Associations between oxidative balance score and chronic kidney disease events in type 2 diabetes mellitus patients: a cross‑sectional study

Associations between oxidative balance score and chronic kidney disease events in type 2 diabetes mellitus patients: a cross‑sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Associations between oxidative balance score and chronic kidney disease events in type 2 diabetes mellitus patients: a cross‑sectional study Yunhe Ding, Bing Liu, Zhen Feng, Xuehua Liu, Zimeng Wei, Lixia Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5778586/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The Oxidative Balance Score (OBS) serves as a comprehensive metric that amalgamates 17 dietary and lifestyle elements to evaluate antioxidant status. Thi research aims to investigate the association between the OBS and the prevalence of chronic kidney disease (CKD) in individuals diagnosed with type 2 diabetes mellitus (T2DM). This cross-sectional study included data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. CKD was determined using the albumin-to-creatinine ratio(ACR)and estimated glomerular filtration rate (eGFR). Patients were grouped into low, moderate, and high-risk categories based on their risk levels. The OBS variable was transformed from a continuous format into quartiles for subsequent analysis. Weighted multivariable logistic regression and restricted cubic spline models were employed to examine the relationship. Subgroup analyses and interaction tests assessed the findings' robustness. The results indicated a negative correlation between OBS and CKD risk. Individuals in higher OBS quartiles exhibited a decreased prevalence of CKD (OR 0.69, 95% CI: 0.57–0.85, P = 0.0003). A notable correlation was identified between OBS and CKD prevalence among the low-risk and moderate-risk groups. The subgroup analysis results were stable, and no significant interactions were detected among the subgroups. Increased OBS levels were correlated with a decreased risk of CKD. Improving antioxidant capacity through dietary and lifestyle modifications to enhance OBS may serve as an effective strategy for CKD prevention. Health sciences/Endocrinology Health sciences/Medical research Health sciences/Nephrology Oxidative balance scores Chronic kidney disease type 2 diabetes mellitus NHANES Cross-sectional study Figures Figure 1 Figure 2 Introduction Diabetes Mellitus (DM) and chronic kidney disease (CKD) are common chronic diseases globally and often occur simultaneously 1 . DM is a key contributing factor to the development of CKD. Research has indicated that diabetic patients are 2 to 4 times more likely to develop CKD compared to non-diabetic individuals, particularly when other comorbidities such as hypertension and lipid metabolism abnormalities are present 2 . Hyperglycemia can lead to various chronic diseases, with diabetic kidney disease (DKD) is among the most serious complications associated with diabetes. It is characterized by chronic proteinuria, tubulointerstitial fibrosis, and renal failure. Prolonged hyperglycemia affects kidney function through multiple pathways and stands as a major contributor to end-stage renal disease (ESRD) 3 , 4 . The coexistence of the two conditions greatly diminishes patients' quality of life, especially in aspects related to physical health, mental well-being, and social interactions 5 . Therefore, early identification and management of the potential risks of kidney damage caused by DM are crucial for delaying disease progression. Oxidative stress is a critical factor in the development and progression of both DM and CKD 6 . Persistent hyperglycemia results in a disruption of the balance between reactive oxygen species (ROS) and the antioxidant defense mechanisms within the body. This condition triggers alterations in numerous metabolic pathways, leading to the accumulation of advanced glycation end products (AGEs), and the production of ROS, oxidative stress levels increase, exacerbating inflammatory responses and promoting enhanced cell apoptosis 7 . At the same time, it triggers inflammatory responses, fibrosis, and tubular interstitial damage in kidney cells, further promoting the loss of kidney function 8 , 9 . Oxidative stress and systemic inflammation are mutually reinforcing processes, creating a vicious cycle that exacerbates their detrimental effects. Consequently, the regulation of oxidative stress is of critical importance in diabetic patients. This regulation is influenced by various antioxidants and pro-oxidants, which are closely associated with dietary choices and lifestyle factors. OBS is a comprehensive assessment tool that integrates the intake of antioxidants and pro-oxidants, as well as dietary and lifestyle factors, to reflect the overall oxidative balance status. It also acts as an indicator of the risk related to the onset and progression of chronic diseases 10 , 11 . A higher OBS reflects greater antioxidant capacity. Accordingly, enhancing oxidative balance through the implementation of antioxidant strategies represents a potentially significant and innovative approach to mitigating oxidative stress in patients with DM and early-stage CKD. Previous studies have investigated the link between OBS and the progression of DKD 12 . However, there is still a lack of research examining the relationship between OBS and various CKD risk stratifications. Furthermore, most current studies focus on oxidative stress responses in advanced stages of kidney disease. The uniqueness of this study lies in its investigation of OBS variations across different CKD stages in diabetic patients, highlighting the role of oxidative balance in CKD progression. This is important for early assessment of renal impairment and developing personalized interventions. Results Characteristics of the Study Population This study included 4,353 DM participants from NHANES (2007–2018), with an average age of 58.13 years. Among them, 2,239 (51.4%) were male and 2,114 (48.6%) were female. Additionally, 1,745 (40.1%) had CKD, while 2,608 (59.9%) did not. Participants in the highest OBS score quartile demonstrated unique demographic and clinical features. These individuals were more likely to be male, non-Hispanic White, with higher educational attainment, non-smokers, non-hypertensive, and had a BMI ≤ 30 kg/m². Participants in higher OBS quartiles exhibited significantly elevated levels of ALB, TBIL, and BUN, while showing lower levels of UCR, TC, and UA. Further analysis identified an inverse relationship between the OBS score and the prevalence of hypertension and CKD, with the prevalence gradually decreasing from Q1 to Q4. There were notable differences among the OBS groups regarding gender, age, race, education, hypertension, smoking status, BMI, ALB, TBIL, TC, BUN, UCR, UA, eGFR, and ACR ( p < 0.05). Table 1 summarizes the baseline demographic characteristics of the study population, grouped according to OBS quartiles. Table 1 Characteristics of participants by quartiles of the OBS in the NHANES 2007–2018 cycles. Total (4353) Q1(1097) Q2 (1037) Q3 (1130) Q4 (1089) p -value Gender, n (%) 0.017 Male 2239(51.4) 536(48.9) 515(49.7) 622(55.0) 566(52.0) Female 2114(48.6) 561(51.1) 522(50.3) 508(45.0) 523(48.0) Age (year) 58.13 ± 15.47 49.79 ± 22.51 50.10 ± 22.84 51.19 ± 23.27 51.67 ± 22.90 0.001 Race, n (%) < 0.001 Mexican American (%) 750(17.2) 155(14.1) 189(18.2) 196(17.3) 210(19.3) Other Hispanic (%) 491(11.3) 112(10.2) 120(11.6) 117(10.4) 142(13.0) Non-Hispanic White (%) 1627(37.4) 440(40.1) 383(36.9) 427(37.8) 377(34.6) Non-Hispanic Black (%) 1040(23.9) 302(27.5) 251(24.2) 274(24.2) 213(19.6) Other race (%) 445(10.2) 88(8.0) 94(9.1) 116(10.3) 147(13.5) Education < 0.001 Less than high school 1440(33.1) 414(37.7) 361(34.8) 360(31.9) 305(28.0) High School graduate 980(22.5) 250(22.8) 233(22.5) 259(22.9) 238(21.9) More than high school 1933(44.4) 433(39.5) 443(42.7) 511(45.2) 546(50.1) Hypertension, n (%) 0.044 Yes 2825(64.9) 717(65.4) 707(68.2) 710(62.8) 691(63.5) No 1528(35.1) 380(34.6) 330(31.8) 420(37.2) 398(36.5) Smoking, n (%) < 0.001 Yes 2170(49.9) 635(57.9) 542(52.3) 533(47.3) 460(42.2) No 2180(50.1) 462(42.1) 494(47.7) 596(52.7) 628(57.8) BMI, n (%) < 0.001 30 kg/m2 2496(57.3) 707(64.4) 627(60.5) 673(59.6) 489(44.9) Chronic kidney disease, n(%) 0.017 Yes 1745(40.1) 476(43.4) 424(40.9) 443(39.2) 402(36.9) No 2608(59.9) 621(56.6) 613(59.1) 687(60.8) 687(63.1) UCR (µmol/L) 118.16 ± 73.77 113.96 ± 81.82 118.23 ± 73.38 115.98 ± 70.55 106.46 ± 66.30 < 0.001 UA (µmol/L) 342.79 ± 90.47 351.97 ± 95.30 347.27 ± 95.14 343.15 ± 90.73 334.93 ± 89.68 < 0.001 ALB (g/L) 41.44 ± 3.40 40.69 ± 3.56 41.14 ± 3.48 41.45 ± 3.31 41.82 ± 3.28 < 0.001 TBIL (µmol/L) 10.88 ± 5.30 10.88 ± 4.95 11.30 ± 4.82 11.49 ± 5.24 11.55 ± 5.88 0.013 TC (mmol/L) 4.80 ± 1.21 4.83 ± 1.26 4.88 ± 1.22 4.78 ± 1.18 4.69 ± 1.17 0.001 BUN (mmol/L) 5.62 ± 2.65 5.38 ± 2.77 5.50 ± 2.55 5.68 ± 2.48 5.91 ± 2.78 < 0.001 eGFR, mL/min/1.73 m2 81.67 ± 25.39 80.09 ± 25.86 81.08 ± 25.66 82.01 ± 24.99 82.53 ± 25.04 < 0.001 ACR, mg/g 12.47(32.64) 11.82(29.52) 11.58(25.22) 11.34(20.65) 10.17(24.64) < 0.001 This table is used to present the basic characteristics of the study population, including the mean ± standard error (Mean ± SE) for continuous variables and percentages (%) for categorical variables. BMI, body mass index; CKD, chronic kidney disease; UCR, urine creatinine; UA, uric acid; ALB, albumin; TBIL, total bilirubin; TC, total cholesterol; BUN, blood urea nitrogen; eGFR, estimates of glomerular filtration rate, ACR, albumin-to-creatinine ratio Association between the OBS and CKD As shown in Table 2 , We developed three weighted logistic regression models to examine the association between OBS and CKD prevalence. In the unadjusted Model 1, every one-unit increase in OBS corresponded to a 3% reduction in CKD risk (OR = 0.97, 95% CI: 0.96–0.99, p < 0.0001). The association persisted across all adjusted models, with the fully adjusted Model 3 demonstrating that each unit increase in OBS was associated with a 4% decrease in CKD risk (OR = 0.96, 95% CI: 0.94–0.98, p < 0.0001). was stratified into quartiles, the highest quartile (Q4) demonstrated the most significant association with a decreased risk of CKD. In comparison to the reference category, which is the first quartile (Q1), the odds ratio (OR) for the other quartiles decreased progressively. Specifically, in Model 3, Q4 had an OR of 0.69 (95% CI: 0.57–0.85, p = 0.0003) compared to Q1, showing a significant trend across the OBS quartiles. Overall, higher OBS scores were associated with a protective effect against CKD risk in all three models ( p for trend < 0.001). The restricted cubic spline analysis demonstrated a linear inverse relationship between OBS and CKD across all samples ( p for nonlinearity = 0.689, Fig. 1 . a ). Table 2 Relationship between OBS and CKD in different models. OR (95% CI); p -value Model 1 Model 2 Model 3 Continuous 0.97 (0.96,0.99) < 0.001 0.96 (0.95,0.98) < 0.001 0.96 (0.94,0.98) < 0.001 Q1 References References References Q2 0.85 (0.72,1.02) 0.07 0.83 (0.69,0.99) 0.04 0.82 (0.68,1.00) 0.04 Q3 0.83 (0.70,0.98) 0.03 0.77 (0.64,0.92) 0.004 0.74 (0.61,0.90) 0.003 Q4 0.79 (0.66,0.94) 0.007 0.72 (0.60,0.86) 0.0003 0.69 (0.57,0.85) 0.0003 p for trend < 0.001 < 0.001 < 0.001 OR, odds ratio;CI, confidence intervals. Model 1 was unadjusted. Model 2 adjusted for age, gender, race and education. Model 3 adjusted for age, gender, race, education, hypertension, smoking, BMI, UCR, UA, ALB, TBIL, TC, BUN. Table 3 illustrates the relationship between OBS and various CKD risk stratifications using weighted logistic regression analysis. CKD patients were divided into three groups: low-risk (G1 and G2), medium-risk (G3a and G3b), and high-risk (G4 and G5). The results showed that in the low-risk CKD group ( p < 0.001), OBS in the third quartile (Q3) was strongly linked to CKD prevalence in Models 1, 2, and 3: (Model 1: OR = 0.83, 95% CI: 0.72–0.96, P = 0.009), (Model 2: OR = 0.80, 95% CI: 0.69–0.92, p = 0.002), (Model 3: OR = 0.81, 95% CI: 0.70–0.94, p = 0.004). In Model 2, participants in the highest quartile (Q4) had an OR of 0.87 (95% CI: 0.75–1.00, p = 0.05), showing a marginally significant association with CKD prevalence. In the medium-risk CKD group ( p < 0.001), participants in Q3 (OR = 0.57, 95% CI: 0.36–0.91, p = 0.017) and Q4 (OR = 0.46, 95% CI: 0.28–0.74, p < 0.0001) had significantly lower CKD prevalence compared to Q1, with a gradually decreasing trend. In Models 1 and 2, Q4 (Model 1: OR = 0.68, 95% CI: 0.49–0.95, p = 0.025) (Model 2: OR = 0.55, 95% CI: 0.38–0.80, p = 0.001) was also significantly associated with lower CKD prevalence. No notable association was found in the high-risk group. The restricted cubic spline analysis demonstrated a linear relationship between OBS and CKD in the low-risk category ( p for nonlinearity = 0.627, Fig. 1 . b ) and medium-risk low-risk category ( p for nonlinearity = 0.654, Fig. 1 . c ). Table 3 Relationship Between Oxidative Balance Score and Chronic Kidney Disease Risk Classification. Grouping factors Observed Cases /Total Cases Model 1 Model 2 Model 3 OR (95% CI)/p-value OR (95% CI)/p-value OR (95% CI)/ p -value Low risk group 848/2608 Q1 References Q2 0.94(0.82,1.08) 0.390 0.91(0.79,1.04) 0.173 0.89(0.77,1.03) 0.122 Q3 0.83(0.72,0.96) 0.009 0.80(0.69,0.92) 0.002 0.81(0.70,0.94) 0.004 Q4 0.89(0.77,1.02) 0.104 0.87(0.75,1.00) 0.050 0.92(0.80,1.07) 0.296 P for trend < 0.001 0.006 Medium risk group 780/2608 Q1 References Q2 0.79(0.57,1.10) 0.158 0.70(0.49,1.01) 0.058 0.67(0.42,1.06) 0.087 Q3 0.84(0.61,1.16) 0.293 0.71(0.50,1.01) 0.056 0.57(0.36,0.91) 0.017 Q4 0.68(0.49,0.95) 0.025 0.55(0.38,0.80) 0.001 0.46(0.28,0.74) < 0.001 P for trend < 0.001 < 0.001 High risk group 117/2608 Q1 References Q2 0.68(0.30,1.55) 0.359 0.58(0.25,1.36) 0.210 0.19(0.03,1.27) 0.087 Q3 0.71(0.32,1.58) 0.404 0.60(0.26,1.38) 0.230 0.32(0.06,1.76) 0.189 Q4 0.81(0.37,1.74) 0.583 0.74(0.33,1.66) 0.464 0.15(0.02,1.07) 0.058 P for trend 0.299 0.092 Multivariable logistic regression analysis was conducted to explore the association between the oxidative balance score and the classification of chronic kidney disease risk. Significant results are highlighted in bold. Model 1 was unadjusted. Model 2 adjusted for age, gender, race and education. Model 3 adjusted for age, gender, race, education, hypertension, smoking, BMI, UCR, UA, ALB, TBIL, TC, BUN. Subgroup Analysis In the subgroup analysis, an inverse relationship between higher OBS and reduced CKD risk was observed across various populations, though the magnitude of this association seemed to differ depending on specific demographic and clinical factors. In males over 60 years old, non-Hispanic Whites, individuals with hypertension, and those with obesity, the correlation between OBS and CKD was evident. However, most analyses of the interactions between OBS and CKD risk stratification factors did not show statistical significance, suggesting that the protective effect of higher OBS against CKD risk was consistent across the entire population. Table 4 shows the stratified analysis and interactions between OBS and CKD risk. Table 4 Associations between OBS and CKD in different subgroups. Character Q1 Q2 Q3 Q4 p for interaction Age 0.280 60 years ref 0.75(0.60,0.95) 0.75(0.60,0.95) 0.73(0.58,0.92) Gender 0.595 Male ref 0.90(0.71.1.14) 0.84(0.66,1.07) 0.73(0.57.0.93) Female ref 0.87(0.68,1.11) 0.80(0.63,1.03) 0.78(0.61,0.99) Race 0.753 Mexican American ref 0.91(0.59,1.3) 0.81(0.53,1.24) 1.05(0.69,1.60) Non-Hispanic White ref 0.78(0.59,1.03) 0.82(0.62,1.09) 0.67(0.50,0.89) Non-Hispanic Black ref 0.81(0.64,1.03) 0.86(0.68,1.08) 0.75(0.59,0.96) Other race ref 1.45(1.00,2.12) 1.07(0.73,1.57) 0.83(0.56,1.23) Education 0.873 Less than high school ref 1.09(0.81,1.46) 0.98(0.73,1.32) 0.74(0.55,1.00) High School Grad/GED or Equivalent ref 0.65(0.45,0.94) 0.63(0.44,0.91) 0.74(0.51,1.01) More than high school ref 1.07(0.83,1.09) 0.85(0.65,1.11) 0.83(0.64,1.08) Hypertension 0.515 Yes ref 0.76(0.61,0.94) 0.73(0.59,0.90) 0.70(0.57,0.87) No ref 1.30(0.96,1.77) 1.09(0.80,1.49) 0.85(0.61,1.17) Smoking 0.185 Yes ref 0.82(0.64,1.04) 0.87(0.68,1.10) 0.68(0.53,0.87) No ref 1.17(0.92,1.49) 0.83(0.65,1.07) 0.96(0.75,1.22) BMI 0.771 30 kg/m2 ref 0.98(0.78,1.23) 0.80(0.64,1.00) 0.79(0.63,0.99) The subgroup analysis was conducted based on stratifications of age, gender, race, education, hypertension, smoking status, BMI. Discussion In this study, data from 4,353 participants in the NHANES dataset (2007–2018) were analyzed, revealing a significant negative correlation between the OBS and CKD prevalence in DM patients, even after adjusting for all relevant covariates. It is important to highlight that, upon stratifying CKD patients according to their risk levels, the correlation demonstrated statistical significance within the low-risk and moderate-risk groups. However, this correlation was not evident in the high-risk group. Our findings indicate that an elevated OBS is linked to a lower prevalence of CKD, especially observed in individuals with low to moderate risk. To the best of our knowledge, this study is one of the few that investigates the association between OBS and CKD risk in DM patients, offering novel insights that may inform both clinical practice and future research directions. Our results align with those reported in previous studies. For instance, Yin et al. demonstrated that an elevated OBS may help reduce the risk of CKD, especially among individuals with a mild to moderate risk profile. However, their study population was not restricted to diabetic patients 13 . Similarly, Liu et al. reported that higher OBS levels were associated with a lower risk of DKD, reduced eGFR, and proteinuria 12 . Another study similarly found that higher OBS levels were inversely associated with CKD prevalence in individuals with metabolic syndrome 14 . Furthermore, research has demonstrated that inflammation and oxidative stress are major driving factors in the onset and progression of CKD 15 , 16 . Curiously, our research revealed that this association predominantly occurs among individuals with mild to moderate CKD risk, whereas no comparable pattern was detected in those classified as high-risk. This also suggests that a healthy diet and lifestyle may reduce the risk of incident CKD by mitigating oxidative stress and enhancing the body’s antioxidant capacity. It highlights the critical role of maintaining a balance between antioxidants and pro-oxidants in preserving kidney health. These findings not only provide a promising strategy for CKD prevention but also offer valuable interventions for enhancing the standard of living in affected patients. Although the etiology of CKD is complex and involves multiple interacting factors, hyperglycemia is a key contributing factor to the development of CKD. Oxidative stress is considered a key mechanism in diabetes-induced kidney damage. Studies have shown that persistent hyperglycemia causes mitochondrial dysfunction, leading to excessive generation of free radicals and ROS. The body's antioxidant defense mechanism fails to clear these ROS in a timely manner, resulting in cellular damage 17 . Oxidative stress also plays a vital role in various stages of CKD progression, causing tubular and glomerular damage, promoting renal fibrosis and inflammation, increasing renal cell apoptosis, and encouraging collagen deposition. These processes ultimately result in structural and functional damage to the kidneys 18 , 19 . In diabetic patients, oxidative stress-induced vascular damage and endothelial dysfunction not only contribute to the progression of diabetes but also serve as key driving factors for related complications. This underscores the intricate relationship between diabetes and CKD, where both conditions exacerbate each other, creating a vicious cycle. The OBS is a tool that integrates the interactions between pro-oxidants and antioxidants from dietary and lifestyle factors, Offering a holistic evaluation of an individual's oxidative stress status. This tool is frequently utilized to evaluate oxidative status and its relationship with chronic disease prevalence 20 . Traditional OBS primarily focuses on the levels of ROS and antioxidants in blood or biological samples, but these measurements often overlook the impact of daily food intake, nutritional habits, and lifestyle factors on oxidative balance 21 . In this study, we investigated the association between 17 sources of oxidative stress and CKD progression in patients with diabetes. We constructed an OBS that included 13 dietary OBS components (9 antioxidants and 4 pro-oxidants) and 4 lifestyle OBS components (1 antioxidant and 3 pro-oxidants). This method enabled a thorough evaluation of the impact of oxidative balance on CKD progression in diabetic patients. In terms of dietary factors, research has demonstrated that non-vitamin A carotenoids, such as lycopene, lutein, and zeaxanthin, demonstrate significant protective effects in preventing diabetic complications. These carotenoids not only protect renal tubules and glomeruli but also reduce oxidative stress and inflammatory responses in the retina, improve neurological function, and alleviate symptoms of diabetic neuropathy 22 . Lycopene has been shown to inhibit high-glucose-induced DNA damage, downregulate apoptosis-related protein expression, reduce intracellular oxidative stress levels, and significantly increase the survival rate of NRK-52E cells, which are rat renal tubular epithelial cells. 23 . Similarly, the protective effects of β-carotene have been validated in diabetic nephropathy (DN) rat models, where β-carotene was found to activate the AMPK/SIRT1 signaling pathway, promote autophagy, and reduce the expression of inflammatory cytokines (e.g. TNF-α and IL-6), thereby significantly improving the inflammatory response in DN 24 . Furthermore, components such as vitamin E, zinc, and selenium have been found to reduce urinary protein levels, improve glomerular filtration rates, and mitigate structural damage to glomeruli by inhibiting oxidative stress and inflammatory responses, which in turn help delay the progression of DN 25 . Research has also identified a strong link between low serum vitamin C levels and a heightened risk of kidney function deterioration, suggesting that improving serum vitamin C levels at early stages may help mitigate further progression of CKD 26 . Cross-sectional studies have further indicated that patients with DM who have higher antioxidant indices exhibit significantly better kidney function-related indicators compared to those with lower indices. However, supplementation with single antioxidant compounds has not demonstrated significant improvement, indicating that a diversified antioxidant-rich dietary pattern is more effective 27 .The relationship between unsaturated fatty acid intake and kidney disease progression in diabetic patients remains controversial. Certain studies propose that unsaturated fatty acids may protect glomerular podocytes from apoptosis by regulating the localization of mTORC1 in lysosomes and inhibiting its overactivation, while saturated fatty acids may increase the risk of podocyte apoptosis by promoting mTORC1 activation 28 . A diet rich in polyunsaturated fatty acids (PUFAs), such as canola oil, may help reduce renal lipid deposition and prevent the progression of diabetic nephropathy 29 . However, other studies have found that genetic variations associated with omega-6 fatty acids significantly modulate the risks of diabetic microvascular complications, while no significant association has been observed between omega-3 fatty acids and microvascular complications 30 . Similarly, Nakamura et al. 29 concluded that the previously reported renal protective effects of n-3 fatty acids have not been confirmed. Additionally, iron is a crucial component of hemoglobin and myoglobin. However, its accumulation in the body can lead to the generation of excessive free radicals and ROS 31 , 32 . In terms of lifestyle factors, research has shown that exercise training can reduce oxidative stress, alleviate inflammatory responses, and improve renal microstructure in type 2 diabetic (T2D) rats, thereby exerting protective effects 33 , 34 . Additionally, smoking 35 and excessive alcohol consumption 36 have been demonstrated to cause severe oxidative stress, immune dysfunction, and cell apoptosis. Obesity can trigger oxidative stress, resulting in the production of ROS, which can impair insulin receptor function and inhibit insulin signaling pathways, contributing to insulin dysfunction and further promoting the development of diabetes 37 . A growing body of research indicates that increasing dietary intake of fiber, vitamin C, carotenoids, riboflavin, lycopene, and vitamin E, along with engaging in regular physical activity, can help mitigate oxidative stress. Conversely, pro-oxidative factors such as excessive iron intake, alcohol consumption, obesity and smoking can enhance the production of ROS and free radicals, leading to cellular damage. Under normal physiological conditions, the body maintains a balance between pro-oxidative factors and antioxidative factors. However, When this balance is disrupted, oxidative stress occurs, leading to damage to cells, tissues, and organs 38 . As oxidative stress significantly impacts the occurrence and progression of diabetes and CKD, antioxidant therapies may serve as an effective intervention for these populations. Previous studies have demonstrated that combining antioxidant supplementation with healthy dietary and exercise habits enables diabetic patients to manage oxidative stress more effectively 39 . Furthermore, a randomized single-blind trial suggested that antioxidants can neutralize ROS and increase the activity of naturally occurring antioxidant enzymes, significantly reducing oxidative stress and improving endothelial function, particularly in individuals with abnormal glucose metabolism 40 . A systematic review and meta-analysis also indicated that a diet low in salt and fat, high in fiber, combined with regular exercise, smoking cessation can significantly reduce the risk of developing CKD 41 . We believe that the OBS could also serve as an effective tool for assessing personal dietary and lifestyle health. It could provide personalized health management advice for high-risk individuals, assist in assessing the effectiveness of public health interventions, and enable early identification and intervention for chronic diseases. There are several limitations to this study. First, it utilized a cross-sectional design, which allowed for the analysis of the relationship between OBS and CKD at a single point in time. However, this approach imposes limitations on our capacity to draw causal inferences. The association between OBS and CKD may involve temporal delays or reverse causation, which future prospective studies are needed to validate. Second, OBS data in this study were collected through 24-hour dietary recall questionnaires, which are susceptible to measurement errors and biases. These limitations may impact the accuracy of the OBS scoring and the reliability of the results. Finally, despite controlling for multiple potential confounding factors such as age, sex, race, educational level, smoking status, and hypertension, there may still be residual confounders that were not accounted for, which might impact the association between OBS and CKD. In conclusion, our study found that OBS is negatively associated with CKD prevalence in DM patients. Specifically, this association was observed in the low to moderate CKD risk groups but not in the high-risk group. Further analysis revealed a linear inverse trend was observed between OBS and CKD. Therefore, increasing OBS levels may serve as an effective strategy for CKD prevention, particularly among individuals with mild to moderate CKD risk. The regulation of oxidative stress and the enhancement of OBS hold potential in CKD prevention, warranting further research and promotion. Materials and Methods Study Population This study utilized data from the National Health and Nutrition Examination Survey (NHANES). NHANES is designed to evaluate the health and nutritional status of the non-institutionalized civilian population in the United States, with data released every two years. It comprises five main components: demographic, dietary, clinical examination, laboratory, and questionnaire information. Data collection for NHANES is conducted in two stages: a household interview followed by physical exams and laboratory tests at mobile examination centers. The survey is supervised by the National Center for Health Statistics (NCHS), with participants provide written informed consent. NHANES data is publicly available, and further details on the data collection and analysis methods can be found at https://www.cdc.gov/nchs/nhanes . In our study, data from five NHANES cycles (2007–2018) were included. The inclusion and exclusion criteria for this study are detailed in Fig. 2 . Initially, 6,397 individuals diagnosed with diabetes were included. After excluding 720 participants under the age of 18, the analysis focused on 5,677 adults aged 18 years or older. Further, 213 participants lacking key measurements of albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), as well as 731 participants with incomplete OBS component data, were further excluded. Additionally, 380 participants with incomplete covariate data were excluded, including 141 lacking educational attainment, 109 missing body mass index (BMI) information, 70 without standard biochemical data, 36 missing blood pressure information, and 24 lacking smoking status. Ultimately, the final sample for this study consisted of 4,353 adults. Oxidative balance score Based on previous studies 42 , 43 , the total OBS was derived by summing the individual scores for each variable, which include 13 dietary nutrients (9 antioxidants and 4 pro-oxidants) and 4 lifestyle factors (1 antioxidant and 3 pro-oxidants). The 13 dietary nutrients include Vitamin C, Vitamin E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein + zeaxanthin, selenium, zinc, iron, total fat, total polyunsaturated fatty acids, and total saturated fatty acids. The 4 lifestyle factors consist of physical activity, BMI, smoking status, and alcohol intake. The study identified pro-oxidant factors, including total fat, total polyunsaturated fatty acids, total saturated fatty acids, total iron intake, cotinine levels, alcohol consumption, and BMI. The other factors were categorized as antioxidants 44 . In NHANES, dietary components of the OBS were assessed using a 24-hour dietary recall interview to collect information on food types and intake amounts. Physical activity, expressed as metabolic equivalent (MET), was calculated based on leisure-time activity data from the past 30 days. Smoking status was assessed by measuring plasma cotinine levels, which serve as an indicator of tobacco use and exposure to environmental tobacco smoke. Additionally, Participants were grouped according to their alcohol consumption levels into non-drinkers and non-heavy drinkers (females: 0–15 g/d, males: 0–30 g/d), and heavy drinkers (females: ≥ 15 g/d, males: ≥ 30 g/d), with scores of 3, 2, and 1, respectively. The remaining components were classified into three categories, with antioxidants receiving scores of 1, 2, and 3 based on the tertiles from lowest to highest, such that greater exposure to antioxidants resulted in higher scores. Conversely, for pro-oxidants, a score of 1 was assigned to the highest tertile, while a score of 3 was assigned to the lowest tertile. Outcome variable Diabetes is defined as: (1) a diagnosis previously reported by a healthcare professional, (2) fasting blood glucose ≥ 7.0 mmol/L, (3) glycated hemoglobin (HbA1c) ≥ 6.5%, (4) 2-hour oral glucose tolerance test (OGTT) blood glucose ≥ 11.1 mmol/L, or (5) use of diabetes medications or insulin. CKD is defined as: (1) eGFR < 60 mL/min/1.73 m², (2) ACR ≥ 30 mg/g. eGFR is calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CKD can be divided into the following stages: G1 (≥ 90 mL/min/1.73 m²), G2 (60–89 mL/min/1.73 m²), G3a (45–59 mL/min/1.73 m²), G3b (30–44 mL/min/1.73 m²), G4 (15–29 mL/min/1.73 m²), and G5 (< 15 mL/min/1.73 m²) 45 . Albuminuria is calculated using the urinary albumin-to-creatinine ratio (ACR). Additionally, we conducted risk stratification based on the stages of CKD. In stages G1 and G2 were classified as low-risk, those in stages G3a and G3b as medium-risk, and those in stages G4 and G5 as high-risk. Covariates Based on existing clinical knowledge and research, we further included several potential covariates that may affect the study outcomes. These factors include age, gender, race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other), education (less than high school, high school graduate, and more than high school). Hypertension is characterized by a systolic blood pressure of 140 mmHg or higher, a diastolic blood pressure of 90 mmHg or higher, or the use of antihypertensive drugs 46 . Smoking status (Yes or No), BMI was calculated as weight (kg) divided by height (m) squared, and classified into two groups: BMI ≤ 30 kg/m², BMI > 30 kg/m². The following laboratory parameters were also included: albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), blood urea nitrogen (BUN), urine creatinine (UCR), and uric acid (UA). Statistical analysis Considering that NHANES utilizes a stratified and complex multistage sampling design, we applied the sample weight calculation procedure recommended by NHANES to adjust the data accordingly. Continuous variables that follow a normal distribution were reported as mean ± standard deviation, whereas those exhibiting a non-normal distribution were presented as median and interquartile range. Categorical variables were reported as numbers and percentages. Participants were categorized into four groups according to the quartiles of OBS.(Q1-Q4) (Q1: ≤ P25; Q2: P25-P50; Q3: P50-P75; Q4: > P75), with Q1 as the reference group. To analyze the differences in variable characteristics across the different OBS groups (quartiles), weighted analysis of variance (ANOVA) was applied for continuous variables, and weighted chi-square tests were used for categorical variables to compare baseline characteristics across the OBS quartiles. A weighted multivariable logistic regression analysis was performed to investigate whether different OBS groups (quartiles) were associated with the prevalence of CKD in diabetic patients. The findings were presented as odds ratios (OR) accompanied by 95% confidence intervals (95% CI), p -values, and trend p -values. Three models were applied in this study. Model 1 reflects the unadjusted raw data. Model 2 adjusted for age, gender, race, and education. Model 3 is the fully adjusted model, further adjusting for potential confounders, including age, gender, race, education, hypertension, smoking status, BMI, albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), blood urea nitrogen (BUN), urine creatinine (UCR), and uric acid (UA). Furthermore, CKD patients were stratified into low-risk, medium-risk, and high-risk groups. Logistic regression models were employed again to evaluate the association between OBS and the various CKD risk stratifications, with the lowest quartile (Q1) of OBS as the reference group. We additionally employed restricted cubic spline regression to evaluate the nonlinear association between OBS and CKD. Finally, subgroup analyses were conducted based on age (≤ 60 years or > 60 years), gender (male or female), race (Mexican American, Non-Hispanic White, Non-Hispanic Black, and Other), education (less than high school, high school graduate, and more than high school), hypertension (yes or no), smoking status (yes or no), and BMI (≤ 30 kg/m² or > 30 kg/m²). We evaluated the consistency of the relationship between OBS and CKD across various demographic and clinical characteristics. To examine the interactions among the covariates, we employed the likelihood ratio test, and the models were adjusted accordingly for the pertinent covariates. All statistical analyses were performed using R Studio (version 4.2.2) for data analysis. A two-tailed p -value < 0.05 was regarded as statistically significant for all tests. Declarations Competing interests The authors declare that competing interests. Author Contribution Y.H.D. were responsible for the data analysis and drafted the manuscript. X.H.L. retrieved and organized the data from the database for analysis. B.L. supervised the analytical methods in collaboration with Z.F. and critically revised the manuscript. Z.M.W. and L.X.F. prepared all figures and the table used in the manuscript. Data Availability The data used in this study were obtained from the NHANES database (https://www.cdc.gov/nchs/nhanes/), which provides publicly available data for researchers. References Wyld, M. L. R. et al. The impact of comorbid chronic kidney disease and diabetes on health-related quality-of-life: a 12-year community cohort study. NephrolDialTransplant .36,1048–1056. (2021). https://doi.org/10.1093/ndt/gfaa031 Fox, C. S. et al. .Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes. meta-analysis Lancet . https://doi.org/10.1016/S0140-6736(12)61350-6 (2012). .380,1662–1673. Singh, H. et al. .Role of oxidative stress in diabetes-induced complications and their management with antioxidants. Arch. Physiol. Biochem. 130 , 616–641. https://doi.org/10.1080/13813455.2023.2243651 (2023). Xue, R. et al. Mechanistic Insight and Management of Diabetic Nephropathy: Recent Progress and Future Perspective. J Diabetes Res .1839809. (2017). https://doi.org/10.1155/2017/1839809 (2017). de Boer, I. H. et al. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care . 45 , 3075–3090. https://doi.org/10.2337/dci22-0027 (2022). Jha, J. C., Banal, C., Chow, B. S. & Cooper, M. E. Jandeleit-Dahm, K.Diabetes and Kidney Disease: Role of Oxidative Stress. Antioxid. Redox Signal. 25 , 657–684. https://doi.org/10.1089/ars.2016.6664 (2016). Kang, Q. & Yang C.Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox Biol .37,101799. (2020). https://doi.org/10.1016/j.redox.2020.101799 Andrade-Sierra, J. et al. The Influence of the Severity of Early Chronic Kidney Disease on Oxidative Stress in Patients with and without Type 2 Diabetes Mellitus.I. nt J. Mol. Sci. 23 https://doi.org/10.3390/ijms231911196 (2022). Wang, N., Zhang, C. O. & Stress A Culprit in the Progression of Diabetic Kidney Disease. Antioxid. (Basel) . 13 https://doi.org/10.3390/antiox13040455 (2024). Hernandez-Ruiz, A. et al. A Review of A Priori Defined Oxidative Balance Scores Relative to Their Components and Impact on Health Outcomes. Nutrients .11, (2019). https://doi.org/10.3390/nu11040774 Moon, M. E., Jung, D. H., Heo, S. J., Park, B. & Lee, Y. J.Oxidative balance score as a useful predictive marker for new-onset type 2 diabetes mellitus in Korean adults aged 60 years or older: The Korean Genome and Epidemiologic Study-Health Examination (KoGES-HEXA) cohort. Exp Gerontol .193,112475. (2024). https://doi.org/10.1016/j.exger.2024.112475 Liu, C. et al. Association between oxidative balance score and diabetic kidney disease, low estimated glomerular filtration rate and albuminuria in type 2 diabetes mellitus patients: a cross-sectional study. Front. Endocrinol. (Lausanne . 151412823. https://doi.org/10.3389/fendo.2024.1412823 (2024). Yin, Y. et al. Associations between oxidative balance score and chronic kidney disease events in US adults: a population-based study. Sci. Rep. https://doi.org/10.1038/s41598-024-64147-9 (2024). 14,13743. Zhu, L. et al. Higher oxidative balance score is linearly associated with reduced prevalence of chronic kidney disease in individuals with metabolic syndrome: evidence from NHANES 1999–2018. Front Nutr .11,1442274. (2024). https://doi.org/10.3389/fnut.2024.1442274 Yilmaz, M. I. et al. The determinants of endothelial dysfunction in CKD: oxidative stress and asymmetric dimethylarginine. Am. J. Kidney Dis. 47 , 42–50. https://doi.org/10.1053/j.ajkd.2005.09.029 (2006). Xu, G. et al. The progress of inflammation and oxidative stress in patients with chronic kidney disease. Ren. Fail. 37 , 45–49. https://doi.org/10.3109/0886022x.2014.964141 (2015). Zhang, Z. et al. The impact of oxidative stress-induced mitochondrial dysfunction on diabetic microvascular complications. Front. Endocrinol. (Lausanne . 14,1112363 (2023). Makuc, J. & Petrovič D.A review of oxidative stress related genes and new antioxidant therapy in diabetic nephropathy. Cardiovasc. Hematol. Agents Med. Chem. 9 , 253–261. https://doi.org/10.2174/187152511798120949 (2011). Oberg, B. P. et al. Increased prevalence of oxidant stress and inflammation in patients with moderate to severe chronic kidney disease. Kidney Int. 65 , 1009–1016. https://doi.org/10.1111/j.1523-1755.2004.00465.x (2004). Chen, X. et al. Association of the oxidative balance score and chronic kidney disease: insights from the national health and nutrition examination survey 2009–2018. Front Nutr .11,1429191. (2024). https://doi.org/10.3389/fnut.2024.1429191 Hernández-Ruiz, Á. et al. Oxidative Balance Scores (OBSs) Integrating Nutrient, Food and Lifestyle Dimensions: Development of the NutrientL-OBS and FoodL-OBS. Antioxidants (Basel) .11, (2022). https://doi.org/10.3390/antiox11020300 Murillo, A. G. & Fernandez, M. L. .Potential of Dietary Non-Provitamin A Carotenoids in the Prevention and Treatment of Diabetic Microvascular Complications. Adv. Nutr. 7 , 14–24. https://doi.org/10.3945/an.115.009803 (2016). Usta, A., Yüksek, V., Çetin, S. & Dede S.Lycopene prevents cell death in NRK-52E cells by inhibition of high glucose-activated DNA damage and apoptotic, autophagic, and necrotic pathways. J. Biochem. Mol. Toxicol. 38 , e23678. https://doi.org/10.1002/jbt.23678 (2024). El-Marasy, S. A., Farouk, H., Khattab, M. S. & Moustafa, P. E.Beta-carotene ameliorates diabetic nephropathy in rats: involvement of AMPK/SIRT1/autophagy pathway. Immunopharmacol Immunotoxicol .46,763–772. (2024). https://doi.org/10.1080/08923973.2024.2402347 Rossing, P., Cooper, M. E. & Parving, H. H.Comparison of the effects of vitamins and/or mineral supplementation on glomerular and tubular dysfunction in type 2 diabetes. Diabetes Care . https://doi.org/10.2337/diacare.29.03.06.dc05-2144 (2006). .29,747–748; author reply 748–749. Wang, C., Zhao, J., Zhou, Q. & Li, J. Serum vitamin C levels and their correlation with chronic kidney disease in adults: a nationwide study. Ren Fail .46,2298079. (2024). https://doi.org/10.1080/0886022x.2023.2298079 Omid, N., Esfahani, E. N., Tabaeifard, R., Montazer, M. & Azadbakht L.Association of dietary antioxidant indices with kidney function indicators in patients with type 2 diabetes: a cross-sectional study. Sci. Rep. https://doi.org/10.1038/s41598-024-71683-x (2024). 14,22991. Yasuda, M. et al. Fatty acids are novel nutrient factors to regulate mTORC1 lysosomal localization and apoptosis in podocytes. Biochim. Biophys. Acta . 1842 , 1097–1108. https://doi.org/10.1016/j.bbadis.2014.04.001 (2014). Nakamura, N. et al. .Dietary Intake of Polyunsaturated Fatty Acids and Diabetic Nephropathy: Cohort Analysis of the Tsugaru Study. Vivo 37 , 1890–1893. https://doi.org/10.21873/invivo.13282 (2023). Liu, B. et al. Polyunsaturated fatty acids and diabetic microvascular complications: a Mendelian randomization study. Front. Endocrinol. (Lausanne . 15,1406382 (2024). Reardon, T. F. & Allen, D. G.Iron injections in mice increase skeletal muscle iron content, induce oxidative stress and reduce exercise performance. Exp. Physiol. 94 , 720–730. https://doi.org/10.1113/expphysiol.2008.046045 (2009). Puntarulo, S. Iron, oxidative stress and human health. Mol. Aspects Med. 26 , 299–312. https://doi.org/10.1016/j.mam.2005.07.001 (2005). Saberi, S. et al. Exercise training improves diabetic renal injury by reducing fetuin-A, oxidative stress and inflammation in type 2. Diabet. rats Heliyon . https://doi.org/10.1016/j.heliyon.2024.e27749 (2024). .10,e27749. Amaral, L. S. et al. Beneficial effects of previous exercise training on renal changes in streptozotocin-induced diabetic female rats. Exp. Biol. Med. (Maywood) . 241 , 437–445. https://doi.org/10.1177/1535370215609696 (2016). van der Vaart, H., Postma, D. S. & Timens, W. & ten Hacken, N. H.Acute effects of cigarette smoke on inflammation and oxidative stress: a review. Thorax .59,713–721. (2004). https://doi.org/10.1136/thx.2003.012468 Ojeda, M. L., Carreras, O., Sobrino, P., Murillo, M. L. & Nogales, F. Biological implications of selenium in adolescent rats exposed to binge drinking: Oxidative, immunologic and apoptotic balance. Toxicol. Appl. Pharmacol. 329 , 165–172. https://doi.org/10.1016/j.taap.2017.05.037 (2017). Furukawa, S. et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. J. Clin. Invest. 114 , 1752–1761. https://doi.org/10.1172/jci21625 (2004). Opara, E. C.Role of oxidative stress in the etiology of type 2 diabetes and the effect of antioxidant supplementation on glycemic control. J. Investig Med. 52 , 19–23. https://doi.org/10.1136/jim-52-01-22 (2004). Caturano, A. et al. .Oxidative Stress in Type 2 Diabetes: Impacts from Pathogenesis to Lifestyle Modifications. Curr. Issues Mol. Biol. 45 , 6651–6666. https://doi.org/10.3390/cimb45080420 (2023). Neri, S. et al. Effects of antioxidant supplementation on postprandial oxidative stress and endothelial dysfunction: a single-blind, 15-day clinical trial in patients with untreated type 2 diabetes, subjects with impaired glucose tolerance, and healthy controls. Clin. Ther. 27 , 1764–1773. https://doi.org/10.1016/j.clinthera.2005.11.006 (2005). Kelly, J. T. et al. Modifiable Lifestyle Factors for Primary Prevention of CKD: A Systematic Review and Meta-Analysis. J. Am. Soc. Nephrol. 32 , 239–253. https://doi.org/10.1681/asn.2020030384 (2021). Zhang, W. et al. Association between the Oxidative Balance Score and Telomere Length from the National Health and Nutrition Examination Survey 1999–2002. Oxid. Med. Cell. Longev. 2022 , 1345071. https://doi.org/10.1155/2022/1345071 (2022). Zhan, F. et al. Higher oxidative balance score decreases risk of stroke in US adults: evidence from a cross-sectional study. Front. Cardiovasc. Med. 10 , 1264923. https://doi.org/10.3389/fcvm.2023.1264923 (2023). Aaby, A., Friis, K., Christensen, B., Rowlands, G. & Maindal, H. T.Health literacy is associated with health behaviour and self-reported health: A large population-based study in individuals with cardiovascular disease. Eur. J. Prev. Cardiol. 24 , 1880–1888. https://doi.org/10.1177/2047487317729538 (2017). Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150 , 604–612. https://doi.org/10.7326/0003-4819-150-9-200905050-00006 (2009). Unger, T. et al. International Society of Hypertension global hypertension practice guidelines. J Hypertens .38,982–1004. (2020). https://doi.org/10.1097/hjh.0000000000002453 (2020). Additional Declarations No competing interests reported. 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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-5778586","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400453770,"identity":"2b013e36-bcd5-47a6-9419-95d3024173c2","order_by":0,"name":"Yunhe Ding","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Yunhe","middleName":"","lastName":"Ding","suffix":""},{"id":400453771,"identity":"e979a544-3019-432a-b24b-7a3ca401cf33","order_by":1,"name":"Bing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNmb+hw8//pFg5mdmPkCcFj72HmZjyQYbdsl2tgTitMjxnGGT4G1I4zc4z2NApMMkcg9ISO44LC3ZzPPxxhsGOzndBoJa8hIMCs8cNuZn5t1sOYch2djsAEEtCQYJEmyHkyWbebdJ8zAcSNxGjJYDPGyH6zcc5nlGpBaeM4YNvG1pzAaHediI1MLelswsccaGWbKZzdhyjgERfpFvZj7+80MFMCr5Dz+88abCTo6gFhQgQWzUIGshVccoGAWjYBSMCAAATXA8kCDpB4kAAAAASUVORK5CYII=","orcid":"","institution":"Hebei Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Liu","suffix":""},{"id":400453772,"identity":"6919490d-acdd-4a5b-b225-f3eea74b9aec","order_by":2,"name":"Zhen Feng","email":"","orcid":"","institution":"Hebei Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Feng","suffix":""},{"id":400453773,"identity":"4559daa3-6e88-4091-b05f-77f4dcda31d8","order_by":3,"name":"Xuehua Liu","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xuehua","middleName":"","lastName":"Liu","suffix":""},{"id":400453774,"identity":"430a5b4e-7d4b-41df-927e-05084e8a58bf","order_by":4,"name":"Zimeng Wei","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Zimeng","middleName":"","lastName":"Wei","suffix":""},{"id":400453775,"identity":"e94bd3d2-1dea-46cd-8889-7969534dc28b","order_by":5,"name":"Lixia Fan","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-01-07 06:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5778586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5778586/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-05281-w","type":"published","date":"2025-07-01T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73784780,"identity":"d55792c4-fd8e-401f-b280-2b21b16b7502","added_by":"auto","created_at":"2025-01-14 16:03:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Linear associations of OBS with CKD; (b) Linear associations of OBS with Low risk CKD group; (c) Linear associations of OBS with Medium risk CKD group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline analysis demonstrated the association between OBS and CKD in all samples and across different risk stratifications. Adjusted restricted cubic spline models were adjusted for age, gender, race, education, hypertension, smoking, BMI, UCR, UA, ALB, TBIL, TC, BUN.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5778586/v1/32816c7d688d9b0be4775e77.png"},{"id":73784781,"identity":"47ee61fb-7bff-4b80-8b68-608a1e605d1e","added_by":"auto","created_at":"2025-01-14 16:03:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62487,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection based on NHANES database from 2007 to 2018.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5778586/v1/3ad9030250aecb2cccbf0147.png"},{"id":86179834,"identity":"4246a53d-6567-43c1-b3d2-f56beaa67200","added_by":"auto","created_at":"2025-07-07 16:19:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5778586/v1/03906729-3bb8-4306-bc71-51e6ea77cc0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between oxidative balance score and chronic kidney disease events in type 2 diabetes mellitus patients: a cross‑sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes Mellitus (DM) and chronic kidney disease (CKD) are common chronic diseases globally and often occur simultaneously\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. DM is a key contributing factor to the development of CKD. Research has indicated that diabetic patients are 2 to 4 times more likely to develop CKD compared to non-diabetic individuals, particularly when other comorbidities such as hypertension and lipid metabolism abnormalities are present\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Hyperglycemia can lead to various chronic diseases, with diabetic kidney disease (DKD) is among the most serious complications associated with diabetes. It is characterized by chronic proteinuria, tubulointerstitial fibrosis, and renal failure. Prolonged hyperglycemia affects kidney function through multiple pathways and stands as a major contributor to end-stage renal disease (ESRD)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The coexistence of the two conditions greatly diminishes patients' quality of life, especially in aspects related to physical health, mental well-being, and social interactions\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Therefore, early identification and management of the potential risks of kidney damage caused by DM are crucial for delaying disease progression.\u003c/p\u003e \u003cp\u003eOxidative stress is a critical factor in the development and progression of both DM and CKD\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Persistent hyperglycemia results in a disruption of the balance between reactive oxygen species (ROS) and the antioxidant defense mechanisms within the body. This condition triggers alterations in numerous metabolic pathways, leading to the accumulation of advanced glycation end products (AGEs), and the production of ROS, oxidative stress levels increase, exacerbating inflammatory responses and promoting enhanced cell apoptosis\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. At the same time, it triggers inflammatory responses, fibrosis, and tubular interstitial damage in kidney cells, further promoting the loss of kidney function\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Oxidative stress and systemic inflammation are mutually reinforcing processes, creating a vicious cycle that exacerbates their detrimental effects. Consequently, the regulation of oxidative stress is of critical importance in diabetic patients. This regulation is influenced by various antioxidants and pro-oxidants, which are closely associated with dietary choices and lifestyle factors.\u003c/p\u003e \u003cp\u003eOBS is a comprehensive assessment tool that integrates the intake of antioxidants and pro-oxidants, as well as dietary and lifestyle factors, to reflect the overall oxidative balance status. It also acts as an indicator of the risk related to the onset and progression of chronic diseases \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. A higher OBS reflects greater antioxidant capacity. Accordingly, enhancing oxidative balance through the implementation of antioxidant strategies represents a potentially significant and innovative approach to mitigating oxidative stress in patients with DM and early-stage CKD. Previous studies have investigated the link between OBS and the progression of DKD \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. However, there is still a lack of research examining the relationship between OBS and various CKD risk stratifications. Furthermore, most current studies focus on oxidative stress responses in advanced stages of kidney disease. The uniqueness of this study lies in its investigation of OBS variations across different CKD stages in diabetic patients, highlighting the role of oxidative balance in CKD progression. This is important for early assessment of renal impairment and developing personalized interventions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Study Population\u003c/h2\u003e \u003cp\u003eThis study included 4,353 DM participants from NHANES (2007\u0026ndash;2018), with an average age of 58.13 years. Among them, 2,239 (51.4%) were male and 2,114 (48.6%) were female. Additionally, 1,745 (40.1%) had CKD, while 2,608 (59.9%) did not. Participants in the highest OBS score quartile demonstrated unique demographic and clinical features. These individuals were more likely to be male, non-Hispanic White, with higher educational attainment, non-smokers, non-hypertensive, and had a BMI\u0026thinsp;\u0026le;\u0026thinsp;30 kg/m\u0026sup2;. Participants in higher OBS quartiles exhibited significantly elevated levels of ALB, TBIL, and BUN, while showing lower levels of UCR, TC, and UA. Further analysis identified an inverse relationship between the OBS score and the prevalence of hypertension and CKD, with the prevalence gradually decreasing from Q1 to Q4.\u003c/p\u003e \u003cp\u003eThere were notable differences among the OBS groups regarding gender, age, race, education, hypertension, smoking status, BMI, ALB, TBIL, TC, BUN, UCR, UA, eGFR, and ACR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline demographic characteristics of the study population, grouped according to OBS quartiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants by quartiles of the OBS in the NHANES 2007\u0026ndash;2018 cycles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (4353)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1(1097)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (1037)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (1130)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (1089)\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\u003eGender, n (%)\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\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2239(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e536(48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e515(49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e622(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e566(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2114(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e561(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e522(50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e508(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e523(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.79\u0026thinsp;\u0026plusmn;\u0026thinsp;22.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.10\u0026thinsp;\u0026plusmn;\u0026thinsp;22.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.19\u0026thinsp;\u0026plusmn;\u0026thinsp;23.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.67\u0026thinsp;\u0026plusmn;\u0026thinsp;22.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\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;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e750(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e491(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1627(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440(40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e383(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e427(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e377(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1040(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e251(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e274(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e213(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e445(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1440(33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e414(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e361(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e360(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e305(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e980(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e259(22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e238(21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1933(44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e443(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e511(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e546(50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\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\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2825(64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e717(65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e707(68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e710(62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e691(63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1528(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e330(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e420(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e398(36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking, n (%)\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;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2170(49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e635(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e542(52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e533(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e460(42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2180(50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e462(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e494(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e596(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e628(57.8)\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, n (%)\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;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=30 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1857(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390(35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e457(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e600(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2496(57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e707(64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e627(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e673(59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e489(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic kidney disease, n(%)\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\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1745(40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e476(43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424(40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e443(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e402(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2608(59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e621(56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e613(59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e687(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e687(63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCR (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.16\u0026thinsp;\u0026plusmn;\u0026thinsp;73.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.96\u0026thinsp;\u0026plusmn;\u0026thinsp;81.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.23\u0026thinsp;\u0026plusmn;\u0026thinsp;73.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115.98\u0026thinsp;\u0026plusmn;\u0026thinsp;70.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106.46\u0026thinsp;\u0026plusmn;\u0026thinsp;66.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342.79\u0026thinsp;\u0026plusmn;\u0026thinsp;90.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e351.97\u0026thinsp;\u0026plusmn;\u0026thinsp;95.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347.27\u0026thinsp;\u0026plusmn;\u0026thinsp;95.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e343.15\u0026thinsp;\u0026plusmn;\u0026thinsp;90.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e334.93\u0026thinsp;\u0026plusmn;\u0026thinsp;89.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.67\u0026thinsp;\u0026plusmn;\u0026thinsp;25.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.09\u0026thinsp;\u0026plusmn;\u0026thinsp;25.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.08\u0026thinsp;\u0026plusmn;\u0026thinsp;25.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.01\u0026thinsp;\u0026plusmn;\u0026thinsp;24.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.53\u0026thinsp;\u0026plusmn;\u0026thinsp;25.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR, mg/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.47(32.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.82(29.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.58(25.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.34(20.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.17(24.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThis table is used to present the basic characteristics of the study population, including the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) for continuous variables and percentages (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index; CKD, chronic kidney disease; UCR, urine creatinine; UA, uric acid; ALB, albumin; TBIL, total bilirubin; TC, total cholesterol; BUN, blood urea nitrogen; eGFR, estimates of glomerular filtration rate, ACR, albumin-to-creatinine ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between the OBS and CKD\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, We developed three weighted logistic regression models to examine the association between OBS and CKD prevalence. In the unadjusted Model 1, every one-unit increase in OBS corresponded to a 3% reduction in CKD risk (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.96\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The association persisted across all adjusted models, with the fully adjusted Model 3 demonstrating that each unit increase in OBS was associated with a 4% decrease in CKD risk (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.94\u0026ndash;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). was stratified into quartiles, the highest quartile (Q4) demonstrated the most significant association with a decreased risk of CKD. In comparison to the reference category, which is the first quartile (Q1), the odds ratio (OR) for the other quartiles decreased progressively. Specifically, in Model 3, Q4 had an OR of 0.69 (95% CI: 0.57\u0026ndash;0.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) compared to Q1, showing a significant trend across the OBS quartiles. Overall, higher OBS scores were associated with a protective effect against CKD risk in all three models (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The restricted cubic spline analysis demonstrated a linear inverse relationship between OBS and CKD across all samples (\u003cem\u003ep\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.689, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ea\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between OBS and CKD in different models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eOR (95% CI); \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.96,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.95,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96 (0.94,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.85 (0.72,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.69,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82 (0.68,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.83 (0.70,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77 (0.64,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.61,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.79 (0.66,0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.60,0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.57,0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eOR, odds ratio;CI, confidence intervals.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 2 adjusted for age, gender, race and education.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 3 adjusted for age, gender, race, education, hypertension, smoking, BMI, UCR, UA, ALB, TBIL, TC, BUN.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the relationship between OBS and various CKD risk stratifications using weighted logistic regression analysis. CKD patients were divided into three groups: low-risk (G1 and G2), medium-risk (G3a and G3b), and high-risk (G4 and G5). The results showed that in the low-risk CKD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), OBS in the third quartile (Q3) was strongly linked to CKD prevalence in Models 1, 2, and 3: (Model 1: OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.72\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.009), (Model 2: OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.69\u0026ndash;0.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), (Model 3: OR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.70\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). In Model 2, participants in the highest quartile (Q4) had an OR of 0.87 (95% CI: 0.75\u0026ndash;1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05), showing a marginally significant association with CKD prevalence. In the medium-risk CKD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), participants in Q3 (OR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.36\u0026ndash;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and Q4 (OR\u0026thinsp;=\u0026thinsp;0.46, 95% CI: 0.28\u0026ndash;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) had significantly lower CKD prevalence compared to Q1, with a gradually decreasing trend. In Models 1 and 2, Q4 (Model 1: OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.49\u0026ndash;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) (Model 2: OR\u0026thinsp;=\u0026thinsp;0.55, 95% CI: 0.38\u0026ndash;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) was also significantly associated with lower CKD prevalence. No notable association was found in the high-risk group. The restricted cubic spline analysis demonstrated a linear relationship between OBS and CKD in the low-risk category (\u003cem\u003ep\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.627, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eb\u003c/b\u003e) and medium-risk low-risk category (\u003cem\u003ep\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.654, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ec\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRelationship Between Oxidative Balance Score and Chronic Kidney Disease Risk Classification.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrouping factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserved Cases /Total Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOR (95% CI)/p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOR (95% CI)/p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOR (95% CI)/\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\u003eLow risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e848/2608\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94(0.82,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91(0.79,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89(0.77,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.83(0.72,0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.80(0.69,0.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.81(0.70,0.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.77,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.87(0.75,1.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92(0.80,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e780/2608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.57,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70(0.49,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67(0.42,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84(0.61,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71(0.50,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.57(0.36,0.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.68(0.49,0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.55(0.38,0.80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.46(0.28,0.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117/2608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.30,1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58(0.25,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19(0.03,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71(0.32,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60(0.26,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32(0.06,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81(0.37,1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74(0.33,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15(0.02,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMultivariable logistic regression analysis was conducted to explore the association between the oxidative balance score and the classification of chronic kidney disease risk. Significant results are highlighted in bold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 2 adjusted for age, gender, race and education.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 3 adjusted for age, gender, race, education, hypertension, smoking, BMI, UCR, UA, ALB, TBIL, TC, BUN.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup Analysis\u003c/h3\u003e\n\u003cp\u003eIn the subgroup analysis, an inverse relationship between higher OBS and reduced CKD risk was observed across various populations, though the magnitude of this association seemed to differ depending on specific demographic and clinical factors. In males over 60 years old, non-Hispanic Whites, individuals with hypertension, and those with obesity, the correlation between OBS and CKD was evident. However, most analyses of the interactions between OBS and CKD risk stratification factors did not show statistical significance, suggesting that the protective effect of higher OBS against CKD risk was consistent across the entire population. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the stratified analysis and interactions between OBS and CKD risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between OBS and CKD in different subgroups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.84,1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.64,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69(0.52,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.60,0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75(0.60,0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73(0.58,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\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 \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.71.1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.66,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73(0.57.0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.68,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.63,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78(0.61,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.59,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81(0.53,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05(0.69,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78(0.59,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.62,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67(0.50,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81(0.64,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.68,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75(0.59,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45(1.00,2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.73,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83(0.56,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09(0.81,1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98(0.73,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74(0.55,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65(0.45,0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63(0.44,0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74(0.51,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07(0.83,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85(0.65,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83(0.64,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\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 \u003cp\u003e0.515\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\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76(0.61,0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73(0.59,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70(0.57,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30(0.96,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09(0.80,1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85(0.61,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\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 \u003cp\u003e0.185\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\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.64,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87(0.68,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68(0.53,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17(0.92,1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83(0.65,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.75,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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 \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=30 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.70,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84(0.65,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75(0.58,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.78,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.64,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79(0.63,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe subgroup analysis was conducted based on stratifications of age, gender, race, education, hypertension, smoking status, BMI.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, data from 4,353 participants in the NHANES dataset (2007\u0026ndash;2018) were analyzed, revealing a significant negative correlation between the OBS and CKD prevalence in DM patients, even after adjusting for all relevant covariates. It is important to highlight that, upon stratifying CKD patients according to their risk levels, the correlation demonstrated statistical significance within the low-risk and moderate-risk groups. However, this correlation was not evident in the high-risk group. Our findings indicate that an elevated OBS is linked to a lower prevalence of CKD, especially observed in individuals with low to moderate risk. To the best of our knowledge, this study is one of the few that investigates the association between OBS and CKD risk in DM patients, offering novel insights that may inform both clinical practice and future research directions.\u003c/p\u003e \u003cp\u003eOur results align with those reported in previous studies. For instance, Yin et al. demonstrated that an elevated OBS may help reduce the risk of CKD, especially among individuals with a mild to moderate risk profile. However, their study population was not restricted to diabetic patients\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Similarly, Liu et al. reported that higher OBS levels were associated with a lower risk of DKD, reduced eGFR, and proteinuria\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Another study similarly found that higher OBS levels were inversely associated with CKD prevalence in individuals with metabolic syndrome\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Furthermore, research has demonstrated that inflammation and oxidative stress are major driving factors in the onset and progression of CKD \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Curiously, our research revealed that this association predominantly occurs among individuals with mild to moderate CKD risk, whereas no comparable pattern was detected in those classified as high-risk. This also suggests that a healthy diet and lifestyle may reduce the risk of incident CKD by mitigating oxidative stress and enhancing the body\u0026rsquo;s antioxidant capacity. It highlights the critical role of maintaining a balance between antioxidants and pro-oxidants in preserving kidney health. These findings not only provide a promising strategy for CKD prevention but also offer valuable interventions for enhancing the standard of living in affected patients.\u003c/p\u003e \u003cp\u003eAlthough the etiology of CKD is complex and involves multiple interacting factors, hyperglycemia is a key contributing factor to the development of CKD. Oxidative stress is considered a key mechanism in diabetes-induced kidney damage. Studies have shown that persistent hyperglycemia causes mitochondrial dysfunction, leading to excessive generation of free radicals and ROS. The body's antioxidant defense mechanism fails to clear these ROS in a timely manner, resulting in cellular damage\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Oxidative stress also plays a vital role in various stages of CKD progression, causing tubular and glomerular damage, promoting renal fibrosis and inflammation, increasing renal cell apoptosis, and encouraging collagen deposition. These processes ultimately result in structural and functional damage to the kidneys\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In diabetic patients, oxidative stress-induced vascular damage and endothelial dysfunction not only contribute to the progression of diabetes but also serve as key driving factors for related complications. This underscores the intricate relationship between diabetes and CKD, where both conditions exacerbate each other, creating a vicious cycle. The OBS is a tool that integrates the interactions between pro-oxidants and antioxidants from dietary and lifestyle factors, Offering a holistic evaluation of an individual's oxidative stress status. This tool is frequently utilized to evaluate oxidative status and its relationship with chronic disease prevalence \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Traditional OBS primarily focuses on the levels of ROS and antioxidants in blood or biological samples, but these measurements often overlook the impact of daily food intake, nutritional habits, and lifestyle factors on oxidative balance\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In this study, we investigated the association between 17 sources of oxidative stress and CKD progression in patients with diabetes. We constructed an OBS that included 13 dietary OBS components (9 antioxidants and 4 pro-oxidants) and 4 lifestyle OBS components (1 antioxidant and 3 pro-oxidants). This method enabled a thorough evaluation of the impact of oxidative balance on CKD progression in diabetic patients.\u003c/p\u003e \u003cp\u003eIn terms of dietary factors, research has demonstrated that non-vitamin A carotenoids, such as lycopene, lutein, and zeaxanthin, demonstrate significant protective effects in preventing diabetic complications. These carotenoids not only protect renal tubules and glomeruli but also reduce oxidative stress and inflammatory responses in the retina, improve neurological function, and alleviate symptoms of diabetic neuropathy\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Lycopene has been shown to inhibit high-glucose-induced DNA damage, downregulate apoptosis-related protein expression, reduce intracellular oxidative stress levels, and significantly increase the survival rate of NRK-52E cells, which are rat renal tubular epithelial cells.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Similarly, the protective effects of β-carotene have been validated in diabetic nephropathy (DN) rat models, where β-carotene was found to activate the AMPK/SIRT1 signaling pathway, promote autophagy, and reduce the expression of inflammatory cytokines (e.g. TNF-α and IL-6), thereby significantly improving the inflammatory response in DN \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Furthermore, components such as vitamin E, zinc, and selenium have been found to reduce urinary protein levels, improve glomerular filtration rates, and mitigate structural damage to glomeruli by inhibiting oxidative stress and inflammatory responses, which in turn help delay the progression of DN\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Research has also identified a strong link between low serum vitamin C levels and a heightened risk of kidney function deterioration, suggesting that improving serum vitamin C levels at early stages may help mitigate further progression of CKD\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Cross-sectional studies have further indicated that patients with DM who have higher antioxidant indices exhibit significantly better kidney function-related indicators compared to those with lower indices. However, supplementation with single antioxidant compounds has not demonstrated significant improvement, indicating that a diversified antioxidant-rich dietary pattern is more effective\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.The relationship between unsaturated fatty acid intake and kidney disease progression in diabetic patients remains controversial. Certain studies propose that unsaturated fatty acids may protect glomerular podocytes from apoptosis by regulating the localization of mTORC1 in lysosomes and inhibiting its overactivation, while saturated fatty acids may increase the risk of podocyte apoptosis by promoting mTORC1 activation\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. A diet rich in polyunsaturated fatty acids (PUFAs), such as canola oil, may help reduce renal lipid deposition and prevent the progression of diabetic nephropathy\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. However, other studies have found that genetic variations associated with omega-6 fatty acids significantly modulate the risks of diabetic microvascular complications, while no significant association has been observed between omega-3 fatty acids and microvascular complications\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Similarly, Nakamura et al. \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e concluded that the previously reported renal protective effects of n-3 fatty acids have not been confirmed. Additionally, iron is a crucial component of hemoglobin and myoglobin. However, its accumulation in the body can lead to the generation of excessive free radicals and ROS\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of lifestyle factors, research has shown that exercise training can reduce oxidative stress, alleviate inflammatory responses, and improve renal microstructure in type 2 diabetic (T2D) rats, thereby exerting protective effects\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Additionally, smoking\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e and excessive alcohol consumption\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e have been demonstrated to cause severe oxidative stress, immune dysfunction, and cell apoptosis. Obesity can trigger oxidative stress, resulting in the production of ROS, which can impair insulin receptor function and inhibit insulin signaling pathways, contributing to insulin dysfunction and further promoting the development of diabetes\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. A growing body of research indicates that increasing dietary intake of fiber, vitamin C, carotenoids, riboflavin, lycopene, and vitamin E, along with engaging in regular physical activity, can help mitigate oxidative stress. Conversely, pro-oxidative factors such as excessive iron intake, alcohol consumption, obesity and smoking can enhance the production of ROS and free radicals, leading to cellular damage. Under normal physiological conditions, the body maintains a balance between pro-oxidative factors and antioxidative factors. However, When this balance is disrupted, oxidative stress occurs, leading to damage to cells, tissues, and organs \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs oxidative stress significantly impacts the occurrence and progression of diabetes and CKD, antioxidant therapies may serve as an effective intervention for these populations. Previous studies have demonstrated that combining antioxidant supplementation with healthy dietary and exercise habits enables diabetic patients to manage oxidative stress more effectively \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Furthermore, a randomized single-blind trial suggested that antioxidants can neutralize ROS and increase the activity of naturally occurring antioxidant enzymes, significantly reducing oxidative stress and improving endothelial function, particularly in individuals with abnormal glucose metabolism \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. A systematic review and meta-analysis also indicated that a diet low in salt and fat, high in fiber, combined with regular exercise, smoking cessation can significantly reduce the risk of developing CKD \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. We believe that the OBS could also serve as an effective tool for assessing personal dietary and lifestyle health. It could provide personalized health management advice for high-risk individuals, assist in assessing the effectiveness of public health interventions, and enable early identification and intervention for chronic diseases.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, it utilized a cross-sectional design, which allowed for the analysis of the relationship between OBS and CKD at a single point in time. However, this approach imposes limitations on our capacity to draw causal inferences. The association between OBS and CKD may involve temporal delays or reverse causation, which future prospective studies are needed to validate. Second, OBS data in this study were collected through 24-hour dietary recall questionnaires, which are susceptible to measurement errors and biases. These limitations may impact the accuracy of the OBS scoring and the reliability of the results. Finally, despite controlling for multiple potential confounding factors such as age, sex, race, educational level, smoking status, and hypertension, there may still be residual confounders that were not accounted for, which might impact the association between OBS and CKD.\u003c/p\u003e \u003cp\u003eIn conclusion, our study found that OBS is negatively associated with CKD prevalence in DM patients. Specifically, this association was observed in the low to moderate CKD risk groups but not in the high-risk group. Further analysis revealed a linear inverse trend was observed between OBS and CKD. Therefore, increasing OBS levels may serve as an effective strategy for CKD prevention, particularly among individuals with mild to moderate CKD risk. The regulation of oxidative stress and the enhancement of OBS hold potential in CKD prevention, warranting further research and promotion.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey (NHANES). NHANES is designed to evaluate the health and nutritional status of the non-institutionalized civilian population in the United States, with data released every two years. It comprises five main components: demographic, dietary, clinical examination, laboratory, and questionnaire information. Data collection for NHANES is conducted in two stages: a household interview followed by physical exams and laboratory tests at mobile examination centers. The survey is supervised by the National Center for Health Statistics (NCHS), with participants provide written informed consent. NHANES data is publicly available, and further details on the data collection and analysis methods can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn our study, data from five NHANES cycles (2007\u0026ndash;2018) were included. The inclusion and exclusion criteria for this study are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Initially, 6,397 individuals diagnosed with diabetes were included. After excluding 720 participants under the age of 18, the analysis focused on 5,677 adults aged 18 years or older. Further, 213 participants lacking key measurements of albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), as well as 731 participants with incomplete OBS component data, were further excluded. Additionally, 380 participants with incomplete covariate data were excluded, including 141 lacking educational attainment, 109 missing body mass index (BMI) information, 70 without standard biochemical data, 36 missing blood pressure information, and 24 lacking smoking status. Ultimately, the final sample for this study consisted of 4,353 adults.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOxidative balance score\u003c/h3\u003e\n\u003cp\u003eBased on previous studies \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, the total OBS was derived by summing the individual scores for each variable, which include 13 dietary nutrients (9 antioxidants and 4 pro-oxidants) and 4 lifestyle factors (1 antioxidant and 3 pro-oxidants). The 13 dietary nutrients include Vitamin C, Vitamin E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein\u0026thinsp;+\u0026thinsp;zeaxanthin, selenium, zinc, iron, total fat, total polyunsaturated fatty acids, and total saturated fatty acids. The 4 lifestyle factors consist of physical activity, BMI, smoking status, and alcohol intake. The study identified pro-oxidant factors, including total fat, total polyunsaturated fatty acids, total saturated fatty acids, total iron intake, cotinine levels, alcohol consumption, and BMI. The other factors were categorized as antioxidants\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In NHANES, dietary components of the OBS were assessed using a 24-hour dietary recall interview to collect information on food types and intake amounts. Physical activity, expressed as metabolic equivalent (MET), was calculated based on leisure-time activity data from the past 30 days. Smoking status was assessed by measuring plasma cotinine levels, which serve as an indicator of tobacco use and exposure to environmental tobacco smoke. Additionally, Participants were grouped according to their alcohol consumption levels into non-drinkers and non-heavy drinkers (females: 0\u0026ndash;15 g/d, males: 0\u0026ndash;30 g/d), and heavy drinkers (females: \u0026ge; 15 g/d, males: \u0026ge; 30 g/d), with scores of 3, 2, and 1, respectively. The remaining components were classified into three categories, with antioxidants receiving scores of 1, 2, and 3 based on the tertiles from lowest to highest, such that greater exposure to antioxidants resulted in higher scores. Conversely, for pro-oxidants, a score of 1 was assigned to the highest tertile, while a score of 3 was assigned to the lowest tertile.\u003c/p\u003e\n\u003ch3\u003eOutcome variable\u003c/h3\u003e\n\u003cp\u003eDiabetes is defined as: (1) a diagnosis previously reported by a healthcare professional, (2) fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, (3) glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, (4) 2-hour oral glucose tolerance test (OGTT) blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, or (5) use of diabetes medications or insulin.\u003c/p\u003e \u003cp\u003eCKD is defined as: (1) eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, (2) ACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g. eGFR is calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CKD can be divided into the following stages: G1 (\u0026ge;\u0026thinsp;90 mL/min/1.73 m\u0026sup2;), G2 (60\u0026ndash;89 mL/min/1.73 m\u0026sup2;), G3a (45\u0026ndash;59 mL/min/1.73 m\u0026sup2;), G3b (30\u0026ndash;44 mL/min/1.73 m\u0026sup2;), G4 (15\u0026ndash;29 mL/min/1.73 m\u0026sup2;), and G5 (\u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;) \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Albuminuria is calculated using the urinary albumin-to-creatinine ratio (ACR). Additionally, we conducted risk stratification based on the stages of CKD. In stages G1 and G2 were classified as low-risk, those in stages G3a and G3b as medium-risk, and those in stages G4 and G5 as high-risk.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eBased on existing clinical knowledge and research, we further included several potential covariates that may affect the study outcomes. These factors include age, gender, race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other), education (less than high school, high school graduate, and more than high school). Hypertension is characterized by a systolic blood pressure of 140 mmHg or higher, a diastolic blood pressure of 90 mmHg or higher, or the use of antihypertensive drugs \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Smoking status (Yes or No), BMI was calculated as weight (kg) divided by height (m) squared, and classified into two groups: BMI\u0026thinsp;\u0026le;\u0026thinsp;30 kg/m\u0026sup2;, BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u0026sup2;. The following laboratory parameters were also included: albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), blood urea nitrogen (BUN), urine creatinine (UCR), and uric acid (UA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eConsidering that NHANES utilizes a stratified and complex multistage sampling design, we applied the sample weight calculation procedure recommended by NHANES to adjust the data accordingly. Continuous variables that follow a normal distribution were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, whereas those exhibiting a non-normal distribution were presented as median and interquartile range. Categorical variables were reported as numbers and percentages. Participants were categorized into four groups according to the quartiles of OBS.(Q1-Q4) (Q1: \u0026le; P25; Q2: P25-P50; Q3: P50-P75; Q4: \u0026gt; P75), with Q1 as the reference group. To analyze the differences in variable characteristics across the different OBS groups (quartiles), weighted analysis of variance (ANOVA) was applied for continuous variables, and weighted chi-square tests were used for categorical variables to compare baseline characteristics across the OBS quartiles. A weighted multivariable logistic regression analysis was performed to investigate whether different OBS groups (quartiles) were associated with the prevalence of CKD in diabetic patients. The findings were presented as odds ratios (OR) accompanied by 95% confidence intervals (95% CI), \u003cem\u003ep\u003c/em\u003e-values, and trend \u003cem\u003ep\u003c/em\u003e-values. Three models were applied in this study. Model 1 reflects the unadjusted raw data. Model 2 adjusted for age, gender, race, and education. Model 3 is the fully adjusted model, further adjusting for potential confounders, including age, gender, race, education, hypertension, smoking status, BMI, albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), blood urea nitrogen (BUN), urine creatinine (UCR), and uric acid (UA). Furthermore, CKD patients were stratified into low-risk, medium-risk, and high-risk groups. Logistic regression models were employed again to evaluate the association between OBS and the various CKD risk stratifications, with the lowest quartile (Q1) of OBS as the reference group. We additionally employed restricted cubic spline regression to evaluate the nonlinear association between OBS and CKD.\u003c/p\u003e \u003cp\u003eFinally, subgroup analyses were conducted based on age (\u0026le;\u0026thinsp;60 years or \u0026gt;\u0026thinsp;60 years), gender (male or female), race (Mexican American, Non-Hispanic White, Non-Hispanic Black, and Other), education (less than high school, high school graduate, and more than high school), hypertension (yes or no), smoking status (yes or no), and BMI (\u0026le;\u0026thinsp;30 kg/m\u0026sup2; or \u0026gt;\u0026thinsp;30 kg/m\u0026sup2;). We evaluated the consistency of the relationship between OBS and CKD across various demographic and clinical characteristics. To examine the interactions among the covariates, we employed the likelihood ratio test, and the models were adjusted accordingly for the pertinent covariates. All statistical analyses were performed using R Studio (version 4.2.2) for data analysis. A two-tailed \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant for all tests.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that competing interests.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.H.D. were responsible for the data analysis and drafted the manuscript. X.H.L. retrieved and organized the data from the database for analysis. B.L. supervised the analytical methods in collaboration with Z.F. and critically revised the manuscript. Z.M.W. and L.X.F. prepared all figures and the table used in the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study were obtained from the NHANES database (https://www.cdc.gov/nchs/nhanes/), which provides publicly available data for researchers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWyld, M. L. R. et al. The impact of comorbid chronic kidney disease and diabetes on health-related quality-of-life: a 12-year community cohort study.\u003cem\u003eNephrolDialTransplant\u003c/em\u003e.36,1048\u0026ndash;1056. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ndt/gfaa031\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfaa031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox, C. S. et al. .Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes. \u003cem\u003emeta-analysis Lancet\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(12)61350-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)61350-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012). .380,1662\u0026ndash;1673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh, H. et al. .Role of oxidative stress in diabetes-induced complications and their management with antioxidants. \u003cem\u003eArch. Physiol. Biochem.\u003c/em\u003e \u003cb\u003e130\u003c/b\u003e, 616\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13813455.2023.2243651\u003c/span\u003e\u003cspan address=\"10.1080/13813455.2023.2243651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue, R. et al. Mechanistic Insight and Management of Diabetic Nephropathy: Recent Progress and Future Perspective.\u003cem\u003eJ Diabetes Res\u003c/em\u003e.1839809. (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2017/1839809\u003c/span\u003e\u003cspan address=\"10.1155/2017/1839809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Boer, I. H. et al. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e, 3075\u0026ndash;3090. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/dci22-0027\u003c/span\u003e\u003cspan address=\"10.2337/dci22-0027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha, J. C., Banal, C., Chow, B. S. \u0026amp; Cooper, M. E. Jandeleit-Dahm, K.Diabetes and Kidney Disease: Role of Oxidative Stress. \u003cem\u003eAntioxid. Redox Signal.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 657\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/ars.2016.6664\u003c/span\u003e\u003cspan address=\"10.1089/ars.2016.6664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, Q. \u0026amp; Yang C.Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications.\u003cem\u003eRedox Biol\u003c/em\u003e.37,101799. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2020.101799\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2020.101799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade-Sierra, J. et al. The Influence of the Severity of Early Chronic Kidney Disease on Oxidative Stress in Patients with and without Type 2 Diabetes Mellitus.I. \u003cem\u003ent J. Mol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms231911196\u003c/span\u003e\u003cspan address=\"10.3390/ijms231911196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, N., Zhang, C. O. \u0026amp; Stress A Culprit in the Progression of Diabetic Kidney Disease. \u003cem\u003eAntioxid. (Basel)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox13040455\u003c/span\u003e\u003cspan address=\"10.3390/antiox13040455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez-Ruiz, A. et al. A Review of A Priori Defined Oxidative Balance Scores Relative to Their Components and Impact on Health Outcomes.\u003cem\u003eNutrients\u003c/em\u003e.11, (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu11040774\u003c/span\u003e\u003cspan address=\"10.3390/nu11040774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon, M. E., Jung, D. H., Heo, S. J., Park, B. \u0026amp; Lee, Y. J.Oxidative balance score as a useful predictive marker for new-onset type 2 diabetes mellitus in Korean adults aged 60 years or older: The Korean Genome and Epidemiologic Study-Health Examination (KoGES-HEXA) cohort.\u003cem\u003eExp Gerontol\u003c/em\u003e.193,112475. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.exger.2024.112475\u003c/span\u003e\u003cspan address=\"10.1016/j.exger.2024.112475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, C. et al. Association between oxidative balance score and diabetic kidney disease, low estimated glomerular filtration rate and albuminuria in type 2 diabetes mellitus patients: a cross-sectional study. \u003cem\u003eFront. Endocrinol. (Lausanne\u003c/em\u003e. 151412823. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2024.1412823\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1412823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, Y. et al. Associations between oxidative balance score and chronic kidney disease events in US adults: a population-based study. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-64147-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-64147-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024). 14,13743.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, L. et al. Higher oxidative balance score is linearly associated with reduced prevalence of chronic kidney disease in individuals with metabolic syndrome: evidence from NHANES 1999\u0026ndash;2018.\u003cem\u003eFront Nutr\u003c/em\u003e.11,1442274. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2024.1442274\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2024.1442274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz, M. I. et al. The determinants of endothelial dysfunction in CKD: oxidative stress and asymmetric dimethylarginine. \u003cem\u003eAm. J. Kidney Dis.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 42\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.ajkd.2005.09.029\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2005.09.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, G. et al. The progress of inflammation and oxidative stress in patients with chronic kidney disease. \u003cem\u003eRen. Fail.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 45\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/0886022x.2014.964141\u003c/span\u003e\u003cspan address=\"10.3109/0886022x.2014.964141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Z. et al. The impact of oxidative stress-induced mitochondrial dysfunction on diabetic microvascular complications. \u003cem\u003eFront. Endocrinol. (Lausanne\u003c/em\u003e. 14,1112363 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakuc, J. \u0026amp; Petrovič D.A review of oxidative stress related genes and new antioxidant therapy in diabetic nephropathy. \u003cem\u003eCardiovasc. Hematol. Agents Med. Chem.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 253\u0026ndash;261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/187152511798120949\u003c/span\u003e\u003cspan address=\"10.2174/187152511798120949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOberg, B. P. et al. Increased prevalence of oxidant stress and inflammation in patients with moderate to severe chronic kidney disease. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 1009\u0026ndash;1016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1755.2004.00465.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1755.2004.00465.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X. et al. Association of the oxidative balance score and chronic kidney disease: insights from the national health and nutrition examination survey 2009\u0026ndash;2018.\u003cem\u003eFront Nutr\u003c/em\u003e.11,1429191. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2024.1429191\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2024.1429191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Ruiz, \u0026Aacute;. et al. Oxidative Balance Scores (OBSs) Integrating Nutrient, Food and Lifestyle Dimensions: Development of the NutrientL-OBS and FoodL-OBS.\u003cem\u003eAntioxidants (Basel)\u003c/em\u003e.11, (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox11020300\u003c/span\u003e\u003cspan address=\"10.3390/antiox11020300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurillo, A. G. \u0026amp; Fernandez, M. L. .Potential of Dietary Non-Provitamin A Carotenoids in the Prevention and Treatment of Diabetic Microvascular Complications. \u003cem\u003eAdv. Nutr.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 14\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/an.115.009803\u003c/span\u003e\u003cspan address=\"10.3945/an.115.009803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUsta, A., Y\u0026uuml;ksek, V., \u0026Ccedil;etin, S. \u0026amp; Dede S.Lycopene prevents cell death in NRK-52E cells by inhibition of high glucose-activated DNA damage and apoptotic, autophagic, and necrotic pathways. \u003cem\u003eJ. Biochem. Mol. Toxicol.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, e23678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jbt.23678\u003c/span\u003e\u003cspan address=\"10.1002/jbt.23678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Marasy, S. A., Farouk, H., Khattab, M. S. \u0026amp; Moustafa, P. E.Beta-carotene ameliorates diabetic nephropathy in rats: involvement of AMPK/SIRT1/autophagy pathway.\u003cem\u003eImmunopharmacol Immunotoxicol\u003c/em\u003e.46,763\u0026ndash;772. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08923973.2024.2402347\u003c/span\u003e\u003cspan address=\"10.1080/08923973.2024.2402347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossing, P., Cooper, M. E. \u0026amp; Parving, H. H.Comparison of the effects of vitamins and/or mineral supplementation on glomerular and tubular dysfunction in type 2 diabetes. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/diacare.29.03.06.dc05-2144\u003c/span\u003e\u003cspan address=\"10.2337/diacare.29.03.06.dc05-2144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006). .29,747\u0026ndash;748; author reply 748\u0026ndash;749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, C., Zhao, J., Zhou, Q. \u0026amp; Li, J. Serum vitamin C levels and their correlation with chronic kidney disease in adults: a nationwide study.\u003cem\u003eRen Fail\u003c/em\u003e.46,2298079. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0886022x.2023.2298079\u003c/span\u003e\u003cspan address=\"10.1080/0886022x.2023.2298079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmid, N., Esfahani, E. N., Tabaeifard, R., Montazer, M. \u0026amp; Azadbakht L.Association of dietary antioxidant indices with kidney function indicators in patients with type 2 diabetes: a cross-sectional study. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-71683-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-71683-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024). 14,22991.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasuda, M. et al. Fatty acids are novel nutrient factors to regulate mTORC1 lysosomal localization and apoptosis in podocytes. \u003cem\u003eBiochim. Biophys. Acta\u003c/em\u003e. \u003cb\u003e1842\u003c/b\u003e, 1097\u0026ndash;1108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbadis.2014.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.bbadis.2014.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura, N. et al. .Dietary Intake of Polyunsaturated Fatty Acids and Diabetic Nephropathy: Cohort Analysis of the Tsugaru Study. \u003cem\u003eVivo\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 1890\u0026ndash;1893. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21873/invivo.13282\u003c/span\u003e\u003cspan address=\"10.21873/invivo.13282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, B. et al. Polyunsaturated fatty acids and diabetic microvascular complications: a Mendelian randomization study. \u003cem\u003eFront. Endocrinol. (Lausanne\u003c/em\u003e. 15,1406382 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReardon, T. F. \u0026amp; Allen, D. G.Iron injections in mice increase skeletal muscle iron content, induce oxidative stress and reduce exercise performance. \u003cem\u003eExp. Physiol.\u003c/em\u003e \u003cb\u003e94\u003c/b\u003e, 720\u0026ndash;730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/expphysiol.2008.046045\u003c/span\u003e\u003cspan address=\"10.1113/expphysiol.2008.046045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuntarulo, S. Iron, oxidative stress and human health. \u003cem\u003eMol. Aspects Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 299\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mam.2005.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.mam.2005.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaberi, S. et al. Exercise training improves diabetic renal injury by reducing fetuin-A, oxidative stress and inflammation in type 2. \u003cem\u003eDiabet. rats Heliyon\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e27749\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e27749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024). .10,e27749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmaral, L. S. et al. Beneficial effects of previous exercise training on renal changes in streptozotocin-induced diabetic female rats. \u003cem\u003eExp. Biol. Med. (Maywood)\u003c/em\u003e. \u003cb\u003e241\u003c/b\u003e, 437\u0026ndash;445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1535370215609696\u003c/span\u003e\u003cspan address=\"10.1177/1535370215609696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Vaart, H., Postma, D. S. \u0026amp; Timens, W. \u0026amp; ten Hacken, N. H.Acute effects of cigarette smoke on inflammation and oxidative stress: a review.\u003cem\u003eThorax\u003c/em\u003e.59,713\u0026ndash;721. (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/thx.2003.012468\u003c/span\u003e\u003cspan address=\"10.1136/thx.2003.012468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjeda, M. L., Carreras, O., Sobrino, P., Murillo, M. L. \u0026amp; Nogales, F. Biological implications of selenium in adolescent rats exposed to binge drinking: Oxidative, immunologic and apoptotic balance. \u003cem\u003eToxicol. Appl. Pharmacol.\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e, 165\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.taap.2017.05.037\u003c/span\u003e\u003cspan address=\"10.1016/j.taap.2017.05.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurukawa, S. et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e, 1752\u0026ndash;1761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/jci21625\u003c/span\u003e\u003cspan address=\"10.1172/jci21625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpara, E. C.Role of oxidative stress in the etiology of type 2 diabetes and the effect of antioxidant supplementation on glycemic control. \u003cem\u003eJ. Investig Med.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 19\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jim-52-01-22\u003c/span\u003e\u003cspan address=\"10.1136/jim-52-01-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaturano, A. et al. .Oxidative Stress in Type 2 Diabetes: Impacts from Pathogenesis to Lifestyle Modifications. \u003cem\u003eCurr. Issues Mol. Biol.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 6651\u0026ndash;6666. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cimb45080420\u003c/span\u003e\u003cspan address=\"10.3390/cimb45080420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeri, S. et al. Effects of antioxidant supplementation on postprandial oxidative stress and endothelial dysfunction: a single-blind, 15-day clinical trial in patients with untreated type 2 diabetes, subjects with impaired glucose tolerance, and healthy controls. \u003cem\u003eClin. Ther.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 1764\u0026ndash;1773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinthera.2005.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.clinthera.2005.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, J. T. et al. Modifiable Lifestyle Factors for Primary Prevention of CKD: A Systematic Review and Meta-Analysis. \u003cem\u003eJ. Am. Soc. Nephrol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 239\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1681/asn.2020030384\u003c/span\u003e\u003cspan address=\"10.1681/asn.2020030384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, W. et al. Association between the Oxidative Balance Score and Telomere Length from the National Health and Nutrition Examination Survey 1999\u0026ndash;2002. \u003cem\u003eOxid. Med. Cell. Longev.\u003c/em\u003e \u003cb\u003e2022\u003c/b\u003e, 1345071. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/1345071\u003c/span\u003e\u003cspan address=\"10.1155/2022/1345071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan, F. et al. Higher oxidative balance score decreases risk of stroke in US adults: evidence from a cross-sectional study. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1264923. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2023.1264923\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1264923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAaby, A., Friis, K., Christensen, B., Rowlands, G. \u0026amp; Maindal, H. T.Health literacy is associated with health behaviour and self-reported health: A large population-based study in individuals with cardiovascular disease. \u003cem\u003eEur. J. Prev. Cardiol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 1880\u0026ndash;1888. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2047487317729538\u003c/span\u003e\u003cspan address=\"10.1177/2047487317729538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey, A. S. et al. A new equation to estimate glomerular filtration rate. \u003cem\u003eAnn. Intern. Med.\u003c/em\u003e \u003cb\u003e150\u003c/b\u003e, 604\u0026ndash;612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7326/0003-4819-150-9-200905050-00006\u003c/span\u003e\u003cspan address=\"10.7326/0003-4819-150-9-200905050-00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnger, T. et al. International Society of Hypertension global hypertension practice guidelines.\u003cem\u003eJ Hypertens\u003c/em\u003e.38,982\u0026ndash;1004. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/hjh.0000000000002453\u003c/span\u003e\u003cspan address=\"10.1097/hjh.0000000000002453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oxidative balance scores, Chronic kidney disease, type 2 diabetes mellitus, NHANES, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-5778586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5778586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Oxidative Balance Score (OBS) serves as a comprehensive metric that amalgamates 17 dietary and lifestyle elements to evaluate antioxidant status. Thi research aims to investigate the association between the OBS and the prevalence of chronic kidney disease (CKD) in individuals diagnosed with type 2 diabetes mellitus (T2DM). This cross-sectional study included data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. CKD was determined using the albumin-to-creatinine ratio(ACR)and estimated glomerular filtration rate (eGFR). Patients were grouped into low, moderate, and high-risk categories based on their risk levels. The OBS variable was transformed from a continuous format into quartiles for subsequent analysis. Weighted multivariable logistic regression and restricted cubic spline models were employed to examine the relationship. Subgroup analyses and interaction tests assessed the findings' robustness. The results indicated a negative correlation between OBS and CKD risk. Individuals in higher OBS quartiles exhibited a decreased prevalence of CKD (OR 0.69, 95% CI: 0.57\u0026ndash;0.85, P\u0026thinsp;=\u0026thinsp;0.0003). A notable correlation was identified between OBS and CKD prevalence among the low-risk and moderate-risk groups. The subgroup analysis results were stable, and no significant interactions were detected among the subgroups. Increased OBS levels were correlated with a decreased risk of CKD. Improving antioxidant capacity through dietary and lifestyle modifications to enhance OBS may serve as an effective strategy for CKD prevention.\u003c/p\u003e","manuscriptTitle":"Associations between oxidative balance score and chronic kidney disease events in type 2 diabetes mellitus patients: a cross‑sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 16:03:34","doi":"10.21203/rs.3.rs-5778586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-06T06:55:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T13:32:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24882073855310995512490941580926148907","date":"2025-05-05T13:28:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310303945846035175027098200156468389794","date":"2025-03-04T13:47:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-04T09:20:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219238506477006170014447539576295129145","date":"2025-03-04T08:57:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-16T03:46:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-13T23:33:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-13T16:41:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-10T11:43:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-07T06:50:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03e0d404-4295-4f4d-a5e2-1fc3bd17a8c9","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42680535,"name":"Health sciences/Endocrinology"},{"id":42680536,"name":"Health sciences/Medical research"},{"id":42680537,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2025-07-07T16:11:12+00:00","versionOfRecord":{"articleIdentity":"rs-5778586","link":"https://doi.org/10.1038/s41598-025-05281-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:57:10","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-01-14 16:03:34","video":"","vorDoi":"10.1038/s41598-025-05281-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-05281-w","workflowStages":[]},"version":"v1","identity":"rs-5778586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5778586","identity":"rs-5778586","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}