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The Effect of Education on Age-Related Changes inThree Cognitive Domains: A Cross-Sectional Study inPrimary CareIsabel Pavão Martins a , Carolina Maruta a , Cláudia Silva a , Pedro Rodrigues a , CatarinaChester a , Sandra Ginó a , Vanda Freitas a , Sara Freitas a & António Gouveia Oliveira ba Language Research Laboratory, Institute of Molecular Medicine and Faculty of Medicine,University of Lisbon, Lisbon, Portugalb Department of Biostatistics, Faculty of Medical Sciences, Universidade Nova de Lisboa,Lisbon, Portugal

Version of record first published: 17 Aug 2012

To cite this article: Isabel Pavão Martins, Carolina Maruta, Cláudia Silva, Pedro Rodrigues, Catarina Chester, SandraGinó, Vanda Freitas, Sara Freitas & António Gouveia Oliveira (2012): The Effect of Education on Age-RelatedChanges in Three Cognitive Domains: A Cross-Sectional Study in Primary Care, Applied Neuropsychology: Adult,DOI:10.1080/09084282.2012.670145

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The Effect of Education on Age-Related Changes in ThreeCognitive Domains: A Cross-Sectional Study in Primary Care

Isabel Pavao Martins, Carolina Maruta, Claudia Silva, Pedro Rodrigues, Catarina Chester,Sandra Gino, Vanda Freitas, and Sara Freitas

Language Research Laboratory, Institute of Molecular Medicine and Faculty of Medicine,University of Lisbon, Lisbon, Portugal

Antonio Gouveia Oliveira

Department of Biostatistics, Faculty of Medical Sciences,Universidade Nova de Lisboa, Lisbon, Portugal

The present study aims to investigate the protective effect of formal education onage-related changes in different cognitive domains with the hypothesis that it mayattenuate the rate of decline. Individuals aged 50 years or older attending primary carephysicians without known brain disease (431 participants, mostly [60.3%] female with66.3 [�9.1] years of age and 7.7 [�4.1] years of education, on average), were evaluatedwith a neuropsychological battery including 28 cognitive measures. Cognitive domainsidentified by factor analysis were subject to repeated multiple regression analyses todetermine the variance explained by age and education controlling for gender, depress-ive symptoms, and vascular risk factors. The slope of the regression equation wascompared between two educational groups with an average of 4 years and 11 yearsof education, respectively. Factors identified corresponded to processing ability(Factor 1), memory (Factor 2), and acquired knowledge (Factor 3). Although educationimproved performance in Factors 1 and 3, it did not change the slope of age-relateddecline in any factor. This study suggests that in culturally heterogeneous groups, smallincrements in education enhance cognition but do not modify the rate of decline ofexecutive functioning with age. These results contradict some clinical findings and needto be confirmed in longitudinal studies.

Key words: cognitive reserve, education in aging, screening battery

INTRODUCTION

Brain aging is an inevitable consequence of time, yet itsimpact in cognition is far from uniform, ranging fromperfect fitness to mental frailty and dementia. This strik-ing variability can be accounted for, in part, by cognitivereserve, a construct that explains the ability to maintain

performance in spite of the presence of brain pathology(Park & Reuter-Lorenz, 2009; Stern, 2002, 2006, 2009).

Indeed, several studies have shown that education orother measures of environmental enrichment maydecrease the rate of conversion to dementia in subjectswith identical degrees of pathological burden ofAlzheimer’s disease (Bennett et al., 2003; EClipSECollaborative Members, 2010); may have a protectiverole against the cognitive impairment associated to brainwhite-matter changes (WMC) or higher ventricularvolume (Brickman et al., 2009; Kuller et al., 1998);

Address correspondence to Isabel Pavao Martins, M.D., Ph.D.,

Language Research Laboratory, Institute of Molecular Medicine and

Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal.

E-mail: labling@fm.ul.pt

APPLIED NEUROPSYCHOLOGY: ADULT, 0: 1–12, 2012

Copyright # Taylor & Francis Group, LLC

ISSN: 0908-4282 print=1532-4826 online

DOI: 10.1080/09084282.2012.670145

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may reduce the progression of memory decline anddeterioration both in Alzheimer’s disease (Koepsellet al., 2008; Le Carret et al., 2005; Stern, Albert, Tang,& Tsai, 1999) and in vascular dementia (Lane, Paul,Moser, Fletcher, & Cohen, 2011); may reduce the riskfor dementia during aging (Stern et al., 1994; Valenzuela& Sachdev, 2006); and may explain cognitive perfor-mance variability in subjects with WMC (Schmidt et al.,2011) or with cognitive impairment and dementia(Vemuri et al., 2011). Those measures, such as readingability, vocabulary, mental stimulation, and occupationalattainment, which reflect lifetime experiences, havetherefore been used as surrogate markers of the constructof cognitive reserve (Jones et al., 2011; Siedlecki et al.,2009). The number of years of formal education isprobably the most consistently used among them.

Contrasting with this recognized protective effectupon the consequences of brain disease is the contro-versy regarding the role of education on the cognitiveeffects of normal aging. The latter has been best charac-terized by morphometric, functional, and neuropsycho-logical changes associated with the frontal lobes(Buckner, 2004), because it is mainly characterized bya dysfunction of executive abilities like processing speed,working memory, inhibitory control, top–down sup-pression (Gazzaley, Cooney, Rissman, & D’Esposito,2005), or shifting ability. Patterns of functional brainactivation during aging are different from those ofyounger adults (Cabeza, Anderson, Locantore, &McIntosh, 2002; Clapp, Rubens, Sabharwal, &Gazzaley, 2011) and depend in part on education(Angel, Fay, Bouazzaoui, Baudouin, & Isingrini, 2010;Springer, McIntosh, Winocur, & Grady, 2005). Yet,despite that evidence and the plausibility that lifelongcognitive stimulation will produce more efficient pat-terns of functioning and a better ability to developnew strategies (or scaffolding) to face age-related decline(Goh & Park, 2009; Satz, Cole, Hardy, & Rassovsky,2010), previous research has shown that education andcognitive stimulation do not protect against aging inall cognitive domains (Batterham, Mackinnon, &Christensen, 2011; Christensen et al., 1997; Siedleckiet al., 2009; Tucker & Stern, 2011). Although therelation between education and age is complex andvariable in different cognitive domains (Ardila,Ostrosky-Solis, Rosselli, & Gomez, 2000; Capitani,Barbarotto, & Laiacana, 1996), apparently its protectiveeffect is much more evident in measures of crystallizedabilities (or ‘‘product’’), like vocabulary, comparedwith fluid abilities (or ‘‘processes’’; Christensen et al.;Salthouse, 2006) like processing speed, memory, or vis-ual–spatial abilities, which is a paradoxical result takinginto account its more widespread protective effect inbrain pathology. Besides, independently of education,crystallized measures are more resistant to aging than

are fluid measures, therefore making any protectiveeffect much more difficult to document. However, someof the extant studies on that topic used brief and generalmeasures, did not evaluate systematically differentaspects of cognition (Christensen et al.; Kuller et al.,1998), focused on cognitive stimulation (Salthouse,2006) or on late-life decline in individuals older than70 years of age (Batterham et al.; Christensen, 2001;Christensen et al.), and did not always control forvascular risk factors or depressive symptoms that canalso influence cognitive performance (Elias, Elias,Robbins, & Budge, 2004). Because cognitive declinemay start early in adulthood (Salthouse, 2009) andaffect selective cognitive abilities, long-term memory,processing speed, reasoning, and spatial ability(Salthouse, 2004; Christensen), it is important to studyit in large samples of the population, heterogeneousfor age and education with more extensive testing andcontrolling for those clinical variables. In addition, itis not known how ‘‘much’’ difference in education isnecessary to produce a cognitive effect in age-relateddecline. A single study estimated the effect of each yearof education on decreasing the risk for Alzheimer’sdisease (Bennett et al., 2003), but most studies address-ing this matter, performed in North America or withEnglish-speaking subjects, compared highly performingsubjects (with university background) to those withlower levels of education, and it is not clear if thoseeffects also stand in samples with overall lowereducation.

The present project (‘‘Mindful Aging: AvoidingAge-Related Cognitive Decline’’) is a prospective longi-tudinal study of a cohort of adults followed in primarycare that aims to study cognitive profiles in aging andto identify the best neuropsychological measures thatpredict cognitive decline. The primary care setting ofrecruitment was chosen considering that primary carephysicians often miss the early stages of dementia(Bradford, Kunik, Schulz, Williams, & Singh, 2009),although this is the most likely place where diagnosiscan be made. This project includes an extensive neuro-psychological assessment, and analysis of its baselineresults allows us to evaluate the effect of education onseveral tests and measures of cognitive performanceacross different decades of age, because the study popu-lation is composed of a rather heterogeneous sample ofindividuals of European origin, speaking the samelanguage and with an identical cultural background,but who had different degrees of cognitive-intellectualstimulation, due to different opportunities to access edu-cation. In addition, most of these individuals have lowlevels of education, which is an opportunity to evaluatethe effects of small educational changes.

In this article, we aim to analyze the putative benefitof education in age-related performance, in different

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cognitive domains. Our hypothesis is that: (a) educationwill be positively related to current performance in allage groups and domains, but (b) it will also decreaseage differences in performance (i.e., it may attenuate dif-ferences across age groups in individuals with highereducation compared with those with lower education,suggesting, therefore, a modifying role in age-relatedcognitive changes). Cognitive aging is measured hereby the average (current) performance obtained by differ-ent age groups (controlled for gender, vascular risk fac-tors, depressive symptoms, and living status), andeducation is used as a surrogate marker of reserve.

METHODS

Study Design, Research Participants, and Setting

The present data refer to the baseline cross-sectionalevaluation of a prospective longitudinal cohort studyon aging and cognition of individuals attending prim-ary care centers in the community. Participants wererecruited from two metropolitan areas belonging tothe National Health Service. All residents of any givengeographic area are entitled to receive health care, anda single physician (general practitioner [GP]) usuallyobserves all family members and knows the familybackground.

Subjects were screened and invited to participate bytheir GP according to the selection criteria: (1) aged 50years old or older; (2) independence on daily livingbased on physician report and self-report; (3) EuropeanPortuguese as their native language; (4) absence of knowbrain disease, namely stroke, head trauma with loss ofconsciousness, epilepsy, dementia (known or suspected),psychosis or uncontrolled severe systemic disease (such

as advanced cancer, renal or hepatic failure, or HIVinfection); (5) lack of substance or alcohol abuse; (6)and willingness and consent to participate. Exclusioncriteria were dementia, mental retardation, or a MiniMental State Examination (MMSE; Folstein, Folstein,& McHugh, 1975) score below education-adjusted cut-off points, which are 22 for individuals with less than12 years of education and 27 for those with 12 or moreyears of education (Guerreiro et al., 1994; Pedrosa et al.,2010).

Patients were required to give institutionally appro-ved informed consent. The study protocol was approvedby the Ethics Committee of Lisbon Faculty of Medicine,by the institutional boards of participating regionalhealth centers, and by the National Committee for DataProtection. Of 544 participants screened, 479 completedthe evaluation. However, because reading is necessary toperform some of the neuropsychological tests, we onlyincluded in the present analysis individuals with a mini-mum of 4 years of formal education (corresponding tothe completion of primary school), which means that48 subjects with very low or no education, who also dif-fered significantly in age and gender from the other part-icipants, were excluded from the present study. Otherreasons for exclusion are presented in Figure 1.

Participants’ ages ranged from 50 to 95 years, with anaverage of 66 (�9.1) years of age and 7.7 (�4.1) years ofeducation. The majority were women (60.3%). Themajority (85.6%) were married or lived with some com-pany, and 14.4% lived alone. The number of years offormal education did not follow a normal distribution(Kolmogorov-Smirnov test¼ 5.1, p< .001); therefore,participants were divided into two categories: a low edu-cation group (LE) ranging from 4 to 6 years of formaleducation (N¼ 219) and a higher education (HE) groupwith more than 6 years of education (N¼ 212), which is

FIGURE 1 Flow diagram of participants’ inclusion.

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TABLE 1

Sample Demographic Characteristics, Mean Test Scores, and Standard Deviations on Each Cognitive Measure, by Education Group

Variables

Low Education

(4–6 yrs)

High Education

(>6 yrs) Statistics Differences 95% CI

Age (M�SD) 67.57� 8.7 64.45� 9.1 T¼ 3.611 p< .001�;Low>High

1.419, 4.808

Education (M�SD) 4.29� 0.69 11.19� 3.0 T¼�32.420 p< .001�;Low<High

�7.320, �6.482

Gender (F:M) 136:83 124:88 X2¼ 0.59 .44;ns —

Hypertension (% yes) 63.4 58.8 X2¼ 0.87 .35;ns —

Diabetes (% yes) 16.7 15.8 X2¼ 0.06 .82;ns —

High Cholesterol (% yes) 60.9 53.3 X2¼ 2.3 .13;ns —

Living Alone (% yes) 15.1 13.7 X2¼ 0.17 .68;ns —

MMSE M�SD (min–max) 27.89� 1.7 (22�30) 29.10� 1.1 (25�30) T¼�8.725 p< .001�;Low<High

�1.475, �0.933

CVLT-9 Trial 1 (M�SD) 4.82� 1.4 5.39� 1.2 T¼�4.543 p< .001�;Low<High

�0.809, �0.321

CVLT-9 Trial 5 (M�SD) 7.67� 1.3 7.72� 1.2 T¼�0.424 .672;ns �0.284, 0.184

CVLT-9 Trials 1–5 (M�SD) 33.22� 5.5 34.65� 4.9 T¼�2.837 .005;

Low<High

�2.424, �0.440

CVLT-9 SDFR (M�SD) 6.46� 1.6 6.82� 1.7 T¼�2.189 .029;

Low<High

�0.673, �0.036

CVLT-9 SDCR (M�SD) 7.17� 1.4 7.46� 1.4 T¼�2.061 .040;

Low<High

�0.555, �0.013

CVLT-9 LDFR (M�SD) 6.62� 1.8 6.91� 1.7 T¼�1.657 .098; ns �0.622, 0.053

CVLT-9 LDCR (M�SD) 7.13� 1.6 7.40� 1.4 T¼�1.846 .066; ns �0.554, 0.017

CVLT-9 Recog. Hits (M�SD) 8.39� 1.0 8.50� 0.8 T¼�1.230 .219; ns �0.291, 0.067

Immediate Visual Reproduction (M�SD) 51.60� 18.2 68.09� 17.7 T¼�9.520 p< .001�;Low<High

�19.898, �13.088

Delayed Visual Reproduction (M�SD) 28.05� 20.2 42.71� 23.0 T¼�7.012 p< .001�;Low<High

�18.770, �10.551

WMS-III Faces (M�SD) 31.33� 4.4 34.72� 4.8 T¼�7.608 p< .001�;Low<High

�4.272, �2.518

Mazes (M�SD) 11.53� 6.4 7.27� 4.3 T¼ 8.120 p< .001�;Low<High

3.233, 5.298

Trail A (M�SD) 81.26� 39.9 51.24� 23.8 T¼ 9.479 p< .001�;Low<High

23.789, 36.244

Trail B (M�SD) 208.18� 76.3 128.72� 63.0 T¼ 11.685 p< .001�;Low<High

66.099, 92.837

Trail B–A difference (M�SD) 128.63� 61.3 77.47� 50.4 T¼ 9.378 p< .001�;Low<High

40.434, 61.882

Symbol Search (M�SD) 14.70� 5.3 22.24� 7.2 T¼�12.327 p< .001�;Low<High

�8.742, �6.337

WASI Matrix Reasoning (M�SD) 10.57� 4.7 16.77� 7.0 T¼�10.725 p< .001�;Low<High

�7.342, �5.067

Stroop Color (M�SD) 47.70� 11.5 56.42� 12.0 T¼�7.624 p< .001�;Low<High

�10.966, �6.471

Stroop Interference (M�SD) 21.93� 8.0 29.06� 9.8 T¼�8.178 p< .001�;Low<High

�8.835, �5.411

Stroop Reading (M�SD) 67.03� 16.4 82.66� 14.8 T¼�10.350 p< .001�;Low<High

�18.600, �12.663

WASI Vocabulary (M�SD) 46.96� 11.2 61.47� 9.7 T¼�14.314 p< .001�;Low<High

�16.503, �12.518

Information (M�SD) 17.12� 2.3 18.82� 1.4 T¼�9.130 p< .001�;Low<High

�2.057, �1.328

Category Fluency (Food) (M�SD) 16.60� 4.3 19.38� 5.0 T¼�6.162 p< .001�;Low<High

�3.674, �1.897

Category Fluency (Animals) (M�SD) 13.85� 3.9 18.06� 4.7 T¼�10.029 p< .001�;Low<High

�5.027, �3.380

Phonemic Fluency (Letter ‘‘p’’) (M�SD) 7.70� 3.9 10.99� 4.2 T¼�8.361 p< .001�;Low<High

�4.062, �2.516

Famous Faces Test (M�SD) 22.80� 8.6 28.60� 7.7 T¼�7.284 p< .001�;Low<High

�7.358, �4.231

(Continued )

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in agreement with the organization of the public schoolsystem. Only 14% of the latter had more than 12 yearsof education, and just 3% had completed university studies(17 or 18 years of education). Table 1 displays demo-graphic and clinical data of the two groups and shows thatsubjects with more education were on average 3 yearsyounger and had a higher score on the MMSE than thoseof LE group. However, they had a similar distribution ofgender, vascular risk factors, and living status.

Construction of the Primary Screening Test Batteryand Neurobehavioral Assessment

The principles underlying the choice of the tests for thebaseline neuropsychological battery were: (1) inter-nationally recognized tests sensitive to cognitive decline;(2) ability to evaluate a wide range of cognitive domains,namely those impaired in Alzheimer’s and vasculardementia (Chen et al., 2000); (3) ability to detectage-related changes (Salthouse, 2009), namely speed ofinformation processing, working memory, and inhibi-tory control; (4) inclusion of measures resistant toage-related decline; and (5) ability to administer the bat-tery in a single visit. The selected battery included 28cognitive tests or measures, summarized and groupedby cognitive domain in the Appendix—namely, SymbolSearch (Wechsler, 1997a), Mazes (Porteus, 1959),Trail-Making Test Parts A and B and B–A (Reitan,1958), Stroop Test (Stroop, 1935), Vocabulary andMatrix Reasoning from the Wechsler Abbreviated Scaleof Intelligence (WASI; Wechsler, 1999), Famous FacesNaming Test (Martins, Loureiro, Rodrigues, & Dias,2005), Information (Garcia, 1984), a 9-item short ver-sion of the California Verbal Learning Test (CVLT;Libon et al., 1996), Digit Span, Faces, and VisualReproduction subtests from the Wechsler MemoryScale-Third Edition (WMS-III; Wechsler, 1997b), andsemantic and phonemic Verbal Fluency tests. Althoughmany different measures could be obtained from thisbattery of tests, we focused on those that are more usedin research and in clinical practice, avoiding overlap

between them. However, it is known that some of theCVLT measures have some correlation among them(Delis, Kramer, Kaplan, & Ober, 1986).

Participants also undertook a 15-item version of theGeriatric Depression Scale (GDS; Yesavage et al., 1983)and questionnaires of subjective memory complaints(Schmand, Jonker, Geerlings, & Lindeboom, 1997), gen-eral health (Ware & Sherbourne, 1992), and autonomy inlocomotion and instrumental activities of daily living(Lawton&Brody, 1969).The latterwill notbe reportedhere.

Procedures

Participating GPs performed the first screening ofparticipants, filled the criteria checklist, and reportedvascular risk factors (hypertension, diabetes and itstype, high serum cholesterol) and current medication.After informed consent, participants undertook theMMSE (Folstein et al., 1975). If score was within theeducation-adjusted normal range, the participants werescheduled an evaluation by a licensed psychologisttrained in this battery. Data recorded included demo-graphic information, current working (retired, emplo-yed, unemployed), and their living status regardingpresent house company (categorized as alone or notalone), and occupation (present or the longestoccupation in the past). The evaluation took place in aprivate office in the local health center.

Data Analysis

Analysis was performed with the Statistical Package forthe Social Sciences Version 19.0. To identify the cogni-tive domains underlying the 28 tests and measures, with-out an a-priori assumption, their raw scores weresubmitted to an exploratory factor analysis with vari-max rotation accepting eigenvalues superior to 1 accord-ing to the scree plot. Identified factors were transformedinto variables, each corresponding to the average stan-dard scores of tests loading on that factor. Therefore,each variable was a composite measure of each cognitive

TABLE 1

Continued

Variables

Low Education

(4–6 yrs)

High Education

(>6 yrs) Statistics Differences 95% CI

Digit Span Forward (M�SD) 4.95� 1.0 5.61� 1.1 T¼�6.498 p< .001�;Low<High

�0.858, �0.460

Digit Span Backward (M�SD) 3.37� 0.9 4.09� 1.1 T¼�7.160 p< .001�;Low <High

�0.923, �0.526

SD¼ standard deviation; CI¼Confidence Interval; SDFR¼ Short-delay free recall; SDCR¼ Short-delay cued recall; LDFR¼Long-delay free

recall; LDCR¼Long-delay cued recall; M¼mean; WMS-III¼Wechsler Memory Scale-Third edition; WASI¼Wechsler Abbreviated Scale of

Intelligence; MMSE¼Mini-mental state examination; CVLT¼ 9¼ 9-item version of the California Verbal Learning Test; F¼ female; M¼male;

T¼ t-test; X2¼Chi-square test; ns¼ not significant at p< .05.�highest level of significance.

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domain. To investigate the effect of education andage on each domain, we conducted multiple linearregression analyses adjusted by gender, presence ofany vascular risk factor, depressive symptoms (definedby 3 or more points on the GDS), and living status(alone=not alone). Due to the multiplicity of tests, andconsidering the exploratory nature of this study, resultswere considered statistically significant at p< .01.

RESULTS

The 28measures presented in the Appendix were includedin an exploratory factor analysis. The scree plot suggestedthat maximum variance was explained by three factors,and this solution explained 53.9% of the variance. Therotated component matrix is presented in Table 2.

Factor 1 (F1) is composed of tests of attention,processing speed, inhibitory control, switching andabstract reasoning, and visual or visuomotor memoryand was designated ‘‘processing ability.’’ Factor 2(F2), ‘‘memory,’’ includes all subtests and measures ofthe CVLT. Factor 3 (F3) is composed of verbal (seman-tic and phonemic) fluency tasks, vocabulary, informa-tion, famous faces, and digit spans and is designated‘‘acquired knowledge.’’

Mean scores and standard deviations obtained ineach test, by education group, are presented in Table 1.A mean standard score was computed from the standardscores of tests loading in each factor. Those were subjectto regression analysis (dependent variable) to analyzethe effect of demographic (age, education, and gender),clinical (significant depressive symptoms or not andpresence or absence of vascular risk factors), and livingstatus (living alone or not) on those domains (Table 3).A dichotomic division of depressive symptoms was usedinstead of the GDS score because the latter did notpresent a normal distribution (Kolmogorov-SmirnovZ¼ 3.843, p< .000). Apart from that, this scale had a

TABLE 2

Exploratory Factor Analysis With a Principal Component Analysis

and Varimax Rotation

Items

Factor 1

(Processing

Ability)

Factor 2

(Memory)

Factor 3

(Acquired

Knowledge)

CVLT-9 Trial 1 .137 .541 .224

CVLT-9 Trial 5 .185 .764 �.010

CVLT-9 Trials 1–5 .232 .820 .093

CVLT-9 SDFR .169 .850 .021

CVLT-9 SDCR .114 .853 .076

CVLT-9 LDFR .083 .871 .110

CVLT-9 LDCR .073 .890 .097

CVLT-9 Recog. hits .001 .523 .066

Immediate Visual

Reproduction

.690 .223 .184

Delayed Visual Reproduction .634 .281 .096

WMS-III Faces .399 .236 .184

Mazes �.510 .035 �.396

Trail A �.625 �.028 �.426

Trail B �.711 �.069 �.481

Trail B–A difference �.579 �.075 �.389

Symbol Search .781 .101 .323

WASI Matrix Reasoning .519 .132 .397

Stroop Color .650 .171 .306

Stroop Interference .741 .173 .167

Stroop Reading .434 .057 .502

WASI Vocabulary .272 .134 .719

Information .172 �.004 .666

Category Fluency (Food) .234 .269 .551

Category Fluency (Animals) .301 .158 .680

Phonemic Fluency (Letter ‘‘p’’) .234 .111 .695

Famous Faces Test .199 .231 .597

Digit Span Forward .302 �.096 .455

Digit Span Backward .387 .051 .433

Eigenvalue 9.777 4.068 1.252

% Cumulative Variance 34.9 49.4 53.9

CVLT-9¼ 9-item version of the California Verbal Learning Test;

SDFR¼Short-delay free recall; SDCR¼Short-delay cued recall;

LDFR¼Long-delay free recall; LDCR¼Long-delay cued recall;

WMS-III¼Weschler Memory Scale-Third Edition; WASI¼Weschler

Weschler Abbreviated Scale of Intelligence.

TABLE 3

Influence of Education, Age, Gender, Depression, Vascular Risk

Factors, and Living Status on Cognitive Performance

Adjusted

R2 Beta t p 95% CI

Processing

Ability (F1)

.407

Age �.499 �11.543 <.001 �0.066, �0.047

Education .319 7.708 <.001 0.473, 0.796

Gender �.086 �2.014 .045 �0.344, �0.004

Depression �.144 �3.422 .001 �0.472, �0.127

Vascular risk

factors

.010 0.241 .810 �0.175, 0.224

Living status .035 0.821 .412 �0.143, 0.347

Memory (F2) .201

Age �.156 �3.108 .002 �0.029, �0.007

Education �.010 �0.204 .839 �0.211, 0.172

Gender .416 8.415 <.001 0.658, 1.060

Depression �.037 �0.765 .444 �0.283, 0.124

Vascular risk

factors

.050 1.028 .304 �0.112, 0.359

Living status �.013 �0.260 .795 �0.328, 0.251

Acquired

knowledge

(F3)

.216

Age .066 1.327 .185 �0.019, 0.004

Education .436 9.162 <.001 0.681, 1.053

Gender �.127 �2.583 .010 �0.452, �0.061

Depression �.066 �1.375 .170 �0.060, 0.336

Vascular risk

factors

.016 0.344 .731 �0.189, 0.269

Living status �.056 �1.168 .244 �0.449, 0.114

Note: Regression analysis: Factor 1 was positively associated with

educational level but negatively associated with age and depression.

Factor 2 was associated with female gender but negatively associated

with age. Factor 3 was positively associated with education level.

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good internal consistency (Cronbach’s alpha¼ .834),presented a negative correlation with education level(Spearman’s rho¼�.22, p< .000), had a higher scoreamong females compared with males (Mann-WhitneyU Test, p< .000), and had no significant correlationwith age (rho¼ .06, p¼ .192, ns).

Multiple regression analysis showed that after adjust-ment by vascular risk factors and depression, F1 wassignificantly and positively associated with educationallevel (p< .001) and was negatively associated with age(p< .001) and depression (p¼ .001). F2 was negativelyassociated with age (p¼ .002) and female gender(p< .001) but not with educational level (p¼ .84). F3was positively associated with education level (p< .001)but not with age (p¼ .19). To evaluate the effect ofeducation on age-related changes, the slope of thedecline was compared between the two main educationalgroups by testing the significance of a term representingthe interaction of age� education. The interaction ofage� education was not statistically significant for anyof the three factors: F1 (beta¼�.497, ns), F2 (beta¼�.251, ns), and F3 (beta¼�.371, ns).

DISCUSSION

This study analyzed the effect of education on age-related cognitive performance, in nondemented adultswith a heterogeneous cultural background followed in

primary care through a comprehensive battery of tests.The two study groups had an average difference of 7years in education, but most individuals in the HEgroup did not have a university background, whichmakes this a comparison between minimum andmedium educational background, thus different frommost previous studies.

Factor analysis yielded three main factors. F1 is com-posed of most tests aimed to evaluate executive abilitiesbut also visuospatial processing, an identical factorloading (executive and visuospatial) of other batteries(Ponton, Gonzalez, Hernandez, Herrera, & Higareda,2000). F2 congregates the different dimensions of verbalepisodic memory, learning, retrieval, and recognition,and F3 includes measures of acquired and consolidatedrote verbal knowledge (crystallized measures) as well asthe digit span tests, possibly because they require roteverbal knowledge. Overall, this factor structure is inagreement with the domains those tests were expectedto evaluate, thus providing a measure of construct val-idity of the selected battery. The small percentage ofvariance explained by this factor solution suggests thatother variables also contribute to the variance observedin this battery of tests.

Regression analysis showed that education and agehad different effects on each cognitive domain. The‘‘acquired knowledge’’ factor, as expected, was veryclosely related to education but did not significantlydecrease with age or vascular risk factors, corroborating

FIGURE 2 Performance on the three cognitive factors by age and education groups. Z scores obtained in the three cognitive measures identified by

factor analysis are displayed by age in decades and education: (A) Processing Ability (F1; top); (B) Memory (F2; middle); and (C) Acquired Knowl-

edge (F3; bottom). Solid line¼Lower Education (4–6 years); dashed line ¼ higher education (>6 years).

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the findings of other studies showing that knowledge-based verbal abilities are less vulnerable to aging orvascular variables (Babcock & Salthouse, 1990; Eliaset al., 2004; McArdle, Ferrer-Caja, Hamagami, &Woodcock, 2002; Salthouse, 2004; Tucker-Drob, 2011)but are closely related to the information acquiredduring life. From the observation of Figure 2, it isapparent that this cognitive domain declines late in life,and therefore, it might be an inadequate compoundmeasure to investigate age-related changes in a span offour decades. In fact, studies that demonstrated a pro-tective effect of education on crystallized measures wereperformed in the eldest range of the population, as partof studies of terminal decline (Batterham et al., 2011).

The ‘‘memory factor,’’ on the contrary, declinedsignificantly with age, starting apparently at (or possiblybefore) the age of inception. Female gender had a signifi-cant and positive effect on this domain, as women areknown to have a better performance on tests of verbalmemory compared with men, from an early age (Martins,Castro-Caldas, et al., 2005). However, education didnot have a significant effect on this factor. This wasalso found in other cohorts (Ardila et al., 2000) butcontradicts findings showing that education may delaythe speed of memory decline (Manly, Touradji, Tang,& Stern, 2003). This lack of effect can have differentexplanations. Firstly, the measure used is a compoundscore that includes learning and delayed recall but alsotests like word recognition that tackle ‘‘familiarity,’’ adistinct cognitive process within memory. Secondly,the short version of the CVLT is relatively easy justlike the memory test used by Ardila et al. and mightbe less sensible to education (Norman, Evans, Miller,& Heaton, 2000). Thirdly, some authors (Salthouse,2004, 2009) postulate that memory decline begins ear-lier than the inception age of this study, and therefore,the main difference might be attenuated at this stage.

The processing factor score, on the other hand,showed a significant and positive effect of educationbut a negative effect of age and depressive symptoms.This is in agreement with previous studies (Clark,Chamberlain, & Sahakian, 2009) and supports thetheory that normal aging corresponds mainly to adecline in executive functions (Salthouse, 1996), inaddition to long-term declarative memory. However,the hypothesis that education could modify the courseof age-related decline was not supported, at least in whatconcerns executive abilities.

In summary, the pattern of age-related performancefound in this analysis corroborates the evidence thatage-related decline varies as a function of cognitivedomain and is enhanced by less education (Brickmanet al., 2009; Park & Reuter-Lorenz, 2009; Vemuri et al.,2011), even among subjects with an overall low edu-cational level. Yet, contrary to what was found by other

authors (Ardila et al., 2000; Capitani et al., 1996), theeffect of education was mostly of a cognitive enhancer,expressed by a higher performance at all ages, but itdid not modify the slope of the regression line represent-ing age-related decline. This result suggests that indivi-duals with higher and longstanding cognitive activity(indirectly measured by the number of years of edu-cation) tend to mitigate decline. They will maintain abetter level of performance over the years comparedwith those with lower education, at least from 50 yearsonward. However, their age-related change in cognitionproceeds at an identical pace. This is intriguing given thefact that education protects or may modify decline asso-ciated with pathological burdens (Manly et al., 2003).

We acknowledge several limitations to this study.Firstly, it is a cross-sectional study where decline isdetermined as a comparison between current age-groupperformance and not as individual decline over time.Secondly, the criteria to exclude dementia consistedmainly in clinical information provided by the GP andthe MMSE score, which may lack sensitivity. Thirdly,it does not include genetic, imaging, or other biologicalmarkers that are known to be associated with decline.This is particularly relevant when we are studying cogni-tive reserve, for they may provide markers of patholo-gies. However, these are not easy to obtain in largesamples of healthy individuals. In addition, we couldnot include subjects with the lowest formal educationin the comparison, nor were we able to include a homo-geneous sample of subjects with the highest educationwho could represent the extreme range of this variable,which limits the conclusions from the data. Due tochanges in educational policies during the last decades,the minimum level of compulsory education hasincreased, and it is now difficult to encounter young,healthy individuals with low levels of education. How-ever, the analysis with the full sample of 479 subjectsreproduced exactly the same results, despite the fact thatilliterate participants could not perform some of thetests requiring reading or paper-and-pencil practice.Finally, some of the tests were not applied in the fullversion (only some subtests were used) or were justtranslated to Portuguese (Vocabulary and CVLT items).With these limitations, we consider these results prelimi-nary, and further research is needed through longitudi-nal studies. We also recognize that despite extensivetesting we decided to compare composite measures,representing general domains to include all cognitivedata collected. It is possible that individual tests directedto more selective areas could produce a different magni-tude of decline in this age span and could be moreadequate for evaluating the effect of education onage-related changes.

We considered the level of education attained as thebest surrogate marker of cognitive reserve. However,

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we acknowledge that this is a complex measure becauseeducation early in life modifies cognitive stimulationirreversibly and contributes to the opportunity of attain-ing cognitively challenging vocations and avocations,leading to a lifelong amplification of its effect upon cog-nition. At the time these participants attended school,some 45 to 70 years ago, it was common for childrennot to attend school or to complete only the 1st yearsof primary school and to begin to work very early in lifeto contribute to the family support. This was not due tolack of intelligence or skills but to poverty and perhapsto lack of recognition of the power of education, leadingtogether to a lack of opportunity. Of course, poverty isassociated with low socioeconomic status, poor diet, andless preventive health measures, which may have conse-quences on brain development and general health. Yet,those variables are difficult to evaluate retrospectively.Education, as it is used here, is probably a very generalmeasure of all those influences.

The strengths of this analysis are the extensive testing,controlling for clinical factors that influence cognition(vascular risk factors and depressive symptoms), andthe education interval studied, which is unusual and repli-cated some results for narrow educational differences.

In conclusion, this study corroborates the positive influ-ence of epigenetic factors in aging in some cognitivedomains, even for small differences in educational level,but it does not support that they delay or modifyage-related changes, at least for this magnitude of edu-cational range (Dotson, Beydoun, & Zonderman, 2010).It underlines the need to understand how cognitive stimu-lation changes brain and cognition as a first step to developpreventive strategies for cognitive decline and dementia.

ACKNOWLEDGEMENTS

The authors thank Fundacao Calouste Gulbenkian forsponsoring the present study (Project 0488). The authorsare indebted to all participants and to GPs and HealthCenter Directors who collaborated in this study, namelyDrs. Teresa Costa, Teresa Mota, Elisabete da Fonseca,Luıs Afonso, Renato Graca, Cristina Galamba, HelenaFebra (Centro de Saude da Lapa); Cecılia Cabral, Euge-nio Oliveira, Paula Freitas, Neto Nogueira, EditeBranco, Helena Ferreira (Centro de Saude de Alcantara);Luısa Romeiro, Rosario Braz, Teresa Liborio, OscarMiranda, Teresa Campos, Aurea Farinha, IsabelSantos, Nave Ferreira, Cristina Bastos, Rita Lourenco,Judite Viana, Manuel Rosmaninho, Luısa Costa, IsabelSantos, Bernardino Costa, Luısa Teixeira (Centro deSaude de Oeiras); Elisabete Serra, Joao Reis, Maria JoseHeleno, Rui Coias, Maria Joao Mendes, Sonia Pereira,Carla Coimbra (Centro de Saude de Paco D’Arcos); AnaPaula Granadeiro, Rosa Oliveira, Analila Cruz, Vıtor

Cardoso (Centro de Saude Moita); Joao Belbut,Manuela Ribeiro, Jose Luıs Gomes, Susete Gomes,Maria Jose Rosa (Centro de Saude do Barreiro); JaimeTorre, Miguel Santos, Luıs Pinto, Paula Dias, RaquelCaetano (Centro de Saude do Lavradio); Manuela Cruz(Centro de Saude de Benfica); Graca Carneiro, AnaMaria Ferreira, Pedro Silva, Elvira Nunes, PaulaAtalaia (Centro de Saude de Alvalade); Maria JoseGalha, Emılia Soares, Rosario Martins, Rogerio Costa,Paula Costa, Teresa Neto, Joao Costa, Vitoria Amaral,Carmo Velez, Luısa Santana (Centro de Saude deEvora); and Fatima Portugal, Maria Joao Palma, MariaJose Luıs (Centro de Saude de Sete Rios).

The authors also thank Profs. Maria Amalia SilveiraBotelho and Manuela Guerreiro and Dr. SofiaMadureira for their contribution in the discussion andorganization of the evaluation battery.

Initial concepts and framework developed by IsabelPavao Martins. Acquisition of subjects and data byCarolina Maruta, Claudia Silva, Pedro Rodrigues,Catarina Chester, Sandra Gino, Vanda Freitas, and SaraFreitas. Data analysis and interpretation by AntonioGouveia de Oliveira, Isabel Pavao Martins, and CarolinaMaruta. Preparation of manuscript by Isabel Pavao Mar-tins, Carolina Maruta, and Antonio Gouveia.

The sponsor (Fundacao Calouste Gulbenkian) hadno participation in any of the scientific steps of the studyor in writing the article.

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APPENDIX

List of Cognitive Tests by Domain Evaluated

Domain Evaluated

Test References

Attention

and

Processing

Speed Language

Semantic

Memory

Episodic

Memory

and

Learning

Visuospatial

Perception

Inhibitory

Control

and

Shifting

Abstract

Reasoning

Symbol Search Wechsler, 1997a þ þMazes Porteus, 1959 þ þTrail A Reitan,1958 þ þTrail B þ þ þTrail difference (B–A) þStroop Reading Stroop, 1935 þ þStroop Color Naming þStroop Interference þWASI Vocabulary Wechsler, 1997b þ þWASI Matrix Reasoning þ þFamous Faces Naming Test Martins, Loureiro, et al.,

2005

þ þ

Information Garcia, 1984 þ þCVLT-9 Trial 1 Libon et al., 1996 þCVLT-9 Trial 5 þCVLT-9 Trials 1–5 þCVLT-9 SDFR þCVLT-9 SDCR þCVLT-9 LDFR þCVLT-9 Recog. þWMS-III Faces Wechsler, 1999 þ þ þWMS-III Visual

Reproduction

þ þ þ

Digit Span þ þVerbal Fluency (Food=

Animals;

Letter ‘‘p’’)

þ þ

CVLT¼California Verbal Learning Test 9-item version; SDFR ¼ Short-Delay Free Recall; SDCR¼Short-Delay Cued Recall; LDFR¼Long-Delay Free Recall; WASI¼Wechsler Abbreviated Scale of Intelligence; WMS–III¼Wechsler Memory Scale-Third Edition.

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