Whole-body aging mediates the association between socioeconomic status disparities and cognition and verbal fluency among U.S. older adults

Whole-body aging mediates the association between socioeconomic status disparities and cognition and verbal fluency among U.S. older adults | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Whole-body aging mediates the association between socioeconomic status disparities and cognition and verbal fluency among U.S. older adults Xiaoqin Yan , Xin Le , Shengling Wang doi: https://doi.org/10.1101/2025.06.16.25329679 Xiaoqin Yan 1 Foreign Languages College, Jiangxi Normal University , Nanchang, Jiangxi, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: xiaoqin_yan1975{at}163.com 18170863606{at}163.com Xin Le 2 Nanchang Minde School , Nanchang, Jiangxi, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shengling Wang 3 The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine , Nanchang, Jiangxi, China . Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: xiaoqin_yan1975{at}163.com 18170863606{at}163.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Cognitive decline is a worldwide public health issue among older populations. Socioeconomic status disparities in income, education, and social status are linked to cognitive function and verbal fluency. Although the etiology of cognitive decline remains unclear, social and biological factors are increasingly recognized as significant risk contributors. This study aims to investigate the potential associations between socioeconomic status and cognitive functions and the mediated effect of whole-body aging. Methods Data on socioeconomic status, whole-bodying aging, cognitive functions, and verbal fluency were collected from 2321 participants aged over 60 years based on the National Health and Nutrition Examination Survey 2011-2014. Independent t-tests, one-way ANOVA tests, Spearman correlation, and multiple logistic regression were used to explore the associations of socioeconomic status, whole-body aging, and cognitive functions, respectively. Mediation analyses were used to explore the mediated effects of whole-body aging on the associations between socioeconomic status and cognitive functions. Results In the multiple regression analysis models, socioeconomic status and whole-body aging were associated with cognitive function and verbal fluency (all p <0.05). Mediation analyses indicated that the associations between socioeconomic status and cognitive function were parallelly mediated by whole-body aging, with the proportion of mediation ranging from 3.21% to 7.65% (all p □<□0.05). Conclusion These findings suggested that socioeconomic status disparities increased the impairment of immediate and delayed learning ability, sustained attention, processing speed, working memory, and verbal fluency, which was possibly and partially mediated by whole-body aging. Download figure Open in new tab 1. Introduction Socioeconomic status (SES) disparities in income, education, and social status have been recognized as critical determinants in physical and cognitive functions, leading to changes in motor function, executive function, attention, learning, and verbal fluency, particularly in older adults (Boa Sorte Silva et al., 2024 ; Schwarz et al., 2024 ; Shan et al., 2024 ). Previous studies revealed that SES fosters brain structure and regulates cognitive functions by shaping gray and white matter volume and intrinsic connectivity (within the brain regions) ( Cox et al., 2016 ; Montemurro et al., 2023 ; Morrison & Baxter, 2012 ). The deficits in cognitive performance have been acknowledged as a crucial concern among survivors of neurodegenerative disorders, such as Parkinsonism and Alzheimer’s disease and related dementias ( Arenaza-Urquijo et al., 2024 ; Livingston et al., 2024 ). A meta-analysis covering 242,804 participants worldwide indicated that over 15% of community dwellers aged 50 years and older suffer from mild cognitive impairment ( Bai et al., 2022 ). As populations age globally, understanding the intricate relationships between SES and cognitive decline becomes increasingly important ( Christensen et al., 2009 ). The aging of the population structure increased vulnerability to cognitive impairment in memory, learning capacity, and verbal fluency, which are essential for role functioning and quality of life ( Christensen et al., 2009 ). Aging is an intricate process involving multiple biological changes, including telomere length shortening, epigenetic modifications, oxidative stress exacerbation, mitochondrial dysfunction, and cellular senescence ( Santoro et al., 2021 ; Shim et al., 2024 ). Chronological age (CA) is generally considered an indicator of individual aging, however, it cannot manifest accurate physiological status and disease vulnerability. Thus, whole-body aging (WBA), which is based on multiple clinical biomarkers, provides a novel and holistic perspective for the recognition and intervention of aging-related disease. Researchers have also developed several clinical measures of WBA, including the Klemera-Doubal Age ( Klemera & Doubal, 2006 ), Phenotypic Age ( Liu et al., 2018 ), and Homeostatic Dysregulation ( Cohen et al., 2013 ). Although WBA has been applied in predicting the incident and mortality risk of several age-related diseases, such as osteoarthritis, hypertensive diseases, diabetes, and cancer ( Chen et al., 2022 ; Tian et al., 2023 ; Williams et al., 2023 ), the associations between WBA and cognitive function and verbal fluency remain unclear. Despite great progress in the socioeconomic conditions and living standards in many countries and regions over recent decades, the gap in wealth inequity between developed and developing countries has become more pronounced. The inequities in SES contribute to disparities in health and survival—longevity, cognitive performance, and quality of life in middle- and high-income populations are better than those in low-income populations ( Bor et al., 2017 ; “Mapping geographical inequalities in oral rehydration therapy coverage in low-income and middle-income countries, 2000-17,” 2020). Therefore, more efforts are warranted to reduce socioeconomic inequities in health and to improve the cognitive functions and quality of life in older populations. Using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, this study aims to explore the associations between SES disparities and cognitive functions and verbal fluency and the mediated effect of WBA among older adults, providing insights into the broader implications of SES-related cognitive declines in aging populations. 2. Methods 2.1. Study design and participants NHANES is a nationally representative survey of the civilian, community-dwelling members in the United States. The survey was conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) using a multistage probability design. Further details on study design have been reported previously ( Centers for Disease Control and Prevention/National Center for Health Statistics. About the National Health and Nutrition Examination Survey. , 2017). Of the 19,931 participants, 9554, 1802, and 6251 were excluded due to SES data, WBA data, and/or cognitive function data missing, respectively. Finally, 2321 participants enrolled in the study ( Figure 1 ). The NHANES was approved by the NCHS Research Ethics Review Board. All the participants provided written informed consent according to the research protocol. Download figure Open in new tab Figure 1. Enrollment process of this study. 2.2. Measurements 2.2.1. Demographic data The participants’ demographic data included their age (years), gender (male or female), ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, or other race), marital status (married, widowed/divorced/separated, never married, or living with a partner), body mass index (BMI) (< 18.5, 18.5-24.9, 25.0-29.9, ≥ 30.0), drinking (never, ever), smoking (never, ever), vigorous-intensity physical activity (yes, no). 2.2.2. Socioeconomic status The participants’ SES was evaluated by self-reported family income level, education, occupation, and health insurance according to previous studies ( Quaglia et al., 2013 ; Zhang et al., 2021 ). These indicators were classified into three levels (low, moderate, and high). The family poverty-to-income ratio (PIR), which indicates the ratio of family annual income to the federal poverty level, was used to evaluate the level of family income. The score S of PIR was categorized as follows: S ≤ 1 = low family income level, 1 < S □< 4 = moderate family income level, and S ≥ 4 = high family income level. Education level was classified into three levels as well: less than senior high school diploma, senior high school graduate/GED, and equivalent, or college/university and above. The socioeconomic index based on the employee’s salary, education, and social prestige was used to evaluate the level of each occupation ( Stevens & Cho, 1985 ). The levels of occupation were also classified into three categories: upper (socioeconomic index ≥ 50), lower (socioeconomic index < 50, including retirees and students), and unemployment ( Zhang et al., 2021 ). Health insurance was classified into three categories: private insurance (including any private health insurance, Medi-Gap, or single-service plans), public insurance only (including Medicare, Medicaid, State Children’s Health Insurance Program, military healthcare, Indian Health Service, state-sponsored health plans, or other government programs), and uninsured ( Le et al., 2020 ). 2.2.3. Whole-body aging WBA was calculated using three composite measures based on blood chemistry and clinical data: the Klemera-Doubal Method biological age (KDM) ( Klemera & Doubal, 2006 ), PhenoAge ( Levine et al., 2018 ), and homeostatic dysregulation (HD) ( Cohen et al., 2013 ). The details of these measures for WBA have been previously reported otherwise ( Graf et al., 2022 ; Kwon & Belsky, 2021 ). Briefly, KDM is derived from regressions of biomarkers on CA and reflects the age at which an individual’s physiology matches the average physiology of participants in the NHANES III. PhenoAge is based on a mortality prediction score using biomarkers and CA, representing the age at which an individual’s mortality risk matches the average risk in NHANES III. Unlike KDM and PhenoAge, HD does not incorporate CA in its calculation. Instead, it uses the Mahalanobis distance to quantify the deviation of a person’s physiology from a healthy population of NHANES III participants aged from 20 to 30. Any age-related biomarkers can be used to construct biological age algorithms generally. We selected the same set of biomarkers for our KDM, PhenoAge, and HD algorithms to ensure comparability using the R package BioAge ( Kwon & Belsky, 2021 ). We first identified 16 potential biomarkers, covering a range of organ systems (e.g., cardiometabolic, inflammatory, and kidney functions), that are routinely collected in clinical practice and available in NHANES III. We considered only biomarkers with ≤ 20% missing data and a correlation with chronological age (|r|□>□0.1, in line with prior research). The 16 age-related biomarkers and distribution for constructing KDM, PhenoAge, and HD algorithms are shown in Figure S1 . Following previous studies, we selected non-pregnant participants aged 30–75 years with complete biomarker data as the reference population for KDM (n□=□7,694) ( Mak et al., 2023 ). The reference population for PhenoAge included participants aged 20–84 years with complete biomarker data (n□=□12,998), while for HD, we selected participants aged 20–30 years who were not obese and had biomarker values within the age- and sex-specific normal range (n□=□258). Each individual had only one measurement occasion in the training set. The newly trained algorithms were then projected onto the NHANES 2011-2014 dataset. The distributions and correlations of the WBA and CA in the present study are shown in Figures S3 and S4 . 2.2.4. Cognitive functions and verbal fluency Immediate and delayed learning ability and inhibition for novel verbal information were assessed using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word List Learning Test (WLLT), Word List Recall Test (WLRT), and Intrusion Word Count Test (WLLT-IC and WLRT-IC). Verbal fluency and executive function were assessed using the Animal Fluency Test (AFT), and sustained attention, processing speed, and working memory were assessed using the Digit Symbol Substitution Test (DSST). Scoring in the CERAD WLLT, WLLT-IC, WLRT, and WLRT-IC ranged between 0 and 10, in AFT from 1 to 40, and in DSST from 0 to 100. Lower scores on cognitive tests above indicate more severe cognitive impairment. These tests have been previously validated for use in research practice. 2.3. Statistical analysis SAS procedure PROC LCA was used to generate an unmeasured variable (SES) from multiple observed exclusive categorical variables ( Lanza et al., 2007 ). An overall SES variable was calculated using latent class analysis based on four individual socioeconomic factors (family income level, education, occupation, and health insurance) ( Quaglia et al., 2013 ). The SES levels of participants were divided into three latent classes (high, moderate, and low SES) according to the posterior probabilities. The details of the latent class analysis can be found in the Table S1 and Figure S2 . Descriptive statistical analyses were conducted to exhibit the participants’ demographic profile and SES levels. Independent t-tests, one-way ANOVA tests, and Spearman correlation analysis were used to examine the associations between demographic variables, SES, whole-body aging, and cognitive functions. Multiple regression analysis was used to investigate the adjusted associations between SES, whole-body aging, and cognitive functions. The potential mediating effects of whole-body aging on the associations of SES and cognitive functions were estimated by simple mediation models using the bootstrap method with 1000 simulations ( Montoya & Hayes, 2017 ; Valente et al., 2020 ). The total effect (TE) indicated the overall effect of SES on cognitive functions. The direct effect (DE) indicated the effects of SES on cognitive functions without whole-body aging. The indirect effect (IE) indicated the effects of SES on cognitive functions through whole-body aging. The mediation proportion = IE/TE. The analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R 4.1.3. Two-sided p values < 0.05 were considered to be significant. 3. Results 3.1. Demographic characteristics Among 2321 participants (mean age 69.3 years, 50.5% female), 561 (24.2%) were of low SES, 1142 (49.4%) were of moderate SES, and 618 (26.5%) were of high SES. The ethnicity of most participants was non-Hispanic White (1151, 49.6%), followed by non-Hispanic Black (527, 22.7%). Most participants were married or cohabiting (889, 57.4%). The demographic characteristics of BMI, drinking, smoking, and physical activity are listed in Table 1 . View this table: View inline View popup Table 1. Socio-demographic characteristics of participants ( n = 2321). 3.2. Associations between demographic characteristics, socioeconomic status, and cognitive functions Table 2 shows the univariate analysis of associations between demographic characteristics and socioeconomic status with cognitive functions. Age, ethnicity, marital status, and SES were associated with CERAD WLLT (all p < 0.01), CERAD WLLRT (all p < 0.01), CERAD WLLT-IC (all p < 0.01), CERAD WLLRT-IC (all p < 0.01), AFT (all p < 0.01), and DSST (all p < 0.01). Gender was associated with CERAD WLLT (all p < 0.01), CERAD WLLRT (all p < 0.01), CERAD WLLT-IC (all p < 0.01), CERAD WLLRT-IC (all p < 0.01) and DSST (all p < 0.01) except for AFT ( t =1.63, p = 0.102). View this table: View inline View popup Download powerpoint Table 2. Univariate analysis of the associations between demographic characteristics and socioeconomic status with cognitive functions. Table 3 shows the correlation analysis of the associations between WBA and cognitive functions. There were statistically significant positive correlations between KDM, PhenoAge, and HD. The WBA was negatively associated with cognitive functions and verbal fluency. The levels of SES were positively associated with CERAD WLLT (β = 0.20, P <0.001), CERAD WLLRT (β = 0.19, P <0.001), CERAD WLLT-IC (β = 0.16, P <0.001), WLLRT-IC (β = 0.17, P <0.001), AFT (β = 0.28, P <0.001), and DSST (β = 0.45, P <0.001) in the adjusted linear regression models. Moreover, the WBA was negatively associated with cognitive function and verbal fluency in the multivariate analysis ( Table 4 ). View this table: View inline View popup Download powerpoint Table 3. Correlation analysis of the associations between whole-body aging and cognitive View this table: View inline View popup Download powerpoint Table 4. Multivariate analysis of the associations between socioeconomic status and whole-body aging with cognitive function. 3.3. Whole-body aging mediates the association between socioeconomic status and cognition and verbal fluency Figure 2 describes the mediation effects of WBA on the association between SES and cognitive functions and verbal fluency. After adjusting gender, ethnicity, marital status, body mass index, smoking, drinking, and physical activity in the mediation analysis, the proportions of indirect effects of SES and cognition and verbal fluency were 3.21%-5.17% for KDM. The proportions of indirect effects of SES and cognition and verbal fluency were 3.42%-7.65% for PhenoAge. The proportions of indirect effects of SES and cognition and verbal fluency were 3.77%-7.40% for HD. Moreover, no mediation effect of KDM on SES and CERAD WLLT was observed. Download figure Open in new tab Figure 2. Whole-body aging mediates the association between socioeconomic status, cognitive functions, and verbal fluency. AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; DSST, Digit Symbol Substitution Test; Homeostatic dysregulation, HD; Klemera-Doubal Method, KDM; Socioeconomic status, SES; WLLT, Word List Learning Test; WLRT, Word List Recall Test.; WLLT-IC, Word List g Test—Intrusion Word Count; WLRT-IC, Word List Recall Learning Test—Intrusion Word Count. Mediation analysis was adjusted by gender, ethnicity, marital status, body mass index, smoking, drinking, and physical activity. A two-tailed p < 0.05 was indicative of statistical significance. 4. Discussion In the present study, we explore the relationship between SES and cognitive functions as well as verbal fluency among U.S. middle-to-old-aged adults. Our study demonstrated that higher SES was significantly associated with better cognitive functions and verbal fluency and accelerated WBA partially mediated the associations between SES and cognitive functions and verbal fluency. The findings reveal a novel pathway linking SES to cognitive decline and the significance of eliminating the SES disparities. Comparison with Previous Studies Our findings are consistent with prior research ( Wang et al., 2023 ), which highlights that SES strongly influences cognitive abilities, including immediate and delayed learning ability, sustained attention, processing speed, working memory, and verbal fluency. SES disparities encompass variations in income, education, occupation, and health insurance, which in turn shape access to resources such as healthcare utilization, nutritious food, and cognitively stimulating environments. Formal education is correlated with higher individual cognitive functions across the lifespan, and prolonging education attenuates aging-related cognitive declines ( Du et al., 2023 ). Improving the educational conditions that shape development during the first decades of life has emerged as a great potential approach for preserving cognitive ability in old age and for reducing public burdens related to cognition-related diseases ( Lövdén et al., 2020 ). In terms of income, a cross-nationally harmonized longitudinal study indicated that wealth loss of 75% or greater in later life is negatively associated with subsequent cognitive functions in the USA and China ( Cho et al., 2023 ). Similarly, economic downturns around retirement increase cognitive decline in later life, with the long-lasting effect persisting for up to 10 years ( Hessel et al., 2018 ). Individuals with higher socioeconomic status perform slower cognitive decline among older Chinese immigrants in Chicago, which underscores the essential role of economic and occupational factors in cognitive functions ( Tang et al., 2023 ). Moreover, health insurance also plays a vital role in aging-related cognitive decline. Studies from China, the USA, and European countries demonstrate that health insurance coverage is associated with a reduced risk of cognitive decline ( Li et al., 2023 ; Peng et al., 2023 ; Wang et al., 2023 ). These findings indicated government policies and social safety nets should protect individuals from wealth losses in later life to reduce cognitive decline and related diseases. Among the 18 mediation analysis models, 17 exhibited a significant mediated effect of WBA between SES and cognitive declines. The findings revealed that WBA accounts for a modest but statistically significant portion of the association between SES and cognitive ability. SES makes an impact on cognition both directly, through psychosocial mechanisms, and indirectly, by accelerating physiological aging. The mediation effect emphasizes the importance of addressing physiological aging in interventions aimed at mitigating SES-related cognitive disparities. Targeting modifiable risk factors such as diet, physical activity, and stress management could potentially slow WBA and preserve cognitive ability. Implications for Policy and Practice The findings have critical implications for public health policies and interventions aimed at reducing health disparities. Policymakers should prioritize strategies to address SES-related inequities, such as expanding access to income, high-quality education, and healthcare utilization for socioeconomically disadvantaged groups. Incorporating WBA evaluation into routine clinical assessments could enable early identification of individuals at risk for accelerated aging and cognitive decline. Furthermore, community-based programs promoting lifestyle modifications—such as physical activity, stress management, and healthy diets—can help mitigate WBA and preserve cognitive function. These efforts should also include targeted initiatives for low-SES populations, addressing both the social determinants of health and the biological mechanisms contributing to disparities. Strengths and Limitations To the best of our knowledge, few studies have examined the mediating role of WBA in the relationship between SES and cognitive functions and verbal fluency. In this study, composite SES ranks and WBA status were constructed using multiple validated and robust measures, including the Klemera-Doubal Method biological aging, phenotypic aging, and homeostatic dysregulation. We investigated both direct and indirect associations between SES disparities, WBA, cognitive function, and verbal fluency in a representative U.S. population. Additionally, the inclusion of multiple cognitive domains allowed for a comprehensive analysis of SES-related disparities in cognitive functions and verbal fluency. These efforts enhance the generalizability and robustness of our findings. However, certain limitations also should be considered in the study. First, the cross-sectional nature of the study design precludes causal inference, limiting our ability to establish temporal relationships between SES, WBA, and cognitive functions. Longitudinal studies are needed to verify the observed associations and explore potential bidirectional effects. Second, self-reported SES measures may introduce reporting bias. Third, although we used a representative U.S. cohort in the study, external validation in other cohorts is warranted given the different developmental status and health-related characteristics across countries and regions. Finally, while our study used well-validated WBA measures, other biological markers, such as epigenetic modification or telomere length, may further elucidate the SES-Aging-Cognition pathway. 5. Conclusion In conclusion, the study suggested that SES disparities were significantly associated with immediate and delayed learning ability, sustained attention, processing speed, working memory, and verbal fluency, which was possibly and partially mediated by WBA. By elucidating the interplay between SES and WBA, the findings contribute to a deeper understanding of cognitive decline and underscore the importance of addressing socioeconomic inequities and physiological aging processes to promote cognitive resilience among older populations. Future research should prioritize longitudinal investigations of these associations and explore further biological mechanisms to explore comprehensive interventions for cognitive health equity. Declaration of Interest Statement The authors have no conflicts to disclose. Data Availability NHANES is an open-access resource. All researchers can apply to use its data for health-related research that is in the public interest ( https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ). https://wwwn.cdc.gov/nchs/nhanes/Default.aspx Funding statement The study was supported by the National Social Science Fund of China to Y.X. (Award number: 13CYY023). Human ethics and consent to participate declarations The NCHS Research Ethics Review Board approved the NHANES in accordance with the Declaration of Helsinki. All the participants provided written informed consent according to the research protocol. Clinical trial number The study is an observational study. Clinical trial registration number is not applicable. Consent for publication Not applicable. Data availability statement NHANES is an open-access resource. All researchers can apply to use its data for health-related research that is in the public interest ( https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ). Author Contributions Y.X. drafted the structure of the present work, conducted data analysis, drafted the first version of the manuscript, and revised subsequent versions critically for important intellectual content. L.X. revised the manuscript. Y.X. and W.S. designed the study. All authors had full access to all data in the study, revised the work critically for important intellectual content, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part were appropriately investigated and resolved. Acknowledgments None. Reference ↵ Arenaza-Urquijo , E. M. , Boyle , R. , Casaletto , K. , Anstey , K. J. , Vila-Castelar , C. , Colverson , A. , Palpatzis , E. , Eissman , J. 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