Association between the Oxidative Balance Score and testosterone levels in males from the National Health and Nutrition Examination Survey 2013-2016

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The Oxidative Balance Score (OBS) is a method used to evaluate the level of systemic oxidative stress. It indicates that higher scores are associated with greater exposure to antioxidants.This study aims to investigate the probable association between OBS and testosterone levels. Methods A total of 5168 male participants from the 2013 to 2016 National Health and Nutrition Examination Survey (NHANES) were included in this study. The OBS was computed using 20 dietary and lifestyle factors. There were three existence forms of testosterone, including total testosterone (TT), free testosterone (FT), and bioavailable testosterone (BAT). The weighted multivariable linear regression, subgroup analyses and restricted cubic splines (RCS) were employed to examine the relationship between OBS and testosterone levels. Additionally, mediation analyses were performed to investigate the potential involvement of oxidative stress inflammation and oxidative stress. Results After accounting for potential confounding factors, a significant positive correlation was observed between OBS and TT, FT, and BAT, and the beta estimates (95% CI) were 0.005 (0.002, 0.008), 0.004 (0.001, 0.007), and 0.005 (0.002, 0.008), respectively. No statistically significant interaction effects were detected in the subgroup analyses. RCS results suggested TT, FT and BAT exhibited a linear positive relationship with an increase in OBS (all p for nonlinear > 0.05). Moreover, WBC counts and albumin mediated the association between OBS and TT by 9.78%, and 10.79%, respectively in model 3. Conclusion There is a positive association between OBS and testosterone levels in males, and this relationship may be partially mediated by inflammation and oxidative stress. Therefore, dietary and lifestyle-related antioxidant therapy for males with low testosterone concentrations should receive attention. NHANES mediation analysis oxidative balance score oxidative stress testosterone concentrations Figures Figure 1 Figure 2 Figure 3 Introduction Testosterone is an essential sex hormone in males. Testosterone deficiency (TD) is a common illness, affecting around 40% of males in the United States, and its prevalence is forecasted to rise over the decades that followed [ 1 , 2 ]. Insufficient levels of testosterone can result in symptoms such as decreased energy, impaired focus, depressive mood, diminished sexual desire, and erectile dysfunction[ 1 , 3 , 4 ]. The causes of testosterone deficiency can be attributed to factors such as infection, trauma, exposure to toxins like chemotherapy, or genetic problems [ 1 ]. Apart from these risk factors, the connection between oxidative stress (OS) and the development of low testosterone has been gradually uncovered [ 5 ]. OS is a complex process that arises from an imbalance between pro-oxidant and anti-oxidant components [ 6 ]. This imbalance may trigger the accumulation of reactive oxygen species (ROS), which can subsequently damage the structure and function of cells through the oxidative degradation of lipids, proteins, or DNA [ 7 , 8 ]. Prior research has demonstrated that an increased intake of certain nutrients as antioxidants, including carotenoids, vitamins C and vitamins E, is essential for protection against OS. Furthermore, adhering to a healthy lifestyle—maintaining a balanced weight, avoiding smoking, engaging in regular physical activity, and limiting alcohol consumption—helps reduce ROS levels [ 9 – 11 ]. Additionally, higher consumption of specific nutrients, including vitamins C, D, and E, as well as calcium, zinc, selenium, and magnesium, has been associated with enhanced resilience to OS. Oxidative balance results from the interaction between pro-oxidant and antioxidant components. Thus, assessing the overall oxidative state based solely on a single component is inadequate [ 12 ]. To address this limitation, the oxidative balance score (OBS) has been developed as a comprehensive tool to evaluate antioxidant status by integrating dietary and lifestyle factors that influence both pro-oxidant and antioxidant elements [ 13 ]. A higher OBS indicates a higher proportion of antioxidants compared to prooxidants. Numerous studies have demonstrated associations between OBS and various health outcomes, including depression[ 14 ], sleep quality[ 15 ], cognitive function[ 16 ], etc. However, the correlation between OBS and testosterone has not been sufficiently examined. Therefore, this study aimed to investigate the association between OBS and testosterone levels. Materials and methods Study population The National Health and Nutrition Examination Survey (NHANES), an ongoing and nationwide survey, was conducted to evaluate health risk factors and nutritional status among noninstitutionalized civilians in the US by the National Center for Health Statistics (NCHS). The NHANES underwent review and approval by the NCHS Research Ethics Review Board, and all the participants signed informed consent. In our study, data from two NHANES cycles (2013–2014 and 2015–2016) were merged, initially comprising 20236 participants. The exclusion criteria were as follows: (1) female (n = 10251); (2) individuals with missing data on OBS components (n = 4193); (3) individuals with missing data on testosterone levels (n = 515); (4) individuals with missing data on WBC and albumin (n = 16); and individuals with abnormal bioavailable testosterone (BAT = 0) (n = 3). After using these screening criteria, a total of 5168 individuals were included in our study (Fig. 1 ). This research followed the Helsinki Declaration. Oxidative Balance Score The OBS was calculated for each participant using prior study methodology [ 17 ]. Based on this approach, the OBS consisted of 16 nutritional elements and four lifestyle factors. Among these, 15 are categorized as antioxidants, while 5 are classified as pro-oxidants. The dietary-related OBS components, such as dietary fiber, carotene, riboflavin, niacin, total folate, calcium, zinc, magnesium, copper, selenium, iron, total fat, and vitamins B12, C, and E, were obtained from the 24-hour dietary recall data of NHANES. The dietary data were computed by averaging the results of two 24-hour recall interviews. If only one interview was available, data from that day was used. The calculation did not take into account dietary supplements and medicine sources. The lifestyle-related OBS components encompassed in the study were physical activity, alcohol use, body mass index (BMI), and cotinine levels. Following the NHANES guidelines, a metabolic equivalent (MET) score of 4 was given to one minute of transportation activity, moderate work-related and leisure-time activity received a MET score of 4, and vigorous work-related and leisure-time activities were assigned a MET score of 8. The physical activity score was calculated based on the cumulative MET minutes per week. Serum cotinine, a major metabolite of nicotine, was utilized to estimate smoking status, as it can assess both the level of tobacco consumption and exposure to secondhand smoke. Except for alcohol, the remaining OBS components were classified and divided into three groups based on weighted tertiles. The antioxidant factors were assigned scores of 0, 1, and 2, representing the lowest, middle, and highest tertiles, respectively. Contrary to antioxidants, pro-oxidants were assigned points in the reverse direction. Ultimately, the OBS was calculated by summing up the points allocated to each component. In addition, alcohol intake was categorized into three levels: non-drinkers, moderate drinkers (0–15 g/day), and heavy drinkers (≥ 15 g/day), with OBS ratings of 2, 1, and 0. Supplementary Table 1 shows the assignment details for OBS components. Testosterone levels The NHANES study utilized isotope dilution high-performance liquid chromatographytandem mass spectrometry (ID-LC-MS/MS), a precise technique, to assess serum total testosterone (TT) levels. The levels of sex hormone-binding globulin (SHBG) were measured by measuring the reaction products with chemo-luminescence measurements and a photomultiplier tube after binding to SHBG with immuno-antibodies. the concentrations of free testosterone (FT) and bioavailable testosterone (BAT) were computed according to the Vermeulen et al. methodology utilized measured levels of total serum testosterone, SHBG, and albumin [ 18 , 19 ]. The computational methodology is provided in the supplementary material. WBC and albumin The WBC counts in whole blood were measured based on the Beckman Coulter method of counting and sizing, using the Beckman Coulter DxH 800 instrument in NHANES 2013–2016. The bromocresol purple dye technique was used to test the concentration of albumin. In this method, a complex was formed between the albumin and Bromcresol Purple reagent. The variation in absorbance of the complex may be used to measure the quantity of albumin in the sample. Covariates The study collected sociodemographic information, which included age (categorized as < 40 and ≥ 40), race (Mexican-American, Other Hispanic, non-Hispanic white, non-Hispanic black, and others), education level (less than high school, high school, and college or higher), and poverty–income ratio (PIR) (categorized as 3.5). Diabetes status was determined based on fasting blood glucose levels ≥ 7 mmol/L, self-reported current use of insulin or oral hypoglycemic agents, or prior diagnosis of diabetes by a physician. Hypertension was determined by either self-reported use of medication for high blood pressure or a previous diagnosis of hypertension by the doctor. Cardiovascular Disease (CVD), including coronary heart disease, stroke, ischemic stroke, and hemorrhagic stroke, was identified based on self-reported physician diagnoses obtained during an individual interview. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [ 20 ], which can be found in the supplementary material. The eGFR is categorized into three groups (< 60 mL/min/1.73 m2, 60–90 mL/min/1.73 m2, and ≥ 90 mL/min/1.73 m2). Statistical analysis The missing values of covariates, including CVD, education levels, hypertension, and PIR, were multiplied and imputed using a multilevel approach designed for survey data through the jomo package in R to generate five imputed data sets after a burn-in of 500 iterations and 100 updates [ 21 , 22 ]. In the present analyse, we randomly selected the data set as the data for primary analysis and the remaining four imputed data sets were used for sensitivity analysis. Sampling weights were employed in all analyses to account for the complex sampling design of the NHANES. We used half of a set of weights (1/2*WTDRD1) derived from the Day 1 dietary recall data as the new sample weight, as recommended by the NHANES. Baseline characteristics of the study population were stratified according to the tertiles of the oxidative balance score (OBS). The right-skewness continuous variables were presented as weighted medians (P25, P75), and the categorical variables were expressed as unweighted frequencies (weighted percentages). Differences in baseline characteristics across OBS tertiles were calculated using chisquare tests for categorical variables and the Wilcoxon rank-sum test for continuous variables. WBC, albumin and all measured and derived testosterone concentrations were ln-transformed to approximate a normal distribution. Firstly, OBS was treated as a continuous variable. Additionally, OBS was converted into categorical variables by tertiles, with tertile 1 as the reference group. We constructed three weighted multivariate line regression models to explore the relationship between OBS and three forms of testosterone concentration. We calculated P for trend by entering the median value of each category of OBS as a continuous variable in the models. Model 1 was unadjusted; Model 2 was adjusted for race, education level, and poverty-income ratio; and Model 3 was further adjusted for diabetes, hypertension, eGFR, and CVD. Subgroup analyses were performed based on age, education level, diabetes, hypertension, eGFR, and CVD to explore potential variations in the relationship between OBS and TT across different subgroups. The potential mediating effects of WBC counts and albumin on the association between OBS and TT were estimated through the R package mediation. All mediation analyses were adjusted for race, education level, PIR, diabetes, hypertension, eGFR, and CVD. Restricted cubic splines were used to examine nonlinear relationships between OBS and three forms of testosterone. In order to evaluate the reliability of the findings, two sensitivity analyses were performed. Firstly, the remaining four imputed data sets were additionally used to investigate the relationship between OBS and three forms of testosterone concentration according to the main analysis methods mentioned earlier. Secondly, each component of the OBS was deleted sequentially to assess the robustness of the results. All statistical analyses were performed using R version 4.2.0, and statistical significance was determined at a two-sided p-value of 0.05. Results Baseline characteristics of the study population A total of 5168 male participants from NHANES (2013–2016) were included in this study. The baseline characteristics of the population, categorized by OBS score tertiles, are presented in Table 1 . Non-Hispanic whites comprised the majority of participants. Participants with higher OBS tended to exhibit characteristics such as younger age, higher PIR, higher education level, higher eGFR, and increased levels of TT, FT, and BAT. With the rise in OBS, there was a corresponding drop in the prevalence of hypertension, diabetes, and CVD. It was worth mentioning that individuals with higher WBC counts generally had lower OBS. Table 1 Basic characteristics of participants by Oxidative Balance Score tertiles. OBS Characteristic T1 N = 1494 (25%) T2 N = 1806 (35%) T3 N = 1870 (40%) P Value Education, n (%) < 0.001 Highschool 628 (51%) 995 (62%) 1,123 (69%) Age 0.3 <40 666 (43%) 845 (45%) 930 (47%) ≥ 40 828 (57%) 961 (55%) 940 (53%) Race < 0.001 Mexican American 228 (9.9%) 310 (11%) 348 (11%) Other Hispanic 156 (5.9%) 191 (5.7%) 205 (6.5%) Non-Hispanic White 507 (59%) 697 (64%) 739 (67%) Non-Hispanic Black 438 (16%) 332 (10%) 262 (6.9%) Other 165 (8.9%) 276 (9.0%) 316 (9.3%) PIR < 0.001 <1.3 619 (31%) 565 (20%) 538 (18%) (1.3–3.5) 540 (37%) 689 (37%) 697 (35%) ≥ 3.5 335 (32%) 552 (43%) 635 (47%) Diabetes, n (%) < 0.001 NO 1,226 (84%) 1,542 (85%) 1,670 (92%) YES 268 (16%) 264 (15%) 200 (7.8%) Hypertension, n (%) 90 881 (60%) 1,116 (61%) 1,255 (65%) (60–90) 432 (31%) 551 (32%) 520 (32%) ≤ 60 181 (8.8%) 139 (6.7%) 95 (3.7%) CVD, n (%) 0.052 NO 1,317 (90%) 1,649 (93%) 1,753 (93%) YES 177 (9.7%) 157 (7.5%) 117 (6.6%) WBC (1000 cells/uL) 7.20 (6.10, 8.40) 7.10 (6.00, 8.50) 6.80 (5.70, 8.00) < 0.001 TT (ng/dL) 382 (280, 495) 381 (284, 519) 409 (301, 522) 0.012 FT (ng/dL) 6.82 (5.07, 8.93) 6.88 (5.33, 8.92) 7.18 (5.58, 9.29) 0.002 BAT (ng/dL) 161 (120, 217) 167 (127, 219) 176 (133, 227) < 0.001 BAT, bioavailable testosterone; CVD, cardiovascular diseases; FT, free testosterone; OBS, oxidative balance score; PIR, poverty–income ratio; TT, total testosterone; WBC, white blood cell count; T1-T3, OBS tertile; Relationship between OBS and testosterone levels As depicted in Table 2 , three weighted line regression models were used to assess the relationship between OBS and testosterone levels, with OBS treated as a continuous variable. Across all three models, OBS was positively associated with three androgen indicators (TT, FT, and BAT) (p < 0.05). In model 3, the analysis of regression coefficients revealed that each unit increase in OBS is significantly associated with rising ln(TT) by 0.005 (95% CI 0.002, 0.008; p = 0.005), ln(FT) by 0.004 (95% CI 0.001, 0.007; p = 0.013), and ln(BAT) by 0.005 (95% CI 0.002, 0.008; p = 0.007). Upon classifying OBS, this significant positive association remained consistent, with all trend analyses being significant (p for trend < 0.05). After all adjustments for confounding factors, comparisons between the highest and lowest tertiles revealed that the higher OBS were associated with the increase in ln(TT) (β = 0.07; 95% CI 0.02, 0.13; p = 0.013), ln(FT)( β = 0.06; 95% CI 0.01, 0.12; p = 0.022), and ln(BAT) (β = 0.07; 95% CI 0.02, 0.12; p = 0.012). Furthermore, the restricted cubic spline analysis revealed linear relationships between OBS and three forms of androgen (TT, FT, and BAT), with the p for non-linear in Models 3 being 0.058, 0.095, and 0.133, respectively (Fig. 2 ). To investigate the robustness of the correlation between OBS and TT, stratified analyses were performed, stratifying by race, education level, PIR, age, hypertension, diabetes, CVD, and eGFR. No significant interaction effects were observed in the subgroup analyses (all p for interaction > 0.05) (Supplementary Table 2). Table 2 Association of OBS with testosterone levels in the US male population, NHANES 2013–2016. model 1 model 2 model 3 Exposure β (95% CI) p.value β (95% CI) p.value β (95% CI) p.value TT Continuous 0.005(0.002,0.008)0.001 0.006(0.003,0.009) < 0.001 0.005(0.002,0.008)0.005 T1 ref ref ref T2 0.024(-0.021,0.069)0.304 0.035(-0.009,0.079)0.138 0.029(-0.015,0.073)0.209 T3 0.078(0.029,0.127)0.004 0.093(0.043,0.143)0.002 0.078(0.028,0.128)0.008 p for trend 0.004 0.002 0.008 FT Continuous 0.006(0.003,0.009) < 0.001 0.006(0.003,0.009) < 0.001 0.004(0.001,0.007)0.013 T1 ref ref ref T2 0.023(-0.026,0.072)0.353 0.027(-0.02,0.074)0.278 0.014(-0.034,0.062)0.586 T3 0.092(0.045,0.139)0.001 0.096(0.049,0.143)0.001 0.069(0.023,0.115)0.010 p for trend < 0.001 < 0.001 0.010 BAT Continuous 0.007(0.004,0.010) < 0.001 0.007(0.004,0.010) < 0.001 0.005(0.002,0.008)0.007 T1 ref ref ref T2 0.037(-0.012,0.086)0.159 0.038(-0.010,0.086)0.139 0.022(-0.027,0.071)0.395 T3 0.109(0.061,0.157) < 0.001 0.111(0.064,0.158) < 0.001 0.078(0.031,0.125)0.005 p for trend < 0.001 < 0.001 0.005 β, beta estimates; CI, confidence interval; OBS, Oxidative Balance Score Continuous: OBS is treated as a continuous value; T1-T3: OBS tertile; Model 1: Unadjusted model; Model 2: Adjusted for race, education, and poverty-income ratio; Model 3: Additionally, adjusted for diabetes, hypertension, eGFR and CVD. As shown in Table 3 , higher levels of OBS were associated with lower levels of WBC and albumin. Additionally, Mediation analysis revealed that WBC counts and albumin had a significant mediating impact on the relationship between OBS and TT, with the mediated proportion 9.78%, and 10.79%, respectively (both P < 0.001) (Fig. 3 ). Table 3 Mediation analyses between OBS and testosterone levels. Indirect effect Direct effect Total effect Prop. Mediated β (95% CI) β (95% CI) β (95% CI) % WBC 0.0006(0.0003,0.0000) * 0.0058(0.0033,0.0100) * 0.0064(0.0039,0.0100) * 9.78 albumin 0.0007(0.0004,0.0000) * 0.0057(0.0033,0.0100) * 0.0064(0.0039,0,0100) * 10.79 *P < 0.001; β, beta estimates; BAT, bioavailable testosterone; CI, confidence interval; FT, free testosterone; TT, total testosterone; All analyses were adjusted for race, education, poverty-income ratio, diabetes, hypertension, eGFR, and CVD. sensitivity analyses One sensitivity analysis conducted using the other four datasets revealed similar findings to the primary conclusion (Table 4 ). The other, performed by removing each OBS component individually, showed that the positive correlation between both OBS and testosterone levels (TT, FT, and BAT) was stable (Supplementary Table 3). Table 4 The correlation between OBS and three types of testosterone in the remaining four sets of multiple imputation data. M1(β(95% CI)p.value) M2(β(95% CI)p.value) M3(β(95% CI)p.value) M4(β(95% CI)p.value) TT Continuous 0.005(0.002,0.008)0.003 0.005(0.002,0.008)0.002 0.005(0.002,0.008)0.003 0.005(0.002,0.008)0.003 T1 ref ref ref ref T2 0.02(-0.03, 0.07)0.30 0.02(-0.03, 0.07)0.50 0.02(-0.03, 0.07)0.50 0.03(-0.02, 0.07)0.30 T3 0.07(0.02, 0.12)0.02 0.07(0.02, 0.12)0.01 0.07(0.02, 0.12)0.01 0.07(0.02, 0.13)0.02 p for trend 0.02 0.01 0.01 0.02 FT Continuous 0.004(0.001,0.007)0.01 0.004(0.001,0.007)0.005 0.005(0.002,0.008)0.006 0.004(0.001,0.007)0.006 T1 ref ref ref ref T2 0.01(-0.05, 0.06)0.80 0.01(-0.04, 0.06)0.70 0.01(-0.04, 0.06)0.70 0.01(-0.04, 0.06)0.70 T3 0.06(0.00, 0.11)0.04 0.06(0.01, 0.11)0.02 0.06(0.01, 0.11)0.02 0.06(0.01, 0.11)0.03 p for trend 0.04 0.02 0.02 0.03 BAT Continuous 0.005(0.002,0.008)0.005 0.005(0.002,0.008)0.002 0.005(0.002,0.008)0.003 0.005(0.002,0.008)0.003 T1 ref ref ref ref T2 0.02(-0.04, 0.07)0.60 0.02(-0.03, 0.07)0.50 0.02(-0.03, 0.07)0.50 0.02(-0.04, 0.07)0.50 T3 0.06(0.01, 0.12)0.02 0.07(0.02, 0.12)0.01 0.07(0.02, 0.12)0.01 0.07(0.02, 0.12)0.01 p for trend 0.02 0.01 0.01 0.01 M1-M4: the remaining four sets of multiple imputation data; Continuous: OBS is treated as a continuous value; T1-T3: OBS tertile; All analyzes were adjusted for race, education, poverty-income ratio, diabetes, hypertension, eGFR and CVD. β, beta estimates; BAT, bioavailable testosterone; CI, confidence intervals; FT, free testosterone; TT, total testosterone; Discussion The relationship between OBS and male testosterone levels was illuminated by the cross-sectional study in the United States. There was a positive correlation between OBS and male testosterone levels (TT, FT, and BAT), indicating that an increase in antioxidant exposure and a decrease in pro-oxidant exposure, as indicated by a higher OBS, may elevate testosterone levels. Furthermore, the restricted cubic spline showed that these positive associations were represented as linear. Mediation analyses revealed that WBC counts and albumin may serve as partial mediators in the relationship between OBS and androgen concentrations. However, WBC counts and albumin were the biomarkers of oxidative stress and inflammation [ 8 ]. We hypothesized that oxidative stress might play a role in the correlation between OBS and testosterone concentrations. This study is the first to investigate the positive association between OBS and testosterone levels in males. According to numerous previous studies, dietary antioxidants may be conducive to higher testosterone concentrations. Prior research on 100 individuals showed that oral antioxidant therapy, such as vitamin E, vitamin C, beta-carotene, and other antioxidants, resulted in a significant increase in levels of sex hormones by reducing the intensity of oxidative stress [ 23 ]. There was evidence that zinc supplementation increased testosterone levels [ 24 ]. An animal study supported a positive association between copper and testosterone [ 25 ]. Similarly, an increased consumption of lycopene and selenium was associated with heightened levels of testosterone [ 26 ]. Additionally, the lifestyle components of OBS were vital in regulating testosterone levels. Several studies have shown that engaging in moderate physical exercise may elevate levels of FSH, LH, and testosterone levels [ 27 – 29 ]. However, in contrast to moderate physical activity, which improves the body's antioxidant defenses, intense exercise may cause a reduction in testosterone levels due to the increased production of ROS [ 30 ]. Furthermore, multiple investigators observed that individuals with detrimental lifestyle behaviors, including obesity [ 31 ], alcohol consumption [ 32 ], and smoking [ 33 ], were susceptible to having lower levels of testosterone. While the biological mechanisms behind the link between OBS and testosterone have been incompletely elucidated, OS may significantly contribute to this correlation. A lower antioxidant and higher pro-oxidant exposure, as indicated by a lower OBS, resulted in an elevation of ROS production, which in turn leads to OS by interfering with the body's antioxidant defense mechanisms. Subsequently, OS might harm the liver cells and germ cells, resulting in a decrease in testosterone production. Furthermore, the hypothalamus and pituitary gland release hormones to control the functioning of the testes [ 34 ]. The hypothalamic-pituitary-adrenal (HPA) axis, activated by OS, released glucocorticoid. Lidocinizing hormone (LH) secretion is inhibited by these hormones via cross-talk between the hypothalamus-pituitary-gonadal (HPG) and HPA axes [ 5 , 35 ]. LH levels were insufficient to stimulate interstitial cells to generate sufficient testosterone [ 36 ]. Consequently, with an increase in OS, there is a corresponding drop in the concentration of testosterone. There were several strengths in the current study. First, we used OBS, a comprehensive score integrating dietary and lifestyle factors, to explore the effects of oxidative stress on testosterone levels in males. Second, due to the use of a sophisticated and multi-step probability sampling methodology in NHANES, the sample chosen for our research was extensive and representative. Third, we performed subgroup analysis and sensitivity analyses to ensure the reliability and validity of our results. However, this study had several limitations. Firstly, the current investigation was just conducted as cross-sectional research, hence making it arduous to establish a causal relationship. Secondly, because of the limited data in NHANES, OBS can’t include all lifestyle and dietary factors associated with OS. Thirdly, this data, collected only from the American population, may be subject to regional bias. Fourthly, the OBS dietary components were derived from self-reported data from 24HR, which is susceptible to measurement error and bias because dietary information was assessed only using a singular 24HR. In this study, we found that higher OBS was associated with higher testosterone concentrations in males in the United States. Furthermore, we found that WBC counts may play a mediating role in the association between OBS and testosterone concentrations. Augmenting antioxidant defenses through dietary supplementation and adopting a healthy lifestyle seems to provide a more rational and feasible strategy to diminish oxidative stress and elevate testosterone levels. Hence, it is essential to conduct further prospective cohort studies in the future to overcome these constraints and get deeper understanding. Conclusion Augmenting antioxidant defenses through dietary supplementation and adopting a healthy lifestyle seems to provide a more rational and feasible strategy to diminish oxidative stress and elevate testosterone levels. Hence, it is essential to conduct further prospective cohort studies in the future to overcome these constraints and get a deeper understanding. Abbreviations β, beta estimates; BAT, bioavailable testosterone; BMI, body mass index; CI, confidence intervals; FT, free testosterone; OBS, oxidative balance score; OS, oxidative stress; PIR, poverty–income ratio; RCS, restricted cubic spline; TT, total testosterone. Declarations Ethics approval and consent to participate The NCHS Research Ethics Review Board (ERB) approved collecting NHANES data. This investigation was conducted under the Declaration of Helsinki (revised 2013). The NHANES data were made freely available to all researchers, and researchers were not required to obtain approval from the Institutional Internal Review Board (IRB). Consent for publication Not applicable. Availability of data and materials The datasets analyzed in this study are opened available from NHANES. This data can be found here: www.cdc.gov/nchs/nhanes/. Conflict of interest The authors report no conflicts of interest. Funding This work was supported by the National Nature Science Foundation of China (81972592, 32200587), the Natural Science Foundation of Sichuan Province, China (2022NSFSC0764), and the Post-Doctor Research Project of Sichuan University (2022SCU12025, 2022SCU12020), and the 1·3·5 project for disciplines of excellence of West China Hospital, Sichuan University (ZYGD23026, ZYJC21003). Authors' contributions Conception, design, investigation, methodology, software, and writing – original draft: Yinchun Lv, Dongsheng Zhang. Analysis, review, and collection and assembly of data: Siying Li, Yutong Nie, Xue Li, Qiaorong Huang. Methodology and software: Ran Lu, Junman Ye, Wentong Meng. Funding acquisition, project administration, supervision, Validation, Writing – review & editing: Xiaolong Chen, Xianming Mo. All authors read and approved the final manuscript Acknowledgments We would like to thank the diligent endeavours of the NHANES in furnishing data of exceptional quality. 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Liu X, Liu X, Wang Y, Zeng B, Zhu B, Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005–2018. J Affect Disord. 2023;337:57–65. Li H, Song L, Cen M, Fu X, Gao X, Zuo Q, Wu J. Oxidative balance scores and depressive symptoms: Mediating effects of oxidative stress and inflammatory factors. J Affect Disord. 2023;334:205–12. Lei X, Xu Z, Chen W. Association of oxidative balance score with sleep quality: NHANES 2007–2014. J Affect Disord. 2023;339:435–42. Song L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. J Nutr. 2023;153(7):1974–83. Zhang W, Peng SF, Chen L, Chen HM, Cheng XE, Tang YH. Association between the Oxidative Balance Score and Telomere Length from the National Health and Nutrition Examination Survey 1999–2002. Oxid Med Cell Longev 2022, 2022:1345071. Vermeulen A, Verdonck L, Kaufman JM. A critical evaluation of simple methods for the estimation of free testosterone in serum. J Clin Endocrinol Metab. 1999;84(10):3666–72. Ho CK, Stoddart M, Walton M, Anderson RA, Beckett GJ. Calculated free testosterone in men: comparison of four equations and with free androgen index. Ann Clin Biochem. 2006;43(Pt 5):389–97. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, Crews DC, Doria A, Estrella MM, Froissart M, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med. 2021;385(19):1737–49. Quartagno M, Grund S, Carpenter J. Jomo: a flexible package for two-level joint modelling multiple imputation. R J 2019, 9(1). Muntner P, Hardy ST, Fine LJ, Jaeger BC, Wozniak G, Levitan EB, Colantonio LD. Trends in Blood Pressure Control Among US Adults With Hypertension, 1999–2000 to 2017–2018. JAMA. 2020;324(12):1190–200. Saylam B, Çayan S. Do antioxidants improve serum sex hormones and total motile sperm count in idiopathic infertile men? Turk J Urol. 2020;46(6):442–8. Te L, Liu J, Ma J, Wang S. Correlation between serum zinc and testosterone: A systematic review. J Trace Elem Med Biol. 2023;76:127124. Chattopadhyay A, Sarkar M, Biswas NM. Dose-dependent effect of copper chloride on male reproductive function in immature rats. Kathmandu Univ Med J (KUMJ). 2005;3(4):392–400. Freni J, Pallio G, Marini HR, Micali A, Irrera N, Romeo C, Puzzolo D, Mannino F, Minutoli L, Pirrotta I et al. Positive Effects of the Nutraceutical Association of Lycopene and Selenium in Experimental Varicocele. Int J Mol Sci 2023, 24(17). Corona G, Vena W, Pizzocaro A, Vignozzi L, Sforza A, Maggi M. Testosterone therapy in diabetes and pre-diabetes. Andrology. 2023;11(2):204–14. D'Andrea S, Spaggiari G, Barbonetti A, Santi D. Endogenous transient doping: physical exercise acutely increases testosterone levels-results from a meta-analysis. J Endocrinol Invest. 2020;43(10):1349–71. Vaamonde D, Da Silva-Grigoletto ME, García-Manso JM, Barrera N, Vaamonde-Lemos R. Physically active men show better semen parameters and hormone values than sedentary men. Eur J Appl Physiol. 2012;112(9):3267–73. Flynn MG, Pizza FX, Brolinson PG. Hormonal responses to excessive training: influence of cross training. Int J Sports Med. 1997;18(3):191–6. Carrageta DF, Oliveira PF, Alves MG, Monteiro MP. Obesity and male hypogonadism: Tales of a vicious cycle. Obes Rev. 2019;20(8):1148–58. Smith SJ, Lopresti AL, Fairchild TJ. The effects of alcohol on testosterone synthesis in men: a review. Expert Rev Endocrinol Metab. 2023;18(2):155–66. Halmenschlager G, Rossetto S, Lara GM, Rhoden EL. Evaluation of the effects of cigarette smoking on testosterone levels in adult men. J Sex Med. 2009;6(6):1763–72. Appasamy M, Muttukrishna S, Pizzey AR, Ozturk O, Groome NP, Serhal P, Jauniaux E. Relationship between male reproductive hormones, sperm DNA damage and markers of oxidative stress in infertility. Reprod Biomed Online. 2007;14(2):159–65. Lane TM, Hines J. The management of mumps orchitis. BJU Int. 2006;97(1):1–2. Zirkin BR, Chen H. Regulation of Leydig cell steroidogenic function during aging. Biol Reprod. 2000;63(4):977–81. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5862685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407587320,"identity":"f2596a13-d3f1-4eda-9957-b43c04afa330","order_by":0,"name":"Yinchun Lv","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yinchun","middleName":"","lastName":"Lv","suffix":""},{"id":407587321,"identity":"749e4c59-2952-457b-8084-3fb10d4cb4b8","order_by":1,"name":"Dongsheng Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Zhang","suffix":""},{"id":407587322,"identity":"1579b67a-61c9-42d3-aa1e-d0870e709717","order_by":2,"name":"Siying Li","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Siying","middleName":"","lastName":"Li","suffix":""},{"id":407587323,"identity":"433f9edc-7db7-48ce-be56-ec5455666c64","order_by":3,"name":"Yutong Nie","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Nie","suffix":""},{"id":407587324,"identity":"86291294-478b-4d9d-acfa-785a0dc501c1","order_by":4,"name":"Xue Li","email":"","orcid":"","institution":"Sichuan 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Meng","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wentong","middleName":"","lastName":"Meng","suffix":""},{"id":407587329,"identity":"7dd83f59-8122-4c12-b578-dd14f68abd09","order_by":9,"name":"Xiaolong Chen","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Chen","suffix":""},{"id":407587330,"identity":"e2330e69-de27-4713-95cd-4e8c5bd6bffc","order_by":10,"name":"Xianming Mo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACxgYQKcfAwAMVOECkFmNStEBAYgPRWgyuHW57+DXHJn3D8bOHP/xsY5Dju5HA+LkAjxbJ2YntxrLb0nI3nMlLk+xtYzCWvJHALD0DjxZ+6cQ2aclth3O33eAxY2ZsY0jccCOBjZkHjxY2qJZ0sxs8xp+BWuoJagHZIvlx2+EEoBYDaaCWBANCWoB+aZNm3JZmuP9MjplkzzkJw5lnHjZL49NicDv9meTPbTbyku1njD/8KLOR5zuefPAzPi0ggOwMCQZY5OIFjD8IKhkFo2AUjIIRDQAfnEp+RGqDdQAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xianming","middleName":"","lastName":"Mo","suffix":""}],"badges":[],"createdAt":"2025-01-20 05:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5862685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5862685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75310437,"identity":"3bcb2462-eb91-433d-90b9-af7d90217abb","added_by":"auto","created_at":"2025-02-03 09:02:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":261113,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection from NHANES 2013–2016\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5862685/v1/50cf60bf255fba3d2d79b6d6.png"},{"id":75310436,"identity":"12a2804c-e282-46d6-9de9-470529227dfb","added_by":"auto","created_at":"2025-02-03 09:02:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219279,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline e curves for the association between OBS and testosterone levels. (A) total testosterone levels (TT). (B) free testosterone levels (FT). (C) bioavailable testosterone levels (BAT). TT, FT, and BAT were transformed using the natural logarithm. The analyses were adjusted according to Model 3. (A) P-non-linear=0.058; (B) P-non-linear=0.095; (C) P-non-linear=0.133\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5862685/v1/4f3fde8d5b9f05b2b9d015c3.png"},{"id":75310438,"identity":"b27ebe09-0ed9-4090-a5ee-6db6a1417ea9","added_by":"auto","created_at":"2025-02-03 09:02:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123041,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated proportion of the association between OBS and total testosterone levels (TT) mediated by (A)WBC counts and (B) albumin\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5862685/v1/f6b0be3b9e5654e53402482f.png"},{"id":75313951,"identity":"0477996d-40c6-499f-a5c3-0035b69db661","added_by":"auto","created_at":"2025-02-03 09:26:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1505802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5862685/v1/18fa1a1a-ddc9-461d-9180-5602fd938b34.pdf"},{"id":75310431,"identity":"a915ab76-99c7-4ebf-a577-4495065f0c33","added_by":"auto","created_at":"2025-02-03 09:02:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30521,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5862685/v1/d1477f704912f045c22b5572.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the Oxidative Balance Score and testosterone levels in males from the National Health and Nutrition Examination Survey 2013-2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTestosterone is an essential sex hormone in males. Testosterone deficiency (TD) is a common illness, affecting around 40% of males in the United States, and its prevalence is forecasted to rise over the decades that followed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Insufficient levels of testosterone can result in symptoms such as decreased energy, impaired focus, depressive mood, diminished sexual desire, and erectile dysfunction[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The causes of testosterone deficiency can be attributed to factors such as infection, trauma, exposure to toxins like chemotherapy, or genetic problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Apart from these risk factors, the connection between oxidative stress (OS) and the development of low testosterone has been gradually uncovered [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. OS is a complex process that arises from an imbalance between pro-oxidant and anti-oxidant components [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This imbalance may trigger the accumulation of reactive oxygen species (ROS), which can subsequently damage the structure and function of cells through the oxidative degradation of lipids, proteins, or DNA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prior research has demonstrated that an increased intake of certain nutrients as antioxidants, including carotenoids, vitamins C and vitamins E, is essential for protection against OS. Furthermore, adhering to a healthy lifestyle\u0026mdash;maintaining a balanced weight, avoiding smoking, engaging in regular physical activity, and limiting alcohol consumption\u0026mdash;helps reduce ROS levels [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, higher consumption of specific nutrients, including vitamins C, D, and E, as well as calcium, zinc, selenium, and magnesium, has been associated with enhanced resilience to OS.\u003c/p\u003e \u003cp\u003eOxidative balance results from the interaction between pro-oxidant and antioxidant components. Thus, assessing the overall oxidative state based solely on a single component is inadequate [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To address this limitation, the oxidative balance score (OBS) has been developed as a comprehensive tool to evaluate antioxidant status by integrating dietary and lifestyle factors that influence both pro-oxidant and antioxidant elements [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA higher OBS indicates a higher proportion of antioxidants compared to prooxidants. Numerous studies have demonstrated associations between OBS and various health outcomes, including depression[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], sleep quality[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], cognitive function[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], etc. However, the correlation between OBS and testosterone has not been sufficiently examined. Therefore, this study aimed to investigate the association between OBS and testosterone levels.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), an ongoing and nationwide survey, was conducted to evaluate health risk factors and nutritional status among noninstitutionalized civilians in the US by the National Center for Health Statistics (NCHS). The NHANES underwent review and approval by the NCHS Research Ethics Review Board, and all the participants signed informed consent. In our study, data from two NHANES cycles (2013\u0026ndash;2014 and 2015\u0026ndash;2016) were merged, initially comprising 20236 participants. The exclusion criteria were as follows: (1) female (n\u0026thinsp;=\u0026thinsp;10251); (2) individuals with missing data on OBS components (n\u0026thinsp;=\u0026thinsp;4193); (3) individuals with missing data on testosterone levels (n\u0026thinsp;=\u0026thinsp;515); (4) individuals with missing data on WBC and albumin (n\u0026thinsp;=\u0026thinsp;16); and individuals with abnormal bioavailable testosterone (BAT\u0026thinsp;=\u0026thinsp;0) (n\u0026thinsp;=\u0026thinsp;3). After using these screening criteria, a total of 5168 individuals were included in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This research followed the Helsinki Declaration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOxidative Balance Score\u003c/h3\u003e\n\u003cp\u003eThe OBS was calculated for each participant using prior study methodology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Based on this approach, the OBS consisted of 16 nutritional elements and four lifestyle factors. Among these, 15 are categorized as antioxidants, while 5 are classified as pro-oxidants. The dietary-related OBS components, such as dietary fiber, carotene, riboflavin, niacin, total folate, calcium, zinc, magnesium, copper, selenium, iron, total fat, and vitamins B12, C, and E, were obtained from the 24-hour dietary recall data of NHANES. The dietary data were computed by averaging the results of two 24-hour recall interviews. If only one interview was available, data from that day was used. The calculation did not take into account dietary supplements and medicine sources. The lifestyle-related OBS components encompassed in the study were physical activity, alcohol use, body mass index (BMI), and cotinine levels. Following the NHANES guidelines, a metabolic equivalent (MET) score of 4 was given to one minute of transportation activity, moderate work-related and leisure-time activity received a MET score of 4, and vigorous work-related and leisure-time activities were assigned a MET score of 8. The physical activity score was calculated based on the cumulative MET minutes per week. Serum cotinine, a major metabolite of nicotine, was utilized to estimate smoking status, as it can assess both the level of tobacco consumption and exposure to secondhand smoke. Except for alcohol, the remaining OBS components were classified and divided into three groups based on weighted tertiles. The antioxidant factors were assigned scores of 0, 1, and 2, representing the lowest, middle, and highest tertiles, respectively. Contrary to antioxidants, pro-oxidants were assigned points in the reverse direction. Ultimately, the OBS was calculated by summing up the points allocated to each component. In addition, alcohol intake was categorized into three levels: non-drinkers, moderate drinkers (0\u0026ndash;15 g/day), and heavy drinkers (\u0026ge;\u0026thinsp;15 g/day), with OBS ratings of 2, 1, and 0. Supplementary Table\u0026nbsp;1 shows the assignment details for OBS components.\u003c/p\u003e\n\u003ch3\u003eTestosterone levels\u003c/h3\u003e\n\u003cp\u003eThe NHANES study utilized isotope dilution high-performance liquid chromatographytandem mass spectrometry (ID-LC-MS/MS), a precise technique, to assess serum total testosterone (TT) levels. The levels of sex hormone-binding globulin (SHBG) were measured by measuring the reaction products with chemo-luminescence measurements and a photomultiplier tube after binding to SHBG with immuno-antibodies. the concentrations of free testosterone (FT) and bioavailable testosterone (BAT) were computed according to the Vermeulen et al. methodology utilized measured levels of total serum testosterone, SHBG, and albumin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The computational methodology is provided in the supplementary material.\u003c/p\u003e\n\u003ch3\u003eWBC and albumin\u003c/h3\u003e\n\u003cp\u003eThe WBC counts in whole blood were measured based on the Beckman Coulter method of counting and sizing, using the Beckman Coulter DxH 800 instrument in NHANES 2013\u0026ndash;2016. The bromocresol purple dye technique was used to test the concentration of albumin. In this method, a complex was formed between the albumin and Bromcresol Purple reagent. The variation in absorbance of the complex may be used to measure the quantity of albumin in the sample.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe study collected sociodemographic information, which included age (categorized as \u0026lt;\u0026thinsp;40 and \u0026ge;\u0026thinsp;40), race (Mexican-American, Other Hispanic, non-Hispanic white, non-Hispanic black, and others), education level (less than high school, high school, and college or higher), and poverty\u0026ndash;income ratio (PIR) (categorized as \u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, and \u0026gt;\u0026thinsp;3.5). Diabetes status was determined based on fasting blood glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;7 mmol/L, self-reported current use of insulin or oral hypoglycemic agents, or prior diagnosis of diabetes by a physician. Hypertension was determined by either self-reported use of medication for high blood pressure or a previous diagnosis of hypertension by the doctor. Cardiovascular Disease (CVD), including coronary heart disease, stroke, ischemic stroke, and hemorrhagic stroke, was identified based on self-reported physician diagnoses obtained during an individual interview. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which can be found in the supplementary material. The eGFR is categorized into three groups (\u0026lt;\u0026thinsp;60 mL/min/1.73 m2, 60\u0026ndash;90 mL/min/1.73 m2, and \u0026ge;\u0026thinsp;90 mL/min/1.73 m2).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe missing values of covariates, including CVD, education levels, hypertension, and PIR, were multiplied and imputed using a multilevel approach designed for survey data through the jomo package in R to generate five imputed data sets after a burn-in of 500 iterations and 100 updates [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the present analyse, we randomly selected the data set as the data for primary analysis and the remaining four imputed data sets were used for sensitivity analysis.\u003c/p\u003e \u003cp\u003eSampling weights were employed in all analyses to account for the complex sampling design of the NHANES. We used half of a set of weights (1/2*WTDRD1) derived from the Day 1 dietary recall data as the new sample weight, as recommended by the NHANES. Baseline characteristics of the study population were stratified according to the tertiles of the oxidative balance score (OBS). The right-skewness continuous variables were presented as weighted medians (P25, P75), and the categorical variables were expressed as unweighted frequencies (weighted percentages). Differences in baseline characteristics across OBS tertiles were calculated using chisquare tests for categorical variables and the Wilcoxon rank-sum test for continuous variables.\u003c/p\u003e \u003cp\u003eWBC, albumin and all measured and derived testosterone concentrations were ln-transformed to approximate a normal distribution. Firstly, OBS was treated as a continuous variable. Additionally, OBS was converted into categorical variables by tertiles, with tertile 1 as the reference group. We constructed three weighted multivariate line regression models to explore the relationship between OBS and three forms of testosterone concentration. We calculated P for trend by entering the median value of each category of OBS as a continuous variable in the models. Model 1 was unadjusted; Model 2 was adjusted for race, education level, and poverty-income ratio; and Model 3 was further adjusted for diabetes, hypertension, eGFR, and CVD. Subgroup analyses were performed based on age, education level, diabetes, hypertension, eGFR, and CVD to explore potential variations in the relationship between OBS and TT across different subgroups. The potential mediating effects of WBC counts and albumin on the association between OBS and TT were estimated through the R package mediation. All mediation analyses were adjusted for race, education level, PIR, diabetes, hypertension, eGFR, and CVD. Restricted cubic splines were used to examine nonlinear relationships between OBS and three forms of testosterone.\u003c/p\u003e \u003cp\u003eIn order to evaluate the reliability of the findings, two sensitivity analyses were performed. Firstly, the remaining four imputed data sets were additionally used to investigate the relationship between OBS and three forms of testosterone concentration according to the main analysis methods mentioned earlier. Secondly, each component of the OBS was deleted sequentially to assess the robustness of the results. All statistical analyses were performed using R version 4.2.0, and statistical significance was determined at a two-sided p-value of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 5168 male participants from NHANES (2013\u0026ndash;2016) were included in this study. The baseline characteristics of the population, categorized by OBS score tertiles, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Non-Hispanic whites comprised the majority of participants. Participants with higher OBS tended to exhibit characteristics such as younger age, higher PIR, higher education level, higher eGFR, and increased levels of TT, FT, and BAT. With the rise in OBS, there was a corresponding drop in the prevalence of hypertension, diabetes, and CVD. It was worth mentioning that individuals with higher WBC counts generally had lower OBS.\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\u003eBasic characteristics of participants by Oxidative Balance Score tertiles.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1494 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1806 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1870 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;Highschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352 (12%)\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\u003eHighschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e414 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395 (19%)\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\u003e\u0026gt;Highschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e628 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e995 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,123 (69%)\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\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\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;40\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e845 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e930 (47%)\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\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e828 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e961 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e940 (53%)\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\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e228 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348 (11%)\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\u003e156 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (6.5%)\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\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e697 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e739 (67%)\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\u003e438 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (6.9%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316 (9.3%)\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\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e619 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538 (18%)\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\u003e(1.3\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e540 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e689 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e697 (35%)\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\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e552 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e635 (47%)\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\u003eDiabetes, n (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,226 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,542 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,670 (92%)\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200 (7.8%)\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\u003eHypertension, n (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e924 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,265 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,395 (76%)\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e570 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e475 (24%)\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\u003eeGFR(mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e881 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,116 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,255 (65%)\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\u003e(60\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520 (32%)\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\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (3.7%)\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\u003eCVD, n (%)\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\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,317 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,649 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,753 (93%)\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (6.6%)\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\u003eWBC (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.20 (6.10, 8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.10 (6.00, 8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80 (5.70, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382 (280, 495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (284, 519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e409 (301, 522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.82 (5.07, 8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88 (5.33, 8.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.18 (5.58, 9.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAT (ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (120, 217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (127, 219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (133, 227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBAT, bioavailable testosterone; CVD, cardiovascular diseases; FT, free testosterone; OBS, oxidative balance score; PIR, poverty\u0026ndash;income ratio; TT, total testosterone; WBC, white blood cell count;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eT1-T3, OBS tertile;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between OBS and testosterone levels\u003c/h2\u003e \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, three weighted line regression models were used to assess the relationship between OBS and testosterone levels, with OBS treated as a continuous variable. Across all three models, OBS was positively associated with three androgen indicators (TT, FT, and BAT) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In model 3, the analysis of regression coefficients revealed that each unit increase in OBS is significantly associated with rising ln(TT) by 0.005 (95% CI 0.002, 0.008; p\u0026thinsp;=\u0026thinsp;0.005), ln(FT) by 0.004 (95% CI 0.001, 0.007; p\u0026thinsp;=\u0026thinsp;0.013), and ln(BAT) by 0.005 (95% CI 0.002, 0.008; p\u0026thinsp;=\u0026thinsp;0.007). Upon classifying OBS, this significant positive association remained consistent, with all trend analyses being significant (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After all adjustments for confounding factors, comparisons between the highest and lowest tertiles revealed that the higher OBS were associated with the increase in ln(TT) (β\u0026thinsp;=\u0026thinsp;0.07; 95% CI 0.02, 0.13; p\u0026thinsp;=\u0026thinsp;0.013), ln(FT)( β\u0026thinsp;=\u0026thinsp;0.06; 95% CI 0.01, 0.12; p\u0026thinsp;=\u0026thinsp;0.022), and ln(BAT) (β\u0026thinsp;=\u0026thinsp;0.07; 95% CI 0.02, 0.12; p\u0026thinsp;=\u0026thinsp;0.012). Furthermore, the restricted cubic spline analysis revealed linear relationships between OBS and three forms of androgen (TT, FT, and BAT), with the p for non-linear in Models 3 being 0.058, 0.095, and 0.133, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To investigate the robustness of the correlation between OBS and TT, stratified analyses were performed, stratifying by race, education level, PIR, age, hypertension, diabetes, CVD, and eGFR. No significant interaction effects were observed in the subgroup analyses (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;2).\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\u003eAssociation of OBS with testosterone levels in the US male population, NHANES 2013\u0026ndash;2016.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI) p.value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI) p.value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI) p.value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005(0.002,0.008)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006(0.003,0.009)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(0.002,0.008)0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024(-0.021,0.069)0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035(-0.009,0.079)0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029(-0.015,0.073)0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.078(0.029,0.127)0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093(0.043,0.143)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078(0.028,0.128)0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006(0.003,0.009)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006(0.003,0.009)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004(0.001,0.007)0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023(-0.026,0.072)0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027(-0.02,0.074)0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014(-0.034,0.062)0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092(0.045,0.139)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096(0.049,0.143)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069(0.023,0.115)0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007(0.004,0.010)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007(0.004,0.010)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(0.002,0.008)0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037(-0.012,0.086)0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038(-0.010,0.086)0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022(-0.027,0.071)0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109(0.061,0.157)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111(0.064,0.158)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078(0.031,0.125)0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eβ, beta estimates; CI, confidence interval; OBS, Oxidative Balance Score\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous: OBS is treated as a continuous value; T1-T3: OBS tertile;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: Unadjusted model;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: Adjusted for race, education, and poverty-income ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: Additionally, adjusted for diabetes, hypertension, eGFR and CVD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, higher levels of OBS were associated with lower levels of WBC and albumin. Additionally, Mediation analysis revealed that WBC counts and albumin had a significant mediating impact on the relationship between OBS and TT, with the mediated proportion 9.78%, and 10.79%, respectively (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analyses between OBS and testosterone levels.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProp. Mediated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006(0.0003,0.0000) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0058(0.0033,0.0100) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0064(0.0039,0.0100) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0007(0.0004,0.0000) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0057(0.0033,0.0100) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0064(0.0039,0,0100) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.001;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eβ, beta estimates; BAT, bioavailable testosterone; CI, confidence interval; FT, free testosterone; TT, total testosterone;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll analyses were adjusted for race, education, poverty-income ratio, diabetes, hypertension, eGFR, and CVD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003esensitivity analyses\u003c/h2\u003e \u003cp\u003eOne sensitivity analysis conducted using the other four datasets revealed similar findings to the primary conclusion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The other, performed by removing each OBS component individually, showed that the positive correlation between both OBS and testosterone levels (TT, FT, and BAT) was stable (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between OBS and three types of testosterone in the remaining four sets of multiple imputation data.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1(β(95% CI)p.value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM2(β(95% CI)p.value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM3(β(95% CI)p.value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM4(β(95% CI)p.value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005(0.002,0.008)0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.002,0.008)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(0.002,0.008)0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005(0.002,0.008)0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02(-0.03, 0.07)0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(-0.03, 0.07)0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(-0.03, 0.07)0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03(-0.02, 0.07)0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07(0.02, 0.13)0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004(0.001,0.007)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004(0.001,0.007)0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(0.002,0.008)0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004(0.001,0.007)0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01(-0.05, 0.06)0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01(-0.04, 0.06)0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(-0.04, 0.06)0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01(-0.04, 0.06)0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.00, 0.11)0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06(0.01, 0.11)0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06(0.01, 0.11)0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06(0.01, 0.11)0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBAT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005(0.002,0.008)0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.002,0.008)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(0.002,0.008)0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005(0.002,0.008)0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02(-0.04, 0.07)0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(-0.03, 0.07)0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(-0.03, 0.07)0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02(-0.04, 0.07)0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.01, 0.12)0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07(0.02, 0.12)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eM1-M4: the remaining four sets of multiple imputation data;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous: OBS is treated as a continuous value; T1-T3: OBS tertile;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll analyzes were adjusted for race, education, poverty-income ratio, diabetes, hypertension, eGFR and CVD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eβ, beta estimates; BAT, bioavailable testosterone; CI, confidence intervals; FT, free testosterone; TT, total testosterone;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between OBS and male testosterone levels was illuminated by the cross-sectional study in the United States. There was a positive correlation between OBS and male testosterone levels (TT, FT, and BAT), indicating that an increase in antioxidant exposure and a decrease in pro-oxidant exposure, as indicated by a higher OBS, may elevate testosterone levels. Furthermore, the restricted cubic spline showed that these positive associations were represented as linear. Mediation analyses revealed that WBC counts and albumin may serve as partial mediators in the relationship between OBS and androgen concentrations. However, WBC counts and albumin were the biomarkers of oxidative stress and inflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We hypothesized that oxidative stress might play a role in the correlation between OBS and testosterone concentrations.\u003c/p\u003e \u003cp\u003eThis study is the first to investigate the positive association between OBS and testosterone levels in males. According to numerous previous studies, dietary antioxidants may be conducive to higher testosterone concentrations. Prior research on 100 individuals showed that oral antioxidant therapy, such as vitamin E, vitamin C, beta-carotene, and other antioxidants, resulted in a significant increase in levels of sex hormones by reducing the intensity of oxidative stress [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There was evidence that zinc supplementation increased testosterone levels [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. An animal study supported a positive association between copper and testosterone [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, an increased consumption of lycopene and selenium was associated with heightened levels of testosterone [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the lifestyle components of OBS were vital in regulating testosterone levels. Several studies have shown that engaging in moderate physical exercise may elevate levels of FSH, LH, and testosterone levels [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, in contrast to moderate physical activity, which improves the body's antioxidant defenses, intense exercise may cause a reduction in testosterone levels due to the increased production of ROS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, multiple investigators observed that individuals with detrimental lifestyle behaviors, including obesity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], alcohol consumption [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and smoking [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], were susceptible to having lower levels of testosterone. While the biological mechanisms behind the link between OBS and testosterone have been incompletely elucidated, OS may significantly contribute to this correlation. A lower antioxidant and higher pro-oxidant exposure, as indicated by a lower OBS, resulted in an elevation of ROS production, which in turn leads to OS by interfering with the body's antioxidant defense mechanisms. Subsequently, OS might harm the liver cells and germ cells, resulting in a decrease in testosterone production. Furthermore, the hypothalamus and pituitary gland release hormones to control the functioning of the testes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The hypothalamic-pituitary-adrenal (HPA) axis, activated by OS, released glucocorticoid. Lidocinizing hormone (LH) secretion is inhibited by these hormones via cross-talk between the hypothalamus-pituitary-gonadal (HPG) and HPA axes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. LH levels were insufficient to stimulate interstitial cells to generate sufficient testosterone [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consequently, with an increase in OS, there is a corresponding drop in the concentration of testosterone.\u003c/p\u003e \u003cp\u003eThere were several strengths in the current study. First, we used OBS, a comprehensive score integrating dietary and lifestyle factors, to explore the effects of oxidative stress on testosterone levels in males. Second, due to the use of a sophisticated and multi-step probability sampling methodology in NHANES, the sample chosen for our research was extensive and representative. Third, we performed subgroup analysis and sensitivity analyses to ensure the reliability and validity of our results.\u003c/p\u003e \u003cp\u003eHowever, this study had several limitations. Firstly, the current investigation was just conducted as cross-sectional research, hence making it arduous to establish a causal relationship. Secondly, because of the limited data in NHANES, OBS can\u0026rsquo;t include all lifestyle and dietary factors associated with OS. Thirdly, this data, collected only from the American population, may be subject to regional bias. Fourthly, the OBS dietary components were derived from self-reported data from 24HR, which is susceptible to measurement error and bias because dietary information was assessed only using a singular 24HR.\u003c/p\u003e \u003cp\u003eIn this study, we found that higher OBS was associated with higher testosterone concentrations in males in the United States. Furthermore, we found that WBC counts may play a mediating role in the association between OBS and testosterone concentrations. Augmenting antioxidant defenses through dietary supplementation and adopting a healthy lifestyle seems to provide a more rational and feasible strategy to diminish oxidative stress and elevate testosterone levels. Hence, it is essential to conduct further prospective cohort studies in the future to overcome these constraints and get deeper understanding.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAugmenting antioxidant defenses through dietary supplementation and adopting a healthy lifestyle seems to provide a more rational and feasible strategy to diminish oxidative stress and elevate testosterone levels. Hence, it is essential to conduct further prospective cohort studies in the future to overcome these constraints and get a deeper understanding.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u0026beta;, beta estimates; BAT, bioavailable testosterone; BMI, body mass index; CI, confidence intervals; FT, free testosterone; OBS, oxidative balance score; OS, oxidative stress; PIR, poverty\u0026ndash;income ratio; RCS, restricted cubic spline; TT, total testosterone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe NCHS Research Ethics Review Board (ERB) approved collecting NHANES data. This investigation was conducted under the Declaration of Helsinki (revised 2013). The NHANES data were made freely available to all researchers, and researchers were not required to obtain approval from the Institutional Internal Review Board (IRB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are opened available from NHANES. This data can be found here: www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Nature Science Foundation of China (81972592,\u0026nbsp;32200587), the Natural Science Foundation of Sichuan Province, China (2022NSFSC0764), and the Post-Doctor Research Project of Sichuan University (2022SCU12025, 2022SCU12020), and the 1\u0026middot;3\u0026middot;5 project for disciplines of excellence of West China Hospital, Sichuan University (ZYGD23026, ZYJC21003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception, design, investigation, methodology, software, and writing \u0026ndash; original draft: Yinchun Lv, Dongsheng Zhang. Analysis, review, and collection and assembly of data: Siying Li, Yutong Nie, Xue Li, Qiaorong Huang. Methodology and software: Ran Lu, Junman Ye, Wentong Meng. \u0026nbsp;Funding acquisition, project administration, supervision, Validation, Writing \u0026ndash; review \u0026amp; editing: Xiaolong Chen, Xianming Mo.\u0026nbsp;All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the diligent endeavours of the NHANES in furnishing data of exceptional quality.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHalpern JA, Brannigan RE. Testosterone Deficiency. JAMA. 2019;322(11):1116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang MY, Parker G, Zarotsky V, Carman W, Morgentaler A, Jones H, Singhal P. 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Biol Reprod. 2000;63(4):977\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NHANES, mediation analysis, oxidative balance score, oxidative stress, testosterone concentrations","lastPublishedDoi":"10.21203/rs.3.rs-5862685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5862685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackgroud:\u003c/h2\u003e \u003cp\u003eLimited research has explored the combined influence of dietary and lifestyle factors on testosterone levels. The Oxidative Balance Score (OBS) is a method used to evaluate the level of systemic oxidative stress. It indicates that higher scores are associated with greater exposure to antioxidants.This study aims to investigate the probable association between OBS and testosterone levels.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 5168 male participants from the 2013 to 2016 National Health and Nutrition Examination Survey (NHANES) were included in this study. The OBS was computed using 20 dietary and lifestyle factors. There were three existence forms of testosterone, including total testosterone (TT), free testosterone (FT), and bioavailable testosterone (BAT). The weighted multivariable linear regression, subgroup analyses and restricted cubic splines (RCS) were employed to examine the relationship between OBS and testosterone levels. Additionally, mediation analyses were performed to investigate the potential involvement of oxidative stress inflammation and oxidative stress.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter accounting for potential confounding factors, a significant positive correlation was observed between OBS and TT, FT, and BAT, and the beta estimates (95% CI) were 0.005 (0.002, 0.008), 0.004 (0.001, 0.007), and 0.005 (0.002, 0.008), respectively. No statistically significant interaction effects were detected in the subgroup analyses. RCS results suggested TT, FT and BAT exhibited a linear positive relationship with an increase in OBS (all p for nonlinear\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Moreover, WBC counts and albumin mediated the association between OBS and TT by 9.78%, and 10.79%, respectively in model 3.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a positive association between OBS and testosterone levels in males, and this relationship may be partially mediated by inflammation and oxidative stress. Therefore, dietary and lifestyle-related antioxidant therapy for males with low testosterone concentrations should receive attention.\u003c/p\u003e","manuscriptTitle":"Association between the Oxidative Balance Score and testosterone levels in males from the National Health and Nutrition Examination Survey 2013-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:02:03","doi":"10.21203/rs.3.rs-5862685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e7a1967-18e4-4b77-bcc8-7f81ddc7173a","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T09:02:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 09:02:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5862685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5862685","identity":"rs-5862685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}