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An Exploratory Study of Pathways from White Matter Hyperintensities to Cognitive Impairment through Depressive Symptoms Using Structural Equation Modeling: A Cross Sectional Study in Patients with Dementia

Published online by Cambridge University Press:  18 March 2020

Chang Hyun Lee  and
Do Hoon Kim Open the ORCID record for Do Hoon Kim [Opens in a new window]
Chang Hyun Lee
Affiliation:
Department of Psychiatry, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea Mind-Neuromodulation laboratory, Chuncheon, Republic of Korea, Hallym University College of Medicine, Chuncheon, Republic of Korea
Do Hoon Kim*
Affiliation:
Department of Psychiatry, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea Mind-Neuromodulation laboratory, Chuncheon, Republic of Korea, Hallym University College of Medicine, Chuncheon, Republic of Korea
*
*Correspondence and reprint requests to: Do Hoon Kim, PhD, Department of Psychiatry, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea. E-mail: [email protected]
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Abstract

Objective:

The aim of this study was to model the relationships among white matter hyperintensities (WMHs), depressive symptoms, and cognitive function and to examine the mediating effect of depressive symptoms on the relationship between WMHs and cognitive impairment.

Methods:

We performed structural equation modeling using cross-sectional data from 1158 patients from the Clinical Research for Dementia of South Korea (CREDOS) registry who were diagnosed with mild-to-moderate dementia. Periventricular white matter hyperintensities (PWMHs) and deep white matter hyperintensities (DWMHs) were obtained separately on the protocol of magnetic resonance imaging (MRI). Depression and cognitive function were assessed using the Korean Form of the Geriatric Depression Scale (KGDS) and the Seoul Neuropsychological Screening Battery (SNSB), respectively.

Results:

The model that best reflected the relationships among the variables was the model in which DWMHs affected cognitive function directly and indirectly through the depressive symptoms; on the other hand, PWMHs only directly affected cognitive function.

Conclusions:

This study presents the mediation model including the developmental pathway from DWMHs to cognitive impairment through depressive symptoms and suggests that the two types of WMHs may affect cognitive impairment through different pathways.

Keywords

DementiaWhite matter hyperintensitiesGeriatric depressionAge-related cognitive impairmentCerebrovascular diseaseStructural equation modeling
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 7 , August 2020 , pp. 679 - 689
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

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References

REFERENCES

Almeida, O.P., Hankey, G.J., Yeap, B.B., Golledge, J., & Flicker, L. (2016). Depression as a risk factor for cognitive impairment in later life: the Health in Men cohort study. International Journal of Geriatric Psychiatry, 31(4), 412–420.CrossRefGoogle ScholarPubMed
Brickman, A.M., Honig, L.S., Scarmeas, N., Tatarina, O., Sanders, L., Albert, M.S., Brandt, J., Blacker, D., & Stern, Y. (2008). Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer disease. Archives of Neurology, 65(9), 1202–1208.CrossRefGoogle ScholarPubMed
Brookes, R.L., Herbert, V., Lawrence, A.J., Morris, R.G., & Markus, H.S. (2014). Depression in small-vessel disease relates to white matter ultrastructural damage, not disability. Neurology, 83(16), 1417–1423.CrossRefGoogle Scholar
Brunet, J., Hudon, C., Macoir, J., Belleville, S., Rousseau, F., Bouchard, R.W., Verret, L., Chertkow, H., Chayer, C., Kergoat, M.J., & Joubert, S. (2011). The relation between depressive symptoms and semantic memory in amnestic mild cognitive impairment and in late-life depression. Journal of the International Neuropsychological Society, 17(5), 865–874.CrossRefGoogle ScholarPubMed
Byers, A.L. & Yaffe, K. (2011). Depression and risk of developing dementia. Nature Reviews Neurology, 7(6), 323–331.CrossRefGoogle ScholarPubMed
Checkley, S (1996). The neuroendocrinology of depression and chronic stress. British Medical Bulletin, 52, 597–617.CrossRefGoogle ScholarPubMed
Cho, M.J., Bae, J.N., Suh, G.H., Hahm, B.J., Kim, J.K., Lee, D.W., & Kang, M.H. (1999). Validation of geriatric depression scale, Korean version (GDS) in the assessment of DSM-III-R major depression. Journal of Korean Neuropsychiatric Association, 38(1), 48–63.Google Scholar
Choi, S.H., Na, D.L., Lee, B.H., Hahm, D.S., Jeong, J.H., Yoon, S.J., Yoo, K.H., Ha, C.K., & Han, I.W. (2001). Estimating the validity of the Korean version of expanded clinical dementia rating (CDR) scale. Journal of the Korean Neurological Association, 19(6), 585.Google Scholar
DeCarli, C., Fletcher, E., Ramey, V., Harvey, D., & Jagust, W.J. (2005). Anatomical mapping of white matter hyperintensities (wmh) exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke, 36(1), 50–55.CrossRefGoogle ScholarPubMed
De Groot, J., De Leeuw, F., Oudkerk, M., Van Gijn, J., Hofman, A., Jolles, J., & Breteler, M.M. (2000). Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Annals of Neurology, 47(2), 145–151.3.0.CO;2-P>CrossRefGoogle ScholarPubMed
De Groot, J., De Leeuw, F., Oudkerk, M., Van Gijn, J., Hofman, A., Jolles, J., & Breteler, M.M. (2002). Periventricular cerebral white matter lesions predict rate of cognitive decline. Annals of Neurology, 52, 335–341. https://doi.org/10.1002/ana.10294CrossRefGoogle ScholarPubMed
Diniz, B.S., Butters, M.A., Albert, S.M., Dew, M.A., & Reynolds, C.F. (2013). Late-life depression and risk of vascular dementia and Alzheimer’s disease: systematic review and meta-analysis of community-based cohort studies. The British Journal of Psychiatry, 202(5), 329–335. https://doi.org/10.1192/bjp.bp.112.118307CrossRefGoogle ScholarPubMed
Dong, H.-S., Han, C., Jeon, S.W., Yoon, S., Jeong, H.-G., Huh, Y.J., Pae, C.U., Patkar, A.A., & Steffens, D.C. (2016). Characteristics of neurocognitive functions in mild cognitive impairment with depression. International Psychogeriatrics, 28(7), 1181–1190. https://doi.org/10.1017/S1041610216000314CrossRefGoogle ScholarPubMed
Frances, A., Pincus, H.A., & First, M.B. (1994). Diagnostic and statistical manual of mental disorders: DSM-IV. Washington, DC: American Psychiatric Association.Google Scholar
Fazekas, F., Chawluk, J., Alavi, A., Hurtig, H., & Zimmerman, R. (1987). MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. American Journal of Roentgenology, 149(2), 351–356. https://doi.org/10.2214/ajr.149.2.351CrossRefGoogle Scholar
Finch, J.F., West, S.G., & MacKinnon, D.P. (1997). Effects of sample size and nonnormality on the estimation of mediated effects in latent variable models. Structural Equation Modeling: A Multidisciplinary Journal, 4(2), 87–107. https://doi.org/10.1080/10705519709540063CrossRefGoogle Scholar
Griffanti, L., Jenkinson, M., Suri, S., Zsoldos, E., Mahmood, A., Filippini, N., Sexton, C.E., Topiwala, A., Allan, C., Kivimäki, M., Singh-Manoux, A., Ebmeier, K.P., Mackay, C.E., & Zamboni, G. (2018). Classification and characterization of periventricular and deep white matter hyperintensities on MRI: a study in older adults. Neuroimage, 170, 174–181.CrossRefGoogle ScholarPubMed
Godin, O., Dufouil, C., Maillard, P., Delcroix, N., Mazoyer, B., Crivello, F., Alpérovitch, A., & Tzourio, C. (2008). White matter lesions as a predictor of depression in the elderly: the 3C-Dijon study. Biological Psychiatry, 63, 663–669.CrossRefGoogle ScholarPubMed
Gorelick, P.B. (2004). Risk factors for vascular dementia and Alzheimer disease. Stroke, 35(11), 2620–2622. https://doi.org/10.1161/01.STR.0000143318.70292.47CrossRefGoogle ScholarPubMed
Hashem, A.H., A Gomaa, M.A., & Khalaf, O.O. (2017). Late versus early onset depression in elderly patients: vascular risk and cognitive impairment. Current aging science, 10(3), 211–216.CrossRefGoogle ScholarPubMed
Heckman, J.J. (1977). Dummy endogenous variables in a simultaneous equation system. National Bureau of Economic Research Cambridge, Mass., USA. Retrieved from http://www.nber.org/papers/w0177.ackCrossRefGoogle Scholar
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2.Google Scholar
Johnson, L.A., Mauer, C., Jahn, D., Song, M., Wyshywaniuk, L., Hall, J.R., Balldin, V.H., & O’Bryant, S.E. (2013). Cognitive differences among depressed and non-depressed MCI participants: a project FRONTIER study. International journal of geriatric psychiatry, 28(4), 377–382.CrossRefGoogle ScholarPubMed
Jorm, A.F. (2000). Is depression a risk factor for dementia or cognitive decline? Gerontology, 46(4), 219–227.CrossRefGoogle ScholarPubMed
Jorm, A.F. (2001). History of depression as a risk factor for dementia: an updated review. Australian & New Zealand Journal of Psychiatry, 35(6), 776–781.CrossRefGoogle ScholarPubMed
Kang, Y.W., Jin, J.H., Na, D.L., Lee, J.H., & Park, J.S. (2000). A normative study of the Korean version of Controlled Oral Word Association Test (COWAT) in the elderly. Korean Journal of Clinical Psychology, 19(2), 385–392.Google Scholar
Kang, Y.W., Jin, J.H., Na, D.L. (2002). A normative study of the digit span test for the elderly. Korean Journal of Clinical Psychology, 21(4), 911–922.Google Scholar
Kang, Y.W., Kim, H.H., & Na, D.L. (1999). A short form of the Korean-Boston Naming Test (K-BNT) for using in dementia patients. Korean Journal of Clinical Psychology, 18(2), 125–138.Google Scholar
Kang, Y.W. & Na, D.L. (2003a). Seoul verbal learning test (SVLT). Seoul: Human Brain Research and Consulting Co.Google Scholar
Kang, Y.W. & Na, D.L. (2003b). go-no-go test. Seoul: Human Brain Research and Consulting Co.Google Scholar
Kang, Y.W. & Na, D.L. (2003c). contrasting programs test. Seoul: Human Brain Research and Consulting Co.Google Scholar
Kang, Y.W., Na, D.L., & Hahn, S.H. (2003). Seoul neuropsychological screening battery. Incheon: Human Brain Research & Consulting Co.Google Scholar
Kim, T.Y., Kim, S.Y., Son, J.E., Lee, E.A., Yu, B.K., Lee, S.C., Hong, T.Y., & Kim, M.J. (2004). Development of the Korean stroop test and study of the validity and the reliability. Journal of the Korean Geriatrics Society, 8(4): 233–240Google Scholar
Kim, K.W., MacFall, J.R., & Payne, M.E. (2008). Classification of white matter lesions on magnetic resonance imaging in the elderly. Biological Psychiatry, 64(4), 273–280. https://doi.org/10.1016/j.biopsych.2008.03.024CrossRefGoogle Scholar
Kim, H.J., Noh, Y., Cho, H., Yoon, C., Ye, B.S., Kim, G.H., Jeon, S., Lee, J.M., Kim, J.H., Choe, Y.S., Lee, K.H., Kim, C.S., Lee, J.H., Na, D.L., & Seo, S.W. (2013). Longitudinal changes in white matter disease and cognition in subcortical vascular mild cognitive impairment. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 9(4), P837.CrossRefGoogle Scholar
Kim, J.M., Prince, M.J., Shin, I.S., & Yoon, J.S. (2001). Validity of Korean form of Geriatric Depression Scale (KGDS) among cognitively impaired Korean elderly and development of a 15-item short version (KGDS-15). International Journal of Methods in Psychiatric Research, 10(4), 204–210.CrossRefGoogle Scholar
Koenig, A.M., Bhalla, R.K., & Butters, M.A. (2014). Cognitive functioning and late-life depression. Journal of the International Neuropsychological Society, 20(5), 461–467.CrossRefGoogle ScholarPubMed
Krishnan, M.S., O’Brien, J.T., Firbank, M.J., Pantoni, L., Carlucci, G., Erkinjuntti, T., Wallin, A., Wahlund, L.O., Scheltens, P., Elisabeth, C.W., & Inzitari, D. (2006). Relationship between periventricular and deep white matter lesions and depressive symptoms in older people. The LADIS Study. International Journal of Geriatric Psychiatry, 21(10), 983–989. https://doi.org/10.1002/gps.1596CrossRefGoogle ScholarPubMed
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E.M. (1984). Clinical diagnosis of Alzheimer’s disease Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–939.CrossRefGoogle ScholarPubMed
Meyers, J.E. & Meyers, K.R. (1995). Rey Complex Figure Test and Recognition Trial Professional Manual. Odessa, Florida: Psychological Assessment Resources.Google Scholar
Ownby, R.L., Crocco, E., Acevedo, A., John, V., & Loewenstein, D. (2006). Depression and risk for Alzheimer disease: systematic review, meta-analysis, and metaregression analysis. Archives of general psychiatry, 63(5), 530–538.CrossRefGoogle ScholarPubMed
Panza, F., Frisardi, V., Capurso, C., D’Introno, A., Colacicco, A.M., Imbimbo, B.P., Santamato, A., Vendemiale, G., Seripa, D., Pilotto, A., Capurso, A., & Solfrizzi, V. (2010). Late-life depression, mild cognitive impairment, and dementia: possible continuum? The American Journal of Geriatric Psychiatry, 18(2), 98–116.CrossRefGoogle ScholarPubMed
Park, K.H., Lee, J.Y., Na, D.L., Kim, S.Y., Cheong, H.K., Moon, S.Y., Shim, Y.S., Park, K.W., Ku, B.D., Choi, S.H., Joo, H., Lee, J.S., Go, S.M., Kim, S.H., Kim, S.Y., Cha, K.R., Lee, J.W., & Seo, S.W. (2011). Different associations of periventricular and deep white matter lesions with cognition, neuropsychiatric symptoms, and daily activities in dementia. J Geriatr Psychiatry Neurol, 24, 84–90. https://doi.org/10.1177/0891988711402351CrossRefGoogle Scholar
Prins, N.D., van Dijk, E.J., den Heijer, T., Vermeer, S.E., Jolles, J., Koudstaal, P.J., Hofman, A., & Breteler, M.M. (2005). Cerebral small-vessel disease and decline in information processing speed, executive function and memory. Brain, 128(9), 2034–2041.CrossRefGoogle ScholarPubMed
Rapp, M.A., Schnaider-Beeri, M., Wysocki, M., Guerrero-Berroa, E., Grossman, H.T., Heinz, A., & Haroutunian, V. (2011). Cognitive in patients with dementia as a function of depression. The American Journal of Geriatric Psychiatry, 19(4), 357–363. https://doi.org/10.1097/JGP.0b013e3181e898d0CrossRefGoogle ScholarPubMed
Reppermund, S. (2016). Depression in old age—The first step to dementia? The Lancet Psychiatry, 3(7), 593–595.CrossRefGoogle Scholar
Selden, N.R., Gitelman, D.R., Salamon-Murayama, N., Parrish, T.B., Mesulam, M.M. (1998). Trajectories of cholinergic pathways within thecerebral hemispheres of the human brain. Brain, 121(12), 2249–2257.CrossRefGoogle Scholar
Sheline, Y.I., Sanghavi, M., Mintun, M.A., & Gado, M.H. (1999). Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression. Journal of Neuroscience, 19(12), 5034–5043.CrossRefGoogle Scholar
Singh-Manoux, A., Dugravot, A., Fournier, A., Abell, J., Ebmeier, K., Kivimäki, M., & Sabia, S. (2017). Trajectories of depressive symptoms before diagnosis of dementia: a 28-year follow-up study. JAMA Psychiatry, 74(7), 712–718. https://doi.org/10.1001/jamapsychiatry.2017.0660CrossRefGoogle ScholarPubMed
Snowden, M.B., Atkins, D.C., Steinman, L.E., Bell, J.F., Bryant, L.L., Copeland, C., & Fitzpatrick, A.L. (2015). Longitudinal association of dementia and depression. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry, 23(9), 897–905. https://doi.org/10.1016/j.jagp.2014.09.002CrossRefGoogle ScholarPubMed
Spalletta, G., Caltagirone, C., Girardi, P., Gianni, W., Casini, A.R., & Palmer, K. (2012). The role of persistent and incident major depression on rate of cognitive deterioration in newly diagnosed Alzheimer’s disease patients. Psychiatry Research, 198(2), 263–268.CrossRefGoogle ScholarPubMed
Steffens, D.C. (2017). Late-life depression and the prodromes of dementia. JAMA Psychiatry, 74(7), 673–674. https://doi.org/10.1001/jamapsychiatry.2017.0658CrossRefGoogle ScholarPubMed
Taylor, W.D., Aizenstein, H.J., & Alexopoulos, G.S. (2013). The vascular depression hypothesis: mechanisms linking vascular disease with depression. Molecular Psychiatry, 18(9), 963–974. https://doi.org/10.1038/mp.2013.20CrossRefGoogle ScholarPubMed
Taylor, W.D., MacFall, J.R., Payne, M.E., McQuoid, D.R., Steffens, D.C., Provenzale, J.M., & Krishnan, R.R. (2005). Greater MRI lesion volumes in elderly depressed subjects than in control subjects. Psychiatry Research: Neuroimaging, 139(1), 1–7.CrossRefGoogle ScholarPubMed
Teodorczuk, A., Firbank, M.J., Pantoni, L., Poggesi, A., Erkinjuntti, T., Wallin, A., Wahlund, L.O., Scheltens, P., Waldemar, G., Schrotter, G., Ferro, J.M., Chabriat, H., Bazner, H., Visser, M., Inzitari, D., & O'Brien, J.T. (2010). Relationship between baseline white-matter changes and development of late-life depressive symptoms: 3-year results from the LADIS study. Psychological Medicine, 40(4), 603–610. https://doi.org/10.1017/S0033291709990857CrossRefGoogle ScholarPubMed
Tupler, L.A., Krishnan, R.R., McDonald, W.M., Dombeck, C.B., D’Souza, S., & Steffens, D.C. (2002). Anatomic location and laterality of MRI signal hyperintensities in late-life depression. Journal of Psychosomatic Research, 53(2), 665–676.CrossRefGoogle ScholarPubMed
Wen, W. & Sachdev, P. (2004). The topography of white matter hyperintensities on brain MRI in healthy 60-to 64-year-old individuals. Neuroimage, 22(1), 144–154.CrossRefGoogle ScholarPubMed
Wen, W., Sachdev, P.S., Li, J.J., Chen, X., & Anstey, K.J. (2009). White matter hyperintensities in the forties: their prevalence and topography in an epidemiological sample aged 44-48. Hum Brain Mapp, 30(4), 1155–1167. https://doi.org/10.1002/hbm.20586CrossRefGoogle Scholar
Winship, C. & Mare, R.D. (1983). Structural equations and path analysis for discrete data. American Journal of Sociology, 89(1), 54–110.CrossRefGoogle Scholar
Yoon, B., Shim, Y.S., Cheong, H.-K., Hong, Y.J., Lee, K.S., Park, K.H., Ahn, K.J., Kim, D.J., Kim, Y.D., Choi, S.H., & Yang, D.W. (2014). White matter hyperintensities in mild cognitive impairment: clinical impact of location and interaction with lacunes and medial temporal atrophy. Journal of Stroke and Cerebrovascular Diseases, 23, 365–372.CrossRefGoogle ScholarPubMed