Remnant Cholesterol and Atherosclerotic Cardiovascular Disease Risk in Populations With Different Low‐Density Lipoprotein Cholesterol Elevations: A Prospective Cohort Study

Hong Zheng; Guanlin Chen; Zhenyu Huo; Yulong Lan; Yuxian Wang; Peng Fu; Weiqiang Wu; Haixiang Zheng; Kuangyi Wu; Zegui Huang; Dan Wu; Shouling Wu; Youren Chen

doi:10.1161/JAHA.125.045376

. 2026 Feb 11;15(4):e045376. doi: 10.1161/JAHA.125.045376

Remnant Cholesterol and Atherosclerotic Cardiovascular Disease Risk in Populations With Different Low‐Density Lipoprotein Cholesterol Elevations: A Prospective Cohort Study

Hong Zheng ^1,², Guanlin Chen ^1,², Zhenyu Huo ³, Yulong Lan ^1,⁴, Yuxian Wang ^1,², Peng Fu ^1,², Weiqiang Wu ¹, Haixiang Zheng ^1,⁵, Kuangyi Wu ¹, Zegui Huang ⁶, Dan Wu ^1,^✉,^#, Shouling Wu ^7,^✉,^#, Youren Chen ^1,^✉,^#

PMCID: PMC13055447 PMID: 41669968

Abstract

Background

Low‐density lipoprotein cholesterol (LDL‐C) and remnant cholesterol (RC) are risk factors for atherosclerotic cardiovascular disease (ASCVD). However, the extent to which differences in RC levels affect ASCVD risk in populations with varying degrees of LDL‐C elevation remains unclear. This study aimed to investigate whether RC can provide additional risk stratification value across different sexes, ages, and elevated LDL‐C statuses.

Methods

This study included 12 743 elevated LDL‐C participants (LDL‐C ≥3.4 mmol/L) and 50 073 age‐ and sex‐matched non‐elevated LDL‐C controls from the Kailuan Study. Elevated LDL‐C participants were categorized by RC levels into <0.5, 0.5 to <1.0, and ≥1.0 mmol/L subgroups. Kaplan‐Meier curves and Cox proportional hazards models were used to assess the relationship between RC levels and ASCVD risk across different sexes, ages, and high LDL‐C statuses.

Results

During a median follow‐up of 12.8 years, 1686 elevated LDL‐C participants (13.2%) and 5252 non‐elevated LDL‐C participants (10.5%) developed ASCVD. In the borderline‐high LDL‐C group (3.4 ≤ LDL‐C < 4.1 mmol/L), those with the lowest RC levels showed no significant risk difference compared with controls (hazard ratio [HR], 1.03 [95% CI, 0.93–1.13]), and this pattern remained consistent across different sexes and ages. In contrast, in the high LDL‐C group (LDL‐C ≥4.1 mmol/L), even when RC was at the lowest level, ASCVD risk remained significantly higher than that of controls (HR, 1.20 [95% CI, 1.02–1.41]).

Conclusions

In the borderline‐high LDL‐C population, those with the lowest RC levels showed no significant risk difference compared with controls, and this pattern remained consistent across different sexes and age subgroups. In the high LDL‐C population, even when RC was at the lowest level, ASCVD risk remained significantly higher than that of controls.

Keywords: atherosclerotic cardiovascular disease, Kailuan Study, low‐density lipoprotein cholesterol, remnant cholesterol

Subject Categories: Coronary Artery Disease

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Nonstandard Abbreviations and Acronyms

RC: remnant cholesterol

Clinical Perspective.

What Is New?

Remnant cholesterol (RC) levels show significant heterogeneity in their impact on atherosclerotic cardiovascular disease risk across populations with varying degrees of low‐density lipoprotein cholesterol (LDL‐C) elevation.
In the borderline‐high LDL‐C population, individuals with the lowest RC levels showed no significant risk difference from controls across sexes and ages, whereas in the high LDL‐C population, significant risk persisted even at the lowest RC levels.

What Are the Clinical Implications?

These findings underscore the importance of jointly assessing LDL‐C and RC levels to improve atherosclerotic cardiovascular disease risk prediction. In the borderline‐high LDL‐C population, RC levels can serve as a key risk stratification tool to help identify high‐risk individuals who need intensive intervention. In the high LDL‐C population, comprehensive management targeting both LDL‐C and RC should be emphasized to effectively reduce cardiovascular risk.

Atherosclerotic cardiovascular disease (ASCVD), encompassing coronary artery disease and related complications such as myocardial infarction, is a leading cause of global mortality and morbidity. ¹ The rising prevalence of obesity and diabetes has increased ASCVD incidence, exacerbating the global cardiovascular disease (CVD) burden. Projections estimate that by 2030, CVD‐related deaths may exceed 23.6 million. ² Major risk factors for ASCVD include hypertension, dyslipidemia, diabetes, smoking, and genetic predisposition, ³ with elevated low‐density lipoprotein cholesterol (LDL‐C) levels considered a primary risk factor. ⁴

Research consistently demonstrates a causal relationship between elevated LDL‐C and ASCVD. ⁵ Statins, by reducing LDL‐C, significantly lower ASCVD risk and associated mortality. ⁶ However, substantial residual cardiovascular risk persists even after significant LDL‐C reduction. Emerging evidence suggests that triglyceride‐rich lipoproteins and their cholesterol content, termed remnant cholesterol (RC), contribute to this residual CVD risk ⁷ , ⁸ and can independently predict ASCVD events. ⁹ Notably, Mendelian randomization analyses have established a strong genetic causal link between RC and cardiovascular events. ¹⁰ Furthermore, elevated RC levels are closely associated with increased risks of atherosclerosis and adverse cardiovascular outcomes. ¹¹ , ¹² , ¹³ Thus, RC may represent a critical ASCVD risk factor.

Although previous studies have fully confirmed that RC is an ASCVD risk factor independent of LDL‐C, ¹⁰ , ¹³ , ¹⁴ , ¹⁵ whether the impact of RC on ASCVD risk differs across populations with varying degrees of LDL‐C elevation remains to be determined. Therefore, this study used discordance analysis using data from the Kailuan cohort to evaluate the risk stratification value of RC in populations with elevated LDL‐C. Specifically, we distinguished between borderline‐high LDL‐C and high LDL‐C levels, and compared individuals with different RC levels within each stratum to age‐ and sex‐matched non‐elevated LDL‐C controls, to verify whether RC can provide additional risk stratification value.

METHODS

Data Availability

The data supporting the findings of this study are available from the Kailuan Study upon reasonable request and with permission from the Kailuan Study Institutional Review Board. Interested researchers may submit written proposals to the corresponding authors, who will facilitate access subject to ethical and data governance requirements.

Study Design and Population

The Kailuan Study is a large, ongoing, prospective cohort study conducted in the Kailuan community, Tangshan, China, with its design and methods previously described. ¹⁶ , ¹⁷ Between 2006 and 2007, 101 510 participants aged 18 to 98 years were recruited. At baseline, participants completed questionnaires, underwent physical examinations, and provided laboratory samples. Follow‐up assessments were conducted every 2 years. The study adheres to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Kailuan Medical Group (approval number 2006‐5). All participants provided written informed consent.

The present study is an age‐ and sex‐matched analysis. We included participants from the 2006 to 2007, 2008 to 2009, and 2010 to 2011 Kailuan health examinations and identified 15 779 participants with elevated LDL‐C (≥3.4 mmol/L). ¹⁸ After excluding 512 with prior CVD (coronary heart disease, stroke, or heart failure) and 2524 with missing or abnormal RC values, 12 743 elevated LDL‐C participants were included. Thereafter, each was matched by age (±2 years) and sex, with up to 4 non‐elevated LDL‐C (<3.4 mmol/L) controls without prior CVD, missing or abnormal RC values, yielding 50 073 controls. The inclusion and exclusion flowchart is shown in Figure S1.

Exposure

Serum total cholesterol, triglycerides, high‐density lipoprotein cholesterol, and LDL‐C were measured separately using the enzymatic end point method with an automated analyzer (Hitachi 747; Hitachi, Tokyo, Japan). Blood samples were collected from the antecubital vein after an overnight fast at each clinical examination. ¹⁹ , ²⁰ RC concentration was calculated as total cholesterol minus high‐density lipoprotein cholesterol and LDL‐C. ²¹ Based on Riis et al’s criteria, ¹² RC levels were categorized as lowest (<0.5 mmol/L), moderate (0.5 to <1.0 mmol/L), and highest (≥1.0 mmol/L).

Data Collection

Trained staff conducted face‐to‐face interviews using standardized questionnaires to collect information on demographic characteristics (age, sex, education level), lifestyle factors (smoking status, drinking status, physical activity), medical conditions (CVD, hypertension, diabetes), medication use (antihypertensive, lipid‐lowering, and antidiabetic agents), and laboratory test results (high‐density lipoprotein cholesterol, LDL‐C, total cholesterol, triglycerides, fasting blood glucose, estimated glomerular filtration rate, high‐sensitivity C‐reactive protein). Weight (kilograms) and height (meters) were measured using calibrated instruments, and body mass index (weight in kilograms divided by height in meters squared) was calculated. Blood pressure was measured using a mercury sphygmomanometer after at least 15 minutes of rest, with the average of 3 readings recorded as systolic blood pressure and diastolic blood pressure. Participants fasted for at least 8 hours before venous blood sample collection, analyzed at the Kailuan Hospital Central Laboratory. Serum creatinine was measured using the enzymatic method with a Beckman Coulter AU5400 analyzer, and estimated glomerular filtration rate was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. ²²

Definitions

Hypertension was defined as a self‐reported history of hypertension, use of antihypertensive agents, or blood pressure ≥140/90 mm Hg. Diabetes was defined as a self‐reported history of diabetes, use of antidiabetic agents, or fasting blood glucose levels ≥7.0 mmol/L. According to the US National Cholesterol Education Program, which is consistent with the 2016 Chinese Guidelines for the Management of Dyslipidemia in Adults, ¹⁸ , ²³ the elevated LDL‐C group was further categorized into borderline‐high LDL‐C (3.4 to <4.1 mmol/L) and high LDL‐C (≥4.1 mmol/L). Lipid‐lowering agents use was defined as long‐term use of statins, niacin, or fibrates. Current smokers were defined as those smoking >1 cigarette per day in the past year. Current alcohol consumers were defined as those consuming ≥100 mL of alcohol (alcohol content >50%) per day in the past year. Education level was categorized as junior high school or below and high school diploma or above. Active physical activity was defined as exercising at least 3 times per week for ≥30 minutes per session. A family history of CVD was defined as self‐reported myocardial infarction or stroke in parents. Insulin resistance was assessed using the Metabolic Score for Insulin Resistance, calculated as {ln[2 × fasting blood glucose(mg/dL) + triglycerides(mg/dL)] × body mass index(kg/m²)}/ln[high‐density lipoprotein cholesterol‐C(mg/dL)]. ²⁴

Outcome Ascertainment

The primary outcome was the first occurrence of ASCVD, including myocardial infarction, ischemic stroke, and coronary revascularization (coronary artery bypass grafting or percutaneous coronary intervention). Myocardial infarction and ischemic stroke were identified using International Classification of Diseases, Tenth Revision (ICD‐10) codes, and coronary revascularization was identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes. A professional team annually reviewed discharge records from 11 local hospitals, verified diagnoses through municipal social insurance and hospital discharge registries, and updated data yearly. Follow‐up time was calculated from baseline to the occurrence of ASCVD, death, or the study end point (December 31, 2021), whichever occurred first.

Statistical Analysis

Participants were divided into 2 groups based on LDL‐C status: non‐elevated LDL‐C and elevated LDL‐C. Elevated LDL‐C participants were further categorized by RC levels into clinically relevant groups: lowest (<0.5 mmol/L), moderate (0.5 to <1.0 mmol/L), and highest (≥1.0 mmol/L). Normally distributed continuous variables were expressed as mean±SD and compared between 2 groups using independent‐samples t tests or among multiple groups using 1‐way ANOVA. Nonnormally distributed continuous variables were presented as median (interquartile range) and compared using the Kruskal‐Wallis test. Categorical variables were presented as frequencies and percentages, with comparisons performed using χ² tests.

The incidence rate of ASCVD was calculated per 1000 person‐years based on follow‐up duration and event counts. Kaplan‐Meier methods were used to estimate cumulative ASCVD incidence rates, with differences between groups assessed using the log‐rank test. The proportional hazards assumption was tested using Schoenfeld residuals, indicating no violation of the assumption. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for ASCVD events in the lowest, moderate, and highest RC levels compared with the non‐elevated LDL‐C group, as well as for moderate and highest RC levels compared with the lowest RC level within the elevated LDL‐C group. All covariates were tested for multicollinearity, and no multicollinearity was detected (Table S1). Model 1 was unadjusted. Model 2 was adjusted for sex (male or female), age (continuous), education level (senior high school or above), physical activity (active or inactive), smoking status (yes or no), drinking status (yes or no), and family history of CVD (yes or no). Model 3 was further adjusted for hypertension (yes or no), use of antihypertensive agents (yes or no), use of antidiabetic agents (yes or no), use of lipid‐lowering agents (yes or no), Metabolic Score for Insulin Resistance (continuous), estimated glomerular filtration rate (continuous), log‐transformed (high‐sensitivity C‐reactive protein) (continuous) and fatty food intake (<1 time per week, 1–3 times per week, or >3 times per week). Missing covariate data were imputed using multiple imputations with chained equations, with details on missing data provided in Table S2, with missing rates for all covariates <15%. Additionally, the relationship between RC and ASCVD risk was evaluated across different sexes, ages, and high LDL‐C statuses (borderline‐high LDL‐C and high LDL‐C). Restricted cubic spline analysis was used to assess the potential nonlinear relationship between RC and ASCVD risk across different elevated LDL‐C groups. A likelihood ratio test was used to evaluate the significance of nonlinearity. Finally, we calculated the population attributable fraction (PAF) for different high LDL‐C statuses to estimate the proportion of ASCVD events that could theoretically be prevented in the study population if RC was maintained at the lowest level, with reference to previous studies. ¹²

To ensure result robustness, several sensitivity analyses were conducted. First, events occurring within the first year of follow‐up were excluded to minimize reverse causation bias. Second, the Fine‐Gray subdistribution hazard model was used to account for the competing risk of death. Third, the data set was reanalyzed after excluding participants with any missing values. Fourth, non‐elevated LDL‐C participants who developed elevated LDL‐C during follow‐up were excluded. Finally, sensitivity analyses were performed by excluding non‐elevated LDL‐C participants who used lipid‐lowering agents and by separately stratifying according to lipid‐lowering agent use status. The results remained consistent across all sensitivity analyses. All statistical analyses were performed using SAS 9.4 (SAS Institute) and R 4.2.2 (R Core Team). A 2‐sided P value <0.05 was considered statistically significant.

RESULTS

Baseline Characteristics

This study included 12 743 elevated LDL‐C participants and 50 073 matched non‐elevated LDL‐C controls. The mean age of participants was 51.2 years, with 83.3% being men. Baseline characteristics of elevated LDL‐C participants grouped by RC levels are shown in Table 1, with the lowest RC level group (<0.5 mmol/L) exhibiting lower body mass index, systolic blood pressure, diastolic blood pressure, LDL‐C, fasting blood glucose, Metabolic Score for Insulin Resistance, triglycerides, and high‐sensitivity C‐reactive protein levels. Additionally, they were less likely to use antihypertensive, antidiabetic, or lipid‐lowering agents. Compared with non‐elevated LDL‐C participants, elevated LDL‐C participants had higher proportions of current smokers, alcohol consumers, fatty food intake, and individuals with a family history of CVD (Table S3).

Table 1.

Baseline Characteristics of Participants Stratified by LDL‐C Status and RC Levels

Characteristics	Non‐elevated LDL‐C (n=50 073)	Elevated LDL‐C			P value
Characteristics	Non‐elevated LDL‐C (n=50 073)	RC <0.5 mmol/L (n=4571)	0.5 ≤ RC < 1.0 mmol/L (n=5496)	RC ≥1.0 mmol/L (n=2676)	P value
Age, y	52.05 (44.20–58.19)	51.93 (43.64–58.04)	52.40 (44.95–58.37)	52.18 (44.70–58.19)	0.02
Men, n (%)	41 762 (83.4)	3776 (82.6)	4576 (83.3)	2218 (82.9)	0.52
BMI, kg/m²	24.84 (22.64–27.17)	24.46 (22.49–26.75)	25.71 (23.59–27.87)	26.12 (24.05–28.25)	<0.01
SBP, mm Hg	129.30 (118.70–140.00)	130.00 (119.70–141.30)	130.70 (120.00–149.33)	132.00 (120.00–150.00)	<0.01
DBP, mm Hg	80.00 (79.30–90.00)	81.00 (78.70–90.00)	84.30 (80.00–92.00)	86.00 (80.00–94.00)	<0.01
FBG, mmol/L	5.20 (4.75–5.79)	5.30 (4.83–5.82)	5.38 (4.90–5.98)	5.55 (5.01–6.31)	<0.01
METS‐IR	35.79 (31.56–40.42)	34.15 (30.84–38.11)	37.25 (33.58–41.30)	39.32 (35.21–43.72)	<0.01
HDL‐C, mmol/L	1.44 (1.23–1.70)	1.46 (1.28–1.66)	1.44 (1.24–1.66)	1.46 (1.23–1.70)	<0.01
LDL‐C, mmol/L	2.30 (1.82–2.74)	3.79 (3.56–4.13)	3.79 (3.57–4.16)	3.72 (3.53–4.08)	<0.01
TC, mmol/L	4.85 (4.30–5.39)	5.65 (5.32–6.09)	6.01 (5.68–6.44)	6.72 (6.30–7.34)	<0.01
Triglycerides, mmol/L	1.27 (0.89–1.92)	0.87 (0.70–1.06)	1.45 (1.21–1.78)	2.33 (1.59–3.00)	<0.01
eGFR, mL/min per 1.73 m²	83.30 (70.03–97.43)	81.78 (69.65–95.22)	77.68 (66.12–91.89)	80.32 (67.50–96.65)	<0.01
hs‐CRP, mg/L	1.09 (0.42–2.88)	0.98 (0.41–2.21)	1.20 (0.54–2.60)	1.30 (0.60–3.07)	<0.01
Current smoker, n (%)	21 645 (43.2)	2251 (49.2)	2772 (50.4)	1384 (51.7)	<0.01
Current drinker, n (%)	21 506 (42.9)	2261 (49.5)	2724 (49.6)	1403 (52.4)	<0.01
Physical activity, n (%)	8333 (16.6)	1167 (25.5)	1484 (27.0)	627 (23.4)	<0.01
High school or above, n (%)	11 324 (22.6)	1203 (26.3)	1381 (25.1)	689 (25.7)	<0.01
Family history of CVD, n (%)	2635 (5.3)	251 (5.5)	346 (6.3)	172 (6.4)	<0.01
Use of antihypertensive agents, n (%)	5505 (11.0)	473 (10.3)	768 (14.0)	518 (19.4)	<0.01
Use of antidiabetic agents, n (%)	1887 (3.8)	145 (3.2)	225 (4.1)	139 (5.2)	<0.01
Use of lipid‐lowering agents, n (%)	1313 (2.6)	117 (2.6)	174 (3.2)	137 (5.1)	<0.01
Fatty food intake, n (%)					<0.01
<1 time/wk	9304.0 (18.6)	840.0 (18.4)	1007.0 (18.3)	508.0 (19.0)
1–3 times/wk	37 107.0 (74.1)	3403.0 (74.4)	4057.0 (73.8)	1893.0 (70.7)
>3 times/wk	3662.0 (7.3)	328.0 (7.2)	432.0 (7.9)	275.0 (10.3)

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Continuous variables are presented as median (interquartile range), and categorical variables are presented as n (%).

BMI indicates body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HDL‐C, high‐density lipoprotein cholesterol; hs‐CRP, high‐sensitivity C‐reactive protein; LDL‐C, low‐density lipoprotein cholesterol; METS‐IR, metabolic score for insulin resistance; RC, remnant cholesterol; SBP, systolic blood pressure; and TC, total cholesterol.

Association of RC With ASCVD Risk According to Elevated LDL‐C Status

During a median follow‐up of 12.8 years (interquartile range, 11.3–14.1), 5252 non‐elevated LDL‐C participants (10.5%) and 1686 elevated LDL‐C participants (13.2%) experienced a first ASCVD event. The cumulative incidence of ASCVD events increased across groups: non‐elevated LDL‐C (8.7%), elevated LDL‐C with lowest RC (8.7%), elevated LDL‐C with moderate RC (11.8%), and elevated LDL‐C with highest RC (14.3%), with statistically significant differences (log‐rank test P < 0.001; Figure 1A). In the fully adjusted model, compared with the non‐elevated LDL‐C group, HRs for ASCVD in the overall elevated LDL‐C group were 1.03 (95% CI, 0.93–1.13) for lowest RC, 1.31 (95% CI, 1.22–1.42) for moderate RC, and 1.53 (95% CI, 1.39–1.69) for highest RC (Table 2). As shown in Table S4, compared with the lowest RC level, the highest RC level in the elevated LDL‐C group had an HR of 1.48 (95% CI, 1.29–1.68; P trend <0.001). The PAF for ASCVD associated with not maintaining the lowest RC level was 7.41% (95% CI, 4.25–10.56), suggesting that 7.41% of ASCVD events could be prevented if all elevated LDL‐C participants maintained the lowest RC level.

A, Men. B, Women. C, Age <65 years. D, Age ≥65 years. ASCVD indicates atherosclerotic cardiovascular disease; LDL‐C, low‐density lipoprotein cholesterol; and RC, remnant cholesterol.

Table 2.

Association Between RC and Atherosclerotic Cardiovascular Disease Incidence According to Elevated LDL‐C Status

	Non‐elevated LDL‐C	Elevated LDL‐C with RC < 0.5 mmol/L	Elevated LDL‐C with 0.5 ≤ RC < 1.0 mmol/L	Elevated LDL‐C with RC ≥ 1.0 mmol/L	P for trend
LDL‐C ≥3.4 mmol/L
Events/total	5252/50 073	478/4571	764/5496	444/2676	…
Incidence, per 1000 person‐years	8.71	8.68	11.75	14.32	…
Model 1	1.00 (Reference)	1.00 (0.91–1.10)	1.37 (1.27–1.48)	1.66 (1.51–1.83)	<0.001
Model 2	1.00 (Reference)	1.02 (0.93–1.12)	1.38 (1.28–1.49)	1.71 (1.55–1.88)	<0.001
Model 3	1.00 (Reference)	1.03 (0.93–1.13)	1.31 (1.22–1.42)	1.53 (1.39–1.69)	<0.001
3.4 ≤ LDL‐C < 4.1 mmol/L
Events/total	5252/50 073	323/3344	515/3927	330/2030	…
Incidence, per 1000 person‐years	8.71	7.99	11.02	13.91	…
Model 1	1.00 (Reference)	0.92 (0.82–1.03)	1.28 (1.17–1.40)	1.61 (1.44–1.80)	<0.001
Model 2	1.00 (Reference)	0.95 (0.85–1.06)	1.31 (1.19–1.43)	1.66 (1.48–1.85)	<0.001
Model 3	1.00 (Reference)	0.97 (0.87–1.09)	1.25 (1.14–1.37)	1.47 (1.32–1.65)	<0.001
LDL‐C ≥ 4.1 mmol/L
Events/total	5252/50 073	155/1227	249/1569	114/646	…
Incidence, per 1000 person‐years	8.71	10.60	13.64	15.49	…
Model 1	1.00 (Reference)	1.23 (1.05–1.45)	1.60 (1.41–1.82)	1.84 (1.53–2.22)	<0.001
Model 2	1.00 (Reference)	1.22 (1.04–1.43)	1.55 (1.37–1.77)	1.90 (1.58–2.29)	<0.001
Model 3	1.00 (Reference)	1.20 (1.02–1.41)	1.45 (1.28–1.65)	1.64 (1.36–1.98)	<0.001

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Model 1: unadjusted model. Model 2: adjusted for age, sex, smoking status, drinking status, education level, physical activity, and family history of CVD. Model 3: adjusted for age, sex, smoking status, drinking status, education level, physical activity, family history of CVD, hypertension, use of antihypertensive agents, use of antidiabetic agents, use of lipid‐lowering agents, METS‐IR, eGFR, log (hs‐CRP), and fatty food intake.

CVD indicates cardiovascular disease; eGFR, estimated glomerular filtration rate; hs‐CRP, high‐sensitivity C‐reactive protein; LDL‐C, low‐density lipoprotein cholesterol; METS‐IR, metabolic score for insulin resistance; and RC, remnant cholesterol.

To assess the risk patterns across different LDL‐C level subgroups, we further stratified the elevated LDL‐C population by LDL‐C levels. In the borderline‐high LDL‐C group, the cumulative incidence of ASCVD events was as follows: elevated LDL‐C with lowest RC (8.0%), non‐elevated LDL‐C (8.7%), elevated LDL‐C with moderate RC (11.0%), and elevated LDL‐C with highest RC (13.9%), with statistically significant differences (log‐rank test, P<0.001; Figure 1B). In the high LDL‐C group, the cumulative incidence of ASCVD events increased as follows: non‐elevated LDL‐C (8.7%), elevated LDL‐C with lowest RC (10.6%), elevated LDL‐C with moderate RC (13.6%), and elevated LDL‐C with highest RC (15.5%), with significant differences (log‐rank test, P<0.001; Figure 1C). Compared with the non‐elevated LDL‐C group, the fully adjusted HRs for the borderline‐high LDL‐C group were 0.97 (95% CI, 0.87–1.09) for the lowest RC, 1.25 (95% CI, 1.14–1.37) for the moderate RC, and 1.47 (95% CI, 1.32–1.65) for the highest RC. In the high LDL‐C group, the HRs were 1.20 (95% CI, 1.02–1.41), 1.45 (95% CI, 1.28–1.65), and 1.64 (95% CI, 1.36–1.98), respectively (Table 2). In the borderline‐high LDL‐C group, compared with the lowest RC level, the highest RC level had an HR of 1.48 (95% CI, 1.26–1.74; P trend <0.001), with a PAF for ASCVD of 7.71% (95% CI, 3.93–11.48). In the high LDL‐C group, the HR was 1.31 (95% CI, 1.03–1.68; P trend=0.027), and the PAF for ASCVD was 4.56% (95% CI, −0.01 to 10.05), which did not reach statistical significance (Table S4). The RCS analysis revealed significant nonlinear associations in the elevated LDL‐C group and the borderline‐high LDL‐C group (P for nonlinearity <0.001; Figure S2A and S2B), whereas a linear relationship was observed in the high LDL‐C group (P for nonlinearity=0.213; Figure S2C).

Association of RC With ASCVD Risk According to Sex and Age

To assess whether the association between RC and ASCVD risk varied across different sex and age groups, we performed stratified analyses. In men, the cumulative incidence of ASCVD events increased as follows: non‐elevated LDL‐C (9.6%), elevated LDL‐C with lowest RC (9.8%), elevated LDL‐C with moderate RC (12.7%), and elevated LDL‐C with highest RC (15.1%). In women across different subgroups, the cumulative incidence of ASCVD events increased in the following order: elevated LDL‐C with lowest RC (3.4%), non‐elevated LDL‐C (4.7%), elevated LDL‐C with moderate RC (7.2%), and elevated LDL‐C with highest RC (10.6%). These differences were statistically significant (log‐rank test, P<0.001; Figure 2A and 2B). After covariate adjustment, HRs in men, compared with the non‐elevated LDL‐C group, were 1.04 (95% CI, 0.94–1.14) for lowest RC, 1.30 (95% CI, 1.20–1.41) for moderate RC, and 1.49 (95% CI, 1.34–1.65) for highest RC. In women, HRs were 0.96 (95% CI, 0.68–1.36), 1.45 (95% CI, 1.14–1.84), and 1.76 (95% CI, 1.34–2.32), respectively (Table 3). The test for interaction between RC levels and sex subgroups was not statistically significant (P _interaction=0.112).

A, Elevated LDL‐C. B, Borderline‐high LDL‐C. C, High LDL‐C. ASCVD indicates atherosclerotic cardiovascular disease; LDL‐C, low‐density lipoprotein cholesterol; and RC, remnant cholesterol.

Table 3.

Association Between RC and Atherosclerotic Cardiovascular Disease Incidence According to Sex

	Non‐elevated LDL‐C	Elevated LDL‐C with RC < 0.5 mmol/L	Elevated LDL‐C with 0.5 ≤ RC < 1.0 mmol/L	Elevated LDL‐C with RC ≥ 1.0 mmol/L	P for trend
Men
Events/total	4767/41762	444/3776	682/4576	385/2218	…
Incidence, per 1000 person‐years	9.55	9.84	12.70	15.13	…
Model 1	1.00 (Reference)	1.03 (0.94–1.13)	1.34 (1.24–1.46)	1.60 (1.44–1.78)	<0.001
Model 2	1.00 (Reference)	1.03 (0.94–1.14)	1.36 (1.25–1.47)	1.66 (1.49–1.84)	<0.001
Model 3	1.00 (Reference)	1.04 (0.94–1.14)	1.30 (1.20–1.41)	1.49 (1.34–1.65)	<0.001
Women
Events/total	485/8311	34/795	82/920	59/458	…
Incidence, per 1000 person‐years	4.66	3.41	7.24	10.60
Model 1	1.00 (Reference)	0.77 (0.55–1.09)	1.63 (1.29–2.06)	2.37 (1.81–3.11)	<0.001
Model 2	1.00 (Reference)	0.93 (0.65–1.31)	1.55 (1.22–1.96)	2.08 (1.59–2.73)	<0.001
Model 3	1.00 (Reference)	0.96 (0.68–1.36)	1.45 (1.14–1.84)	1.76 (1.34–2.32)	<0.001

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In the age‐stratified analysis, the cumulative incidence of ASCVD events in younger participants (aged <65 years) increased as follows: elevated LDL‐C with lowest RC (8.1%), non‐elevated LDL‐C (8.1%), elevated LDL‐C with moderate RC (11.1%), and elevated LDL‐C with highest RC (13.2%). In older participants (aged ≥65 years), the cumulative incidence of ASCVD events increased in the following order: elevated LDL‐C with lowest RC (14.6%), non‐elevated LDL‐C (14.6%), elevated LDL‐C with moderate RC (18.2%), and elevated LDL‐C with highest RC (24.8%). These differences were statistically significant (log‐rank test, P<0.001; Figure 2C and 2D). In younger participants, compared with the non‐elevated LDL‐C group, the fully adjusted HRs were 1.04 (95% CI, 0.94–1.15) for the lowest RC, 1.28 (95% CI, 1.18–1.39) for the moderate RC, and 1.42 (95% CI, 1.28–1.58) for the highest RC. In older participants, HRs were 1.00 (95% CI, 0.79–1.27), 1.23 (95% CI, 1.01–1.50), and 1.71 (95% CI, 1.34–2.18), respectively (Table 4). RC levels showed no significant interaction with age subgroups (P _interaction=0.644).

Table 4.

Association Between RC and Atherosclerotic Cardiovascular Disease Incidence According to Age

	Non‐elevated LDL‐C	Elevated LDL‐C with RC < 0.5 mmol/L	Elevated LDL‐C with 0.5 ≤ RC < 1.0 mmol/L	Elevated LDL‐C with RC ≥ 1.0 mmol/L	P for trend
Age <65 y
Events/total	4405/44 576	401/4067	653/4889	373/2380	…
Incidence, per 1000 person‐years	8.08	8.05	11.08	13.24
Model 1	1.00 (Reference)	1.00 (0.91–1.11)	1.39 (1.28–1.51)	1.65 (1.49–1.84)	<0.001
Model 2	1.00 (Reference)	1.01 (0.91–1.12)	1.39 (1.28–1.51)	1.67 (1.50–1.86)	<0.001
Model 3	1.00 (Reference)	1.04 (0.94–1.15)	1.28 (1.18–1.39)	1.42 (1.28–1.58)	<0.001
Age ≥65 y
Events/total	847/5497	77/504	111/607	71/296	…
Incidence, per 1000 person‐years	14.62	14.61	18.22	24.84	…
Model 1	1.00 (Reference)	1.00 (0.80–1.27)	1.25 (1.03–1.53)	1.76 (1.38–2.24)	<0.001
Model 2	1.00 (Reference)	0.98 (0.78–1.25)	1.25 (1.02–1.53)	1.83 (1.43–2.33)	<0.001
Model 3	1.00 (Reference)	1.00 (0.79–1.27)	1.23 (1.01–1.50)	1.71 (1.34–2.18)	<0.001

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Association of RC With ASCVD Risk According to Elevated LDL‐C Status Stratified by Sex and Age

Given the above findings, we further assessed whether the association between RC and ASCVD risk differed between borderline‐high and high LDL‐C subgroups by sex and age. In the sex‐stratified analysis (Table S5), compared with the non‐elevated LDL‐C group, HRs for the lowest RC level in men were 0.98 (95% CI, 0.87–1.10) for borderline‐high LDL‐C and 1.22 (95% CI, 1.03–1.44) for high LDL‐C, whereas in women, HRs were 0.91 (95% CI, 0.60–1.37) and 1.05 (95% CI, 0.56–1.97), respectively. In the age‐stratified analysis (Table S6), compared with the non‐elevated LDL‐C group, the HRs for the lowest RC level in younger participants (aged <65 years) were 1.01 (95% CI, 0.89–1.14) for borderline‐high LDL‐C and 1.24 (95% CI, 1.03–1.50) for high LDL‐C, whereas in older participants (aged ≥65 years), the HRs were 0.82 (95% CI, 0.59–1.13) for borderline‐high LDL‐C and 1.39 (95% CI, 0.89–2.17) for high LDL‐C.

Sensitivity Analyses

Multiple sensitivity analyses were conducted to assess result robustness. First, excluding participants with ASCVD events within the first year of follow‐up yielded consistent results (Tables S7 through S9). Second, analyses using the Fine‐Gray model to account for competing risks of death showed similar findings (Tables S10 through S12). Third, excluding participants with any missing data did not substantially alter results (Tables S13 through S15). Fourth, excluding non‐elevated LDL‐C participants who developed elevated LDL‐C during follow‐up maintained result stability (Tables S16 through S18). Finally, excluding non‐elevated LDL‐C participants who used lipid‐lowering agents during follow‐up, as well as performing stratified analyses by lipid‐lowering agent use status, yielded consistent results (Tables S19 through S22).

DISCUSSION

In this large prospective study of 12 743 elevated LDL‐C participants and 50 073 matched non‐elevated LDL‐C controls from the Kailuan cohort, we analyzed data stratified by sex, age, and high LDL‐C status. Results showed that the impact of RC levels on ASCVD risk differed significantly across populations with varying degrees of LDL‐C elevation. In the borderline‐high LDL‐C group, those with the lowest RC levels showed no statistically significant risk difference compared with controls, whereas in the high LDL‐C group, even when RC was at the lowest level, ASCVD risk remained significantly higher than that of controls. These findings were consistent across sensitivity analyses. Additionally, a significant positive association was observed between RC levels and ASCVD risk. Notably, RCS analyses revealed that this association exhibited nonlinear patterns in the elevated LDL‐C group and borderline‐high LDL‐C group, whereas a predominantly linear relationship was observed in the high LDL‐C group. PAF analysis indicated that up to 7.71% of ASCVD events in the borderline‐high LDL‐C group could be prevented by maintaining the lowest RC level, an effect not observed in the high LDL‐C group.

Multiple large‐scale prospective studies have reported that elevated RC increases CVD risk across all age groups in the general population. ¹¹ , ¹² , ¹³ Furthermore, higher RC levels are associated with adverse cardiovascular outcomes in high‐risk patients and patients with ASCVD. ²⁵ , ²⁶ , ²⁷ Recently, increasing attention has focused on the interaction between RC and LDL‐C in CVD. However, findings on CVD risk in high LDL‐C populations with low RC compared with low LDL‐C populations with low RC are inconsistent. Studies from the United States and Spain reported no significant difference in CVD risk, ¹³ , ¹⁴ whereas studies from Korea and China observed increased risk, potentially due to regional differences, study populations, or variations in LDL‐C and RC classification methods. ²⁷ , ²⁸ Our study results emphasize that the risk stratification value of RC differs significantly across populations with different LDL‐C levels, and this differentiated impact pattern provides new evidence supporting individualized risk assessment and management strategies.

LDL‐C is a well‐established atherogenic factor for ASCVD. ²⁹ However, RC contributes to atherosclerosis by accumulating in the arterial intima due to its large size and high cholesterol content, promoting foam cell formation via macrophage uptake and accelerating inflammation through free fatty acid release. ³⁰ , ³¹ , ³² , ³³ Emerging evidence also suggests a causal relationship between RC and ASCVD risk. ¹⁰ However, inconsistent results from intervention trials targeting RC reduction ³⁰ and the lack of guideline‐defined RC target values have limited its routine clinical monitoring and management. Our study found that in the elevated LDL‐C population, RC levels provided important additional risk stratification information.

Further analyses showed that across different sexes and age groups, those with elevated LDL‐C maintaining the lowest RC level had no statistically significant difference in ASCVD risk compared with the non‐elevated LDL‐C population. However, at moderate and highest RC levels, ASCVD risk was significantly increased compared with controls. Stratified analyses by high LDL‐C status revealed that, in the borderline‐high LDL‐C group, maintaining the lowest RC level resulted in no statistically significant difference in ASCVD risk compared with matched non‐elevated LDL‐C controls, which could prevent 7.71% of ASCVD events. In the high LDL‐C group, even when RC was at the lowest level, ASCVD risk remained significantly higher than that of controls, suggesting the presence of other incompletely controlled metabolic risk factors in this population.

Although the mechanisms underlying the differential association between RC and ASCVD risk across different high LDL‐C statuses remain to be fully elucidated, several factors may explain these findings. LDL‐C is a primary driver of atherosclerosis, promoting foam cell formation through intimal retention, oxidative modification, and macrophage uptake, while also inducing inflammation and necrotic core formation. ³⁴ In contrast, RC is directly taken up by macrophages without requiring oxidation, and its larger particles carry more cholesterol, enhancing its atherogenic potential. Additionally, RC hydrolysis produces oxidized free fatty acids and remnants that induce reactive oxygen species, TNF‐α (tumor necrosis factor α), IL (interleukin)‐1β, and other proinflammatory factors, promoting endothelial dysfunction, monocyte adhesion, and coagulation cascade activation, thereby amplifying inflammation and thrombotic risk. Notably, Mendelian randomization studies support a causal link between elevated RC and low‐grade inflammation, whereas LDL‐C is primarily associated with cholesterol deposition rather than inflammation. ³¹ The differentiated impact pattern we observed may reflect different proportions of lipid‐related risk components at different LDL‐C levels: in the borderline‐high LDL‐C group, the contribution of RC‐related inflammatory and metabolic pathways to overall risk may be more prominent; thus, lower RC levels are associated with more significant risk reduction. In contrast, in the high LDL‐C group, multiple lipid and nonlipid risk factors may act together; although reduced RC levels are beneficial, they cannot fully offset the impact of other risk factors.

Notably, this study found that the risk stratification value of RC exhibits significant heterogeneity across different sex and age groups. In women and the older population, individuals with high LDL‐C but RC at the lowest level had relatively low ASCVD risk approaching that of the control group, whereas in men and younger individuals, even with RC at the lowest level, risk remained significantly elevated as long as LDL‐C was elevated. This differentiated risk pattern is consistent with previously reported sex‐ and age‐related metabolic heterogeneity in the literature. Postmenopausal women, due to decreased estrogen levels, experience significant changes in lipid metabolism patterns, with insulin resistance and triglyceride metabolic abnormalities having more prominent effects on ASCVD. ³⁵ Similarly, with increasing age, the rising prevalence of metabolic syndrome and decreased lipoprotein remnant clearance capacity lead to more significant contribution from RC in the older population. ³⁶ , ³⁷ In contrast, men often have higher levels of small, dense LDL particles, and lacking estrogen protection, their cardiovascular risk is more predominantly driven by LDL‐C‐mediated atherosclerotic processes. ³⁸ Likewise, in younger populations, LDL‐C is the primary pathogenic factor for cardiovascular risk, with studies showing that LDL‐C elevation starting from young to middle age is significantly associated with subsequent cardiovascular events. ³⁹ Therefore, even with RC at the lowest level, high LDL‐C itself remains the major pathogenic factor.

Based on the above observational findings and literature evidence on metabolic heterogeneity, we recommend adopting differentiated lipid management strategies in clinical practice. For men and younger patients, LDL‐C is the primary pathogenic factor; therefore, priority should be given to lipid‐lowering therapy titration, optimizing statin dosage or combining ezetimibe/proprotein convertase subtilisin/kexin type 9 inhibitors to adequately reduce LDL‐C. ⁴⁰ , ⁴¹ For women and older patients, although LDL‐C reduction remains important, metabolic dysfunction and RC‐related risks may play a more prominent role in the presence of obesity, diabetes, or metabolic syndrome. In these cases, in addition to adequate lipid‐lowering therapy, additional benefits may be obtained through lifestyle interventions or, when necessary, combined treatment targeting insulin resistance and triglyceride metabolism such as glucagon‐like peptide‐1 receptor agonists. ³¹ , ⁴² It should be emphasized that these clinical implications based on observational studies require validation through prospective randomized controlled trials to clarify the actual clinical benefits of differentiated treatment strategies across different populations.

Even under lipid‐lowering therapy, simultaneous elevation of both LDL‐C and RC reflects poorly controlled lipid metabolic disorders. This high‐risk phenotype may stem from genetic susceptibility, severe metabolic dysfunction, poor drug response, or inadequate adherence. In this study, residual risk in such patients remained significant, supporting the use of RC as an important marker for identifying residual risk. For these high‐risk patients, multitarget comprehensive lipid management strategies should be considered, including optimizing RC control through lifestyle interventions and, when necessary, combining drugs targeting triglyceride metabolism with intensive lipid‐lowering therapy.

In summary of the above findings, it should be emphasized that the observational findings of this study should not be interpreted as challenging the status of LDL‐C as the core pathogenic factor and primary therapeutic target for cardiovascular disease. Numerous randomized controlled trials and Mendelian randomization studies have established the causal pathogenicity of elevated LDL‐C. ⁴³ The value of this study lies in demonstrating that RC may provide additional risk stratification information in individuals with already elevated LDL‐C, thereby providing observational evidence for future multidimensional lipid risk assessment and individualized treatment strategies.

STRENGTHS AND LIMITATIONS

This study used a large‐scale prospective cohort design to systematically evaluate the risk stratification value of RC in populations with varying degrees of LDL‐C elevation through matched controls and refined LDL‐C stratification analysis. Additional strengths include large sample size and long‐term follow‐up. However, several limitations should be noted. First, the observational study design, despite adjustment for potential covariates, may be subject to residual confounding, limiting causal inference. Second, RC levels were calculated rather than directly measured, potentially introducing measurement bias. However, the strong correlation between calculated and directly measured RC supports its applicability in most cohort studies. ⁴⁴ Third, LDL‐C and RC levels were measured only at baseline, which does not account for dynamic changes during follow‐up. Due to limitations in longitudinal data completeness, we were unable to construct RC trajectory models or incorporate time‐dependent covariates. This represents an important limitation, because lipid levels may change substantially during follow‐up, potentially influencing the observed associations. As highlighted by DeSantis and Li, long‐term proportional hazards modeling faces challenges when baseline covariates may not adequately represent exposures throughout extended follow‐up. ⁴⁵ Although our formal testing using Schoenfeld residuals showed no violation of the proportional hazards assumption, we acknowledge that over our median follow‐up of 12.80 years, time‐varying confounding may still influence our estimates. Future studies should consider using repeated‐measures designs and time‐dependent Cox models to more comprehensively assess the association between RC and ASCVD risk. Fourth, due to the lack of detailed medication information on lipid‐lowering agents, we were unable to adjust baseline LDL‐C for medication use. However, we controlled for potential medication confounding as much as possible through sensitivity analysis excluding lipid‐lowering agent users from the non‐elevated LDL‐C group. Finally, because the study population consisted of Chinese adults from the Kailuan community, the generalizability of findings to other racial or geographic populations may be limited.

CONCLUSIONS

In this prospective cohort study of 12 743 elevated LDL‐C participants, we found that in the borderline‐high LDL‐C group, those with the lowest RC levels showed no significant risk difference compared with controls, and this pattern remained consistent across different sex and age subgroups. However, in the high LDL‐C group, even when RC was at the lowest level, ASCVD risk remained significantly higher than that of controls. These findings suggest that LDL‐C determines baseline risk, whereas RC provides additional stratification value, and jointly assessing both can help improve cardiovascular risk prediction and guide individualized management in the elevated LDL‐C population.

Sources of Funding

This work was supported by the 2023 Guangdong Provincial Science and Technology Innovation Strategic Special Fund (number STKJ2023003).

Disclosures

None.

Supporting information

Tables S1–S22

Figures S1–S2

JAH3-15-e045376-s002.pdf^{(544.1KB, pdf)}

STROBE Checklist

JAH3-15-e045376-s001.pdf^{(114.7KB, pdf)}

Acknowledgments

Th authors express their sincere gratitude to all participants of the Kailuan Study and to the members of the research team for their contributions.

This article was sent to Olufunmilayo H. Obisesan, MD, MPH, Assistant Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.125.045376

For Sources of Funding and Disclosures, see page 12.

Contributor Information

Dan Wu, Email: 07dwu@stu.edu.cn.

Shouling Wu, Email: drwusl@163.com.

Youren Chen, Email: yrchen3@stu.edu.cn.

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45. DeSantis SM, Li R. The association between remnant cholesterol levels and the risk of atrial fibrillation: a deeper dive into a dramatic reversal in findings. Heart Rhythm. 2025;22:2243–2244. doi: 10.1016/j.hrthm.2024.11.019 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Tables S1–S22

Figures S1–S2

JAH3-15-e045376-s002.pdf^{(544.1KB, pdf)}

STROBE Checklist

JAH3-15-e045376-s001.pdf^{(114.7KB, pdf)}

Data Availability Statement

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[jah370280-bib-0045] 45. DeSantis SM, Li R. The association between remnant cholesterol levels and the risk of atrial fibrillation: a deeper dive into a dramatic reversal in findings. Heart Rhythm. 2025;22:2243–2244. doi: 10.1016/j.hrthm.2024.11.019 [DOI] [PubMed] [Google Scholar]

PERMALINK

Remnant Cholesterol and Atherosclerotic Cardiovascular Disease Risk in Populations With Different Low‐Density Lipoprotein Cholesterol Elevations: A Prospective Cohort Study

Hong Zheng, MBBS

Guanlin Chen, MBBS

Zhenyu Huo, MD

Yulong Lan, PhD

Yuxian Wang, MBBS

Peng Fu, MD

Weiqiang Wu, PhD

Haixiang Zheng, MD

Kuangyi Wu, MD

Zegui Huang, MD

Dan Wu, PhD

Shouling Wu, PhD

Youren Chen, MD

Abstract

Background

Methods

Results

Conclusions

Nonstandard Abbreviations and Acronyms

Clinical Perspective.

What Is New?

What Are the Clinical Implications?

METHODS

Data Availability

Study Design and Population

Exposure

Data Collection

Definitions

Outcome Ascertainment

Statistical Analysis

RESULTS

Baseline Characteristics

Table 1.

Association of RC With ASCVD Risk According to Elevated LDL‐C Status

Figure 1. Kaplan–Meier curves depicting the cumulative incidence of ASCVD events by RC levels, stratified by sex and age.

Table 2.

Association of RC With ASCVD Risk According to Sex and Age

Figure 2. Kaplan‐Meier curves depicting the cumulative incidence of ASCVD events by RC levels, stratified by elevated LDL‐C status.

Table 3.

Table 4.

Association of RC With ASCVD Risk According to Elevated LDL‐C Status Stratified by Sex and Age

Sensitivity Analyses

DISCUSSION

STRENGTHS AND LIMITATIONS

CONCLUSIONS

Sources of Funding

Disclosures

Supporting information

Acknowledgments

Contributor Information

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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