Abstract
Structural equation modeling (SEM) was used to examine the development of intellectual functioning in 145 school-age pairs of siblings. Each pair included one child with Fragile X syndrome (FXS) and one unaffected sibling. All pairs of children were evaluated on the Wechsler Intelligence Scale for Children—Third Edition (WISC-III) at time 1 and 80 pairs of children received a second evaluation at time 2 approximately 4 years later. Compared to their unaffected siblings, children with FXS obtained significantly lower percentage correct scores on all subtests of the WISC at both time points. During the time between the first and second assessments, the annual rate of intellectual development was approximately 2.2 times faster in the unaffected children compared to the children with FXS. Levels of the fragile X mental retardation protein (FMRP) were highly associated with intellectual ability scores of the children with FXS at both time points (r = 0.55 and 0.64 respectively). However, when gender, age, and the time between assessments were included as covariates in the structural equation model, FMRP accounted for only 5% of the variance in intellectual ability scores at time 1 and 13% of the variance at time 2. The results of this study suggest that slower learning contributes to the low and declining standardized IQ scores observed in children with FXS.
Similar content being viewed by others
Notes
Some children with FXS also had siblings with FXS, although diagnosis was not always confirmed.
In the study by Hall et al. (2007), 150 pairs of siblings were included. However, five boys with FXS were unable to complete any of the subtests on the WISC-III at either time point and refused blood draws for the FMRP analysis. These boys, and their unaffected siblings, were therefore excluded from the data analysis in the present study.
This method can provide consistent parameter estimates in the presence of missing data, even when the data are not missing completely at random. Three alternative methods of estimating models with missing data include mean substitution, listwise deletion, and pairwise deletion. These methods are less efficient and provide consistent estimates only under the stronger assumption that any missing data are missing completely at random.
Some investigators prefer to call these terms “other causes,” since they may contain systematic variance as well as random measurement errors.
There were no significant differences in intellectual ability scores between male and female unaffected siblings. Therefore, the gender of the unaffected siblings was not included in the model.
In a super parallel factor, the regression coefficients, error variances, and intercepts for all the indicators are equal.
The error terms between the intellectual ability factors are correlated at time 1 and time 2. The magnitude depends in large part on the R 2 values for the factors. At time 2, they are very high, so only a small amount of systematic variance is left in the error terms. Hence, the error terms are highly correlated.
In the boys with FXS, these values were obtained by multiplying the total effect of FMRP on intellectual ability at each time point times 80, an increase that would bring the mean FMRP level from 13% to 93%. Corresponding values for the girls were obtained by multiplying the total effect of FMRP on intellectual ability at each time point times 40, an increase that would bring the mean FMRP level from 43% to 93%.
References
Abrams, M. T., Kaufmann, W. E., Rousseau, F., Oostra, B. A., Wolozin, B., Taylor, C. V., et al. (1999). FMR1 gene expression in olfactory neuroblasts from two males with fragile X syndrome. American Journal of Medical Genetics, 82, 25–30.
Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides, & R. E. Schumacker (Eds.) Advanced structural equation modeling. Mahwah, New Jersey: Erlbaum.
Arbuckle, J. L. (2005). Amos 6.0 user’s guide. Chicago, IL: SPSS Inc.
Bailey Jr., D. B., Hatton, D. D., & Skinner, M. (1998). Early developmental trajectories of males with fragile X syndrome. American Journal on Mental Retardation, 103, 29–39.
Bailey Jr., D. B., Hatton, D. D., Tassone, F., Skinner, M., & Taylor, A. K. (2001). Variability in FMRP and early development in males with fragile X syndrome. American Journal on Mental Retardation, 106, 16–27.
Bennetto, L., Pennington, B. F., Porter, D., Taylor, A. K., & Hagerman, R. J. (2001). Profile of cognitive functioning in women with the fragile X mutation. Neuropsychology, 15, 290–299.
Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.) Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
Burns, D. D., & Nolen-Hoeksema, S. (1992). Therapeutic empathy and recovery from depression in cognitive-behavioral therapy: A structural equation model. Journal of Consulting and Clinical Psychology, 60, 441–449.
Cornish, K., Swainson, R., Cunnington, R., Wilding, J., Morris, P., & Jackson, G. (2004). Do women with fragile X syndrome have problems in switching attention: Preliminary findings from ERP and fMRI. Brain & Cognition, 54, 235–239.
Dykens, E. M., Hodapp, R. M., & Leckman, J. F. (1989a). Adaptive and maladaptive functioning of institutionalized and noninstitutionalized fragile X males. Journal of the American Academy of Child and Adolescent Psychiatry, 28, 427–430.
Dykens, E., Leckman, J., Paul, R., & Watson, M. (1988). Cognitive, behavioral, and adaptive functioning in fragile X and non-fragile X retarded men. Journal of Autism and Developmental Disorders, 18, 41–52.
Dykens, E. M., Hodapp, R. M., Ort, S., Finucane, B., Shapiro, L. R., & Leckman, J. F. (1989b). The trajectory of cognitive development in males with fragile X syndrome. Journal of the American Academy of Child and Adolescent Psychiatry, 28, 422–426.
Fisch, G. S. (2006). Cognitive-behavioral profiles of females with the fragile X mutation. American Journal of Medical Genetics, 140A, 673–677.
Fisch, G. S., Carpenter, N. J., Holden, J. J., Simensen, R., Howard-Peebles, P. N., Maddalena, A., et al. (1999a). Longitudinal assessment of adaptive and maladaptive behaviors in fragile X males: growth, development, and profiles. American Journal of Medical Genetics, 83, 257–263.
Fisch, G. S., Carpenter, N. J., Simensen, R., Smits, A. P., van Roosmalen, T., & Hamel, B. C. (1999b). Longitudinal changes in cognitive-behavioral levels in three children with FRAXE. American Journal of Medical Genetics, 84, 291–292.
Fisch, G. S., Simensen, R., Arinami, T., Borghgraef, M., & Fryns, J. P. (1994). Longitudinal changes in IQ among fragile X females: a preliminary multicenter analysis. American Journal of Medical Genetics, 51, 353–357.
Fisch, G. S., Simensen, R., Tarleton, J., Chalifoux, M., Holden, J. J., Carpenter, N., et al. (1996). Longitudinal study of cognitive abilities and adaptive behavior levels in fragile X males: A prospective multicenter analysis. American Journal of Medical Genetics, 64, 356–361.
Freund, L. S., & Reiss, A. L. (1991). Cognitive profiles associated with the fra(X) syndrome in males and females. American Journal of Medical Genetics, 38, 542–547.
Hagerman, R. J., Schreiner, R. A., Kemper, M. B., Wittenberger, M. D., Zahn, B., & Habicht, K. (1989). Longitudinal IQ changes in fragile X males. American Journal of Medical Genetics, 33, 513–518.
Hall, S. S., Burns, D. D., & Reiss, A. L. (2007). Modeling family dynamics in children with fragile X syndrome. Journal of Abnormal Child Psychology, 35, 29–42.
Hay, D. A. (1994). Does IQ decline with age in fragile-X? A methodological critique. American Journal of Medical Genetics, 51, 358–363.
Kemper, M. B., Hagerman, R. J., Ahmad, R. S., & Mariner, R. (1986). Cognitive profiles and the spectrum of clinical manifestations in heterozygous fra (X) females. American Journal of Medical Genetics, 23, 139–156.
Loesch, D. Z., Huggins, R. M., & Hagerman, R. J. (2004). Phenotypic variation and FMRP levels in fragile X. Mental Retardation and Developmental Disabilities Research Reviews, 10, 31–41.
Mazzocco, M. M., Hagerman, R. J., & Pennington, B. F. (1992). Problem solving limitations among cytogenetically expressing fragile X women. American Journal of Medical Genetics, 43, 78–86.
Miezejeski, C. M., Jenkins, E. C., Hill, A. L., Wisniewski, K., French, J. H., & Brown, W. T. (1986). A profile of cognitive deficit in females from fragile X families. Neuropsychologia, 24(3), 405–409.
Munir, F., Cornish, K. M., & Wilding, J. (2000). A neuropsychological profile of attention deficits in young males with fragile X syndrome. Neuropsychologia, 38(9), 1261–1270.
Reiss, A. L., Freund, L. S., Baumgardner, T. L., Abrams, M. T., & Denckla, M. B. (1995). Contribution of the FMR1 gene mutation to human intellectual dysfunction. Nature Genetics, 11, 331–334.
Roberts, J. E., Mirrett, P., & Burchinal, M. (2001). Receptive and expressive communication development of young males with fragile X syndrome. American Journal on Mental Retardation, 106, 216–230.
Skinner, M., Hooper, S., Hatton, D. D., Roberts, J., Mirrett, P., Schaaf, J., et al. (2005). Mapping nonverbal IQ in young boys with fragile X syndrome. American Journal of Medical Genetics, 132A, 25–32.
Tassone, F., Hagerman, R. J., Gane, L. W., & Taylor, A. K. (1999a). Strong similarities of the FMR1 mutation in multiple tissues: Postmortem studies of a male with a full mutation and a male carrier of a premutation. American Journal of Medical Genetics, 84, 240–244.
Tassone, F., Hagerman, R. J., Ikle, D. N., Dyer, P. N., Lampe, M., Willemsen, R., et al. (1999b). FMRP expression as a potential prognostic indicator in fragile X syndrome. American Journal of Medical Genetics, 84, 250–261.
Tomarken, A. J., & Waller, N. G. (2003). Potential problems with “well fitting” models. Journal of Abnormal Psychology, 112, 578–598.
Verkerk, A. J., Pieretti, M., Sutcliffe, J. S., Fu, Y. H., Kuhl, D. P., Pizzuti, A., et al. (1991). Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell, 65, 905–914.
Wechsler, D. (1991). Wechsler Intelligence Scale for Children - Third Edition. Manual. San Antonio: The Psychological Corporation.
Wiegers, A. M., Curfs, L. M., Vermeer, E. L., & Fryns, J. P. (1993). Adaptive behavior in the fragile X syndrome: Profile and development. American Journal of Medical Genetics, 47, 216–220.
Willemsen, R., Anar, B., Otero, Y. D., de Vries, B. B., Hilhorst-Hofstee, Y., Smits, A., et al. (1999). Noninvasive test for fragile X syndrome, using hair root analysis. American Journal of Human Genetics, 65, 98–103.
Willemsen, R., Smits, A., Mohkamsing, S., van Beerendonk, H., de Haan, A., de Vries, B., et al. (1997). Rapid antibody test for diagnosing fragile X syndrome: A validation of the technique. Human Genetics, 99(3), 308–311.
Wright-Talamante, C., Cheema, A., Riddle, J. E., Luckey, D. W., Taylor, A. K., & Hagerman, R. J. (1996). A controlled study of longitudinal IQ changes in females and males with fragile X syndrome. American Journal of Medical Genetics, 64, 350–355.
Acknowledgements
The authors would like to thank the families for their participation in this project. This research was supported by NIH grants MH50047 and MH01142.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hall, S.S., Burns, D.D., Lightbody, A.A. et al. Longitudinal Changes in Intellectual Development in Children with Fragile X Syndrome. J Abnorm Child Psychol 36, 927–939 (2008). https://doi.org/10.1007/s10802-008-9223-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10802-008-9223-y