Predictive Value of Non-High-Density to High-Density Lipoprotein Ratio for Coronary Heart Disease and Mortality in Adults

Predictive Value of Non-High-Density to High-Density Lipoprotein Ratio for Coronary Heart Disease and Mortality in Adults | 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 Article Predictive Value of Non-High-Density to High-Density Lipoprotein Ratio for Coronary Heart Disease and Mortality in Adults Haibin Xu, Zhou Liu, Baohong Yao, Ziqi Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4456196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Coronary heart disease (CHD) significantly impacts public health worldwide. Non-high-density lipoprotein cholesterol (non-HDL-C) and high-density lipoprotein cholesterol (HDL-C) are vital in lipid metabolism. The ratio of non-HDL-C to HDL-C (NHHR) may predict CHD and mortality. Objective This study investigates the relationship between NHHR and CHD incidence and its predictive value for all-cause and cardiovascular mortality in adults. Methods Data were collected from NHANES (2005–2016), including participants aged 18–80 years. NHHR was calculated by dividing non-HDL-C by HDL-C. Multivariable logistic regression and Cox proportional hazards models assessed associations between NHHR, CHD prevalence, and mortality outcomes. Results Higher NHHR levels were significantly associated with lower CHD prevalence in all adjusted models. The highest NHHR quartile showed the strongest inverse association with CHD prevalence (Model 3: Q4, OR = 0.93, 95% CI: 0.31–0.50, P < 0.0001). Higher NHHR quartiles also correlated with reduced all-cause and cardiovascular mortality risks. The restricted cubic spline analysis indicated a non-linear relationship, with the most significant protective effect at an NHHR value of approximately 3. Conclusions NHHR is a robust predictor of CHD and mortality. Incorporating NHHR into cardiovascular risk assessments could better identify high-risk individuals, guiding personalized prevention and treatment strategies. NHHR Coronary Heart Disease Cardiovascular Mortality Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Coronary heart disease (CHD) is caused by atherosclerosis of the coronary arteries, characterized primarily by the narrowing or blockage of these arteries, which leads to insufficient blood supply to the myocardium. This insufficient supply can cause chest pain (angina), myocardial infarction, and even sudden cardiac arrest. CHD is one of the leading causes of death globally, significantly impacting public health. According to the World Health Organization, approximately 17 million people die from cardiovascular diseases each year, with CHD accounting for a substantial portion 1 . In recent years, with changes in lifestyle (such as unhealthy diets, lack of physical activity, smoking, etc.) and an aging population, the incidence of CHD has been on the rise, posing an increasing threat to public health. Non-HDL-C(non-high-density lipoprotein cholesterol) includes all atherogenic lipoproteins, namely low-density lipoprotein cholesterol (LDL-C), very-low-density lipoprotein cholesterol (VLDL-C), and intermediate-density lipoprotein cholesterol (IDL-C). LDL-C is often referred to as "bad" cholesterol because it deposits in the arterial walls, leading to atherosclerosis. VLDL-C and IDL-C are also considered contributors to atherosclerosis. On the other hand, HDL-C is known as "good" cholesterol because it helps transport cholesterol from the arterial walls to the liver for metabolism and excretion, thereby slowing the process of atherosclerosis.Studies have shown that higher levels of Non-HDL-C are significantly associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD) 2 , 6 . Non-HDL-C, as the sum of all atherogenic cholesterols, is considered a better predictor than LDL-C alone. Higher levels of HDL-C are associated with a reduced risk of cardiovascular diseases. HDL-C protects through a reverse cholesterol transport mechanism, helping to reduce cholesterol accumulation in the arterial walls 3 . The NHHR is an indicator that comprehensively reflects the status of lipoprotein metabolism, providing a more comprehensive assessment of cardiovascular disease risk. Recent studies have shown that NHHR is a better predictor of CHD incidence and mortality than either Non-HDL-C or HDL-C alone. For instance, a large epidemiological study found that higher NHHR was significantly associated with an increased incidence of CHD, and this association remained even after adjusting for traditional risk factors such as age, gender, smoking, and blood pressure 4 .Moreover, NHHR has been used to assess the risk of all-cause mortality and cardiovascular mortality. Some studies have found that higher NHHR is associated not only with an increased risk of CHD but also with a significant association with cardiovascular-related mortality 5 . These findings suggest that NHHR has important clinical applications and can serve as a powerful tool for identifying high-risk populations, thereby guiding personalized prevention and treatment strategies.In conclusion, NHHR as a new predictor of CHD and mortality risk has significant clinical implications. It can provide a more comprehensive assessment of cardiovascular disease risk and help clinicians identify high-risk patients early, allowing for appropriate interventions to reduce the incidence of cardiovascular events and mortality. This study aims to explore the relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and the incidence of coronary heart disease (CHD) in adults, providing a comprehensive understanding of how this ratio impacts CHD risk. The novelty of this research lies in its focus on NHHR, a relatively underexplored yet promising indicator that may offer superior predictive value compared to traditional lipid measures. In addition to examining CHD incidence, the study also evaluates the predictive value of NHHR for all-cause mortality and cardiovascular mortality. Despite numerous studies on individual lipid components, the combined effect as reflected by NHHR remains less understood. Addressing this gap, our research highlights the potential of NHHR as a robust marker for mortality risk, providing critical insights that could enhance risk stratification and guide clinical decision-making in the management of cardiovascular health. 2. METHODS 2.1 Data collection The National Health and Nutrition Examination Survey (NHANES) is a comprehensive program designed to assess the health and nutritional status of adults and children in the United States. It uniquely combines interviews with physical examinations, covering demographic, socioeconomic, dietary, and health-related topics. The survey uses cross-sectional data to represent the entire U.S. population, ensuring broad applicability and relevance. This research solely utilizes publicly available NHANES data, eliminating the need for additional ethical approval. For more details on ethical oversight, refer to the NCHS Ethics Review Board (ERB) Approval:NCHS ERB Approval at ( https://www.cdc.gov/nchs/nhanes/irba98.htm).Th e data can be freely accessed through the CDC's NHANES website:NHANES Data Access at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx . In this study, to clarify the relationship between NHHR and the incidence and mortality of coronary heart disease (CHD), the dataset was collected from NHANES during six cycles (2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, and 2015–2016). Initially, a total of 60,936 participants were identified across these six cycles. Through a methodological screening process, certain demographic characteristics were excluded as follows: individuals under 18 years old (24,649), pregnant women (684), individuals lacking total cholesterol (TC) or high-density lipoprotein cholesterol (HDL) data (3,449), those who did not provide CHD information (1,953), and those lacking other covariate data. After a rigorous screening process, 27,091 participants met the inclusion criteria (Fig. 1 ). 2.2 Exposure measurement methods The focal exposure variable of this study is the NHHR, which is the independent variable in the exposure assessment. It represents the ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C) 7 . To derive non-HDL-C, HDL-C was subtracted from total cholesterol (TC), and the lipid profiles of fasting individuals were analyzed. An automated biochemical analyzer performed enzymatic tests to assess TC and HDL-C levels. For the determination of TC concentration, the study used the Roche Cobas 6000 and Roche Modular P chemistry analyzers in the analytical procedures. 2.3 Outcome variable selections The outcomes selected for this study are coronary heart disease (CHD) and mortality. CHD prevalence was assessed using an online questionnaire. Trained interviewers asked participants, "Has a doctor or other health professional ever told you that you have coronary heart disease?" Participants responded with a yes or no answer. If they could not respond or did not know, they were considered missing data. As of December 31, 2019, we used the NHANES Public-use Linked Mortality File ( https://www.cdc.gov/nchs/data-linkage/mortality-public.htm ) and linked it to the National Death Index (NDI) data using a probabilistic matching algorithm to determine mortality status. Mortality outcomes were determined according to the International Classification of Diseases, 10th Revision (ICD-10). In ICD-10, various codes specify cardiovascular deaths, such as congestive heart failure (I50), stroke or cerebrovascular accident (CVA), and others. The follow-up period for each participant was calculated from the date of the baseline examination to the date of the last known survival status or the date of removal from the mortality file due to death. 2.4 Covariates extraction Covariates that could potentially influence the relationship between NHHR and coronary heart disease (CHD) and mortality were collected through interviews and medical examinations. These included sociodemographic and lifestyle characteristics such as age, gender, race/ethnicity, educational level, marital status, smoking status, drinking status, body mass index (BMI), hypertension, diabetes, total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C). Demographic data on gender (male, female), age (in years), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Races), marital status (Married/Living with partner, Widowed/Divorced/Separated, Never married), educational level (less than high school, high school, more than high school), smoking status (every day, some days, not at all), and drinking status (heavy drinking, moderate drinking) were obtained from interviews. Participants were asked questions such as, "Has a doctor or other health professional ever told you that you have high blood pressure, also called hypertension?", "Has a doctor or health professional ever told you that you have diabetes or sugar diabetes?", "In your lifetime, have you had at least 5 drinks of any alcoholic beverage almost every day for 2 weeks or more?", and "Do you now smoke cigarettes?". Participants responded with a "yes" or "no" answer, and if they refused to respond or were unable to provide an answer, their data were classified as missing. BMI was determined by considering participants' height and weight and categorized into three ranges: 0–25 kg/m² (normal), 25–30 kg/m² (overweight), and > 30 kg/m² (obese). TC and HDL-C levels were measured using an automated biochemical analyzer, with total cholesterol (TC) and HDL-C levels determined using enzymatic methods with the Beckman Synchron LX20. Detailed measurement methods for these variables are publicly available at www.cdc.gov/nchs/nhanes/ . 3. DATA ANALYSIS Statistical analyses were conducted using R software (version 4.3.0). Following NHANES guidelines, appropriate weighting procedures were applied to obtain accurate estimates representative of the U.S. population within the complex survey design. The weights for 2005–2016 were calculated by sampling a portion of each participant from the 2005–2016 data set. Continuous variables are presented as means and standard errors (SE), while categorical variables are expressed as frequencies and percentages. To compare different variables, t-tests and Mann-Whitney U tests were used for continuous variables, and chi-square tests were employed for categorical variables. Weighted multivariable logistic regression was used to estimate the odds ratios (OR) and 95% confidence intervals (CI) of NHHR in relation to the prevalence of coronary heart disease (CHD). Three models were applied: Model 1 was unadjusted; Model 2 was adjusted for age, gender, race, and educational level; Model 3 was further adjusted for BMI, smoking status, drinking status, and diabetes. Additionally, NHHR was analyzed in quartiles, with the lowest quartile (Q1) serving as the reference category, to examine its association with the prevalence of coronary heart disease (CHD). To estimate the hazard ratios (HR) for all-cause and cardiovascular disease mortality, Cox proportional hazards regression models were used, assessing the impact of NHHR on the time to event (death), accounting for risk review and time dependency. These models included three levels of adjustment: Model 1 was unadjusted; Model 2 was adjusted for demographic variables; and Model 3 included further adjustments for clinical and lifestyle factors. For mortality analysis, nonlinear relationship detection using restricted cubic splines (RCS) curves was included to explore potential nonlinear associations between NHHR and mortality outcomes. Additionally, subgroup analyses were performed to investigate the consistency of associations across various subpopulations. All analyses were utilized in a cross-sectional analysis for this study, with a significance level of P < 0.05 considered statistically significant. 4. RESULTS 4.1 Characteristics of the Study Population The table presents the baseline characteristics of participants categorized by the presence or absence of coronary heart disease (CHD). It reveals that lower NHHR is associated with the CHD group compared to the non-CHD group (P = 0.01). Total cholesterol (TC) is significantly lower in the CHD group (P < 0.0001), while no significant difference in HDL levels was observed (P = 0.75). Older age is significantly associated with CHD (P < 0.0001), with a higher prevalence among those aged ≥ 60. There are significant differences across racial/ethnic groups (P < 0.0001), with a higher prevalence of CHD among Non-Hispanic Whites. Males show a higher prevalence of CHD (P < 0.0001). Lower education levels are associated with a higher prevalence of CHD (P < 0.0001). Diabetes, smoking, and heavy alcohol use are significantly associated with CHD, with higher prevalence among former and current smokers and heavy alcohol users (P < 0.0001 for all). A history of stroke, angina, congestive heart failure, and heart attack are all significantly associated with CHD (P < 0.0001). Lower NHHR quartiles are linked to a higher prevalence of CHD (P = 0.03). Participants with cardiovascular disease (CVD) and hypertension also show a significantly higher prevalence of CHD (P < 0.0001)(see table 1). Table 1.The general characteristics of the population in NHANESE 4.2 Association Between NHHR and the Prevalence of Coronary Heart Disease (CHD) in Adults. The analysis of Table 2 demonstrates a consistent inverse association between NHHR quartiles and the prevalence of coronary heart disease (CHD) in adults. In the unadjusted model (Model 1), there is a trend towards lower CHD prevalence with higher NHHR quartiles, with the strongest association observed in the highest quartile (Q4, B = -0.32, 95% CI: -0.60, 0.88, P = 0.001) and a significant P for trend (P = 0.002). After adjusting for age, sex, ethnicity, education level, and BMI in Model 2, the association becomes more pronounced and statistically significant across all quartiles (Q2: B = -0.40, 95% CI: -0.54, -0.84, P < 0.001; Q3: B = -0.69, 95% CI: -0.39, -0.64, P < 0.0001; Q4: B = -1.00, 95% CI: -0.29, -0.46, P < 0.0001) with a highly significant P for trend (< 0.0001). Further adjustment for smoking, drinking status, hypertension, and diabetes in Model 3 maintains the significant inverse association, particularly in Q4 (B = -0.93, 95% CI: -0.31, -0.50, P < 0.0001), indicating that higher NHHR quartiles are associated with a lower prevalence of CHD. The consistent P for trend values across all models underscore the robustness of this inverse relationship(see Table 2 ). 4.3 Establish Cox proportional hazard model The analysis of Table 3 demonstrates a consistent inverse association between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) quartiles and both all-cause and cardiovascular disease (CVD) mortality among adults during the follow-up period. In the unadjusted model (Model 1), higher NHHR quartiles (Q3 and Q4) are significantly associated with lower all-cause mortality (Q3: HR 0.79, 95% CI: 0.69, 0.91, P < 0.001; Q4: HR 0.77, 95% CI: 0.68, 0.87, P < 0.0001) and lower CVD mortality (Q3: HR 0.72, 95% CI: 0.58, 0.89, P = 0.002; Q4: HR 0.75, 95% CI: 0.61, 0.93, P = 0.01). After adjusting for age, sex, ethnicity, education level, and BMI in Model 2, the inverse associations remain significant and become stronger (all-cause mortality Q3: HR 0.69, 95% CI: 0.59, 0.80, P < 0.0001; Q4: HR 0.62, 95% CI: 0.54, 0.72, P < 0.0001; CVD mortality Q3: HR 0.61, 95% CI: 0.48, 0.78, P < 0.0001; Q4: HR 0.59, 95% CI: 0.46, 0.76, P < 0.0001). Further adjustments for smoking, drinking status, hypertension, and diabetes in Model 3 continue to show significant inverse associations (all-cause mortality Q3: HR 0.66, 95% CI: 0.57, 0.77, P < 0.0001; Q4: HR 0.61, 95% CI: 0.53, 0.70, P < 0.0001; CVD mortality Q3: HR 0.58, 95% CI: 0.46, 0.73, P < 0.0001; Q4: HR 0.58, 95% CI: 0.45, 0.75, P < 0.0001), indicating that higher NHHR is protective against both all-cause and CVD mortality. The significant P for trend values in all models underscore the robustness of this inverse relationship(see Table 3 ) Table 3 Mortality Outcomes by Dietary Copper Intake (Quartiles) Among Participants During the Follow-up Period The quartile of NHHR Q1(0.205–1.755] Q2(1.755–2.396] Q3(2.396–3.333] Q4(3.333,25.813] P for trend All-Cause Mortality Number of deaths 654 726 825 946 Model 1 h(95% CI)P Value 1 0.90(0.78,1.03) 0.12 0.79(0.69,0.91) < 0.001 0.77(0.68,0.87) < 0.0001 < 0.0001 Model 2 h(95% CI)P Value 1 0.84(0.74,0.97) 0.02 0.69(0.59,0.80) < 0.0001 0.62(0.54,0.72) < 0.0001 < 0.0001 Model 3 h(95% CI)P Value 1 0.81(0.70,0.95) 0.01 0.66(0.57,0.77) < 0.0001 0.61(0.53,0.70) < 0.0001 < 0.0001 CVD Mortality Number of deaths 227 227 278 321 Model 1 h(95% CI)P Value 1 0.86(0.70,1.06) 0.16 0.72(0.58,0.89) 0.002 0.75(0.61,0.93) 0.01 0.006 Model 2 h(95% CI)P Value 1 0.80(0.65,0.99) 0.04 0.61(0.48,0.78) < 0.0001 0.59(0.46,0.76) < 0.0001 < 0.0001 Model 3 h(95% CI)P Value 1 0.77(0.61,0.95) 0.02 0.58(0.46,0.73) < 0.0001 0.58(0.45,0.75) < 0.0001 < 0.0001 4.4 Nonlinear Relationship Detection The analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and all-cause mortality, modeled using restricted cubic splines (RCS), reveals a non-linear association. The RCS curve demonstrates an initial steep decline in the hazard ratio with increasing NHHR levels, suggesting a strong protective effect against all-cause mortality. This protective effect is most pronounced at an NHHR value of approximately 3, where the log hazard ratio reaches its lowest point. Beyond this value, the hazard ratio gradually increases, indicating a diminishing protective effect and a potential rise in mortality risk at very high NHHR levels. The optimal number of knots for the RCS model was determined to be 3, providing the best fit with the lowest AIC value. The non-linearity test was significant (P < 0.05), confirming that the non-linear model fits the data better than a linear model. These findings highlight the complex relationship between NHHR and mortality, emphasizing the importance of considering non-linear associations in evaluating lipid ratios and their impact on health outcomes(see firgure 2). Firgure 2.Impact of NHHR on All-Cause Mortality Risk: A Restricted Cubic Spline Analysis The relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and cardiovascular mortality was assessed using restricted cubic splines (RCS) to model the non-linear associations. The RCS curve demonstrates a non-linear relationship, showing a sharp decline in the hazard ratio as NHHR increases from very low levels, indicating a protective effect against cardiovascular mortality. The log hazard ratio stabilizes as NHHR continues to increase, suggesting that higher NHHR levels do not significantly increase mortality risk beyond a certain point. The optimal number of knots for the RCS model was determined to be 3, providing the best fit with the lowest AIC value of 12818.72. The non-linearity test was significant (P = 0.0004), confirming that the non-linear model fits the data better than a linear model. These findings highlight that while higher NHHR levels are initially protective against cardiovascular mortality, the risk does not significantly change beyond an NHHR value of approximately 3, emphasizing the importance of considering non-linear relationships in evaluating lipid ratios and their impact on cardiovascular health outcomes(see Fig. 2 ). 4.5 subgroup analysis The subgroup analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and all-cause mortality reveals significant variations across different demographic and clinical subgroups. The protective effect of higher NHHR is notably stronger in males (HR -0.29, 95% CI: -0.40, -0.18, P < 0.0001) compared to females (HR 0.11, 95% CI: 0.00, 0.23, P = 0.05), and in older adults (≥ 60 years) (HR -0.79, 95% CI: -1.51, -0.06, P = 0.04). Additionally, individuals with impaired glucose tolerance (IGT) (HR -0.41, 95% CI: -0.60, -0.21, P < 0.0001) and those who are overweight (HR -0.21, 95% CI: -0.32, -0.10, P < 0.001) show significant protective effects. Furthermore, the analysis indicates stronger protective effects in individuals with a history of angina, congestive heart failure, heart attack, or cardiovascular disease (CVD). These findings suggest that the impact of NHHR on mortality risk is influenced by specific population characteristics, emphasizing the need for targeted interventions in these high-risk groups(see table 4). Table 4. Subgroup Analysis of the Association Between NHHR and All-Cause Mortality The subgroup analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and cardiovascular mortality reveals significant variations across different demographic and clinical factors. The protective effect of higher NHHR is notably stronger in males (HR -0.35, 95% CI: -0.51, -0.19, P < 0.0001) compared to females (HR 0.11, 95% CI: -0.04, 0.26, P = 0.14). There is no significant difference by ethnicity, but individuals with a high school education (HR -0.32, 95% CI: -0.48, -0.15, P < 0.001) show significant protective effects. Those with impaired glucose tolerance (IGT) (HR -0.52, 95% CI: -0.80, -0.25, P < 0.001) and impaired fasting glucose (IFG) (HR -0.51, 95% CI: -0.89, -0.14, P = 0.01) also exhibit stronger protective effects. Among alcohol users, former drinkers (HR -0.23, 95% CI: -0.41, -0.06, P = 0.01) and mild drinkers (HR -0.18, 95% CI: -0.35, -0.01, P = 0.04) benefit more significantly. The protective effect of NHHR is more pronounced in younger adults (< 60 years) (HR -0.11, 95% CI: -0.21, 0.00, P = 0.04). Overweight (HR -0.29, 95% CI: -0.45, -0.12, P < 0.001) and obese individuals (HR -0.19, 95% CI: -0.35, -0.04, P = 0.02) also show significant protective effects. Furthermore, significant protective effects are observed in individuals with a history of angina (HR -0.50, 95% CI: -0.83, -0.17, P = 0.003) and congestive heart failure (HR -0.36, 95% CI: -0.63, -0.09, P = 0.01). Those with hypertension also benefit significantly from higher NHHR levels (HR -0.27, 95% CI: -0.40, -0.14, P < 0.0001). These findings suggest that the impact of NHHR on cardiovascular mortality risk is influenced by specific population characteristics, emphasizing the need for targeted interventions in these high-risk groups(see Table 5 ). 5. DISCUSSION The findings of this study underscore the significance of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of coronary heart disease (CHD) incidence and mortality in adults. Our results align with recent literature, which highlights NHHR as a valuable marker for cardiovascular risk assessment and offers several noteworthy contributions to the field. Our study confirms that higher NHHR levels are significantly associated with a lower prevalence of CHD and reduced risk of all-cause and cardiovascular mortality. This relationship persists even after adjusting for various demographic, clinical, and lifestyle factors, demonstrating NHHR's robustness as a predictive marker. This finding is consistent with the study by Yu et al. 8 , which found that higher NHHR levels were linked to lower all-cause and cardiovascular mortality in a cohort of US adults with diabetes or prediabetes. The restricted cubic spline analysis revealed a non-linear association between NHHR and mortality, indicating that the relationship is more complex than a simple linear trend. The significant P-value for non-linearity underscores the importance of considering non-linear effects in clinical assessments. This complexity suggests that NHHR's protective effects may plateau or even diminish at very high levels, highlighting the need for careful evaluation in clinical settings. Sheng et al. 10 also reported a non-linear relationship between NHHR and diabetes risk, further emphasizing the importance of non-linear models in predicting health outcomes. The protective effect of higher NHHR was notably stronger in specific subgroups, including males, older adults, individuals with impaired glucose tolerance, and those with a history of cardiovascular conditions. These groups exhibited a stronger inverse relationship between NHHR and mortality risk, suggesting that NHHR could be particularly useful for identifying high-risk individuals who may benefit from more intensive lipid management and lifestyle interventions. Prior research by Saito et al. 9 supports these findings, indicating sex-specific differences in lipid profiles and cardiovascular outcomes. Our study highlights NHHR's potential utility in clinical practice. By integrating NHHR into routine cardiovascular risk assessments, healthcare providers could improve risk stratification and develop more personalized prevention and treatment strategies. This approach could be especially beneficial for managing patients with comorbidities, providing a more nuanced understanding of their cardiovascular risk and guiding targeted interventions. Gao et al. 11 demonstrated that NHHR is an effective predictor of coronary artery disease severity, further supporting its clinical relevance. This study adds to the growing body of evidence supporting the use of composite lipid measures like NHHR in cardiovascular risk assessment. The consistent associations observed across various subgroups and the detailed analysis of non-linear relationships contribute to a deeper understanding of how NHHR impacts cardiovascular health. Liu et al. 12 found that a higher NHHR was associated with increased risk of progression of non-culprit coronary lesions in patients with acute coronary syndrome, reinforcing the significance of NHHR in cardiovascular research. Limitations Despite the strengths of this study, there are several limitations to consider. The observational nature of the study precludes establishing causality. Additionally, the study population is limited to adults in the United States, which may affect the generalizability of the findings to other populations. Future research should include diverse populations and consider longitudinal designs to better understand the causal relationships. 6. CONCLUSIONS This study In conclusion, this study reinforces NHHR's relevance as a predictor of CHD and mortality. The robust associations, significant subgroup insights, and the non-linear relationship with mortality underscore NHHR's potential utility in clinical practice. Integrating NHHR into routine cardiovascular risk assessments could enhance the identification of high-risk individuals and support more personalized prevention and treatment strategies. Declarations Data Availability Statement The data that support the findings of this study are available from the National Health and Nutrition Examination Survey (NHANES), which is publicly accessible through the Centers for Disease Control and Prevention (CDC). These data can be accessed at https://www.cdc.gov/nchs/nhanes/. Author contributions B.Y. and H.X. designed the research. H.X., Z.L., B.Y., and Z.X. collected, analyzed the data, and drafted the manuscript. H.X., Z.L., B.Y., and Z.X. revised the manuscript. All authors contributed to the article and approved the submitted version. Competing interests The authors declare no competing interests. References World Health Organization. (2021). Cardiovascular diseases (CVDs). WHO. Cheang, I., Zhu, X., Lu, X., Shi, S., Tang, Y., Yue, X., ... & Li, X. (2022). Association of remnant cholesterol and non-high density lipoprotein cholesterol with risk of cardiovascular mortality among US general population. Heliyon, 8(8). Guan, C. L., Liu, H. T., Chen, D. H., Quan, X. Q., Gao, W. L., & Duan, X. Y. (2022). Is elevated triglyceride/high-density lipoprotein cholesterol ratio associated with poor prognosis of coronary heart disease? A meta-analysis of prospective studies. Medicine, 101(45), e31123. You, J., Wang, Z., Lu, G., & Chen, Z. (2020). Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and the risk of coronary artery disease. BioMed Research International, 2020. Huang, Y., Yan, M. Q., Zhou, D., Chen, C. L., & Feng, Y. Q. (2023). The U-shaped association of non-high-density lipoprotein cholesterol with all-cause and cardiovascular mortality in general adult population. Frontiers in Cardiovascular Medicine, 10, 1065750. Saito, I., Yamagishi, K., Kokubo, Y., Yatsuya, H., Iso, H., Sawada, N., ... & Tsugane, S. (2020). Non-high-density lipoprotein cholesterol and risk of stroke subtypes and coronary heart disease: the Japan public health center-based prospective (JPHC) study. Journal of atherosclerosis and thrombosis, 27(4), 363-374. Wang, A., Li, Y., Zhou, L., Liu, K., Li, S., Zong, C., ... & Wang, L. (2022). Non-HDL-C/HDL-C ratio is associated with carotid plaque stability in general population: a cross-sectional study. Frontiers in Neurology, 13, 875134. Yu, B., Li, M., Yu, Z., Zheng, T., Feng, X., Gao, A., ... & Gao, R. (2024). The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018. Saito, I., Yamagishi, K., Kokubo, Y., Yatsuya, H., Iso, H., Sawada, N., ... & Tsugane, S. (2020). Non-high-density lipoprotein cholesterol and risk of stroke subtypes and coronary heart disease: the Japan public health center-based prospective (JPHC) study. Journal of atherosclerosis and thrombosis, 27(4), 363-374. Sheng, G., Liu, D., Kuang, M., Zhong, Y., Zhang, S., & Zou, Y. (2022). Utility of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio in evaluating incident diabetes risk. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 1677-1686. Gao, P., Zhang, J., & Fan, X. (2022). NHHR: An Important Independent Risk Factor for Patients with STEMI. Reviews in Cardiovascular Medicine, 23(12), 398. Liu, J., Zhao, L., Zhang, Y., Wang, L., Feng, Q., Cui, J., ... & Chen, Y. (2024). A higher non‐HDL‐C/HDL‐C ratio was associated with an increased risk of progression of nonculprit coronary lesion in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Clinical Cardiology, 47(2), e24243. Table 1 and 4,5 Table 1 and 4,5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1.xlsx Table4.SubgroupAnalysisoftheAssociationBetweenNHHRandAllCauseMortality.xlsx Table5.SubgroupAnalysisoftheAssociationBetweenNHHRandCardiovascularMortalityRate.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4456196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309576536,"identity":"78abb304-98da-45e4-bfb7-d80c90afdd1c","order_by":0,"name":"Haibin Xu","email":"","orcid":"","institution":"First People's Hospital Affiliated to Huzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Xu","suffix":""},{"id":309576537,"identity":"49a76ff8-f21c-434d-9a35-b62524dda2c9","order_by":1,"name":"Zhou Liu","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Liu","suffix":""},{"id":309576538,"identity":"177472b2-0209-4f19-a79d-3c3cf247ede5","order_by":2,"name":"Baohong Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3PMQrCMBSA4RShXQJZE1rwCgGhKgQ8iMsrQiadXDu8A/QAFU/RGwQCTgXXuBW8gI5dxIKTU9NNMP/8Pt57hIRCPxibX59d8eKUJehJRE0WsotVJirjSWRNctHFWkkHviRF4EAtFad740iptqNkmRkjgVvKUn1ckYs+4BhZY4EActhy3uc8QjtOpNkRA2CpvLW+xOkIwWgqHfUkompnpEBFRTX8Aj6/sKRK+h75hiW2cY9SjZOvIzlMGf+QqSIUCoX+ozchw0Aa5EfMAAAAAABJRU5ErkJggg==","orcid":"","institution":"First People's Hospital Affiliated to Huzhou University","correspondingAuthor":true,"prefix":"","firstName":"Baohong","middleName":"","lastName":"Yao","suffix":""},{"id":309576539,"identity":"a3a8181f-d0eb-4fbc-8197-197114fba516","order_by":3,"name":"Ziqi Xu","email":"","orcid":"","institution":"The First People's Hospital of Lin'an District","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-05-21 16:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4456196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4456196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57954458,"identity":"ddfed4f5-f8ab-480c-9241-e5b9a7a4a430","added_by":"auto","created_at":"2024-06-07 23:16:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":460751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of study sample selection form NHANES\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/8922665c8a56402bfda23da5.png"},{"id":57954890,"identity":"144d5055-1931-4b63-82f7-b84b4c46394c","added_by":"auto","created_at":"2024-06-07 23:24:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of NHHR on All-Cause Mortality Risk: A Restricted Cubic Spline Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/c5d0c92bcd915075612637b4.png"},{"id":57954462,"identity":"5ebd614f-1c14-4b19-9535-2cd4756b257e","added_by":"auto","created_at":"2024-06-07 23:16:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of NHHR on CVD Mortality Risk: A Restricted Cubic Spline Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/f35c948e48dff3da174202bc.png"},{"id":58107746,"identity":"49bc7fe1-dfdc-4542-b48a-206c803e4bc7","added_by":"auto","created_at":"2024-06-11 08:18:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/5c78e8f0-f4cc-443f-ae41-e13f70f96c68.pdf"},{"id":57955370,"identity":"adb48fc6-55e9-4429-ba8e-876a4e36701e","added_by":"auto","created_at":"2024-06-07 23:32:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13066,"visible":true,"origin":"","legend":"","description":"","filename":"table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/770c6496d3bf282adb12ca4b.xlsx"},{"id":57954463,"identity":"3c54005e-e898-4ca1-8295-c138b72640de","added_by":"auto","created_at":"2024-06-07 23:16:04","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12275,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.SubgroupAnalysisoftheAssociationBetweenNHHRandAllCauseMortality.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/8bfc149f93edef30eced4249.xlsx"},{"id":57954459,"identity":"7198f16d-d144-4754-b8f5-bf3c01a4e3d8","added_by":"auto","created_at":"2024-06-07 23:16:03","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12344,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.SubgroupAnalysisoftheAssociationBetweenNHHRandCardiovascularMortalityRate.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4456196/v1/e6b5ee759687c41191893971.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Non-High-Density to High-Density Lipoprotein Ratio for Coronary Heart Disease and Mortality in Adults","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCoronary heart disease (CHD) is caused by atherosclerosis of the coronary arteries, characterized primarily by the narrowing or blockage of these arteries, which leads to insufficient blood supply to the myocardium. This insufficient supply can cause chest pain (angina), myocardial infarction, and even sudden cardiac arrest. CHD is one of the leading causes of death globally, significantly impacting public health. According to the World Health Organization, approximately 17 million people die from cardiovascular diseases each year, with CHD accounting for a substantial portion\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent years, with changes in lifestyle (such as unhealthy diets, lack of physical activity, smoking, etc.) and an aging population, the incidence of CHD has been on the rise, posing an increasing threat to public health.\u003c/p\u003e\n\u003cp\u003eNon-HDL-C(non-high-density lipoprotein cholesterol) includes all atherogenic lipoproteins, namely low-density lipoprotein cholesterol (LDL-C), very-low-density lipoprotein cholesterol (VLDL-C), and intermediate-density lipoprotein cholesterol (IDL-C). LDL-C is often referred to as \u0026quot;bad\u0026quot; cholesterol because it deposits in the arterial walls, leading to atherosclerosis. VLDL-C and IDL-C are also considered contributors to atherosclerosis. On the other hand, HDL-C is known as \u0026quot;good\u0026quot; cholesterol because it helps transport cholesterol from the arterial walls to the liver for metabolism and excretion, thereby slowing the process of atherosclerosis.Studies have shown that higher levels of Non-HDL-C are significantly associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Non-HDL-C, as the sum of all atherogenic cholesterols, is considered a better predictor than LDL-C alone. Higher levels of HDL-C are associated with a reduced risk of cardiovascular diseases. HDL-C protects through a reverse cholesterol transport mechanism, helping to reduce cholesterol accumulation in the arterial walls\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe NHHR is an indicator that comprehensively reflects the status of lipoprotein metabolism, providing a more comprehensive assessment of cardiovascular disease risk. Recent studies have shown that NHHR is a better predictor of CHD incidence and mortality than either Non-HDL-C or HDL-C alone. For instance, a large epidemiological study found that higher NHHR was significantly associated with an increased incidence of CHD, and this association remained even after adjusting for traditional risk factors such as age, gender, smoking, and blood pressure \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.Moreover, NHHR has been used to assess the risk of all-cause mortality and cardiovascular mortality. Some studies have found that higher NHHR is associated not only with an increased risk of CHD but also with a significant association with cardiovascular-related mortality\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These findings suggest that NHHR has important clinical applications and can serve as a powerful tool for identifying high-risk populations, thereby guiding personalized prevention and treatment strategies.In conclusion, NHHR as a new predictor of CHD and mortality risk has significant clinical implications. It can provide a more comprehensive assessment of cardiovascular disease risk and help clinicians identify high-risk patients early, allowing for appropriate interventions to reduce the incidence of cardiovascular events and mortality.\u003c/p\u003e\n\u003cp\u003eThis study aims to explore the relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and the incidence of coronary heart disease (CHD) in adults, providing a comprehensive understanding of how this ratio impacts CHD risk. The novelty of this research lies in its focus on NHHR, a relatively underexplored yet promising indicator that may offer superior predictive value compared to traditional lipid measures. In addition to examining CHD incidence, the study also evaluates the predictive value of NHHR for all-cause mortality and cardiovascular mortality. Despite numerous studies on individual lipid components, the combined effect as reflected by NHHR remains less understood. Addressing this gap, our research highlights the potential of NHHR as a robust marker for mortality risk, providing critical insights that could enhance risk stratification and guide clinical decision-making in the management of cardiovascular health.\u003c/p\u003e\n"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data collection\u003c/h2\u003eThe National Health and Nutrition Examination Survey (NHANES) is a comprehensive program designed to assess the health and nutritional status of adults and children in the United States. It uniquely combines interviews with physical examinations, covering demographic, socioeconomic, dietary, and health-related topics. The survey uses cross-sectional data to represent the entire U.S. population, ensuring broad applicability and relevance. This research solely utilizes publicly available NHANES data, eliminating the need for additional ethical approval. For more details on ethical oversight, refer to the NCHS Ethics Review Board (ERB) Approval:NCHS ERB Approval at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/irba98.htm).Th\u003c/span\u003e\u003c/span\u003ee data can be freely accessed through the CDC\u0026apos;s NHANES website:NHANES Data Access at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/Default.aspx\u003c/span\u003e\u003c/span\u003e.\u003cp\u003eIn this study, to clarify the relationship between NHHR and the incidence and mortality of coronary heart disease (CHD), the dataset was collected from NHANES during six cycles (2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, and 2015\u0026ndash;2016). Initially, a total of 60,936 participants were identified across these six cycles. Through a methodological screening process, certain demographic characteristics were excluded as follows: individuals under 18 years old (24,649), pregnant women (684), individuals lacking total cholesterol (TC) or high-density lipoprotein cholesterol (HDL) data (3,449), those who did not provide CHD information (1,953), and those lacking other covariate data. After a rigorous screening process, 27,091 participants met the inclusion criteria (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Exposure measurement methods\u003c/h2\u003e\n \u003cp\u003eThe focal exposure variable of this study is the NHHR, which is the independent variable in the exposure assessment. It represents the ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. To derive non-HDL-C, HDL-C was subtracted from total cholesterol (TC), and the lipid profiles of fasting individuals were analyzed. An automated biochemical analyzer performed enzymatic tests to assess TC and HDL-C levels. For the determination of TC concentration, the study used the Roche Cobas 6000 and Roche Modular P chemistry analyzers in the analytical procedures.\u003c/p\u003e\n \u003ch2\u003e2.3 Outcome variable selections\u003c/h2\u003e\n \u003cp\u003eThe outcomes selected for this study are coronary heart disease (CHD) and mortality. CHD prevalence was assessed using an online questionnaire. Trained interviewers asked participants, \u0026quot;Has a doctor or other health professional ever told you that you have coronary heart disease?\u0026quot; Participants responded with a yes or no answer. If they could not respond or did not know, they were considered missing data. As of December 31, 2019, we used the NHANES Public-use Linked Mortality File (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/span\u003e\u003c/span\u003e) and linked it to the National Death Index (NDI) data using a probabilistic matching algorithm to determine mortality status. Mortality outcomes were determined according to the International Classification of Diseases, 10th Revision (ICD-10). In ICD-10, various codes specify cardiovascular deaths, such as congestive heart failure (I50), stroke or cerebrovascular accident (CVA), and others. The follow-up period for each participant was calculated from the date of the baseline examination to the date of the last known survival status or the date of removal from the mortality file due to death.\u003c/p\u003e\n \u003ch2\u003e2.4 Covariates extraction\u003c/h2\u003eCovariates that could potentially influence the relationship between NHHR and coronary heart disease (CHD) and mortality were collected through interviews and medical examinations. These included sociodemographic and lifestyle characteristics such as age, gender, race/ethnicity, educational level, marital status, smoking status, drinking status, body mass index (BMI), hypertension, diabetes, total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C). Demographic data on gender (male, female), age (in years), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Races), marital status (Married/Living with partner, Widowed/Divorced/Separated, Never married), educational level (less than high school, high school, more than high school), smoking status (every day, some days, not at all), and drinking status (heavy drinking, moderate drinking) were obtained from interviews. Participants were asked questions such as, \u0026quot;Has a doctor or other health professional ever told you that you have high blood pressure, also called hypertension?\u0026quot;, \u0026quot;Has a doctor or health professional ever told you that you have diabetes or sugar diabetes?\u0026quot;, \u0026quot;In your lifetime, have you had at least 5 drinks of any alcoholic beverage almost every day for 2 weeks or more?\u0026quot;, and \u0026quot;Do you now smoke cigarettes?\u0026quot;. Participants responded with a \u0026quot;yes\u0026quot; or \u0026quot;no\u0026quot; answer, and if they refused to respond or were unable to provide an answer, their data were classified as missing. BMI was determined by considering participants\u0026apos; height and weight and categorized into three ranges: 0\u0026ndash;25 kg/m\u0026sup2; (normal), 25\u0026ndash;30 kg/m\u0026sup2; (overweight), and \u0026gt;\u0026thinsp;30 kg/m\u0026sup2; (obese). TC and HDL-C levels were measured using an automated biochemical analyzer, with total cholesterol (TC) and HDL-C levels determined using enzymatic methods with the Beckman Synchron LX20. Detailed measurement methods for these variables are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003c/span\u003e.\n\u003c/div\u003e"},{"header":"3. DATA ANALYSIS","content":"\u003cp\u003eStatistical analyses were conducted using R software (version 4.3.0). Following NHANES guidelines, appropriate weighting procedures were applied to obtain accurate estimates representative of the U.S. population within the complex survey design. The weights for 2005\u0026ndash;2016 were calculated by sampling a portion of each participant from the 2005\u0026ndash;2016 data set. Continuous variables are presented as means and standard errors (SE), while categorical variables are expressed as frequencies and percentages. To compare different variables, t-tests and Mann-Whitney U tests were used for continuous variables, and chi-square tests were employed for categorical variables. Weighted multivariable logistic regression was used to estimate the odds ratios (OR) and 95% confidence intervals (CI) of NHHR in relation to the prevalence of coronary heart disease (CHD). Three models were applied: Model 1 was unadjusted; Model 2 was adjusted for age, gender, race, and educational level; Model 3 was further adjusted for BMI, smoking status, drinking status, and diabetes. Additionally, NHHR was analyzed in quartiles, with the lowest quartile (Q1) serving as the reference category, to examine its association with the prevalence of coronary heart disease (CHD). To estimate the hazard ratios (HR) for all-cause and cardiovascular disease mortality, Cox proportional hazards regression models were used, assessing the impact of NHHR on the time to event (death), accounting for risk review and time dependency. These models included three levels of adjustment: Model 1 was unadjusted; Model 2 was adjusted for demographic variables; and Model 3 included further adjustments for clinical and lifestyle factors. For mortality analysis, nonlinear relationship detection using restricted cubic splines (RCS) curves was included to explore potential nonlinear associations between NHHR and mortality outcomes. Additionally, subgroup analyses were performed to investigate the consistency of associations across various subpopulations. All analyses were utilized in a cross-sectional analysis for this study, with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\n"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Characteristics of the Study Population\u003c/h2\u003eThe table presents the baseline characteristics of participants categorized by the presence or absence of coronary heart disease (CHD). It reveals that lower NHHR is associated with the CHD group compared to the non-CHD group (P\u0026thinsp;=\u0026thinsp;0.01). Total cholesterol (TC) is significantly lower in the CHD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while no significant difference in HDL levels was observed (P\u0026thinsp;=\u0026thinsp;0.75). Older age is significantly associated with CHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with a higher prevalence among those aged\u0026thinsp;\u0026ge;\u0026thinsp;60. There are significant differences across racial/ethnic groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with a higher prevalence of CHD among Non-Hispanic Whites. Males show a higher prevalence of CHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Lower education levels are associated with a higher prevalence of CHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Diabetes, smoking, and heavy alcohol use are significantly associated with CHD, with higher prevalence among former and current smokers and heavy alcohol users (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for all). A history of stroke, angina, congestive heart failure, and heart attack are all significantly associated with CHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Lower NHHR quartiles are linked to a higher prevalence of CHD (P\u0026thinsp;=\u0026thinsp;0.03). Participants with cardiovascular disease (CVD) and hypertension also show a significantly higher prevalence of CHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)(see table 1).\u003cp\u003e\u003cstrong\u003eTable 1.The general characteristics of the population in NHANESE\u003c/strong\u003e\u003c/p\u003e\n \u003ch2\u003e4.2 Association Between NHHR and the Prevalence of Coronary Heart Disease (CHD) in Adults.\u003c/h2\u003e\n \u003cp\u003eThe analysis of Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates a consistent inverse association between NHHR quartiles and the prevalence of coronary heart disease (CHD) in adults. In the unadjusted model (Model 1), there is a trend towards lower CHD prevalence with higher NHHR quartiles, with the strongest association observed in the highest quartile (Q4, B = -0.32, 95% CI: -0.60, 0.88, P\u0026thinsp;=\u0026thinsp;0.001) and a significant P for trend (P\u0026thinsp;=\u0026thinsp;0.002). After adjusting for age, sex, ethnicity, education level, and BMI in Model 2, the association becomes more pronounced and statistically significant across all quartiles (Q2: B = -0.40, 95% CI: -0.54, -0.84, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Q3: B = -0.69, 95% CI: -0.39, -0.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4: B = -1.00, 95% CI: -0.29, -0.46, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) with a highly significant P for trend (\u0026lt;\u0026thinsp;0.0001). Further adjustment for smoking, drinking status, hypertension, and diabetes in Model 3 maintains the significant inverse association, particularly in Q4 (B = -0.93, 95% CI: -0.31, -0.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that higher NHHR quartiles are associated with a lower prevalence of CHD. The consistent P for trend values across all models underscore the robustness of this inverse relationship(see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1717576577.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e4.3 Establish Cox proportional hazard model\u003cp\u003eThe analysis of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates a consistent inverse association between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) quartiles and both all-cause and cardiovascular disease (CVD) mortality among adults during the follow-up period. In the unadjusted model (Model 1), higher NHHR quartiles (Q3 and Q4) are significantly associated with lower all-cause mortality (Q3: HR 0.79, 95% CI: 0.69, 0.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Q4: HR 0.77, 95% CI: 0.68, 0.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and lower CVD mortality (Q3: HR 0.72, 95% CI: 0.58, 0.89, P\u0026thinsp;=\u0026thinsp;0.002; Q4: HR 0.75, 95% CI: 0.61, 0.93, P\u0026thinsp;=\u0026thinsp;0.01). After adjusting for age, sex, ethnicity, education level, and BMI in Model 2, the inverse associations remain significant and become stronger (all-cause mortality Q3: HR 0.69, 95% CI: 0.59, 0.80, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4: HR 0.62, 95% CI: 0.54, 0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; CVD mortality Q3: HR 0.61, 95% CI: 0.48, 0.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4: HR 0.59, 95% CI: 0.46, 0.76, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Further adjustments for smoking, drinking status, hypertension, and diabetes in Model 3 continue to show significant inverse associations (all-cause mortality Q3: HR 0.66, 95% CI: 0.57, 0.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4: HR 0.61, 95% CI: 0.53, 0.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; CVD mortality Q3: HR 0.58, 95% CI: 0.46, 0.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Q4: HR 0.58, 95% CI: 0.45, 0.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that higher NHHR is protective against both all-cause and CVD mortality. The significant P for trend values in all models underscore the robustness of this inverse relationship(see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMortality Outcomes by Dietary Copper Intake (Quartiles) Among Participants During the Follow-up Period\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003eThe quartile of NHHR\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eQ1(0.205\u0026ndash;1.755]\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eQ2(1.755\u0026ndash;2.396]\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eQ3(2.396\u0026ndash;3.333]\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eQ4(3.333,25.813]\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eP for trend\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAll-Cause Mortality\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNumber of deaths\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e654\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e726\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e825\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e946\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 1\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.90(0.78,1.03)\u003cbr\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.79(0.69,0.91)\u003cbr\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.77(0.68,0.87)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 2\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84(0.74,0.97)\u003cbr\u003e0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.69(0.59,0.80)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.62(0.54,0.72)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 3\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.81(0.70,0.95)\u003cbr\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.66(0.57,0.77)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.61(0.53,0.70)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCVD Mortality\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNumber of deaths\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e227\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e227\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e278\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e321\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 1\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.86(0.70,1.06)\u003cbr\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.72(0.58,0.89)\u003cbr\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.75(0.61,0.93)\u003cbr\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 2\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.80(0.65,0.99)\u003cbr\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.61(0.48,0.78)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.59(0.46,0.76)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eModel 3\u003cbr\u003eh(95% CI)P Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.77(0.61,0.95)\u003cbr\u003e0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.58(0.46,0.73)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.58(0.45,0.75)\u003cbr\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.0001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Nonlinear Relationship Detection\u003c/h2\u003e\n \u003cp\u003eThe analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and all-cause mortality, modeled using restricted cubic splines (RCS), reveals a non-linear association. The RCS curve demonstrates an initial steep decline in the hazard ratio with increasing NHHR levels, suggesting a strong protective effect against all-cause mortality. This protective effect is most pronounced at an NHHR value of approximately 3, where the log hazard ratio reaches its lowest point. Beyond this value, the hazard ratio gradually increases, indicating a diminishing protective effect and a potential rise in mortality risk at very high NHHR levels. The optimal number of knots for the RCS model was determined to be 3, providing the best fit with the lowest AIC value. The non-linearity test was significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming that the non-linear model fits the data better than a linear model. These findings highlight the complex relationship between NHHR and mortality, emphasizing the importance of considering non-linear associations in evaluating lipid ratios and their impact on health outcomes(see firgure 2).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFirgure 2.Impact of NHHR on All-Cause Mortality Risk: A Restricted Cubic Spline Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and cardiovascular mortality was assessed using restricted cubic splines (RCS) to model the non-linear associations. The RCS curve demonstrates a non-linear relationship, showing a sharp decline in the hazard ratio as NHHR increases from very low levels, indicating a protective effect against cardiovascular mortality. The log hazard ratio stabilizes as NHHR continues to increase, suggesting that higher NHHR levels do not significantly increase mortality risk beyond a certain point. The optimal number of knots for the RCS model was determined to be 3, providing the best fit with the lowest AIC value of 12818.72. The non-linearity test was significant (P\u0026thinsp;=\u0026thinsp;0.0004), confirming that the non-linear model fits the data better than a linear model. These findings highlight that while higher NHHR levels are initially protective against cardiovascular mortality, the risk does not significantly change beyond an NHHR value of approximately 3, emphasizing the importance of considering non-linear relationships in evaluating lipid ratios and their impact on cardiovascular health outcomes(see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e4.5 subgroup analysis\u003cbr\u003e\n \u003cp\u003eThe subgroup analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and all-cause mortality reveals significant variations across different demographic and clinical subgroups. The protective effect of higher NHHR is notably stronger in males (HR -0.29, 95% CI: -0.40, -0.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to females (HR 0.11, 95% CI: 0.00, 0.23, P\u0026thinsp;=\u0026thinsp;0.05), and in older adults (\u0026ge;\u0026thinsp;60 years) (HR -0.79, 95% CI: -1.51, -0.06, P\u0026thinsp;=\u0026thinsp;0.04). Additionally, individuals with impaired glucose tolerance (IGT) (HR -0.41, 95% CI: -0.60, -0.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and those who are overweight (HR -0.21, 95% CI: -0.32, -0.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) show significant protective effects. Furthermore, the analysis indicates stronger protective effects in individuals with a history of angina, congestive heart failure, heart attack, or cardiovascular disease (CVD). These findings suggest that the impact of NHHR on mortality risk is influenced by specific population characteristics, emphasizing the need for targeted interventions in these high-risk groups(see table 4).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;4. Subgroup Analysis of the Association Between NHHR and All-Cause Mortality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe subgroup analysis of the relationship between NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio) levels and cardiovascular mortality reveals significant variations across different demographic and clinical factors. The protective effect of higher NHHR is notably stronger in males (HR -0.35, 95% CI: -0.51, -0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to females (HR 0.11, 95% CI: -0.04, 0.26, P\u0026thinsp;=\u0026thinsp;0.14). There is no significant difference by ethnicity, but individuals with a high school education (HR -0.32, 95% CI: -0.48, -0.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) show significant protective effects. Those with impaired glucose tolerance (IGT) (HR -0.52, 95% CI: -0.80, -0.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and impaired fasting glucose (IFG) (HR -0.51, 95% CI: -0.89, -0.14, P\u0026thinsp;=\u0026thinsp;0.01) also exhibit stronger protective effects. Among alcohol users, former drinkers (HR -0.23, 95% CI: -0.41, -0.06, P\u0026thinsp;=\u0026thinsp;0.01) and mild drinkers (HR -0.18, 95% CI: -0.35, -0.01, P\u0026thinsp;=\u0026thinsp;0.04) benefit more significantly. The protective effect of NHHR is more pronounced in younger adults (\u0026lt;\u0026thinsp;60 years) (HR -0.11, 95% CI: -0.21, 0.00, P\u0026thinsp;=\u0026thinsp;0.04). Overweight (HR -0.29, 95% CI: -0.45, -0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and obese individuals (HR -0.19, 95% CI: -0.35, -0.04, P\u0026thinsp;=\u0026thinsp;0.02) also show significant protective effects. Furthermore, significant protective effects are observed in individuals with a history of angina (HR -0.50, 95% CI: -0.83, -0.17, P\u0026thinsp;=\u0026thinsp;0.003) and congestive heart failure (HR -0.36, 95% CI: -0.63, -0.09, P\u0026thinsp;=\u0026thinsp;0.01). Those with hypertension also benefit significantly from higher NHHR levels (HR -0.27, 95% CI: -0.40, -0.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings suggest that the impact of NHHR on cardiovascular mortality risk is influenced by specific population characteristics, emphasizing the need for targeted interventions in these high-risk groups(see Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eThe findings of this study underscore the significance of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of coronary heart disease (CHD) incidence and mortality in adults. Our results align with recent literature, which highlights NHHR as a valuable marker for cardiovascular risk assessment and offers several noteworthy contributions to the field.\u003c/p\u003e\n\u003cp\u003eOur study confirms that higher NHHR levels are significantly associated with a lower prevalence of CHD and reduced risk of all-cause and cardiovascular mortality. This relationship persists even after adjusting for various demographic, clinical, and lifestyle factors, demonstrating NHHR\u0026apos;s robustness as a predictive marker. This finding is consistent with the study by Yu et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, which found that higher NHHR levels were linked to lower all-cause and cardiovascular mortality in a cohort of US adults with diabetes or prediabetes.\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline analysis revealed a non-linear association between NHHR and mortality, indicating that the relationship is more complex than a simple linear trend. The significant P-value for non-linearity underscores the importance of considering non-linear effects in clinical assessments. This complexity suggests that NHHR\u0026apos;s protective effects may plateau or even diminish at very high levels, highlighting the need for careful evaluation in clinical settings. Sheng et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003ealso reported a non-linear relationship between NHHR and diabetes risk, further emphasizing the importance of non-linear models in predicting health outcomes.\u003c/p\u003e\n\u003cp\u003eThe protective effect of higher NHHR was notably stronger in specific subgroups, including males, older adults, individuals with impaired glucose tolerance, and those with a history of cardiovascular conditions. These groups exhibited a stronger inverse relationship between NHHR and mortality risk, suggesting that NHHR could be particularly useful for identifying high-risk individuals who may benefit from more intensive lipid management and lifestyle interventions. Prior research by Saito et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003esupports these findings, indicating sex-specific differences in lipid profiles and cardiovascular outcomes.\u003c/p\u003e\n\u003cp\u003eOur study highlights NHHR\u0026apos;s potential utility in clinical practice. By integrating NHHR into routine cardiovascular risk assessments, healthcare providers could improve risk stratification and develop more personalized prevention and treatment strategies. This approach could be especially beneficial for managing patients with comorbidities, providing a more nuanced understanding of their cardiovascular risk and guiding targeted interventions. Gao et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003edemonstrated that NHHR is an effective predictor of coronary artery disease severity, further supporting its clinical relevance.\u003c/p\u003e\n\u003cp\u003eThis study adds to the growing body of evidence supporting the use of composite lipid measures like NHHR in cardiovascular risk assessment. The consistent associations observed across various subgroups and the detailed analysis of non-linear relationships contribute to a deeper understanding of how NHHR impacts cardiovascular health. Liu et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e found that a higher NHHR was associated with increased risk of progression of non-culprit coronary lesions in patients with acute coronary syndrome, reinforcing the significance of NHHR in cardiovascular research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the strengths of this study, there are several limitations to consider. The observational nature of the study precludes establishing causality. Additionally, the study population is limited to adults in the United States, which may affect the generalizability of the findings to other populations. Future research should include diverse populations and consider longitudinal designs to better understand the causal relationships.\u003c/p\u003e\n"},{"header":"6. CONCLUSIONS","content":"\u003cp\u003eThis study In conclusion, this study reinforces NHHR\u0026apos;s relevance as a predictor of CHD and mortality. The robust associations, significant subgroup insights, and the non-linear relationship with mortality underscore NHHR\u0026apos;s potential utility in clinical practice. Integrating NHHR into routine cardiovascular risk assessments could enhance the identification of high-risk individuals and support more personalized prevention and treatment strategies.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the National Health and Nutrition Examination Survey (NHANES), which is publicly accessible through the Centers for Disease Control and Prevention (CDC). These data can be accessed at https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.Y. and H.X. designed the research. H.X., Z.L., B.Y., and Z.X. collected, analyzed the data, and drafted the manuscript. H.X., Z.L., B.Y., and Z.X. revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. (2021). Cardiovascular diseases (CVDs). WHO. \u003c/li\u003e\n\u003cli\u003eCheang, I., Zhu, X., Lu, X., Shi, S., Tang, Y., Yue, X., ... \u0026amp; Li, X. (2022). Association of remnant cholesterol and non-high density lipoprotein cholesterol with risk of cardiovascular mortality among US general population. Heliyon, 8(8).\u003c/li\u003e\n\u003cli\u003eGuan, C. L., Liu, H. T., Chen, D. H., Quan, X. Q., Gao, W. L., \u0026amp; Duan, X. Y. (2022). Is elevated triglyceride/high-density lipoprotein cholesterol ratio associated with poor prognosis of coronary heart disease? A meta-analysis of prospective studies. Medicine, 101(45), e31123.\u003c/li\u003e\n\u003cli\u003eYou, J., Wang, Z., Lu, G., \u0026amp; Chen, Z. (2020). Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and the risk of coronary artery disease. BioMed Research International, 2020.\u003c/li\u003e\n\u003cli\u003eHuang, Y., Yan, M. Q., Zhou, D., Chen, C. L., \u0026amp; Feng, Y. Q. (2023). The U-shaped association of non-high-density lipoprotein cholesterol with all-cause and cardiovascular mortality in general adult population. Frontiers in Cardiovascular Medicine, 10, 1065750.\u003c/li\u003e\n\u003cli\u003eSaito, I., Yamagishi, K., Kokubo, Y., Yatsuya, H., Iso, H., Sawada, N., ... \u0026amp; Tsugane, S. (2020). Non-high-density lipoprotein cholesterol and risk of stroke subtypes and coronary heart disease: the Japan public health center-based prospective (JPHC) study. Journal of atherosclerosis and thrombosis, 27(4), 363-374.\u003c/li\u003e\n\u003cli\u003eWang, A., Li, Y., Zhou, L., Liu, K., Li, S., Zong, C., ... \u0026amp; Wang, L. (2022). Non-HDL-C/HDL-C ratio is associated with carotid plaque stability in general population: a cross-sectional study. Frontiers in Neurology, 13, 875134.\u003c/li\u003e\n\u003cli\u003eYu, B., Li, M., Yu, Z., Zheng, T., Feng, X., Gao, A., ... \u0026amp; Gao, R. (2024). The Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (NHHR) as a Predictor of All-Cause and Cardiovascular Mortality in US Adults with Diabetes or Prediabetes: NHANES 1998-2018.\u003c/li\u003e\n\u003cli\u003eSaito, I., Yamagishi, K., Kokubo, Y., Yatsuya, H., Iso, H., Sawada, N., ... \u0026amp; Tsugane, S. (2020). Non-high-density lipoprotein cholesterol and risk of stroke subtypes and coronary heart disease: the Japan public health center-based prospective (JPHC) study. Journal of atherosclerosis and thrombosis, 27(4), 363-374.\u003c/li\u003e\n\u003cli\u003eSheng, G., Liu, D., Kuang, M., Zhong, Y., Zhang, S., \u0026amp; Zou, Y. (2022). Utility of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio in evaluating incident diabetes risk. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 1677-1686.\u003c/li\u003e\n\u003cli\u003eGao, P., Zhang, J., \u0026amp; Fan, X. (2022). NHHR: An Important Independent Risk Factor for Patients with STEMI. Reviews in Cardiovascular Medicine, 23(12), 398.\u003c/li\u003e\n\u003cli\u003eLiu, J., Zhao, L., Zhang, Y., Wang, L., Feng, Q., Cui, J., ... \u0026amp; Chen, Y. (2024). A higher non‐HDL‐C/HDL‐C ratio was associated with an increased risk of progression of nonculprit coronary lesion in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Clinical Cardiology, 47(2), e24243.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1 and 4,5","content":"\u003cp\u003eTable 1 and 4,5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NHHR, Coronary Heart Disease, Cardiovascular Mortality","lastPublishedDoi":"10.21203/rs.3.rs-4456196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4456196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary heart disease (CHD) significantly impacts public health worldwide. Non-high-density lipoprotein cholesterol (non-HDL-C) and high-density lipoprotein cholesterol (HDL-C) are vital in lipid metabolism. The ratio of non-HDL-C to HDL-C (NHHR) may predict CHD and mortality.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study investigates the relationship between NHHR and CHD incidence and its predictive value for all-cause and cardiovascular mortality in adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were collected from NHANES (2005\u0026ndash;2016), including participants aged 18\u0026ndash;80 years. NHHR was calculated by dividing non-HDL-C by HDL-C. Multivariable logistic regression and Cox proportional hazards models assessed associations between NHHR, CHD prevalence, and mortality outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher NHHR levels were significantly associated with lower CHD prevalence in all adjusted models. The highest NHHR quartile showed the strongest inverse association with CHD prevalence (Model 3: Q4, OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.31\u0026ndash;0.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Higher NHHR quartiles also correlated with reduced all-cause and cardiovascular mortality risks. The restricted cubic spline analysis indicated a non-linear relationship, with the most significant protective effect at an NHHR value of approximately 3.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNHHR is a robust predictor of CHD and mortality. Incorporating NHHR into cardiovascular risk assessments could better identify high-risk individuals, guiding personalized prevention and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Non-High-Density to High-Density Lipoprotein Ratio for Coronary Heart Disease and Mortality in Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:15:59","doi":"10.21203/rs.3.rs-4456196/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4778f9d-9568-40dc-a6c1-acde939a1401","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T12:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 23:15:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4456196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4456196","identity":"rs-4456196","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}