Remnant Cholesterol as a Predictor of Cardiovascular Outcomes in Acute Coronary Syndrome Patients with Low-Density Lipoprotein Cholesterol Levels Below 1.8 mmol/L: Insights from the MPCS-ACS Study

doi:10.21203/rs.3.rs-4856821/v1

Aims: This study aimed to investigate the influence of remnant cholesterol (RC) on the risk of cardiovascular events in patients with acute coronary syndrome (ACS) who have tightly controlled low-density lipoprotein cholesterol (LDL-C) levels.

Methods: Analyzing data from the MPCS-ACS study, this investigation targeted individuals aged 18 to 79 diagnosed with ACS, who were admitted to three Chinese medical centers between June 2016 and May 2021, and who maintained LDL-C levels below 1.8 mmol/L.

Results: Out of 17,500 screened patients, 4,329 were analyzed. RC levels were calculated, with patients then categorized into quartiles. The primary focus was on all-cause and cardiovascular mortality. Secondary objectives involved assessing the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) as well as major adverse cardiovascular events (MACE). Through analysis of outcome events across different groups, coupled with multivariable adjustments and the use of restricted cubic splines, findings revealed that RC is a significant, independent risk factor for adverse outcomes in ACS patients when LDL-C levels are strictly controlled below 1.8 mmol/L, and this association remains significant even when LDL-C levels are further controlled below 1.4 mmol/L. Restricted cubic splines analysis illustrated a U-shaped, non-linear relationship between RC levels and endpoint events (all-cause and cardiovascular mortality, MACE, and MACCE), with the lowest risk observed at RC levels ranging from 0.29 to 0.45 mmol/L.

Conclusions: The study identifies RC as an independent risk factor for cardiovascular complications and highlights its U-shaped correlation with adverse outcomes.

Acute Coronary Syndrome

Cardiovascular Risk Management

Cholesterol Management Strategies

Low-Density Lipoprotein Cholesterol

Remnant cholesterol

Acute Coronary Syndrome (ACS) is a leading cause of death from cardiovascular diseases. Improving treatment strategies remains a core focus of medical research. Effective management of low-density lipoprotein cholesterol (LDL-C) is crucial for reducing the recurrence of cardiovascular events [1, 2]. Current medical guidelines recommend controlling LDL-C levels below 1.4 mmol/L to improve ACS patients' prognosis [3, 4]. Despite achieving strict LDL-C targets, residual cardiovascular risks persist, underscoring the importance of addressing other potential risk factors [5, 6].

Recently, remnant cholesterol (RC) has emerged as a significant risk factor. It is primarily present in triglyceride-rich, non-high-density lipoprotein particles, including very low-density and intermediate-density lipoproteins [7–9]. Due to their larger size, RC particles are more likely to accumulate in arterial walls, accelerating atherosclerosis and increasing cardiovascular disease risk [10–12]. Studies indicate that when LDL-C levels are below 1.8 mmol/L, RC acts as an independent risk factor for cardiovascular diseases in non-ACS populations [13, 14]. However, the relevance of this finding for ACS patients, especially those with LDL-C levels strictly controlled below 1.8 mmol/L or 1.4 mmol/L, remains to be fully tested. Although recent studies have explored the potential role of RC in patients with ST-segment elevation myocardial infarction, they have not conclusively determined whether RC is a prognostic risk factor in the ACS population after achieving LDL-C target levels. Therefore, this further highlights the necessity of studying the impact of RC under different LDL-C level controls [15].

In this context, the study aims to explore the role of RC in ACS patients with stringent LDL-C control. The hypothesis posits that RC is an independent cardiovascular risk factor in this specific patient group, which will be validated through stratified studies across different LDL-C levels. Unlike existing research, this study focuses on a specific group of ACS patients with LDL-C levels below 1.8 mmol/L, and further stratifies patients with LDL-C levels below either 1.8 mmol/L or 1.4 mmol/L. This approach enables a more precise elucidation of the relationship between RC and cardiovascular risk, thereby highlighting the potential value of RC in managing cardiovascular event risk.

Ethics statement

The experiments were authorized by the Third Affiliated Hospital of Jinzhou Medical University (Approval No. KX2024024). All procedures strictly adhered to the Declaration of Helsinki. All subjects involved were fully informed of the study objectives and provided written informed consent before sampling.

Study Design and Participants

This post hoc analysis of the MPCS-ACS study (ClinicalTrials.gov NCT05164601, 01/6/2016) analyzed data from a large, multi-center cohort of ACS patients aged 18 to 79, hospitalized across three Chinese research centers from June 2016 to May 2021. From the study's inception, participants underwent regular follow-ups every 6 to 12 months. The inclusion criteria were consistent with those of the MPCS-ACS study: 1) Individuals aged 18 to 79. 2) A diagnosis of ACS, including Unstable Angina and Acute Myocardial Infarction, based on clinical symptoms, ECG changes, and elevated cardiac biomarkers. 3) Admission for ACS treatment at participating hospitals from June 1, 2016, to May 31, 2021. To examine the impact of LDL-C and RC levels on cardiovascular outcomes, 17,500 patients were initially screened. The exclusion criteria included: 1) Refusal of recommended treatment at admission. 2) LDL-C levels ≥ 1.8 mmol/L at admission. 3) LDL-C levels ≥ 1.8 mmol/L during two subsequent follow-ups. After screening, 4,329 ACS patients were included. The study's flow chart is depicted in Fig. 1.

Data collection

Basic characteristics and medical histories of ACS patients were collected, including gender, age, smoking status, alcohol consumption, hypertension, diabetes, stroke history, ACS types, and family history of cardiovascular disease (CVD). After fasting for at least 12 hours after admission, morning venous blood was drawn for biochemical indicator testing using equipment for chemical analysis (Dimension AR/AVL Clinical Chemistry System, Newark, NJ, USA), which included Blood Urea Nitrogen (BUN), Uric Acid, Serum Creatinine (SCr), Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), Triglycerides (TG), Total Cholesterol (TC); High-Density Lipoprotein-C (HDL-C) and LDL-C. Echocardiography was performed on all participants during their hospitalization, and the measurements of the Right Atrial Diameter (RAD), Left Ventricular Diastolic Dimension (LVDD), Interventricular Septal Thickness (IVST), and Left Ventricular Ejection Fraction (LVEF) were recorded. Medication data were obtained from medical records and self-reports, while smoking and alcohol consumption statuses were assessed via self-report.

Smoking status referred to individuals who had smoked for six months or more, either continuously or cumulatively, classifying them as smokers. Alcohol consumption was classified based on drinking frequency, with individuals drinking more than three times weekly considered as alcohol consumers [16–18]. Diabetes Mellitus was defined by a glycosylated hemoglobin level over 6.5%, treated with oral drugs or insulin [19]. Hypertension was identified by blood pressure exceeding 140/90 mmHg or antihypertensive drug use [19]. Stroke was confirmed by brain imaging (CT or MRI) indicating tissue damage from vascular occlusion (ischemic) or rupture (hemorrhagic) [20]. Family history of CVD involved cardiovascular diseases in direct relatives before age 60 [21].

Remnant Cholesterol measurement

Levels of RC were estimated as TC minus HDL-C minus calculated LDL-C [13]. Participants were categorized into four groups based on baseline RC levels: Q1: < 0.29 mmol/L, Q2: 0.29–0.45 mmol/L, Q3: 0.46–0.71 mmol/L, Q4: > 0.71 mmol/L.

Endpoints

This study tracked CAD patients over periods ranging from 6 to 90 months, using clinic visits, calls, and surveys conducted by trained investigators for evaluations. Key evaluations encompassed lipid profiles, medication adherence, and recording of adverse events. The primary objective was to study long-term mortality, specifically all-cause mortality (ACM) and cardiac mortality (CM), with the latter arising from coronary heart disease, cardiogenic shock, or sudden cardiac events.

Secondary objectives included evaluating the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) and major adverse cardiovascular events (MACE). MACE comprised cardiac death, non-fatal myocardial infarction, major aortic pathologies (aortic dissection, aortic aneurysm rupture), unstable angina needing medical treatment, acute heart failure requiring hospitalization (Killip Class IV), and target vessel revascularization. MACCE expanded on MACE to include cerebrovascular complications: ischemic stroke, hemorrhagic stroke, transient ischemic attacks (TIAs), and subarachnoid hemorrhage.

Statistical analyses

Statistical analyses were performed using SPSS 29.0 for Windows (SPSS Inc., Chicago, IL, USA) and R version 4.1.2. RC levels were treated as continuous data and divided into four categories. Continuous variables were reported as mean or median, and categorical variables as percentages. Differences in normally distributed variables were analyzed using a t-test, and non-normally distributed variables with the Mann–Whitney U test. Categorical variables were compared using a χ² test. Kaplan–Meier analysis was employed to calculate the cumulative incidence rates of long-term events, with group comparisons made via a log-rank test. The models were adjusted for the following variables: basic characteristics (sex, age, smoking status, drinking status), medical history (hypertension, diabetes, stroke, type of acute coronary syndrome, family history of cardiovascular disease), medication history (ACEI/ARBs, β-blockers, CCBs, and nitrates use), biochemical indicators (BUN, uric acid, SCr, AST, ALT, TG, TC, HDL-C, LDL-C), and echocardiogram indices (RAD, LVDD, IVST, LVEF). Cox proportional hazards models calculated hazard ratios (HR) and 95% confidence intervals for adverse outcomes. The R package 'smoothHR' was utilized for this analysis. HR curves, allowing for non-linear associations, examined the relationship between RC and adverse outcomes, with Q2 as the reference category and adjustments for covariates.

Baseline characteristics of patients

The study included 4,329 ACS patients with LDL-C levels below 1.8 mmol/L, among whom 1,770 had levels consistently under 1.4 mmol/L. Patients were categorized by RC levels, with baseline characteristics detailed in Table 1. The average participant age was around 55 years, with males comprising about 64%. The Q4 group, which had elevated RC levels, showed higher percentages of patients with hypertension (37.8%), diabetes (31.05%), stroke (16.33%), unstable angina (78.92%), and a family history of CVD (24.50%) compared to other groups. The baseline characteristics of ACS patients with LDL-C levels between 1.8 to 1.4 mmol/L and those under 1.4 mmol/L were examined to highlight variations at different LDL-C thresholds. Baseline characteristics were similar across these three groups when compared to the overall population (Tables S1 and S2).

Clinical outcomes in patients

Over a median follow-up of 54 months, 205 cases of CM, 483 cases of ACM, 807 MACE cases, and 1050 MACCE cases were recorded. Outcome events were documented across the overall population and for patients with LDL-C levels from 1.8 to 1.4 mmol/L and those below 1.4 mmol/L, segmented by RC groups. Notable differences in outcome event incidence were seen across the four groups (Table 2). Kaplan-Meier curves were used to compare outcome event occurrences over time among the four groups for the overall population, and specifically for those with LDL-C levels from 1.8 to 1.4 mmol/L and those below 1.4 mmol/L. The analysis showed that higher RC levels correlated with increased cumulative risk, whereas the Q2 group (RC levels 0.29–0.45 mmol/L) demonstrated lower cumulative risks (Figs. 2, S1, and S2). Concurrently, comparing the incidence rates and HRs among the four groups revealed a U-shaped curve in outcome event incidence with increasing RC levels (Figs. 3, S3, and S4).

Association Between Remnant Cholesterol Levels and Prognostic Outcomes in ACS Patients

To elucidate the link between RC levels and prognosis in ACS patients, adjustments were made for cardiovascular risk factors, including basic features, previous history, medication history, biochemical indicators, and echocardiogram indices. Findings revealed significant increases in CM event hazards for both Q1 and Q4 groups, with HRs of 1.750 (1.062–2.884) and 4.331 (2.599–7.216) respectively, referencing Q2. Similarly, ACM event HRs were 1.765 (1.304–2.390) for Q1 and 3.424 (2.451–4.783) for Q4, compared to Q2. MACE risk also increased for Q1 and Q4, with HRs of 1.859 (1.477–2.339) and 3.206 (2.470–4.162) respectively, compared to Q2. Additionally, MACCE risks rose for Q1, Q3, and Q4 compared to Q2, with HRs of 1.956 (1.600–2.392), 1.392 (1.121–1.729), and 3.064 (2.429–3.865) respectively. Importantly, analyses of patients with LDL-C levels from 1.8 to 1.4 mmol/L and those below 1.4 mmol/L showed that RC continues to be a critical risk factor for adverse outcomes, even with LDL-C at 1.4 mmol/L, as detailed in Table 3. Sensitivity analysis results, shown in Table S3, reveal that after adjusting for adverse events within 3 years and liver function abnormalities, the association between RC and adverse outcomes remained consistent. Furthermore, the analysis using restricted cubic splines revealed a U-shaped, non-linear relationship between RC levels and endpoint events, including all-cause mortality, cardiovascular mortality, MACE, and MACCE across the population. The RC concentrations associated with the lowest risk for these outcomes ranged from 0.29 mmol/L to 0.45 mmol/L (Fig. 4).

Despite numerous studies highlighting RC's role in cardiovascular diseases, few examine its impact under strict LDL-C control below 1.8 mmol/L or 1.4 mmol/L, resulting in notable selection bias [13, 22–24]. This study, which analyzed ACS patients in the MPCS-ACS cohort with LDL-C levels consistently below 1.8 mmol/L, demonstrates a significant correlation between RC levels and adverse outcomes, despite strict LDL-C control. This finding not only aligns with previous studies but also highlights the crucial role of RC level management in cardiovascular risk assessment, extending beyond current LDL-C strategies. Importantly, the analysis revealed a U-shaped, non-linear relationship between RC levels and adverse outcomes, indicating that extremely low or high RC levels could elevate the risk for ACS patients. Notably, the RC concentrations associated with the lowest risk for these outcomes ranged from 0.29 mmol/L to 0.45 mmol/L. This discovery suggests a potential target range for RC that is crucial for developing future specific treatment guidelines.

Although previous studies have identified RC's role in cardiovascular diseases, this study uniquely focuses on patients with strictly controlled LDL-C levels. This focus on strictly controlled LDL-C levels enabled a more precise assessment of RC's role as an independent risk factor. The mechanism of RC in driving adverse cardiovascular outcomes is gaining attention as it emerges as a novel CVD risk factor. However, LDL-C continues to be a key marker of atherosclerosis and the primary pathophysiological process leading to cardiovascular events [2, 25–28]. Without strict LDL-C targets, focusing solely on RC does not effectively guide individualized clinical treatment. Grouping analysis of patients with LDL-C levels below 1.8 mmol/L conclusively demonstrated RC's close relationship to the adverse prognosis in ACS patients.

Including participants with lower baseline lipid levels led to the discovery of a non-linear, U-shaped relationship between RC levels and adverse outcomes. This offers a fresh perspective on cardiovascular risk management, underscoring the need to keep RC within an optimal range. Analysis in this study of patients with LDL-C levels consistently below 1.8 mmol/L revealed that even when LDL-C meets treatment targets, RC remains closely linked to adverse ACS outcomes. This underscores the need in clinical practice to not only focus on LDL-C levels but also assess and manage RC levels for a more comprehensive approach to reducing cardiovascular event risks.

This study highlights the crucial role of assessing and managing RC in ACS patients with tightly controlled LDL-C levels. However, additional research is required to establish the causal relationship between RC and cardiovascular events and to identify how RC intervention could enhance cardiovascular outcomes [29–31]. Considering the limited prevalence of RC measurement techniques, future studies should focus on creating simpler, more precise testing methods. Moreover, exploring the role of RC across various populations is essential to gauge its global value in preventing cardiovascular diseases. Nonetheless, applying these findings requires consideration of costs, test availability, and comprehensive treatment strategies [32–35]. As insights deepen and targeted RC reduction strategies emerge, RC is set to play a significant role in cardiovascular risk management.

This study provides valuable insights by leveraging the data from the large, multicenter MPCS-ACS cohort, specifically examining the impact of RC in patients with tightly controlled LDL-C levels. The focus on a subgroup with LDL-C levels consistently below 1.8 mmol/L, particularly those below 1.4 mmol/L, offers a unique perspective on the implications of RC levels under stringent lipid management, a critical area that has not been extensively studied previously. However, the study also has limitations. As an observational study, it is prone to confounding and cannot definitively establish causality. The rarity of patients with LDL-C levels below 1.8 mmol/L, especially those below 1.4 mmol/L within the cohort, leads to larger confidence intervals for some findings and introduces selection bias. This highlights the need for additional large-scale multicenter prospective cohort studies to validate these findings and explore the mechanisms further.

This study highlights the significance of RC as an independent risk factor for adverse cardiovascular outcomes in ACS patients with well-controlled LDL-C levels. Our findings advocate for the inclusion of RC in routine lipid management, suggesting that assessing both RC and LDL-C could enhance risk prediction and lead to more personalized treatment approaches. Future guidelines should consider incorporating RC targets, based on the identified U-shaped relationship with cardiovascular events. Further research is needed to develop accessible RC measurement techniques and to elucidate the underlying mechanisms by which RC influences cardiovascular risk. Ultimately, this study calls for a broader approach to lipid management in ACS patients to improve overall cardiovascular health outcomes.

Acknowledgements

The authors would like to express their gratitude to all the nurses and doctors at the Heart Centre of the First Affiliated Hospital of Xinjiang Medical University, the Traditional Chinese Medicine Hospital of the Xinjiang Uygur Autonomous Region, and the Third Affiliated Hospital of Jinzhou Medical University for their dedication and hard work. Furthermore, gratitude is extended to all the patients who provided blood samples for this research.

Author contributions

JS.Z and JZ conceptualized the study. HT.Y and YY conducted the research, encompassing data collection, analysis, and interpretation. HT.Y and JK.L contributed to the preparation of figures, and both drafted and revised the manuscript. HT.Y and JZ offered academic guidance. All authors reviewed and approved the final manuscript.

Funding

NA

Data availability

Data of this study were available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

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Table.1 Baseline characteristics of all patients

Variables	Q1	Q2	Q3	Q4
	(< 0.29 mmol/L)	(0.29 - 0.45 mmol/L)	(0.46 - 0.71 mmol/L)	(> 0.71 mmol/L)
Basic Features
Male, N (%)	638 (62.3)	677 (62.17)	760 (65.35)	677 (64.29)
Age, year^#	55.73 ± 11.49	55.08 ± 11.44	54.58 ± 11.29	55.31 ± 11.71
Smoking, N (%)	407 (39.75)	432 (39.67)	443 (38.09)	372 (35.33)
Drinking, N (%)	179 (17.48)	190 (17.45)	197 (16.94)	165 (15.67)
Previous History
Hypertension, N (%)	369 (36.04)	410 (37.65)	432 (37.15)	398 (37.80)
Diabetes, N (%)	306 (29.88)	331 (30.39)	356 (30.61)	327 (31.05)
Stroke, N (%)	179 (17.48)	176 (16.16)	195 (16.77)	172 (16.33)
Type of ACS (UA), N (%)	774 (75.59)	825 (75.76)	897 (77.13)	831 (78.92)
Family history of CVD, N (%)	277 (27.05)	291 (26.72)	290 (24.94)	258 (24.50)
History of medications
ACEI/ARB use, N (%)	845 (82.52)	899 (82.55)	966 (83.06)	888 (84.33)
β-blockers use, N (%)	907 (88.57)	967 (88.80)	1045 (89.85)	941 (89.36)
CCB use, N (%)	742 (72.46)	813 (74.66)	886 (76.18)	803 (76.26)
Nitrates use, N (%)	747 (72.95)	798 (73.28)	873 (75.06)	795 (75.50)
Biochemical indicator
BUN, mmol/L^#	5.83 ± 1.80	5.95 ± 2.55	5.80 ± 1.22	5.78 ± 1.38
Uric Acid, µmol/L^*	349.80 (300.00-396.35)	346.00 (296.00-392.00)	345.00 (301.30-401.00)	348.70 (294.90-403.00)
SCr, µmol/L^#	79.26 ± 25.87	78.00 ± 10.94	78.31 ± 10.62	78.30 ± 10.94
AST, IU/L^#	29.22 ± 24.48	29.45 ± 20.18	28.47 ± 16.30	29.12 ± 16.68
ALT, IU/L^#	34.83 ± 22.11	35.05 ± 35.70	35.57 ± 40.64	35.13 ± 25.29
TG, mmol/L^*	1.47 (1.05-2.06)	1.43 (1.05-2.07)	1.44 (1.05-2.05)	1.79 (1.05-2.10)
TC, mmol/L^#	2.75 ± 0.28	2.79 ± 0.30	3.36 ± 0.85	3.31 ± 0.46
HDL-C, mmol/L^#	1.04 ± 0.23	0.94 ± 0.93	1.33 ± 0.96	0.84 ± 0.28
LDL-C, mmol/L^#	1.51 ± 0.20	1.48 ± 0.22	1.46 ± 0.23	1.23 ± 0.34
RC, mmol/L^#	0.21 ± 0.05	0.36 ± 0.05	0.57 ± 0.06	1.25 ± 0.36
Echocardiogram
RAD, mm^#	34.51 ± 4.21	34.61 ± 4.72	34.20 ± 5.03	34.52 ± 4.41
LVDD, mm^#	49.62 ± 5.61	49.66 ± 6.01	49.67 ± 13.30	49.54 ± 6.07
IVST, mm^#	9.12 ± 1.08	9.19 ± 0.99	9.12 ± 1.16	9.12 ± 1.11
LVEF, %^#	54.18 ± 5.57	54.28 ± 5.38	54.22 ± 5.50	54.14 ± 5.72

* median (Q1-Q3),^#mean ± SD, # n (%)

UA, Unstable Angina; ACEI, Angiotensin-converting Enzyme Inhibitor; ARB, Angiotensin Receptor Blocker; CCB, Calcium Channel Blockers; BUN, Blood Urea Nitrogen; SCr, Serum Creatinine; AST, Aspartate Aminotransferase; ALT, Alanine Aminotransferase; TG, Triglycerides; TC, Total cholesterol; HDL-C, High-density lipoprotein-C; LDL-C, Low-density lipoprotein-C; RC, Remnant cholesterol; RAD, Right Atrial Diameter; LVDD, Left Ventricular Diastolic Dimension; IVST,Interventricular Septal Thickness; LVEF, Left Ventricular Ejection Fraction.

Table.2 Clinical outcomes of patients

Variables	Q1	Q2	Q3	Q4	P value
Variables	(< 0.29 mmol/L)	(0.29 - 0.45 mmol/L)	(0.46 - 0.71 mmol/L)	(> 0.71 mmol/L)	P value
All patients (N = 4329)
CM, N (%)	40 (3.91)	25 (2.30)	48 (4.13)	92 (8.74)	< 0.001
ACM, N (%)	112 (10.94)	70 (6.43)	101 (8.68)	200 (18.99)	< 0.001
MACE, N (%)	204 (19.92)	118 (10.84)	163 (14.02)	322 (30.58)	< 0.001
MACCE, N (%)	275 (26.86)	152 (13.96)	223 (19.17)	400 (37.99)	< 0.001
1.4 mmol/L ≤ LDL-C level < 1.8 mmol/L (N = 2559)
CM, N (%)	32 (4.20)	22 (3.01)	34 (4.80)	31 (8.66)	< 0.001
ACM, N (%)	80 (10.50)	52 (7.11)	70 (9.88)	68 (18.99)	< 0.001
MACE, N (%)	144 (18.90)	81 (11.08)	107 (15.11)	111 (31.01)	< 0.001
MACCE, N (%)	199 (26.12)	100 (13.68)	141 (19.92)	137 (38.27)	< 0.001
LDL-C level < 1.4 mmol/L (N = 1770)
CM, N (%)	8 (3.05)	3 (0.84)	14 (3.08)	61 (8.78)	< 0.001
ACM, N (%)	32 (12.21)	18 (5.03)	31 (6.81)	132 (18.99)	< 0.001
MACE, N (%)	60 (22.90)	37 (10.34)	56 (12.31)	211 (30.36)	< 0.001
MACCE, N (%)	76 (29.01)	52 (14.53)	82 (18.02)	263 (37.84)	< 0.001

CM, cardiac mortality; ACM, all-cause mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events.

Table.3 Association between residual cholesterol levels and adverse outcomes

Variables	Q1		Q2		Q3		Q4
Variables	HR (95% Cl)	P *value*	HR (95% Cl)	P *value*	HR (95% Cl)	P *value*	HR (95% Cl)	P value
All patients (N = 4329)
CM	1.750 (1.062 - 2.884)	0.028	Ref	/	1.595 (0.966 - 2.634)	0.068	4.331 (2.599 - 7.216)	< 0.001
ACM	1.765 (1.304 - 2.390)	< 0.001	Ref	/	1.313 (0.951 - 1.813)	0.097	3.424 (2.451 - 4.783)	< 0.001
MACE	1.859 (1.477 - 2.339)	< 0.001	Ref	/	1.261 (0.982 - 1.619)	0.069	3.206 (2.470 - 4.162)	< 0.001
MACCE	1.956 (1.600 - 2.392)	< 0.001	Ref	/	1.392 (1.121 - 1.729)	0.003	3.064 (2.429 - 3.865)	< 0.001
1.4 mmol/L ≤ LDL-C level < 1.8 mmol/L (N = 2559)
CM	1.493 (0.857 - 2.600)	0.157	Ref	/	1.359 (0.754 - 2.450)	0.308	2.659 (1.166 - 6.060)	0.020
ACM	1.594 (1.108 - 2.292)	0.012	Ref	/	1.335 (0.896 - 1.989)	0.156	2.716 (1.545 - 4.775)	< 0.001
MACE	1.757 (1.323 - 2.334)	< 0.001	Ref	/	1.327 (0.966 - 1.822)	0.080	2.998 (1.935 - 4.646)	< 0.001
MACCE	1.985 (1.545 - 2.550)	< 0.001	Ref	/	1.456 (1.101 - 1.925)	0.008	2.867 (1.932 - 4.255)	< 0.001
LDL-C level < 1.4 mmol/L (N = 1770)
CM	3.690 (0.869 - 14.059)	0.056	Ref	/	3.586 (1.008 - 12.760)	0.049	14.733 (4.072 - 53.311)	< 0.001
ACM	2.340 (1.299 - 4.214)	0.005	Ref	/	1.354 (0.739 - 2.482)	0.327	5.035 (2.665 - 9.514)	< 0.001
MACE	2.143 (1.412 - 3.252)	< 0.001	Ref	/	1.187 (0.766 - 1.839)	0.442	3.865 (2.402 - 6.219)	< 0.001
MACCE	1.964 (1.370 - 2.815)	< 0.001	Ref	/	1.2.86 (0.891 - 1.855)	0.179	3.421 (2.263 - 5.174)	< 0.001

CM, cardiac mortality; ACM, all-cause mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events.

No competing interests reported.

supFigure1.pdf
Figure S1. Cumulative Kaplan-Meier curves of the time to the occurrence of clinical outcomes in 1.4 mmol/L ≤ LDL-C level < 1.8 mmol/L patients according to four RC categories. ACM, all-cause mortality; CM, cardiac mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiac and cerebrovascular events.
supFigure2.pdf
Figure S2. Cumulative Kaplan-Meier curves of the time to the occurrence of clinical outcomes in LDL-C level < 1.4 mmol/L patients according to four RC categories. ACM, all-cause mortality; CM, cardiac mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiac and cerebrovascular events.
supFigure3.pdf
Figure S3. Clinical outcomes according to four RC categories in 1.4 mmol/L ≤ LDL-C level < 1.8 mmol/L patients (A-D). Endpoint event Incidence rate (left), hazard ratio (right). ACM, all-cause mortality; CM, cardiac mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiac and cerebrovascular events.
supFigure4.pdf
Figure S4. Clinical outcomes according to four RC categories in LDL-C level < 1.4 mmol/L patients (A-D). Endpoint event Incidence rate (left), hazard ratio (right). ACM, all-cause mortality; CM, cardiac mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiac and cerebrovascular events.
supTable.docx

Remnant Cholesterol as a Predictor of Cardiovascular Outcomes in Acute Coronary Syndrome Patients with Low-Density Lipoprotein Cholesterol Levels Below 1.8 mmol/L: Insights from the MPCS-ACS Study

Status:

Version 1

Abstract

Figures

Introduction

Methods

Ethics statement

Study Design and Participants

Data collection

Remnant Cholesterol measurement

Endpoints

Statistical analyses

Results

Baseline characteristics of patients

Clinical outcomes in patients

Association Between Remnant Cholesterol Levels and Prognostic Outcomes in ACS Patients

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1