Association between monocyte-to-high-density lipoprotein-cholesterol ratio and chronic obstruc tive pulmonary disease findings from the National Health and Nutrition Examination Survey 2001–2023

Association between monocyte-to-high-density lipoprotein-cholesterol ratio and chronic obstruc tive pulmonary disease findings from the National Health and Nutrition Examination Survey 2001–2023 | 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 between monocyte-to-high-density lipoprotein-cholesterol ratio and chronic obstruc tive pulmonary disease findings from the National Health and Nutrition Examination Survey 2001–2023 Li Chen, Mi Yu, Ximei Ni, Yimin Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6640631/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: Chronic obstructive pulmonary disease (COPD) is a global public health challenge, with its pathogenesis driven by complex inflammatory pathways. This study aimed to investigate the potential association between MHR and COPD risk while evaluating its utility as a predictive marker for COPD progression. Methods: This study utilized data from the National Health and Nutrition Survey (NHANES) spanning from 2001 to 2023. Control for con-founders by the propensity score matching (PSM) method. The relationship between monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and chronic obstructive pulmonary disease (COPD) was analyzed using multivariate logistic regression models. A restricted cubic spline（RCS）model was applied to explore the dose-response relationship between MHR and COPD risk. Subgroup analyses revealed significant heterogeneity in the associations across different predefined groups, indicating varying degrees of correlation between MHR and COPD depending on specific subgroup characteristics. To evaluate the relative contribution of MHR as a predictor of COPD risk, an XGBoost model was constructed to assess its predictive importance. Results: The cross-sectional analysis included a total of 15,047 participants, among whom 582 were diagnosed with chronic obstructive pulmonary disease (COPD). The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) of COPD patients was significantly higher compared to non-COPD participants (0.58 ± 0.38 vs. 0.49 ± 0.25, P < 0.001). Multivariate logistic regression analysis revealed that MHR was an independent risk factor for COPD diagnosis (odds ratio = 2.134; 95% confidence interval: 1.115–4.378; P = 0.0327). The restricted cubic spline (RCS) model demonstrated a significant linear trend in the association between MHR and COPD risk (P for overall trend < 0.001), as well as a similarly significant nonlinear relationship (P for non-linear term = 0.02). Furthermore, the XGBoost model identified MHR as an important predictor of COPD diagnosis, with an average SHAP score of 0.0174. Conclusions: There was an association between MHR and COPD, indicating its potential utility as a clinical marker in assessing disease risk. Monocyte-to-high-density lipoprotein-cholesterol ratio Chronic obstructive pulmonary disease NHANES Limiting cubic spline Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Chronic Obstructive Pulmonary Disease (COPD) represents a diverse lung disorder. It is distinguished by chronic airflow limitation caused by abnormalities in the airways or alveoli. This condition gives rise to respiratory manifestations including dyspnea, cough, and expectoration [ 1 ] . Currently, Chronic Obstructive Pulmonary Disease (COPD) ranks as the third foremost cause of global mortality, simultaneously placing substantial economic strains. In the United States, COPD-related healthcare costs are projected to rise to $ 800.9 billion over the next 20 years, or approximately $ 40 billion annually [ 2 ] . Additionally, the heterogeneity and complex pathophysiology of COPD present substantial challenges in diagnosis and prognosis [ 3 ] . The Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) has recently introduced a "treatable feature" (TTSs), which aims to identify patients with COPD through biomarkers [ 4 ] . The discovery of an easily measurable and reliable biomarker will be invaluable in diagnosing COPD patients. Currently available biomarkers such as peripheral blood eosinophil count and CRP can guide the pharmacological treatment of COPD. While MDA and SOD in peripheral blood, IL-6 and MPO in sputum, and GSH in bronchoalveolar lavage fluid have been extensively studied, they have not yet been fully validated [ 5 ] . Previous studies have shown a close relationship between COPD and inflammation; however, research on the correlation between inflammatory markers and COPD is still insufficient. Therefore, exploring the relationship between MHR and COPD can further enrich the biomarkers for COPD. The pathogenesis of Chronic Obstructive Pulmonary Disease (COPD) is intricate, with the participation of numerous inflammatory mediators and cytokines [ 6 ] . The monocyte - to - HDL - cholesterol ratio (MHR), a newly - established composite, has been employed as a potential clinical indicator for atherosclerosis coronary heart disease, and other diseases due to its simple availability and clinical relevance [ 7 , 8 ] . High-density lipoprotein cholesterol (HDL-C) acts as an anti-inflammatory factor by inhibiting macrophage migration [ 9 ] . Monocytes are inflammatory cells that can migrate to sites of inflammation and accelerate oxidative stress [ 10 ] . MHR is based on the anti-inflammatory properties of HDL-C and the pro-inflammatory properties of monocytes and may be a potential pathogenesis of COPD. Nevertheless, the connection between the monocyte - to - HDL - cholesterol ratio (MHR) and Chronic Obstructive Pulmonary Disease (COPD) remains ambiguous. Thus, a profound investigation into the association of MHR with COPD could offer precious perspectives for subsequent research. As far as we are aware, no population - based research has been carried out to assess the link between MHR levels and the development of COPD. A deep exploration of the intrinsic connection between MRH and chronic obstructive pulmonary disease (COPD) holds significant clinical and social value. This research not only helps uncover the pathogenesis of COPD and identify key stages in its development but also provides new directions for biomarker studies in COPD, uncovering more potential early diagnostic indicators. Through these findings, we can achieve precise early diagnosis of COPD, enabling timely and effective intervention measures. This can significantly improve patients' respiratory function, enhance their quality of life and long-term survival rates, while effectively reducing the medical burden on families and society, providing strong support for the development of public health. Consequently, we undertook this research by leveraging the data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2001 to 2023. Materials and methods Study population The National Health and Nutrition Examination Survey (NHANES), an ongoing cross - sectional survey with national representativeness in the United States, is a key project of the National Center for Health Statistics (NCHS) research. It has been sanctioned and funded by the Centers for Disease Control and Prevention (CDC). The primary objective of NHANES is to evaluate the health and nutritional condition of non - institutionalized U.S. civilian populations Data is amassed biannually via a sophisticated multi - tiered probability sampling technique. which includes face-to-face interviews at participants' homes followed by physical examinations at the National Center for Mobile Health (MEC), where blood and urine samples are collected. All data from this survey are publicly accessible [ 11 ] . For our study, we analyzed data from NHANES participants spanning from 2001 to 2023, including all individuals who tested positive for COPD. Initially, a sample of 103,284 participants was extracted; however, the following exclusions were applied: (1) samples lacking MHC data (N = 45,994), (2) samples lacking data on COPD (N = 42,095), and (3) individuals who did not fulfill the inclusion requirements for covariates including age, gender, and ethnic background. smoking history, BMI, PIR, diabetes, hypertension, weight, alcohol consumption, etc. (N = 148). Consequently, the final dataset included 15,047 participants, comprising 582 individuals with COPD and 14,465 individuals without COPD ( Fig. 1 ). Assessment of COPD COPD is defined as a positive response to any one of the following inquiries: (1) "Has a medical doctor or other healthcare professional ever informed you that you have emphysema?" (2) "Has a medical doctor or other healthcare professional ever informed you that you have COPD? ”[ 11 ] MHR calculations The monocyte - to - HDL - cholesterol ratio (MHR) was designated as the exposure variable. It was computed by dividing the monocyte count by the HDL - C level (expressed in mmol/L). Covariates definition A number of potential confounders, including ethnicity, household poverty-income ratio, diabetes mellitus, hypertension, HDL cholesterol, urea nitrogen, total protein, total bilirubin, calcium, alanine aminotransferase, lactate, albumin, sodium, and triglycerides, were analyzed. Ethnicity was determined through the Demographic Variable File (DEMO) categorizes racial groups into Mexican - American, non - Hispanic Black, non - Hispanic White, other Hispanic, and other races. This classification is based on data regarding household economic status. The household poverty - income ratio was grouped as low - income (less than 1.3), middle - income (ranging from 1.3 to 3.5), and high - income (greater than 3.5). Identification of diabetes mellitus and hypertension was achieved via a questionnaire - based survey. Blood biomarkers such as HDL cholesterol, urea nitrogen, total protein, total bilirubin, calcium, alanine aminotransferase, lactate, albumin, sodium, and triglycerides were measured using biochemical test methods. Statistical analyses Data processing and all analyses were executed with R software (version 4.1.1). For non - normally distributed continuous variables, baseline characteristics were depicted by means of the median and interquartile range (IQR). Categorical variables were shown as sample counts and weighted percentages. To evaluate the disparities in variable features across the MHR groups (quartiles), the Wilcoxon rank - sum test was utilized for continuous variables, while the Rao - Scott chi - square test was applied to the weighted percentages of categorical variables. thus providing a comprehensive profile of the entire population. When the proportion of missing values in the data was less than 20%, multiple imputation was employed for data filling, with the number of imputed datasets set to 5. The Extreme Gradient Boosting (XGBoost) model was utilized to evaluate the importance of each factor, identify the most crucial factors influencing COPD, and assist in pinpointing the most predictive variables. A two - tailed statistical analysis was conducted, and statistical significance was defined as a p - value of less than 0.05. Results Basic characteristics of the participants The study addressed missing data through multiple imputation, ensuring that the pre- and post-imputation distributions remained consistent (Table 1). To address the imbalance in baseline characteristics, Propensity Score Matching (PSM) was utilized. Initially, 15,047 participants (including 582 COPD patients and 14,465 control individuals) were analyzed. Post-PSM, the age, sex, ethnicity, and poverty-to-income ratio showed differences within 10% (Figure 2). A comprehensive comparison of baseline characteristics revealed significant disparities in age, gender, ethnicity, and PIR between groups prior to PSM. Notably, some variables remained insignificant after matching, suggesting potential confounding factors. Variables such as smoking status, diabetes prevalence, hypertension incidence, cardiovascular disease presence, BMI measurements, white blood cell count, total cholesterol levels, and monocyte-to-HDL ratio exhibited significant differences both before and after PSM (Table 2). These findings imply that certain baseline characteristics may influence study outcomes, warranting further investigation. Relationship between COPD and MHR A multiple linear regression analysis was carried out to evaluate the relationship between the monocyte - to - HDL - cholesterol ratio (MHR) and the risk of Chronic Obstructive Pulmonary Disease (COPD). The findings demonstrated a notable positive association between MHR and the occurrence of COPD. Across all models, statistical significance was observed with a P - value less than 0.001. In detail, a unit increment in MHR corresponded to a heightened risk of COPD, as manifested by the odds ratios (OR) derived from both the unadjusted and adjusted models. The unadjusted model disclosed that elevated MHR levels substantially augmented the risk of COPD (OR = 3.166, 95% CI: 2.031 - 5.026, P < 0.001). Following the adjustment for potential confounding factors in Model 1 and Model 2, this relationship retained its statistical significance in both models. (Model 1: OR = 3.468, 95% CI: 2.162–5.673, P < 0.001; Model 2: OR = 2.134, 95% CI: 1.115–4.378, P = 0.0327). Further analysis of MHR by quartiles demonstrated that the risk of COPD progressively increased with higher MHR levels (Crude Model Q4 OR = 2.363; Model 1 Q4 OR = 2.572; Model 2 Q4 OR = 1.902, all P < 0.001). In both the unadjusted and adjusted models, the tendency observed across quartiles maintained its statistical significance. (Crude Model: P < 0.001 for trend; Model 1: P < 0.001; Model 2: P = 0.0155).These findings suggest that elevated MHR levels may independently contribute to the risk of COPD, with augmented risks linked to the higher quartiles of MHR (Crude Model Q4 OR = 2.363; Model 1 Q4 OR = 2.572; Model 2 Q4 OR = 1.902). The observed trend across quartiles further supports the potential role of MHR as a risk factor for COPD (P < 0.001 in all models).See Table 3 for detailed results. Machine learning analysis of COPD-related factors based on XGBoost model In the present research, the XGBoost machine - learning model was applied to assess the relative contributions of multiple variables to the development of COPD. The SHAP (Shapley Additive Explanations) approach was adopted to offer in - depth understanding of variable significance and their precise contributions to model forecasts. The performance of the XGBoost model was evaluated using MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). An MSE value of 0.08 implies a minor discrepancy between predicted and actual values. with an RMSE of 0.28 further demonstrating the high accuracy of the model in predicting COPD outcomes.Variable importance rankings, as determined by SHAP analysis, revealed that MHR emerged as a significant predictor of COPD (mean SHAP value = 0.0174). This finding underscores MHR's role in disease progression, with its impact being higher than red blood cell count and total cholesterol but lower than other variables such as white blood cell count (mean SHAP value = 0.0171), total protein (0.0114), high-density lipoprotein cholesterol (0.0104) and potassium (0.0111), Furthermore, MHR demonstrated its importance as a composite index in the model, highlighting its close association with COPD progression (as shown in Figure 3A).The SHAP-dependent plot further elucidated the specific contributions of individual variables to the model's predictions (Figure 3B), revealing that MHR exhibited a positive correlation with COPD risk. As the SHAP value increased with rising MHR levels, this indicated an enhanced risk of COPD. Additionally, red blood cell count was found to positively correlate with COPD progression, while total bilirubin exhibited a reduced contribution to total protein levels Among the variables analyzed, age, gender, PIR (Parental Income Ratio), and ethnicity exhibited relatively minor effects. Although their average SHAP values were low—e.g., Total Bilirubin (0.0065), Age Group (0.0003), Gender (0.001), Diabetes-related PIR (0.0012), and Ethnicity (0.0012)—these attributes still contributed to the model's predictive capacity for COPD, albeit with lesser significance compared to other variables These findings underscore the potential of MHR as a key predictor in understanding and modeling COPD risk, while also highlighting between various demographic and physiological factors in disease etiology and dependence plots. Group statistics Group statistical results Fig 4 A show that the COPD incidence gradually increases with the quartile of MHR, with the fourth quartile (Q4) having the highest incidence at 61.51%, and the first quartile (Q1) having the lowest at 40.34%. Fig 4 B shows the stratified COPD incidence by gender, indicating an overall trend of increasing COPD incidence in individuals under 75 years old as age increases. Notably, among those under 60 years old, women have a higher COPD incidence than men, but in those over 60 years old, women have a lower COPD incidence than men. RCS curves of COPD versus MHR and its subgroups The relationship between MHR and COPD was investigated using a confined cubic spline (RCS) curve, and all relevant covariates were adjusted. The findings indicated that, within the entire population, there was a significant linear trend (P for overall < 0.001) and a non - linear relationship (P non - linear = 0.02) between MHR and the risk of COPD. The curve showed that the risk of COPD increased significantly with the increase of MHR (Fig.5 A). Subsequent subgroup analysis revealed that no significant linear tendency was present. (P for overall = 0.267) and a nonlinear relationship (P non linear = 0.367) in young people (Fig.5 B). In the older population, the association between MHR and COPD risk showed a significant linear trend (P for overall < 0.001) and a nonlinear relationship (P non linear = 0.019) (Fig.5 C). In the non-smokers, there was no significant linear trend between MHR and COPD risk (P for overall = 0.078), and there was also no significant nonlinear relationship (P non linear = 0.182) (Fig.5 D). Within the smoking population, a significant linear trend (P for overall = 0.002) and a notable non - linear relationship (P non - linear = 0.023) were observed between MHR and the risk of COPD, as depicted in Fig. 5E. The subgroup analysis results further indicated that MHR served as a risk factor for COPD. Relationship between MHR and subgroups of baseline characteristics In this research, multivariate logistic regression was applied to conduct a more in - depth analysis of the relationship between MHR and COPD across diverse subgroups based on baseline characteristics (Fig. 6). The outcomes demonstrated that, in the overall assessment, the risk of COPD in Q4, which represented the highest MHR group, was markedly higher than that in Q1, the lowest group (Q4: OR = 2.36, 95%CI: 1.69 - 3.3, P < 0.001), thereby indicating that MHR was a risk factor for COPD. Specifically, in the elderly group, Q4 had an OR of 2.91 (95%CI: 1.91 - 4.44, P < 0.001), while in the young population, Q4 had an OR of 1.68 (95%CI: 0.97 - 2.9, P = 0.062). Among females, the OR was 2.02 (95%CI: 1.21 - 3.37, P = 0.007), and among males, it was 2.95 (95%CI: 1.77 - 4.92, P < 0.001). Regarding ethnic groups, Mexican - Americans had an OR of 2.75 (95%CI: 0.51 - 14.86, P = 0.24), Non - Hispanic Blacks had an OR of 2.3 (95%CI: 0.95 - 5.56, P = 0.063), Non - Hispanic Whites had an OR of 2.15 (95%CI: 1.41 - 3.27, P < 0.001), other Hispanics had an OR of 3.2 (95%CI: 0.67 - 15.38, P = 0.146), and other races had an OR of 4.55 (95%CI: 1.44 - 14.38, P = 0.01).For groups classified by the Poverty - Income Ratio (PIR), Q4 in the group with PIR ≤ 1.3 had an OR of 2.54 (95%CI: 1.58 - 4.1, P 3.5 group had an OR of 2.45 (95%CI: 1.06 - 5.67, P = 0.037). Among non - smokers, the OR was 1.7 (95%CI: 1.13 - 2.55, P = 0.011), and among smokers, it was 1.8 (95%CI: 0.85 - 3.83, P = 0.126).Considering the diabetes mellitus index, the borderline population had an OR of 1.43 (95%CI: 0.27 - 7.52, P = 0.674), the non - diabetic population had an OR of 1.99 (95%CI: 1.33 - 2.97, P = 0.001), and the diabetic population had an OR of 3.26 (95%CI: 1.64 - 6.47, P = 0.001). In the non - hypertensive population, the OR was 2.2 (95%CI: 1.32 - 3.67, P = 0.002), and in the hypertensive population, it was 2.37 (95%CI: 1.52 - 3.7, P < 0.001). Finally, in the non - cardiovascular disease population, the OR was 2.18 (95%CI: 1.53 - 3.12, P < 0.001), while in the cardiovascular disease population, it was 0.82 (95%CI: 0.2 - 3.31, P = 0.778). Discussion The elevated incidence and mortality rate among COPD patients present a substantial challenge to public health [ 12 , 13 ] . Early detection, diagnosis, and timely intervention among COPD patients are crucial for improving their quality of life and survival [ 14 ] . Establishing a clear relationship between MHR levels and COPD prevalence not only provides a robust medical rationale but also enhances the prognosis assessment for COPD patients. This study aims to investigate the relationship between monocytes and high-density lipoprotein cholesterol ratio (MHR) with chronic obstructive pulmonary disease (COPD), while controlling for multiple confounding factors. A cross-sectional analysis included 15,047 participants, of whom 582 had COPD. After matching for age, sex, and other factors, the MHR in the COPD group (0.58 ± 0.38) was significantly higher than that in the control group (0.49 ± 0.25; P < 0.001). Further adjustment for multiple blood indicators confirmed that MHR is an independent risk factor for COPD. Quartile analysis of MHR showed a positive trend with the risk of COPD, and subgroup analysis revealed differences in the risk of COPD across different age and sex groups. XGBoost machine learning models indicated that MHR is one of the top five variables influencing COPD risk, and restricted cubic spline analysis showed a negative correlation between low MHR values and COPD risk. The study suggests that MHR may serve as a predictive marker for COPD, which is significant for identifying high-risk populations. This research reveals that MHR serves as a crucial risk determinant for the onset of chronic obstructive pulmonary disease (COPD).This discovery emphasizes the pivotal part played by inflammation and oxidative stress in the pathogenesis of COPD [ 15 ] . COPD characterized by persistent airway inflammation and alveolar damage, involves infiltration of monocytes that differentiate into macrophages with predominant M1 phenotype, Pro - inflammatory cytokines, including TNF - α and IL − 6, are secreted by these cells, which promote oxidative stress (a 2-fold increase in reactive oxygen species (ROS) and imbalance between proteases and antiproteases (a 40% decrease in alpha-1 anti-trypsin activity) [ 16 , 17 ] . Monocytes act as inflammatory cells involved in including the release of inflammatory mediators and the repair of tissue damage. High-density lipoprotein cholesterol (HDL-C), as an anti-inflammatory factor, is anti-inflammatory by inhibiting macrophage migration and activating [ 9 ] . An elevated MHR could signify heightened systemic inflammation, which plays a role in the development of COPD. Additionally, the association between MHR and COPD could be mediated through oxidative stress. Oxidative stress is a key pathogenic mechanism in COPD, with monocytes accelerating its progression by reaching inflammatory sites and producing ROS. Simultaneously, monocytes generate large amounts of ROS, further exacerbating oxidative stress [ 18 , 19 ] . HDL-C exhibits antioxidant properties, neutralizing ROS from the body [ 20 ] . A higher MHR might suggest augmented systemic oxidative stress, thereby facilitating the progression of COPD.​ In subgroup analysis, smoking emerged as a significant modifier. Cigarette smoking is one of the most important risk factors for COPD [ 21 ] . suggesting that combining smoking with MHR testing could aid earlier detection of COPD. This may be due to harmful tobacco constituents (tar, nicotine, and carbon monoxide) irritating airway epithelial cells, triggering inflammation in the lungs [ 22 ] . Monocytes are central to the inflammatory response, with elevated levels potentially reflecting an active inflammatory process. Inflammatory infiltrates are common in COPD lung tissue [ 23 ] , indicative of the disease's inflammatory nature. HDL-C plays a regulatory role in balancing inflammation and antioxidant processes [ 20 , 24 , 25 ] . Subgroup analyses revealed higher incidence of COPD in women under 60 years of age, possibly due to enhanced inflammatory pathways driven by increased adipokines and IL-16 levels observed in female COPD patients [ 26 , 27 ] . Estrogen has been implicated in enhancing the inflammatory response [ 28 , 29 ], suggesting that hormonal imbalances may influence disease progression.Differences between the results of this study and previous studies have focused on the relationship between MHR and cardiovascular disease, and some studies using the NHANES database have identified MHR as an indicator of various diseases. As an example, in a study of more than 30,000 participants, MHR was significantly associated with all-cause mortality and cardiovascular mortality in the general population [ 30 ] . Another study from the NHANES database showed a positive correlation between MHR and coronary artery disease prevalence at an MHR < of 0.6 [ 31 ] . This study explores the relationship between MHR and COPD for the first time. Compared with previous studies, this study revealed a significant association between MHR and COPD based on large-sample data analysis, and explored the potential of MHR as a predictor of COPD. In addition, the XGBoost machine learning model and RCS analysis were used to further validate the nonlinear relationship between MHR and COPD risk. Advantages of this research: First, this is the first time that the relationship between MHR levels and COPD risk has been investigated in a representative population in the United States. In addition, it verified a significant positive correlation between MHR and COPD through a variety of analysis methods, enhancing its practicability. Secondly, the control of confounding variables enhanced the reliability and representativeness of the research. Thirdly, the subgroup analysis led to robust and trustworthy conclusions. Nevertheless, this investigation has its drawbacks. Given the cross - sectional design of NHANES, causal relationships cannot be established. Additionally, although we accounted for some confounders, residual and unmeasured confounding factors might still exist. Third, the diagnostic definition of COPD is based on self-report rather than a more specific diagnostic approach, which can lead to recall bias. Conclusion This study shows that MRH is associated with COPD. MRH can serve as a predictive indicator for the onset of COPD. Declarations Acknowledgements All authors are grateful to the investigators and participants of the NHANES database. We also sincerely thank the developers of Empower Stats. Ethics statement Ethics approval is not required for this study because the summary data are publicly available. Consent for publication Not applicable. Data availability This study is based on the public database, and all related datasets are available at https://www.cdc.gov/nchs/nhanes/ Author contributions LC: Data curation, Formal analysis, Writing – original draft. MY: Software,Writing – original draft. XN: Reviewing and editing. TY: Data curation. All authors approved the manuscript and agreed to publish. 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Gruppen, et al., High-Density Lipoprotein Anti-Inflammatory Capacity and Incident Cardiovascular Events. Circulation, 2021. 143 (20): p. 1935-1945.DOI: 10.1161/circulationaha.120.050808. Tanaka, S., D. Couret, A. Tran-Dinh, et al., High-density lipoproteins during sepsis: from bench to bedside. Crit Care, 2020. 24 (1): p. 134.DOI: 10.1186/s13054-020-02860-3. Balgoma, D., M. Yang, M. Sjödin, et al., Linoleic acid-derived lipid mediators increase in a female-dominated subphenotype of COPD. Eur Respir J, 2016. 47 (6): p. 1645-1656.DOI: 10.1183/13993003.01080-2015. Diaz, A.A., L. Zhou, T.P. Young, et al., Chest CT measures of muscle and adipose tissue in COPD: gender-based differences in content and in relationships with blood biomarkers. Acad Radiol, 2014. 21 (10): p. 1255-1261.DOI: 10.1016/j.acra.2014.05.013. Reyes-García, J., L.M. Montaño, A. Carbajal-García, et al., Sex Hormones and Lung Inflammation. Adv Exp Med Biol, 2021. 1304 : p. 259-321.DOI: 10.1007/978-3-030-68748-9_15. van der Plaat, D.A., A. Lenoir, S. Dharmage, et al., Effects of testosterone and sex hormone binding globulin on lung function in males and females: a multivariable Mendelian Randomisation study. Thorax, 2024. 79 (6): p. 564-572.DOI: 10.1136/thorax-2023-220374. Jiang, M., J. Yang, H. Zou, et al., Monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and the risk of all-cause and cardiovascular mortality: a nationwide cohort study in the United States. Lipids Health Dis, 2022. 21 (1): p. 30.DOI: 10.1186/s12944-022-01638-6. Yan, S., S. Sha, D. Wang, et al., Association between monocyte to high-density lipoprotein ratio and coronary heart disease in US adults in the National Health and Nutrition Examination Surveys 2009-2018. Coron Artery Dis, 2023. 34 (2): p. 111-118.DOI: 10.1097/mca.0000000000001208. Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Jun, 2025 Editor invited by journal 22 May, 2025 Editor assigned by journal 22 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 11 May, 2025 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. 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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-6640631","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471484138,"identity":"a4141c75-e657-461a-af50-353962cc2563","order_by":0,"name":"Li Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACfvnHBx98/GMjx8/eQKQWyYa0ZMOZDWnGkj0HiNRicCBHTZqz4XDihhsJxLrswBkGacYdh40Zbj7eeIOhxiaaoA7Gxt4DxoVn0uUYZ6cVWzAcS8ttIKSFmZkvIXkGm7Uxs3SOmQRjw2HCWtjYeAwO87AxJ7ZJniFSCw8Pj2Ezb5tzYo8ED5FaJCTYkhlnnEkzluAB+iWBGL/Y32A+/uNDhY2c/fHDG298qLEhrAUZGEgkkKIcooVUHaNgFIyCUTAyAACddj/wC2gQrQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Chengdu Qingbaijiang District People's Hospital.","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":471484139,"identity":"7766d01b-b3c8-47d1-846a-15acbad6d4a6","order_by":1,"name":"Mi Yu","email":"","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Chengdu Qingbaijiang District People's Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Mi","middleName":"","lastName":"Yu","suffix":""},{"id":471484140,"identity":"51cd119a-5d99-47e8-bf03-945c777dbce4","order_by":2,"name":"Ximei Ni","email":"","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Chengdu Qingbaijiang District People's Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Ximei","middleName":"","lastName":"Ni","suffix":""},{"id":471484141,"identity":"84054787-4692-417a-8d6d-a6ca083a5aa9","order_by":3,"name":"Yimin Tan","email":"","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Chengdu Qingbaijiang District People's Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2025-05-11 16:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6640631/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6640631/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84919239,"identity":"f21c9b31-d4e3-4dea-a32c-1e81954bd6b2","added_by":"auto","created_at":"2025-06-18 19:33:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140730,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection from the NHANES 2001–2023\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/369c483b8b963a5dd510f966.png"},{"id":84920954,"identity":"5a87aa0c-8404-410e-b06b-793019acc3ec","added_by":"auto","created_at":"2025-06-18 19:41:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46476,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluating covariate equilibrium pre- and post-PSM\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/a17d45064d4cdfc3c239c01d.png"},{"id":84919235,"identity":"5256eeb2-72cd-4cfd-9e9c-c16e17d0a5b1","added_by":"auto","created_at":"2025-06-18 19:33:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116487,"visible":true,"origin":"","legend":"\u003cp\u003eClarification of machine learning models through SHAP summary and dependence plots\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/6521c6a25df861086bad2c49.png"},{"id":84919241,"identity":"c320e542-587d-43dc-83f5-4eaef94e9c2e","added_by":"auto","created_at":"2025-06-18 19:33:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51869,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;A. Bar chart showing COPD incidence across various bicarbonate\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/e6538e4628f328ded98902bd.png"},{"id":84921296,"identity":"7a623b15-64fc-4ce0-9a4f-35f391954f08","added_by":"auto","created_at":"2025-06-18 19:49:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123341,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios for COPD in relation to MHR Score levels within the entire population\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/19aa624cff18805ef947c285.png"},{"id":84920955,"identity":"eb972ea0-d79d-4596-92ec-c2d41a15e65f","added_by":"auto","created_at":"2025-06-18 19:41:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":337752,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the relationship between MHR Score and COPD. MHR Score was analyzed as a continuous variable\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/5453373623b9899b728aab41.png"},{"id":84921768,"identity":"00881a60-7c70-4df5-ac74-1f5e64675bff","added_by":"auto","created_at":"2025-06-18 19:57:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1380764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/cccd48ef-aa25-416a-aa5f-a5e393b1ae57.pdf"},{"id":84919232,"identity":"33239c93-ddfe-4fd2-8da6-f89e76e5eb76","added_by":"auto","created_at":"2025-06-18 19:33:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50229,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6640631/v1/9c5d4a6b6d52dc7d8e628ff3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation between monocyte-to-high-density lipoprotein-cholesterol ratio and chronic obstruc tive pulmonary disease findings from the National Health and Nutrition Examination Survey 2001–2023\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e Chronic Obstructive Pulmonary Disease (COPD) represents a diverse lung disorder. It is distinguished by chronic airflow limitation caused by abnormalities in the airways or alveoli. This condition gives rise to respiratory manifestations including dyspnea, cough, and expectoration \u003csup\u003e [ \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e ] \u003c/sup\u003e . Currently, Chronic Obstructive Pulmonary Disease (COPD) ranks as the third foremost cause of global mortality, simultaneously placing substantial economic strains. In the United States, COPD-related healthcare costs are projected to rise to \u003cspan\u003e$\u003c/span\u003e800.9\u0026nbsp;billion over the next 20 years, or approximately \u003cspan\u003e$\u003c/span\u003e40\u0026nbsp;billion annually \u003csup\u003e [ \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e ] \u003c/sup\u003e . Additionally, the heterogeneity and complex pathophysiology of COPD present substantial challenges in diagnosis and prognosis \u003csup\u003e [ \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e ] \u003c/sup\u003e . The Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) has recently introduced a \"treatable feature\" (TTSs), which aims to identify patients with COPD through biomarkers \u003csup\u003e [ \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e ] \u003c/sup\u003e . The discovery of an easily measurable and reliable biomarker will be invaluable in diagnosing COPD patients. \u003c/p\u003e \u003cp\u003eCurrently available biomarkers such as peripheral blood eosinophil count and CRP can guide the pharmacological treatment of COPD. While MDA and SOD in peripheral blood, IL-6 and MPO in sputum, and GSH in bronchoalveolar lavage fluid have been extensively studied, they have not yet been fully validated\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown a close relationship between COPD and inflammation; however, research on the correlation between inflammatory markers and COPD is still insufficient. Therefore, exploring the relationship between MHR and COPD can further enrich the biomarkers for COPD.\u003c/p\u003e \u003cp\u003eThe pathogenesis of Chronic Obstructive Pulmonary Disease (COPD) is intricate, with the participation of numerous inflammatory mediators and cytokines\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The monocyte - to - HDL - cholesterol ratio (MHR), a newly - established composite, has been employed as a potential clinical indicator for atherosclerosis coronary heart disease, and other diseases due to its simple availability and clinical relevance \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. High-density lipoprotein cholesterol (HDL-C) acts as an anti-inflammatory factor by inhibiting macrophage migration\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Monocytes are inflammatory cells that can migrate to sites of inflammation and accelerate oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. MHR is based on the anti-inflammatory properties of HDL-C and the pro-inflammatory properties of monocytes and may be a potential pathogenesis of COPD. Nevertheless, the connection between the monocyte - to - HDL - cholesterol ratio (MHR) and Chronic Obstructive Pulmonary Disease (COPD) remains ambiguous. Thus, a profound investigation into the association of MHR with COPD could offer precious perspectives for subsequent research. As far as we are aware, no population - based research has been carried out to assess the link between MHR levels and the development of COPD.\u003c/p\u003e \u003cp\u003eA deep exploration of the intrinsic connection between MRH and chronic obstructive pulmonary disease (COPD) holds significant clinical and social value. This research not only helps uncover the pathogenesis of COPD and identify key stages in its development but also provides new directions for biomarker studies in COPD, uncovering more potential early diagnostic indicators. Through these findings, we can achieve precise early diagnosis of COPD, enabling timely and effective intervention measures. This can significantly improve patients' respiratory function, enhance their quality of life and long-term survival rates, while effectively reducing the medical burden on families and society, providing strong support for the development of public health. Consequently, we undertook this research by leveraging the data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2001 to 2023.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), an ongoing cross - sectional survey with national representativeness in the United States, is a key project of the National Center for Health Statistics (NCHS) research. It has been sanctioned and funded by the Centers for Disease Control and Prevention (CDC). The primary objective of NHANES is to evaluate the health and nutritional condition of non - institutionalized U.S. civilian populations Data is amassed biannually via a sophisticated multi - tiered probability sampling technique. which includes face-to-face interviews at participants' homes followed by physical examinations at the National Center for Mobile Health (MEC), where blood and urine samples are collected. All data from this survey are publicly accessible\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor our study, we analyzed data from NHANES participants spanning from 2001 to 2023, including all individuals who tested positive for COPD. Initially, a sample of 103,284 participants was extracted; however, the following exclusions were applied: (1) samples lacking MHC data (N\u0026thinsp;=\u0026thinsp;45,994), (2) samples lacking data on COPD (N\u0026thinsp;=\u0026thinsp;42,095), and (3) individuals who did not fulfill the inclusion requirements for covariates including age, gender, and ethnic background. smoking history, BMI, PIR, diabetes, hypertension, weight, alcohol consumption, etc. (N\u0026thinsp;=\u0026thinsp;148). Consequently, the final dataset included 15,047 participants, comprising 582 individuals with COPD and 14,465 individuals without COPD ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of COPD\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eCOPD is defined as a positive response to any one of the following inquiries: (1) \"Has a medical doctor or other healthcare professional ever informed you that you have emphysema?\" (2) \"Has a medical doctor or other healthcare professional ever informed you that you have COPD?\u003csup\u003e\u0026rdquo;[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eMHR calculations\u003c/h3\u003e\n\u003cp\u003eThe monocyte - to - HDL - cholesterol ratio (MHR) was designated as the exposure variable. It was computed by dividing the monocyte count by the HDL - C level (expressed in mmol/L).\u003c/p\u003e\n\u003ch3\u003eCovariates definition\u003c/h3\u003e\n\u003cp\u003eA number of potential confounders, including ethnicity, household poverty-income ratio, diabetes mellitus, hypertension, HDL cholesterol, urea nitrogen, total protein, total bilirubin, calcium, alanine aminotransferase, lactate, albumin, sodium, and triglycerides, were analyzed. Ethnicity was determined through the Demographic Variable File (DEMO) categorizes racial groups into Mexican - American, non - Hispanic Black, non - Hispanic White, other Hispanic, and other races. This classification is based on data regarding household economic status. The household poverty - income ratio was grouped as low - income (less than 1.3), middle - income (ranging from 1.3 to 3.5), and high - income (greater than 3.5). Identification of diabetes mellitus and hypertension was achieved via a questionnaire - based survey. Blood biomarkers such as HDL cholesterol, urea nitrogen, total protein, total bilirubin, calcium, alanine aminotransferase, lactate, albumin, sodium, and triglycerides were measured using biochemical test methods.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eData processing and all analyses were executed with R software (version 4.1.1). For non - normally distributed continuous variables, baseline characteristics were depicted by means of the median and interquartile range (IQR). Categorical variables were shown as sample counts and weighted percentages. To evaluate the disparities in variable features across the MHR groups (quartiles), the Wilcoxon rank - sum test was utilized for continuous variables, while the Rao - Scott chi - square test was applied to the weighted percentages of categorical variables. thus providing a comprehensive profile of the entire population. When the proportion of missing values in the data was less than 20%, multiple imputation was employed for data filling, with the number of imputed datasets set to 5. The Extreme Gradient Boosting (XGBoost) model was utilized to evaluate the importance of each factor, identify the most crucial factors influencing COPD, and assist in pinpointing the most predictive variables. A two - tailed statistical analysis was conducted, and statistical significance was defined as a p - value of less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study addressed missing data through multiple imputation, ensuring that the pre- and post-imputation distributions remained consistent (Table 1). To address the imbalance in baseline characteristics, Propensity Score Matching (PSM) was utilized. Initially, 15,047 participants (including 582 COPD patients and 14,465 control individuals) were analyzed. Post-PSM, the age, sex, ethnicity, and poverty-to-income ratio showed differences within 10% (Figure 2). A comprehensive comparison of baseline characteristics revealed significant disparities in age, gender, ethnicity, and PIR between groups prior to PSM. Notably, some variables remained insignificant after matching, suggesting potential confounding factors. Variables such as smoking status, diabetes prevalence, hypertension incidence, cardiovascular disease presence, BMI measurements, white blood cell count, total cholesterol levels, and monocyte-to-HDL ratio exhibited significant differences both before and after PSM (Table 2). These findings imply that certain baseline characteristics may influence study outcomes, warranting further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between COPD and MHR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multiple linear regression analysis was carried out to evaluate the relationship between the monocyte - to - HDL - cholesterol ratio (MHR) and the risk of Chronic Obstructive Pulmonary Disease (COPD). The findings demonstrated a notable positive association between MHR and the occurrence of COPD. Across all models, statistical significance was observed with a P - value less than 0.001. In detail, a unit increment in MHR corresponded to a heightened risk of COPD, as manifested by the odds ratios (OR) derived from both the unadjusted and adjusted models. The unadjusted model disclosed that elevated MHR levels substantially augmented the risk of COPD (OR = 3.166, 95% CI: 2.031 - 5.026, P \u0026lt; 0.001). Following the adjustment for potential confounding factors in Model 1 and Model 2, this relationship retained its statistical significance in both models. (Model 1: OR = 3.468, 95% CI: 2.162\u0026ndash;5.673, P \u0026lt; 0.001; Model 2: OR = 2.134, 95% CI: 1.115\u0026ndash;4.378, P = 0.0327). Further analysis of MHR by quartiles demonstrated that the risk of COPD progressively increased with higher MHR levels (Crude Model Q4 OR = 2.363; Model 1 Q4 OR = 2.572; Model 2 Q4 OR = 1.902, all P \u0026lt; 0.001). In both the unadjusted and adjusted models, the tendency observed across quartiles maintained its statistical significance. (Crude Model: P \u0026lt; 0.001 for trend; Model 1: P \u0026lt; 0.001; Model 2: P = 0.0155).These findings suggest that elevated MHR levels may independently contribute to the risk of COPD, with augmented risks linked to the higher quartiles of MHR (Crude Model Q4 OR = 2.363; Model 1 Q4 OR = 2.572; Model 2 Q4 OR = 1.902). The observed trend across quartiles further supports the potential role of MHR as a risk factor for COPD (P \u0026lt; 0.001 in all models).See Table 3 for detailed results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine learning analysis of COPD-related factors based on XGBoost model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present research, the XGBoost machine - learning model was applied to assess the relative contributions of multiple variables to the development of COPD. The SHAP (Shapley Additive Explanations) approach was adopted to offer in - depth understanding of variable significance and their precise contributions to model forecasts. The performance of the XGBoost model was evaluated using MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). An MSE value of 0.08 implies a minor discrepancy between predicted and actual values. with an RMSE of 0.28 further demonstrating the high accuracy of the model in predicting COPD outcomes.Variable importance rankings, as determined by SHAP analysis, revealed that MHR emerged as a significant predictor of COPD (mean SHAP value = 0.0174). This finding underscores MHR\u0026apos;s role in disease progression, with its impact being higher than red blood cell count and total cholesterol but lower than other variables such as white blood cell count (mean SHAP value = 0.0171), total protein (0.0114), high-density lipoprotein cholesterol (0.0104) and potassium (0.0111), \u0026nbsp;Furthermore, MHR demonstrated its importance as a composite index in the model, highlighting its close association with COPD progression (as shown in Figure 3A).The SHAP-dependent plot further elucidated the specific contributions of individual variables to the model\u0026apos;s predictions (Figure 3B), revealing that MHR exhibited a positive correlation with COPD risk. As the SHAP value increased with rising MHR levels, this indicated an enhanced risk of COPD. Additionally, red blood cell count was found to positively correlate with COPD progression, while total bilirubin exhibited a reduced contribution to total protein levels Among the variables analyzed, age, gender, PIR (Parental Income Ratio), and ethnicity exhibited relatively minor effects. Although their average SHAP values were low\u0026mdash;e.g.,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal Bilirubin (0.0065), Age Group (0.0003), Gender (0.001), Diabetes-related PIR (0.0012), and Ethnicity (0.0012)\u0026mdash;these attributes still contributed to the model\u0026apos;s predictive capacity for COPD, albeit with lesser significance compared to other variables These findings underscore the potential of MHR as a key predictor in understanding and modeling COPD risk, while \u0026nbsp; also highlighting between \u0026nbsp;various demographic and physiological factors in disease etiology and dependence plots.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eGroup statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup statistical results Fig 4 A show that the COPD incidence gradually increases with the quartile of MHR, with the fourth quartile (Q4) having the highest incidence at 61.51%, and the first quartile (Q1) having the lowest at 40.34%. Fig 4 B shows the stratified COPD incidence by gender, indicating an overall trend of increasing COPD incidence in individuals under 75 years old as age increases. Notably, among those under 60 years old, women have a higher COPD incidence than men, but in those over 60 years old, women have a lower COPD incidence than men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS curves of COPD versus MHR and its subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between MHR and COPD was investigated using a confined cubic spline (RCS) curve, and all relevant covariates were adjusted. The findings indicated that, within the entire population, there was a significant linear trend (P for overall \u0026lt; 0.001) and a non - linear relationship (P non - linear = 0.02) between MHR and the risk of COPD. The curve showed that the risk of COPD increased significantly with the increase of MHR (Fig.5 A). Subsequent subgroup analysis revealed that no significant linear tendency was present. (P for overall = 0.267) and a nonlinear relationship (P non linear = 0.367) in young people (Fig.5 B). In the older population, the association between MHR and COPD risk showed a significant linear trend (P for overall \u0026lt; 0.001) and a nonlinear relationship (P non linear = 0.019) (Fig.5 C). In the non-smokers, there was no significant linear trend between MHR and COPD risk (P for overall = 0.078), and there was also no significant nonlinear relationship (P non linear = 0.182) (Fig.5 D). Within the smoking population, a significant linear trend (P for overall = 0.002) and a notable non - linear relationship (P non - linear = 0.023) were observed between MHR and the risk of COPD, as depicted in Fig. 5E. The subgroup analysis results further indicated that MHR served as a risk factor for COPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between MHR and subgroups of baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this research, multivariate logistic regression was applied to conduct a more in - depth analysis of the relationship between MHR and COPD across diverse subgroups based on baseline characteristics (Fig. 6). The outcomes demonstrated that, in the overall assessment, the risk of COPD in Q4, which represented the highest MHR group, was markedly higher than that in Q1, the lowest group (Q4: OR = 2.36, 95%CI: 1.69 - 3.3, P \u0026lt; 0.001), thereby indicating that MHR was a risk factor for COPD. Specifically, in the elderly group, Q4 had an OR of 2.91 (95%CI: 1.91 - 4.44, P \u0026lt; 0.001), while in the young population, Q4 had an OR of 1.68 (95%CI: 0.97 - 2.9, P = 0.062). Among females, the OR was 2.02 (95%CI: 1.21 - 3.37, P = 0.007), and among males, it was 2.95 (95%CI: 1.77 - 4.92, P \u0026lt; 0.001). Regarding ethnic groups, Mexican - Americans had an OR of 2.75 (95%CI: 0.51 - 14.86, P = 0.24), Non - Hispanic Blacks had an OR of 2.3 (95%CI: 0.95 - 5.56, P = 0.063), Non - Hispanic Whites had an OR of 2.15 (95%CI: 1.41 - 3.27, P \u0026lt; 0.001), other Hispanics had an OR of 3.2 (95%CI: 0.67 - 15.38, P = 0.146), and other races had an OR of 4.55 (95%CI: 1.44 - 14.38, P = 0.01).For groups classified by the Poverty - Income Ratio (PIR), Q4 in the group with PIR \u0026le; 1.3 had an OR of 2.54 (95%CI: 1.58 - 4.1, P \u0026lt; 0.001), in the PIR 1.3 - 3.5 group had an OR of 2.26 (95%CI: 1.27 - 4, P = 0.005), and in the PIR \u0026gt; 3.5 group had an OR of 2.45 (95%CI: 1.06 - 5.67, P = 0.037). Among non - smokers, the OR was 1.7 (95%CI: 1.13 - 2.55, P = 0.011), and among smokers, it was 1.8 (95%CI: 0.85 - 3.83, P = 0.126).Considering the diabetes mellitus index, the borderline population had an OR of 1.43 (95%CI: 0.27 - 7.52, P = 0.674), the non - diabetic population had an OR of 1.99 (95%CI: 1.33 - 2.97, P = 0.001), and the diabetic population had an OR of 3.26 (95%CI: 1.64 - 6.47, P = 0.001). In the non - hypertensive population, the OR was 2.2 (95%CI: 1.32 - 3.67, P = 0.002), and in the hypertensive population, it was 2.37 (95%CI: 1.52 - 3.7, P \u0026lt; 0.001). Finally, in the non - cardiovascular disease population, the OR was 2.18 (95%CI: 1.53 - 3.12, P \u0026lt; 0.001), while in the cardiovascular disease population, it was 0.82 (95%CI: 0.2 - 3.31, P = 0.778).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe elevated incidence and mortality rate among COPD patients present a substantial challenge to public health \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Early detection, diagnosis, and timely intervention among COPD patients are crucial for improving their quality of life and survival \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Establishing a clear relationship between MHR levels and COPD prevalence not only provides a robust medical rationale but also enhances the prognosis assessment for COPD patients.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the relationship between monocytes and high-density lipoprotein cholesterol ratio (MHR) with chronic obstructive pulmonary disease (COPD), while controlling for multiple confounding factors. A cross-sectional analysis included 15,047 participants, of whom 582 had COPD. After matching for age, sex, and other factors, the MHR in the COPD group (0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38) was significantly higher than that in the control group (0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further adjustment for multiple blood indicators confirmed that MHR is an independent risk factor for COPD. Quartile analysis of MHR showed a positive trend with the risk of COPD, and subgroup analysis revealed differences in the risk of COPD across different age and sex groups. XGBoost machine learning models indicated that MHR is one of the top five variables influencing COPD risk, and restricted cubic spline analysis showed a negative correlation between low MHR values and COPD risk. The study suggests that MHR may serve as a predictive marker for COPD, which is significant for identifying high-risk populations.\u003c/p\u003e \u003cp\u003eThis research reveals that MHR serves as a crucial risk determinant for the onset of chronic obstructive pulmonary disease (COPD).This discovery emphasizes the pivotal part played by inflammation and oxidative stress in the pathogenesis of COPD\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. COPD characterized by persistent airway inflammation and alveolar damage, involves infiltration of monocytes that differentiate into macrophages with predominant M1 phenotype, Pro - inflammatory cytokines, including TNF - α and IL \u0026minus;\u0026thinsp;6, are secreted by these cells, which promote oxidative stress (a 2-fold increase in reactive oxygen species (ROS) and imbalance between proteases and antiproteases (a 40% decrease in alpha-1 anti-trypsin activity)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Monocytes act as inflammatory cells involved in including the release of inflammatory mediators and the repair of tissue damage. High-density lipoprotein cholesterol (HDL-C), as an anti-inflammatory factor, is anti-inflammatory by inhibiting macrophage migration and activating \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. An elevated MHR could signify heightened systemic inflammation, which plays a role in the development of COPD.\u003c/p\u003e \u003cp\u003eAdditionally, the association between MHR and COPD could be mediated through oxidative stress. Oxidative stress is a key pathogenic mechanism in COPD, with monocytes accelerating its progression by reaching inflammatory sites and producing ROS. Simultaneously, monocytes generate large amounts of ROS, further exacerbating oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. HDL-C exhibits antioxidant properties, neutralizing ROS from the body \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. A higher MHR might suggest augmented systemic oxidative stress, thereby facilitating the progression of COPD.​\u003c/p\u003e \u003cp\u003eIn subgroup analysis, smoking emerged as a significant modifier. Cigarette smoking is one of the most important risk factors for COPD\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. suggesting that combining smoking with MHR testing could aid earlier detection of COPD. This may be due to harmful tobacco constituents (tar, nicotine, and carbon monoxide) irritating airway epithelial cells, triggering inflammation in the lungs\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Monocytes are central to the inflammatory response, with elevated levels potentially reflecting an active inflammatory process. Inflammatory infiltrates are common in COPD lung tissue\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, indicative of the disease's inflammatory nature. HDL-C plays a regulatory role in balancing inflammation and antioxidant processes\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubgroup analyses revealed higher incidence of COPD in women under 60 years of age, possibly due to enhanced inflammatory pathways driven by increased adipokines and IL-16 levels observed in female COPD patients\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Estrogen has been implicated in enhancing the inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e],\u003c/sup\u003e suggesting that hormonal imbalances may influence disease progression.Differences between the results of this study and previous studies have focused on the relationship between MHR and cardiovascular disease, and some studies using the NHANES database have identified MHR as an indicator of various diseases. As an example, in a study of more than 30,000 participants, MHR was significantly associated with all-cause mortality and cardiovascular mortality in the general population \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Another study from the NHANES database showed a positive correlation between MHR and coronary artery disease prevalence at an MHR\u0026thinsp;\u0026lt;\u0026thinsp;of 0.6\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. This study explores the relationship between MHR and COPD for the first time. Compared with previous studies, this study revealed a significant association between MHR and COPD based on large-sample data analysis, and explored the potential of MHR as a predictor of COPD. In addition, the XGBoost machine learning model and RCS analysis were used to further validate the nonlinear relationship between MHR and COPD risk.\u003c/p\u003e \u003cp\u003eAdvantages of this research: First, this is the first time that the relationship between MHR levels and COPD risk has been investigated in a representative population in the United States. In addition, it verified a significant positive correlation between MHR and COPD through a variety of analysis methods, enhancing its practicability. Secondly, the control of confounding variables enhanced the reliability and representativeness of the research. Thirdly, the subgroup analysis led to robust and trustworthy conclusions. Nevertheless, this investigation has its drawbacks. Given the cross - sectional design of NHANES, causal relationships cannot be established. Additionally, although we accounted for some confounders, residual and unmeasured confounding factors might still exist. Third, the diagnostic definition of COPD is based on self-report rather than a more specific diagnostic approach, which can lead to recall bias.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that MRH is associated with COPD. MRH can serve as a predictive indicator for the onset of COPD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are grateful to the investigators and participants of the NHANES database. We also sincerely thank the developers of Empower Stats.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval is not required for this study because the summary data are publicly available. Consent for publication Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on the public database, and all related datasets are available at https://www.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC: Data curation, Formal analysis, Writing – original draft. MY: Software,Writing – original draft. XN: Reviewing and editing. TY: Data curation. All authors approved the manuscript and agreed to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.In this study, written informed consent was obtained from all participants prior to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicate.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgust\u0026iacute;, A., B.R. Celli, G.J. 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Young, et al., \u003cem\u003eChest CT measures of muscle and adipose tissue in COPD: gender-based differences in content and in relationships with blood biomarkers.\u003c/em\u003e Acad Radiol, 2014. \u003cstrong\u003e21\u003c/strong\u003e(10): p. 1255-1261.DOI: 10.1016/j.acra.2014.05.013.\u003c/li\u003e\n \u003cli\u003eReyes-Garc\u0026iacute;a, J., L.M. Monta\u0026ntilde;o, A. Carbajal-Garc\u0026iacute;a, et al., \u003cem\u003eSex Hormones and Lung Inflammation.\u003c/em\u003e Adv Exp Med Biol, 2021. \u003cstrong\u003e1304\u003c/strong\u003e: p. 259-321.DOI: 10.1007/978-3-030-68748-9_15.\u003c/li\u003e\n \u003cli\u003evan der Plaat, D.A., A. Lenoir, S. Dharmage, et al., \u003cem\u003eEffects of testosterone and sex hormone binding globulin on lung function in males and females: a multivariable Mendelian Randomisation study.\u003c/em\u003e Thorax, 2024. \u003cstrong\u003e79\u003c/strong\u003e(6): p. 564-572.DOI: 10.1136/thorax-2023-220374.\u003c/li\u003e\n \u003cli\u003eJiang, M., J. Yang, H. Zou, et al., \u003cem\u003eMonocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and the risk of all-cause and cardiovascular mortality: a nationwide cohort study in the United States.\u003c/em\u003e Lipids Health Dis, 2022. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 30.DOI: 10.1186/s12944-022-01638-6.\u003c/li\u003e\n \u003cli\u003eYan, S., S. Sha, D. Wang, et al., \u003cem\u003eAssociation between monocyte to high-density lipoprotein ratio and coronary heart disease in US adults in the National Health and Nutrition Examination Surveys 2009-2018.\u003c/em\u003e Coron Artery Dis, 2023. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 111-118.DOI: 10.1097/mca.0000000000001208.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\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":"Monocyte-to-high-density lipoprotein-cholesterol ratio, Chronic obstructive pulmonary disease, NHANES, Limiting cubic spline","lastPublishedDoi":"10.21203/rs.3.rs-6640631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6640631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eChronic obstructive pulmonary disease (COPD) is a global public health challenge, with its pathogenesis driven by complex inflammatory pathways. This study aimed to investigate the potential association between MHR and COPD risk while evaluating its utility as a predictive marker for COPD progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study utilized data from the National Health and Nutrition Survey (NHANES) spanning from 2001 to 2023. Control for con-founders by the propensity score matching (PSM) method. The relationship between monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and chronic obstructive pulmonary disease (COPD) was analyzed using multivariate logistic regression models. A restricted cubic spline（RCS）model was applied to explore the dose-response relationship between MHR and COPD risk. Subgroup analyses revealed significant heterogeneity in the associations across different predefined groups, indicating varying degrees of correlation between MHR and COPD depending on specific subgroup characteristics. To evaluate the relative contribution of MHR as a predictor of COPD risk, an XGBoost model was constructed to assess its predictive importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The cross-sectional analysis included a total of 15,047 participants, among whom 582 were diagnosed with chronic obstructive pulmonary disease (COPD). The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) of COPD patients was significantly higher compared to non-COPD participants (0.58 ± 0.38 vs. 0.49 ± 0.25, P \u0026lt; 0.001). Multivariate logistic regression analysis revealed that MHR was an independent risk factor for COPD diagnosis (odds ratio = 2.134; 95% confidence interval: 1.115–4.378; P = 0.0327). The restricted cubic spline (RCS) model demonstrated a significant linear trend in the association between MHR and COPD risk (P for overall trend \u0026lt; 0.001), as well as a similarly significant nonlinear relationship (P for non-linear term = 0.02). Furthermore, the XGBoost model identified MHR as an important predictor of COPD diagnosis, with an average SHAP score of 0.0174.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e There was an association between MHR and COPD, indicating its potential utility as a clinical marker in assessing disease risk.\u003c/p\u003e","manuscriptTitle":"Association between monocyte-to-high-density lipoprotein-cholesterol ratio and chronic obstruc tive pulmonary disease findings from the National Health and Nutrition Examination Survey 2001–2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 19:32:59","doi":"10.21203/rs.3.rs-6640631/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-13T08:34:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-22T12:04:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T06:20:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-22T06:15:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-05-11T16:04:08+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":"399db9b2-2d6b-4605-b3a4-aeebc63858bf","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T19:33:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 19:32:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6640631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6640631","identity":"rs-6640631","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}