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Does the Environment Have an Enduring Effect on ADHD? A Longitudinal Study of Monozygotic Twin Differences in Children

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Abstract

Environmental factors play a key role in the development of Attention-Deficit/Hyperactivity Disorder (ADHD), but the long-term effects of these factors are still unclear. This study analyses data from 1024 monozygotic (identical) twins in Australia, the United States, and Scandinavia who were assessed for ADHD in Preschool, Kindergarten, Grade 1, and Grade 2. Differences within each twin pair were used as a direct measure of non-shared environmental effects. The Trait-State-Occasion (TSO) model developed by Cole et al. (Psychological Methods, 10, 3–20, 2005) was used to separate the non-shared environmental effects into stable factors, and transient factors that excluded measurement error. Stable factors explained, on average, 44 % and 39 % of the environmental variance in hyperactive-impulsive and inattentive symptoms, respectively. Transient effects explained the remaining 56 % and 60 % of variance. The proportion of stable variance was higher than expected based on previous research, suggesting promise for targeted interventions if future research identifies these stable risk factors.

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Acknowledgments

This research is being conducted with the support of the Australian Research Council (A79906201, DP0770805, and DP0663498), the National Institutes of Health (2 P50 HD27802 and 1 R01 HD38526), the Research Council of Norway (154715/330), the Swedish Research Council (345-2002-3701), and the University of Stavanger. We are grateful to the many twins, their families, and the twins’ teachers for their participation, and to the Colorado and Australian Twin Registries.

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Authors and Affiliations

  1. School of Behavioural, Cognitive, and Social Sciences, University of New England, Armidale, NSW, Australia

    Luisa T. Livingstone, William L. Coventry & Brian Byrne

  2. Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA

    Robin P. Corley, Erik G. Willcutt & Richard K. Olson

  3. Department of Behavioral Sciences, Linköping University, Linköping, Sweden

    Stefan Samuelsson

  4. National Centre for Reading Education and Research, Stavanger University, Stavanger, Norway

    Stefan Samuelsson

Authors
  1. Luisa T. Livingstone
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  2. William L. Coventry
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  3. Robin P. Corley
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  4. Erik G. Willcutt
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  5. Stefan Samuelsson
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  6. Richard K. Olson
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  7. Brian Byrne
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Correspondence to Luisa T. Livingstone.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

Appendices

Appendix A

Parameter Estimates for Hyperactivity and Inattention Models

Table 8 Complete list of model parameters estimated in Amos
Full size table

Appendix B

Variance Decomposition for Hyperactivity and Inattention Models

The variance at each time-point was decomposed into proportions accounted for by trait, state, occasion, and measurement error factors, using the steps below. These are based on the principles of path analysis, which are outlined in Plomin et al. (2008). Figure 1 shows the parameters used in this explanation.

Decomposing Observed Variables into State and Measurement Error Components

Variance (half-scale x at time t) = ftx 2 Variance (State t ) + Variance (e tx )

Percentage of variance in half-scale x at time t explained by the State factor = ftx 2 Variance (State t ) / Variance (half-scale x at time t)

Percentage of variance in half-scale x at time t explained by measurement error = Variance (e tx ) / Variance (half-scale x at time t)

Decomposing State Factors into Trait and Occasion Variance

Variance (State t ) = Variance (Trait) + Variance (Occ t )

Percentage of variance in Statet explained by the Trait factor = Variance (Trait) / Variance (State t )

Percentage of variance in Statet explained by the Occasion factor = Variance (Occ t ) / Variance (State t )

Decomposing Occasion Factors into Autoregressive and Disturbance Variance

Variance (Occt) = at-1 2 Variance (Occt-1) + Variance (ut-1)

Percentage of variance in Occt explained by the previous Occasion factor (autoregressive effect) = at-1 2 Variance (Occt-1) / Variance (Occt)

Percentage of variance in Occt explained by new variance (disturbance factor) = Variance (ut-1) / Variance (Occt)

Decomposing State Factors into Trait, Autoregressive, and Disturbance Variance

Variance (State t ) = Variance (Trait) + Variance (Occ t )

Variance (State t ) = Variance (Trait) + at-1 2 Variance (Occt-1) + Variance (ut-1)

Percentage of variance in Statet explained by the Trait factor = Variance (Trait) / Variance (State t )

Percentage of variance in Statet explained by the autoregressive effects = at-1 2 Variance (Occt-1) / Variance (State t )

Percentage of variance in Statet explained by the disturbance factor = Variance (ut-1) / Variance (State t )

Decomposing Observed Variables into Trait, Autoregressive, Disturbance, and Measurement Error Components

Variance (half-scale x at time t) = ftx 2 Variance (State t ) + Variance (e tx )

Variance (half-scale x at time t) = ftx 2 Variance (Trait) + ftx 2 at-1 2 Variance (Occt-1) + ftx 2 Variance (ut-1) + Variance (e tx )

Percentage of variance in half-scale x at time t explained by the Trait factor = ftx 2 (Variance (Trait) / Variance (half-scale x at time t))

Percentage of variance in half-scale x at time t explained by autoregressive effects = ftx 2 (at-1 2 Variance (Occt-1) / Variance (half-scale x at time t))

Percentage of variance in half-scale x at time t explained by the disturbance factor = ftx 2 (Variance (ut-1) / Variance (half-scale x at time t))

Percentage of variance in half-scale x at time t explained by measurement error = ftx 2 (Variance (e tx ) / Variance (half-scale x at time t))

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Livingstone, L.T., Coventry, W.L., Corley, R.P. et al. Does the Environment Have an Enduring Effect on ADHD? A Longitudinal Study of Monozygotic Twin Differences in Children. J Abnorm Child Psychol 44, 1487–1501 (2016). https://doi.org/10.1007/s10802-016-0145-9

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