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Depression and worry symptoms predict future executive functioning impairment via inflammation

Published online by Cambridge University Press:  03 March 2021

Nur Hani Zainal Open the ORCID record for Nur Hani Zainal [Opens in a new window]  and
Michelle G. Newman Open the ORCID record for Michelle G. Newman [Opens in a new window]
Nur Hani Zainal*
Affiliation:
Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA
Michelle G. Newman
Affiliation:
Department of Psychology, The Pennsylvania State University, University Park, PA 16802, USA
*
Author for correspondence: Nur Hani Zainal, E-mail: nvz5057@psu.edu
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Abstract

Background

Scar models posit that heightened anxiety and depression can increase the risk for subsequent reduced executive function (EF) through increased inflammation across months. However, the majority of past research on this subject used cross-sectional designs. We therefore examined if elevated generalized anxiety disorder (GAD), major depressive disorder (MDD), and panic disorder (PD) symptoms forecasted lower EF after 20 months through heightened inflammation.

Methods

Community-dwelling adults partook in this study (n = 614; MAGE = 51.80 years, 50% females). Time 1 (T1) symptom severity (Composite International Diagnostic Interview – Short Form), T2 (2 months after T1) inflammation serum levels (C-reactive protein, fibrinogen, interleukin-6), and T3 (20 months after T1) EF (Brief Test of Adult Cognition by Telephone) were assessed. Structural equation mediation modeling was performed.

Results

Greater T1 MDD and GAD (but not PD) severity predicted increased T2 inflammation (Cohen's d = 0.21–1.92). Moreover, heightened T2 inflammation forecasted lower T3 EF (d = −1.98 to −1.87). T2 inflammation explained 25–32% of the negative relations between T1 MDD or GAD and T3 EF. T1 GAD severity predicting T3 EF via T2 inflammation path was stronger among younger (v. older) adults. Direct effects of T1 MDD, GAD, and PD forecasting decreased T3 EF were found (d = −2.02 to −1.92). Results remained when controlling for socio-demographic, physical health, and lifestyle factors.

Conclusions

Inflammation can function as a mechanism of the T1 MDD or GAD–T3 EF associations. Interventions that successfully treat depression, anxiety, and inflammation-linked disorders may avert EF decrements.

Keywords

executive functiongeneralized anxiety disorderinflammationlongitudinalmajor depressive disorderpanic disorderscar theorystructural equation model
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 15 , November 2022 , pp. 3625 - 3635
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Abel, J. L. (1994, November). Alexithymia in an analogue sample of generalized anxiety disorder and non-anxious matched controls. Paper presented at the 28th annual meeting of the Association for Advancement of Behavior Therapy, San Diego, CA.Google Scholar
Allott, K., Fisher, C. A., Amminger, G. P., Goodall, J., & Hetrick, S. (2016). Characterizing neurocognitive impairment in young people with major depression: State, trait, or scar? Brain and Behavior, 6, e00527. doi: 10.1002/brb3.527.CrossRefGoogle ScholarPubMed
Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: A meta-analytic review. Neuropsychology Review, 16, 17–42. doi: 10.1007/s11065-006-9002-x.CrossRefGoogle ScholarPubMed
American Psychiatric Association (1987). Diagnostic and statistical manual of mental disorders (3rd, rev. ed.). Washington, DC: American Psychiatric Association.Google Scholar
Averill, L. A., Abdallah, C. G., Levey, D. F., Han, S., Harpaz-Rotem, I., Kranzler, H. R., … Pietrzak, R. H. (2019). Apolipoprotein E gene polymorphism, posttraumatic stress disorder, and cognitive function in older U.S. veterans: Results from the National Health and Resilience in Veterans Study. Depression and Anxiety, 36, 834–845. doi: 10.1002/da.22912.CrossRefGoogle ScholarPubMed
Balter, L. J. T., Bosch, J. A., Aldred, S., Drayson, M. T., Veldhuijzen van Zanten, J. J. C. S., Higgs, S., … Mazaheri, A. (2019). Selective effects of acute low-grade inflammation on human visual attention. Neuroimage, 202, 116098. doi: 10.1016/j.neuroimage.2019.116098.CrossRefGoogle ScholarPubMed
Bennett, S., & Thomas, A. J. (2014). Depression and dementia: Cause, consequence or coincidence? Maturitas, 79, 184–190. doi: 10.1016/j.maturitas.2014.05.009.CrossRefGoogle ScholarPubMed
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi: 10.1037/0033-2909.107.2.238.CrossRefGoogle ScholarPubMed
Beydoun, M. A., Weiss, J., Obhi, H. K., Beydoun, H. A., Dore, G. A., Liang, H., … Zonderman, A. B. (2019). Cytokines are associated with longitudinal changes in cognitive performance among urban adults. Brain, Behavior, and Immunity, 80, 474–487. doi: 10.1016/j.bbi.2019.04.027.CrossRefGoogle ScholarPubMed
Bollen, J., Trick, L., Llewellyn, D., & Dickens, C. (2017). The effects of acute inflammation on cognitive functioning and emotional processing in humans: A systematic review of experimental studies. Journal of Psychosomatic Research, 94, 47–55. doi: 10.1016/j.jpsychores.2017.01.002.CrossRefGoogle ScholarPubMed
Bosaipo, N. B., Foss, M. P., Young, A. H., & Juruena, M. F. (2017). Neuropsychological changes in melancholic and atypical depression: A systematic review. Neuroscience & Biobehavioral Reviews, 73, 309–325. doi: 10.1016/j.neubiorev.2016.12.014.CrossRefGoogle ScholarPubMed
Brüünsgaard, H., & Pedersen, B. K. (2003). Age-related inflammatory cytokines and disease. Immunology and Allergy Clinics of North America, 23, 15–39. doi: 10.1016/S0889-8561(02)00056-5.CrossRefGoogle ScholarPubMed
Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershfield, H., Samanez-Larkin, G. R., … Nesselroade, J. R. (2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26, 21–33. doi: 10.1037/a0021285.CrossRefGoogle ScholarPubMed
Clauss, A. (1957). Gerinnungsphysiologische Schnellmethode zur Bestimmung des Fibrinogens. Acta Haematologica, 17, 237–246. doi: 10.1159/000205234.CrossRefGoogle Scholar
Cohen, J. (1988). Statistical power for the social sciences. Hillsdale, NJ: Laurence Erlbaum and Associates.Google Scholar
Copeland, W. E., Shanahan, L., Worthman, C., Angold, A., & Costello, E. J. (2012a). Cumulative depression episodes predict later C-reactive protein levels: A prospective analysis. Biological Psychiatry, 71, 15–21. doi: 10.1016/j.biopsych.2011.09.023.CrossRefGoogle ScholarPubMed
Copeland, W. E., Shanahan, L., Worthman, C., Angold, A., & Costello, E. J. (2012b). Generalized anxiety and C-reactive protein levels: A prospective, longitudinal analysis. Psychological Medicine, 42, 2641–2650. doi: 10.1017/S0033291712000554.CrossRefGoogle Scholar
Daniels, T. E., Olsen, E. M., & Tyrka, A. R. (2020). Stress and psychiatric disorders: The role of mitochondria. Annual Review of Clinical Psychology, 16, 165–186. doi: 10.1146/annurev-clinpsy-082719-104030.CrossRefGoogle ScholarPubMed
Darweesh, S. K. L., Wolters, F. J., Ikram, M. A., de Wolf, F., Bos, D., & Hofman, A. (2018). Inflammatory markers and the risk of dementia and Alzheimer's disease: A meta-analysis. Alzheimer's & Dementia, 14, 1450–1459. doi: 10.1016/j.jalz.2018.02.014.CrossRefGoogle ScholarPubMed
Deng, L., Yang, M., & Marcoulides, K. M. (2018). Structural equation modeling with many variables: A systematic review of issues and developments. Frontiers in Psychology, 9, 580. doi: 10.3389/fpsyg.2018.00580.CrossRefGoogle ScholarPubMed
Deverts, D. J., Cohen, S., DiLillo, V. G., Lewis, C. E., Kiefe, C., Whooley, M., & Matthews, K. A. (2010). Depressive symptoms, race, and circulating C-reactive protein: The coronary artery risk development in young adults (CARDIA) study. Psychosomatic Medicine, 72, 734–741. doi: 10.1097/PSY.0b013e3181ec4b98.CrossRefGoogle ScholarPubMed
Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychological Methods, 1, 170–177. doi: 10.1037/1082-989x.1.2.170.CrossRefGoogle Scholar
Dunst, C. J., Hamby, D. W., & Trivette, C. M. (2004). Guidelines for calculating effect sizes for practice-based research syntheses. Centerscope, 3, 1–10.Google Scholar
Eyre, H., & Baune, B. T. (2012). Neuroimmunological effects of physical exercise in depression. Brain, Behavior, and Immunity, 26, 251–266. doi: 10.1016/j.bbi.2011.09.015.CrossRefGoogle ScholarPubMed
Follmer, D. J. (2018). Executive function and reading comprehension: A meta-analytic review. Educational Psychologist, 53, 42–60. doi: 10.1080/00461520.2017.1309295.CrossRefGoogle Scholar
Friedman, E. M., & Herd, P. (2010). Income, education, and inflammation: Differential associations in a national probability sample (The MIDUS Study). Psychosomatic Medicine, 72, 290–300. doi: 10.1097/psy.0b013e3181cfe4c2.CrossRefGoogle Scholar
Furtado, M., & Katzman, M. A. (2015). Neuroinflammatory pathways in anxiety, posttraumatic stress, and obsessive compulsive disorders. Psychiatry Research, 229, 37–48. doi: 10.1016/j.psychres.2015.05.036.CrossRefGoogle ScholarPubMed
Gallacher, J., Bayer, A., Lowe, G., Fish, M., Pickering, J., Pedro, S., … Ben-Shlomo, Y. (2010). Is sticky blood bad for the brain? Arteriosclerosis, Thrombosis, and Vascular Biology, 30, 599–604. doi: 10.1161/ATVBAHA.109.197368.CrossRefGoogle ScholarPubMed
Gimson, A., Schlosser, M., Huntley, J. D., & Marchant, N. L. (2018). Support for midlife anxiety diagnosis as an independent risk factor for dementia: A systematic review. BMJ Open, 8, e019399. doi: 10.1136/bmjopen-2017-019399.CrossRefGoogle ScholarPubMed
Glaus, J., von Känel, R., Lasserre, A. M., Strippoli, M.-P. F., Vandeleur, C. L., Castelao, E., … Merikangas, K. R. (2018). The bidirectional relationship between anxiety disorders and circulating levels of inflammatory markers: Results from a large longitudinal population-based study. Depression and Anxiety, 35, 360–371. doi: 10.1002/da.22710.CrossRefGoogle ScholarPubMed
Goldstein, S., & Naglieri, J. A. (2014). Handbook of executive functioning. New York: Springer.CrossRefGoogle Scholar
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. doi: 10.1146/annurev.psych.58.110405.085530.CrossRefGoogle ScholarPubMed
Hartman, J., & Frishman, W. H. (2014). Inflammation and atherosclerosis: A review of the role of interleukin-6 in the development of atherosclerosis and the potential for targeted drug therapy. Cardiology in Review, 22, 147–151. doi: 10.1097/crd.0000000000000021.CrossRefGoogle ScholarPubMed
Hostinar, C. E., Lachman, M., Mroczek, D., Seeman, T., & Miller, G. (2015). Additive contributions of childhood adversity and recent stressors to inflammation at midlife: Findings from the MIDUS study. Developmental Psychology, 51(11), 1630–1644. doi: 10.1037/dev0000049.CrossRefGoogle ScholarPubMed
Jacobson, N. C., & Newman, M. G. (2014). Avoidance mediates the relationship between anxiety and depression over a decade later. Journal of Anxiety Disorders, 28, 437–445. doi: 10.1016/j.janxdis.2014.03.007.CrossRefGoogle Scholar
Kessler, R. C., Andrews, G., Mroczek, D., Ustun, B., & Wittchen, H.-U. (1998). The World Health Organization Composite International Diagnostic Interview short-form (CIDI-SF). International Journal of Methods in Psychiatric Research, 7, 171–185. doi: 10.1002/mpr.47.CrossRefGoogle Scholar
Krogh, J., Benros, M. E., Jørgensen, M. B., Vesterager, L., Elfving, B., & Nordentoft, M. (2014). The association between depressive symptoms, cognitive function, and inflammation in major depression. Brain, Behavior, and Immunity, 35, 70–76. doi: 10.1016/j.bbi.2013.08.014.CrossRefGoogle ScholarPubMed
Lachman, M. E., Agrigoroaei, S., Tun, P. A., & Weaver, S. L. (2014). Monitoring cognitive functioning: Psychometric properties of the brief test of adult cognition by telephone. Assessment, 21, 404–417. doi: 10.1177/1073191113508807.CrossRefGoogle ScholarPubMed
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. doi: 10.3389/fpsyg.2013.00863.CrossRefGoogle Scholar
Li, C.-H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48, 936–949. doi: 10.3758/s13428-015-0619-7.CrossRefGoogle ScholarPubMed
Liao, H., Li, Y., & Brooks, G. (2016). Outlier impact and accommodation methods: Multiple comparisons of type I error rates. Journal of Modern Applied Statistical Methods, 15, 452–471. doi: 10.22237/jmasm/1462076520.CrossRefGoogle Scholar
Liukkonen, T., Räsänen, P., Jokelainen, J., Leinonen, M., Järvelin, M. R., Meyer-Rochow, V. B., & Timonen, M. (2011). The association between anxiety and C-reactive protein (CRP) levels: Results from the Northern Finland 1966 Birth Cohort Study. European Psychiatry, 26, 363–369. doi: 10.1016/j.eurpsy.2011.02.001.CrossRefGoogle ScholarPubMed
Liukkonen, T., Silvennoinen-Kassinen, S., Jokelainen, J., Räsänen, P., Leinonen, M., Meyer-Rochow, V. B., & Timonen, M. (2006). The association between C-reactive protein levels and depression: Results from the Northern Finland 1966 Birth Cohort Study. Biological Psychiatry, 60, 825–830. doi: 10.1016/j.biopsych.2006.02.016.CrossRefGoogle ScholarPubMed
Love, G. D., Seeman, T. E., Weinstein, M., & Ryff, C. D. (2010). Bioindicators in the MIDUS national study: Protocol, measures, sample, and comparative context. Journal of Aging and Health, 22, 1059–1080. doi: 10.1177/0898264310374355.CrossRefGoogle Scholar
Lucassen, P. J., Pruessner, J., Sousa, N., Almeida, O. F. X., Van Dam, A. M., Rajkowska, G., … Czéh, B. (2014). Neuropathology of stress. Acta Neuropathologica, 127, 109–135. doi: 10.1007/s00401-013-1223-5.CrossRefGoogle ScholarPubMed
Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434–445. doi: 10.1038/nrn2639.CrossRefGoogle ScholarPubMed
Mac Giollabhui, N., Swistun, D., Murray, S., Moriarity, D. P., Kautz, M. M., Ellman, L. M., … Alloy, L. B. (2020). Executive dysfunction in depression in adolescence: The role of inflammation and higher body mass. Psychological Medicine, 50, 683–691. doi: 10.1017/S0033291719000564.CrossRefGoogle Scholar
Majd, M., Saunders, E. F. H., & Engeland, C. G. (2020). Inflammation and the dimensions of depression: A review. Frontiers in Neuroendocrinology, 56, 100800. doi: 10.1016/j.yfrne.2019.100800.CrossRefGoogle ScholarPubMed
Maslowsky, J., Jager, J., & Hemken, D. (2015). Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method. International Journal of Behavioral Development, 39, 87–96. doi: 10.1177/0165025414552301.CrossRefGoogle ScholarPubMed
Matthews, K. A., Schott, L. L., Bromberger, J., Cyranowski, J., Everson-Rose, S. A., & Sowers, M. F. (2007). Associations between depressive symptoms and inflammatory/hemostatic markers in women during the menopausal transition. Psychosomatic Medicine, 69, 124–130. doi: 10.1097/01.psy.0000256574.30389.1b.CrossRefGoogle ScholarPubMed
McEwen, B. S. (2007). Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews, 87, 873–904. doi: 10.1152/physrev.00041.2006.CrossRefGoogle ScholarPubMed
McEwen, B. S., & Gianaros, P. J. (2011). Stress- and allostasis-induced brain plasticity. Annual Review of Medicine, 62, 431–445. doi: 10.1146/annurev-med-052209-100430.CrossRefGoogle ScholarPubMed
Miller, A. H., & Raison, C. L. (2016). The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nature Reviews Immunology, 16, 22–34. doi: 10.1038/nri.2015.5.CrossRefGoogle Scholar
Mooijaart, S. P., Sattar, N., Trompet, S., Lucke, J., Stott, D. J., Ford, I., … de Craen, A. J. M. (2013). Circulating interleukin-6 concentration and cognitive decline in old age: The PROSPER study. Journal of Internal Medicine, 274, 77–85. doi: 10.1111/joim.12052.CrossRefGoogle ScholarPubMed
Moriarity, D. P., Ng, T., Titone, M. K., Chat, I. K. Y., Nusslock, R., Miller, G. E., & Alloy, L. B. (2020). Reward responsiveness and ruminative styles interact to predict inflammation and mood symptomatology. Behavior Therapy, 51, 829–842. doi: 10.1016/j.beth.2019.11.007.CrossRefGoogle ScholarPubMed
Morozink, J. A., Friedman, E. M., Coe, C. L., & Ryff, C. D. (2010). Socioeconomic and psychosocial predictors of interleukin-6 in the MIDUS national sample. Health Psychology, 29, 626–635. doi: 10.1037/a0021360.CrossRefGoogle ScholarPubMed
Niles, A. N., Smirnova, M., Lin, J., & O'Donovan, A. (2018). Gender differences in longitudinal relationships between depression and anxiety symptoms and inflammation in the health and retirement study. Psychoneuroendocrinology, 95, 149–157. doi: 10.1016/j.psyneuen.2018.05.035.CrossRefGoogle ScholarPubMed
Ottaviani, C., Watson, D. R., Meeten, F., Makovac, E., Garfinkel, S. N., & Critchley, H. D. (2016). Neurobiological substrates of cognitive rigidity and autonomic inflexibility in generalized anxiety disorder. Biological Psychology, 119, 31–41. doi: 10.1016/j.biopsycho.2016.06.009.CrossRefGoogle ScholarPubMed
Pariante, C. M. (2017). Why are depressed patients inflamed? A reflection on 20 years of research on depression, glucocorticoid resistance and inflammation. European Neuropsychopharmacology, 27, 554–559. doi: 10.1016/j.euroneuro.2017.04.001CrossRefGoogle ScholarPubMed
Petersen, M. A., Ryu, J. K., & Akassoglou, K. (2018). Fibrinogen in neurological diseases: Mechanisms, imaging and therapeutics. Nature Reviews Neuroscience, 19, 283–301. doi: 10.1038/nrn.2018.13.CrossRefGoogle ScholarPubMed
Prince, M., Ali, G.-C., Guerchet, M., Prina, A. M., Albanese, E., & Wu, Y.-T. (2016). Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimer's Research & Therapy, 8, 23. doi: 10.1186/s13195-016-0188-8.CrossRefGoogle ScholarPubMed
Renna, M. E., O'Toole, M. S., Spaeth, P. E., Lekander, M., & Mennin, D. S. (2018). The association between anxiety, traumatic stress, and obsessive–compulsive disorders and chronic inflammation: A systematic review and meta-analysis. Depression and Anxiety, 35, 1081–1094. doi: 10.1002/da.22790.CrossRefGoogle ScholarPubMed
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.CrossRefGoogle Scholar
Ryff, C., Almeida, D., Ayanian, J., Binkley, N., Carr, D. S., Coe, C., … Williams, D. (2017). Midlife in the United States (MIDUS Refresher), 2011-2014: Inter-university Consortium for Political and Social Research [distributor].Google Scholar
Ryff, C. D., & Lachman, M. E. (2018). Midlife in the United States (MIDUS Refresher): Cognitive Project, 2011-2014: Inter-university Consortium for Political and Social Research [distributor].Google Scholar
Snyder, H. R., Miyake, A., & Hankin, B. L. (2015). Advancing understanding of executive function impairments and psychopathology: Bridging the gap between clinical and cognitive approaches. Frontiers in Psychology, 6, 328. doi: 10.3389/fpsyg.2015.00328.CrossRefGoogle ScholarPubMed
Spyridaki, E. C., Avgoustinaki, P. D., & Margioris, A. N. (2016). Obesity, inflammation and cognition. Current Opinion in Behavioral Sciences, 9, 169–175. doi: 10.1016/j.cobeha.2016.05.004.CrossRefGoogle Scholar
Steel, Z., Marnane, C., Iranpour, C., Chey, T., Jackson, J. W., Patel, V., & Silove, D. (2014). The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. International Journal of Epidemiology, 43, 476–493. doi: 10.1093/ije/dyu038.CrossRefGoogle ScholarPubMed
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. doi: 10.1207/s15327906mbr2502_4.CrossRefGoogle ScholarPubMed
Stewart, J. C., Rand, K. L., Muldoon, M. F., & Kamarck, T. W. (2009). A prospective evaluation of the directionality of the depression–inflammation relationship. Brain, Behavior, and Immunity, 23, 936–944. doi: 10.1016/j.bbi.2009.04.011.CrossRefGoogle ScholarPubMed
Strawbridge, R., Arnone, D., Danese, A., Papadopoulos, A., Herane Vives, A., & Cleare, A. J. (2015). Inflammation and clinical response to treatment in depression: A meta-analysis. European Neuropsychopharmacology, 25, 1532–1543. doi: 10.1016/j.euroneuro.2015.06.007.CrossRefGoogle ScholarPubMed
Su, K.-P., Tseng, P.-T., Lin, P.-Y., Okubo, R., Chen, T.-Y., Chen, Y.-W., & Matsuoka, Y. J. (2018). Association of use of omega-3 polyunsaturated fatty acids with changes in severity of anxiety symptoms: A systematic review and meta-analysis. JAMA Network Open, 1, e182327. doi: 10.1001/jamanetworkopen.2018.2327.CrossRefGoogle ScholarPubMed
Sumner, J. A., Chen, Q., Roberts, A. L., Winning, A., Rimm, E. B., Gilsanz, P., … Kubzansky, L. D. (2017). Cross-sectional and longitudinal associations of chronic posttraumatic stress disorder with inflammatory and endothelial function markers in women. Biological Psychiatry, 82, 875–884. doi: 10.1016/j.biopsych.2017.06.020.CrossRefGoogle ScholarPubMed
Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: Strengths, limitations, and misconceptions. Annual Review of Clinical Psychology, 1, 31–65. doi: 10.1146/annurev.clinpsy.1.102803.144239.CrossRefGoogle ScholarPubMed
Von Känel, R., Bellingrath, S., & Kudielka, B. M. (2009). Association between longitudinal changes in depressive symptoms and plasma fibrinogen levels in school teachers. Psychophysiology, 46, 473–480. doi: 10.1111/j.1469-8986.2009.00788.x.CrossRefGoogle ScholarPubMed
Wagner, E.-Y. N., Wagner, J. T., Glaus, J., Vandeleur, C. L., Castelao, E., Strippoli, M.-P. F., … von Känel, R. (2015). Evidence for chronic low-grade systemic inflammation in individuals with agoraphobia from a population-based prospective study. PLoS ONE, 10, e0123757. doi: 10.1371/journal.pone.0123757.CrossRefGoogle ScholarPubMed
Warren, K. N., Beason-Held, L. L., Carlson, O., Egan, J. M., An, Y., Doshi, J., … Resnick, S. M. (2018). Elevated markers of inflammation are associated with longitudinal changes in brain function in older adults. Journals of Gerontology: Medical Sciences, 73, 770–778. doi: 10.1093/gerona/glx199.CrossRefGoogle Scholar
Weaver, J. D., Huang, M. H., Albert, M., Harris, T., Rowe, J. W., & Seeman, T. E. (2002). Interleukin-6 and risk of cognitive decline. Neurology, 59, 371. doi: 10.1212/WNL.59.3.371.CrossRefGoogle ScholarPubMed
Weinstein, M., Ryff, C. D., & Seeman, T. E. (2019). Midlife in the United States (MIDUS Refresher): Biomarker Project, 2012-2016: Inter-university Consortium for Political and Social Research [distributor].Google Scholar
Wen, Z., & Fan, X. (2015). Monotonicity of effect sizes: Questioning kappa-squared as mediation effect size measure. Psychological Methods, 20, 193–203. doi: 10.1037/met0000029.CrossRefGoogle ScholarPubMed
Wittchen, H.-U. (1994). Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): A critical review. Journal of Psychiatric Research, 28, 57–84. doi: 10.1016/0022-3956(94)90036-1.CrossRefGoogle ScholarPubMed
Zahodne, L. B., Kraal, A. Z., Zaheed, A., Farris, P., & Sol, K. (2019). Longitudinal effects of race, ethnicity, and psychosocial disadvantage on systemic inflammation. SSM – Population Health, 7, 100391. doi: 10.1016/j.ssmph.2019.100391.CrossRefGoogle ScholarPubMed
Zainal, N. H., & Newman, M. G. (2018). Executive function and other cognitive deficits are distal risk factors of generalized anxiety disorder 9 years later. Psychological Medicine, 48, 2045–2053. doi: 10.1017/S0033291717003579.CrossRefGoogle ScholarPubMed
Zainal, N. H., & Newman, M. G. (2020). Mindfulness enhances cognitive functioning: A meta-analysis of 95 randomized controlled trials. Manuscript submitted for publication. doi: 10.31234/osf.io/vzxw7.CrossRefGoogle Scholar
Zainal, N. H., & Newman, M. G. (2021). Larger increase in trait negative affect is associated with greater future cognitive decline and vice versa across 23 years. Depression and Anxiety, 38, 146–160. doi:10.1002/da.23093.CrossRefGoogle ScholarPubMed
Zainal, N. H., & Newman, M. G. (in press). Within-person increase in pathological worry predicts future depletion of unique executive functioning domains. Psychological Medicine, 1–11. doi:10.1017/S0033291720000422.Google Scholar
Zhang, Z. (2014). Monte Carlo based statistical power analysis for mediation models: Methods and software. Behavior Research Methods, 46, 1184–1198. doi: 10.3758/s13428-013-0424-0.CrossRefGoogle ScholarPubMed
Zheng, F., & Xie, W. (2018). High-sensitivity C-reactive protein and cognitive decline: The English longitudinal study of ageing. Psychological Medicine, 48, 1381–1389. doi: 10.1017/S0033291717003130.CrossRefGoogle ScholarPubMed