Abstract
Background
The residual risk of myocardial infarction (MI) persists despite the management of traditional risk factors. Although remnant cholesterol and systemic inflammatory activity, reflected by high-sensitivity C-reactive protein (hs-CRP), may contribute to atherothrombosis, their long-term combined impact on MI remains unclear.
Methods
In all, 42,303 participants from the Kailuan prospective cohort (2006–2010) with repeated remnant cholesterol and hs-CRP measurements were included. Cumulative exposure to remnant cholesterol and hs-CRP was calculated across three visits. Participants were stratified into six groups according to cumulative remnant cholesterol (below/above median) and cumulative hs-CRP concentrations (< 1, 1–3, ≥ 3 mg/L). Incident MI was ascertained through insurance and hospital records, with a median follow-up period of 10.99 years.
Results
During a median follow-up of approximately 11.0 years, 741 MI events were identified. The risk of MI increased progressively with increasing cumulative hs-CRP levels in both cumulative remnant cholesterol strata. Participants with concurrent cumulative remnant cholesterol and high cumulative hs-CRP levels had the greatest risk (HR 3.16, 95% CI 2.37–4.20). The associations remained robust across age, sex, body mass index, hypertension, and diabetes, and sensitivity analyses excluded early events, cancer, acute infection and major cardiovascular diseases at baseline. Women appeared to demonstrate a more pronounced relative risk under dual high exposure.
Conclusions
Long-term cumulative exposure to elevated RC and systemic inflammation was associated with markedly increased MI risk. Simultaneous assessment of cumulative lipid-related and inflammatory burden may improve long-term risk stratification, help identify individuals who may benefit from intensive preventive care, and support combined lipid-lowering and inflammation-targeted strategies for MI prevention.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-026-02992-5.
Keywords: Myocardial infarction, Inflammation, Lipoproteins, Cohort studies
Introduction
Despite substantial progress in controlling traditional risk factors such as hypertension, dyslipidaemia, and diabetes mellitus, myocardial infarction (MI) remains a leading contributor to global morbidity and mortality [1]. However, a high residual risk of MI persists even among individuals who have achieved the recommended targets, which suggests the involvement of additional atherothrombotic pathways beyond those associated with low-density lipoprotein cholesterol (LDL-C) [2–4].
Remnant cholesterol (RC), defined as the cholesterol content of triglyceride-rich lipoproteins (TRLs), including very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and chylomicron remnants, has emerged as a potentially important contributor to residual cardiovascular disease risk. Such particles are able to enter the arterial wall, undergo uptake by macrophages independent of LDL receptors, and promote plaque inflammation and thrombogenicity [5, 6]. In parallel, chronic low-grade inflammation, indicated by circulating C-reactive protein (CRP), is a well-established predictor of atherosclerotic events [7, 8] and a plausible therapeutic target [9, 10]. These two pathways are biologically interconnected: RC may intensify vascular inflammation, whereas inflammatory signalling impairs RC clearance, potentially resulting in synergistic atherogenesis.
Prior epidemiologic studies have primarily relied on single time-point measurements of RC or CRP, which are vulnerable to short-term variability, and thus measurements may underestimate long-term exposure. Moreover, the independent and joint long-term effects of cumulative RC and CRP exposure on incident MI have not been fully elucidated [11, 12]. The present study addresses this gap by examining the long-term cumulative burden of RC and inflammation simultaneously, rather than considering each pathway in isolation or at a single baseline assessment. It was hypothesized that higher cumulative RC and higher cumulative hs-CRP would be associated with a greater risk of incident MI and that concurrent exposure to both high cumulative RC and high cumulative hs-CRP would confer the greatest risk. Furthermore, even with partial control of traditional risk factors, concurrent exposure to high RC and high CRP levels would consistently confer the highest MI risk.
Methods
Study population
The Kailuan Study is a large, ongoing, community-based prospective cohort study initiated in 2006–2007 that enrolled 101,510 participants at baseline and subsequently included newly enrolled individuals each year [13, 14]. Follow-up examinations are conducted every two years. For the present analysis, individuals who entered the cohort between 2006 and 2010 were considered eligible (n = 133,076). The exposure assessment window consisted of three serial examinations conducted from 2006 to 2010. Follow-up for incident MI began after the third examination was completed and continued until December 31, 2021. Participants were excluded if any of the following criteria were met: [1] absence from any of the three examinations during the exposure assessment period (n = 75,586); [2] missing data for total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), LDL-C, or hs-CRP during the exposure assessment period (n = 14,951); or [3] a history of MI or death before the start of follow-up (n = 236). After application of these exclusions, 42,303 participants were ultimately included in the final analysis (Supplementary Fig. 1). The study protocol was approved by the Ethics Committee of Kailuan General Hospital, and all participants provided written informed consent.
Data collection and definitions
The study population was drawn from the Kailuan community, and participants completed standardized surveys at 11 affiliated hospitals. Demographic characteristics, medical history, lifestyle factors, anthropometric indices, and blood pressure data were collected by trained staff at each examination using standardized protocols. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure ≥ 90 mmHg, a self-reported physician diagnosis of hypertension, or the use of antihypertensive medication. Diabetes mellitus was defined as a fasting blood glucose (FBG) concentration ≥ 7.0 mmol/L, self-reported physician diagnosis, or use of glucose-lowering medication. Smoking status and alcohol consumption were categorized as present or absent on the basis of questionnaire responses. Physical activity was defined as exercising ≥ 4 times per week for a minimum of 20 min each session. Participants were classified as inactive (≤ 3 times/week) or active (> 3 times/week). Body mass index (BMI) was computed as weight in kilograms divided by the square of height (kg/m²). Acute infection during the exposure assessment period was defined a priori as any hs-CRP measurement > 10 mg/L. In stratified analyses, traditional risk factor control was defined according to achievement of the following targets during the exposure period: blood pressure < 140/90 mmHg, FBG < 6.1 mmol/L, and LDL-C < 2.6 mmol/L.
Exposure
Cumulative hs-CRP (CumCRP) was calculated using a time-weighted approach as follows: {[(hs-CRP1 + hs-CRP2)/2 × (Visit2 − Visit1)] + [(hs-CRP2 + hs-CRP3)/2 × (Visit3 − Visit2)]}/(Visit3 − Visit1) [15, 16], where hs-CRP1, hs-CRP2, and hs-CRP3 are the values at each visit. This approach estimates a time-weighted average exposure, in which each measurement is weighted according to the exact interval between consecutive visits within the predefined 2006–2010 exposure window. The cumulative RC (CumRC) was calculated using the same algorithm. RC was calculated by subtracting LDL-C and HDL-C from TC [5, 17, 18]. CumCRP was classified using clinical thresholds for hs-CRP in Asian populations (< 1, 1–3, ≥ 3 mg/L) [19, 20]. Elevated CumRC was defined as a concentration at or above the population median (1.0080 mmol/L). For the joint exposure analysis, the participants were grouped into six categories according to their CumCRP (< 1, 1–3, ≥ 3 mg/L) and CumRC concentrations (< 1.0080 or ≥ 1.0080 mmol/L). In addition to the primary analysis using the population median of CumRC, sensitivity analyses were performed using an alternative clinically relevant CumRC cut-off value of 0.78 mmol/L. This threshold has been widely adopted in prior clinical and epidemiological studies to reflect elevated RC levels [21, 22]. Participants were reclassified accordingly, and joint exposure analyses with CumCRP categories were repeated.
Biochemical measurements
Laboratory analyses were performed using a Hitachi 747 automatic analyser (Hitachi, Tokyo, Japan) to measure TC, HDL-C, LDL-C, triglycerides (TG), FBG, and hs-CRP. The estimated glomerular filtration rate (eGFR) was calculated using the modified Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, with adjustment for the Chinese population.
Assessment of myocardial infarction
Diagnoses were obtained through municipal health insurance and hospital discharge records and underwent annual review by an expert panel. Outcomes were continuously ascertained independently of scheduled study visits. The diagnosis of MI adhered to the criteria established by the World Health Organization Monitoring Trends and Determinants in Cardiovascular Disease (WHO MONICA), incorporating clinical symptoms, electrocardiographic features, and serial changes in myocardial enzyme levels. The mortality data were updated annually from provincial vital statistics. Follow-up continued until the first MI event, death, or December 31, 2021, whichever occurred first.
Statistical analysis
Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and a two-sided P value < 0.05 was considered statistically significant. Continuous variables are presented as means±standard deviation (SD) or medians with interquartile range (IQR), as appropriate, whereas categorical variables are presented as counts and percentages. Baseline characteristics across six groups defined by combinations of CumRC and CumCRP were compared using the chi-square test for categorical variables and analysis of variance (ANOVA) or the Kruskal–Wallis test for continuous variables. Participants with missing TC, HDL-C, LDL-C, or hs-CRP data during the exposure assessment period were excluded because CumRC and CumCRP could not be calculated. Missing covariate data (Supplementary Table S1) were handled by multiple imputation under a missing-at-random assumption, and the estimates were pooled across imputed datasets using Rubin’s rules. Cumulative MI incidence was evaluated using Kaplan–Meier methods and compared across groups with the log-rank test. The incidence rates were reported per 1000 person-years (Fig. 1). Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations among CumCRP, CumRC, and incident MI. For the joint exposure analysis, participants were divided into six groups according to thresholds for CumCRP (< 1, 1–3, ≥ 3 mg/L) and CumRC (below or above the median), and the groups for which CumCRP was < 1 mg/L and CumRC was < 1.0080 mmol/L served as the reference. Multiplicative interactions were assessed by including an interaction term in the Cox model and statistical significance was determined using the likelihood ratio test. Three models were constructed: Model 1 was adjusted for age and sex; Model 2 was further adjusted for FBG, systolic blood pressure (SBP), eGFR, BMI, smoking status, drinking status, physical activity, and education level; and Model 3 further accounted for the use of antidiabetic, antihypertensive, and lipid-lowering treatments. Stratified analyses were carried out according to age (< 60 vs. ≥60 years), sex, obesity status (BMI < 24 vs. ≥24 kg/m²), hypertension, and diabetes mellitus. Several prespecified sensitivity analyses were conducted to assess the robustness of the findings: (1) events occurring within the first year of follow-up were excluded to reduce potential reverse causation (2), participants diagnosed with cancer during the exposure period were excluded (3), participants with hs-CRP > 10 mg/L during the exposure period were removed to limit the influence of acute inflammation (4), participants with major cardiovascular diseases at baseline, including stroke, heart failure, and atrial fibrillation were excluded.
Fig. 1.
As the Kaplan–Meier curves represent the cumulative probability of myocardial infarction rather than an incidence rate
Results
Baseline characteristics according to joint cumulative hs-CRP and RC exposure groups
The baseline characteristics across the six joint CumCRP and CumRC exposure groups are shown in Table 1. In all, 42,303 participants were included (mean age: 53.6 ± 11.8 years; 76.5% males). With increasing CumCRP and CumRC levels, participants tended to be older and had higher BMI and SBP as well as higher FBG, and LDL-C levels, along with lower HDL-C levels and eGFR (all P < 0.01). High-exposure groups were also associated with a greater prevalence of smoking; physical inactivity; and the use of antihypertensive, antidiabetic, or lipid-lowering medications, as well as lower educational attainment. All differences between groups were statistically significant (all P < 0.01).
Table 1.
Baseline characteristics by joint cumulative hs-CRP and RC exposure groups
| Variables | Total | G1 | G2 | G3 | G4 | G5 | G6 | P |
|---|---|---|---|---|---|---|---|---|
| N | 42,303 | 8,604 | 10,211 | 4,770 | 5,015 | 7,591 | 6,112 | |
| Age, y | 53.6 ± 11.8 | 50.6 ± 11.8 | 53.4 ± 12.3 | 56.1 ± 12.8 | 52.1 ± 10.1 | 53.6 ± 10.8 | 57.6 ± 10.9 | <0.01 |
| Male, (n%) | 32,371(76.5) | 6,357(73.9) | 8,001(78.4) | 3,654(76.6) | 3,883(77.4) | 5,972(78.7) | 4,504(73.7) | <0.01 |
| CumRC, mmol/L | 1.09 ± 0.56 | 0.68 ± 0.24 | 0.70 ± 0.23 | 0.72 ± 0.23 | 1.53 ± 0.42 | 1.57 ± 0.45 | 1.65 ± 0.48 | <0.01 |
| CumCRP, mg/L | 1.54(0.83,3.07) | 0.63(0.44,0.81) | 1.64(1.28,2.14) | 4.85(3.69,7.28) | 0.64(0.46,0.82) | 1.69(1.31,2.23) | 5.03(3.88,6.79) | <0.01 |
| BMI, kg/m² | 25.1 ± 3.2 | 23.9 ± 2.9 | 25.2 ± 3.1 | 25.8 ± 3.5 | 24.4 ± 2.9 | 25.6 ± 3.0 | 25.9 ± 3.3 | <0.01 |
| SBP, mmHg | 130.4 ± 17.2 | 125.6 ± 16.4 | 130.7 ± 17.1 | 133.8 ± 18.0 | 127.2 ± 15.7 | 131.6 ± 16.3 | 135.3 ± 17.5 | <0.01 |
| DBP, mmHg | 84.0 ± 9.3 | 81.6 ± 9.0 | 84.0 ± 9.2 | 85.0 ± 9.6 | 83.0 ± 9.0 | 85.1 ± 9.0 | 85.9 ± 9.3 | <0.01 |
| FBG, mmol/L | 5.56 ± 1.34 | 5.34 ± 1.04 | 5.50 ± 1.21 | 5.66 ± 1.44 | 5.49 ± 1.21 | 5.73 ± 1.50 | 5.74 ± 1.66 | <0.01 |
| eGFR, ml/min/1.73 m² | 87.0 ± 16.6 | 87.4 ± 17.0 | 83.7 ± 17.4 | 81.1 ± 17.5 | 92.2 ± 14.1 | 90.0 ± 15.4 | 87.9 ± 14.9 | <0.01 |
| LDL-C, mmol/L | 2.60 ± 0.80 | 2.53 ± 0.66 | 2.69 ± 0.71 | 2.64 ± 0.83 | 2.64 ± 0.72 | 2.78 ± 0.81 | 2.26 ± 1.03 | <0.01 |
| HDL-C, mmol/L | 1.53 ± 0.43 | 1.61 ± 0.44 | 1.52 ± 0.40 | 1.49 ± 0.40 | 1.57 ± 0.48 | 1.49 ± 0.44 | 1.43 ± 0.40 | <0.01 |
| Smoking, (n%) | 16,196(38.3) | 3,101(36.0) | 3,642(35.7) | 1,568(32.9) | 2,204(43.9) | 3,411(44.9) | 2,270(37.1) | <0.01 |
| Alcohol Consumption, (n%) | 39,995(94.5) | 8,126(94.4) | 9,733(95.3) | 4,567(95.7) | 4,653(92.8) | 7,090(93.4) | 5,826(95.3) | <0.01 |
| Physical Activity | <0.01 | |||||||
| Inactive, (n%) | 36,253(85.7) | 7,341(85.3) | 8,742(85.6) | 4,110(86.2) | 4,336(86.5) | 6,400(84.3) | 5.324(87.1) | |
| Active, (n%) | 6,050(14.3) | 1,263(14.7) | 1,469(14.4) | 660(13.8) | 679(13.5) | 1,191(15.7) | 788(12.9) | |
| Educational Level | <0.01 | |||||||
| Junior High School or Below, (n%) | 3,090(7.30) | 465(5.40) | 713(6.98) | 440(9.22) | 272(5.42) | 475(6.26) | 725(11.90) | |
| High School, (n%) | 34,787(82.2) | 6,857(79.7) | 8,363(81.9) | 3,893(81.6) | 4,244(84.6) | 6,399(84.3) | 5,031(82.3) | |
| College or above, (n%) | 4,426(10.5) | 1,282(14.9) | 1,135(11.1) | 437(9.2) | 499(10.0) | 717(9.5) | 356(5.8) | |
| History of stroke (n%) | 940.00(2.22) | 97.00 (1.13) | 188.00(1.84) | 182.00(3.82) | 71.00(1.42) | 178.00(2.34) | 224.00(3.66) | <0.01 |
| History of atrial fibrillation (n%) | 99.00 (0.23) | 17.00 (0.20) | 23.00(0.23) | 16.00(0.34) | 8.00(0.16) | 19.00(0.25) | 16.00(0.26) | 0.53 |
| History of heart failure (n%) | 371.00(0.88) | 35.00(0.41) | 75.00(0.73) | 87.00(1.82) | 25.00(0.50) | 59.00(0.78) | 90.00(1.47) | <0.01 |
| History of antihyperlipidemic drugs, (n%) | 413(0.98) | 32(0.37) | 79(0.77) | 46(0.96) | 39(0.78) | 91(1.20) | 126(2.06) | <0.01 |
| History of antidiabetic drugs, (n%) | 1,814(4.29) | 208(2.42) | 293(2.87) | 206(4.32) | 193(3.85) | 437(5.76) | 477(7.80) | <0.01 |
| History of taking antihypertensive drugs, (n%) | 5,252(12.4) | 633(7.36) | 1014(9.93) | 599(12.6) | 571(11.4) | 1,231(16.2) | 1,204(19.7) | <0.01 |
Values are n (%) or mean±SD
Abbreviations: CumRC Cumulative remnant cholesterol, CumCRP Cumulative c-reactive protein, BMI Body mass index, SBP Systolic blood pressure, DBP Diastolic Blood Pressure, FBG Fasting blood glucose, eGFR estimated glomerular filtration rate, LDL-C Low-density lipoprotein, HDL-C High-density lipoprotein
G1: CumRC < 1.0080 mmol/L and CumCRP < 1 mg/L; G2: CumRC < 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G3: CumRC < 1.0080 mmol/L and CumCRP ≥ 3 mg/L; G4: CumRC ≥ 1.0080 mmol/L and CumCRP < 1 mg/L; G5: CumRC ≥ 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G6: CumRC ≥1.0080 mmol/L and CumCRP ≥ 3 mg/L
The risk of incident MI upon coexposure to CumCRP and CumRC
After a median follow-up of 11.0 years (IQR: 10.6–11.3), 741 participants developed MI. Participants were grouped into six categories on the basis of CumCRP thresholds (1 and 3 mg/L) and the median CumRC concentration (1.0080 mmol/L). Group 1 (G1; CumRC < 1.0080 and CumCRP < 1 mg/L) served as the reference. Among individuals with low CumRC, increasing hs-CRP levels were associated with progressively higher MI risk: the HRs were 1.33 (0.99–1.80) for G2 (CRP concentration of 1–3 mg/L) and 2.04 (1.49–2.78) for G3 (CRP concentration ≥ 3 mg/L). Among individuals with higher CumRC levels (≥ 1.0080 mmol/L), an elevated MI risk was consistently observed across all CumCRP strata. The corresponding HRs were 1.50 (95% CI: 1.05–2.13) for G4 (CumCRP < 1 mg/L), 2.31 (95% CI: 1.73–3.08) for G5 (CumCRP 1–3 mg/L), and 3.16 (95% CI: 2.37–4.20) for G6 (CumCRP ≥ 3 mg/L) (Table 2).
Table 2.
MI risk upon co-exposure stratified by CumCRP thresholds (1, 3 mg/L) and CumRC (median)
| Combination of CumCRP and CumRC, HRs (95% CIs) | ||||||
|---|---|---|---|---|---|---|
| G1 | G2 | G3 | G4 | G5 | G6 | |
| Event/Total | 67/8,604 | 130/10,211 | 110/4,770 | 59/5,015 | 162/7,591 | 213/6,112 |
|
Incident rate, Per 1000 person-y |
0.73 | 1.21 | 2.28 | 1.10 | 2.03 | 3.42 |
| Model 1 | Ref. | 1.48 (1.10,1.99) | 2.40 (1.76,3.26) | 1.51 (1.06,2.15) | 2.57 (1.93,3.42) | 3.62 (2.74,4.78) |
| Model 2 | Ref. | 1.33 (0.99,1.79) | 2.04 (1.50,2.78) | 1.51 (1.06,2.14) | 2.32 (1.74,3.11) | 3.18 (2.39,4.22) |
| Model 3 | Ref. | 1.33 (0.99,1.80) | 2.04 (1.49,2.78) | 1.50 (1.05,2.13) | 2.31 (1.73,3.08) | 3.16 (2.37,4.20) |
G1: CumRC < 1.0080 mmol/L and CumCRP < 1 mg/L; G2: CumRC < 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G3: CumRC < 1.0080 mmol/L and CumCRP ≥ 3 mg/L; G4: CumRC ≥ 1.0080 mmol/L and CumCRP < 1 mg/L; G5: CumRC ≥ 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G6: CumRC ≥ 1.0080 mmol/L and CumCRP ≥ 3 mg/L
Model 1: Adjusted for age and gender
Model 2: Adjusted for age and gender, fasting blood glucose, systolic blood pressure, estimated glomerular filtration rate, BMI, smoking, drinking, physical activity, education level
Model 3: Adjusted for all variables in model 2 and antidiabetic treatment, antihypertensive treatment and antihyperlipidemic treatment
Results of the subgroup and sensitivity analyses
To assess whether associations varied by conventional risk factor control, participants were stratified into four groups according to the number of risk factors that met guideline targets. These factors included blood pressure (< 140/90 mmHg), FBG concentration (< 6.1 mmol/L), and LDL-C concentration (< 2.6 mmol/L) during the exposure period. After multivariable adjustment, the associations between joint high CumCRP and CumRC exposure and MI risk remained significant across all strata (Table 3). In each stratum, the group characterized by both high CumCRP and high CumRC (G6) consistently exhibited the highest risk. The HRs ranged from 2.65 (95% CI: 1.54–4.56) among participants with no controlled risk factors to 5.84 (95% CI: 3.07–11.09) among those with two controlled factors. Although the fully controlled group had wider confidence intervals because of fewer events, the graded risk pattern from G1 to G6 remained evident. Additional stratified analyses by age, sex, BMI, hypertension status, and diabetes mellitus status revealed similar risk patterns across the six joint exposure groups (Supplementary Fig. 2). No statistically significant interactions were observed across these subgroups. The sensitivity analyses yielded results consistent with the main findings. After the exclusion of participants who developed MI during the first year of follow-up, those diagnosed with cancer, those with acute infection during the exposure period, and those with major cardiovascular diseases at baseline, including stroke, heart failure, and atrial fibrillation, the overall dose–response pattern across the joint exposure groups remained similar to that observed in the primary analysis (Supplementary Table 2). In the sensitivity analyses using an alternative clinically relevant CumRC cut-off of 0.78 mmol/L, the overall pattern of associations remained consistent. Participants with concurrent high CumRC and high CumCRP levels continued to exhibit the highest risk of MI across CumCRP categories (Supplementary Table 3; Supplementary Fig. 3). Similar results were observed in analyses stratified by traditional risk factor control status (Supplementary Table 4). Among the intermediate exposure groups, the risk associated with elevated CumCRP alone (G3) was consistently greater than that associated with elevated CumRC alone (G4) across all the models (Model 3: HR = 2.04 for G3 vs. 1.50 for G4). To further investigate this difference, additional subgroup analyses were conducted (Supplementary Tables 5 and 6). Regardless of whether G3 or G4 was used as the reference group, the HRs for the other groups did not reach statistical significance, which indicates that no statistically significant difference was observed between the two groups.
Table 3.
Stratified associations of combined CumCRP and CumRC exposure with MI risk by traditional risk factor control status
| Combination of CumCRP and CumRC, HRs (95% CIs) | ||||||
|---|---|---|---|---|---|---|
| G1 | G2 | G3 | G4 | G5 | G6 | |
| Event/Total | 67/8,604 | 130/10,211 | 110/4,770 | 59/5,015 | 162/7,591 | 213/6,112 |
|
Incident rate, Per 1000 person-y |
0.73 | 1.21 | 2.28 | 1.10 | 2.03 | 3.42 |
| All controlled | Ref. | 2.09 (0.51,8.58) | 0.72 (0.07,6.98) | 3.29 (0.77,14.13) | 1.82 (0.36,9.32) | 3.74 (0.86,16.24) |
| 2 Factors controlled | Ref. | 1.07 (0.52,2.22) | 2.60 (1.25,5.39) | 1.46 (0.67,3.21) | 3.06 (1.57,5.96) | 5.84 (3.07,11.09) |
| 1 Factor controlled | Ref. | 1.23 (0.80,1.89) | 2.05 (1.31,3.20) | 1.59 (0.95,2.65) | 2.32 (1.53,3.51) | 2.71 (1.79,4.09) |
| None Factors controlled | Ref. | 1.45 (0.83,2.53) | 1.86 (1.04,3.35) | 1.09 (0.53,2.25) | 1.67 (0.96,2.94) | 2.65 (1.54,4.56) |
G1: CumRC < 1.0080 mmol/L and CumCRP < 1 mg/L; G2: CumRC < 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G3: CumRC < 1.0080 mmol/L and CumCRP ≥ 3 mg/L; G4: CumRC ≥ 1.0080 mmol/L and CumCRP < 1 mg/L; G5: CumRC ≥ 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G6: CumRC ≥ 1.0080 mmol/L and CumCRP ≥ 3 mg/L
Model 1: Adjusted for age and gender
Model 2: Adjusted for age and gender, fasting blood glucose, systolic blood pressure, estimated glomerular filtration rate, BMI, smoking, drinking, physical activity, education level
Model 3: Adjusted for all variables in model 2 and antidiabetic treatment, antihypertensive treatment and antihyperlipidemic treatment
G1: CumRC < 1.0080 mmol/L and CumCRP < 1 mg/L; G2: CumRC < 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G3: CumRC < 1.0080 mmol/L and CumCRP ≥ 3 mg/L; G4: CumRC ≥ 1.0080 mmol/L and CumCRP < 1 mg/L; G5: CumRC ≥ 1.0080 mmol/L and 1 ≤ CumCRP < 3 mg/L; G6: CumRC ≥ 1.0080 mmol/L and CumCRP ≥ 3 mg/L
Discussion
In this prospective cohort of 42,303 participants, the associations between long-term cumulative exposure to RC and systemic inflammation and MI risk were examined. The findings demonstrated that, in individuals with similar RC exposure, higher cumulative hs-CRP levels were linked to higher MI risk. The greatest risk was observed in individuals with persistently elevated levels in both RC and hs-CRP. This association remained robust after traditional risk factors, including blood pressure, FBG, and LDL-C, were considered. These findings suggest that persistent inflammation and elevated remnant lipoproteins may synergistically promote coronary heart disease. The assessment of both factors in combination could improve cardiovascular risk prediction and support more precise prevention strategies.
RC, a cholesterol component of triglyceride-rich lipoproteins, can penetrate the arterial intima [23, 24]. Higher levels of RC have been associated with increased cardiovascular risk, as each 39 mg/dL increase is linked to a nearly threefold increase in ischaemic heart disease risk [25]. Studies such as the Jackson Heart Study and Framingham Offspring Cohorts have confirmed the predictive value of RC for incident cardiovascular events [26], while Mendelian randomization analyses support a causal role for RC in MI [27]. Compared with LDL-C, RC not only shares similar atherogenic potential but also possesses distinct proinflammatory properties. Evidence from Mendelian randomization analyses demonstrated a causal association between elevated RC and systemic low-grade inflammation, an association not observed for LDL-C [28]. Furthermore, the EPIC-Norfolk study revealed that each 1 mmol/L increase in RC was associated with a 29.5% increase in the hs-CRP level and that, up to 5.9% of cardiovascular risk was mediated through inflammatory pathways [29].
Systemic inflammation is also a key contributor to atherosclerotic disease. CRP is widely recognized as a marker of systemic inflammation and has been linked to atherogenesis and plaque destabilization [30]. Residual inflammatory risk persists in some patients despite intensive statin therapy [31, 32], which highlights the limitations of lipid-centred prevention strategies. This notion was further supported by the CANTOS trial, in which inhibition of interleukin-1β with canakinumab significantly reduced recurrent MI and cardiovascular events among patients with prior MI and elevated hs-CRP levels [33].
Recent clinical studies have provided critical insights into the interplay between lipid-related and inflammatory pathways in driving residual cardiovascular risk [34, 35]. Kraler et al. reported that, among patients with recent acute coronary syndrome, residual lipid risk, residual inflammatory risk, and the combination of the two were independently associated with an elevated risk of major adverse cardiovascular events (MACE) within one year [36]. Bay et al. reported that in statin-treated patients who underwent percutaneous coronary intervention (PCI), residual inflammatory risk was independently associated with adverse cardiovascular events, whereas residual cholesterol risk defined by LDL-C was not [37]. The present findings are consistent with these studies. Furthermore, this study addressed residual risk at an earlier, pre-event stage by examining incident MI in a large community-based cohort. In addition, compared with a single on-treatment assessment, repeated measurements were used to quantify cumulative exposure over time, which offers a more accurate representation of the long-term biological burden relevant to atherosclerotic progression. Finally, this study focused on RC rather than LDL-C, which may be clinically relevant. LDL-C may incompletely capture the atherogenic burden attributable to triglyceride-rich remnant particles, particularly in populations treated intensively with secondary-prevention measures, whereas cumulative RC better reflects prolonged exposure to these lipoproteins. Accordingly, the findings complement those of prior studies by suggesting that the combined burden of remnant lipoproteins and inflammation may provide additional insight for long-term MI risk stratification.
The biological plausibility of this association is supported by mechanistic evidence. RC can penetrate the arterial intima and be taken up by macrophages, leading to foam cell formation. In addition, RC directly induces endothelial dysfunction and promotes the release of proinflammatory and prothrombotic mediators, thereby amplifying local vascular inflammation [38, 39]. RC-induced inflammation further disrupts lipid metabolism, which increases VLDL secretion and consequently increases circulating RC levels [40]. This interplay suggests a self-reinforcing cycle of lipid accumulation and inflammation that accelerates the progression of atherosclerosis.
Sensitivity analyses confirmed the robustness of these associations. These associations remained robust after the exclusion of participants who experienced MI within the first year of follow-up, those diagnosed with cancer during the exposure period, those with evidence of acute infection (defined as any hs-CRP measurement > 10 mg/L during the exposure period) and those with major cardiovascular diseases at baseline, including stroke, heart failure, and atrial fibrillation. Subgroup analyses further demonstrated that joint exposure to elevated RC and hs-CRP was consistently associated with increased MI risk across strata defined by age, sex, BMI, hypertension, and diabetes status. No significant interactions were detected (all P values for interaction > 0.05), which underscores the generalizability of these findings. Notably, a stronger relative association was observed in women than in men, which suggests that women may be more vulnerable to the long-term combined burden of RC and systemic inflammation. This vulnerability may be partly explained by stronger associations between CRP levels and coronary events in women [41], as well as the loss of oestrogen-related vascular protection after menopause [42, 43]. When lipid abnormalities and systemic inflammation coexist, these factors synergistically amplify cardiovascular risk, leading to a more pronounced relative risk in women.
The findings from this study suggest that long-term cumulative exposure to both systemic inflammation and RC markedly increases the risk of MI. While the management of traditional cardiovascular risk factors remains essential, clinical practice should also consider patients’ inflammatory status and RC burden. Existing evidence suggests that combined lipid-lowering and anti-inflammatory interventions may represent a promising strategy for mitigating atherosclerotic risk. These results further underscore the clinical value of incorporating cumulative RC and hs-CRP levels into cardiovascular risk assessment. Joint evaluation of these markers not only identifies individuals at extremely high risk due to prolonged dual elevation of RC and hs-CRP but also reveals residual risk in populations with traditional risk factors that are otherwise well controlled. This combined assessment enables more refined risk stratification and may inform the optimization of primary prevention strategies.
Strengths and limitations
This study has several notable strengths, including its large sample size, prospective design, and repeated measurements over time. Nonetheless, several limitations should be noted. First, this study did not differentiate between ST-segment elevation and non-ST-segment elevation MI, which may limit mechanistic insights. Second, RC was calculated as TC minus LDL-C and HDL-C and was not directly measured. Although this approach is widely used in large-scale epidemiological studies, it may lead to potential measurement imprecision and potential exposure misclassification. Third, the fully controlled risk factor groups in the subgroup analyses were small, which may have limited statistical power to detect differences between groups, although overall the trends were consistent with the main findings. Fourth, despite multivariable adjustment, residual confounding cannot be entirely excluded. Finally, as the cohort was predominantly male, the generalizability of our findings to females and to individuals of other ethnicities or younger populations requires further validation.
Conclusion
Long-term concurrent exposure to elevated RC and systemic inflammation was associated with a substantially increased risk of MI. Joint assessment of cumulative RC and CRP may improve long-term cardiovascular risk stratification, particularly through the identification of individuals with substantial residual risk despite otherwise acceptable control of traditional risk factors. These findings support a more comprehensive preventive approach that considers both lipid-related and inflammatory burden, and may help guide earlier, more individualized strategies for myocardial infarction prevention in clinical practice.
Supplementary Information
Acknowledgements
We extend our heartfelt thanks to everyone involved in the Kailuan Study, including the members of Kailuan General Hospital and its affiliated hospitals.
Role of the funder/sponsor
National High Level Hospital Clinical Research Funding had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Access to data and data analysis
Wei Wu and Shouling Wu had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Abbreviations
- MI
Myocardial Infarction
- LDL-C
Low-Density Lipoprotein Cholesterol
- HDL-C
High-Density Lipoprotein Cholesterol
- RC
Remnant Cholesterol
- CVD
Cardiovascular Disease
- TRLs
Triglyceride-Rich Lipoproteins
- VLDL
Very Low-Density Lipoprotein
- IDL
Intermediate-Density Lipoprotein
- TG
Triglycerides
- TC
Total Cholesterol
- hs-CRP
High-Sensitivity C-Reactive Protein
- eGFR
Estimated Glomerular Filtration Rate
- FBG
Fasting Blood Glucose
- SBP
Systolic Blood Pressure
- DBP
Diastolic Blood Pressure
- BMI
Body Mass Index
- HR
Hazard Ratio
- CI
Confidence Interval
- ANOVA
Analysis of Variance
- CKD-EPI
Chronic Kidney Disease Epidemiology Collaboration
- SD
Standard Deviation
- IQR
Interquartile Range
Authors’ contributions
Kanghao Zhou, Wei Wu and Shouling Wu were integral to the conception, design, and conduct of the study. Kanghao Zhou, Guangcheng Liu, Bin Zhang and Xiong Zhang were involved in the acquisition, analysis, and interpretation of the data. Kanghao Zhou contributed to the drafting the manuscript. All authors participated in reviewing, editing, and providing approval for the final version of the manuscript. Wei Wu and Shouling Wu assume the role of guarantors for this work, thereby possessing full access to all study data, shouldering responsibility for data integrity, and ensuring the precision of data analysis.
Funding
This work was supported by the National High Level Hospital Clinical Research Funding(2022-PUMCH-B-098).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The ethics committee of Kailuan General Hospital approved this study, and it followed the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from participants or their legal representatives.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Wei Wu is the primary corresponding author.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Wei Wu, Email: camsww@163.com.
Shouling Wu, Email: drwusl@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.