The association between cognitive function and subsequent depression: a systematic review and meta-analysis.
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2017-01
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Despite a growing interest in understanding the cognitive deficits associated with major depressive disorder (MDD), it is largely unknown whether such deficits exist before disorder onset or how they might influence the severity of subsequent illness. The purpose of the present study was to conduct a systematic review and meta-analysis of longitudinal datasets to determine whether cognitive function acts as a predictor of later MDD diagnosis or change in depression symptoms. Eligible studies included longitudinal designs with baseline measures of cognitive functioning, and later unipolar MDD diagnosis or symptom assessment. The systematic review identified 29 publications, representing 34 unique samples, and 121 749 participants, that met the inclusion/exclusion criteria. Quantitative meta-analysis demonstrated that higher cognitive function was associated with decreased levels of subsequent depression (r = -0.088, 95% confidence interval. -0.121 to -0.054, p < 0.001). However, sensitivity analyses revealed that this association is likely driven by concurrent depression symptoms at the time of cognitive assessment. Our review and meta-analysis indicate that the association between lower cognitive function and later depression is confounded by the presence of contemporaneous depression symptoms at the time of cognitive assessment. Thus, cognitive deficits predicting MDD likely represent deleterious effects of subclinical depression symptoms on performance rather than premorbid risk factors for disorder.
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Scult, MA, AR Paulli, ES Mazure, TE Moffitt, AR Hariri and TJ Strauman (2017). The association between cognitive function and subsequent depression: a systematic review and meta-analysis. Psychol Med, 47(1). pp. 1–17. 10.1017/S0033291716002075 Retrieved from https://hdl.handle.net/10161/13834.
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Terrie E. Moffitt
Terrie E. Moffitt, Ph.D., is the Nannerl O. Keohane University Professor of Psychology at Duke University, and Professor of Social Development at King’s College London. Her expertise is in the areas of longitudinal methods, developmental theory, mental disorders and antisocial behaviors, neuropsychology, and genomics in behavioral science. She is currently uncovering the consequences of a lifetime of mental and behavioral disorder on processes of aging. She is the Associate Director of the Dunedin Longitudinal Study, which follows a 1972 birth cohort in New Zealand. She also co-founded the Environmental Risk Longitudinal Twin Study (E-Risk), which follows a 1994 birth cohort in the UK. Dr. Moffitt also is a licensed clinical psychologist, with specialization in neuropsychological assessment. She collaborates with criminologists, economists, geneticists, epidemiologists, sociologists, demographers, gerontologists, statisticians, neuroscientists, medical scientists, opthalmologists, and dentists. Dr. Moffitt is a fellow of the National Academy of Medicine and the American Academy of Arts and Sciences , as well as the British Academy, Academy of Medical Sciences (UK), Academia Europa, Association of Psychological Science, and the American Society of Criminology. She holds honorary doctorates from the Katholieke Universiteit Leuven, Belgium, and Universitat Basel, Switzerland. For her research, Dr. Moffitt has received both the American Psychological Association's Early Career Contribution Award and Distinguished Career Award. Dr. Moffitt was also awarded a Royal Society-Wolfson Merit Award, the Klaus-Grawe Prize, and was a recipient of the Stockholm Prize in Criminology, NARSAD Ruane Prize, the Klaus J. Jacobs Research Prize, and in 2022 the Grawemeyer Prize. Her service includes serving as chair of the Board on Behavioral, Cognitive, and Sensory Science at NASEM, Chair of the NIA Data Monitoring Committee for the Health and Retirement Study, and Chair of the Jury for the Klaus J. Jacobs Prize in Switzerland. Dr. Moffitt attended the University of North Carolina at Chapel Hill for her undergraduate degree in psychology. She continued her training in psychology at the University of Southern California, receiving an M.A. in experimental animal behavior, and a Ph.D. in clinical psychology. She also completed postdoctoral training in geriatrics and neuropsychology at the University of California Los Angeles Neuropsychiatric Institute. In her spare time, she works on her poison-ivy farm in North Carolina.
Ahmad Hariri
Integrating psychology, neuroimaging, pharmacology and molecular genetics in the search for biological pathways mediating individual differences in behavior and related risk for psychopathology.
Timothy J. Strauman
Professor Strauman’s work is grounded in the premise that mental health and well-being are fundamentally shaped by self-regulation—how individuals pursue goals, respond to challenges, and adapt over time. His research integrates clinical psychology, affective neuroscience, and behavioral science to characterize the psychological and neurobiological systems that support self-regulation, and to understand how disruptions in these systems contribute to vulnerability to depression and related conditions.
Across a program of experimental, clinical, and neuroimaging research, his work has examined self-regulation as a multi-level system, including its cognitive and motivational mechanisms, its development through socialization, and its links to affective and immunological processes. This work has also informed the development and evaluation of novel interventions targeting self-regulatory dysfunction.
More recently, his work has focused on translating this science of self-regulation into scalable approaches to intervention and prevention. This includes the development of new models of treatment that target regulatory processes across disorders, as well as efforts to extend effective self-regulation skills beyond traditional clinical settings and into everyday contexts. This translational focus reflects a broader aim of building integrated, system-level approaches to mental health that can improve outcomes at population scale.
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