The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and Prostate specific antigen (PSA): a cross-sectional study

The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and Prostate specific antigen (PSA): a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and Prostate specific antigen (PSA): a cross-sectional study Guoqiang Huang, Shuangquan Lin, Kaiwen Xiao, Lingxing Duan, Xiongbing Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4395346/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 There is mounting evidence that prostate-specific antigen (PSA) levels can be influenced by lipid metabolism. However, there is still no clear relationship between PSA levels and the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR). This study's goal is to evaluate the association between PSA and NHHR by using data from the National Health and Nutrition Examination Survey (NHANES). Methods We used data from the NHANES from 2003 to 2010 to examine the connection between PSA and NHHR. Out of 41,156 participants, 6,015 met our inclusion criteria. Serum triglycerides served as our independent variable, while PSA was the dependent variable. Results The study's participants were 59.7 years old on average, with a standard deviation of 12.7 years. After controlling for potential confounding variables, a one-unit increment in NHHR was associated with a decrease of 0.15 ng/mL in PSA levels, with a 95% confidence interval ranging from − 0.22 to -0.08, indicating a statistically significant inverse relationship. Moreover, a restricted cubic spline analysis further corroborated the presence of a statistically significant, largely inverse association between NHHR and PSA concentrations. Conclusions Among American males, NHHR is inversely correlated with PSA. This negative correlation might make it challenging for men with higher triglycerides to detect asymptomatic prostate cancer and achieve early diagnosis. PSA NHHR NHANES prostate cancer cross-sectional study Figures Figure 1 Figure 2 Figure 3 Introduction The most often diagnosed disease and the second largest cause of cancer-related mortality in males is prostate cancer (PCa)[ 1 ]. The introduction of prostate-specific antigen (PSA) testing has revolutionized the landscape of PCa diagnosis and management[ 2 ]. This diagnostic tool empowers clinicians to detect PCa at earlier stages when they are typically small, low-grade, and localized[ 2 ]. Furthermore, a growing body of evidence suggests that various factors beyond the presence of cancer can influence PSA levels. Notably, blood cholesterol levels have been implicated in the progression of PCa, with studies indicating that hypercholesterolemia, arising from diets heavy in fat and cholesterol, not only raises the chance of PCa in older men but also promotes the disease's progression and metastatic spread [ 3 – 5 ]. Moreover, research has highlighted the role of a diet heavy in fat as a modifiable risk factor associated with the progression of the condition. Recent research increasingly suggests that lipid metabolism is crucial in the progression of PCa. There are signs that cholesterol's impact on inflammatory processes may have a role in the onset, severity, and progression of PCa[ 6 ]. Prior investigations have uncovered an inverse relationship between serum triglyceride levels and PSA in adult male populations[ 7 ]. A novel marker of atherosclerosis risk, NHHR has exhibited superior diagnostic performance in assessing the risk of cerebrovascular diseases, insulin resistance, and metabolic syndrome compared to traditional lipid parameters [ 8 – 10 ]. But the connection between the PSA and NHHR is still unknown. As a result, this study aims to clarify the connection between NHHR and PSA levels through data from the NHANES collected between 2003 and 2010. Methods Data Availability Initiated by the National Center for Health Statistics (NCHS) and conducted biennially by the Centers for Disease Control and Prevention (CDC) since 1960, NHANES is a comprehensive, nationwide survey that assesses the public of the United States in terms of wellness and dietary habits. NHANES utilizes interviews to obtain data, which includes a variety of information, including demographics, socioeconomics, dietary habits, and health-related issues, with the aim of representing diverse ethnic groups and addressing various health concerns. The insights gleaned from this extensive survey are essential for understanding current disease trends and informing the development of public health policies in the United States. Moreover, NHANES serves as an indispensable tool for scholars, policymakers, and healthcare workers who are committed to tackling the nation's health challenges and enhancing the well-being of its citizens by providing a rich repository of data. Study population Information from four cycles of the NHANES survey, covering the period from 2003 to 2010, was carefully chosen for this investigation. The initial cohort consisted of 41,156 participants. Exclusion criteria were applied to the initial cohort, the following: (1) being female (n = 20,785); (2) being under 40 years old (n = 13,231); (3) being diagnosed with prostate cancer (n = 314); (4) the presence of factors influencing PSA levels, including a prostatitis diagnosis (n = 76) and having undergone a rectal examination within the previous week (n = 61); (5) a lack of PSA data (n = 674); and (6) missing NHHR information (n = 0). These exclusions were implemented sequentially, culminating in the removal of 35,141 individuals from the original pool. Consequently, the final analytical cohort was composed of 6,015 participants, as depicted in Fig. 1. Figure1. Flowchart utilized to select the research participants. Exposure definition The focus of this study was on NHHR, which was identified as the key variable. Calculation of NHHR employed methodologies derived from previous research [ 11 ]. Total cholesterol (TC) and HDL cholesterol (HDL-C) levels were evaluated using enzymatic assays conducted on automated biochemistry analyzers [ 12 ]. By analyzing the lipid profiles of fasting individuals, Non-HDL cholesterol (Non-HDL-C) was obtained by subtracting HDL-C from TC. Individuals were then separated into four groups according to the NHHR quartile ranges. Outcome definition In this research, the concentration of serum PSA, quantified in nanograms per milliliter (ng/mL), was determined through the use of the Hybritech Total PSA Assay, implemented on the Beckman Access Immunoassay System[ 13 ]. Our study categorized PSA data into two groups: individuals with PSA levels at or below 4 ng/mL and those above 4 ng/mL. These groups functioned as the outcome variables of the investigation. Covariates The analytical study involved examining several continuous variables, such as age, the poverty-to-income ratio (PIR), C-reactive protein (CRP) levels, the ratio of neutrophils to lymphocytes (NLR), and glycohemoglobin percentages. The categorical variables included classifications of race/ethnicity, educational attainment, and marital status. Specifically, individuals were grouped by marital status into cohabitating/partnered and single, encompassing categories such as never married, widowed, divorced, or separated. Furthermore, the study addressed binary variables such as alcohol usage and smoking history, denoting individuals as never, former, or current smokers, and examining physical activity ranging from inactive to various intensities including moderate and vigorous efforts. Participants reporting alcohol usage during any two 24-hour periods were considered alcohol users. Smokers were defined based on lifetime cigarette consumption: non-smokers, former smokers, or current smokers. Physical activity evaluation considered both intense activities, like running and basketball, and regular moderate activities such as brisk walking, swimming, and cycling. To ensure a robust and reliable dataset, the NHANES database was meticulously curated; the analysis's accuracy was enhanced by utilizing the MICE package for managing missing data[ 14 ] Statistical analysis. Rigorously adhering to the CDC guidelines outlined in the CDC’s NHANES Tutorials, our statistical analysis enhanced the robustness of our findings. Initially, the analysis involved a descriptive review of participant data, summarizing continuous variables using means with standard deviations and medians coupled with interquartile ranges, while categorical variables were detailed through frequencies and percentages. We employed the chi-square (X²) test for assessing differences among categorical groups and conducted continuous data analysis using one-way analysis of variance for normally distributed data and the Kruskal-Wallis H test for skewed data. The sensitivity of the analysis was increased by segmenting PSA levels into categories using a cutoff of 4 ng/mL. To investigate the links between PSA levels and NHHR, logistic regression analyses were carried out, yielding models that were non-adjusted, slightly adjusted, and completely adjusted to illuminate the complicated connections. To investigate the connection between NHHR and PSA within particular participant segments, stratified multivariate logistic regression was employed. We also utilized restricted cubic spline (RCS) regression analysis to investigate potential non-linear associations between NHHR and PSA levels. The statistical calculations were utilized using R version 4.3.3, and P-values below 0.05 were regarded as significant, employing a two-tailed testing approach. Results The baseline characteristics of the participants Table 1 outlines the initial profiles of the individuals involved in the study, as obtained from NHANES spanning 2003–2010, and organizes them based on NHHR quartile divisions. The study's participants were 59.7 years old on average, with a standard deviation of 12.7 years. Observations revealed significant differences across NHHR quartiles in several variables, all showing p-values less than 0.05. These variables include age, race, education, PIR, alcohol consumption, TC, HDL, diabetes status, glycohemoglobin levels, NLR, marital status, PSA levels, smoking status, BMI, and hypertension. In the lowest NHHR quartile (Q1), participants were generally younger, and a higher proportion were non-Hispanic White individuals. This quartile also reported higher levels of educational attainment and higher PIR. Furthermore, participants in Q1 exhibited lower TC and higher HDL levels, a lower prevalence of diabetes, lower glycohemoglobin and NLR values, and fewer reported being current or former smokers compared to those in higher NHHR quartiles. Conversely, participants in the highest NHHR quartile (Q4) were typically older and had a higher representation of Mexican American and other Hispanic individuals. This quartile also displayed lower educational levels and lower PIR. Clinically, Q4 participants exhibited higher TC and lower HDL levels, an increased prevalence of diabetes, higher glycohemoglobin and NLR values, and a greater proportion were current or former smokers. Table 1 Features of the population under investigation [ALL] Q1 Q2 Q3 Q4 p.overall N = 6015 N = 1505 N = 1506 N = 1500 N = 1504 Age 59.7 (12.7) 63.6 (12.8) 60.9 (12.6) 58.8 (12.7) 55.7 (11.5) <0.001 Race: <0.001 Mexican American 1039 (17.3%) 172 (11.4%) 225 (14.9%) 289 (19.3%) 353 (23.5%) Other Hispanic 389 (6.47%) 71 (4.72%) 84 (5.58%) 102 (6.80%) 132 (8.78%) Non-Hispanic White 3252 (54.1%) 805 (53.5%) 843 (56.0%) 822 (54.8%) 782 (52.0%) Non-Hispanic Black 1105 (18.4%) 398 (26.4%) 300 (19.9%) 233 (15.5%) 174 (11.6%) Other Race - Including Multi-Racial 230 (3.82%) 59 (3.92%) 54 (3.59%) 54 (3.60%) 63 (4.19%) EDUcation: <0.001 High school 4131 (68.7%) 1037 (68.9%) 1085 (72.0%) 1021 (68.1%) 988 (65.7%) PIR 2.80 (1.64) 2.86 (1.61) 2.93 (1.63) 2.82 (1.65) 2.58 (1.63) <0.001 Alcohol: <0.001 No 5054 (84.0%) 1149 (76.3%) 1251 (83.1%) 1317 (87.8%) 1337 (88.9%) Yes 961 (16.0%) 356 (23.7%) 255 (16.9%) 183 (12.2%) 167 (11.1%) TC 5.11 (1.12) 4.46 (0.91) 4.81 (0.89) 5.20 (0.89) 5.97 (1.14) 0.000 HDL 1.26 (0.38) 1.64 (0.43) 1.30 (0.25) 1.13 (0.20) 0.95 (0.19) 0.000 level: 0.087 Inactive 1926 (32.0%) 519 (34.5%) 487 (32.3%) 462 (30.8%) 458 (30.5%) Moderate 2375 (39.5%) 594 (39.5%) 591 (39.2%) 601 (40.1%) 589 (39.2%) Vigorous 432 (7.18%) 108 (7.18%) 94 (6.24%) 105 (7.00%) 125 (8.31%) Both moderate and vigorous 1282 (21.3%) 284 (18.9%) 334 (22.2%) 332 (22.1%) 332 (22.1%) Diabetes: <0.001 Yes 979 (16.3%) 304 (20.2%) 232 (15.4%) 226 (15.1%) 217 (14.4%) No 4891 (81.3%) 1175 (78.1%) 1232 (81.8%) 1234 (82.3%) 1250 (83.1%) Borderline 145 (2.41%) 26 (1.73%) 42 (2.79%) 40 (2.67%) 37 (2.46%) Glycohemoglobin 5.90 (1.16) 5.84 (1.10) 5.83 (1.02) 5.90 (1.11) 6.04 (1.37) <0.001 CRP 0.43 (0.96) 0.38 (0.93) 0.43 (1.09) 0.43 (1.00) 0.47 (0.78) 0.070 NLR 2.36 (1.35) 2.56 (1.54) 2.42 (1.41) 2.27 (1.11) 2.21 (1.27) <0.001 Marital_new: <0.001 Married/Living with partner 4348 (72.3%) 1009 (67.0%) 1109 (73.6%) 1122 (74.8%) 1108 (73.7%) Widowed/Divorced/Separated/ 1667 (27.7%) 496 (33.0%) 397 (26.4%) 378 (25.2%) 396 (26.3%) tPSA_new: <0.001 tPSA 4 565 (9.39%) 182 (12.1%) 146 (9.69%) 148 (9.87%) 89 (5.92%) Smoke: <0.001 Never 2289 (38.1%) 545 (36.2%) 584 (38.8%) 616 (41.1%) 544 (36.2%) Former 2356 (39.2%) 620 (41.2%) 626 (41.6%) 570 (38.0%) 540 (35.9%) Current 1370 (22.8%) 340 (22.6%) 296 (19.7%) 314 (20.9%) 420 (27.9%) BMI_new: <0.001 Normal(18.5 to < 25) 1348 (22.4%) 542 (36.0%) 350 (23.2%) 261 (17.4%) 195 (13.0%) Obese(30 or greater) 2075 (34.5%) 336 (22.3%) 507 (33.7%) 577 (38.5%) 655 (43.6%) Overweight(25 to < 30) 2528 (42.0%) 581 (38.6%) 638 (42.4%) 655 (43.7%) 654 (43.5%) Underweight(< 18.5) 64 (1.06%) 46 (3.06%) 11 (0.73%) 7 (0.47%) 0 (0.00%) hyptersion: 0.014 Yes 1512 (25.1%) 404 (26.8%) 359 (23.8%) 343 (22.9%) 406 (27.0%) No 4503 (74.9%) 1101 (73.2%) 1147 (76.2%) 1157 (77.1%) 1098 (73.0%) Q1–Q4: According to the NHHR, grouped by quartile. Associations between the NHHR and PSA The purpose of this study was to determine whether NHHR and PSA levels are correlated. The results, presented in Table 2 , reveal a consistently significant negative association across diverse statistical models. In the unadjusted model, a higher NHHR showed a significant inverse relationship with PSA levels, indicated by a regression coefficient (β) of -0.21 and a 95% Confidence Interval (CI) ranging from − 0.28 to -0.14, corroborated by a highly significant p-value (< 0.001). This basic model solely correlates NHHR and PSA without considering other variables. The connection remained strong (p < 0.001) when PIR, race, education level, and marital status were taken into account in the minimally adjusted model. The fully adjusted model confirmed the association's strength by taking into consideration a broader variety of factors, including race, PIR, CRP, education level, marital status, NLR, glycohemoglobin, diabetic status, physical activity, BMI, smoking status, alcohol use, and hypertension. In all models—unadjusted, minimally adjusted, and fully adjusted—higher NHHR quartiles were associated with lower PSA levels. The most pronounced negative association appeared in the highest quartile (Q4), evident in three models (p < 0.001). These findings clarify that a higher NHHR is strongly and inversely related to PSA levels, consistently across various methodological modifications. Table 2 Relationship between PSA level and NHHR Exposure Non-Adjusted Model Minimally Adjusted Model Fully Adjusted Model NHHR -0.21 (-0.28, -0.14), < 0.001 -0.20 (-0.27, -0.13), < 0.001 -0.15 (-0.22, -0.08), < 0.001 NHHR Q1 Q2 -0.25 (-0.48, -0.02), 0.035 -0.20 (-0.44, 0.03), 0.087 -0.12 (-0.36, 0.12), 0.3 Q3 -0.23 (-0.46, 0.00), 0.051 -0.18 (-0.42, 0.05), 0.12 -0.02 (-0.27, 0.22), 0.9 Q4 -0.78 (-1.1, -0.52), < 0.001 -0.73 (-1.0, -0.47), < 0.001 -0.54 (-0.83, -0.26), < 0.001 Unadjusted model makes no adjustments. Minimally adjusted model considers poverty income ratio, race/ethnicity, education level, and marital status. Fully adjusted model accounts for race, PIR, CRP, education level, marital status, NLR, glycohemoglobin, diabetic status, physical activity, BMI, smoking status, alcohol consumption, and hypertension. Subgroup analysis A rigorous subgroup analysis was performed to determine the strength of the connection between NHHR and PSA levels. This analysis was depicted in Fig. 2 and it focused on an array of demographic and clinical characteristics to determine if any specific variable influenced the observed association between NHHR and PSA. In the subgroup analyses, a comprehensive set of variables such as age, race, BMI, and so on was meticulously evaluated. The outcome of these analyses indicated no statistically significant interaction among these variables; all interaction p-values exceeded 0.05, confirming that these factors did not significantly modify the relationship between NHHR and PSA. This result implies that the negative correlation between NHHR and PSA is consistent across various subgroups, highlighting its reliability and broad applicability. By showing that the correlation holds true irrespective of diverse demographic backgrounds, lifestyle habits, and health-related factors, the findings enhance the clinical relevance of NHHR as a potential biomarker. Specifically, it suggests that NHHR could serve as a universally applicable indicator of PSA levels—a critical factor in prostate health assessment—across different population groups and clinical conditions. Figure 2. Subgroup and interaction analyses of the relationship between NHHR and PSA. A nonlinear relationship between NHHR and PSA A restricted cubic spline (RCS) analysis investigated the relationship between PSA levels and NHHR. As illustrated by the RCS curve in Fig. 3, there is a clear association between NHHR and the odds ratio (OR) of PSA levels. The overall p-value for this association is less than 0.001, indicating a statistically significant relationship between NHHR and PSA levels. However, the p-value for nonlinearity stands at 0.496, which suggests that the relationship between NHHR and PSA appears relatively linear within the range of NHHR values assessed. The curve delineates a decreasing trend in the OR of PSA as NHHR increases. With lower NHHR values, the OR of PSA is elevated, signaling a higher likelihood of increased PSA levels. Conversely, as NHHR values rise, the OR of PSA progressively diminishes, pointing to a reduced probability of elevated PSA levels. This pattern reveals an inverse relationship where higher NHHR levels correlate with lower PSA levels. Figure 3. Analysis of the PSA-NHHR association using the RCS model. Discussion A cross-sectional analysis using the NHANES database provides the most comprehensive assessment of the potential link between NHHR and PSA levels to date. Analyzing data from 6,015 participants, we noted a significant inverse correlation between elevated NHHR and PSA levels. Remarkably, this association persisted even after stratifying for variables such as age, BMI, PIR, diabetes, and hypertension. Subsequent logistic regression analyses indicated a decline of 0.15 in PSA levels per unit increase in NHHR, as per the fully adjusted model. The goal of this study is to look at the probable link between NHHR and PSA levels. Increasing research supports the notion that NHHR reliably predicts the likelihood of developing lipid-associated conditions[ 9 , 15 ]. While direct investigations are sparse, extensive evidence has surfaced revealing links between PCa and multiple lipid-associated factors. Higher BMI, high cholesterol levels, and other metabolic syndrome components have been linked to a significantly increased risk of developing PCa[ 16 – 19 ]. A 6-year retrospective study identified a significant correlation between serum triglycerides and PCa, suggesting that hypertriglyceridemia could elevate the risk of developing PCa[ 20 ]. There has been a correlation between PCa and high levels of serum TC and LDL-C[ 21 , 22 ]. Despite substantial study on the link between dyslipidemia and PCa, the basic processes are still not fully understood[ 23 ]. Furthermore, animal research has indicated that the proliferation of androgen-sensitive PCa cells is decreased in mice following low-fat diets and reduced serum triglyceride levels[ 24 ]. Several studies have linked raised LDL-C to an increased risk of disease[ 25 – 27 ]. It has been discovered that LDL-C activates the JAK1/JAK2/STAT3 pathway and increases the production of oncogenic proteins, which in turn promotes the growth, migration, and invasion of prostate cancer cells[ 28 ]. As a significant lipid marker for plaque prevention as well as a novel risk factor for the development of atherosclerotic plaque, NHHR is considered an emerging lipid biomarker for atherosclerosis[ 11 , 29 ]. According to a meta-analysis, individuals with PCa who have metabolic syndrome are more likely to have inferior oncological outcomes, particularly if their tumors have aggressive characteristics and biochemical recurrence[ 30 ]. The advancement of PCa may be associated with markers of insulin resistance, such as increased glucose and glycohemoglobin levels. Research has indicated that males with increased levels of glucose, insulin, and glycated hemoglobin are more vulnerable to PCa-related death or metastases[ 31 – 35 ]. Studies have shown that individuals with higher plasma glucose, insulin, and glycated hemoglobin levels have a higher risk of developing metastatic disease or dying from PCa[ 36 – 42 ]. In comparison to traditional lipid indicators, NHHR has shown higher diagnostic effectiveness in identifying the start of insulin resistance and metabolic syndrome[ 10 , 43 ]. The robust correlation between NHHR and a range of disorders further underscores its effectiveness as a lipid management tool[ 44 ]. Moreover, our research disclosed a non-linear relationship between NHHR and PSA, suggesting NHHR's potential as a crucial indicator of how lipid metabolism may influence PCa development. Study strengths and limitations Our study offers significant advantages over previously published work, contributing important new insights to this field. Firstly, by incorporating a large sample size of 6,015 participants, we substantially enhanced the generalizability and statistical power of our findings. Furthermore, through meticulous control of confounding covariates, we ensured a significant improvement in the reliability and validity of our results. These methodological innovations and refinements provided a robust foundation for evaluating the relationship between PSA and NHHR. To further elucidate the link between PSA and NHHR, our study innovatively employed RCS curve analysis. This approach enabled us to more precisely delineate the pattern of association between these two variables, offering new perspectives for future research directions and clinical practice. However, despite the study's multifaceted strengths, we acknowledge certain limitations in interpreting the results. A primary limitation stems from the inherent constraints of using the NHANES database for a cross-sectional survey, which precludes the establishment of causal relationships. Consequently, prospective cohort studies will be crucial in future research to confirm this association. We may have had a limited understanding of the disease landscape if participants diagnosed with PCa were excluded from the study, as well as factors that may influence PSA concentrations. Moreover, although we adjusted for numerous known confounders, the potential influence of unmeasured or unknown confounders cannot be eliminated. Finally, it is noteworthy that, as our study relied on the NHANES database, our findings are primarily applicable to the American population. This geographic limitation may restrict the generalizability of our results, necessitating further validation across different regions and populations. In conclusion, future research should address these limitations through broader sampling, diverse populations, and alternative study designs to deepen our understanding of this significant medical issue. Although our study made significant methodological and analytical advancements, offering new insights into the relationship between PSA and NHHR, these limitations should not be overlooked. Conclusion According to our research, PSA levels and NHHR are inversely correlated among American men. Future diagnoses of advanced PCa are more common in people with higher NHHR values. Further prospective studies are, however, warranted to corroborate the potential role of dyslipidemia in the pathogenesis of PCa. While our findings advance our understanding of the association between lipid metabolism and PCa, more study is needed to unravel the underlying processes and demonstrate causation. Nonetheless, our results underscore the importance of monitoring lipid profiles and their potential implications for PCa risk stratification and management strategies. Ultimately, a multidisciplinary approach integrating lipid management and PCa screening may lead to improved patient outcomes and personalized treatment approaches. Abbreviations PSA Prostate Specific Antigen NHANES National Health and Nutrition Examination Survey BMI Body mass index LDL-C Low-density lipoprotein cholesterol CRP C-reactive protein TC Total cholesterol CI Confidence Interval NHHR Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio HDL-C High-density lipoprotein cholesterol PIR Poverty income ratio RCS restricted cubic spline PCa Prostate Cancer NLR the ratio of neutrophils to lymphocytes Declarations Ethics approval and consent to participate The NCHS Ethics Review Board evaluated and gave its approval to this project. Written informed permission was given by the patients/participants to take part in this study. Consent for publication Not applicable . Availability of data and material The NHANES website, located at https://www.cdc.gov/nchs/nhanes/index.htm, has the dataset that was used for this investigation. Competing interests The authors declare no competing interests. Funding The National Natural Science of China (No. 82060465) provided a grant for this work. Authors' contributions The following people contributed to the study and manuscript: HGQ was in charge of designing the study and generating the report. LSQ and XKW contributed to data gathering and analysis. DLX examined the data, and LXB contributed to the article's revision. Co-first authors HGQ, LSQ, and XKW should be acknowledged for their equal contributions to this work. Acknowledgments. The authors respectfully thank all of the participants for their selfless dedication and effort, as well as the NHANES for providing important data. Contribute: The following people contributed to the study and manuscript: HGQ was in charge of designing the study and generating the report. LSQ and XKW contributed to data gathering and analysis. DLX examined the data, and LXB contributed to the article's revision. 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Decreased growth of established human prostate LNCaP tumors in nude mice fed a low-fat diet. J Natl Cancer Inst. 1995;87:1456–62. Jung YY, Ko J-H, Um J-Y, Chinnathambi A, Alharbi SA, Sethi G, et al. LDL cholesterol promotes the proliferation of prostate and pancreatic cancer cells by activating the STAT3 pathway. Journal of Cellular Physiology. 2021;236:5253–64. Raftopulos NL, Washaya TC, Niederprüm A, Egert A, Hakeem-Sanni MF, Varney B, et al. Prostate cancer cell proliferation is influenced by LDL-cholesterol availability and cholesteryl ester turnover. Cancer Metab. 2022;10:1–15. Cheng S, Zheng Q, Ding G, Li G. Influence of serum total cholesterol, LDL, HDL, and triglyceride on prostate cancer recurrence after radical prostatectomy. Cancer Management and Research. 2019;11:6651–61. Jung YY, Ko J, Um J, Chinnathambi A, Alharbi SA, Sethi G, et al. LDL cholesterol promotes the proliferation of prostate and pancreatic cancer cells by activating the STAT3 pathway. Journal Cellular Physiology. 2021;236:5253–64. Sheng G, Liu D, Kuang M, Zhong Y, Zhang S, Zou Y. Utility of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio in evaluating incident diabetes risk. DMSO. 2022;Volume 15:1677–86. Gacci M, Russo GI, De Nunzio C, Sebastianelli A, Salvi M, Vignozzi L, et al. Meta-analysis of metabolic syndrome and prostate cancer. Prostate Cancer Prostatic Dis. 2017;20:146–55. Guo Z-L, Weng X-T, Chan F-L, Gong L-L, Xiang S-T, Gan S, et al. Serum C-peptide concentration and prostate cancer: A meta-analysis of observational studies. Medicine. 2018;97:e11771. Saboori S, Rad EY, Birjandi M, Mohiti S, Falahi E. Serum insulin level, HOMA-IR and prostate cancer risk: A systematic review and meta-analysis. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13:110–5. Burton AJ, Gilbert R, Tilling K, Langdon R, Donovan JL, Holly JMP, et al. Circulating adiponectin and leptin and risk of overall and aggressive prostate cancer: A systematic review and meta-analysis. Sci Rep. 2021;11:320. Monroy‐Iglesias MJ, Russell B, Crawley D, Allen NE, Travis RC, Perez‐Cornago A, et al. Metabolic syndrome biomarkers and prostate cancer risk in the UK biobank. Intl Journal of Cancer. 2021;148:825–34. Jochems SHJ, Fritz J, Häggström C, Stattin P, Stocks T. Prediagnostic markers of insulin resistance and prostate cancer risk and death: A pooled study. Cancer Medicine. 2023;12:13732–44. Hammarsten J, Högstedt B. Hyperinsulinaemia: A prospective risk factor for lethal clinical prostate cancer. European Journal of Cancer. 2005;41:2887–95. Ma J, Li H, Giovannucci E, Mucci L, Qiu W, Nguyen PL, et al. Prediagnostic body-mass index, plasma C-peptide concentration, and prostate cancer-specific mortality in men with prostate cancer: A long-term survival analysis. Lancet Oncol. 2008;9:1039–47. Nik‐Ahd F, Howard LE, Eisenberg AT, Aronson WJ, Terris MK, Cooperberg MR, et al. Poorly controlled diabetes increases the risk of metastases and castration‐resistant prostate cancer in men undergoing radical prostatectomy: Results from the SEARCH database. Cancer. 2019;125:2861–7. Cai H, Xu Z, Xu T, Yu B, Zou Q. Diabetes mellitus is associated with elevated risk of mortality amongst patients with prostate cancer: A meta‐analysis of 11 cohort studies. Diabetes Metabolism Res. 2015;31:336–43. Bensimon L, Yin H, Suissa S, Pollak MN, Azoulay L. Type 2 diabetes and the risk of mortality among patients with prostate cancer. Cancer Causes Control. 2014;25:329–38. Arthur R, Møller H, Garmo H, Häggström C, Holmberg L, Stattin P, et al. Serum glucose, triglycerides, and cholesterol in relation to prostate cancer death in the swedish AMORIS study. Cancer Causes Control. 2019;30:195–206. Marrone MT, Selvin E, Barber JR, Platz EA, Joshu CE. Hyperglycemia, classified with multiple biomarkers simultaneously in men without diabetes, and risk of fatal prostate cancer. Cancer Prevention Research. 2019;12:103–12. Lin D, Qi Y, Huang C, Wu M, Wang C, Li F, et al. Associations of lipid parameters with insulin resistance and diabetes: A population-based study. Clinical Nutrition. 2018;37:1423–9. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and kidney stones: A cross-sectional study [Internet]. [cited 2024 Apr 17]. Available from: https://github.com/MuiseDestiny/zotero-style Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx 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. 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Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Shuangquan","middleName":"","lastName":"Lin","suffix":""},{"id":306034430,"identity":"e3c53f2e-7262-45c6-9ea6-a3e7ffce6d42","order_by":2,"name":"Kaiwen Xiao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Kaiwen","middleName":"","lastName":"Xiao","suffix":""},{"id":306034431,"identity":"38325e40-06c3-4c1e-89ca-3970fc4bb1a4","order_by":3,"name":"Lingxing Duan","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lingxing","middleName":"","lastName":"Duan","suffix":""},{"id":306034432,"identity":"d3b7b1cc-3e67-4c1c-b5a9-ac8ae149cdc3","order_by":4,"name":"Xiongbing Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfvb+x4//8EjIsbE3EKlFsucMmwGPjIUxP88BIrUY3MhhkOCxqUiUnJFAtJbcAwYSORIJBjcfb7zBUGMTTdhhZ94lPDA4I5FncDut2ILhWFpuAyEtfMcTDAwSeySKDW7nmEkwNhwmrIXhQIKBxMF/Eokbbp4hUovAiRwDyQYeicSZM3iI1CLZcyzNmIFHAhjIQL8kEOMXfvbmw48ZeOqAUXl4440PNTZE+AUJGEgkkKIcooVUHaNgFIyCUTAyAAD4t0BHnGQJhAAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Xiongbing","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-05-09 13:07:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4395346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4395346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57447408,"identity":"11cd3050-0b65-4530-aef5-b8f7ee091722","added_by":"auto","created_at":"2024-05-30 19:42:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113605,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart utilized to select the research participants.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4395346/v1/c092ac9101bed9a869f7396c.png"},{"id":57447409,"identity":"21ab64c1-5e91-4a10-add4-b056b944aa33","added_by":"auto","created_at":"2024-05-30 19:42:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105285,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses of the relationship between NHHR and \u0026nbsp;\u0026nbsp;PSA.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4395346/v1/b0ccea8d27e0e19f68162f38.png"},{"id":57447412,"identity":"2c686163-3c1b-49a4-903d-0a964f2f1441","added_by":"auto","created_at":"2024-05-30 19:42:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17230,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the PSA-NHHR association using the RCS \u0026nbsp;\u0026nbsp;model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4395346/v1/5ebd302320f863d820e3ffa4.png"},{"id":58023964,"identity":"cda0147d-0e39-420d-8d2d-3da5f6166bfc","added_by":"auto","created_at":"2024-06-10 05:59:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4395346/v1/03a23a68-9b9c-4602-9c62-bb997010a13f.pdf"},{"id":57447411,"identity":"d9c47dcf-6058-4798-9c94-2e04f1ef7bb0","added_by":"auto","created_at":"2024-05-30 19:42:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29332,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4395346/v1/35a70f688fc993160dc6c40e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and Prostate specific antigen (PSA): a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe most often diagnosed disease and the second largest cause of cancer-related mortality in males is prostate cancer (PCa)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The introduction of prostate-specific antigen (PSA) testing has revolutionized the landscape of PCa diagnosis and management[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This diagnostic tool empowers clinicians to detect PCa at earlier stages when they are typically small, low-grade, and localized[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, a growing body of evidence suggests that various factors beyond the presence of cancer can influence PSA levels. Notably, blood cholesterol levels have been implicated in the progression of PCa, with studies indicating that hypercholesterolemia, arising from diets heavy in fat and cholesterol, not only raises the chance of PCa in older men but also promotes the disease's progression and metastatic spread [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, research has highlighted the role of a diet heavy in fat as a modifiable risk factor associated with the progression of the condition.\u003c/p\u003e \u003cp\u003eRecent research increasingly suggests that lipid metabolism is crucial in the progression of PCa. There are signs that cholesterol's impact on inflammatory processes may have a role in the onset, severity, and progression of PCa[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prior investigations have uncovered an inverse relationship between serum triglyceride levels and PSA in adult male populations[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A novel marker of atherosclerosis risk, NHHR has exhibited superior diagnostic performance in assessing the risk of cerebrovascular diseases, insulin resistance, and metabolic syndrome compared to traditional lipid parameters [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. But the connection between the PSA and NHHR is still unknown. As a result, this study aims to clarify the connection between NHHR and PSA levels through data from the NHANES collected between 2003 and 2010.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eInitiated by the National Center for Health Statistics (NCHS) and conducted biennially by the Centers for Disease Control and Prevention (CDC) since 1960, NHANES is a comprehensive, nationwide survey that assesses the public of the United States in terms of wellness and dietary habits. NHANES utilizes interviews to obtain data, which includes a variety of information, including demographics, socioeconomics, dietary habits, and health-related issues, with the aim of representing diverse ethnic groups and addressing various health concerns. The insights gleaned from this extensive survey are essential for understanding current disease trends and informing the development of public health policies in the United States. Moreover, NHANES serves as an indispensable tool for scholars, policymakers, and healthcare workers who are committed to tackling the nation's health challenges and enhancing the well-being of its citizens by providing a rich repository of data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eInformation from four cycles of the NHANES survey, covering the period from 2003 to 2010, was carefully chosen for this investigation. The initial cohort consisted of 41,156 participants. Exclusion criteria were applied to the initial cohort, the following: (1) being female (n\u0026thinsp;=\u0026thinsp;20,785); (2) being under 40 years old (n\u0026thinsp;=\u0026thinsp;13,231); (3) being diagnosed with prostate cancer (n\u0026thinsp;=\u0026thinsp;314); (4) the presence of factors influencing PSA levels, including a prostatitis diagnosis (n\u0026thinsp;=\u0026thinsp;76) and having undergone a rectal examination within the previous week (n\u0026thinsp;=\u0026thinsp;61); (5) a lack of PSA data (n\u0026thinsp;=\u0026thinsp;674); and (6) missing NHHR information (n\u0026thinsp;=\u0026thinsp;0). These exclusions were implemented sequentially, culminating in the removal of 35,141 individuals from the original pool. Consequently, the final analytical cohort was composed of 6,015 participants, as depicted in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure1. Flowchart utilized to select the research participants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposure definition\u003c/h2\u003e \u003cp\u003eThe focus of this study was on NHHR, which was identified as the key variable. Calculation of NHHR employed methodologies derived from previous research [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Total cholesterol (TC) and HDL cholesterol (HDL-C) levels were evaluated using enzymatic assays conducted on automated biochemistry analyzers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By analyzing the lipid profiles of fasting individuals, Non-HDL cholesterol (Non-HDL-C) was obtained by subtracting HDL-C from TC. Individuals were then separated into four groups according to the NHHR quartile ranges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome definition\u003c/h2\u003e \u003cp\u003eIn this research, the concentration of serum PSA, quantified in nanograms per milliliter (ng/mL), was determined through the use of the Hybritech Total PSA Assay, implemented on the Beckman Access Immunoassay System[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our study categorized PSA data into two groups: individuals with PSA levels at or below 4 ng/mL and those above 4 ng/mL. These groups functioned as the outcome variables of the investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe analytical study involved examining several continuous variables, such as age, the poverty-to-income ratio (PIR), C-reactive protein (CRP) levels, the ratio of neutrophils to lymphocytes (NLR), and glycohemoglobin percentages. The categorical variables included classifications of race/ethnicity, educational attainment, and marital status. Specifically, individuals were grouped by marital status into cohabitating/partnered and single, encompassing categories such as never married, widowed, divorced, or separated. Furthermore, the study addressed binary variables such as alcohol usage and smoking history, denoting individuals as never, former, or current smokers, and examining physical activity ranging from inactive to various intensities including moderate and vigorous efforts. Participants reporting alcohol usage during any two 24-hour periods were considered alcohol users. Smokers were defined based on lifetime cigarette consumption: non-smokers, former smokers, or current smokers. Physical activity evaluation considered both intense activities, like running and basketball, and regular moderate activities such as brisk walking, swimming, and cycling. To ensure a robust and reliable dataset, the NHANES database was meticulously curated; the analysis's accuracy was enhanced by utilizing the MICE package for managing missing data[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis.\u003c/h2\u003e \u003cp\u003e Rigorously adhering to the CDC guidelines outlined in the CDC\u0026rsquo;s NHANES Tutorials, our statistical analysis enhanced the robustness of our findings. Initially, the analysis involved a descriptive review of participant data, summarizing continuous variables using means with standard deviations and medians coupled with interquartile ranges, while categorical variables were detailed through frequencies and percentages. We employed the chi-square (X\u0026sup2;) test for assessing differences among categorical groups and conducted continuous data analysis using one-way analysis of variance for normally distributed data and the Kruskal-Wallis H test for skewed data. The sensitivity of the analysis was increased by segmenting PSA levels into categories using a cutoff of 4 ng/mL. To investigate the links between PSA levels and NHHR, logistic regression analyses were carried out, yielding models that were non-adjusted, slightly adjusted, and completely adjusted to illuminate the complicated connections. To investigate the connection between NHHR and PSA within particular participant segments, stratified multivariate logistic regression was employed. We also utilized restricted cubic spline (RCS) regression analysis to investigate potential non-linear associations between NHHR and PSA levels. The statistical calculations were utilized using R version 4.3.3, and P-values below 0.05 were regarded as significant, employing a two-tailed testing approach.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe baseline characteristics of the participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the initial profiles of the individuals involved in the study, as obtained from NHANES spanning 2003\u0026ndash;2010, and organizes them based on NHHR quartile divisions. The study's participants were 59.7 years old on average, with a standard deviation of 12.7 years. Observations revealed significant differences across NHHR quartiles in several variables, all showing p-values less than 0.05. These variables include age, race, education, PIR, alcohol consumption, TC, HDL, diabetes status, glycohemoglobin levels, NLR, marital status, PSA levels, smoking status, BMI, and hypertension. In the lowest NHHR quartile (Q1), participants were generally younger, and a higher proportion were non-Hispanic White individuals. This quartile also reported higher levels of educational attainment and higher PIR. Furthermore, participants in Q1 exhibited lower TC and higher HDL levels, a lower prevalence of diabetes, lower glycohemoglobin and NLR values, and fewer reported being current or former smokers compared to those in higher NHHR quartiles. Conversely, participants in the highest NHHR quartile (Q4) were typically older and had a higher representation of Mexican American and other Hispanic individuals. This quartile also displayed lower educational levels and lower PIR. Clinically, Q4 participants exhibited higher TC and lower HDL levels, an increased prevalence of diabetes, higher glycohemoglobin and NLR values, and a greater proportion were current or former smokers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFeatures of the population under investigation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ALL]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.overall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;6015\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1505\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1506\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1500\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1504\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.7 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.6 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.9 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.8 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1039 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e225 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e289 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e353 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389 (6.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (4.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (5.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102 (6.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e132 (8.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3252 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e805 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e843 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e822 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e782 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1105 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e398 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e233 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e174 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230 (3.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (3.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (3.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54 (3.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63 (4.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDUcation:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1012 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e270 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e306 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e872 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e209 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4131 (68.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1037 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1085 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1021 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e988 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.86 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.93 (1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.82 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.58 (1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5054 (84.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1149 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1251 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1317 (87.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1337 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e961 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e356 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e167 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.11 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.46 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.81 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.20 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.97 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elevel:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1926 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e487 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e462 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e458 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2375 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e591 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e601 (40.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e589 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (7.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (7.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (6.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105 (7.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e125 (8.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth moderate and vigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1282 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e284 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e334 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e332 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e332 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e979 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e217 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4891 (81.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1175 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1232 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1234 (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1250 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145 (2.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (1.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (2.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (2.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37 (2.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycohemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.90 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.84 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.83 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.90 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.04 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56 (1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.42 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.27 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21 (1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital_new:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4348 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1009 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1109 (73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1122 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1108 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1667 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e496 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e397 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e378 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e396 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA_new:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5450 (90.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1323 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1360 (90.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1352 (90.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1415 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e565 (9.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146 (9.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148 (9.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89 (5.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2289 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e545 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e584 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e616 (41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e544 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2356 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e620 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e626 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e570 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e540 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1370 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e314 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e420 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI_new:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal(18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1348 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e542 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e350 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e261 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e195 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese(30 or greater)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2075 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e577 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e655 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight(25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2528 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e581 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e638 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e655 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e654 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (1.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (3.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (0.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (0.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyptersion:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1512 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e359 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e343 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e406 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4503 (74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1101 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1147 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1157 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1098 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eQ1\u0026ndash;Q4: According to the NHHR, grouped by quartile.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between the NHHR and PSA\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to determine whether NHHR and PSA levels are correlated. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal a consistently significant negative association across diverse statistical models. In the unadjusted model, a higher NHHR showed a significant inverse relationship with PSA levels, indicated by a regression coefficient (β) of -0.21 and a 95% Confidence Interval (CI) ranging from \u0026minus;\u0026thinsp;0.28 to -0.14, corroborated by a highly significant p-value (\u0026lt;\u0026thinsp;0.001). This basic model solely correlates NHHR and PSA without considering other variables. The connection remained strong (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when PIR, race, education level, and marital status were taken into account in the minimally adjusted model. The fully adjusted model confirmed the association's strength by taking into consideration a broader variety of factors, including race, PIR, CRP, education level, marital status, NLR, glycohemoglobin, diabetic status, physical activity, BMI, smoking status, alcohol use, and hypertension. In all models\u0026mdash;unadjusted, minimally adjusted, and fully adjusted\u0026mdash;higher NHHR quartiles were associated with lower PSA levels. The most pronounced negative association appeared in the highest quartile (Q4), evident in three models (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings clarify that a higher NHHR is strongly and inversely related to PSA levels, consistently across various methodological modifications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between PSA level and NHHR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimally Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFully Adjusted Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.21 (-0.28, -0.14), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.20 (-0.27, -0.13), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.15 (-0.22, -0.08), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.25 (-0.48, -0.02), 0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.20 (-0.44, 0.03), 0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.12 (-0.36, 0.12), 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.23 (-0.46, 0.00), 0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.18 (-0.42, 0.05), 0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.27, 0.22), 0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-0.78 (-1.1, -0.52), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.73 (-1.0, -0.47), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.54 (-0.83, -0.26), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eUnadjusted model makes no adjustments.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMinimally adjusted model considers poverty income ratio, race/ethnicity, education level, and marital status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFully adjusted model accounts for race, PIR, CRP, education level, marital status, NLR, glycohemoglobin, diabetic status, physical activity, BMI, smoking status, alcohol consumption, and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eA rigorous subgroup analysis was performed to determine the strength of the connection between NHHR and PSA levels. This analysis was depicted in Fig.\u0026nbsp;2 and it focused on an array of demographic and clinical characteristics to determine if any specific variable influenced the observed association between NHHR and PSA. In the subgroup analyses, a comprehensive set of variables such as age, race, BMI, and so on was meticulously evaluated. The outcome of these analyses indicated no statistically significant interaction among these variables; all interaction p-values exceeded 0.05, confirming that these factors did not significantly modify the relationship between NHHR and PSA. This result implies that the negative correlation between NHHR and PSA is consistent across various subgroups, highlighting its reliability and broad applicability. By showing that the correlation holds true irrespective of diverse demographic backgrounds, lifestyle habits, and health-related factors, the findings enhance the clinical relevance of NHHR as a potential biomarker. Specifically, it suggests that NHHR could serve as a universally applicable indicator of PSA levels\u0026mdash;a critical factor in prostate health assessment\u0026mdash;across different population groups and clinical conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 2. Subgroup and interaction analyses of the relationship between NHHR and PSA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eA nonlinear relationship between NHHR and PSA\u003c/h2\u003e \u003cp\u003eA restricted cubic spline (RCS) analysis investigated the relationship between PSA levels and NHHR. As illustrated by the RCS curve in Fig.\u0026nbsp;3, there is a clear association between NHHR and the odds ratio (OR) of PSA levels. The overall p-value for this association is less than 0.001, indicating a statistically significant relationship between NHHR and PSA levels. However, the p-value for nonlinearity stands at 0.496, which suggests that the relationship between NHHR and PSA appears relatively linear within the range of NHHR values assessed. The curve delineates a decreasing trend in the OR of PSA as NHHR increases. With lower NHHR values, the OR of PSA is elevated, signaling a higher likelihood of increased PSA levels. Conversely, as NHHR values rise, the OR of PSA progressively diminishes, pointing to a reduced probability of elevated PSA levels. This pattern reveals an inverse relationship where higher NHHR levels correlate with lower PSA levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 3. Analysis of the PSA-NHHR association using the RCS model.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA cross-sectional analysis using the NHANES database provides the most comprehensive assessment of the potential link between NHHR and PSA levels to date. Analyzing data from 6,015 participants, we noted a significant inverse correlation between elevated NHHR and PSA levels. Remarkably, this association persisted even after stratifying for variables such as age, BMI, PIR, diabetes, and hypertension. Subsequent logistic regression analyses indicated a decline of 0.15 in PSA levels per unit increase in NHHR, as per the fully adjusted model.\u003c/p\u003e \u003cp\u003eThe goal of this study is to look at the probable link between NHHR and PSA levels. Increasing research supports the notion that NHHR reliably predicts the likelihood of developing lipid-associated conditions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While direct investigations are sparse, extensive evidence has surfaced revealing links between PCa and multiple lipid-associated factors. Higher BMI, high cholesterol levels, and other metabolic syndrome components have been linked to a significantly increased risk of developing PCa[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A 6-year retrospective study identified a significant correlation between serum triglycerides and PCa, suggesting that hypertriglyceridemia could elevate the risk of developing PCa[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. There has been a correlation between PCa and high levels of serum TC and LDL-C[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite substantial study on the link between dyslipidemia and PCa, the basic processes are still not fully understood[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, animal research has indicated that the proliferation of androgen-sensitive PCa cells is decreased in mice following low-fat diets and reduced serum triglyceride levels[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several studies have linked raised LDL-C to an increased risk of disease[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It has been discovered that LDL-C activates the JAK1/JAK2/STAT3 pathway and increases the production of oncogenic proteins, which in turn promotes the growth, migration, and invasion of prostate cancer cells[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a significant lipid marker for plaque prevention as well as a novel risk factor for the development of atherosclerotic plaque, NHHR is considered an emerging lipid biomarker for atherosclerosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. According to a meta-analysis, individuals with PCa who have metabolic syndrome are more likely to have inferior oncological outcomes, particularly if their tumors have aggressive characteristics and biochemical recurrence[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The advancement of PCa may be associated with markers of insulin resistance, such as increased glucose and glycohemoglobin levels. Research has indicated that males with increased levels of glucose, insulin, and glycated hemoglobin are more vulnerable to PCa-related death or metastases[\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies have shown that individuals with higher plasma glucose, insulin, and glycated hemoglobin levels have a higher risk of developing metastatic disease or dying from PCa[\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In comparison to traditional lipid indicators, NHHR has shown higher diagnostic effectiveness in identifying the start of insulin resistance and metabolic syndrome[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The robust correlation between NHHR and a range of disorders further underscores its effectiveness as a lipid management tool[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, our research disclosed a non-linear relationship between NHHR and PSA, suggesting NHHR's potential as a crucial indicator of how lipid metabolism may influence PCa development.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eOur study offers significant advantages over previously published work, contributing important new insights to this field. Firstly, by incorporating a large sample size of 6,015 participants, we substantially enhanced the generalizability and statistical power of our findings. Furthermore, through meticulous control of confounding covariates, we ensured a significant improvement in the reliability and validity of our results. These methodological innovations and refinements provided a robust foundation for evaluating the relationship between PSA and NHHR. To further elucidate the link between PSA and NHHR, our study innovatively employed RCS curve analysis. This approach enabled us to more precisely delineate the pattern of association between these two variables, offering new perspectives for future research directions and clinical practice. However, despite the study's multifaceted strengths, we acknowledge certain limitations in interpreting the results. A primary limitation stems from the inherent constraints of using the NHANES database for a cross-sectional survey, which precludes the establishment of causal relationships. Consequently, prospective cohort studies will be crucial in future research to confirm this association. We may have had a limited understanding of the disease landscape if participants diagnosed with PCa were excluded from the study, as well as factors that may influence PSA concentrations. Moreover, although we adjusted for numerous known confounders, the potential influence of unmeasured or unknown confounders cannot be eliminated. Finally, it is noteworthy that, as our study relied on the NHANES database, our findings are primarily applicable to the American population. This geographic limitation may restrict the generalizability of our results, necessitating further validation across different regions and populations. In conclusion, future research should address these limitations through broader sampling, diverse populations, and alternative study designs to deepen our understanding of this significant medical issue. Although our study made significant methodological and analytical advancements, offering new insights into the relationship between PSA and NHHR, these limitations should not be overlooked.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e According to our research, PSA levels and NHHR are inversely correlated among American men. Future diagnoses of advanced PCa are more common in people with higher NHHR values. Further prospective studies are, however, warranted to corroborate the potential role of dyslipidemia in the pathogenesis of PCa. While our findings advance our understanding of the association between lipid metabolism and PCa, more study is needed to unravel the underlying processes and demonstrate causation. Nonetheless, our results underscore the importance of monitoring lipid profiles and their potential implications for PCa risk stratification and management strategies. Ultimately, a multidisciplinary approach integrating lipid management and PCa screening may lead to improved patient outcomes and personalized treatment approaches.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" \u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProstate Specific Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNHHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoverty income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erestricted cubic spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProstate Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ethe ratio of neutrophils to lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NCHS Ethics Review Board evaluated and gave its approval to this project. Written informed permission was given by the patients/participants to take part in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES website, located at https://www.cdc.gov/nchs/nhanes/index.htm, has the dataset that was used for this investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science of China (No. 82060465) provided a grant for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following people contributed to the study and manuscript: HGQ was in charge of designing the study and generating the report. LSQ and XKW contributed to data gathering and analysis. DLX examined the data, and LXB contributed to the article\u0026apos;s revision. Co-first authors HGQ, LSQ, and XKW should be acknowledged for their equal contributions to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors respectfully thank all of the participants for their selfless dedication and effort, as well as the NHANES for providing important data.\u003c/p\u003e\n\u003cp\u003eContribute:\u0026nbsp;The following people contributed to the study and manuscript: HGQ was in charge of designing the study and generating the report. LSQ and XKW contributed to data gathering and analysis. DLX examined the data, and LXB contributed to the article\u0026apos;s revision. Co-first authors HGQ, LSQ, and XKW should be acknowledged for their equal contributions to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71:7\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eLilja H, Ulmert D, Vickers AJ. Prostate-specific antigen and prostate cancer: Prediction, detection and monitoring. Nat Rev Cancer. 2008;8:268\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003ePardo JC, Ruiz de Porras V, Gil J, Font A, Puig-Domingo M, Jord\u0026agrave; M. Lipid metabolism and epigenetics crosstalk in prostate cancer. Nutrients. 2022;14:851. \u003c/li\u003e\n\u003cli\u003eLocke JA, Guns EST, Lehman ML, Ettinger S, Zoubeidi A, Lubik A, et al. Arachidonic acid activation of intratumoral steroid synthesis during prostate cancer progression to castration resistance. Prostate. 2010;70:239\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eAllott EH, Freedland SJ. 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Poorly controlled diabetes increases the risk of metastases and castration‐resistant prostate cancer in men undergoing radical prostatectomy: Results from the SEARCH database. Cancer. 2019;125:2861\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eCai H, Xu Z, Xu T, Yu B, Zou Q. Diabetes mellitus is associated with elevated risk of mortality amongst patients with prostate cancer: A meta‐analysis of 11 cohort studies. Diabetes Metabolism Res. 2015;31:336\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eBensimon L, Yin H, Suissa S, Pollak MN, Azoulay L. Type 2 diabetes and the risk of mortality among patients with prostate cancer. Cancer Causes Control. 2014;25:329\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eArthur R, M\u0026oslash;ller H, Garmo H, H\u0026auml;ggstr\u0026ouml;m C, Holmberg L, Stattin P, et al. Serum glucose, triglycerides, and cholesterol in relation to prostate cancer death in the swedish AMORIS study. Cancer Causes Control. 2019;30:195\u0026ndash;206. \u003c/li\u003e\n\u003cli\u003eMarrone MT, Selvin E, Barber JR, Platz EA, Joshu CE. Hyperglycemia, classified with multiple biomarkers simultaneously in men without diabetes, and risk of fatal prostate cancer. Cancer Prevention Research. 2019;12:103\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eLin D, Qi Y, Huang C, Wu M, Wang C, Li F, et al. Associations of lipid parameters with insulin resistance and diabetes: A population-based study. Clinical Nutrition. 2018;37:1423\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eThe association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and kidney stones: A cross-sectional study [Internet]. [cited 2024 Apr 17]. Available from: https://github.com/MuiseDestiny/zotero-style\u003c/li\u003e\n\u003c/ol\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":"PSA, NHHR, NHANES, prostate cancer, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4395346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4395346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is mounting evidence that prostate-specific antigen (PSA) levels can be influenced by lipid metabolism. However, there is still no clear relationship between PSA levels and the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR). This study's goal is to evaluate the association between PSA and NHHR by using data from the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used data from the NHANES from 2003 to 2010 to examine the connection between PSA and NHHR. Out of 41,156 participants, 6,015 met our inclusion criteria. Serum triglycerides served as our independent variable, while PSA was the dependent variable.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study's participants were 59.7 years old on average, with a standard deviation of 12.7 years. After controlling for potential confounding variables, a one-unit increment in NHHR was associated with a decrease of 0.15 ng/mL in PSA levels, with a 95% confidence interval ranging from \u0026minus;\u0026thinsp;0.22 to -0.08, indicating a statistically significant inverse relationship. Moreover, a restricted cubic spline analysis further corroborated the presence of a statistically significant, largely inverse association between NHHR and PSA concentrations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong American males, NHHR is inversely correlated with PSA. This negative correlation might make it challenging for men with higher triglycerides to detect asymptomatic prostate cancer and achieve early diagnosis.\u003c/p\u003e","manuscriptTitle":"The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and Prostate specific antigen (PSA): a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 19:42:10","doi":"10.21203/rs.3.rs-4395346/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":"f684916a-aef6-48f9-9a81-d342946c6042","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T09:02:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 19:42:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4395346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4395346","identity":"rs-4395346","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}