Behavior Genetics

, Volume 46, Issue 1, pp 100–113 | Cite as

Widowhood and the Stability of Late Life Depressive Symptomatology in the Swedish Adoption Twin Study of Aging

  • Christopher R. Beam
  • Robert E. Emery
  • Chandra A. Reynolds
  • Margaret Gatz
  • Eric Turkheimer
  • Nancy L. Pedersen
Original Research


Although the Swedish Adoption Twin of Aging (SATSA) has been used to investigate phenotypic stability of late life depressive symptoms, the biometric processes underlying this stability have not been studied. Under a reciprocal effects modeling framework, we used SATSA twins’ Center for Epidemiologic Studies Depression (CES-D) Scale data across 5 waves (from 1987–2007) to test whether the reciprocal exchange between twins within a family and their nonshared environments (P<=>E) promote the accumulation of gene-environment correlation (rGE) over time. The model generates increasing rGE that produces subsequent stable environmental differences between twins within a family—a process hypothesized to explain stability in chronic late life depressive symptoms. Widowhood is included as a stressful life experience that may introduce an additional nonshared source of variability in CES-D scores. Genetic effects and nonshared environmental effects are primary sources of stability of late life depressive symptoms without evidence of underlying rGE processes. Additionally, widowhood explained stable differences in CES-D scores between twins within a family up to 3 years after spousal loss.


Depressive symptomatology Late life depression Reciprocal effects model Gene-environment correlation Nonshared environment 



This work was supported by the National Institute of Child Health and Human Development (1R01HD056354-01) and the National Institute on Aging (1F31AG044047-01A1 and T32AG020500).

Conflict of Interest


Human and Animal Rights and Informed Consent

This report does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Supplementary material

10519_2015_9733_MOESM1_ESM.docx (244 kb)
Supplementary material 1 (DOCX 244 kb)


  1. Alexopoulos GS (2005) Depression in the elderly. Lancet 365(9475):1961–1970PubMedCrossRefGoogle Scholar
  2. Beam CR, Turkheimer E (2013) Phenotype-environment correlations in longitudinal twin models. Dev Psychopathol 25(1):7–16PubMedCrossRefGoogle Scholar
  3. Beekman ATF, Geerlings SW, Deeg DJ, Smit JH, Schoevers RS, de Beurs E et al (2002) The natural history of late-life depression. Arch Gen Psychiatry 59(2002):605–611PubMedCrossRefGoogle Scholar
  4. Blazer DG (2003) Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci 58(3):249–265PubMedCrossRefGoogle Scholar
  5. Bonanno GA, Wortman CB, Lehman DR, Tweed RG, Haring M, Sonnega J et al (2002) Resilience to loss and chronic grief: A prospective study from preloss to 18-months postloss. J Pers Soc Psychol 83(5):1150–1164PubMedCrossRefGoogle Scholar
  6. Bonanno GA, Wortman CB, Nesse RM (2004) Prospective patterns of resilience and maladjustment during widowhood. Psychol Aging 19(2):260–271PubMedCrossRefGoogle Scholar
  7. Boomsma DI, Molenaar PCM (1987) The genetic analysis of repeated measures. I simplex models. Behav Genet 17(2):111–123PubMedCrossRefGoogle Scholar
  8. Browne MW, Cudeck R (1992) Alternative ways of assessing model fit. Soc Methods Res 21(2):230–258CrossRefGoogle Scholar
  9. Bruce ML (1980) Depression and disability in late life: directions for future research. Adv Pathobiol 7:247–248Google Scholar
  10. Bruce ML (2002) Psychosocial risk factors for depressive disorders in late life. Biol Psychiatry 52:175–184PubMedCrossRefGoogle Scholar
  11. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Soc Methods Res 33(2):261–304CrossRefGoogle Scholar
  12. Carmelli D, Swan GE, Kelly-Hayes M, Wolf PA, Reed T, Miller B (2000) Longitudinal changes in the contribution of genetic and environmental influences to symptoms of depression in older male twins. Psychol Aging 15(3):505–510PubMedCrossRefGoogle Scholar
  13. Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  14. De Kort J, Dolan C, Kan KJ, van Beijsterveldt C, Bartels M, Boomsma D (2014) Can GE-covariance originating in phenotype to environment transmission account for the Flynn Effect? J Intell 2(3):82–105CrossRefGoogle Scholar
  15. Dickens WT, Flynn JR (2001) Heritability estimates versus large environmental effects: the IQ paradox resolved. Psychol Rev 108(2):346–369PubMedCrossRefGoogle Scholar
  16. Dickens WT, Turkheimer E, Beam CR (2011) The social dynamics of the expression of genes for cognitive ability. In: Kendler KS, Jaffee S, Romer D (eds) The dynamic genome and mental health: The role of genes and environments in youth development. Oxford, New York, pp 103–127Google Scholar
  17. Dolan CV, de Kort JM, van Beijsterveldt TCEM, Bartels M, Boomsma DI (2014) GE Covariance through phenotype to environment transmission: an assessment in longitudinal twin data and application to childhood anxiety. Behav Genet 44(3):240–253PubMedPubMedCentralCrossRefGoogle Scholar
  18. Eaves LJ, Krystyna L, Martin NG, Jinks JL (1977) A progressive approach to non-additivity and genotype-environmental covariance in the analysis of human differences. Brit J Math Stat Psy 30:1–42CrossRefGoogle Scholar
  19. Eaves LJ, Long J, Heath AC (1986) A theory of developmental change in quantitative phenotypes applied to cognitive development. Behav Genet 16(1):143–162PubMedCrossRefGoogle Scholar
  20. Finkel D, Pedersen NL (2004) Processing speed and longitudinal trajectories of change for cognitive abilities: the Swedish Adoption/Twin Study of Aging. Aging, Neuropsychol, Cognit 11(2–3):325–345CrossRefGoogle Scholar
  21. Fiske A, Gatz M, Pedersen NL (2003) Depressive symptoms and aging: the effects of illness and non-health-related events. J Gerontrol 58(6):320–328CrossRefGoogle Scholar
  22. Fiske A, Wetherell JL, Gatz M (2009) Depression in older adults. Annu Rev Clin Psychol 5:363–389PubMedPubMedCentralCrossRefGoogle Scholar
  23. Galatzer-Levy IR, Bonanno GA (2012) Beyond normality in the study of bereavement: heterogeneity in depression outcomes following loss in older adults. Soc Sci Med 74(12):1987–1994PubMedPubMedCentralCrossRefGoogle Scholar
  24. Gatz M, Pedersen NL, Plomin R, Nesselroade JR, McClearn GE (1992) Importance of shared genes and shared environments for symptoms of depression in older adults. J Abnorm Psychol 101(4):701–708PubMedCrossRefGoogle Scholar
  25. Guttman L (1954) Some necessary conditions for common-factor analysis. Psychometrika 19:149–161CrossRefGoogle Scholar
  26. Hertzog C, Alstine JV, Usala PD, Hultsch DF, Dixon R (1990) Measurement properties of the Center for Epidemiological Studies Depression Scale (CES-D) in older populations. Psychol Assess 2(1):64–72CrossRefGoogle Scholar
  27. Jaffee SR, Price TS, Reyes TM (2013) Behavior genetics: past, present, future. Dev Psychopathol 25:1225–1242PubMedCrossRefGoogle Scholar
  28. Kendler KS, Baker JH (2007) Genetic influences on measures of the environment: a systematic review. Psych Med 37:615–626CrossRefGoogle Scholar
  29. Kendler KS, Prescott CA (1999) A population-based twin study of lifetime major depression in men and women. Arch Gen Psychiatry 56:39–44PubMedCrossRefGoogle Scholar
  30. Kline RB (2005) Principles and practice of structural equation modeling, 2nd edn. Guilford Press, New YorkGoogle Scholar
  31. Lee GR, DeMaris A (2007) Widowhood, gender, and depression: a longitudinal analysis. Res Aging 29(1):56–72CrossRefGoogle Scholar
  32. Lichtenstein P, Gatz M, Pedersen NL, Berg S, McClearn GE (1996) A co-twin-control study of response to widowhood. J Gerontrol 51B(5):P279–P289CrossRefGoogle Scholar
  33. Lucas RE, Clark AE, Georgellis Y, Diener E (2003) Reexamining adaptation and the set point model of happiness: reactions to changes in marital status. J Pers Soc Psychol 84(3):527–539PubMedCrossRefGoogle Scholar
  34. Martin-Matthews A (2011) Revisiting widowhood in later life: changes in patterns and profiles, advances in research and understanding. Can J Aging 30(3):339–354PubMedCrossRefGoogle Scholar
  35. McArdle JJ, Prescott CA (2005) Mixed-effects variance components models for biometric family analyses. Behav Genet 35(5):631–652PubMedCrossRefGoogle Scholar
  36. McDonald RP (1999) Test theory: a unified treatment. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  37. McGue M, Christensen K (2003) The heritability of depression symptoms in elderly Danish twins: occasion-specific versus general effects. Behav Genet 33(2):83–93PubMedCrossRefGoogle Scholar
  38. McGue M, Christensen K (2013) Growing old but not growing apart: twin similarity in the latter half of the lifespan. Behav Genet 43:1–12PubMedPubMedCentralCrossRefGoogle Scholar
  39. Muthén BO, Kaplan D (1985) A comparison of some methodologies for the factor analysis of non-normal Likert variables: a note on the size of the model. Br J Math Stat Psychol 38:171–189CrossRefGoogle Scholar
  40. Muthén LK, Muthén BO (1998–2012) Mplus user’s guide. Muthén and Muthén, Los AngelesGoogle Scholar
  41. Neale MC, Cardon LR (1992) Methodology for genetic studies of twins and families. Springer, NetherlandsCrossRefGoogle Scholar
  42. Neiss M, Almeida DA (2004) Age differences in the heritability of mean and intraindividual variation of psychological distress. Gerontology 50(1):22–27PubMedCrossRefGoogle Scholar
  43. Osler M, McGue M, Lund R, Christensen K (2008) Marital status and twins’ health and behavior: an analysis of middle-aged Danish twins. Psychosom Med 70(4):482–487PubMedPubMedCentralCrossRefGoogle Scholar
  44. Radloff LS (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1(3):385–401CrossRefGoogle Scholar
  45. Raykov T (2005) Analysis of longitudinal studies with missing data using covariance structure modeling with full-information maximum likelihood. Struct Equ Model 12(3):493–505CrossRefGoogle Scholar
  46. Satorra A, Bentler PM (2001) A scaled difference Chi square test statistic for moment structure analysis. Psychometrika 66(4):507–519CrossRefGoogle Scholar
  47. Shafer AB (2006) Meta-analysis of the factor structures of four depression questionnaires: beck, CES-D, Hamilton, and Zung. J Clin Psychol 62(1):123–146PubMedCrossRefGoogle Scholar
  48. Wetherell JL, Gatz M, Pedersen NL (2001) A longitudinal analysis of anxiety and depressive symptoms. Psychol Aging 16(2):187–195PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University of VirginiaCharlottesvilleUSA
  2. 2.University of CaliforniaRiversideUSA
  3. 3.Department of PsychologyUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Karolinska InstituteStockholmSweden

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