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Sex Differences After All Those Years? Heritability of Cognitive Abilities in Old Age

¹ School of Health Sciences, Institute of Gerontology, Jönköping, Sweden.
² Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
³ Department of Psychology, University of Southern California, Los Angeles.
⁴ Department of Public Health, University of Helsinki, Finland.
⁵ Department of Psychology, University of Göteborg, Sweden.
⁶ Department of Biobehavioral Health and Center for Developmental and Health Genetics, The Pennsylvania State University, University Park.

Address correspondence to Sanna Read, Institute of Gerontology, School of Health Sciences, PO Box 1026, 551 11 Jönköping, Sweden. E-mail: sata{at}hhj.hj.se

	Abstract

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We investigated sex differences in genetic and environmental effects on cognitive abilities among older adult twins. We drew participants from the Swedish Twin Registry; our sample included 647 twin pairs. Our cognitive measures included Synonyms, Block Design, Digit Span, Thurstone's Picture Memory, Symbol Digit, and general cognitive ability tests. Higher age was related to lower performance in all cognitive measures, except synonyms. For digit span forward, symbol digit, and general cognitive ability tasks, there was a Sex x Age interaction, with greater deficits in the performance of women compared with those of men at higher ages. We found no sex-specific genetic influences. In other words, the same genetic effects were operating for men and women. Furthermore, the magnitude of genetic effect was similar for men and women.

SEX differences in cognitive functioning have received considerable attention. Generally, men perform better in some spatial ability tests, whereas women do better in some verbal ability tasks (see reviews by Halpern, 2004; Kimura 1992; Linn & Petersen, 1985; Voyer, Voyer, & Bryden, 1995). The same pattern has also been found among older people (Deary, Whiteman, Starr, Whalley & Fox, 2004; Ho, Woo, Sham, Chan, & Yu, 2001; Maitland, Intrieri, Schaie, & Willis, 2000; Portin, Saarijärvi, Joukamaa, & Salokangas, 1995). Age-related decline is more pronounced among women than among men (Deary et al.; Ho et al.; Meinz & Salthouse, 1998), although not all studies have found a Sex x Age interaction (Maitland et al.; Schaie, 1996). A number of studies have found that women are more prone than men to develop dementia, especially in very old age (Borjesson-Hanson, Edin, Gislason, & Skoog, 2004; Launer et al., 1999; Ruitenberg, Ott, van Swieten, Hofman, & Breteler, 2001). However, other large studies have not found sex differences in rates of dementia (e.g., Fitzpatrick et al., 2004; Gatz et al., 2003; and discussion of the issue by Swanwick & Lawlor, 1999).

Although gender differences in cognitive abilities in older adults have been examined, there has been a dearth of theoretical models to direct hypothesis testing and discussion of the findings (Sinnott & Shifren, 2001). Explanations for gender differences include sex steroid hormone differences (McKeever, 1995), structural and functional differences in brains of men and women (Gur et al., 1999; Harasty, Double, Halliday, Krill, & McRitchie, 1997; Levy & Reid, 1978), and differential life experiences and social expectations for men and women (Richardson, 1997). In older people, gender differences in health and physical functioning may be reflected in cognitive performance (Jorm, Anstey, Christensen, & Rodgers, 2004).

Most studies of gender differences have focused on group differences in mean levels. There are likely to be substantial individual differences about the means within groups. Quantitative genetic analyses evaluate the extent to which these individual differences reflect genetic and environmental variance. The factors that influence mean levels may be very different from those that influence variances. For example, estrogen levels may be important predictors of mean level differences between men and women for some traits. However, one would expect that variation in estrogen is more important for individual differences in the trait of interest among women than among men. Thus, in order for us to fully understand gender differences, we must evaluate not only influences on means but also those on variances.

A number of twin studies have demonstrated that genetic factors explain more than half (60–80%) of the variation in most cognitive abilities in adults (Pedersen, Plomin, Nesselroade, & McClearn, 1992; Finkel & McGue, 1993; Finkel, Pedersen, McGue, & McClearn, 1995; McClearn et al., 1997; McGue & Christensen, 2001). Shared environmental influences, those contributing to familial similarity, explain a modest amount of variance in elderly adults, at most 20%, for measures of crystallized abilities such as the Synonyms and Information tests (Pedersen et al.; McClearn et al.). Although in a broad sense men and women age differently, none of these studies have investigated gender differences in heritability of cognition in old age.

One of the key reasons that twin studies of cognitive abilities in old age have not examined gender differences has been the lack of access to opposite-sexed twin pairs. In standard twin designs, one can stratify by gender and test whether the relative importance of genetic influences (heritability) differs for men and women. This type of difference is a quantitative difference in heritability. With the inclusion of opposite-sexed pairs, one can also evaluate qualitative differences, that is, test the extent to which the genetic (or environmental) influences are correlated or sex specific. In other words, one can evaluate whether the same genes are expressed in men and women.

In one of the earliest efforts to evaluate sex-linked genetic influences, DeFries and colleagues (1979) studied parent–child and sibling correlation patterns in specific cognitive abilities, but they did not find evidence of sex-linked genetic influences. Knopik and DeFries (1998) reported a somewhat higher heritability estimate for general cognitive ability in 8- to 20-year-old male individuals than in comparably aged female individuals, although the difference was not statistically significant. The same genetic influences were found to be operating for male and female individuals. Using parental evaluations of verbal and nonverbal cognitive abilities, Galsworthy, Dionne, Dale, and Plomin (2000) studied a large sample of 2-year-old twins and found a greater heritability in verbal measures for boys than for girls. Verbal ability also showed some sex-specific genetic effects. For nonverbal cognitive ability, heritability did not differ between boys and girls. To our knowledge, the only studies on sex differences in the heritability of cognitive ability in old age deal with dementia. There is some evidence that, in dementia, different genes may be involved for men and women (Gatz et al., 2003).

Our aim in this study is to assess gender-based individual differences in cognitive abilities in a sample of older Swedish twins. We predict higher mean performance for men in measures of spatial ability and higher mean performance for women in verbal ability. We also expect an Age x Sex interaction such that the performance of women is worse than that of men in older age groups. To our knowledge, no studies on sex-limited genetic influence have been carried out in older populations. On the basis of the earlier findings of higher heritability for verbal measures and general cognitive abilities in boys than in girls (Galsworthy et al., 2000; Knopik & DeFries, 1998), we expect to see similar patterns among older people such that there are sex differences in the relative importance of genetic influences on verbal ability and general cognitive ability, with greater heritability for male individuals than for female individuals.

	METHODS

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Participants
A total of 742 twin pairs of individuals who were 65 years of age or older participated in the cognitive assessment in three largely parallel longitudinal studies of aging, all drawn from the population-based Swedish Twin Registry (Lichtenstein et al., 2002). The studies included the OCTO-Twin study (McClearn et al., 1997), the Swedish Adoption/Twin Study of Aging (SATSA; Pedersen et al., 1991; Finkel & Pedersen, 2004) and the Gender study (Gold, Malmberg, McClearn, Pedersen, & Berg, 2002). From SATSA—where the study includes a larger age range—only twins 65 or older were included in the analyses. Thus, the age range was between 65 and 98 years in our combined sample of participants at baseline (Table 1). The participants were tested in their home or a location near to their home by nurses who had finished an extensive training for collecting data for this research purpose, and whose work was regularly monitored throughout the study. We excluded a total of 95 pairs because one or both members of the twin pair had dementia as defined by the Diagnostic and Statistical Manual of Mental Disorders, third edition, revised (DSM-III-R; American Psychiatric Association, 1987); had a total score of 24 or less on the Mini-Mental State Examination (Folstein, Folstein, & McHugh, 1975); or had sensory and motor handicaps. The final sample included 647 twin pairs, although the number varied somewhat for each cognitive measure (Table 2). To our knowledge, the combined sample is the largest twin sample ever reported for studying aging in both general and specific cognitive abilities.

Using the Synonyms, Block Design, Thurstone's Picture Memory, and Symbol Digit tests, we calculated a one-factor principal component analysis for general cognitive ability (G factor). We omitted the Digit Span test because it was not evaluated at the first in-person testing for all three samples. Because the measures had different metrics, we used the z scores for each measure in the principal component analysis. All four measures loaded highly (> 0.80) and explained 71% of the variance of the first principal component. We used the factor scores for the first principal component as an index of general cognitive ability. To make the factor score positive and easier to read, we added 10 to the original score.

We used three zygosity groups: 1 = monozygotic (MZ)–same sex, 2 = dizygotic (DZ)–same sex, and 3 = DZ–opposite sex. We use sample to refer to the origin of the data (1 = OCTO-Twin, 2 = SATSA, 3 = gender). Educational level consisted of four categories: 1 = elementary school; 2 = 0 level, vocational school, or folk high school; 3 = gymnasium (A level); and 4 = university or higher. For self-rated health, we used a sum score of four standardized items: How would you rate your general health status? (1 = bad, 2 = reasonable, 3 = good); How would you rate your present health status compared with 3 years ago? (1 = worse, 2 = about the same, 3 = better); How would you rate your health status compared with that of others in your age group? (1 = worse, 2 = about the same, 3 = better); and Do you think your health prevents you from doing things you would like to do? (1 = to a great extent, 2 = partly, 3 = not at all). The internal consistency of the score was satisfactory (Cronbach's = 0.71).

Statistical Method
We tested sex and age differences in mean levels of cognitive abilities by using linear mixed models in the SPSS 11.5 program (SPSS, 2003). Mixed models, based on general linear modeling, are suitable for analyzing multivariate data with correlated and nonconstant variability (e.g., data including twin pairs). The testing of differences in means (fixed effects) and differences in covariance structure (random effects) is possible. We treated sex and age as fixed main effects. Moreover, we added zygosity, sample, educational level, and self-rated health as fixed effects to control for their possible mean level effects. To take into account the correlated variance between the twins, we treated a twin pair as a random effect in the model, and we assumed the covariance matrix between the members of a twin pair to have a constant structure. We carried out the model estimation by using the restricted maximum likelihood method, which is adjusted for the fixed effects in the model and takes into account the degrees of freedom that are used to estimate the fixed effects.

We calculated intraclass correlations for five groups created by the combination of sex and zygosity: MZ men, MZ women, DZ men, DZ women, and DZ opposite-sex twin pairs. A higher intraclass correlation among MZ twins compared with DZ twins indicates additive genetic effects. A MZ intraclass correlation more than twice the size of the DZ correlation suggests nonadditive genetic effects. Sex-specific influences are indicated when the intraclass correlation for the same-sex DZ pairs differs from that of opposite-sex pairs.

To estimate the importance of genetic and environmental variance components for cognitive abilities, we used the Mx structural equation modeling software (Neale, Boker, Xie, & Maes, 1999). In the present study, we decomposed the total phenotype variance, V, to four sources of variance: additive genetic, or A, effects; nonadditive (dominance) genetic, or D, effects; shared environmental, or C, effects; and nonshared environmental, or E, effects. The assumptions for the decomposition of phenotype variance in MZ twins is V_MZ = A + D + C + E, and in DZ twins it is V_DZ = A + D + C + E. The estimated genetic correlation, r_g, was 0.5 for same-sex DZ twins. For opposite-sex twin pairs, r_g can vary between 0 and 0.5. A value smaller than 0.5 suggests differences in the genes for men and women.

The separation of the nonadditive genetic effect and the shared environmental effect in the same model is not possible (Neale & Cardon, 1992). Therefore, we tested two separate models, ACE or ADE, on the basis of the patterns of intraclass correlations. Because age effects can inflate twin resemblance in same-sex twins, we took age into account by using it as a covariate in the model. We first allowed the sex-specific genetic (r_g) effects to estimate freely among men and women (assumption of different genes among men and women). Next, we fixed r_g to 0.5 for opposite-sex twins to test the model, assuming the same genetic influences for men and women. We first estimated the magnitude of genetic and environmental effects independently for men and women and then constrained them to be equal.

Covariance matrices were the basis of the structural equation modeling analyses. We assessed the fit of the model by chi-square test and root mean square error of approximation (RMSEA; Steiger, 1990). A model with acceptable fit had chi-square test p >.05 and RMSEA < 0.08. We assessed the comparison of the nested models by the difference in chi-square values: a significant difference indicates that the reduction of the model makes the fit of the model worse (i.e., the dropped term is significant). We also compared the models according to their Akaike Information Criteria (AIC; Akaike, 1987): a smaller value indicates a better model.

	RESULTS

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Descriptive Statistics
The mean scores and standard deviations for cognitive abilities are shown in Table 1. There were no main effects of sex or age for the Synonyms test. For the Block Design, Digit Span Forward, Thurstone's Picture Memory, and Symbol Digit tests, as well as general cognitive ability, there were both main effects of sex and age as well as a Sex x Age interaction in which advanced age was more strongly related to lower performance in women than it was in men. There was a main effect for age for the Digit Span Backward test.

Of the covariates, higher educational level and better self-rated health were significantly related to higher mean performance in all cognitive tests, whereas zygosity and sample origin were not related to cognitive test scores.

Intraclass Correlations
The patterns of the intraclass correlations among MZ and DZ twins suggest additive genetic effects for the Block Design test, Digit Span Forward test, and general cognitive ability, and nonadditive genetic effects for the Digit Span Backward test, Thurstone's Picture Memory test, and the Symbol Digit test (Table 2). For synonyms, the similarity between MZ and DZ intraclass correlations indicates the presence of shared environmental effects. Men and women had similar intraclass correlations, suggesting no sex difference in the magnitude of genetic and environmental effects for all tests except the Digit Span Forward test. The intraclass correlations for the opposite-sex twins were similar to the correlations for DZ men and women, indicating no sex-specific genetic influences.

Sex-Limitation Genetic Model
The percentage of total variation caused by A, D or C, and E, goodness-of-fit parameters for the full model and best-fitting model are shown in Table 3. A nonsignificant chi-square difference indicates that the reduced model fit the data significantly better than did the full model. A lower AIC value also indicated better fit of the reduced model. For all measures, r_g could be fixed without reducing the fit of the model, indicating that the same genes are operating in men and women. Except for the Digit Span Forward test, ²(df = 2) = 8.11, p <.05, the parameter estimates could be set equal for men and women without reducing the fit of the models. In other words, the heritabilities were the same for men and women. The last step included testing the AE model (dropping D or C). This model provided the best fit for all measures except synonyms, where dropping either C, ²(df = 1) = 7.31, p <.01, or A, ²(df = 1) = 8.76, p <.01, resulted in a reduced fit. Thus, the best model for synonyms included both A and C. Across measures, additive genetic effects accounted for between 34% and 68% of the total variance, being highest for general cognitive ability and somewhat lower for specific abilities.

	DISCUSSION

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Finding significant differences in mean levels does not, a priori, suggest that there should be differences in variances or variance components. Nevertheless, a number of the explanations offered for mean differences between men and women, such as differences in sex hormones, health histories, and life experiences, could well lead to different heritabilities in men and women. If, for example, life experiences are more variable for men, then one might expect proportionately lower genetic variance for performance on cognitive traits influenced by those experiences. Similarly, variation in hormone levels is likely to be greater in women than men, although perhaps of less importance very late in life. It is clear that if there are variance differences for trait-relevant contributors to gender differences, then these are either evenly distributed among genetic and environmental sources or have not been substantial enough to influence the heritability analyses based on these elderly adults.

We found only hypothesized differentials in mean levels of cognitive abilities in interactions with age, which remained after we controlled for educational level and self-rated health. This interaction indicates that the difference between men and women was larger in older age; younger women had equal or higher scores than younger men, but older women scored lower than older men. The result is congruent with findings in previous longitudinal studies (Deary et al., 2004; Ho et al., 2001; Meinz & Salthouse, 1998). Cohort differences or greater selectivity among men in survival can be possible explanations for the interaction. Finally, higher age was related to lower performance in all tests except the Synonyms test. This is expected as the Synonyms test measures crystallized abilities that are not as sensitive to decline with increasing age as fluid abilities and memory (Horn & Donaldson, 1976; Park et al., 1996; Salthouse, 1996).

It should be noted that, in the present study, we excluded people with dementia from the analysis. Thus, the lower scores of older women cannot be attributed to the inclusion of individuals with dementia. Excluding those with dementia furthermore makes the sample more homogeneous and reduces possible confounding caused by the differential heritability of normal and pathological manifestations of a trait.

Sex-limitation genetic models require a substantial sample size. Because the five gender by zygosity subgroups are analyzed together in one model, the total sample size is substantial. Power analyses indicated that detecting a genetic effect for men and women was over 0.80 for all tests, except the Digit Span Forward test and Thurstone's Picture Memory test. In evaluating the power of the study, we also find it particularly important to notice the pattern of intraclass correlations and whether they are consistent with the results of modeling. To expect the models to find differences in heritability or a sex-specific genetic influence, there have to be different correlations in men and women or between same-sex and opposite-sex DZ twins. In the present study, intraclass correlations suggested no substantial differences in heritability or sex-specific genetic influences, a finding confirmed by the modeling results.

In conclusion, although life trajectories, mortality, and health may vary among men and women (Arber & Ginn, 1995) and there is evidence of differential heritability in some domains of cognitive abilities in early childhood (Galsworthy et al., 2000; Knopik & DeFries, 1998), the patterns of twin correlations and the sex-limitation model testing did not give support to sex differences in relative contributions of genetic and environmental influences on cognitive abilities in old age. The results suggest that men and women would benefit equally from environmental interventions to enhance good cognitive functioning in old age. Further studies are needed to confirm whether this pattern of genetic and environmental influence in older Swedish twins is also found in other populations.

	Acknowledgments

 
The SATSA is supported by the National Institute on Aging (under Grants AG04563 and AG10175), The McArthur Foundation Research Network on Successful Aging, and the Swedish Council for Social Research (Grant 97:0147:1B).

The OCTO-Twin Study (Origins of Variance in the Old-Old: Octogenarian Twins) is supported by the National Institute on Aging (under Grant AG08861).

The Gender study is supported by the MacArthur Foundation Research Network on Successful Aging, The Axel and Margaret Ax:son Johnson's Foundation, The Swedish Council for Social Research, and the Swedish Foundation for Health Care Sciences and Allergy Research.

Dementia diagnoses were obtained with support by the Study of Dementia in Swedish Twins (Grant AG08724).

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication February 16, 2005. Accepted for publication October 3, 2005.

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