Association of Remnant Cholesterol with All-Cause and Cause-Specific Mortality in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study

Association of Remnant Cholesterol with All-Cause and Cause-Specific Mortality in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Remnant Cholesterol with All-Cause and Cause-Specific Mortality in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study zheyuan Wang, Wangdong Xu, Ye Xuan, Mengting Shen, Chen Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9058704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Although remnant cholesterol (RC) has been established as a causal risk factor for atherosclerotic cardiovascular disease in the general population, whether this pro-atherogenic lipid component holds the same prognostic significance in patients with chronic obstructive pulmonary disease (COPD)—a group characterized by systemic inflammation and metabolic disorders—remains unexplored. Methods This cohort study utilized data from ten consecutive cycles (1999–2018) of the National Health and Nutrition Examination Survey (NHANES), linked to National Death Index (NDI) mortality records through December 31, 2019. Adults with self-reported COPD diagnosis and complete fasting lipid profiles were included. RC was calculated as total cholesterol minus HDL-C minus LDL-C estimated via the Sampson formula. Analyses employed complex sampling-weighted Cox regression (with stepwise covariate adjustment), restricted cubic splines (RCS), Kaplan-Meier survival analysis, and pre-specified subgroup and sensitivity analyses. Results Among 1,563 eligible patients (431 deaths, median follow-up 7.4 years, IQR 3.5–12.3), after full adjustment, the highest RC quartile (Q4) was associated with a significantly lower risk of all-cause mortality compared to the lowest quartile (Q1) (HR 0.62; 95% CI 0.42–0.91; P = 0.014; P-trend = 0.028). The association was most pronounced for chronic lower respiratory disease (CLRD) mortality (HR 0.29; 95% CI 0.10–0.88; P = 0.029). Cardiovascular mortality showed a similar direction but did not reach statistical significance (HR 0.68; P = 0.274). The RCS model identified a non-linear dose-response curve (P-nonlinear < 0.001), with the steepest risk reduction observed below 20 mg/dL. Subgroup analyses suggested potential effect modification by sex (male: HR 0.70, P = 0.036) and diabetes status (diabetes: HR 0.54, P = 0.026), although no formal interaction terms reached significance. Results were consistent across four sensitivity analyses (Q4 HR range 0.60–0.67; all P < 0.05). Conclusions In this nationally representative cohort, higher RC levels were independently and inversely associated with all-cause and respiratory mortality in COPD patients—a finding that reverses the established detrimental role of RC in the general population and parallels the metabolic paradox described in other wasting diseases. If prospectively validated, RC may serve as an inexpensive and routinely accessible prognostic marker in COPD. Clinical trial number: not applicable. remnant cholesterol chronic obstructive pulmonary disease all-cause mortality respiratory mortality NHANES cholesterol paradox Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition characterized by persistent airflow limitation and systemic inflammation. Statistically, COPD has become the third leading cause of death globally, accounting for over 3 million deaths annually [ 1 ]. The global mortality burden among COPD patients remains substantial, with prognosis influenced not only by airflow limitation itself but also by systemic comorbidities including cardiovascular disease, diabetes, and sarcopenia [ 2 ]. Standard risk stratification tools such as the BODE index incorporate limited metabolic information [ 3 ], highlighting the need for simple and inexpensive biomarkers to improve prognostic assessment. Remnant cholesterol (RC), defined as total cholesterol minus HDL-C minus LDL-C, represents the cholesterol content of triglyceride-rich lipoproteins [ 4 ]. Mendelian randomization studies have established RC as a causal risk factor for ischemic heart disease [ 5 , 6 ], and recent population-based studies have linked elevated RC to increased all-cause and cardiovascular mortality in the general population [ 8 – 11 ]. However, COPD is characterized by chronic airway inflammation, accelerated catabolism, and metabolic disorders [ 7 , 12 , 13 ]—conditions under which the “obesity paradox” and “cholesterol paradox” have been repeatedly reported [ 14 – 17 ]. Whether RC exhibits a similar paradoxical association in COPD patients remains unknown. As a dynamic parameter reflecting the metabolism of triglyceride-rich lipoproteins, RC can be conveniently calculated from routine lipid panels without additional cost. Clinical studies have confirmed associations between RC and various diseases and outcomes [ 5 , 8 , 9 ]. In recent years, several NHANES-based studies have explored the relationship between RC and mortality in the general population [ 10 , 11 ], but no study has examined this association in a specific disease population characterized by catabolism. Furthermore, the relationship between RC and cause-specific mortality, including cardiovascular disease mortality and chronic lower respiratory disease mortality, in COPD patients has not been explored. Therefore, the primary objective of this study was to evaluate the associations between RC levels and all-cause, cardiovascular disease (CVD), and CLRD mortality among COPD patients, using data from ten consecutive cycles of NHANES (1999–2018) linked to the National Death Index through December 31, 2019. Cox proportional hazards regression, Kaplan-Meier survival curves, restricted cubic spline models, and multiple sensitivity strategies were employed to investigate these associations. Methods Study Design and Data Source NHANES is a cross-sectional survey of the non-institutionalized civilian U.S. population using a stratified, multistage probability sampling design. Data from ten 2-year cycles between 1999 and 2018 were pooled. The NCHS Research Ethics Review Board approved all protocols; all participants provided written informed consent. As the data are publicly available and de-identified, this secondary analysis required no additional ethical approval. Study Population Eligible participants were adults aged ≥ 20 years with self-reported physician diagnosis of emphysema (variable MCQ160G) or chronic bronchitis (MCQ160K), valid measurements of fasting total cholesterol, HDL-C, and triglycerides, and linkage to mortality follow-up files. Pregnant individuals, those with triglycerides exceeding 400 mg/dL (due to unreliable LDL-C estimation [ 18 ]), and those missing key covariates were excluded. The final analytical cohort comprised 1,563 patients (Fig. 1 ). Exposure Variable: Remnant Cholesterol LDL-C was uniformly recalculated for all cycles using the Sampson formula [ 18 ] (which outperforms the Friedewald formula at higher triglyceride levels): LDL-C = TC/0.948 − HDL-C/0.971 − (TG/8.56 + TG×non-HDL-C/2140 − TG²/16100) − 9.44. RC = TC − HDL-C − LDL-C (Sampson). RC was categorized into quartiles for primary analysis, with Q1 as the reference group. Outcome Variables The primary outcome was all-cause mortality. Secondary outcomes were cardiovascular mortality (UCOD_LEADING codes 1 and 5, corresponding to heart disease and cerebrovascular disease) and chronic lower respiratory disease (CLRD) mortality (UCOD_LEADING code 3). Follow-up extended from the NHANES examination date to the date of death or the administrative censoring date of December 31, 2019. Covariates Covariates were selected a priori based on directed acyclic graph reasoning and previous NHANES literature [ 19 ]. Demographic variables included age (continuous), sex, and race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Other). Behavioral variables included education level, smoking status (never/former/current), alcohol consumption, and body mass index (BMI). Comorbidities included hypertension, diabetes (self-reported diagnosis, fasting glucose ≥ 126 mg/dL, or HbA1c ≥ 6.5%), coronary heart disease, congestive heart failure, stroke, and chronic kidney disease (eGFR < 60 mL/min/1.73 m², CKD-EPI 2021 formula). Statin use was obtained from the prescription medication questionnaire. Statistical Analysis All analyses adhered to NHANES analytical guidelines, accounting for the complex sampling design with sample weights, strata, and clusters. Fasting subsample weights were divided by 10 to accommodate pooling across ten cycles. All respondent data were weighted using the recommended NHANES examination weights to account for differential sampling probabilities and non-response. Comparisons of baseline characteristics used weighted linear regression (continuous variables) and Rao-Scott chi-square tests (categorical variables). Weighted Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) at three adjustment levels: Model 1 (unadjusted), Model 2 (age, sex, race/ethnicity), Model 3 (all aforementioned covariates). The proportional hazards assumption was verified via Schoenfeld residuals (global P = 1.00). Restricted cubic splines with 4 knots were used to explore continuous dose-response relationships and test for non-linearity. P-for-trend was obtained by modeling the median RC value within each quartile as a continuous variable in the fully adjusted model. Pre-specified subgroup analyses were stratified by sex, age (< 65 vs ≥ 65 years), smoking status, BMI category, hypertension, diabetes, and statin use; P-for-interaction was calculated using multiplicative interaction terms. Four sensitivity analyses tested robustness: (i) excluding participants who died within the first two years of follow-up, (ii) restricting to non-statin users, (iii) additionally adjusting for C-reactive protein, (iv) modeling RC as a continuous variable per 10 mg/dL increment. All analyses were performed in R 4.3 using the survey, survival, and rms packages. A two-sided P < 0.05 was considered the threshold for statistical significance. Results Participant Characteristics The screening process (Fig. 1 ) ultimately included 1,563 COPD patients; their weighted baseline characteristics are presented in Table 1 . With increasing RC quartiles, participants were more likely to have diabetes (Q1: 5.2% vs Q4: 19.2%; P < 0.001), had higher BMI (28.1 vs 31.7 kg/m²; P < 0.001), and had higher statin use (17.6% vs 29.1%; P = 0.007). Non-Hispanic whites were also more prevalent in the higher quartiles (73.4% vs 82.4%; P < 0.001). No significant difference in mean age was observed across groups (P = 0.149). Table 1 Weighted baseline characteristics stratified by RC quartiles Characteristic Q1 (n = 391) Q2 (n = 391) Q3 (n = 391) Q4 (n = 390) P Age, years 52.3 ± 1.0 54.7 ± 0.9 54.9 ± 1.0 54.8 ± 1.1 0.149 Male, % 33.4 30.8 43.3 38.8 0.031 NH White, % 73.4 74.5 78.9 82.4 < 0.001 NH Black, % 16.1 11.3 6.8 4.7 Other race, % 10.5 14.2 14.3 12.9 BMI, kg/m² 28.1 ± 0.7 30.0 ± 0.6 31.9 ± 0.5 31.7 ± 0.5 < 0.001 Current smoker, % 32.0 35.2 32.7 33.4 0.347 Diabetes, % 5.2 10.9 10.7 19.2 < 0.001 Hypertension, % 45.4 48.2 52.3 53.4 0.331 CHD, % 8.1 11.8 9.2 8.7 0.562 CHF, % 8.2 8.3 9.8 9.8 0.848 Statin use, % 17.6 31.0 27.4 29.1 0.007 TC, mg/dL 184.6 190.4 193.8 218.9 < 0.001 HDL-C, mg/dL 65.4 56.3 49.7 42.7 < 0.001 TG, mg/dL 63.3 98.7 144.0 258.1 < 0.001 RC, mg/dL 12.1 ± 2.1 17.8 ± 1.6 25.5 ± 2.6 45.4 ± 20.5 < 0.001 CRP, mg/L 3.4 2.4 2.3 2.6 0.825 Follow-up, yrs 8.2 ± 5.7 8.5 ± 5.5 8.9 ± 5.5 9.6 ± 5.6 0.165 Death, n(%) 102(22.0%) 100(20.0%) 121(25.0%) 108(22.2%) 0.595 Values are weighted mean ± SE or weighted proportion. P from survey-weighted regression or Rao-Scott χ². RC and All-Cause Mortality During a median follow-up of 7.4 years (IQR 3.5–12.3), a total of 431 deaths were accumulated (Q1: 102; Q2: 100; Q3: 121; Q4: 108). In the fully adjusted model, all-cause mortality in Q4 was 38% lower compared to Q1 (HR 0.62; 95% CI 0.42–0.91; P = 0.014), with a significant trend across quartiles (P-trend = 0.028). The intermediate quartiles showed directionally consistent but non-significant reductions (Q2 HR 0.77; Q3 HR 0.85). Analyzed as a continuous exposure variable, each 10 mg/dL increase in RC was associated with a 10% reduction in mortality (HR 0.90; 0.82–1.00; P = 0.040). Complete results from the multivariable models are shown in Table 2 . Table 2 Survey-weighted Cox regression: RC quartiles and mortality Outcome / Model Q1 Q2 HR (95%CI) Q3 HR (95%CI) Q4 HR (95%CI) P Q4 All-cause mortality Model 1 Ref 0.88 (0.54–1.41) 1.06 (0.73–1.53) 0.86 (0.56–1.33) 0.508 Model 2 Ref 0.78 (0.50–1.21) 0.87 (0.63–1.21) 0.72 (0.49–1.06) 0.094 Model 3 Ref 0.77 (0.49–1.21) 0.85 (0.60–1.20) 0.62 (0.42–0.91) 0.014 CVD mortality Model 1 Ref 1.69 (0.84–3.39) 1.38 (0.55–3.49) 1.16 (0.52–2.57) 0.723 Model 2 Ref 1.48 (0.79–2.78) 1.17 (0.51–2.67) 1.02 (0.51–2.03) 0.953 Model 3 Ref 1.30 (0.70–2.41) 0.93 (0.40–2.16) 0.68 (0.34–1.36) 0.274 CLRD mortality Model 1 Ref 0.93 (0.30–2.93) 1.35 (0.57–3.21) 0.55 (0.17–1.79) 0.322 Model 2 Ref 0.85 (0.26–2.74) 1.10 (0.44–2.73) 0.44 (0.15–1.27) 0.128 Model 3 Ref 1.09 (0.33–3.65) 1.02 (0.42–2.50) 0.29 (0.10–0.88) 0.029 Model 1: unadjusted. Model 2: age, sex, race. Model 3: all covariates. RC and Cause-Specific Mortality Cardiovascular mortality (151 events) did not show an inverse association in the crude model (Q4 HR 1.16; P = 0.723). After full adjustment, the point estimate shifted to 0.68 (P = 0.274), a pattern consistent with positive confounding by diabetes and statin use. For CLRD mortality (78 events), the fully adjusted HR for Q4 was 0.29 (0.10–0.88; P = 0.029; P-trend = 0.010), representing the strongest cause-specific finding in this study. Dose-Response Analysis Restricted cubic spline modeling confirmed a significant overall association (P < 0.001) and a significant deviation from linearity (P-nonlinear < 0.001; Fig. 3 ). The curve was steepest below approximately 20 mg/dL, crossed the reference line (HR = 1.0) around 20–25 mg/dL, and continued to decline gradually at higher RC levels. Subgroup and Sensitivity Analyses The protective association was most pronounced in males (HR 0.70; P = 0.036) and in patients with comorbid diabetes (HR 0.54; P = 0.026), although no interaction term reached formal significance (all P-interaction > 0.05; Table 3 ). Sensitivity analyses consistently confirmed the primary finding: Q4 HRs ranged from 0.60 to 0.67 and remained significant regardless of excluding early deaths, removing statin users, or adjusting for CRP (Table 4 ). Table 3 Subgroup analyses: high vs low RC and all-cause mortality Subgroup N Events HR (95% CI) P P-interaction Male 622 239 0.70 (0.50–0.98) 0.036 0.151 Female 941 192 1.21 (0.81–1.81) 0.360 Age < 65 963 117 0.88 (0.60–1.28) 0.502 0.874 Age ≥ 65 600 314 0.93 (0.68–1.26) 0.631 Diabetes: No 1322 344 0.88 (0.65–1.19) 0.404 0.257 Diabetes: Yes 241 87 0.54 (0.32–0.93) 0.026 HTN: No 700 142 1.04 (0.64–1.69) 0.882 0.661 HTN: Yes 863 289 0.81 (0.59–1.09) 0.165 Statin: No 1127 297 0.84 (0.59–1.20) 0.338 0.232 Statin: Yes 436 134 1.14 (0.66–1.96) 0.634 High RC = above median. Adjusted for age, sex, race, smoking, BMI (minus stratification variable). Table 4 Sensitivity analyses: Q4 vs Q1 and all-cause mortality Analysis N Events Q4 HR (95% CI) P Main analysis 1,563 431 0.62 (0.42–0.91) 0.014 Exclude death < 2 yr 1,471 339 0.67 (0.48–0.94) 0.020 Exclude statin users 1,127 297 0.60 (0.39–0.93) 0.022 Adjusted for CRP 1,240 366 0.62 (0.43–0.90) 0.011 RC per 10 mg/dL 1,563 431 0.90 (0.82–1.00) 0.040 Discussion In this large population-based cohort study utilizing US NHANES data from 1999–2018, a clear independent inverse association was demonstrated between RC and both all-cause mortality (Q4 vs Q1: HR 0.62, P = 0.014) and CLRD-related mortality (HR 0.29, P = 0.029) in COPD patients. Conversely, the association between RC and CVD mortality was directionally consistent but did not reach statistical significance (HR 0.68, P = 0.274). Further exploratory subgroup analyses indicated that the protective association was most prominent in male patients and those with comorbid diabetes, with no significant interactions observed. Our analysis also identified a non-linear dose-response relationship between RC and all-cause mortality, with the steepest risk reduction observed at lower RC levels. Furthermore, four sensitivity analyses confirmed the robustness of the primary findings (Q4 HR range: 0.60–0.67, all P < 0.05). These findings appear superficially paradoxical in light of RC’s established role as a causal atherogenic factor in the general population [ 5 , 8 ]. In the Copenhagen General Population Study, elevated RC was associated with a doubling of cardiovascular mortality [ 8 ], and Mendelian randomization data have confirmed that genetically elevated RC promotes ischemic heart disease [ 5 , 6 ]. However, accumulating evidence suggests that the direction of the lipid-mortality association can reverse once chronic catabolic disease is established, a phenomenon termed the “cholesterol paradox” or “obesity paradox” [ 20 ]. In advanced heart failure, higher total cholesterol independently predicts better survival, likely because lipoproteins buffer endotoxins and serve as an energy substrate [ 21 ]. A similar pattern has been reported following COPD exacerbations, where survivors had significantly higher mean total cholesterol than non-survivors [ 16 ]. These results align with our findings, suggesting that RC may play a different prognostic role in COPD patients compared to the general population. Interestingly, a recent Mendelian randomization study demonstrated that genetically elevated RC increases the risk of developing COPD [ 29 ], yet our findings suggest that once COPD is established, higher RC levels are associated with improved survival—a pattern consistent with the risk–survival paradox observed across multiple chronic diseases. To our knowledge, the underlying mechanisms primarily involve the following aspects. COPD is characterized by accelerated catabolism, with resting energy expenditure 15–20% higher than predicted even during stable periods [ 12 ]. Up to 40% of patients with moderate-to-severe COPD experience fat-free mass depletion, an independent predictor of mortality regardless of BMI [ 13 , 22 ]. The European Respiratory Society has positioned nutritional status as an intervenable determinant of COPD prognosis [ 23 ]. In this context, higher RC may reflect preserved hepatic synthetic capacity and adequate caloric reserves. RC is carried by VLDL and chylomicron remnants and is closely linked to the flux of triglyceride-rich lipoproteins, which reflect recent dietary fat absorption and hepatic VLDL secretion [ 4 ]. Therefore, very low RC levels may signal advanced cachexia, reduced oral intake, or impaired liver function, all of which increase mortality risk in COPD. The particularly strong protective association in the COPD subgroup with diabetes (HR 0.54) warrants attention. COPD patients with diabetes represent a phenotype with a significantly elevated mortality risk [ 2 , 24 ]. Insulin resistance promotes hepatic VLDL overproduction and is a major determinant of circulating RC concentration [ 25 ]. Paradoxically, in the setting of COPD-related catabolism, the ability to sustain high VLDL output may signify relative metabolic resilience. This interpretation is consistent with data from the NHANES 1999–2018 diabetic sub-cohort, which showed a U-shaped RC-mortality curve with the lowest risk at moderately elevated levels [ 26 ]. The sex-specific pattern—significant protection in males (HR 0.70) but not in females (HR 1.21)—may reflect multiple interacting factors. Male COPD patients are more likely to have an emphysema-dominant phenotype, associated with more severe lean mass depletion and metabolic stress [ 27 ]. Estrogen modulates hepatic lipase and VLDL clearance, potentially altering the metabolic significance of a given RC level in premenopausal women [ 28 ]. Furthermore, female COPD patients tend to have a greater burden of airway disease with less parenchymal destruction and thus may be less susceptible to the catabolic pathways through which RC exerts its apparent protective effect. Given that the interaction term did not reach statistical significance (P = 0.151), this sex difference should be interpreted with caution. The reversal of the CVD mortality point estimate between adjustment models—from a non-significant positive association (crude HR 1.16) to a non-significant negative association (adjusted HR 0.68)—merits methodological comment. Diabetes and statin use were positively associated with RC but independently predicted CVD death; their inclusion in Model 3 attenuated the positive confounding effect, revealing an underlying (albeit underpowered) negative trend. With only 151 cardiovascular events distributed across four quartiles, our study was underpowered for CVD-specific effects. Larger cohorts or pooled analyses are needed to address this issue. This study has several notable strengths. First, to our knowledge, it is the first to explore the association between RC and mortality in COPD patients. Second, our study included a large, nationally representative sample from ten consecutive NHANES cycles with a follow-up period of up to 20 years and employed weighted analyses accounting for the complex sampling design. Third, we examined three distinct mortality endpoints (all-cause, CVD, and CLRD) and utilized multiple analytical approaches including Cox regression, Kaplan-Meier survival curves, and restricted cubic spline modeling. Furthermore, comprehensive subgroup and sensitivity analyses, including CRP adjustment and exclusion of statin users and early deaths, were conducted, enhancing the credibility of the findings. Nevertheless, several limitations should be acknowledged. First, COPD was defined by self-report rather than spirometry, potentially introducing misclassification bias. However, this definition is widely used in NHANES-based COPD studies and has been successfully implemented elsewhere [ 14 ]. Second, single-timepoint RC measurement cannot capture longitudinal trajectories or treatment-induced changes. Third, data on COPD severity (FEV1, GOLD stage), inhaled corticosteroid use, and exacerbation frequency were lacking. Fourth, CRP data were unavailable for the 2011–2014 NHANES cycles; however, sensitivity analyses adjusting for CRP yielded results consistent with the main analysis. Fifth, despite efforts to adjust for relevant confounders in multivariable models, residual confounding from unmeasured or unknown factors cannot be entirely excluded. Finally, generalizability is limited to the non-institutionalized US adult population. Future well-designed prospective multicenter studies with spirometry-confirmed COPD are needed to validate these findings. Conclusions In this large cohort of US adult COPD patients, higher RC levels were found to be inversely associated with all-cause mortality and CLRD-related mortality after adjusting for various potential confounders. The protective association was particularly prominent in male patients and those with comorbid diabetes. Further exploratory subgroup and sensitivity analyses showed consistent results, supporting the robustness of these findings. Therefore, measuring RC from routine lipid panels may aid in risk assessment and prognosis prediction for COPD patients. Prospective studies with spirometry-confirmed COPD and serial RC measurements are warranted to validate these results. Declarations Ethics approval and consent to participate The National Center for Health Statistics (NCHS) Research Ethics Review Board approved all NHANES protocols. All participants provided written informed consent. As NHANES data are publicly available and de-identified, this secondary analysis did not require additional ethical approval. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution WZY conceptualized the study, performed data curation and formal analysis, and wrote the original draft. XY and XWD contributed to data collection and methodology. SMT, WC and WZY participated in data interpretation and manuscript revision. SJL and WHB supervised the research and provided critical revision. All authors read and approved the final manuscript. Acknowledgements The authors thank the National Center for Health Statistics for designing and conducting the NHANES survey and making the data publicly available. We also thank all NHANES participants for their contributions. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the NCHS or the Centers for Disease Control and Prevention. Data Availability All data analyzed in this study are publicly available from the NHANES website (https://www.cdc.gov/nchs/nhanes) and the NCHS linked mortality files (https://www.cdc.gov/nchs/linked-data/mortality-files/index.html). References Christenson SA, Smith BM, Bafadhel M, Putcha N. Chronic obstructive pulmonary disease. Lancet. 2022;399(10342):2227–42. Divo M, Cote C, de Torres JP, et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;186(2):155–61. Celli BR, Cote CG, Marin JM, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med. 2004;350(10):1005–12. 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Overproduction of very low-density lipoproteins is the hallmark of the dyslipidemia in the metabolic syndrome. Arterioscler Thromb Vasc Biol. 2008;28(7):1225–36. Wang H, Guo Y, Zhang H, Wang X, Zheng X. The U-shaped association between remnant cholesterol and risk of all-cause and cardiovascular deaths in diabetic adults: findings from NHANES 1999–2018. Nutr Metab Cardiovasc Dis. 2024;34(10):2282–8. Martinez FJ, Curtis JL, Sciurba F, et al. Sex differences in severe pulmonary emphysema. Am J Respir Crit Care Med. 2007;176(3):243–52. DeMeo DL, Ramagopalan S, Kavati A, et al. Women manifest more severe COPD symptoms across the life course. Int J Chron Obstruct Pulmon Dis. 2018;13:3021–9. Feng WY, Zheng JH, Xiao JQ, Wang ZX, Zheng YF, Jin XN, Xie JY, Liao WZ, Guo XG, Guan WJ. Risk of remnant cholesterol and chronic obstructive pulmonary disease: a mendelian randomization study. J Thorac Dis. 2025;17(7):5122–32. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9058704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622336178,"identity":"30235b90-6198-4180-ad6a-b875d28e4cbf","order_by":0,"name":"zheyuan Wang","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"zheyuan","middleName":"","lastName":"Wang","suffix":""},{"id":622336179,"identity":"de8252c8-7c46-41bb-8dba-bb986a1c8cb2","order_by":1,"name":"Wangdong Xu","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wangdong","middleName":"","lastName":"Xu","suffix":""},{"id":622336180,"identity":"35d55096-648d-4d24-a5f4-2450f7996b44","order_by":2,"name":"Ye Xuan","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Xuan","suffix":""},{"id":622336181,"identity":"b7dbfb87-b189-42fb-9fb9-ce649c373423","order_by":3,"name":"Mengting Shen","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengting","middleName":"","lastName":"Shen","suffix":""},{"id":622336182,"identity":"0bcf2153-0cc5-48c1-aef6-3f4796317e10","order_by":4,"name":"Chen Wang","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":622336183,"identity":"c30338ba-6693-4f55-95bd-1df7b6d3b37f","order_by":5,"name":"Jianliang Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACxgYQyWMhx8befoAkLRLGfDxnEkiyTCJxnoSDAXFqmWdkJ3/8ISOR3ibBkMDwo2IbEQ7rObvBmIdHIrdNuvEAY8+Z20Roae/dkMwA0iJzIIGZsY0YLc28Gw7+4JFIZ5NIMCBSS3vvxgagwxJI0NJzdjMzUIthGzCQDxLlF8MZuZs//uyxkZdvbz/44EcFMVoawFZBOAcIqwcCeTD5gyi1o2AUjIJRMFIBANWoOGKT0tkTAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianliang","middleName":"","lastName":"Sun","suffix":""},{"id":622336184,"identity":"fb62df90-b58c-47d9-aeed-b151497afaf0","order_by":6,"name":"Hanbing Wang","email":"","orcid":"","institution":"Hangzhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanbing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-07 13:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9058704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9058704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107241961,"identity":"e4ec7468-c7d8-45c9-a43c-74eec1cd2e50","added_by":"auto","created_at":"2026-04-19 07:19:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78127,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection flowchart. NHANES 1999–2018, from 101,316 participants to 1,563 eligible COPD patients with complete data.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9058704/v1/bd72e20c3bc6109f10808263.png"},{"id":107482439,"identity":"9334fa0b-c771-4296-bef9-13d9ab2a89ac","added_by":"auto","created_at":"2026-04-22 02:23:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":875540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Kaplan–Meier curves for all-cause mortality by RC quartile. Survey-weighted Cox P-trend = 0.028.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Kaplan–Meier curves for cardiovascular mortality by RC quartile. Survey-weighted Cox P-trend = 0.067.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Kaplan–Meier curves for CLRD mortality by RC quartile. Survey-weighted Cox P-trend = 0.010.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9058704/v1/4707ffe8ad630a5f1a017137.png"},{"id":107241963,"identity":"4666cacd-53d1-4a47-a8e6-aefb44a6727c","added_by":"auto","created_at":"2026-04-19 07:19:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":349361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Restricted cubic spline of the dose–response relationship between RC and all-cause mortality (fully adjusted). P-nonlinearity \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Restricted cubic spline: RC and CVD mortality. P-nonlinearity = 0.342.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Restricted cubic spline: RC and CLRD mortality. P-nonlinearity = 0.262.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9058704/v1/6fd6b78839764651a1f670b8.png"},{"id":107241964,"identity":"3078431f-6a26-4c8b-9abf-9abd5e94284a","added_by":"auto","created_at":"2026-04-19 07:19:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45648,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analyses for the association between high vs low RC and all-cause mortality.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9058704/v1/2d9141b17c2b2ef26ae2b1a8.png"},{"id":107704911,"identity":"6c8f6601-6e96-428f-9cbd-9d1516279eea","added_by":"auto","created_at":"2026-04-24 09:03:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9058704/v1/6888a1d4-06ea-4772-9210-269a4d88d77b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Remnant Cholesterol with All-Cause and Cause-Specific Mortality in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a heterogeneous condition characterized by persistent airflow limitation and systemic inflammation. Statistically, COPD has become the third leading cause of death globally, accounting for over 3\u0026nbsp;million deaths annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global mortality burden among COPD patients remains substantial, with prognosis influenced not only by airflow limitation itself but also by systemic comorbidities including cardiovascular disease, diabetes, and sarcopenia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Standard risk stratification tools such as the BODE index incorporate limited metabolic information [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], highlighting the need for simple and inexpensive biomarkers to improve prognostic assessment. Remnant cholesterol (RC), defined as total cholesterol minus HDL-C minus LDL-C, represents the cholesterol content of triglyceride-rich lipoproteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mendelian randomization studies have established RC as a causal risk factor for ischemic heart disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and recent population-based studies have linked elevated RC to increased all-cause and cardiovascular mortality in the general population [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, COPD is characterized by chronic airway inflammation, accelerated catabolism, and metabolic disorders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u0026mdash;conditions under which the \u0026ldquo;obesity paradox\u0026rdquo; and \u0026ldquo;cholesterol paradox\u0026rdquo; have been repeatedly reported [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Whether RC exhibits a similar paradoxical association in COPD patients remains unknown.\u003c/p\u003e \u003cp\u003eAs a dynamic parameter reflecting the metabolism of triglyceride-rich lipoproteins, RC can be conveniently calculated from routine lipid panels without additional cost. Clinical studies have confirmed associations between RC and various diseases and outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In recent years, several NHANES-based studies have explored the relationship between RC and mortality in the general population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], but no study has examined this association in a specific disease population characterized by catabolism. Furthermore, the relationship between RC and cause-specific mortality, including cardiovascular disease mortality and chronic lower respiratory disease mortality, in COPD patients has not been explored.\u003c/p\u003e \u003cp\u003eTherefore, the primary objective of this study was to evaluate the associations between RC levels and all-cause, cardiovascular disease (CVD), and CLRD mortality among COPD patients, using data from ten consecutive cycles of NHANES (1999\u0026ndash;2018) linked to the National Death Index through December 31, 2019. Cox proportional hazards regression, Kaplan-Meier survival curves, restricted cubic spline models, and multiple sensitivity strategies were employed to investigate these associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eNHANES is a cross-sectional survey of the non-institutionalized civilian U.S. population using a stratified, multistage probability sampling design. Data from ten 2-year cycles between 1999 and 2018 were pooled. The NCHS Research Ethics Review Board approved all protocols; all participants provided written informed consent. As the data are publicly available and de-identified, this secondary analysis required no additional ethical approval.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eEligible participants were adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years with self-reported physician diagnosis of emphysema (variable MCQ160G) or chronic bronchitis (MCQ160K), valid measurements of fasting total cholesterol, HDL-C, and triglycerides, and linkage to mortality follow-up files. Pregnant individuals, those with triglycerides exceeding 400 mg/dL (due to unreliable LDL-C estimation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), and those missing key covariates were excluded. The final analytical cohort comprised 1,563 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExposure Variable: Remnant Cholesterol\u003c/h3\u003e\n\u003cp\u003eLDL-C was uniformly recalculated for all cycles using the Sampson formula [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (which outperforms the Friedewald formula at higher triglyceride levels): LDL-C\u0026thinsp;=\u0026thinsp;TC/0.948\u0026thinsp;\u0026minus;\u0026thinsp;HDL-C/0.971 \u0026minus; (TG/8.56\u0026thinsp;+\u0026thinsp;TG\u0026times;non-HDL-C/2140\u0026thinsp;\u0026minus;\u0026thinsp;TG\u0026sup2;/16100)\u0026thinsp;\u0026minus;\u0026thinsp;9.44. RC\u0026thinsp;=\u0026thinsp;TC\u0026thinsp;\u0026minus;\u0026thinsp;HDL-C\u0026thinsp;\u0026minus;\u0026thinsp;LDL-C (Sampson). RC was categorized into quartiles for primary analysis, with Q1 as the reference group.\u003c/p\u003e\n\u003ch3\u003eOutcome Variables\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality. Secondary outcomes were cardiovascular mortality (UCOD_LEADING codes 1 and 5, corresponding to heart disease and cerebrovascular disease) and chronic lower respiratory disease (CLRD) mortality (UCOD_LEADING code 3). Follow-up extended from the NHANES examination date to the date of death or the administrative censoring date of December 31, 2019.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates were selected a priori based on directed acyclic graph reasoning and previous NHANES literature [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Demographic variables included age (continuous), sex, and race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Other). Behavioral variables included education level, smoking status (never/former/current), alcohol consumption, and body mass index (BMI). Comorbidities included hypertension, diabetes (self-reported diagnosis, fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%), coronary heart disease, congestive heart failure, stroke, and chronic kidney disease (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, CKD-EPI 2021 formula). Statin use was obtained from the prescription medication questionnaire.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e All analyses adhered to NHANES analytical guidelines, accounting for the complex sampling design with sample weights, strata, and clusters. Fasting subsample weights were divided by 10 to accommodate pooling across ten cycles. All respondent data were weighted using the recommended NHANES examination weights to account for differential sampling probabilities and non-response. Comparisons of baseline characteristics used weighted linear regression (continuous variables) and Rao-Scott chi-square tests (categorical variables).\u003c/p\u003e \u003cp\u003eWeighted Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) at three adjustment levels: Model 1 (unadjusted), Model 2 (age, sex, race/ethnicity), Model 3 (all aforementioned covariates). The proportional hazards assumption was verified via Schoenfeld residuals (global P\u0026thinsp;=\u0026thinsp;1.00). Restricted cubic splines with 4 knots were used to explore continuous dose-response relationships and test for non-linearity. P-for-trend was obtained by modeling the median RC value within each quartile as a continuous variable in the fully adjusted model.\u003c/p\u003e \u003cp\u003ePre-specified subgroup analyses were stratified by sex, age (\u0026lt;\u0026thinsp;65 vs\u0026thinsp;\u0026ge;\u0026thinsp;65 years), smoking status, BMI category, hypertension, diabetes, and statin use; P-for-interaction was calculated using multiplicative interaction terms. Four sensitivity analyses tested robustness: (i) excluding participants who died within the first two years of follow-up, (ii) restricting to non-statin users, (iii) additionally adjusting for C-reactive protein, (iv) modeling RC as a continuous variable per 10 mg/dL increment. All analyses were performed in R 4.3 using the survey, survival, and rms packages. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered the threshold for statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eThe screening process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) ultimately included 1,563 COPD patients; their weighted baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. With increasing RC quartiles, participants were more likely to have diabetes (Q1: 5.2% vs Q4: 19.2%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had higher BMI (28.1 vs 31.7 kg/m\u0026sup2;; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and had higher statin use (17.6% vs 29.1%; P\u0026thinsp;=\u0026thinsp;0.007). Non-Hispanic whites were also more prevalent in the higher quartiles (73.4% vs 82.4%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference in mean age was observed across groups (P\u0026thinsp;=\u0026thinsp;0.149).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted baseline characteristics stratified by RC quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;390)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin use, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up, yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE or weighted proportion. P from survey-weighted regression or Rao-Scott χ\u0026sup2;.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRC and All-Cause Mortality\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 7.4 years (IQR 3.5\u0026ndash;12.3), a total of 431 deaths were accumulated (Q1: 102; Q2: 100; Q3: 121; Q4: 108). In the fully adjusted model, all-cause mortality in Q4 was 38% lower compared to Q1 (HR 0.62; 95% CI 0.42\u0026ndash;0.91; P\u0026thinsp;=\u0026thinsp;0.014), with a significant trend across quartiles (P-trend\u0026thinsp;=\u0026thinsp;0.028). The intermediate quartiles showed directionally consistent but non-significant reductions (Q2 HR 0.77; Q3 HR 0.85). Analyzed as a continuous exposure variable, each 10 mg/dL increase in RC was associated with a 10% reduction in mortality (HR 0.90; 0.82\u0026ndash;1.00; P\u0026thinsp;=\u0026thinsp;0.040). Complete results from the multivariable models are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey-weighted Cox regression: RC quartiles and mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome / Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Q4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.54\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.73\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.56\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.50\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.63\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.49\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.49\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.60\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62 (0.42\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69 (0.84\u0026ndash;3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38 (0.55\u0026ndash;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.52\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48 (0.79\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.51\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.51\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30 (0.70\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.40\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68 (0.34\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCLRD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.30\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35 (0.57\u0026ndash;3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.17\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.26\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.44\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.15\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.33\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.42\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29 (0.10\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eModel 1: unadjusted. Model 2: age, sex, race. Model 3: all covariates.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRC and Cause-Specific Mortality\u003c/h2\u003e \u003cp\u003eCardiovascular mortality (151 events) did not show an inverse association in the crude model (Q4 HR 1.16; P\u0026thinsp;=\u0026thinsp;0.723). After full adjustment, the point estimate shifted to 0.68 (P\u0026thinsp;=\u0026thinsp;0.274), a pattern consistent with positive confounding by diabetes and statin use. For CLRD mortality (78 events), the fully adjusted HR for Q4 was 0.29 (0.10\u0026ndash;0.88; P\u0026thinsp;=\u0026thinsp;0.029; P-trend\u0026thinsp;=\u0026thinsp;0.010), representing the strongest cause-specific finding in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDose-Response Analysis\u003c/h2\u003e \u003cp\u003eRestricted cubic spline modeling confirmed a significant overall association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant deviation from linearity (P-nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The curve was steepest below approximately 20 mg/dL, crossed the reference line (HR\u0026thinsp;=\u0026thinsp;1.0) around 20\u0026ndash;25 mg/dL, and continued to decline gradually at higher RC levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eThe protective association was most pronounced in males (HR 0.70; P\u0026thinsp;=\u0026thinsp;0.036) and in patients with comorbid diabetes (HR 0.54; P\u0026thinsp;=\u0026thinsp;0.026), although no interaction term reached formal significance (all P-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sensitivity analyses consistently confirmed the primary finding: Q4 HRs ranged from 0.60 to 0.67 and remained significant regardless of excluding early deaths, removing statin users, or adjusting for CRP (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analyses: high vs low RC and all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.50\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21 (0.81\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.60\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.68\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.65\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.32\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.64\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.59\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.59\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.66\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eHigh RC\u0026thinsp;=\u0026thinsp;above median. Adjusted for age, sex, race, smoking, BMI (minus stratification variable).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analyses: Q4 vs Q1 and all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ4 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.42\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExclude death\u0026thinsp;\u0026lt;\u0026thinsp;2 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.48\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExclude statin users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.39\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted for CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.43\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC per 10 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.82\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large population-based cohort study utilizing US NHANES data from 1999\u0026ndash;2018, a clear independent inverse association was demonstrated between RC and both all-cause mortality (Q4 vs Q1: HR 0.62, P\u0026thinsp;=\u0026thinsp;0.014) and CLRD-related mortality (HR 0.29, P\u0026thinsp;=\u0026thinsp;0.029) in COPD patients. Conversely, the association between RC and CVD mortality was directionally consistent but did not reach statistical significance (HR 0.68, P\u0026thinsp;=\u0026thinsp;0.274). Further exploratory subgroup analyses indicated that the protective association was most prominent in male patients and those with comorbid diabetes, with no significant interactions observed. Our analysis also identified a non-linear dose-response relationship between RC and all-cause mortality, with the steepest risk reduction observed at lower RC levels. Furthermore, four sensitivity analyses confirmed the robustness of the primary findings (Q4 HR range: 0.60\u0026ndash;0.67, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese findings appear superficially paradoxical in light of RC\u0026rsquo;s established role as a causal atherogenic factor in the general population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the Copenhagen General Population Study, elevated RC was associated with a doubling of cardiovascular mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and Mendelian randomization data have confirmed that genetically elevated RC promotes ischemic heart disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, accumulating evidence suggests that the direction of the lipid-mortality association can reverse once chronic catabolic disease is established, a phenomenon termed the \u0026ldquo;cholesterol paradox\u0026rdquo; or \u0026ldquo;obesity paradox\u0026rdquo; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In advanced heart failure, higher total cholesterol independently predicts better survival, likely because lipoproteins buffer endotoxins and serve as an energy substrate [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A similar pattern has been reported following COPD exacerbations, where survivors had significantly higher mean total cholesterol than non-survivors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These results align with our findings, suggesting that RC may play a different prognostic role in COPD patients compared to the general population. Interestingly, a recent Mendelian randomization study demonstrated that genetically elevated RC increases the risk of developing COPD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], yet our findings suggest that once COPD is established, higher RC levels are associated with improved survival\u0026mdash;a pattern consistent with the risk\u0026ndash;survival paradox observed across multiple chronic diseases.\u003c/p\u003e \u003cp\u003eTo our knowledge, the underlying mechanisms primarily involve the following aspects. COPD is characterized by accelerated catabolism, with resting energy expenditure 15\u0026ndash;20% higher than predicted even during stable periods [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Up to 40% of patients with moderate-to-severe COPD experience fat-free mass depletion, an independent predictor of mortality regardless of BMI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The European Respiratory Society has positioned nutritional status as an intervenable determinant of COPD prognosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this context, higher RC may reflect preserved hepatic synthetic capacity and adequate caloric reserves. RC is carried by VLDL and chylomicron remnants and is closely linked to the flux of triglyceride-rich lipoproteins, which reflect recent dietary fat absorption and hepatic VLDL secretion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, very low RC levels may signal advanced cachexia, reduced oral intake, or impaired liver function, all of which increase mortality risk in COPD.\u003c/p\u003e \u003cp\u003eThe particularly strong protective association in the COPD subgroup with diabetes (HR 0.54) warrants attention. COPD patients with diabetes represent a phenotype with a significantly elevated mortality risk [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Insulin resistance promotes hepatic VLDL overproduction and is a major determinant of circulating RC concentration [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Paradoxically, in the setting of COPD-related catabolism, the ability to sustain high VLDL output may signify relative metabolic resilience. This interpretation is consistent with data from the NHANES 1999\u0026ndash;2018 diabetic sub-cohort, which showed a U-shaped RC-mortality curve with the lowest risk at moderately elevated levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sex-specific pattern\u0026mdash;significant protection in males (HR 0.70) but not in females (HR 1.21)\u0026mdash;may reflect multiple interacting factors. Male COPD patients are more likely to have an emphysema-dominant phenotype, associated with more severe lean mass depletion and metabolic stress [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Estrogen modulates hepatic lipase and VLDL clearance, potentially altering the metabolic significance of a given RC level in premenopausal women [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, female COPD patients tend to have a greater burden of airway disease with less parenchymal destruction and thus may be less susceptible to the catabolic pathways through which RC exerts its apparent protective effect. Given that the interaction term did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.151), this sex difference should be interpreted with caution.\u003c/p\u003e \u003cp\u003eThe reversal of the CVD mortality point estimate between adjustment models\u0026mdash;from a non-significant positive association (crude HR 1.16) to a non-significant negative association (adjusted HR 0.68)\u0026mdash;merits methodological comment. Diabetes and statin use were positively associated with RC but independently predicted CVD death; their inclusion in Model 3 attenuated the positive confounding effect, revealing an underlying (albeit underpowered) negative trend. With only 151 cardiovascular events distributed across four quartiles, our study was underpowered for CVD-specific effects. Larger cohorts or pooled analyses are needed to address this issue.\u003c/p\u003e \u003cp\u003eThis study has several notable strengths. First, to our knowledge, it is the first to explore the association between RC and mortality in COPD patients. Second, our study included a large, nationally representative sample from ten consecutive NHANES cycles with a follow-up period of up to 20 years and employed weighted analyses accounting for the complex sampling design. Third, we examined three distinct mortality endpoints (all-cause, CVD, and CLRD) and utilized multiple analytical approaches including Cox regression, Kaplan-Meier survival curves, and restricted cubic spline modeling. Furthermore, comprehensive subgroup and sensitivity analyses, including CRP adjustment and exclusion of statin users and early deaths, were conducted, enhancing the credibility of the findings.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. First, COPD was defined by self-report rather than spirometry, potentially introducing misclassification bias. However, this definition is widely used in NHANES-based COPD studies and has been successfully implemented elsewhere [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Second, single-timepoint RC measurement cannot capture longitudinal trajectories or treatment-induced changes. Third, data on COPD severity (FEV1, GOLD stage), inhaled corticosteroid use, and exacerbation frequency were lacking. Fourth, CRP data were unavailable for the 2011\u0026ndash;2014 NHANES cycles; however, sensitivity analyses adjusting for CRP yielded results consistent with the main analysis. Fifth, despite efforts to adjust for relevant confounders in multivariable models, residual confounding from unmeasured or unknown factors cannot be entirely excluded. Finally, generalizability is limited to the non-institutionalized US adult population. Future well-designed prospective multicenter studies with spirometry-confirmed COPD are needed to validate these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this large cohort of US adult COPD patients, higher RC levels were found to be inversely associated with all-cause mortality and CLRD-related mortality after adjusting for various potential confounders. The protective association was particularly prominent in male patients and those with comorbid diabetes. Further exploratory subgroup and sensitivity analyses showed consistent results, supporting the robustness of these findings. Therefore, measuring RC from routine lipid panels may aid in risk assessment and prognosis prediction for COPD patients. Prospective studies with spirometry-confirmed COPD and serial RC measurements are warranted to validate these results.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The National Center for Health Statistics (NCHS) Research Ethics Review Board approved all NHANES protocols. All participants provided written informed consent. As NHANES data are publicly available and de-identified, this secondary analysis did not require additional ethical approval.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWZY conceptualized the study, performed data curation and formal analysis, and wrote the original draft. XY and XWD contributed to data collection and methodology. SMT, WC and WZY participated in data interpretation and manuscript revision. SJL and WHB supervised the research and provided critical revision. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the National Center for Health Statistics for designing and conducting the NHANES survey and making the data publicly available. We also thank all NHANES participants for their contributions. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the NCHS or the Centers for Disease Control and Prevention.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data analyzed in this study are publicly available from the NHANES website (https://www.cdc.gov/nchs/nhanes) and the NCHS linked mortality files (https://www.cdc.gov/nchs/linked-data/mortality-files/index.html).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChristenson SA, Smith BM, Bafadhel M, Putcha N. Chronic obstructive pulmonary disease. 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Eur Heart J. 2023;44(16):1432\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian Y, Wu Y, Qi M, et al. Associations of remnant cholesterol with cardiovascular and cancer mortality in a nationwide cohort. Sci Bull. 2024;69(4):526\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai M, Liao J, Wang Y, et al. Remnant cholesterol and all-cause mortality risk: findings from NHANES, 2003\u0026ndash;2015. Front Endocrinol. 2024;15:1417228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M, Chen Z, Ye H, et al. Long-term association of remnant cholesterol with all-cause and cardiovascular disease mortality: a nationally representative cohort study. Front Cardiovasc Med. 2024;11:1286091.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchols AMWJ, Ferreira IM, Franssen FME, et al. Nutritional assessment and therapy in COPD: a European Respiratory Society statement. Eur Respir J. 2014;44(6):1504\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVestbo J, Prescott E, Almdal T, et al. Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample. Am J Respir Crit Care Med. 2006;173(1):79\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao C, Wang R, Wang J, Bunjhoo H, Xu Y, Xiong W. Body mass index and mortality in chronic obstructive pulmonary disease: a meta-analysis. PLoS ONE. 2012;7(8):e43892.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert AA, Putcha N, Drummond MB, et al. Obesity is associated with increased morbidity in moderate to severe COPD. Chest. 2017;151(1):68\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShteinberg M, Segal-Trabelsky M, Adir Y, et al. Lipid profile and statin use: the paradox of survival after acute exacerbation of chronic obstructive pulmonary disease. Isr Med Assoc J. 2015;17(2):95\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauchhaus M, Clark AL, Doehner W, et al. The relationship between cholesterol and survival in patients with chronic heart failure. J Am Coll Cardiol. 2003;42(11):1933\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson M, Ling C, Sun Q, et al. A new equation for calculation of low-density lipoprotein cholesterol in patients with normolipidemia and/or hypertriglyceridemia. JAMA Cardiol. 2020;5(5):540\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Textor J, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package \u0026lsquo;dagitty\u0026rsquo;. Int J Epidemiol. 2016;45(6):1887\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavie CJ, De Schutter A, Parto P, et al. Obesity and prevalence of cardiovascular diseases and prognosis\u0026mdash;the obesity paradox updated. Prog Cardiovasc Dis. 2016;58(5):537\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauchhaus M, Coats AJ, Anker SD. The endotoxin-lipoprotein hypothesis. Lancet. 2000;356(9233):930\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchols AMWJ, Broekhuizen R, Weling-Scheepers CA, Wouters EF. Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr. 2005;82(1):53\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogelmeier CF, Criner GJ, Martinez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Am J Respir Crit Care Med. 2017;195(5):557\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannino DM, Thorn D, Swensen A, Holguin F. Prevalence and outcomes of diabetes, hypertension and cardiovascular disease in COPD. Eur Respir J. 2008;32(4):962\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdiels M, Olofsson SO, Taskinen MR, Bor\u0026eacute;n J. Overproduction of very low-density lipoproteins is the hallmark of the dyslipidemia in the metabolic syndrome. Arterioscler Thromb Vasc Biol. 2008;28(7):1225\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Guo Y, Zhang H, Wang X, Zheng X. The U-shaped association between remnant cholesterol and risk of all-cause and cardiovascular deaths in diabetic adults: findings from NHANES 1999\u0026ndash;2018. Nutr Metab Cardiovasc Dis. 2024;34(10):2282\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez FJ, Curtis JL, Sciurba F, et al. Sex differences in severe pulmonary emphysema. Am J Respir Crit Care Med. 2007;176(3):243\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeMeo DL, Ramagopalan S, Kavati A, et al. Women manifest more severe COPD symptoms across the life course. Int J Chron Obstruct Pulmon Dis. 2018;13:3021\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng WY, Zheng JH, Xiao JQ, Wang ZX, Zheng YF, Jin XN, Xie JY, Liao WZ, Guo XG, Guan WJ. Risk of remnant cholesterol and chronic obstructive pulmonary disease: a mendelian randomization study. J Thorac Dis. 2025;17(7):5122\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"remnant cholesterol, chronic obstructive pulmonary disease, all-cause mortality, respiratory mortality, NHANES, cholesterol paradox","lastPublishedDoi":"10.21203/rs.3.rs-9058704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9058704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough remnant cholesterol (RC) has been established as a causal risk factor for atherosclerotic cardiovascular disease in the general population, whether this pro-atherogenic lipid component holds the same prognostic significance in patients with chronic obstructive pulmonary disease (COPD)\u0026mdash;a group characterized by systemic inflammation and metabolic disorders\u0026mdash;remains unexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cohort study utilized data from ten consecutive cycles (1999\u0026ndash;2018) of the National Health and Nutrition Examination Survey (NHANES), linked to National Death Index (NDI) mortality records through December 31, 2019. Adults with self-reported COPD diagnosis and complete fasting lipid profiles were included. RC was calculated as total cholesterol minus HDL-C minus LDL-C estimated via the Sampson formula. Analyses employed complex sampling-weighted Cox regression (with stepwise covariate adjustment), restricted cubic splines (RCS), Kaplan-Meier survival analysis, and pre-specified subgroup and sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 1,563 eligible patients (431 deaths, median follow-up 7.4 years, IQR 3.5\u0026ndash;12.3), after full adjustment, the highest RC quartile (Q4) was associated with a significantly lower risk of all-cause mortality compared to the lowest quartile (Q1) (HR 0.62; 95% CI 0.42\u0026ndash;0.91; P\u0026thinsp;=\u0026thinsp;0.014; P-trend\u0026thinsp;=\u0026thinsp;0.028). The association was most pronounced for chronic lower respiratory disease (CLRD) mortality (HR 0.29; 95% CI 0.10\u0026ndash;0.88; P\u0026thinsp;=\u0026thinsp;0.029). Cardiovascular mortality showed a similar direction but did not reach statistical significance (HR 0.68; P\u0026thinsp;=\u0026thinsp;0.274). The RCS model identified a non-linear dose-response curve (P-nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the steepest risk reduction observed below 20 mg/dL. Subgroup analyses suggested potential effect modification by sex (male: HR 0.70, P\u0026thinsp;=\u0026thinsp;0.036) and diabetes status (diabetes: HR 0.54, P\u0026thinsp;=\u0026thinsp;0.026), although no formal interaction terms reached significance. Results were consistent across four sensitivity analyses (Q4 HR range 0.60\u0026ndash;0.67; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this nationally representative cohort, higher RC levels were independently and inversely associated with all-cause and respiratory mortality in COPD patients\u0026mdash;a finding that reverses the established detrimental role of RC in the general population and parallels the metabolic paradox described in other wasting diseases. If prospectively validated, RC may serve as an inexpensive and routinely accessible prognostic marker in COPD.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Association of Remnant Cholesterol with All-Cause and Cause-Specific Mortality in Patients with Chronic Obstructive Pulmonary Disease: A Population-Based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 07:19:02","doi":"10.21203/rs.3.rs-9058704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-08T04:45:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-13T07:12:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T04:47:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T04:46:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-03-07T13:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c052458-63c8-4a33-a727-9ace1475da17","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T07:19:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 07:19:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9058704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9058704","identity":"rs-9058704","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}