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Brain Volume Decline in Aging: Evidence for a Relation Between Socioeconomic Status, Preclinical Alzheimer Disease, and Reserve

Brain Volume Decline in Aging: Evidence for a Relation Between Socioeconomic Status, Preclinical... Abstract Objectives To assess the relation between socioeconomic status (SES) and structural brain change in nondemented older adults and to ascertain the potential role of preclinical Alzheimer disease (AD). Design Cross-sectional and longitudinal observation. Setting Alzheimer's Disease Research Center, St Louis, Missouri. Participants Volunteer sample of 362 nondemented adults aged 18 to 93 years. The main cohort of 100 was evaluated for dementia and SES; a Clinical Dementia Rating (CDR) of 0 (no dementia) and middle, high-middle, or high SES was required for eligibility. All 362 received magnetic resonance imaging; of the main 100, 91 received follow-up clinical assessment, and 33 received follow-up magnetic resonance imaging over at least a 3-year interval. A separate sample of 58 CDR 0 participants (aged 47 to 86 years) took part in amyloid imaging with Pittsburgh Compound B (PiB) labeled with radioactive carbon (11C). Main Outcome Measures Whole-brain volume adjusted for head size (aWBV) and change per year. Results aWBV declined by 0.22% per year between the ages of 20 and 80 years with accelerated decline in advanced aging. Controlling for effects of age and sex in older adults (>65 years) with CDR 0, higher SES was associated with smaller aWBV (3.8% difference spanning the sample range from middle to high privilege, P < .01) and more rapid volume loss (0.39% per year to 0.68% per year from middle to high privilege, P < .05). aWBV was reduced by 2.5% in individuals positive for PiB binding (n = 9) as compared with individuals negative for PiB binding (n = 49, P < .05), supporting an influence of undetected preclinical AD. Follow-up clinical data revealed that brain volume reduction associated with SES was greater in those who later developed very mild dementia (preclinical CDR 0 group, n = 19) compared with those who remained nondemented (stable CDR 0 group, n = 64; group × SES interaction, P < .05). Conclusions Privileged nondemented older adults harbor more preclinical brain atrophy, consistent with their having greater reserve against the expression of AD. The observation that normal cognitive function can be present in some older adults with Alzheimer disease (AD) pathology has led to an interest in between-subject differences that may protect against dementia.1 One variable that has been associated with reduced risk for dementia is high socioeconomic status (SES), representing both educational and occupational attainment.2 Clinicopathological investigation has found that older adults respond differently to the same burden of AD pathology with the more educated showing less severe cognitive decline.3 These studies suggest that SES is associated with an individual's reserve of protective function against the expression of AD. Reserve, as operationally defined for the present article, refers to the delay in time between pathology (as an independent process) and disease expression (as a dependent process).4 Here we characterize the relation between SES and age-related brain volume change in a large cohort of nondemented older adults. Brain volume decline begins in adolescence or earlier and continues into advanced aging.5,6 Volume loss accelerates markedly in the earliest stages of AD.7,8 These and related observations suggest that whole-brain volume is determined by a constellation of factors that include normal developmental as well as pathological processes.9 The relations between SES, dementia, and brain volume suggest that the extent of pathological atrophy nondemented older adults can accommodate increases with SES.10 This hypothesized outcome could alternatively reflect a direct relation between SES and brain aging without any involvement of preclinical pathology.11 To explore the contribution of preclinical pathology, we supplemented our main analysis with subject stratification based on amyloid binding with Pittsburgh Compound B (PiB) labeled with radioactive carbon (11C) and longitudinal clinical assessment. Support for a reserve explanation would come from finding that greater volume decline was accounted for by privileged individuals on the threshold of clinically detectable dementia. Methods Participants Magnetic resonance imaging (MRI) scans were obtained from 362 individuals (aged 18 to 93 years) participating in the longitudinal studies of the Washington University Alzheimer's Disease Research Center (ADRC) or other studies of younger adult aging and development. Detailed selection and attrition characteristics of this population have been described previously.8 All participants were scanned using identical procedures. The main cohort comprised 100 clinically screened ADRC participants aged 65 to 93 years. Of these 100, 33 were followed up with MRI for an extended interval to allow for longitudinal data analysis (mean, 3.1 times over a 3.1- to 6.5-year interval; mean time, 4.3 years). Participants were classified as initially nondemented if their Clinical Dementia Rating (CDR) nearest the time of baseline MRI was 0. Specialist clinicians determined the CDR, blind to the results of neuropsychological testing and prior clinical assessment, through examination of the participant and interview with an informant (usually a family member) who knew the participant well and could provide information regarding decline from the participant's normal cognitive and functional abilities.12 The designation of CDR 0.5 (very mild dementia) thus indicates very early clinical impairment relative to an individual's own baseline rather than impaired test performance relative to group norms.13 The CDR has been validated by clinicopathological studies.14 At the participant's initial clinical evaluation, SES was assessed using the Hollingshead 2-factor index of social position.15 The Hollingshead index represents a linear combination of educational and household occupational attainment with occupation almost doubly weighted. The highest attained occupation was used for indexing. The index ranges from 11 to 77 grouped into 5 SES categories (I-V). To control for potential health confounds related to deprivation in the underprivileged,16 and because the cohort from which data were drawn contains too few low-SES individuals to disentangle these effects, this study focused on variation of the Hollingshead index within the range of the high-privilege to middle-SES groups (I-III).17 Neither participation in follow-up MRI nor any of the measured health variables differed between these groups (Table 1). Estimation of whole-brain volume Our method of image acquisition and estimation of total intracranial volume (eTIV) and whole-brain volume (WBV) has been described previously.8,18 Head-size differences were corrected using a covariance procedure.18,19 The term adjusted whole-brain volume (aWBV) is used to denote covariance-adjusted volumes as distinct from proportionally normalized whole-brain volume (nWBV). aWBV was defined as aWBV = WBV − b(eTIV − mean eTIV) where WBV is the uncorrected (native) whole-brain volume, b is the slope of the volume regression on eTIV, eTIV is the head-size estimate derived from atlas scaling, and mean eTIV is the sample mean. When the relations of multiple variables to WBV were being explored simultaneously, eTIV was always entered as a covariate, and the dependent variable is denoted as aWBV to reflect this adjustment. Cross-sectional analysis To explore differences in brain volume across the full life span, aWBV was plotted cross-sectionally vs age for the entire sample of 362 individuals, including the cohort of 100 clinically screened nondemented older participants (aged 65 to 93 years) and the young and middle-aged volunteers from the community (aged 18 to 64 years). Statistical analysis was conducted with both JMP and SAS software packages (SAS Institute, Cary, NC). Analysis of covariance and hierarchical polynomial regression were used to test for additional effects of age and sex. To test for a cross-sectional relation between SES and brain volume, analysis (including recomputation of aWBV) was restricted to the main, carefully screened older adult sample of 100, and SES was entered as the predictor variable with age and sex as covariates. Longitudinal analysis To test for a longitudinal relation between SES and brain volume, we used multilevel modeling (SAS PROC MIXED, full maximum likelihood estimation) with aWBV as the dependent measure and the time × SES term as the predictor; covariates were baseline age, time (expressed as years from baseline), SES, and sex. For visualization, the most precise ordinary-least-squared regressions of aWBV against time were plotted per individual with individuals ranked by SES (via the Hollingshead index). Preclinical ad Amyloid was visualized by positron emission tomography (PET) scanning with [11C]PiB, a radiotracer with high affinity for amyloid in β amyloid plaques.20,21 Pittsburgh Compound B was imaged with PET in a sample of 58 nondemented ADRC participants that partially overlapped with this study's main MRI sample. Characteristics of the PiB sample are described in Table 2. Other articles describe PiB-PET image acquisition and analysis.22,23,26 Uptake of PiB in 4 cortical brain regions (prefrontal, lateral temporal, precuneus, and gyrus rectus) was obtained by manual drawing of regions of interest on the coregistered MRI and application to the dynamic PET data. Binding potential was calculated using Logan graphical analysis with a cerebellar reference region of interest24 (descriptions of regions of interest and regional distribution of binding potential values appear elsewhere22,23). A mean binding potential for these 4 regions greater than 0.2 was used to classify individuals with higher relative cortical binding as PiB+ based on the demonstrated association between CDR, cerebrospinal fluid β amyloid 42, and quantitative PiB uptake.22 Baseline MRIs from individuals classified as PiB+ were then compared against PiB− MRIs in a separate analysis of the PiB sample using aWBV as the dependent measure. Age and sex were covariates. Later development of dementia was assessed by examining the longitudinal history of clinical examinations. A large overlapping clinical sample (in contrast to the limited PiB sample) allowed us to explore interactions with SES. Specifically, 91 of the 100 participants characterized in Table 1 as CDR 0 around the time of baseline MRI received at least 1 subsequent clinical evaluation (mean, 2.9; range, 1 to 7; mean follow-up interval, 3.1 years, range, 0.3 to 6.9 years). Participants were classified relative to their initial MRI as preclinical if they progressed to CDR 0.5 at any subsequent clinical examination. Group status (preclinical vs stable CDR 0) was added as an additional term in the cross-sectional analysis of SES described here. Results Brain volume reductions in nondemented aging Cross-sectional brain volumes in nondemented individuals, aged 18 to 93 years, are illustrated in Figure 1 (using covariance-adjusted whole-brain volume; aWBV). Parameter estimates for age, age2, sex, and age × sex were all significant in the model (F5,356 = 1394.14, P < .001, R2 = 0.95). Between ages 20 and 80 years, aWBV was estimated to decline from 1199 cm3 to 1025 cm3 in men and from 1195 cm3 to 1050 cm3 in women (decline in annualized percentage terms, 0.24% per year and 0.20% per year, respectively). Initial aWBV is similar in men and women, reflecting the adjustment's ability to accommodate head-size differences.18 The quadratic age term reflects acceleration of volume decline in advanced aging. Within the age range between 65 and 80 years, estimated declines were 0.40% per year (men) and 0.35% per year (women). Privileged older adults and reduced brain volume Figure 2A shows the relation between SES and brain volume in nondemented older adults. After accounting for effects of age, sex, and age × sex on aWBV (model F5,94 = 218.74, P < .001, R2 = 0.92), more privileged individuals were associated with lower volume estimates (β = 1.3 cm3 per Hollingshead unit, P < .01). Spanning the sample range from middle privilege (Hollingshead 43) to highest privilege (Hollingshead 11), aWBV was estimated to decrease from 1066 cm3 to 1026 cm3 (3.8% difference). Privileged older adults and accelerated longitudinal volume loss To determine whether cross-sectional differences associated with SES relate to aging, volume change was estimated within participants using longitudinal MRI (Figure 2B and Table 3). More privileged individuals exhibited accelerated loss of aWBV (time × SES β = 0.11 cm3 per year per Hollingshead, P < .05), controlling for sex × time and main effects of SES and time within the multilevel model (χ23 = 191.96, P < .001; adding baseline age did not contribute). Spanning the longitudinal sample range from middle privilege (Hollingshead 40) to highest privilege (Hollingshead 11), model estimates of aWBV loss increased from 4.3 cm3 per year to 7.4 cm3 per year (0.39% per year to 0.68% per year, relative to model intercept). Evidence that reserve may be an important factor in ad Figure 3 and Figure 4 display results that explore aWBV in relation to amyloid imaging with PiB and follow-up clinical assessments. Nine of 58 individuals (16%) within the separate CDR 0 PiB sample (aged 47 to 86 years) were positive for PiB binding. Figure 3 shows that there was a main effect (P < .05) of positive PiB binding on brain volume: aWBV was estimated to decline 27 cm3 (2.5%, from 1066 cm3 to 1039 cm3) in the CDR 0 PiB+ group after adjusting for effects of age and sex (model F3,54 = 151.62, P < .001, R2 = 0.89). Figure 4 suggests that preclinical status contributes to the effect of SES. Participants were grouped as preclinical if subsequent clinical evaluation indicated very mild dementia (CDR 0.5). Adding group status to the cross-sectional model (F7,83 = 151.38, P < .001, R2 = 0.93) revealed a group × privilege interaction (β = 2.2 cm3 per Hollingshead per clinical conversion, P < .05). The magnitude of the interaction predicts that the cross-sectional decline in aWBV with privilege (β = 1.3 cm3 per Hollingshead unit overall) will increase by 2.2 cm3 per Hollingshead unit in individuals with subsequent dementia. Comment Nondemented participants with high SES (the most privileged individuals) were found to have reduced brain volume (cross-sectional analysis) and accelerated volume loss (longitudinal analysis). The capacity for more privileged individuals to cope longer with brain pathology before manifesting dementia may contribute to this association. Ses and brain volume reduction in nondemented aging This study's main result is that high SES is associated with lower aWBV in nondemented older adults (Figure 2). It is worth emphasizing that, by design, this study concerns individual differences in long-term structural change (Figure 1), not early established differences such as in head size.25 This focus on change is most clear in the longitudinal result that shows accelerated volume loss in more privileged individuals. Moreover, in the present sample, we did not find significant head-size differences attributable to SES. Our main cross-sectional result (Figure 2A) extends and strengthens the findings of the study by Coffey and colleagues.10 The longitudinal finding illustrated in Figure 2B confirms the direction of the cross-sectional association between volume and privilege and provides novel evidence that this association is related to aging and present in older age. Role of preclinical ad and cognitive reserve To explore whether the observed relation between brain aging and SES was associated with preclinical pathology, we conducted supplementary analyses on available amyloid imaging and clinical follow-up data. At least 3 results implicate preclinical AD as a possible factor. First, 16% of our nondemented PiB sample showed high levels of binding indicative of amyloid plaque presence, suggesting a number of individuals may harbor preclinical pathology.23,26 Second, high PiB binding was associated with reduced aWBV27 (Figure 3), suggesting that preclinical pathology is already having an influence on brain volume in some individuals.28 Third, in the full sample with follow-up clinical data, a group × SES interaction was observed with reduced aWBV associated with more privileged individuals who subsequently showed signs of very mild dementia (Figure 4). Together, these results suggest individuals with high SES are more likely to remain clinically nondemented in the early stages of AD relative to their less privileged peers even though AD is causing brain atrophy. Preclinical neurodegeneration might affect more privileged individuals less because of relations between SES and AD pathology,29 between SES and the structural response to pathology,30 or between SES and the clinical response to pathology.10 The latter reserve explanation is supported by studies that reveal similar plaque burden leads to lessened cognitive decline in the most educated individuals.3 The recent observation that more educated individuals decline more rapidly on neuropsychological tests several years prior to AD diagnosis has also been interpreted in terms of cognitive susceptibility and reserve.31,32 The present data thus are consistent with SES influencing the ability to detect cognitive impairment in the presence of pathology. It is unclear whether there is any modification of underlying structural or disease processes by life experiences associated with SES. Education and occupational attainment may protect against AD through a “use it and hide it” mechanism in comparison with the more traditionally assumed “use it or lose it” explanation. Limitations and caveats Limitations of this study highlight open questions and may help guide future research. For example, the “use it and hide it” interpretation of our results implies that CDR 0 status is insensitive to some degree to AD pathology and associated cognitive variation, particularly in individuals with high SES. Development of sensitive neuropsychological markers to capture this cognitive variance is an active area of research at our ADRC.1,33 Future research should also aim to increase the precision of the presently characterized relation between SES and brain volume, both in terms of regional anatomy and analysis of the multiple factors that contribute to SES.10,34 The present study explored SES between the middle and high range in older ADRC volunteers. This range is higher than most, but not all,35 epidemiological studies that have found protective demographic factors against AD2 and favors a gradient over threshold model of SES.36 Our sample was not randomly assigned from the population and not all participants were followed up longitudinally. Thus, broader and more prospective sampling could help establish the generality of these findings. A final point to raise is that a reserve explanation for the present findings does not exclude the possibility that additional factors are at work. Specifically, it remains difficult to account fully for the magnitude of the SES-related volume difference unless SES-related protection against AD is greater and/or CDR 0 pathology more burdensome than recent research suggests.1,3,31 We thus conclude that reserve likely explains some, but perhaps not all, of the novel association reported here between SES and structural brain aging. Correspondence: Anthony F. Fotenos, BS, Washington University Medical Scientist Training Program, Campus Box 8826, 660 S Euclid Ave, St Louis, MO 63110 (anthony.fotenos@wustl.edu). Accepted for Publication: March 18, 2007. Author Contributions:Study concept and design: Fotenos and Buckner. Acquisition of data: Mintun, Morris, and Buckner. Analysis and interpretation of data: Fotenos, Mintun, Snyder, Morris, and Buckner. Drafting of the manuscript: Fotenos. Critical revision of the manuscript for important intellectual content: Fotenos, Mintun, Snyder, Morris, and Buckner. Statistical analysis: Fotenos. Obtained funding: Mintun, Morris, and Buckner. Administrative, technical, and material support: Fotenos, Mintun, Snyder, Morris, and Buckner. Study supervision: Fotenos, Morris, and Buckner. Financial Disclosure: None reported. Funding/Support: This study was supported by grants P50 AG-05681 and P01 AG-03991 (Dr Morris) and R01 AG-21910 (Dr Buckner) from the National Institute on Aging, Bethesda, Maryland; grant P30 NS-048056 from the National Institute of Neurological Disorders and Stroke, Bethesda (Dr Mintun); grant IIRG-00-1944 from the Alzheimer's Association, Chicago, Illinois; the James S. McDonnell Foundation, St Louis, Missouri (Dr Buckner); and the Howard Hughes Medical Institute, Chevy Chase, Maryland (Dr Buckner). Additional Information: The Washington University Alzheimer's Disease Research Center and the Conte Center recruited and assessed the participants; Elizabeth Grant provided database assistance; Daniel Marcus provided database development and support; Dana Sacco, Erin Laciny, Jamie Parker, Susan Larson, Laura Williams, and Glenn Foster assisted with MRI and PET data collection; and Martha Storandt, Denise Head, Julie Bugg, David Johnson, and Cathy Roe discussed statistical procedures and results. References 1. Galvin JEPowlishta KKWilkins K et al. Predictors of preclinical Alzheimer disease and dementia: a clinicopathologic study. Arch Neurol 2005;62 (5) 758- 765PubMedGoogle ScholarCrossref 2. Valenzuela MJSachdev P Brain reserve and dementia: a systematic review. Psychol Med 2006;36 (4) 441- 454PubMedGoogle ScholarCrossref 3. Bennett DAWilson RSSchneider JA et al. Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology 2003;60 (12) 1909- 1915PubMedGoogle ScholarCrossref 4. Stern Y Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord 2006;20 (2) 112- 117PubMedGoogle ScholarCrossref 5. Giedd JNBlumenthal JJeffries NO et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999;2 (10) 861- 863PubMedGoogle ScholarCrossref 6. Raz NLindenberger URodrigue KM et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex 2005;15 (11) 1676- 1689PubMedGoogle ScholarCrossref 7. Jack CRShiung MMGunter JL et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 2004;62 (4) 591- 600PubMedGoogle ScholarCrossref 8. Fotenos AFSnyder AZGirton LEMorris JCBuckner RL Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64 (6) 1032- 1039PubMedGoogle ScholarCrossref 9. Buckner RL Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 2004;44 (1) 195- 208PubMedGoogle ScholarCrossref 10. Coffey CESaxton JARatcliff GBryan RNLucke JF Relation of education to brain size in normal aging: implications for the reserve hypothesis. Neurology 1999;53 (1) 189- 196PubMedGoogle ScholarCrossref 11. Van Petten C Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: review and meta-analysis. Neuropsychologia 2004;42 (10) 1394- 1413PubMedGoogle ScholarCrossref 12. Morris JC The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43 (11) 2412- 2414PubMedGoogle ScholarCrossref 13. Storandt MGrant EMiller JMorris J Longitudinal course and neuropathologic outcomes in original vs revised MCI and in pre-MCI. Neurology 2006;67 (3) 467- 473PubMedGoogle ScholarCrossref 14. Berg LMcKeel DWMiller JP et al. Clinicopathologic studies in cognitively healthy aging and Alzheimer disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Arch Neurol 1998;55 (3) 326- 335PubMedGoogle ScholarCrossref 15. Hollingshead ABRedlich FC Social Class and Mental Illness. New York, NY John Wiley & Sons1958; 16. House JSLepkowski JMKinney AMMero RPKessler RCHerzog AR The social stratification of aging and health. J Health Soc Behav 1994;35 (3) 213- 234PubMedGoogle ScholarCrossref 17. Fratiglioni LDe Ronchi DAguero-Torres H Worldwide prevalence and incidence of dementia. Drugs Aging 1999;15 (5) 365- 375PubMedGoogle ScholarCrossref 18. Buckner RLHead DParker J et al. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 2004;23 (2) 724- 738PubMedGoogle ScholarCrossref 19. Mathalon DHSullivan EVRawles JMPfefferbaum A Correction for head size in brain-imaging measurements. Psychiatry Res 1993;50 (2) 121- 139PubMedGoogle ScholarCrossref 20. Mathis CAWang YMHolt DPHuang GFDebnath MLKlunk WE Synthesis and evaluation of C-11-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem 2003;46 (13) 2740- 2754PubMedGoogle ScholarCrossref 21. Klunk WEEngler HNordberg A et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 2004;55 (3) 306- 319PubMedGoogle ScholarCrossref 22. Fagan AMMintun MAMach RH et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid a beta(42) in humans. Ann Neurol 2006;59 (3) 512- 519PubMedGoogle ScholarCrossref 23. Mintun MLaRossa GSheline Y et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology 2006;67 (3) 446- 452PubMedGoogle ScholarCrossref 24. Logan JFowler JSVolkow NDWang GJDing YSAlexoff DL Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab 1996;16 (5) 834- 840PubMedGoogle ScholarCrossref 25. Wickett JCVernon PALee DH Relationships between factors of intelligence and brain volume. Pers Individ Differ 2000;291095- 1122Google ScholarCrossref 26. Buckner RLSnyder AZShannon BJ et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005;25 (34) 7709- 7717PubMedGoogle ScholarCrossref 27. Archer HAEdison PBrooks DJ et al. Amyloid load and cerebral atrophy in Alzheimer's disease: an C-11-PIB positron emission tomography study. Ann Neurol 2006;60 (1) 145- 147PubMedGoogle ScholarCrossref 28. Fox NCWarrington EKRossor MN Serial magnetic resonance imaging of cerebral atrophy in preclinical Alzheimer's disease. Lancet 1999;353 (9170) 2125PubMedGoogle ScholarCrossref 29. D’Andrea MRNagele RGGumula NA et al. Lipofuscin and A beta 42 exhibit distinct distribution patterns in normal and Alzheimer's disease brains. Neurosci Lett 2002;323 (1) 45- 49PubMedGoogle ScholarCrossref 30. Snowdon DAGreiner LHMarkesbery WR Linguistic ability in early life and the neuropathology of Alzheimer's disease and cerebrovascular disease: findings from the Nun study. Ann N Y Acad Sci 2000;90334- 38PubMedGoogle ScholarCrossref 31. Amieva HJacqmin-Gadda HOrgogozo JM et al. The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study. Brain 2005;128 (pt 5) 1093- 1101PubMedGoogle ScholarCrossref 32. Scarmeas NAlbert SMManly JJStern Y Education and rates of cognitive decline in incident Alzheimer's disease. J Neurol Neurosurg Psychiatry 2006;77 (3) 308- 316PubMedGoogle ScholarCrossref 33. Goldman WPPrice JLStorandt M et al. Absence of cognitive impairment or decline in preclinical Alzheimer's disease. Neurology 2001;56 (3) 361- 367PubMedGoogle ScholarCrossref 34. Smyth KAFritsch TCook TBMcClendon MJSantillan CEFriedland RP Worker functions and traits associated with occupations and the development of AD. Neurology 2004;63 (3) 498- 503PubMedGoogle ScholarCrossref 35. De Ronchi DFratiglioni LRucci PPaternico AGraziani SDalmonte E The effect of education on dementia occurrence in an Italian population with middle to high socioeconomic status. Neurology 1998;50 (5) 1231- 1238PubMedGoogle ScholarCrossref 36. Sapolsky RM Social status and health in humans and other animals. Annu Rev Anthropol 2004;33393- 418Google ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Neurology American Medical Association

Brain Volume Decline in Aging: Evidence for a Relation Between Socioeconomic Status, Preclinical Alzheimer Disease, and Reserve

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American Medical Association
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Copyright © 2008 American Medical Association. All Rights Reserved.
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0003-9942
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1538-3687
DOI
10.1001/archneurol.2007.27
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Abstract

Abstract Objectives To assess the relation between socioeconomic status (SES) and structural brain change in nondemented older adults and to ascertain the potential role of preclinical Alzheimer disease (AD). Design Cross-sectional and longitudinal observation. Setting Alzheimer's Disease Research Center, St Louis, Missouri. Participants Volunteer sample of 362 nondemented adults aged 18 to 93 years. The main cohort of 100 was evaluated for dementia and SES; a Clinical Dementia Rating (CDR) of 0 (no dementia) and middle, high-middle, or high SES was required for eligibility. All 362 received magnetic resonance imaging; of the main 100, 91 received follow-up clinical assessment, and 33 received follow-up magnetic resonance imaging over at least a 3-year interval. A separate sample of 58 CDR 0 participants (aged 47 to 86 years) took part in amyloid imaging with Pittsburgh Compound B (PiB) labeled with radioactive carbon (11C). Main Outcome Measures Whole-brain volume adjusted for head size (aWBV) and change per year. Results aWBV declined by 0.22% per year between the ages of 20 and 80 years with accelerated decline in advanced aging. Controlling for effects of age and sex in older adults (>65 years) with CDR 0, higher SES was associated with smaller aWBV (3.8% difference spanning the sample range from middle to high privilege, P < .01) and more rapid volume loss (0.39% per year to 0.68% per year from middle to high privilege, P < .05). aWBV was reduced by 2.5% in individuals positive for PiB binding (n = 9) as compared with individuals negative for PiB binding (n = 49, P < .05), supporting an influence of undetected preclinical AD. Follow-up clinical data revealed that brain volume reduction associated with SES was greater in those who later developed very mild dementia (preclinical CDR 0 group, n = 19) compared with those who remained nondemented (stable CDR 0 group, n = 64; group × SES interaction, P < .05). Conclusions Privileged nondemented older adults harbor more preclinical brain atrophy, consistent with their having greater reserve against the expression of AD. The observation that normal cognitive function can be present in some older adults with Alzheimer disease (AD) pathology has led to an interest in between-subject differences that may protect against dementia.1 One variable that has been associated with reduced risk for dementia is high socioeconomic status (SES), representing both educational and occupational attainment.2 Clinicopathological investigation has found that older adults respond differently to the same burden of AD pathology with the more educated showing less severe cognitive decline.3 These studies suggest that SES is associated with an individual's reserve of protective function against the expression of AD. Reserve, as operationally defined for the present article, refers to the delay in time between pathology (as an independent process) and disease expression (as a dependent process).4 Here we characterize the relation between SES and age-related brain volume change in a large cohort of nondemented older adults. Brain volume decline begins in adolescence or earlier and continues into advanced aging.5,6 Volume loss accelerates markedly in the earliest stages of AD.7,8 These and related observations suggest that whole-brain volume is determined by a constellation of factors that include normal developmental as well as pathological processes.9 The relations between SES, dementia, and brain volume suggest that the extent of pathological atrophy nondemented older adults can accommodate increases with SES.10 This hypothesized outcome could alternatively reflect a direct relation between SES and brain aging without any involvement of preclinical pathology.11 To explore the contribution of preclinical pathology, we supplemented our main analysis with subject stratification based on amyloid binding with Pittsburgh Compound B (PiB) labeled with radioactive carbon (11C) and longitudinal clinical assessment. Support for a reserve explanation would come from finding that greater volume decline was accounted for by privileged individuals on the threshold of clinically detectable dementia. Methods Participants Magnetic resonance imaging (MRI) scans were obtained from 362 individuals (aged 18 to 93 years) participating in the longitudinal studies of the Washington University Alzheimer's Disease Research Center (ADRC) or other studies of younger adult aging and development. Detailed selection and attrition characteristics of this population have been described previously.8 All participants were scanned using identical procedures. The main cohort comprised 100 clinically screened ADRC participants aged 65 to 93 years. Of these 100, 33 were followed up with MRI for an extended interval to allow for longitudinal data analysis (mean, 3.1 times over a 3.1- to 6.5-year interval; mean time, 4.3 years). Participants were classified as initially nondemented if their Clinical Dementia Rating (CDR) nearest the time of baseline MRI was 0. Specialist clinicians determined the CDR, blind to the results of neuropsychological testing and prior clinical assessment, through examination of the participant and interview with an informant (usually a family member) who knew the participant well and could provide information regarding decline from the participant's normal cognitive and functional abilities.12 The designation of CDR 0.5 (very mild dementia) thus indicates very early clinical impairment relative to an individual's own baseline rather than impaired test performance relative to group norms.13 The CDR has been validated by clinicopathological studies.14 At the participant's initial clinical evaluation, SES was assessed using the Hollingshead 2-factor index of social position.15 The Hollingshead index represents a linear combination of educational and household occupational attainment with occupation almost doubly weighted. The highest attained occupation was used for indexing. The index ranges from 11 to 77 grouped into 5 SES categories (I-V). To control for potential health confounds related to deprivation in the underprivileged,16 and because the cohort from which data were drawn contains too few low-SES individuals to disentangle these effects, this study focused on variation of the Hollingshead index within the range of the high-privilege to middle-SES groups (I-III).17 Neither participation in follow-up MRI nor any of the measured health variables differed between these groups (Table 1). Estimation of whole-brain volume Our method of image acquisition and estimation of total intracranial volume (eTIV) and whole-brain volume (WBV) has been described previously.8,18 Head-size differences were corrected using a covariance procedure.18,19 The term adjusted whole-brain volume (aWBV) is used to denote covariance-adjusted volumes as distinct from proportionally normalized whole-brain volume (nWBV). aWBV was defined as aWBV = WBV − b(eTIV − mean eTIV) where WBV is the uncorrected (native) whole-brain volume, b is the slope of the volume regression on eTIV, eTIV is the head-size estimate derived from atlas scaling, and mean eTIV is the sample mean. When the relations of multiple variables to WBV were being explored simultaneously, eTIV was always entered as a covariate, and the dependent variable is denoted as aWBV to reflect this adjustment. Cross-sectional analysis To explore differences in brain volume across the full life span, aWBV was plotted cross-sectionally vs age for the entire sample of 362 individuals, including the cohort of 100 clinically screened nondemented older participants (aged 65 to 93 years) and the young and middle-aged volunteers from the community (aged 18 to 64 years). Statistical analysis was conducted with both JMP and SAS software packages (SAS Institute, Cary, NC). Analysis of covariance and hierarchical polynomial regression were used to test for additional effects of age and sex. To test for a cross-sectional relation between SES and brain volume, analysis (including recomputation of aWBV) was restricted to the main, carefully screened older adult sample of 100, and SES was entered as the predictor variable with age and sex as covariates. Longitudinal analysis To test for a longitudinal relation between SES and brain volume, we used multilevel modeling (SAS PROC MIXED, full maximum likelihood estimation) with aWBV as the dependent measure and the time × SES term as the predictor; covariates were baseline age, time (expressed as years from baseline), SES, and sex. For visualization, the most precise ordinary-least-squared regressions of aWBV against time were plotted per individual with individuals ranked by SES (via the Hollingshead index). Preclinical ad Amyloid was visualized by positron emission tomography (PET) scanning with [11C]PiB, a radiotracer with high affinity for amyloid in β amyloid plaques.20,21 Pittsburgh Compound B was imaged with PET in a sample of 58 nondemented ADRC participants that partially overlapped with this study's main MRI sample. Characteristics of the PiB sample are described in Table 2. Other articles describe PiB-PET image acquisition and analysis.22,23,26 Uptake of PiB in 4 cortical brain regions (prefrontal, lateral temporal, precuneus, and gyrus rectus) was obtained by manual drawing of regions of interest on the coregistered MRI and application to the dynamic PET data. Binding potential was calculated using Logan graphical analysis with a cerebellar reference region of interest24 (descriptions of regions of interest and regional distribution of binding potential values appear elsewhere22,23). A mean binding potential for these 4 regions greater than 0.2 was used to classify individuals with higher relative cortical binding as PiB+ based on the demonstrated association between CDR, cerebrospinal fluid β amyloid 42, and quantitative PiB uptake.22 Baseline MRIs from individuals classified as PiB+ were then compared against PiB− MRIs in a separate analysis of the PiB sample using aWBV as the dependent measure. Age and sex were covariates. Later development of dementia was assessed by examining the longitudinal history of clinical examinations. A large overlapping clinical sample (in contrast to the limited PiB sample) allowed us to explore interactions with SES. Specifically, 91 of the 100 participants characterized in Table 1 as CDR 0 around the time of baseline MRI received at least 1 subsequent clinical evaluation (mean, 2.9; range, 1 to 7; mean follow-up interval, 3.1 years, range, 0.3 to 6.9 years). Participants were classified relative to their initial MRI as preclinical if they progressed to CDR 0.5 at any subsequent clinical examination. Group status (preclinical vs stable CDR 0) was added as an additional term in the cross-sectional analysis of SES described here. Results Brain volume reductions in nondemented aging Cross-sectional brain volumes in nondemented individuals, aged 18 to 93 years, are illustrated in Figure 1 (using covariance-adjusted whole-brain volume; aWBV). Parameter estimates for age, age2, sex, and age × sex were all significant in the model (F5,356 = 1394.14, P < .001, R2 = 0.95). Between ages 20 and 80 years, aWBV was estimated to decline from 1199 cm3 to 1025 cm3 in men and from 1195 cm3 to 1050 cm3 in women (decline in annualized percentage terms, 0.24% per year and 0.20% per year, respectively). Initial aWBV is similar in men and women, reflecting the adjustment's ability to accommodate head-size differences.18 The quadratic age term reflects acceleration of volume decline in advanced aging. Within the age range between 65 and 80 years, estimated declines were 0.40% per year (men) and 0.35% per year (women). Privileged older adults and reduced brain volume Figure 2A shows the relation between SES and brain volume in nondemented older adults. After accounting for effects of age, sex, and age × sex on aWBV (model F5,94 = 218.74, P < .001, R2 = 0.92), more privileged individuals were associated with lower volume estimates (β = 1.3 cm3 per Hollingshead unit, P < .01). Spanning the sample range from middle privilege (Hollingshead 43) to highest privilege (Hollingshead 11), aWBV was estimated to decrease from 1066 cm3 to 1026 cm3 (3.8% difference). Privileged older adults and accelerated longitudinal volume loss To determine whether cross-sectional differences associated with SES relate to aging, volume change was estimated within participants using longitudinal MRI (Figure 2B and Table 3). More privileged individuals exhibited accelerated loss of aWBV (time × SES β = 0.11 cm3 per year per Hollingshead, P < .05), controlling for sex × time and main effects of SES and time within the multilevel model (χ23 = 191.96, P < .001; adding baseline age did not contribute). Spanning the longitudinal sample range from middle privilege (Hollingshead 40) to highest privilege (Hollingshead 11), model estimates of aWBV loss increased from 4.3 cm3 per year to 7.4 cm3 per year (0.39% per year to 0.68% per year, relative to model intercept). Evidence that reserve may be an important factor in ad Figure 3 and Figure 4 display results that explore aWBV in relation to amyloid imaging with PiB and follow-up clinical assessments. Nine of 58 individuals (16%) within the separate CDR 0 PiB sample (aged 47 to 86 years) were positive for PiB binding. Figure 3 shows that there was a main effect (P < .05) of positive PiB binding on brain volume: aWBV was estimated to decline 27 cm3 (2.5%, from 1066 cm3 to 1039 cm3) in the CDR 0 PiB+ group after adjusting for effects of age and sex (model F3,54 = 151.62, P < .001, R2 = 0.89). Figure 4 suggests that preclinical status contributes to the effect of SES. Participants were grouped as preclinical if subsequent clinical evaluation indicated very mild dementia (CDR 0.5). Adding group status to the cross-sectional model (F7,83 = 151.38, P < .001, R2 = 0.93) revealed a group × privilege interaction (β = 2.2 cm3 per Hollingshead per clinical conversion, P < .05). The magnitude of the interaction predicts that the cross-sectional decline in aWBV with privilege (β = 1.3 cm3 per Hollingshead unit overall) will increase by 2.2 cm3 per Hollingshead unit in individuals with subsequent dementia. Comment Nondemented participants with high SES (the most privileged individuals) were found to have reduced brain volume (cross-sectional analysis) and accelerated volume loss (longitudinal analysis). The capacity for more privileged individuals to cope longer with brain pathology before manifesting dementia may contribute to this association. Ses and brain volume reduction in nondemented aging This study's main result is that high SES is associated with lower aWBV in nondemented older adults (Figure 2). It is worth emphasizing that, by design, this study concerns individual differences in long-term structural change (Figure 1), not early established differences such as in head size.25 This focus on change is most clear in the longitudinal result that shows accelerated volume loss in more privileged individuals. Moreover, in the present sample, we did not find significant head-size differences attributable to SES. Our main cross-sectional result (Figure 2A) extends and strengthens the findings of the study by Coffey and colleagues.10 The longitudinal finding illustrated in Figure 2B confirms the direction of the cross-sectional association between volume and privilege and provides novel evidence that this association is related to aging and present in older age. Role of preclinical ad and cognitive reserve To explore whether the observed relation between brain aging and SES was associated with preclinical pathology, we conducted supplementary analyses on available amyloid imaging and clinical follow-up data. At least 3 results implicate preclinical AD as a possible factor. First, 16% of our nondemented PiB sample showed high levels of binding indicative of amyloid plaque presence, suggesting a number of individuals may harbor preclinical pathology.23,26 Second, high PiB binding was associated with reduced aWBV27 (Figure 3), suggesting that preclinical pathology is already having an influence on brain volume in some individuals.28 Third, in the full sample with follow-up clinical data, a group × SES interaction was observed with reduced aWBV associated with more privileged individuals who subsequently showed signs of very mild dementia (Figure 4). Together, these results suggest individuals with high SES are more likely to remain clinically nondemented in the early stages of AD relative to their less privileged peers even though AD is causing brain atrophy. Preclinical neurodegeneration might affect more privileged individuals less because of relations between SES and AD pathology,29 between SES and the structural response to pathology,30 or between SES and the clinical response to pathology.10 The latter reserve explanation is supported by studies that reveal similar plaque burden leads to lessened cognitive decline in the most educated individuals.3 The recent observation that more educated individuals decline more rapidly on neuropsychological tests several years prior to AD diagnosis has also been interpreted in terms of cognitive susceptibility and reserve.31,32 The present data thus are consistent with SES influencing the ability to detect cognitive impairment in the presence of pathology. It is unclear whether there is any modification of underlying structural or disease processes by life experiences associated with SES. Education and occupational attainment may protect against AD through a “use it and hide it” mechanism in comparison with the more traditionally assumed “use it or lose it” explanation. Limitations and caveats Limitations of this study highlight open questions and may help guide future research. For example, the “use it and hide it” interpretation of our results implies that CDR 0 status is insensitive to some degree to AD pathology and associated cognitive variation, particularly in individuals with high SES. Development of sensitive neuropsychological markers to capture this cognitive variance is an active area of research at our ADRC.1,33 Future research should also aim to increase the precision of the presently characterized relation between SES and brain volume, both in terms of regional anatomy and analysis of the multiple factors that contribute to SES.10,34 The present study explored SES between the middle and high range in older ADRC volunteers. This range is higher than most, but not all,35 epidemiological studies that have found protective demographic factors against AD2 and favors a gradient over threshold model of SES.36 Our sample was not randomly assigned from the population and not all participants were followed up longitudinally. Thus, broader and more prospective sampling could help establish the generality of these findings. A final point to raise is that a reserve explanation for the present findings does not exclude the possibility that additional factors are at work. Specifically, it remains difficult to account fully for the magnitude of the SES-related volume difference unless SES-related protection against AD is greater and/or CDR 0 pathology more burdensome than recent research suggests.1,3,31 We thus conclude that reserve likely explains some, but perhaps not all, of the novel association reported here between SES and structural brain aging. Correspondence: Anthony F. Fotenos, BS, Washington University Medical Scientist Training Program, Campus Box 8826, 660 S Euclid Ave, St Louis, MO 63110 (anthony.fotenos@wustl.edu). Accepted for Publication: March 18, 2007. Author Contributions:Study concept and design: Fotenos and Buckner. Acquisition of data: Mintun, Morris, and Buckner. Analysis and interpretation of data: Fotenos, Mintun, Snyder, Morris, and Buckner. Drafting of the manuscript: Fotenos. Critical revision of the manuscript for important intellectual content: Fotenos, Mintun, Snyder, Morris, and Buckner. Statistical analysis: Fotenos. Obtained funding: Mintun, Morris, and Buckner. Administrative, technical, and material support: Fotenos, Mintun, Snyder, Morris, and Buckner. Study supervision: Fotenos, Morris, and Buckner. Financial Disclosure: None reported. Funding/Support: This study was supported by grants P50 AG-05681 and P01 AG-03991 (Dr Morris) and R01 AG-21910 (Dr Buckner) from the National Institute on Aging, Bethesda, Maryland; grant P30 NS-048056 from the National Institute of Neurological Disorders and Stroke, Bethesda (Dr Mintun); grant IIRG-00-1944 from the Alzheimer's Association, Chicago, Illinois; the James S. McDonnell Foundation, St Louis, Missouri (Dr Buckner); and the Howard Hughes Medical Institute, Chevy Chase, Maryland (Dr Buckner). Additional Information: The Washington University Alzheimer's Disease Research Center and the Conte Center recruited and assessed the participants; Elizabeth Grant provided database assistance; Daniel Marcus provided database development and support; Dana Sacco, Erin Laciny, Jamie Parker, Susan Larson, Laura Williams, and Glenn Foster assisted with MRI and PET data collection; and Martha Storandt, Denise Head, Julie Bugg, David Johnson, and Cathy Roe discussed statistical procedures and results. References 1. Galvin JEPowlishta KKWilkins K et al. Predictors of preclinical Alzheimer disease and dementia: a clinicopathologic study. Arch Neurol 2005;62 (5) 758- 765PubMedGoogle ScholarCrossref 2. Valenzuela MJSachdev P Brain reserve and dementia: a systematic review. Psychol Med 2006;36 (4) 441- 454PubMedGoogle ScholarCrossref 3. Bennett DAWilson RSSchneider JA et al. Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology 2003;60 (12) 1909- 1915PubMedGoogle ScholarCrossref 4. Stern Y Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord 2006;20 (2) 112- 117PubMedGoogle ScholarCrossref 5. Giedd JNBlumenthal JJeffries NO et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999;2 (10) 861- 863PubMedGoogle ScholarCrossref 6. Raz NLindenberger URodrigue KM et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex 2005;15 (11) 1676- 1689PubMedGoogle ScholarCrossref 7. Jack CRShiung MMGunter JL et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 2004;62 (4) 591- 600PubMedGoogle ScholarCrossref 8. Fotenos AFSnyder AZGirton LEMorris JCBuckner RL Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64 (6) 1032- 1039PubMedGoogle ScholarCrossref 9. Buckner RL Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 2004;44 (1) 195- 208PubMedGoogle ScholarCrossref 10. Coffey CESaxton JARatcliff GBryan RNLucke JF Relation of education to brain size in normal aging: implications for the reserve hypothesis. Neurology 1999;53 (1) 189- 196PubMedGoogle ScholarCrossref 11. Van Petten C Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: review and meta-analysis. Neuropsychologia 2004;42 (10) 1394- 1413PubMedGoogle ScholarCrossref 12. Morris JC The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43 (11) 2412- 2414PubMedGoogle ScholarCrossref 13. Storandt MGrant EMiller JMorris J Longitudinal course and neuropathologic outcomes in original vs revised MCI and in pre-MCI. Neurology 2006;67 (3) 467- 473PubMedGoogle ScholarCrossref 14. Berg LMcKeel DWMiller JP et al. Clinicopathologic studies in cognitively healthy aging and Alzheimer disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Arch Neurol 1998;55 (3) 326- 335PubMedGoogle ScholarCrossref 15. Hollingshead ABRedlich FC Social Class and Mental Illness. New York, NY John Wiley & Sons1958; 16. House JSLepkowski JMKinney AMMero RPKessler RCHerzog AR The social stratification of aging and health. J Health Soc Behav 1994;35 (3) 213- 234PubMedGoogle ScholarCrossref 17. Fratiglioni LDe Ronchi DAguero-Torres H Worldwide prevalence and incidence of dementia. Drugs Aging 1999;15 (5) 365- 375PubMedGoogle ScholarCrossref 18. Buckner RLHead DParker J et al. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 2004;23 (2) 724- 738PubMedGoogle ScholarCrossref 19. Mathalon DHSullivan EVRawles JMPfefferbaum A Correction for head size in brain-imaging measurements. Psychiatry Res 1993;50 (2) 121- 139PubMedGoogle ScholarCrossref 20. Mathis CAWang YMHolt DPHuang GFDebnath MLKlunk WE Synthesis and evaluation of C-11-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem 2003;46 (13) 2740- 2754PubMedGoogle ScholarCrossref 21. Klunk WEEngler HNordberg A et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 2004;55 (3) 306- 319PubMedGoogle ScholarCrossref 22. Fagan AMMintun MAMach RH et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid a beta(42) in humans. Ann Neurol 2006;59 (3) 512- 519PubMedGoogle ScholarCrossref 23. Mintun MLaRossa GSheline Y et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology 2006;67 (3) 446- 452PubMedGoogle ScholarCrossref 24. Logan JFowler JSVolkow NDWang GJDing YSAlexoff DL Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab 1996;16 (5) 834- 840PubMedGoogle ScholarCrossref 25. Wickett JCVernon PALee DH Relationships between factors of intelligence and brain volume. Pers Individ Differ 2000;291095- 1122Google ScholarCrossref 26. Buckner RLSnyder AZShannon BJ et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005;25 (34) 7709- 7717PubMedGoogle ScholarCrossref 27. Archer HAEdison PBrooks DJ et al. Amyloid load and cerebral atrophy in Alzheimer's disease: an C-11-PIB positron emission tomography study. Ann Neurol 2006;60 (1) 145- 147PubMedGoogle ScholarCrossref 28. Fox NCWarrington EKRossor MN Serial magnetic resonance imaging of cerebral atrophy in preclinical Alzheimer's disease. Lancet 1999;353 (9170) 2125PubMedGoogle ScholarCrossref 29. D’Andrea MRNagele RGGumula NA et al. Lipofuscin and A beta 42 exhibit distinct distribution patterns in normal and Alzheimer's disease brains. Neurosci Lett 2002;323 (1) 45- 49PubMedGoogle ScholarCrossref 30. Snowdon DAGreiner LHMarkesbery WR Linguistic ability in early life and the neuropathology of Alzheimer's disease and cerebrovascular disease: findings from the Nun study. Ann N Y Acad Sci 2000;90334- 38PubMedGoogle ScholarCrossref 31. Amieva HJacqmin-Gadda HOrgogozo JM et al. The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study. Brain 2005;128 (pt 5) 1093- 1101PubMedGoogle ScholarCrossref 32. Scarmeas NAlbert SMManly JJStern Y Education and rates of cognitive decline in incident Alzheimer's disease. J Neurol Neurosurg Psychiatry 2006;77 (3) 308- 316PubMedGoogle ScholarCrossref 33. Goldman WPPrice JLStorandt M et al. Absence of cognitive impairment or decline in preclinical Alzheimer's disease. Neurology 2001;56 (3) 361- 367PubMedGoogle ScholarCrossref 34. Smyth KAFritsch TCook TBMcClendon MJSantillan CEFriedland RP Worker functions and traits associated with occupations and the development of AD. Neurology 2004;63 (3) 498- 503PubMedGoogle ScholarCrossref 35. De Ronchi DFratiglioni LRucci PPaternico AGraziani SDalmonte E The effect of education on dementia occurrence in an Italian population with middle to high socioeconomic status. Neurology 1998;50 (5) 1231- 1238PubMedGoogle ScholarCrossref 36. Sapolsky RM Social status and health in humans and other animals. Annu Rev Anthropol 2004;33393- 418Google ScholarCrossref

Journal

Archives of Neurology – American Medical Association

Published: Jan 1, 2008

Keywords: aging,alzheimer's disease,socioeconomic factors,brain volume,follow-up,head size,dementia,older adult,amyloid,diagnostic imaging,pittsburgh compound b,brain,magnetic resonance imaging

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