Background Limited data regarding the correlation between oxidative balance score (OBS) and hyperuricemia highlights the necessity for thorough investigations. This study aims to examine the link between OBS, which incorporates dietary and lifestyle factors, and the occurrence of hyperuricemia. Methods We conducted a cross-sectional study involving 13,636 participants from the 2007-2018 National Health and Nutrition Examination Survey (NHANES). The oxidative balance score (OBS) was determined based on four lifestyle factors and sixteen dietary nutrients. We assessed the levels of serum uric acid (SUA) and the occurrence of hyperuricemia as outcomes. Weighted logistic regression and linear models were used for statistical analysis, using Restricted Cubic Splines (RCS) to examine potential nonlinear associations. Subgroup analysis and sensitivity assessments were performed to identify any variations and ensure the robustness of the findings. Results Higher OBS was consistently correlated with decreased SUA levels and a reduced prevalence of hyperuricemia. RCS highlighted a significant negative nonlinear association, particularly in females. Subgroup analysis revealed gender-based differences and interactive correlation, providing additional insights regarding OBS and hyperuricemia relationship. Conclusion This study underscores a robust negative correlation between OBS and SUA levels as well as the incidence of hyperuricemia, emphasizing the importance of dietary and lifestyle factors. Incorporating RCS, subgroup analysis, and sensitivity assessments enhances the depth of our findings, providing valuable insights for further research. 背景关于氧化平衡评分（OBS）与高尿酸血症之间关联的数据有限，这突出了深入研究的必要性。本研究旨在探讨包含饮食和生活方式因素的 OBS 与高尿酸血症发生之间的联系。方法我们进行了一项横断面研究，涉及 2007-2018 年国家健康与营养调查（NHANES）中的 13,636 名参与者。氧化平衡评分（OBS）基于四种生活方式因素和十六种膳食营养素确定。我们评估了血清尿酸（SUA）水平和高尿酸血症的发生作为结果。采用加权逻辑回归和线性模型进行统计分析，使用限制性三次样条（RCS）检查潜在的非线性关联。进行亚组分析和敏感性评估，以识别任何变化并确保结果的稳健性。结果较高的 OBS 始终与较低的 SUA 水平和高尿酸血症患病率的降低相关。RCS 突显了显著的负非线性关联，尤其是在女性中。 亚组分析揭示了基于性别的差异和交互相关性，为 OBS 与高尿酸血症的关系提供了额外见解。结论本研究强调了 OBS 与 SUA 水平以及高尿酸血症发病率之间的显著负相关性，突出了饮食和生活方式因素的影响。结合 RCS、亚组分析和敏感性评估，增强了我们研究结果的深度，为后续研究提供了有价值的见解。
Uric acid is the final byproduct of purine metabolism in humans. The kidneys eliminate around two-thirds of uric acid as free sodium urate in urine, while the remaining is either excreted through the intestines or broken down by intestinal bacteria [1]. In a healthy individual, the generation and excretion of uric acid are balanced, maintaining stable blood uric acid levels. However, increased factors contributing to uric acid production or impaired renal excretion can result in elevated blood uric acid levels [2]. In clinical practice, hyperuricemia is defined as a serum uric acid level of >= 7mg//dL\geq 7 \mathrm{mg} / \mathrm{dL} in males and >= 6mg//dL\geq 6 \mathrm{mg} / \mathrm{dL} in female [3]. The rise in living standards in recent years has led to an increasing occurrence of hyperuricemia, making it the second most prevalent metabolic disorder after diabetes and posing a significant threat to human health [4]. Statistical data reveals more than 12%12 \% increase in the prevalence of hyperuricemia in the United States between 1988 and 2016 [5]. Hyperuricemia is associated with the development of gout, urinary tract stones, and kidney damage [6]. Furthermore, emerging evidence suggests a close link between hyperuricemia and cardiovascular disease, diabetes, obesity, and other conditions [7, 8]. 尿酸是人体嘌呤代谢的最终产物。肾脏通过尿液排出约三分之二的尿酸，其余部分则通过肠道排出或被肠道细菌分解[1]。在健康个体中，尿酸的生成与排泄保持平衡，维持稳定的血尿酸水平。然而，尿酸生成因素的增加或肾脏排泄功能的损害会导致血尿酸水平升高[2]。在临床实践中，高尿酸血症被定义为男性血清尿酸水平为 >= 7mg//dL\geq 7 \mathrm{mg} / \mathrm{dL} ，女性为 >= 6mg//dL\geq 6 \mathrm{mg} / \mathrm{dL} [3]。近年来生活水平的提高导致高尿酸血症的发生率不断增加，使其成为继糖尿病之后最常见的代谢性疾病，对人类健康构成严重威胁[4]。统计数据显示，1988 年至 2016 年间美国高尿酸血症的患病率增加了超过 12%12 \% [5]。高尿酸血症与痛风、尿路结石和肾脏损伤的发生相关[6]。 此外，越来越多的证据表明高尿酸血症与心血管疾病、糖尿病、肥胖症和其他疾病之间存在密切联系[7, 8]。
Oxidative stress is a pathological condition characterized by a disruption in the equilibrium between prooxidant and antioxidant factors, with a predominance of pro-oxidants. This disruption can result in cellular and tissue damage through oxidative mechanisms, leading to inflammation, cellular injury, and various diseases [9]. Besides dietary factors, the body’s oxidative stress status is also influenced by various lifestyle elements such as obesity, alcohol consumption, smoking, and physical activity [10-12]. . 氧化应激是一种病理状态，其特征是促氧化剂和抗氧化剂因素之间的平衡被破坏，促氧化剂占主导地位。这种破坏可以通过氧化机制导致细胞和组织损伤，进而引发炎症、细胞损伤和各种疾病[9]。除了饮食因素外，身体的氧化应激状态也受到肥胖、饮酒、吸烟和身体活动等多种生活方式因素的影响[10-12]。
The Oxidative Balance Score (OBS) is a refined indicator for evaluating an individual’s state of oxidative balance primarily constituted by dietary and lifestyle elements [13]. Elevated OBS typically suggests a predominance of antioxidants, surpassing pro-oxidants [13]. Numerous epidemiological investigations have demonstrated an inverse correlation between OBS and different inflammation-related diseases, including cardiovascular diseases, chronic kidney disease, as well as type 2 diabetes [14, 15]. However, current studies do not provide sufficient evidence to confirm the relationship between OBS and hyperuricemia. Our study aimed to perform a crosssectional analysis utilizing data from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 to investigate the association between OBS, integrated with lifestyle and dietary components, and hyperuricemia. 氧化平衡评分（OBS）是一种用于评估个体氧化平衡状态的精细指标，主要由饮食和生活习惯因素构成[13]。OBS 升高通常表明抗氧化剂占主导地位，超过促氧化物[13]。大量流行病学研究表明，OBS 与多种炎症相关疾病（包括心血管疾病、慢性肾脏病以及 2 型糖尿病）之间存在负相关关系[14, 15]。然而，目前的研究尚未提供足够的证据来证实 OBS 与高尿酸血症之间的关系。本研究旨在利用 2007 年至 2018 年国家健康与营养调查（NHANES）的数据进行横断面分析，以探讨 OBS（结合生活方式和饮食成分）与高尿酸血症的关联。
Materials and methodology 材料和方法的
Study design and population 研究设计和人群
This study utilized data from 2007 to 2018 obtained from the NHANES, which conducted a national cross-sectional survey and employed a multistage stratified probability sample. These specific cycles were chosen due to the consistent measurement of the variables required, particularly the physical activity questionnaire (PAQ). Among the initially extracted 59,842 participants, we made exclusions for the following reasons: (1) younger than 20 years old ( n=25,072n=25,072 ), ( 2 ) missing SUA level data (n=3,501)(n=3,501), (3) a total count of less than 16 out of the 20 components of the OBS (n=2,145)(n=2,145), (4) loss of WTSAF2YR data ( n=14,965n=14,965 ), and (5) further exclusion of 1,714 participants with extreme energy intake (total energy intakes below 800 or above 4,200kcal//4,200 \mathrm{kcal} / day for males and below 500 or above 3,500kcal//3,500 \mathrm{kcal} / day for females) [16]. Ultimately, the study encompassed 13,636 participants (Fig. 1). 这项研究使用了 2007 年至 2018 年从 NHANES 获取的数据，NHANES 进行了一项全国性横断面调查，并采用多阶段分层概率抽样。选择这些特定周期是因为所需变量的一致测量，特别是身体活动问卷（PAQ）。在最初提取的 59,842 名参与者中，我们因以下原因进行了排除：(1)年龄小于 20 岁 ( n=25,072n=25,072 )，(2)缺少 SUA 水平数据 (n=3,501)(n=3,501) ，(3)OBS 的 20 个组成部分总数少于 16 个 (n=2,145)(n=2,145) ，(4)WTSAF2YR 数据丢失 ( n=14,965n=14,965 )，以及(5)进一步排除 1,714 名极端能量摄入的参与者（男性总能量摄入低于 800 或高于 4,200kcal//4,200 \mathrm{kcal} / 天，女性总能量摄入低于 500 或高于 3,500kcal//3,500 \mathrm{kcal} / 天）[16]。最终，这项研究涵盖了 13,636 名参与者（图 1）。
Serum uric acid level and hyperuricemia 血清尿酸水平和高尿酸血症
We defined the outcomes as serum uric acid (SUA) level and the prevalence of hyperuricemia to investigate their association with the oxidative balance score. Serum samples were collected from the participants and stored at -30^(@)C-30^{\circ} \mathrm{C} before being transmitted to the CDC/NCEH/DLS for examination. The SUA level was measured as part of the routine serum biochemistry profile using the Beckman Coulter UniCel ^("® "){ }^{\text {® }} DxC800. 我们将结果定义为血清尿酸（SUA）水平和高尿酸血症的患病率，以研究它们与氧化平衡评分的相关性。从参与者中收集血清样本，并在传输至 CDC/NCEH/DLS 进行检测前储存在 -30^(@)C-30^{\circ} \mathrm{C} 。SUA 水平作为常规血清生化指标的一部分，使用 Beckman Coulter UniCel ^("® "){ }^{\text {® }} DxC800 进行测量。
Oxidative balance score 氧化平衡评分
The Oxidative Balance Score (OBS) was determined by integrating 16 dietary factors and 4 lifestyle components, comprising 5 pro-oxidants and 15 antioxidants [17]. Dietary OBS, including dietary fiber, total fat, total folate, vitamins (B6, B12, C and E), niacin, carotene, riboflavin, calcium, iron, selenium, copper, magnesium, and zinc, which were collected from dietary recalls. The lifestyle OBS consists of four components: smoking, alcohol consumption, physical activity, and body mass index (BMI). Serum cotinine levels were used to quantify the smoking factor, which reflects both direct smoking and exposure to second-hand smoking. Alcohol consumption and BMI were categorized into three groups based on different gender. Total physical activity was quantified as the metabolic equivalent of task (MET) [18], calculated based on the accumulated time of transportation and moderate and vigorous activities per week. Except for alcohol consumption and BMI, the other OBS components were categorized by gender and divided into tertiles. Scores were assigned to antioxidants in tertiles 1 to 3 as 0,1 , and 2 , respectively, while scores were assigned to pro-oxidants in the opposite order (Table S1). A higher OBS score indicates a more substantial antioxidant effect. This study 氧化平衡评分（OBS）是通过整合 16 个膳食因素和 4 个生活方式成分确定的，包括 5 种促氧化剂和 15 种抗氧化剂[17]。膳食 OBS 包括从膳食回顾中收集的膳食纤维、总脂肪、总叶酸、维生素（B6、B12、C 和 E）、烟酸、胡萝卜素、核黄素、钙、铁、硒、铜、镁和锌。生活方式 OBS 由四个成分组成：吸烟、饮酒、身体活动和体重指数（BMI）。血清可替宁水平用于量化吸烟因素，它反映了直接吸烟和二手烟暴露。饮酒和 BMI 根据不同性别分为三组。总身体活动以代谢当量（MET）[18]量化，基于每周交通和中等及剧烈活动累积时间计算。除饮酒和 BMI 外，其他 OBS 成分按性别分类并分为三分位数。 抗氧化剂在第一至第三层级分别被赋予 0、1 和 2 分，而促氧化剂则按相反顺序被赋予分数（表 S1）。OBS 分数越高，表明抗氧化效果越显著。本研究
Fig. 1 Flowchart showing the selection of the studied population 图 1 显示研究人群选择的流程图
included participants with a total count of at least 16 out of the 20 OBS components. In cases where OBS components were missing, the corresponding component was assigned a score of 0 , regardless of its antioxidant or prooxidant nature. 包括了至少有 16 个 OBS 成分的参与者。在 OBS 成分缺失的情况下，无论其是抗氧化还是促氧化性质，对应的成分均被赋予 0 分。
Covariates 协变量
Prior research and clinical considerations informed the selection of covariates. Demographic characteristics, such as age, race, gender, educational level, and poverty income ratio (PIR), were obtained through standardized household interviews. Age was divided into four categories, each representing a twenty-year range. Race was categorized as Hispanic, non-Hispanic white, non-Hispanic black, and other races. Educational level was classified as below, equal to, and above high school. Poverty was classified into three groups based on PIR (<1.3, [1.3, 3.5 ), and >= 3.5\geq 3.5 ). We also included commonly observed outcomes of metabolic disorders, namely cardiovascular disease (CVD), diabetes, chronic kidney disease (CKD), hypertension, as well as hyperlipidemia. Participants with CKD were defined by a questionnaire: “Ever been told by a doctor or other health professional that you had weak or failing kidneys?” or diagnosed by estimated Glomerular Filtration Rate (eGFR) < 60mL//min*1.73m^(2)<60 \mathrm{~mL} / \min \cdot 1.73 \mathrm{~m}^{2} and albumin-to-creatinine ratio (ACR) >= 30mg//g\geq 30 \mathrm{mg} / \mathrm{g} [16]. The definition of the other comorbidities was collected in Table S2. Daily intake of energy, caffeine, protein, and sugar were also recruited in our study. Values of dietary intake in this study were summarized for each variable. In cases of missing data in the second recall, the average dietary intake from both recalls or sole data from the first 24-hour interview was utilized. 既往研究和临床考量指导了协变量的选择。通过标准化家庭访谈获取了人口统计学特征，如年龄、种族、性别、教育程度和贫困收入比（PIR）。年龄分为四个类别，每类代表 20 年范围。种族分为西班牙裔、非西班牙裔白人、非西班牙裔黑人及其他种族。教育程度分为高中以下、高中同等和高中以上。根据 PIR 将贫困分为三组（<1.3、[1.3, 3.5)和 >= 3.5\geq 3.5 ）。我们还纳入了常见的代谢综合征结果，即心血管疾病（CVD）、糖尿病、慢性肾脏病（CKD）、高血压以及高脂血症。CKD 患者通过问卷定义：“是否曾被医生或其他健康专业人士告知肾脏功能弱或衰竭？”或通过估算肾小球滤过率（eGFR） < 60mL//min*1.73m^(2)<60 \mathrm{~mL} / \min \cdot 1.73 \mathrm{~m}^{2} 和尿白蛋白肌酐比（ACR） >= 30mg//g\geq 30 \mathrm{mg} / \mathrm{g} [16]。其他合并症的定义见表 S2。本研究还收集了每日能量、咖啡因、蛋白质和糖的摄入量。 本研究中各变量的膳食摄入量进行了汇总。在第二次回顾中存在缺失数据的情况下，使用了两次回顾的平均膳食摄入量或仅使用了第一次 24 小时访谈的数据。
Statistical analysis 统计分析
According to the NHANES recommended sample weight on Fasting Subsample 2 Year MEC Weight (WTSAF2YR) records, sample weights of individuals were determined by WTSAF2YR/6. Nonnormally distributed continuous variables were described using median and interquartile range (IQR) to analyze baseline characteristics. Meanwhile, categorical variables were reported as sample counts and weighted percentages. To examine variations in variable characteristics among OBS groups (quartiles), we employed the Wilcoxon rank-sum test for continuous variables and the Rao-Scott chi-squared test for weighted percentages of categorical variables, comprehensively describing the entire population. To compare OBS with different outcomes (hyperuricemia and SUA level), weighted logistic and linear regression were employed. Model 1 represented the crude model, while Model 2 included adjustments for grouped age, gender, race, PIR, and dietary intake of energy, caffeine, protein, and sugar. Model 3 further adjusted for CKD, diabetes, CVDs, hypertension, and hyperlipidemia upon the adjustments in model 2. All regressions incorporated survey weights, and non-normally distributed continuous covariates were transformed using weighted quartiles. We further categorized OBS into dietary and lifestyle subtypes to explore their correlation with outcomes. Additionally, interaction analyses were conducted to assess potential interactions between each subgroup and OBS. Furthermore, a weighted restricted cubic spline (RCS) curve was performed to investigate the potentially nonlinear association between exposure and outcome. Sensitivity assessments were performed by iteratively removing each OBS component from model 3. Statistical analyses were two-sided, and p < 0.05p<0.05 was considered statistically 根据 NHANES 推荐的空腹亚样本 2 年 MEC 权重记录（WTSAF2YR），个体的样本权重由 WTSAF2YR/6 确定。非正态分布的连续变量使用中位数和四分位距（IQR）描述以分析基线特征。同时，分类变量以样本计数和加权百分比报告。为检验 OBS 组（四分位数）中变量特征的差异，我们采用 Wilcoxon 秩和检验分析连续变量，采用 Rao-Scott 卡方检验分析分类变量的加权百分比，全面描述整个人群。为比较具有不同结局（高尿酸血症和 SUA 水平）的 OBS，采用加权逻辑回归和线性回归进行比较。模型 1 代表原始模型，模型 2 在模型 1 的基础上调整了分组年龄、性别、种族、PIR 以及能量、咖啡因、蛋白质和糖的膳食摄入。模型 3 在模型 2 的基础上进一步调整了 CKD、糖尿病、CVDs、高血压和血脂异常。 所有回归分析均包含了调查权重，且非正态分布的连续协变量通过加权四分位数进行了转换。我们进一步将 OBS 分为饮食和生活方式亚型，以探索其与结果的相关性。此外，进行了交互分析，以评估每个亚型与 OBS 之间潜在的相互作用。此外，还进行了加权限制性三次样条（RCS）曲线分析，以研究暴露与结果之间潜在的非线性关联。通过迭代地从模型 3 中移除每个 OBS 成分进行敏感性评估。统计分析为双尾检验， p < 0.05p<0.05 被认为具有统计学意义。
^(†){ }^{\dagger} Fengmin Liu and Fangqin You contributed equally to this work. ^(†){ }^{\dagger} 冯民刘和游方琴对这项工作贡献相同。
*Correspondence: *通讯：
Geng Chen 陈耿 339729173@qq.com
Diya Xie diego4_2@outlook.com ^(1){ }^{1} Department of Endocrinology, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian 350009, China ^(1){ }^{1} 福建医科大学附属福州第一医院内分泌科，福州，福建 350009，中国 ^(2){ }^{2} Department of General Surgery, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian 350009, China ^(2){ }^{2} 福建医科大学附属福州第一医院普外科，福州，福建 350009，中国 ^(3){ }^{3} Nursing Department, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian 350009, China ^(3){ }^{3} 福建医科大学附属福州第一医院护理部，福州，福建 350009，中国
