Abstract
Objective
Although remnant cholesterol (RC) is an established risk factor for atherosclerotic cardiovascular diseases, its role in chronic kidney disease (CKD) progression remains unclear. We aimed to investigate the association between RC levels and adverse kidney outcomes in patients with CKD.
Methods
We analyzed data from the KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease, a prospective multicenter cohort study. Patients were categorized into quartiles according to their RC levels, calculated as total cholesterol level minus low-density lipoprotein cholesterol and high-density lipoprotein cholesterol levels. The primary outcome was the initiation of kidney replacement therapy (KRT).
Results
Among 2,087 patients, 31% underwent KRT during a median follow-up of 8.0 years. Higher RC levels were significantly associated with an increased risk of KRT initiation. Compared with the lowest quartile (Q1), the adjusted hazard ratios (aHRs) for KRT initiation were 1.22 (95% confidence interval [CI], 0.95–1.57) for Q2, 1.32 (95% CI, 1.03–1.70) for Q3, and 1.42 (95% CI, 1.07–1.89) for Q4. A 1–standard deviation increase in RC level was associated with 16% higher risk of KRT initiation (aHR, 1.16; 95% CI, 1.05–1.29). This association remained significant, particularly among patients with diabetes and those with early-stage CKD.
Conclusion
Elevated RC levels were independently associated with a higher risk of KRT initiation in CKD patients. These findings suggest that RC is a risk factor for CKD progression and underscore the need for further studies to evaluate the potential benefits of RC-targeted lipid-modifying strategies for preserving kidney function.
Keywords: Chronic kidney diseases, Dyslipidemia, Kidney replacement therapy, Cholesterol
INTRODUCTION
Dyslipidemia in patients with chronic kidney disease (CKD) is characterized by increased triglyceride (TG) levels, reduced high-density lipoprotein cholesterol (HDL-C) levels, and variable levels of low-density lipoprotein cholesterol (LDL-C).1,2 Lipid metabolism in patients with CKD exhibits distinct characteristics, and dyslipidemia has continuously received attention, both with respect to cardiovascular disease risk and the preservation of kidney function.2,3,4,5
Remnant cholesterol (RC) is the cholesterol content of triglyceride-rich lipoprotein (TRL) remnants, including partially lipolyzed very-low-density lipoprotein, intermediate-density lipoprotein, and chylomicron remnants. RC is calculated as the total cholesterol (TC) level excluding HDL-C and LDL-C levels. Elevated RC levels promote cholesterol accumulation within arterial walls by facilitating macrophage uptake and foam cell formation, thereby accelerating atherosclerosis.6,7,8 Based on this pathophysiology, RC is a well-established risk factor for ischemic heart disease with a robust causal relationship6,8; it also increases the risk of atherosclerotic cardiovascular disease in patients with CKD.9,10 Increased RC levels are associated with a decline in kidney function,11,12,13,14 particularly in patients with diabetes mellitus (DM).15,16,17 However, the role of RC in the progression of CKD remains unclear.
In this study, we aimed to investigate the effect of RC levels on the deterioration of kidney function in patients with CKD using prospective cohort data from a Korean population.
We hypothesized that higher RC levels would be associated with a greater risk of KRT initiation in patients with CKD.
MATERIALS AND METHODS
1. Study design
The KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) (NCT01630486 at http://clinicaltrials.gov) is a large-scale prospective cohort study involving 9 tertiary care hospitals in Korea.18 Between 2011 and 2016, 2,238 patients aged 20–75 years with pre-dialysis CKD stages G1–G5 were enrolled by nephrologists based on clinical assessment of the chronicity of kidney dysfunction to exclude acute kidney injury. Patients with a history of hemodialysis or peritoneal dialysis, organ transplantation, heart failure (New York Heart Association class 3 or 4), liver cirrhosis (Child–Pugh class B or C), or cancer; pregnant women; and individuals who were unable or unwilling to provide written consent were excluded. Among the 2,238 patients who were initially eligible for analysis, 115 with missing values for lipid parameters or information on lipid-lowering agent use, 31 who were lost to follow-up, and 26 with negative calculated RC values were excluded. Consequently, 2,087 patients were included in the final analysis (Fig. 1). All patients were followed up until March 2022. This study was approved by the Institutional Review Board of each participating clinical center: Seoul National University Hospital (H-1704-025-842), Seoul National University Bundang Hospital (B-1106/129-008), Yonsei University Severance Hospital (4-2011-0163), Kangbuk Samsung Medical Center (2011-01-076), Seoul St. Mary’s Hospital (KC11OIMI0441), Gil Hospital (GIRBA2553), Eulji General Hospital (201105-01), Chonnam National University Hospital (CNUH-2011-092), and Pusan Paik Hospital (11-091). This study was conducted and reported in accordance with the principles of the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Written informed consent was obtained from all participants.
Fig. 1. Study flow chart.
TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; RC, remnant cholesterol.
2. Data collection and measurements
Demographic and medical data, including age and sex; lifestyle factors, including smoking status, alcohol consumption, and physical activity; socioeconomic characteristics, including economic and educational status; comorbidities, including DM, hypertension (HTN), and cardiovascular disease; and medication use, including angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and lipid-lowering agents such as statins, ezetimibe, fibrate, and niacin, were collected through self-report questionnaires and review of medical records by trained medical staff. The intensity of statin therapy was defined according to the 2018 American Heart Association/American College of Cardiology guideline on the management of blood cholesterol.19 The conventional Charlson Comorbidity Index (CCI) and the age-adjusted CCI score, with additional points assigned for age, were calculated at baseline. Anthropometric measurements, including height, weight, body mass index (BMI), waist circumference, and systolic and diastolic blood pressure, were obtained. Blood samples were collected after an overnight fast of at least 8 hours. Serum creatinine (Cr), urine Cr, and urine protein and albumin levels were measured at a central laboratory (Lab Genomics). Serum Cr levels were quantified using an isotope dilution mass spectrometry–traceable method. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation.20 CKD stages were classified according to eGFR. Random midstream urine samples were used to measure the urine protein-to-Cr ratio and urine albumin-to-Cr ratio (UACR).
3. Measurement of RC
The exposure of interest was the RC level. RC was calculated as the TC level minus LDL-C and HDL-C levels, as defined by Nordestgaard and Varbo.6 Lipid parameters, including TC, LDL-C, HDL-C, and TG levels, were measured using the direct enzyme method at the local laboratory of each participating center. Patients were categorized according to quartiles of RC levels: Q1, <16.6; Q2, ≥16.6 to <24.2; Q3, ≥24.2 to <35.0; Q4, ≥35.0 mg/dL.
4. Study outcome
The primary outcome was the initiation of kidney replacement therapy (KRT), defined as maintenance dialysis for at least 3 months or kidney transplantation during the follow-up period. The secondary outcomes included: 1) composite kidney events, defined as a composite of KRT initiation and kidney function decline, defined as the first occurrence of a 2-fold increase in serum Cr level or a ≥50% decline in eGFR from baseline; 2) slope of eGFR decline; and 3) all-cause mortality. All patients were closely monitored, and adverse events were reported by each participating center. Adverse events were identified through review of electronic medical records or patient interviews conducted during annual follow-up visits. All events were adjudicated by an independent adjudication committee. Patients who were lost to follow-up were censored at the date of their last visit.
5. Statistical analysis
For descriptive analyses across RC quartiles, continuous variables are presented as means ± standard deviations (SDs), and categorical variables are presented as numbers (percentages). Incidence rates were calculated as events per 1,000 person-years. Cox proportional hazards (PH) models were used to assess the risk of study outcomes according to RC quartiles or per 1–SD increase in RC level. Results from the Cox PH models are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). To account for potential confounding effects, a multivariable model was constructed with adjustment for age; sex; smoking status; DM; HTN; BMI; use of ACEI, ARB, fibrates, or statins; CKD stage; and baseline levels of LDL-C, HDL-C, and TG. The intensity of statin therapy was additionally adjusted for in the final multivariable model. Among these covariates, missing values were observed for smoking status (n=2, 0.1%) and BMI (n=15, 0.7%). Given the negligible proportion of missing data, patients with missing values were excluded from the multivariable analyses. Kaplan–Meier curves were generated to describe the probability of study outcomes over time. Restricted cubic spline curves were constructed to evaluate the risk of study outcomes according to RC as a continuous variable and to explore potential nonlinear relationships between RC and outcome risk.21 Linear mixed-effects models with random intercepts and slopes were used to estimate the slope of eGFR decline.22 For this analysis, 1,849 patients who underwent at least 3 serum Cr measurements were included. Effect modification was examined in prespecified subgroups, including age (<60 and ≥60 years), sex (male and female), primary kidney disease (DM- or HTN-related CKD and others), DM (yes and no), BMI (<25 and ≥25 kg/m2), statin use (yes and no), CKD stage (stages 1–3 and stages 4–5), TG level (<150 and ≥150 mg/dL), LDL-C level (<100 and ≥100 mg/dL), HDL-C level (<40 and ≥40 mg/dL for males; <50 and ≥50 mg/dL for females), and UACR (<30 and ≥30 mg/g), to assess their impact on the association between RC and the primary outcome.
We performed additional sensitivity analyses to confirm the robustness of these findings. First, patients with eGFR ≥90 mL/min/1.73 m2 were excluded, as individuals with CKD stage 1 generally have near-normal kidney function and may not adequately represent the CKD population. Second, patients with eGFR <15 mL/min/1.73 m2 were excluded, as CKD stage 5 is considered equivalent to kidney failure requiring KRT in the near future. Third, a sensitivity analysis restricted to patients with eGFR 15–60 mL/min/1.73 m2 (CKD stages 3–4) was performed, as this group represents a key target population for preventing progression to kidney failure. Fourth, patients receiving any lipid-lowering therapy were excluded to reduce potential confounding by lipid-lowering agents. Fifth, a competing-risk analysis for adverse kidney events was conducted using a cause-specific hazards model, in which death events occurring before the study outcome were treated as competing events and censored at the time of death.23 Cumulative incidence functions accounting for the competing risk of death were estimated and plotted. Sixth, an additional competing-risk analysis was performed with non-cardiovascular mortality treated as a competing event for adverse kidney outcomes.
All statistical analyses were performed using R software (version 4.4.2; R Foundation for Statistical Computing), and 2-sided p-values <0.05 were considered statistically significant.
RESULTS
1. Baseline characteristics
The baseline characteristics of the 2,087 patients according to RC quartiles are presented in Table 1. The mean age was 54 years, and 1,284 patients (62%) were male. A total of 722 patients (35%) had DM, whereas 2,003 patients (96%) had HTN, with 1,792 (86%) receiving ACEI or ARB. Regarding lipid-lowering therapy, 1,088 (52%), 135 (7%), and 58 (3%) patients used statins, ezetimibe, and fibrates, respectively. The mean eGFR was 53 mL/min/1.73 m2, and the mean RC level was 28.6 mg/dL. Patients with higher RC levels were older and had higher BMI values. These patients also showed a higher prevalence of DM and HTN, higher scores on both the conventional and age-adjusted CCI, and a tendency toward lower eGFR values. Patients in higher RC quartiles had higher TC and TG levels and lower HDL-C levels, whereas LDL-C levels did not differ significantly across RC quartiles at baseline.
Table 1. Baseline characteristics of the study population according to quartiles of remnant cholesterol levels.
| Variable | Total | Remnant cholesterol (mg/dL) | p | ||||
|---|---|---|---|---|---|---|---|
| Q1 (<16.6) | Q2 (≥16.6 to <24.2) | Q3 (≥24.2 to <35.0) | Q4 (≥35.0) | ||||
| Number | 2,087 | 522 | 522 | 522 | 521 | ||
| Remnant cholesterol level (mg/dL) | 28.6±18.7 | 11.4±3.8 | 20.5±2.3 | 29.3±3.2 | 53.1±20.1 | <0.001 | |
| Age (yr) | 53.8±12.3 | 52.2±13.5 | 54.1±12.3 | 54.3±11.9 | 54.5±11.4 | 0.011 | |
| Sex, male | 1,284 (61.5) | 304 (58.2) | 325 (62.3) | 322 (61.7) | 333 (63.9) | 0.288 | |
| Economic status | 0.373 | ||||||
| High | 457 (22.5) | 121 (23.8) | 125 (24.5) | 101 (20.0) | 110 (21.8) | ||
| Middle | 1,097 (54.1) | 279 (54.9) | 274 (53.7) | 272 (53.8) | 272 (54.0) | ||
| Low | 474 (23.4) | 108 (21.3) | 111 (21.8) | 133 (26.3) | 122 (24.2) | ||
| Educational status | 0.125 | ||||||
| <High school | 505 (24.2) | 115 (22.0) | 125 (24.0) | 134 (25.8) | 131 (25.2) | ||
| High school | 728 (34.9) | 190 (36.4) | 160 (30.7) | 185 (35.6) | 193 (37.1) | ||
| >High school | 850 (40.8) | 217 (41.6) | 236 (45.3) | 201 (38.7) | 196 (37.7) | ||
| Smoking status | 0.092 | ||||||
| Never | 1,112 (53.3) | 302 (57.9) | 285 (54.6) | 261 (50.1) | 264 (50.8) | ||
| Current | 330 (15.8) | 68 (13.0) | 75 (14.4) | 93 (17.9) | 94 (18.1) | ||
| Former | 643 (30.8) | 152 (29.1) | 162 (31.0) | 167 (32.1) | 162 (31.2) | ||
| Alcohol consumption | 0.242 | ||||||
| Non-drinker | 742 (39.4) | 170 (36.8) | 178 (37.3) | 197 (41.6) | 197 (41.7) | ||
| Current | 1,143 (60.6) | 292 (63.2) | 299 (62.7) | 277 (58.4) | 275 (58.3) | ||
| Regular physical activity | 669 (36.3) | 173 (38.5) | 183 (39.1) | 157 (34.1) | 156 (33.5) | 0.160 | |
| Primary kidney disease | <0.001 | ||||||
| DM | 540 (25.9) | 90 (17.2) | 121 (23.2) | 146 (28.0) | 183 (35.1) | ||
| HTN | 410 (19.7) | 94 (18.0) | 103 (19.7) | 101 (19.4) | 112 (21.5) | ||
| GN | 657 (31.5) | 176 (33.7) | 158 (30.3) | 171 (32.8) | 152 (29.2) | ||
| PKD | 332 (15.9) | 124 (23.8) | 104 (19.9) | 61 (11.7) | 43 (8.3) | ||
| Others | 147 (7.0) | 38 (7.3) | 36 (6.9) | 42 (8.1) | 31 (6.0) | ||
| Comorbidities | |||||||
| DM | 722 (34.6) | 126 (24.1) | 171 (32.8) | 194 (37.2) | 231 (44.3) | <0.001 | |
| HTN | 2,003 (96.0) | 481 (92.1) | 500 (95.8) | 507 (97.1) | 515 (98.8) | <0.001 | |
| CVD | 325 (15.6) | 76 (14.6) | 75 (14.4) | 85 (16.3) | 89 (17.1) | 0.554 | |
| Conventional CCI | 2.3±1.6 | 1.9±1.6 | 2.3±1.6 | 2.4±1.6 | 2.7±1.5 | <0.001 | |
| Age-adjusted CCI | 3.4±2.2 | 2.9±2.3 | 3.4±2.3 | 3.6±2.1 | 3.8±2.1 | <0.001 | |
| Anthropometric measurement | |||||||
| BMI (kg/m2) | 24.6±3.4 | 23.7±3.3 | 24.4±3.3 | 24.8±3.2 | 25.6±3.6 | <0.001 | |
| Waist circumference (cm) | 87.6±9.7 | 84.9±9.4 | 87.0±9.9 | 88.0±9.0 | 90.6±9.7 | <0.001 | |
| SBP (mmHg) | 128.0±16.3 | 124.9±14.8 | 127.7±15.8 | 128.4±16.4 | 130.9±17.5 | <0.001 | |
| DBP (mmHg) | 77.0±11.2 | 76.0±11.2 | 76.3±10.8 | 76.8±11.1 | 78.7±11.4 | 0.001 | |
| Drugs | |||||||
| ACEI or ARB | 1,792 (85.9) | 426 (81.6) | 453 (86.8) | 449 (86.0) | 464 (89.1) | 0.006 | |
| Statin | 1,088 (52.1) | 243 (46.6) | 277 (53.1) | 295 (56.5) | 273 (52.4) | 0.013 | |
| Ezetimibe | 135 (6.5) | 23 (4.4) | 30 (5.7) | 39 (7.5) | 43 (8.3) | 0.052 | |
| Fibrate | 58 (2.8) | 8 (1.5) | 13 (2.5) | 11 (2.1) | 26 (5.0) | 0.004 | |
| Niacin | 3 (0.1) | 1 (0.2) | 0 (0.0) | 1 (0.2) | 1 (0.2) | 0.801 | |
| Statin intensity | 0.066 | ||||||
| Non-user | 999 (47.9) | 279 (53.4) | 245 (46.9) | 227 (43.5) | 248 (47.6) | ||
| Low | 212 (10.2) | 47 (9.0) | 44 (8.4) | 63 (12.1) | 58 (11.1) | ||
| Moderate | 827 (39.6) | 188 (36.0) | 221 (42.3) | 218 (41.8) | 200 (38.4) | ||
| High | 49 (2.3) | 8 (1.5) | 12 (2.3) | 14 (2.7) | 15 (2.9) | ||
| Kidney function | |||||||
| Creatinine (mg/dL) | 1.8±1.2 | 1.7±1.1 | 1.8±1.3 | 1.9±1.2 | 2.0±1.1 | <0.001 | |
| eGFR (mL/min/1.73 m2) | 53.0±30.6 | 60.2±33.8 | 54.9±29.7 | 50.9±29.4 | 45.9±27.4 | <0.001 | |
| UPCR (g/g) | 1.3±2.1 | 0.8±1.6 | 1.0±1.8 | 1.3±1.8 | 2.1±2.9 | <0.001 | |
| CKD stage | <0.001 | ||||||
| Stage 1 | 334 (16.0) | 129 (24.7) | 83 (15.9) | 72 (13.8) | 50 (9.6) | ||
| Stage 2 | 393 (18.8) | 103 (19.7) | 111 (21.3) | 98 (18.8) | 81 (15.5) | ||
| Stage 3A | 347 (16.6) | 84 (16.1) | 96 (18.4) | 84 (16.1) | 83 (15.9) | ||
| Stage 3B | 442 (21.2) | 80 (15.3) | 118 (22.6) | 114 (21.8) | 130 (25.0) | ||
| Stage 4 | 437 (20.9) | 95 (18.2) | 82 (15.7) | 120 (23.0) | 140 (26.9) | ||
| Stage 5 | 134 (6.4) | 31 (5.9) | 32 (6.1) | 34 (6.5) | 37 (7.1) | ||
| Laboratory measurements | |||||||
| Total CO2 (mmol/L) | 25.7±3.6 | 25.9±3.7 | 26.1±3.6 | 25.7±3.6 | 25.3±3.7 | 0.009 | |
| Hemoglobin (g/dL) | 12.8±2.0 | 12.9±1.9 | 13.0±2.0 | 12.8±2.1 | 12.7±2.0 | 0.259 | |
| Albumin (g/dL) | 4.18±0.43 | 4.22±0.36 | 4.23±0.38 | 4.17±0.40 | 4.09±0.52 | <0.001 | |
| hs-CRP (mg/L) | 2.0±5.1 | 1.8±4.5 | 1.8±5.3 | 2.0±5.2 | 2.3±5.4 | 0.483 | |
| Fasting glucose (mg/dL) | 111.3±40.1 | 101.2±23.2 | 106.6±34.4 | 114.0±42.8 | 123.5±51.2 | <0.001 | |
| Lipid parameters | |||||||
| Total cholesterol (mg/dL) | 174.5±39.3 | 162.5±33.2 | 167.7±35.7 | 173.3±37.8 | 194.5±42.3 | <0.001 | |
| LDL-C (mg/dL) | 96.7±31.5 | 94.8±28.6 | 96.2±30.8 | 96.9±32.3 | 99.1±34.0 | 0.166 | |
| HDL-C (mg/dL) | 49.2±15.2 | 56.3±15.5 | 51.1±14.5 | 47.1±14.3 | 42.2±12.7 | <0.001 | |
| Triglycerides (mg/dL) | 157.2±96.8 | 90.8±37.1 | 120.6±42.8 | 156.9±54.5 | 260.7±122.1 | <0.001 | |
Values are presented as number (%) or mean ± standard deviation.
DM, diabetes mellitus; HTN, hypertension; GN, glomerulonephritis; PKD, polycystic kidney disease; CVD, cardiovascular disease; CCI, Charlson comorbidity index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; eGFR, estimated glomerular filtration rate; UPCR, urinary protein-creatinine ratio; CKD, chronic kidney disease; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
2. Risk of KRT initiation according to RC level
During a median follow-up of 8.0 years (interquartile range, 6.0–9.5 years), KRT initiation and composite kidney events occurred in 654 (31%) and 850 (41%) patients, respectively. The incidence rates and HRs of adverse kidney outcomes according to RC quartiles are presented in Table 2. Compared with Q1, the unadjusted HRs for KRT initiation were 1.16 (95% CI, 0.91–1.48) in Q2, 1.64 (95% CI, 1.30–2.06) in Q3, and 2.02 (95% CI, 1.62–2.52) in Q4, indicating a progressive increase in KRT risk across higher RC quartiles. Consistent with this pattern, the risks of KRT initiation in Q3 and Q4 were significantly higher than those in Q1 (Fig. 2). In the multivariable model adjusted for age, sex, comorbidities, medication use, and other clinicodemographic factors, the aHRs for KRT initiation were 1.22 (95% CI, 0.95–1.57) in Q2, 1.32 (95% CI, 1.03–1.70) in Q3, and 1.42 (95% CI, 1.07–1.89) in Q4. These associations remained consistent after additional adjustment for statin therapy intensity. When RC was analyzed as a continuous variable, each 1–SD increase in RC level was associated with a 16% higher risk of KRT initiation (aHR, 1.16; 95% CI, 1.05–1.29). A restricted cubic spline analysis demonstrated a positive linear association between RC level and the risk of KRT initiation (p for nonlinearity=0.253) (Fig. 3), indicating a progressively higher risk of KRT initiation with increasing RC levels.
Table 2. Risk of adverse kidney outcomes according to RC levels.
| RC | Number | Event (%) | Duration (PY) | IR (/1,000 PY) | Model 1* | Model 2† | Model 3‡ | Model 4§ | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | ||||||
| KRT initiation | |||||||||||||
| Q1 | 522 | 124 (24) | 3,337 | 37.2 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||||
| Q2 | 522 | 141 (27) | 3,265 | 43.2 | 1.16 (0.91–1.48) | 0.227 | 1.13 (0.89–1.44) | 0.305 | 1.22 (0.95–1.57) | 0.123 | 1.21 (0.94–1.56) | 0.135 | |
| Q3 | 522 | 181 (35) | 2,996 | 60.4 | 1.64 (1.30–2.06) | <0.001 | 1.60 (1.27–2.01) | <0.001 | 1.32 (1.03–1.70) | 0.026 | 1.32 (1.03–1.70) | 0.026 | |
| Q4 | 521 | 208 (40) | 2,797 | 74.4 | 2.02 (1.62–2.52) | <0.001 | 1.96 (1.57–2.46) | <0.001 | 1.42 (1.07–1.89) | 0.015 | 1.40 (1.06–1.86) | 0.020 | |
| RC per 1–SD increase | 1.22 (1.16–1.29) | <0.001 | 1.22 (1.15–1.29) | <0.001 | 1.16 (1.05–1.29) | 0.003 | 1.16 (1.05–1.29) | 0.004 | |||||
| Composite kidney event | |||||||||||||
| Q1 | 522 | 177 (34) | 3,149 | 56.2 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||||
| Q2 | 522 | 189 (36) | 3,081 | 61.3 | 1.09 (0.89–1.34) | 0.395 | 1.08 (0.88–1.32) | 0.468 | 1.03 (0.84–1.28) | 0.754 | 1.03 (0.83–1.27) | 0.806 | |
| Q3 | 522 | 227 (43) | 2,790 | 81.4 | 1.46 (1.20–1.78) | <0.001 | 1.43 (1.18–1.74) | <0.001 | 1.12 (0.91–1.39) | 0.283 | 1.12 (0.91–1.39) | 0.291 | |
| Q4 | 521 | 257 (49) | 2,585 | 99.4 | 1.80 (1.48–2.17) | <0.001 | 1.76 (1.46–2.14) | <0.001 | 1.25 (0.98–1.60) | 0.076 | 1.23 (0.96–1.58) | 0.095 | |
| RC per 1–SD increase | 1.20 (1.14–1.26) | <0.001 | 1.20 (1.14–1.26) | <0.001 | 1.14 (1.04–1.25) | 0.004 | 1.14 (1.04–1.25) | 0.004 | |||||
PY, person-years; IR, incidence rate; HR, hazard ratio; CI, confidence interval; KRT, kidney replacement therapy; SD, standard deviation; RC, remnant cholesterol.
*Model 1 is unadjusted.
† Model 2 is adjusted for age and sex.
‡ Model 3 is adjusted for age, sex, smoking status, diabetes mellitus, hypertension, body mass index, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, statin use, fibrate use, stage of chronic kidney disease, and baseline levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides.
§ Model 4 is further adjusted for statin intensity.
Fig. 2. Risks of adverse kidney outcomes according to quartile of RC levels. (A) KRT initiation; (B) Composite kidney event. The x-axes indicate time (years). The y-axes indicate the probability of incidence for adverse kidney outcomes. The tables below the survival curves show the numbers of people at risk. Black, blue, yellow, and red lines represent Q1, Q2, Q3, and Q4 group, respectively. The p-values were calculated using log-rank test.
RC, remnant cholesterol; KRT, kidney replacement therapy.
Fig. 3. Restricted cubic spline curve plotting the associations between remnant cholesterol and the risks of adverse kidney outcomes. (A) KRT initiation; (B) Composite kidney events. The x-axis indicates the remnant cholesterol levels as continuous variable. The y-axis indicates the adjusted HR of adverse kidney outcomes calculated by multivariable Cox regression analysis. In the multivariable model, age, sex, smoking status, diabetes mellitus, hypertension, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, statin use, fibrate use, stage of chronic kidney disease, and baseline levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides were adjusted. The red lines indicate the adjusted HRs and the pink shade indicates the 95% confidence intervals.
KRT, kidney replacement therapy; HR, hazard ratio.
3. Secondary outcome analysis
For other adverse kidney outcomes, Q4 was associated with a significantly higher risk of composite kidney events in the unadjusted model (HR, 1.80; 95% CI, 1.48–2.17). However, after adjustment for potential confounding factors, the risk of composite kidney events in Q4 was no longer significantly higher than that in Q1 (aHR, 1.25; 95% CI, 0.98–1.60) (Table 2). In contrast, when RC was analyzed as a continuous variable, each 1–SD increase in RC level was associated with a 14% higher risk of composite kidney events (aHR, 1.14; 95% CI, 1.04–1.25) (Table 2). The association between RC level and the risk of composite kidney events showed a linear pattern similar to that observed for KRT initiation (p for nonlinearity=0.918) (Fig. 3). In analyses of eGFR slopes by RC quartile, higher RC quartiles were associated with steeper declines in eGFR, although differences between Q1 and other quartiles did not reach statistical significance (Supplementary Table 1). Regardless of whether RC was analyzed as a categorical or continuous variable, no significant association was observed between RC level and all-cause mortality (Supplementary Table 2, Supplementary Fig. 1).
4. Subgroup analysis
In subgroup analyses, significant interactions were observed between RC quartiles and DM (interaction p=0.019) as well as early-stage CKD (interaction p=0.024) in relation to the risk of KRT initiation (Supplementary Table 3). Among patients with DM and those with CKD stages 1–3, higher RC quartiles were associated with a significantly increased risk of KRT initiation, which rose progressively across RC categories. Although no statistically significant interactions were identified for other prespecified factors, stronger associations between higher RC quartiles and KRT risk were consistently observed among men, individuals aged <60 years, those not using statins, those with higher LDL-C and lower HDL-C levels, and those with a UACR ≥30 mg/g.
5. Sensitivity analysis
After excluding patients with CKD stage 1, 1,753 patients were included in the sensitivity analysis evaluating the association between RC quartiles and KRT initiation (Supplementary Table 4). Consistent with the primary analysis, the risk of KRT initiation increased across higher RC quartiles, with significantly higher risks observed in Q3 (aHR, 1.34; 95% CI, 1.04–1.71) and Q4 (aHR, 1.43; 95% CI, 1.08–1.90) compared with Q1. Similarly, each 1–SD increase in RC level was associated with a 16% higher risk of KRT initiation (aHR, 1.16; 95% CI, 1.05–1.29). These findings were consistent in additional sensitivity analyses excluding patients with CKD stage 5 (n=1,953) or restricting the cohort to those with CKD stages 3–4 (n=1,226). Comparable results were also observed among patients not receiving any lipid-lowering therapy at baseline (n=957) (Supplementary Table 5).
Among the 173 all-cause deaths recorded during follow-up, 32 (18%) were attributable to cardiovascular causes, whereas 141 (82%) were due to non-cardiovascular causes (Supplementary Table 6). When competing risk analyses were conducted using a cause-specific hazards model with all-cause mortality treated as a competing event, the associations between RC and adverse kidney outcomes were consistent with the primary analysis (Supplementary Table 7). The cumulative incidence function curves showed patterns similar to those of the Kaplan–Meier curves (Supplementary Fig. 2). When non-cardiovascular mortality was treated as the competing event, the results likewise remained consistent with the primary findings.
DISCUSSION
In this study, we demonstrated that elevated RC levels were significantly associated with a higher risk of KRT initiation in patients with CKD. This association was independent of traditional lipid parameters, including LDL-C and HDL-C levels, as well as the underlying etiology of CKD. Our findings are consistent with previous studies reporting a link between dyslipidemia and CKD progression; however, the present study provides novel insights by leveraging a prospective cohort specifically focused on patients with CKD.
The role of RC in CKD progression is an emerging area of interest. Elevated RC levels contribute to atherosclerosis, cardiovascular disease, and metabolic complications in patients with CKD.1,2,3 A recent population-based study in South Korea reported that high RC levels were associated with an increased risk of kidney failure in the general population.12 In contrast, findings from the Chronic Renal Insufficiency Cohort study suggested that RC may not be independently associated with CKD progression.24 Our study differs from previous investigations in that we used a well-characterized prospective CKD cohort with long-term follow-up, which enabled a focused evaluation of the association between RC levels and KRT initiation.
Several mechanisms may explain the association between RC and declining kidney function. Unlike LDL-C, which is primarily cleared via hepatic LDL receptors, RC-rich lipoproteins are removed through alternative pathways involving direct uptake by macrophages and vascular endothelial cells.25 This process can lead to foam cell formation and increased vascular inflammation, which may, in turn, exacerbate glomerular injury. In addition, RC is associated with increased oxidative stress and endothelial dysfunction, both of which are key drivers of CKD progression.6,7 Furthermore, TRLs and their remnants are strong risk factors for atherosclerotic cardiovascular disease and are closely linked to systemic inflammation.8 These particles accumulate in the circulation as a result of impaired lipolysis or hepatic clearance, leading to increased vascular permeability and lipid deposition within kidney tissue.9 Moreover, TRL remnants induce pro-inflammatory responses through macrophage activation, which may promote chronic inflammation and fibrosis in patients with CKD.8,9 Emerging genetic evidence further supports the causal role of TRLs and RC in cardiovascular disease. Mendelian randomization studies have demonstrated that genetically elevated RC levels are causally associated with an increased risk of atherosclerotic cardiovascular disease.7,26,27,28,29 Although observational studies suggest a potential link between RC and CKD progression, further research, including Mendelian randomization analyses, is required to clarify causality.
Elevated RC levels are also positively correlated with insulin resistance (IR).30,31 Increased RC may exacerbate IR through interactions between TRLs and very-low-density lipoprotein receptors, which also function as RC receptors and play a key role in the development of IR.32,33 Given that increased IR potentiates the adverse effects of RC on prevalent DM and cardiovascular mortality34,35 and is a well-established risk factor for CKD progression,36 the correlation between elevated RC levels and IR may represent a plausible mechanism underlying the association between RC and CKD progression. This mechanism may help explain the findings of our subgroup analysis, which demonstrated a more pronounced association between higher RC levels and increased risk of KRT initiation in patients with DM compared with those without DM.
In another subgroup analysis, patients with early-stage CKD (stages 1–3) exhibited a more pronounced association between higher RC levels and increased risk of KRT initiation. In advanced CKD, malnutrition and inflammation may attenuate the relationship between cholesterol levels and adverse outcomes, including cardiovascular disease and CKD progression, and may paradoxically increase the risk of adverse outcomes among patients with low cholesterol levels.24,37,38 Accordingly, patients with advanced CKD stages 4–5 demonstrated a less prominent positive association between RC levels and KRT risk. In addition, although no statistically significant interaction was observed, the positive association between RC levels and KRT initiation appeared relatively stronger among patients not receiving statin therapy. Given the established efficacy of statins in reducing LDL-C and RC levels,39 clinicians may consider statin initiation in early-stage CKD with the aim of reducing RC levels. This observation is consistent with prior evidence showing that lipid-lowering therapy reduced major vascular events in patients with an eGFR ≥30 mL/min/1.73 m2,40 thereby supporting the hypothesis that lipid-lowering therapy may exert kidney-protective effects in early CKD. Nevertheless, because statistical heterogeneity in subgroup analyses does not establish pathophysiologic causality, further studies are needed to evaluate the association between RC and KRT risk in specific subpopulations.
Our study leveraged a large-scale prospective CKD cohort, enabling a robust analysis of the impact of RC on kidney function. This study is the first to demonstrate that elevated RC may be a risk factor for KRT initiation in patients with CKD. Although RC was significantly associated with KRT initiation, elevated RC levels were not significantly associated with the risk of composite kidney events or with the slope of eGFR decline. In a previous study, patterns of kidney function decline were heterogeneous, particularly in advanced stages of CKD.41 Most patients with advanced CKD exhibited trajectories of eGFR decline that differed from the conventional linear pattern observed over time. Owing to this heterogeneity, in the KNOW-CKD cohort, which is assessed annually, KRT initiation may have been recorded before confirmation of serum Cr doubling or eGFR halving at the time of scheduled evaluation. This circumstance may explain the higher number of KRT initiation events relative to documented declines in kidney function during follow-up, potentially leading to an underestimation of the association between RC and kidney function decline. Although the observed trend toward a steeper eGFR slope among patients with higher RC levels did not reach statistical significance, the significant findings related to the more definitive hard outcome of KRT initiation support the conclusion that elevated RC levels are associated with an increased risk of CKD progression.
Despite the strengths of a prospective cohort design, this study has several limitations. First, although we adjusted for multiple confounders, residual confounding due to unmeasured factors, such as genetic predisposition and duration of lipid-lowering therapy, cannot be fully excluded. In addition, medication allocation, including lipid-lowering agents and ACEI/ARB, was not randomized because of the observational design. Nevertheless, we attempted to mitigate this limitation by adjusting for comprehensive covariates and conducting multiple sensitivity analyses. Second, longitudinal changes in RC levels over time were not evaluated, which may have provided additional insight into the dynamic role of RC in CKD progression. Third, the sample size was relatively modest compared with large population-based studies. However, the cohort was carefully phenotyped and included detailed clinical assessments, allowing for more precise estimation of associations. Fourth, the study population consisted exclusively of Korean patients, which may limit generalizability to populations with different genetic backgrounds or healthcare systems. Fifth, RC was calculated rather than directly measured. Given that direct measurement of RC is feasible, future studies evaluating measured RC in relation to adverse kidney outcomes are warranted to improve precision. Finally, although RC was associated with a higher risk of KRT initiation, adding RC to traditional risk factors—including baseline eGFR, UACR, DM, and TG levels—did not significantly improve risk discrimination (C-statistic, 0.88 [95% CI, 0.87–0.89]). This finding is likely attributable to a ceiling effect driven by the strong predictive performance of baseline eGFR and UACR alone (C-statistic, 0.88 [95% CI, 0.87–0.89]).
In our study, the graded increase in KRT initiation risk across RC quartiles suggests a dose-dependent relationship, further supporting the hypothesis that RC is independently associated with CKD progression. These findings underscore the need for future research on lipid-modifying strategies that specifically target TRLs and RC to mitigate CKD progression.
Footnotes
Funding: This work was supported by the New Faculty Startup Fund from Seoul National University (50%) and by the National Institute of Health (NIH) research project (2025E110100) (50%). The funding agency had no role in the design, collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.
Conflict of Interest: The authors have no conflicts of interest to declare.
Data Availability Statement: The datasets generated during and/or analyzed during the current study are not publicly available due to ethical and confidentiality considerations related to patient data but are available from the corresponding author on reasonable request.
- Conceptualization: Kim M, Cho S.
- Data curation: Kim M, Cho S, Kim J.
- Formal analysis: Kim M, Cho S, Kim J, Park SK.
- Funding acquisition: Oh KH.
- Investigation: Kang G, Kang E.
- Resources: Han SH, Kim Y, Kim SW, Oh KH.
- Supervision: Kang E, Park SK, Han SH, Kim Y, Kim SW, Oh KH.
- Writing - original draft: Kim M, Cho S.
- Writing - review & editing: Kang G, Kang E, Park SK, Han SH, Kim Y, Kim SW, Oh KH.
SUPPLEMENTARY MATERIALS
Slopes of eGFR decline according to quartiles of remnant cholesterol levels
Risk of all-cause mortality according to RC levels
Subgroup analysis of primary outcome according to RC levels
Risk of primary outcome according to RC levels in various CKD stage sub-populations
Risk of primary outcome according to RC levels in patients not receiving lipid-lowering therapy at baseline
Distribution of cause of death according to remnant cholesterol levels
Cause-specific hazard ratios for adverse kidney outcomes according to remnant cholesterol level in competing risk analysis
Risk of all-cause mortality according to quartiles of remnant cholesterol level. The x-axis indicates time (years). The y-axis represents the probability of all-cause mortality. The table below the survival curves shows the number of at-risk individuals. The black, blue, yellow, and red lines represent groups Q1, Q2, Q3, and Q4, respectively. The p-values were calculated using the log-rank test.
Cumulative incidence function for adverse kidney outcomes according to quartiles of remnant cholesterol levels. (A) KRT initiation; (B) Composite kidney event. The x-axis represents time (years). The y-axis indicates the cumulative incidence function of adverse kidney outcomes calculated using the cause-specific hazards model.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Slopes of eGFR decline according to quartiles of remnant cholesterol levels
Risk of all-cause mortality according to RC levels
Subgroup analysis of primary outcome according to RC levels
Risk of primary outcome according to RC levels in various CKD stage sub-populations
Risk of primary outcome according to RC levels in patients not receiving lipid-lowering therapy at baseline
Distribution of cause of death according to remnant cholesterol levels
Cause-specific hazard ratios for adverse kidney outcomes according to remnant cholesterol level in competing risk analysis
Risk of all-cause mortality according to quartiles of remnant cholesterol level. The x-axis indicates time (years). The y-axis represents the probability of all-cause mortality. The table below the survival curves shows the number of at-risk individuals. The black, blue, yellow, and red lines represent groups Q1, Q2, Q3, and Q4, respectively. The p-values were calculated using the log-rank test.
Cumulative incidence function for adverse kidney outcomes according to quartiles of remnant cholesterol levels. (A) KRT initiation; (B) Composite kidney event. The x-axis represents time (years). The y-axis indicates the cumulative incidence function of adverse kidney outcomes calculated using the cause-specific hazards model.