Abstract
Introduction
Emerging evidence suggests a potential association between dyslipidemia and renal dysfunction. Remnant cholesterol, defined as the cholesterol content of triglyceride-rich lipoproteins, has not been thoroughly investigated in relation to rapid renal function decline in individuals with preserved kidney function. Therefore, our study aimed to clarify this association in a nationwide Chinese cohort.
Methods
Our study used data from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study. Remnant cholesterol level was derived using the following equation: total cholesterol minus low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol. The study outcomes were rapid kidney function decline or the progression to chronic kidney disease (CKD). To determine the associations between remnant cholesterol and CKD outcomes, we used multivariable logistic regression, subgroup analyses, restricted cubic spline models, and receiver operating characteristic curve analysis.
Results
A total of 4,039 individuals (59 ± 8.7 years, 52.5% female) were enrolled in our study. Over a 4-year follow-up, 195 (4.8%) individuals experienced rapid renal function decline, and 367(9.1%) individuals experienced progression to CKD. Elevated remnant cholesterol levels were strongly linked to an elevated risk of rapid renal function decline (OR = 1.89, 95% CI: 1.59–2.26, p value <0.001) and the progression to CKD (OR = 1.76, 95% CI: 1.51–2.04, p value <0.001). The restricted cubic spline analysis revealed that these associations were nonlinear. Additionally, subgroup analysis demonstrated that this relationship was more evident in individuals with LDL-C >2.6 mmol/L than those in LDL-C ≤2.6 mmol/L.
Conclusion
Our study demonstrated that elevated remnant cholesterol concentrations were associated with a higher risk of rapid renal function decline among middle-aged and older Chinese adults. These findings suggest that remnant cholesterol may serve as a valuable biomarker for identifying individuals at elevated risk for renal dysfunction.
Keywords: Remnant cholesterol, Chronic kidney disease, Rapid renal function decline, Nonlinear trends
Introduction
Chronic kidney disease (CKD) is a progressive degenerative disorder characterized by a gradual decline in kidney function, affecting nearly 10% of the global population [1]. As estimated glomerular filtration rate (eGFR) decreases or albuminuria increases, renal dysfunction progresses, resulting in poor prognosis, substantial economic burden, and worse quality of life [2]. Moreover, most adults with renal function decline remain asymptomatic until the very late stages, leading to approximately 90% of cases being undiagnosed, especially among the elderly population [3]. Therefore, early identification of potential risk factors for rapid renal function decline is essential to prevent kidney dysfunction and promote healthy aging.
Previous studies have identified multiple risk factors for renal function decline, including age, blood glucose, hypertension, and depressive symptoms [4, 5]. In particular, abnormal lipid profiles are well-established risk factors for the progression of kidney dysfunction [6–9]. However, research on the relationship between dyslipidemia and CKD has largely focused on conventional lipid parameters. Whether dyslipidemia directly affects renal function and which specific lipid components contribute to kidney dysfunction remain unclear [6–9]. Remnant cholesterol, a recently recognized component of triglyceride-rich lipoproteins, consists of cholesterol derived from very low-density lipoproteins and intermediate-density lipoproteins in the fasting condition, as well as from chylomicron cholesterol in the non-fasting condition [10]. Although accumulating evidence suggests that elevated remnant cholesterol is closely linked to an increased risk of cardiovascular diseases, its role in CKD has been less extensively studied [11–13]. Existing studies examining the association between remnant cholesterol and kidney dysfunction have primarily focused on its association with CKD rather than rapid renal function decline [14–16]. Furthermore, these studies lacked comprehensive prognostic data and detailed subgroup analyses [14, 15]. Accordingly, our study aimed to evaluate the associations of remnant cholesterol with rapid kidney function decline and the progression to CKD among middle-aged and older individuals with preserved renal function, using data from the China Health and Retirement Longitudinal Study (CHARLS).
Methods
Study Population
The CHARLS is a nationally representative longitudinal cohort that recruited individuals aged 45 years and above from 150 counties across 28 provinces in China [17]. CHARLS was designed to collect comprehensive information on demographics, lifestyle behaviors, socioeconomic status, and health conditions. Baseline data were collected through face-to-face interviews in 2011, with follow-up surveys conducted biennially [17]. For this study, we used data from the 2011 and 2015 waves [17]. A total of 5,191 individuals with normal renal function at baseline were initially enrolled. Individuals were excluded if they had missing data on depressive symptoms, body weight, height, handgrip strength, blood pressure, residence area, education level, marital status, or medical insurance. Finally, 4,039 adults were analyzed in our present study (online suppl. Fig. 1; for all online suppl. material, see https://doi.org/10.1159/000547526). The CHARLS study protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and written informed consent was obtained from all participants.
Laboratory Measurements
Venous blood samples were conducted by trained researchers and subsequently transported to the Chinese Center for Disease Control and Prevention in Beijing for storage and analysis [18]. Lipid and glucose levels were determined using enzymatic colorimetric assays. High-sensitivity C-reactive protein, creatinine, and cystatin C levels were measured using immunoturbidimetric assay, rate-blanked and compensated Jaffe method, and particle-enhanced turbidimetric assay, respectively. Renal function was determined by cystatin C-based eGFR (eGFRcys), creatinine-based eGFR (eGFRcr), or their combined measure (eGFRcys-cr), calculated using the Chronic Kidney Disease Epidemiology Collaboration equation [19]. Remnant cholesterol was determined using the following formula: total cholesterol minus low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol [20]. Individuals were classified into 3 groups according to the tertiles of remnant cholesterol levels: tertile 1 (<0.37 mmol/L), tertile 2 (0.37 mmol/L–0.70 mmol/L), and tertile 3 (≥0.70 mmol/L).
Study Outcome
Our primary outcome was a rapid renal function decline, defined as an annualized reduction in eGFR of ≥5 mL/min/1.73 m2 [4]. The annualized eGFR decline was calculated using the formula: (eGFR at the 2011 wave − eGFR at the 2015 wave)/follow-up duration. The secondary outcome was progression to CKD, defined as either decline in eGFR from baseline >30%, to a level of eGFR <60 mL/min/1.73 m2 in 2015, or a physician-diagnosed CKD during follow-up [21].
Covariates
Demographic information, including age, sex, marital status, residential location, educational background, smoking, and drinking consumption, was acquired using structured face-to-face interviews. Blood pressure (both systolic and diastolic) was recorded 3 times, and the average value was used. Body mass index was calculated as body weight in kilograms divided by height in meters squared (kg/m2). Handgrip strength was measured using a handheld dynamometer, and the maximum recorded values of two hands were used in our analysis. Hypertension was defined as a self-reported physician diagnosis, current use of anti-hypertensive medications, or measured blood pressure ≥140/90 mm Hg. Diabetes mellitus was defined as a self-reported physician diagnosis, use of glucose-lowering medications, fasting plasma glucose concentration ≥7.0 mmol/L, or glycosylated hemoglobin ≥6.5%. Depressive symptoms were screened via the ten-item Center for Epidemiologic Studies Depression scale [22, 23].
Statistical Analyses
For quantitative variables, mean ± standard deviation or median (interquartile range) was employed for summary statistics, with group comparisons performed by one-way analysis of variance or Kruskal-Wallis test. For categorical variables, frequencies (percentages) were employed for summary statistics, with groups comparisons performed by chi-squared test or Fisher’s exact test. Logistic regression models were applied to evaluate the impact of remnant cholesterol on CKD outcomes over the 4-year follow-up period. Covariates included variables that were significantly associated with rapid renal function decline in the univariate logistic regression model (online suppl. Table 1) or were established risk factors for renal dysfunction were chosen as covariables [4, 24, 25]. Model 1 was adjusted for age and sex; model 2 was further adjusted for marital status, current smoker, current drinker, hypertension, diabetes mellitus, systolic blood pressure, diastolic blood pressure, depressive symptoms, glucose, triglycerides, LDL-C, high-density lipoprotein cholesterol, and eGFR. To reinforce the reliability of our findings, we used creatinine-based eGFR or cystatin C-based eGFR to determine the associations between remnant cholesterol and incident CKD outcomes. Nonlinear trends between remnant cholesterol and CKD outcomes were modeled using restricted cubic splines. Additionally, to examine whether the relationship between remnant cholesterol and CKD outcomes differed in some specific populations, subgroup analyses were performed. To evaluate whether incorporating remnant cholesterol into the original risk factors model enhances the prediction of CKD outcomes, we calculated discrimination measures, including Harrell’s C‐statistic, the continuous net reclassification improvement, and integrated discrimination improvement. We further used receiver operating characteristic curve analysis to determine the threshold value of remnant cholesterol to predict CKD outcomes incidence. All statistical analyses were executed using GraphPad Prism 8.2.1 software (USA) and R 4.0.3 software (Austria). A significance threshold of two-tailed p value <0.05 was applied.
Result
Participant Characteristics
A total of 4,039 individuals from the CHARLS were analyzed in this prospective study, with a mean age of 59 ± 8.7 years. The median remnant cholesterol level was 0.52 mmol/L (0.30–0.84 mmol/L) (online suppl. Fig. 2). Baseline characteristics of enrolled individuals, stratified by remnant cholesterol tertiles, are outlined in Table 1. Compared with individuals in the lowest tertile, those with higher remnant cholesterol were more likely to be female, less likely to be alcohol consumption, and had a higher prevalence of hypertension and diabetes mellitus. They also exhibited elevated levels of high-sensitivity C-reactive protein, glucose, triglycerides, and total cholesterol.
Table 1.
Baseline characteristics of participants stratified by tertile of remnant cholesterol
| Variable | Tertile 1 (n = 1,348) | Tertile 2 (n = 1,345) | Tertile 3 (n = 1,346) | p value |
|---|---|---|---|---|
| Age, years | 59±8.9 | 59±8.7 | 59±8.5 | 0.757 |
| Female | 666 (49.4%) | 697 (51.8%) | 757 (56.2%) | 0.002 |
| Marital status | | | | 0.222 |
| Married | 1,130 (83.8%) | 1,138 (84.6%) | 1,160 (86.2%) | |
| Residence | | | | 0.160 |
| Rural area | 1,190 (88.3%) | 1,181 (87.8%) | 1,157 (86.0%) | |
| Medical insurance | | | | 0.420 |
| Uninsured | 60 (4.5%) | 74 (5.5%) | 73 (5.4%) | |
| Public | 1,238 (91.8%) | 1,234 (91.7%) | 1,226 (91.1%) | |
| Private | 50 (3.7%) | 37 (2.8%) | 47 (3.5%) | |
| Education level | | | | 0.567 |
| High school or above | 132 (9.8%) | 126 (9.4%) | 116 (8.6%) | |
| Current smoker | 417 (30.9%) | 427 (31.7%) | 373 (27.7%) | 0.054 |
| Current drinker | 434 (32.2%) | 390 (29.0%) | 372 (27.6%) | 0.029 |
| Comorbidity | | | | |
| Hypertension | 452 (33.5%) | 541 (40.2%) | 669 (49.7%) | <0.001 |
| Diabetes mellitus | 148 (11.0%) | 233 (17.3%) | 380 (28.2%) | <0.001 |
| Heart-related disease | 149 (11.1%) | 165 (12.3%) | 179 (13.3%) | 0.204 |
| Stroke | 22 (1.6%) | 22 (1.6%) | 29 (2.2%) | 0.504 |
| Systolic blood pressure, mm Hg | 123 (112–138) | 127 (115–142) | 130 (117–145) | <0.001 |
| Diastolic blood pressure, mm Hg | 73 (66–81) | 75 (67–83) | 76 (69–85) | <0.001 |
| Body mass index, kg/m2 | 24.5±4.2 | 23.8±4.8 | 25.5±5.3 | <0.001 |
| Handgrip strength, kg | 31 (25–38) | 30 (24–38) | 30 (24–38) | 0.449 |
| Depressive symptoms | 11 (8–15) | 11 (8–14) | 11 (8–14) | 0.946 |
| Laboratory test | ||||
| eGFRcys, mL/min/1.73 m2 | 77 (65–89) | 79 (67–93) | 85 (71–100) | <0.001 |
| eGFRcr, mL/min/1.73 m2 | 97 (88–102) | 95 (87–103) | 94 (85–102) | <0.001 |
| eGFRcys-cr, mL/min/1.73 m2 | 86 (76–97) | 87 (77–97) | 90 (78–101) | <0.001 |
| Glucose, mmol/L | 5.7±1.2 | 6.0±2.0 | 6.5±2.2 | <0.001 |
| Hs-CRP, mg/L | 0.8 (0.5–1.7) | 1.0 (0.6–2.0) | 1.2 (0.6–2.3) | <0.001 |
| TG, mmol/L | 0.8 (0.6–1.0) | 1.2 (1.0–1.4) | 2.1 (1.7–2.8) | <0.001 |
| TC, mmol/L | 4.7 (4.1–5.3) | 4.9 (4.3–5.5) | 5.2 (4.6–5.8) | <0.001 |
| LDL-C, mmol/L | 3.0 (2.5–3.6) | 3.0 (2.5–3.6) | 2.9 (2.3–3.5) | 0.072 |
| HDL-C, mmol/L | 1.5 (1.3–1.7) | 1.3 (1.1–1.5) | 1.0 (0.9–1.3) | <0.001 |
Continuous variables are expressed as mean ± standard deviation or median (interquartile range). Categorical variables are expressed as number (percentage).
eGFRcys, cystatin C-based estimated glomerular filtration rate; eGFRcr, creatinine-based estimated glomerular filtration rate; eGFRcys-cr, cystatin C and creatinine-based estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
Association of Remnant Cholesterol with CKD Outcomes
Over a 4-year follow-up period, 195 participants (4.8%) developed rapid renal function decline, and 367 participants (9.1%) developed progression to CKD. When remnant cholesterol was analyzed as a continuous variable, the fully adjusted model showed that elevated remnant cholesterol levels were associated with an increased risk of rapid renal function decline (odds ratio = 1.89, 95% confidence interval: 1.59–2.26, p value <0.001) and progression to CKD (odds ratio = 1.76, 95% confidence interval: 1.51–2.04, p value <0.001) (Table 2). Consistent association trends were detected when individuals were analyzed across tertiles of remnant cholesterol. Besides, similar patterns were found in the relationship between remnant cholesterol and renal outcomes, as measured by eGFRcys (online suppl. Table 2) or eGFRcr (online suppl. Table 3), although some comparisons did not reach statistical significance.
Table 2.
Association between remnant cholesterol and CKD outcomes
| | Events/total | Model 1 | Model 2 | ||
|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | ||
| Rapid kidney function decline | |||||
| Continuous remnant cholesterol, mmol/L | 195/4,039 (4.8%) | 2.02 (1.74–2.35) | <0.001 | 1.89 (1.59–2.26) | <0.001 |
| Categorical remnant cholesterol | |||||
| Tertile 1 | 38/1,348 (2.8%) | Reference | | Reference | |
| Tertile 2 | 48/1,345 (3.6%) | 1.28 (0.83–1.97) | 0.262 | 1.23 (0.79–1.89) | 0.361 |
| Tertile 3 | 109/1,346 (8.1%) | 3.06 (2.10–4.47) | <0.001 | 2.64 (1.79–3.90) | <0.001 |
| Progression to CKD | |||||
| Continuous remnant cholesterol, mmol/L | 367/4,039 (9.1%) | | | | |
| Categorical remnant cholesterol | | | | 1.76 (1.51–2.04) | <0.001 |
| Tertile 1 | 78/1,348 (5.8%) | Reference | | Reference | |
| Tertile 2 | 101/1,345 (7.5%) | 1.34 (0.99–1.82) | 0.061 | 1.29 (0.95–1.75) | 0.108 |
| Tertile 3 | 188/1,346 (14.0%) | 2.73 (2.07–3.61) | <0.001 | 2.47 (1.86–3.29) | <0.001 |
Model 1: adjusted for age and sex.
Model 2: adjusted for age, sex, marital status, current smoker, current drinker, hypertension, diabetes mellitus, systolic blood pressure, diastolic blood pressure, depressive symptoms, glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and estimated glomerular filtration rate.
CKD, chronic kidney disease; OR, odds ratio; CI, confidence interval.
Nonlinear associations between remnant cholesterol and CKD outcomes are displayed in Figure 1. Restricted cubic spline analysis revealed significant nonlinearity for both rapid renal function decline (p value for nonlinearity = 0.001) and progression to CKD (p value for nonlinearity <0.001).
Fig. 1.
Restricted cubic spline plot analysis of the association between remnant cholesterol and the risk of rapid kidney function decline (a) or progression to CKD (b). The multivariate model included age, sex, marital status, current smoker, current drinker, hypertension, diabetes mellitus, systolic blood pressure, diastolic blood pressure, depressive symptoms, glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and estimated glomerular filtration rate. CKD, chronic kidney disease; OR, odds ratio; CI, confidence interval.
When the analyses were stratified by age (< and ≥65 years), sex (male and female), married status (unmarried and married), current smoker (no and yes), current drinker (no and yes), hypertension (no and yes), diabetes mellitus (no and yes), depressive symptoms (< and ≥10), body mass index(< and ≥28 kg/m2), and LDL-C (≤ and >2.6 mmol/L), a significant interaction was detected only between remnant cholesterol and LDL-C (p value for interaction = 0.039 for rapid renal function decline and <0.001 for progression to CKD) (Fig. 2). This association was more pronounced in individuals with LDL-C >2.6 mmol/L than those with LDL-C ≤2.6 mmol/L.
Fig. 2.
Subgroup analysis of the association of remnant cholesterol and rapid kidney function decline (a) or progression to CKD (b). The multivariate model included age, sex, marital status, current smoker, current drinker, hypertension, diabetes mellitus, systolic blood pressure, diastolic blood pressure, depressive symptoms, glucose, triglycerides, LDL-C, high-density lipoprotein cholesterol, and estimated glomerular filtration rate. CKD, chronic kidney disease; BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; CI, confidence interval.
Role of Remnant Cholesterol for Predicting CKD Outcomes
As shown in online supplementary Table 4, adding remnant cholesterol to the original risk model notably improved its predictive performance, as supported by increases in Harrell’s C-statistic, net reclassification improvement, and integrated discrimination improvement. Moreover, the optimal threshold for remnant cholesterol in predicting rapid renal function decline was determined to be 0.67 mmol/L (sensitivity 60.0%, specificity 65.1%), and 0.64 mmol/L (sensitivity 55.3%, specificity 65.8%) for predicting the progression to CKD (online suppl. Fig. 3).
Discussion
In this community-based longitudinal cohort, we found that elevated remnant cholesterol was positively associated with subsequent rapid kidney function decline and CKD in individuals without clinical CKD. This association still holds after adjusting for baseline eGFR and LDL-C levels, suggesting the independent contribution of remnant cholesterol to rapid renal function decline. Furthermore, this association was more evident in individuals with LDL-C >2.6 mmol/L than those in LDL-C ≤2.6 mmol/L.
Remnant cholesterol is a novel marker of lipoproteins, identified as triglyceride-rich lipoprotein cholesterol [10], which has been extensively studied in the cardiovascular field [11–13]. However, its role in predicting CKD remains relatively unexplored. Previous cross-sectional studies have provided some insights into this relationship. For example, a study of 7,356 Chinese participants aged ≥40 years reported that higher remnant cholesterol was independently associated with prevalent CKD [14]. Similarly, another cross-sectional study also found a significant correlation between higher remnant cholesterol and lower eGFR in individuals aged 18–70 years [15]. While these findings suggest a potential link, their cross-sectional design inherently limits causal inference. To address this gap, several longitudinal investigations have examined the influence of remnant cholesterol on kidney function. However, these studies primarily focused on populations with diabetes or pre-existing CKD, rather than the middle-aged and older population with normal renal function [26, 27]. Moreover, most of them used the conventional CKD definition (eGFR <60 mL/min/1.73 m2) as the study outcome, which may reflect relatively advanced kidney dysfunction rather than the early stages of renal function decline. Our study specifically focused on rapid renal function decline, which captured an earlier detection of kidney dysfunction. To our knowledge, only one prospective study has investigated the association between baseline remnant cholesterol and rapid kidney function decline, but it did not observe a significant relationship [28]. This discrepancy may be attributable to differences in study design, including a relatively younger population and a shorter follow-up duration compared to our cohort. In contrast, our study demonstrated that elevated remnant cholesterol is strongly associated with an increased risk of both rapid kidney function decline and incident CKD. Importantly, these associations remained robust even after adjusting for well-known risk factors such as LDL-C and eGFR, suggesting an independent contribution of remnant cholesterol to renal dysfunction. These findings highlight the potential benefit of incorporating remnant cholesterol assessment into routine clinical practice to enable earlier identification of individuals at high risk for renal function decline and to inform lipid management strategies beyond LDL-C control for CKD prevention.
The underlying mechanisms between remnant cholesterol and rapid kidney function decline remains indeterminate. However, it may be elucidated by several plausible biological mechanisms. First, this relationship might be contributed to lipid nephrotoxicity [29]. Renal histopathological analyses from 34 patients with diabetic nephropathy and 12 control subjects have demonstrated substantial lipid accumulation, particularly triglycerides and cholesterol, within proximal tubular epithelial cells [30]. This lipid deposition is closely associated with the severity of tubulointerstitial injury and fibrosis [30]. Second, the mechanisms linking remnant cholesterol to CKD outcomes involve the accumulation of pro-inflammatory cytokines, oxidative stress, and endoplasmic reticulum stress. The Copenhagen General Population study demonstrated that elevated remnant cholesterol is causally associated with low-grade inflammation, evidenced by a 37% higher C-reactive protein levels for each 1 mmol/L increase in remnant cholesterol concentration [31]. Furthermore, Yuan et al. [32] reported that the relationship between remnant cholesterol and renal dysfunction was partially mediated by pre-inflammatory state, as reflected by elevated high-sensitivity C-reactive protein or white blood cell counts. Inflammation further exacerbates reactive oxygen species production and endoplasmic reticulum stress, thereby promoting renal lipid accumulation and foam cell formation [33]. Third, emerging evidence indicates that remnant cholesterol contributes to kidney dysfunction through aldosterone-mediated pathways. Specifically, Xing et al. [34] demonstrated that very-low-density lipoprotein, a major component of remnant cholesterol, can directly stimulate aldosterone production in adrenocortical cells. Elevated aldosterone levels, in turn, promote tubulointerstitial injury, accelerate nephron loss, and exacerbate renal fibrosis and functional decline [35]. Future research is warranted to clarify the precise mechanisms of the association between remnant cholesterol and rapid kidney function decline, which may provide insights into potential therapeutic strategies for mitigating renal deterioration.
Our subgroup analysis revealed that the association between remnant cholesterol and CKD outcomes was generally consistent across various specific populations, except for a significant interaction observed with LDL-C. Whereas LDL-C is a well-established lipid marker for kidney dysfunction, our results reveal that the impact of remnant cholesterol on CKD outcomes is more pronounced in individuals with LDL-C >2.6 mmol/L compared to those with LDL-C ≤2.6 mmol/L. This finding is consistent with previous research reporting that the influence of remnant cholesterol on progression to end-stage renal disease was stronger in individuals with dyslipidemia than those without dyslipidemia [16]. Similarly, Yan et al. demonstrated an interaction between remnant cholesterol and LDL-C for CKD risk, with the highest risk observed among individuals with LDL-C ≥4.1 mmol/L [14]. One plausible explanation for this interaction is a synergistic effect between remnant cholesterol and high LDL-C levels, which may jointly amplify the risk for renal dysfunction. In addition, people with higher LDL-C levels are likely to have a higher burden of other CKD-related risk factors, making them more susceptible to the detrimental effects of remnant cholesterol.
Our study identified a remnant cholesterol cutoff value of 0.67 mmol/L for predicting the long-term risk of rapid renal function decline. This threshold is slightly lower than the 0.8 mmol/L cutoff recommended by the European Atherosclerosis Society for assessing cardiovascular disease risk in the fasting state [36]. Regarding kidney function outcomes, several studies have reported comparable but lower cutoff values [15, 37]. A US community-based study reported that remnant cholesterol levels above 0.52 mmol/L were associated with decreased kidney function [15]. Li et al. identified a cutoff value of 0.56 mmol/L for predicting kidney dysfunction in patients with type 2 diabetes mellitus [37]. These differences in cutoff values may be attributed to variations in study design, population characteristics, and metabolic conditions. Although the sensitivity and specificity of our identified cutoff was modest, it may still serve as an initial reference point for risk stratification. It is not intended for immediate clinical application but may guide future research aimed at developing remnant cholesterol-based predictive models. Currently, remnant cholesterol is not included in standard lipid management guidelines, which primarily target LDL-C. However, our findings suggest that remnant cholesterol could serve as a complementary biomarker, especially for individuals with preserved kidney function who may be at risk but not flagged by traditional lipid metrics. Future studies are needed to validate clinically meaningful remnant cholesterol thresholds and evaluate their potential integration into routine clinical practice for the early prevention of kidney dysfunction.
Previous lipid-lowering therapies have primarily focused on reducing LDL-C levels, but substantial residual adverse clinical events risk remains even after achieving optimal LDL-C control [38]. This has prompted increasing attention toward triglyceride-rich lipoproteins as emerging therapeutic targets. For example, peroxisome proliferator-activated receptor alpha agonists, such as fibrates, mainly target triglycerides reduction. Evidence suggests that fibrate therapy can reduce remnant cholesterol levels by 25.6%, but it has also been associated with a higher incidence of adverse renal events [39]. In contrast, a nationwide Korean cohort study reported that fenofibrate users had a lower incidence of adverse renal outcomes compared to non-users, with this protective effect being particularly prominent among individuals with pre-existing CKD [40]. These seemingly contradictory findings suggest that the impact of fibrates on renal outcomes remains inconclusive and warrants further investigation. In addition to fibrates, several novel lipid-lowering agents, including angiopoietin-like protein 3 inhibitors and apolipoprotein C-III inhibitors, have demonstrated potent remnant cholesterol-lowering effects in early-phase clinical trials [41, 42]. However, whether these emerging therapies can translate into meaningful renal benefits remains uncertain and requires further investigation in large-scale, well-designed clinical trials.
Limitation
Our study has some limitations that deserve attention. First, although we adjusted for a range of confounding variables, residual confounding cannot be entirely ruled out, particularly from factors such as dietary habits, genetic predisposition, and medication use. In addition, despite the prospective design, reverse causality remains a possibility as early kidney impairment at baseline may have influenced remnant cholesterol levels. Second, our study focused on Chinese adults aged 45 years and older, which may limit the generalizability of the findings to younger individuals or other ethnic groups. Third, renal function was assessed using solely calculated eGFR due to the absence of albuminuria or other CKD-specific markers in the CHARLS. This limitation could possibly influence the precise assessment of renal function. However, the assessment of renal function using eGFR is most common and convenient in medical practice [43]. Fourth, remnant cholesterol levels were estimated using a calculated equation rather than direct measurement, which may lead to discrepancies from actual values. However, calculated remnant cholesterol is widely recognized as a cost-effective and practical alternative in clinical assessment [44]. Additionally, a previous study has demonstrated that calculated and measured remnant cholesterol show comparable associations with atherosclerotic risk, supporting the validity of the calculated approach for large-scale studies [45]. Therefore, future research is warranted to further investigate the potential causal association between remnant cholesterol and rapid renal function decline.
Conclusions
Among a population-based cohort of middle-aged and older adults with normal kidney function, remnant cholesterol was associated with rapid renal function decline and progression to CKD beyond conventional risk factors. This association was particularly pronounced in subjects with LDL-C >2.6 mmol/L than those without. Further research is needed to explore how the intensity of remnant cholesterol will confer a greater benefit in CKD outcomes reduction.
Acknowledgments
We thank the participants and staff of the CHARLS study for their contributions.
Statement of Ethics
All procedures performed in CHARLS were approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015) and written informed consent was acquired from each participant.
Conflict of Interest Statement
We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding Sources
This work was supported by the Nature Science Foundation of Sichuan Province (2023NSFSC1630 and 2023NSFSC1339).
Author Contributions
Qing Li: conceptualization, methodology, formal analysis, investigation, writing – original draft, and writing – reviewing and editing; Chengxiang Song: methodology, formal analysis, investigation, and writing – original draft; Hao Zhou: data curation and validation; Qiang Luo: data curation and writing – original draft; Junli Li and Mao Chen: conceptualization, project administration, supervision, and writing – review and editing. All authors provided critical revisions and approved the final version of the submitted manuscript.
Funding Statement
This work was supported by the Nature Science Foundation of Sichuan Province (2023NSFSC1630 and 2023NSFSC1339).
Data Availability Statement
Data from the CHARLS are available to all researchers upon reasonable application at http://charls.pku.edu.cn. The authors do not have special access privileges and confirm that others would be able to access the data in the same manner.
Supplementary Material.
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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
Data from the CHARLS are available to all researchers upon reasonable application at http://charls.pku.edu.cn. The authors do not have special access privileges and confirm that others would be able to access the data in the same manner.