Age and Ageing 1996:25:392-397

Does Education Level Determine the Course of Cognitive Decline? DIDIER LEIBOVICI, KAREN RITCHIE, BERNARD LEDESERT, JACQUES TOUCHON

Summary Many studies have implicated low education as a risk factor for cognitive impairment in elderly people. Findings are, however, inconsistent and the mechanism by which education level may intervene in senescent cognitive change is uncertain. The present study examines cognitive change over a 1-year period in 283 elderly persons manifesting recent subclinical deterioration in at least one area of cognitive functioning. The results suggest that the impact of both education level and young adult IQ on the degree of cognitive change over the year is greater in the older age groups. Secondary memory and language functions were found to be more resistant to decline in the high-education group, while attention, implicit memory and visuospatial skills are found to decline irrespective of education level. Keywords: Cognitive impairment, Dementia, Education.

Introduction The impact of education level on cognitive ageing is of interest to both epidemiologists seeking risk factors for dementia and gerontologists attempting to define the normal ageing process. Many studies have observed low education to be consistently associated with a high prevalence of cognitive impairment [1-4]. While this observation derived initially from cross-sectional studies in which cohort effects undoubtedly played an important role, confirmation has more recently been provided by longitudinal studies [5, 6]. Several epidemiological studies have also attempted to determine whether low education may carry an increased risk of dementia. Most report an inverse relationship between dementia (both its presence and severity) and education level [7-13]. In a smaller number of studies no relationship was found between age and education [14-16]. Teri et al. [17] have reported an association between younger age and higher education and a faster rate of decline in Alzheimer's disease patients. The education effect may be limited to specific types of dementia. Fratiglioni et al. [15] have attributed the higher prevalence of dementia in the poorly educated to cases of alcoholic dementia. Mortel et al. [9] have also reported differences in levels of education in patients with Alzheimer's disease and patients with vascular dementia. Ritchie et al. [18] observed that subjects with 'age-associated memory impairment' and a lower young adult IQ had a much greater risk of evolving towards dementia over a 2-year observation period than

subjects with a high premorbid IQ level. Some evidence has also been presented to demonstrate that elderly persons with a lower level of education have a later age of onset of dementia [19], however, this may in part be due to delays in contacting a health professional by poorly educated people. Three possible mechanisms may explain lower rates of cognitive decline in elderly persons with higher levels of education. First, persons with lower education are at higher risk of central nervous system damage (illness, injury, dietary deficiency, alcoholism); second, persons with higher education may have greater neuronal reserve capacity and/or reduced risk of neuronal damage; third, persons with higher levels of education may be better able to generate compensatory strategies at both a behavioural and neuronal level. There may also be an interaction between these factors. It has not been possible to conclude in favour of any of the above hypotheses because of shortcomings in the studies conducted to date. First, with regard to the measures of cognitive functioning themselves, these have tended to be global measures (such as screening tests or a single test of secondary memory functioning) and little consideration has been given to the more likely hypothesis that specific cognitive functions may be differentially affected. The outcome of studies examining the relationship between age and education would thereby be determined by the type of tests selected. Second, studies have been predominantly cross-sectional, permitting comparisons of age cohorts but unable to examine the rate of deterioration

EDUCATION LEVEL AND THE COURSE OF COGNITIVE DECLINE manifested by different education groups over time. The present study attempts to clarify findings relating to education and cognitive change by examining changes in a wide range of cognitive functions over time. Subjects Subjects were recruited from a general-practitioner research network in the South of France as part of the Eugeria longitudinal study of cognitive ageing. A proxy screening questionnaire on cognitive functioning over the past year was sent to all persons over 60 years of age in each general practice. This screening instrument, DECO (Deterioration Cognitive Observee), has been shown in previous studies to be highly sensitive to early changes in cognitive functioning due to various causes [20-22]. DECO is a 19-item Likert scale in which degree of change in behaviour over the past year is estimated by a proxy, who has had at least monthly contact with the subject over the past 3 years. Three hundred and ninety-seven of the subjects included in the cross-sectional study were found to have a score of less than 38 on DECO; 38 being the maximum total score. These persons were thus considered by an observer to have shown some degree of deterioration in at least one area of cognitive functioning over the past year. Of these, 283 have so far completed the second year and are the subjects included in the present study. Four levels of education were differentiated: 1 = no formal education; 2 = primary school education; 3 = secondary school education; 4 = tertiary education. Methods Each subject was visited at home annually by one of the project interviewers and a computerized neuropsychometric examination was administered. This examination, ECO (Examen Cognitif par Ordinateur) was used to assess working memory, verbal and visuospatial secondary memory, implicit memory, language skills (words and syntax comprehension, naming, verbal fluency, articulation), visuospatial performance (ideational, ideo-motor and constructional apraxia, functional and semantic categorization of visual data, visual reasoning and form perception), and focused and divided attention (visual and auditory modalities). The development of ECO and the theoretical basis for test selection is described in detail in the report by Ritchie et al. [23]. From the 159 ECO variables, eight summary scores representing six cognitive domains were used in the analysis. The eight summary scores were derived from the mean of the rescaled scores (between 0 and 100) representing six cognitive domains: Attention: measured by response time on a dual task (simultaneous visual selection and counting of auditory stimuli). Primary memory (verbal and visual span): assessed by immediate recall of first names which had the highest frequency in the French language fifty years ago, and visual trials of increasing length. Secondary memory: measured by delayed recall of first names and their associated faces, and prose recall. Implicit memory: measured by reference to the number of trials required to recognize previously presented items compared with novel stimuli reconstructed progressively on the computer screen. Visuospatial ability: measured by reference to two scores; number of correct responses on tasks of shape-matching, semantic and functional categorization and reproduction of

393

three-dimensional figures (CR), and response time on shape, functional and semantic matching tasks (RT). Language: assessed by tests of word and syntax comprehension, naming and verbal fluency. Two scores are derived; total number of correct responses (CR), and word and syntax comprehension response time (RT). All subjects completed the National Adult Reading Test (NART). The scores obtained from this simple reading test correlate highly with intelligence quotients obtained from formal IQ batteries [24]. The present study examines changes occurring in cognitive functioning between wave one and wave two of the studv.

Results The early cognitive impairment sample of 283 subjects who fully completed both waves had a mean age of 74.7 years (SD = 7.0) and consisted of 87 men and 196 women. Of the sample, 3.5% had no education, 44.5% had primary education, 35.7% secondary education and 16.3% tertiary education. Subjects not having yet completed the second wave were not found to differ from the present sample in distribution of either age or educational status. In order to assess the effect of education on change in global cognitive functioning, a 'g' or overall change factor was extracted by Principal Components Analysis. The g factor is the first principal component from the difference scores (that is, the difference between summary scores obtained on the first and second wave of the study). With g as the dependent variable, the independent variables age, NART, and education were entered into a linear regression model. The regression of g on age gave an R of9.3%(p < 0.01) suggesting that age is a significant determinant of cognitive change over the past year. Comparing subjects with low education (education groups 1 + 2 ) with those with high education (education groups 3 + 4), the age effect was found to be significant for the low education group (R = 4.5%; p < 0.01) but not for the high education group (R = 1.4%; NS). Age thus appears to have a more significant impact on rate of deterioration over a 1-year period in persons with little education. Regression on the NART score was not significant for the cohort as a whole (R2 = 0.8%; NS). However, a significant effect was found for subjects over 75 years (R 2 = 3.5%; p < 0.03) but not for those under 75 (R = 1.0%; NS). Premorbid intelligence thus appears to have little influence on the rate of cognitive decline in younger cohorts but becomes significant at higher ages. In order to answer the question of whether age or education plays the most important role in cognitive performance, correspondence analysis was used to determine the factors which best explained separation between the scores obtained on the second wave. The second wave was chosen on the basis that improvements in scores due to learning by unimpaired subjects would increase variability. The first axis, explaining 62.5% of the separation between groups of individuals in terms of their cognitive profile, separates high and low levels of education. The order of modalities on this

D. LEIBOVICI ET AL.

394

Table I. Mean rank scores and Kruskal-Wallis tests for each cognitive function assessed, for two age ranges and two levels of education Education group Wl Attention Language RT RC Memory primary secondary

4

3

2

1

K-W( P )

OL 119 OL 126 OL 92 OL 106 OL 108

OH 137 OH

YL 154 YH 162 YH

OH 136 YL 140

YH 151 YL 138 YL 148 YL 144 OH 150

8.09 (0.04) 8.4 (0.03) 32.6 (0) 23.2 (0) 16.8

OL 97 OL 109

OH 130 OH 138

YL

YH 178 YH

OL OL

OH 128 YL

101

139

YL 158 OH 148

OL 103 OL 102 OL 101

OH

134

OH 147

169

YH 171 YH 162

implicit Visuospatial RT RC

149

YL 141 '

172

(0) 3 NS

34.1 (0) 25.1 (0)

W2

Attention

YH 169 YH 158 YH 162 YH 168

17.9 (0) 22.6 (0) 19.8 (0) 20.6 (0) 26 (0)

implicit

5.5

Visuospatial RT RC

134

YL 159 YL 143

YH 165 YH 174

NS 31.7 (0) 26.3 (0)

2

3

4

K-W(p)

112

Language RT RC Memory primary secondary

Education group

OL 95

OL 105 1

143

OH 146

YL 134 OH 138 OH

YL 155 YL 148 OH

152

YH 160

W1-W2 Attention Language RT RC Memory primary secondary

OL 170

YL 136

YH 135

OH 129

OH 163

OL 147

YH 136

YL 124

implicit Visuospatial RT RC

6.14 NS 9.2 (0.02) 1.31 NS 2.09 NS 9.1 (0.02) 6

YH 163

OL 135

YL

OH

134

129

NS 9.1 (0.02) 1.6 NS

YL = young and low (n = 73), YH = young and high (n = 83), OL = old and low (n = 63), OH = old and high (n = 64).

axis is low education/80-89 years; low education/70-79 years; low education/60-69 years; high education/8089 years; high education/70-79 years; high education/ 60-69 years. From this general analysis, education would appear to have a greater impact on cognitive change than age. In order to investigate this finding further, KruskalWallis tests were then conducted for each cognitive function for the following age and education groups: young (60-75 years)/low education (groups 1 + 2); young/high education (groups 3 + 4); old (76+ years)/ low education; old/high education (see Table I). Different age groups were chosen to obtain roughly equal group size. This analysis demonstrated that the effects of age and education vary according to the cognitive domain examined. The education effect was found to be greater for secondary memory and language (RT) tasks only. It is interesting to note that the effect is reversed with respect to visuospatial (RT) tasks, with the young high-education group deteriorating more rapidly than either the old group with high education or the young group with low education. Differences over the two waves for individual cognitive tests are given in Table II. Generally speaking, it can be seen that education has its greatest effect at higher ages on most tests. The Kruskal—Wallis test was used to evaluate the significance of differences between the two education groups with age. A decrease in performance in the low-education group is seen with age in wave one in attention, language (RC), primary memory and visuospatial functioning. In the higheducation group, attention and primary memory also decrease with age, there is no effect on the language scores and only visuospatial reaction time (rather than response accuracy) is seen to fall with age. A year later, a more exaggerated version of the same pattern is observed. At the second wave the high-education group is now showing significant change on language (RT) but not on language (RC). Wilcoxon tests conducted between the two education levels at each age show no significant differences in the lowest age group apart from language (RT) where the low-education group is already doing worse. Significant differences are found on all tests in the age group 70—79 except attention and language (RT) in wave one, and primary memory and implicit memory in wave two. A repeated measure analysis of variance by age group and global levels of education (Table I) indicated first a time effect, showing an overall decline over the year in attention (p < 0.001), but with some improvement in primary (p < 0.001) and implicit memory (p < 0.001). Second, an age-time interaction was observed with significant decreases in attention (p < 0.05) and secondary memory (p < 0.001) being observed in the oldest groups, and some improvement in secondary memory in the youngest. Third, a significant education—time interaction could be observed with language (RT) scores (p < 0.05) improving in the high education group and decreasing in the low education group. Finally, an education—time—age interaction was

EDUCATION LEVEL AND THE COURSE OF COGNITIVE DECLINE

395

Table II. Mean scaled scores (and standard deviations) by age group and education for each cognitive function Wilcoxon-Z 60-69 years Cognitive function

70-79 years

^ 80 years

Kruskal-Wallis

High (n = 44)

Low

(n = 63)

High (n = 64)

Low

(n = 33)

(n = 40)

High (n = 39)

85(13)

84(12)

82 (15)

84 (14)

74 (20)

77 (18)

8.1 (0.02)

3.1 (0.2)

64 (20)

1.4(0.5)

5 (0.07)

70(16)

13 (0.001)

1.8(0.4)

53 (16)

8.2 (0.02)

9.4 (0.01)

62 (20)

2.7 (0.3)

0.9 (0.6)

46(17)

0.6 (0.7)

0.08 (0.95)

Low

Low

High

Wl

Attention

NS

Language RT RC

69(17)

NS

72(17)

NS

72 (14) 59(18)

76(12) 62 (17)

NS

62(18) 45(11)

64(19)

RT RC

71 (18)

44(13)

55 (19) 46 (15)

76 (14)

67 (14)

62 (17)

83(11)

69(21)

51 (16) NS

66(17)

55 (20) NS

46(13)

49(18) NS

72 (14)

•

54 (20)

63 (20)

17 (0.0002)

8.9 (0.01)

75(17)

8.6 (0.01)

5.4 (0.07)

70(21)

11 (0.005)

11 (0.003)

65 (20)

2.5 (0.3)

6.9 (0.03)

70(18)

11 (0.005)

2.25 (0.3)

62 (18)

5.4 (0.07)

3.4 (0.2)

58 (18)

10 (0.007)

6.7 (0.03)

46 (17)

0.25 (0.9)

2.2 (0.3)

68 (15)

18 (0.0001)

5.5 (0.06)

72(15)

9.6 (0.008)

11 (0.004)

•

77 (14)

•

NS

60(17) •••

NS

NS

76(21)

74(11)

•••

NS

Visuospatial

67 (17) •• 51 (16)

64(19) NS

•••

NS

implicit

73(15)

NS

NS

Memory primary secondary

69(17)

NS

63 (24) •

W2

Attention

82(19)

81 (16)

Language RT RC

66 (20)

75(13)

•

73 (19)

75(13)

NS

Memory primary secondary

63 (21)

69 (18)

51 (14)

68(16)

RT RC

75(11)

46(16)

NS

57 (18) 59 (16) 51 (17)

73(13)

70(15)

71 (13)

75(15)

58 (23) ••

62(18)

53(19) •

64(17)

51 (20) NS

49 (14)

51 (20) NS

75(11)

•

82(11)

58 (23) NS

NS

NS

76(19)

73(12)

•

NS

Visuospatial

64(17) • •• 68 (17)

64 (29) NS

NS

NS

implicit

84(13)

NS

NS

65 (18)

76 (18) •

NS

59(18) ••

80(12)

••

66(17) NS

NS = not significant; • < 0.05; • • < 0.01; • • • < 0.001. observed in relation to primary memory (p < 0.05), with improvement being observed for the youngest and oldest for the highly educated group.

Discussion Examination of changes over one year in a cohort of elderly persons with early signs of cognitive deterioration suggests that the impact of education is complex. It would seem that while education does play a significant role in the evolution of cognitive deficit, its impact varies greatly according to the age of the subject at onset of the impairment and the type of cognitive function. Age would seem to have a far more significant effect on rate of cognitive deterioration over a 1-year period in

persons with low education. With regard to premorbid intelligence levels, young adult IQ seems to have little effect on the rate of cognitive deterioration in the younger elderly, but begins to exert an important protective role over the age of 75. It might be suggested therefore that IQ level becomes crucial at the age where neurobiologists note a significant drop in neuronal reserve capacity [25, 26]. The amount of variance explained by the regression analyses conducted here are admittedly very small, but the time over which change is observed is very short and significance is none the less obtained. With regard to the question of whether age or education level is the most significant determinant of cognitive change, the study suggests that over the brief

396

D. LEIBOVICI ET AL.

time period examined here, education may have a more important impact on changes in secondary memory and language functioning, but that elsewhere age is the more important factor. Elderly persons with a high level of education appear to show greatest resistance to change but only on tests with a high learned component—that is, tests of language and secondary memory. The results also suggest that on cognitive functions such as attention, implicit memory and visuospatial analysis, which might be postulated to have a higher 'nature' rather than 'nurture' component, level of education seems to make relatively little difference to the rate of change over time. These latter functions have been attributed to older nervous system structures. Corsini et al. [27] have demonstrated, for example, that in ontogeny, visual functions appear to precede verbal functions. If this is so it would suggest that 'memory training' programmes designed for elderly people might have a significant role to play in helping to maintain skills with a high learned component but may have little impact on cognitive abilities, which are largely determined by genetic and physiological factors and are not significantly modified by learning. These results tend to support a modification of our third hypothesis, that is that elderly persons with higher education have developed and are perhaps also better able to continue developing explicit verbal cognitive skills in the face of deterioration in other areas. In the higher education groups, a higher average score across cognitive tasks will therefore be masking a greater underlying cross-task variability.

7.

8.

9. 10. 11. 12. 13.

14.

15.

A cknowledgemen ts The authors wish to thank the Fondation de France, the French Social Security (CNAM-TS), and the Direction Generale de la Sante for their financial support of this project and Joelle Faurobert and Catherine Gonzalez, the project interviewers, for their important role in data collection.

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