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Meta-analysis of CSF and MRI biomarkers for detecting preclinical Alzheimer's disease

Published online by Cambridge University Press:  29 October 2009

B. Schmand ,
H. M. Huizenga  and
W. A. van Gool
B. Schmand*
Affiliation:
Department of Neurology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
H. M. Huizenga
Affiliation:
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
W. A. van Gool
Affiliation:
Department of Neurology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
*
*Address for correspondence: B. Schmand, Ph.D., Department of Neurology, H2-222, Academic Medical Centre, PO Box 22660, 1100 DD Amsterdam, The Netherlands. (Email: [email protected])
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Abstract

Background

Abnormal levels of biomarkers in cerebrospinal fluid (CSF) and atrophy of medial temporal lobe (MTL) structures on magnetic resonance imaging (MRI) are being used increasingly to diagnose early Alzheimer's disease (AD). We evaluated the claim that these biomarkers can detect preclinical AD before behavioural (i.e. memory) symptoms arise.

Method

We included all relevant longitudinal studies of CSF and MRI biomarkers published between January 2003 and November 2008. Subjects were not demented at baseline but some declined to mild cognitive impairment (MCI) or to AD during follow-up. Measures of tau and beta-amyloid in CSF, MTL atrophy on MRI, and performance on delayed memory tasks were extracted from the papers or obtained from the investigators.

Results

Twenty-one MRI studies and 14 CSF studies were retrieved. The effect sizes of total tau (t-tau), phosphorylated tau (p-tau) and amyloid beta 42 (aβ42) ranged from 0.91 to 1.11. The effect size of MTL atrophy was 0.75. Memory performance had an effect size of 1.06. MTL atrophy and memory impairment tended to increase when assessed closer to the moment of diagnosis, whereas effect sizes of CSF biomarkers tended to increase when assessed longer before the diagnosis.

Conclusions

Memory impairment is a more accurate predictor of early AD than atrophy of MTL on MRI, whereas CSF abnormalities and memory impairment are about equally predictive. Consequently, the CSF and MRI biomarkers are not very sensitive to preclinical AD. CSF markers remain promising, but studies with long follow-up periods in elderly subjects who are normal at baseline are needed to evaluate this promise.

Keywords

Alzheimer's diseasebeta-amyloidcerebrospinal fluiddelayed recallhippocampuslongitudinal studiesmedial temporal lobememorymeta-analysismeta-regressionmild cognitive impairmentMRIphosphorylated tautotal tau
Type
Original Articles
Information
Psychological Medicine , Volume 40 , Issue 1 , January 2010 , pp. 135 - 145
Copyright
Copyright © Cambridge University Press 2009

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References

Andersson, C, Blennow, K, Almkvist, O, Andreasen, N, Engfeldt, P, Johansson, SE, Lindau, M, Eriksdotter-Jonhagen, M (2008). Increasing CSF phospho-tau levels during cognitive decline and progression to dementia. Neurobiology of Aging 29, 1466–1473.CrossRefGoogle ScholarPubMed
Apostolova, LG, Mosconi, L, Thompson, PM, Green, AE, Hwang, KS, Ramirez, A, Mistur, R, Tsui, WH, de Leon, MJ (2008). Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal. Neurobiology of Aging doi:10.1016/j.neurobiolaging.2008.08.008.Google ScholarPubMed
Bäckman, L, Jones, S, Berger, AK, Laukka, EJ, Small, BJ (2005). Cognitive impairment in preclinical Alzheimer's disease: a meta-analysis. Neuropsychology 19, 520–531.CrossRefGoogle ScholarPubMed
Blackwell, AD, Sahakian, BJ, Vesey, R, Semple, JM, Robbins, TW, Hodges, JR (2004). Detecting dementia: novel neuropsychological markers of preclinical Alzheimer's disease. Dementia and Geriatric Cognitive Disorders 17, 42–48.CrossRefGoogle ScholarPubMed
Blennow, K, Hampel, H (2003). CSF markers for incipient Alzheimer's disease. Lancet Neurology 2, 605–613.CrossRefGoogle ScholarPubMed
Bossuyt, PM, Reitsma, JB, Bruns, DE, Gatsonis, CA, Glasziou, PP, Irwig, LM, Lijmer, JG, Moher, D, Rennie, D, de Vet, HC (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. British Medical Journal 326, 41–44.CrossRefGoogle ScholarPubMed
Bouwman, FH, Schoonenboom, SN, van der Flier, WM, van Elk, EJ, Kok, A, Barkhof, F, Blankenstein, MA, Scheltens, P (2007). CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment. Neurobiology of Aging 28, 1070–1074.CrossRefGoogle ScholarPubMed
Brys, M, Pirraglia, E, Rich, K, Rolstad, S, Mosconi, L, Switalski, R, Glodzik-Sobanska, L, De Santi, S, Zinkowski, R, Mehta, P, Pratico, D, Saint Louis, LA, Wallin, A, Blennow, K, de Leon, MJ (2009). Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment. Neurobiology of Aging 30, 682–690.CrossRefGoogle ScholarPubMed
Buschke, H, Sliwinski, MJ, Kuslansky, G, Lipton, RB (1997). Diagnosis of early dementia by the Double Memory Test: encoding specificity improves diagnostic sensitivity and specificity. Neurology 48, 989–997.CrossRefGoogle ScholarPubMed
Carlson, NE, Moore, MM, Dame, A, Howieson, D, Silbert, LC, Quinn, JF, Kaye, JA (2008). Trajectories of brain loss in aging and the development of cognitive impairment. Neurology 70, 828–833.CrossRefGoogle ScholarPubMed
Carmichael, OT, Kuller, LH, Lopez, OL, Thompson, PM, Dutton, RA, Lu, A, Lee, SE, Lee, JY, Aizenstein, HJ, Meltzer, CC, Liu, Y, Toga, AW, Becker, JT (2007). Ventricular volume and dementia progression in the Cardiovascular Health Study. Neurobiology of Aging 28, 389–397.CrossRefGoogle ScholarPubMed
Csernansky, JG, Wang, L, Swank, J, Miller, JP, Gado, M, McKeel, D, Miller, MI, Morris, JC (2005). Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly. NeuroImage 25, 783–792.CrossRefGoogle ScholarPubMed
Davatzikos, C, Fan, Y, Wu, X, Shen, D, Resnick, SM (2008). Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging 29, 514–523.CrossRefGoogle ScholarPubMed
de Leon, MJ, DeSanti, S, Zinkowski, R, Mehta, PD, Pratico, D, Segal, S, Clark, C, Kerkman, D, DeBernardis, J, Li, J, Lair, L, Reisberg, B, Tsui, W, Rusinek, H (2004). MRI and CSF studies in the early diagnosis of Alzheimer's disease. Journal of Internal Medicine 256, 205–223.CrossRefGoogle ScholarPubMed
de Leon, MJ, Mosconi, L, Blennow, K, DeSanti, S, Zinkowski, R, Mehta, PD, Pratico, D, Tsui, W, Saint Louis, LA, Sobanska, L, Brys, M, Li, Y, Rich, K, Rinne, J, Rusinek, H (2007 a). Imaging and CSF studies in the preclinical diagnosis of Alzheimer's disease. Annals of the New York Academy of Sciences 109, 114–145.CrossRefGoogle Scholar
de Leon, MJ, Mosconi, L, Li, J, De Santi, S, Yao, Y, Tsui, WH, Pirraglia, E, Rich, K, Javier, E, Brys, M, Glodzik, L, Switalski, R, Saint Louis, LA, Pratico, D (2007 b). Longitudinal CSF isoprostane and MRI atrophy in the progression to AD. Journal of Neurology 254, 1666–1675.CrossRefGoogle ScholarPubMed
DeCarli, C, Mungas, D, Harvey, D, Reed, B, Weiner, M, Chui, H, Jagust, W (2004). Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology 63, 220–227.CrossRefGoogle Scholar
den Heijer, T, Geerlings, MI, Hoebeek, FE, Hofman, A, Koudstaal, PJ, Breteler, MM (2006). Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people. Archives of General Psychiatry 63, 57–62.CrossRefGoogle ScholarPubMed
Devanand, DP, Pradhaban, G, Liu, X, Khandji, A, De Santi, S, Segal, S, Rusinek, H, Pelton, GH, Honig, LS, Mayeux, R, Stern, Y, Tabert, MH, de Leon, MJ (2007). Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 68, 828–836.CrossRefGoogle ScholarPubMed
Eckerstrom, C, Olsson, E, Borga, M, Ekholm, S, Ribbelin, S, Rolstad, S, Starck, G, Edman, A, Wallin, A, Malmgren, H (2008). Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: the Goteborg MCI study. Journal of the Neurological Sciences 272, 48–59.CrossRefGoogle ScholarPubMed
Ewers, M, Buerger, K, Teipel, SJ, Scheltens, P, Schroder, J, Zinkowski, RP, Bouwman, FH, Schonknecht, P, Schoonenboom, NS, Andreasen, N, Wallin, A, DeBernardis, JF, Kerkman, DJ, Heindl, B, Blennow, K, Hampel, H (2007). Multicenter assessment of CSF-phosphorylated tau for the prediction of conversion of MCI. Neurology 69, 2205–2212.CrossRefGoogle ScholarPubMed
Fagan, AM, Roe, CM, Xiong, C, Mintun, MA, Morris, JC, Holtzman, DM (2007). Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Archives of Neurology 64, 343–349.CrossRefGoogle ScholarPubMed
Fellgiebel, A, Scheurich, A, Bartenstein, P, Muller, MJ (2007). FDG-PET and CSF phospho-tau for prediction of cognitive decline in mild cognitive impairment. Psychiatry Research 155, 167–171.CrossRefGoogle ScholarPubMed
Fleisher, AS, Sun, S, Taylor, C, Ward, CP, Gamst, AC, Petersen, RC, Jack, CR Jr., Aisen, PS, Thal, LJ (2008). Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment. Neurology 70, 191–199.CrossRefGoogle ScholarPubMed
Forsberg, A, Engler, H, Almkvist, O, Blomquist, G, Hagman, G, Wall, A, Ringheim, A, Langstrom, B, Nordberg, A (2008). PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiology of Aging 29, 1456–1465.CrossRefGoogle ScholarPubMed
Galton, CJ, Erzinclioglu, S, Sahakian, BJ, Antoun, N, Hodges, JR (2005). A comparison of the Addenbrooke's Cognitive Examination (ACE), conventional neuropsychological assessment, and simple MRI-based medial temporal lobe evaluation in the early diagnosis of Alzheimer's disease. Cognitive and Behavioral Neurology 18, 144–150.CrossRefGoogle ScholarPubMed
Geroldi, C, Rossi, R, Calvagna, C, Testa, C, Bresciani, L, Binetti, G, Zanetti, O, Frisoni, GB (2006). Medial temporal atrophy but not memory deficit predicts progression to dementia in patients with mild cognitive impairment. Journal of Neurology, Neurosurgery, and Psychiatry 77, 1219–1222.CrossRefGoogle Scholar
Growdon, JH (1999). Biomarkers of Alzheimer disease. Archives of Neurology 56, 281–283.CrossRefGoogle ScholarPubMed
Hall, AM, Moore, RY, Lopez, OL, Kuller, L, Becker, JT (2008). Basal forebrain atrophy is a presymptomatic marker for Alzheimer's disease. Alzheimer's and Dementia 4, 271–279.CrossRefGoogle ScholarPubMed
Hampel, H, Burger, K, Teipel, SJ, Bokde, AL, Zetterberg, H, Blennow, K (2008). Core candidate neurochemical and imaging biomarkers of Alzheimer's disease. Alzheimer's and Dementia 4, 38–48.CrossRefGoogle ScholarPubMed
Hansson, O, Zetterberg, H, Buchhave, P, Londos, E, Blennow, K, Minthon, L (2006). Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurology 5, 228–234.CrossRefGoogle ScholarPubMed
Herukka, SK, Helisalmi, S, Hallikainen, M, Tervo, S, Soininen, H, Pirttila, T (2007). CSF Abeta42, Tau and phosphorylated Tau, APOE epsilon4 allele and MCI type in progressive MCI. Neurobiology of Aging 28, 507–514.CrossRefGoogle ScholarPubMed
Herukka, SK, Pennanen, C, Soininen, H, Pirttila, T (2008). CSF Abeta42, tau and phosphorylated tau correlate with medial temporal lobe atrophy. Journal of Alzheimer's Disease 14, 51–57.CrossRefGoogle ScholarPubMed
Higgins, JP, Thompson, SG (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21, 1539–1558.CrossRefGoogle ScholarPubMed
Karas, G, Sluimer, J, Goekoop, R, van der Flier, W, Rombouts, SA, Vrenken, H, Scheltens, P, Fox, N, Barkhof, F (2008). Amnestic mild cognitive impairment: structural MR imaging findings predictive of conversion to Alzheimer disease. AJNR. American Journal of Neuroradiology 29, 944–949.CrossRefGoogle Scholar
Korf, ES, Wahlund, LO, Visser, PJ, Scheltens, P (2004). Medial temporal lobe atrophy on MRI predicts dementia in patients with mild cognitive impairment. Neurology 63, 94–100.CrossRefGoogle ScholarPubMed
Li, G, Sokal, I, Quinn, JF, Leverenz, JB, Brodey, M, Schellenberg, GD, Kaye, JA, Raskind, MA, Zhang, J, Peskind, ER, Montine, TJ (2007). CSF tau/Abeta42 ratio for increased risk of mild cognitive impairment: a follow-up study. Neurology 69, 631–639.CrossRefGoogle ScholarPubMed
Lindeboom, J, Schmand, B, Tulner, L, Walstra, G, Jonker, C (2002). Visual association test to detect early dementia of the Alzheimer type. Journal of Neurology, Neurosurgery, and Psychiatry 73, 126–133.CrossRefGoogle ScholarPubMed
Maruyama, M, Matsui, T, Tanji, H, Nemoto, M, Tomita, N, Ootsuki, M, Arai, H, Sasaki, H (2004). Cerebrospinal fluid tau protein and periventricular white matter lesions in patients with mild cognitive impairment: implications for 2 major pathways. Archives of Neurology 61, 716–720.CrossRefGoogle ScholarPubMed
McKhann, G, Drachman, D, Folstein, M, Katzman, R, Price, D, Stadlan, EM (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, 939–944.CrossRefGoogle ScholarPubMed
Morris, JC, McKeel, DW Jr., Fulling, K, Torack, RM, Berg, L (1988). Validation of clinical diagnostic criteria for Alzheimer's disease. Annals of Neurology 24, 17–22.CrossRefGoogle ScholarPubMed
Mosconi, L, Brys, M, Glodzik-Sobanska, L, De Santi, S, Rusinek, H, de Leon, MJ (2007). Early detection of Alzheimer's disease using neuroimaging. Experimental Gerontology 42, 129–138.CrossRefGoogle ScholarPubMed
Neuropathology Group of MRC CFAS (2001). Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS). Lancet 357, 169–175.Google Scholar
Parker, RI, Hagan-Burke, S (2007). Useful effect size interpretations for single case research. Behavior Therapy 38, 95–105.CrossRefGoogle ScholarPubMed
Parnetti, L, Lanari, A, Silvestrelli, G, Saggese, E, Reboldi, P (2006). Diagnosing prodromal Alzheimer's disease: role of CSF biochemical markers. Mechanisms of Ageing and Development 127, 129–132.CrossRefGoogle ScholarPubMed
Petersen, RC, Doody, R, Kurz, A, Mohs, RC, Morris, JC, Rabins, PV, Ritchie, K, Rossor, M, Thal, L, Winblad, B (2001). Current concepts in mild cognitive impairment. Archives of Neurology 58, 1985–1992.CrossRefGoogle ScholarPubMed
Petersen, RC, Smith, GE, Waring, SC, Ivnik, RJ, Tangalos, EG, Kokmen, E (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology 56, 303–308.CrossRefGoogle ScholarPubMed
Reagan Institute Working Group (1998). Consensus report of the Working Group on: ‘Molecular and Biochemical Markers of Alzheimer's Disease’. The Ronald and Nancy Reagan Research Institute of the Alzheimer's Association and the National Institute on Aging Working Group. Neurobiology of Aging 19, 109–116.CrossRefGoogle Scholar
Rosenberg, MS, Adams, DC, Gurevitch, J (2000). MetaWin. Statistical Software for Meta-Analysis. Version 2. Sinauer Associates: Sunderland, MA.Google Scholar
Rosenthal, R (1991). Meta-Analytic Procedures for Social Research. Sage: Newbury Park, CA.CrossRefGoogle Scholar
Rusinek, H, De Santi, S, Frid, D, Tsui, WH, Tarshish, CY, Convit, A, de Leon, MJ (2003). Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology 229, 691–696.CrossRefGoogle ScholarPubMed
Schonknecht, P, Pantel, J, Kaiser, E, Thomann, P, Schroder, J (2007). Increased tau protein differentiates mild cognitive impairment from geriatric depression and predicts conversion to dementia. Neuroscience Letters 416, 39–42.CrossRefGoogle ScholarPubMed
Shaw, LM, Korecka, M, Clark, CM, Lee, VM, Trojanowski, JQ (2007). Biomarkers of neurodegeneration for diagnosis and monitoring therapeutics. Nature Reviews Drug Discovery 6, 295–303.CrossRefGoogle ScholarPubMed
Skoog, I, Davidsson, P, Aevarsson, O, Vanderstichele, H, Vanmechelen, E, Blennow, K (2003). Cerebrospinal fluid beta-amyloid 42 is reduced before the onset of sporadic dementia: a population-based study in 85-year-olds. Dementia and Geriatric Cognitive Disorders 15, 169–176.CrossRefGoogle ScholarPubMed
Smith, CD, Chebrolu, H, Wekstein, DR, Schmitt, FA, Jicha, GA, Cooper, G, Markesbery, WR (2007). Brain structural alterations before mild cognitive impairment. Neurology 68, 1268–1273.CrossRefGoogle ScholarPubMed
Tapiola, T, Pennanen, C, Tapiola, M, Tervo, S, Kivipelto, M, Hanninen, T, Pihlajamaki, M, Laakso, MP, Hallikainen, M, Hamalainen, A, Vanhanen, M, Helkala, EL, Vanninen, R, Nissinen, A, Rossi, R, Frisoni, GB, Soininen, H (2008). MRI of hippocampus and entorhinal cortex in mild cognitive impairment: a follow-up study. Neurobiology of Aging 29, 31–38.CrossRefGoogle ScholarPubMed
Tarkowski, E, Andreasen, N, Tarkowski, A, Blennow, K (2003). Intrathecal inflammation precedes development of Alzheimer's disease. Journal of Neurology, Neurosurgery, and Psychiatry 74, 1200–1205.CrossRefGoogle ScholarPubMed
Teipel, SJ, Born, C, Ewers, M, Bokde, AL, Reiser, MF, Moller, HJ, Hampel, H (2007). Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage 38, 13–24.CrossRefGoogle ScholarPubMed
Thompson, SG, Higgins, JP (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine 21, 1559–1573.CrossRefGoogle ScholarPubMed
Twamley, EW, Ropacki, SA, Bondi, MW (2006). Neuropsychological and neuroimaging changes in preclinical Alzheimer's disease. Journal of the International Neuropsychological Society 12, 707–735.CrossRefGoogle ScholarPubMed
van Houwelingen, HC, Arends, LR, Stijnen, T (2002). Advanced methods in meta-analysis: multivariate approach and meta-regression. Statistics in Medicine 21, 589–624.CrossRefGoogle ScholarPubMed
Viechtbauer, W (2006). MiMa: An S-plus/R Function to Fit Meta-Analytic Mixed-, Random-, and Fixed-Effects Models. Computer software and manual (www.wvbauer.com).Google Scholar
Wang, PN, Lirng, JF, Lin, KN, Chang, FC, Liu, HC (2006). Prediction of Alzheimer's disease in mild cognitive impairment: a prospective study in Taiwan. Neurobiology of Aging 27, 1797–1806.CrossRefGoogle ScholarPubMed
Zaccai, J, Ince, P, Brayne, C (2006). Population-based neuropathological studies of dementia: design, methods and areas of investigation – a systematic review. BMC Neurology 6, 2.CrossRefGoogle ScholarPubMed
Zakzanis, KK (1998). Quantitative evidence for neuroanatomic and neuropsychological markers in dementia of the Alzheimer's type. Journal of Clinical and Experimental Neuropsychology 20, 259–269.CrossRefGoogle ScholarPubMed
Zakzanis, KK, Graham, SJ, Campbell, Z (2003). A meta-analysis of structural and functional brain imaging in dementia of the Alzheimer's type: a neuroimaging profile. Neuropsychological Review 13, 1–18.CrossRefGoogle ScholarPubMed
Zamrini, E, De Santi, S, Tolar, M (2004). Imaging is superior to cognitive testing for early diagnosis of Alzheimer's disease. Neurobiology of Aging 25, 685–691.CrossRefGoogle ScholarPubMed