The association between the ALBI score and cognitive impairment as assessed by various diagnostic methods

The association between the ALBI score and cognitive impairment as assessed by various diagnostic methods | 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 The association between the ALBI score and cognitive impairment as assessed by various diagnostic methods Xiufeng Wang, Cailin Wang, Shangqi Sun, Hongxiu Guo, Siyi Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6475951/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 association between the Albumin-Bilirubin (ALBI) score and the risk of cognitive impairment remains unclear. This study aims to analyze the relationship between ALBI score and cognitive function. Methods : Data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) were analyzed using multivariable logistic regression and subgroup analyses to assess the relationship between the ALBI score and cognitive impairment diagnosed through various methods. Restricted cubic spline (RCS) models were employed to evaluate potential nonlinear associations between ALBI and cognitive function. Results : A total of 2,215 participants were included. In fully adjusted models, each 1-unit increase in the ALBI score was associated with a 2.61-fold higher risk of cognitive impairment, as measured by the Digit Symbol Substitution Test (DSST) (adjusted OR = 2.61; 95% CI: 1.17–5.82; P = 0.023). Associations with cognitive impairment identified using the other two assessment methods were not statistically significant. Age appeared to differentially affect various domains of cognitive function. RCS analysis indicated a linear association between the ALBI score and cognitive impairment across all three diagnostic approaches after adjustment for confounders. Conclusion : A higher ALBI score may be associated with increased risk of cognitive impairment. Further research is warranted to investigate potential causal pathways linking liver function and cognitive decline. ALBI cognitive impairment CERAD Animal Fluency Digit Symbol NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cognitive impairment—including mild cognitive impairment (MCI) and dementia—is a growing public health concern, especially among older adults. It involves deficits in memory, attention, language, and problem-solving, often reducing quality of life and independence. A recent meta-analysis estimated that the global prevalence of MCI among adults aged 50 and older is approximately 15.56% [ 1 ]. In the United States, data from the 2016 Health and Retirement Study revealed substantial rates of MCI and dementia among individuals aged 65 years and above[ 2 ]. Multiple factors contribute to cognitive decline, including advanced age, genetic predisposition, low educational attainment, cardiovascular disease, diabetes, smoking, and physical inactivity [ 3 , 4 ]. These findings highlight the multifactorial nature of cognitive impairment and the importance of comprehensive evaluation. Common cognitive assessments include the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery for memory, the Animal Fluency Test(AFT) for language, and the Digit Symbol Substitution Test (DSST) for processing speed[ 5 ]. These tools are essential for detecting early cognitive deficits and guiding further diagnostics. The Albumin-Bilirubin (ALBI) score is an objective measure of liver function, initially developed to predict survival in patients with hepatocellular carcinoma (HCC)[ 6 ]. It is calculated using serum albumin and bilirubin levels and avoids subjective parameters like ascites or encephalopathy, which are part of other scoring systems [ 7 ]. The ALBI score has been validated in multiple studies and is widely recognized for its prognostic value in liver disease, particularly HCC[ 8 , 9 ]. Although severe liver disease is known to impair cognition—primarily through hepatic encephalopathy—the broader relationship between liver function, as quantified by the ALBI score, and cognitive health in the general population remains underexplored. Hepatic encephalopathy involves the accumulation of neurotoxic substances such as ammonia, leading to cognitive dysfunction [ 10 ]. However, most prior research has focused on patients with cirrhosis or advanced hepatic conditions, limiting its applicability to the general population[ 11 ]. Nonalcoholic fatty liver disease (NAFLD), which is highly prevalent in the general population, has been linked to cognitive decline in observational studies[ 12 ]. These findings suggest that even mild-to-moderate liver dysfunction may negatively impact cognitive health[ 13 , 14 ]. Proposed mechanisms include systemic inflammation, insulin resistance, and endothelial dysfunction, all of which can affect brain function[ 15 ]. Given that the ALBI score reflects key metabolic markers—serum albumin and bilirubin—it is plausible that it may be associated with cognitive performance. Nonetheless, this relationship has not been directly investigated in population-based studies, representing a significant gap in the literature. Given the current uncertainty regarding the relationship between ALBI and cognitive function, this study aims to explore their association using nationally representative data from National Health and Nutrition Examination Survey (NHANES). Additionally, the study examines whether age, sex, and chronic conditions modify this relationship. By leveraging NHANES data, this research provides evidence that may inform future studies and clinical strategies for identifying liver-related risk factors in cognitive decline and developing targeted interventions. Methods Study Population The National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS), is designed to assess the health and nutritional status of the non-institutionalized civilian population in the United States. This study utilized data from NHANES 2011–2014, initially including 19,931 participants. After applying stringent exclusion criteria, 17,716 participants were removed. Specifically, exclusions were based on the following missing or unknown data: (1) CERAD score, AFT Score, and DSST Score (n = 16,997); (2) ALBI data (n = 168); (3) education level (n = 2); (4) family income to poverty ratio (n = 229); (5) body mass index (BMI) (n = 37); (6) hypertension status (n = 4); (7) diabetes status (n = 1); (8) smoking information (n = 2); (9) alcohol consumption (n = 269); and (10) cardiovascular disease status (n = 7). After exclusions, 2,215 participants remained for the final analysis (Fig. 1 ). Cognitive Function Assessment A comprehensive battery of cognitive tests was administered to assess various cognitive domains. The CERAD tests evaluated immediate and delayed memory. Immediate memory reflects short-term retention, while delayed memory involves consolidation and retrieval processes. The AFT assessed executive function and language by recording the number of animal names generated within one minute. The DSST measured processing speed, sustained attention, and working memory. Collectively, these assessments provided an integrated evaluation of cognitive performance, particularly relevant to aging and neurological conditions. Participants were classified as cognitively impaired if any individual test score was below the 25th percentile[ 16 – 18 ]. ALBI Score Assessment ALBI score was calculated using serum albumin and bilirubin levels, with specific weightings applied. Blood samples were collected at the Mobile Examination Center (MEC) and maintained at 2–8°C during transport to a certified laboratory in Ottumwa, Iowa. The ALBI score was computed using the following formula[ 19 ]: $$\:ALBI=\left({log}_{10}\times\:Bilirubin\right)+\left(Albumin\times\:\:-0.085\right)$$ Cardiovascular Disease and Chronic Kidney Disease Cardiovascular disease (CVD) was defined based on participants' affirmative responses to any of the following conditions in the NHANES Medical Conditions Questionnaire: congestive heart failure, coronary heart disease, angina/angina pectoris, or myocardial infarction[ 20 , 21 ]. Kidney function was evaluated using the estimated glomerular filtration rate (eGFR) and the Urine albumin-to-creatinine ratio (UACR). The 2021 CKD-EPI equation, adjusted for age, sex, and race, was used to estimate eGFR based on serum creatinine (Scr). Chronic kidney disease (CKD) was defined as eGFR < 60 ml/min/1.73 m² or UACR ≥ 30 mg/g. The eGFR was calculated using the following equation[ 22 , 23 ]: $$\:eGFR\left(ml/min/1.73{m}^{2}\right)=142\times\:{\left(\frac{Scr}{A}\right)}^{B}\times\:{0.9938}^{Age}\times\:C$$ If gender was female: C = 1.012, When Scr ≤ 0.7mg/dL, A = 0.7, B=-0.241; otherwise, Scr > 0.7mg/dL, A = 0.7, B=-1.2. If gender was male: C = 1, When Scr ≤ 0.9mg/dL, A = 0.9, B=-0.302; otherwise, Scr > 0.9mg/dL, A = 0.9, B=-1.2. Covariates Demographic and lifestyle covariates included age, sex, race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race), education level (< 9th grade, 9–11th grade, high school diploma/GED, some college/associate degree, college graduate or higher), family income to poverty ratio, BMI, smoking status, drinking status, hypertension, and diabetes. Hypertension was defined as self-reported diagnosis and current use of antihypertensive medications(24). Diabetes was identified through self-report, a two-hour glucose level ≥ 11.1 mmol/L, or fasting glucose ≥ 7.0 mmol/L. Prediabetes was defined as self-report, a two-hour glucose level between 7.8 and 11.1 mmol/L, or fasting glucose between 6.1 and 6.9 mmol/L[ 24 ]. Smoking status was categorized as: (1) non-smokers (never smoked or quit smoking for over one year), and (2) current smokers (those who smoked in the past 30 days or regularly smoked more than two cigarettes daily after quitting)[ 25 ]. Drinking status was defined as: (1) never drinkers (fewer than 12 drinks in a lifetime), and (2) current drinkers (at least 12 drinks per year or more than six drinks in the past 12 months)[ 26 ]. BMI was calculated as weight (kg) divided by height squared (m²)[ 24 ]. Statistical Analyses All analyses adhered to NHANES analytical protocols. The WTMEC2YR sample weight variable was applied and adjusted for the number of survey cycles (2011–2014) so that each sample represented approximately 9,600 U.S. individuals. Continuous variables were expressed as means with standard errors (SE), while categorical variables were presented as counts and weighted percentages. Between-group differences were assessed using chi-square tests or Student's t-tests, as appropriate. Multivariable logistic regression models were employed to evaluate associations between ALBI score and cognitive impairment, based on three diagnostic criteria. Model 1 was unadjusted. Model 2 adjusted for age, sex, race/ethnicity, education, family income to poverty ratio, and BMI. Model 3 additionally included smoking status, drinking status, hypertension, and diabetes. Model 4 further adjusted for cardiovascular disease and chronic kidney disease. Restricted cubic spline (RCS) analysis was conducted to explore potential non-linear relationships between ALBI score and cognitive impairment. Subgroup and interaction analyses were also performed to examine heterogeneity across different population strata. All statistical analyses were performed using IBM SPSS Statistics (version 24.0) and R software (version 4.3.0). A two-tailed p-value < 0.05 was considered statistically significant. Results Participant Characteristics by ALBI Score Quartiles Table 1 presents participant characteristics stratified by ALBI score quartiles, based on data from 2,215 individuals from the NHANES 2011–2014 cycles, representing 21,264,249 U.S. adults after weighting. Of these, 549 individuals were in the first quartile (weighted n = 5,361,356), 527 in the second (n = 6,075,158), 540 in the third (n = 5,308,445), and 599 in the fourth (n = 4,519,289). The overall mean age was 68.86 years (SE = 6.61), with 1,093 females (52%) and 1,122 males (48%). The prevalence of cognitive impairment, assessed using the CERAD, AFT, and DSST tests, varied by ALBI quartile. Participants in the highest ALBI quartile showed the highest prevalence of cognitive impairment. Statistically significant differences were observed for the Animal Fluency (p = 0.042) and Digit Symbol (p = 0.001) tests, but not for CERAD-based diagnoses (p = 0.316). Association Between ALBI Score and Cognitive Impairment Diagnosed by Different Methods Table 2 displays the associations between ALBI scores and cognitive impairment based on multiple logistic regression models using CERAD, AFT, and DSST tests. In Model 1 (unadjusted), a positive association was observed between ALBI score and cognitive impairment across all three tests: CERAD (OR = 3.42, 95% CI: 0.95–12.27, p = 0.059), Animal Fluency (OR = 2.15, 95% CI: 1.16–3.96, p = 0.01), and Digit Symbol (OR = 3.66, 95% CI: 2.06–6.51, p < 0.001). The association for CERAD, however, did not reach statistical significance. In Model 4, after adjusting for potential confounders (sex, age, race/ethnicity, education, family income-to-poverty ratio, BMI, alcohol consumption, smoking, hypertension, diabetes, cardiovascular disease, and chronic kidney disease), the associations remained positive but only the DSST showed statistical significance: CERAD (OR = 2.05, 95% CI: 0.37–11.39, p = 0.381), Animal Fluency (OR = 1.51, 95% CI: 0.81–2.79, p = 0.174), and Digit Symbol (OR = 2.61, 95% CI: 1.17–5.82, p = 0.023). Participants were grouped into ALBI score quartiles. After multivariable adjustment, no statistically significant differences in cognitive impairment risk were found when comparing higher quartiles to the lowest: second quartile—CERAD (adjusted OR = 0.87, 95% CI: 0.19–4.05, p = 0.847); Animal Fluency (adjusted OR = 0.98, 95% CI: 0.49–1.99, p = 0.957); Digit Symbol (adjusted OR = 0.98, 95% CI: 0.58–1.68, p = 0.950); third quartile—CERAD (adjusted OR = 1.14, 95% CI: 0.45–2.94, p = 0.760); AFT (adjusted OR = 1.14, 95% CI: 0.64–2.03, p = 0.635); DSST (adjusted OR = 1.40, 95% CI: 0.95–2.06, p = 0.083); fourth quartile—CERAD (adjusted OR = 1.54, 95% CI: 0.43–5.49, p = 0.474); AFT (adjusted OR = 1.42, 95% CI: 0.78–2.58, p = 0.229); DSST (adjusted OR = 1.58, 95% CI: 0.96–2.61, p = 0.069). Subgroup Analysis of ALBI Score and Cognitive Impairment Subgroup analyses examined whether associations between ALBI score and cognitive impairment varied by age, sex, and chronic conditions (Tables 3 – 5 ). In the CERAD model, a stronger association was observed among participants aged ≤ 60 years, though interaction terms did not reach statistical significance (Fig. 2 ). In the AFT model, stronger associations were found in individuals aged ≤ 68 years, and significant interactions were detected with diabetes, smoking, and BMI (Fig. 3 ). In the DSST model, the association between ALBI score and cognitive impairment remained stable among participants older than 68 years, females, non-diabetics, those with BMI ≤ 28, and those with chronic kidney disease. Significant interactions were observed with diabetes, smoking, and BMI (Fig. 4 ). Linear and Nonlinear Associations RCS analyses were used to visualize the relationship between ALBI scores and cognitive impairment (Figs. 5 – 7 ). Prior to adjustment, a linear association was found for CERAD (p for nonlinearity = 0.074; p for overall = 0.002) and AFT (p for nonlinearity = 0.814; p for overall = 0.100). A nonlinear association was observed for DSST (p for nonlinearity = 0.003; p for overall < 0.001). After adjusting for potential confounders, the relationships became more linear and consistent across all tests: CERAD (p for nonlinearity = 0.108; p for overall = 0.051); AFT (p for nonlinearity = 0.690; p for overall = 0.578); DSST (p for nonlinearity = 0.286; p for overall = 0.031). Discussion This study investigated the association between the ALBI score and cognitive impairment using three established diagnostic tools: the CERAD Word Learning Test, the Animal Fluency Test, and the Digit Symbol Substitution Test. Higher ALBI scores were positively associated with cognitive impairment, particularly when assessed using the DSST. This association remained significant after adjusting for a wide range of potential confounders. Restricted cubic spline (RCS) analysis demonstrated a stable linear relationship between the ALBI score and cognitive impairment across all three tests in adjusted models. Subgroup analyses indicated that diabetes, smoking status, and body mass index (BMI) may modify this relationship. Our findings contribute to the growing literature on the liver–brain axis. Although the ALBI score is primarily used to evaluate liver function in patients with hepatocellular carcinoma (HCC), we applied it to a general population to examine its association with cognitive performance. Previous studies have established links between liver dysfunction—particularly NAFLD—and cognitive decline[ 27 , 28 ]. For instance, analyses of NHANES III data showed that NAFLD was associated with poorer outcomes on tests of reaction time, processing speed, and memory[ 29 ]. A systematic review also supported the association between NAFLD and cognitive dysfunction, with impairments observed across domains such as general cognition and memory[ 30 ]. Our study, however, is the first to assess the association between the ALBI score—derived from serum albumin and bilirubin—and cognitive impairment in the general population. The significant association between higher ALBI scores and lower DSST performance, which measures processing speed and visuomotor coordination, aligns with findings from prior NAFLD studies using the Symbol Digit Substitution Test (SDST), a comparable tool[ 31 , 32 ]. This consistency suggests that liver dysfunction may affect cognitive processing speed, possibly through mechanisms involving impaired neurotransmission. In contrast, we found no significant associations between the ALBI score and outcomes from the CERAD or Animal Fluency tests, which assess memory and language functions, respectively. These findings diverge from prior research linking liver enzyme abnormalities to Alzheimer’s disease and memory-related changes[ 33 ]. The lack of association in our study may reflect differences in cognitive domains affected, the generally healthier status of our population, or insufficient sensitivity of the ALBI score to detect subtler impairments in memory and language among non-clinical individuals. Alternatively, our comprehensive adjustment for confounders may have attenuated the observed relationships compared with studies focused on patients with clinically evident liver or neurodegenerative disease. An unexpected yet important finding was the selective association between the ALBI score and processing speed, but not with memory or language. Processing speed is a fundamental cognitive domain involved in higher-order functions such as reaction time and decision-making[ 34 ]. It is also highly sensitive to systemic and neurological insults[ 35 ]. The specificity of this association suggests that processing speed may be more susceptible to subclinical liver dysfunction, potentially due to inflammation or metabolic disturbances that affect neural transmission. Age also appeared to influence this association. Younger individuals exhibited stronger associations between ALBI scores and cognitive impairment[ 36 , 37 ]. This age-specific effect may reflect the protective influence of cognitive reserve among older adults or increased vulnerability in younger populations[ 38 ]. A similar age-stratified pattern was observed for the Animal Fluency Test, with more pronounced associations among participants aged ≤ 68 years. Furthermore, subgroup differences by diabetes status, smoking, and BMI highlight the role of metabolic and lifestyle factors in modulating the relationship between liver function and cognitive performance[ 39 – 41 ]. These findings offer important implications for both research and clinical practice. The consistent association between the ALBI score and processing speed raises the possibility of using this domain as an early marker of cognitive decline in individuals with impaired liver function. Subgroup differences suggest that risk stratification based on age and comorbidities may enhance screening and intervention strategies. Our results extend current understanding by showing that liver function, as reflected by the ALBI score, may influence specific cognitive domains in the general population. This contrasts with earlier research that focused primarily on severe liver disease and hepatic encephalopathy. One mechanistic theory proposes that liver dysfunction leads to increased systemic inflammation and oxidative stress, which impair neurotransmission and synaptic plasticity[ 27 , 42 ]. Given its reliance on rapid neuronal processing, processing speed may be especially vulnerable to such disruptions. Another hypothesis involves liver-mediated alterations in lipid metabolism and cholesterol homeostasis, both essential for maintaining neuronal membrane integrity and synaptic function[ 43 – 45 ]. Impairments in these processes could selectively affect domains like processing speed by altering the brain’s metabolic environment. Our findings support these proposed mechanisms and suggest that even mild liver dysfunction can impact cognitive health. Limitations of this study should be noted. First, the cross-sectional design precludes causal inference. Second, cognitive impairment was classified using standardized test cutoffs rather than clinical diagnoses, which may introduce misclassification. Third, although we adjusted for multiple confounders, residual confounding cannot be excluded. Finally, the ALBI score may be influenced by transient physiological states, such as acute illness or dehydration, which could affect its precision in non-hospitalized populations. Conclusion In conclusion, this study provides novel evidence linking the ALBI score to cognitive performance, particularly processing speed, in the general population. While no associations were observed with memory or language function, the consistent relationship with DSST performance suggests that liver function may influence specific cognitive domains. These findings highlight the importance of considering subclinical liver dysfunction in cognitive health assessments. Future longitudinal studies are warranted to confirm these associations and explore the underlying biological mechanisms, which may ultimately inform preventive strategies aimed at preserving cognitive function through liver health optimization. Abbreviations ALBI Albumin-Bilirubin NHANES National Health and Nutrition Examination Survey MCI mild cognitive impairment CERAD Consortium to Establish a Registry for Alzheimer's Disease AFT Animal Fluency Test DSST Digit Symbol Substitution Test HCC hepatocellular carcinoma NAFLD Nonalcoholic fatty liver disease NCHS National Center for Health Statistics BMI body mass index MEC Mobile Examination Center CVD Cardiovascular disease eGFR estimated glomerular filtration rate UACR Urine albumin-to-creatinine ratio Scr serum creatinine CKD Chronic kidney disease SE standard errors SDST Symbol Digit Substitution Test 95%CI 95% Confidence Interval ANOVA Analysis of Variance OR Odds Ratio Q Quartiles RCS Restricted Cubic Spline Declarations Acknowledgements We thank the National Health and Nutrition Examination Survey participants and staff and the National Center for Health Statistics for their valuable contributions. Authors' contributions Manuscript draft and data analysis: WXF; conception and design: WCL, SSQ, GHX, ZSY, SXY; All authors revised and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (No. 82271230). Clinical trial number Not applicable. Availability of data and materials Data described in the manuscript are publicly and freely available without restriction at https://www.cdc.gov/nchs/nhanes/index.htm Ethics approval and consent to participate The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional study carried out by the National Center for Health Statistics (NCHS), a branch of the Centers for Disease Control and Prevention (CDC), with the goal of gathering data on the health and nutritional status of both adults and children in the United States. The survey protocols received approval from the Ethics Review Board of the CDC’s NCHS, and all participants provided written informed consent prior to participation. Consent for publication Not applicable. Competing interests All authors have no conflict of interest. References Bai W, Chen P, Cai H, Zhang Q, Su Z, Cheung T, et al. Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies. Age and Ageing. 2022;51(8):afac173. Manly JJ, Jones RN, Langa KM, Ryan LH, Levine DA, McCammon R, et al. Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol Project. JAMA Neurology. 2022;79(12):1242-9. Gil-Peinado M, Alacreu M, Ramos H, Sendra-Lillo J, García C, García-Lluch G, et al. The A-to-Z factors associated with cognitive impairment. Results of the DeCo study. Frontiers in psychology. 2023;14:1152527. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396(10248):413-46. Braconnier ML, Siper PM. Neuropsychological Assessment in Autism Spectrum Disorder. Current psychiatry reports. 2021;23(10):63. Ho SY, Liu PH, Hsu CY, Huang YH, Liao JI, Su CW, et al. A New Tumor Burden Score and Albumin-Bilirubin Grade-Based Prognostic Model for Hepatocellular Carcinoma. Cancers. 2022;14(3). Zhang HY, Li WB, Ye H, Xiao ZY, Peng YR, Wang J. Long-term results of the paraesophagogastric devascularization with or without esophageal transection: which is more suitable for variceal bleeding? World journal of surgery. 2014;38(8):2105-12. Hiraoka A, Michitaka K, Kumada T, Izumi N, Kadoya M, Kokudo N, et al. Validation and Potential of Albumin-Bilirubin Grade and Prognostication in a Nationwide Survey of 46,681 Hepatocellular Carcinoma Patients in Japan: The Need for a More Detailed Evaluation of Hepatic Function. Liver cancer. 2017;6(4):325-36. Marasco G, Alemanni LV, Colecchia A, Festi D, Bazzoli F, Mazzella G, et al. Prognostic value of the albumin-bilirubin grade for the prediction of post-hepatectomy liver failure: a systematic review and meta-analysis. 2021;10(9):2011. Vilstrup H, Amodio P, Bajaj J, Cordoba J, Ferenci P, Mullen KD, et al. Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. Hepatology (Baltimore, Md). 2014;60(2):715-35. Adekanle O, Sunmonu TA, Komolafe MA, Ndububa DA. Cognitive functions in patients with liver cirrhosis: assessment using community screening interview for dementia. Annals of African medicine. 2012;11(4):222-9. Cushman M, Callas PW, Alexander KS, Wadley V, Zakai NA, Lidofsky SD, et al. Nonalcoholic fatty liver disease and cognitive impairment: A prospective cohort study. PloS one. 2023;18(4):e0282633. López-Franco Ó, Morin JP, Cortés-Sol A, Molina-Jiménez T, Del Moral DI, Flores-Muñoz M, et al. Cognitive Impairment After Resolution of Hepatic Encephalopathy: A Systematic Review and Meta-Analysis. Frontiers in neuroscience. 2021;15:579263. Seo SW, Gottesman RF, Clark JM, Hernaez R, Chang Y, Kim C, et al. Nonalcoholic fatty liver disease is associated with cognitive function in adults. Neurology. 2016;86(12):1136-42. Powell A, Sumnall H, Smith J, Kuiper R, Montgomery C. Recovery of neuropsychological function following abstinence from alcohol in adults diagnosed with an alcohol use disorder: Systematic review of longitudinal studies. PloS one. 2024;19(1):e0296043. Casagrande SS, Lee C, Stoeckel LE, Menke A, Cowie CC. Cognitive function among older adults with diabetes and prediabetes, NHANES 2011-2014. Diabetes research and clinical practice. 2021;178:108939. Song S, Liu N, Wang G, Wang Y, Zhang X, Zhao X, et al. Sex Specificity in the Mixed Effects of Blood Heavy Metals and Cognitive Function on Elderly: Evidence from NHANES. Nutrients. 2023;15(13). Wei B, Dong Q, Ma J, Zhang A. The association between triglyceride-glucose index and cognitive function in nondiabetic elderly: NHANES 2011-2014. Lipids in health and disease. 2023;22(1):188. Du L, Xu H, Fang L, Qiao L, Xie Y, Yang C, et al. Albumin-bilirubin score as a predictor of all-cause mortality in patients with hepatitis B virus infection: An analysis of National Health and Nutrition Examination Survey (NHANES) 1999-2018. Preventive medicine reports. 2024;39:102639. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003-2018. Cardiovascular diabetology. 2024;23(1):8. Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. Cardiovascular diabetology. 2023;22(1):279. Huang D, Wu H. Association between the aggregate index of systemic inflammation and CKD: evidence from NHANES 1999-2018. Frontiers in medicine. 2025;12:1506575. Liang J, An H, Hu X, Gao Y, Zhou J, Gong X, et al. Correlation between chronic kidney disease and all-cause mortality in diabetic foot ulcers: evidence from the 1999-2004 national health and nutrition examination survey (NHANES). Frontiers in endocrinology. 2025;16:1533087. Jiang J, Zhao H, Chen J, Du J, Ni W, Zheng B, et al. The association between dietary creatine intake and cancer in U.S. adults: insights from NHANES 2007-2018. Frontiers in nutrition. 2024;11:1460057. Baltic S, Grasaas E, Ostojic SM. Creatine and sleep habits and disorders in the general population aged 16 years and over: NHANES 2007-2008. Nutrition and health. 2024:2601060241299958. Ostojic SM, Stea TH, Ellery SJ, Smith-Ryan AE. Association between dietary intake of creatine and female reproductive health: Evidence from NHANES 2017-2020. Food science & nutrition. 2024;12(7):4893-8. Medina-Julio D, Ramírez-Mejía MM, Cordova-Gallardo J, Peniche-Luna E, Cantú-Brito C, Mendez-Sanchez N. From Liver to Brain: How MAFLD/MASLD Impacts Cognitive Function. Medical science monitor : international medical journal of experimental and clinical research. 2024;30:e943417. Peng X, Zhang X, Xu Z, Li L, Mo X, Peng Z, et al. Peripheral amyloid-β clearance mediates cognitive impairment in non-alcoholic fatty liver disease. EBioMedicine. 2024;102:105079. Jung I, Park SY, Lee DY, Cho HJ, Lee SK, Seo JA, et al. Association of metabolic dysfunction-associated fatty liver disease with white matter hyperintensity and cognitive decline: A longitudinal cohort study. Diabetes, obesity & metabolism. 2025;27(4):2271-9. George ES, Sood S, Daly RM, Tan S-Y. Is there an association between non-alcoholic fatty liver disease and cognitive function? A systematic review. BMC Geriatrics. 2022;22(1):47. Colognesi M, Gabbia D, De Martin S. Depression and Cognitive Impairment-Extrahepatic Manifestations of NAFLD and NASH. Biomedicines. 2020;8(7). Wernberg CW, Grønkjær LL, Gade Jacobsen B, Indira Chandran V, Krag A, Graversen JH, et al. The prevalence and risk factors for cognitive impairment in obesity and NAFLD. Hepatology communications. 2023;7(7). Nho K, Kueider-Paisley A, Ahmad S, MahmoudianDehkordi S, Arnold M, Risacher SL, et al. Association of Altered Liver Enzymes With Alzheimer Disease Diagnosis, Cognition, Neuroimaging Measures, and Cerebrospinal Fluid Biomarkers. JAMA Network Open. 2019;2(7):e197978-e. Baune BT, Brignone M, Larsen KG. A Network Meta-Analysis Comparing Effects of Various Antidepressant Classes on the Digit Symbol Substitution Test (DSST) as a Measure of Cognitive Dysfunction in Patients with Major Depressive Disorder. The international journal of neuropsychopharmacology. 2018;21(2):97-107. Gajewski B, Karlińska I, Stasiołek M. Symbol Digit Modalities Test in progressive multiple sclerosis. Neurologia i neurochirurgia polska. 2024;58(3):221-32. Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. Jama. 2014;312(23):2551-61. Ma Y, Liang L, Zheng F, Shi L, Zhong B, Xie W. Association Between Sleep Duration and Cognitive Decline. JAMA Netw Open. 2020;3(9):e2013573. Pettigrew C, Soldan A. Defining Cognitive Reserve and Implications for Cognitive Aging. Current neurology and neuroscience reports. 2019;19(1):1. Srikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ. Type 2 diabetes and cognitive dysfunction-towards effective management of both comorbidities. The lancet Diabetes & endocrinology. 2020;8(6):535-45. Benito-León J, Ghosh R, Lapeña-Motilva J, Martín-Arriscado C, Bermejo-Pareja F. Association between cumulative smoking exposure and cognitive decline in non-demented older adults: NEDICES study. Scientific reports. 2023;13(1):5754. Xu X, Xu Y, Shi R. Association between obesity, physical activity, and cognitive decline in Chinese middle and old-aged adults: a mediation analysis. BMC Geriatr. 2024;24(1):54. Wen W, Fan H, Zhang S, Hu S, Chen C, Tang J, et al. Associations between metabolic dysfunction-associated fatty liver disease and atherosclerotic cardiovascular disease. The American journal of the medical sciences. 2024;368(6):557-68. Maity S, Farrell K, Navabpour S, Narayanan SN, Jarome TJ. Epigenetic Mechanisms in Memory and Cognitive Decline Associated with Aging and Alzheimer's Disease. International journal of molecular sciences. 2021;22(22). Sekhar RV. GlyNAC Supplementation Improves Glutathione Deficiency, Oxidative Stress, Mitochondrial Dysfunction, Inflammation, Aging Hallmarks, Metabolic Defects, Muscle Strength, Cognitive Decline, and Body Composition: Implications for Healthy Aging. The Journal of nutrition. 2021;151(12):3606-16. Weng ZB, Chen YR, Lv JT, Wang MX, Chen ZY, Zhou W, et al. A Review of Bile Acid Metabolism and Signaling in Cognitive Dysfunction-Related Diseases. Oxidative medicine and cellular longevity. 2022;2022:4289383. Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6475951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463519397,"identity":"82dcccb2-e201-40fe-a7da-be6a17a7c446","order_by":0,"name":"Xiufeng Wang","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiufeng","middleName":"","lastName":"Wang","suffix":""},{"id":463519398,"identity":"306868db-ee3f-4108-a8b2-9c5eddf8e6a6","order_by":1,"name":"Cailin Wang","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Cailin","middleName":"","lastName":"Wang","suffix":""},{"id":463519399,"identity":"a3a990b3-7898-4cd0-b74d-8a6353b605dd","order_by":2,"name":"Shangqi Sun","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shangqi","middleName":"","lastName":"Sun","suffix":""},{"id":463519400,"identity":"10eabcdc-69f4-4217-8e9d-3082bb6a4266","order_by":3,"name":"Hongxiu Guo","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongxiu","middleName":"","lastName":"Guo","suffix":""},{"id":463519401,"identity":"1db6b7a9-ba4b-4d4d-8a1f-1a47ce1fed24","order_by":4,"name":"Siyi Zheng","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Siyi","middleName":"","lastName":"Zheng","suffix":""},{"id":463519402,"identity":"76ac47bb-9680-4dbb-9c32-6879695a69fc","order_by":5,"name":"Xueying Shi","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Shi","suffix":""},{"id":463519403,"identity":"480234f2-464a-4373-b710-a341ef0a6b5c","order_by":6,"name":"Rong Ma","email":"","orcid":"","institution":"Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Ma","suffix":""},{"id":463519404,"identity":"f92a3458-926f-4019-8132-713d3a99c260","order_by":7,"name":"Gang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYLCCBwY2PGzszceg3AQitCQYpMnx8xxLAzINiNXCcNhYckaOGXFa5N3PHn6RUMCcuOHMmW+Pef78YeBnzzFg+LkDtxbDM3lpFgkGbIkbjvduN+ZtM2CQ7HljwNh7Bo+WhhwzgwQDHqAtZ7dJ8zYYMBjcyDFgZmzDo6X/DUiLROKGGznPpHn+GDDYE9IiL5Fj/CDBwADkfTZpHjagLRIEtBhIvDEDBnICKJDNJOe2GfNInHlWcLAXny39OcYfPvz5D4rKZxJv/sjJ8bcnb3zwE58tBxjYJJAFeEDEAdwagLY0MDB/wKdgFIyCUTAKRgEDANV0UK0jcSxcAAAAAElFTkSuQmCC","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-18 04:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6475951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6475951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83766512,"identity":"cadf0c15-34c7-4877-a91a-d54324829db6","added_by":"auto","created_at":"2025-06-02 11:14:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20407,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants selection in the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e","description":"","filename":"Binder31.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/ce57c032f30d4c71e21c56ee.png"},{"id":83766517,"identity":"e27f3b7b-d529-4562-b39d-fe278445de7c","added_by":"auto","created_at":"2025-06-02 11:14:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76038,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of CERAD and Cognitive Impairment. Stratified analyses of the association between cognitive function and ALBI score according to baseline characteristics in CERAD test. The P value for interaction represents the likelihood of interaction between the variable and ALBI score. CI confidence interval. BMI: body mass index.\u003c/p\u003e","description":"","filename":"Binder32.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/ff8fde4843e39cc0e121cc8c.png"},{"id":83767464,"identity":"c587bab4-7b0a-4026-903d-5c0903482ed9","added_by":"auto","created_at":"2025-06-02 11:30:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74226,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of AFT and Cognitive Impairment. Stratified analyses of the association between cognitive function and ALBI score according to baseline characteristics in AFT test. The P value for interaction represents the likelihood of interaction between the variable and ALBI score. CI confidence interval. BMI: body mass index.\u003c/p\u003e","description":"","filename":"Binder33.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/e4b0b6bb81641ab0c7b97818.png"},{"id":83766515,"identity":"e29bb76b-6c41-4643-8cdc-4f01ee28792d","added_by":"auto","created_at":"2025-06-02 11:14:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75509,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of DSST and Cognitive Impairment. Stratified analyses of the association between cognitive function and ALBI score according to baseline characteristics in DSST test. The P value for interaction represents the likelihood of interaction between the variable and ALBI score. CI confidence interval. BMI: body mass index.\u003c/p\u003e","description":"","filename":"Binder34.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/9b125457108c5ba49f68569d.png"},{"id":83766516,"identity":"fda721e0-118f-43fa-a940-cfe8992209e1","added_by":"auto","created_at":"2025-06-02 11:14:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83180,"visible":true,"origin":"","legend":"\u003cp\u003eRCS Analysis betweenCERAD and Cognitive Impairment. Relationship between ALBI score and cognitive performance in CERAD test. Figure A is before adjustment. Figure B is after adjustment.\u003c/p\u003e","description":"","filename":"Binder35.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/5641303d35d78ed6c4933eff.png"},{"id":83767759,"identity":"e5bf7783-f67e-40af-a748-4db8cb6868f6","added_by":"auto","created_at":"2025-06-02 11:38:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":87138,"visible":true,"origin":"","legend":"\u003cp\u003eRCS Analysis between AFT and Cognitive Impairment. Relationship between ALBI score and cognitive performance in AFT test. Figure A is before adjustment. Figure B is after adjustment.\u003c/p\u003e","description":"","filename":"Binder36.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/39d03c6b190209849a9aee0f.png"},{"id":83766831,"identity":"8eb25772-78fc-4b26-8de7-224d077df7bc","added_by":"auto","created_at":"2025-06-02 11:22:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":79920,"visible":true,"origin":"","legend":"\u003cp\u003eRCS Analysis between DSST and Cognitive Impairment. Relationship between ALBI score and cognitive performance in DSST test. Figure A is before adjustment. Figure B is after adjustment.\u003c/p\u003e","description":"","filename":"Binder37.png","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/63d53410ee1481c16dc00270.png"},{"id":90636784,"identity":"ce46dcac-e0e3-431e-b74e-7d0b6cfef276","added_by":"auto","created_at":"2025-09-05 05:08:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/9e2f3661-a509-45a5-b8aa-0fbac10069c5.pdf"},{"id":83766513,"identity":"01ff70d1-ac15-426c-9758-51c2d17ea59c","added_by":"auto","created_at":"2025-06-02 11:14:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":60663,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6475951/v1/d822f17d2facf454b110ba6c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between the ALBI score and cognitive impairment as assessed by various diagnostic methods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCognitive impairment\u0026mdash;including mild cognitive impairment (MCI) and dementia\u0026mdash;is a growing public health concern, especially among older adults. It involves deficits in memory, attention, language, and problem-solving, often reducing quality of life and independence. A recent meta-analysis estimated that the global prevalence of MCI among adults aged 50 and older is approximately 15.56% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, data from the 2016 Health and Retirement Study revealed substantial rates of MCI and dementia among individuals aged 65 years and above[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple factors contribute to cognitive decline, including advanced age, genetic predisposition, low educational attainment, cardiovascular disease, diabetes, smoking, and physical inactivity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These findings highlight the multifactorial nature of cognitive impairment and the importance of comprehensive evaluation. Common cognitive assessments include the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery for memory, the Animal Fluency Test(AFT) for language, and the Digit Symbol Substitution Test (DSST) for processing speed[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These tools are essential for detecting early cognitive deficits and guiding further diagnostics.\u003c/p\u003e \u003cp\u003eThe Albumin-Bilirubin (ALBI) score is an objective measure of liver function, initially developed to predict survival in patients with hepatocellular carcinoma (HCC)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is calculated using serum albumin and bilirubin levels and avoids subjective parameters like ascites or encephalopathy, which are part of other scoring systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The ALBI score has been validated in multiple studies and is widely recognized for its prognostic value in liver disease, particularly HCC[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough severe liver disease is known to impair cognition\u0026mdash;primarily through hepatic encephalopathy\u0026mdash;the broader relationship between liver function, as quantified by the ALBI score, and cognitive health in the general population remains underexplored. Hepatic encephalopathy involves the accumulation of neurotoxic substances such as ammonia, leading to cognitive dysfunction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, most prior research has focused on patients with cirrhosis or advanced hepatic conditions, limiting its applicability to the general population[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonalcoholic fatty liver disease (NAFLD), which is highly prevalent in the general population, has been linked to cognitive decline in observational studies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings suggest that even mild-to-moderate liver dysfunction may negatively impact cognitive health[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Proposed mechanisms include systemic inflammation, insulin resistance, and endothelial dysfunction, all of which can affect brain function[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given that the ALBI score reflects key metabolic markers\u0026mdash;serum albumin and bilirubin\u0026mdash;it is plausible that it may be associated with cognitive performance. Nonetheless, this relationship has not been directly investigated in population-based studies, representing a significant gap in the literature.\u003c/p\u003e \u003cp\u003eGiven the current uncertainty regarding the relationship between ALBI and cognitive function, this study aims to explore their association using nationally representative data from National Health and Nutrition Examination Survey (NHANES). Additionally, the study examines whether age, sex, and chronic conditions modify this relationship. By leveraging NHANES data, this research provides evidence that may inform future studies and clinical strategies for identifying liver-related risk factors in cognitive decline and developing targeted interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS), is designed to assess the health and nutritional status of the non-institutionalized civilian population in the United States. This study utilized data from NHANES 2011\u0026ndash;2014, initially including 19,931 participants. After applying stringent exclusion criteria, 17,716 participants were removed. Specifically, exclusions were based on the following missing or unknown data: (1) CERAD score, AFT Score, and DSST Score (n\u0026thinsp;=\u0026thinsp;16,997); (2) ALBI data (n\u0026thinsp;=\u0026thinsp;168); (3) education level (n\u0026thinsp;=\u0026thinsp;2); (4) family income to poverty ratio (n\u0026thinsp;=\u0026thinsp;229); (5) body mass index (BMI) (n\u0026thinsp;=\u0026thinsp;37); (6) hypertension status (n\u0026thinsp;=\u0026thinsp;4); (7) diabetes status (n\u0026thinsp;=\u0026thinsp;1); (8) smoking information (n\u0026thinsp;=\u0026thinsp;2); (9) alcohol consumption (n\u0026thinsp;=\u0026thinsp;269); and (10) cardiovascular disease status (n\u0026thinsp;=\u0026thinsp;7). After exclusions, 2,215 participants remained for the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive Function Assessment\u003c/h3\u003e\n\u003cp\u003eA comprehensive battery of cognitive tests was administered to assess various cognitive domains. The CERAD tests evaluated immediate and delayed memory. Immediate memory reflects short-term retention, while delayed memory involves consolidation and retrieval processes. The AFT assessed executive function and language by recording the number of animal names generated within one minute. The DSST measured processing speed, sustained attention, and working memory. Collectively, these assessments provided an integrated evaluation of cognitive performance, particularly relevant to aging and neurological conditions. Participants were classified as cognitively impaired if any individual test score was below the 25th percentile[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eALBI Score Assessment\u003c/h3\u003e\n\u003cp\u003eALBI score was calculated using serum albumin and bilirubin levels, with specific weightings applied. Blood samples were collected at the Mobile Examination Center (MEC) and maintained at 2\u0026ndash;8\u0026deg;C during transport to a certified laboratory in Ottumwa, Iowa. The ALBI score was computed using the following formula[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ALBI=\\left({log}_{10}\\times\\:Bilirubin\\right)+\\left(Albumin\\times\\:\\:-0.085\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eCardiovascular Disease and Chronic Kidney Disease\u003c/h3\u003e\n\u003cp\u003eCardiovascular disease (CVD) was defined based on participants' affirmative responses to any of the following conditions in the NHANES Medical Conditions Questionnaire: congestive heart failure, coronary heart disease, angina/angina pectoris, or myocardial infarction[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKidney function was evaluated using the estimated glomerular filtration rate (eGFR) and the Urine albumin-to-creatinine ratio (UACR). The 2021 CKD-EPI equation, adjusted for age, sex, and race, was used to estimate eGFR based on serum creatinine (Scr). Chronic kidney disease (CKD) was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73 m\u0026sup2; or UACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g. The eGFR was calculated using the following equation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:eGFR\\left(ml/min/1.73{m}^{2}\\right)=142\\times\\:{\\left(\\frac{Scr}{A}\\right)}^{B}\\times\\:{0.9938}^{Age}\\times\\:C$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf gender was female: C\u0026thinsp;=\u0026thinsp;1.012,\u003c/p\u003e \u003cp\u003eWhen Scr\u0026thinsp;\u0026le;\u0026thinsp;0.7mg/dL, A\u0026thinsp;=\u0026thinsp;0.7, B=-0.241; otherwise, Scr\u0026thinsp;\u0026gt;\u0026thinsp;0.7mg/dL, A\u0026thinsp;=\u0026thinsp;0.7, B=-1.2.\u003c/p\u003e \u003cp\u003eIf gender was male: C\u0026thinsp;=\u0026thinsp;1,\u003c/p\u003e \u003cp\u003eWhen Scr\u0026thinsp;\u0026le;\u0026thinsp;0.9mg/dL, A\u0026thinsp;=\u0026thinsp;0.9, B=-0.302; otherwise, Scr\u0026thinsp;\u0026gt;\u0026thinsp;0.9mg/dL, A\u0026thinsp;=\u0026thinsp;0.9, B=-1.2.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic and lifestyle covariates included age, sex, race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race), education level (\u0026lt;\u0026thinsp;9th grade, 9\u0026ndash;11th grade, high school diploma/GED, some college/associate degree, college graduate or higher), family income to poverty ratio, BMI, smoking status, drinking status, hypertension, and diabetes. Hypertension was defined as self-reported diagnosis and current use of antihypertensive medications(24). Diabetes was identified through self-report, a two-hour glucose level\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, or fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L. Prediabetes was defined as self-report, a two-hour glucose level between 7.8 and 11.1 mmol/L, or fasting glucose between 6.1 and 6.9 mmol/L[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Smoking status was categorized as: (1) non-smokers (never smoked or quit smoking for over one year), and (2) current smokers (those who smoked in the past 30 days or regularly smoked more than two cigarettes daily after quitting)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Drinking status was defined as: (1) never drinkers (fewer than 12 drinks in a lifetime), and (2) current drinkers (at least 12 drinks per year or more than six drinks in the past 12 months)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eAll analyses adhered to NHANES analytical protocols. The WTMEC2YR sample weight variable was applied and adjusted for the number of survey cycles (2011\u0026ndash;2014) so that each sample represented approximately 9,600 U.S. individuals. Continuous variables were expressed as means with standard errors (SE), while categorical variables were presented as counts and weighted percentages. Between-group differences were assessed using chi-square tests or Student's t-tests, as appropriate. Multivariable logistic regression models were employed to evaluate associations between ALBI score and cognitive impairment, based on three diagnostic criteria. Model 1 was unadjusted. Model 2 adjusted for age, sex, race/ethnicity, education, family income to poverty ratio, and BMI. Model 3 additionally included smoking status, drinking status, hypertension, and diabetes. Model 4 further adjusted for cardiovascular disease and chronic kidney disease. Restricted cubic spline (RCS) analysis was conducted to explore potential non-linear relationships between ALBI score and cognitive impairment. Subgroup and interaction analyses were also performed to examine heterogeneity across different population strata. All statistical analyses were performed using IBM SPSS Statistics (version 24.0) and R software (version 4.3.0). A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant Characteristics by ALBI Score Quartiles\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents participant characteristics stratified by ALBI score quartiles, based on data from 2,215 individuals from the NHANES 2011\u0026ndash;2014 cycles, representing 21,264,249 U.S. adults after weighting. Of these, 549 individuals were in the first quartile (weighted n\u0026thinsp;=\u0026thinsp;5,361,356), 527 in the second (n\u0026thinsp;=\u0026thinsp;6,075,158), 540 in the third (n\u0026thinsp;=\u0026thinsp;5,308,445), and 599 in the fourth (n\u0026thinsp;=\u0026thinsp;4,519,289). The overall mean age was 68.86 years (SE\u0026thinsp;=\u0026thinsp;6.61), with 1,093 females (52%) and 1,122 males (48%). The prevalence of cognitive impairment, assessed using the CERAD, AFT, and DSST tests, varied by ALBI quartile. Participants in the highest ALBI quartile showed the highest prevalence of cognitive impairment. Statistically significant differences were observed for the Animal Fluency (p\u0026thinsp;=\u0026thinsp;0.042) and Digit Symbol (p\u0026thinsp;=\u0026thinsp;0.001) tests, but not for CERAD-based diagnoses (p\u0026thinsp;=\u0026thinsp;0.316).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cstrong\u003eAssociation Between ALBI Score and Cognitive Impairment Diagnosed by Different Methods\u003c/strong\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the associations between ALBI scores and cognitive impairment based on multiple logistic regression models using CERAD, AFT, and DSST tests. In Model 1 (unadjusted), a positive association was observed between ALBI score and cognitive impairment across all three tests: CERAD (OR\u0026thinsp;=\u0026thinsp;3.42, 95% CI: 0.95\u0026ndash;12.27, p\u0026thinsp;=\u0026thinsp;0.059), Animal Fluency (OR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.16\u0026ndash;3.96, p\u0026thinsp;=\u0026thinsp;0.01), and Digit Symbol (OR\u0026thinsp;=\u0026thinsp;3.66, 95% CI: 2.06\u0026ndash;6.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The association for CERAD, however, did not reach statistical significance. In Model 4, after adjusting for potential confounders (sex, age, race/ethnicity, education, family income-to-poverty ratio, BMI, alcohol consumption, smoking, hypertension, diabetes, cardiovascular disease, and chronic kidney disease), the associations remained positive but only the DSST showed statistical significance: CERAD (OR\u0026thinsp;=\u0026thinsp;2.05, 95% CI: 0.37\u0026ndash;11.39, p\u0026thinsp;=\u0026thinsp;0.381), Animal Fluency (OR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 0.81\u0026ndash;2.79, p\u0026thinsp;=\u0026thinsp;0.174), and Digit Symbol (OR\u0026thinsp;=\u0026thinsp;2.61, 95% CI: 1.17\u0026ndash;5.82, p\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eParticipants were grouped into ALBI score quartiles. After multivariable adjustment, no statistically significant differences in cognitive impairment risk were found when comparing higher quartiles to the lowest: second quartile\u0026mdash;CERAD (adjusted OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.19\u0026ndash;4.05, p\u0026thinsp;=\u0026thinsp;0.847); Animal Fluency (adjusted OR\u0026thinsp;=\u0026thinsp;0.98, 95% CI: 0.49\u0026ndash;1.99, p\u0026thinsp;=\u0026thinsp;0.957); Digit Symbol (adjusted OR\u0026thinsp;=\u0026thinsp;0.98, 95% CI: 0.58\u0026ndash;1.68, p\u0026thinsp;=\u0026thinsp;0.950); third quartile\u0026mdash;CERAD (adjusted OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.45\u0026ndash;2.94, p\u0026thinsp;=\u0026thinsp;0.760); AFT (adjusted OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.64\u0026ndash;2.03, p\u0026thinsp;=\u0026thinsp;0.635); DSST (adjusted OR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 0.95\u0026ndash;2.06, p\u0026thinsp;=\u0026thinsp;0.083); fourth quartile\u0026mdash;CERAD (adjusted OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 0.43\u0026ndash;5.49, p\u0026thinsp;=\u0026thinsp;0.474); AFT (adjusted OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 0.78\u0026ndash;2.58, p\u0026thinsp;=\u0026thinsp;0.229); DSST (adjusted OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 0.96\u0026ndash;2.61, p\u0026thinsp;=\u0026thinsp;0.069).\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup Analysis of ALBI Score and Cognitive Impairment\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses examined whether associations between ALBI score and cognitive impairment varied by age, sex, and chronic conditions (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the CERAD model, a stronger association was observed among participants aged\u0026thinsp;\u0026le;\u0026thinsp;60 years, though interaction terms did not reach statistical significance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the AFT model, stronger associations were found in individuals aged\u0026thinsp;\u0026le;\u0026thinsp;68 years, and significant interactions were detected with diabetes, smoking, and BMI (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the DSST model, the association between ALBI score and cognitive impairment remained stable among participants older than 68 years, females, non-diabetics, those with BMI\u0026thinsp;\u0026le;\u0026thinsp;28, and those with chronic kidney disease. Significant interactions were observed with diabetes, smoking, and BMI (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eLinear and Nonlinear Associations\u003c/h2\u003e\n \u003cp\u003eRCS analyses were used to visualize the relationship between ALBI scores and cognitive impairment (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Prior to adjustment, a linear association was found for CERAD (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.074; p for overall\u0026thinsp;=\u0026thinsp;0.002) and AFT (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.814; p for overall\u0026thinsp;=\u0026thinsp;0.100). A nonlinear association was observed for DSST (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.003; p for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for potential confounders, the relationships became more linear and consistent across all tests: CERAD (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.108; p for overall\u0026thinsp;=\u0026thinsp;0.051); AFT (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.690; p for overall\u0026thinsp;=\u0026thinsp;0.578); DSST (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.286; p for overall\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the association between the ALBI score and cognitive impairment using three established diagnostic tools: the CERAD Word Learning Test, the Animal Fluency Test, and the Digit Symbol Substitution Test. Higher ALBI scores were positively associated with cognitive impairment, particularly when assessed using the DSST. This association remained significant after adjusting for a wide range of potential confounders. Restricted cubic spline (RCS) analysis demonstrated a stable linear relationship between the ALBI score and cognitive impairment across all three tests in adjusted models. Subgroup analyses indicated that diabetes, smoking status, and body mass index (BMI) may modify this relationship.\u003c/p\u003e \u003cp\u003eOur findings contribute to the growing literature on the liver\u0026ndash;brain axis. Although the ALBI score is primarily used to evaluate liver function in patients with hepatocellular carcinoma (HCC), we applied it to a general population to examine its association with cognitive performance. Previous studies have established links between liver dysfunction\u0026mdash;particularly NAFLD\u0026mdash;and cognitive decline[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For instance, analyses of NHANES III data showed that NAFLD was associated with poorer outcomes on tests of reaction time, processing speed, and memory[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A systematic review also supported the association between NAFLD and cognitive dysfunction, with impairments observed across domains such as general cognition and memory[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study, however, is the first to assess the association between the ALBI score\u0026mdash;derived from serum albumin and bilirubin\u0026mdash;and cognitive impairment in the general population. The significant association between higher ALBI scores and lower DSST performance, which measures processing speed and visuomotor coordination, aligns with findings from prior NAFLD studies using the Symbol Digit Substitution Test (SDST), a comparable tool[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This consistency suggests that liver dysfunction may affect cognitive processing speed, possibly through mechanisms involving impaired neurotransmission.\u003c/p\u003e \u003cp\u003eIn contrast, we found no significant associations between the ALBI score and outcomes from the CERAD or Animal Fluency tests, which assess memory and language functions, respectively. These findings diverge from prior research linking liver enzyme abnormalities to Alzheimer\u0026rsquo;s disease and memory-related changes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The lack of association in our study may reflect differences in cognitive domains affected, the generally healthier status of our population, or insufficient sensitivity of the ALBI score to detect subtler impairments in memory and language among non-clinical individuals. Alternatively, our comprehensive adjustment for confounders may have attenuated the observed relationships compared with studies focused on patients with clinically evident liver or neurodegenerative disease.\u003c/p\u003e \u003cp\u003eAn unexpected yet important finding was the selective association between the ALBI score and processing speed, but not with memory or language. Processing speed is a fundamental cognitive domain involved in higher-order functions such as reaction time and decision-making[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is also highly sensitive to systemic and neurological insults[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The specificity of this association suggests that processing speed may be more susceptible to subclinical liver dysfunction, potentially due to inflammation or metabolic disturbances that affect neural transmission.\u003c/p\u003e \u003cp\u003eAge also appeared to influence this association. Younger individuals exhibited stronger associations between ALBI scores and cognitive impairment[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This age-specific effect may reflect the protective influence of cognitive reserve among older adults or increased vulnerability in younger populations[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A similar age-stratified pattern was observed for the Animal Fluency Test, with more pronounced associations among participants aged\u0026thinsp;\u0026le;\u0026thinsp;68 years. Furthermore, subgroup differences by diabetes status, smoking, and BMI highlight the role of metabolic and lifestyle factors in modulating the relationship between liver function and cognitive performance[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings offer important implications for both research and clinical practice. The consistent association between the ALBI score and processing speed raises the possibility of using this domain as an early marker of cognitive decline in individuals with impaired liver function. Subgroup differences suggest that risk stratification based on age and comorbidities may enhance screening and intervention strategies.\u003c/p\u003e \u003cp\u003eOur results extend current understanding by showing that liver function, as reflected by the ALBI score, may influence specific cognitive domains in the general population. This contrasts with earlier research that focused primarily on severe liver disease and hepatic encephalopathy. One mechanistic theory proposes that liver dysfunction leads to increased systemic inflammation and oxidative stress, which impair neurotransmission and synaptic plasticity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Given its reliance on rapid neuronal processing, processing speed may be especially vulnerable to such disruptions. Another hypothesis involves liver-mediated alterations in lipid metabolism and cholesterol homeostasis, both essential for maintaining neuronal membrane integrity and synaptic function[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Impairments in these processes could selectively affect domains like processing speed by altering the brain\u0026rsquo;s metabolic environment. Our findings support these proposed mechanisms and suggest that even mild liver dysfunction can impact cognitive health.\u003c/p\u003e \u003cp\u003eLimitations of this study should be noted. First, the cross-sectional design precludes causal inference. Second, cognitive impairment was classified using standardized test cutoffs rather than clinical diagnoses, which may introduce misclassification. Third, although we adjusted for multiple confounders, residual confounding cannot be excluded. Finally, the ALBI score may be influenced by transient physiological states, such as acute illness or dehydration, which could affect its precision in non-hospitalized populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides novel evidence linking the ALBI score to cognitive performance, particularly processing speed, in the general population. While no associations were observed with memory or language function, the consistent relationship with DSST performance suggests that liver function may influence specific cognitive domains. These findings highlight the importance of considering subclinical liver dysfunction in cognitive health assessments. Future longitudinal studies are warranted to confirm these associations and explore the underlying biological mechanisms, which may ultimately inform preventive strategies aimed at preserving cognitive function through liver health optimization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALBI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Albumin-Bilirubin\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; \u0026nbsp; \u0026nbsp;National Health and Nutrition Examination Survey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;mild cognitive impairment\u003c/p\u003e\n\u003cp\u003eCERAD \u0026nbsp; \u0026nbsp; \u0026nbsp; Consortium to Establish a Registry for Alzheimer's Disease\u003c/p\u003e\n\u003cp\u003eAFT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Animal Fluency Test\u003c/p\u003e\n\u003cp\u003eDSST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Digit Symbol Substitution Test\u003c/p\u003e\n\u003cp\u003eHCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003eNAFLD \u0026nbsp; \u0026nbsp; \u0026nbsp; Nonalcoholic fatty liver disease\u003c/p\u003e\n\u003cp\u003eNCHS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eMEC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mobile Examination Center\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eeGFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eUACR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Urine albumin-to-creatinine ratio\u003c/p\u003e\n\u003cp\u003eScr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; serum creatinine\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eSE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; standard errors\u003c/p\u003e\n\u003cp\u003eSDST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Symbol Digit Substitution Test\u003c/p\u003e\n\u003cp\u003e95%CI \u0026nbsp; \u0026nbsp; \u0026nbsp; 95% Confidence Interval\u003c/p\u003e\n\u003cp\u003eANOVA \u0026nbsp; \u0026nbsp; \u0026nbsp;Analysis of Variance\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Quartiles\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Restricted Cubic Spline\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Health and Nutrition Examination Survey participants and staff and the National Center for Health Statistics for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManuscript draft and data analysis: WXF; conception and design: WCL, SSQ, GHX, ZSY, SXY; All authors revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82271230).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData described in the manuscript are publicly and freely available without restriction at https://www.cdc.gov/nchs/nhanes/index.htm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a cross-sectional study carried out by the National Center for Health Statistics (NCHS), a branch of the Centers for Disease Control and Prevention (CDC), with the goal of gathering data on the health and nutritional status of both adults and children in the United States. The survey protocols received approval from the Ethics Review Board of the CDC\u0026rsquo;s NCHS, and all participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBai W, Chen P, Cai H, Zhang Q, Su Z, Cheung T, et al. Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies. Age and Ageing. 2022;51(8):afac173.\u003c/li\u003e\n \u003cli\u003eManly JJ, Jones RN, Langa KM, Ryan LH, Levine DA, McCammon R, et al. Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol Project. JAMA Neurology. 2022;79(12):1242-9.\u003c/li\u003e\n \u003cli\u003eGil-Peinado M, Alacreu M, Ramos H, Sendra-Lillo J, Garc\u0026iacute;a C, Garc\u0026iacute;a-Lluch G, et al. The A-to-Z factors associated with cognitive impairment. Results of the DeCo study. Frontiers in psychology. 2023;14:1152527.\u003c/li\u003e\n \u003cli\u003eLivingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the \u0026lt;em\u0026gt;Lancet\u0026lt;/em\u0026gt; Commission. The Lancet. 2020;396(10248):413-46.\u003c/li\u003e\n \u003cli\u003eBraconnier ML, Siper PM. Neuropsychological Assessment in Autism Spectrum Disorder. Current psychiatry reports. 2021;23(10):63.\u003c/li\u003e\n \u003cli\u003eHo SY, Liu PH, Hsu CY, Huang YH, Liao JI, Su CW, et al. A New Tumor Burden Score and Albumin-Bilirubin Grade-Based Prognostic Model for Hepatocellular Carcinoma. Cancers. 2022;14(3).\u003c/li\u003e\n \u003cli\u003eZhang HY, Li WB, Ye H, Xiao ZY, Peng YR, Wang J. Long-term results of the paraesophagogastric devascularization with or without esophageal transection: which is more suitable for variceal bleeding? World journal of surgery. 2014;38(8):2105-12.\u003c/li\u003e\n \u003cli\u003eHiraoka A, Michitaka K, Kumada T, Izumi N, Kadoya M, Kokudo N, et al. Validation and Potential of Albumin-Bilirubin Grade and Prognostication in a Nationwide Survey of 46,681 Hepatocellular Carcinoma Patients in Japan: The Need for a More Detailed Evaluation of Hepatic Function. Liver cancer. 2017;6(4):325-36.\u003c/li\u003e\n \u003cli\u003eMarasco G, Alemanni LV, Colecchia A, Festi D, Bazzoli F, Mazzella G, et al. Prognostic value of the albumin-bilirubin grade for the prediction of post-hepatectomy liver failure: a systematic review and meta-analysis. 2021;10(9):2011.\u003c/li\u003e\n \u003cli\u003eVilstrup H, Amodio P, Bajaj J, Cordoba J, Ferenci P, Mullen KD, et al. Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. Hepatology (Baltimore, Md). 2014;60(2):715-35.\u003c/li\u003e\n \u003cli\u003eAdekanle O, Sunmonu TA, Komolafe MA, Ndububa DA. Cognitive functions in patients with liver cirrhosis: assessment using community screening interview for dementia. Annals of African medicine. 2012;11(4):222-9.\u003c/li\u003e\n \u003cli\u003eCushman M, Callas PW, Alexander KS, Wadley V, Zakai NA, Lidofsky SD, et al. Nonalcoholic fatty liver disease and cognitive impairment: A prospective cohort study. PloS one. 2023;18(4):e0282633.\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez-Franco \u0026Oacute;, Morin JP, Cort\u0026eacute;s-Sol A, Molina-Jim\u0026eacute;nez T, Del Moral DI, Flores-Mu\u0026ntilde;oz M, et al. Cognitive Impairment After Resolution of Hepatic Encephalopathy: A Systematic Review and Meta-Analysis. Frontiers in neuroscience. 2021;15:579263.\u003c/li\u003e\n \u003cli\u003eSeo SW, Gottesman RF, Clark JM, Hernaez R, Chang Y, Kim C, et al. Nonalcoholic fatty liver disease is associated with cognitive function in adults. Neurology. 2016;86(12):1136-42.\u003c/li\u003e\n \u003cli\u003ePowell A, Sumnall H, Smith J, Kuiper R, Montgomery C. Recovery of neuropsychological function following abstinence from alcohol in adults diagnosed with an alcohol use disorder: Systematic review of longitudinal studies. PloS one. 2024;19(1):e0296043.\u003c/li\u003e\n \u003cli\u003eCasagrande SS, Lee C, Stoeckel LE, Menke A, Cowie CC. Cognitive function among older adults with diabetes and prediabetes, NHANES 2011-2014. Diabetes research and clinical practice. 2021;178:108939.\u003c/li\u003e\n \u003cli\u003eSong S, Liu N, Wang G, Wang Y, Zhang X, Zhao X, et al. Sex Specificity in the Mixed Effects of Blood Heavy Metals and Cognitive Function on Elderly: Evidence from NHANES. Nutrients. 2023;15(13).\u003c/li\u003e\n \u003cli\u003eWei B, Dong Q, Ma J, Zhang A. The association between triglyceride-glucose index and cognitive function in nondiabetic elderly: NHANES 2011-2014. Lipids in health and disease. 2023;22(1):188.\u003c/li\u003e\n \u003cli\u003eDu L, Xu H, Fang L, Qiao L, Xie Y, Yang C, et al. Albumin-bilirubin score as a predictor of all-cause mortality in patients with hepatitis B virus infection: An analysis of National Health and Nutrition Examination Survey (NHANES) 1999-2018. Preventive medicine reports. 2024;39:102639.\u003c/li\u003e\n \u003cli\u003eDang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003-2018. Cardiovascular diabetology. 2024;23(1):8.\u003c/li\u003e\n \u003cli\u003eZhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. Cardiovascular diabetology. 2023;22(1):279.\u003c/li\u003e\n \u003cli\u003eHuang D, Wu H. Association between the aggregate index of systemic inflammation and CKD: evidence from NHANES 1999-2018. Frontiers in medicine. 2025;12:1506575.\u003c/li\u003e\n \u003cli\u003eLiang J, An H, Hu X, Gao Y, Zhou J, Gong X, et al. Correlation between chronic kidney disease and all-cause mortality in diabetic foot ulcers: evidence from the 1999-2004 national health and nutrition examination survey (NHANES). Frontiers in endocrinology. 2025;16:1533087.\u003c/li\u003e\n \u003cli\u003eJiang J, Zhao H, Chen J, Du J, Ni W, Zheng B, et al. The association between dietary creatine intake and cancer in U.S. adults: insights from NHANES 2007-2018. Frontiers in nutrition. 2024;11:1460057.\u003c/li\u003e\n \u003cli\u003eBaltic S, Grasaas E, Ostojic SM. Creatine and sleep habits and disorders in the general population aged 16 years and over: NHANES 2007-2008. Nutrition and health. 2024:2601060241299958.\u003c/li\u003e\n \u003cli\u003eOstojic SM, Stea TH, Ellery SJ, Smith-Ryan AE. Association between dietary intake of creatine and female reproductive health: Evidence from NHANES 2017-2020. Food science \u0026amp; nutrition. 2024;12(7):4893-8.\u003c/li\u003e\n \u003cli\u003eMedina-Julio D, Ram\u0026iacute;rez-Mej\u0026iacute;a MM, Cordova-Gallardo J, Peniche-Luna E, Cant\u0026uacute;-Brito C, Mendez-Sanchez N. From Liver to Brain: How MAFLD/MASLD Impacts Cognitive Function. Medical science monitor : international medical journal of experimental and clinical research. 2024;30:e943417.\u003c/li\u003e\n \u003cli\u003ePeng X, Zhang X, Xu Z, Li L, Mo X, Peng Z, et al. Peripheral amyloid-\u0026beta; clearance mediates cognitive impairment in non-alcoholic fatty liver disease. EBioMedicine. 2024;102:105079.\u003c/li\u003e\n \u003cli\u003eJung I, Park SY, Lee DY, Cho HJ, Lee SK, Seo JA, et al. Association of metabolic dysfunction-associated fatty liver disease with white matter hyperintensity and cognitive decline: A longitudinal cohort study. Diabetes, obesity \u0026amp; metabolism. 2025;27(4):2271-9.\u003c/li\u003e\n \u003cli\u003eGeorge ES, Sood S, Daly RM, Tan S-Y. Is there an association between non-alcoholic fatty liver disease and cognitive function? A systematic review. BMC Geriatrics. 2022;22(1):47.\u003c/li\u003e\n \u003cli\u003eColognesi M, Gabbia D, De Martin S. Depression and Cognitive Impairment-Extrahepatic Manifestations of NAFLD and NASH. Biomedicines. 2020;8(7).\u003c/li\u003e\n \u003cli\u003eWernberg CW, Gr\u0026oslash;nkj\u0026aelig;r LL, Gade Jacobsen B, Indira Chandran V, Krag A, Graversen JH, et al. The prevalence and risk factors for cognitive impairment in obesity and NAFLD. Hepatology communications. 2023;7(7).\u003c/li\u003e\n \u003cli\u003eNho K, Kueider-Paisley A, Ahmad S, MahmoudianDehkordi S, Arnold M, Risacher SL, et al. Association of Altered Liver Enzymes With Alzheimer Disease Diagnosis, Cognition, Neuroimaging Measures, and Cerebrospinal Fluid Biomarkers. JAMA Network Open. 2019;2(7):e197978-e.\u003c/li\u003e\n \u003cli\u003eBaune BT, Brignone M, Larsen KG. A Network Meta-Analysis Comparing Effects of Various Antidepressant Classes on the Digit Symbol Substitution Test (DSST) as a Measure of Cognitive Dysfunction in Patients with Major Depressive Disorder. The international journal of neuropsychopharmacology. 2018;21(2):97-107.\u003c/li\u003e\n \u003cli\u003eGajewski B, Karlińska I, Stasiołek M. Symbol Digit Modalities Test in progressive multiple sclerosis. Neurologia i neurochirurgia polska. 2024;58(3):221-32.\u003c/li\u003e\n \u003cli\u003eLanga KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. Jama. 2014;312(23):2551-61.\u003c/li\u003e\n \u003cli\u003eMa Y, Liang L, Zheng F, Shi L, Zhong B, Xie W. Association Between Sleep Duration and Cognitive Decline. JAMA Netw Open. 2020;3(9):e2013573.\u003c/li\u003e\n \u003cli\u003ePettigrew C, Soldan A. Defining Cognitive Reserve and Implications for Cognitive Aging. Current neurology and neuroscience reports. 2019;19(1):1.\u003c/li\u003e\n \u003cli\u003eSrikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ. Type 2 diabetes and cognitive dysfunction-towards effective management of both comorbidities. The lancet Diabetes \u0026amp; endocrinology. 2020;8(6):535-45.\u003c/li\u003e\n \u003cli\u003eBenito-Le\u0026oacute;n J, Ghosh R, Lape\u0026ntilde;a-Motilva J, Mart\u0026iacute;n-Arriscado C, Bermejo-Pareja F. Association between cumulative smoking exposure and cognitive decline in non-demented older adults: NEDICES study. Scientific reports. 2023;13(1):5754.\u003c/li\u003e\n \u003cli\u003eXu X, Xu Y, Shi R. Association between obesity, physical activity, and cognitive decline in Chinese middle and old-aged adults: a mediation analysis. BMC Geriatr. 2024;24(1):54.\u003c/li\u003e\n \u003cli\u003eWen W, Fan H, Zhang S, Hu S, Chen C, Tang J, et al. Associations between metabolic dysfunction-associated fatty liver disease and atherosclerotic cardiovascular disease. The American journal of the medical sciences. 2024;368(6):557-68.\u003c/li\u003e\n \u003cli\u003eMaity S, Farrell K, Navabpour S, Narayanan SN, Jarome TJ. Epigenetic Mechanisms in Memory and Cognitive Decline Associated with Aging and Alzheimer\u0026apos;s Disease. International journal of molecular sciences. 2021;22(22).\u003c/li\u003e\n \u003cli\u003eSekhar RV. GlyNAC Supplementation Improves Glutathione Deficiency, Oxidative Stress, Mitochondrial Dysfunction, Inflammation, Aging Hallmarks, Metabolic Defects, Muscle Strength, Cognitive Decline, and Body Composition: Implications for Healthy Aging. The Journal of nutrition. 2021;151(12):3606-16.\u003c/li\u003e\n \u003cli\u003eWeng ZB, Chen YR, Lv JT, Wang MX, Chen ZY, Zhou W, et al. A Review of Bile Acid Metabolism and Signaling in Cognitive Dysfunction-Related Diseases. Oxidative medicine and cellular longevity. 2022;2022:4289383.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\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":"ALBI, cognitive impairment, CERAD, Animal Fluency, Digit Symbol, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6475951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6475951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The association between the Albumin-Bilirubin (ALBI) score and the risk of cognitive impairment remains unclear. This study aims to analyze the relationship between ALBI score and cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) were analyzed using multivariable logistic regression and subgroup analyses to assess the relationship between the ALBI score and cognitive impairment diagnosed through various methods. Restricted cubic spline (RCS) models were employed to evaluate potential nonlinear associations between ALBI and cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 2,215 participants were included. In fully adjusted models, each 1-unit increase in the ALBI score was associated with a 2.61-fold higher risk of cognitive impairment, as measured by the Digit Symbol Substitution Test (DSST) (adjusted OR = 2.61; 95% CI: 1.17–5.82; P = 0.023). Associations with cognitive impairment identified using the other two assessment methods were not statistically significant. Age appeared to differentially affect various domains of cognitive function. RCS analysis indicated a linear association between the ALBI score and cognitive impairment across all three diagnostic approaches after adjustment for confounders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: A higher ALBI score may be associated with increased risk of cognitive impairment. Further research is warranted to investigate potential causal pathways linking liver function and cognitive decline.\u003c/p\u003e","manuscriptTitle":"The association between the ALBI score and cognitive impairment as assessed by various diagnostic methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 11:13:57","doi":"10.21203/rs.3.rs-6475951/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":"2cc21a5f-d6e9-4a6e-ad3c-dc5cea0c5778","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-05T05:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 11:13:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6475951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6475951","identity":"rs-6475951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}