From Diet to Oxidative Stress: Obesity as a Key Mediator in Postmenopausal Women

From Diet to Oxidative Stress: Obesity as a Key Mediator in Postmenopausal Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Diet to Oxidative Stress: Obesity as a Key Mediator in Postmenopausal Women Bingli Zuo, Chen Liang, Mengmeng Wang, Hao Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7061650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in BMC Women's Health → Version 1 posted 15 You are reading this latest preprint version Abstract Background Oxidative stress plays a critical role in age-related pathophysiology, and postmenopausal women are particularly vulnerable due to hormonal and metabolic changes. Although dietary quality has been implicated in modulating oxidative balance, the potential mediating role of obesity in this relationship remains insufficiently explored. Objectives This study aimed to examine the associations between dietary quality and oxidative stress among postmenopausal women using data from the National Health and Nutrition Examination Survey (NHANES) and to assess whether obesity mediates this relationship. Methods A total of 2,391 postmenopausal women from NHANES cycles 2005–2020 were included. Dietary quality was assessed using the Healthy Eating Index (HEI-2015), Dietary Inflammatory Index (DII), and Composite Dietary Antioxidant Index (CDAI). Oxidative stress status was measured using the Oxidative Balance Score (OBS), while obesity was evaluated using body mass index (BMI) and waist circumference. Weighted multivariable regression and restricted cubic spline models were employed to investigate associations. Mediation analysis was performed to assess the potential mediating role of obesity. Results Higher HEI and CDAI scores were significantly associated with higher OBS, while higher DII was associated with lower OBS (all P < 0.01). Similarly, healthier dietary profiles were inversely associated with both BMI and waist circumference. Obesity indicators were negatively associated with OBS. Mediation analysis suggested that BMI and waist circumference explained a small but statistically significant proportion of the associations between dietary indices and OBS. Conclusions Among postmenopausal women, healthier dietary patterns were associated with more favorable oxidative stress profiles. Obesity may partly mediate these associations. These findings highlight the potential value of dietary and weight management strategies in mitigating oxidative stress in this population, warranting further longitudinal and interventional studies to clarify underlying mechanisms. dietary quality oxidative stress obesity postmenopausal women mediation analysis antioxidant index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Oxidative stress is a crucial factor implicated in the pathophysiology of aging and the onset of numerous age-related diseases. In postmenopausal women, the interplay between metabolic and hormonal alterations fosters an environment conducive to increased oxidative burden [ 1 – 2 ] . The decline in estrogen levels, a hallmark of menopause, has been associated with impaired antioxidant defense mechanisms and heightened susceptibility to oxidative damage, which, in turn, contributes to a greater risk of metabolic dysfunction, cardiovascular disease, and neurodegenerative disorders [ 3 ] . Given the fundamental role of oxidative stress in these pathological processes, identifying modifiable factors that influence oxidative balance is of significant scientific and clinical interest. Dietary components exert a dual role in oxidative stress, acting as either promoters or inhibitors. High-sugar and high-fat diets contribute to increased reactive oxygen species (ROS) production, thereby elevating oxidative stress levels. For instance, a high-sucrose diet has been shown to significantly elevate oxidative stress biomarkers and impair antioxidant defense mechanisms in Drosophila [ 4 ] . In contrast, diets rich in antioxidants—such as polyphenols, vitamins C and E, and carotenoids—effectively scavenge free radicals and enhance antioxidant enzyme activity, thereby mitigating oxidative stress. The Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, abundant in antioxidant compounds, have been associated with reduced oxidative stress levels, whereas the Western diet, characterized by high sugar, high fat, and ultra-processed foods, has been linked to increased oxidative stress [ 5 – 6 ] . Furthermore, a high-carbohydrate diet is correlated with an elevated hemoglobin glycation index (HGI), a potential marker of oxidative stress and inflammation, indicating that dietary patterns not only influence short-term oxidative stress but may also have long-term health implications [ 6 ] . At the molecular level, polyphenols activate the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, upregulating the expression of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GPx), thereby enhancing the body's antioxidative capacity [ 7 ] . Additionally, dietary components such as iron chelators can reduce ROS production via iron-mediated pathways, further alleviating oxidative stress [ 8 ] . These mechanisms collectively regulate oxidative stress status and may play a pivotal role in the pathogenesis and progression of chronic diseases. Obesity has emerged as a major contributor to oxidative stress due to the pro-inflammatory and metabolic disruptions associated with excess adiposity. Adipose tissue, particularly visceral fat, functions as a metabolically active organ that secretes a range of inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1), which collectively promote chronic inflammation and oxidative damage [ 8 – 10 ] . Additionally, obesity is linked to mitochondrial dysfunction, impaired adipokine signaling, and increased oxidative phosphorylation, all of which contribute to elevated ROS production [ 11 – 14 ] . Given that postmenopausal women are particularly vulnerable to obesity-induced metabolic dysregulation, it is plausible that obesity acts as a key mediator in the relationship between dietary quality and oxidative stress. Understanding this mediation effect is critical, as it may provide new avenues for targeted dietary and lifestyle interventions aimed at reducing oxidative stress burden in this high-risk population. The National Health and Nutrition Examination Survey (NHANES) provides a robust and nationally representative dataset that enables a comprehensive investigation of these intricate relationships. In the present study, we employ a cross-sectional analytical approach to examine the association between dietary quality and oxidative stress among postmenopausal women, with a specific focus on the potential mediating role of obesity. Although the observational nature of this study precludes causal inferences, identifying significant associations can contribute to the growing body of evidence informing future longitudinal and interventional research. Such insights may aid in the development of precision nutrition and weight management strategies aimed at mitigating oxidative stress-related health risks in postmenopausal women. 2. Materials and methods 2.1 Study Design and Sample This study was based on the National Health and Nutrition Examination Survey (NHANES) database, with all data approved by the Institutional Review Board. Informed consent and written documentation were obtained from all participants. A total of 8,345 NHANES participants from 2005 to 2020 were included. Initially, individuals with incomplete laboratory data (n = 2,917) and missing dietary information (n = 1,473) were excluded, leaving 3,955 participants. Further exclusion of individuals with missing demographic data (n = 1,240) resulted in 2,715 participants. Finally, 324 individuals with insufficient parameters for calculating the Oxidative Balance Score (OBS) were excluded, yielding a final analytical sample of 2,391 participants. The detailed selection process is illustrated in Fig. 1 . 2.2 Sources of Core Variables In this study, three dietary indices were used to assess healthy eating patterns: the Healthy Eating Index (HEI), the Dietary Inflammatory Index (DII), and the Composite Dietary Antioxidant Index (CDAI). Dietary data were derived from the "What We Eat in America" (WWEIA) project, conducted by the U.S. Department of Agriculture (USDA). The automated multiple-pass method (AMPM) developed by the USDA was used to obtain 24-hour dietary recall data [ 15 ] . Food categories and nutrient compositions were calculated based on the USDA Food Patterns Equivalents Database (FPED) [ 16 ] . The analysis used total nutrient intake data from the first day of NHANES dietary assessment. HEI-2015 is a scoring system ranging from 0 to 100, where a higher score indicates greater adherence to the Dietary Guidelines for Americans (DGA) and better dietary quality. HEI-2015 is calculated based on energy density (per 1,000 kcal) rather than absolute intake and consists of 13 components: 9 adequacy components (total fruits, whole fruits, total vegetables, greens and beans, total protein, seafood and plant proteins, whole grains, dairy, and fatty acids) and 4 moderation components (sodium, refined grains, added sugars, and saturated fats), with scoring ranges of 0–5 or 0–10 points. The final HEI score is obtained by summing all component scores [ 17 ] . DII quantifies the inflammatory potential of dietary intake based on 45 dietary components, including 36 anti-inflammatory components (e.g., fiber, omega-3 fatty acids, vitamin C) and 9 pro-inflammatory components (e.g., saturated fat, sugar, cholesterol). Intake levels of these components are compared with a global reference database and weighted based on their influence on six inflammatory markers (e.g., C-reactive protein, IL-6). A higher DII score indicates a more pro-inflammatory diet, whereas a lower score reflects greater anti-inflammatory potential [ 18 ] . CDAI evaluates dietary antioxidant capacity by integrating intake levels of key antioxidant compounds, including vitamins A, C, and E, selenium, zinc, and carotenoids. Intakes are standardized against global reference values and summed using weighted scores to represent overall dietary antioxidant capacity [ 19 ] . The OBS is calculated by integrating pro-oxidant and antioxidant factors. Pro-oxidant factors (e.g., smoking, obesity, high-fat diet, environmental pollutants) are assigned negative scores, decreasing with higher exposure levels. Antioxidant factors (e.g., dietary intake of vitamin C, vitamin E, selenium, polyphenols, regular physical activity, and antioxidant-related genetic polymorphisms) receive positive scores, increasing with higher intake or activity levels. The total OBS score reflects the balance between oxidative stress and antioxidant defense, with a higher score indicating lower oxidative stress and reduced pathological risk [ 20 ] . Body mass index (BMI) and waist circumference were used to assess obesity. Anthropometric data were collected by trained health professionals at the NHANES Mobile Examination Center (MEC). Participants were classified as obese if BMI ≥ 30 and as centrally obese if waist circumference ≥ 88 cm. 2.3 Covariates Covariates included age, race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), marital status (married/partnered, divorced/widowed/separated, never married), smoking status (never, former, current), alcohol consumption (never, former, light, moderate, heavy), the poverty-to-income ratio (PIR), total energy intake (total kcal on the first dietary recall day), hypertension status, non-high-density lipoprotein cholesterol (NHDL), and homeostatic model assessment for insulin resistance (HOMA-IR). 2.4 Statistical Analyses Multiple imputation was applied to address missing at-random data, minimizing potential bias and enhancing analytical robustness. Systematically missing data, which showed specific patterns related to sample or variable characteristics, were excluded to maintain sample representativeness. Filtered datasets were analyzed using R (v.4.3.3). Continuous variables were presented as means (standard deviation) and compared using t-tests, while categorical variables were presented as counts (percentages) and compared using chi-square tests. Sensitivity analyses were conducted using multivariate linear regression to examine the associations between HEI, DII, CDAI, OBS, BMI, and waist circumference. Three models were constructed to test robustness by progressively adjusting for 12 covariates: Model 1 adjusted for age and race; Model 2 further adjusted for marital status, PIR, total energy intake, smoking, and alcohol consumption; and Model 3 additionally controlled for hypertension status, NHDL, and HOMA-IR. Restricted cubic spline (RCS) models with three knots were used to explore nonlinear relationships between HEI, DII, CDAI, and OBS, with subgroup analyses stratified by obesity status. All 10 covariates were adjusted in this analysis. We used mediation analysis to explore whether the risk of oxidative stress in postmenopausal women is mediated by obesity. The mediation ratio was calculated using the coefficient product method, which was proposed by Baron and Kenny (1986) and further developed by VanderWeele (2009, 2016) for causal mediation analysis [ 21 – 23 ] . This method decomposes the total effect (TE) of HEI, DII, and CDAI on OBS into a direct effect (DE) and an indirect effect (IE) through a mediating factor (obesity). The mediation ratio was calculated as follows: Mediation ratio = IE/TE =(β1 × β2)/β3 Where β1 represents the association between HEI, DII, and CDAI on OBS, β2 represents the association between obesity and OBS (controlling for HEI, DII, and CDAI), and β3 represents the total effect of HEI, DII, and CDAI on OBS. To ensure robust estimates of the indirect effect and confidence intervals, we used nonparametric bootstrapping with 1,000 resampling, a widely accepted technique in mediation analysis. Bootstrapping is a resampling method that repeatedly draws random samples from the original dataset to approximate the sampling distribution of the mediation effect. This approach does not rely on the normality assumption and provides more reliable confidence intervals for the indirect effect, thereby enhancing the robustness of the findings [ 23 ] . The selection of 1,000 bootstrap samples is in line with standard practice in mediation analysis to ensure that the estimates are stable and reliable. All analyses incorporated NHANES' complex, multistage sampling design and applied appropriate sampling weights to ensure national representativeness. Weighted multivariable logistic regression was performed, and statistical significance was set at P < 0.05. 3. Result 3.1 Characteristics of the Included Population Our study included 2,391 participants from eight NHANES cycles spanning 2005 to 2020. The baseline characteristics of the study population, stratified by OBS quartiles, are presented in Table 1 . Overall, individuals with higher OBS were predominantly non-Hispanic White, whereas those with lower OBS had a higher proportion of non-Hispanic Black and Hispanic individuals (P < 0.01). BMI and waist circumference showed a decreasing trend with increasing OBS (P < 0.01), with significantly lower values in the Q4 group compared to the Q1 group. Additionally, individuals with higher OBS had higher household income levels (P < 0.01). Regarding dietary and lifestyle factors, higher OBS was associated with greater energy intake (P < 0.01), a higher proportion of never smokers, and a greater prevalence of light alcohol consumption (P < 0.01). Moreover, hypertension prevalence was lower in the high OBS group (P < 0.01). Biochemical indicators revealed that higher OBS was associated with lower non-HDL cholesterol levels (P = 0.03), lower HOMA-IR (P < 0.01), and better dietary quality (P < 0.01). Table.1 Baseline Characteristics of Menopausal Women Table 2005–2020. Variable Oxidative Balance Score Total Q1 Q2 Q3 Q4 P -value Total 2391(100.00) 642(23.22) 558(23.37) 696(30.88) 495(22.53) Age ~ years 59.89(0.31) 58.63(0.68) 60.00(0.68) 60.11(0.52) 60.77(0.49) 0.11 Race~% < 0.01 Non-Hispanic White 1214(78.94) 290(74.70) 279(77.19) 363(80.53) 282(82.92) Non-Hispanic Black 470(8.26) 170(12.51) 105(7.99) 130(7.85) 65(4.73) Mexican American 267(3.84) 70(4.25) 65(4.63) 82(3.73) 50(2.74) Other Hispanic 238(3.89) 70(4.59) 62(4.73) 69(3.56) 37(2.75) Other Race 202(5.07) 42(3.95) 47(5.45) 52(4.32) 61(6.87) Marital Status 0.01 Married/Living with partner 1292(62.00) 305(53.22) 302(60.86) 372(65.46) 313(67.51) Widowed/Divorced/Separated 892(31.87) 273(38.98) 213(34.02) 264(28.87) 142(26.40) Never married 207(6.13) 64(7.80) 43(5.12) 60(5.67) 40(6.09) BMI ~ kg/cm2 29.04(0.21) 30.41(0.35) 29.79(0.35) 28.82(0.37) 27.14(0.35) < 0.01 Waist ~ cm 98.09(0.49) 101.07(0.80) 99.51(0.82) 97.79(0.90) 93.99(0.87) < 0.01 Family PIR 3.32(0.05) 2.74(0.08) 3.25(0.08) 3.50(0.08) 3.74(0.07) < 0.01 Energy intake ~ kcal 1770.12(20.91) 1331.83(26.12) 1615.02(30.96) 1959.32(33.79) 2123.32(41.50) < 0.01 Smoking behavior~% < 0.01 Never 1405(56.88) 328(47.04) 323(54.18) 439(62.73) 315(61.80) Former 623(27.71) 158(24.81) 140(29.16) 176(26.40) 149(31.00) Now 363(15.41) 156(28.14) 95(16.66) 81(10.87) 31(7.20) Alcohol consumption~% < 0.01 Never 432(13.40) 142(18.82) 109(12.61) 109(11.63) 72(11.06) Former 391(14.13) 126(19.10) 86(11.09) 106(13.90) 73(12.49) Mild 862(40.71) 181(26.74) 188(38.64) 264(42.91) 229(54.22) Moderate 449(21.87) 123(24.71) 100(23.36) 143(23.12) 83(15.69) Heavy 257(9.89) 70(10.63) 75(14.31) 74(8.43) 38(6.54) Hypertension < 0.01 No 1012(48.79) 246(42.39) 213(45.03) 319(52.61) 234(54.03) Yes 1379(51.21) 396(57.61) 345(54.97) 377(47.39) 261(45.97) Non-HDL ~ mg/dL 145.12(1.20) 148.17(2.18) 147.82(2.40) 145.24(1.98) 139.02(3.00) 0.03 HOMA-IR 3.39(0.11) 3.94(0.23) 3.78(0.27) 3.04(0.12) 2.89(0.20) < 0.01 HEI 53.50(0.47) 45.41(0.64) 51.52(0.65) 55.06(0.74) 61.76(0.96) < 0.01 DII 1.65(0.05) 3.33(0.06) 2.31(0.08) 1.25(0.07) -0.20(0.11) < 0.01 CDAI 0.46(0.09) -2.52(0.09) -0.64(0.13) 1.07(0.11) 3.83(0.22) < 0.01 3.2 Relationship Between Healthy Diet and Oxidative Stress Table 2 presents the association between healthy diet and oxidative stress. HEI was positively associated with OBS (P < 0.01, β = 0.20, 95% CI: 0.18, 0.23), and this association remained significant after adjusting for multiple covariates (P < 0.01, β = 0.20, 95% CI: 0.18, 0.22). Conversely, DII was inversely associated with OBS (P < 0.01, β = -2.66, 95% CI: -2.81, -2.51), and this inverse association remained significant after covariate adjustment (P < 0.01, β = -2.24, 95% CI: -2.40, -2.08). Similarly, CDAI demonstrated a significant positive association with OBS (P < 0.01, β = 1.29, 95% CI: 1.17, 1.42), which remained robust after covariate adjustments (P < 0.01, β = 1.09, 95% CI: 0.95, 1.23). Table. 2 Association between HEI, DII, CDAI and OBS. Variable Model OBS β (95%CI) P -value HEI Model1 0.20(0.18,0.23) < 0.01 Model2 0.20(0.18,0.22) < 0.01 Model3 0.20(0.18,0.22) < 0.01 DII Model1 -2.66(-2.81, -2.51) < 0.01 Model2 -2.26(-2.41, -2.10) < 0.01 Model3 -2.24(-2.40, -2.08) < 0.01 CDAI Model1 1.29(1.17,1.42) < 0.01 Model2 1.09(0.96,1.23) < 0.01 Model3 1.09(0.95,1.23) < 0.01 3.3 Relationship Between Healthy Diet and Obesity Table 3 shows the association between healthy diet and obesity. HEI was inversely correlated with BMI (P < 0.01, β = -0.09, 95% CI: -0.11, -0.07), and this negative association persisted after adjusting for covariates (P < 0.01, β = -0.07, 95% CI: -0.10, -0.05). DII was positively correlated with BMI (P < 0.01, β = 0.40, 95% CI: 0.25, 0.55), which remained significant after covariate adjustment (P < 0.01, β = 0.45, 95% CI: 0.27, 0.64). CDAI exhibited a significant negative correlation with BMI (P = 0.02, β = -0.10, 95% CI: -0.18, -0.02), which strengthened after covariate adjustments (P < 0.01, β = -0.20, 95% CI: -0.31, -0.09). A similar pattern was observed for waist circumference, where HEI showed a significant negative association (P < 0.01, β = -0.23, 95% CI: -0.28, -0.17), while DII was positively associated (P < 0.01, β = 0.77, 95% CI: 0.39, 1.16). After adjusting for covariates, these associations remained significant. CDAI was not significantly correlated with waist circumference (P = 0.20, β = -0.14, 95% CI: -0.34, 0.07), but after adjustment, a significant negative association was observed (P < 0.01, β = -0.38, 95% CI: -0.67, -0.10). Table.3 Association between HEI, DII, CDAI and BMI, Waist. Variable Model BMI Waist β (95%CI) P -value β (95%CI) P -value HEI Model1 -0.09(-0.11, -0.07) < 0.01 -0.23(-0.28, -0.17) < 0.01 Model2 -0.09(-0.11, -0.07) < 0.01 -0.21(-0.26, -0.16) < 0.01 Model3 -0.07(-0.10, -0.05) < 0.01 -0.17(-0.22, -0.12) < 0.01 DII Model1 0.40(0.25,0.55) < 0.01 0.77(0.39,1.16) < 0.01 Model2 0.53(0.34,0.73) < 0.01 1.12(0.65,1.60) < 0.01 Model3 0.45(0.27,0.64) < 0.01 0.89(0.43,1.34) < 0.01 CDAI Model1 -0.10(-0.18, -0.02) 0.02 -0.14(-0.34,0.07) 0.20 Model2 -0.20(-0.32, -0.09) < 0.01 -0.42(-0.71, -0.12) < 0.01 Model3 -0.20(-0.31, -0.09) < 0.01 -0.38(-0.67, -0.10) < 0.01 3.4 Relationship Between Obesity and Oxidative Stress Table 4 presents the association between obesity and oxidative stress. BMI was negatively associated with OBS (P < 0.01, β = -0.16, 95% CI: -0.21, -0.11), and this inverse relationship persisted after adjusting for covariates (P < 0.01, β = -0.17, 95% CI: -0.22, -0.12). Compared to non-obese individuals, obesity was significantly associated with lower OBS (P < 0.01, β = -2.27, 95% CI: -3.03, -1.52), and this association remained significant after adjustment (P < 0.01, β = -2.43, 95% CI: -3.07, -1.78). Similarly, waist circumference exhibited a negative correlation with OBS (P < 0.01, β = -0.07, 95% CI: -0.09, -0.04), which remained significant after adjustment. Central obesity, compared to non-central obesity, was also significantly associated with lower OBS (P < 0.01, β = -2.15, 95% CI: -3.04, -1.26), which persisted after adjustment (P < 0.01, β = -1.89, 95% CI: -2.72, -1.07). Table.4 Association between obesity and OBS. Independent Variable Model1 Model2 Model3 β (95%CI) P -value β (95%CI) P -value β (95%CI) P -value BMI Continuous -0.16(-0.21, -0.11) < 0.01 -0.18(-0.23, -0.14) < 0.01 -0.17(-0.22, -0.12) < 0.01 Obesity No Reference Yes -2.27(-3.03, -1.52) < 0.01 -2.58(-3.20, -1.96) < 0.01 -2.43(-3.07, -1.78) < 0.01 Waist Continuous -0.07(-0.09, -0.04) < 0.01 -0.08(-0.10, -0.06) < 0.01 -0.07(-0.09, -0.05) < 0.01 Abdominal Obesity No Reference Yes -2.15(-3.04, -1.26) < 0.01 -2.15(-2.94, -1.37) < 0.01 -1.89(-2.72, -1.07) < 0.01 3.5 Non-Linear Relationship Between Healthy Diet and Oxidative Stress Figure 2 illustrates the non-linear associations between dietary indices and OBS. After adjusting for all covariates, HEI exhibited a significant linear positive relationship with OBS (P for overall < 0.01, P for non-linear = 0.99). In contrast, DII showed a significant inverse non-linear association with OBS (P for overall < 0.01, P for non-linear < 0.01). CDAI displayed a significant positive non-linear relationship with OBS (P for overall < 0.01, P for non-linear < 0.01). Stratified analyses based on obesity (Fig. 3 ) and central obesity (Fig. 4 ) revealed consistent patterns. 3.6 Mediating Effects The results of the mediation analysis for BMI are presented in Fig. 5 . After adjusting for all covariates, BMI mediated 5.271% of the association between HEI and OBS (IE = 0.012, P < 0.001; DE = 0.207, P < 0.001), 3.350% of the association between DII and OBS (IE = -0.074, P < 0.001; DE = -2.135, P < 0.001), and 3.920% of the association between CDAI and OBS (IE = 0.046, P < 0.001; DE = 1.118, P < 0.001). Similarly, the results of the mediation analysis for Waist Circumference are shown in Fig. 6 . After adjusting for all covariates, Waist Circumference mediated 2.464% of the association between HEI and OBS (IE = 0.029, P < 0.001; DE = 1.140, P < 0.001), 0.827% of the association between DII and OBS (IE = -0.048, P < 0.001; DE = -2.331, P < 0.001), and 3.769% of the association between CDAI and OBS (IE = 0.001, P < 0.001; DE = 0.188, P < 0.001). 4. Discussion In this study, we examined the association between dietary quality and oxidative stress in postmenopausal women, emphasizing the potential mediating role of obesity. Our findings indicate that higher adherence to a healthy dietary pattern, as reflected by HEI and CDAI, is associated with lower oxidative stress, while a pro-inflammatory diet (higher DII) is linked to increased oxidative burden. Additionally, our results suggest that obesity may influence oxidative balance, supporting its potential role as a mediator in the diet-oxidative stress relationship. These findings provide insights into possible dietary and weight management strategies for reducing oxidative stress in postmenopausal women. Our study aligns with prior research indicating that diets rich in antioxidants and anti-inflammatory components may help mitigate oxidative damage by reducing ROS generation and enhancing endogenous antioxidant defense systems [ 24 – 26 ] . Conversely, pro-inflammatory diets, characterized by high intake of saturated fats, refined sugars, and processed foods, have been linked to increased oxidative stress through mechanisms such as lipid peroxidation and activation of inflammatory pathways [ 27 – 28 ] . These findings reinforce the relevance of dietary quality in oxidative stress modulation. Obesity is associated with increased oxidative stress, primarily due to its link to chronic low-grade inflammation and metabolic dysregulation. Our results indicate a negative correlation between BMI, waist circumference, and OBS, even after adjusting for multiple covariates. The potential link between obesity and oxidative stress may be attributed to the pro-inflammatory secretory profile of adipose tissue, which includes cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), both of which contribute to oxidative stress by promoting ROS production and impairing mitochondrial function [ 29 – 31 ] . Additionally, obesity-related insulin resistance and dyslipidemia may further exacerbate oxidative damage, underscoring the importance of weight management in oxidative stress reduction [ 32 – 34 ] . Our mediation analysis suggests that obesity may partially mediate the relationship between dietary quality and oxidative stress. Specifically, a healthier diet is associated with lower BMI and waist circumference, which in turn may contribute to a more favorable oxidative balance. This finding implies that dietary interventions aimed at improving diet quality may not only have direct antioxidative effects but may also influence oxidative stress indirectly by preventing obesity. Notably, while our analysis indicates a significant mediation effect, residual direct associations between diet and oxidative stress suggest that additional pathways, such as gut microbiota modulation and epigenetic modifications, may also play a role [ 35 ] . The non-linear relationships observed between dietary indices and oxidative stress further support the complexity of these interactions. While HEI exhibited a predominantly linear positive association with OBS, DII and CDAI demonstrated non-linear trends, indicating potential threshold effects. These findings imply that while improvements in dietary quality generally correspond to better oxidative balance, the magnitude of benefits may vary across different levels of dietary adherence [ 36 – 37 ] . Future studies should explore these non-linear effects in greater detail to refine dietary recommendations for oxidative stress management. Despite the strengths of our study, including the use of a nationally representative dataset, rigorous statistical adjustments, and comprehensive dietary assessment, certain limitations should be acknowledged. First, the cross-sectional design limits causal inference, and longitudinal studies are needed to clarify the temporal relationships between diet, obesity, and oxidative stress. Second, dietary intake was assessed using 24-hour dietary recalls, which are subject to recall bias and day-to-day variability. Third, while OBS integrates multiple pro- and anti-oxidative factors, it remains an indirect measure of oxidative stress and may not fully capture all relevant biological processes. Lastly, residual confounding from unmeasured factors, such as genetic predispositions, cannot be ruled out. Future research should focus on longitudinal and interventional studies to better understand causality and explore additional biological mechanisms underlying these associations. 5. Conclusion The study suggests a role for dietary quality in oxidative stress regulation among postmenopausal women, with obesity acting as a possible mediator in this relationship. These findings highlight the importance of promoting healthy dietary habits and weight management strategies to mitigate oxidative stress-related health risks in this population. Declarations Acknowledgments The author thanks the staff and the participants of the NHANES study for their valuable contributions. Availability of data and materials The NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data. Author Contributions HS: Methodology, Data Analysis; CL and MW managed and cleaned the data. BZ criticized and revised the manuscript. All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest. Funding No funded. Data Availability Statement The NHANES datasets analyzed during the current study are publicly available from the National Center for Health Statistics (NCHS) (https:// www. cdc.gov/ nchs/ nhanes/ index. htm), except for geographic data (latitude) that are restricted to use through the NCHS Research Data Center (http:// www. cdc.gov/ rdc/) per NCHS, Centers for Disease Control and Prevention policy. Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. The data used in this study were obtained from the NHANES, which is publicly available and de-identified. NHANES is approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent. No additional ethical approval was required for this secondary analysis. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable Human Ethics and Consent to Participate Not applicable Clinical trial number Not applicable References Anklam, C. F. V., Lissarassa, et al. (2021). Oxidative and Cellular Stress Markers in Postmenopause Women with Diabetes: The Impact of Years of Menopause. Journal of Diabetes Research , 2021 , 1–9. https://doi.org/10.1155/2021/3314871 Montoya-Estrada, A., García-Cortés, et al. (2024). The Administration of Resveratrol and Vitamin C Reduces Oxidative Stress in Postmenopausal Women—A Pilot Randomized Clinical Trial. Nutrients , 16 (21), 3775. https://doi.org/10.3390/nu16213775 Lin, K., Li, Y., et al. (2021). 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(2023). Obesity‐induced oxidative stress and mitochondrial dysfunction negatively affect sperm quality. FEBS Open Bio , 13 (4), 763–778. https://doi.org/10.1002/2211-5463.13589 Moshfegh A.J., Rhodes D.G., Baer D.J., Murayi T., Clemens J.C., Rumpler W.V., Paul D.R., Sebastian R.S., Kuczynski K.J., Ingwersen L.A., et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am. J. Clin. Nutr. 2008;88:324–332. doi: 10.1093/ajcn/88.2.324. Zhang Y., Lu C., Li X., Fan Y., Li J., Liu Y., Yu Y., Zhou L. Healthy Eating Index-2015 and Predicted 10-Year Cardiovascular Disease Risk, as Well as Heart Age. Front. Nutr. 2022;9:888966. doi: 10.3389/fnut.2022.888966. Li X.Y., Wen M.Z., Xu Y.H., Shen Y.C., Yang X.T. The association of healthy eating index with periodontitis in NHANES 2013–2014. Front. Nutr. 2022;9:968073. doi: 10.3389/fnut.2022.968073. Shivappa, N., Steck, et al. (2014). Designing and developing a literature-derived, population-based dietary inflammatory index. Public health nutrition , 17 (8), 1689–1696. https://doi.org/10.1017/S1368980013002115 Maugeri, A., Hruskova, et al. (2019). Dietary antioxidant intake decreases carotid intima media thickness in women but not in men: A cross-sectional assessment in the Kardiovize study. Free radical biology & medicine , 131 , 274–281. https://doi.org/10.1016/j.freeradbiomed.2018.12.018 Zhang, W., Peng, et al. (2022). Association between the Oxidative Balance Score and Telomere Length from the National Health and Nutrition Examination Survey 1999-2002. Oxidative medicine and cellular longevity , 2022 , 1345071. https://doi.org/10.1155/2022/1345071 Baron, R. M., & Kenny, et al. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of personality and social psychology , 51 (6), 1173–1182. https://doi.org/10.1037//0022-3514.51.6.1173 VanderWeele T. J. (2009). Mediation and mechanism. European journal of epidemiology , 24 (5), 217–224. https://doi.org/10.1007/s10654-009-9331-1 VanderWeele T. J. (2016). Mediation Analysis: A Practitioner's Guide. Annual review of public health , 37 , 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402 Wang, Y. B., Page, et al. (2022). Association of dietary and nutrient patterns with systemic inflammation in community dwelling adults. Frontiers in Nutrition , 9 . https://doi.org/10.3389/fnut.2022.977029 Ucar, A., Yeltekin, A. Ç., et al. (2023). Has PdCu@GO effect on oxidant/antioxidant balance? Using zebrafish embryos and larvae as a model. Chemico-Biological Interactions , 378 , 110484. https://doi.org/10.1016/j.cbi.2023.110484 Vezzoli, A., Mrakic-Sposta, et al. (2023). Chelation Therapy Associated with Antioxidant Supplementation Can Decrease Oxidative Stress and Inflammation in Multiple Sclerosis: Preliminary Results. Antioxidants , 12 (7), 1338. https://doi.org/10.3390/antiox12071338 Pengrattanachot, N., Thongnak, et al. (2022). The impact of prebiotic fructooligosaccharides on gut dysbiosis and inflammation in obesity and diabetes related kidney disease. Food & Function , 13 (11), 5925–5945. https://doi.org/10.1039/d1fo04428a Gürel, S., Pak, et al. (2024). Aging Processes Are Affected by Energy Balance: Focused on the Effects of Nutrition and Physical Activity on Telomere Length. Current Nutrition Reports , 13 (2), 264–279. https://doi.org/10.1007/s13668-024-00529-9 Codoñer-Franch, P., Valls-Bellés, et al. (2011). Oxidant mechanisms in childhood obesity: the link between inflammation and oxidative stress. Translational Research , 158 (6), 369–384. https://doi.org/10.1016/j.trsl.2011.08.004 Gaman, M.-A., Epingeac, et al. (2021). OXIDATIVE STRESS AND INFLAMMATION LEVELS ARE INCREASED IN OBESE SUBJECTS. Journal of Hypertension , 39 (Supplement 1), e334. https://doi.org/10.1097/01.hjh.0000748372.32094.15 Tanaka, S., Watanabe, et al. (2020). Indoxyl Sulfate Contributes to Adipose Tissue Inflammation through the Activation of NADPH Oxidase. Toxins , 12 (8), 502. https://doi.org/10.3390/toxins12080502 Jiang, S., Liu, A., et al. (2023). Lactobacillus gasseri CKCC1913 mediated modulation of the gut–liver axis alleviated insulin resistance and liver damage induced by type 2 diabetes. Food & Function , 14 (18), 8504–8520. https://doi.org/10.1039/d3fo01701j Navarro-Ruiz, M. C., Soler-Vázquez, et al. (2022). Influence of Protein Carbonylation on Human Adipose Tissue Dysfunction in Obesity and Insulin Resistance. Biomedicines , 10 (12), 3032. https://doi.org/10.3390/biomedicines10123032 Li, S., Eguchi, et al. (2020). The Role of the Nrf2 Signaling in Obesity and Insulin Resistance. International Journal of Molecular Sciences , 21 (18), 6973. https://doi.org/10.3390/ijms21186973 Ávila‐Escalante, M. L., Coop‐Gamas, et al. (2020). The effect of diet on oxidative stress and metabolic diseases—Clinically controlled trials. Journal of Food Biochemistry , 44 (5). https://doi.org/10.1111/jfbc.13191 Mao, Z., & Bostick, et al. (2021). Associations of dietary, lifestyle, other participant characteristics, and oxidative balance scores with plasma F2-isoprostanes concentrations in a pooled cross-sectional study. European Journal of Nutrition , 61 (3), 1541–1560. https://doi.org/10.1007/s00394-021-02754-2 Kong, W., Jiang, et al. (2022). Dietary diversity, diet quality, and oxidative stress in older adults. Geriatric Nursing , 48 , 158–163. https://doi.org/10.1016/j.gerinurse.2022.09.013 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 23 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 20 Jul, 2025 Reviews received at journal 19 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers invited by journal 13 Jul, 2025 Editor invited by journal 08 Jul, 2025 Editor assigned by journal 07 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 07 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7061650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485382989,"identity":"d5f4a4c2-7fa7-4897-9c75-3cf27b78e844","order_by":0,"name":"Bingli Zuo","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bingli","middleName":"","lastName":"Zuo","suffix":""},{"id":485382990,"identity":"7da9f97b-53e2-4af2-bab0-08a0f4948c59","order_by":1,"name":"Chen Liang","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Liang","suffix":""},{"id":485382991,"identity":"282dafc2-9b40-4236-8d13-c0cfeea7852f","order_by":2,"name":"Mengmeng Wang","email":"","orcid":"","institution":"Chinese Academy of traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Wang","suffix":""},{"id":485382992,"identity":"97cab141-d08f-47dc-ae5f-12693000163b","order_by":3,"name":"Hao Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3RMYvCMBTA8ReEuATr+OSk/QoBQRH8MAmFuNyJ4NKhQ6WSDuLux3B0VAqZ4u7YfgS3m46L8x1t3Rzyn/ODl/cAfL43jEbltX78LMIgqreVSNJ2MkAaT5CqySiLc15Z005CZNMho6Xk2VKP6l2vw2AfWgEyJThcdSIzCkGxF81kXJpqjYvVjGz1XZ7HgPZ2aiaglhy52sxz4oilwPGrjXxOkYlSngzRa6l7HQg+ycURSzR0I8zEbsFuyUeSo7CGtf4lKnJ3ysydEvv14ztJw6A4NJM/sdee+3w+n+/ffgFnfUqO4s1LBgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-07-07 05:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7061650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7061650/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-04026-1","type":"published","date":"2025-09-29T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86776309,"identity":"56002807-e827-4a09-b8d9-e217e945aadd","added_by":"auto","created_at":"2025-07-15 12:39:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71427,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for screening the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/204a12bfdef8785cde14514d.png"},{"id":86776312,"identity":"c624e5aa-9268-4abb-b92d-0815d0c4e824","added_by":"auto","created_at":"2025-07-15 12:39:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54380,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve (RCS) showing the relationship between HEI, DII, CDAI and OBS.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/7fd8667dd091636cdc74aa5a.jpeg"},{"id":86776915,"identity":"da0c9552-0b4e-41c6-947a-62294ebaefa8","added_by":"auto","created_at":"2025-07-15 12:47:44","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55479,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve (RCS) showing the relationship between HEI, DII, CDAI and OBS, grouped according to obesity.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/4fbd7632405663437d9ce72b.jpeg"},{"id":86776916,"identity":"62a07534-1ede-4844-98d6-0ba5d95302b0","added_by":"auto","created_at":"2025-07-15 12:47:44","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62386,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve (RCS) showing the relationship between HEI, DII, CDAI and OBS, grouped according to abdominal obesity.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/c837c4aa8f318225bc191bb1.jpeg"},{"id":86776316,"identity":"cf9c2b06-b458-425a-bb75-acd6a278d0cd","added_by":"auto","created_at":"2025-07-15 12:39:44","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36448,"visible":true,"origin":"","legend":"\u003cp\u003eMediating effects between BMI-mediated HEI, DII, CDAI and OBS.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/5a7a957cec67f88fbc7989d2.jpeg"},{"id":86776322,"identity":"9c180eed-7884-402c-8ae0-1eb76051f082","added_by":"auto","created_at":"2025-07-15 12:39:44","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":36224,"visible":true,"origin":"","legend":"\u003cp\u003eMediating effects between Waist-mediated HEI, DII, CDAI and OBS.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/3a126c0a0769b70424c98fa3.jpeg"},{"id":92883614,"identity":"7e909c66-03c6-401f-8556-03d440ff0175","added_by":"auto","created_at":"2025-10-06 16:05:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7061650/v1/bac12bdf-ba70-43b4-9998-1a09973ec3c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Diet to Oxidative Stress: Obesity as a Key Mediator in Postmenopausal Women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOxidative stress is a crucial factor implicated in the pathophysiology of aging and the onset of numerous age-related diseases. In postmenopausal women, the interplay between metabolic and hormonal alterations fosters an environment conducive to increased oxidative burden \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The decline in estrogen levels, a hallmark of menopause, has been associated with impaired antioxidant defense mechanisms and heightened susceptibility to oxidative damage, which, in turn, contributes to a greater risk of metabolic dysfunction, cardiovascular disease, and neurodegenerative disorders \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Given the fundamental role of oxidative stress in these pathological processes, identifying modifiable factors that influence oxidative balance is of significant scientific and clinical interest.\u003c/p\u003e\u003cp\u003eDietary components exert a dual role in oxidative stress, acting as either promoters or inhibitors. High-sugar and high-fat diets contribute to increased reactive oxygen species (ROS) production, thereby elevating oxidative stress levels. For instance, a high-sucrose diet has been shown to significantly elevate oxidative stress biomarkers and impair antioxidant defense mechanisms in Drosophila \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In contrast, diets rich in antioxidants\u0026mdash;such as polyphenols, vitamins C and E, and carotenoids\u0026mdash;effectively scavenge free radicals and enhance antioxidant enzyme activity, thereby mitigating oxidative stress. The Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, abundant in antioxidant compounds, have been associated with reduced oxidative stress levels, whereas the Western diet, characterized by high sugar, high fat, and ultra-processed foods, has been linked to increased oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Furthermore, a high-carbohydrate diet is correlated with an elevated hemoglobin glycation index (HGI), a potential marker of oxidative stress and inflammation, indicating that dietary patterns not only influence short-term oxidative stress but may also have long-term health implications \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt the molecular level, polyphenols activate the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, upregulating the expression of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GPx), thereby enhancing the body's antioxidative capacity \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Additionally, dietary components such as iron chelators can reduce ROS production via iron-mediated pathways, further alleviating oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These mechanisms collectively regulate oxidative stress status and may play a pivotal role in the pathogenesis and progression of chronic diseases.\u003c/p\u003e\u003cp\u003eObesity has emerged as a major contributor to oxidative stress due to the pro-inflammatory and metabolic disruptions associated with excess adiposity. Adipose tissue, particularly visceral fat, functions as a metabolically active organ that secretes a range of inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1), which collectively promote chronic inflammation and oxidative damage \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Additionally, obesity is linked to mitochondrial dysfunction, impaired adipokine signaling, and increased oxidative phosphorylation, all of which contribute to elevated ROS production \u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Given that postmenopausal women are particularly vulnerable to obesity-induced metabolic dysregulation, it is plausible that obesity acts as a key mediator in the relationship between dietary quality and oxidative stress. Understanding this mediation effect is critical, as it may provide new avenues for targeted dietary and lifestyle interventions aimed at reducing oxidative stress burden in this high-risk population.\u003c/p\u003e\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) provides a robust and nationally representative dataset that enables a comprehensive investigation of these intricate relationships. In the present study, we employ a cross-sectional analytical approach to examine the association between dietary quality and oxidative stress among postmenopausal women, with a specific focus on the potential mediating role of obesity. Although the observational nature of this study precludes causal inferences, identifying significant associations can contribute to the growing body of evidence informing future longitudinal and interventional research. Such insights may aid in the development of precision nutrition and weight management strategies aimed at mitigating oxidative stress-related health risks in postmenopausal women.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Sample\u003c/h2\u003e\u003cp\u003eThis study was based on the National Health and Nutrition Examination Survey (NHANES) database, with all data approved by the Institutional Review Board. Informed consent and written documentation were obtained from all participants. A total of 8,345 NHANES participants from 2005 to 2020 were included. Initially, individuals with incomplete laboratory data (n\u0026thinsp;=\u0026thinsp;2,917) and missing dietary information (n\u0026thinsp;=\u0026thinsp;1,473) were excluded, leaving 3,955 participants. Further exclusion of individuals with missing demographic data (n\u0026thinsp;=\u0026thinsp;1,240) resulted in 2,715 participants. Finally, 324 individuals with insufficient parameters for calculating the Oxidative Balance Score (OBS) were excluded, yielding a final analytical sample of 2,391 participants. The detailed selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sources of Core Variables\u003c/h2\u003e\u003cp\u003eIn this study, three dietary indices were used to assess healthy eating patterns: the Healthy Eating Index (HEI), the Dietary Inflammatory Index (DII), and the Composite Dietary Antioxidant Index (CDAI). Dietary data were derived from the \"What We Eat in America\" (WWEIA) project, conducted by the U.S. Department of Agriculture (USDA). The automated multiple-pass method (AMPM) developed by the USDA was used to obtain 24-hour dietary recall data \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Food categories and nutrient compositions were calculated based on the USDA Food Patterns Equivalents Database (FPED) \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The analysis used total nutrient intake data from the first day of NHANES dietary assessment.\u003c/p\u003e\u003cp\u003e HEI-2015 is a scoring system ranging from 0 to 100, where a higher score indicates greater adherence to the Dietary Guidelines for Americans (DGA) and better dietary quality. HEI-2015 is calculated based on energy density (per 1,000 kcal) rather than absolute intake and consists of 13 components: 9 adequacy components (total fruits, whole fruits, total vegetables, greens and beans, total protein, seafood and plant proteins, whole grains, dairy, and fatty acids) and 4 moderation components (sodium, refined grains, added sugars, and saturated fats), with scoring ranges of 0\u0026ndash;5 or 0\u0026ndash;10 points. The final HEI score is obtained by summing all component scores \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDII quantifies the inflammatory potential of dietary intake based on 45 dietary components, including 36 anti-inflammatory components (e.g., fiber, omega-3 fatty acids, vitamin C) and 9 pro-inflammatory components (e.g., saturated fat, sugar, cholesterol). Intake levels of these components are compared with a global reference database and weighted based on their influence on six inflammatory markers (e.g., C-reactive protein, IL-6). A higher DII score indicates a more pro-inflammatory diet, whereas a lower score reflects greater anti-inflammatory potential \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCDAI evaluates dietary antioxidant capacity by integrating intake levels of key antioxidant compounds, including vitamins A, C, and E, selenium, zinc, and carotenoids. Intakes are standardized against global reference values and summed using weighted scores to represent overall dietary antioxidant capacity \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe OBS is calculated by integrating pro-oxidant and antioxidant factors. Pro-oxidant factors (e.g., smoking, obesity, high-fat diet, environmental pollutants) are assigned negative scores, decreasing with higher exposure levels. Antioxidant factors (e.g., dietary intake of vitamin C, vitamin E, selenium, polyphenols, regular physical activity, and antioxidant-related genetic polymorphisms) receive positive scores, increasing with higher intake or activity levels. The total OBS score reflects the balance between oxidative stress and antioxidant defense, with a higher score indicating lower oxidative stress and reduced pathological risk \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBody mass index (BMI) and waist circumference were used to assess obesity. Anthropometric data were collected by trained health professionals at the NHANES Mobile Examination Center (MEC). Participants were classified as obese if BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 and as centrally obese if waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;88 cm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Covariates\u003c/h2\u003e\u003cp\u003eCovariates included age, race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), marital status (married/partnered, divorced/widowed/separated, never married), smoking status (never, former, current), alcohol consumption (never, former, light, moderate, heavy), the poverty-to-income ratio (PIR), total energy intake (total kcal on the first dietary recall day), hypertension status, non-high-density lipoprotein cholesterol (NHDL), and homeostatic model assessment for insulin resistance (HOMA-IR).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analyses\u003c/h2\u003e\u003cp\u003eMultiple imputation was applied to address missing at-random data, minimizing potential bias and enhancing analytical robustness. Systematically missing data, which showed specific patterns related to sample or variable characteristics, were excluded to maintain sample representativeness. Filtered datasets were analyzed using R (v.4.3.3). Continuous variables were presented as means (standard deviation) and compared using t-tests, while categorical variables were presented as counts (percentages) and compared using chi-square tests.\u003c/p\u003e\u003cp\u003eSensitivity analyses were conducted using multivariate linear regression to examine the associations between HEI, DII, CDAI, OBS, BMI, and waist circumference. Three models were constructed to test robustness by progressively adjusting for 12 covariates: Model 1 adjusted for age and race; Model 2 further adjusted for marital status, PIR, total energy intake, smoking, and alcohol consumption; and Model 3 additionally controlled for hypertension status, NHDL, and HOMA-IR.\u003c/p\u003e\u003cp\u003eRestricted cubic spline (RCS) models with three knots were used to explore nonlinear relationships between HEI, DII, CDAI, and OBS, with subgroup analyses stratified by obesity status. All 10 covariates were adjusted in this analysis.\u003c/p\u003e\u003cp\u003eWe used mediation analysis to explore whether the risk of oxidative stress in postmenopausal women is mediated by obesity. The mediation ratio was calculated using the coefficient product method, which was proposed by Baron and Kenny (1986) and further developed by VanderWeele (2009, 2016) for causal mediation analysis \u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This method decomposes the total effect (TE) of HEI, DII, and CDAI on OBS into a direct effect (DE) and an indirect effect (IE) through a mediating factor (obesity). The mediation ratio was calculated as follows:\u003c/p\u003e\u003cp\u003eMediation ratio\u0026thinsp;=\u0026thinsp;IE/TE =(β1\u0026thinsp;\u0026times;\u0026thinsp;β2)/β3\u003c/p\u003e\u003cp\u003eWhere β1 represents the association between HEI, DII, and CDAI on OBS, β2 represents the association between obesity and OBS (controlling for HEI, DII, and CDAI), and β3 represents the total effect of HEI, DII, and CDAI on OBS.\u003c/p\u003e\u003cp\u003eTo ensure robust estimates of the indirect effect and confidence intervals, we used nonparametric bootstrapping with 1,000 resampling, a widely accepted technique in mediation analysis. Bootstrapping is a resampling method that repeatedly draws random samples from the original dataset to approximate the sampling distribution of the mediation effect. This approach does not rely on the normality assumption and provides more reliable confidence intervals for the indirect effect, thereby enhancing the robustness of the findings \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The selection of 1,000 bootstrap samples is in line with standard practice in mediation analysis to ensure that the estimates are stable and reliable.\u003c/p\u003e\u003cp\u003eAll analyses incorporated NHANES' complex, multistage sampling design and applied appropriate sampling weights to ensure national representativeness. Weighted multivariable logistic regression was performed, and statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Characteristics of the Included Population\u003c/h2\u003e\u003cp\u003e Our study included 2,391 participants from eight NHANES cycles spanning 2005 to 2020. The baseline characteristics of the study population, stratified by OBS quartiles, are presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Overall, individuals with higher OBS were predominantly non-Hispanic White, whereas those with lower OBS had a higher proportion of non-Hispanic Black and Hispanic individuals (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). BMI and waist circumference showed a decreasing trend with increasing OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with significantly lower values in the Q4 group compared to the Q1 group. Additionally, individuals with higher OBS had higher household income levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Regarding dietary and lifestyle factors, higher OBS was associated with greater energy intake (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), a higher proportion of never smokers, and a greater prevalence of light alcohol consumption (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Moreover, hypertension prevalence was lower in the high OBS group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Biochemical indicators revealed that higher OBS was associated with lower non-HDL cholesterol levels (P\u0026thinsp;=\u0026thinsp;0.03), lower HOMA-IR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and better dietary quality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.1\u003c/b\u003e Baseline Characteristics of Menopausal Women Table\u0026nbsp;2005\u0026ndash;2020.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eOxidative Balance Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-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\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2391(100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e642(23.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e558(23.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e696(30.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e495(22.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.89(0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.63(0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.00(0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.11(0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.77(0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1214(78.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e290(74.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e279(77.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e363(80.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e282(82.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e470(8.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170(12.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105(7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e130(7.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65(4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267(3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70(4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65(4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82(3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50(2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e238(3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70(4.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62(4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37(2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202(5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42(3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47(5.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52(4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61(6.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1292(62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e305(53.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e302(60.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e372(65.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e313(67.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e892(31.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e273(38.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e213(34.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e264(28.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e142(26.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e207(6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64(7.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43(5.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60(5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40(6.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u0026thinsp;~\u0026thinsp;kg/cm2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.04(0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.41(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.79(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.82(0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.14(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist\u0026thinsp;~\u0026thinsp;cm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98.09(0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.07(0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.51(0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97.79(0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.99(0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.32(0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.25(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.50(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.74(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnergy intake\u0026thinsp;~\u0026thinsp;kcal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1770.12(20.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1331.83(26.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1615.02(30.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1959.32(33.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2123.32(41.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking behavior~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1405(56.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e328(47.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e323(54.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e439(62.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e315(61.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e623(27.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158(24.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140(29.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e176(26.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e149(31.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e363(15.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156(28.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95(16.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81(10.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31(7.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e432(13.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142(18.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109(12.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e109(11.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e72(11.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e391(14.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126(19.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86(11.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106(13.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73(12.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e862(40.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181(26.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188(38.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e264(42.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e229(54.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e449(21.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e123(24.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100(23.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e143(23.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83(15.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e257(9.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70(10.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75(14.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74(8.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38(6.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1012(48.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246(42.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e213(45.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e319(52.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e234(54.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1379(51.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e396(57.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e345(54.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e377(47.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e261(45.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-HDL\u0026thinsp;~\u0026thinsp;mg/dL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145.12(1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148.17(2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147.82(2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e145.24(1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139.02(3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.39(0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.94(0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.78(0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.04(0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.89(0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHEI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.50(0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.41(0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.52(0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.06(0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.76(0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.65(0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.33(0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.31(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.25(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.20(0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCDAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46(0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.52(0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.64(0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.07(0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.83(0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Relationship Between Healthy Diet and Oxidative Stress\u003c/h2\u003e\u003cp\u003eTable 2 presents the association between healthy diet and oxidative stress. HEI was positively associated with OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.18, 0.23), and this association remained significant after adjusting for multiple covariates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.18, 0.22). Conversely, DII was inversely associated with OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -2.66, 95% CI: -2.81, -2.51), and this inverse association remained significant after covariate adjustment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -2.24, 95% CI: -2.40, -2.08). Similarly, CDAI demonstrated a significant positive association with OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.17, 1.42), which remained robust after covariate adjustments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 0.95, 1.23). \u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003e\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eTable. 2\u003c/b\u003e Association between HEI, DII, CDAI and OBS.\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eOBS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eHEI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20(0.18,0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20(0.18,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20(0.18,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.66(-2.81, -2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.26(-2.41, -2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.24(-2.40, -2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCDAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29(1.17,1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09(0.96,1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09(0.95,1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Relationship Between Healthy Diet and Obesity\u003c/h2\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e shows the association between healthy diet and obesity. HEI was inversely correlated with BMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.09, 95% CI: -0.11, -0.07), and this negative association persisted after adjusting for covariates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.07, 95% CI: -0.10, -0.05). DII was positively correlated with BMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;0.40, 95% CI: 0.25, 0.55), which remained significant after covariate adjustment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;0.45, 95% CI: 0.27, 0.64). CDAI exhibited a significant negative correlation with BMI (P\u0026thinsp;=\u0026thinsp;0.02, β = -0.10, 95% CI: -0.18, -0.02), which strengthened after covariate adjustments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.20, 95% CI: -0.31, -0.09).\u003c/p\u003e\u003cp\u003eA similar pattern was observed for waist circumference, where HEI showed a significant negative association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.23, 95% CI: -0.28, -0.17), while DII was positively associated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.39, 1.16). After adjusting for covariates, these associations remained significant. CDAI was not significantly correlated with waist circumference (P\u0026thinsp;=\u0026thinsp;0.20, β = -0.14, 95% CI: -0.34, 0.07), but after adjustment, a significant negative association was observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.38, 95% CI: -0.67, -0.10).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.3\u003c/b\u003e Association between HEI, DII, CDAI and BMI, Waist.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eWaist\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eHEI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09(-0.11, -0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.23(-0.28, -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09(-0.11, -0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.21(-0.26, -0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.07(-0.10, -0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.17(-0.22, -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40(0.25,0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77(0.39,1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53(0.34,0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12(0.65,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45(0.27,0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89(0.43,1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCDAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.10(-0.18, -0.02)\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\u003e-0.14(-0.34,0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.20(-0.32, -0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.42(-0.71, -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eModel3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.20(-0.31, -0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.38(-0.67, -0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Relationship Between Obesity and Oxidative Stress\u003c/h2\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e presents the association between obesity and oxidative stress. BMI was negatively associated with OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.16, 95% CI: -0.21, -0.11), and this inverse relationship persisted after adjusting for covariates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.17, 95% CI: -0.22, -0.12). Compared to non-obese individuals, obesity was significantly associated with lower OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -2.27, 95% CI: -3.03, -1.52), and this association remained significant after adjustment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -2.43, 95% CI: -3.07, -1.78).\u003c/p\u003e\u003cp\u003eSimilarly, waist circumference exhibited a negative correlation with OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -0.07, 95% CI: -0.09, -0.04), which remained significant after adjustment. Central obesity, compared to non-central obesity, was also significantly associated with lower OBS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -2.15, 95% CI: -3.04, -1.26), which persisted after adjustment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, β = -1.89, 95% CI: -2.72, -1.07).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.4\u003c/b\u003e Association between obesity and OBS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndependent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.16(-0.21, -0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.18(-0.23, -0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.17(-0.22, -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.27(-3.03, -1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.58(-3.20, -1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.43(-3.07, -1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e-0.07(-0.09, -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.08(-0.10, -0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.07(-0.09, -0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal Obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.15(-3.04, -1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.15(-2.94, -1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.89(-2.72, -1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Non-Linear Relationship Between Healthy Diet and Oxidative Stress\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the non-linear associations between dietary indices and OBS. After adjusting for all covariates, HEI exhibited a significant linear positive relationship with OBS (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P for non-linear\u0026thinsp;=\u0026thinsp;0.99). In contrast, DII showed a significant inverse non-linear association with OBS (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.01). CDAI displayed a significant positive non-linear relationship with OBS (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Stratified analyses based on obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and central obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed consistent patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Mediating Effects\u003c/h2\u003e\u003cp\u003eThe results of the mediation analysis for BMI are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. After adjusting for all covariates, BMI mediated 5.271% of the association between HEI and OBS (IE\u0026thinsp;=\u0026thinsp;0.012, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE\u0026thinsp;=\u0026thinsp;0.207, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 3.350% of the association between DII and OBS (IE = -0.074, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE = -2.135, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3.920% of the association between CDAI and OBS (IE\u0026thinsp;=\u0026thinsp;0.046, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE\u0026thinsp;=\u0026thinsp;1.118, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eSimilarly, the results of the mediation analysis for Waist Circumference are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. After adjusting for all covariates, Waist Circumference mediated 2.464% of the association between HEI and OBS (IE\u0026thinsp;=\u0026thinsp;0.029, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE\u0026thinsp;=\u0026thinsp;1.140, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.827% of the association between DII and OBS (IE = -0.048, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE = -2.331, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3.769% of the association between CDAI and OBS (IE\u0026thinsp;=\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DE\u0026thinsp;=\u0026thinsp;0.188, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we examined the association between dietary quality and oxidative stress in postmenopausal women, emphasizing the potential mediating role of obesity. Our findings indicate that higher adherence to a healthy dietary pattern, as reflected by HEI and CDAI, is associated with lower oxidative stress, while a pro-inflammatory diet (higher DII) is linked to increased oxidative burden. Additionally, our results suggest that obesity may influence oxidative balance, supporting its potential role as a mediator in the diet-oxidative stress relationship. These findings provide insights into possible dietary and weight management strategies for reducing oxidative stress in postmenopausal women.\u003c/p\u003e\u003cp\u003eOur study aligns with prior research indicating that diets rich in antioxidants and anti-inflammatory components may help mitigate oxidative damage by reducing ROS generation and enhancing endogenous antioxidant defense systems \u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Conversely, pro-inflammatory diets, characterized by high intake of saturated fats, refined sugars, and processed foods, have been linked to increased oxidative stress through mechanisms such as lipid peroxidation and activation of inflammatory pathways \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. These findings reinforce the relevance of dietary quality in oxidative stress modulation.\u003c/p\u003e\u003cp\u003eObesity is associated with increased oxidative stress, primarily due to its link to chronic low-grade inflammation and metabolic dysregulation. Our results indicate a negative correlation between BMI, waist circumference, and OBS, even after adjusting for multiple covariates. The potential link between obesity and oxidative stress may be attributed to the pro-inflammatory secretory profile of adipose tissue, which includes cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), both of which contribute to oxidative stress by promoting ROS production and impairing mitochondrial function \u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, obesity-related insulin resistance and dyslipidemia may further exacerbate oxidative damage, underscoring the importance of weight management in oxidative stress reduction \u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR33\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur mediation analysis suggests that obesity may partially mediate the relationship between dietary quality and oxidative stress. Specifically, a healthier diet is associated with lower BMI and waist circumference, which in turn may contribute to a more favorable oxidative balance. This finding implies that dietary interventions aimed at improving diet quality may not only have direct antioxidative effects but may also influence oxidative stress indirectly by preventing obesity. Notably, while our analysis indicates a significant mediation effect, residual direct associations between diet and oxidative stress suggest that additional pathways, such as gut microbiota modulation and epigenetic modifications, may also play a role \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe non-linear relationships observed between dietary indices and oxidative stress further support the complexity of these interactions. While HEI exhibited a predominantly linear positive association with OBS, DII and CDAI demonstrated non-linear trends, indicating potential threshold effects. These findings imply that while improvements in dietary quality generally correspond to better oxidative balance, the magnitude of benefits may vary across different levels of dietary adherence \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Future studies should explore these non-linear effects in greater detail to refine dietary recommendations for oxidative stress management.\u003c/p\u003e\u003cp\u003eDespite the strengths of our study, including the use of a nationally representative dataset, rigorous statistical adjustments, and comprehensive dietary assessment, certain limitations should be acknowledged. First, the cross-sectional design limits causal inference, and longitudinal studies are needed to clarify the temporal relationships between diet, obesity, and oxidative stress. Second, dietary intake was assessed using 24-hour dietary recalls, which are subject to recall bias and day-to-day variability. Third, while OBS integrates multiple pro- and anti-oxidative factors, it remains an indirect measure of oxidative stress and may not fully capture all relevant biological processes. Lastly, residual confounding from unmeasured factors, such as genetic predispositions, cannot be ruled out. Future research should focus on longitudinal and interventional studies to better understand causality and explore additional biological mechanisms underlying these associations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study suggests a role for dietary quality in oxidative stress regulation among postmenopausal women, with obesity acting as a possible mediator in this relationship. These findings highlight the importance of promoting healthy dietary habits and weight management strategies to mitigate oxidative stress-related health risks in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the staff and the participants of the NHANES study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHS:\u0026nbsp;Methodology,\u0026nbsp;Data Analysis; CL and MW managed and cleaned the data. BZ criticized and revised the manuscript.\u0026nbsp;All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES datasets analyzed during the current study are publicly available from the National Center for Health Statistics (NCHS) (https:// www. cdc.gov/ nchs/ nhanes/ index. htm), except for geographic data (latitude) that are restricted to use through the NCHS Research Data Center (http:// www. cdc.gov/ rdc/) per NCHS, Centers for Disease Control and Prevention policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The data used in this study were obtained from the NHANES, which is publicly available and de-identified. NHANES is approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent. No additional ethical approval was required for this secondary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnklam, C. F. V., Lissarassa, et al. (2021). 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Dietary diversity, diet quality, and oxidative stress in older adults. \u003cem\u003eGeriatric Nursing\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 158\u0026ndash;163. https://doi.org/10.1016/j.gerinurse.2022.09.013\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dietary quality, oxidative stress, obesity, postmenopausal women, mediation analysis, antioxidant index","lastPublishedDoi":"10.21203/rs.3.rs-7061650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7061650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOxidative stress plays a critical role in age-related pathophysiology, and postmenopausal women are particularly vulnerable due to hormonal and metabolic changes. Although dietary quality has been implicated in modulating oxidative balance, the potential mediating role of obesity in this relationship remains insufficiently explored.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis study aimed to examine the associations between dietary quality and oxidative stress among postmenopausal women using data from the National Health and Nutrition Examination Survey (NHANES) and to assess whether obesity mediates this relationship.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 2,391 postmenopausal women from NHANES cycles 2005\u0026ndash;2020 were included. Dietary quality was assessed using the Healthy Eating Index (HEI-2015), Dietary Inflammatory Index (DII), and Composite Dietary Antioxidant Index (CDAI). Oxidative stress status was measured using the Oxidative Balance Score (OBS), while obesity was evaluated using body mass index (BMI) and waist circumference. Weighted multivariable regression and restricted cubic spline models were employed to investigate associations. Mediation analysis was performed to assess the potential mediating role of obesity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher HEI and CDAI scores were significantly associated with higher OBS, while higher DII was associated with lower OBS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, healthier dietary profiles were inversely associated with both BMI and waist circumference. Obesity indicators were negatively associated with OBS. Mediation analysis suggested that BMI and waist circumference explained a small but statistically significant proportion of the associations between dietary indices and OBS.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAmong postmenopausal women, healthier dietary patterns were associated with more favorable oxidative stress profiles. Obesity may partly mediate these associations. These findings highlight the potential value of dietary and weight management strategies in mitigating oxidative stress in this population, warranting further longitudinal and interventional studies to clarify underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"From Diet to Oxidative Stress: Obesity as a Key Mediator in Postmenopausal Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 12:39:39","doi":"10.21203/rs.3.rs-7061650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-23T06:38:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T09:10:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192172352906790489105708500153494047648","date":"2025-07-20T04:34:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-19T16:39:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258806136648367296460298956836470102045","date":"2025-07-19T06:56:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T21:46:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204645917841096484503550387550522199694","date":"2025-07-17T01:33:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73880819147509265217439236678611708187","date":"2025-07-13T22:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79010096131657102657633536417681019512","date":"2025-07-13T18:51:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122396728990017453836605525691606761056","date":"2025-07-13T18:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-13T18:10:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-08T09:50:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T07:12:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T07:10:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-07-07T05:47:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c2bc97f-3a65-488a-87f5-e4474ded733c","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T15:59:02+00:00","versionOfRecord":{"articleIdentity":"rs-7061650","link":"https://doi.org/10.1186/s12905-025-04026-1","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-09-29 15:56:57","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-07-15 12:39:39","video":"","vorDoi":"10.1186/s12905-025-04026-1","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04026-1","workflowStages":[]},"version":"v1","identity":"rs-7061650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7061650","identity":"rs-7061650","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}