1 Danish Twin Registry, Institute of Public Health, University of Southern Denmark, DK-5000 Odense, Denmark.
2 Danish Center for Demographic Research, Odense University, DK-5000 Odense, Denmark.
3 Max Planck Institute for Demographic Research, Rostock D-18057, Germany.
4 Department of Psychology, University of Minnesota, Minneapolis, MN.
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ABSTRACT |
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activities of daily living; aging; genetics; twin studies; twins; variation (genetics)
Abbreviations: LSADT, Longitudinal Study of Aging Danish Twins
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INTRODUCTION |
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The apolipoprotein E polymorphism is the only common polymorphism known to influence the aging process in humans. However, previous studies (including studies of twins) have suggested that in contemporary populations in the industrial world, approximately one quarter of the variation in life-span can be attributed to genetic factors (68
). There is evidence that cognitive and functional abilities have an even larger genetic component: A Swedish twin study showed that approximately half of the variation in cognitive abilities among persons aged
80 years was due to genetic factors (9
), and a recent Danish twin study showed that one third to one half of the variation in functional abilities among women aged
80 years could be attributed to genetic variation (10
).
Both of these studies were cross-sectional, and it has been suggested that rate-of-change patterns underlie such observationsi.e., that genetic factors influence capabilities more through the rate of decline than through the "starting value" or "level value" (11). If this is the case, rate of change may be a more heritable and therefore more powerful phenotype than level phenotypes for research aimed at identifying genes that influence aging processes. However, at present, few data on the genetic contribution to rate-of-change phenotypes are available, and the data have generally shown lower heritability of rate of change than of the phenotypes in cross-sectional analyses (12
15
).
In the present study, we sought to estimate the genetic contribution to rate of change in functional abilities among the elderly by using data from the Longitudinal Study of Aging Danish Twins (LSADT). The LSADT started out with assessment of 2,401 Danish twins aged 75 years in 1995. The survivors were revisited after 2 years and again after 4 years. A total of 984 individuals, including 127 twin pairs, participated in all three ability assessments.
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MATERIALS AND METHODS |
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An intrinsic problem in studies of functional and cognitive disabilities among the elderly is that persons with more severe disabilities may not be able to participate in the study interview. Therefore, in this study, proxy interviews were sought when the twin was unable to participate. The fraction of proxy interviews was less than 10 percent in all three surveys.
Of the 2,401 participants in LSADT-95, 421 (18 percent) died before LSADT-97, and 385 (16 percent) refused participation in LSADT-97; this resulted in 1,595 reinterviews. This meant that 81 percent of the surviving 1995 participants were reinterviewed in 1997. Of the 1,595 two-wave participants, 265 (17 percent) died before LSADT-99 and 346 (22 percent) were nonresponders. Thus, 984 individuals participated in all three waves of the study, corresponding to a reinterview rate of 74 percent for the surviving participants from the first two waves (figure 1). These 984 individuals included 133 intact twin pairs and 718 twins who had a deceased or nonparticipating co-twin.
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Functional abilities
In this study, we used an instrument (Avlund) that has previously been validated in Denmark (10, 22
, 23
). Assessment of functional abilities was based on self-report, which has generally been found to be reliable and valid (24
, 25
). The Avlund instrument has been described in detail previously, and it has been shown to discriminate between levels of functional ability among community-dwelling elderly persons through questions about tiredness and the need for personal assistance in relation to functional abilities (22
, 23
). The Avlund instrument was extended to include assessment of the need for medical equipment or aids in relation to functional abilities, based on results showing that equipment and aids can improve functional abilities among the elderly (26
). All of the items from the Katz index of Activities of Daily Living were included (27
), as well as questions about the ability to see and hear and the ability to engage in more demanding activities such as running (10
).
For identification of meaningful quantitative subscales, the 26 items were factor-analyzed in the total LSADT-95 twin sample. All functional ability items were rated on a scale of 1 to 4, with the following response options: 4 = can do without fatigue; 3 = can do with fatigue or minor difficulties; 2 = can do with an aid or with major difficulties; and 1 = cannot do. In the factor analyses, three factors had an eigenvalue of more than 1, but few of the items loaded on the third factor. Therefore, a two-factor solution was adopted. The first factor loaded highest on items dealing with the ability to walk, run, climb stairs, and carry weights and was interpreted to reflect a dimension of strength. The second factor loaded highest on items dealing with the ability to dress and wash oneself and get into and out of bed; this factor was interpreted to reflect a dimension of agility.
Scores for the two dimensions were calculated by taking the average response of items that loaded highest on the factor or had been judged to be relevant for that dimension. The internal consistency reliability estimate for the strength scale was 0.93 in both the male and female samples for both the in-person interviews and the proxy interviews. The reliability estimates for the agility scale were also the same for men and women and equaled 0.91 for the in-person interview and 0.93 for the proxy interview. These values indicate very reliable scales. The correlation between the strength scale and the agility scale was 0.77. The later surveys yielded very similar results.
In this study, we focused on the strength scale, since the small variability in the agility scale led to its being dropped from the assessment in 1999. Rate of change for an individual was measured as the regression slope of the three strength scores on time of assessment separately for each individual. Because the three assessments were equally spaced 2 years apart, the slope is mathematically equivalent to the difference between the 1999 and 1995 scores.
Analysis of twin similarity
Classical analyses. In humans, two types of twinning occur: monozygotic and dizygotic. Monozygotic (identical) twins share all of their genetic material, and dizygotic (fraternal) twins, like ordinary siblings, share, on average, 50 percent of their genes (i.e., 50 percent of their genes are identical by descent). In the classical twin study, monozygotic and dizygotic intraclass correlations for a given trait are compared. A significantly higher correlation in monozygotic twins indicates that genetic factors play an etiologic role.
To estimate the heritability of the functional ability scales (i.e., the proportion of the population variance attributable to genetic variation) in this study, we analyzed the twin data using standard biometric models (28). In such a case, the total variance (V) in a scale is decomposed as V = A + D + C + E, where A refers to the variance contribution of additive genetic effects, D refers to the variance contribution of genetic effects due to dominance (intralocus interaction), C refers to the variance contribution of shared environmental effects (i.e., environmental factors that are shared by twins reared together and are thus a source of their similarity), and E refers to the variance contribution of nonshared environmental effects (i.e., environmental factors that are not shared by twins reared together and are thus a source of their dissimilarity). Assuming that shared environmental effects contribute equally to the resemblance of monozygotic (MZ) and dizygotic (DZ) twins, the expected twin covariances under this model are given by Cov(MZ) = A + D + C and Cov(DZ) = (1/2)A + (1/4)D + C. Previous analysis of the 1995 data indicated that the functional ability data could be fitted adequately with a model that included only additive genetic (A) and nonshared environmental effects (E) that varied in magnitude in the male and female subsamples (10
). Consequently, in the current analysis, only AE models were fitted to the twin data separately in the male and female subsamples. Prior to model-fitting analysis, the observed twin variances and covariances were stratified by sex. For correction of unequal variances between twin 1 and twin 2 in the smaller subgroups, the data were double-entered, and the degrees of freedom were adjusted accordingly.
Growth model. For assessment of the contribution of genetic factors to functional ability at each of the individual assessments as well as rate of change in functional ability across assessments, a "growth" model was fitted to the observed twin data (29). The model we fitted is depicted in figure 2 at the phenotypic level. Specifically, variation at each assessment was decomposed into the contribution of up to three factors: a level effect (equivalent to the average functional ability across assessments), a slope effect (equivalent to the difference in functional ability between the last and first assessments), and a residual effect.
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RESULTS |
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Table 1 documents that even at the single-item level, functional abilities showed a systematic decline in mean score over time accompanied by a high within-person correlation. As expected, this pattern is even more clear for the summary strength score, which showed a decline of one quarter to one third of a standard deviation for every 2-year period and a within-person correlation of approximately 0.8 for both the 2-year interval and the 4-year interval. A similar pattern was found for all of the age and sex groups shown in table 2.
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Table 3 shows the strength scores stratified for participation in the follow-up studies. The twins who died between two interview waves had, in their last assessment, an average strength score approximately one standard deviation below that of the individuals who did not die between two waves. Furthermore, twins who died between 1997 and 1999 showed a greater decline between 1995 and 1997 than twins who survived, which is consistent with a pattern of terminal decline. Alternatively, the surviving nonparticipants, in their last assessment, had scores only slightly lower than those of the individuals who participated in the next wave.
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The results of fitting the growth model to the twin data are summarized in table 5 in terms of the estimates of heritability (with associated confidence intervals). Because there was a significant difference in parameter estimates in the male and female samples (2 = 27.9, 12 df; p = 0.006), estimates are given separately for men and women. Heritability estimates were uniformly low and nonsignificant in the male sample. In the female sample, heritability was 4344 percent for individual assessments and 48 percent for the level parameter. However, estimated heritability for the slope parameter was statistically nonsignificant in both the female sample and the male sample.
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DISCUSSION |
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Twin studies in the very old can be influenced by selective survival. Functional abilities are correlated with survival; furthermore, the survival of twins is correlated, which can produce spurious results (33). However, the correlation in life-span for twins is modest (about 0.25 for monozygotic twins and about 0.05 for dizygotic twins (7
). As expected, the twins who died between two waves had a lower average strength score at their last assessment than the twins who participated in the subsequent wave (see table 3). It was reassuring, however, that the surviving nonparticipants ("refusers") were quite similar to the twins who stayed in the study, which suggests that there was no substantial selective nonparticipation among the survivors. In any case, all twin pairs who completed the initial assessment were included in the biometric analysis of the longitudinal data, so selective survival is not a likely explanation for our failure to observe heritable influences on rate of change in functional abilities.
Functional abilities among the elderly can be conceptualized as being determined by both a "starting level" and rate of change, and genetic influences on both might be expected. Several cross-sectional studies in the elderly have demonstrated substantial genetic influences on physical and cognitive functioning over a broad range of phenotypes. However, there have been few published studies on the heritability of rate-of-change traits. A longitudinal analysis of middle-aged female twins over a 10-year interval found substantial heritability for changes in body mass index and moderate heritability for changes in coronary heart disease risk factors (14, 15
). Studies of male twins have also indicated a genetic influence on change in weight over a 43-year interval (13
) but not on bone loss over a 16-year interval (12
). A common feature of all of these studies is that the heritability of rate of change is not larger than the heritability of the phenotypes in cross-sectional analyses.
It has recently been suggested that rate-of-change phenotypes may be especially appropriate targets for molecular genetic investigations aimed at identifying the specific genes influencing human aging (11). This recommendation does not appear to have received support in previous investigations of rate-of-change phenotypes that have reported relatively low levels of heritability (12
15
). To be successful, a large-scale molecular genetic study of a rate-of-change phenotype should, at a minimum, satisfy the following criteria: 1) rate of change is inexpensively and reliably assessed; 2) attrition over the multiple waves of assessment is minimal; and 3) rate of change is at least moderately heritable.
Our analysis of the strength measure illustrates some of the difficulties geneticists will have in attempting to identify suitable rate-of-change phenotypes for molecular genetic investigation. There is, of course, overwhelming evidence that functional ability declines with age, and our analysis of the strength measure confirmed that decline over a 4-year period in a sample of persons who were 80 years old, on average, at initial assessment. Importantly, decline in strength scores was observed at the individual level as well as the aggregate level. Nearly 80 percent of the sample of individuals who participated in all three waves of assessment showed a negative slope of strength score against assessment wave. The costs associated with administering the strength scale are relatively low, yet the resulting scale has impressive reliability and validity properties. Item-level responses paralleled the decline observed at the aggregate level, and a very high intercorrelation of responses over assessments was observed at the scale level as well as at the individual item level.
Nonetheless, it would be difficult to argue that rate of change on the strength scale would be a suitable phenotype for molecular genetic investigation. Biometric analysis of the twin data indicated that the heritability of rate of change on the strength scale was modest and not statistically significant in both the male and female samples. It may be that retest intervals longer than 4 years would produce rate-of-change phenotypes that were more heritable than was observed in the 4-year interval used in the present study. For example, Fabsitz et al. (13) reported that change in body mass index over a 43-year interval was 70 percent heritable in a sample of men aged 23 years (on average) at initial assessment. Identifying heritable influences on rate-of-change phenotypes may require initial samples of persons who are middle-aged or younger at initial assessment, with retest intervals that are measured in decades rather than single years. However, molecular geneticists are not likely to want to wait 40, 20, or even 10 years to obtain material for their investigations, and long retest intervals are likely to be associated with relatively high levels of attrition (loss to mortality was a major factor in our sample of predominantly octogenarians). Nonetheless, follow-up of large middle-aged samples originally assessed many years previously may be an attractive alternative to trying to measure rate of change in an elderly sample.
Experience with the LSADT provides further evidence of the difficulty of assessing rate of change in elderly samples for molecular genetic investigation. In the LSADT, participation rates at each wave of assessment were relatively high, probably because the assessment was relatively brief (less than 2 hours) and was completed in the respondent's residence. Nonetheless, 60 percent of the sample members who completed the 1995 survey did not participate in the 1999 survey, half being lost to mortality. Sample loss was even more pronounced at the twin-pair level, since only 127 (28 percent) of the 451 pairs who completed the strength assessment in 1995 completed it again in 1999. Thus, researchers conducting candidate gene studies of rate-of-change phenotypes (which would be based on samples of individuals) might expect that 60 percent or more of a sample of very old persons would be lost to follow-up, while in genetic linkage studies of rate of change (which would be based on samples of relatives), there might be a sample loss to follow-up as high as 70 percent. Clearly, attrition rates like these represent a significant challenge to large-scale molecular genetic studies of rate-of-change phenotypes, even when they are based on long retest intervals.
Our analysis of the strength scale at the individual waves of assessment suggests an alternative approach for identifying phenotypes for molecular genetic analysis in human aging research. At each wave of assessment, strength scores were moderately heritable, at least in the female sample, and aggregating scale scores over multiple waves of assessment had little impact on heritability. This suggests that a single assessment would suffice in identifying all of the relevant genetic variance. We believe that the strength score in this sample of very old persons probably does reflect rate of change, albeit over a very long interval. Even though our sample was not assessed earlier in life, we can confidently say that the vast majority of LSADT participants were able to walk up two flights of stairs, engage in light exercise, and run 100 m when they were young and middle-aged. The difficulties they are having with these activities in late life thus reflect a change from a higher level of functioning. Consequently, a single-wave assessment of physical functioning might be an attractive alternative to assessing rate of change over multiple waves of assessment.
Understanding the genetic contribution to human aging should be a priority in gerontologic research over the next decade. Nonetheless, our analysis of the multiple waves of functional ability data in the LSADT serves to highlight some of the difficulties human molecular geneticists will encounter as they turn their attention to aging. At this initial stage of inquiry, it is essential that a range of phenotypes and research designs be critically evaluated for their suitability to support molecular genetic analysis.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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