Neighborhood Socioeconomic Deprivation is Associated with Worse Cognitive Performance and in vivo Amyloid Accumulation

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Mecca, David Matuskey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8546338/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract INTRODUCTION Socioeconomic disadvantage (SED) has been associated with poorer brain health, yet its underlying pathology remains incompletely understood. We examined whether neighborhood-level SED, measured using the Area Deprivation Index (ADI), relates to amyloid deposition assessed with amyloid positron emission tomography (PET). METHODS Participants (n = 1,110) underwent cognitive assessment using the mini mental state examination (MMSE) and PET scanning with amyloid-specific tracers. Associations between national and state ADI and MMSE and global amyloid burden were evaluated using linear models adjusting for age, sex, and APOE-ε4 carrier status. RESULTS In 1,110 participants, higher neighborhood socioeconomic deprivation was associated with lower MMSE scores, with both national and state ADI measures showing significant inverse associations independent of age and sex (all p < 0.001). Higher ADI was significantly associated with greater amyloid burden among cognitively unimpaired participants (β = 0.18, p = 0.006, d = 0.27), indicating early AD-related pathology. DISCUSSION Neighborhood socioeconomic disadvantage is associated with worse cognitive performance and for the first time were shown to be associated with amyloid accumulation during the preclinical phase of AD. These findings underscore the need to consider socioeconomic context in early-stage risk assessment and may help inform targeted prevention strategies aimed at reducing disparities in dementia outcomes. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Area Deprivation index MMSE Positron Emission Tomography Alzheimer’s disease Figures Figure 1 Figure 2 Highlights • Higher area deprivation is associated with worse cognition. • First study linking Area Deprivation Index (ADI) to amyloid PET measures. • Higher ADI was associated with greater amyloid burden in cognitively unimpaired adults. • Associations remained significant after adjusting for age, sex, and APOE-ε4. • Findings suggest socioeconomic context may influence preclinical AD pathology. Introduction Socioeconomic disadvantage is consistently linked to poorer health outcomes, including increased rates of cardiovascular disease, diabetes, and cognitive decline [ 1 ] − [ 2 ]. The Area Deprivation Index (ADI) is a validated, census-based composite measure that captures neighborhood-level socioeconomic disadvantage through indicators such as income, education, employment, and housing quality [ 3 ]. Prior studies using ADI and related metrics show that dementias disproportionately affect individuals from economically disadvantaged communities [ 4 ] and higher ADI scores have been associated with lower cognitive performance [ 1 , 5 – 7 ]. Alzheimer’s dementia (AD), characterized by multidomain cognitive decline [ 8 ], currently affects an estimated 6.9 million Americans [ 8 ]. Although genetic factors such as the apolipoprotein E ε4 (APOE-ε4) allele are well-established contributors to AD risk and pathology, growing evidence suggests that environmental and socioeconomic exposures may also shape vulnerability to AD [ 9 , 10 ], potentially through mechanisms involving chronic stress, dietary patterns, toxin exposures, and infectious processes [ 9 – 11 ]. Despite these emerging links, the neurobiological pathways connecting socioeconomic disadvantage to cognitive impairment remain poorly understood. We examined whether neighborhood-level disadvantage is associated with Alzheimer’s-related pathology by assessing the relationship between ADI and brain amyloid-β (Aβ) burden using positron emission tomography (PET) imaging in cognitively normal (CN) and cognitively impaired (CI) participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods Participants ADNI participants with complete ADI, amyloid PET data, and clinical diagnosis were included (Fig. 1 A). Written informed consent was obtained from all participants, and study procedures were approved by the institutional review boards of all participating institutions. CN individuals had no memory complaints, demonstrated normal memory performance, and showed no cognitive or functional impairment. The CI group consisted of individuals who reported memory concerns, exhibited abnormal memory performance ranging from mild to more pronounced deficits, but retained functional independence [ 12 ]. Cognitive Assessment Global cognitive function was assessed using the Mini-Mental State Examination (MMSE) [ 13 ], a widely used screening tool that evaluates orientation, attention, memory, language, and visuospatial abilities. MMSE scores range from 0 to 30, with higher scores indicating better cognitive performance. MMSE was administered according to standardized procedures by trained study personnel. MMSE scores were treated as a continuous outcome variable in all analyses. Associations between neighborhood socioeconomic deprivation and cognitive performance were examined using multivariable linear regression models, adjusting for age and sex. Participants with missing MMSE or covariate data were excluded from the respective analyses. Area Deprivation Index The ADI data was extracted from ADNI. ADI was calculated using the United States Census indicators of poverty, education, housing, and employment and neighborhood socioeconomic status was ranked by disadvantage at the state and national level. Each census block/neighborhood was split into state deciles and national percentiles, with lower percentile scores indicating less socioeconomic disadvantage. APOE Genotyping APOE genotyping was performed using baseline blood samples in accordance with ADNI protocols. Participants were categorized based on APOE ε4 carrier status. Individuals with at least one ε4 allele were classified as APOE ε4 positive. Imaging Data Acquisition and Preprocessing Imaging data processed using the ADNI pipelines ( https://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/ ) were downloaded for these analyses. In brief, two amyloid tracers of [ 18 F]florbetapir (FBP) and [ 18 F]florbetaben (FBB) were used. PET images were motion corrected before averaging all frames into a single static image. The MRI was segmented and parcellated with Free Surfer v7.1.1 to define a global cortical measure that is made up of frontal, anterior/posterior cingulate, lateral parietal, lateral temporal regions. To generate the standardized uptake value ratios (SUVRs), each amyloid PET scan was co-registered to the corresponding segmented MRI and normalized by the whole cerebellum. SUVR was then calculated for the cortical summary region. Centiloids Click or tap here to enter text.were calculated from cortical summary region SUVRs as described previously [ 14 ]. The centiloids scale provides a standardized metric for amyloid PET quantification, where 0 corresponds to amyloid-negative young controls and 100 represents typical amyloid levels in patients with AD. Centiloids calculated based on FBP or FBB PET scans were both included [ 15 ]. Additional details can be found at https://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/ . Statistics Analysis Data were analyzed in R (v. 4.4.2.). The distribution of all continuous variables was checked for normality using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots and parametric or non-parametric approaches were selected accordingly. Outlier centiloids (± 3 standard deviations) were removed. Descriptive statistics are reported to summarize the characteristics of the study sample and the distribution of key variables. To examine the predictive value of the ADI in relation to amyloid deposition, linear regression models were employed. Age, sex, and APOE ε4 carrier status were used as covariates (significance: p-value < 0.05). Results In multivariable linear regression models including 1,110 participants and adjusted for age and sex, higher neighborhood socioeconomic deprivation was significantly associated with lower global cognitive performance as measured by the MMSE. Specifically, higher national ADI was associated with lower MMSE scores (β = −0.013, SE = 0.002, p < 0.001), and a similar association was observed for state ADI (β = −0.100, SE = 0.023, p < 0.001). In both models, older age was independently associated with lower MMSE performance (p < 0.001), while sex was significantly associated with MMSE scores (p < 0.001). The overall models explained a modest but significant proportion of variance in MMSE scores (national ADI model: R² = 0.066; state ADI model: R² = 0.060, Table 1 ). Table 1 Associations of National and State Area Deprivation Index (ADI) With MMSE Performance in Models Adjusted for Age and Sex N = 1,110 Model / Predictor β SE t value p value R² Adj. R² F statistic National ADI ADI (national) −0.012 0.002 −5.10 < 0.001 0.066 0.064 26.12 Age (years) −0.054 0.009 −5.88 < 0.001 Sex 0.581 0.136 4.27 < 0.001 State ADI ADI (state) −0.100 0.023 −4.28 < 0.001 0.060 0.057 23.45 Age (years) −0.054 0.009 −5.83 < 0.001 Sex 0.573 0.137 4.19 < 0.001 A total of 737 participants (CN n = 518; CI n = 219) were included (Fig. 1 ). CN participants were younger than those with CI ( p = 0.001) and had a lower proportion of males (CN: 189 males, 329 females; CI: 108 males, 111 females; p = 0.001). The frequency of APOE ε4 carriers did not differ significantly between groups (CN: 56.7%; CI: 66.7%; p = 0.41). Overall amyloid positivity in our study population with a centiloids cutoff of 10 was 32.83%. Linear regression analyses assessed the relationship between ADI and global amyloid burden (centiloids), controlling for age, sex, and APOE ε polymorphism. In the CN group, both state and national ADI were significantly associated with higher centiloids values (β = 1.38, p = 0.01; β = 0.18, p = 0.006, respectively). Effect sizes for both state and national ADI were moderate (Cohen’s d = 0.23, 0.27, respectively). In contrast, these associations were not significant in the CI group for state (β = − 1.45, p = 0.24) or national (β = − 1.14, p = 0.21) ADI (Table 2 , Fig. 2 ). No significant interactions were found between ADI and APOE-ε4 carrier status (p = 0.09). Table 2 Results with ADI as the predictor, centiloids as the outcome and age, gender, and APOE ε polymorphisms as covariates. Group Variable Estimate (β) SE t-value p-value Cohen’s d CN (n = 518) Intercept 78.05 14.75 5.29 0.002 0.30 ADI-State 1.38 0.68 2.05 0.016 0.23 Model 75.44 14.80 5.10 0.004 0.28 ADI-National 0.18 0.07 2.62 0.006 0.27 CI (n = 219) Intercept 72.52 22.24 3.26 0.740 0.04 ADI-State -1.45 0.82 -1.76 0.242 -0.17 Intercept 68.18 22.54 3.02 0.807 0.03 ADI-National -0.14 0.08 -1.69 0.210 -0.18 ADI: Area Deprivation Index; CN: Cognitively Normal; CI: Cognitive Impairment (mild cognitive impairment or mild dementia); SE: Standard Error Discussion We investigated the associations between socioeconomic disadvantage and cognition and brain amyloid accumulations. Our findings showed that ADI is significantly associated with worse cognition, and with amyloid burden among CN individuals, even after adjusting for covariates. Previous studies have addressed the associations between neighborhood deprivation and worse cognition and showed higher odds of cognitive impairment in those participants living in worse neighborhoods [ 16 ]. In agreement with previous findings and using the national level ADNI data, we found significant associations between higher state and national ADI and worse MMSE scores. However, we found no significant associations between ADI and amyloid burden in participants with CI. This could be due to a ceiling effect, meaning that the associations between neighborhood disadvantage and amyloid accumulations cannot be detected as the disease progresses. These results extend prior work demonstrate that socioeconomic disparities contribute to increased risk of cognitive decline and dementia [ 2 , 4 , 6 ]. Neuropathological studies have reported higher odds of AD pathology with increasing neighborhood disadvantage. Our findings suggest that these associations are detectable through amyloid PET imaging, at least in the preclinical phase of disease. The lack of association in the CI group may reflect that other disease associated processes exert stronger effects when the clinical symptoms have emerged and as disease processes advance, biological and clinical factors may dominate, attenuating the observable influence of socioeconomic context. In addition, although the APOE-ε4 allele is a well-established genetic risk factor for AD and is associated with greater amyloid accumulation in the brain [17], we did not find any significant interactions between APOE-ε4 carrier status and ADI. Future research should examine potential interactions between polygenic susceptibility and environmental disadvantage and employ longitudinal designs to characterize differential trajectories of amyloid accumulation and the combined effects of gene–environment interactions on disease progression. Overall, these results reinforce the multifactorial nature of AD risk, highlighting the importance of incorporating social determinants of health into predictive models of neurodegeneration. Future studies integrating environmental, biological, and machine learning approaches may improve our understanding of the pathways through which disadvantage contributes to AD risk and progression. Our main limitation was the cross-sectional study design, which precludes causal inference. Additionally, larger and more demographically diverse samples are needed to enhance statistical power and explore subgroup-specific effects. Declarations Consent to Participate Written informed consent was obtained from all participants or their legally authorized representatives prior to participation in ADNI. Statement of Ethics The study was conducted in accordance with the ethical standards of the institutional research committees and with the Declaration of Helsinki and its later amendments. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) study was approved by the approved by the Institutional Review Board of the University of Southern California and local IRBs at each participating site, and all participants provided written informed consent prior to enrollment. Funding Sources The work in this manuscript was supported through funding in part from NIH (Matuskey, 1R01NS124819). Author Contribution Conceptualization: M.M., D.M.; Methodology: M.M., D.M.; Data Curation: M.M.; Formal Analysis: M.M.; Visualization: M.M.; Writing – Original Draft: M.M.; Writing – Review & Editing: M.M., R.S., A.P.M., D.M.; Supervision: D.M. Acknowledgement Data collection and sharing for the Alzheimer's Disease Neuroimaging Initiative (ADNI) is funded by the National Institute on Aging (National Institutes of Health Grant U19AG024904). The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health (FNIH) including generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; BristolMyers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. Data Availability Data were obtained from the ADNI and are available to those researchers with approved data use agreements. References Matuskey D, Dias M, Naganawa M, et al (2019) Social status and demographic effects of the kappa opioid receptor: a PET imaging study with a novel agonist radiotracer in healthy volunteers. Neuropsychopharmacology 44:1714. https://doi.org/10.1038/S41386-019-0379-7 Williams VJ, Trane R, Sicinski K, et al (2024) Midlife and late-life environmental exposures on dementia risk in the Wisconsin Longitudinal Study: The modifying effects of ApoE. Alzheimer’s and Dementia 20:8263–8278. https://doi.org/10.1002/ALZ.14216 ;PAGE:STRING:ARTICLE/CHAPTER Zuelsdorff M, Larson JL, Hunt JFV, et al (2020) The Area Deprivation Index: A novel tool for harmonizable risk assessment in Alzheimer’s disease research. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 6:e12039. https://doi.org/10.1002/TRC2.12039 Kim Y, Jang S (2025) Socioeconomic Disparities in Cognitive Impairment: The Role of Neighborhood Social Cohesion. Innov Aging. https://doi.org/10.1093/GERONI/IGAF031 Clarke PJ, Weuve J, Barnes L, et al (2015) Cognitive decline and the neighborhood environment. Ann Epidemiol 25:849–854. https://doi.org/10.1016/J.ANNEPIDEM.2015.07.001 Gogniat MA, Khan OA, Bolton CJ, et al (2023) Area Deprivation Index is associated with biomarkers of inflammation cross-sectionally and longitudinally over a 9-year follow-up period. Alzheimer’s & Dementia 19:e080029. https://doi.org/10.1002/ALZ.080029 Kim B, Yannatos I, Blam K, et al (2024) Neighborhood disadvantage reduces cognitive reserve independent of neuropathologic change. Alzheimer’s & Dementia 20:2707. https://doi.org/10.1002/ALZ.13736 (2024) 2024 Alzheimer’s disease facts and figures. Alzheimers Dement 20:3708–3821. https://doi.org/10.1002/ALZ.13809 Reuben A, Richmond-Rakerd LS, Milne B, et al (2024) Dementia, dementia’s risk factors and premorbid brain structure are concentrated in disadvantaged areas: National register and birth-cohort geographic analyses. Alzheimer’s & Dementia 20:3167. https://doi.org/10.1002/ALZ.13727 Powell WR, Buckingham WR, Larson JL, et al (2020) Association of Neighborhood-Level Disadvantage With Alzheimer Disease Neuropathology. JAMA Netw Open 3:e207559–e207559. https://doi.org/10.1001/JAMANETWORKOPEN.2020.7559 Armstrong RA (2019) Risk factors for Alzheimer’s disease. Folia Neuropathol 57:87–105. https://doi.org/10.5114/FN.2019.85929 Morris JC, Ernesto C, Schafer K, et al (1997) Clinical dementia rating training and reliability in multicenter studies: The Alzheimer’s Disease Cooperative Study experience. Neurology 48:1508–1510. https://doi.org/10.1212/WNL.48.6.1508 Folstein MF, Robins LN, Helzer JE (1983) The Mini-Mental State Examination. Arch Gen Psychiatry 40:812–812. https://doi.org/10.1001/ARCHPSYC.1983.01790060110016 Klunk WE, Koeppe RA, Price JC, et al (2015) The Centiloid project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimer’s and Dementia 11:1–15.e4. https://doi.org/10.1016/J.JALZ.2014.07.003 , Klunk WE, Koeppe RA, Price JC, et al (2015) The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement 11:1–15.e4. https://doi.org/10.1016/J.JALZ.2014.07.003 Mares J, Kumar G, Sharma A, et al (2025) APOE ε4-associated heterogeneity of neuroimaging biomarkers across the Alzheimer’s disease continuum. Alzheimers Dement 21:. https://doi.org/10.1002/ALZ.14392 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 07 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8546338","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584173837,"identity":"1ee86519-aaa7-4087-824a-ea091a674dcd","order_by":0,"name":"Mahsa Mayeli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFACHhiDueHABwYGGRBTgkgtjA0HZ0C5xGth5iFGi2772cMffjBskzc43th42LbNjoefgfngbR48WszO5KVJ9jDcNtxw5mDD4dy2ZB7JBrZka7xaDuSYAR1zm3HDjUSQFmYegwM8ZtJ4tZx/Y/zxD8Nt+w33HzYctmyr57E/wP8Nv5YbOQZABbcTN9xgbDjM2HaYx4CBh42Aljdm0jIGt5NnnklsONhz7jiPxGE2Y8s5eB2WY/zxTcVt277jh4FBV1Ytx9/e/PDGGzxaIMCAgUHhAJBmZAMSzASVQ4F8A4j8Q6zyUTAKRsEoGEkAALbfUa4H4K96AAAAAElFTkSuQmCC","orcid":"","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Mayeli","suffix":""},{"id":584173838,"identity":"35a86425-5835-4054-83a4-2465aab3b3a8","order_by":1,"name":"Riya Saraiya","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Riya","middleName":"","lastName":"Saraiya","suffix":""},{"id":584173841,"identity":"a51c9837-5b60-43c1-9e12-c561efa88db3","order_by":2,"name":"Adam P. Mecca","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"P.","lastName":"Mecca","suffix":""},{"id":584173842,"identity":"7fafa8ad-47d8-4ad4-b728-56449f89a764","order_by":3,"name":"David Matuskey","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Matuskey","suffix":""}],"badges":[],"createdAt":"2026-01-08 03:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8546338/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8546338/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101790450,"identity":"f7f56550-9ecd-4f8d-b2ca-e05f0e48270e","added_by":"auto","created_at":"2026-02-03 16:05:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132807,"visible":true,"origin":"","legend":"\u003cp\u003eStudy population flow and distribution of Area Deprivation Index (ADI) scores.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8546338/v1/989e9fce6a8ca8529d244088.png"},{"id":101880668,"identity":"4fec5b49-00a4-4837-89ec-e589a7a784b7","added_by":"auto","created_at":"2026-02-04 15:05:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96081,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between Area Deprivation Index (ADI) Study Measures: A. National ADI and MMSE score; B. National ADI and amyloid burden (Centiloids) in cognitively unimpaired (navy) and cognitively impaired (orange) participants.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8546338/v1/d4efbbf6985a5a0e76c5f0f4.png"},{"id":101882122,"identity":"de4a388a-76fd-4c50-9362-316538d66b44","added_by":"auto","created_at":"2026-02-04 15:21:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8546338/v1/39b4af62-2181-4330-9243-a5dafd7d2540.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neighborhood Socioeconomic Deprivation is Associated with Worse Cognitive Performance and in vivo Amyloid Accumulation","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Higher area deprivation is associated with worse cognition.\u003c/p\u003e\u003cp\u003e\u0026bull; First study linking Area Deprivation Index (ADI) to amyloid PET measures.\u003c/p\u003e\u003cp\u003e\u0026bull; Higher ADI was associated with greater amyloid burden in cognitively unimpaired adults.\u003c/p\u003e\u003cp\u003e\u0026bull; Associations remained significant after adjusting for age, sex, and APOE-ε4.\u003c/p\u003e\u003cp\u003e\u0026bull; Findings suggest socioeconomic context may influence preclinical AD pathology.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSocioeconomic disadvantage is consistently linked to poorer health outcomes, including increased rates of cardiovascular disease, diabetes, and cognitive decline [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003csup\u003e\u0026minus;\u003c/sup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Area Deprivation Index (ADI) is a validated, census-based composite measure that captures neighborhood-level socioeconomic disadvantage through indicators such as income, education, employment, and housing quality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prior studies using ADI and related metrics show that dementias disproportionately affect individuals from economically disadvantaged communities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and higher ADI scores have been associated with lower cognitive performance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlzheimer\u0026rsquo;s dementia (AD), characterized by multidomain cognitive decline [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], currently affects an estimated 6.9\u0026nbsp;million Americans [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although genetic factors such as the apolipoprotein E ε4 (APOE-ε4) allele are well-established contributors to AD risk and pathology, growing evidence suggests that environmental and socioeconomic exposures may also shape vulnerability to AD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], potentially through mechanisms involving chronic stress, dietary patterns, toxin exposures, and infectious processes [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite these emerging links, the neurobiological pathways connecting socioeconomic disadvantage to cognitive impairment remain poorly understood.\u003c/p\u003e \u003cp\u003eWe examined whether neighborhood-level disadvantage is associated with Alzheimer\u0026rsquo;s-related pathology by assessing the relationship between ADI and brain amyloid-β (Aβ) burden using positron emission tomography (PET) imaging in cognitively normal (CN) and cognitively impaired (CI) participants from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eADNI participants with complete ADI, amyloid PET data, and clinical diagnosis were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Written informed consent was obtained from all participants, and study procedures were approved by the institutional review boards of all participating institutions. CN individuals had no memory complaints, demonstrated normal memory performance, and showed no cognitive or functional impairment. The CI group consisted of individuals who reported memory concerns, exhibited abnormal memory performance ranging from mild to more pronounced deficits, but retained functional independence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive Assessment\u003c/h3\u003e\n\u003cp\u003eGlobal cognitive function was assessed using the Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a widely used screening tool that evaluates orientation, attention, memory, language, and visuospatial abilities. MMSE scores range from 0 to 30, with higher scores indicating better cognitive performance. MMSE was administered according to standardized procedures by trained study personnel. MMSE scores were treated as a continuous outcome variable in all analyses. Associations between neighborhood socioeconomic deprivation and cognitive performance were examined using multivariable linear regression models, adjusting for age and sex. Participants with missing MMSE or covariate data were excluded from the respective analyses.\u003c/p\u003e\n\u003ch3\u003eArea Deprivation Index\u003c/h3\u003e\n\u003cp\u003eThe ADI data was extracted from ADNI. ADI was calculated using the United States Census indicators of poverty, education, housing, and employment and neighborhood socioeconomic status was ranked by disadvantage at the state and national level. Each census block/neighborhood was split into state deciles and national percentiles, with lower percentile scores indicating less socioeconomic disadvantage.\u003c/p\u003e\n\u003ch3\u003eAPOE Genotyping\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eAPOE\u003c/em\u003e genotyping was performed using baseline blood samples in accordance with ADNI protocols. Participants were categorized based on APOE ε4 carrier status. Individuals with at least one ε4 allele were classified as APOE ε4 positive.\u003c/p\u003e\n\u003ch3\u003eImaging Data Acquisition and Preprocessing\u003c/h3\u003e\n\u003cp\u003eImaging data processed using the ADNI pipelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/\u003c/span\u003e\u003cspan address=\"https://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were downloaded for these analyses. In brief, two amyloid tracers of [\u003csup\u003e18\u003c/sup\u003eF]florbetapir (FBP) and [\u003csup\u003e18\u003c/sup\u003eF]florbetaben (FBB) were used. PET images were motion corrected before averaging all frames into a single static image. The MRI was segmented and parcellated with Free Surfer v7.1.1 to define a global cortical measure that is made up of frontal, anterior/posterior cingulate, lateral parietal, lateral temporal regions. To generate the standardized uptake value ratios (SUVRs), each amyloid PET scan was co-registered to the corresponding segmented MRI and normalized by the whole cerebellum. SUVR was then calculated for the cortical summary region. Centiloids Click or tap here to enter text.were calculated from cortical summary region SUVRs as described previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The centiloids scale provides a standardized metric for amyloid PET quantification, where 0 corresponds to amyloid-negative young controls and 100 represents typical amyloid levels in patients with AD. Centiloids calculated based on FBP or FBB PET scans were both included [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additional details can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/\u003c/span\u003e\u003cspan address=\"https://adni.loni.usc.edu/data-samples/adni-data/neuroimaging/pet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed in R (v. 4.4.2.). The distribution of all continuous variables was checked for normality using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots and parametric or non-parametric approaches were selected accordingly. Outlier centiloids (\u0026plusmn;\u0026thinsp;3 standard deviations) were removed. Descriptive statistics are reported to summarize the characteristics of the study sample and the distribution of key variables. To examine the predictive value of the ADI in relation to amyloid deposition, linear regression models were employed. Age, sex, and APOE ε4 carrier status were used as covariates (significance: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e In multivariable linear regression models including 1,110 participants and adjusted for age and sex, higher neighborhood socioeconomic deprivation was significantly associated with lower global cognitive performance as measured by the MMSE. Specifically, higher national ADI was associated with lower MMSE scores (β = \u0026minus;0.013, SE\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a similar association was observed for state ADI (β = \u0026minus;0.100, SE\u0026thinsp;=\u0026thinsp;0.023, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In both models, older age was independently associated with lower MMSE performance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while sex was significantly associated with MMSE scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall models explained a modest but significant proportion of variance in MMSE scores (national ADI model: R\u0026sup2; = 0.066; state ADI model: R\u0026sup2; = 0.060, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of National and State Area Deprivation Index (ADI) With MMSE Performance in Models Adjusted for Age and Sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,110\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel / Predictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF statistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNational ADI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eADI (national)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eState ADI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eADI (state)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e23.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 737 participants (CN n\u0026thinsp;=\u0026thinsp;518; CI n\u0026thinsp;=\u0026thinsp;219) were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CN participants were younger than those with CI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and had a lower proportion of males (CN: 189 males, 329 females; CI: 108 males, 111 females; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). The frequency of APOE ε4 carriers did not differ significantly between groups (CN: 56.7%; CI: 66.7%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41). Overall amyloid positivity in our study population with a centiloids cutoff of 10 was 32.83%.\u003c/p\u003e \u003cp\u003eLinear regression analyses assessed the relationship between ADI and global amyloid burden (centiloids), controlling for age, sex, and APOE ε polymorphism. In the CN group, both state and national ADI were significantly associated with higher centiloids values (β\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; β\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, respectively). Effect sizes for both state and national ADI were moderate (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.23, 0.27, respectively). In contrast, these associations were not significant in the CI group for state (β = \u0026minus;\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24) or national (β = \u0026minus;\u0026thinsp;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21) ADI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant interactions were found between ADI and APOE-ε4 carrier status (p\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults with ADI as the predictor, centiloids as the outcome and age, gender, and APOE ε polymorphisms as covariates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCN (n\u0026thinsp;=\u0026thinsp;518)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADI-State\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADI-National\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCI (n\u0026thinsp;=\u0026thinsp;219)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADI-State\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADI-National\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eADI: Area Deprivation Index; CN: Cognitively Normal; CI: Cognitive Impairment (mild cognitive impairment or mild dementia); SE: Standard Error\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the associations between socioeconomic disadvantage and cognition and brain amyloid accumulations. Our findings showed that ADI is significantly associated with worse cognition, and with amyloid burden among CN individuals, even after adjusting for covariates.\u003c/p\u003e \u003cp\u003ePrevious studies have addressed the associations between neighborhood deprivation and worse cognition and showed higher odds of cognitive impairment in those participants living in worse neighborhoods [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In agreement with previous findings and using the national level ADNI data, we found significant associations between higher state and national ADI and worse MMSE scores.\u003c/p\u003e \u003cp\u003eHowever, we found no significant associations between ADI and amyloid burden in participants with CI. This could be due to a ceiling effect, meaning that the associations between neighborhood disadvantage and amyloid accumulations cannot be detected as the disease progresses.\u003c/p\u003e \u003cp\u003eThese results extend prior work demonstrate that socioeconomic disparities contribute to increased risk of cognitive decline and dementia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Neuropathological studies have reported higher odds of AD pathology with increasing neighborhood disadvantage. Our findings suggest that these associations are detectable through amyloid PET imaging, at least in the preclinical phase of disease. The lack of association in the CI group may reflect that other disease associated processes exert stronger effects when the clinical symptoms have emerged and as disease processes advance, biological and clinical factors may dominate, attenuating the observable influence of socioeconomic context.\u003c/p\u003e \u003cp\u003eIn addition, although the APOE-ε4 allele is a well-established genetic risk factor for AD and is associated with greater amyloid accumulation in the brain [17], we did not find any significant interactions between APOE-ε4 carrier status and ADI. Future research should examine potential interactions between polygenic susceptibility and environmental disadvantage and employ longitudinal designs to characterize differential trajectories of amyloid accumulation and the combined effects of gene\u0026ndash;environment interactions on disease progression.\u003c/p\u003e \u003cp\u003eOverall, these results reinforce the multifactorial nature of AD risk, highlighting the importance of incorporating social determinants of health into predictive models of neurodegeneration. Future studies integrating environmental, biological, and machine learning approaches may improve our understanding of the pathways through which disadvantage contributes to AD risk and progression.\u003c/p\u003e \u003cp\u003eOur main limitation was the cross-sectional study design, which precludes causal inference. Additionally, larger and more demographically diverse samples are needed to enhance statistical power and explore subgroup-specific effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent to Participate\u003c/h2\u003e \u003cp\u003e Written informed consent was obtained from all participants or their legally authorized representatives prior to participation in ADNI.\u003c/p\u003e\u003ch2\u003eStatement of Ethics\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the ethical standards of the institutional research committees and with the Declaration of Helsinki and its later amendments. The Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) study was approved by the approved by the Institutional Review Board of the University of Southern California and local IRBs at each participating site, and all participants provided written informed consent prior to enrollment.\u003c/p\u003e\u003ch2\u003eFunding Sources\u003c/h2\u003e \u003cp\u003eThe work in this manuscript was supported through funding in part from NIH (Matuskey, 1R01NS124819).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: M.M., D.M.; Methodology: M.M., D.M.; Data Curation: M.M.; Formal Analysis: M.M.; Visualization: M.M.; Writing \u0026ndash; Original Draft: M.M.; Writing \u0026ndash; Review \u0026amp; Editing: M.M., R.S., A.P.M., D.M.; Supervision: D.M.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eData collection and sharing for the Alzheimer's Disease Neuroimaging Initiative (ADNI) is funded by the National Institute on Aging (National Institutes of Health Grant U19AG024904). The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health (FNIH) including generous contributions from the following: AbbVie, Alzheimer\u0026rsquo;s Association; Alzheimer\u0026rsquo;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; BristolMyers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research \u0026amp; Development, LLC.; Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC.; Lumosity; Lundbeck; Merck \u0026amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData were obtained from the ADNI and are available to those researchers with approved data use agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMatuskey D, Dias M, Naganawa M, et al (2019) Social status and demographic effects of the kappa opioid receptor: a PET imaging study with a novel agonist radiotracer in healthy volunteers. 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Alzheimers Dement 21:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ALZ.14392\u003c/span\u003e\u003cspan address=\"10.1002/ALZ.14392\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-dementia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Dementia](https://www.nature.com/npjdementia/)","snPcode":"44400","submissionUrl":"https://submission.springernature.com/new-submission/44400/3","title":"npj Dementia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Area Deprivation index, MMSE, Positron Emission Tomography, Alzheimer’s disease","lastPublishedDoi":"10.21203/rs.3.rs-8546338/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8546338/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eINTRODUCTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocioeconomic disadvantage (SED) has been associated with poorer brain health, yet its underlying pathology remains incompletely understood. We examined whether neighborhood-level SED, measured using the Area Deprivation Index (ADI), relates to amyloid deposition assessed with amyloid positron emission tomography (PET).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants (n = 1,110) underwent cognitive assessment using the mini mental state examination (MMSE) and PET scanning with amyloid-specific tracers. Associations between national and state ADI and MMSE and global amyloid burden were evaluated using linear models adjusting for age, sex, and APOE-ε4 carrier status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 1,110 participants, higher neighborhood socioeconomic deprivation was associated with lower MMSE scores, with both national and state ADI measures showing significant inverse associations independent of age and sex (all p \u0026lt; 0.001). Higher ADI was significantly associated with greater amyloid burden among cognitively unimpaired participants (β = 0.18, \u003cem\u003ep\u003c/em\u003e = 0.006, \u003cem\u003ed\u003c/em\u003e = 0.27), indicating early AD-related pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISCUSSION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeighborhood socioeconomic disadvantage is associated with worse cognitive performance and for the first time were shown to be associated with amyloid accumulation during the preclinical phase of AD. These findings underscore the need to consider socioeconomic context in early-stage risk assessment and may help inform targeted prevention strategies aimed at reducing disparities in dementia outcomes.\u003c/p\u003e","manuscriptTitle":"Neighborhood Socioeconomic Deprivation is Associated with Worse Cognitive Performance and in vivo Amyloid Accumulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:05:21","doi":"10.21203/rs.3.rs-8546338/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T14:46:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T16:07:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T19:28:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141685545151190487015277789698523489670","date":"2026-02-02T09:04:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211347192522540915986007927866204320216","date":"2026-01-31T15:40:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323195222526049032594546038321129761348","date":"2026-01-30T20:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T19:31:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T00:57:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T06:55:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Dementia","date":"2026-01-08T03:03:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-dementia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Dementia](https://www.nature.com/npjdementia/)","snPcode":"44400","submissionUrl":"https://submission.springernature.com/new-submission/44400/3","title":"npj Dementia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"70b91619-f45a-4bce-a6f0-2a68d27d5811","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62142900,"name":"Health sciences/Biomarkers"},{"id":62142901,"name":"Health sciences/Diseases"},{"id":62142902,"name":"Health sciences/Medical research"},{"id":62142903,"name":"Health sciences/Neurology"},{"id":62142904,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-03T01:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:05:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8546338","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8546338","identity":"rs-8546338","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}