{"paper_id":"1d693e40-697b-46dc-9c65-0202a2e81703","body_text":"Association between High-Density Lipoproteins (HDLs) and Prostate Specific Antigen (PSA): a cross-sectional study from NHANES database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between High-Density Lipoproteins (HDLs) and Prostate Specific Antigen (PSA): a cross-sectional study from NHANES database Mohamed Mohamoud Adan, Bowen Hu, Minbo Yan Yan, Gonghui Li, Sakarie Mustafe Hidig This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3925868/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: According to growing evidence, high-density lipoproteins (HDLs ) may raise PSA levels in the prostate. The link between HDL-C and PSA, on the other hand, is debatable and challenging. Hence, this research examined the relationship between HDL-C and PSA in men using the National Health and Nutrition Examination Survey (NHANES) database. Methods: We extracted NHANES data for five cycles from 2001 to 2010. The data used for analysis included PSA concentrations, sociodemographic data, and laboratory data. After the screening, 6,669 of 52,195 participants were included in the study. Participants were divided into 4 groups based on HDL-C quartiles. We analyzed categorical and continuous variables using weighted chi-square tests and linear regression models to compare differences between groups. We constructed 3 weighted multivariate linear regression models and assessed the association between HDL-C and PSA using a smoothed curve fit. Results: In our study, unadjusted and adjusted multivariate linear regression models revealed a significant positive association between prostate-specific antigen (PSA) concentrations and serum high-density lipoprotein cholesterol (HDL-C) levels. Specifically, each unit increase in HDL-C ratio was associated with an increase in PSA concentration by 0.470 ng/mL (P < 0.001) in the unadjusted model. In minimally adjusted models, accounting for socioeconomic and demographic factors, this correlation remained significant, with an increase of 0.408 ng/mL per unit increase in serum HDL-C (P < 0.001). Furthermore, the stratified analysis revealed various impacts based on socioeconomic status and HDL-C levels, with a significant interaction between household income and HDL-C levels (P = 0.037). Exclusion of subjects with low HDL-C levels strengthened the association, showing a significant increase in PSA concentration with higher HDL-C levels (0.50 ng/mL per 1 mmol/L increase, P = 0.009). Our findings suggest a nuanced relationship between HDL-C levels, socioeconomic factors, and PSA concentrations, highlighting the potential importance of considering these factors in prostate cancer screening and risk assessment. Conclusion: This study found a positive association between serum HDL-C and PSA concentrations in adult men in the United States without a prostate cancer diagnosis. Moreover, People with low HDL-C are more likely to be diagnosed with prostate cancer in the late stage of the disease. Hence, people with high levels of HDL-C should be tested for PSA to help early detection of prostate cancer. Prostate-Specific Antigen (PSA) High-Density Lipoproteins (HDL) Prostate Cancer (PCa) Triglycerides National Health and Nutrition Examination Survey (NHANES) Figures Figure 1 Figure 2 1. Introduction Prostate cancer is the second most diagnosed cancer and the fifth leading cause of cancer-related deaths among men worldwide [ 1 ]. In the United States alone, it is estimated that there were about 288,300 new cases of prostate cancer and 34,700 deaths from the disease in 2021, and in 2022 the number of incidences increased to 3,523,230 [ 2 ][ 3 ]. Prostate cancer is the most frequently diagnosed cancer[ 2 ] Early detection and treatment are crucial for improving prostate cancer patient's prognosis and survival rates [ 4 ]. Prostate-specific antigen (PSA) is a protein produced by the prostate gland, and its levels can be measured through a simple blood test. The PSA test is widely used for prostate cancer screening[ 5 ][ 6 ], but its effectiveness in reducing mortality from prostate cancer remains controversial [ 7 ]. Elevated PSA levels can also be caused by non-cancerous conditions such as prostatitis and benign prostatic hyperplasia (BPH), leading to false-positive results and unnecessary biopsies[ 6 ][ 8 ][ 9 ] [ 10 ]. High-density lipoprotein cholesterol (HDL-C) is a lipoprotein that transports cholesterol from peripheral tissues to the liver for excretion. HDL-C is commonly referred to as \"good\" cholesterol because it has been shown to have protective effects against cardiovascular disease [ 11 ]. However, in recent years, several studies have investigated the association between HDL-C levels and prostate cancer risk. Several hypotheses have been proposed to explain the potential link between HDL-C levels and prostate cancer. One theory is that high levels of HDL-C may be protective against prostate cancer by removing excess cholesterol from prostate cells and reducing oxidative stress [ 12 ]. Another hypothesis is that low HDL-C levels may be a result of inflammation, which has been linked to the development and progression of prostate cancer [ 13 ].In addition to its potential role in prostate cancer risk, HDL-C levels may also be related to PSA levels. Some studies have reported an inverse association between HDL-C levels and PSA levels, suggesting that men with higher HDL-C levels may have lower PSA levels[ 14 ]. However, the relationship between HDL-C and PSA levels is not fully understood, and additional research is needed to clarify this association. Therefore, the purpose of this article is to we examine the relationship between HDL-C and PSA in U.S. men using the National Health and Nutrition Examination Survey (NHANES) database and to discuss the potential implications of this relationship for prostate cancer screening and diagnosis. 2. Materials and Methods 2.1. Data Availability The NHANES, initially established in 1960, is a survey conducted by the National Center for Disease Control (CDC) and the Prevention National Center for Health Statistics. The objective of this assessment is to evaluate the physical well-being and dietary condition of both adults and children living in the United States. For demographic and methodological information, please refer to the NHANES website www.cdc.gov/nchs/nhanes [ 15 ], viewed on October 7, 2022. The NHANES protocols have been authorized by the National Center for the Health Statistics Research Ethics Review Board. 2.2. Study Population NHANES employs a stratified, multi-stage random sampling design, serving as a nationally representative nutrition survey of the broader U.S. population. Our research incorporates five cycles of NHANES data covering the period from 2001 to 2010. The dataset utilized for our secondary analysis encompasses Prostate-Specific Antigen (PSA) concentrations, socio-demographic data, and laboratory information. Our research systematically excluded participants based on specific criteria: (1) Age below 40 years (n = 34,634); (2) Individuals diagnosed with Prostate Cancer (PCa) (n = 377); (3) Factors impacting PSA levels, including prostatitis diagnosis, statin drug usage, recent prostate biopsy within one week, and urinary system surgery within one month (n = 492); (4) Instances of missing PSA data (n = 10,023); (5) No instances of missing High-Density Lipoprotein Cholesterol (HDL-C) data (n = 0). Following this meticulous screening process, 6,669 individuals out of the initial 52,195 participants were deemed suitable for inclusion in our study, as illustrated in (Fig. 1 ). Furthermore, our study strictly adheres to the ethical guidelines delineated in the World Medical Association's Declaration of Helsinki concerning the design and implementation of the study. The foundation of our data analysis rests upon the utilization of NHANES data in our research. 2.3. Statistical Analysis We conducted all statistical analyses using the R Package and EmpowerStats ( http://www.empowerstats.com ), with a complex weighted sampling design from NHANES. Participants were characterized according to the quartiles of HDL-C (Category1: 19–40; Category2: 40–46; Category 3: 46–56; Category4: 56–148). We used percentages for categorical variables and means ± standard deviations for continuous variables. To investigate group differences, we performed weighted χ2 tests for categorical variables and linear regression models for continuous variables. The link between HDL-C and PSA was evaluated using a weighted multivariate linear regression model. We established three models: an unadjusted model (Model 1), a minimally adjusted model (Model 2) considering factors like poverty-income ratio, race, military participation, marriage, and education, and a fully adjusted model (Model 3) incorporating additional adjustments for triglycerides, LDL cholesterol, monocyte count, neutrophil count, red blood cell count, hemoglobin, platelet count, and C-reactive protein, Then Stratified analyses were done based on age, family income, race, military status, marital status, and education, with interactions examined. Additionally, assuming the normal reference value range of HDL-C as 1.04 mmol/L, we did a weighted multivariate linear regression analysis, after removing people with HDL-C levels below 1.04 ug/dl to limit the impact of very low HDL-C in U.S. men. In our study, a p-value < 0.05 was considered statistically significant. 3. Results 3.1-Baseline characteristics of the selected participant We performed statistical analyses of HDL, triglyceride, and PSA levels. HDL-C was grouped into four quartiles (Q1-Q4), and there were no significant differences in military participation and platelet count between the groups. People with higher PSA, older age, higher household income, higher level of education, married, high in vitamin D, high LDL-C, and high in total cholesterol have high HDL-C. On the other hand, those with elevated serum HDL-C have lower triglycerides, lower WBC, lower LY, lower MONO, lower neutrophils, lower red blood cells, lower hemoglobin, and lower C-reactive protein. In our study, non-Hispanic whites were the main participants. Table 1 Baseline characteristics of the selected participants. HDL-Cholesterol (mmol/L) Q1 Q2 Q3 Q4 P-value N 1458 1682 1797 1732 Total prostste specific antigen(ng/ml) 1.56 ± 2.97 1.64 ± 2.18 1.73 ± 2.78 1.94 ± 3.67 0.002 Age(years) 57.24 ± 11.76 58.43 ± 11.74 58.93 ± 12.09 59.44 ± 11.73 < 0.001 Family income 2.62 ± 1.61 2.78 ± 1.59 2.95 ± 1.64 2.91 ± 1.66 < 0.001 Military Status 0.285 Yes 467 (32.03%) 583 (34.66%) 613 (34.13%) 608 (35.10%) No 991(67.97%) 1099 (65.34%) 1183 (65.87%) 1124(64.90%) Education < 0.001 Less than High School Grad 505 (34.66%) 503 (29.90%) 541(30.16%) 500 (28.90%) High School Grad 353 (24.23%) 408 (24.26%) 398 (22.19%) 389 (22.49%) More than High School Grad 599 (41.11%) 771(45.84%) 855 (47.66%) 841(48.61%) Marital status < 0.001 More than high school 1015 (69.66%) 1163 (69.27%) 1287(71.62%) 1101(63.64%) More than high school 361(24.78%) 439 (26.15%) 429 (23.87%) 535 (30.92%) Living with a partner 81(5.56%) 77(4.59%) 81(4.51%) 94 (5.43%) Race < 0.001 Mexican American 305 (20.92%) 332(19.74%) 338(18.81%) 238(13.74%) Other Hispanic 116 (7.96%) 90(5.35%) 111(6.18%) 99 (5.72%) Non-Hispanic White 825 (56.58%) 942(56.00%) 950 (52.87%) 859 (49.60%) Non-Hispanic Black 157(10.77%) 264(15.70%) 341(18.98%) 483 (27.89%) Other race/ethnicity 55 (3.77%) 54(3.21%) 57(3.17%) 53 (3.06%) Vitamin D(nmol/L) 58.73 ± 19.95 59.66 ± 20.19 61.41 ± 20.90 60.87 ± 23.90 0.002 Triglyceride(mmol/L) 2.96 ± 2.55 1.97 ± 2.08 1.58 ± 1.50 1.15 ± 0.64 < 0.001 LDL-Cholesterol (mmol/L) 2.87 ± 0.95 3.14 ± 0.90 3.20 ± 0.90 3.12 ± 093 < 0.001 Total Cholesterol (mmol/L) 5.07 ± 1.23 5.34 ± 1.29 5.33 ± 1.10 5.37 ± 1.01 < 0.001 White blood cell count (1000 cells/ul) 7.78 ± 3.31 7.33 ± 3.03 6.93 ± 2.03 6.69 ± 2.20 < 0.001 lymphocyte number (1000 cells/ul) 2.25 ± 1.06 2.13 ± 2.28 1.97 ± 0.76 1.92 ± 1.02 < 0.001 Monocyte number (1000 cells/ul) 0.61 ± 0.21 0.58 ± 0.21 0.55 ± 0.18 0.56 ± 0.19 < 0.001 Segmented neutrophils number (1000 cells/ul) 4.62 ± 2.69 4.34 ± 1.58 4.15 ± 1.62 3.96 ± 1.62 < 0.001 Red blood cell count(million cells/ul) 4.92 ± 0.48 4.91 ± 0.47 4.90 ± 0.46 4.77 ± 0.48 < 0.001 Hemoglobin(g/dL) 15.13 ± 1.38 15.08 ± 1.33 15.04 ± 1.25 14.84 ± 1.32 < 0.001 Platelet count (1000 cells/uL) 242.19 ± 68.64 245.41 ± 66.04 242.26 ± 60.19 239.95 ± 62.17 0.182 C-reactive protein(mg/dL) 0.55 ± 1.17 0.46 ± 0.96 0.37 ± 0.86 0.33 ± 0.82 < 0.001 Q1–Q4: Grouped by quartile according to the HDL-Cholesterol concentrations. Our data included PSA concentrations, sociodemographic data, laboratory dat for the second analysis. 3.2. Link between PSA concentration and serum HDL-C We performed univariate and multivariate analyses using weighted linear models, and the results showed that there were 3 weighted univariate and multivariate linear regression models. First, in the unadjusted model, for each unit increase in the HDL-C ratio, PSA concentration increased by 0.470 ng/ml (0.281, 0.659), P < 0.001. In a minimum adjustment model that adjusted for poverty income ratio, ethnicity, military enlistment, marriage, and education, PSA concentrations increased by 0.408 ng/mL (0.227, 0.589), P for each unit increase in serum HDL-C P < 0.001。 In addition, on poverty income ratios, race, military enlistment, marriage and education, triglycerides; LDL cholesterol; number of monocytes; Number of neutrophils; number of red blood cells; Haemoglobin; Number of platelets; In the C-reactive protein model, PSA concentrations rose to 0.400 ng/mL (0.099, 0.701), P < 0.009 for each additional unit of serum HDL-C. The results of linear relationship exploration are shown in Table 2 . Table 2 Univariate and multivariate analyses by the weighted linear model. Exposure Non-Adjusted Model Minimally Adjusted Model Fully Adjusted Model HDL-Cholesterol 0.470(0.281,0.659) < 0.001 0.408(0.227,0.589) < 0.001 0.400 (0.099,0.701)0.009 HDL-Cholesterol Q1 Ref Ref Ref Q2 0.077(-0.130,0.284)0.467 0.108(-0.086,0.303)0.275 -0.001(-0.312.0.310)0.995 Q3 0.166(-0.038,0.370)0.109 0.196(0.004,0.389)0.045 0.064(-0.249,0.377)0.689 Q4 0.377(0.172,0.583)0.001 0.351(0.156,0.546)0.001 0.228(-0.112.0.568)0.189 p for trend < 0.001 < 0.001 0.026 Non-adjusted model adjusts for none. Minimally adjusted model adjusts for Family income; Race; Military Status; Marital status ; Education. Fully adjusted model adjusts for Adjust model adjust for : Family income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride; LDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive; 3.3. Sensitivity analysis We performed sensitivity analyses to confirm the accuracy and robustness of the results. First, we convert serum HDL-C as a continuous variable to a categorical variable in the quartile value and then calculate the p-value of the trend (Table 2 ). Surprisingly, the results of the categorical variable were consistent with the effect of serum HDL-C as a continuous variable. We constructed a smoothed curve based on a fully tuned model to investigate a possible linear relationship between serum HDL-C and PSA concentration. According to the fully tuned model strategy (Fig. 2 ), the results showed that for every 1 mmol/L increase in serum HDL-C, PSA concentrations increased by 0.400 ng/mL. These results suggest a positive correlation between serum HDL-C and PSA concentration. Figure 2 : Representation of the fully adjusted smooth curve fitting model. 3.4. Stratified Association Between HDL-C and PSA We performed an analysis to determine the hierarchical relationship between PSA and HDL-C. Our finding was a 25.2% reduction in PSA concentrations in people with low HDL-C in people with low household income (HR = 0.748, P < 0.001). Among people with higher household incomes, PSA concentrations were reduced by 58.0% in people with low HDL-C (HR = 0.420, P = 0.011). PSA concentrations were reduced by 6.8% among those with lower than lower secondary education (HR = 0.932, P < 0.001). The interaction test showed that the effect of household income on PSA concentration was significantly affected by HDL-C (P = 0.037). These findings suggest a link between low HDL-C levels and lower PSA concentrations, and that this link is stronger in people with higher household incomes. Table 3 Effect size of HDL-Cholesterol on PSA in the prespecified and exploratory subgroup. HDL-Cholesterol N β 95%CI Low 95%CI High p-Value p for Interaction Stratified by age 0.190 < 60 3630 0.266 0.020 0.511 0.034 60–80 2796 0.618 0.329 0.907 < 0.001 > 80 243 0.399 -0.626 1.424 0.446 Stratified by ratio of family income 0.037 Low group 2075 0.748 0.453 1.043 < 0.001 Median group 2082 0.194 -0.116 0.504 0.220 High group 2079 0.420 0.094 0.746 0.011 Stratified by race 0.663 Mexican American 1213 0.268 -0.217 0.753 0.278 Other Hispanic 416 0.429 -0.411 1.268 0.317 Non-Hispanic White 3576 0.314 0.038 0.590 0.025 Non-Hispanic Black 1245 0.646 0.279 1.013 0.001 Other race/ethnicity 219 0.447 -0.663 1.557 0.430 Stratified by military Status 0.249 Yes 2271 0.305 -0.026 0.635 0.071 No 4397 0.541 0.311 0.771 < 0.001 Stratified by marital status 0.310 Married 4566 0.345 0.100 0.590 0.005 Single 1764 0.662 0.333 0.991 < 0.001 Living with a partner 333 0.380 -0.348 1.108 0.306 Stratified by education 0.006 Less Than 9th Grade 2049 0.932 0.599 1.264 < 0.001 High School Grad 1548 0.264 -0.117 0.645 0.174 More Than 9th Grade 3066 0.279 -0.009 0.567 0.057 Note 1 : Above adjusts for Family income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride; LDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive; Note 2 : In each case, the model was not adjusted for the stratification variable itself. 3.5. Results of weighted linear regression modeling of the association between serum HDL-C and PSA after excluding individuals with HDL-C abnormalities (< 1.04 mmol/L). First, in the unadjusted model, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.53 ng/mL, P < 0.001. Second, in the minimum adjustment model for poverty income ratio, race, military enlistment, marriage, and education, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.42 ng/ml, P = 0.01. Third, after completely adjusting the poverty income ratio, race, joining the army, marriage, education, triglycerides; LDL cholesterol; Monocytes count; neutrophil count; red blood cell count; Haemoglobin; and platelet count; In the C-reactive protein model, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.50ng/mL, P = 0.009. After excluding participants with HDL-C < 1.04 mmol/L, this association remained evident in adult men in the United States. Such findings mean that HDL-C is still positively correlated with PSA in adult men, even at a reference concentration of > 1.04 mmol/L. See Table 4 for details Table 4 Results of weighted linear regression modeling for associations of the HDL-Cholesterol with PSA after excluding individual blood HDL-Cholesterol concentrations less than 1.04 mmol/L. Exposure Non-Adjusted Model Minimally Adjusted Model Fully Adjusted Model HDL-Cholesterol 0.53(0.27,0.79) < 0.001 0.42(0.16,0.67)0.001 0.50(0.12,0.87)0.009 HDL-Cholesterol Q1 Ref Ref Ref Q2 0.00(-0.25,0.26)0.9777 0.04(-0.21,0.28)0.7748 -0.06(-0.39,0.28)0.7381 Q3 0.21(-0.05,0.47)0.1078 0.22(-0.03,0.47)0.0910 0.20(-0.15,0.54)0.2601 Q4 0.39(0.14,0.65)0.0027 0.31(0.06,0.56)0.0163 0.35(-0.01,0.72)0.0568 p for trend 0.003 0.010 0.046 Non-adjusted model adjusts for none. Minimally adjusted model adjusts for Family income; Race; Military Status; Marital status ; Education. Fully adjusted model adjusts for Adjust model adjust for : Family income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride; LDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive; 4. Discussion Our study is one of the most comprehensive cross-sectional types of research to determine the association between HDL-C and prostate-specific antigen (PSA). This study is also the first to study the relationship between men without any history of prostate cancer (PCa) in the U.S. based on the NHANES database. Our first analysis relied on checking the stratified connection between HDL-C and PSA. In this analysis, the study classified participants based on various factors, such as family income (low group, median group, and high group), education, race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other race/ethnicity), military status, and marital status (married, single, or living with a partner). The positive association between serum HDL-C and PSA concentrations observed in this study is consistent with previous studies. A study by Platz et al. [ 16 ] found that men with higher HDL-C levels had a higher risk of developing prostate cancer. Another study by Mondul et al. [ 17 ]. found that high HDL-C levels were associated with an increased risk of advanced prostate cancer [ 17 ]. Furthermore, the univariable randomization analysis indicated that the genetic prediction of Lp(a) had a statistically negligible correlation with the overall occurrence of prostate cancer[ 18 ]. However, the weighted median approach indicated the possibility of a potential correlation [ 17 ], and the mechanism behind this association remains unclear. One theory is that high levels of HDL-C increase the production of androgen hormones, which are known to promote the growth of prostate cancer cells[ 19 ]. Moreover, an alternative hypothesis suggests that HDL-C could function as a biomarker for chronic inflammation, a recognized component that promotes the progression of prostate cancer [ 19 ]. Remarkably, the stratified analysis revealed a stronger correlation between lower HDL-C levels and reduced PSA concentrations, especially among those with higher family incomes. This may be due to differences in lifestyle factors such as diet and physical activity, as well as access to healthcare services. A study by Vidal et al. [ 20 ] found that low HDL-C levels were associated with a higher risk of prostate cancer in men with low education levels, but not in men with higher education levels. Furthermore, our findings diverge from previous studies that focused on clarifying the correlation between HDL-C and PSA. Several studies have shown that lipids have an impact on the likelihood of developing prostate cancer [ 21 ], with some indicating a favorable link and others suggesting a negative relationship between the two factors. A PubMed-based search engine was used to select 13 relevant publications for an epidemiological investigation exploring the association between HDL-C and prostate cancer [ 21 ], Out of these, three publications established a direct connection between high-density lipoprotein cholesterol (HDL-C) levels and the occurrence of cancer, whereas five articles indicated a marginal correlation between HDL-C and cancer [ 22 ]. While these studies are consistent with our findings, it is important to not dismiss those that suggest a negative or no correlation relationship between HDL-C and PSA. Hence, it is necessary to conduct more data analysis to verify this association and to determine if other risk factors are closely related to the occurrence and development of prostate cancer. Overall, this study supports the conception that HDL has a positive relationship with PSA[ 23 ]. It means that with higher levels of HDL-C in the body, the PSA concentrations also increase, which raises the probability of diagnosing prostate cancer (PCa). Besides, these findings also mean that the established link could be used as a viable approach for cancer screening. With this, it is possible to improve the specificity of PSA tests among men with high triglycerides. Most importantly, this study has an additional advantage in that it helps save lives and money. With the implementation of this PCa detection mechanism, the affected could begin early treatment and prevent cancer from spreading to other parts of the body. In most cases, identifying cancer earlier means there will be a reduced need for aggressive treatment[ 24 ], which can help the patients save their lives. Furthermore, the benefits of this research study outweigh earlier research on the same topic. First, this study comprises 6,669 male population data, resulting in more accurate and convincing results than other small sample studies, as well as more objective research findings. Second, this study is the first large-scale cross-sectional study, which found a poor relationship between serum HDL cholesterol and PSA in American men with no history of prostate cancer. This lays the foundation for other extensive and in-depth research on this topic, to obtain more conclusive results. Third, the sample in our study is a multi-level random sample, representing the general population in the United States, and has highly reliable and standardized data. At the same time, multi-layer random sampling technology gives researchers more freedom and flexibility in selecting samples, which is also conducive to the data collection of geographically dispersed populations. Fourth, this study has made a certain contribution to the supplement of the knowledge base. Research shows that not only in the United States but also globally, the increase of early HDL is instructive for the risk of PCa. With the implementation of this PCa detection mechanism to improve the early detection rate of cancer, such patients can start early treatment to prevent the cancer from spreading to other parts of the body. Finally, we conducted a linear and nonlinear exploratory sensitivity analysis of the relationship between serum HDL-C and PSA. We considered and evaluated the influence of other factors that may affect the results. Combined with the application of generalized estimation equation (GEE) analysis, these technologies help to find reliable results consistent with assumptions. Limitations of this study include the cross-sectional design, which limits the ability to establish causality, and the use of a single PSA measurement, which may not accurately reflect long-term PSA levels. Furthermore, the study was based on the NHANES database, which is restricted to the U.S. population. However, the population consisted mainly of non-Hispanic whites, which may limit the generalizability of the findings to other racial/ethnic groups. 5. Conclusion This study found a positive association between serum HDL-C and PSA concentrations in adult men in the United States without a prostate cancer diagnosis. Moreover, People with low HDL-C are more likely to be diagnosed with prostate cancer in the late stage of the disease. Hence, people with a high level of HDL-C should be tested for PSA to help early detection of prostate cancer. Abbreviations Pca prostate cancer HDL-C HDL cholesterol PIR Poverty income ratio PSA Prostate-specific antigen CI Confidence interval Declarations Author Contributions: Conceptualization and Writing—original draft, Adan.M; Acquisition and analysis of the data, Hu B;and Adan.M; Interpretation of the data, Yan M and Adan.M; Review and editing, Hu B and Hidig S; review and editing and critical analysis of the results, ............ and Abdi M All authors have read and agreed to the published version of the manuscript. Funding: No funding was received to assist with the preparation of this manuscript. Informed Consent Statement Not applicable. Data Availability Statement All data are available at the NHANES website https://www.cdc.gov/nchs/nhanes/index.htm (accessed on 7 October 2022). 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Beyond Prostate Specific Antigen: New Prostate Cancer Screening Options. World Journal of Men’s Health. 2022;40. Schröder FH, Hugosson J, Roobol MJ, Tammela TLJ, Zappa M, Nelen V, et al. Screening and prostate cancer mortality: Results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. The Lancet. 2014;384:2027–35. Carlsson S V., Vickers AJ. Screening for Prostate Cancer. Medical Clinics of North America. 2020;104:1051–62. Parekh DJ, Punnen S, Sjoberg DD, Asroff SW, Bailen JL, Cochran JS, et al. A Multi-institutional Prospective Trial in the USA Confirms that the 4Kscore Accurately Identifies Men with High-grade Prostate Cancer. Eur Urol. 2015;68. Buddingh KT, Maatje MGF, Putter H, Kropman RF, Pelger RCM. Do antibiotics decrease prostate-specific antigen levels and reduce the need for prostate biopsy in type IV prostatitis? A systematic literature review. Canadian Urological Association Journal. 2018;12. Stattin P, Carlsson S, Holmstrom B, Vickers A, Hugosson J, Lilja H, et al. Prostate cancer mortality in areas with high and low prostate cancer incidence. J Natl Cancer Inst. 2014;106. Tall AR. Cholesterol efflux pathways and other potential mechanisms involved in the athero-protective effect of high density lipoproteins. Journal of Internal Medicine. 2008;263. Murtola TJ, Syvälä H, Pennanen P, Bläuer M, Solakivi T, Ylikomi T, et al. The importance of LDL and Cholesterol metabolism for prostate epithelial cell growth. PLoS One. 2012;7. Platz EA, Clinton SK, Giovannucci E. Association between plasma cholesterol and prostate cancer in the PSA era. Int J Cancer. 2008;123:1693–8. Mondul AM, Clipp SL, Helzlsouer KJ, Platz EA. Association between plasma total cholesterol concentration and incident prostate cancer in the CLUE II cohort. Cancer Causes and Control. 2010;21. https://www.cdc.gov/nchs/nhanes/index.htm. Platz EA, Leitzmann MF, Visvanathan K, Rimm EB, Stampfer MJ, Willett WC, et al. Statin drugs and risk of advanced prostate cancer. J Natl Cancer Inst. 2006;98:1819–25. Mondul AM, Weinstein SJ, Virtamo J, Albanes D. Serum total and HDL cholesterol and risk of prostate cancer. Cancer Causes & Control. 2011;22:1545–52. Ioannidou A, Watts EL, Perez-Cornago A, Platz EA, Mills IG, Key TJ, et al. The relationship between lipoprotein A and other lipids with prostate cancer risk: A multivariable Mendelian randomisation study. PLoS Med. 2022;19. Senapati D, Sharma V, Rath SK, Rai U, Panigrahi N. Functional implications and therapeutic targeting of androgen response elements in prostate cancer. Biochimie. 2023;214:188–98. Jamnagerwalla J, Howard LE, Allott EH, Vidal AC, Moreira DM, Castro-Santamaria R, et al. Serum cholesterol and risk of high-grade prostate cancer: results from the REDUCE study. Prostate Cancer Prostatic Dis. 2018;21:252–9. Van Hemelrijck M, Garmo H, Holmberg L, Walldius G, Jungner I, Hammar N, et al. Prostate cancer risk in the Swedish AMORIS study. Cancer. 2011;117:2086–95. Kotani K, Sekine Y, Ishikawa S, Ikpot IZ, Suzuki K, Remaley AT. High-density lipoprotein and prostate cancer: An overview. J Epidemiol. 2013;23:313–9. Van Hemelrijck M, Walldius G, Jungner I, Hammar N, Garmo H, Binda E, et al. Low levels of apolipoprotein A-I and HDL are associated with risk of prostate cancer in the Swedish AMORIS study. Cancer Causes & Control. 2011;22:1011–9. Albertsen PC. Prostate cancer screening and treatment: where have we come from and where are we going? BJU Int. 2020;126:218–24. Additional Declarations No competing interests reported. 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3925868\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":270882998,\"identity\":\"4ba1fc97-809e-4a5f-9103-6ef6bcdac849\",\"order_by\":0,\"name\":\"Mohamed Mohamoud Adan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDCCAwwMEkDEwy8B5krIEKvFQkZyBgNjA0gvsVoqbAxugLUwENbCd/vwwxs/d0jwGN9uPv7oRo0FDwP74aMb8GmRPJdmbNl7RoLH7M6xxOacY0CH8aSl3cCnxeAMg5kEbxtQy40cw+YcNqAWEBu/FvZvkn+BWoxngLT8I0oLj5k0yBYDCaCW3DYitEie4Sm2lgWqlLiRljg7t0+Ch42QX/jOsG+8+batzp5/RvKBzznf6uT42Q8fw6sFE7CRpnwUjIJRMApGATYAAJCPQ7p3l9h+AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mohamed\",\"middleName\":\"Mohamoud\",\"lastName\":\"Adan\",\"suffix\":\"\"},{\"id\":270882999,\"identity\":\"963856c1-98a1-4835-b3ae-a0882f4a1bbf\",\"order_by\":1,\"name\":\"Bowen Hu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Urology, the People's Hospital of Longhua, the Affiliated Hospital of Southern Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bowen\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":270883000,\"identity\":\"9939479a-4416-48b2-a246-b9ce2253df7e\",\"order_by\":2,\"name\":\"Minbo Yan Yan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Urology, The Fifth Affiliated Hospital of Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Minbo\",\"middleName\":\"Yan\",\"lastName\":\"Yan\",\"suffix\":\"\"},{\"id\":270883001,\"identity\":\"c30821ea-5cc6-42c6-9408-8242da4d3ce4\",\"order_by\":3,\"name\":\"Gonghui Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gonghui\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":270883002,\"identity\":\"6de48080-81b6-4004-94f0-38fd3559cbcd\",\"order_by\":4,\"name\":\"Sakarie Mustafe Hidig\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Hepatobiliary and Pancreatic Surgery at the fourth affiliated hospital of Zhejiang University, Yiwu\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sakarie\",\"middleName\":\"Mustafe\",\"lastName\":\"Hidig\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-02-04 02:14:07\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3925868/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3925868/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":50751618,\"identity\":\"8b865c10-5ad4-49b8-9671-8f11942b42c8\",\"added_by\":\"auto\",\"created_at\":\"2024-02-06 17:39:20\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":126458,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy participant inclusion and exclusion criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNote: According to the inclusion and exclusion criteria, a total of 6669 eligible cases were included, and they were divided into four groups according to HDL-C quartile.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3925868/v1/9f1c77cb32ca89e1c37c5b74.png\"},{\"id\":50751617,\"identity\":\"41e6a968-88fb-4a08-b82f-bb43d0ec66ba\",\"added_by\":\"auto\",\"created_at\":\"2024-02-06 17:39:20\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":115694,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRepresentation of the fully adjusted smooth curve fitting model\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNote: Each black dot represents a sample. (B) The red line represents the smooth curve fit between the variables. The blue line represents the 95% confidence interval for the fit. Adjusted for poverty-income ratio, race, military service, marriage, education, triglycerides, low-density lipoprotein cholesterol, monocyte count, neutrophil count, red blood cell count, hemoglobin, platelet count, and C-reactive protein. Figure 3: Representation of a fully adjusted smoothed curve-fitting model\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3925868/v1/9e9efb826b1cef0bb03454ba.png\"},{\"id\":51102417,\"identity\":\"2ef37b2d-1d00-491a-ae4c-7ee89fbf386a\",\"added_by\":\"auto\",\"created_at\":\"2024-02-14 06:54:13\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":849537,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3925868/v1/6b9f1df5-f691-45ab-a5b7-f053ad629243.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association between High-Density Lipoproteins (HDLs) and Prostate Specific Antigen (PSA): a cross-sectional study from NHANES database\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eProstate cancer is the second most diagnosed cancer and the fifth leading cause of cancer-related deaths among men worldwide [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. In the United States alone, it is estimated that there were about 288,300 new cases of prostate cancer and 34,700 deaths from the disease in 2021, and in 2022 the number of incidences increased to 3,523,230 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Prostate cancer is the most frequently diagnosed cancer[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e] Early detection and treatment are crucial for improving prostate cancer patient's prognosis and survival rates [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eProstate-specific antigen (PSA) is a protein produced by the prostate gland, and its levels can be measured through a simple blood test. The PSA test is widely used for prostate cancer screening[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], but its effectiveness in reducing mortality from prostate cancer remains controversial [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Elevated PSA levels can also be caused by non-cancerous conditions such as prostatitis and benign prostatic hyperplasia (BPH), leading to false-positive results and unnecessary biopsies[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e] [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eHigh-density lipoprotein cholesterol (HDL-C) is a lipoprotein that transports cholesterol from peripheral tissues to the liver for excretion. HDL-C is commonly referred to as \\\"good\\\" cholesterol because it has been shown to have protective effects against cardiovascular disease [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. However, in recent years, several studies have investigated the association between HDL-C levels and prostate cancer risk. Several hypotheses have been proposed to explain the potential link between HDL-C levels and prostate cancer. One theory is that high levels of HDL-C may be protective against prostate cancer by removing excess cholesterol from prostate cells and reducing oxidative stress [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Another hypothesis is that low HDL-C levels may be a result of inflammation, which has been linked to the development and progression of prostate cancer [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].In addition to its potential role in prostate cancer risk, HDL-C levels may also be related to PSA levels. Some studies have reported an inverse association between HDL-C levels and PSA levels, suggesting that men with higher HDL-C levels may have lower PSA levels[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. However, the relationship between HDL-C and PSA levels is not fully understood, and additional research is needed to clarify this association. Therefore, the purpose of this article is to we examine the relationship between HDL-C and PSA in U.S. men using the National Health and Nutrition Examination Survey (NHANES) database and to discuss the potential implications of this relationship for prostate cancer screening and diagnosis.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Data Availability\\u003c/h2\\u003e \\u003cp\\u003eThe NHANES, initially established in 1960, is a survey conducted by the National Center for Disease Control (CDC) and the Prevention National Center for Health Statistics. The objective of this assessment is to evaluate the physical well-being and dietary condition of both adults and children living in the United States. For demographic and methodological information, please refer to the NHANES website \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.cdc.gov/nchs/nhanes\\\" target=\\\"_blank\\\"\\u003ewww.cdc.gov/nchs/nhanes\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.cdc.gov/nchs/nhanes\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], viewed on October 7, 2022. The NHANES protocols have been authorized by the National Center for the Health Statistics Research Ethics Review Board.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Study Population\\u003c/h2\\u003e \\u003cp\\u003eNHANES employs a stratified, multi-stage random sampling design, serving as a nationally representative nutrition survey of the broader U.S. population. Our research incorporates five cycles of NHANES data covering the period from 2001 to 2010. The dataset utilized for our secondary analysis encompasses Prostate-Specific Antigen (PSA) concentrations, socio-demographic data, and laboratory information. Our research systematically excluded participants based on specific criteria: (1) Age below 40 years (n\\u0026thinsp;=\\u0026thinsp;34,634); (2) Individuals diagnosed with Prostate Cancer (PCa) (n\\u0026thinsp;=\\u0026thinsp;377); (3) Factors impacting PSA levels, including prostatitis diagnosis, statin drug usage, recent prostate biopsy within one week, and urinary system surgery within one month (n\\u0026thinsp;=\\u0026thinsp;492); (4) Instances of missing PSA data (n\\u0026thinsp;=\\u0026thinsp;10,023); (5) No instances of missing High-Density Lipoprotein Cholesterol (HDL-C) data (n\\u0026thinsp;=\\u0026thinsp;0). Following this meticulous screening process, 6,669 individuals out of the initial 52,195 participants were deemed suitable for inclusion in our study, as illustrated in (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Furthermore, our study strictly adheres to the ethical guidelines delineated in the World Medical Association's Declaration of Helsinki concerning the design and implementation of the study. The foundation of our data analysis rests upon the utilization of NHANES data in our research.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Statistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eWe conducted all statistical analyses using the R Package and EmpowerStats (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.empowerstats.com\\u003c/span\\u003e\\u003cspan address=\\\"http://www.empowerstats.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), with a complex weighted sampling design from NHANES. Participants were characterized according to the quartiles of HDL-C\\u003c/p\\u003e \\u003cp\\u003e(Category1: 19\\u0026ndash;40; Category2: 40\\u0026ndash;46; Category 3: 46\\u0026ndash;56; Category4: 56\\u0026ndash;148). We used percentages for categorical variables and means\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviations for continuous variables. To investigate group differences, we performed weighted χ2 tests for categorical variables and linear regression models for continuous variables. The link between HDL-C and PSA was evaluated using a weighted multivariate linear regression model. We established three models: an unadjusted model (Model 1), a minimally adjusted model (Model 2) considering factors like poverty-income ratio, race, military participation, marriage, and education, and a fully adjusted model (Model 3) incorporating additional adjustments for triglycerides, LDL cholesterol, monocyte count, neutrophil count, red blood cell count, hemoglobin, platelet count, and C-reactive protein, Then Stratified analyses were done based on age, family income, race, military status, marital status, and education, with interactions examined. Additionally, assuming the normal reference value range of HDL-C as 1.04 mmol/L, we did a weighted multivariate linear regression analysis, after removing people with HDL-C levels below 1.04 ug/dl to limit the impact of very low HDL-C in U.S. men.\\u003c/p\\u003e \\u003cp\\u003eIn our study, a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1-Baseline characteristics of the selected participant\\u003c/h2\\u003e \\u003cp\\u003eWe performed statistical analyses of HDL, triglyceride, and PSA levels. HDL-C was grouped into four quartiles (Q1-Q4), and there were no significant differences in military participation and platelet count between the groups. People with higher PSA, older age, higher household income, higher level of education, married, high in vitamin D, high LDL-C, and high in total cholesterol have high HDL-C. On the other hand, those with elevated serum HDL-C have lower triglycerides, lower WBC, lower LY, lower MONO, lower neutrophils, lower red blood cells, lower hemoglobin, and lower C-reactive protein. In our study, non-Hispanic whites were the main participants.\\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\\u003eBaseline characteristics of the selected participants.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHDL-Cholesterol (mmol/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1458\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1682\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1797\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1732\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal prostste specific antigen(ng/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge(years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e58.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e59.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFamily income\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMilitary Status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.285\\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=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e467 (32.03%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e583 (34.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e613 (34.13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e608 (35.10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e991(67.97%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1099 (65.34%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1183 (65.87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1124(64.90%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLess than High School Grad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e505 (34.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e503 (29.90%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e541(30.16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e500 (28.90%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh School Grad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e353 (24.23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e408 (24.26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e398 (22.19%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e389 (22.49%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMore than High School Grad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e599 (41.11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e771(45.84%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e855 (47.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e841(48.61%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMore than high school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1015 (69.66%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1163 (69.27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1287(71.62%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1101(63.64%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMore than high school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e361(24.78%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e439 (26.15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e429 (23.87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e535 (30.92%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLiving with a partner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e81(5.56%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e77(4.59%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e81(4.51%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94 (5.43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRace\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMexican American\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e305 (20.92%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e332(19.74%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e338(18.81%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e238(13.74%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther Hispanic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e116 (7.96%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90(5.35%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e111(6.18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99 (5.72%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e825 (56.58%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e942(56.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e950 (52.87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e859 (49.60%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-Hispanic Black\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e157(10.77%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e264(15.70%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e341(18.98%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e483 (27.89%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther race/ethnicity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e55 (3.77%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e54(3.21%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57(3.17%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 (3.06%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVitamin D(nmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e58.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;19.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e61.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;23.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTriglyceride(mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-Cholesterol\\u003c/p\\u003e \\u003cp\\u003e(mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.20\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;093\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal Cholesterol\\u003c/p\\u003e \\u003cp\\u003e(mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite blood cell count\\u003c/p\\u003e \\u003cp\\u003e(1000 cells/ul)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003elymphocyte number\\u003c/p\\u003e \\u003cp\\u003e(1000 cells/ul)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocyte number\\u003c/p\\u003e \\u003cp\\u003e(1000 cells/ul)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSegmented neutrophils number (1000 cells/ul)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRed blood cell count(million cells/ul)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHemoglobin(g/dL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelet count\\u003c/p\\u003e \\u003cp\\u003e(1000 cells/uL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e242.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;68.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e245.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;66.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e242.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;60.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e239.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;62.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.182\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC-reactive protein(mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003cb\\u003eQ1\\u0026ndash;Q4: Grouped by quartile according to the HDL-Cholesterol concentrations. Our data included PSA concentrations, sociodemographic data, laboratory dat for the second analysis.\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Link between PSA concentration and serum HDL-C\\u003c/h2\\u003e \\u003cp\\u003eWe performed univariate and multivariate analyses using weighted linear models, and the results showed that there were 3 weighted univariate and multivariate linear regression models. First, in the unadjusted model, for each unit increase in the HDL-C ratio, PSA concentration increased by 0.470 ng/ml (0.281, 0.659), P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. In a minimum adjustment model that adjusted for poverty income ratio, ethnicity, military enlistment, marriage, and education, PSA concentrations increased by 0.408 ng/mL (0.227, 0.589), P for each unit increase in serum HDL-C P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001。 In addition, on poverty income ratios, race, military enlistment, marriage and education, triglycerides; LDL cholesterol; number of monocytes; Number of neutrophils; number of red blood cells; Haemoglobin; Number of platelets; In the C-reactive protein model, PSA concentrations rose to 0.400 ng/mL (0.099, 0.701), P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.009 for each additional unit of serum HDL-C. The results of linear relationship exploration are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnivariate and multivariate analyses by the weighted linear model.\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\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\\u003e\\u003cb\\u003eHDL-Cholesterol\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.470(0.281,0.659)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.408(0.227,0.589)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.400 (0.099,0.701)0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHDL-Cholesterol\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.077(-0.130,0.284)0.467\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.108(-0.086,0.303)0.275\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.001(-0.312.0.310)0.995\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.166(-0.038,0.370)0.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.196(0.004,0.389)0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.064(-0.249,0.377)0.689\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.377(0.172,0.583)0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.351(0.156,0.546)0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.228(-0.112.0.568)0.189\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep for trend\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.026\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cb\\u003eNon-adjusted model adjusts for none. Minimally adjusted model adjusts for\\u003c/b\\u003e Family income; Race; Military Status; Marital status ; Education. \\u003cb\\u003eFully adjusted model adjusts for Adjust model adjust for\\u003c/b\\u003e:\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eFamily income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eLDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Sensitivity analysis\\u003c/h2\\u003e \\u003cp\\u003eWe performed sensitivity analyses to confirm the accuracy and robustness of the results. First, we convert serum HDL-C as a continuous variable to a categorical variable in the quartile value and then calculate the p-value of the trend (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Surprisingly, the results of the categorical variable were consistent with the effect of serum HDL-C as a continuous variable. We constructed a smoothed curve based on a fully tuned model to investigate a possible linear relationship between serum HDL-C and PSA concentration. According to the fully tuned model strategy (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), the results showed that for every 1 mmol/L increase in serum HDL-C, PSA concentrations increased by 0.400 ng/mL. These results suggest a positive correlation between serum HDL-C and PSA concentration.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e: Representation of the fully adjusted smooth curve fitting model.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. Stratified Association Between HDL-C and PSA\\u003c/h2\\u003e \\u003cp\\u003eWe performed an analysis to determine the hierarchical relationship between PSA and HDL-C. Our finding was a 25.2% reduction in PSA concentrations in people with low HDL-C in people with low household income (HR\\u0026thinsp;=\\u0026thinsp;0.748, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Among people with higher household incomes, PSA concentrations were reduced by 58.0% in people with low HDL-C (HR\\u0026thinsp;=\\u0026thinsp;0.420, P\\u0026thinsp;=\\u0026thinsp;0.011). PSA concentrations were reduced by 6.8% among those with lower than lower secondary education (HR\\u0026thinsp;=\\u0026thinsp;0.932, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The interaction test showed that the effect of household income on PSA concentration was significantly affected by HDL-C (P\\u0026thinsp;=\\u0026thinsp;0.037). These findings suggest a link between low HDL-C levels and lower PSA concentrations, and that this link is stronger in people with higher household incomes.\\u003c/p\\u003e \\n\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eEffect size of HDL-Cholesterol on PSA in the prespecified and exploratory subgroup.\\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 \\u003cp\\u003eHDL-Cholesterol\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eβ\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95%CI Low\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95%CI High\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep-Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ep for Interaction\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStratified by age\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.190\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3630\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.266\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.511\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.034\\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\\u003e60\\u0026ndash;80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2796\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.618\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.329\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.907\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e243\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.399\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.626\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.424\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.446\\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\\u003cb\\u003eStratified by ratio of family income\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2075\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.748\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.453\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.043\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMedian group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2082\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.194\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.504\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.220\\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\\u003eHigh group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2079\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.420\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.746\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.011\\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\\u003cb\\u003eStratified by race\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.663\\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\\u003e1213\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.753\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.278\\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\\u003e416\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.429\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.411\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.317\\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\\u003e3576\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.314\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.590\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.025\\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\\u003e1245\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.646\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.279\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.001\\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/ethnicity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e219\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.447\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.663\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.557\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.430\\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\\u003cb\\u003eStratified by military Status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.249\\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\\u003e2271\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.305\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.026\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.635\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.071\\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\\u003e4397\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.541\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.771\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStratified by marital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.310\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4566\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.345\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.590\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.005\\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\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1764\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.662\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.991\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLiving with a partner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.380\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.348\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.108\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.306\\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\\u003cb\\u003eStratified by education\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLess Than 9th Grade\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2049\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.932\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.599\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.264\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh School Grad\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1548\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.264\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.645\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.174\\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\\u003eMore Than 9th Grade\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.279\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.567\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.057\\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\\\"\\u003e\\u003cb\\u003eNote 1\\u003c/b\\u003e: Above adjusts for Family income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride; LDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive; \\u003cb\\u003eNote 2\\u003c/b\\u003e: In each case, the model was not adjusted for the stratification variable itself.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\n\\u003cp\\u003e \\u003cb\\u003e3.5. Results of weighted linear regression modeling of the association between serum HDL-C and PSA after excluding individuals with HDL-C abnormalities (\\u0026lt;\\u0026thinsp;1.04 mmol/L).\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eFirst, in the unadjusted model, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.53 ng/mL, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. Second, in the minimum adjustment model for poverty income ratio, race, military enlistment, marriage, and education, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.42 ng/ml, P\\u0026thinsp;=\\u0026thinsp;0.01. Third, after completely adjusting the poverty income ratio, race, joining the army, marriage, education, triglycerides; LDL cholesterol; Monocytes count; neutrophil count; red blood cell count; Haemoglobin; and platelet count; In the C-reactive protein model, for every 1 mmol/L increase in HDL-C concentration, PSA concentration increased by 0.50ng/mL, P\\u0026thinsp;=\\u0026thinsp;0.009. After excluding participants with HDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.04 mmol/L, this association remained evident in adult men in the United States. Such findings mean that HDL-C is still positively correlated with PSA in adult men, even at a reference concentration of \\u0026gt;\\u0026thinsp;1.04 mmol/L. See Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e for details\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResults of weighted linear regression modeling for associations of the HDL-Cholesterol with PSA after excluding individual blood HDL-Cholesterol concentrations less than 1.04 mmol/L.\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\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\\u003e\\u003cb\\u003eHDL-Cholesterol\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.53(0.27,0.79)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.42(0.16,0.67)0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.50(0.12,0.87)0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHDL-Cholesterol\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.00(-0.25,0.26)0.9777\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.04(-0.21,0.28)0.7748\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.06(-0.39,0.28)0.7381\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.21(-0.05,0.47)0.1078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.22(-0.03,0.47)0.0910\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.20(-0.15,0.54)0.2601\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.39(0.14,0.65)0.0027\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.31(0.06,0.56)0.0163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.35(-0.01,0.72)0.0568\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ep for trend\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cb\\u003eNon-adjusted model adjusts for none. Minimally adjusted model adjusts for\\u003c/b\\u003e Family income; Race; Military Status; Marital status ; Education. \\u003cb\\u003eFully adjusted model adjusts for Adjust model adjust for\\u003c/b\\u003e:\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eFamily income; Race; Military Status; Marital status ; Education; Mononuclear count ; Triglyceride;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eLDL-Cholesterol; Monocyte number; Segmented neutrophils number; Red blood cell count; Hemoglobin; Platelet count; C-reactive;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eOur study is one of the most comprehensive cross-sectional types of research to determine the association between HDL-C and prostate-specific antigen (PSA). This study is also the first to study the relationship between men without any history of prostate cancer (PCa) in the U.S. based on the NHANES database. Our first analysis relied on checking the stratified connection between HDL-C and PSA. In this analysis, the study classified participants based on various factors, such as family income (low group, median group, and high group), education, race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other race/ethnicity), military status, and marital status (married, single, or living with a partner).\\u003c/p\\u003e \\u003cp\\u003eThe positive association between serum HDL-C and PSA concentrations observed in this study is consistent with previous studies. A study by Platz et al. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] found that men with higher HDL-C levels had a higher risk of developing prostate cancer. Another study by Mondul et al. [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. found that high HDL-C levels were associated with an increased risk of advanced prostate cancer [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Furthermore, the univariable randomization analysis indicated that the genetic prediction of Lp(a) had a statistically negligible correlation with the overall occurrence of prostate cancer[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. However, the weighted median approach indicated the possibility of a potential correlation [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], and the mechanism behind this association remains unclear. One theory is that high levels of HDL-C increase the production of androgen hormones, which are known to promote the growth of prostate cancer cells[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Moreover, an alternative hypothesis suggests that HDL-C could function as a biomarker for chronic inflammation, a recognized component that promotes the progression of prostate cancer [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Remarkably, the stratified analysis revealed a stronger correlation between lower HDL-C levels and reduced PSA concentrations, especially among those with higher family incomes. This may be due to differences in lifestyle factors such as diet and physical activity, as well as access to healthcare services. A study by Vidal et al. [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e] found that low HDL-C levels were associated with a higher risk of prostate cancer in men with low education levels, but not in men with higher education levels.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, our findings diverge from previous studies that focused on clarifying the correlation between HDL-C and PSA. Several studies have shown that lipids have an impact on the likelihood of developing prostate cancer [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], with some indicating a favorable link and others suggesting a negative relationship between the two factors. A PubMed-based search engine was used to select 13 relevant publications for an epidemiological investigation exploring the association between HDL-C and prostate cancer [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], Out of these, three publications established a direct connection between high-density lipoprotein cholesterol (HDL-C) levels and the occurrence of cancer, whereas five articles indicated a marginal correlation between HDL-C and cancer [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. While these studies are consistent with our findings, it is important to not dismiss those that suggest a negative or no correlation relationship between HDL-C and PSA. Hence, it is necessary to conduct more data analysis to verify this association and to determine if other risk factors are closely related to the occurrence and development of prostate cancer.\\u003c/p\\u003e \\u003cp\\u003eOverall, this study supports the conception that HDL has a positive relationship with PSA[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. It means that with higher levels of HDL-C in the body, the PSA concentrations also increase, which raises the probability of diagnosing prostate cancer (PCa). Besides, these findings also mean that the established link could be used as a viable approach for cancer screening. With this, it is possible to improve the specificity of PSA tests among men with high triglycerides. Most importantly, this study has an additional advantage in that it helps save lives and money. With the implementation of this PCa detection mechanism, the affected could begin early treatment and prevent cancer from spreading to other parts of the body. In most cases, identifying cancer earlier means there will be a reduced need for aggressive treatment[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], which can help the patients save their lives.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, the benefits of this research study outweigh earlier research on the same topic. First, this study comprises 6,669 male population data, resulting in more accurate and convincing results than other small sample studies, as well as more objective research findings. Second, this study is the first large-scale cross-sectional study, which found a poor relationship between serum HDL cholesterol and PSA in American men with no history of prostate cancer. This lays the foundation for other extensive and in-depth research on this topic, to obtain more conclusive results. Third, the sample in our study is a multi-level random sample, representing the general population in the United States, and has highly reliable and standardized data. At the same time, multi-layer random sampling technology gives researchers more freedom and flexibility in selecting samples, which is also conducive to the data collection of geographically dispersed populations. Fourth, this study has made a certain contribution to the supplement of the knowledge base. Research shows that not only in the United States but also globally, the increase of early HDL is instructive for the risk of PCa. With the implementation of this PCa detection mechanism to improve the early detection rate of cancer, such patients can start early treatment to prevent the cancer from spreading to other parts of the body. Finally, we conducted a linear and nonlinear exploratory sensitivity analysis of the relationship between serum HDL-C and PSA. We considered and evaluated the influence of other factors that may affect the results. Combined with the application of generalized estimation equation (GEE) analysis, these technologies help to find reliable results consistent with assumptions.\\u003c/p\\u003e \\u003cp\\u003eLimitations of this study include the cross-sectional design, which limits the ability to establish causality, and the use of a single PSA measurement, which may not accurately reflect long-term PSA levels. Furthermore, the study was based on the NHANES database, which is restricted to the U.S. population. However, the population consisted mainly of non-Hispanic whites, which may limit the generalizability of the findings to other racial/ethnic groups.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study found a positive association between serum HDL-C and PSA concentrations in adult men in the United States without a prostate cancer diagnosis. Moreover, People with low HDL-C are more likely to be diagnosed with prostate cancer in the late stage of the disease. Hence, people with a high level of HDL-C should be tested for PSA to help early detection of prostate cancer.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePca\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eprostate cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHDL-C\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHDL cholesterol\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePIR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePoverty income ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePSA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eProstate-specific antigen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eConfidence interval\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contributions:\\u003c/h2\\u003e\\n\\u003cp\\u003eConceptualization and Writing\\u0026mdash;original draft, Adan.M; Acquisition and analysis of the data, Hu B;and Adan.M; Interpretation of the data, Yan M and Adan.M; Review and editing, Hu B and Hidig S; review and editing and critical analysis of the results, ............ and Abdi M All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding:\\u003c/h2\\u003e\\n\\u003cp\\u003eNo funding was received to assist with the preparation of this manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003eInformed Consent Statement\\u003c/h2\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003ch2\\u003eData Availability Statement\\u003c/h2\\u003e\\n\\u003cp\\u003eAll data are available at the NHANES website https://www.cdc.gov/nchs/nhanes/index.htm (accessed on 7 October 2022).\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgments\\u003c/h2\\u003e\\n\\u003cp\\u003eThe NHANES protocol was approved by the NCHS Research Ethics Review Board and thanks so much to Little Bear who supported us the most.\\u003c/p\\u003e\\n\\u003ch2\\u003eConflicts of Interest\\u003c/h2\\u003e\\n\\u003cp\\u003eThe authors declare no conflict of interest.\\u003c/p\\u003e\\n\\u003ch2\\u003eSTTOBE statement: \\u003c/h2\\u003e\\n\\u003cp\\u003ewe have read the strobe statement checklist and followed the manuscript accordingly.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWang L, Lu B, He M, Wang Y, Wang Z, Du L. Prostate Cancer Incidence and Mortality: Global Status and Temporal Trends in 89 Countries From 2000 to 2019. Front Public Health. 2022;10.\\u003c/li\\u003e\\n\\u003cli\\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209\\u0026ndash;49.\\u003c/li\\u003e\\n\\u003cli\\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72.\\u003c/li\\u003e\\n\\u003cli\\u003eGrubb RL. Prostate Cancer: Update on Early Detection and New Biomarkers. Missouri medicine. 2018;115.\\u003c/li\\u003e\\n\\u003cli\\u003eNarain TA, Sooriakumaran P. Beyond Prostate Specific Antigen: New Prostate Cancer Screening Options. World Journal of Men\\u0026rsquo;s Health. 2022;40.\\u003c/li\\u003e\\n\\u003cli\\u003eSchr\\u0026ouml;der FH, Hugosson J, Roobol MJ, Tammela TLJ, Zappa M, Nelen V, et al. Screening and prostate cancer mortality: Results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. The Lancet. 2014;384:2027\\u0026ndash;35.\\u003c/li\\u003e\\n\\u003cli\\u003eCarlsson S V., Vickers AJ. Screening for Prostate Cancer. Medical Clinics of North America. 2020;104:1051\\u0026ndash;62.\\u003c/li\\u003e\\n\\u003cli\\u003eParekh DJ, Punnen S, Sjoberg DD, Asroff SW, Bailen JL, Cochran JS, et al. A Multi-institutional Prospective Trial in the USA Confirms that the 4Kscore Accurately Identifies Men with High-grade Prostate Cancer. Eur Urol. 2015;68.\\u003c/li\\u003e\\n\\u003cli\\u003eBuddingh KT, Maatje MGF, Putter H, Kropman RF, Pelger RCM. Do antibiotics decrease prostate-specific antigen levels and reduce the need for prostate biopsy in type IV prostatitis? A systematic literature review. Canadian Urological Association Journal. 2018;12.\\u003c/li\\u003e\\n\\u003cli\\u003eStattin P, Carlsson S, Holmstrom B, Vickers A, Hugosson J, Lilja H, et al. Prostate cancer mortality in areas with high and low prostate cancer incidence. J Natl Cancer Inst. 2014;106.\\u003c/li\\u003e\\n\\u003cli\\u003eTall AR. Cholesterol efflux pathways and other potential mechanisms involved in the athero-protective effect of high density lipoproteins. Journal of Internal Medicine. 2008;263.\\u003c/li\\u003e\\n\\u003cli\\u003eMurtola TJ, Syv\\u0026auml;l\\u0026auml; H, Pennanen P, Bl\\u0026auml;uer M, Solakivi T, Ylikomi T, et al. The importance of LDL and Cholesterol metabolism for prostate epithelial cell growth. PLoS One. 2012;7.\\u003c/li\\u003e\\n\\u003cli\\u003ePlatz EA, Clinton SK, Giovannucci E. Association between plasma cholesterol and prostate cancer in the PSA era. Int J Cancer. 2008;123:1693\\u0026ndash;8.\\u003c/li\\u003e\\n\\u003cli\\u003eMondul AM, Clipp SL, Helzlsouer KJ, Platz EA. Association between plasma total cholesterol concentration and incident prostate cancer in the CLUE II cohort. Cancer Causes and Control. 2010;21.\\u003c/li\\u003e\\n\\u003cli\\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm.\\u003c/li\\u003e\\n\\u003cli\\u003ePlatz EA, Leitzmann MF, Visvanathan K, Rimm EB, Stampfer MJ, Willett WC, et al. Statin drugs and risk of advanced prostate cancer. J Natl Cancer Inst. 2006;98:1819\\u0026ndash;25.\\u003c/li\\u003e\\n\\u003cli\\u003eMondul AM, Weinstein SJ, Virtamo J, Albanes D. Serum total and HDL cholesterol and risk of prostate cancer. Cancer Causes \\u0026amp; Control. 2011;22:1545\\u0026ndash;52.\\u003c/li\\u003e\\n\\u003cli\\u003eIoannidou A, Watts EL, Perez-Cornago A, Platz EA, Mills IG, Key TJ, et al. The relationship between lipoprotein A and other lipids with prostate cancer risk: A multivariable Mendelian randomisation study. PLoS Med. 2022;19.\\u003c/li\\u003e\\n\\u003cli\\u003eSenapati D, Sharma V, Rath SK, Rai U, Panigrahi N. Functional implications and therapeutic targeting of androgen response elements in prostate cancer. Biochimie. 2023;214:188\\u0026ndash;98.\\u003c/li\\u003e\\n\\u003cli\\u003eJamnagerwalla J, Howard LE, Allott EH, Vidal AC, Moreira DM, Castro-Santamaria R, et al. Serum cholesterol and risk of high-grade prostate cancer: results from the REDUCE study. Prostate Cancer Prostatic Dis. 2018;21:252\\u0026ndash;9.\\u003c/li\\u003e\\n\\u003cli\\u003eVan Hemelrijck M, Garmo H, Holmberg L, Walldius G, Jungner I, Hammar N, et al. Prostate cancer risk in the Swedish AMORIS study. Cancer. 2011;117:2086\\u0026ndash;95.\\u003c/li\\u003e\\n\\u003cli\\u003eKotani K, Sekine Y, Ishikawa S, Ikpot IZ, Suzuki K, Remaley AT. High-density lipoprotein and prostate cancer: An overview. J Epidemiol. 2013;23:313\\u0026ndash;9.\\u003c/li\\u003e\\n\\u003cli\\u003eVan Hemelrijck M, Walldius G, Jungner I, Hammar N, Garmo H, Binda E, et al. Low levels of apolipoprotein A-I and HDL are associated with risk of prostate cancer in the Swedish AMORIS study. Cancer Causes \\u0026amp; Control. 2011;22:1011\\u0026ndash;9.\\u003c/li\\u003e\\n\\u003cli\\u003eAlbertsen PC. Prostate cancer screening and treatment: where have we come from and where are we going? BJU Int. 2020;126:218\\u0026ndash;24.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Prostate-Specific Antigen (PSA), High-Density Lipoproteins (HDL), Prostate Cancer (PCa), Triglycerides, National Health and Nutrition Examination Survey (NHANES)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3925868/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3925868/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground:\\u003c/h2\\u003e \\u003cp\\u003eAccording to growing evidence, high-density lipoproteins (HDLs ) may raise PSA levels in the prostate. The link between HDL-C and PSA, on the other hand, is debatable and challenging. Hence, this research examined the relationship between HDL-C and PSA in men using the National Health and Nutrition Examination Survey (NHANES) database.\\u003c/p\\u003e\\u003ch2\\u003eMethods:\\u003c/h2\\u003e \\u003cp\\u003eWe extracted NHANES data for five cycles from 2001 to 2010. The data used for analysis included PSA concentrations, sociodemographic data, and laboratory data. After the screening, 6,669 of 52,195 participants were included in the study. Participants were divided into 4 groups based on HDL-C quartiles. We analyzed categorical and continuous variables using weighted chi-square tests and linear regression models to compare differences between groups. We constructed 3 weighted multivariate linear regression models and assessed the association between HDL-C and PSA using a smoothed curve fit.\\u003c/p\\u003e\\u003ch2\\u003eResults:\\u003c/h2\\u003e \\u003cp\\u003eIn our study, unadjusted and adjusted multivariate linear regression models revealed a significant positive association between prostate-specific antigen (PSA) concentrations and serum high-density lipoprotein cholesterol (HDL-C) levels. Specifically, each unit increase in HDL-C ratio was associated with an increase in PSA concentration by 0.470 ng/mL (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) in the unadjusted model. In minimally adjusted models, accounting for socioeconomic and demographic factors, this correlation remained significant, with an increase of 0.408 ng/mL per unit increase in serum HDL-C (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Furthermore, the stratified analysis revealed various impacts based on socioeconomic status and HDL-C levels, with a significant interaction between household income and HDL-C levels (P\\u0026thinsp;=\\u0026thinsp;0.037). Exclusion of subjects with low HDL-C levels strengthened the association, showing a significant increase in PSA concentration with higher HDL-C levels (0.50 ng/mL per 1 mmol/L increase, P\\u0026thinsp;=\\u0026thinsp;0.009). Our findings suggest a nuanced relationship between HDL-C levels, socioeconomic factors, and PSA concentrations, highlighting the potential importance of considering these factors in prostate cancer screening and risk assessment.\\u003c/p\\u003e\\u003ch2\\u003eConclusion:\\u003c/h2\\u003e \\u003cp\\u003eThis study found a positive association between serum HDL-C and PSA concentrations in adult men in the United States without a prostate cancer diagnosis. Moreover, People with low HDL-C are more likely to be diagnosed with prostate cancer in the late stage of the disease. Hence, people with high levels of HDL-C should be tested for PSA to help early detection of prostate cancer.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association between High-Density Lipoproteins (HDLs) and Prostate Specific Antigen (PSA): a cross-sectional study from NHANES database\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-06 17:39:15\",\"doi\":\"10.21203/rs.3.rs-3925868/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"858ab9f2-9e04-4daa-9fd0-bf15fcc19cfc\",\"owner\":[],\"postedDate\":\"February 6th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-02-21T18:23:41+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-02-06 17:39:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3925868\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3925868\",\"identity\":\"rs-3925868\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}