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1. Author Correction: Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease (Nature Communications, (2021), 12, 1, (721), 10.1038/s41467-021-21057-y)

Author

Roe, JM, Vidal-Piñeiro, D, Sørensen, Ø, Brandmaier, AM, Düzel, S, Gonzalez, HA, Kievit, RA, Knights, E, Kühn, S, Lindenberger, U, Mowinckel, AM, Nyberg, L, Park, DC, Pudas, S, Rundle, MM, Walhovd, KB, Fjell, AM, Westerhausen, R, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, Vacher, M, Roe, JM, Vidal-Piñeiro, D, Sørensen, Ø, Brandmaier, AM, Düzel, S, Gonzalez, HA, Kievit, RA, Knights, E, Kühn, S, Lindenberger, U, Mowinckel, AM, Nyberg, L, Park, DC, Pudas, S, Rundle, MM, Walhovd, KB, Fjell, AM, Westerhausen, R, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, and Vacher, M

Abstract

In this article the affiliation details for Ulman Lindenberger were incorrectly given as ‘Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany’ but should have been ‘Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany’, ‘Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany’. The original article has been corrected.

Published
2022

3. Fifteen years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and observations from 2,359 older adults spanning the spectrum from cognitive normality to Alzheimer’s Disease

Author

Fowler, C., Rainey-Smith, S.R., Bird, S., Bomke, J., Bourgeat, P., Brown, B.M., Burnham, S.C., Bush, A.I., Chadunow, C., Collins, S., Doecke, J., Doré, V., Ellis, K.A., Evered, L., Fazlollahi, A., Fripp, J., Gardener, S.L., Gibson, S., Grenfell, R., Harrison, E., Head, R., Jin, L., Kamer, A., Lamb, F., Lautenschlager, N.T., Laws, S.M., Li, Q-X, Lim, L., Lim, Y.Y., Louey, A., Macaulay, S.L., Mackintosh, L., Martins, R.N., Maruff, P., Masters, C.L., McBride, S., Milicic, L., Peretti, M., Pertile, K., Porter, T., Radler, M., Rembach, A., Robertson, J., Rodrigues, M., Rowe, C.C., Rumble, R., Salvado, O., Savage, G., Silbert, B., Soh, M., Sohrabi, H.R., Taddei, K., Taddei, T., Thai, C., Trounson, B., Tyrrell, R., Vacher, M., Varghese, S., Villemagne, V.L., Weinborn, M., Woodward, M., Xia, Y., Ames, D., Fowler, C., Rainey-Smith, S.R., Bird, S., Bomke, J., Bourgeat, P., Brown, B.M., Burnham, S.C., Bush, A.I., Chadunow, C., Collins, S., Doecke, J., Doré, V., Ellis, K.A., Evered, L., Fazlollahi, A., Fripp, J., Gardener, S.L., Gibson, S., Grenfell, R., Harrison, E., Head, R., Jin, L., Kamer, A., Lamb, F., Lautenschlager, N.T., Laws, S.M., Li, Q-X, Lim, L., Lim, Y.Y., Louey, A., Macaulay, S.L., Mackintosh, L., Martins, R.N., Maruff, P., Masters, C.L., McBride, S., Milicic, L., Peretti, M., Pertile, K., Porter, T., Radler, M., Rembach, A., Robertson, J., Rodrigues, M., Rowe, C.C., Rumble, R., Salvado, O., Savage, G., Silbert, B., Soh, M., Sohrabi, H.R., Taddei, K., Taddei, T., Thai, C., Trounson, B., Tyrrell, R., Vacher, M., Varghese, S., Villemagne, V.L., Weinborn, M., Woodward, M., Xia, Y., and Ames, D.

Abstract

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Published
2021

4. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Author

Zhang, Q, Sidorenko, J, Couvy-Duchesne, B, Marioni, RE, Wright, MJ, Goate, AM, Marcora, E, Huang, KL, Porter, T, Laws, SM, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, Vacher, M, Sachdev, PS, Mather, KA, Armstrong, NJ, Thalamuthu, A, Brodaty, H, Yengo, L, Yang, J, Wray, NR, McRae, AF, Visscher, PM, Zhang, Q, Sidorenko, J, Couvy-Duchesne, B, Marioni, RE, Wright, MJ, Goate, AM, Marcora, E, Huang, KL, Porter, T, Laws, SM, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, Vacher, M, Sachdev, PS, Mather, KA, Armstrong, NJ, Thalamuthu, A, Brodaty, H, Yengo, L, Yang, J, Wray, NR, McRae, AF, and Visscher, PM

Abstract

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

Published
2020

5. A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Author

Pedrini, Steve, Gupta, Veer B, Hone, Eugene, Doecke, James, O'Bryant, Sid, James, Ian, Bush, Ashley I, Rowe, Christopher C, Villemagne, Victor L, Ames, David, Masters, Colin L, Martins, Ralph N, Savage, G, Wilson, B, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, McBride, S, Salvado, O, Fenech, M, Francois, M, Barnes, M, Baker, J, Barnham, K, Bellingham, S, Bomke, J, Pejoska, SB, Buckley, R, Cheng, L, Collins, S, Cooke, I, Cyarto, E, Darby, D, Dore, V, El-Sheikh, D, Faux, N, Fowler, C, Harrington, K, Hill, A, Horne, M, Jones, G, Kamer, A, Killeen, N, Korrel, H, Lamb, F, Lautenschlager, N, Lennon, K, Li, QX, Lim, YY, Louey, A, Macaulay, L, Mackintosh, L, Maruff, P, McIlroy, A, Nigro, J, Perez, K, Pertile, K, Restrepo, C, Rita Cardoso, Barbara, Rembach, A, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Sach, J, Silbert, B, Thai, C, Trounson, B, Volitakis, I, Vovos, M, Ward, L, Watt, A, Williams, R, Woodward, M, Yates, P, Ugarte, FY, Zhang, P, Bird, S, Brown, B, Burnham, S, Chatterjee, P, Cox, K, Fernandez, S, Fernando, B, Gardener, S, Laws, S, Lim, F, Lim, L, Tegg, M, Lucas, K, Martins, G, Pedrini, Steve, Gupta, Veer B, Hone, Eugene, Doecke, James, O'Bryant, Sid, James, Ian, Bush, Ashley I, Rowe, Christopher C, Villemagne, Victor L, Ames, David, Masters, Colin L, Martins, Ralph N, Savage, G, Wilson, B, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, McBride, S, Salvado, O, Fenech, M, Francois, M, Barnes, M, Baker, J, Barnham, K, Bellingham, S, Bomke, J, Pejoska, SB, Buckley, R, Cheng, L, Collins, S, Cooke, I, Cyarto, E, Darby, D, Dore, V, El-Sheikh, D, Faux, N, Fowler, C, Harrington, K, Hill, A, Horne, M, Jones, G, Kamer, A, Killeen, N, Korrel, H, Lamb, F, Lautenschlager, N, Lennon, K, Li, QX, Lim, YY, Louey, A, Macaulay, L, Mackintosh, L, Maruff, P, McIlroy, A, Nigro, J, Perez, K, Pertile, K, Restrepo, C, Rita Cardoso, Barbara, Rembach, A, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Sach, J, Silbert, B, Thai, C, Trounson, B, Volitakis, I, Vovos, M, Ward, L, Watt, A, Williams, R, Woodward, M, Yates, P, Ugarte, FY, Zhang, P, Bird, S, Brown, B, Burnham, S, Chatterjee, P, Cox, K, Fernandez, S, Fernando, B, Gardener, S, Laws, S, Lim, F, Lim, L, Tegg, M, Lucas, K, and Martins, G

Abstract

Alzheimer's Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer's participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ϵ4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ϵ4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial.

Published
2017

6. Concordance between cerebrospinal fluid biomarkers with Alzheimer’s disease pathology between three independent assay platforms

Author

Doecke, J.D., Rembach, A., Villemagne, V.L., Varghese, S., Rainey-Smith, S., Sarros, S., Evered, L.A., Fowler, C.J., Pertile, K.K., Rumble, R.L., Trounson, B., Taddei, K., Laws, S.M., Macaulay, S.L., Bush, A.I., Ellis, K.A., Martins, R., Ames, D., Silbert, B., Vanderstichele, H., Masters, C.L., Darby, D.G., Li, Q-X, Collins, S., Kuiperij, H.B., Doecke, J.D., Rembach, A., Villemagne, V.L., Varghese, S., Rainey-Smith, S., Sarros, S., Evered, L.A., Fowler, C.J., Pertile, K.K., Rumble, R.L., Trounson, B., Taddei, K., Laws, S.M., Macaulay, S.L., Bush, A.I., Ellis, K.A., Martins, R., Ames, D., Silbert, B., Vanderstichele, H., Masters, C.L., Darby, D.G., Li, Q-X, Collins, S., and Kuiperij, H.B.

Abstract

Background:To enhance the accuracy of clinical diagnosis for Alzheimer’s disease (AD), pre-mortem biomarkers have become increasingly important for diagnosis and for participant recruitment in disease-specific treatment trials. Cerebrospinal fluid (CSF) biomarkers provide a low-cost alternative to positron emission tomography (PET) imaging for in vivo quantification of different AD pathological hallmarks in the brains of affected subjects; however, consensus around the best platform, most informative biomarker and correlations across different methodologies are controversial. Objective:Assessing levels of Aβ-amyloid and tau species determined using three different versions of immunoassays, the current study explored the ability of CSF biomarkers to predict PET Aβ-amyloid (32 Aβ-amyloid–and 45 Aβ-amyloid+), as well as concordance between CSF biomarker levels and PET Aβ-amyloid imaging. Methods:Prediction and concordance analyses were performed using a sub-cohort of 77 individuals (48 healthy controls, 15 with mild cognitive impairment, and 14 with AD) from the Australian Imaging Biomarker and Lifestyle study of aging. Results:Across all three platforms, the T-tau/Aβ42 ratio biomarker had modestly higher correlation with SUVR/BeCKeT (ρ= 0.69–0.8) as compared with Aβ42 alone (ρ= 0.66–0.75). Differences in CSF biomarker levels between the PET Aβ-amyloid–and Aβ-amyloid+ groups were strongest for the Aβ42/Aβ40 and T-tau/Aβ42 ratios (p < 0.0001); however, comparison of predictive models for PET Aβ-amyloid showed no difference between Aβ42 alone and the T-tau/Aβ42 ratio. Conclusion:This study confirms strong concordance between CSF biomarkers and PET Aβ-amyloid status is independent of immunoassay platform, supporting their utility as biomarkers in clinical practice for the diagnosis of AD and for participant enrichment in clinical trials.

Published
2017

7. High content, multi-parameter analyses in buccal cells to identify Alzheimer's disease

Author

François, M., Fenech, M., Thomas, P., Hor, M., Rembach, A., Martins, R.N., Rainey-Smith, S.R., Masters, C.L., Ames, D., Rowe, C.C., Macaulay, S.L., Hill, A.F., Leifert, W.R., Appannah, A., Barnes, M., Barnham, K., Bedo, J., Bellingham, S., Bon, L., Bourgeat, P., Brown, B., Buckley, R., Burnham, S., Bush, A., Chandler, G., Chen, K., Clarnette, R., Collins, S., Cooke, I., Cowie, T., Cox, K., Cuningham, E., Cyarto, E., Dang, P.A.V., Darby, D., Desmond, P., Doecke, J., Dore, V., Downing, H., Dridan, B., Duesing, K., Fahey, M., Farrow, M., Faux, N., Fernandez, S., Fernando, B., Fowler, C., Fripp, J., Frost, S., Gardener, S., Gibson, S., Graham, P., Gupta, V., Hansen, D., Harrington, K., Hone, E., Horne, M., Huckstepp, B., Jones, A., Jones, G., Kamer, A., Kanagasingam, Y., Keam, L., Kowalczyk, A., Krivdic, B., Lam, C.P., Lamb, F., Lautenschlager, N., Laws, S., Lenzo, N., Leroux, H., Lftikhar, F., Li, Q-X, Lim, F., Lim, L., Lockett, L., Lucas, K., Mano, M., Marczak, C., Martins, G., Matsumoto, Y., Bird, S., McBride, S., McKay, R., Mulligan, R., Nash, T., Nigro, J., O'Keefe, G., Ong, K., Parker, B., Pedrini, S., Peiffer, J., Pejoska, S., Penny, L., Perez, K., Pertile, K., Phal, P., Porter, T., Raniga, P., Restrepo, C., Riley, M., Roberts, B., Robertson, J., Rodrigues, M., Rooney, A., Rumble, R., Ryan, T., Salvado, O., Samuel, M., Saunders, I., Savage, G., Silbert, B., Sohrabi, H.R., Syrette, J., Szoeke, C., Taddei, K., Taddei, T., Tan, S., Tegg, M., Trivedi, D., Trounson, B., Veljanovski, R., Verdile, G., Villemagne, V., Volitakis, I., Vockler, C., Vovos, M., Vrantsidis, F., Walker, S., Watt, A., Weinborn, M., Wilson, B., Woodward, M., Yastrubetskaya, O., Yates, P., Zhang, P., Chatterjee, P., Creegan, R., De Ruyck, K., Ding, H., Groth, D., Head, R., Krause, D., Lachovitzki, R., Lim, Y.Y., Lintern, T., Mondal, A., Nuttall, S., O'Callaghan, N., Osborne, L., Pang, C., Patten, G., Tuckfield, A., Varghese, J., Wilson, A., Zhang, Q., François, M., Fenech, M., Thomas, P., Hor, M., Rembach, A., Martins, R.N., Rainey-Smith, S.R., Masters, C.L., Ames, D., Rowe, C.C., Macaulay, S.L., Hill, A.F., Leifert, W.R., Appannah, A., Barnes, M., Barnham, K., Bedo, J., Bellingham, S., Bon, L., Bourgeat, P., Brown, B., Buckley, R., Burnham, S., Bush, A., Chandler, G., Chen, K., Clarnette, R., Collins, S., Cooke, I., Cowie, T., Cox, K., Cuningham, E., Cyarto, E., Dang, P.A.V., Darby, D., Desmond, P., Doecke, J., Dore, V., Downing, H., Dridan, B., Duesing, K., Fahey, M., Farrow, M., Faux, N., Fernandez, S., Fernando, B., Fowler, C., Fripp, J., Frost, S., Gardener, S., Gibson, S., Graham, P., Gupta, V., Hansen, D., Harrington, K., Hone, E., Horne, M., Huckstepp, B., Jones, A., Jones, G., Kamer, A., Kanagasingam, Y., Keam, L., Kowalczyk, A., Krivdic, B., Lam, C.P., Lamb, F., Lautenschlager, N., Laws, S., Lenzo, N., Leroux, H., Lftikhar, F., Li, Q-X, Lim, F., Lim, L., Lockett, L., Lucas, K., Mano, M., Marczak, C., Martins, G., Matsumoto, Y., Bird, S., McBride, S., McKay, R., Mulligan, R., Nash, T., Nigro, J., O'Keefe, G., Ong, K., Parker, B., Pedrini, S., Peiffer, J., Pejoska, S., Penny, L., Perez, K., Pertile, K., Phal, P., Porter, T., Raniga, P., Restrepo, C., Riley, M., Roberts, B., Robertson, J., Rodrigues, M., Rooney, A., Rumble, R., Ryan, T., Salvado, O., Samuel, M., Saunders, I., Savage, G., Silbert, B., Sohrabi, H.R., Syrette, J., Szoeke, C., Taddei, K., Taddei, T., Tan, S., Tegg, M., Trivedi, D., Trounson, B., Veljanovski, R., Verdile, G., Villemagne, V., Volitakis, I., Vockler, C., Vovos, M., Vrantsidis, F., Walker, S., Watt, A., Weinborn, M., Wilson, B., Woodward, M., Yastrubetskaya, O., Yates, P., Zhang, P., Chatterjee, P., Creegan, R., De Ruyck, K., Ding, H., Groth, D., Head, R., Krause, D., Lachovitzki, R., Lim, Y.Y., Lintern, T., Mondal, A., Nuttall, S., O'Callaghan, N., Osborne, L., Pang, C., Patten, G., Tuckfield, A., Varghese, J., Wilson, A., and Zhang, Q.

Abstract

Alzheimer’s disease (AD) is a degenerative brain disorder and is the most common form of dementia. Minimally invasive approaches are required that combine biomarkers to identify individuals who are at risk of developing mild cognitive impairment (MCI) and AD, to appropriately target clinical trials for therapeutic discovery as well as lifestyle strategies aimed at prevention. Buccal mucosa cells from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing cohort (n=60) were investigated for cytological markers that could be used to identify both MCI and AD individuals. Visual scoring of the buccal cytome demonstrated a significantly lower frequency of basal and karyorrhectic cells in the MCI group compared with controls. A high content, automated assay was developed using laser scanning cytometry to simultaneously measure cell types, nuclear DNA content and aneuploidy, neutral lipid content, putative Tau and amyloid-β (Aβ) in buccal cells. DNA content, aneuploidy, neutral lipids and Tau were similar in all groups. However, there was significantly lower Tau protein in both basal and karyolytic buccal cell types compared with differentiated buccal cells. Aβ, as measured by frequency of cells containing Aβ signal, as well as area and integral of Aβ signal, was significantly higher in the AD group compared with the control group. Buccal cell Aβ was correlated with mini-mental state examination (MMSE) scores (r = -0.436, P=0.001) and several blood-based biomarkers. Combining newly identified biomarkers from buccal cells with those already established may offer a potential route for more specific biomarker panels which may substantially increase the likelihood of better predictive markers for earlier diagnosis of AD.

Published
2016

8. An anemia of Alzheimer's disease

Author

Faux, NG, Rembach, A, Wiley, J, Ellis, KA, Ames, D, Fowler, CJ, Martins, RN, Pertile, KK, Rumble, RL, Trounson, B, Masters, CL, Bush, AI, Faux, NG, Rembach, A, Wiley, J, Ellis, KA, Ames, D, Fowler, CJ, Martins, RN, Pertile, KK, Rumble, RL, Trounson, B, Masters, CL, and Bush, AI

Abstract

Lower hemoglobin is associated with cognitive impairment and Alzheimer's disease (AD). Since brain iron homeostasis is perturbed in AD, we investigated whether this is peripherally reflected in the hematological and related blood chemistry values from the Australian Imaging Biomarker and Lifestyle (AIBL) study (a community-based, cross-sectional cohort comprising 768 healthy controls (HC), 133 participants with mild cognitive impairment (MCI) and 211 participants with AD). We found that individuals with AD had significantly lower hemoglobin, mean cell hemoglobin concentrations, packed cell volume and higher erythrocyte sedimentation rates (adjusted for age, gender, APOE-ɛ4 and site). In AD, plasma iron, transferrin, transferrin saturation and red cell folate levels exhibited a significant distortion of their customary relationship to hemoglobin levels. There was a strong association between anemia and AD (adjusted odds ratio (OR)=2.43, confidence interval (CI) (1.31, 4.54)). Moreover, AD emerged as a strong risk factor for anemia on step-down regression, even when controlling for all other available explanations for anemia (adjusted OR=3.41, 95% CI (1.68, 6.92)). These data indicated that AD is complicated by anemia, which may itself contribute to cognitive decline.

Published
2014

9. Blood-Based Protein Biomarkers for Diagnosis of Alzheimer Disease

Author

Doecke, J.D., Laws, S.M., Faux, N.G., Wilson, W., Burnham, S.C., Lam, C.P., Mondal, A., Bedo, J., Bush, A.I., Brown, B., De Ruyck, K., Ellis, K.A., Fowler, C., Gupta, V.B., Head, R., Macaulay, S.L., Pertile, K., Rowe, C.C., Rembach, A., Rodrigues, M., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ames, D., Masters, C.L., Martins, R.N., Doecke, J.D., Laws, S.M., Faux, N.G., Wilson, W., Burnham, S.C., Lam, C.P., Mondal, A., Bedo, J., Bush, A.I., Brown, B., De Ruyck, K., Ellis, K.A., Fowler, C., Gupta, V.B., Head, R., Macaulay, S.L., Pertile, K., Rowe, C.C., Rembach, A., Rodrigues, M., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ames, D., Masters, C.L., and Martins, R.N.

Abstract

Objective To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). Design Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. Setting General community-based, prospective, longitudinal study of aging. Participants A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. Results A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β2 microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. Conclusions This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis. Alzheimer disease (AD) is the most common form of dementia, affecting more than 27 million persons worldwide and predicted to affect 86 million people by the

Published
2012

10. Blood-based protein biomarkers for diagnosis of Alzheimer disease

Author

Doecke, JD, Laws, SM, Faux, NG, Wilson, W, Burnham, SC, Lam, CP, Mondal, A, Bedo, J, Bush, AI, Brown, B, De Ruyck, K, Ellis, KA, Fowler, C, Gupta, Veer, Head, R, Macaulay, SL, Pertile, K, Rowe, CC, Rembach, A, Rodrigues, M, Rumble, R, Szoeke, C, Taddei, K, Taddei, T, Trounson, B, Ames, D, Masters, CL, Martins, RN, Doecke, JD, Laws, SM, Faux, NG, Wilson, W, Burnham, SC, Lam, CP, Mondal, A, Bedo, J, Bush, AI, Brown, B, De Ruyck, K, Ellis, KA, Fowler, C, Gupta, Veer, Head, R, Macaulay, SL, Pertile, K, Rowe, CC, Rembach, A, Rodrigues, M, Rumble, R, Szoeke, C, Taddei, K, Taddei, T, Trounson, B, Ames, D, Masters, CL, and Martins, RN

Published
2012

11. Plasma Amyloid-β as a Biomarker in Alzheimer's Disease: The AIBL Study of Aging

Author

Lui, J.K., Laws, S.M., Li, Q-X, Villemagne, V.L., Ames, D., Brown, B., Bush, A.I., De Ruyck, K., Dromey, J., Ellis, K.A., Faux, N.G., Foster, J., Fowler, C., Gupta, V., Hudson, P., Laughton, K., Masters, C.L., Pertile, K., Rembach, A., Rimajova, M., Rodrigues, M., Rowe, C.C., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ward, V., Martins, R.N., Lui, J.K., Laws, S.M., Li, Q-X, Villemagne, V.L., Ames, D., Brown, B., Bush, A.I., De Ruyck, K., Dromey, J., Ellis, K.A., Faux, N.G., Foster, J., Fowler, C., Gupta, V., Hudson, P., Laughton, K., Masters, C.L., Pertile, K., Rembach, A., Rimajova, M., Rodrigues, M., Rowe, C.C., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ward, V., and Martins, R.N.

Abstract

Amyloid-β (Aβ) plays a central role in the pathogenesis of Alzheimer's disease (AD) and has been postulated as a potential biomarker for AD. However, there is a lack of consensus as to its suitability as an AD biomarker. The objective of this study was to determine the significance of plasma Aβ as an AD biomarker and its relationship with Aβ load and to determine the effect of different assay methods on the interpretation of Aβ levels. Plasma Aβ1-40, Aβ1-42, and N-terminal cleaved fragments were measured using both a commercial multiplex assay and a well-documented ELISA in 1032 individuals drawn from the well-characterized Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Further, Aβ levels were compared to Aβ load derived from positron-emission tomography (PET) with the Pittsburgh compound B (PiB). Lower Aβ1-42 and Aβ1-42/1-40 ratio were observed in patients with AD and inversely correlated with PiB-PET derived Aβ load. However, assay methodology significantly impacted the interpretation of data. The cross-sectional analysis of plasma Aβ isoforms suggests that they may not be sufficient per se to diagnose AD. The value of their measurement in prognosis and monitoring of AD interventions needs further study, in addition to future longitudinal comparisons together with other predictors, which will determine whether plasma Aβ has diagnostic value in a panel of biomarkers.

Published
2010

12. Plasma Amyloid-beta as a Biomarker in Alzheimer's Disease: The AIBL Study of Aging

Author

Lui, J., Laws, S., Li, Q., Villemagne, V., Ames, D., Brown, B., Bush, A., De Ruyck, K., Dromey, J., Ellis, K., Faux, N., Foster, Jonathan, Fowlor, C., Gupta, V., Hudson, P., Laughton, K., Masters, C., Pertile, K., Rembach, A., Rimajova, M., Rodrigues, M., Rowe, C., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ward, V., Martins, R., Lui, J., Laws, S., Li, Q., Villemagne, V., Ames, D., Brown, B., Bush, A., De Ruyck, K., Dromey, J., Ellis, K., Faux, N., Foster, Jonathan, Fowlor, C., Gupta, V., Hudson, P., Laughton, K., Masters, C., Pertile, K., Rembach, A., Rimajova, M., Rodrigues, M., Rowe, C., Rumble, R., Szoeke, C., Taddei, K., Taddei, T., Trounson, B., Ward, V., and Martins, R.

Abstract

Amyloid-β (Aβ) plays a central role in the pathogenesis of Alzheimer's disease (AD) and has been postulated as a potential biomarker for AD. However, there is a lack of consensus as to its suitability as an AD biomarker. The objective of this study was to determine the significance of plasma Aβ as an AD biomarker and its relationship with Aβ load and to determine the effect of different assay methods on the interpretation of Aβ levels. Plasma Aβ1-40, Aβ1-42, and N-terminal cleaved fragments were measured using both a commercial multiplex assay and a well-documented ELISA in 1032 individuals drawn from the well-characterized Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Further, Aβ levels were compared to Aβ load derived from positron-emission tomography (PET) with the Pittsburgh compound B (PiB). Lower Aβ1-42 and Aβ1-42/1-40 ratio were observed in patients with AD and inversely correlated with PiB-PET derived Aβ load. However, assay methodology significantly impacted the interpretation of data. The cross-sectional analysis of plasma Aβ isoforms suggests that they may not be sufficient per se to diagnose AD. The value of their measurement in prognosis and monitoring of AD interventions needs further study, in addition to future longitudinal comparisons together with other predictors, which will determine whether plasma Aβ has diagnostic value in a panel of biomarkers.

Published
2010

13. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease

Author

Ellis, K., Bush, A., Darby, D., de Fazio, D., Foster, Jonathan, Hudson, P., Lautenschlager, N., Lenzo, N., Martins, R., Maruff, P., Masters, C., Milner, A., Pike, K., Rowe, C., Savage, G., Szoeke, C., Taddei, K., Villemagne, V., Woodward, M., Ames, D., Acosta, O., Ames, J., Agarwal, M., Bahar-Fuchs, A., Baxendale, D., Bechta-Metti, K., Bevage, C., Bevege, L., Bourgeat, P., Brown, B., Clarnette, R., Cowie, T., Crowley, K., Currie, A., El- Sheikh, D., Dickinson, K., Farrow, M., Faux, N., Fripp, J., Fowler, C., Gupta, V., Jones, G., Khoo, J., Killedar, A., Killeen, N., Kim, T.W., Kotsopoulos, E., Lachovitzki, R., Li, Q., Liang, X., Lucas, K., Lui, J., Martins, G., Montague, C., Moore, L., Muir, A., O’Halloran, C., O’Keefe, G., Panayiotou, A., Paton, A., Paton, J., Peiffer, J., Pejoska, S., Pertile, K., Porter, L., Price, R., Raniga, P., Rees, G., Rembach, A., Rimajova, M., Robins, P., Ronsisvalle, E., Rumble, R., Rodrigues, M., Salvado, O., Sach, J., Samuel, M., Sittironnarit, G., Taddei, T., Trivedi, D., Trounson, B., Tsikkos, M., Walker, S., Ward, V., Yastrubetskaya, O., Ellis, K., Bush, A., Darby, D., de Fazio, D., Foster, Jonathan, Hudson, P., Lautenschlager, N., Lenzo, N., Martins, R., Maruff, P., Masters, C., Milner, A., Pike, K., Rowe, C., Savage, G., Szoeke, C., Taddei, K., Villemagne, V., Woodward, M., Ames, D., Acosta, O., Ames, J., Agarwal, M., Bahar-Fuchs, A., Baxendale, D., Bechta-Metti, K., Bevage, C., Bevege, L., Bourgeat, P., Brown, B., Clarnette, R., Cowie, T., Crowley, K., Currie, A., El- Sheikh, D., Dickinson, K., Farrow, M., Faux, N., Fripp, J., Fowler, C., Gupta, V., Jones, G., Khoo, J., Killedar, A., Killeen, N., Kim, T.W., Kotsopoulos, E., Lachovitzki, R., Li, Q., Liang, X., Lucas, K., Lui, J., Martins, G., Montague, C., Moore, L., Muir, A., O’Halloran, C., O’Keefe, G., Panayiotou, A., Paton, A., Paton, J., Peiffer, J., Pejoska, S., Pertile, K., Porter, L., Price, R., Raniga, P., Rees, G., Rembach, A., Rimajova, M., Robins, P., Ronsisvalle, E., Rumble, R., Rodrigues, M., Salvado, O., Sach, J., Samuel, M., Sittironnarit, G., Taddei, T., Trivedi, D., Trounson, B., Tsikkos, M., Walker, S., Ward, V., and Yastrubetskaya, O.

Abstract

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) flagship study of aging aimed to recruit 1000 individuals aged over 60 to assist with prospective research into Alzheimer's disease (AD). This paper describes the recruitment of the cohort and gives information about the study methodology, baseline demography, diagnoses, medical comorbidities, medication use, and cognitive function of the participants. Methods: Volunteers underwent a screening interview, had comprehensive cognitive testing, gave 80 ml of blood, and completed health and lifestyle questionnaires. One quarter of the sample also underwent amyloid PET brain imaging with Pittsburgh compound B (PiB PET) and MRI brain imaging, and a subgroup of 10% had ActiGraph activity monitoring and body composition scanning. Results: A total of 1166 volunteers were recruited, 54 of whom were excluded from further study due to comorbid disorders which could affect cognition or because of withdrawal of consent. Participants with AD (211) had neuropsychological profiles which were consistent with AD, and were more impaired than participants with mild cognitive impairment (133) or healthy controls (768), who performed within expected norms for age on neuropsychological testing. PiB PET scans were performed on 287 participants, 100 had DEXA scans and 91 participated in ActiGraph monitoring. Conclusion: The participants comprising the AIBL cohort represent a group of highly motivated and well-characterized individuals who represent a unique resource for the study of AD. They will be reassessed at 18-month intervals in order to determine the predictive utility of various biomarkers, cognitive parameters and lifestyle factors as indicators of AD, and as predictors of future cognitive decline.

Published
2009

14. High content, multi-parameter analyses in buccal cells to identify alzheimer’s disease

Author

François, M., Fenech, M. F., Thomas, P., Hor, M., Rembach, A., Martins, R. N., Rainey-Smith, S. R., Masters, C. L., Ames, D., Rowe, C. C., Lance Macaulay, S., Hill, A. F., Leifert, W. R., Appannah, A., Barnes, M., Barnham, K., Bedo, J., Bellingham, S., Bon, L., Bourgeat, P., Brown, B., Buckley, R., Burnham, S., Bush, A., Chandler, G., Chen, K., Clarnette, R., Collins, S., Cooke, I., Cowie, T., Cox, K., Cuningham, E., Cyarto, E., Dang, P. A. V., Darby, D., Desmond, P., Doecke, J., Dore, V., Downing, H., Dridan, B., Duesing, K., Fahey, M., Farrow, M., Faux, N., Fenech, M., Fernandez, S., Fernando, B., Fowler, C., Francois, M., Fripp, J., Frost, S., Gardener, S., Gibson, S., Graham, P., Gupta, V., Hansen, D., Harrington, K., Hill, A., Hone, E., Horne, M., Huckstepp, B., Jones, A., Jones, G., Kamer, A., Kanagasingam, Y., Keam, L., Kowalczyk, A., Krivdic, B., Lam, C. P., Lamb, F., Lautenschlager, N., Laws, S., Leifert, W., Lenzo, N., Leroux, H., Lftikhar, F., Li, Q. -X, Lim, F., Lim, L., Lockett, L., Lucas, K., Mano, M., Marczak, C., Martins, G., Maruff, P., Matsumoto, Y., Bird, S., Mcbride, S., Mckay, R., Mulligan, R., Nash, T., Nigro, J., O Keefe, G., Ong, K., Parker, B., Pedrini, S., Peiffer, J., Pejoska, S., Penny, L., Perez, K., Pertile, K., Phal, P., Porter, T., Rainey-Smith, S., Raniga, P., Restrepo, C., Riley, M., Roberts, B., Robertson, J., Rodrigues, M., Rooney, A., Rumble, R., Ryan, T., Salvado, O., Samuel, M., Saunders, I., Savage, G., Silbert, B., Sohrabi, H., Syrette, J., Cassandra Szoeke, Taddei, K., Taddei, T., Tan, S., Tegg, M., Trivedi, D., Trounson, B., Veljanovski, R., Verdile, G., Villemagne, V., Volitakis, I., Vockler, C., Vovos, M., Vrantsidis, F., Walker, S., Watt, A., Weinborn, M., Wilson, B., Woodward, M., Yastrubetskaya, O., Yates, P., Zhang, P., Chatterjee, P., Creegan, R., Ruyck, K., Ding, H., Groth, D., Head, R., Krause, D., Lachovitzki, R., Lim, Y. Y., Lintern, T., Mondal, A., Nuttall, S., O Callaghan, N., Osborne, L., Pang, C., Patten, G., Tuckfield, A., Varghese, J., Wilson, A., and Zhang, Q.

15. An anemia of Alzheimer's disease

Author

Faux, N G, Rembach, A, Wiley, J, Ellis, K A, Ames, D, Fowler, C J, Martins, Ralph N, Pertile, K K, Rumble, R L, Trounson, B, Masters, C L, Bush, A I, Faux, N G, Rembach, A, Wiley, J, Ellis, K A, Ames, D, Fowler, C J, Martins, Ralph N, Pertile, K K, Rumble, R L, Trounson, B, Masters, C L, and Bush, A I

Abstract

This is an Author's Accepted Manuscript of: Faux N.G., Rembach A., Wiley J., Ellis K.A., Ames D., Fowler C.J., Martins R.N., Pertile K.K., Rumble R.L., Trounson B., Masters C.L., & Bush A.I. (2014). An anemia of Alzheimer's disease. Molecular Psychiatry, 19(11), 1227-1234. Available here

16. Leukocyte surface biomarkers implicate deficits of innate immunity in sporadic Alzheimer's disease.

Author

Huang X, Li Y, Fowler C, Doecke JD, Lim YY, Drysdale C, Zhang V, Park K, Trounson B, Pertile K, Rumble R, Pickering JW, Rissman RA, Sarsoza F, Abdel-Latif S, Lin Y, Doré V, Villemagne V, Rowe CC, Fripp J, Martins R, Wiley JS, Maruff P, Mintzer JE, Masters CL, and Gu BJ

Subjects
Humans, Amyloid beta-Peptides metabolism, Biomarkers, Leukocytes metabolism, Immunity, Innate, Alzheimer Disease diagnosis
Abstract

Introduction: Blood-based diagnostics and prognostics in sporadic Alzheimer's disease (AD) are important for identifying at-risk individuals for therapeutic interventions., Methods: In three stages, a total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses., Results: We identified leukocyte markers differentially expressed in the patients with AD. Pathway analysis revealed a complex network involving upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance. A proposed panel including four leukocyte markers - CD11c, CD59, CD91, and CD163 - predicts patients' PET Aβ status with an area under the curve (AUC) of 0.93 (0.88 to 0.97). CD163 was the top performer in preclinical models. These findings have been validated in two independent cohorts., Conclusion: Our finding of changes on peripheral leukocyte surface antigens in AD implicates the deficit in innate immunity. Leukocyte-based biomarkers prove to be both sensitive and practical for AD screening and diagnosis., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2023
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17. Systemic perturbations of the kynurenine pathway precede progression to dementia independently of amyloid-β.

Author

Cespedes M, Jacobs KR, Maruff P, Rembach A, Fowler CJ, Trounson B, Pertile KK, Rumble RL, Rainey-Smith SR, Rowe CC, Villemagne VL, Bourgeat P, Lim CK, Chatterjee P, Martins RN, Ittner A, Masters CL, Doecke JD, Guillemin GJ, and Lovejoy DB

Subjects
3-Hydroxyanthranilic Acid, Amyloid beta-Peptides cerebrospinal fluid, Australia, Biomarkers, Cross-Sectional Studies, Disease Progression, Humans, Kynurenine, Peptide Fragments cerebrospinal fluid, tau Proteins cerebrospinal fluid, Alzheimer Disease metabolism, Cognitive Dysfunction cerebrospinal fluid, Neurodegenerative Diseases
Abstract

Increasing evidence suggests that kynurenine pathway (KP) dyshomeostasis may promote disease progression in dementia. Studies in Alzheimer's disease (AD) patients confirm KP dyshomeostasis in plasma and cerebrospinal fluid (CSF) which correlates with amyloid-β and tau pathology. Herein, we performed the first comprehensive study assessing baseline levels of KP metabolites in participants enrolling in the Australian Imaging Biomarkers Flagship Study of Aging. Our purpose was to test the hypothesis that changes in KP metabolites may be biomarkers of dementia processes that are largely silent. We used a cross-sectional analytical approach to assess non-progressors (N = 73); cognitively normal (CN) or mild cognitive impairment (MCI) participants at baseline and throughout the study, and progressors (N = 166); CN or MCI at baseline but progressing to either MCI or AD during the study. Significant KP changes in progressors included increased 3-hydroxyanthranilic acid (3-HAA) and 3-hydroxyanthranilic acid/anthranilic acid (3-HAA/AA) ratio, the latter having the largest effect on the odds of an individual being a progressor (OR 35.3; 95% CI between 14 and 104). 3-HAA levels were hence surprisingly bi-phasic, high in progressors but low in non-progressors or participants who had already transitioned to MCI or dementia. This is a new, unexpected and interesting result, as most studies of the KP in neurodegenerative disease show reduced 3-HAA/AA ratio after diagnosis. The neuroprotective metabolite picolinic acid was also significantly decreased while the neurotoxic metabolite 3-hydroxykynurenine increased in progressors. These results were significant even after adjustment for confounders. Considering the magnitude of the OR to predict change in cognition, it is important that these findings are replicated in other populations. Independent validation of our findings may confirm the utility of 3-HAA/AA ratio to predict change in cognition leading to dementia in clinical settings., (Copyright © 2022. Published by Elsevier Inc.)

Published
2022
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18. Identification of Leukocyte Surface P2X7 as a Biomarker Associated with Alzheimer's Disease.

Author

Li Y, Huang X, Fowler C, Lim YY, Laws SM, Faux N, Doecke JD, Trounson B, Pertile K, Rumble R, Doré V, Villemagne VL, Rowe CC, Wiley JS, Maruff P, Masters CL, and Gu BJ

Subjects
Amyloid beta-Peptides cerebrospinal fluid, Biomarkers cerebrospinal fluid, Humans, Integrins, Leukocytes pathology, Peptide Fragments cerebrospinal fluid, tau Proteins cerebrospinal fluid, Alzheimer Disease pathology
Abstract

Alzheimer's disease (AD) has shown altered immune responses in the periphery. We studied P2X7 (a proinflammatory receptor and a scavenger receptor) and two integrins, CD11b and CD11c, on the surface of circulating leukocytes and analysed their associations with Aβ-PET, brain atrophy, neuropsychological assessments, and cerebrospinal fluid (CSF) biomarkers. Total 287 age-matched, sex-balanced participants were recruited in a discovery cohort and two validation cohorts through the AIBL study and studied using tri-colour flow cytometry. Our results demonstrated reduced expressions of P2X7, CD11b, and CD11c on leukocytes, particularly monocytes, in Aβ +ve cases compared with Aβ -ve controls. P2X7 and integrin downregulation was observed at pre-clinical stage of AD and stayed low throughout disease course. We further constructed a polygenic risk score (PRS) model based on 12 P2RX7 risk alleles to assess the genetic impact on P2X7 function in AIBL and ADNI cohorts. No significant association was identified between the P2RX7 gene and AD, indicating that P2X7 downregulation in AD is likely caused by environmental changes rather than genetic factors. In conclusion, the downregulation of P2X7 and integrins at pre-clinical stage of AD indicates altered pro-inflammatory responses, phagocytic functions, and migrating capabilities of circulating monocytes in early AD pathogenesis. Our study not only improves our understanding of peripheral immune involvement in early stage of AD but also provides more insights into novel biomarker development, diagnosis, and prognosis of AD.

Published
2022
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19. Analysis of plasma proteins using 2D gels and novel fluorescent probes: in search of blood based biomarkers for Alzheimer's disease.

Author

Laffoon SB, Doecke JD, Roberts AM, Vance JA, Reeves BD, Pertile KK, Rumble RL, Fowler CJ, Trounson B, Ames D, Martins R, Bush AI, Masters CL, Grieco PA, Dratz EA, and Roberts BR

Abstract

Background: The Australian Imaging and Biomarker Lifestyle (AIBL) study of aging is designed to aid the discovery of biomarkers. The current study aimed to discover differentially expressed plasma proteins that could yield a blood-based screening tool for Alzheimer's disease., Methods: The concentration of proteins in plasma covers a vast range of 12 orders of magnitude. Therefore, to search for medium to low abundant biomarkers and elucidate mechanisms of AD, we immuno-depleted the most abundant plasma proteins and pre-fractionated the remaining proteins by HPLC, prior to two-dimensional gel electrophoresis. The relative levels of approximately 3400 protein species resolved on the 2D gels were compared using in-gel differential analysis with spectrally resolved fluorescent protein detection dyes (Zdyes™). Here we report on analysis of pooled plasma samples from an initial screen of a sex-matched cohort of 72 probable AD patients and 72 healthy controls from the baseline time point of AIBL., Results: We report significant changes in variants of apolipoprotein E, haptoglobin, α1 anti-trypsin, inter-α trypsin inhibitor, histidine-rich glycoprotein, and a protein of unknown identity. α1 anti-trypsin and α1 anti-chymotrypsin demonstrated plasma concentrations that were dependent on APOE ε4 allele dose. Our analysis also identified an association with the level of Vitamin D binding protein fragments and complement factor I with sex. We then conducted a preliminary validation study, on unique individual samples compared to the discovery cohort, using a targeted LC-MS/MS assay on a subset of discovered biomarkers. We found that targets that displayed a high degree of isoform specific changes in the 2D gels were not changed in the targeted MS assay which reports on the total level of the biomarker., Conclusions: This demonstrates that further development of mass spectrometry assays is needed to capture the isoform complexity that exists in theses biological samples. However, this study indicates that a peripheral protein signature has potential to aid in the characterization of AD., (© 2022. The Author(s).)

Published
2022
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20. Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease.

Author

Fowler C, Rainey-Smith SR, Bird S, Bomke J, Bourgeat P, Brown BM, Burnham SC, Bush AI, Chadunow C, Collins S, Doecke J, Doré V, Ellis KA, Evered L, Fazlollahi A, Fripp J, Gardener SL, Gibson S, Grenfell R, Harrison E, Head R, Jin L, Kamer A, Lamb F, Lautenschlager NT, Laws SM, Li QX, Lim L, Lim YY, Louey A, Macaulay SL, Mackintosh L, Martins RN, Maruff P, Masters CL, McBride S, Milicic L, Peretti M, Pertile K, Porter T, Radler M, Rembach A, Robertson J, Rodrigues M, Rowe CC, Rumble R, Salvado O, Savage G, Silbert B, Soh M, Sohrabi HR, Taddei K, Taddei T, Thai C, Trounson B, Tyrrell R, Vacher M, Varghese S, Villemagne VL, Weinborn M, Woodward M, Xia Y, and Ames D

Abstract

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer's disease dementia (AD)) as an 'Inception cohort' who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an 'Enrichment cohort' (as of 10 April 2019)., Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation., Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations., Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression., Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims., Competing Interests: The following authors have no CoI to declare: CF, SRRS, SB, JB, PB, BMB, SCB, CC, JD, VD, KAE, LE, AF, JF, SLG, SG, RG, EH, RH, LJ, AK, FL, NTL, QXL, LL, YYL, AL, SLM, LMa, SM, LMi, MP, KP, TP, MRa, AR, JR, MRo, RR, OS, GS, BS, MS, HRS, KT, TT, CT, BT, RT, MV, MWe, MWo, YX. AIB is a shareholder in Alterity Therapeutics Ltd, Cogstate Ltd, Mesoblast Ltd, and is a paid consultant for, and has a profit share interest in, Collaborative Medicinal Development Pty Ltd. SC has received payments from Biogen for advice in relation to CSF biomarkers of Alzheimer’s disease. SML has previously been a paid consultant to Alzhyme. RNM is founder of, and owns stock in, Alzhyme, and is a co-founder of the KaRa Institute of Neurological Diseases. PM is a full-time employee of Cogstate Ltd. CLM is an advisor to Prana Biotechnology Ltd and a consultant to Eli Lilly. CCR has served on scientific advisory boards for Bayer Pharma, Elan Corporation, GE Healthcare and AstraZeneca, has received speaker honoraria from Bayer Pharma and GE Healthcare, and has received research support from Bayer Pharma, GE Healthcare, Piramal Lifesciences and Avid Radiopharmaceuticals. VLV has served as a consultant for Bayer Pharma and received research support from a NEDO grant from Japan. DA has served on scientific advisory boards for Novartis, Eli Lilly, Janssen, and Pfizer Inc., (© 2021 – The authors. Published by IOS Press.)

Published
2021
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21. Concordance Between Cerebrospinal Fluid Biomarkers with Alzheimer's Disease Pathology Between Three Independent Assay Platforms.

Author

Doecke JD, Rembach A, Villemagne VL, Varghese S, Rainey-Smith S, Sarros S, Evered LA, Fowler CJ, Pertile KK, Rumble RL, Trounson B, Taddei K, Laws SM, Macaulay SL, Bush AI, Ellis KA, Martins R, Ames D, Silbert B, Vanderstichele H, Masters CL, Darby DG, Li QX, and Collins S

Subjects
Aged, Aged, 80 and over, Alzheimer Disease diagnostic imaging, Amyloid beta-Peptides cerebrospinal fluid, Cognition Disorders cerebrospinal fluid, Cognition Disorders diagnostic imaging, Cognition Disorders pathology, Female, Humans, Male, Mental Status Schedule, Peptide Fragments cerebrospinal fluid, Positron-Emission Tomography, ROC Curve, tau Proteins cerebrospinal fluid, Alzheimer Disease cerebrospinal fluid, Alzheimer Disease pathology, Biomarkers cerebrospinal fluid
Abstract

Background: To enhance the accuracy of clinical diagnosis for Alzheimer's disease (AD), pre-mortem biomarkers have become increasingly important for diagnosis and for participant recruitment in disease-specific treatment trials. Cerebrospinal fluid (CSF) biomarkers provide a low-cost alternative to positron emission tomography (PET) imaging for in vivo quantification of different AD pathological hallmarks in the brains of affected subjects; however, consensus around the best platform, most informative biomarker and correlations across different methodologies are controversial., Objective: Assessing levels of Aβ-amyloid and tau species determined using three different versions of immunoassays, the current study explored the ability of CSF biomarkers to predict PET Aβ-amyloid (32 Aβ-amyloid-and 45 Aβ-amyloid+), as well as concordance between CSF biomarker levels and PET Aβ-amyloid imaging., Methods: Prediction and concordance analyses were performed using a sub-cohort of 77 individuals (48 healthy controls, 15 with mild cognitive impairment, and 14 with AD) from the Australian Imaging Biomarker and Lifestyle study of aging., Results: Across all three platforms, the T-tau/Aβ42 ratio biomarker had modestly higher correlation with SUVR/BeCKeT (ρ= 0.69-0.8) as compared with Aβ42 alone (ρ= 0.66-0.75). Differences in CSF biomarker levels between the PET Aβ-amyloid-and Aβ-amyloid+ groups were strongest for the Aβ42/Aβ40 and T-tau/Aβ42 ratios (p < 0.0001); however, comparison of predictive models for PET Aβ-amyloid showed no difference between Aβ42 alone and the T-tau/Aβ42 ratio., Conclusion: This study confirms strong concordance between CSF biomarkers and PET Aβ-amyloid status is independent of immunoassay platform, supporting their utility as biomarkers in clinical practice for the diagnosis of AD and for participant enrichment in clinical trials.

Published
2018
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22. Blood-based protein biomarkers for diagnosis of Alzheimer disease.

Author

Doecke JD, Laws SM, Faux NG, Wilson W, Burnham SC, Lam CP, Mondal A, Bedo J, Bush AI, Brown B, De Ruyck K, Ellis KA, Fowler C, Gupta VB, Head R, Macaulay SL, Pertile K, Rowe CC, Rembach A, Rodrigues M, Rumble R, Szoeke C, Taddei K, Taddei T, Trounson B, Ames D, Masters CL, and Martins RN

Subjects
Age Factors, Aged, Aged, 80 and over, Alzheimer Disease genetics, Apolipoproteins E genetics, Australia, Brain pathology, Cohort Studies, Female, Humans, Male, Mental Status Schedule, Middle Aged, Neuroimaging, Reproducibility of Results, Residence Characteristics, Sensitivity and Specificity, Statistics, Nonparametric, Alzheimer Disease blood, Alzheimer Disease diagnosis, Biomarkers blood, Blood Proteins metabolism, Gene Expression Regulation physiology
Abstract

Objective: To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD)., Design: Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data., Setting: General community-based, prospective, longitudinal study of aging., Participants: A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort., Results: A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve., Conclusions: This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.

Published
2012
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23. Plasma amyloid-beta as a biomarker in Alzheimer's disease: the AIBL study of aging.

Author

Lui JK, Laws SM, Li QX, Villemagne VL, Ames D, Brown B, Bush AI, De Ruyck K, Dromey J, Ellis KA, Faux NG, Foster J, Fowler C, Gupta V, Hudson P, Laughton K, Masters CL, Pertile K, Rembach A, Rimajova M, Rodrigues M, Rowe CC, Rumble R, Szoeke C, Taddei K, Taddei T, Trounson B, Ward V, Martins RN, and AIBL Research Group

Subjects
Aged, Alzheimer Disease pathology, Apolipoproteins E metabolism, Australia, Biomarkers, Brain pathology, Cohort Studies, Cross-Sectional Studies, Enzyme-Linked Immunosorbent Assay, Female, Humans, Life Style, Male, Neuropsychological Tests, Risk Assessment, Socioeconomic Factors, Aging physiology, Alzheimer Disease blood, Amyloid beta-Peptides blood
Abstract

Amyloid-beta (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD) and has been postulated as a potential biomarker for AD. However, there is a lack of consensus as to its suitability as an AD biomarker. The objective of this study was to determine the significance of plasma Abeta as an AD biomarker and its relationship with Abeta load and to determine the effect of different assay methods on the interpretation of Abeta levels. Plasma Abeta1-40, Abeta1-42, and N-terminal cleaved fragments were measured using both a commercial multiplex assay and a well-documented ELISA in 1032 individuals drawn from the well-characterized Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Further, Abeta levels were compared to Abeta load derived from positron-emission tomography (PET) with the Pittsburgh compound B (PiB). Lower Abeta1-42 and Abeta1-42/1-40 ratio were observed in patients with AD and inversely correlated with PiB-PET derived Abeta load. However, assay methodology significantly impacted the interpretation of data. The cross-sectional analysis of plasma Abeta isoforms suggests that they may not be sufficient per se to diagnose AD. The value of their measurement in prognosis and monitoring of AD interventions needs further study, in addition to future longitudinal comparisons together with other predictors, which will determine whether plasma Abeta has diagnostic value in a panel of biomarkers.

Published
2010
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