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3 results on '"Celmins, D."'

1. A blood-based signature of cerebrospinal fluid A beta(1-42) status

Author

Goudey, B., Fung, B.J., Schieber, C., Faux, N.G., Weiner, M.W., Aisen, P., Petersen, R., Jack, C.R., Jagust, W., Trojanowki, J.Q., Toga, A.W., Beckett, L., Green, R.C., Saykin, A.J., Morris, J., Shaw, L.M., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L.S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B.M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J.L., Lord, J.L., Mason, S.S., Albers, C.S., Knopman, D., Johnson, K., Doody, R.S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L.S., Bell, K.L., Ances, B., Morris, J.C., Carroll, M., Creech, M.L., Franklin, E., Mintun, M.A., Schneider, S., Oliver, A., Marson, D., Griffth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Natelson Love, M., Grossman, H., Mitsis, E., Shah, R.C., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M.T., Roberts, P., Albert, M., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J.E., Cerbone, B., Michel, C.A., Pogorelec, D.M., Rusinek, H., Leon, M.J. de, Glodzik, L., De Santi, S., Doraiswamy, P.M., Petrella, J.R., Borges-Neto, S., Wong, T.Z., Coleman, E., Smith, C.D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A.P., Goldstein, B.S., Martin, K., Makino, K.M., Ismail, M.S., Brand, C., Mulnard, R.A., Thai, G., McAdams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A.I., Lah, J.J., Cellar, J.S., Burns, J.M., Swerdlow, R.H., Brooks, W.M., Apostolova, L., Tingus, K., Woo, E., Silverman, D.H.S., Lu, P.H., Bartzokis, G., Graff-Radford, N.R., Parftt, F., Kendall, T., Johnson, H., Farlow, M.R., Hake, A.M., Matthews, B.R., Brosch, J.R., Herring, S., Hunt, C., Dyck, .H. van, Carson, R.E., MacAvoy, M.G., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, Ging-Yuek Robin, Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R.S., Reynolds, B., Sperling, R.A., Johnson, K.A., Marshall, G., Yesavage, J., Taylor, J.L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M.N., Belden, C.M., Jacobson, S.A., Sirrel, S.A., Kowall, N., Killiany, R., Budson, A.E., Norbash, A., Johnson, P.L., Obisesan, T.O., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C.M., Potkin, S.G., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D.W., Kataki, M., Adeli, A., Zimmerman, E.A., Celmins, D., Brown, A.D., Pearlson, G.D., Blank, K., Anderson, K., Flashman, L.A., Seltzer, M., Hynes, M.L., Santulli, R.B., Sink, K.M., Gordineer, L., Williamson, J.D., Garg, P., Watkins, F., Ott, B.R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H.J., Miller, B.L., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B.A., Fargher, K., Saykin, A., Nho, K., Kling, M., Toledo, J., Shaw, L., Trojanowski, J., Farrer, L., Kastsenmueller, G., Arnold, M., Wishart, D., Wurtz, P., Bhattcharyya, S., Duijin, C. van, Mangravite, L., Han, X., Hankemeier, T., Fiehn, O., Barupal, D., Thiele, I., Heinken, A., Meikle, P., Price, N., Funk, C., Jia, W., Kueider-Paisley, A., Tenebaum, J., Black, C., Moseley, A., Thompson, W., Mahmoudiandehkorki, S., Baillie, R., Welsh-Bohmer, K., Plassman, B., and Epidemiology

Subjects
Male, 0301 basic medicine, Apolipoprotein E, Oncology, medicine.medical_specialty, Amyloid, Amyloid beta, lcsh:Medicine, Article, 03 medical and health sciences, Apolipoproteins E, 0302 clinical medicine, Cerebrospinal fluid, Alzheimer Disease, Predictive Value of Tests, Internal medicine, medicine, Humans, Dementia, Cognitive decline, lcsh:Science, Aged, Aged, 80 and over, Amyloid beta-Peptides, Multidisciplinary, biology, Chemokine CCL26, business.industry, lcsh:R, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative, medicine.disease, Peptide Fragments, 3. Good health, 030104 developmental biology, biology.protein, Chromogranin A, Female, lcsh:Q, Alzheimer's disease, business, Biomarkers, 030217 neurology & neurosurgery, Alzheimer's Disease Neuroimaging Initiative
Abstract

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.

Published
2019

2. Sex-dependent association of common variants of microcephaly genes with brain structure

Author

Rimol, L. M., Agartz, I., Djurovic, S., Brown, A. A., Roddey, J. C., Kahler, A. K., Mattingsdal, M., Athanasiu, L., Joyner, A. H., Schork, N. J., Halgren, E., Sundet, K., Melle, I., Dale, A. M., Andreassen, O. A., Weiner, M., Thal, L., Petersen, R., Jack, C. R., Jagust, W., Trojanowki, J., Toga, A. W., Beckett, L., Green, R. C., Gamst, A., Potter, W. Z., Montine, T., Anders, D., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., Alexander, G., Bandy, D., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Shaw, L., Lee, V. M.- Y., Korecka, M., Crawford, K., Neu, S., Harvey, D., Kornak, J., Kachaturian, Z., Frank, R., Snyder, P. J., Molchan, S., Kaye, J., Vorobik, R., Quinn, J., Schneider, L., Pawluczyk, S., Spann, B., Fleisher, A. S., Vanderswag, H., Heidebrink, J. L., Lord, J. L., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Stern, Yaakov, Honig, L. S., Bell, K. L., Morris, J. C., Mintun, M. A., Schneider, S., Marson, D., Griffith, R., Badger, B., Grossman, H., Tang, C., Stern, J., deToledo-Morrell, L., Shah, R. C., Bach, J., Duara, R., Isaacson, R., Strauman, S., Albert, M. S., Pedroso, J., Toroney, J., Rusinek, H., de Leon, M. J., De Santi, S. M., Doraiswamy, P. M., Petrella, J. R., Aiello, M., Clark, C. M., Pham, C., Nunez, J., Smith, C. D., Given II, C. A., Hardy, P., DeKosky, S. T., Oakley, M., Simpson, D. M., Ismail, M. S., Porsteinsson, A., McCallum, C., Cramer, S. C., Mulnard, R. A., McAdams-Ortiz, C., Diaz-Arrastia, R., Martin-Cook, K., DeVous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Laubinger, M. M., Bartzokis, G., Silverman, D. H. S., Lu, P. H., Fletcher, R., Parfitt, F., Johnson, H., Farlow, M., Herring, S., Hake, A. M., van Dyck, C. H., MacAvoy, M. G., Bifano, L. A., Chertkow, H., Bergman, H., Hosein, C., Black, S., Graham, S., Caldwell, C., Feldman, H., Assaly, M., Hsiung, G.-Y. R., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Gitelman, D., Johnson, N., Mesulam, M., Sadowsky, C., Villena, T., Mesner, S., Aisen, P. S., Johnson, K. B., Behan, K. E., Sperling, R. A., Rentz, D. M., Johnson, K. A., Rosen, A., Tinklenberg, J., Ashford, W., Sabbagh, M., Connor, D., Obradov, S., Killiany, R., Norbash, A., Obisesan, T. O., Jayam-Trouth, A., Wang, P., Auchus, A. P., Huang, J., Friedland, R. P., DeCarli, C., Fletcher, E., Carmichael, O., Kittur, S., Mirje, S., Johnson, S. C., Borrie, M., Lee, T.-Y., Asthana, S., Carlsson, C. M., Potkin, S. G., Highum, D., Preda, A., Nguyen, D., Tariot, P. N., Hendin, B. A., Scharre, D. W., Kataki, M., Beversdorf, D. Q., Zimmerman, E. A., Celmins, D., Brown, A. D., Gandy, S., Marenberg, M. E., Rovner, B. W., Pearlson, G., Blank, K., Anderson, K., Saykin, A. J., Santulli, R. B., Pare, N., Williamson, J. D., Sink, K. M., Potter, H., Ashok Raj, B., Giordano, A., Ott, B. R., Wu, C.-K., Cohen, R., Wilks, K. L., and Alzheimer's Disease Neuroimaging Initiative

Subjects
Adult, Male, Microcephaly, Molecular Sequence Data, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Brain mapping, ASPM, Sex Factors, medicine, Animals, Humans, SNP, Genetic Predisposition to Disease, Genetics, Brain Mapping, Multidisciplinary, CDK5RAP2, Brain morphometry, Brain, Middle Aged, Biological Sciences, medicine.disease, Magnetic Resonance Imaging, Phenotype, Brain size, Female
Abstract

Loss-of-function mutations in the genes associated with primary microcephaly (MCPH) reduce human brain size by about two-thirds, without producing gross abnormalities in brain organization or physiology and leaving other organs largely unaffected [Woods CG, et al. (2005) Am J Hum Genet 76:717–728]. There is also evidence suggesting that MCPH genes have evolved rapidly in primates and humans and have been subjected to selection in recent human evolution [Vallender EJ, et al. (2008) Trends Neurosci 31:637–644]. Here, we show that common variants of MCPH genes account for some of the common variation in brain structure in humans, independently of disease status. We investigated the correlations of SNPs from four MCPH genes with brain morphometry phenotypes obtained with MRI. We found significant, sex-specific associations between common, nonexonic, SNPs of the genes CDK5RAP2 , MCPH1 , and ASPM , with brain volume or cortical surface area in an ethnically homogenous Norwegian discovery sample ( n = 287), including patients with mental illness. The most strongly associated SNP findings were replicated in an independent North American sample ( n = 656), which included patients with dementia. These results are consistent with the view that common variation in brain structure is associated with genetic variants located in nonexonic, presumably regulatory, regions.

Published
2009

3. Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans

Author

Bakken, TE, Roddey, JC, Djurovic, S, Akshoomoff, N, Amaral, DG, Bloss, CS, Casey, BJ, Chang, L, Ernst, TM, Gruen, JR, Jernigan, TL, Kaufmann, WE, Kenet, T, Kennedy, DN, Kuperman, JM, Murray, SS, Sowell, ER, Rimol, LM, Mattingsdal, M, Melle, I, Agartz, I, Andreassen, OA, Schork, NJ, Dale, AM, Alzheimer’s Disease Neuroimaging Initiative, Pediatric Imaging, Neurocognition, Genetics Study, Weiner, M, Aisen, P, Petersen, R, Jack, CR, Jr, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, J, Liu, E, Montine, T, Gamst, A, Thomas, RG, Donohue, M, Walter, S, Gessert, D, Sather, T, Harvey, D, Kornak, J, Dale, A, Bernstein, M, Felmlee, J, Fox, N, Thompson, P, Schuff, N, Alexander, G, DeCarli, C, Bandy, D, Koeppe, RA, Foster, N, Reiman, EM, Chen, K, Mathis, C, Cairns, NJ, Taylor-Reinwald, L, Shaw, L, Lee, VM, Korecka, M, Crawford, K, Neu, S, Foroud, TM, Potkin, S, Shen, L, Kachaturian, Z, Frank, R, Snyder, PJ, Molchan, S, Kaye, J, Quinn, J, Lind, B, Dolen, S, Schneider, LS, Pawluczyk, S, Spann, BM, Brewer, J, Vanderswag, H, Heidebrink, JL, Lord, JL, Johnson, K, Doody, RS, Villanueva-Meyer, J, Chowdhury, M, Stern, Yaakov, Honig, LS, Bell, KL, Morris, JC, Ances, B, Carroll, M, Leon, S, Mintun, MA, Schneider, S, Marson, D, Griffith, R, Clark, D, Grossman, H, Mitsis, E, Romirowsky, A, deToledo-Morrell, L, Shah, RC, Duara, R, Varon, D, Roberts, P, Albert, M, Onyike, C, Kielb, S, Rusinek, H, de, Leon, MJ, Glodzik, L, De, Santi, S, Doraiswamy, PM, Petrella, JR, Coleman, RE, Arnold, SE, Karlawish, JH, Wolk, D, Smith, CD, Jicha, G, Hardy, P, Lopez, OL, Oakley, M, Simpson, DM, Porsteinsson, AP, Goldstein, BS, Martin, K, Makino, KM, Ismail, MS, Brand, C, Mulnard, RA, Thai, G, Mc-Adams-Ortiz, C, Womack, K, Mathews, D, Quiceno, M, Diaz-Arrastia, R, King, R, Martin-Cook, K, DeVous, M, Levey, AI, Lah, JJ, Cellar, JS, Burns, JM, Anderson, HS, Swerdlow, RH, Apostolova, L, Lu, PH, Bartzokis, G, Silverman, DH, Graff-Radford, NR, Parfitt, F, Johnson, H, Farlow, MR, Hake, AM, Matthews, BR, Herring, S, van, Dyck, CH, Carson, RE, MacAvoy, MG, Chertkow, H, Bergman, H, Hosein, C, Black, S, Stefanovic, B, Caldwell, C, Ging-Yuek, Hsiung, R, Feldman, H, Mudge, B, Assaly, M, Kertesz, A, Rogers, J, Trost, D, Bernick, C, Munic, D, Kerwin, D, Mesulam, MM, Lipowski, K, Wu, CK, Johnson, N, Sadowsky, C, Martinez, W, Villena, T, Turner, RS, Reynolds, B, Sperling, RA, Johnson, KA, Marshall, G, Frey, M, Yesavage, J, Taylor, JL, Lane, B, Rosen, A, Tinklenberg, J, Sabbagh, M, Belden, C, Jacobson, S, Kowall, N, Killiany, R, Budson, AE, Norbash, A, Johnson, PL, Obisesan, TO, Wolday, S, Bwayo, SK, Lerner, A, Hudson, L, Ogrocki, P, Fletcher, E, Carmichael, O, Olichney, J, Kittur, S, Borrie, M, Lee, TY, Bartha, R, Johnson, S, Asthana, S, Carlsson, CM, Potkin, SG, Preda, A, Nguyen, D, Tariot, P, Fleisher, A, Reeder, S, Bates, V, Capote, H, Rainka, M, Scharre, DW, Kataki, M, Zimmerman, EA, Celmins, D, Brown, AD, Pearlson, GD, Blank, K, Anderson, K, Santulli, RB, Schwartz, ES, Sink, KM, Williamson, JD, Garg, P, Watkins, F, Ott, BR, Querfurth, H, Tremont, G, Salloway, S, Malloy, P, Correia, S, Rosen, HJ, Miller, BL, Mintzer, J, Longmire, CF, Spicer, K, Finger, E, Rachinsky, I, Drost, D, Jernigan, T, McCabe, C, Grant, E, Ernst, T, Kuperman, J, Chung, Y, Murray, S, Bloss, C, Darst, B, Pritchett, L, Saito, A, Amaral, D, DiNino, M, Eyngorina, B, Sowell, E, Houston, S, Soderberg, L, Kaufmann, W, van, Zijl, P, Rizzo-Busack, H, Javid, M, Mehta, N, Ruberry, E, Powers, A, Rosen, B, Gebhard, N, Manigan, H, Frazier, J, Kennedy, D, Yakutis, L, Hill, M, Gruen, J, Bosson-Heenan, J, and Carlson, H

Subjects
anatomy & histology, pathology [Visual Cortex], Adult, Diagnostic Imaging, Male, Linkage disequilibrium, Visual perception, genetic structures, Adolescent, Genotype, Imaging genetics, methods [Diagnostic Imaging], Single-nucleotide polymorphism, Genome-wide association study, Saccharomyces cerevisiae, Biology, Polymorphism, Single Nucleotide, Cohort Studies, methods [Brain Mapping], pathology [Brain], Cortex (anatomy), Genetic variation, Medicine and Health Sciences, medicine, Humans, genetics [Phosphoric Diester Hydrolases], Aged, Visual Cortex, Genetics, Brain Mapping, Multidisciplinary, Models, Genetic, Phosphoric Diester Hydrolases, metabolism [Saccharomyces cerevisiae], Brain, Genetic Variation, Genomics, Middle Aged, Biological Sciences, Visual cortex, medicine.anatomical_structure, Female, Genome-Wide Association Study
Abstract

Visual cortical surface area varies two- to threefold between human individuals, is highly heritable, and has been correlated with visual acuity and visual perception. However, it is still largely unknown what specific genetic and environmental factors contribute to normal variation in the area of visual cortex. To identify SNPs associated with the proportional surface area of visual cortex, we performed a genome-wide association study followed by replication in two independent cohorts. We identified one SNP (rs6116869) that replicated in both cohorts and had genome-wide significant association ( P combined = 3.2 × 10 −8 ). Furthermore, a metaanalysis of imputed SNPs in this genomic region identified a more significantly associated SNP (rs238295; P = 6.5 × 10 −9 ) that was in strong linkage disequilibrium with rs6116869. These SNPs are located within 4 kb of the 5′ UTR of GPCPD1 , glycerophosphocholine phosphodiesterase GDE1 homolog ( Saccharomyces cerevisiae ), which in humans, is more highly expressed in occipital cortex compared with the remainder of cortex than 99.9% of genes genome-wide. Based on these findings, we conclude that this common genetic variation contributes to the proportional area of human visual cortex. We suggest that identifying genes that contribute to normal cortical architecture provides a first step to understanding genetic mechanisms that underlie visual perception.

Published
2012

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