The effects of estimated remnant-like particle cholesterol on incident cardiovascular disease: Insights from a general Chinese population

doi:10.21203/rs.3.rs-531321/v1

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The effects of estimated remnant-like particle cholesterol on incident cardiovascular disease: Insights from a general Chinese population

https://doi.org/10.21203/rs.3.rs-531321/v1

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Background

Growing evidence suggests that remnant cholesterol (RC) may contribute to residual atherosclerotic cardiovascular disease (ASCVD) risk. But the cut-off point to treat RC for reducing ASCVD is still not known. This study was aimed to investigate the relationships between RC and combined cardiovascular diseases (CVDs) in a general China cohort, with 11,956 subjects aged ≥ 35 years.

Methods

Baseline RC was estimated with the Friedewald formula for 8782 subjects. The outcome was the incidence of combined CVD, including fatal and nonfatal stroke and coronary heart disease (CHD). Cox proportional hazards model was used to calculate hazard ratios (HRs) with 95% confidence intervals. Restricted cubic spline model was used to evaluate dose-response relationship between continuous RC and the natural log of HRs.

Results

After a mean follow-up of 4.66 years, 431 CVD events occurred. In the cox proportional models, participants with high level of categorial RC had significantly higher risk for combined CVD (HR: 1.37; 95% CI: 1.07–1.74) and CHD (HR: 1.63; 95% CI: 1.06–2.53), compared to those with medium level of RC. In the stratification analyses, high level of RC significantly increased combined CVD risk for subgroups females, age < 65years, non-current smokers, non-current drinkers, normal weight, renal dysfunction, and no hyperuricemia. Same trends were found for CHD among subgroups males, age < 65 years, overweight, renal dysfunction, and normal uric acid; stroke for subgroup females. In the restricted cubic spline models, significantly linear association between RC and combined CVD and non-linear association between RC and CHD were shown. The risk of outcomes was relatively flat until 0.84mmol/l of RC and increased rapidly afterwards, with HR of 1.308 (1.102 to 1.553) for combined CVD and 1.411 (1.061 to 1.876) for CHD. Stratified analyses showed significant nonlinearity association between RC and CVD outcomes in subgroup aged < 65 years or subgroup diabetes.

Conclusions

In this large-scale and long-term follow-up cohort study, participants with higher RC levels had significantly worse prognosis, especially for subgroup aged 35–65 years or subgroup diabetes mellitus.

Cardiothoracic Surgery

Cardiac & Cardiovascular Systems

Remnant cholesterol

Diabetes mellitus

Cardiovascular disease

Dyslipidemia

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality worldwide and remains a major public health challenge.[1] Lipid abnormalities play a central role in the pathogenesis of ASCVD.[2] Lowering plasma levels of low-density lipoprotein (LDL) cholesterol (LDL-C) has been reported an important modality to prevent ASCVD for decades.[3, 4] However, substantial residual risk remains despite achieving the LDL-C level as a mean of 30–40 mg/dl with statins, ezetimibe, and/or the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors.[5] Due to the negative results of high-density lipoprotein (HDL) raising drugs (niacin, CETP inhibitors), regulating other lipid components to reduce residual risk of ASCVD becomes the new focus for lipid intervention.[6] The REDUCE-IT (Reduction of Cardiovascular Event Icosapent Ethyl Intervention Trial) found that reduction in triglycerides by 20% led to a 25% reduction in atherosclerotic cardiovascular events.[7] However, roughly one-half of the risk reduction can be explained by reduction in remnant cholesterol (RC).[8]

RC is the cholesterol content of the triglyceride-rich lipoproteins (TRLs) which are formed when TRLs are partly depleted of triglyceride (TG) by lipoprotein lipase and are composed of very-low density (VLDLs) and intermediate-density lipoproteins (IDLs) in the fasting state, and by these two lipoproteins together with chylomicron remnants in the non-fasting state.[9] The cholesterol content of remnant lipoproteins is defined as remnant-like particle cholesterol (RLP-C).[10] In recent years, growing evidence suggests that RC is a causal risk factor for cardiovascular events and all-cause mortality. [11–14] The starting point, and the target to treat RC for reducing CVD are still not known. More studies are needed to investigate the association between RC with CVD events, and further determine the individual target of RC for primary and secondary prevention of CVD risk. This study was aimed to investigate the relationships between RC and combined CVDs in a rural general China cohort, with ≥ 35 years adults.

Study Design and population

The Northeast China Rural Cardiovascular Health Study (NCRCHS) is a community-based prospective cohort study conducted in rural areas of Northeast China. The study design has been described previously.[15] From January 2012 to August 2013, a total of 11,956 subjects aged ≥ 35 years, was recruited as baseline from three counties (Dawa, Zhangwu, and Liaoyang) in Liaoning province, using a multi-stage, randomly stratified cluster-sampling scheme. Detailed information was collected for each subject. In 2015 and 2017, all subjects were invited to attend two stages of follow-up. Detailed cardiovascular examination was repeated in 2015, and incident CVD events were collected in 2017- 2018. Of the 11,956 subjects, 10,700 participants consented and were qualified for our follow-up study. A total of 10,349 participants completed at least one follow-up visit. The study was approved by the Ethics Committee of China Medical University (Shenyang, China). Written informed consent was obtained from all participants, in accordance with principles of Helsinki Declaration.

In the present analyses, we excluded participants with CVD at baseline (n=821) and participants with missing baseline lipid profile (n=73) or using lipid lowering agents at baseline (n=248). Participants with ineligible values of HDL cholesterol (HDL-C), LDL-C, and TG (>4.5mmol/l) were also excluded (n=415). Data were therefore available for 8782 participants. The flow chart of study is shown in the online Supplemental Figure 1.

Study variables

At baseline, detailed information on demographic characteristics, dietary and lifestyle factors, and medical history was obtained by interview with a standardized questionnaire. History of CHD was defined by self-reported history of myocardial infarction or prior coronary revascularization and electrocardiography evidence of myocardial infarction at baseline and confirmed by medical records. History of stroke was similarly defined by self-reported history of stroke at baseline and confirmed by medical records. Current uses of antihypertensive medications, and hypoglycemic agents, and lipid lowering agents were self-reported. Medication use over the prior 2 weeks was verified by reviewing medication containers that participants brought to the visit. Lipid lowering agents included statins, bile sequestrants, fibrates, niacin, and antihyperlipidemic medications. Family history of hypertension, diabetes mellitus, stroke, and CHD were defined as that more than one first-degree relative was involved.

All study participants underwent physical examination including measurements of weight and standing height, systolic and diastolic blood pressure, as previously reported in detail.[15] Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. According to the World Health Organization (WHO) criteria,[16] low and normal weight, overweight, and obesity was defined as BMI < 25kg/m², BMI 25-29.9kg/m², and BMI ≥ 30kg/m², respectively. Blood pressure was measured three times with participants seated after at least 5 min rest using a standardized automatic electronic sphygmomanometer (HEM-907; Omron, Tokyo, Japan). Hypertension (HTN) was defined as systolic blood pressure (SBP) ≥140 mm Hg, and/or diastolic blood pressure (DBP) ≥ 90 mm Hg, or use of antihypertensive medications. Smoking status and alcohol using status were defined as current or not.

Other variates

Blood samples were collected in the morning from participants who had fasted for at least 12 h. Biochemical analyses were performed automatically (Olympus AU 640, Tokyo, Japan) for the measurement of circulating concentrations of fasting blood glucose (FBG), total cholesterol (TC), LDL-C, HDL- C, TG, uric acid (UA), serum creatinine, and other routine blood biochemical indexes. As previously proposed, [17] fasting RC was calculated as TC minus HDL-C minus LDL-C, and expressed as mmol/l. Diabetes mellitus (DM) was defined according to WHO criterion [18]: FBG ≥ 7.0mmol/l (126 mg/dl), self-reported physician diagnosis, or use of antidiabetic medications. Estimated glomerular filtration rate (eGFR) was calculated with MDRD equation as previously proposed.[19] Renal dysfunction was defined as reduced eGFR < 60 ml/min/1.73 m². Hyperuricemia was defined as serum uric acid level > 420 umol/L (7 mg/dl) in males and > 360 umol/L (> 6 mg/dl) in females.[20]

Outcomes ascertainment

The outcome of the present study was CVD incidence. Incident CVD was defined as fatal and nonfatal stroke and CHD according to adjudication by a physician panel. The specific incidences of all-cause mortality, stroke, and CHD were also determined. Hospital records or death certificates were also collected. The diagnosis was classified and coded according to the International Classification of Diseases-Tenth Revision (ICD-10). Stroke was defined following the World Health Organization Multinational Monitoring of Trends and Determinants in Cardiovascular Disease criteria.[21,22] as rapidly developing signs of focal or global disturbance of cerebral function, lasting more than 24 h (unless interrupted by surgery or death) with no apparent non-vascular cause. The ICD-10 codes for stroke were I60.x-I69.x. Transient ischemic attack and chronic cerebral vascular disease were excluded. CHD was defined as a diagnosis of hospitalized angina pectoris, hospitalized myocardial infarction, any coronary revascularization, or CHD death,[23] and the ICD-10 codes for CHD were I20.x-25.x. All materials were independently evaluated and adjudicated by the endpoint assessment committee. The date of the ﬁrst participant recruited was January 2013, and the last follow-up date was January 2018. Follow-up time was calculated as the interval between the date of randomization and the date of death, the date of the last visit, or the last recorded clinical event of participants still alive, whichever occurred ﬁrst.

Statistical Analysis

Data are reported as means and standard deviations for normally distributed variables or as medians (tertiles) for non-normally distributed variable. The baseline characteristics were assessed by tertiles of RC. Differences among groups were assessed with the analysis of variance for parametric variables or Kruskal-Wallis test for nonparametric variables. Nominal variables were expressed as absolute numbers and proportions, and compared by χ²test or Fisher’s exact test when appropriate. The correlations between continuous RC and adjustment variables were assessed by Pearson or Spearman’s rank correlation test as applicable.

The cumulative incidence for cardiovascular outcomes in subjects with different level of estimated RC at baseline was evaluated by Kaplan-Meier curves, and compared with the log-rank test. Cox proportional hazards models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between RC (considered as both continuous and categorical variables) and CVD events. Proportionality of hazards was assessed for each variable by using Schoenfeld residuals. Three adjustment models were founded for cox analysis. Model 1 is adjusted for age, gender, and ethnicity. Model 2 is further adjusted for current smoking, current drinking, and BMI (normal, overweight or obesity). Model 3 is further adjusted for serum level of triglyceride (continuous), eGFR (dichotomous), uric acid (dichotomous), DM or not, and HTN or not.

Restricted cubic spline (RCS) regression [24] was applied to flexibly model the non-linear association between continuous RC and the natural log of HRs (lnHRs) of outcomes. The knots were placed at the 5th, 50th, and 95th percentiles. Variables in cox model 3 were adjusted. Stratified analyses were used to explore whether the association of RC with CVD risk varied across age and diabetes or not. Departure from linearity of the final cubic spline model was assessed using the Wald test for nonlinearity.[25] The median value of RC was considered as the reference duo to its skew distribution. As the associations of RC and outcomes were approximately log linear below and above their medians (50th percentile), a linear model was used to calculate HRs per standard deviation increase of RC in outcome prediction.[26]

Statistical analyses were performed using SPSS (version 23.0; IBM, IL, USA), SAS (version 9.3; Institute Inc., Cary, NC, USA), and Graphpad prism (V.8.4.2; SanDiego, USA). Two-sided P < 0.05 were considered statistically significant. P-values were adjusted for multiple comparisons using the Bonferroni correction.

Patient involvement

No patients were involved in setting the research question or the outcome measures, nor were they involved in the design and implementation of the study. There are no plans to involve patients in dissemination.

Baseline characteristics

The characteristics of the participants were assessed by the tertiles of RC in Table 1. In the whole cohort of 8782 subjects (46.4% males), mean age was 53.2 ± 10.4 years. The mean BMI was 24.7 ± 3.6 kg/m², 47.3% had HTN, 8.4% had diabetes.

Table 1. Baseline Characteristics by Tertiles of remnant cholesterol

	Total n=8782	Remnant cholesterol			p-value
	Total n=8782	Tertile I (n=2949)	Tertile II (n=2932)	Tertile III (n=2901)	p-value
Age(year)	53.2±10.4	52.2±10.6	52.7±10.4	54.7±10.0	<0.001
Male (%)	4075(46.4)	1432(48.6)	1343(45.8)	1300(44.8)	0.012
Ethnicity of Han (%)	8277(94.2)	2664(90.3)	2820(96.2)	2793(96.3)	<0.001
Current smoking (%)	3118(35.5)	1121(38.0)	1022(34.9)	975(33.6)	0.001
Current drinking alcohol (%)	1992(22.7)	755(25.6)	619(21.1)	618(21.3)	<0.001
Physical activity
Low	2964(33.8)	819(27.8)	1033(35.2)	1112(38.3)	<0.001
Medium	1652(18.8)	566(19.2)	581(19.8)	505(17.4)
High	4091(46.6)	1534(52.0)	1290(44.0)	1267(44.0)
BMI (kg/m²)	24.7±3.6	24.7±3.8	24.4±3.6	24.8±3.5	<0.001
HTN (%)	4154(47.3)	1141(38.7)	965(32.9)	911(31.4)	<0.001
DM (%)	738(8.4)	201(6.8)	212(7.2)	325(11.2)	<0.001
SBP (mmHg)	140.5±22.7	144.0±24.3	137.1±21.6	140.3±21.5	<0.001
DBP (mmHg)	81.6±11.5	81.1±11.8	81.1±11.5	82.6±11.3	<0.001
FBG (mmol/l)	5.8±1.5	5.6±1.4	5.8±1.7	5.8±1.3	<0.001
TC (mmol/l)	5.2±1.0	4.7±0.9	5.0±0.8	5.8±1.0	<0.001
TG (mmol/l)	1.4±0.8	1.1±0.5	1.3±0.7	1.8±0.8	<0.001
HDL-C(mmol/l)	1.4±0.3	1.6±0.4	1.3±0.3	1.3±0.3	<0.001
LDL-C (mmol/l)	2.9±0.8	2.8±0.8	2.8±0.7	3.1±0.8	<0.001
Estimated eGFR (ml/min/1.73m²)	94.1±15.1	101.6±13.2	92.3±14.3	88.2±14.4	<0.001
Serum uric acid (μmol/l)	285.6±81.5	260.7±73.8	284.8±79.5	290.4±78.5	<0.001
RC (mmol/l)	0.83±0.44	0.36±0.16	0.84±0.10	1.32±0.30	<0.001

BMI: body mass index; DBP: diastolic blood pressure; DM: diabetes mellitus; FPG: fasting plasma glucose; GFR: glomerular; filtration rate; HDL-C: high-density lipoprotein cholesterol; HTN, hypertension; LDL-C: low-density lipoprotein cholesterol; RC: remnant cholesterol; SBP: systolic blood pressure; SD: standard deviation; TC: total cholesterol; TG: triglyceride.

Data are expressed as mean±SD or as n (%).

The mean concentration of RC was 0.8 ± 0.4 mmol/l. According to RC level, all participants were divided into three groups: tertile I (RC < 0.65 mmol/l), tertile II (RC 0.65-1.00mmol/l), and tertile III (RC ≥ 1.00mmol/l). Across tertiles of RC, age, serum level of lipids (TC and TG), uric acid, and proportion of DM increased gradually (all P < 0.001), whereas the level of eGFR, proportions of male, HTN, and current smoking significantly declined (all P < 0.05). HDL-C concentration and the proportion of current alcohol was significantly higher (both P < 0.001) in tertile I than those of other two groups (P < 0.001). LDL-C concentration was significantly higher in tertile III.

Survival analyses for different level of RC

After the median follow-up time of 4.66 years, a total of 431 CVD events occurred in the studied population (293 stroke cases, 150 CHD cases, and 71 myocardial infarction cases), including 148 fatal CVD cases. The Kaplan-Meier curves for each endpoint in participants with different levels of RC are shown in Figure 1. Participants in high level of RC (tertile III) had significantly higher cumulative incidences of combined CVD (P = 0.0019), CHD (P = 0.0101), stroke (P = 0.0448), and fatal CVD (P = 0.0465) compared to those in tertile II.

Table 2 shows the multivariable adjusted HRs for combined CVD, fatal CVD, stroke, and CHD incidence by RC concentration. In the categorial analysis of RC, risks for combined CVD (HR: 1.37; 95% CI: 1.07-1.74) and CHD (HR: 1.63; 95% CI: 1.06-2.53) were significantly higher among participants in tertile III, compared with those in tertile II after full adjustment. Whether adjustment or not, categorial concentration of RC had no significant influence on stroke or fatal CVD. In the continuous analysis, high level of RC significantly related with a 28% increased risk of combined CVD (HR: 1.28; 95% CI: 1.02-1.62) and a 51% increased risk of fatal CVD (HR: 1.51; 95% CI: 1.05-2.17) after full adjustment. Significantly higher stroke risk was found for participants in tertile III after adjustment for model 1(HR: 1.31; 95% CI: 1.03-1.67) and model 2 (HR: 1.30; 95% CI: 1.02-1.67). No significant association was found between continuous RC and CHD. The results were similar when intensity of physical activity was additionally adjusted (data not shown).

Table 2. Multivariate-adjusted hazard ratios and 95% confidence intervals for cardiovascular outcomes associated with baseline remnant cholesterol

	RC Q₁	RC Q₂	RC Q₃	RC continuous(1-SD†)
Combined CVD
n/N	139/2191	117/2932	175/2901	431/8782
Model 1	1.23(0.96-1.57)	1.00 (ref)	1.49(1.18-1.89)**	1.33 (1.09-1.63)**
Model 2	1.22(0.95-1.57)	1.00 (ref)	1.49(1.18-1.89)**	1.32 (1.08-1.62)**
Model 3	1.16 (0.90-1.50)	1.00 (ref)	1.37(1.07-1.74)*	1.28(1.02-1.62)*
CHD
n/N	55/2191	34/2932	61/2901	150/8782
Model 1	1.77(1.15-2.74)*	1.00 (ref)	1.71(1.12-2.61)*	1.17(0.82-1.67)
Model 2	1.74(1.12-2.70)*	1.00 (ref)	1.71(1.12-2.61)*	1.16(0.81-1.66)
Model 3	1.68(1.08-2.62)*	1.00 (ref)	1.63(1.06-2.53)*	1.15(0.76-1.74)
Stroke
n/N	88/2191	87/2932	118/2901	293/8782
Model 1	1.04(0.77-1.40)	1.00 (ref)	1.30(0.98-1.72)	1.31(1.03-1.67)*
Model 2	1.04(0.77-1.41)	1.00 (ref)	1.31(0.98-1.73)	1.30(1.02-1.67)*
Model 3	0.99(0.73-1.35)	1.00 (ref)	1.19(0.89-1.59)	1.25(0.94-1.66)
Fatal CVD
n/N	41/2191	44/2932	63/2901	148/8782
Model 1	0.97(0.64-1.49)	1.00 (ref)	1.394(0.95-2.05)	1.44(1.05-1.97)*
Model 2	0.95(0.62-1.46)	1.00 (ref)	1.421(0.97-2.09)	1.47(1.07-2.01)*
Model 3	0.90(0.58-1.39)	1.00 (ref)	1.37(0.92-2.05)	1.51(1.05-2.17)*

CHD: coronary heart disease; CVD: cardiovascular disease; RC remnant cholesterol

Model 1: adjusted for age (<65 years vs. ≥ 65 years), sex and ethnicity (Han or not). Model 2: adjusted for factors in model 1 and smoking status, drinking status, body mass index (normal, overweight, obesity). Model 3: adjusted for factors in model 2 and estimated glomerular filtration rate (< 60ml/min/1.73m² vs. ≥ 60 ml/min/1.73m²), diabetes mellites (yes or no), hypertension (yes or no), triglyceride (continuous), hyperuricemia (yes or no).

†HR for continuous 1-SD increment. *P < 0.05; **P < 0.01

Stratification analyses

The relationships between RC and outcomes were assured between RC categorical groups (tertile II as reference), stratified with sex, age (<65 years vs. ≥ 65 years), smoking status (current smoker or no), drinking status (current drinker or no), BMI subgroups (normal, overweight, obesity), DM (yes or no), HTN (yes or no), renal dysfunction (eGFR < 60 ml/min/1.73m² vs. eGFR ≥ 60 ml/min/1.73m²), hyperuricemia (yes or no). Due to small number of nationality other than Han, there was no subgroup comparisons for nationality. After full adjustment of other factors, CVD were more likely to be happened in the following subgroups of tertile III (Figure 2): females (HR:1.515, 95%CI:1.043-2.201), participants with age < 65years (HR:1.547, 95%CI:1.127-2.123), non-current smokers (HR:1.482, 95%CI:1.064-2.062), non-current drinkers (HR:1.431, 95%CI:1.079-1.898), normal BMI subgroup (HR:1.486, 95%CI:1.046-2.110), renal dysfunction subgroup (HR:1.357, 95%CI:1.050-1.752), and no hyperuricemia subgroup (HR:1.439, 95%CI:1.101-1.880). Same trends for CHD were found among subgroups in tertile III: participants with age <65 years, overweight subgroup, renal dysfunction subgroup, and normal uric acid subgroup (Supplementary Figure 2). For participants in tertile III, males had significantly higher risk for CHD (HR:2.170, 95%CI:1.038-4.537), and females had more probability of stroke (HR:1.643, 95%CI:1.010-2.686) compared to those in tertile II (Supplementary Figure 3). Compared with the reference, low level of RC (tertile I) also increased the risk for CHD in subgroups under 65 years, no current smoking, no current drinking, BMI 25-30kg/m², renal dysfunction, normal uric acid (Supplementary Figure 4). No significant differences were found for fatal CVD between tertile II and tertile III, and combined CVD, stroke, and fatal CVD between tertile I and tertile II (data not shown).

Dose-response analyses of RC with cardiovascular outcomes

Dose-response analyses were implemented with RCS to investigate optimal level of RC for each outcome (Figure 3). Multivariable adjusted RCS analyses showed significant overall associations between RC and combined CVD (P_overall = 0.0324), or RC and CHD (P_overall = 0.0398). Significantly linear relationship (P_linear = 0.0225) between RC and combined CVD, and non-linear relationship (P_non-linear= 0.0221) between RC and CHD are found for all participants. The risk of combined CVD was relatively flat until around 0.84mmol/l (32.76mg/dl) of RC and then started to increase rapidly afterwards. For participants with higher level of RC than 0.84mmol/l, the HR of combined CVD and CHD per standard deviation was 1.308 (1.102 to 1.553) and 1.411 (1.061 to 1.876), respectively.

Non-linear associations between RC and lnHRs for outcomes stratified by subgroups (age < 65 years or ≥ 65 years, DM or not) are shown in Figure 4. Significant nonlinearity association and “J” shape curves were shown for combined CVD (P_non-linear= 0.0059) and CHD (P_non-linear= 0.0002) in participants with age < 65 years (Figure 4. A). Meanwhile, linear association was found between RC and lnHR for fatal CVD (P_linear= 0.0104). A 50.7% increase risk for combined CVD, a 57.0% increase risk for CHD, and a 2.0% increase risk for fatal CVD were found for participants under 65 years with serum level of RC > 0.83mmol/l. The same trend was found for participants with DM and serum level of RC > 0.95mmol/l (Figure 4. B).

The current study for general Chinese population confirmed an independent relationship between estimated RC and the incidence of cardiovascular outcomes. The Kaplan-Meier curves explored significantly higher cumulative incidences of combined CVD, CHD, stroke, and fatal CVD for participants with high level of RC than those with medium level of RC. Cox models confirmed a 37% increased risk for combined CVD and a 63% increased risk for CHD (HR: 1.63; 95% CI: 1.06–2.53) in categorial analysis; a 28% increased risk of combined CVD and a 51% increased risk of fatal CVD in continuous analysis of RC. Significantly higher risk of stroke was also investigated for participants in tertile III after part adjustment. Further dose-response association analyses and subgroup analyses elucidated significant associations between continuous RC and the incidence of combined CVD and CHD, not only for whole population, but also for subgroup with age < 65 years or subgroup DM. Median levels of RC of each group showed lowest HRs for corresponding CVD outcomes.

RC refers to the cholesterol content of TRLs, which is composed of chylomicron remnants, VLDL, and IDL. A growing amount of population studies, epidemiological and genetic evidence suggests that high concentrations of RC closely associated with high risk of ischemic heart disease (IHD),[11, 27, 28] myocardial infarction (MI)[29, 30] and all-cause mortality.[12, 31] A recent study showed close relationship between RC and coronary atheroma progression independent of conventional lipid parameters.[13] A cardiovascular benefit of statin therapy, independently of LDL-C reduction, was suggested in reducing TRL-C levels among those with high TRL-C levels.[32]

The onset age of CVD gets younger and younger in the world.[2, 33] Dyslipidemia is a prominently traditional risk factor in the development of early-onset CVD.[34, 35] Unhealthy lifestyle behaviors such as poor diet, decreased physical activity, and increased rates of smoking contribute to the increased prevalence of dyslipidemia in the young to middle-aged US population.[33, 36] Ideal levels of key cardiovascular health factors are also important in reducing the risk of early-onset cardiovascular-related death.[37, 38] Healthy lifestyle changes at the age of 20 years would reduce odds of atherosclerosis 30 years later.[39] However, the currently employed CVD risk assessment tools are heavily age-weighted and have been shown to underestimate CVD risk in the young to middle-aged populations.[40–42] Early identification and modification of atherogenic dyslipidemia can improve primary and secondary prevention of CVD outcomes. Until now, no optimal RC cutoff for risk of CVD in the young to middle-aged population is accessed. The present study suggested increased risks of CVD, CHD, and fatal CVD for participants with 35 to 65 years of age and RC level > 0.83mmol/l. This may contribute to risk stratification for dyslipidemia and constructing the following treatment strategies.

DM confers at least two to threefold excess risk for ASCVD.[43] Data has revealed that serum RC concentrations are elevated in patients with DM or pre-DM, and can increase the risk for CHD and future coronary outcomes. [44–46] That suggests RC a possible treatment target for patients with impaired glucose metabolism.[17] A baseline RC level ≥ 30 mg/dl (0.78mmol/l) identifies a high risk of major adverse cardiovascular event for individuals (men: 55–80 years of age, women: 60–80 years of age), having type 2 DM (T2DM) or ≥ 3 CVD risk factors.[47] And the risk is independent of whether LDL-C levels were on target at ≤ 100 mg/dl (2.59 mmol/l). For patients with T2DM underwent previously percutaneous coronary stents, baseline RLP-C level at 0.505 mmol/L was identified as the optimal cutoff point to predict in-stent restenosis.[48] Several guidelines proposed a stratification of cardiovascular risk among people with diabetes, and recommend an intensive therapy for dyslipidemia in diabetes.[49, 50] But, the target for regulating RC concentration is still not assured among Chinese population with diabetes. In this study, the incidence of combined CVD and CHD significantly increased for general Chinese participants with T2DM and serum level of RC > 0.95mmol/l (Fig. 4).

The mechanism behind this cardiovascular benefit for RC is not fully understood. There is a complex link among RC, inflammation, and CVD events.[51] Balling revealed that one third of TC in plasma was present in RC by using direct measurements.[52] Autopsy cases analysis showed coronary atherosclerosis in the young(< 40years) commonly exhibiting eccentric plaques with associated inflammation.[53] The atherosclerotic plaques of younger patients are rich in foam cells, which reduce plaque stability and induce early onset ACS.[54] Mazzone reviewed the important roles of diabetic dyslipidemia (beyond LDL cholesterol level) and inflammation for accelerating vascular injury and increasing the rates of cardiovascular disease in T2DM patients.[55] High levels of RC can cause a variety of proatherogenic effects, including monocyte activation, upregulation of proinflammatory cytokines, and increased prothrombotic factors production.[27, 56] Remnant lipoproteins can enter and get trapped in the intima of the arterial wall, promoting endothelial dysfunction and inflammation through increased secretion of various cytokines and adhesion molecules.[57] Without oxidative modification by macrophages,[58] RC are accumulated in the arterial wall and may play a causal role in the development of atherosclerosis and ultimate ASCVD.[27, 28] RC also accelerate the onset of endothelial progenitor cells senescence via increased oxidative stress, and induce endothelial dysfunction through inhibiting nitric oxide production. [59]

The present study demonstrated that high levels of RC were significant predictors of increased risk of CVD events, especially for 35 to 65 years aged population and DM patients. This suggests that assessing RC levels in these populations might be likely to have clinical utility in terms of CVD risk stratification and future intervention. Further investigations into RC lowering interventions to reduce residual ASCVD risk are necessary. This study had several limitations. Firstly, RC was estimated with the Friedewald formula, but not measure directly. A simple and widely available assay is needed to be developed to measure the cholesterol content of RC. Secondly, the effect of different constructions of diet on RC concentration is lack. Finally, this was a study among general Chinese population. Studies of RC in other country with different diet culture need further investigation.

In conclusion, in this large-scale population-based study, we found high level of RC significantly related with worse prognosis. For the first time, we revealed that the risk of cardiovascular disease significantly increased after the median values of RC for young to middle-aged populations (0.83mmol/l) and subgroup DM (0.95mmol/l). RC as a causative risk factor for CVD events deserves further attention and may constitute a prominent target for interventions to reduce vascular risk after LDL cholesterol lowering, especially for young to middle-aged populations and DM.

Ethics approval and consent to participate

The study was approved by the institutional ethics review committee of the First Affiliated Hospital of China Medical University (AF-SOP-07-1.0-01). All patients were given the opportunity to withdraw their participation using an opt-out procedure.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article (and its Supplementary Information files). The datasets generated during and/or analysed during the current study are not publicly available due to the lack of a specific patients’ consent but are made available by corresponding author based on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Professor YXS had the funds of National Key Research and Development Program from the Ministry of Science and Technology of China (Project Grant # 2018YFC1312400, Sub-project Grant # 2018YFC1312403).

Author contributions

YLC contributed to the conception or design of the work and wrote the manuscript. YLC and GXL performed the data analyses. XFG and GZS critically revised the manuscript. YXS directed the study and obtained the Funds. ZL, NY, HMY and SSY took part in data collection. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.

Acknowledgments

The authors thank the staff and participants of the NCRCHS study for their important contributions. This study was funded by the National Key Research and Development Program from the Ministry of Science and Technology of China (Project Grant # 2018YFC1312400, Sub-project Grant # 2018YFC1312403).

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The effects of estimated remnant-like particle cholesterol on incident cardiovascular disease: Insights from a general Chinese population

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