Glycemic Control and Prostate Antigen Levels in Individuals with Diabetes Based on NHANES Data

Glycemic Control and Prostate Antigen Levels in Individuals with Diabetes Based on NHANES Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Glycemic Control and Prostate Antigen Levels in Individuals with Diabetes Based on NHANES Data Xiao Huo, Zhi Wang, Nan Huang, Jie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5674422/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background The relationship between diabetes and prostate-specific antigen (PSA) levels is complex, with potential implications for prostate cancer screening. This study examined the association between glycemic control and total PSA (tPSA) levels in patients with diabetes. Methods We analyzed data from the 2001–2010 NHANES to assess the relationship between glycated hemoglobin (HbA1c) and tPSA in adults with diabetes, categorizing HbA1c as <7% (good glycemic control) or ≥7% (poor glycemic control). Multivariable regression models were used, adjusting for key demographic, clinical, and lifestyle factors, including age, race/ethnicity, marital status, body mass index (BMI), smoking status, alcohol use, hypertension, CAD, and insulin use. Adjustments for multiple comparisons were made using the Bonferroni correction, and missing data were handled using multiple imputation. Results Participants with poor glycemic control were younger, less likely to be married or partnered, and had higher rates of insulin use but lower hypertension incidence than those with good glycemic control (P < 0.05). The median tPSA level was greater in the good control group (1.10 ng/mL vs. 0.90 ng/mL; P = 0.0014). Multivariate analysis revealed no overall association between HbA1c and tPSA (β= -0.022, P = 0.917). However, significant inverse associations were observed across subgroups, including those aged ≤59 years (β = -0.71, P = 0.033), married individuals (β= -0.55, P < 0.001), participants without coronary artery disease(CAD) (β= -0.49, P = 0.015), and insulin users (β= -0.80, P = 0.031). Conclusions Although no significant overall association was found between glycemic control and tPSA levels, subgroup analyses revealed an inverse relationship between HbA1c and tPSA in younger individuals (≤59 years), insulin users, and those without coronary artery disease (CAD). These findings suggest that glycemic control may have subgroup-specific effects on prostate health in individuals with diabetes. Health sciences/Diseases/Endocrine system and metabolic diseases Health sciences/Diseases/Urogenital diseases Diabetes Glycemic Control Prostate-Specific Antigen (tPSA) NHANES HbA1c Prostate Health Figures Figure 1 Figure 2 Figure 3 1. Introduction The relationship between blood glucose control and prostate-specific antigen levels in patients with diabetes has drawn increasing attention because of its potential impact on prostate cancer screening ( 1 , 2 ). Diabetes, characterized by chronic hyperglycemia, can alter metabolic processes that may influence PSA expression ( 3 ). Given the increasing prevalence of diabetes worldwide, understanding its interaction with PSA levels is crucial for improving prostate cancer detection strategies ( 4 ). PSA is a widely used biomarker for prostate cancer detection, but its level can be influenced by several factors other than cancer, including inflammation and benign prostate hyperplasia ( 5 ). In patients with diabetes, the interplay between hyperglycemia, insulin resistance, and inflammatory responses may complicate PSA interpretation( 6 ). Prior studies have yielded conflicting results regarding the impact of glycemic control on PSA levels, necessitating further investigation. Studies suggest that poor glycemic control, as indicated by an HbA1c level ≥ 7%, is associated with higher PSA levels, potentially leading to false positives in cancer screening ( 7 , 8 ). On the other hand, maintaining better glucose control may reduce PSA levels and improve the accuracy of prostate cancer screening ( 9 ). The molecular mechanisms underlying this relationship are not fully understood, but several pathways have been proposed. Insulin resistance, a hallmark of type 2 diabetes, is often accompanied by increased production of growth factors and inflammatory cytokines, which can promote prostate cell proliferation and increase PSA levels ( 10 ). Additionally, studies suggest that hyperglycemia-induced oxidative stress in diabetes may alter prostate gene expression and cellular function, potentially impacting the secretion of PSA ( 11 ). These findings underscore the need for a nuanced approach when PSA is used as a screening tool in individuals with diabetes. Emerging research has also explored how treatments for diabetes, such as metformin, may influence PSA levels ( 12 , 13 ). Some studies suggest that metformin, which improves insulin sensitivity, may reduce the risk of prostate cancer and lower PSA levels by modulating glucose metabolism and inflammatory responses ( 14 , 15 ). While preliminary, these findings highlight the potential role of diabetes management in improving the accuracy of PSA testing. However, larger, more robust studies are needed to validate these effects and establish clearer clinical guidelines for the management of prostate cancer risk in patients with diabetes ( 16 ). In this context, the current study utilized data from the National Health and Nutrition Examination Survey (NHANES) to investigate the possible correlations between blood glucose management and tPSA levels in individuals with diabetes. We hypothesize that glycemic control may differentially influence tPSA levels, with possible variations across age groups and clinical subpopulations. 2. Materials and methods 2.1. Survey description The data for this study were obtained from the NHANES, a program conducted by the National Center for Health Statistics (NCHS) to assess the nutritional and health status of the U.S. population (17). The NHANES employs a complex, multistage, stratified probability sampling design and is conducted biennially, ensuring a representative sample. All NHANES research protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from all participants or their parents and/or legal guardians for those under 16 years of age. The detailed NHANES research designs and data are publicly available at www.cdc.gov/nchs/nhanes/. Additionally, all methods were performed in accordance with the relevant guidelines and regulations to ensure the ethical conduct of this study. 2.2. Study population This study utilized data from the NHANES 2001‒2010 survey cycles, which included relevant data on PSA, HbA1c, and diabetes-related questionnaires. After screening 52,195 participants, a total of 1,025 male participants aged 40 years and older, who had undergone PSA laboratory testing and had available HbA1c data, were included in the analysis (Figure 1). Participants were excluded if they met any of the following criteria: current prostate infection or inflammation, a rectal examination in the past week, prostate biopsy or cystoscopy in the past month, a history of malignancy (including prostate cancer), missing PSA data, or if their diabetes diagnosis could not be confirmed through questionnaires or related tests. 2.3. Diabetes definition and classification In this study, diabetes status was defined based on the American Diabetes Association (ADA) criteria. A participant was classified as having diabetes if they met one or more of the following conditions: They self-reported a diabetes diagnosis through the NHANES questionnaire, or They met any of the following criteria: The HbA1c level was ≥6.5%, or The fasting blood glucose level was ≥7 mmol/L, or The 2-hour post-OGTT glucose level exceeded 11.1 mmol/L. A review of the relevant literature, revealed that an HbA1c threshold of 7% is widely recognized as a reliable marker for assessing glycemic control in diabetes management. Numerous studies have established a strong correlation between HbA1c levels and blood glucose control. An HbA1c value above 7% is generally associated with poor glycemic control and an increased risk of both microvascular and macrovascular complications in patients with diabetes (18, 19). Conversely, an HbA1c level less than 7% is linked to better glycemic control and a reduced risk of diabetes-related complications(20). Clinical research and guidelines support the idea that maintaining an HbA1c ≤7% lowers complication risks and improves quality of life for patients with diabetes, making it an internationally recognized target for optimal glycemic control. 2.4. Covariates Several covariates were included in the analysis to adjust for potential confounders. These covariates included the following: Demographic variables: Age (years), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other), education level (College graduate or above, some college, high school or less), poverty income ratio (PIR), and marital status (married or living with a partner, widowed/divorced/separated/never married). Clinical and lifestyle variables: Body mass index (BMI) (categorized as ≤25, 25–30, and ≥30 kg/m²), smoking status (yes/no), alcohol consumption (yes/no), hypertension (yes/no), enlarged prostate (yes/no), and CAD (yes/no). Insulin and oral hypoglycemic use were identified from the diabetes questionnaire. Biological markers : C-reactive protein (CRP), OGTT, and fasting glucose levels, were obtained from laboratory data. The PIR data used in this study were obtained from the NHANES database. These data are publicly accessible through the NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm. BMI was categorized as normal weight (≤25 kg/m²), overweight (25–30 kg/m²), or obese (≥30 kg/m²). The detailed measurement protocols for these variables can be found at www.cdc.gov/nchs/nhanes/. Marital status was included as a key variable due to its potential influence on health outcomes and social support, as evidenced by previous literature (21, 22). We hypothesized that marital status might serve as a confounding factor, impacting participants' psychological and physical health. Marital status data was collected through self-reported questionnaires. 2.5. Statistical analysis All statistical analyses were performed via SPSS 26.0 and Python 3.10. Descriptive statistics were used to calculate means and standard deviations (SDs). The normality of the data was assessed via the Kolmogorov‒Smirnov (KS) test. Nonparametric methods were used due to the non-normal distribution of PSA values (confirmed by the KS normality test). For inter-group comparisons, Mann-Whitney U tests were used for non-normally distributed continuous variables, and chi-square tests were used for categorical variables. Spearman’s rank correlation was used for correlation analysis. To examine the relationship between tPSA levels and other variables, multiple linear regression analysis was conducted to adjust for potential confounders, including demographic variables, clinical and lifestyle factors, and biological markers. This method was chosen because the dependent variable (tPSA) is continuous, and linear regression is appropriate for continuous outcomes. Missing data were handled via multiple imputation. Statistical significance was set at p < 0.05. Given the multiple subgroup comparisons in our analysis, findings should be interpreted with caution due to the risk of Type I error. To control for multiple comparisons, we applied the Bonferroni correction to reduce the risk of Type I errors. 3. Results 3.1. Baseline characteristics of the participants Table 1 presents the baseline characteristics of the 1,025 adults with diabetes included in this study, categorized by glycemic control status. Participants had a mean age of 62.72 years (± 11.41), and those with HbA1c < 7% (good glycemic control) differed significantly from those with HbA1c ≥ 7% (poor glycemic control) across multiple clinical and demographic variables (P < 0.05). As expected, markers of glucose metabolism diverged sharply between the two groups, with significantly lower OGTT (14.27 mmol/L vs. 17.29 mmol/L, P < 0.001) and fasting plasma glucose levels (7.71 mmol/L vs. 10.08 mmol/L, P < 0.001) in the well-controlled group. Socioeconomic factors mirrored these clinical distinctions, with the poorly controlled group having a greater proportion of individuals with high school or less (66.2% vs. 59.5%, P = 0.049) and fewer with college-level education or higher (13.1% vs. 18.3%). Marital status differed as well, with fewer married or partnered individuals in the well-controlled group (66.5% vs. 78.7%, P < 0.001). Hypertension was unexpectedly more prevalent in the good glycemic control group (64.7% vs. 57.2%, P = 0.015), whereas insulin use was significantly higher in those with poor control (33.0% vs. 12.5%, P < 0.001). As shown in supplementary table 1 , a more detailed breakdown of the baseline characteristics across these groups is provided. Table 1 Baseline characteristics of the selected participants. Variables n (%) HbA1c < 7 HbA1c ≥ 7 P All patients 1025 546 479 Age, mean SD (years) 62.72 ± 11.41 62.85 ± 11.44 62.57 ± 11.40 0.696 PIR, mean SD 2.51 ± 1.50 2.58 ± 1.51 2.44 ± 1.48 0.148 BMI, mean SD 30.85 ± 6.16 30.70 ± 6.08 31.01 ± 6.26 0.421 HbA1c, mean SD (%) 7.34 ± 1.79 6.11 ± 0.54 8.73 ± 1.70 < 0.001 CRP, mean SD (mg/dL) 0.50 ± 0.97 0.52 ± 0.98 0.49 ± 0.96 0.636 tPSA, mean SD (ng/mL) 1.85 ± 3.05 2.02 ± 3.01 1.65 ± 3.08 0.052 OGTT, mean SD (mmol/L) 15.68 ± 3.51 14.27 ± 2.11 17.29 ± 4.06 < 0.001 Fasting plasma glucose, 8.82 ± 2.77 7.71 ± 1.52 10.08 ± 3.28 < 0.001 mean SD (mmol/L) Race/Ethnicity 0.335 Mexican American 231 (22.5%) 116 (21.3%) 115 (24.0%) Other Hispanic 92 (9.0%) 42 (7.7%) 50 (10.4%) Non-Hispanic White 424 (41.4%) 231 (42.3%) 193 (40.3%) Non-Hispanic Black 237 (23.1%) 135 (24.7%) 102 (21.3%) Other Race 41 (4.0%) 22 (4.0%) 19 (4.0%) Education level 0.049 High school or less 644 (62.8%) 327 (59.9%) 317(66.2%) Some college 218 (21.3%) 119 (21.8%) 99 (20.7%) College or above 163 (15.9%) 100 (18.3%) 63 (13.1%) Marital Status < 0.001 Married/Partnered 740 (72.2%) 363 (66.5%) 377 (78.7%) Unpartnered 285 (27.8%) 183 (33.5%) 102 (21.3%) Hypertension 0.015 Yes 627 (61.2%) 353 (64.7%) 274 (57.2%) No 398 (38.8%) 193 (35.3%) 205 (42.8%) CAD 0.402 Yes 147 (14.3%) 83 (15.2%) 64 (13.4%) No 878 (85.7%) 463 (84.8%) 415 (86.6%) Enlarged prostate 0.114 Yes 136 (13.3%) 81 (14.8%) 55 (11.5%) No 889 (86.7%) 465 (85.2%) 424 (88.5%) Use of insulin < 0.001 Yes 226 (22.1%) 68 (12.5%) 158 (33.0%) No 799 (77.9%) 478 (87.5%) 321 (67.0%) Use of diabetes pills 0.380 Yes 781 (76.2%) 422 (77.3%) 359 (74.9%) No 244 (23.8%) 124 (22.7%) 120 (25.1%) Had ≥ 12 alcoholic drinks in the past year 0.297 Yes 798 (77.9%) 432 (79.1%) 366 (76.4%) No 227 (22.1%) 114 (20.9%) 113 (23.6%) Smoked at least 100 cigarettes in life 0.512 Yes 657 (64.1%) 355 (65.0%) 302 (63.0%) No 368 (35.9%) 191 (35.0%) 177 (37.0%) 3.2. HbA1c and its association with tPSA levels To explore the potential relationship between HbA1c levels and tPSA, we first conducted a correlation analysis, represented in the heatmap (Fig. 2 ). The analysis revealed a very weak negative correlation (r = -0.061) between HbA1c and tPSA, indicating a minimal linear association. To assess the tPSA distribution, we conducted a KS normality test, which revealed that tPSA significantly deviated from a normal distribution (D = 0.280, p < 0.0001). As a result, we used nonparametric methods for subsequent analyses. Next, we performed the Mann‒Whitney U test to compare tPSA levels between the HbA1c < 7% group and the HbA1c ≥ 7% group. The results revealed that the median tPSA in the HbA1c < 7% group was significantly greater than that in the HbA1c ≥ 7% group (P = 0.0014). Specifically, the median tPSA in the HbA1c < 7% group was 1.10 ng/mL (95% CI: 0.96 to 1.28), which was significantly higher than the 0.90 ng/mL (95% CI: 0.87 to 1.02) in the HbA1c ≥ 7% group. To assess the distribution of tPSA across different glycemic control groups, a boxplot was generated (Fig. 3 ). In the HbA1c < 7% group, tPSA values were higher, with a median value of 1.10 ng/mL. In contrast, the HbA1c ≥ 7% group had a lower median tPSA of 0.90 ng/mL. We also conducted a multivariate linear regression analysis, adjusting for demographic variables, clinical and lifestyle variables and biological markers as covariates. The results revealed that the HbA1c group had no statistically significant effect on tPSA levels (β = -0.022, 95% CI: -0.448 to 0.403, P = 0.917). However, marital status (β = 1.362, 95% CI: 0.935 to 1.789, P < 0.001) and BMI (β = -0.037, 95% CI: -0.072 to -0.002, P = 0.040) were significantly associated with tPSA levels. 3.3. Subgroup analysis To further investigate potential moderating factors, we conducted subgroup analyses (Table 2 ). Significant interactions between HbA1c and tPSA were identified in several subgroups. In the subgroup of participants aged ≤ 59 years, a notable negative correlation between HbA1c and tPSA was observed (β = -0.71, 95% CI: -1.36 to -0.06, P = 0.033), suggesting a modest clinical impact. Additionally, in individuals who were married or partnered, a significant inverse association between HbA1c and tPSA was observed (β = -0.55, 95% CI: -0.81 to -0.29, P < 0.001), whereas no significant correlation was detected in those who were single, divorced, or widowed. Among participants without a history of CAD, HbA1c was also negatively associated with tPSA (β = -0.49, 95% CI: -0.88 to -0.10, P = 0.015), whereas no such association was found in individuals with CAD. Furthermore, in the insulin-using subgroup, a significant negative correlation between HbA1c and tPSA was identified (β = -0.80, 95% CI: -1.53 to -0.08, P = 0.031), suggesting that insulin therapy may moderate the relationship between HbA1c and tPSA. As shown in supplementary table 2 , a detailed breakdown of these subgroup analyses and the corresponding beta coefficients (β) with 95% confidence intervals (CI) is provided. Subgroup-specific effects require cautious interpretation and validation in larger prospective studies before clinical recommendations can be made, as the clinical relevance of these associations may vary across different subgroups. Table 2 Subgroup analysis of the HbA1c and tPSA association in patients with diabetes with good and poor glycemic control Variables n (%) HbA1c < 7 HbA1c ≥ 7 β (95%CI) P P for interaction All patients 1025 (100.00) 2.02 ± 3.01 1.65 ± 3.08 -0.37 (-0.74 ~ 0.00) 0.052 Age 0.183 ≤ 59 376 (36.68) 2.06 ± 3.76 1.35 ± 2.47 -0.71 (-1.36 ~ -0.06) 0.033 60–74 464 (45.27) 1.81 ± 2.18 1.83 ± 2.94 0.02 (-0.45 ~ 0.49) 0.937 ≥ 75 185 (18.05) 2.47 ± 3.15 1.84 ± 4.33 -0.63 (-1.71 ~ 0.45) 0.253 Education level 0.357 High school or less 644 (62.83) 1.86 ± 2.66 1.54 ± 2.97 -0.31 (-0.75 ~ 0.12) 0.158 Some college 218 (21.27) 2.33 ± 3.79 1.56 ± 1.55 -0.77 (-1.56 ~ 0.03) 0.060 College or above 163 (15.90) 2.19 ± 3.05 2.33 ± 4.86 0.14 (-1.07 ~ 1.36) 0.818 Marital Status 0.001 Married/Partnered 740 (72.20) 1.69 ± 2.29 1.15 ± 1.16 -0.55 (-0.81 ~ -0.29) < .001 Unpartnered 285 (27.80) 2.67 ± 4.01 3.51 ± 5.95 0.85 (-0.31 ~ 2.01) 0.154 PIR 0.563 0–1 169 (16.49) 1.64 ± 2.53 1.68 ± 4.14 0.04 (-1.00 ~ 1.09) 0.936 1–2 306 (29.85) 1.91 ± 2.80 1.64 ± 2.74 -0.26 (-0.88 ~ 0.36) 0.410 2–3 195 (19.02) 2.16 ± 2.62 1.96 ± 4.08 -0.20 (-1.15 ~ 0.76) 0.688 3–4 135 (13.17) 2.58 ± 4.71 1.51 ± 1.72 -1.06 (-2.31 ~ 0.18) 0.097 ≥ 4 220 (21.46) 1.97 ± 2.55 1.41 ± 1.65 -0.56 (-1.15 ~ 0.03) 0.065 BMI 0.890 ≤ 25 147 (14.34) 2.09 ± 3.25 1.55 ± 1.40 -0.54 (-1.37 ~ 0.28) 0.198 25–30 381 (37.17) 2.41 ± 3.84 2.08 ± 4.36 -0.33 (-1.16 ~ 0.49) 0.430 ≥ 30 497 (48.49) 1.66 ± 1.88 1.39 ± 2.25 -0.27 (-0.63 ~ 0.10) 0.149 Smoked at least 100 cigarettes in life 0.546 Yes 657 (64.10) 2.04 ± 2.79 1.76 ± 3.69 -0.28 (-0.78 ~ 0.22) 0.269 No 368 (35.90) 1.99 ± 3.39 1.47 ± 1.53 -0.52 (-1.07 ~ 0.03) 0.062 Had ≥ 12 alcoholic drinks in the past year 0.793 Yes 798 (77.85) 2.02 ± 3.15 1.62 ± 3.35 -0.40 (-0.85 ~ 0.05) 0.084 No 227 (22.15) 2.01 ± 2.43 1.74 ± 1.98 -0.28 (-0.85 ~ 0.30) 0.346 Hypertension 0.589 Yes 627 (61.17) 2.05 ± 2.69 1.77 ± 3.42 -0.27 (-0.75 ~ 0.20) 0.262 No 398 (38.83) 1.97 ± 3.53 1.49 ± 2.54 -0.49 (-1.09 ~ 0.12) 0.115 CAD 0.119 Yes 147 (14.34) 1.84 ± 2.27 2.20 ± 4.74 0.36 (-0.80 ~ 1.53) 0.540 No 878 (85.66) 2.05 ± 3.13 1.56 ± 2.73 -0.49 (-0.88 ~ -0.10) 0.015 Enlarged prostate 0.611 Yes 136 (13.27) 2.94 ± 4.81 2.36 ± 4.35 -0.59 (-2.17 ~ 1.00) 0.468 No 889 (86.73) 1.86 ± 2.55 1.56 ± 2.86 -0.30 (-0.66 ~ 0.06) 0.098 Use of insulin 0.279 Yes 226 (22.05) 2.34 ± 3.99 1.54 ± 1.59 -0.80 (-1.53 ~ -0.08) 0.031 No 799 (77.95) 1.97 ± 2.85 1.70 ± 3.59 -0.27 (-0.72 ~ 0.18) 0.239 Use of diabetes pills 0.629 Yes 781 (76.20) 2.00 ± 2.97 1.68 ± 2.98 -0.32 (-0.74 ~ 0.10) 0.137 No 244 (23.80) 2.09 ± 3.17 1.56 ± 3.36 -0.53 (-1.35 ~ 0.29) 0.203 Covariates used for adjusting the model include the following : demographic variables (age, race/ethnicity, education level, PIR, and marital status); clinical and lifestyle variables (BMI, smoking status, alcohol consumption, hypertension, CAD, enlarged prostate, insulin, and oral hypoglycemic usage); and biological markers: CRP, OGTT, and fasting glucose levels. 4. Discussion This study explored the relationship between glycemic control, as measured by HbA1c, and tPSA levels in individuals with diabetes, an area that has been relatively underexplored in the literature. While numerous studies have compared tPSA levels between individuals with diabetes and nondiabetic populations, the impact of glycemic control on tPSA levels within the population with diabetes itself remains less well understood. By specifically examining how varying levels of glycemic control affect tPSA levels, this study provides valuable insights into potential mechanisms influencing prostate health in individuals with diabetes. In contrast to our hypothesis, the results revealed no significant association between poor glycemic control and elevated tPSA levels, nor was better glycemic control clearly linked to lower tPSA levels. These findings suggest that, while the relationship between glycemic control and prostate health is likely complex, blood glucose management alone does not appear to be a major determinant of tPSA levels in men with diabetes ( 23 , 24 ). These findings contrast with those of previous studies suggesting that glycemic control may play a role in regulating tPSA levels and improving the early detection of prostate diseases in populations with diabetes ( 1 , 8 , 25 ). For example, Sarma et al. ( 26 ) found that in men with type 1 diabetes, a 10% increase in HbA1c was associated with an 11% reduction in PSA levels, suggesting that poorer glycemic control could lead to lower PSA concentrations. Similarly, Atalay et al. ( 27 )reported that poor glycemic control in men with type 2 diabetes was associated with significantly lower PSA levels and smaller prostate volumes, supporting the idea that glycemic control might impact prostate health. Unlike previous studies that associate hyperglycemia with elevated PSA levels, our findings suggest that other metabolic factors, such as obesity and inflammation, may have a more significant impact on tPSA. Discrepancies between our results and earlier studies could stem from differences in study design, sample characteristics, or unaccounted confounders. The lack of a clear association between glycemic control and tPSA in our study aligns with some prior research. For example, some studies have reported no significant correlation between uncontrolled blood glucose and elevated tPSA in patients with diabetes, indicating that other factors may be more influential ( 7 , 28 ). Similarly, Choi et al. reported no significant correlation between elevated HbA1c and tPSA, suggesting that insulin resistance and metabolic disturbances may not directly impact tPSA levels in all patients with diabetes ( 29 ). However, other studies have identified a positive relationship between poor glycemic control and elevated tPSA levels, indicating that differences in study designs, population characteristics, or measurement methods may explain these discrepancies( 1 , 25 , 30 ). This underscores the need for larger, more robust studies to clarify the relationship between glycemic control and tPSA levels in individuals with diabetes ( 1 , 28 ). Despite the absence of a clear association, several biological mechanisms could still explain the potential relationship between glycemic control and tPSA. Insulin resistance, which is commonly observed in poorly controlled diabetes, leads to hyperinsulinemia, which can promote prostate cell proliferation through androgenic and insulin-like growth factor (IGF) signaling pathways ( 10 , 31 ). Moreover, chronic hyperglycemia activates inflammatory pathways that may produce proinflammatory cytokines, influencing prostate tissue and potentially increasing tPSA secretion( 32 , 33 ). Additionally, elevated visceral fat, often observed in patients with diabetes, contributes to both metabolic disturbances and prostate disease ( 34 – 36 ). Visceral fat secretes adipokines and inflammatory cytokines, exacerbating the effects of hyperglycemia on prostate health by promoting inflammation and altering hormonal signaling, which could influence tPSA levels ( 37 , 38 ). These findings suggest that managing broader metabolic factors, such as insulin resistance and visceral adiposity, may improve prostate health in individuals with diabetes, even if glycemic control alone does not directly affect tPSA ( 39 , 40 ). Interestingly, our subgroup analyses revealed that the relationship between HbA1c and tPSA varied by age, marital status, CAD, and insulin use. In the subgroup of individuals aged ≤ 59 years, a significant inverse association between HbA1c and tPSA was observed (β = -0.71, 95% CI: -1.36 to -0.06, P = 0.033), suggesting that younger individuals with diabetes may be more susceptible to the effects of glycemic control on tPSA levels ( 28 ). However, this association was not detected in older age groups, which may be influenced by other factors such as aging-related changes in hormone levels or comorbidities that obscure the glycemic control–tPSA relationship ( 41 ). Additionally, in participants who were married or living with a partner, a significant negative correlation between HbA1c and tPSA was observed. This finding may reflect the broader impact of psychosocial factors, such as social support and lifestyle behaviors, on both glycemic control and prostate health, warranting further exploration ( 42 – 44 ). These findings highlight the complexity of the glycemic control–tPSA relationship and suggest that other variables may modulate this association in certain subgroups of the population with diabetes. Despite the lack of a direct link between HbA1c and tPSA, our study contributes to the growing body of literature on the potential role of metabolic factors in prostate health. Future research should aim to explore the underlying mechanisms through longitudinal studies that explore the interactions between glycemic control, insulin resistance, inflammation, and other metabolic factors in relation to tPSA levels. Moreover, clinical trials assessing the effects of comprehensive metabolic management on prostate health outcomes in patients with diabetes could provide valuable insights. In conclusion, while this study did not establish a clear relationship between glycemic control and tPSA levels in men with diabetes, it emphasizes the importance of considering broader metabolic factors, such as insulin resistance and visceral adiposity, in managing prostate health in this population. Future research should aim to clarify the role of glycemic control in prostate health and develop more effective strategies to reduce the risk of prostate diseases among individuals with diabetes. Additionally, enhancing the accuracy and clinical relevance of prostate cancer screening methods for patients with diabetes remains a crucial objective. 5. Limitations Several limitations should be acknowledged in this study. First, the cross-sectional nature of the NHANES data limits our ability to draw causal inferences between glycemic control and tPSA levels. Second, unmeasured variables, including genetic predispositions, prostate size, medication use, and lifestyle factors, may confound the observed relationships. Moreover, NHANES data lacks prostate volume and medication use data, which may influence PSA levels and should be considered a limitation. Additionally, using HbA1c as a proxy for long-term metabolic status might oversimplify the complex dynamics of glycemic regulation over time. Finally, longitudinal studies are necessary to confirm our findings and better understand the biological mechanisms linking glycemic control with tPSA levels. 6. Conclusion The present study demonstrated that overall glycemic control was not significantly associated with tPSA levels in patients with diabetes after adjusting for multiple covariates. However, significant associations were observed within specific subgroups, including younger individuals, married or partnered participants, those without CAD, and insulin users. These findings suggest that clinicians should consider closely monitoring PSA trends in these subgroups, as they may exhibit distinct biomarker profiles. Targeted monitoring of prostate health in younger individuals with diabetes and insulin users, in particular, may offer a more tailored approach to identifying prostate health risks. Nonetheless, further longitudinal studies are needed to validate these associations and explore the mechanisms involved, including the potential role of hormonal and inflammatory mediators. Declarations Ethics approval and consent to participate This study utilized data from the NHANES, which was approved by the Institutional Review Board (IRB) of the National Center for Health Statistics, Centers for Disease Control and Prevention (CDC). Written informed consent was obtained from all participants. Consent for publication All participants provided informed consent for the publication of their data. Competing interests The authors declare no competing interests. Conflict of interest The authors confirm that there are no conflicts of interest regarding the publication of this manuscript. Funding This study did not receive any specific funding from public, commercial, or non-profit organizations. Author Contribution X.H. and J.Z. conceptualized and designed the study, supervised the overall content, and revised the manuscript. X.H. also performed the statistical analysis and prepared the initial manuscript draft. N.H. assisted in drafting the manuscript and revising it. N.H. and Z.W. was responsible for data curation and preprocessing, while Z.W. assisted with data analysis.All authors reviewed and approved the final manuscript. Acknowledgement We would like to sincerely thank the staff of the National Health and Nutrition Examination Survey (NHANES) for their invaluable work in collecting, maintaining, and providing access to this essential dataset. We are also grateful to Dr. Jie Zhang (Corresponding Author) for his thoughtful guidance and revisions throughout the study. Our thanks to Dr. Zhi Wang and Dr. Nan Huang for their contributions to data curation and manuscript preparation. Data Availability The datasets generated and/or analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) repository at https://www.cdc.gov/nchs/nhanes/. References Bharti A, Shekhar R, Prakash P, Kumari S, Kumar S. Poor Glycemic Control Affecting Screening of Prostate Carcinoma. Cureus. 2024;16(4):e58680. Bernal-Soriano MC, Lumbreras B, Hernández-Aguado I, Pastor-Valero M, López-Garrigos M, Parker LA. Untangling the association between prostate-specific antigen and diabetes: a systematic review and meta-analysis. Clinical chemistry and laboratory medicine. 2020;59(1):11-26. Franko A, Berti L, Hennenlotter J, Rausch S, Scharpf MO, de Angelis MH, et al. Transcript Levels of Aldo-Keto Reductase Family 1 Subfamily C (AKR1C) Are Increased in Prostate Tissue of Patients with Type 2 Diabetes. Journal of personalized medicine. 2020;10(3). Kasper JS, Giovannucci E. A meta-analysis of diabetes mellitus and the risk of prostate cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2006;15(11):2056-62. Romero Otero J, Garcia Gomez B, Campos Juanatey F, Touijer KA. Prostate cancer biomarkers: an update. Urologic oncology. 2014;32(3):252-60. Zhou B, Wang P, Xu WJ, Li YM, Tong DL, Jiang J, et al. Correlations of glucose metabolism, insulin resistance and inflammatory factors with symptom score of patients with benign prostatic hyperplasia. European review for medical and pharmacological sciences. 2018;22(16):5077-81. Satir A, Demirci H. Total Prostate Specific Antigen in Prostate Cancer Screening in Hyperglycemic Individuals. Clinical genitourinary cancer. 2023;21(2):e53-e7. Müller H, Raum E, Rothenbacher D, Stegmaier C, Brenner H. Association of diabetes and body mass index with levels of prostate-specific antigen: implications for correction of prostate-specific antigen cutoff values? Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2009;18(5):1350-6. Beckmann K, Crawley D, Nordström T, Aly M, Olsson H, Lantz A, et al. Association Between Antidiabetic Medications and Prostate-Specific Antigen Levels and Biopsy Results. JAMA network open. 2019;2(11):e1914689. Vikram A, Jena G, Ramarao P. Insulin-resistance and benign prostatic hyperplasia: the connection. European journal of pharmacology. 2010;641(2-3):75-81. Ye C, Li X, Wang Y, Zhang Y, Cai M, Zhu B, et al. Diabetes causes multiple genetic alterations and downregulates expression of DNA repair genes in the prostate. Laboratory investigation; a journal of technical methods and pathology. 2011;91(9):1363-74. Park JS, Lee KS, Ham WS, Chung BH, Koo KC. Impact of metformin on serum prostate-specific antigen levels: Data from the national health and nutrition examination survey 2007 to 2008. Medicine. 2017;96(51):e9427. Liu X, Li J, Schild SE, Schild MH, Wong W, Vora S, et al. Statins and Metformin Use Is Associated with Lower PSA Levels in Prostate Cancer Patients Presenting for Radiation Therapy. Journal of cancer therapy. 2017;8(2):73-85. Ahn HK, Lee YH, Koo KC. Current Status and Application of Metformin for Prostate Cancer: A Comprehensive Review. International journal of molecular sciences. 2020;21(22). Tseng CH. The Effect of Metformin on Male Reproductive Function and Prostate: An Updated Review. The world journal of men's health. 2022;40(1):11-29. Jian Gang P, Mo L, Lu Y, Runqi L, Xing Z. Diabetes mellitus and the risk of prostate cancer: an update and cumulative meta-analysis. Endocrine research. 2015;40(1):54-61. Borrud L, Chiappa MM, Burt VL, Gahche J, Zipf G, Johnson CL, et al. National Health and Nutrition Examination Survey: national youth fitness survey plan, operations, and analysis, 2012. Vital and health statistics Series 2, Data evaluation and methods research. 2014(163):1-24. Shichiri M, Kishikawa H, Ohkubo Y, Wake N. Long-term results of the Kumamoto Study on optimal diabetes control in type 2 diabetic patients. Diabetes care. 2000;23 Suppl 2:B21-9. King P, Peacock I, Donnelly R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. British journal of clinical pharmacology. 1999;48(5):643-8. Wang P, Huang R, Lu S, Xia W, Sun H, Sun J, et al. HbA1c below 7% as the goal of glucose control fails to maximize the cardiovascular benefits: a meta-analysis. Cardiovascular diabetology. 2015;14:124. Trief PM, Himes CL, Orendorff R, Weinstock RS. The marital relationship and psychosocial adaptation and glycemic control of individuals with diabetes. Diabetes care. 2001;24(8):1384-9. Haines L, Coppa N, Harris Y, Wisnivesky JP, Lin JJ. The Impact of Partnership Status on Diabetes Control and Self-Management Behaviors. Health education & behavior : the official publication of the Society for Public Health Education. 2018;45(5):668-71. Onitilo AA, Stankowski RV, Berg RL, Engel JM, Glurich I, Williams GM, et al. Type 2 diabetes mellitus, glycemic control, and cancer risk. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP). 2014;23(2):134-40. Waters KM, Henderson BE, Stram DO, Wan P, Kolonel LN, Haiman CA. Association of diabetes with prostate cancer risk in the multiethnic cohort. American journal of epidemiology. 2009;169(8):937-45. Park J, Cho SY, Lee YJ, Lee SB, Son H, Jeong H. Poor glycemic control of diabetes mellitus is associated with higher risk of prostate cancer detection in a biopsy population. PloS one. 2014;9(9):e104789. Sarma AV, Hotaling J, Dunn RL, Cleary PA, Braffett BH, Kim C, et al. Poor glycemic control is associated with reduced prostate specific antigen concentrations in men with type 1 diabetes. The Journal of urology. 2015;193(3):786-93. Atalay HA, Akarsu M, Canat L, Ülker V, Alkan İ, Ozkuvancı U. Impact of poor glycemic control of type 2 diabetes mellitus on serum prostate-specific antigen concentrations in men. Prostate international. 2017;5(3):104-9. Ainahi A, Brakat A, Wakrim L, Mohammadi H, ElMdaghri N, Ezzikouri S. Prostate-specific Antigen Levels in Moroccan Diabetic Males: A Cross-sectional Study. Current diabetes reviews. 2018;14(3):286-90. Choi HC, Park JH, Cho BL, Son KY, Yoo YJ, Kwon HT. The illusion of prostate-specific antigen decline in patients with metabolic syndrome and insulin resistance. BJU international. 2011;108(11):1756-61. Ohwaki K, Endo F, Muraishi O, Yano E. Relationship between changes in haemoglobin A1C and prostate-specific antigen in healthy men. European journal of cancer (Oxford, England : 1990). 2011;47(2):262-6. Wang Z, Olumi AF. Diabetes, growth hormone-insulin-like growth factor pathways and association to benign prostatic hyperplasia. Differentiation; research in biological diversity. 2011;82(4-5):261-71. Chang SC, Yang WV. Hyperglycemia, tumorigenesis, and chronic inflammation. Critical reviews in oncology/hematology. 2016;108:146-53. Franko A, Berti L, Hennenlotter J, Rausch S, Scharpf MO, Angelis MH, et al. Increased Expressions of Matrix Metalloproteinases (MMPs) in Prostate Cancer Tissues of Men with Type 2 Diabetes. Biomedicines. 2020;8(11). AlZaim I, Al-Saidi A, Hammoud SH, Darwiche N, Al-Dhaheri Y, Eid AH, et al. Thromboinflammatory Processes at the Nexus of Metabolic Dysfunction and Prostate Cancer: The Emerging Role of Periprostatic Adipose Tissue. Cancers. 2022;14(7). McGrowder DA, Jackson LA, Crawford TV. Prostate cancer and metabolic syndrome: is there a link? Asian Pacific journal of cancer prevention : APJCP. 2012;13(1):1-13. Rhee H, Vela I, Chung E. Metabolic Syndrome and Prostate Cancer: a Review of Complex Interplay Amongst Various Endocrine Factors in the Pathophysiology and Progression of Prostate Cancer. Hormones & cancer. 2016;7(2):75-83. Parikesit D, Mochtar CA, Umbas R, Hamid AR. The impact of obesity towards prostate diseases. Prostate international. 2016;4(1):1-6. Fujita K, Hayashi T, Matsushita M, Uemura M, Nonomura N. Obesity, Inflammation, and Prostate Cancer. Journal of clinical medicine. 2019;8(2). Sousa AP, Costa R, Alves MG, Soares R, Baylina P, Fernandes R. The Impact of Metabolic Syndrome and Type 2 Diabetes Mellitus on Prostate Cancer. Frontiers in cell and developmental biology. 2022;10:843458. Lee JJ, Beretvas SN, Freeland-Graves JH. Abdominal adiposity distribution in diabetic/prediabetic and nondiabetic populations: a meta-analysis. Journal of obesity. 2014;2014:697264. Cannarella R, Condorelli RA, Barbagallo F, La Vignera S, Calogero AE. Endocrinology of the Aging Prostate: Current Concepts. Frontiers in endocrinology. 2021;12:554078. Cosansu G, Erdogan S. Influence of psychosocial factors on self-care behaviors and glycemic control in Turkish patients with type 2 diabetes mellitus. Journal of transcultural nursing : official journal of the Transcultural Nursing Society. 2014;25(1):51-9. Stone AA, Mezzacappa ES, Donatone BA, Gonder M. Psychosocial stress and social support are associated with prostate-specific antigen levels in men: results from a community screening program. Health psychology : official journal of the Division of Health Psychology, American Psychological Association. 1999;18(5):482-6. Cuevas AG, Trudel-Fitzgerald C, Cofie L, Zaitsu M, Allen J, Williams DR. Placing prostate cancer disparities within a psychosocial context: challenges and opportunities for future research. Cancer causes & control : CCC. 2019;30(5):443-56. Additional Declarations No competing interests reported. Supplementary Files TableLegends.docx HuoGlycemicControlPSARawData.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 30 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 28 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5674422","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434971118,"identity":"379c759c-74e5-4a8d-ae7a-da832d76701c","order_by":0,"name":"Xiao Huo","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Huo","suffix":""},{"id":434971119,"identity":"ab2e9cc3-2a2e-4321-b761-1e7ac0e22fea","order_by":1,"name":"Zhi Wang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Wang","suffix":""},{"id":434971120,"identity":"c705e39c-6f39-4d8d-ab45-7712298a4dfb","order_by":2,"name":"Nan Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Huang","suffix":""},{"id":434971121,"identity":"68f2fc29-3862-4add-8e50-1d212585d85b","order_by":3,"name":"Jie Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3RsQrCMBCA4QuFusR2vS72FU4cFX2VQKEuBZ2cM+niA1R8Ct8gJavUTYQ6FATnji4VixVcpNbNIT+3BO4bjgCYTH+Ya0FwKe+j19NuQbyVvBK3wx8I7ZMcua1/IHAKBCE/+M5xT1AsNLhb2SxYHIqcMOtvsohYnGrAs2omFkaCBGXVRGR1lxoIRTOxcVagEmlNyjaEcyU8qVRNWBuCHRkOmAyqW8J5sk6nHE9fyERXX8nk2HeyYJffFsOeG38h79RzgLfdr4nJZDKZPvYAtOVFT23CHqYAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-12-19 07:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5674422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5674422/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-00853-2","type":"published","date":"2025-05-06T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79550263,"identity":"b9cbaa6b-2ee7-40ff-99c0-88721301417b","added_by":"auto","created_at":"2025-03-31 06:31:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42238,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart illustrates the entire inclusion and exclusion process. Participants were excluded based on the following criteria: women, age \u0026lt; 40 years, missing data on malignancies, missing HbA1c or PSA data, non-diabetic status, unclear diabetes diagnosis, and factors affecting PSA levels, including current prostate infection or inflammation, a rectal examination in the past week, prostate biopsy or cystoscopy in the past month, or a history of malignancy (including prostate cancer). The final sample size for the analysis was 1,025 participants.\u003c/p\u003e","description":"","filename":"Figure1FlowchartofparticipantselectionfromtheNHANES20012010dataset.png","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/a963b09e35845748aeb21c97.png"},{"id":79551338,"identity":"628a23ac-e0a3-454e-bc4d-c2cea02b65e1","added_by":"auto","created_at":"2025-03-31 06:39:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67150,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap illustrating the relationship between total tPSA and HbA1c. The correlation coefficient between tPSA and HbA1c is shown as -0.061, indicating a very weak inverse correlation between the two variables.\u003c/p\u003e","description":"","filename":"Figure2CorrelationHeatmapBetweentPSAandHbA1cLevels.png","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/1e33b5f241be6b20ca1dc19b.png"},{"id":79551342,"identity":"c0563073-61d3-4a05-801f-165010987af6","added_by":"auto","created_at":"2025-03-31 06:39:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88586,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot showing the distribution of total tPSA levels by glycemic control group, categorized by HbA1c \u0026lt; 7% (good glycemic control) and HbA1c ≥ 7% (poor glycemic control). The boxplot highlights the central tendency and spread of tPSA levels within each group, with outliers represented as individual points.\u003c/p\u003e","description":"","filename":"Figure3ComparisonoftPSALevelsAcrossGlycemicControlGroupsBasedonHbA1cThresholds.png","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/2e9a53ef075c85e0e11bdd23.png"},{"id":82537526,"identity":"4de57d24-79a7-4534-bab5-0afa83b26908","added_by":"auto","created_at":"2025-05-12 16:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1167807,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/49a631ca-cd4e-4dc7-8ed4-c2ae1b9063d9.pdf"},{"id":79550265,"identity":"57852081-5f46-4b3d-818b-19795984c770","added_by":"auto","created_at":"2025-03-31 06:31:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18177,"visible":true,"origin":"","legend":"","description":"","filename":"TableLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/0e8d0978523ea8c89c6b3751.docx"},{"id":79550279,"identity":"61843b68-01fd-4e8e-8cf4-202f8877bd0d","added_by":"auto","created_at":"2025-03-31 06:31:10","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":152772,"visible":true,"origin":"","legend":"","description":"","filename":"HuoGlycemicControlPSARawData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5674422/v1/052d096d5eaf4c3fe9b19bb2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Glycemic Control and Prostate Antigen Levels in Individuals with Diabetes Based on NHANES Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe relationship between blood glucose control and prostate-specific antigen levels in patients with diabetes has drawn increasing attention because of its potential impact on prostate cancer screening (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Diabetes, characterized by chronic hyperglycemia, can alter metabolic processes that may influence PSA expression (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given the increasing prevalence of diabetes worldwide, understanding its interaction with PSA levels is crucial for improving prostate cancer detection strategies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). PSA is a widely used biomarker for prostate cancer detection, but its level can be influenced by several factors other than cancer, including inflammation and benign prostate hyperplasia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In patients with diabetes, the interplay between hyperglycemia, insulin resistance, and inflammatory responses may complicate PSA interpretation(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior studies have yielded conflicting results regarding the impact of glycemic control on PSA levels, necessitating further investigation. Studies suggest that poor glycemic control, as indicated by an HbA1c level\u0026thinsp;\u0026ge;\u0026thinsp;7%, is associated with higher PSA levels, potentially leading to false positives in cancer screening (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). On the other hand, maintaining better glucose control may reduce PSA levels and improve the accuracy of prostate cancer screening (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The molecular mechanisms underlying this relationship are not fully understood, but several pathways have been proposed. Insulin resistance, a hallmark of type 2 diabetes, is often accompanied by increased production of growth factors and inflammatory cytokines, which can promote prostate cell proliferation and increase PSA levels (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, studies suggest that hyperglycemia-induced oxidative stress in diabetes may alter prostate gene expression and cellular function, potentially impacting the secretion of PSA (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These findings underscore the need for a nuanced approach when PSA is used as a screening tool in individuals with diabetes.\u003c/p\u003e \u003cp\u003eEmerging research has also explored how treatments for diabetes, such as metformin, may influence PSA levels (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Some studies suggest that metformin, which improves insulin sensitivity, may reduce the risk of prostate cancer and lower PSA levels by modulating glucose metabolism and inflammatory responses (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). While preliminary, these findings highlight the potential role of diabetes management in improving the accuracy of PSA testing. However, larger, more robust studies are needed to validate these effects and establish clearer clinical guidelines for the management of prostate cancer risk in patients with diabetes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the current study utilized data from the National Health and Nutrition Examination Survey (NHANES) to investigate the possible correlations between blood glucose management and tPSA levels in individuals with diabetes. We hypothesize that glycemic control may differentially influence tPSA levels, with possible variations across age groups and clinical subpopulations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Survey description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the NHANES, a program conducted by the National Center for Health Statistics (NCHS) to assess the nutritional and health status of the U.S. population (17). The NHANES employs a complex, multistage, stratified probability sampling design and is conducted biennially, ensuring a representative sample. All NHANES research protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from all participants or their parents and/or legal guardians for those under 16 years of age. The detailed NHANES research designs and data are publicly available at www.cdc.gov/nchs/nhanes/. Additionally, all methods were performed in accordance with the relevant guidelines and regulations to ensure the ethical conduct of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the NHANES 2001‒2010 survey cycles, which included relevant data on PSA, HbA1c, and diabetes-related questionnaires. After screening 52,195 participants, a total of 1,025 male participants aged 40 years and older, who had undergone PSA laboratory testing and had available HbA1c data, were included in the analysis (Figure 1). Participants were excluded if they met any of the following criteria: current prostate infection or inflammation, a rectal examination in the past week, prostate biopsy or cystoscopy in the past month, a history of malignancy (including prostate cancer), missing PSA data, or if their diabetes diagnosis could not be confirmed through questionnaires or related tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Diabetes definition and classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, diabetes status was defined based on the American Diabetes Association (ADA) criteria. A participant was classified as having diabetes if they met one or more of the following conditions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThey self-reported a diabetes diagnosis through the NHANES questionnaire, or\u003c/p\u003e\n\u003cp\u003eThey met any of the following criteria:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eThe HbA1c level was \u0026ge;6.5%, or\u003c/li\u003e\n \u003cli\u003eThe fasting blood glucose level was \u0026ge;7 mmol/L, or\u003c/li\u003e\n \u003cli\u003eThe 2-hour post-OGTT glucose level exceeded 11.1 mmol/L.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA review of the relevant literature, revealed that an HbA1c threshold of 7% is widely recognized as a reliable marker for assessing glycemic control in diabetes management. Numerous studies have established a strong correlation between HbA1c levels and blood glucose control. An HbA1c value above 7% is generally associated with poor glycemic control and an increased risk of both microvascular and macrovascular complications in patients with diabetes (18, 19). Conversely, an HbA1c level less than 7% is linked to better glycemic control and a reduced risk of diabetes-related complications(20). Clinical research and guidelines support the idea that maintaining an HbA1c\u0026nbsp;\u0026le;7% lowers complication risks and improves quality of life for patients with diabetes, making it an internationally recognized target for optimal glycemic control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral covariates were included in the analysis to adjust for potential confounders. These covariates included the following:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic variables:\u0026nbsp;\u003c/strong\u003eAge (years), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other), education level (College graduate or above, some college, high school or less), poverty income ratio (PIR), and marital status (married or living with a partner, widowed/divorced/separated/never married).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and lifestyle variables:\u0026nbsp;\u003c/strong\u003eBody mass index (BMI) (categorized as \u0026le;25, 25\u0026ndash;30, and \u0026ge;30 kg/m\u0026sup2;), smoking status (yes/no), alcohol consumption (yes/no), hypertension (yes/no), enlarged prostate (yes/no), and CAD (yes/no). Insulin and oral hypoglycemic use were identified from the diabetes questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological markers\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eC-reactive protein (CRP), OGTT, and fasting glucose levels, were obtained from laboratory data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PIR data used in this study were obtained from the NHANES database. These data are publicly accessible through the NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003eBMI was categorized as normal weight (\u0026le;25 kg/m\u0026sup2;), overweight (25\u0026ndash;30 kg/m\u0026sup2;), or obese (\u0026ge;30 kg/m\u0026sup2;). The detailed measurement protocols for these variables can be found at www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003eMarital status was included as a key variable due to its potential influence on health outcomes and social support, as evidenced by previous literature (21, 22). We hypothesized that marital status might serve as a confounding factor, impacting participants\u0026apos; psychological and physical health. Marital status data was collected through self-reported questionnaires.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed via SPSS 26.0 and Python 3.10. Descriptive statistics were used to calculate means and standard deviations (SDs). The normality of the data was assessed via the Kolmogorov‒Smirnov (KS) test. Nonparametric methods were used due to the non-normal distribution of PSA values (confirmed by the KS normality test). For inter-group comparisons, Mann-Whitney U tests were used for non-normally distributed continuous variables, and chi-square tests were used for categorical variables. Spearman\u0026rsquo;s rank correlation was used for correlation analysis.\u003c/p\u003e\n\u003cp\u003eTo examine the relationship between tPSA levels and other variables, multiple linear regression analysis was conducted to adjust for potential confounders, including demographic variables, clinical and lifestyle factors, and biological markers. This method was chosen because the dependent variable (tPSA) is continuous, and linear regression is appropriate for continuous outcomes. Missing data were handled via multiple imputation. Statistical significance was set at p \u0026lt; 0.05. Given the multiple subgroup comparisons in our analysis, findings should be interpreted with caution due to the risk of Type I error. To control for multiple comparisons, we applied the Bonferroni correction to reduce the risk of Type I errors.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics of the participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the 1,025 adults with diabetes included in this study, categorized by glycemic control status. Participants had a mean age of 62.72 years (\u0026plusmn;\u0026thinsp;11.41), and those with HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% (good glycemic control) differed significantly from those with HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% (poor glycemic control) across multiple clinical and demographic variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As expected, markers of glucose metabolism diverged sharply between the two groups, with significantly lower OGTT (14.27 mmol/L vs. 17.29 mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fasting plasma glucose levels (7.71 mmol/L vs. 10.08 mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the well-controlled group. Socioeconomic factors mirrored these clinical distinctions, with the poorly controlled group having a greater proportion of individuals with high school or less (66.2% vs. 59.5%, P\u0026thinsp;=\u0026thinsp;0.049) and fewer with college-level education or higher (13.1% vs. 18.3%). Marital status differed as well, with fewer married or partnered individuals in the well-controlled group (66.5% vs. 78.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertension was unexpectedly more prevalent in the good glycemic control group (64.7% vs. 57.2%, P\u0026thinsp;=\u0026thinsp;0.015), whereas insulin use was significantly higher in those with poor control (33.0% vs. 12.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a more detailed breakdown of the baseline characteristics across these groups is provided.\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=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\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\u003eHbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, mean SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, mean SD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mean SD (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA, mean SD (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT, mean SD (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eFasting\u0026nbsp;plasma\u0026nbsp;glucose,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003emean SD (mmol/L)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\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 \u003cp\u003e0.335\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\u003e231 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e92 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e424 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e237 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\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 \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e644 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327 (59.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317(66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003cp\u003e0.015\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\u003e627 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e353 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274 (57.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e398 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\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 \u003cp\u003e0.402\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\u003e147 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e878 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnlarged prostate\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 \u003cp\u003e0.114\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\u003e136 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e889 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465 (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of insulin\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e799 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUse of diabetes pills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.380\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\u003e781 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e359 (74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e244 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHad\u0026thinsp;\u0026ge;\u0026thinsp;12 alcoholic drinks in the past year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.297\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\u003e798 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432 (79.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e227 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSmoked at least 100 cigarettes in life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\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\u003e657 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e368 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. HbA1c and its association with tPSA levels\u003c/h2\u003e \u003cp\u003eTo explore the potential relationship between HbA1c levels and tPSA, we first conducted a correlation analysis, represented in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis revealed a very weak negative correlation (r = -0.061) between HbA1c and tPSA, indicating a minimal linear association. To assess the tPSA distribution, we conducted a KS normality test, which revealed that tPSA significantly deviated from a normal distribution (D\u0026thinsp;=\u0026thinsp;0.280, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). As a result, we used nonparametric methods for subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we performed the Mann‒Whitney U test to compare tPSA levels between the HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% group and the HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% group. The results revealed that the median tPSA in the HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% group was significantly greater than that in the HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% group (P\u0026thinsp;=\u0026thinsp;0.0014). Specifically, the median tPSA in the HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% group was 1.10 ng/mL (95% CI: 0.96 to 1.28), which was significantly higher than the 0.90 ng/mL (95% CI: 0.87 to 1.02) in the HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% group.\u003c/p\u003e \u003cp\u003eTo assess the distribution of tPSA across different glycemic control groups, a boxplot was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% group, tPSA values were higher, with a median value of 1.10 ng/mL. In contrast, the HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% group had a lower median tPSA of 0.90 ng/mL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also conducted a multivariate linear regression analysis, adjusting for demographic variables, clinical and lifestyle variables and biological markers as covariates. The results revealed that the HbA1c group had no statistically significant effect on tPSA levels (β = -0.022, 95% CI: -0.448 to 0.403, P\u0026thinsp;=\u0026thinsp;0.917). However, marital status (β\u0026thinsp;=\u0026thinsp;1.362, 95% CI: 0.935 to 1.789, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI (β = -0.037, 95% CI: -0.072 to -0.002, P\u0026thinsp;=\u0026thinsp;0.040) were significantly associated with tPSA levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Subgroup analysis\u003c/h2\u003e \u003cp\u003eTo further investigate potential moderating factors, we conducted subgroup analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant interactions between HbA1c and tPSA were identified in several subgroups. In the subgroup of participants aged\u0026thinsp;\u0026le;\u0026thinsp;59 years, a notable negative correlation between HbA1c and tPSA was observed (β = -0.71, 95% CI: -1.36 to -0.06, P\u0026thinsp;=\u0026thinsp;0.033), suggesting a modest clinical impact. Additionally, in individuals who were married or partnered, a significant inverse association between HbA1c and tPSA was observed (β = -0.55, 95% CI: -0.81 to -0.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no significant correlation was detected in those who were single, divorced, or widowed. Among participants without a history of CAD, HbA1c was also negatively associated with tPSA (β = -0.49, 95% CI: -0.88 to -0.10, P\u0026thinsp;=\u0026thinsp;0.015), whereas no such association was found in individuals with CAD. Furthermore, in the insulin-using subgroup, a significant negative correlation between HbA1c and tPSA was identified (β = -0.80, 95% CI: -1.53 to -0.08, P\u0026thinsp;=\u0026thinsp;0.031), suggesting that insulin therapy may moderate the relationship between HbA1c and tPSA. As shown in supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a detailed breakdown of these subgroup analyses and the corresponding beta coefficients (β) with 95% confidence intervals (CI) is provided. Subgroup-specific effects require cautious interpretation and validation in larger prospective studies before clinical recommendations can be made, as the clinical relevance of these associations may vary across different subgroups.\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\u003eSubgroup analysis of the HbA1c and tPSA association in patients with diabetes with good and poor glycemic control\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=\"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=\"left\" 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\u003eVariables\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\u003eHbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1025 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.37 (-0.74\u0026thinsp;~\u0026thinsp;0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\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\u003eAge\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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376 (36.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.71 (-1.36 ~ -0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\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;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e464 (45.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (-0.45\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.937\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\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185 (18.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.63 (-1.71\u0026thinsp;~\u0026thinsp;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e644 (62.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.31 (-0.75\u0026thinsp;~\u0026thinsp;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158\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\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218 (21.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.77 (-1.56\u0026thinsp;~\u0026thinsp;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\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\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163 (15.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14 (-1.07\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.818\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\u003eMarital Status\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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e740 (72.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.55 (-0.81 ~ -0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.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\u003eUnpartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e285 (27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (-0.31\u0026thinsp;~\u0026thinsp;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 (16.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (-1.00\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.936\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\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306 (29.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.26 (-0.88\u0026thinsp;~\u0026thinsp;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.410\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\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195 (19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.20 (-1.15\u0026thinsp;~\u0026thinsp;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.688\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\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135 (13.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.06 (-2.31\u0026thinsp;~\u0026thinsp;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\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\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220 (21.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.56 (-1.15\u0026thinsp;~\u0026thinsp;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147 (14.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.54 (-1.37\u0026thinsp;~\u0026thinsp;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.198\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\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e381 (37.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.33 (-1.16\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e497 (48.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27 (-0.63\u0026thinsp;~\u0026thinsp;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSmoked at least 100 cigarettes in life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.546\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\u003e657 (64.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.28 (-0.78\u0026thinsp;~\u0026thinsp;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.269\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\u003e368 (35.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.52 (-1.07\u0026thinsp;~\u0026thinsp;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eHad\u0026thinsp;\u0026ge;\u0026thinsp;12 alcoholic drinks in the past year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.793\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\u003e798 (77.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.40 (-0.85\u0026thinsp;~\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\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\u003e227 (22.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.28 (-0.85\u0026thinsp;~\u0026thinsp;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.346\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\u003eHypertension\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=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.589\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\u003e627 (61.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27 (-0.75\u0026thinsp;~\u0026thinsp;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.262\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\u003e398 (38.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.49 (-1.09\u0026thinsp;~\u0026thinsp;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\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\u003e147 (14.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (-0.80\u0026thinsp;~\u0026thinsp;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.540\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\u003e878 (85.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.49 (-0.88 ~ -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eEnlarged prostate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.611\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\u003e136 (13.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.59 (-2.17\u0026thinsp;~\u0026thinsp;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\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\u003e889 (86.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.30 (-0.66\u0026thinsp;~\u0026thinsp;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUse of insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.279\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\u003e226 (22.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.80 (-1.53 ~ -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\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\u003e799 (77.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27 (-0.72\u0026thinsp;~\u0026thinsp;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUse of diabetes pills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.629\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\u003e781 (76.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.32 (-0.74\u0026thinsp;~\u0026thinsp;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137\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\u003e244 (23.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.53 (-1.35\u0026thinsp;~\u0026thinsp;0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.203\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 \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates used for adjusting the model include the following\u003c/b\u003e: demographic variables (age, race/ethnicity, education level, PIR, and marital status); clinical and lifestyle variables (BMI, smoking status, alcohol consumption, hypertension, CAD, enlarged prostate, insulin, and oral hypoglycemic usage); and biological markers: CRP, OGTT, and fasting glucose levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study explored the relationship between glycemic control, as measured by HbA1c, and tPSA levels in individuals with diabetes, an area that has been relatively underexplored in the literature. While numerous studies have compared tPSA levels between individuals with diabetes and nondiabetic populations, the impact of glycemic control on tPSA levels within the population with diabetes itself remains less well understood. By specifically examining how varying levels of glycemic control affect tPSA levels, this study provides valuable insights into potential mechanisms influencing prostate health in individuals with diabetes.\u003c/p\u003e \u003cp\u003eIn contrast to our hypothesis, the results revealed no significant association between poor glycemic control and elevated tPSA levels, nor was better glycemic control clearly linked to lower tPSA levels. These findings suggest that, while the relationship between glycemic control and prostate health is likely complex, blood glucose management alone does not appear to be a major determinant of tPSA levels in men with diabetes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These findings contrast with those of previous studies suggesting that glycemic control may play a role in regulating tPSA levels and improving the early detection of prostate diseases in populations with diabetes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For example, Sarma et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) found that in men with type 1 diabetes, a 10% increase in HbA1c was associated with an 11% reduction in PSA levels, suggesting that poorer glycemic control could lead to lower PSA concentrations. Similarly, Atalay et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)reported that poor glycemic control in men with type 2 diabetes was associated with significantly lower PSA levels and smaller prostate volumes, supporting the idea that glycemic control might impact prostate health.\u003c/p\u003e \u003cp\u003eUnlike previous studies that associate hyperglycemia with elevated PSA levels, our findings suggest that other metabolic factors, such as obesity and inflammation, may have a more significant impact on tPSA. Discrepancies between our results and earlier studies could stem from differences in study design, sample characteristics, or unaccounted confounders. The lack of a clear association between glycemic control and tPSA in our study aligns with some prior research. For example, some studies have reported no significant correlation between uncontrolled blood glucose and elevated tPSA in patients with diabetes, indicating that other factors may be more influential (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, Choi et al. reported no significant correlation between elevated HbA1c and tPSA, suggesting that insulin resistance and metabolic disturbances may not directly impact tPSA levels in all patients with diabetes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, other studies have identified a positive relationship between poor glycemic control and elevated tPSA levels, indicating that differences in study designs, population characteristics, or measurement methods may explain these discrepancies(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This underscores the need for larger, more robust studies to clarify the relationship between glycemic control and tPSA levels in individuals with diabetes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the absence of a clear association, several biological mechanisms could still explain the potential relationship between glycemic control and tPSA. Insulin resistance, which is commonly observed in poorly controlled diabetes, leads to hyperinsulinemia, which can promote prostate cell proliferation through androgenic and insulin-like growth factor (IGF) signaling pathways (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Moreover, chronic hyperglycemia activates inflammatory pathways that may produce proinflammatory cytokines, influencing prostate tissue and potentially increasing tPSA secretion(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Additionally, elevated visceral fat, often observed in patients with diabetes, contributes to both metabolic disturbances and prostate disease (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Visceral fat secretes adipokines and inflammatory cytokines, exacerbating the effects of hyperglycemia on prostate health by promoting inflammation and altering hormonal signaling, which could influence tPSA levels (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings suggest that managing broader metabolic factors, such as insulin resistance and visceral adiposity, may improve prostate health in individuals with diabetes, even if glycemic control alone does not directly affect tPSA (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, our subgroup analyses revealed that the relationship between HbA1c and tPSA varied by age, marital status, CAD, and insulin use. In the subgroup of individuals aged\u0026thinsp;\u0026le;\u0026thinsp;59 years, a significant inverse association between HbA1c and tPSA was observed (β = -0.71, 95% CI: -1.36 to -0.06, P\u0026thinsp;=\u0026thinsp;0.033), suggesting that younger individuals with diabetes may be more susceptible to the effects of glycemic control on tPSA levels (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, this association was not detected in older age groups, which may be influenced by other factors such as aging-related changes in hormone levels or comorbidities that obscure the glycemic control\u0026ndash;tPSA relationship (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, in participants who were married or living with a partner, a significant negative correlation between HbA1c and tPSA was observed. This finding may reflect the broader impact of psychosocial factors, such as social support and lifestyle behaviors, on both glycemic control and prostate health, warranting further exploration (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These findings highlight the complexity of the glycemic control\u0026ndash;tPSA relationship and suggest that other variables may modulate this association in certain subgroups of the population with diabetes.\u003c/p\u003e \u003cp\u003eDespite the lack of a direct link between HbA1c and tPSA, our study contributes to the growing body of literature on the potential role of metabolic factors in prostate health. Future research should aim to explore the underlying mechanisms through longitudinal studies that explore the interactions between glycemic control, insulin resistance, inflammation, and other metabolic factors in relation to tPSA levels. Moreover, clinical trials assessing the effects of comprehensive metabolic management on prostate health outcomes in patients with diabetes could provide valuable insights.\u003c/p\u003e \u003cp\u003eIn conclusion, while this study did not establish a clear relationship between glycemic control and tPSA levels in men with diabetes, it emphasizes the importance of considering broader metabolic factors, such as insulin resistance and visceral adiposity, in managing prostate health in this population. Future research should aim to clarify the role of glycemic control in prostate health and develop more effective strategies to reduce the risk of prostate diseases among individuals with diabetes. Additionally, enhancing the accuracy and clinical relevance of prostate cancer screening methods for patients with diabetes remains a crucial objective.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eSeveral limitations should be acknowledged in this study. First, the cross-sectional nature of the NHANES data limits our ability to draw causal inferences between glycemic control and tPSA levels. Second, unmeasured variables, including genetic predispositions, prostate size, medication use, and lifestyle factors, may confound the observed relationships. Moreover, NHANES data lacks prostate volume and medication use data, which may influence PSA levels and should be considered a limitation. Additionally, using HbA1c as a proxy for long-term metabolic status might oversimplify the complex dynamics of glycemic regulation over time. Finally, longitudinal studies are necessary to confirm our findings and better understand the biological mechanisms linking glycemic control with tPSA levels.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe present study demonstrated that overall glycemic control was not significantly associated with tPSA levels in patients with diabetes after adjusting for multiple covariates. However, significant associations were observed within specific subgroups, including younger individuals, married or partnered participants, those without CAD, and insulin users. These findings suggest that clinicians should consider closely monitoring PSA trends in these subgroups, as they may exhibit distinct biomarker profiles. Targeted monitoring of prostate health in younger individuals with diabetes and insulin users, in particular, may offer a more tailored approach to identifying prostate health risks. Nonetheless, further longitudinal studies are needed to validate these associations and explore the mechanisms involved, including the potential role of hormonal and inflammatory mediators.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study utilized data from the NHANES, which was approved by the Institutional Review Board (IRB) of the National Center for Health Statistics, Centers for Disease Control and Prevention (CDC). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eAll participants provided informed consent for the publication of their data.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors confirm that there are no conflicts of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study did not receive any specific funding from public, commercial, or non-profit organizations.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eX.H. and J.Z. conceptualized and designed the study, supervised the overall content, and revised the manuscript. X.H. also performed the statistical analysis and prepared the initial manuscript draft. N.H. assisted in drafting the manuscript and revising it. N.H. and Z.W. was responsible for data curation and preprocessing, while Z.W. assisted with data analysis.All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to sincerely thank the staff of the National Health and Nutrition Examination Survey (NHANES) for their invaluable work in collecting, maintaining, and providing access to this essential dataset. We are also grateful to Dr. Jie Zhang (Corresponding Author) for his thoughtful guidance and revisions throughout the study. Our thanks to Dr. Zhi Wang and Dr. Nan Huang for their contributions to data curation and manuscript preparation.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) repository at https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBharti A, Shekhar R, Prakash P, Kumari S, Kumar S. Poor Glycemic Control Affecting Screening of Prostate Carcinoma. Cureus. 2024;16(4):e58680.\u003c/li\u003e\n\u003cli\u003eBernal-Soriano MC, Lumbreras B, Hern\u0026aacute;ndez-Aguado I, Pastor-Valero M, L\u0026oacute;pez-Garrigos M, Parker LA. Untangling the association between prostate-specific antigen and diabetes: a systematic review and meta-analysis. 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Obesity, Inflammation, and Prostate Cancer. Journal of clinical medicine. 2019;8(2).\u003c/li\u003e\n\u003cli\u003eSousa AP, Costa R, Alves MG, Soares R, Baylina P, Fernandes R. The Impact of Metabolic Syndrome and Type 2 Diabetes Mellitus on Prostate Cancer. Frontiers in cell and developmental biology. 2022;10:843458.\u003c/li\u003e\n\u003cli\u003eLee JJ, Beretvas SN, Freeland-Graves JH. Abdominal adiposity distribution in diabetic/prediabetic and nondiabetic populations: a meta-analysis. Journal of obesity. 2014;2014:697264.\u003c/li\u003e\n\u003cli\u003eCannarella R, Condorelli RA, Barbagallo F, La Vignera S, Calogero AE. Endocrinology of the Aging Prostate: Current Concepts. Frontiers in endocrinology. 2021;12:554078.\u003c/li\u003e\n\u003cli\u003eCosansu G, Erdogan S. Influence of psychosocial factors on self-care behaviors and glycemic control in Turkish patients with type 2 diabetes mellitus. 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Cancer causes \u0026amp; control : CCC. 2019;30(5):443-56.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, Glycemic Control, Prostate-Specific Antigen (tPSA), NHANES, HbA1c, Prostate Health","lastPublishedDoi":"10.21203/rs.3.rs-5674422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5674422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between diabetes and prostate-specific antigen (PSA) levels is complex, with potential implications for prostate cancer screening. This study examined the association between glycemic control and total PSA (tPSA) levels in patients with diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\n We analyzed data from the 2001–2010 NHANES to assess the relationship between glycated hemoglobin (HbA1c) and tPSA in adults with diabetes, categorizing HbA1c as \u0026lt;7% (good glycemic control) or ≥7% (poor glycemic control). Multivariable regression models were used, adjusting for key demographic, clinical, and lifestyle factors, including age, race/ethnicity, marital status, body mass index (BMI), smoking status, alcohol use, hypertension, CAD, and insulin use. Adjustments for multiple comparisons were made using the Bonferroni correction, and missing data were handled using multiple imputation.\u003cbr\u003e\n \u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with poor glycemic control were younger, less likely to be married or partnered, and had higher rates of insulin use but lower hypertension incidence than those with good glycemic control (P \u0026lt; 0.05). The median tPSA level was greater in the good control group (1.10 ng/mL vs. 0.90 ng/mL; P = 0.0014). Multivariate analysis revealed no overall association between HbA1c and tPSA (β= -0.022, P = 0.917). However, significant inverse associations were observed across subgroups, including those aged ≤59 years (β = -0.71, P = 0.033), married individuals (β= -0.55, P \u0026lt; 0.001), participants without coronary artery disease(CAD) (β= -0.49, P = 0.015), and insulin users (β= -0.80, P = 0.031).\u003cbr\u003e\n \u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough no significant overall association was found between glycemic control and tPSA levels, subgroup analyses revealed an inverse relationship between HbA1c and tPSA in younger individuals (≤59 years), insulin users, and those without coronary artery disease (CAD). These findings suggest that glycemic control may have subgroup-specific effects on prostate health in individuals with diabetes.\u003c/p\u003e","manuscriptTitle":"Glycemic Control and Prostate Antigen Levels in Individuals with Diabetes Based on NHANES Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 06:31:04","doi":"10.21203/rs.3.rs-5674422/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-30T17:20:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T22:35:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286793993049021907865201186206386597351","date":"2025-04-01T03:38:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-28T05:10:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171504643748699858255351509576326708588","date":"2025-03-27T17:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T17:00:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T11:59:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T05:58:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6eaa7f71-130a-40d0-a077-6b4a447a7c6d","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46317699,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"},{"id":46317700,"name":"Health sciences/Diseases/Urogenital diseases"}],"tags":[],"updatedAt":"2025-05-12T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-5674422","link":"https://doi.org/10.1038/s41598-025-00853-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-06 15:57:47","publishedOnDateReadable":"May 6th, 2025"},"versionCreatedAt":"2025-03-31 06:31:04","video":"","vorDoi":"10.1038/s41598-025-00853-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-00853-2","workflowStages":[]},"version":"v1","identity":"rs-5674422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5674422","identity":"rs-5674422","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}