Association between Remnant Cholesterol and All-Cause and Cardiovascular Mortality Risk in Chronic Kidney Disease Patients Over 60: A Cohort Study Based on NHANES 2001-2018

Association between Remnant Cholesterol and All-Cause and Cardiovascular Mortality Risk in Chronic Kidney Disease Patients Over 60: A Cohort Study Based on NHANES 2001-2018 | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Association between Remnant Cholesterol and All-Cause and Cardiovascular Mortality Risk in Chronic Kidney Disease Patients Over 60: A Cohort Study Based on NHANES 2001-2018 Jiechun YAO , Chun Lin , Qingbo Xu , Guode Li , Yan Lin doi: https://doi.org/10.1101/2025.02.12.25322142 Jiechun YAO 1 Department of Cardiovascular Medicine, Maoming People’s Hospital , Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: 673589060{at}qq.com Chun Lin 2 General Department, Maoming People’s Hospital , Guangdong Province, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qingbo Xu 1 Department of Cardiovascular Medicine, Maoming People’s Hospital , Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guode Li 1 Department of Cardiovascular Medicine, Maoming People’s Hospital , Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan Lin 3 Affiliated Hospital of Guangdong Medical University,Guangdong , China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Objective Chronic kidney disease (CKD) patients face significant mortality risks, and remnant cholesterol (RC) may be a critical prognostic indicator. This study aimed to explore the association between RC and all-cause and cardiovascular mortality risks in CKD patients. Methods This prospective cohort study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2001-2018, including 1,606 CKD patients aged over 60. Cox proportional hazards models were employed to assess the association between RC and all-cause and cardiovascular mortality, with multi-model adjustments and subgroup analyses. Results During 93.9 months of follow-up, 932 all-cause mortality events (58.0%) and 283 cardiovascular mortality events (17.6%) were observed. Each logarithmic unit increase in RC was associated with a significant 41% reduction in all-cause mortality risk (adjusted HR: 0.59, 95% CI: 0.40-0.87, P=0.0079). Subgroup analyses revealed significantly reduced all-cause mortality risks in women (HR=0.71), hypertensive patients (HR=0.75), and type 2 diabetes patients (HR=0.66). Conclusion In CKD patients, RC is significantly associated with reduced all-cause mortality risk, providing a new perspective for individualized mortality risk assessment. Introduction Chronic Kidney Disease (CKD) is a global public health concern, especially among individuals over 60( 1 ). Recent studies show a CKD prevalence of 13.4% worldwide, with rates in China between 11.3% and 13.7% ( 2 , 3 ). CKD patients face alarming outcomes, with all-cause mortality rates up to 40% and cardiovascular events as a primary cause of death( 4 ). Cardiovascular disease incidence is significantly higher in CKD patients, particularly among the elderly( 5 ). Remnant Cholesterol (RC) represents cholesterol remnants remaining after triglyceride-rich lipoprotein metabolism( 6 ). Increasingly recognized for cardiovascular risk, recent literature links elevated RC levels to adverse outcomes like atherosclerosis, heart attacks, and strokes( 7 , 8 ). In CKD patients, high RC levels are a critical marker for increased atherosclerotic cardiovascular disease (ASCVD) risk( 9 ). However, the RC-disease relationship remains complex, with studies showing inconsistent associations( 10 ). The relationship between RC and mortality in CKD patients remains controversial. Supporting studies correlate higher RC levels with increased cardiovascular and all-cause mortality, particularly in kidney transplant recipients and diabetic CKD patients( 11 ). Conversely, some research suggests varying mortality impacts across different disease states, with heart failure patients showing different RC-mortality relationships( 12 ). While most studies suggest a positive correlation between high RC levels and increased mortality in CKD patients, conflicting results underscore the need for further investigation. This study employed a prospective cohort research method, analyzing data from the NHANES database between 2001-2018 to evaluate the association between red cell distribution width RC and all-cause and cardiovascular mortality rates among CKD patients aged 60 and above. Methods Data and study participants The research data were sourced from the National Health and Nutrition Examination Survey (NHANES, https://www.cdc.gov/nchs/nhanes/ ), an authoritative public health database managed by the National Center for Health Statistics (NCHS). NHANES ensures scientific rigor and representativeness through complex stratified and multi-stage sampling techniques( 13 , 14 ). The database comprehensively captures demographic characteristics, physical assessments, biochemical markers, and detailed dietary and health history, providing an invaluable resource for investigating the health status and lifestyle of the U.S. population.The study strictly adhered to the ethical guidelines of the 1975 Helsinki Declaration and obtained approval from the NCHS Research Ethics Review Committee. All participants provided voluntary informed consent, thereby safeguarding research ethics and participant rights. This study systematically collected data covering 91,351 participants between 2001 and 2018. Initially, individuals under 60 years old (n=74,098) were excluded, followed by the removal of individuals lacking relevant RC data (n=12,841) and those with missing CKD data or classified as non-CKD (n=2,804). Subsequently, we further excluded individuals without all-cause mortality information (n=2). After this rigorous screening process, 1,606 participants ultimately met the inclusion criteria for this study, as illustrated in Fig. 1 . Download figure Open in new tab Fig 1. A flow chart of the sample selection from NHANES 2001–2018.RC, Remnant Cholesterol; CKD, chronic kidney disease Exposed Variables and Outcomes RC is a critical blood lipid assessment indicator calculated from a standard lipid profile in the fasting state. The calculation method is: RC = Total Cholesterol (TC) - Low-Density Lipoprotein Cholesterol (LDL-C) - High-Density Lipoprotein Cholesterol (HDL-C)( 15 – 17 ). The primary study endpoints were all-cause and cardiovascular-specific mortality. We employed advanced data linkage techniques to match NHANES participant records with the National Death Index, tracking participant vital status from the survey date until December 31, 2006. Mortality classification followed the International Classification of Diseases, 10th Revision (ICD-10), with specific focus on cardiovascular mortality categories (I00-I09, I11, I13, I20-I51, I60-I69). Participants without matched death records were considered alive at the end of the follow-up period( 18 , 19 ). Covariates We comprehensively assessed participants’ health status using standardized diagnostic criteria and validated assessment tools. Hypertension was defined by three criteria: ongoing antihypertensive medication, physician-confirmed diagnosis, or persistent blood pressure measurements ≥140/90 mmHg across three consecutive readings. Diabetes mellitus was diagnosed through multiple parameters, including antidiabetic medication use, physician diagnosis, or meeting specific laboratory thresholds: glycated hemoglobin A1c ≥6.5%, fasting blood glucose ≥126 mg/dL, or 2-hour postprandial glucose ≥200 mg/dL( 20 ). The diagnosis of coronary artery disease (CAD) was determined through self-reported physician diagnoses obtained during personal interviews using a standardized medical condition questionnaire. Subjects were asked, “Has a doctor or other health professional ever told you that you have congestive heart failure/coronary heart disease/angina pectoris/heart attack/stroke?” Answering yes to any of these questions was coded as positive for coronary artery disease (CAD)( 21 ). Demographic and socioeconomic variables were meticulously documented to control for potential confounding factors. Racial categories included Mexican Americans, other Hispanic, non-Hispanic Black, non-Hispanic White, and other races (including multiracial). Education levels were stratified into five comprehensive categories ( less than 9th grade,9-11th grade,high school graduate/GED equivalent,some college or Associate’s degree and college graduate or above). Lifestyle characteristics were systematically assessed across three domains: smoking status (never smoked, current smokers, former smokers), alcohol consumption (drinkers versus non-drinkers), and anthropometric measurements, with body mass index (BMI) serving as a key indicator of physical health. We employed rigorous biochemical analysis techniques to quantify key biomarkers with high precision. Serum and plasma creatinine concentrations were determined using the Jaffe rate method (kinetic alkaline picrate), a standard spectrophotometric technique known for its analytical accuracy. Urinary albumin was quantified through solid-phase fluorescence immunoassay (FIA), while urinary creatinine levels were measured using standardized enzymatic methods. Urine samples were collected randomly to minimize sampling bias and ensure representative measurement. The albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) were calculated using established mathematical formulas ( 22 , 23 ), providing comprehensive insights into renal function and metabolic parameters. ACR (mg/g) = urinary albumin(mg/dL)/urine creatinine(g/dL) eGFR (ml/min/1.73m²) = 141.9 × (Scr/κ) α × max(Scr/κ, 1) - ¹·²⁰⁹ × 0.993ᴬᵍᵉ × 1.018(if female) × 1.159(if black) It is important to note that Scr denotes serum creatinine concentration (mg/dL), and κ is 0.9 for males and 0.7 for females,and α is −0.411 for males and −0.329 for females. The diagnosis of CKD is based on the following criteria( 24 – 26 ): eGFR 30 mg/g. Additionally, the study included various blood markers such as triglycerides (TG) (mg/dL), total cholesterol (TC) (mg/dL), high-density lipoprotein cholesterol (HDL-C) (mg/dL), low-density lipoprotein cholesterol (LDL-C) (mg/dL), serum creatinine (μmol/L), fasting blood glucose (mg/dL), and glycated hemoglobin (HbA1C (%)). Statistical analysis The statistical analyses of this study were performed EmpowerStats ( http://www.empowerstats.com , X&Y Solutions, Inc., Boston, MA).In this study, to enhance the representativeness of results and reduce potential bias, researchers used sampling weights as an important supplement to the analytical method. Continuous variables were presented as mean values with 95% confidence intervals, while categorical variables were described by occurrence frequency and corresponding percentages, and statistical significance was calculated using weighted χ2 tests. To investigate the relationship between RC and all-cause and cardiovascular mortality, we employed a weighted Cox regression model for survival analysis of the study population, using hazard ratios (HR) with 95% confidence intervals (CI) to assess the impact of RC on survival rates. We constructed three models, with the first model having no covariate adjustments; Model 2 incorporated adjustments for factors such as age, sex, and race; Model 3 added covariates from Table 1 on the basis of Model 2. Additionally, the dose-response effect between RC and all-cause and cardiovascular mortality was evaluated using a generalized additive model and fitted curves (using penalized spline method). Finally, to ensure the robustness of results, we conducted sensitivity analyses stratified by age (< 75 years or ≥ 75 years), sex, smoking status, hypertension, coronary heart disease, and diabetes. In all hypothesis tests, p-values < 0.05 were considered statistically significant. View this table: View inline View popup Table 1 Baseline characteristics of study population according to RC(mg/dl) tertiles, weighted Note: Continuous data: the mean with 95% confidence intervals, P value was calculated by weighted linear regression model. Categorical data: the number of occurrences with respective percentages, P value was calculated by weighted χ 2 test. Abbreviations: PIR,Ratio of family income to poverty;BMI,body mass index;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;Scr,Creatinine; TC,Total cholesterol;FBG,Fasting blood glucose;HbA1C,Hemoglobin A1c;CAD, coronary atherosclerotic heart disease; T2DM,diabetess;HDL-C, high-density lipoprotein-cholesterol;ACR,the urinary albumin to creatinine ratio;eGFR,estimated glomerular filtration rate. Results Baseline characteristics of study participants In this stratified analysis of RC, significant demographic and clinical characteristic variations were observed across tertiles (5.96-19.97 mg/dL to 29.95-78.91 mg/dL), as shown in Table 1 . The high RC group showed a lower mean age (72.73 vs. 74.51 years, P=0.0019) and increased body mass index (27.26 to 30.64 kg/m 2 , P<0.0001). Lipid profile analysis revealed escalating triglyceride levels (75.47 to 212.97 mg/dL, P<0.0001) and incrementally rising LDL-C (100.36 to 113.67 mg/dL, P<0.0001), while HDL-C significantly declined (63.40 to 47.22 mg/dL, P<0.0001). Glucose metabolism showed increased fasting blood glucose (112.94 to 123.44 mg/dL, P=0.0013) and higher Type 2 diabetes mellitus prevalence (36.60% to 47.57%, P=0.0040). Racial distribution demonstrated a significant association with RC levels (P<0.0001). Renal function assessment indicated slight but statistically significant eGFR differences (P=0.0052), with follow-up time increasing from 85.80 to 102.27 months (P=0.0002). Association of RC With All-Cause and CVD Mortality In this study, we investigated the association between RC levels and mortality risks using Cox proportional hazards models with progressive covariate adjustments, as illustrated in Table 2 . During the follow-up period of 93.9 months (approximately 7.8 years), a total of 932 all-cause mortality events were observed (representing 58.0% of the total population) and 283 cardiovascular mortality events (representing 17.6% of the total population).Our findings reveal a significant inverse relationship between RC and mortality, particularly noteworthy in all-cause mortality analysis. Specifically, for each logarithmic unit increase in RC, we observed a substantial 41% reduction in total mortality risk (adjusted HR: 0.59, 95% CI: 0.40-0.87, P=0.0079). Quartile-based analysis further substantiated this trend, demonstrating that the highest RC group (Q3) exhibited a 21% lower mortality risk compared to the lowest group (Q1, HR: 0.79, 95% CI: 0.65-0.96, P=0.0153), with a statistically significant dose-response relationship (P trend = 0.0163). Regarding cardiovascular mortality, while the results showed a promising 48% risk reduction (HR: 0.52, 95% CI: 0.27-1.015, P=0.0552), the findings did not reach conventional statistical significance. The quartile analysis of cardiovascular mortality mirrored this pattern, with the highest RC group showing a 26% risk reduction (HR: 0.74, 95% CI: 0.52-1.06, P=0.1018), though again without achieving statistical significance. Moreover, as shown in Fig. 2 , generalized additive model analysis indicates that there is no nonlinear relationship between RC and total cause and cardiovascular mortality rates. Download figure Open in new tab Fig 2. The generalized additive model analyses for the association between log-transformed RC and the risk of cardiovascular disease (CVD) and all-cause mortality. All covariates from model 3 were adjusted. (A) The relationship between log-transformed RC levels and all-cause mortality. The red line represents the estimated probability of all-cause mortality, and the blue dashed lines represent the 95% confidence intervals. The P-value for non-linearity was 0.0945, suggesting a potential non-linear association between RC levels and all-cause mortality risk, although not reaching statistical significance.(B) The relationship between log-transformed RC levels and cardiovascular disease (CVD) mortality. The red line indicates the estimated probability of CVD mortality, with blue dashed lines showing the 95% confidence intervals. The P-value for non-linearity was 0.0876, indicating a possible non-linear relationship between RC levels and CVD mortality risk, though not statistically significant. View this table: View inline View popup Table 2 Association of log-transformed RC levels With All-Cause and CVD Mortality Model 1: no covariates were adjusted. Model 2: age, gender, and race were adjusted. Model 3: age, gender, race,BMI,Hypertensive,SCr,smooking, Alcohol,FBG, CAD,T2DM,Education level and PIR were adjusted. PIR,Ratio of family income to poverty;BMI,body mass index;T2DM,diabetess;SCr,Creatinine; FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;survey-weighted percentage;HR, hazard ratio.Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analysis As shown in Table 3 , after adjusting for confounding factors in Model 3, we observed significant associations in this comprehensive stratified analysis of residual cholesterol (RC) and mortality risk within specific subgroups. Women (HR=0.71, 95% CI: 0.54-0.93, P=0.0132), patients with hypertension (HR=0.75, 95% CI: 0.59-0.95, P=0.0151), and patients with type 2 diabetes (HR=0.66, 95% CI: 0.48-0.91, P=0.0111) exhibited statistically significant reductions in all-cause mortality risk. Notably, non-smokers also demonstrated a significant decrease in cardiovascular mortality (HR=0.57, 95% CI: 0.33-0.99, P=0.0449). Although differences among subgroups were observed, the interaction P-values (all >0.05) indicate that the effects of RC are consistent across different population strata, demonstrating that the strong association between RC and mortality risk remains relatively stable across various demographic and clinical characteristics. View this table: View inline View popup Table 3 Stratified analyses of the associations between log-transformed RC levels and all-cause mortality and CVD mortality Note:CAD, coronary atherosclerotic heart disease; T2DM,diabetess;HR, Hazard ratio; CI, confidence interval; Ref, reference. survey-weighted percentage.age, gender, race,BMI,Hypertensive,SCr,smooking, Alcohol,FBG, CAD,T2DM,Education level and PIR were adjusted. PIR,Ratio of family income to poverty;BMI,body mass index;T2DM,diabetess;SCr,Creatinine; FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;Cox proportional hazards models were used to estimate HRs and 95% 95% CIs. Discussion In this NHANES-based cohort study (2001–2018), we examined the association between RC and mortality in CKD patients over 60. Among 1,606 participants followed for 7.8 years, 932 all-cause and 283 cardiovascular mortality events occurred. Notably, each logarithmic RC unit increase was linked to a 41% reduction in all-cause mortality risk (adjusted HR: 0.59, 95% CI: 0.40-0.87, P=0.0079), with the highest RC group showing a 21% lower mortality risk compared to the lowest group (HR: 0.79, 95% CI: 0.65-0.96, P=0.0153). Our study revealed a significant negative correlation between RC and all-cause mortality risk in CKD patients, partially consistent with the findings of Yu et al.( 11 ) published in the Journal of Clinical Endocrinology & Metabolism in 2021. Yu’s study also focused on patients with type 2 diabetes and nephropathy, discovering a complex association between RC and mortality risk. However, our study offers notable methodological advantages: first, we utilized a larger NHANES database (1,606 patients) with a follow-up period of 93.9 months, compared to the relatively smaller sample in Yu’s research. Second, our study population comprised CKD patients over 60, providing a more representative sample and reducing potential bias through multi-model adjustments. Notably, we observed a 41% reduction in all-cause mortality risk for each logarithmic unit increase in RC, a finding that subtly differs from Zhao et al.’s( 12 ) research in Frontiers in Cardiovascular Medicine on heart failure patients. While Zhao’s study highlighted RC’s predictive value for mortality in heart failure patients, our research unveiled a protective effect of RC in CKD patients. These differences may stem from metabolic characteristics and inflammatory states across different disease populations. For instance, Yuan et al.’s( 8 )research indicates that RC is closely associated with the pre-inflammatory state in kidney disease patients, potentially explaining the observed association between RC and reduced mortality risk. Our subgroup analysis further revealed that the mortality risk-reducing effect of RC was more pronounced in women, hypertensive, and type 2 diabetes patients, providing crucial insights for personalized medicine.Furthermore,the intricate physiological mechanisms of RC in CKD patients unveil its complex association with mortality risk. Hua et al. elucidated the intricate interplay between inflammation and lipid metabolism in CKD, revealing the multifaceted metabolic regulatory pathways of RC( 27 ). Chen et al. investigated the mechanistic role of remnant lipoproteins in cardiovascular disease, demonstrating that RC transcends its traditional role as a mere lipid transport molecule, emerging as a critical modulator of inflammatory and metabolic processes( 28 ). Butcko et al. emphasized that inflammatory responses in CKD patients represent a pivotal perspective for comprehending the physiological implications of RC( 29 ). Our study reveals a non-linear relationship between RC and mortality risk, arising from its nuanced regulatory functions in inflammation, energy metabolism, and vascular homeostasis, thereby providing a novel theoretical framework for personalized risk stratification. Through an in-depth exploration of RC and mortality risk in CKD patients, our study offers a crucial new clinical risk assessment perspective. We first systematically revealed the inverse association between RC and all-cause mortality in a large population, challenging traditional risk perceptions. The significant RC-related mortality risk reduction in women, hypertensive, and type 2 diabetes subgroups provides critical guidance for targeted interventions. Future research should investigate RC’s biological mechanisms and differential impacts across chronic disease populations, developing risk stratification and personalized prediction models. We recommend incorporating RC into routine lipid profile and mortality risk assessments, using multi-dimensional tracking to enhance individualized medical management for CKD patients. The study’s strengths lie in its rigorous methodological design and comprehensive data analysis. Utilizing the NHANES database with complex stratified multi-stage sampling, we ensured high representativeness and scientific rigor. The research included 1,606 CKD patients over 60, with a 7.8-year follow-up, providing a stable observation window for exploring RC and mortality risk. We employed a multi-layered Cox proportional hazards model, progressively adjusting for potential confounding factors like age, gender, race, BMI, hypertension, smoking, and diabetes to minimize bias. Innovatively using generalized additive models and penalized spline methods, we explored the dose-response relationship between RC and mortality risk. Subgroup analysis revealed RC’s differential impacts across population characteristics, uniquely demonstrating its significant role in reducing mortality risk among women, hypertensive, and type 2 diabetes patients, offering new theoretical and practical insights for personalized medicine. While providing important insights into RC and mortality risk in CKD patients, our study has notable limitations. Based on NHANES data focusing on American patients over 60, our findings may have limited generalizability across different races, age groups, and geographical regions. As an observational cohort study, we can only demonstrate an association, not establish a causal mechanism. Despite adjusting for known confounders, potential unmeasured factors like genetic background and lifestyle details may influence result accuracy. The 7.8-year follow-up may not fully capture the long-term impact of RC dynamics on mortality risk. We did not extensively explore the specific biological mechanisms underlying this association, providing a crucial direction for future research. We recommend subsequent multi-center, large-sample prospective studies combined with molecular biological investigations to further validate and expand our findings. Conclusions This study reveals a novel association between RC and mortality risk in CKD patients. Our findings suggest RC as a potential prognostic indicator, expanding understanding of its physiological role and offering new perspectives for personalized clinical management. Data Availability All data are freely available through the NHANES database ( https://www.cdc.gov/nchs/nhanes/ ) https://www.cdc.gov/nchs/nhanes/ Abbreviations PIR Ratio of family income to poverty BMI Body mass index TG Triglycerides LDL-C Low-density lipoprotein TC Total cholesterol HDL-C High-density lipoprotein-cholesterol SCr Creatinine FBG Fasting blood glucose HbA1C Hemoglobin A1c CAD Coronary atherosclerotic heart disease T2DM Diabetess CKD Chronic kidney disease RC Remnant cholesterol NHANES Nutrition Examination Survey Data Availability All data are freely available through the NHANES database ( https://www.cdc.gov/nchs/nhanes/ ) Funding Statement Note. Author contributions J.C.Y. conceived and designed the study, and wrote the manuscript. C.L. analyzed the data. Q.B.X. took the quality control of data. G.D.L. and Y.L. critically revised the manuscript. All authors read and approved the final manuscript. 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Share Association between Remnant Cholesterol and All-Cause and Cardiovascular Mortality Risk in Chronic Kidney Disease Patients Over 60: A Cohort Study Based on NHANES 2001-2018 Jiechun YAO , Chun Lin , Qingbo Xu , Guode Li , Yan Lin medRxiv 2025.02.12.25322142; doi: https://doi.org/10.1101/2025.02.12.25322142 Share This Article: Copy Citation Tools Association between Remnant Cholesterol and All-Cause and Cardiovascular Mortality Risk in Chronic Kidney Disease Patients Over 60: A Cohort Study Based on NHANES 2001-2018 Jiechun YAO , Chun Lin , Qingbo Xu , Guode Li , Yan Lin medRxiv 2025.02.12.25322142; doi: https://doi.org/10.1101/2025.02.12.25322142 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Endocrinology (including Diabetes Mellitus and Metabolic Disease) Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff534aa18ebe2c5',t:'MTc3OTM4MzU0Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();