation using bootstrapping was conducted to evaluate the model's generalisability. Statistical significance was set at p < 0.05. Every analysis conducted was bilateral. The statistical analyses were performed using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline characteristics

Patients with ACS were categorised into three groups based on the non-HDL-c tertile method. Patients in the T3 group were younger and exhibited higher lipid levels and BMI than those in the T1 and T2 groups (both p < 0.05). The combined initial characteristics are presented in Table 1.

Table 1

Fundamental traits categorised based on the tertiles of non-HDL-C
	Total (N = 1,695)	T1 (N = 577)	T2 (N = 553)	T3 (N = 565)	P
Glycemia(mmol/L)	7.71(5.83,11.45)	7.24(5.62,11.20)	7.76(5.86,11.39)	8.24(6.02,11.66)	0.006
Age(years)	62 (54,71)	64 (56,72)	63 (54,71)	58 (52,69)	< 0.001
BMI(kg/m²)	23.88 (21.80,26.42)	23.51(21.45,26.11)	23.88 (21.93,26.12)	24.04 (22.04,26.89)	0.017
Creatinine(µmol/L)	78 (66,94)	81 (69,99)	78 (65,92)	74 (63,92)	< 0.001
hs-CRP(mg/L)	3.77(1.21,14.30)	3.46(1.10,14.22)	4.10(1.49,17.07)	3.77(1.20,11.99)	0.086
SBP (mmHg)	123 (111,136)	122 (111,135)	122 (111,135)	125 (112,138)	0.254
DBP (mmHg)	75 (67,84)	75 (68,83)	75 (67,84)	76 (68,86)	0.107
LVEF (%)	56 (46,61)	56 (47,61)	55 (44,60)	56 (47,60)	0.174
eGFR (mL/min/1.73 m²)	87.73(70.43,105.03)	84.30(68.13,99.87)	88.35(70.97,104.67)	92.06(73.07,108.71)	< 0.001
Hb (g/L)	138 (125,151)	136 (122,147)	138 (125,150)	142 (130,154)	< 0.001
HbA1c (%)	6.50 (5.80,8.00)	6.50 (5.80,7.60)	6.50 (5.70,8.30)	6.70 (5.80,8.40)	0.018
HDL-C (mmol/L)	1.09 (0.93,1.27)	1.03 (0.87,1.20)	1.09 (0.93,1.26)	1.16 (1.01,1.31)	< 0.001
LDL-C (mmol/L)	2.96 (2.35,3.60)	2.13 (1.81,2.38)	3.00 (2.76,3.25)	3.89 (3.60,4.29)	< 0.001
TC (mmol/L)	4.85 (3.97,5.76)	3.65 (3.20,4.01)	4.86 (4.56,5.18)	6.19 (5.74,6.83)	< 0.001
TG (mmol/L)	1.88 (1.26,2.89)	1.39 (1.02,2.16)	1.83 (1.30,2.83)	2.47 (1.71,3.95)	< 0.001
Uric acid(µmol/l)	353 (294,431)	349 (295,425)	350 (291,428)	364 (299,441)	0.129
Male	1,208 (71.3)	445 (77.1)	382 (69.1)	381 (67.4)	< 0.001
Smoking					< 0.001
Current	714 (42.1)	235 (40.7)	233 (42.1)	246 (43.5)
Former	210 (12.4)	108 (18.7)	51 (9.2)	51 (9.0)
Never	771 (45.5)	234 (40.6)	269 (48.6)	268 (47.4)
Previous MI	559 (33)	249 (43.2)	160 (28.9)	150 (26.5)	< 0.001
Previous CABG	14 (0.8)	3 (0.5)	6 (1.1)	5 (0.9)	0.567
Previous PCI	190 (11.2)	115 (19.9)	45 (8.1)	30 (5.3)	< 0.001
Hypertension grade					0.468
1	97 (5.7)	31 (5.4)	33 (6.0)	33 (5.8)
2	294 (17.3)	109 (18.9)	91 (16.5)	94 (16.6)
3	643 (37.9)	226 (39.2)	218 (39.4)	199 (35.2)
Dyslipidaemia	754 (44.5)	126 (21.8)	226 (40.9)	402 (71.2)	< 0.001
Previous stroke	216 (12.7)	101 (17.5)	59 (10.7)	56 (9.9)	< 0.001
Family history	8 (0.5)	0 (0.0)	2 (0.4)	6 (1.1)	0.029
Insulin	433 (25.5)	146 (25.3)	136 (24.6)	151 (26.7)	0.706
Oral hypoglycaemic drugs	763 (45)	264 (45.8)	238 (43.0)	261 (46.2)	0.517
DM	959 (56.6)	321 (55.6)	305 (55.2)	333 (58.9)	0.378
Diagnosis on admission					< 0.001
STEMI	429 (25.3)	100 (17.3)	157 (28.4)	172 (30.4)
NSTEMI	434 (25.6)	126 (21.8)	152 (27.5)	156 (27.6)
UA	832 (49.1)	351 (60.8)	244 (44.1)	237 (41.9)
Gensini score	50 (32,78)	45 (25,72)	52 (32,80)	52 (35,80)	< 0.001
The data are displayed in the form of averages ± standard deviations, median values (interquartile ranges), or percentages.
Hb, haemoglobin; MI, myocardial infarction; NSTEMI, non-ST-segment elevation myocardial infarction; LDL-C, low-density lipoprotein cholesterol; STEMI, ST-segment elevation myocardial infarction; LVEF, left ventricular ejection fraction; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; DM, diabetes mellitus; PCI, percutaneous coronary intervention; TC, total cholesterol; TG, triglycerides; CABG, coronary artery bypass grafting; BMI, body mass index; SBP, systolic blood pressure; hs-CRP, high-sensitivity C-reactive protein; non-HDL-C, non-high-density lipoprotein; HbA1c, glycosylated haemoglobin; eGFR, estimated glomerular filtration rate; UA, unstable angina

Clinical outcomes

Table 2 presents the correlations between all patients with ACS and elevated Gensini scores. The single-factor logistic regression analysis in Model 1 revealed a correlation between non-HDL-C and elevated Gensini scores (OR = 1.09; 95%CI = 1.02–1.18; p = 0.016). The T3 group exhibited a risk of elevated Gensini scores that were 1.37 times higher than those of the T1 group (OR = 1.37; 95%CI = 1.08–1.73; p = 0.008). The multifactorial logistic regression analysis in Model 2, accounting for possible risk elements, revealed non-HDL-C as a standalone risk factor for elevated Gensini scores (OR = 1.20; 95%CI = 1.10–1.31; p < 0.001). The T2 group had a 1.61 times higher risk (OR = 1.61; 95%CI = 1.25–2.07; p < 0.001), while the T3 group had a 1.8 times higher risk (OR = 1.80; 95%CI = 1.37–2.36; p < 0.001) of elevated Gensini scores than the T1 group.

Table 2

Correlations between non-HDL-C and the Gensini score
	Events/N	Model 1			Model 2
		OR	95CI%	P	OR	95CI%	P
non-HDL-C	834/1,695	1.09	1.02–1.18	0.016	1.20	1.10–1.31	< 0.001
T1	251/577	Reference			Reference
T2	293/553	1.46	1.16–1.85	0.001	1.61	1.25–2.07	< 0.001
T3	290/565	1.37	1.08–1.73	0.008	1.80	1.37–2.36	< 0.001
P for trend				0.008			< 0.001

Non-HDL-C, non-high-density lipoprotein; OR, odds ratio; CI, confidence interval; DM, diabetes mellitus; non-HDL-C, non-high-density lipoprotein

The results of the restricted cubic spline analysis are shown in Fig. 2, which demonstrates a dose-response relationship between non-HDL-C and high Gensini scores in patients with ACS (nonlinear p = 0.513).

Subgroup analyses

Further categorisation was conducted according to sex, age groups, and DM status (diabetic or non-diabetic) to explore the correlation between non-HDL-C and high Gensini scores in the subgroups. The results of analysing patients with ACS with varying DM statuses are presented in Table 3. No significant interaction was noted between DM status and non-HDL cholesterol levels (interaction p-value = 0.110). After adjusting for potential risk factors, multifactorial logistic regression analysis (OR = 1.15; 95%CI = 1.02–1.29; p = 0.024) revealed non-HDL-C as an independent risk factor for elevated Gensini scores in patients with DM. Within the T2 (OR = 1.73; 95%CI = 1.22–2.44; p = 0.002) and T3 (OR = 1.65; 95%CI = 1.14–2.38; p = 0.007) groups, indicating that the risks were 1.73 and 1.65 times greater than those in the T1 group, respectively. Among patients without DM, after adjusting for potential risk factors, non-HDL-C continued to be an independent risk factor for elevated Gensini scores (OR = 1.29; 95%CI = 1.11–1.49; p < 0.001), with the T3 group showing a risk 1.98 times greater (OR = 1.98; 95%CI = 1.30–3.04; p = 0.002) than the T1 group.

Table 3

Correlations between non-HDL-C and Gensini scores across various diabetes conditions
	Events/N	Mode 1	P		Model 2
		OR	95CI%	P	OR	95CI%	P	P for interaction
Glucose metabolism state								0.110
DM	461/959	1.03	0.94–1.13	0.528	1.15	1.02–1.29	0.024
T1	138/321	Reference			Reference
T2	165/305	1.56	1.14–2.14	0.006	1.73	1.22–2.44	0.002
T3	158/333	1.20	0.88–1.63	0.252	1.65	1.14–2.38	0.007
Non-DM	373/736	1.20	1.07–1.35	0.002	1.29	1.11–1.49	< 0.001
T1	113/256	Reference			Reference
T2	128/248	1.35	0.95–1.92	0.093	1.43	0.97–2.10	0.069
T3	132/232	1.67	1.17–2.39	0.005	1.98	1.30–3.04	0.002

non-HDL-C, non-high-density lipoprotein; OR, odds ratio; CI, confidence interval; DM, diabetes mellitus

Results from the examination of patients with ACS across different age groups are presented in Table 4. No significant interaction was observed between age and degree of non-HDL cholesterol (interaction p = 0.258). In elderly patients, after accounting for potential risk factors, the complex logistic regression analysis (OR = 1.18; 95%CI = 1.04–1.33; p = 0.009) revealed that non-HDL-C independently resulted in higher Gensini scores, and the risks of attaining high Gensini scores in the T3 group (OR = 1.45; 95%CI = 1.00–2.10; p = 0.048) was 1.45 times higher than that in the T1 group. In the younger group, after accounting for potential risk factors, the complex logistic regression analysis revealed non-HDL-C as an independent risk factor for increased Gensini scores (OR = 1.21; 95%CI = 1.06–1.40; p = 0.007). In contrast to the T1 group, the T2 (OR = 2.23; 95%CI = 1.47–3.39; p < 0.001) and T3 (OR = 2.56; 95%CI = 1.66–3.95; p < 0.001) groups exhibited 2.23 and 2.56 times higher risks, respectively, in attaining elevated Gensini scores.

Table 4

Correlations between non-HDL-C and Gensini scores across various age groups
	Events/N	Model 1			Model 2
		OR	95CI%	P	OR	95CI%	P	P for interaction
Age-years								0.258
Age ≥ 60	477 /937	1.07	0.97–1.18	0.192	1.18	1.04–1.33	0.009
T1	180/369	Reference			Reference
T2	166/312	1.19	0.88–1.61	0.250	1.36	0.98–1.89	0.069
T3	131/256	1.10	0.80–1.51	0.557	1.45	1.00-2.10	0.048
Age < 60	357/758	1.15	1.03–1.29	0.012	1.21	1.06–1.40	0.007
T1	71/208	Reference			Reference
T2	127/241	2.15	1.47–3.15	< 0.001	2.23	1.47–3.39	< 0.001
T3	159/309	2.05	1.42–2.94	< 0.001	2.56	1.66–3.95	< 0.001

non-HDL-C, non-high-density lipoprotein; OR, odds ratio; CI, confidence interval

Table 5 shows the results of the sex-focused examination of patients with ACS. No significant interaction was found between sex and the levels of non-HDL-C (interaction p = 0.491). In the case of male patients, after accounting for potential risk factors, the comprehensive logistic regression analysis identified non-HDL-C as an independent risk factor for increased Gensini scores (OR = 1.15; 95%CI = 1.04–1.28; p = 0.010). Individuals in both the T2 (OR = 1.57; CI = 1.16–2.11; p = 0.003) and T3 (OR = 1.60; CI = 1.16–2.22; p = 0.004) groups exhibited an increase of 1.57 and 1.60 times higher, respectively, in attaining elevated Gensini scores than those in the T1 group. For female patients, after accounting for potential risk factors, the complex logistic regression analysis indicated that non-HDL-C independently led to higher Gensini scores (OR = 1.31; 95%CI = 1.10–1.57; p = 0.002). The T2 (OR = 1.90; CI = 1.14–3.17; p = 0.014) and T3 (OR = 2.54; CI = 1.48–4.35; p < 0.001) groups exhibited 1.90 and 2.54 times higher risks of having elevated Gensini scores than the T1 group.

Table 5

Correlations between non-HDL-C and Gensini scores across various sexes
	Events/N	Model 1			Model 2
		OR	95CI%	P	OR	95CI%	P	P for interaction
Sex								0.491
Male	601 /1208	1.07	0.98–1.17	0.116	1.15	1.04–1.28	0.010
T1	199 /445	Reference			Reference
T2	207 /382	1.46	1.11–1.92	0.007	1.57	1.16–2.11	0.003
T3	195 /381	1.30	0.99–1.71	0.064	1.60	1.16–2.22	0.004
Female	233 /487	1.16	1.01–1.33	0.034	1.31	1.10–1.57	0.002
T1	52 /132	Reference			Reference
T2	86 /171	1.56	0.98–2.47	0.059	1.90	1.14–3.17	0.014
T3	95 /184	1.64	1.04–2.58	0.032	2.54	1.48–4.35	< 0.001

non-HDL-C, non-high-density lipoprotein; OR, odds ratio; CI, confidence interval

Non-HDL-C predicts the incremental effect of high Gensini scores in patients with ACS

To assess patients with ACS, an ROC curve was formulated (Fig. 3), and the effectiveness of the initial risk, non-HDL-C (baseline risk model + non-HDL-C), and TC (baseline risk model + TC) models in predicting higher Gensini scores in patients with ACS was also examined. Table 6 illustrates the C-statistics, NRI, and IDI for different models. The C-statistic revealed that the non-HDL-C model showed an advancing predictive effect over the original risk model (0.6584 [95%CI; 0.6326–0.6841] versus 0.6483 [95%CI; 0.6224–0.6743], p = 0.043). Calculations of the NRI (0.0382 [95% CI = 0.0065–0.0699]; p = 0.018) and IDI (0.0091 [95% CI = 0.0047–0.0136]; p < 0.001] demonstrated a significant incremental predictive effect of the inclusion of non-HDL-C in the initial risk assessment.

Table 7 shows the outcomes of internally validating the model using the bootstrap technique involving 1000 resamplings, yielding an AUC of 0.6234; the AUC for the non-HDL-C model developed for this study was 0.6584, a comparable finding, indicative of the model's robustness level.

Table 6

Incremental predictive power and predictive strength of various models incorporating NRI, IDI, and C statistics
Model	C-statistic (95%Cl)	P	NRI (95%Cl)	P	IDI (95%Cl)	P
Baseline risk model	0.6483(0.6224–0.6743)	Ref	Ref		Ref
+TC	0.6566(0.6308–0.6824)	0.065	0.0300(-0.0005-0.0604)	0.054	0.0073(0.0033–0.0113)	< 0.001
+Non-HDL-C	0.6584(0.6326–0.6841)	0.043	0.0382(0.0065–0.0699)	0.018	0.0091(0.0047–0.0136)	< 0.001

The baseline risk model includes age, BMI, SBP, DBP, TG, eGFR, creatinine, glycemia, uric acid, Hb, hs-CRP, HbA1c, LVEF, sex, smoking, previous MI, previous CABG, previous PCI, hypertension, previous stroke, family history, and insulin.

Hb, haemoglobin; MI, myocardial infarction; LVEF, left ventricular ejection fraction; DBP, diastolic blood pressure; PCI, percutaneous coronary intervention; TC, total cholesterol; TG, triglycerides; CABG, coronary artery bypass grafting; BMI, body mass index; SBP, systolic blood pressure; hs-CRP, high-sensitivity C-reactive protein; HbA1c, glycosylated haemoglobin; eGFR, estimated glomerular filtration rate; CI, confidence interval; IDI, Integrated Discriminant Improvement Index; NRI, Net Reclassification Index

Table 7

Results of bootstrap-based internal validation of models
AUC	Sensitivity	Specificity
0.6182	0.6234	0.4513
AUC, area under the curve

This study explored the association between non-HDL-C and coronary artery disease in individuals with ACS, aiming to collect vital data to mitigate ACS's impact and create a non-intrusive serological index for predicting coronary artery disease in these cases. The primary outcomes of our study reveal that 1) non-HDL-C independently contributes to the elevated risk of coronary artery disease in patients with ACS, as evidenced by uniform findings across various subgroup analyses; 2) non-HDL-C levels are dose-dependently related to coronary artery disease in patients with ACS; and 3) non-HDL-C has significant incremental predictive power for coronary artery disease in patients with ACS.

Currently, CVD caused by atherosclerosis significantly impacts life expectancy, and its incidence is increasing in underdeveloped areas. Managing dyslipidaemia is important in preventing CVD [8]. Recent research suggests that lowering LDL-C levels significantly reduces the incidence of adverse cardiovascular events [13]. However, LDL-C is affected by individual differences and metabolic abnormalities, and it does not accurately reflect the true burden of atherogenic lipids, whereas non-HDL-C reflects cholesterol concentrations in all atherogenic lipoprotein particles and is considered superior to LDL-C in predicting cardiovascular events [14]. Reduction in non-HDL-C levels is believed to reduce the risk of CVD, with consensus recognising it as an important target for coronary heart disease prevention [15]. ACS is a high-risk subtype of coronary atherosclerotic heart disease, with rupture of atherosclerotic plaques serving as an essential pathogenic mechanism. Experimental and clinical studies have shown that lowering lipid levels can reduce the incidence of plaque rupture [16]. However, the association between non-HDL-C and the intensity of coronary artery disease in patients with ACS remains insufficiently investigated. Therefore, this study clarified this association, aiming to furnish proof for lipid-reducing treatments in patients with ACS. To the best of our knowledge, this is the first study to discover a dose-response correlation between non-HDL-C and coronary artery lesions in patients with ACS using restricted cubic spline analysis. We observed a significant incremental predictive value of non-HDL-C for coronary artery lesions in patients with ACS. Furthermore, a forward-looking cohort analysis using the Multinational Cardiovascular Risk Consortium revealed a significant association between non-HDL-C and the likelihood of ASCVD development, with uniform correlations observed among different sexes and ages, particularly in younger individuals [17]. In our study, we also found that the independent correlation between non-HDL-C and coronary artery disease in patients with ACS did not change with sex and age. In the elderly/non-elderly subgroup analysis, non-elderly patients with ACS were at a greater risk of coronary artery disease. This finding is consistent with that of previous studies. These findings emphasise the potential benefits of tighter control over non-HDL-C levels in young individuals to prevent coronary artery disease. Moreover, prior studies have demonstrated that non-HDL-C is a significant contributor to and predictor of coronary atherosclerotic heart disease in both patients with and without diabetes [18–20]. This is consistent with our observations in patients with ACS. In this study, we found that non-HDL-C was strongly associated with coronary artery disease in patients with ACS and showed a dose-response relationship with coronary artery disease. Current guidelines recommend non-HDL-C as a secondary target for lipid-lowering therapy in patients with dyslipidaemia to prevent CVD [8]. These findings can further increase awareness of non-HDL-C in patients with ACS and provide insights into lipid-lowering therapeutic strategies. They also serve as a reference for selecting non-invasive serologic predictors of coronary artery lesions in this group of patients.

However, this study has some limitations. First, it was a retrospective study, limited by its inevitable inherent shortcomings, from which causality cannot be inferred. Second, this was a single-centre study that included only Asian populations and did not perform external validation of the model. Hence, careful consideration is advised in the interpretation and extrapolation of the study's findings. Further studies should focus on several key areas across multiple centres to corroborate these results. Longitudinal studies are needed to confirm the robustness of non-HDL-C as a predictor of coronary artery disease severity over time and its impact on long-term patient outcomes.

This study demonstrated that non-HDL-C is dose-dependently associated with the severity of coronary artery disease in patients with ACS and can serve as a valuable tool for predicting its severity.

ACS, acute coronary syndromes

AUC, area under the curve

BMI, body mass index

CI, confidence interval

Cr, creatinine

CVD, cardiovascular disease

DBP, diastolic blood pressure

DM, diabetes mellitus

eGFR, estimated glomerular filtration rate

FBG, fasting blood glucose

Hb, haemoglobin

HbA1c, glycosylated haemoglobin

HDL-C, high-density lipoprotein cholesterol

IDI, Integrated Discriminant Improvement Index

IR, insulin resistance

LDL-C, low-density lipoprotein cholesterol

LVEF, left ventricular ejection fraction

MI, myocardial infarction

Non-HDL-C, non-high-density lipoprotein cholesterol

NRI, Net Reclassification Index

NSTEMI, non-ST-segment elevation myocardial infarction

OR, odds ratio

PCI, percutaneous coronary intervention

ROC, receiver operating characteristic

SBP, systolic blood pressure

STEMI, ST-segment elevation myocardial infarction

TC, total cholesterol

TG, triglycerides

Ethics approval and consent to participate

This study was performed in accordance with the Declaration of Helsinki guidelines and approved by the Ethics Committee of Zunyi Medical University's Affiliated Hospital. The requirement for informed consent was waived owing to the study's retrospective design.

Consent for publication

Not applicable.

Availability of data and materials

Data produced or examined in this study can be obtained from the lead author upon a justified request.

Competing interests

The authors declare that they have no competing interests.

Funding

The Biomedical Joint Funding for this project came from the Guizhou Provincial Science and Technology Department (No. Qiankehe LH Zi [2017] 7109).

Authors' contributions

XYY and KL were responsible for the study's design, data analysis, and manuscript composition; XYY, KL, LC, HX, CLY, YXC, YML, and JJW were in charge of data collection and interpretation; GXX and YCZ oversaw the analytical process. BS and YM meticulously edited the manuscript to enhance its intellectual substance. Every author reviewed and sanctioned the ultimate version of the manuscript.

Acknowledgements

The authors would like to thank the Medical Big Data Research Platform at Zunyi Medical University's Affiliated Hospital for its assistance.

Clinical trial number

Not applicable

Bergmark BA, Mathenge N, Merlini PA, Lawrence-Wright MB, Giugliano RP. Acute coronary syndromes. Lancet (London England). 2022;399(10332):1347–58.
Deng P, Fu Y, Chen M, Wang D, Si L. Temporal trends in inequalities of the burden of cardiovascular disease across 186 countries and territories. Int J Equity Health. 2023;22(1):164.
Li Y, Cao GY, Jing WZ, Liu J, Liu M. Global trends and regional differences in incidence and mortality of cardiovascular disease, 1990–2019: findings from 2019 global burden of disease study. Eur J Prev Cardiol. 2023;30(3):276–86.
Pagidipati NJ, Peterson ED. Acute coronary syndromes in women and men. Nat reviews Cardiol. 2016;13(8):471–80.
Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, Claeys MJ, Dan GA, Dweck MR, Galbraith M, et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720–826.
Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, McKenney JM, Grundy SM, Gill EA, Wild RA, et al. National lipid association recommendations for patient-centered management of dyslipidemia: part 1–full report. J Clin Lipidol. 2015;9(2):129–69.
Çimen S, Gooya A, Grass M, Frangi AF. Reconstruction of coronary arteries from X-ray angiography: A review. Med Image Anal. 2016;32:46–68.
Reiner Z, Catapano AL, De Backer G, Graham I, Taskinen MR, Wiklund O, Agewall S, Alegria E, Chapman MJ, Durrington P, et al. ESC/EAS Guidelines for the management of dyslipidaemias: the Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS). Eur Heart J. 2011;32(14):1769–818.
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130(6):461–70.
Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel JP, Lloyd-Sherlock P, Epping-Jordan JE, Peeters G, Mahanani WR, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet (London England). 2016;387(10033):2145–54.
Yang X, Li K, Wen J, Yang C, Li Y, Xu G, Ma Y. Association of the triglyceride glucose-body mass index with the extent of coronary artery disease in patients with acute coronary syndromes. Cardiovasc Diabetol. 2024;23(1):24.
Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019;287:181–3.
Sigamani A, Gupta R. Revisiting secondary prevention in coronary heart disease. Indian Heart J. 2022;74(6):431–40.
Brunzell JD, Davidson M, Furberg CD, Goldberg RB, Howard BV, Stein JH, Witztum JL. Lipoprotein management in patients with cardiometabolic risk: consensus conference report from the American Diabetes Association and the American College of Cardiology Foundation. Journal of the American College of Cardiology 2008, 51(15):1512–1524.
Robinson JG, Wang S, Smith BJ, Jacobson TA. Meta-analysis of the relationship between non-high-density lipoprotein cholesterol reduction and coronary heart disease risk. J Am Coll Cardiol. 2009;53(4):316–22.
Libby P, Pasterkamp G, Crea F, Jang IK. Reassessing the Mechanisms of Acute Coronary Syndromes. Circul Res. 2019;124(1):150–60.
Brunner FJ, Waldeyer C, Ojeda F, Salomaa V, Kee F, Sans S, Thorand B, Giampaoli S, Brambilla P, Tunstall-Pedoe H, et al. Application of non-HDL cholesterol for population-based cardiovascular risk stratification: results from the Multinational Cardiovascular Risk Consortium. Lancet (London England). 2019;394(10215):2173–83.
Lehto S, Rönnemaa T, Haffner SM, Pyörälä K, Kallio V, Laakso M. Dyslipidemia and hyperglycemia predict coronary heart disease events in middle-aged patients with NIDDM. Diabetes. 1997;46(8):1354–9.
Jiang R, Schulze MB, Li T, Rifai N, Stampfer MJ, Rimm EB, Hu FB. Non-HDL cholesterol and apolipoprotein B predict cardiovascular disease events among men with type 2 diabetes. Diabetes Care. 2004;27(8):1991–7.
Al-Daghri NM, Al-Attas OS, Al-Rubeaan K. The atherogenic and metabolic impact of non-HDL cholesterol versus other lipid sub-components among non-diabetic and diabetic Saudis. Lipids Health Dis. 2007;6:9.

No competing interests reported.

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Association between non-high-density lipoprotein cholesterol and coronary artery disease in patients with acute coronary syndrome: a single-centre retrospective cross-sectional analysis

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Abstract

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Methods

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Background

Methods

Study design and population

Data measurement and definitions

Statistical analysis

Results

Baseline characteristics

Clinical outcomes

Subgroup analyses

Non-HDL-C predicts the incremental effect of high Gensini scores in patients with ACS

Discussion

Conclusions

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