Early Life Conditions and Cognitive Functioning in Later Life

Susan A. Everson-Rose1,2 , Carlos F. Mendes de Leon1,2,3, Julia L. Bienias1,3, Robert S. Wilson1,4,5 and Denis A. Evans1,3

1 Rush Institute for Healthy Aging, Rush University Medical Center, Chicago, IL.
2 Department of Preventive Medicine, Rush University Medical Center, Chicago, IL.
3 Department of Internal Medicine, Rush University Medical Center, Chicago, IL.
4 Department of Neurological Sciences, Rush University Medical Center, Chicago, IL.
5 Department of Psychology, Rush University Medical Center, Chicago, IL.

Received for publication January 14, 2003; accepted for publication June 10, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Early life conditions are related to cognitive development and abilities in childhood and cognitive function in adulthood. However, the association between early life conditions and cognitive change in old age is unknown. The authors examined the relation between socioeconomic position (SEP) and cognitive milieu in childhood and change in cognitive function in a population-based sample of 4,398 community-dwelling adults (62.1% female; 61.7% Black) aged 65 years or older from Chicago, Illinois. Baseline data were collected in 1993–1997. Change in cognitive function was assessed by means of a global cognitive index derived from measures of memory, perceptual speed, and overall cognitive function administered at three in-home assessments between 1993 and 2003, with an average of 5.3 years of follow-up. After results were controlled for age, sex, race, and education, interactions of time with childhood SEP (ß = –0.003, t = –1.0, p = 0.32) and childhood cognitive milieu (ß = –0.0008, t = –0.5, p = 0.62) were nonsignificant, indicating that early life conditions were not related to cognitive change. SEP (ß = 0.034, t = 2.4, p = 0.01) and cognitive milieu (ß = 0.017, t = 2.2, p = 0.03) were associated with absolute level of cognitive function, with better performance being related to higher SEP and a better cognitive milieu. A better SEP and a more stimulating cognitive milieu in childhood have small but significant effects on absolute level of cognitive function; however, they do not seem to protect against cognitive decline in old age.

aging; child; cognition; longitudinal studies; social class

Abbreviations: Abbreviations: CI, confidence interval; SD, standard deviation; SEP, socioeconomic position.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Cognitive decline with aging has been widely reported, but the origin of this decline is still poorly understood. Low educational attainment and other markers of low socioeconomic position (SEP) in adulthood are associated with poorer cognitive function in adulthood and age-related cognitive decline (13), greater risk of dementia and Alz-heimer’s disease in the elderly (46), and overall worse health (7). Indeed, the observation that health status and functioning are inversely related to SEP is one of the most enduring in public health. It is now recognized that the origins of this relation, in many instances, can be traced to early life conditions (8, 9) and, in particular, socioeconomic conditions in early childhood (10, 11).

Several lines of evidence demonstrate that childhood socioeconomic factors and early life conditions influence cognitive development and abilities. Children from poor backgrounds show poorer cognitive development as infants (12) and worse verbal and achievement outcomes in the first 5 years of life (13). Disadvantaged children are also more likely to experience cognitive impairment and have worse school performance than their more advantaged peers (14). Low SEP in childhood is related to functional illiteracy in young adulthood (15), and childhood SEP has been associated with cognitive function in middle age, net of years of education completed (16). Well-documented evidence links the educational attainment of parents to their children’s level of intelligence (17). Other research indicates that early life home environment, particularly the provision of learning experiences and cognitive stimulation, significantly influences cognitive development and abilities in childhood (18, 19). Adverse early life conditions are predictive of poorer health in adulthood, including excess cardiovascular disease morbidity and worse behavioral risk profiles (2023), which may have an impact on cognitive functioning in adults (3, 24, 25). Whether early life conditions related to socioeconomic factors and/or home environment influence change in cognitive function in old age has not been fully evaluated.

Accordingly, the purpose of this study was to examine the association between early life conditions and change in cognitive functioning in a geographically defined community population of adults aged 65 years or older. To address this question, we first examined the association of early life conditions with absolute level of cognitive function among older adults. Next, we tested our main hypothesis that more advantageous early life conditions would be associated with less rapid decline in cognitive function in old age. This hypothesis was tested using two separate indicators of early life conditions: 1) SEP in childhood and 2) exposure to a cognitively stimulating environment in childhood, referred to as a favorable "cognitive milieu" (details below).


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
Participants were men and women from the Chicago Health and Aging Project, an ongoing longitudinal study of risk factors for incident Alzheimer’s disease and other common chronic conditions of old age. The study population of the Chicago Health and Aging Project includes 6,158 persons aged 65 years or older (78.7 percent of those eligible) residing in three adjacent neighborhoods on the south side of Chicago, Illinois. The study population was predominantly Black (61.6 percent) or White (38.2 percent), with 0.2 percent reporting another racial category. For the purpose of analysis, respondents were categorized as Black or non-Black. Details on this study have been provided elsewhere (26, 27).

Baseline interviews, conducted between 1993 and 1997, included questions on medical and medication history, anthropometric measurements, assessments of psychosocial factors, health behaviors, socioeconomic factors in childhood and adulthood, and health and cognitive activities in childhood, and a battery of cognitive tests. Two follow-up assessments were conducted at 3-year intervals, on average (January 1997–March 2000 and April 2000–January 2003). Among the persons lost to follow-up, 1,175 died before follow-up, 149 relocated, 341 refused, 34 could not be contacted, and 61 were missing for other reasons. The study was approved by the Institutional Review Board of Rush University Medical Center, and all participants gave written, informed consent at each assessment.

The present analyses were limited to 4,398 respondents who had scores for the tests of cognitive function from at least two of the three interviews. A comparison of these 4,398 persons with the 436 who refused or were otherwise unable to participate showed no difference in age, education, sex, cognitive score at baseline, childhood SEP index, or childhood cognitive milieu; however, non-Blacks were more likely than Blacks (p < 0.001) to be excluded from the analyses.

Measurement of childhood SEP
SEP in childhood was measured by a composite index of standardized scores on four questions assessing 1) paternal and 2) maternal educational attainment (highest grade or year of regular schooling completed, as reported by respondents), 3) paternal occupational prestige, and 4) self-reported family financial status when the respondent was a child. Occupational prestige was coded on the basis of respondent reports of father’s primary occupation using the method reported by Hauser and Warren (28), which is based on 1990 US Census data on occupational earnings and educational requirements. Prior research has shown that occupational prestige ratings have been highly stable across historical periods (29, 30) and that the relative standings of occupations change extremely slowly over time (30). Data from the 1990 Census, which were the most recent data available at the time the Chicago Health and Aging Project originated, were used to code the occupational prestige of respondents (not reported here); given the reported stability of prestige ratings, we used the same coding scheme for the prestige ratings for respondents’ fathers. Family financial status was coded using a five-point Likert response format ranging from "very poor" to "very well off," in response to the question, "How would you describe your family’s financial situation when you were a very young child?" Responses to each of the four questions were converted to standardized scores with a mean of 0 and a standard deviation of 1.0 (z scores), and the z scores were then averaged. Scores on the composite index of childhood SEP were modeled continuously. If respondents were missing data on one or two items, the average of the nonmissing items was used to calculate the childhood SEP index score. Scores for respondents with missing data on more than two items were set to missing in analyses of the childhood SEP index (n = 65).

Measurement of childhood cognitive milieu
"Cognitive milieu" in childhood was measured by means of a composite index of three questionnaire items that assessed how frequently someone in the home read to, told stories to, or played games with the respondent as a child. Responses to each item used a five-point Likert format with scores ranging from 0 ("once a year or less") to 4 ("every day or nearly every day"). Scores on the cognitive milieu index were averaged across the three items, centered to the mean of the population at baseline, and modeled continuously, with higher scores representing a better cognitive milieu. If respondents were missing data on any one item, the average of the other two items was used to calculate the cognitive milieu index score. Scores for respondents with missing data on two or more items were set to missing in analyses of childhood cognitive milieu (n = 95).

Assessment of covariates
Age at baseline was assessed via self-reported birth date and was modeled continuously, centered at age 75 years. To account for nonlinear effects of age on cognitive function, we also entered an age-squared term into every model. Sex was modeled dichotomously, with female sex as the referent. Race, measured by the same questions as used in the 1990 Census, was modeled dichotomously, with non-Black designated the referent group. Respondent’s education was reported as the highest grade or year of regular schooling completed and was modeled continuously, centered at 12. We also included an education-squared term in relevant models to account for nonlinear effects of education on cognitive abilities.

Assessment of cognitive function
The oral version of the Symbol Digit Modalities Test (31), a test of perceptual speed; the immediate and delayed recall portions of the East Boston Story (32, 33); and the Mini-Mental State Examination (34) were administered at each wave of data collection. To reduce overall measurement error, and especially floor and ceiling effects that might be particularly problematic in longitudinal analyses, we created a global index of cognitive function by averaging z scores from the four individual tests. This approach has been used in previous analyses of data from the Chicago Health and Aging Project (26, 35), and prior principal-components factor analyses showed that all four tests loaded on a single factor (26). We based all z scores on the mean values and standard deviations of the cognitive test scores from the entire population at baseline so we could examine change in the global cognitive index over time.

Data analyses
We used mixed-effects regression models (36) to model cognitive function across the in-home assessments as a function of early life conditions. Such models allow the estimation of group-level effects of variables on change over time ("fixed effects") while simultaneously accounting for individual differences from the group estimate ("random effects"). We modeled cognitive function as a linear function of time, allowing each person to have his or her own initial level ("intercept") and rate of change ("slope"). These models allow varying numbers of observations per participant and varying lengths of time between observations.

Cognitive function was first modeled as a function of (follow-up) time, sex, age, age2, race, and four interaction terms representing the effects of age x sex, age x time, sex x time, and race x time. The core terms in the model were included on the basis of previous examination of the effects of age, race, and sex on change in cognitive function in this data set. The interaction terms were added to fit additional heterogeneity in cognitive function scores among men and women of different ages and additional heterogeneity in change in cognitive function over time by age, sex, and race, respectively. To this base model, we first added a main-effect term for childhood SEP or cognitive milieu. These models tested the degree to which the childhood variable was associated with an absolute difference in cognitive function scores, averaged across interviews. In other words, these models tested the cross-sectional effect of early life conditions on cognitive function. Next, we added a term for the interaction of each early life variable with time to the previous model to test the effect of childhood variables on rate of change in cognitive function, or the longitudinal effect of childhood SEP and cognitive milieu. The interaction terms formed the formal test of the main hypothesis that advantageous early life conditions would be associated with less rapid decline in cognitive function over time.

Because education is an important determinant of cognitive function in old age and is also related to early life conditions, we reestimated the effects of childhood SEP and cognitive milieu on cognitive function and change in cognitive function in models that included terms for education and the two-way interactions between education and age, race, sex, and time. These models tested the degree to which childhood SEP and cognitive milieu were associated with late-life cognitive function, independent of the respondents’ own education. Finally, we fitted additional models to examine whether the associations between cognitive function and childhood SEP or cognitive milieu were consistent across demographic subgroups. To that end, we reestimated the models with the inclusion of two-way interaction terms between childhood SEP or cognitive milieu and age, race, sex, and education. We fitted separate models for each childhood variable.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Participant characteristics
A total of 4,398 respondents participated in at least two of the three in-home assessments (2,731 females (62.1 percent) and 1,667 males (37.9 percent); 2,713 Blacks (61.7 percent) and 1,685 non-Blacks (38.3 percent)). On average, participants were aged 73.9 years (standard deviation (SD), 6.4) at baseline and had completed 12 years (SD, 3.7) of schooling. Average follow-up time was 5.3 years (range, 1.8–9.0). The mean baseline global cognitive index z score was 0.12 (SD, 0.78; range, –3.50 to 1.54). Mean childhood SEP score was 0.00 (SD, 0.72; range, –1.83 to 3.25), and mean childhood milieu score was 2.37 (SD, 1.23; range, 0–4).

Table 1 presents mean levels of childhood SEP and cognitive milieu by age, sex, and race. SEP scores did not differ by age or sex, but non-Blacks had significantly higher mean SEP index scores than Blacks (t = 23.0, p < 0.0001). Cognitive milieu scores did not differ by sex or race, but participants who were less than 75 years of age at the start of the study had significantly higher childhood cognitive milieu scores than older participants (t = 5.5, p < 0.0001).


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TABLE 1. Scores for childhood socioeconomic position and childhood cognitive milieu, by age, sex, and race, Chicago Health and Aging Project baseline examination, 1993–1997
 
Early life conditions and cognitive function
Results from models assessing the impact of early life conditions and cognitive function are summarized in table 2. The section labeled "model 1" presents the coefficients from the random-effects regression models demonstrating the main or fixed effects of childhood SEP and cognitive milieu on absolute level of cognitive function, after the effects of age, sex, and race were taken into consideration. Each 1-unit increase in childhood SEP was associated with a 0.158-standard-unit (95 percent confidence interval (CI): 0.130, 0.186) higher absolute level of cognitive function (p < 0.0001). Similarly, each 1-unit increase in childhood cognitive milieu was related to a 0.055-standard-unit (95 percent CI: 0.039, 0.070) higher absolute level of cognitive function (p < 0.0001). Overall, cognitive function declined an average of 0.055 standard unit (95 percent CI: –0.061, –0.049) per year in the study.


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TABLE 2. Results from random-effects models for the relation of socioeconomic position and childhood cognitive milieu to cognitive function in older adulthood, Chicago Health and Aging Project, 1993–2003*
 
The second section in table 2 ("model 2") presents the coefficients from the longitudinal models assessing whether early life conditions were associated with change in cognitive function over time. The interactions between time and our two indicators of early life conditions were nonsignificant, indicating that neither childhood SEP (ß = –0.005, 95 percent CI: –0.01, 0.0008; p < 0.10) nor childhood cognitive milieu (ß = –0.001, 95 percent CI: –0.004, 0.002; p = 0.39) was associated with cognitive change over time.

Influence of respondents’ education
The third section in table 2 ("model 3") presents the coefficients from the random-effects models that examined whether respondents’ education influenced the observed relations between early life experiences and cognitive function. Including education as a covariate weakened the nonsignificant interactions between time and childhood SEP (ß = –0.003, 95 percent CI: –0.009, 0.003; p = 0.32) and cognitive milieu (ß = –0.0008, 95 percent CI: –0.004, 0.002; p = 0.62). Adding education to the models decreased the coefficients for the effects of childhood SEP and cognitive milieu on absolute level of cognitive function by 70 percent or more, although these effects remained statistically significant.

Interactions with demographic variables
There were no significant interactions between childhood SEP and sex, age, race, or education, indicating that the effects of childhood SEP on absolute level of cognitive function did not differ significantly between men and women, younger and older respondents, Blacks and non-Blacks, and persons with differing levels of education. Childhood cognitive milieu did not interact with age, sex, or education, showing that the effect of cognitive milieu on absolute level of cognitive function did not differ across these demographic characteristics. However, a significant cross-sectional interaction between cognitive milieu and race was observed (ß = –0.036, t = –2.3, p = 0.02). A more stimulating cognitive milieu was related to a better absolute level of cognitive function more strongly among non-Blacks than among Blacks.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Previous research has demonstrated that socioeconomic conditions in childhood and early life experiences influence cognitive development and abilities in childhood and adolescence, as well as in young and middle adulthood (1216, 19). To our knowledge, however, few systematic investigations have previously examined whether early life conditions influence cognitive function in old age or protect against cognitive decline. Our findings do not support the hypothesis that advantageous early life conditions, as defined by higher SEP during childhood or more exposure to a cognitively stimulating environment, are associated with less rapid decline in cognitive function in old age. In other words, these early life conditions do not seem to provide a reserve or buffer against decline in cognition with age. As expected, however, we did find that higher SEP and greater exposure to a cognitively stimulating environment during childhood were associated with a higher absolute level of cognitive function in old age. This absolute effect appeared to be mostly mediated by a person’s own educational level, although a small but significant residual effect remained after we controlled for education.

Several factors may have influenced our results. Although the longitudinal design of this study, with three waves of data collection over an average of 5 years of follow-up, was an important strength, it is possible that the follow-up period was too short, with insufficient change in cognitive function to reliably ascertain the proposed relations. It is possible that with continued follow-up a protective effect of early life conditions on cognitive decline could emerge. However, we did observe a small but consistent decline in cognitive function in our population over the 5 years of follow-up, indicating that there was indeed a measurable decline in this population. In addition, we used a summary measure of cognitive function with strong psychometric properties designed to maximize our ability to characterize change in cognitive function (26).

Another potential limitation is recall bias. Self-reported information on childhood conditions may be affected by inaccurate recall, especially among persons with poorer memory. This probably affected indicators of a cognitively stimulating environment more so than childhood SES, since information on parental education and occupation is less subjective and easier to recall. Recall bias is not easily overcome in the context of large-scale epidemiologic studies, since studies that follow participants from early childhood to old age are generally not feasible. An alternative strategy would be to use archival indicators of childhood cognitive experiences, such as school records, that could be used in lieu of or to validate self-reported recall of childhood conditions. However, no such archival data are available for Chicago Health and Aging Project participants. We did observe robust cross-sectional associations between both measures of early life conditions and absolute cognitive function, which suggests that our measures of early life conditions, even with imperfect assessment, were sensitive to differences in childhood experiences hypothesized to relate to cognitive function.

Conceptually, the pattern of associations observed in the present study is consistent with the extensive body of literature showing that childhood conditions are strongly associated with cognitive development in early life (3740). It is possible that the cognitive advantages of good early life conditions are manifest early in and consistently across the life course but are simply unrelated to the processes that drive the cognitive decline seen in aging populations. Alternatively, it may be that the morphologic and functional changes in the brain that lead to cognitive decline are less influenced by early life cognitive stimulation and socioeconomic factors, or that the biologic influences governing these changes override any protective effects of early life conditions. More proximal experiences in adulthood may be more strongly related to cognitive decline in older adulthood. For example, prior work has shown that cognitive activity in adulthood, defined as time spent in activities involving a high degree of information processing, is protective against Alzheimer’s disease and cognitive decline in elderly men and women (41).

Our findings showing significant effects of early life conditions on absolute level of cognitive function in old age complement the existing literature by extending these observations to an older population. To our knowledge, most published work in this area has examined populations younger than ours, who were 73 years old on average at the initial examination. Another aspect of our findings suggests that much of the influence of early life conditions on absolute level of cognitive function is mediated by education. A better SEP in childhood probably allows for more and better educational opportunities and greater exposure to cognitive stimulation in the home environment (through toys, play, and language) (37, 39, 42). Similarly, a better cognitive milieu in early life may lay the cognitive foundation for educational success by contributing to better reading ability and better verbal skills in childhood and adolescence—skills and abilities that probably carry through to older ages (43). Early exposure to a stimulating cognitive environment may enhance information processing skills and other cognitive abilities (38) and may provide or increase motivation to succeed in school and to pursue educational goals and opportunities.

Summary and future directions
Results from this study suggest that socioeconomic and cognitive conditions in early life contribute to absolute levels of cognitive function but do not protect against cognitive decline in later life. These findings are consistent with published literature demonstrating the importance of the effect of early life experiences on cognitive skills and abilities at younger ages. To our knowledge, this is the first population-based study of the relation between conditions in early life and cognitive function in old age. Thus, replication of these findings is warranted. Additional work is needed to understand why the cognitive advantage conferred by childhood SEP and exposure to a stimulating cognitive milieu in early life does not slow the rate of decline in cognitive function experienced in old age.


    ACKNOWLEDGMENTS
 
This work was supported by grants AG11101 and ES10902 from the National Institutes of Health.

The authors thank Ann Marie Lane for community development and oversight of project coordination; Michelle Bos, Flavio Lamorticella, and Jennifer Tarpey for study coordination; and Todd Beck for data analysis.


    NOTES
 
Reprint requests to Dr. Susan A. Everson-Rose, Department of Preventive Medicine, Rush University Medical Center, 1700 West Van Buren Street, Suite 470, Chicago, IL 60612 (e-mail: Susan_Everson{at}rush.edu). Back


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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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