Widowhood and the Stability of Late Life Depressive Symptomatology in the Swedish Adoption Twin Study of Aging
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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.
KeywordsDepressive 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.
- Bruce ML (1980) Depression and disability in late life: directions for future research. Adv Pathobiol 7:247–248Google Scholar
- Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- 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
- Kline RB (2005) Principles and practice of structural equation modeling, 2nd edn. Guilford Press, New YorkGoogle Scholar
- McDonald RP (1999) Test theory: a unified treatment. Lawrence Erlbaum Associates, MahwahGoogle Scholar
- Muthén LK, Muthén BO (1998–2012) Mplus user’s guide. Muthén and Muthén, Los AngelesGoogle Scholar