Skip to main content
Log in

Genetic and Environmental Variation in Lung Function Drives Subsequent Variation in Aging of Fluid Intelligence

  • Original Research
  • Published:
Behavior Genetics Aims and scope Submit manuscript

Abstract

Longitudinal studies document an association of pulmonary function with cognitive function in middle-aged and older adults. Previous analyses have identified a genetic contribution to the relationship between pulmonary function with fluid intelligence. The goal of the current analysis was to apply the biometric dual change score model to consider the possibility of temporal dynamics underlying the genetic covariance between aging trajectories for pulmonary function and fluid intelligence. Longitudinal data from the Swedish Adoption/Twin Study of Aging were available from 808 twins ranging in age from 50 to 88 years at the first wave. Participants completed up to six assessments covering a 19-year period. Measures at each assessment included spatial and speed factors and pulmonary function. Model-fitting indicated that genetic variance for FEV1 was a leading indicator of variation in age changes for spatial and speed factors. Thus, these data indicate a genetic component to the directional relationship from decreased pulmonary function to decreased function of fluid intelligence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Albert MS, Jones K, Savage CR, Berkman L, Seeman T, Blazer D et al (1995) Predictors of cognitive change in older persons: MacArthur studies of successful aging. Psychol Aging 10(4):578–589

    Article  PubMed  Google Scholar 

  • Anstey KJ, Windsor TD, Jorm AF, Christensen H, Rodgers B (2004) Association of pulmonary function with cognitive performance in early, middle and late adulthood. Gerontology 50(4):230–234

    Article  PubMed  Google Scholar 

  • Bryk AS, Raudenbush SW (1992) Hierarchical linear models. Sage Publications, London

    Google Scholar 

  • Chowdhary R, Tan SL, Pavesi G, Jin J, Dong D, Mathur SK, Burkart A, Narang V, Glurich I, Raby BA, Weiss ST, Wong L, Liu JS, Bajic VB (2012) A database of annotated promoters of genes associated with common respiratory and related diseases. Am J Respir Cell Mol Biol 47:112–119

    Article  PubMed  Google Scholar 

  • Chyou PH, White LR, Yano K, Sharp DS, Burchfiel CM, Chen R et al (1996) Pulmonary function measures as predictors and correlates of cognitive functioning in later life. Am J Epidemiol 143(8):750–756

    Article  PubMed  Google Scholar 

  • Colcombe S, Kramer AF (2003) Fitness effects on the cognitive function of older adults: a metal-analytic study. Psychol Sci 14:125–130

    Article  PubMed  Google Scholar 

  • Davies G, Tenesa A, Payton A, Yang J, Harris SE, Liewald D et al (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol psychiatry 16(10):996–1005

    Google Scholar 

  • Deary IJ, Johnson W, Gow AJ, Pattie A, Brett CE, Bates TC, Starr JM (2011) Losing one’s grip: a bivariate growth curve model of grip strength and nonverbal reasoning from age 79 to 87 years in the Lothian Birth Cohort 1921. J Gerontol 66B:699–707

    Article  Google Scholar 

  • Emery CF, Huppert FA, Schein RL (1997) Do smoking and pulmonary function predict cognitive function? Findings from a British sample. Psychol Health 12:265–275

    Article  Google Scholar 

  • Emery CF, Pedersen NL, Svartengren M, McClearn GE (1998) Longitudinal and genetic effects in the relationship between pulmonary function and cognitive performance. J Gerontol 53(5):P311–P317

    Google Scholar 

  • Emery CF, Finkel D, Pedersen NL (2012) Pulmonary function as a leading cause of cognitive aging. Psychol Sci 23:1024–1032

    Article  PubMed  Google Scholar 

  • Finkel D, Pedersen NL (2004) Processing speed and longitudinal trajectories of change for cognitive abilities: the Swedish Adoption/Twin Study of Aging. Aging Neuropsychol Cogn 11:325–345

    Article  Google Scholar 

  • Finkel D, Reynolds CA, McArdle JJ, Pedersen NL (2005) The longitudinal relationship between processing speed and cognitive ability: genetic and environmental influences. Behav Genet 35:535–549

    Article  PubMed  Google Scholar 

  • Finkel D, Reynolds CA, McArdle JJ, Pedersen NL (2007) Age changes in processing speed as a leading indicator of cognitive aging. Psychol Aging 22:558–568

    Article  PubMed  Google Scholar 

  • Finkel D, Reynolds CA, McArdle JJ, Hamagami F, Pedersen NL (2009) Genetic variance in processing speed drives variation in aging of spatial and memory abilities. Dev Psychol 45:820–834

    Article  PubMed  Google Scholar 

  • Gatz M, Pedersen NL, Berg S, Johansson B, Johansson K, Mortimer JA, Posner SF, Viitanen M, Winblad B, Ahlbom A (1997) Heritability for Alzheimer’s disease: the study of dementia in Swedish twins. J Gerontol 52A:M117–M125

    Article  Google Scholar 

  • Ghisletta P, de Ribaupierre A (2005) A dynamic investigation of cognitive dedifferentiation with control for retest: evidence from the Swiss Interdisciplinary Longitudinal Study of the Oldest Old. Psychol Aging 20:671–682

    Article  PubMed  Google Scholar 

  • Laird NM, Ware H (1982) Random-effects models for longitudinal data. Biometrics 38:963–974

    Article  PubMed  Google Scholar 

  • Lövdén M, Ghisletta P, Lindenberger U (2005) Social participation attenuates decline in perceptual speed in old and very old age. Psychol Aging 20:423–434

    Article  PubMed  Google Scholar 

  • McArdle JJ (2001) A latent difference score approach to longitudinal dynamic structural analyses. In: Cudeck R, duToit S, Sorbom D (eds) Structural equation modeling: present and future. Scientific Software International, Lincolnwood, pp 342–380

    Google Scholar 

  • McArdle JJ, Anderson E (1990) Latent variable growth models for research on aging. In: Birren JE, Schaie KW (eds) Handbook of the psychology of aging, 3rd edn. Academic Press, New York, pp 21–44

    Google Scholar 

  • McArdle JJ, Hamagami F (2003) Structural equation models for evaluating dynamic concepts within longitudinal twin analyses. Behav Genet 33:137–159

    Article  PubMed  Google Scholar 

  • McArdle JJ, Prescott CA, Hamagami F, Horn JL (1998) A contemporary method for developmental-genetic analyses of age changes in intellectual abilities. Dev Neuropsychol 14:69–114

    Article  Google Scholar 

  • McArdle JJ, Hamagami F, Meredith W, Bradway KP (2000) Modeling the dynamic hypotheses of Gf–Gc theory using longitudinal life-span data. Learn Individ Differ 12:53–79

    Article  Google Scholar 

  • McArdle JJ, Hamagami F, Jones K, Jolesz F, Kikinis R, Spiro A III, Albert MS (2004) Structural modeling of dynamic changes in memory and brain structure using longitudinal data form the normative aging study. J Gerontol: Psychol Sci 59B:294–304

    Google Scholar 

  • McGue M, Christensen K (2001) The heritability of cognitive functioning in very old adults: evidence from Danish twins aged 75 years and older. Psychol Aging 16:272–280

    Article  PubMed  Google Scholar 

  • McGue M, Johnson W (2007) Genetics of cognitive aging. In: Craik FIM, Salthouse TA (eds) Handbook of aging and cognition, 3rd edn. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  • Neale MC, Cardon LR (1992) Methodology for genetic studies of twins and families. Kluwer Academic Publishers, London

    Book  Google Scholar 

  • Neale MC, Boker SM, Xie G, Maes HH (2003) Mx: statistical modeling, 6th edn. Department of Psychiatry, Richmond

    Google Scholar 

  • Nesselroade JR, Pedersen NL, McClearn GE, Plomin R, Bergeman CS (1988) Factorial and criterion validities of telephone-assessed cognitive ability measures: age and gender comparisons in adult twins. Res Aging 10:220–234

    Article  PubMed  Google Scholar 

  • Pedersen NL, Reynolds CA (1998) Stability and change in adult personality: genetic and environmental components. Eur J Pers 12:365–386

    Google Scholar 

  • Pedersen NL, Plomin R, Nesselroade JR, McClearn GE (1992) Quantitative genetic analysis of cognitive abilities during the second half of the lifespan. Psychol Sci 3:346–353

    Article  Google Scholar 

  • Plomin R, DeFries JC, McClearn GE, McGuffin P (2001) Behavioral genetics, 4th edn. Worth Publishers, New York

    Google Scholar 

  • Reynolds CA, Finkel D, Gatz M, Pedersen NL (2002) Sources of influences on rate of cognitive change over time in Swedish twins: an application of latent growth models. Exp Aging Res 28:407–433

    Article  PubMed  Google Scholar 

  • Reynolds CA, Finkel D, McArdle JJ, Gatz M, Berg S, Pedersen NL (2005) Quantitative genetic analysis of latent growth curve models of cognitive abilities in adulthood. Dev Psychol 41:3–16

    Article  PubMed  Google Scholar 

  • Richards M, Strachan D, Hardy R, Kuh D, Wadsworth M (2005) Lung function and cognitive ability in a longitudinal birth cohort study. Psychosom Med 67(4):602–608

    Article  PubMed  Google Scholar 

  • Schaie KW (1965) A general model for the study of developmental problems. Psychol Bull 64:91–107

    Article  Google Scholar 

  • Schaie KW (1970) A reinterpretation of age related changes in cognitive structure and functioning. In: Goulet LR, Baltes PB (eds) Life-span developmental psychology: research and theory. Academic Press, New York, pp 485–507

    Google Scholar 

  • Smiley-Oyen A, Lowry K, Francois S, Kohut M, Ekkekakis P (2008) Exercise, fitness, and neurocognitive function in older adults: the “selective improvement” and “cardiovascular fitness” hypotheses. Ann Behav Med 36:280–291

    Article  PubMed  Google Scholar 

  • Tucker-Drob E, Reynolds CA, Finkel D, Pedersen NL Shared and unique genetic and environmental influences on changes in multiple cognitive abilities over 16 years of late adulthood. Dev Psychol (in press)

  • Wicherts JM, Dolan CV, Hessen DJ, Oosterveld P, van Baal GCM, Boomsma DI, Span MM (2004) Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect. Intelligence 32:509–537

    Article  Google Scholar 

  • Zimprich D, Martin M (2002) Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? Psychol Aging 17(4):690–695

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The Swedish Adoption/Twin Study of Aging (SATSA) is supported by NIA (AG04563, AG10175), The MacArthur Foundation Research Network on Successful Aging, the Swedish Council for Social Research (97:0147:1B), and the Swedish Research Council.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deborah Finkel.

Additional information

Edited by Kristen Jacobson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Finkel, D., Reynolds, C.A., Emery, C.F. et al. Genetic and Environmental Variation in Lung Function Drives Subsequent Variation in Aging of Fluid Intelligence. Behav Genet 43, 274–285 (2013). https://doi.org/10.1007/s10519-013-9600-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10519-013-9600-3

Keywords

Navigation