Oxidative Balance, Nutrition, and Cognitive Function in Older Adults: A Mediation Analysis Using NHANES 2011–2014 Data

Oxidative Balance, Nutrition, and Cognitive Function in Older Adults: A Mediation Analysis Using NHANES 2011–2014 Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Oxidative Balance, Nutrition, and Cognitive Function in Older Adults: A Mediation Analysis Using NHANES 2011–2014 Data Shouxin Wei, Sijia Yu, Chuan Qian, Yunsheng Lan, Yindong Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6407269/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The global rise in cognitive function impairment (CFI) parallels an aging population. While oxidative stress is considered a contributing factor, its role in CFI remains underexplored. This study examines the relationship between oxidative balance score (OBS) and CFI using 2011–2014 National Health and Nutrition Examination Survey (NHANES) data. Methods Data from 2,089 participants aged 60 and above were analyzed. OBS was calculated from 16 dietary and 4 lifestyle components. Cognitive function was evaluated using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Multivariable weighted logistic regression, restricted cubic splines (RCS), subgroup, and mediation analyses assessed the association between OBS and CFI, adjusting for age, sex, and comorbidities. Sensitivity analysis tested result robustness. Results Higher OBS was significantly associated with a lower CFI risk after adjusting for confounders. RCS indicated a linear negative correlation between OBS and CFI. Subgroup analysis showed stronger associations in non-Hispanic Black and non-Hispanic White populations. Mediation analysis revealed the Geriatric Nutritional Risk Index (GNRI) and albumin/globulin ratio (AGR) had opposing mediating effects. Sensitivity analysis confirmed the stability of these findings in unweighted models. Conclusion Higher oxidative balance is significantly linked to reduced CFI risk in older adults. Enhancing oxidative balance through diet and lifestyle modifications could be a promising strategy to maintain cognitive health. Oxidative Balance Score Cognitive Function Impairment Oxidative Stress NHANES Geriatric Nutritional Risk Index. Figures Figure 1 Figure 2 Figure 3 1. Introduction Cognitive function impairment (CFI) refers to deficits in various critical brain functions, including memory, learning, attention, and decision-making[ 1 , 2 ]. With the accelerating global aging population, the incidence of CFI has significantly increased, particularly in relation to Alzheimer’s disease (AD) and its related dementias (ADRD) [ 3 ]. Currently, approximately 50 million individuals worldwide are affected by AD, and this number is projected to rise to 139 million by 2050 [ 4 ]. Mild cognitive impairment (MCI) represents an intermediate stage between normal aging and dementia. While MCI leads to cognitive decline, its impact on daily life is relatively minor. It is estimated that approximately 15% of MCI patients will progress to AD within two years[ 5 ]. Although dementia is generally considered irreversible, studies indicate that around 16% of MCI patients may regain normal cognitive function [ 6 ]. CFI imposes a significant burden on individuals, families, and society as a whole. Therefore, early identification and intervention are crucial for slowing the progression of CFI. Research suggests that CFI may be associated with factors such as aging, genetic predisposition, lifestyle, chronic diseases, and infections, with oxidative stress playing a key role in cognitive decline [ 7 ]. Aging and disease conditions disrupt the balance between the production and clearance of reactive oxygen species (ROS), leading to oxidative stress [ 8 ]. Neuronal cells in the brain are highly susceptible to oxidative stress, and excessive oxygen free radicals cause lipid and protein peroxidation, ultimately resulting in neuronal cell death [ 9 ]. The accumulation of such damage adversely affects memory and learning abilities, contributing to the onset of CFI. The Oxidative Balance Score (OBS) is a quantitative index used to assess exposure to antioxidants and pro-oxidants in diet and lifestyle. By comprehensively analyzing the quantity and types of antioxidants and pro-oxidants consumed, OBS reflects the overall oxidative stress level [ 10 ]. A higher OBS value indicates greater antioxidant exposure, whereas a lower OBS value signifies increased pro-oxidant exposure. Key components used to evaluate OBS include dietary antioxidants such as vitamin C, vitamin E, and carotenoids, as well as lifestyle factors such as smoking and alcohol consumption, which have been shown to influence cognitive function [ 11 – 13 ]. However, no studies to date have established a direct relationship between OBS and cognitive function. This study utilizes data from two consecutive cycles (2011–2014) of the National Health and Nutrition Examination Survey (NHANES), incorporating various demographic and disease-related factors to explore the potential association between OBS and CFI. The findings of this study will provide scientific evidence for a deeper understanding of the protective role of antioxidant exposure on cognitive function and may serve as a valuable reference for public health policy development. 2. Methods 2.1. Study population The National Health and Nutrition Examination Survey (NHANES) is conducted by the National Center for Health Statistics (NCHS) and has been assessing the health and nutritional status of U.S. adults and children since the early 1960s. NHANES data and study design are highly transparent, with all public data and detailed survey methodologies available on the NHANES official website. The study protocol was approved by the NCHS Ethics Review Board to ensure compliance with ethical standards. All participants provided written informed consent before participating in the survey.This study utilized data from two consecutive NHANES cycles (2011–2012 and 2013–2014), comprising a total of 19,931 participants. The exclusion criteria were as follows: (1) age < 60 years (n = 16,299); (2) individuals missing dietary data required for OBS calculation (n = 1,319); (3) individuals lacking CFI assessment data (n = 210); and (4) individuals missing albumin/globulin ratio (AGR) data (n = 14). Ultimately, 2,089 participants were included in the final analysis, consisting of 1,013 males and 1,076 females (Figure S1 ). 2.2. Exposure assessment OBS is a comprehensive index that quantifies an individual's exposure to antioxidants and pro-oxidants by integrating dietary and lifestyle factors. The calculation of OBS is based on 16 dietary components and four lifestyle factors. Dietary data were obtained from two 24-hour dietary recall interviews conducted as part of NHANES. The included dietary components are dietary fiber, carotenoids, riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E, calcium, magnesium, zinc, copper, selenium, total fat, and iron. The lifestyle factors considered are physical activity, alcohol consumption, smoking status, and body mass index (BMI).Among these components, total fat, iron, BMI, alcohol consumption, and smoking are classified as pro-oxidants, whereas the remaining components are categorized as antioxidants. Cotinine, a primary metabolite of nicotine, was used to assess smoking status through serum cotinine levels. Physical activity was quantified by multiplying the frequency and duration of each activity performed per week by the corresponding metabolic equivalent of task (MET) score. Alcohol consumption was categorized into three groups: non-drinkers, moderate drinkers (0–15 g/day for women and 0–30 g/day for men), and heavy drinkers (≥ 15 g/day for women and ≥ 30 g/day for men), with assigned scores of 2, 1, and 0, respectively. Other components were scored based on their tertile distribution within sex-specific groups, where antioxidants were assigned scores ranging from 0 to 2, and pro-oxidants were scored inversely. The total OBS score was derived by summing the individual component scores. 2.3. Outcome assessment In the NHANES (2011–2014) study, participants underwent multiple assessments, including the CERAD Word Learning and Recall Module, the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). The CERAD test consists of three consecutive learning trials and a delayed recall trial, where participants are asked to read 10 unrelated words and recall as many as possible. The delayed recall section is conducted after the AFT and DSST. The maximum possible score for the CERAD test is 40. In the AFT, participants are required to name as many different animals as possible within one minute. The AFT score is determined by the total number of animals named within the time limit, reflecting participants' ability to rapidly generate words within a specific semantic category. The DSST is a performance subtest of the Wechsler Adult Intelligence Scale (WAIS-III), designed to assess processing speed, sustained attention, and working memory. In this test, participants must match 133 numbers with their corresponding symbols within two minutes. The DSST assesses not only cognitive processing speed but also visuomotor coordination and cognitive flexibility, with a maximum score of 133.To account for individual differences across tests and mitigate scale attenuation effects, we standardized the test results by computing scores for each cognitive assessment and integrating them into a global cognition score, providing a more comprehensive evaluation of participants’ overall cognitive function. The global cognition score was calculated using the formula: Global.Cognition = (x - m) / σ, where x represents the individual's test score, m denotes the mean test score, and σ represents the standard deviation. Currently, there is no universally established threshold for identifying low cognitive performance. Based on previous studies, we defined the 25th percentile as the cutoff: CERAD < 21, AFT < 13, DSST < 35, or Global.Cognition < -1.43. Participants falling below any of these thresholds were classified as having cognitive impairment[ 14 ]. 2.4. Covariates To minimize potential confounding factors in our study, we reviewed the literature and incorporated relevant covariates into our analysis [ 1 , 15 ]. These covariates included age, sex, race/ethnicity, education level, marital status, poverty-to-income ratio (PIR), hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. Sex was categorized as male or female, while race/ethnicity was classified into Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, and other races. Marital status was classified as married/cohabiting, divorced/separated/widowed, or never married. Education level was categorized as below high school, high school graduate, or above high school. PIR was categorized into low (< 1.3), medium (1.3–3.5), and high (≥ 3.5).Hypertension was defined as: (1) a physician-diagnosed condition; (2) an average systolic blood pressure of ≥ 130 mmHg or an average diastolic blood pressure of ≥ 80 mmHg; or (3) current use of antihypertensive medication. Diabetes was defined as: (1) a physician-diagnosed condition; (2) a hemoglobin A1c (HbA1c) level of ≥ 6.5%; or (3) current use of diabetes medication or insulin. Depression was defined as a Patient Health Questionnaire-9 (PHQ-9) score of ≥ 10. Diagnoses of liver disease, stroke, and arthritis were based on self-reported data. Individuals diagnosed with congestive heart failure, coronary heart disease, myocardial infarction, or angina were classified as having heart disease, whereas those diagnosed with asthma, chronic bronchitis, or emphysema were classified as having lung disease.Mediator variables included the Geriatric Nutritional Risk Index (GNRI) and the albumin-to-globulin ratio (AGR); detailed calculation formulas are provided in the supplementary materials. 2.5. Statistical analysis Based on NHANES guidelines on weighting, we accounted for complex sampling weights and incorporated one-half of the laboratory weights. Continuous variables were expressed as means, while categorical variables were presented as percentages. Statistical significance was assessed using the t-test and chi-square test.To analyze the association between OBS and CFI, we conducted multivariable weighted logistic regression. The crude model was unadjusted, while Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was further adjusted for age, sex, race/ethnicity, education level, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. Results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs).We also applied restricted cubic spline models to fit the association between OBS and CFI and conducted stratified analyses to further validate the robustness of the results. Additionally, we performed stepwise regression-based mediation analysis using the "mediation" R package to examine the mediating effect of GNRI and AGR in the OBS-CFI association. Finally, we conducted a sensitivity analysis by exploring the OBS-CFI association using unweighted data to assess the robustness of our findings. All analyses were performed using R (version 4.4.1, http://www.R-project.org ) and EmpowerStats (version 4.2, http://www.empowerstats.com ). A p-value < 0.05 was considered statistically significant. 3. Results 3.1.Participant characteristics This study included 2,089 participants aged 60 years and older. As shown in Table 1 , the participants had a mean age of 69.1 years, with 48.49% being male. A total of 895 individuals met the CFI criteria, accounting for 42.84% of the sample.Compared to the non-CFI group, participants in the CFI group were older, had a higher proportion of males, a higher percentage of non-Hispanic Black individuals, and lower education levels. They were more likely to be divorced, separated, or widowed, had lower economic status (lower PIR), and exhibited a higher prevalence of hypertension, diabetes, depression, heart disease, stroke, and arthritis. Additionally, they had relatively lower GNRI and AGR levels.Compared to other OBS groups, participants in the highest OBS group had a higher proportion of non-Hispanic White individuals, higher education levels, and better economic status (higher PIR). They also had lower prevalence rates of hypertension, diabetes, and heart disease, along with lower GNRI levels and higher AGR levels (Table S1 ). Table 1 Baseline characteristics of participants by CFI in NHANES 2011–2014. Characteristics Total (n = 2089) non CFI(n = 1194) CFI (n = 895) P-value AGE(years) 69.10 (68.73 ,69.48) 67.73 (67.34 ,68.12) 72.15 (71.37 ,72.94) < 0.0001 GNRI 121.94 (120.99 ,122.88) 122.51 (121.39 ,123.63) 120.67 (119.37 ,121.96) 0.0205 AGR 1.60 (1.57 ,1.63) 1.63 (1.59 ,1.66) 1.53 (1.50 ,1.56) < 0.0001 Sex 0.0032 Male 1013 (48.49%) 521 (43.63%) 492 (54.97%) Female 1076 (51.51%) 673 (56.37%) 403 (45.03%) Race < 0.0001 Mexican American 176 (8.43%) 85 (7.12%) 91 (10.17%) Other Hispanic 199 (9.53%) 74 (6.20%) 125 (13.97%) Non-Hispanic White 1084 (51.89%) 714 (59.80%) 370 (41.34%) Non-Hispanic Black 458 (21.92%) 217 (18.17%) 241 (26.93%) Other Race 172 (8.23%) 104 (8.71%) 68 (7.60%) Education level < 0.0001 Below high school 483 (23.12%) 132 (11.06%) 351 (39.22%) High school 499 (23.89%) 267 (22.36%) 232 (25.92%) Above high school 1107 (52.99%) 795 (66.58%) 312 (34.86%) Marital status 0.0007 Married or living with partner 1247 (59.69%) 748 (62.65%) 499 (55.75%) Divorced, separated, or widowed 728 (34.85%) 380 (31.83%) 348 (38.88%) Never married 114 (5.46%) 66 (5.53%) 48 (5.36%) PIR < 0.0001 < 1.3 536 (25.66%) 212 (17.76%) 324 (36.20%) 1.3–3.5 914 (43.75%) 503 (42.13%) 411 (45.92%) ≥ 3.5 639 (30.59%) 479 (40.12%) 160 (17.88%) Hypertension 0.0002 Yes 430 (20.58%) 284 (23.79%) 146 (16.31%) NO 1659 (79.42%) 910 (76.21%) 749 (83.69%) Diabetes 0.0248 Yes 1501 (71.85%) 905 (75.80%) 596 (66.59%) NO 588 (28.15%) 289 (24.20%) 299 (33.41%) PHQ Score 0.0014 < 10 179 (8.57%) 69 (5.78%) 110 (12.29%) ≥ 10 1910 (91.43%) 1125 (94.22%) 785 (87.71%) Liver disease 0.4117 Yes 62 (2.97%) 34 (2.85%) 28 (3.13%) NO 2027 (97.03%) 1160 (97.15%) 867 (96.87%) Pulmonary disease 0.6106 Yes 362 (17.33%) 209 (17.50%) 153 (17.09%) NO 1727 (82.67%) 985 (82.50%) 742 (82.91%) Heart disease 0.0002 Yes 370 (17.71%) 171 (14.32%) 199 (22.23%) NO 1719 (82.29%) 1023 (85.68%) 696 (77.77%) Stroke 0.0004 Yes 129 (6.18%) 47 (3.94%) 82 (9.16%) NO 1960 (93.82%) 1147 (96.06%) 813 (90.84%) Arthritis 0.0033 Yes 1042 (49.88%) 576 (48.24%) 466 (52.07%) NO 1047 (50.12%) 618 (51.76%) 429 (47.93%) n: Number of study samples.PIR: Poverty Income Ratio;OBS:Oxidative Balance Score;GNRI:Geriatric Nutritional Risk Index;AGR:Albumin-to-Globulin Ratio. 3.2.Association Between OBS and CFI OBS was analyzed as both a continuous variable and a categorical variable (quartiles) using weighted logistic regression with CFI as the outcome variable. As shown in Table 2 , a significant negative association was observed between OBS and CFI (p < 0.05).In the fully adjusted Model 2, each standard deviation increase in OBS was associated with a 4.4% reduction in CFI risk [OR (95% CI) = 0.956 (0.934, 0.978)]. This negative association became more pronounced as OBS increased. Compared to the lowest quartile, in the highest quartile, each standard deviation increase in OBS was associated with a 59.6% reduction in CFI risk [OR (95% CI) = 0.404 (0.249, 0.655), ptrend = 0.005]. Further analysis using restricted cubic spline (RCS) regression, as shown in Fig. 1, indicated a linear negative association between OBS and CFI (P for nonlinearity = 0.1). Table 2 . Association between OBS and CFI. Characteristics Crude Model OR (95% CI) Model 1 OR (95% CI) Model 2 OR (95% CI) Continuous OBS 0.934 (0.916, 0.953) < 0.001 0.938 (0.917, 0.960) <0.001 0.956 (0.934, 0.978) 0.003 OBS quartiles Q1 1[Ref] 1[Ref] 1[Ref] Q2 0.487 (0.347, 0.684) < 0.001 0.489 (0.318, 0.750) 0.003 0.567 (0.386, 0.834) 0.018 Q3 0.425 (0.312, 0.580) < 0.001 0.467 (0.314, 0.696) 0.001 0.583 (0.399, 0.853) 0.021 Q4 0.273 (0.180, 0.415) < 0.001 0.291 (0.175, 0.484) <0.001 0.404 (0.249, 0.655) 0.005 P for trend < 0.001 < 0.001 0.005 OR, odd ratio; CI, confidence interval. Crude model: Unadjusted for covariates. Model 1: Adjusted for age, sex, and race. Model 2: Adjusted for age, sex, race ,education, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. Additionally, linear regression analysis was performed to examine the association between OBS and various cognitive function test scores. The results showed a significant positive association between OBS and all cognitive function test scores. Specifically, in the fully adjusted model, each standard deviation increase in OBS was associated with a 0.094-point increase in CERAD score [β(95% CI) = 0.094 (0.051, 0.137)], a 0.107-point increase in AFT score [β(95% CI) = 0.107 (0.068, 0.147)], a 0.213-point increase in DSST score [β(95% CI) = 0.213 (0.097, 0.329)], and a 0.046-point increase in Global Cognition score [β(95% CI) = 0.046 (0.030, 0.062)] (Table 3 ). Table 3 Association between OBS and test scores of cognitive function. Characteristics CERAD test AFT DSST Global.Cognition β (95% CI) p-value β (95% CI) p-value β (95% CI) p-value β (95% CI) p-value OBS 0.094 (0.051, 0.137) 0.001 0.107 (0.068, 0.147) < 0.001 0.213 (0.097, 0.329) 0.004 0.046 (0.030, 0.062) < 0.001 β : regression coefficient; 95% CI: 95% Confidence Interval;CERAD:the Consortium to Establish a Registry for Alzheimer’s Disease; AFT:Animal Fluency Test; DSST:Digit Symbol Substitution Test.Adjusted for age, sex, race ,education, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. 3.3.Subgroup Analysis We conducted a subgroup analysis stratified by age, sex, race/ethnicity, and the presence of hypertension or diabetes to determine whether the association between OBS and CFI remained consistent across different subgroups. As shown in Fig. 2, the association between OBS and CFI remained generally stable across age, sex, hypertension, and diabetes subgroups (P for interaction > 0.05).A significant interaction was observed across different racial/ethnic groups. Compared to other racial groups, the negative association between OBS and CFI was more pronounced among non-Hispanic Black and non-Hispanic White individuals (P for interaction < 0.05). 3.4.Association of GNRI and AGR with OBS and CFI The weighted linear regression analysis of OBS with GNRI and AGR is presented in Table S2. In the fully adjusted model, OBS was negatively associated with GNRI [β(95% CI) = -0.153 (-0.244, -0.061)] but positively associated with AGR [β(95% CI) = 0.005 (0.003, 0.008)].Table S3 presents the multivariable weighted logistic regression results examining the association between GNRI, AGR, and CFI. In the fully adjusted model, GNRI was negatively associated with CFI risk [OR (95% CI) = 0.989 (0.982, 0.995)], and AGR was also negatively associated with CFI risk [OR (95% CI) = 0.578 (0.375, 0.892)]. 3.5.Mediation Effect of GNRI and AGR We further conducted a mediation analysis to explore the potential mechanisms underlying the effect of OBS on CFI (Table S4). After adjusting for all covariates, OBS was significantly associated with CFI, with a total effect coefficient of -0.052 (p < 0.001). The indirect effect of GNRI was 0.002 (p = 0.038), accounting for − 4.46% of the total effect (p = 0.038), while the indirect effect of AGR was − 0.003 (p = 0.004), contributing to 5.79% of the total effect (p = 0.004) (Fig. 3). 3.6.Sensitivity Analysis To assess the robustness of our results, we conducted a sensitivity analysis. After removing sample weighting, OBS remained significantly negatively associated with CFI (Table S5).In the fully adjusted model, each standard deviation increase in OBS was associated with a 5.5% reduction in CFI risk [OR (95% CI) = 0.975 (0.960, 0.990)]. Compared to the lowest quartile, in the highest quartile, each standard deviation increase in OBS was associated with a 37.2% reduction in CFI risk [OR (95% CI) = 0.628 (0.468, 0.841), ptrend = 0.004]. 4. Discussion With global population aging, CFI has emerged as a significant public health challenge.OBS is a novel composite indicator designed to assess an individual's overall oxidative balance by measuring multiple oxidative and antioxidative factors. Previous studies have demonstrated an inverse association between OBS and the incidence of conditions such as stroke, kidney stones, and depression [ 10 , 16 , 17 ]. To our knowledge, this study is the first comprehensive retrospective analysis investigating the association between OBS and CFI. Data were obtained from 2,089 participants aged 60 years and older from the 2011–2014 NHANES. The results indicated an inverse relationship between OBS and CFI after adjusting for all potential confounding factors. These findings underscore the importance of maintaining antioxidative balance in preventing cognitive decline. The impact of oxidative stress on cognitive function involves multiple complex biological mechanisms. The brain consumes approximately 20% of total oxygen intake to maintain normal function, a high level of oxygen consumption that generates free radicals [ 18 ]. Under aging and pathological conditions, the production of ROS significantly increases, directly attacking and damaging the cell membranes of central nervous system neurons, ultimately leading to impaired cellular function. Moreover, ROS can impair mitochondrial DNA and membrane proteins in neurons, disrupting energy supply and ultimately leading to neuronal death [ 19 ]. Oxidative stress can induce inflammatory responses in the brain. Inflammatory responses activate microglia and astrocytes in the brain, which normally function to maintain neuronal homeostasis [ 20 ]. However, when these cells undergo sustained activation, they release pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and chemokines (C-C motif), mediating neuroinflammation and neurodegeneration. Additionally, peripheral inflammatory mediators cross a compromised blood-brain barrier, exacerbating inflammation [ 21 ]. These inflammatory responses further damage neurons, ultimately resulting in neural network dysfunction. AD is the most common neurodegenerative disorder, with cognitive decline as its core clinical symptom. Current etiological theories of AD primarily include the cholinergic hypothesis and the extracellular β-amyloid plaque deposition hypothesis [ 22 ]. Acetylcholine is the primary neurotransmitter associated with learning and memory, playing a critical role in neuronal activity, plasticity, and network connectivity. Oxidative stress can impair choline acetyltransferase, leading to reduced acetylcholine levels [ 23 ]. Additionally, oxidative stress can damage acetylcholine receptors, further impairing synaptic transmission and resulting in deficits in learning and memory [ 24 ]. ROS in the brain can disrupt the metabolic processing of amyloid precursor protein, leading to the production of β-amyloid. This aberrant metabolite tends to aggregate into insoluble plaques that deposit around neurons, interfering with normal synaptic transmission, causing neuronal dysfunction, and ultimately triggering cognitive decline [ 25 ]. Previous studies have demonstrated an association between dietary components and cognitive function. A meta-analysis indicated that B vitamins, vitamin C, and vitamin E help protect the brain from oxidative stress, and dietary supplementation with these vitamins may improve cognitive function during aging [ 26 ]. Wang et al. [ 27 ] reported that patients with dementia had significantly lower blood carotenoid levels compared to controls, suggesting that low carotenoid levels may be a risk factor for dementia and MCI. Meng et al. [ 28 ] demonstrated an L-shaped inverse association between dietary zinc intake and cognitive decline in older adults, indicating that maintaining adequate dietary zinc levels may help prevent cognitive deterioration. A cross-sectional study found that higher dietary intakes of iron and copper were associated with poorer cognitive function [ 29 ]. Similarly, Xu et al. [ 30 ] confirmed this finding, showing that higher copper exposure may be associated with cognitive impairment. However, another prospective cohort study reported a nonlinear inverse association between dietary copper intake and cognitive decline in older adults [ 31 ], possibly due to differences in the proportion of copper forms present in the body. Copper can act as both an antioxidant and a pro-oxidant within the body. Chan et al. [ 32 ] proposed that copper serves as a cofactor for CuZn-superoxide dismutase, specifically scavenging superoxide radicals. In contrast, Valko et al. [ 33 ] found that copper ions catalyze the conversion of hydrogen peroxide into highly reactive hydroxyl radicals, leading to oxidative damage to lipids, proteins, and DNA. The association between dietary magnesium and cognitive impairment remains inconsistent. A cohort study from China showed that high dietary magnesium intake was associated with an increased risk of dementia, while a Japanese cohort study suggested that higher magnesium intake reduced the risk of all-cause dementia in the general population [ 34 , 35 ]. Chen et al. [ 36 ] proposed that the dietary calcium-to-magnesium (Ca:Mg) intake ratio may explain these discrepancies, with magnesium acting as a risk factor for dementia under low calcium and high magnesium conditions, and as a protective factor under high calcium and low magnesium conditions. Fat is a major pro-oxidant component in OBS. Animal studies have shown that prolonged high-fat diet consumption leads to excessive phosphorylation of Tau protein in the hippocampus, activation of microglia, and expression of inflammatory factors, ultimately resulting in cognitive decline [ 37 ]. Multiple studies have confirmed that physical activity serves as a protective factor for cognitive function, and encouraging older adults to remain physically active may help prevent cognitive decline and the onset of dementia [ 38 ]. Smoking exacerbates the burden of cerebrovascular diseases such as cerebral infarction, impairs cerebral blood flow, and aggravates neuronal damage. Furthermore, smoking may promote white matter degeneration by inhibiting myelin synthesis, thereby increasing the risk of AD [ 39 ]. Similarly, alcohol consumption is positively associated with cognitive decline in older adults. Even alcohol consumption exceeding one standard unit adversely affects cognitive performance [ 40 ]. Evidence regarding the relationship between body mass index (BMI) and cognitive function remains conflicting. Naomi et al. [ 41 ]suggested that obesity induces oxidative stress and inflammation, thereby increasing the risk of cognitive impairment and dementia. In contrast, Sun et al. [ 42 ] found that obesity in late life was associated with improved cognitive function and a reduced risk of dementia. This phenomenon may reflect the "obesity paradox," wherein weight loss in older adults is directly linked to worsening health and increased mortality, while obese individuals may possess greater physiological reserves and a lower likelihood of severe comorbidities [ 43 ]. Our study revealed significant associations of both the GNRI and AGR with the oxidative balance score (OBS) and CFI. Further mediation analysis highlighted the pivotal role of GNRI and AGR in the relationship between OBS and CFI, underscoring nutrition as a critical bridge linking oxidative balance and cognitive function. However, the direction of mediation effects differed between GNRI and AGR, which may be attributed to their distinct associations with OBS. A seven-year retrospective cohort study found that lower baseline serum albumin levels were associated with an increased risk of MCI [ 44 ]. Maeda [ 45 ] and Yang [ 46 ] demonstrated a positive association between AGR and cognitive function in Japanese and American populations, respectively, while Liu [ 14 ] also reported a positive association between GNRI and cognitive function. In contrast to the consistent impact of AGR and GNRI on cognitive function, their associations with OBS were inconsistent. Albumin, a classic antioxidant, has been widely recognized for its antioxidative properties in numerous studies [ 47 , 48 ]. However, GNRI was negatively associated with OBS, possibly due to the significant weight component within the GNRI calculation. Individuals with higher OBS typically exhibit lower BMI and body fat percentage, as higher antioxidant intake may contribute to weight reduction [ 49 , 50 ]. This finding highlights the complexity of the relationship between OBS and CFI. This study possesses several strengths. We integrated all available continuous NHANES cycle data, encompassing a large sample of older adults from across the United States, which enhanced the statistical robustness and generalizability of our findings. Our study design accounted for multiple potential confounders, including sex, race, income, and comorbidities, with adjustments made using multivariable analytical methods to ensure the accuracy and reliability of the results. The OBS incorporates various oxidative stress-related indicators, providing a comprehensive reflection of the body's overall oxidative status. Compared with single biomarkers, OBS offers greater comprehensiveness and representativeness. For the first time, we identified a significant association between OBS and CFI and revealed the mediating roles of GNRI and AGR. This finding provides new insights into the potential role of oxidative stress in CFI and may facilitate the identification of populations at risk for cognitive impairment, enabling the development of personalized interventions. Nevertheless, this study has several limitations. First, as a cross-sectional study, it does not capture the dynamic changes over time between OBS and cognitive function. Second, cognitive assessment is inherently complex, and the cognitive tests in the NHANES database may not comprehensively cover all cognitive domains. Although multiple confounders were adjusted for, it remains challenging to eliminate all external variables that may influence the results. These unmeasured confounders could potentially affect the accuracy of the study. Future prospective longitudinal studies are warranted to further validate the relationship between OBS and CFI, thereby enhancing the stability and reliability of the findings. 5. Conclusion Our findings indicate that higher OBS levels are generally associated with a lower risk of CFI among older adults in the United States, with GNRI and AGR mediating this association. Antioxidant-rich dietary and lifestyle modifications may contribute to the improvement of cognitive function in older adults. However, the underlying mechanisms through which OBS influences cognitive function warrant further investigation. Abbreviations Abbreviation Full form AD Alzheimer’s disease AFT Animal Fluency Test AGR Albumin/globulin ratio BMI Body Mass Index CERAD Consortium to Establish a Registry for Alzheimer’s Disease CFI Cognitive function impairment DSST Digit Symbol Substitution Test GNRI Geriatric Nutritional Risk Index MCI Mild cognitive impairment MET Metabolic equivalent of task NCHS The National Center for Health Statistics NHANES National Health and Nutrition Examination Survey OBS Oxidative balance score PIR Poverty-to-Income Ratio RCS Restricted cubic splines ROS Reactive oxygen species Declarations Ethics approval and consent to participate ： The ethical review board of the National Center for Health Statistics granted approval to the NHANES protocols, and ethical approval was exempted in this study. Consent for publication ： Not applicable. Availability of data and materials: The data backing the findings of this article can be found in the NHANES repository (https://wwwn.cdc.gov/nchs/nhanes/default.aspx, accessed 04/07/2025). The code for statistical analysis can be provided upon request, and the raw data backing the conclusions of this paper can be obtained from the corresponding author. Competing interests: The authors declare that they have no competing interests Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions: Shouxin Wei: Conceptualization, Data Curation, Formal Analysis, Software, Writing – Original Draft. Sijia Yu: Conceptualization, Methodology,Writing – Review & Editing. Chuan Qian:Resources, Validation, Writing – review & editing.Yunsheng Lan: Formal Analysis, Data Curation, Visualization. Yindong Jia: Writing – Review & Editing, Project administration. Acknowledgements: The authors acknowledge the important contributions of all the staff and participants in this study. Clinical trial number: not applicable. References Zhao C, Pu M, Wu C, Ding J, Guo J, Zhang G. Association between composite dietary antioxidant index and cognitive function impairment among the US older adults: a cross-sectional study based on the NHANES 2011-2014. Frontiers in nutrition. 2024;11:1471981.doi:10.3389/fnut.2024.1471981. Yen FS, Wang SI, Lin SY, Chao YH, Wei JC. The Impact of Alcohol Consumption on Cognitive Impairment in Patients With Diabetes, Hypertension, or Chronic Kidney Disease. Frontiers in medicine. 2022;9:861145.doi:10.3389/fmed.2022.861145. Li X, Feng X, Sun X, Hou N, Han F, Liu Y. Global, regional, and national burden of Alzheimer's disease and other dementias, 1990-2019. Frontiers in aging neuroscience. 2022;14:937486.doi:10.3389/fnagi.2022.937486. Deng Y, Wang H, Gu K, Song P. Alzheimer's disease with frailty: Prevalence, screening, assessment, intervention strategies and challenges. Bioscience trends. 2023;17(4):283-92.doi:10.5582/bst.2023.01211. Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, Gronseth GS, Marson D, Pringsheim T, Day GS, Sager M, Stevens J, Rae-Grant A. Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(3):126-35.doi:10.1212/wnl.0000000000004826. McGirr A, Nathan S, Ghahremani M, Gill S, Smith EE, Ismail Z. Progression to Dementia or Reversion to Normal Cognition in Mild Cognitive Impairment as a Function of Late-Onset Neuropsychiatric Symptoms. Neurology. 2022;98(21):e2132-e9.doi:10.1212/wnl.0000000000200256. Yaffe K, Fiocco AJ, Lindquist K, Vittinghoff E, Simonsick EM, Newman AB, Satterfield S, Rosano C, Rubin SM, Ayonayon HN, Harris TB. Predictors of maintaining cognitive function in older adults: the Health ABC study. Neurology. 2009;72(23):2029-35.doi:10.1212/WNL.0b013e3181a92c36. Colita E, Mateescu VO, Olaru DG, Popa-Wagner A. Cognitive Decline in Ageing and Disease: Risk factors, Genetics and Treatments. Current health sciences journal. 2024;50(2):170-80.doi:10.12865/chsj.50.02.02. Angelova PR, Esteras N, Abramov AY. Mitochondria and lipid peroxidation in the mechanism of neurodegeneration: Finding ways for prevention. Medicinal research reviews. 2021;41(2):770-84.doi:10.1002/med.21712. Zhan F, Lin G, Duan K, Huang B, Chen L, Ni J. Higher oxidative balance score decreases risk of stroke in US adults: evidence from a cross-sectional study. Frontiers in cardiovascular medicine. 2023;10:1264923.doi:10.3389/fcvm.2023.1264923. Nooyens AC, Milder IE, van Gelder BM, Bueno-de-Mesquita HB, van Boxtel MP, Verschuren WM. Diet and cognitive decline at middle age: the role of antioxidants. The British journal of nutrition. 2015;113(9):1410-7.doi:10.1017/s0007114515000720. Qiu T, Zeng Q, Luo X, Xu T, Shen Z, Xu X, Wang C, Li K, Huang P, Li X, Xie F, Dai S, Zhang M. Effects of Cigarette Smoking on Resting-State Functional Connectivity of the Nucleus Basalis of Meynert in Mild Cognitive Impairment. Frontiers in aging neuroscience. 2021;13:755630.doi:10.3389/fnagi.2021.755630. Topiwala A, Ebmeier KP. Effects of drinking on late-life brain and cognition. Evidence-based mental health. 2018;21(1):12-5.doi:10.1136/eb-2017-102820. Liu J, Oorloff MD, Nadella A, Zhou N, Ye M, Tang Y, Wang YJFiN. Association between lower geriatric nutritional risk index and low cognitive functions in United States older adults: a cross-sectional study. 2024;11:1483790 Song L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. The Journal of nutrition. 2023;153(7):1974-83.doi:10.1016/j.tjnut.2023.05.014. Ke R, He Y, Chen C. Association between oxidative balance score and kidney stone in United States adults: analysis from NHANES 2007-2018. Frontiers in physiology. 2023;14:1275750.doi:10.3389/fphys.2023.1275750. Liu X, Liu X, Wang Y, Zeng B, Zhu B, Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005-2018. Journal of affective disorders. 2023;337:57-65.doi:10.1016/j.jad.2023.05.071. Gebert M, Sławski J, Kalinowski L, Collawn JF, Bartoszewski R. The Unfolded Protein Response: A Double-Edged Sword for Brain Health. Antioxidants (Basel, Switzerland). 2023;12(8).doi:10.3390/antiox12081648. Ashok A, Andrabi SS, Mansoor S, Kuang Y, Kwon BK, Labhasetwar V. Antioxidant Therapy in Oxidative Stress-Induced Neurodegenerative Diseases: Role of Nanoparticle-Based Drug Delivery Systems in Clinical Translation. Antioxidants (Basel, Switzerland). 2022;11(2).doi:10.3390/antiox11020408. Bellavite P. Neuroprotective Potentials of Flavonoids: Experimental Studies and Mechanisms of Action. Antioxidants (Basel, Switzerland). 2023;12(2).doi:10.3390/antiox12020280. Kempuraj D, Thangavel R, Natteru PA, Selvakumar GP, Saeed D, Zahoor H, Zaheer S, Iyer SS, Zaheer A. Neuroinflammation Induces Neurodegeneration. Journal of neurology, neurosurgery and spine. 2016;1(1) Abdelbaset S, Ayoub IM, Mohamed OG, Tripathi A, Eldahshan OA, El-Kersh DM. Metabolic profiling of Vitex Pubescens Vahl bark via UPLC-ESI-QTOF/MS/MS analysis and evaluation of its antioxidant and acetylcholinesterase inhibitory activities. BMC complementary medicine and therapies. 2024;24(1):232.doi:10.1186/s12906-024-04520-3. Iliyasu MO, Musa SA, Oladele SB, Iliya AI. Amyloid-beta aggregation implicates multiple pathways in Alzheimer's disease: Understanding the mechanisms. Frontiers in neuroscience. 2023;17:1081938.doi:10.3389/fnins.2023.1081938. Kumar A, Yegla B, Foster TC. Redox Signaling in Neurotransmission and Cognition During Aging. Antioxidants & redox signaling. 2018;28(18):1724-45.doi:10.1089/ars.2017.7111. Ali J, Choe K, Park JS, Park HY, Kang H, Park TJ, Kim MO. The Interplay of Protein Aggregation, Genetics, and Oxidative Stress in Alzheimer's Disease: Role for Natural Antioxidants and Immunotherapeutics. Antioxidants (Basel, Switzerland). 2024;13(7).doi:10.3390/antiox13070862. Fekete M, Lehoczki A, Tarantini S, Fazekas-Pongor V, Csípő T, Csizmadia Z, Varga JT. Improving Cognitive Function with Nutritional Supplements in Aging: A Comprehensive Narrative Review of Clinical Studies Investigating the Effects of Vitamins, Minerals, Antioxidants, and Other Dietary Supplements. Nutrients. 2023;15(24).doi:10.3390/nu15245116. Wang L, Zhao T, Zhu X, Jiang Q. Low blood carotenoid status in dementia and mild cognitive impairment: A systematic review and meta-analysis. BMC geriatrics. 2023;23(1):195.doi:10.1186/s12877-023-03900-7. Meng Q, Liu M, Zu C, Su X, Wei Y, Gan X, Zhang Y, He P, Zhou C, Ye Z, Liu C, Qin X. L-shaped association between dietary zinc intake and cognitive decline in Chinese older people. Age and ageing. 2024;53(1).doi:10.1093/ageing/afae008. Zhao D, Huang Y, Wang B, Chen H, Pan W, Yang M, Xia Z, Zhang R, Yuan C. Dietary Intake Levels of Iron, Copper, Zinc, and Manganese in Relation to Cognitive Function: A Cross-Sectional Study. Nutrients. 2023;15(3).doi:10.3390/nu15030704. Xu X, Mo C, Qin J, Cai J, Liu Q, Tang X, Zhang H, Zhang Z. Association between Copper Exposure and Cognitive Function: A Cross-Sectional Study in a County, Guangxi, China. Biological trace element research. 2024.doi:10.1007/s12011-024-04296-0. Meng Q, Liu C, Zu C, Wei Y, Su X, Gan X, Zhang Y, He P, Zhou C, Liu M, Ye Z, Qin X. Association Between Dietary Copper Intake and Cognitive Decline: A Perspective Cohort Study in Chinese Elderly. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry. 2023;31(10):753-63.doi:10.1016/j.jagp.2023.05.001. Chan PH. Antioxidant-dependent amelioration of brain injury: role of CuZn-superoxide dismutase. Journal of neurotrauma. 1992;9 Suppl 2:S417-23 Valko M, Jomova K, Rhodes CJ, Kuča K, Musílek KJAot. Redox-and non-redox-metal-induced formation of free radicals and their role in human disease. 2016;90:1-37 Luo J, Zhang C, Zhao Q, Wu W, Liang X, Xiao Z, Mortimer JA, Borenstein AR, Dai Q, Ding DJAs, Research DT, Interventions C. Dietary calcium and magnesium intake and risk for incident dementia: The Shanghai Aging Study. 2022;8(1):e12362 Ozawa M, Ninomiya T, Ohara T, Hirakawa Y, Doi Y, Hata J, Uchida K, Shirota T, Kitazono T, Kiyohara YJJotAGS. Self‐Reported Dietary Intake of Potassium, Calcium, and Magnesium and Risk of Dementia in the J apanese: The H isayama Study. 2012;60(8):1515-20 Chen F, Wang J, Cheng Y, Li R, Wang Y, Chen Y, Scott T, Tucker KLJAiN. Magnesium and cognitive health in adults: a systematic review and meta-analysis. 2024:100272 Liang Z, Gong X, Ye R, Zhao Y, Yu J, Zhao Y, Bao JJN. Long-term high-fat diet consumption induces cognitive decline accompanied by tau hyper-phosphorylation and microglial activation in aging. 2023;15(1):250 Song H, Park J-HJJomh. Effects of changes in physical activity with cognitive decline in Korean home-dwelling older adults. 2022:333-41 Benito-León J, Ghosh R, Lapeña-Motilva J, Martín-Arriscado C, Bermejo-Pareja FJSr. Association between cumulative smoking exposure and cognitive decline in non-demented older adults: NEDICES study. 2023;13(1):5754 Piumatti G, Moore SC, Berridge DM, Sarkar C, Gallacher JJJoPH. The relationship between alcohol use and long-term cognitive decline in middle and late life: a longitudinal analysis using UK Biobank. 2018;40(2):304-11 Naomi R, Teoh SH, Embong H, Balan SS, Othman F, Bahari H, Yazid MDJA. The role of oxidative stress and inflammation in obesity and its impact on cognitive impairments—a narrative review. 2023;12(5):1071 Sun Z, Wang Z-T, Sun F-R, Shen X-N, Xu W, Ma Y-H, Dong Q, Tan L, Yu J-T, Aging AsDNIJ. Late-life obesity is a protective factor for prodromal Alzheimer’s disease: a longitudinal study. 2020;12(2):2005 Hamer M, O’Donovan GJTAjocn. Sarcopenic obesity, weight loss, and mortality: the English Longitudinal Study of Ageing. 2017;106(1):125-9 Wang L, Wang F, Liu J, Zhang Q, Lei PJTTjoem. Inverse relationship between baseline serum albumin levels and risk of mild cognitive impairment in elderly: a seven-year retrospective cohort study. 2018;246(1):51-7 Maeda S, Takeya Y, Oguro R, Akasaka H, Ryuno H, Kabayama M, Yokoyama S, Nagasawa M, Fujimoto T, Takeda MJG, International G. Serum albumin/globulin ratio is associated with cognitive function in community‐dwelling older people: The Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians study. 2019;19(10):967-71 Yang H, Liao Z, Zhou Y, Gao Z, Mao YJFiPH. Non-linear relationship of serum albumin-to-globulin ratio and cognitive function in American older people: a cross-sectional national health and nutrition examination survey 2011–2014 (NHANES) study. 2024;12:1375379 Lv L, Sun X, Liu B, Song J, Wu DJ, Gao Y, Li A, Hu X, Mao Y, Ye DJCE. Genetically predicted serum albumin and risk of colorectal cancer: A bidirectional Mendelian randomization study. 2022:771-8 Han M, Lee HW, Lee HC, Kim HJ, Seong EY, Song SHJSR. Impact of nutritional index on contrast-associated acute kidney injury and mortality after percutaneous coronary intervention. 2021;11(1):7123 Lu Y, Wang M, Bao J, Chen D, Jiang HJFin. Association between oxidative balance score and metabolic syndrome and its components in US adults: A cross-sectional study from NHANES 2011–2018. 2024;11:1375060 Zhu Z, Bai H, Li Z, Fan M, Li G, Chen LJFiN. Association of the oxidative balance score with obesity and body composition among young and middle-aged adults. 2024;11:1373709 Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.docx FigureS1.jpg Figure S1. 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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-6407269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452057143,"identity":"d0fad0f4-a8e1-4eb3-bd7f-e52a636971f8","order_by":0,"name":"Shouxin Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNmb+jw8+/rGR45c/fIA4LXzsDcaGMxvSjCVnsCUQp0WO54CZNGfDoUSDGTwGRDpMIiFNmnHHgQQD6Z6PN94w2MnpNhDWcti68MydPHOZs5st5zAkG5sdIKglsfH2DLZnxZYNudukeRgOJG4jrCWZQZqH7XDihgM5z4jUwnOMSZq3DajlRg4bkVrYe5gNZ5wBBnLPMWPLOQZE+EW+mYfxwYcKYFSyNz+88abCTo6gFhQgQWzUIGshVccoGAWjYBSMCAAAU41EHy9hGC0AAAAASUVORK5CYII=","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shouxin","middleName":"","lastName":"Wei","suffix":""},{"id":452057144,"identity":"2832f124-043f-4220-bfb0-3fd3a6ad671a","order_by":1,"name":"Sijia Yu","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Yu","suffix":""},{"id":452057145,"identity":"05947f64-80e5-4255-8ce4-a15f3ec27828","order_by":2,"name":"Chuan 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02:09:50","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":108536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eFlowchart of the study\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6407269/v1/efe03dee4460d17d28adf7a7.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oxidative Balance, Nutrition, and Cognitive Function in Older Adults: A Mediation Analysis Using NHANES 2011–2014 Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCognitive function impairment (CFI) refers to deficits in various critical brain functions, including memory, learning, attention, and decision-making[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the accelerating global aging population, the incidence of CFI has significantly increased, particularly in relation to Alzheimer\u0026rsquo;s disease (AD) and its related dementias (ADRD) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, approximately 50\u0026nbsp;million individuals worldwide are affected by AD, and this number is projected to rise to 139\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mild cognitive impairment (MCI) represents an intermediate stage between normal aging and dementia. While MCI leads to cognitive decline, its impact on daily life is relatively minor. It is estimated that approximately 15% of MCI patients will progress to AD within two years[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although dementia is generally considered irreversible, studies indicate that around 16% of MCI patients may regain normal cognitive function [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CFI imposes a significant burden on individuals, families, and society as a whole. Therefore, early identification and intervention are crucial for slowing the progression of CFI.\u003c/p\u003e \u003cp\u003eResearch suggests that CFI may be associated with factors such as aging, genetic predisposition, lifestyle, chronic diseases, and infections, with oxidative stress playing a key role in cognitive decline [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Aging and disease conditions disrupt the balance between the production and clearance of reactive oxygen species (ROS), leading to oxidative stress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Neuronal cells in the brain are highly susceptible to oxidative stress, and excessive oxygen free radicals cause lipid and protein peroxidation, ultimately resulting in neuronal cell death [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The accumulation of such damage adversely affects memory and learning abilities, contributing to the onset of CFI.\u003c/p\u003e \u003cp\u003eThe Oxidative Balance Score (OBS) is a quantitative index used to assess exposure to antioxidants and pro-oxidants in diet and lifestyle. By comprehensively analyzing the quantity and types of antioxidants and pro-oxidants consumed, OBS reflects the overall oxidative stress level [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A higher OBS value indicates greater antioxidant exposure, whereas a lower OBS value signifies increased pro-oxidant exposure. Key components used to evaluate OBS include dietary antioxidants such as vitamin C, vitamin E, and carotenoids, as well as lifestyle factors such as smoking and alcohol consumption, which have been shown to influence cognitive function [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, no studies to date have established a direct relationship between OBS and cognitive function.\u003c/p\u003e \u003cp\u003eThis study utilizes data from two consecutive cycles (2011\u0026ndash;2014) of the National Health and Nutrition Examination Survey (NHANES), incorporating various demographic and disease-related factors to explore the potential association between OBS and CFI. The findings of this study will provide scientific evidence for a deeper understanding of the protective role of antioxidant exposure on cognitive function and may serve as a valuable reference for public health policy development.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is conducted by the National Center for Health Statistics (NCHS) and has been assessing the health and nutritional status of U.S. adults and children since the early 1960s. NHANES data and study design are highly transparent, with all public data and detailed survey methodologies available on the NHANES official website. The study protocol was approved by the NCHS Ethics Review Board to ensure compliance with ethical standards. All participants provided written informed consent before participating in the survey.This study utilized data from two consecutive NHANES cycles (2011\u0026ndash;2012 and 2013\u0026ndash;2014), comprising a total of 19,931 participants. The exclusion criteria were as follows: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;60 years (n\u0026thinsp;=\u0026thinsp;16,299); (2) individuals missing dietary data required for OBS calculation (n\u0026thinsp;=\u0026thinsp;1,319); (3) individuals lacking CFI assessment data (n\u0026thinsp;=\u0026thinsp;210); and (4) individuals missing albumin/globulin ratio (AGR) data (n\u0026thinsp;=\u0026thinsp;14). Ultimately, 2,089 participants were included in the final analysis, consisting of 1,013 males and 1,076 females (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Exposure assessment\u003c/h2\u003e \u003cp\u003eOBS is a comprehensive index that quantifies an individual's exposure to antioxidants and pro-oxidants by integrating dietary and lifestyle factors. The calculation of OBS is based on 16 dietary components and four lifestyle factors. Dietary data were obtained from two 24-hour dietary recall interviews conducted as part of NHANES. The included dietary components are dietary fiber, carotenoids, riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E, calcium, magnesium, zinc, copper, selenium, total fat, and iron. The lifestyle factors considered are physical activity, alcohol consumption, smoking status, and body mass index (BMI).Among these components, total fat, iron, BMI, alcohol consumption, and smoking are classified as pro-oxidants, whereas the remaining components are categorized as antioxidants. Cotinine, a primary metabolite of nicotine, was used to assess smoking status through serum cotinine levels. Physical activity was quantified by multiplying the frequency and duration of each activity performed per week by the corresponding metabolic equivalent of task (MET) score. Alcohol consumption was categorized into three groups: non-drinkers, moderate drinkers (0\u0026ndash;15 g/day for women and 0\u0026ndash;30 g/day for men), and heavy drinkers (\u0026ge;\u0026thinsp;15 g/day for women and \u0026ge;\u0026thinsp;30 g/day for men), with assigned scores of 2, 1, and 0, respectively. Other components were scored based on their tertile distribution within sex-specific groups, where antioxidants were assigned scores ranging from 0 to 2, and pro-oxidants were scored inversely. The total OBS score was derived by summing the individual component scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Outcome assessment\u003c/h2\u003e \u003cp\u003eIn the NHANES (2011\u0026ndash;2014) study, participants underwent multiple assessments, including the CERAD Word Learning and Recall Module, the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). The CERAD test consists of three consecutive learning trials and a delayed recall trial, where participants are asked to read 10 unrelated words and recall as many as possible. The delayed recall section is conducted after the AFT and DSST. The maximum possible score for the CERAD test is 40. In the AFT, participants are required to name as many different animals as possible within one minute. The AFT score is determined by the total number of animals named within the time limit, reflecting participants' ability to rapidly generate words within a specific semantic category. The DSST is a performance subtest of the Wechsler Adult Intelligence Scale (WAIS-III), designed to assess processing speed, sustained attention, and working memory. In this test, participants must match 133 numbers with their corresponding symbols within two minutes. The DSST assesses not only cognitive processing speed but also visuomotor coordination and cognitive flexibility, with a maximum score of 133.To account for individual differences across tests and mitigate scale attenuation effects, we standardized the test results by computing scores for each cognitive assessment and integrating them into a global cognition score, providing a more comprehensive evaluation of participants\u0026rsquo; overall cognitive function. The global cognition score was calculated using the formula: Global.Cognition = (x - m) / σ, where x represents the individual's test score, m denotes the mean test score, and σ represents the standard deviation. Currently, there is no universally established threshold for identifying low cognitive performance. Based on previous studies, we defined the 25th percentile as the cutoff: CERAD\u0026thinsp;\u0026lt;\u0026thinsp;21, AFT\u0026thinsp;\u0026lt;\u0026thinsp;13, DSST\u0026thinsp;\u0026lt;\u0026thinsp;35, or Global.Cognition \u0026lt; -1.43. Participants falling below any of these thresholds were classified as having cognitive impairment[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariates\u003c/h2\u003e \u003cp\u003eTo minimize potential confounding factors in our study, we reviewed the literature and incorporated relevant covariates into our analysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These covariates included age, sex, race/ethnicity, education level, marital status, poverty-to-income ratio (PIR), hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. Sex was categorized as male or female, while race/ethnicity was classified into Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, and other races. Marital status was classified as married/cohabiting, divorced/separated/widowed, or never married. Education level was categorized as below high school, high school graduate, or above high school. PIR was categorized into low (\u0026lt;\u0026thinsp;1.3), medium (1.3\u0026ndash;3.5), and high (\u0026ge;\u0026thinsp;3.5).Hypertension was defined as: (1) a physician-diagnosed condition; (2) an average systolic blood pressure of \u0026ge;\u0026thinsp;130 mmHg or an average diastolic blood pressure of \u0026ge;\u0026thinsp;80 mmHg; or (3) current use of antihypertensive medication. Diabetes was defined as: (1) a physician-diagnosed condition; (2) a hemoglobin A1c (HbA1c) level of \u0026ge;\u0026thinsp;6.5%; or (3) current use of diabetes medication or insulin. Depression was defined as a Patient Health Questionnaire-9 (PHQ-9) score of \u0026ge;\u0026thinsp;10. Diagnoses of liver disease, stroke, and arthritis were based on self-reported data. Individuals diagnosed with congestive heart failure, coronary heart disease, myocardial infarction, or angina were classified as having heart disease, whereas those diagnosed with asthma, chronic bronchitis, or emphysema were classified as having lung disease.Mediator variables included the Geriatric Nutritional Risk Index (GNRI) and the albumin-to-globulin ratio (AGR); detailed calculation formulas are provided in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003e Based on NHANES guidelines on weighting, we accounted for complex sampling weights and incorporated one-half of the laboratory weights. Continuous variables were expressed as means, while categorical variables were presented as percentages. Statistical significance was assessed using the t-test and chi-square test.To analyze the association between OBS and CFI, we conducted multivariable weighted logistic regression. The crude model was unadjusted, while Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was further adjusted for age, sex, race/ethnicity, education level, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis. Results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs).We also applied restricted cubic spline models to fit the association between OBS and CFI and conducted stratified analyses to further validate the robustness of the results. Additionally, we performed stepwise regression-based mediation analysis using the \"mediation\" R package to examine the mediating effect of GNRI and AGR in the OBS-CFI association. Finally, we conducted a sensitivity analysis by exploring the OBS-CFI association using unweighted data to assess the robustness of our findings.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R (version 4.4.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and EmpowerStats (version 4.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Participant characteristics\u003c/h2\u003e \u003cp\u003eThis study included 2,089 participants aged 60 years and older. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the participants had a mean age of 69.1 years, with 48.49% being male. A total of 895 individuals met the CFI criteria, accounting for 42.84% of the sample.Compared to the non-CFI group, participants in the CFI group were older, had a higher proportion of males, a higher percentage of non-Hispanic Black individuals, and lower education levels. They were more likely to be divorced, separated, or widowed, had lower economic status (lower PIR), and exhibited a higher prevalence of hypertension, diabetes, depression, heart disease, stroke, and arthritis. Additionally, they had relatively lower GNRI and AGR levels.Compared to other OBS groups, participants in the highest OBS group had a higher proportion of non-Hispanic White individuals, higher education levels, and better economic status (higher PIR). They also had lower prevalence rates of hypertension, diabetes, and heart disease, along with lower GNRI levels and higher AGR levels (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants by CFI in NHANES 2011\u0026ndash;2014.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;2089)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon CFI(n\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI (n\u0026thinsp;=\u0026thinsp;895)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.10 (68.73 ,69.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.73 (67.34 ,68.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.15 (71.37 ,72.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.94 (120.99 ,122.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.51 (121.39 ,123.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.67 (119.37 ,121.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.57 ,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63 (1.59 ,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.50 ,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0032\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\u003e1013 (48.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e521 (43.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e492 (54.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1076 (51.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673 (56.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e403 (45.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e176 (8.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (7.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91 (10.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199 (9.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (6.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125 (13.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1084 (51.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e714 (59.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e370 (41.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458 (21.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217 (18.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e241 (26.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172 (8.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104 (8.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (7.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e483 (23.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (11.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e351 (39.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499 (23.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267 (22.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232 (25.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1107 (52.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e795 (66.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312 (34.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1247 (59.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e748 (62.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e499 (55.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced, separated, or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e728 (34.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380 (31.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (38.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (5.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (5.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (5.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e536 (25.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212 (17.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324 (36.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e914 (43.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e503 (42.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e411 (45.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e639 (30.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e479 (40.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160 (17.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\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\u003e430 (20.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e284 (23.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146 (16.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1659 (79.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e910 (76.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e749 (83.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0248\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\u003e1501 (71.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e905 (75.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e596 (66.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e588 (28.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289 (24.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299 (33.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ Score\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179 (8.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69 (5.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (12.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1910 (91.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1125 (94.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e785 (87.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4117\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\u003e62 (2.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (2.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (3.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2027 (97.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1160 (97.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e867 (96.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary disease\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6106\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\u003e362 (17.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209 (17.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153 (17.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1727 (82.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e985 (82.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e742 (82.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\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\u003e370 (17.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171 (14.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199 (22.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1719 (82.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1023 (85.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e696 (77.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0004\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\u003e129 (6.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (3.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (9.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1960 (93.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1147 (96.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e813 (90.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0033\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\u003e1042 (49.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e576 (48.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e466 (52.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1047 (50.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e618 (51.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e429 (47.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en: Number of study samples.PIR: Poverty Income Ratio;OBS:Oxidative Balance Score;GNRI:Geriatric Nutritional Risk Index;AGR:Albumin-to-Globulin Ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Association Between OBS and CFI\u003c/h2\u003e \u003cp\u003eOBS was analyzed as both a continuous variable and a categorical variable (quartiles) using weighted logistic regression with CFI as the outcome variable. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a significant negative association was observed between OBS and CFI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).In the fully adjusted Model 2, each standard deviation increase in OBS was associated with a 4.4% reduction in CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.956 (0.934, 0.978)]. This negative association became more pronounced as OBS increased. Compared to the lowest quartile, in the highest quartile, each standard deviation increase in OBS was associated with a 59.6% reduction in CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.404 (0.249, 0.655), ptrend\u0026thinsp;=\u0026thinsp;0.005]. Further analysis using restricted cubic spline (RCS) regression, as shown in Fig.\u0026nbsp;1, indicated a linear negative association between OBS and CFI (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.1).\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\u003e\u003cb\u003e.\u003c/b\u003eAssociation between OBS and CFI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Model OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous OBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.934 (0.916, 0.953)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.938 (0.917, 0.960) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956 (0.934, 0.978) 0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBS quartiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1[Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1[Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1[Ref]\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 \u003cp\u003e0.487 (0.347, 0.684)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.489 (0.318, 0.750) 0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.567 (0.386, 0.834) 0.018\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 \u003cp\u003e0.425 (0.312, 0.580)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467 (0.314, 0.696) 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583 (0.399, 0.853) 0.021\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 \u003cp\u003e0.273 (0.180, 0.415)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.291 (0.175, 0.484) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.404 (0.249, 0.655) 0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eOR, odd ratio; CI, confidence interval. Crude model: Unadjusted for covariates. Model 1: Adjusted for age, sex, and race. Model 2: Adjusted for age, sex, race ,education, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, linear regression analysis was performed to examine the association between OBS and various cognitive function test scores. The results showed a significant positive association between OBS and all cognitive function test scores. Specifically, in the fully adjusted model, each standard deviation increase in OBS was associated with a 0.094-point increase in CERAD score [β(95% CI)\u0026thinsp;=\u0026thinsp;0.094 (0.051, 0.137)], a 0.107-point increase in AFT score [β(95% CI)\u0026thinsp;=\u0026thinsp;0.107 (0.068, 0.147)], a 0.213-point increase in DSST score [β(95% CI)\u0026thinsp;=\u0026thinsp;0.213 (0.097, 0.329)], and a 0.046-point increase in Global Cognition score [β(95% CI)\u0026thinsp;=\u0026thinsp;0.046 (0.030, 0.062)] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between OBS and test scores of cognitive function.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCERAD test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eGlobal.Cognition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094 (0.051, 0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107 (0.068, 0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213 (0.097, 0.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.046 (0.030, 0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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=\"9\"\u003eβ : regression coefficient; 95% CI: 95% Confidence Interval;CERAD:the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease; AFT:Animal Fluency Test; DSST:Digit Symbol Substitution Test.Adjusted for age, sex, race ,education, marital status, PIR, hypertension, diabetes, depression, liver disease, lung disease, heart disease, stroke, and arthritis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3.Subgroup Analysis\u003c/h2\u003e \u003cp\u003eWe conducted a subgroup analysis stratified by age, sex, race/ethnicity, and the presence of hypertension or diabetes to determine whether the association between OBS and CFI remained consistent across different subgroups. As shown in Fig.\u0026nbsp;2, the association between OBS and CFI remained generally stable across age, sex, hypertension, and diabetes subgroups (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).A significant interaction was observed across different racial/ethnic groups. Compared to other racial groups, the negative association between OBS and CFI was more pronounced among non-Hispanic Black and non-Hispanic White individuals (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4.Association of GNRI and AGR with OBS and CFI\u003c/h2\u003e \u003cp\u003eThe weighted linear regression analysis of OBS with GNRI and AGR is presented in Table S2. In the fully adjusted model, OBS was negatively associated with GNRI [β(95% CI) = -0.153 (-0.244, -0.061)] but positively associated with AGR [β(95% CI)\u0026thinsp;=\u0026thinsp;0.005 (0.003, 0.008)].Table S3 presents the multivariable weighted logistic regression results examining the association between GNRI, AGR, and CFI. In the fully adjusted model, GNRI was negatively associated with CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.989 (0.982, 0.995)], and AGR was also negatively associated with CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.578 (0.375, 0.892)].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5.Mediation Effect of GNRI and AGR\u003c/h2\u003e \u003cp\u003eWe further conducted a mediation analysis to explore the potential mechanisms underlying the effect of OBS on CFI (Table S4). After adjusting for all covariates, OBS was significantly associated with CFI, with a total effect coefficient of -0.052 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The indirect effect of GNRI was 0.002 (p\u0026thinsp;=\u0026thinsp;0.038), accounting for \u0026minus;\u0026thinsp;4.46% of the total effect (p\u0026thinsp;=\u0026thinsp;0.038), while the indirect effect of AGR was \u0026minus;\u0026thinsp;0.003 (p\u0026thinsp;=\u0026thinsp;0.004), contributing to 5.79% of the total effect (p\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6.Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of our results, we conducted a sensitivity analysis. After removing sample weighting, OBS remained significantly negatively associated with CFI (Table S5).In the fully adjusted model, each standard deviation increase in OBS was associated with a 5.5% reduction in CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.975 (0.960, 0.990)]. Compared to the lowest quartile, in the highest quartile, each standard deviation increase in OBS was associated with a 37.2% reduction in CFI risk [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.628 (0.468, 0.841), ptrend\u0026thinsp;=\u0026thinsp;0.004].\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith global population aging, CFI has emerged as a significant public health challenge.OBS is a novel composite indicator designed to assess an individual's overall oxidative balance by measuring multiple oxidative and antioxidative factors. Previous studies have demonstrated an inverse association between OBS and the incidence of conditions such as stroke, kidney stones, and depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To our knowledge, this study is the first comprehensive retrospective analysis investigating the association between OBS and CFI. Data were obtained from 2,089 participants aged 60 years and older from the 2011\u0026ndash;2014 NHANES. The results indicated an inverse relationship between OBS and CFI after adjusting for all potential confounding factors. These findings underscore the importance of maintaining antioxidative balance in preventing cognitive decline.\u003c/p\u003e \u003cp\u003eThe impact of oxidative stress on cognitive function involves multiple complex biological mechanisms. The brain consumes approximately 20% of total oxygen intake to maintain normal function, a high level of oxygen consumption that generates free radicals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Under aging and pathological conditions, the production of ROS significantly increases, directly attacking and damaging the cell membranes of central nervous system neurons, ultimately leading to impaired cellular function. Moreover, ROS can impair mitochondrial DNA and membrane proteins in neurons, disrupting energy supply and ultimately leading to neuronal death [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Oxidative stress can induce inflammatory responses in the brain. Inflammatory responses activate microglia and astrocytes in the brain, which normally function to maintain neuronal homeostasis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, when these cells undergo sustained activation, they release pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and chemokines (C-C motif), mediating neuroinflammation and neurodegeneration. Additionally, peripheral inflammatory mediators cross a compromised blood-brain barrier, exacerbating inflammation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These inflammatory responses further damage neurons, ultimately resulting in neural network dysfunction.\u003c/p\u003e \u003cp\u003eAD is the most common neurodegenerative disorder, with cognitive decline as its core clinical symptom. Current etiological theories of AD primarily include the cholinergic hypothesis and the extracellular β-amyloid plaque deposition hypothesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Acetylcholine is the primary neurotransmitter associated with learning and memory, playing a critical role in neuronal activity, plasticity, and network connectivity. Oxidative stress can impair choline acetyltransferase, leading to reduced acetylcholine levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, oxidative stress can damage acetylcholine receptors, further impairing synaptic transmission and resulting in deficits in learning and memory [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. ROS in the brain can disrupt the metabolic processing of amyloid precursor protein, leading to the production of β-amyloid. This aberrant metabolite tends to aggregate into insoluble plaques that deposit around neurons, interfering with normal synaptic transmission, causing neuronal dysfunction, and ultimately triggering cognitive decline [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated an association between dietary components and cognitive function. A meta-analysis indicated that B vitamins, vitamin C, and vitamin E help protect the brain from oxidative stress, and dietary supplementation with these vitamins may improve cognitive function during aging [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Wang et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reported that patients with dementia had significantly lower blood carotenoid levels compared to controls, suggesting that low carotenoid levels may be a risk factor for dementia and MCI. Meng et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] demonstrated an L-shaped inverse association between dietary zinc intake and cognitive decline in older adults, indicating that maintaining adequate dietary zinc levels may help prevent cognitive deterioration. A cross-sectional study found that higher dietary intakes of iron and copper were associated with poorer cognitive function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, Xu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] confirmed this finding, showing that higher copper exposure may be associated with cognitive impairment. However, another prospective cohort study reported a nonlinear inverse association between dietary copper intake and cognitive decline in older adults [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], possibly due to differences in the proportion of copper forms present in the body. Copper can act as both an antioxidant and a pro-oxidant within the body. Chan et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] proposed that copper serves as a cofactor for CuZn-superoxide dismutase, specifically scavenging superoxide radicals. In contrast, Valko et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] found that copper ions catalyze the conversion of hydrogen peroxide into highly reactive hydroxyl radicals, leading to oxidative damage to lipids, proteins, and DNA. The association between dietary magnesium and cognitive impairment remains inconsistent. A cohort study from China showed that high dietary magnesium intake was associated with an increased risk of dementia, while a Japanese cohort study suggested that higher magnesium intake reduced the risk of all-cause dementia in the general population [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Chen et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] proposed that the dietary calcium-to-magnesium (Ca:Mg) intake ratio may explain these discrepancies, with magnesium acting as a risk factor for dementia under low calcium and high magnesium conditions, and as a protective factor under high calcium and low magnesium conditions. Fat is a major pro-oxidant component in OBS. Animal studies have shown that prolonged high-fat diet consumption leads to excessive phosphorylation of Tau protein in the hippocampus, activation of microglia, and expression of inflammatory factors, ultimately resulting in cognitive decline [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple studies have confirmed that physical activity serves as a protective factor for cognitive function, and encouraging older adults to remain physically active may help prevent cognitive decline and the onset of dementia [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Smoking exacerbates the burden of cerebrovascular diseases such as cerebral infarction, impairs cerebral blood flow, and aggravates neuronal damage. Furthermore, smoking may promote white matter degeneration by inhibiting myelin synthesis, thereby increasing the risk of AD [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, alcohol consumption is positively associated with cognitive decline in older adults. Even alcohol consumption exceeding one standard unit adversely affects cognitive performance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Evidence regarding the relationship between body mass index (BMI) and cognitive function remains conflicting. Naomi et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]suggested that obesity induces oxidative stress and inflammation, thereby increasing the risk of cognitive impairment and dementia. In contrast, Sun et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] found that obesity in late life was associated with improved cognitive function and a reduced risk of dementia. This phenomenon may reflect the \"obesity paradox,\" wherein weight loss in older adults is directly linked to worsening health and increased mortality, while obese individuals may possess greater physiological reserves and a lower likelihood of severe comorbidities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed significant associations of both the GNRI and AGR with the oxidative balance score (OBS) and CFI. Further mediation analysis highlighted the pivotal role of GNRI and AGR in the relationship between OBS and CFI, underscoring nutrition as a critical bridge linking oxidative balance and cognitive function. However, the direction of mediation effects differed between GNRI and AGR, which may be attributed to their distinct associations with OBS. A seven-year retrospective cohort study found that lower baseline serum albumin levels were associated with an increased risk of MCI [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Maeda [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and Yang [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] demonstrated a positive association between AGR and cognitive function in Japanese and American populations, respectively, while Liu [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] also reported a positive association between GNRI and cognitive function. In contrast to the consistent impact of AGR and GNRI on cognitive function, their associations with OBS were inconsistent. Albumin, a classic antioxidant, has been widely recognized for its antioxidative properties in numerous studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, GNRI was negatively associated with OBS, possibly due to the significant weight component within the GNRI calculation. Individuals with higher OBS typically exhibit lower BMI and body fat percentage, as higher antioxidant intake may contribute to weight reduction [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This finding highlights the complexity of the relationship between OBS and CFI.\u003c/p\u003e \u003cp\u003eThis study possesses several strengths. We integrated all available continuous NHANES cycle data, encompassing a large sample of older adults from across the United States, which enhanced the statistical robustness and generalizability of our findings. Our study design accounted for multiple potential confounders, including sex, race, income, and comorbidities, with adjustments made using multivariable analytical methods to ensure the accuracy and reliability of the results. The OBS incorporates various oxidative stress-related indicators, providing a comprehensive reflection of the body's overall oxidative status. Compared with single biomarkers, OBS offers greater comprehensiveness and representativeness. For the first time, we identified a significant association between OBS and CFI and revealed the mediating roles of GNRI and AGR. This finding provides new insights into the potential role of oxidative stress in CFI and may facilitate the identification of populations at risk for cognitive impairment, enabling the development of personalized interventions.\u003c/p\u003e \u003cp\u003eNevertheless, this study has several limitations. First, as a cross-sectional study, it does not capture the dynamic changes over time between OBS and cognitive function. Second, cognitive assessment is inherently complex, and the cognitive tests in the NHANES database may not comprehensively cover all cognitive domains. Although multiple confounders were adjusted for, it remains challenging to eliminate all external variables that may influence the results. These unmeasured confounders could potentially affect the accuracy of the study. Future prospective longitudinal studies are warranted to further validate the relationship between OBS and CFI, thereby enhancing the stability and reliability of the findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur findings indicate that higher OBS levels are generally associated with a lower risk of CFI among older adults in the United States, with GNRI and AGR mediating this association. Antioxidant-rich dietary and lifestyle modifications may contribute to the improvement of cognitive function in older adults. However, the underlying mechanisms through which OBS influences cognitive function warrant further investigation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eFull form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eAFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAnimal Fluency Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eAGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAlbumin/globulin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eCERAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eConsortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCognitive function impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eDSST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eDigit Symbol Substitution Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eGeriatric Nutritional Risk Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eMild cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eMetabolic equivalent of task\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNCHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eThe National Center for Health Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eOBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eOxidative balance score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003ePoverty-to-Income Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eRestricted cubic splines\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eROS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eReactive oxygen species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe ethical review board of the National Center for Health Statistics granted approval to the NHANES protocols, and ethical approval was exempted in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003eThe data backing the findings of this article can be found in the NHANES repository (https://wwwn.cdc.gov/nchs/nhanes/default.aspx, accessed 04/07/2025). The code for statistical analysis can be provided upon request, and the raw data backing the conclusions of this paper can be obtained from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003eShouxin Wei: Conceptualization, Data Curation, \u0026nbsp; Formal Analysis, Software, Writing – Original Draft. Sijia Yu: Conceptualization, Methodology,Writing – Review \u0026amp; Editing. \u0026nbsp;Chuan Qian:Resources, Validation, Writing – review \u0026amp; editing.Yunsheng Lan: Formal Analysis, Data Curation, Visualization. Yindong Jia: Writing – Review \u0026amp; Editing, Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003eThe authors acknowledge the important contributions of all the staff and participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao C, Pu M, Wu C, Ding J, Guo J, Zhang G. Association between composite dietary antioxidant index and cognitive function impairment among the US older adults: a cross-sectional study based on the NHANES 2011-2014. Frontiers in nutrition. 2024;11:1471981.doi:10.3389/fnut.2024.1471981.\u003c/li\u003e\n\u003cli\u003eYen FS, Wang SI, Lin SY, Chao YH, Wei JC. The Impact of Alcohol Consumption on Cognitive Impairment in Patients With Diabetes, Hypertension, or Chronic Kidney Disease. Frontiers in medicine. 2022;9:861145.doi:10.3389/fmed.2022.861145.\u003c/li\u003e\n\u003cli\u003eLi X, Feng X, Sun X, Hou N, Han F, Liu Y. Global, regional, and national burden of Alzheimer\u0026apos;s disease and other dementias, 1990-2019. Frontiers in aging neuroscience. 2022;14:937486.doi:10.3389/fnagi.2022.937486.\u003c/li\u003e\n\u003cli\u003eDeng Y, Wang H, Gu K, Song P. Alzheimer\u0026apos;s disease with frailty: Prevalence, screening, assessment, intervention strategies and challenges. Bioscience trends. 2023;17(4):283-92.doi:10.5582/bst.2023.01211.\u003c/li\u003e\n\u003cli\u003ePetersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, Gronseth GS, Marson D, Pringsheim T, Day GS, Sager M, Stevens J, Rae-Grant A. Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(3):126-35.doi:10.1212/wnl.0000000000004826.\u003c/li\u003e\n\u003cli\u003eMcGirr A, Nathan S, Ghahremani M, Gill S, Smith EE, Ismail Z. Progression to Dementia or Reversion to Normal Cognition in Mild Cognitive Impairment as a Function of Late-Onset Neuropsychiatric Symptoms. Neurology. 2022;98(21):e2132-e9.doi:10.1212/wnl.0000000000200256.\u003c/li\u003e\n\u003cli\u003eYaffe K, Fiocco AJ, Lindquist K, Vittinghoff E, Simonsick EM, Newman AB, Satterfield S, Rosano C, Rubin SM, Ayonayon HN, Harris TB. Predictors of maintaining cognitive function in older adults: the Health ABC study. Neurology. 2009;72(23):2029-35.doi:10.1212/WNL.0b013e3181a92c36.\u003c/li\u003e\n\u003cli\u003eColita E, Mateescu VO, Olaru DG, Popa-Wagner A. Cognitive Decline in Ageing and Disease: Risk factors, Genetics and Treatments. Current health sciences journal. 2024;50(2):170-80.doi:10.12865/chsj.50.02.02.\u003c/li\u003e\n\u003cli\u003eAngelova PR, Esteras N, Abramov AY. Mitochondria and lipid peroxidation in the mechanism of neurodegeneration: Finding ways for prevention. Medicinal research reviews. 2021;41(2):770-84.doi:10.1002/med.21712.\u003c/li\u003e\n\u003cli\u003eZhan F, Lin G, Duan K, Huang B, Chen L, Ni J. Higher oxidative balance score decreases risk of stroke in US adults: evidence from a cross-sectional study. Frontiers in cardiovascular medicine. 2023;10:1264923.doi:10.3389/fcvm.2023.1264923.\u003c/li\u003e\n\u003cli\u003eNooyens AC, Milder IE, van Gelder BM, Bueno-de-Mesquita HB, van Boxtel MP, Verschuren WM. Diet and cognitive decline at middle age: the role of antioxidants. The British journal of nutrition. 2015;113(9):1410-7.doi:10.1017/s0007114515000720.\u003c/li\u003e\n\u003cli\u003eQiu T, Zeng Q, Luo X, Xu T, Shen Z, Xu X, Wang C, Li K, Huang P, Li X, Xie F, Dai S, Zhang M. Effects of Cigarette Smoking on Resting-State Functional Connectivity of the Nucleus Basalis of Meynert in Mild Cognitive Impairment. Frontiers in aging neuroscience. 2021;13:755630.doi:10.3389/fnagi.2021.755630.\u003c/li\u003e\n\u003cli\u003eTopiwala A, Ebmeier KP. Effects of drinking on late-life brain and cognition. Evidence-based mental health. 2018;21(1):12-5.doi:10.1136/eb-2017-102820.\u003c/li\u003e\n\u003cli\u003eLiu J, Oorloff MD, Nadella A, Zhou N, Ye M, Tang Y, Wang YJFiN. Association between lower geriatric nutritional risk index and low cognitive functions in United States older adults: a cross-sectional study. 2024;11:1483790\u003c/li\u003e\n\u003cli\u003eSong L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. The Journal of nutrition. 2023;153(7):1974-83.doi:10.1016/j.tjnut.2023.05.014.\u003c/li\u003e\n\u003cli\u003eKe R, He Y, Chen C. Association between oxidative balance score and kidney stone in United States adults: analysis from NHANES 2007-2018. Frontiers in physiology. 2023;14:1275750.doi:10.3389/fphys.2023.1275750.\u003c/li\u003e\n\u003cli\u003eLiu X, Liu X, Wang Y, Zeng B, Zhu B, Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005-2018. Journal of affective disorders. 2023;337:57-65.doi:10.1016/j.jad.2023.05.071.\u003c/li\u003e\n\u003cli\u003eGebert M, Sławski J, Kalinowski L, Collawn JF, Bartoszewski R. The Unfolded Protein Response: A Double-Edged Sword for Brain Health. Antioxidants (Basel, Switzerland). 2023;12(8).doi:10.3390/antiox12081648.\u003c/li\u003e\n\u003cli\u003eAshok A, Andrabi SS, Mansoor S, Kuang Y, Kwon BK, Labhasetwar V. Antioxidant Therapy in Oxidative Stress-Induced Neurodegenerative Diseases: Role of Nanoparticle-Based Drug Delivery Systems in Clinical Translation. Antioxidants (Basel, Switzerland). 2022;11(2).doi:10.3390/antiox11020408.\u003c/li\u003e\n\u003cli\u003eBellavite P. Neuroprotective Potentials of Flavonoids: Experimental Studies and Mechanisms of Action. Antioxidants (Basel, Switzerland). 2023;12(2).doi:10.3390/antiox12020280.\u003c/li\u003e\n\u003cli\u003eKempuraj D, Thangavel R, Natteru PA, Selvakumar GP, Saeed D, Zahoor H, Zaheer S, Iyer SS, Zaheer A. Neuroinflammation Induces Neurodegeneration. Journal of neurology, neurosurgery and spine. 2016;1(1)\u003c/li\u003e\n\u003cli\u003eAbdelbaset S, Ayoub IM, Mohamed OG, Tripathi A, Eldahshan OA, El-Kersh DM. Metabolic profiling of Vitex Pubescens Vahl bark via UPLC-ESI-QTOF/MS/MS analysis and evaluation of its antioxidant and acetylcholinesterase inhibitory activities. BMC complementary medicine and therapies. 2024;24(1):232.doi:10.1186/s12906-024-04520-3.\u003c/li\u003e\n\u003cli\u003eIliyasu MO, Musa SA, Oladele SB, Iliya AI. Amyloid-beta aggregation implicates multiple pathways in Alzheimer\u0026apos;s disease: Understanding the mechanisms. Frontiers in neuroscience. 2023;17:1081938.doi:10.3389/fnins.2023.1081938.\u003c/li\u003e\n\u003cli\u003eKumar A, Yegla B, Foster TC. Redox Signaling in Neurotransmission and Cognition During Aging. Antioxidants \u0026amp; redox signaling. 2018;28(18):1724-45.doi:10.1089/ars.2017.7111.\u003c/li\u003e\n\u003cli\u003eAli J, Choe K, Park JS, Park HY, Kang H, Park TJ, Kim MO. The Interplay of Protein Aggregation, Genetics, and Oxidative Stress in Alzheimer\u0026apos;s Disease: Role for Natural Antioxidants and Immunotherapeutics. Antioxidants (Basel, Switzerland). 2024;13(7).doi:10.3390/antiox13070862.\u003c/li\u003e\n\u003cli\u003eFekete M, Lehoczki A, Tarantini S, Fazekas-Pongor V, Cs\u0026iacute;pő T, Csizmadia Z, Varga JT. Improving Cognitive Function with Nutritional Supplements in Aging: A Comprehensive Narrative Review of Clinical Studies Investigating the Effects of Vitamins, Minerals, Antioxidants, and Other Dietary Supplements. Nutrients. 2023;15(24).doi:10.3390/nu15245116.\u003c/li\u003e\n\u003cli\u003eWang L, Zhao T, Zhu X, Jiang Q. Low blood carotenoid status in dementia and mild cognitive impairment: A systematic review and meta-analysis. BMC geriatrics. 2023;23(1):195.doi:10.1186/s12877-023-03900-7.\u003c/li\u003e\n\u003cli\u003eMeng Q, Liu M, Zu C, Su X, Wei Y, Gan X, Zhang Y, He P, Zhou C, Ye Z, Liu C, Qin X. L-shaped association between dietary zinc intake and cognitive decline in Chinese older people. Age and ageing. 2024;53(1).doi:10.1093/ageing/afae008.\u003c/li\u003e\n\u003cli\u003eZhao D, Huang Y, Wang B, Chen H, Pan W, Yang M, Xia Z, Zhang R, Yuan C. Dietary Intake Levels of Iron, Copper, Zinc, and Manganese in Relation to Cognitive Function: A Cross-Sectional Study. Nutrients. 2023;15(3).doi:10.3390/nu15030704.\u003c/li\u003e\n\u003cli\u003eXu X, Mo C, Qin J, Cai J, Liu Q, Tang X, Zhang H, Zhang Z. Association between Copper Exposure and Cognitive Function: A Cross-Sectional Study in a County, Guangxi, China. Biological trace element research. 2024.doi:10.1007/s12011-024-04296-0.\u003c/li\u003e\n\u003cli\u003eMeng Q, Liu C, Zu C, Wei Y, Su X, Gan X, Zhang Y, He P, Zhou C, Liu M, Ye Z, Qin X. Association Between Dietary Copper Intake and Cognitive Decline: A Perspective Cohort Study in Chinese Elderly. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry. 2023;31(10):753-63.doi:10.1016/j.jagp.2023.05.001.\u003c/li\u003e\n\u003cli\u003eChan PH. Antioxidant-dependent amelioration of brain injury: role of CuZn-superoxide dismutase. Journal of neurotrauma. 1992;9 Suppl 2:S417-23\u003c/li\u003e\n\u003cli\u003eValko M, Jomova K, Rhodes CJ, Kuča K, Mus\u0026iacute;lek KJAot. Redox-and non-redox-metal-induced formation of free radicals and their role in human disease. 2016;90:1-37\u003c/li\u003e\n\u003cli\u003eLuo J, Zhang C, Zhao Q, Wu W, Liang X, Xiao Z, Mortimer JA, Borenstein AR, Dai Q, Ding DJAs, Research DT, Interventions C. Dietary calcium and magnesium intake and risk for incident dementia: The Shanghai Aging Study. 2022;8(1):e12362\u003c/li\u003e\n\u003cli\u003eOzawa M, Ninomiya T, Ohara T, Hirakawa Y, Doi Y, Hata J, Uchida K, Shirota T, Kitazono T, Kiyohara YJJotAGS. Self‐Reported Dietary Intake of Potassium, Calcium, and Magnesium and Risk of Dementia in the J apanese: The H isayama Study. 2012;60(8):1515-20\u003c/li\u003e\n\u003cli\u003eChen F, Wang J, Cheng Y, Li R, Wang Y, Chen Y, Scott T, Tucker KLJAiN. Magnesium and cognitive health in adults: a systematic review and meta-analysis. 2024:100272\u003c/li\u003e\n\u003cli\u003eLiang Z, Gong X, Ye R, Zhao Y, Yu J, Zhao Y, Bao JJN. Long-term high-fat diet consumption induces cognitive decline accompanied by tau hyper-phosphorylation and microglial activation in aging. 2023;15(1):250\u003c/li\u003e\n\u003cli\u003eSong H, Park J-HJJomh. Effects of changes in physical activity with cognitive decline in Korean home-dwelling older adults. 2022:333-41\u003c/li\u003e\n\u003cli\u003eBenito-Le\u0026oacute;n J, Ghosh R, Lape\u0026ntilde;a-Motilva J, Mart\u0026iacute;n-Arriscado C, Bermejo-Pareja FJSr. Association between cumulative smoking exposure and cognitive decline in non-demented older adults: NEDICES study. 2023;13(1):5754\u003c/li\u003e\n\u003cli\u003ePiumatti G, Moore SC, Berridge DM, Sarkar C, Gallacher JJJoPH. The relationship between alcohol use and long-term cognitive decline in middle and late life: a longitudinal analysis using UK Biobank. 2018;40(2):304-11\u003c/li\u003e\n\u003cli\u003eNaomi R, Teoh SH, Embong H, Balan SS, Othman F, Bahari H, Yazid MDJA. The role of oxidative stress and inflammation in obesity and its impact on cognitive impairments\u0026mdash;a narrative review. 2023;12(5):1071\u003c/li\u003e\n\u003cli\u003eSun Z, Wang Z-T, Sun F-R, Shen X-N, Xu W, Ma Y-H, Dong Q, Tan L, Yu J-T, Aging AsDNIJ. Late-life obesity is a protective factor for prodromal Alzheimer\u0026rsquo;s disease: a longitudinal study. 2020;12(2):2005\u003c/li\u003e\n\u003cli\u003eHamer M, O\u0026rsquo;Donovan GJTAjocn. Sarcopenic obesity, weight loss, and mortality: the English Longitudinal Study of Ageing. 2017;106(1):125-9\u003c/li\u003e\n\u003cli\u003eWang L, Wang F, Liu J, Zhang Q, Lei PJTTjoem. Inverse relationship between baseline serum albumin levels and risk of mild cognitive impairment in elderly: a seven-year retrospective cohort study. 2018;246(1):51-7\u003c/li\u003e\n\u003cli\u003eMaeda S, Takeya Y, Oguro R, Akasaka H, Ryuno H, Kabayama M, Yokoyama S, Nagasawa M, Fujimoto T, Takeda MJG, International G. Serum albumin/globulin ratio is associated with cognitive function in community‐dwelling older people: The Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians study. 2019;19(10):967-71\u003c/li\u003e\n\u003cli\u003eYang H, Liao Z, Zhou Y, Gao Z, Mao YJFiPH. Non-linear relationship of serum albumin-to-globulin ratio and cognitive function in American older people: a cross-sectional national health and nutrition examination survey 2011\u0026ndash;2014 (NHANES) study. 2024;12:1375379\u003c/li\u003e\n\u003cli\u003eLv L, Sun X, Liu B, Song J, Wu DJ, Gao Y, Li A, Hu X, Mao Y, Ye DJCE. Genetically predicted serum albumin and risk of colorectal cancer: A bidirectional Mendelian randomization study. 2022:771-8\u003c/li\u003e\n\u003cli\u003eHan M, Lee HW, Lee HC, Kim HJ, Seong EY, Song SHJSR. Impact of nutritional index on contrast-associated acute kidney injury and mortality after percutaneous coronary intervention. 2021;11(1):7123\u003c/li\u003e\n\u003cli\u003eLu Y, Wang M, Bao J, Chen D, Jiang HJFin. Association between oxidative balance score and metabolic syndrome and its components in US adults: A cross-sectional study from NHANES 2011\u0026ndash;2018. 2024;11:1375060\u003c/li\u003e\n\u003cli\u003eZhu Z, Bai H, Li Z, Fan M, Li G, Chen LJFiN. Association of the oxidative balance score with obesity and body composition among young and middle-aged adults. 2024;11:1373709\u003c/li\u003e\n\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oxidative Balance Score, Cognitive Function Impairment, Oxidative Stress, NHANES, Geriatric Nutritional Risk Index.","lastPublishedDoi":"10.21203/rs.3.rs-6407269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6407269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe global rise in cognitive function impairment (CFI) parallels an aging population. While oxidative stress is considered a contributing factor, its role in CFI remains underexplored. This study examines the relationship between oxidative balance score (OBS) and CFI using 2011\u0026ndash;2014 National Health and Nutrition Examination Survey (NHANES) data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 2,089 participants aged 60 and above were analyzed. OBS was calculated from 16 dietary and 4 lifestyle components. Cognitive function was evaluated using the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Multivariable weighted logistic regression, restricted cubic splines (RCS), subgroup, and mediation analyses assessed the association between OBS and CFI, adjusting for age, sex, and comorbidities. Sensitivity analysis tested result robustness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher OBS was significantly associated with a lower CFI risk after adjusting for confounders. RCS indicated a linear negative correlation between OBS and CFI. Subgroup analysis showed stronger associations in non-Hispanic Black and non-Hispanic White populations. Mediation analysis revealed the Geriatric Nutritional Risk Index (GNRI) and albumin/globulin ratio (AGR) had opposing mediating effects. Sensitivity analysis confirmed the stability of these findings in unweighted models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigher oxidative balance is significantly linked to reduced CFI risk in older adults. Enhancing oxidative balance through diet and lifestyle modifications could be a promising strategy to maintain cognitive health.\u003c/p\u003e","manuscriptTitle":"Oxidative Balance, Nutrition, and Cognitive Function in Older Adults: A Mediation Analysis Using NHANES 2011–2014 Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 01:53:43","doi":"10.21203/rs.3.rs-6407269/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b61123b-0cfa-483e-8cd3-86c9ecfec19d","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T06:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 01:53:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6407269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6407269","identity":"rs-6407269","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}