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160 results on '"Kłoszewska, I."'

1. FASTKD2 is associated with memory and hippocampal structure in older adults

Author

Ramanan, V K, Nho, K, Shen, L, Risacher, S L, Kim, S, McDonald, B C, Farlow, M R, Foroud, T M, Gao, S, Soininen, H, Kłoszewska, I, Mecocci, P, Tsolaki, M, Vellas, B, Lovestone, S, Aisen, P S, Petersen, R C, Jack, Jr, C R, Shaw, L M, Trojanowski, J Q, Weiner, M W, Green, R C, Toga, A W, De Jager, P L, Yu, L, Bennett, D A, and Saykin, A J

Published
2015
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4. Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Author

Nabais, M.F., Laws, S.M., Lin, T., Vallerga, C.L., Armstrong, N.J., Blair, I.P., Kwok, J.B., Mather, K.A., Mellick, G.D., Sachdev, P.S., Wallace, L., Henders, A.K., Zwamborn, R.A.J., Hop, P.J., Lunnon, K., Pishva, E., Roubroeks, J.A.Y., Soininen, H., Tsolaki, M., Mecocci, P., Lovestone, S., Kłoszewska, I., Vellas, B., Furlong, S., Garton, F.C., Henderson, R.D., Mathers, S., McCombe, P.A., Needham, M., Ngo, S.T., Nicholson, G., Pamphlett, R., Rowe, D.B., Steyn, F.J., Williams, K.L., Anderson, T.J., Bentley, S.R., Dalrymple-Alford, J., Fowder, J., Gratten, J., Halliday, G., Hickie, I.B., Kennedy, M., Lewis, S.J.G., Montgomery, G.W., Pearson, J., Pitcher, T.L., Silburn, P., Zhang, F., Visscher, P.M., Yang, J., Stevenson, A.J., Hillary, R.F., Marioni, R.E., Harris, S.E., Deary, I.J., Jones, A.R., Shatunov, A., Iacoangeli, A., van Rheenen, W., van den Berg, L.H., Shaw, P.J., Shaw, C.E., Morrison, K.E., Al-Chalabi, A., Veldink, J.H., Hannon, E., Mill, J., Wray, N.R., McRae, A.F., Nabais, M.F., Laws, S.M., Lin, T., Vallerga, C.L., Armstrong, N.J., Blair, I.P., Kwok, J.B., Mather, K.A., Mellick, G.D., Sachdev, P.S., Wallace, L., Henders, A.K., Zwamborn, R.A.J., Hop, P.J., Lunnon, K., Pishva, E., Roubroeks, J.A.Y., Soininen, H., Tsolaki, M., Mecocci, P., Lovestone, S., Kłoszewska, I., Vellas, B., Furlong, S., Garton, F.C., Henderson, R.D., Mathers, S., McCombe, P.A., Needham, M., Ngo, S.T., Nicholson, G., Pamphlett, R., Rowe, D.B., Steyn, F.J., Williams, K.L., Anderson, T.J., Bentley, S.R., Dalrymple-Alford, J., Fowder, J., Gratten, J., Halliday, G., Hickie, I.B., Kennedy, M., Lewis, S.J.G., Montgomery, G.W., Pearson, J., Pitcher, T.L., Silburn, P., Zhang, F., Visscher, P.M., Yang, J., Stevenson, A.J., Hillary, R.F., Marioni, R.E., Harris, S.E., Deary, I.J., Jones, A.R., Shatunov, A., Iacoangeli, A., van Rheenen, W., van den Berg, L.H., Shaw, P.J., Shaw, C.E., Morrison, K.E., Al-Chalabi, A., Veldink, J.H., Hannon, E., Mill, J., Wray, N.R., and McRae, A.F.

Abstract

Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Published
2021

5. Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease

Author

Whiley, L., Chappell, K.E., D’Hondt, E., Lewis, M.R., Jiménez, B., Snowden, S.G., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Swann, J.R., Hye, A., Lovestone, S., Legido-Quigley, C., Holmes, E., Whiley, L., Chappell, K.E., D’Hondt, E., Lewis, M.R., Jiménez, B., Snowden, S.G., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Swann, J.R., Hye, A., Lovestone, S., Legido-Quigley, C., and Holmes, E.

Abstract

Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine

Published
2021

7. The genetic architecture of human brainstem structures and their involvement in common brain disorders

Author

Elvsåshagen, T., Bahrami, S., Meer, D. van der, Agartz, I., Alnæs, D., Barch, D.M., Baur-Streubel, R., Bertolino, A., Beyer, M.K., Blasi, G., Borgwardt, S., Boye, B., Buitelaar, J.K., Bøen, E., Celius, E.G., Cervenka, S., Conzelmann, A., Coynel, D., Carlo, P. di, Djurovic, S., Eisenacher, S., Espeseth, T., Fatouros-Bergman, H., Flyckt, L., Franke, B., Frei, O., Gelao, B., Harbo, H.F., Hartman, Catharina A., Håberg, A., Heslenfeld, D., Hoekstra, P.J., Høgestøl, E.A., Jonassen, R., Jönsson, E.G., Kirsch, P., Kłoszewska, I., Lagerberg, T.V., Landrø, N.I., Hellard, S. Le, Lesch, K.P., Maglanoc, L.A., Malt, U.F., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Moberget, T., Nordvik, J.E., Nyberg, L., Connell, K.S.O., Oosterlaan, J., Papalino, M., Papassotiropoulos, A., Pauli, P., Pergola, G., Persson, K., Quervain, D. de, Reif, A., Rokicki, J., Rooij, D. van, Shadrin, A.A., Schmidt, A., Schwarz, E., Selbæk, G., Soininen, H., Sowa, P., Steen, V.M., Tsolaki, M., Vellas, B., Wang, L, Westman, E., Ziegler, G.C., Zink, M., Andreassen, O.A., Westlye, L.T., Kaufmann, T., Elvsåshagen, T., Bahrami, S., Meer, D. van der, Agartz, I., Alnæs, D., Barch, D.M., Baur-Streubel, R., Bertolino, A., Beyer, M.K., Blasi, G., Borgwardt, S., Boye, B., Buitelaar, J.K., Bøen, E., Celius, E.G., Cervenka, S., Conzelmann, A., Coynel, D., Carlo, P. di, Djurovic, S., Eisenacher, S., Espeseth, T., Fatouros-Bergman, H., Flyckt, L., Franke, B., Frei, O., Gelao, B., Harbo, H.F., Hartman, Catharina A., Håberg, A., Heslenfeld, D., Hoekstra, P.J., Høgestøl, E.A., Jonassen, R., Jönsson, E.G., Kirsch, P., Kłoszewska, I., Lagerberg, T.V., Landrø, N.I., Hellard, S. Le, Lesch, K.P., Maglanoc, L.A., Malt, U.F., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Moberget, T., Nordvik, J.E., Nyberg, L., Connell, K.S.O., Oosterlaan, J., Papalino, M., Papassotiropoulos, A., Pauli, P., Pergola, G., Persson, K., Quervain, D. de, Reif, A., Rokicki, J., Rooij, D. van, Shadrin, A.A., Schmidt, A., Schwarz, E., Selbæk, G., Soininen, H., Sowa, P., Steen, V.M., Tsolaki, M., Vellas, B., Wang, L, Westman, E., Ziegler, G.C., Zink, M., Andreassen, O.A., Westlye, L.T., and Kaufmann, T.

Abstract

Contains fulltext : 225402.pdf (publisher's version ) (Open Access), Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

Published
2020

8. Urinary metabolic phenotyping for Alzheimer’s disease

Author

Kurbatova, N., Garg, M., Whiley, L., Chekmeneva, E., Jiménez, B., Gómez-Romero, M., Pearce, J., Kimhofer, T., D’Hondt, E., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Aarsland, D., Nevado-Holgado, A., Liu, B., Snowden, S., Proitsi, P., Ashton, N.J., Hye, A., Legido-Quigley, C., Lewis, M.R., Nicholson, J.K., Holmes, E., Brazma, A., Lovestone, S., Kurbatova, N., Garg, M., Whiley, L., Chekmeneva, E., Jiménez, B., Gómez-Romero, M., Pearce, J., Kimhofer, T., D’Hondt, E., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Aarsland, D., Nevado-Holgado, A., Liu, B., Snowden, S., Proitsi, P., Ashton, N.J., Hye, A., Legido-Quigley, C., Lewis, M.R., Nicholson, J.K., Holmes, E., Brazma, A., and Lovestone, S.

Abstract

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

Published
2020

12. The interactive effect of demographic and clinical factors on hippocampal volume: A multicohort study on 1958 cognitively normal individuals

Author

Ferreira, D, Hansson, O, Barroso, J, Molina, Y, Machado, A, Hernández-Cabrera, J, Muehlboeck, J, Stomrud, E, Nägga, K, Lindberg, O, Ames, D, Kalpouzos, G, Fratiglioni, L, Bäckman, L, Graff, C, Mecocci, P, Vellas, B, Tsolaki, M, Kłoszewska, I, Soininen, H, Lovestone, S, Ahlström, H, Lind, L, Larsson, E, Wahlund, L, Simmons, A, Westman, E, consortium, AddNeuroMed, (ADNI), Alzheimer's Disease Neuroimaging Initiative, and group, Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) research

Subjects
Aging, medicine.medical_specialty, Neurology, Cognitive Neuroscience, Multicohort, Population, Disease, Hippocampus, 050105 experimental psychology, Cohort Studies, 03 medical and health sciences, Magnetic resonance imaging, 0302 clinical medicine, Global brain atrophy, medicine, Humans, 0501 psychology and cognitive sciences, education, Analysis of covariance, education.field_of_study, Alzheimer's disease, Hippocampal volume, 05 social sciences, Contrast (statistics), Organ Size, Analysis of variance, Psychology, Neuroscience, 030217 neurology & neurosurgery, Cohort study
Abstract

Alzheimer's disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer's disease and predementia stages.

Published
2017
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13. Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease

Author

Sims, R., van der Lee, S.J., Naj, A.C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B.W., Boland, A., Raybould, R., Bis, J.C., Martin, E.R., Grenier-Boley, B., Heilmann-Heimbach, S., Chouraki, V., Kuzma, A.B., Sleegers, K., Vronskaya, M., Ruiz, A., Graham, R.R., Olaso, R., Hoffmann, P., Grove, M.L., Vardarajan, B.N., Hiltunen, M., Nöthen, M.M., White, C.C., Hamilton-Nelson, K.L., Epelbaum, J., Maier, W., Choi, S.H., Beecham, G.W., Dulary, C., Herms, S., Smith, A.V., Funk, C.C., Derbois, Forstner, A.J., Ahmad, S., Li, H., Bacq, D., Harold, D., Satizabal, C.L., Valladares, O., Squassina, A., Thomas, R., Brody, J.A., Qu, L., Sánchez-Juan, P., Morgan, T., Wolters, F.J., Zhao, Y., Garcia, F.S., Denning, N., Fornage, M., Malamon, J., Naranjo, M.C.D., Majounie, E., Mosley, T.H., Dombroski, B., Wallon, D., Lupton, M.K., Dupuis, J., Whitehead, P., Fratiglioni, L., Medway, C., Jian, X., Mukherjee, S., Keller, L., Brown, K., Lin, H., Cantwell, L.B., Panza, F., McGuinness, B., Moreno-Grau, S., Burgess, J.D., Solfrizzi, V., Proitsi, P., Adams, H.H., Allen, M., Seripa, D., Pastor, P., Cupples, L.A., Price, N.D., Hannequin, D., Frank-García, A., Levy, D., Chakrabarty, P., Caffarra, P., Giegling, I., Beiser, A.S., Giedraitis, V., Hampel, H., Garcia, M.E., Wang, X., Lannfelt, L., Mecocci, P., Eiriksdottir, G., Crane, P.K., Pasquier, F., Boccardi, V., Henández, I., Barber, R.C., Scherer, M., Tarraga, L., Adams, P.M., Leber, M., Chen, Y., Albert, M.S., Riedel-Heller, S., Emilsson, V., Beekly, D., Braae, A., Schmidt, R., Blacker, D., Masullo, C., Schmidt, H., Doody, R.S., Spalletta, G., Longstreth, W.T., Jr., Fairchild, T.J., Bossù, P., Lopez, O.L., Frosch, M.P., Sacchinelli, E., Ghetti, B., Yang, Q., Huebinger, R.M., Jessen, F., Li, S., Kamboh, M.I., Morris, J., Sotolongo-Grau, O., Katz, M.J., Corcoran, C., Dunstan, M., Braddel, A., Thomas, C., Meggy, A., Marshall, R., Gerrish, A., Chapman, J., Aguilar, M., Taylor, S., Hill, M., Fairén, M.D., Hodges, A., Vellas, B., Soininen, H., Kloszewska, I., Daniilidou, M., Uphill, J., Patel, Y., Hughes, J.T., Lord, J., Turton, J., Hartmann, A.M., Cecchetti, R., Fenoglio, C., Serpente, M., Arcaro, M., Caltagirone, C., Orfei, M.D., Ciaramella, A., Pichler, S., Mayhaus, M., Gu, W., Lleó, A., Fortea, J., Blesa, R., Barber, I.S., Brookes, K., Cupidi, C., Maletta, R.G., Carrell, D., Sorbi, S., Moebus, S., Urbano, M., Pilotto, A., Kornhuber, J., Bosco, P., Todd, S., Craig, D., Johnston, J., Gill, M., Lawlor, B., Lynch, A., Fox, N.C., Hardy, J., Albin, R.L., Apostolova, L.G., Arnold, S.E., Asthana, S., Atwood, C.S., Baldwin, C.T., Barnes, L.L., Barral, S., Beach, T.G., Becker, J.T., Bigio, E.H., Bird, T.D., Boeve, B.F., Bowen, J.D., Boxer, A., Burke, J.R., Burns, J.M., Buxbaum, J.D., Cairns, N.J., Cao, C., Carlson, C.S., Carlsson, C.M., Carney, R.M., Carrasquillo, M.M., Carroll, S.L., Diaz, C.C., Chui, H.C., Clark, D.G., Cribbs, D.H., Crocco, E.A., DeCarli, C., Dick, M., Duara, R., Evans, D.A., Faber, K.M., Fallon, K.B., Fardo, D.W., Farlow, M.R., Ferris, S., Foroud, T.M., Galasko, D.R., Gearing, M., Geschwind, D.H., Gilbert, J.R., Graff-Radford, N.R., Green, R.C., Growdon, J.H., Hamilton, R.L., Harrell, L.E., Honig, L.S., Huentelman, M.J., Hulette, C.M., Hyman, B.T., Jarvik, G.P., Abner, E., Jin, L.W., Jun, G., Karydas, A., Kaye, J.A., Kim, R., Kowall, N.W., Kramer, J.H., LaFerla, F.M., Lah, J.J., Leverenz, J.B., Levey, A.I., Li, G., Lieberman, A.P., Lunetta, K.L., Lyketsos, C.G., Marson, D.C., Martiniuk, F., Mash, D.C., Masliah, E., McCormick, W.C., McCurry, S.M., McDavid, A.N., McKee, A.C., Mesulam, M., Miller, B.L., Miller, C.A., Miller, J.W., Morris, J.C., Murrell, J.R., Myers, A.J., O'Bryant, S., Olichney, J.M., Pankratz, V.S., Parisi, J.E., Paulson, H.L., Perry, W., Peskind, E., Pierce, A., Poon, W.W., Potter, H., Quinn, J.F., Raj, A., Raskind, M., Reisberg, B., Reitz, C., Ringman, J.M., Roberson, E.D., Rogaeva, E., Rosen, H.J., Rosenberg, R.N., Sager, M.A., Saykin, A.J., Schneider, J.A., Schneider, L.S., Seeley, W.W., Smith, A.G., Sonnen, J.A., Spina, S., Stern, R.A., Swerdlow, R.H., Tanzi, R.E., Thornton-Wells, T.A., Trojanowski, J.Q., Troncoso, J.C., Van Deerlin, V.M., Van Eldik, L.J., Vinters, H.V., Vonsattel, J.P., Weintraub, S., Welsh-Bohmer, K.A., Wilhelmsen, K.C., Williamson, J., Wingo, T.S., Woltjer, R.L., Wright, C.B., Yu, C.E., Yu, L., Garzia, F., Golamaully, F., Septier, G., Engelborghs, S., Vandenberghe, R., De Deyn, P.P., Fernadez, C.M., Benito, Y.A., Thonberg, H., Forsell, C., Lilius, L., Kinhult-Stählbom, A., Kilander, L., Brundin, R., Concari, L., Helisalmi, S., Koivisto, A.M., Haapasalo, A., Dermecourt, V., Fievet, N., Hanon, O., Dufouil, C., Brice, A., Ritchie, K., Dubois, B., Himali, J.J., Keene, C.D., Tschanz, J., Fitzpatrick, A.L., Kukull, W.A., Norton, M., Aspelund, T., Larson, E.B., Munger, R., Rotter, J.I., Lipton, R.B., Bullido, M.J., Hofman, A., Montine, T.J., Coto, E., Boerwinkle, E., Petersen, R.C., Alvarez, V., Rivadeneira, F., Reiman, E.M., Gallo, M., O'Donnell, C.J., Reisch, J.S., Bruni, A.C., Royall, D.R., Dichgans, M., Sano, M., Galimberti, D., St George-Hyslop, P., Scarpini, E., Tsuang, D.W., Mancuso, M., Bonuccelli, U., Winslow, A.R., Daniele, A., Wu, C.K., Peters, O., Nacmias, B., Riemenschneider, M., Heun, R., Brayne, C., Rubinsztein, D.C., Bras, J., Guerreiro, R., Al-Chalabi, A., Shaw, C.E., Collinge, J., Mann, D., Tsolaki, M., Clarimón, J., Sussams, R., Lovestone, S., O'Donovan, M.C., Owen, M.J., Behrens, T.W., Mead, S., Goate, A.M., Uitterlinden, A.G., Holmes, C., Cruchaga, C., Ingelsson, M., Bennett, D.A., Powell, J., Golde, T.E., Graff, C., De Jager, P.L., Morgan, K., Ertekin-Taner, N., Combarros, O., Psaty, B.M., Passmore, P., Younkin, S.G., Berr, C., Gudnason, V., Rujescu, D., Dickson, D.W., Dartigues, J.F., DeStefano, A.L., Ortega-Cubero, S., Hakonarson, H., Campion, D., Boada, M., Kauwe, J.K., Farrer, L.A., Van Broeckhoven, C., Ikram, M.A., Jones, L., Haines, J.L., Tzourio, C., Launer, L.J., Escott-Price, V., Mayeux, R., Deleuze, J.F., Amin, N., Holmans, P.A., Pericak-Vance, M.A., Amouyel, P., van Duijn, C.M., Ramirez, A., Wang, L.S., Lambert, J.C., Seshadri, S., Williams, J., Schellenberg, G.D., Peloso, Gina M., van der Lee, Sven J., Destefano, Anita L., and Seshardi, Sudha

Published
2018
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15. Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

Author

Lebedev, A.V., primary, Westman, E., additional, Van Westen, G.J.P., additional, Kramberger, M.G., additional, Lundervold, A., additional, Aarsland, D., additional, Soininen, H., additional, Kłoszewska, I., additional, Mecocci, P., additional, Tsolaki, M., additional, Vellas, B., additional, Lovestone, S., additional, and Simmons, A., additional

Published
2014
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19. Frontotemporal atrophy associated with paranoid delusions in women with Alzheimer's disease.

Author

Whitehead, D., Tunnard, C., Hurt, C., Wahlund, L. O., Mecocci, P., Tsolaki, M., Vellas, B., Spenger, C., Kłoszewska, I., Soininen, H., Cromb, D., Lovestone, S., and Simmons, A.

Abstract

Background: Paranoid delusions are a common and difficult-to-manage feature of Alzheimer's disease (AD). We investigated the neuroanatomical correlates of paranoid delusions in a cohort of AD patients, using magnetic resonance imaging (MRI) to measure regional volume and regional cortical thickness.Methods: 113 participants with probable AD were assessed for severity of disease, cognitive and functional impairment. Presence and type of delusions were assessed using the Neuropsychiatric Inventory (NPI). Structural MRI images were acquired on a 1.5T scanner, and were analyzed using an automated analysis pipeline.Results: Paranoid delusions were experienced by 23 (20.4%) of the participants. Female participants with paranoid delusions showed reduced cortical thickness in left medial orbitofrontal and left superior temporal regions, independently of cognitive decline. Male participants with delusions did not show any significant differences compared to males without delusions. An exploratory whole brain analysis of non-hypothesized regions showed reduced cortical thickness in the left insula for female participants only.Conclusion: Frontotemporal atrophy is associated with paranoid delusions in females with AD. Evidence of sex differences in the neuroanatomical correlates of delusions as well as differences in regional involvement in different types of delusions may be informative in guiding management and treatment of delusions in AD. [ABSTRACT FROM AUTHOR]

Published
2012
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20. Apathy and cortical atrophy in Alzheimer's disease.

Author

Tunnard, C., Whitehead, D., Hurt, C., Wahlund, LO., Mecocci, P., Tsolaki, M., Vellas, B., Spenger, C., Kłoszewska, I., Soininen, H., Lovestone, S., and Simmons, A.

Subjects
ALZHEIMER'S disease, ANALYSIS of covariance, CAREGIVERS, CEREBRAL cortex, MENTAL depression, INTERVIEWING, MAGNETIC resonance imaging, RESEARCH methodology, RESEARCH funding, T-test (Statistics), ATROPHY
Abstract

Objectives: Apathy has been reported as the most prevalent behavioural symptom experienced in Alzheimer’s disease (AD), associated with greater functional decline and caregiver distress. The aim of the current study was to investigate structural correlates of apathy in AD using magnetic resonance imaging (MRI) regional volume and regional cortical thickness measures. Methods: Semi-structured interviews were conducted with 111 AD patients and their caregivers as part of the European multi-centre study AddNeuroMed. Apathy was measured using the apathy domain of the Neuropsychiatric Inventory (NPI). All AD patients were scanned using a 1.5T MRI scanner and the images analysed using an automated analysis pipeline. Results: We found apathy to be the most prevalent neuropsychiatric symptom occurring in 57% of patients. Apathetic patients had significantly greater cortical thinning in left caudal anterior cingulate cortex (ACC) and left lateral orbitofrontal cortex (OFC), as well as left superior and ventrolateral frontal regions, than those without apathy symptoms. Conclusions: Apathy is mediated by frontocortical structures but this is specific to the left hemisphere at least for patients in the mild to moderate stages of AD.

Published
2011
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21. The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months.

Author

Simmons A, Westman E, Muehlboeck S, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Wahlund L, Soininen H, Lovestone S, Evans A, Spenger C, Simmons, Andrew, Westman, Eric, Muehlboeck, Sebastian, Mecocci, Patrizia, Vellas, Bruno, Tsolaki, Magda, Kłoszewska, Iwona, and Wahlund, Lars-Olof

Abstract

Objective: To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimer's disease and report on early results from the first 24 months of the project.Methods: A multi-centre study similarly to a faux clinical trial has been established to assess longitudinal MRI changes in Alzheimer disease (AD), mild cognitive impairment (MCI) and healthy control subjects using an image acquisition protocol compatible with Alzheimer disease neuroimaging initiative (ADNI). Comprehensive quality control (QC) measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline.Results: A total of 378 subjects have been recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the QC criteria. Measurements of normalized whole brain volume and whole brain cortical thickness showed significant differences between AD and controls, AD and MCI and MCI and controls.Conclusions: A framework for multi-centre MRI studies of Alzheimer's disease has been established consisting of a harmonized MRI acquisition protocol across centres, rigorous QC at both the sites and central data analysis hub and an automated image analysis pipeline. Early results demonstrate the high quality of the images acquired and the applicability of the automated image analysis techniques employed. [ABSTRACT FROM AUTHOR]

Published
2011
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40. Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders.

Author

Nabais MF, Laws SM, Lin T, Vallerga CL, Armstrong NJ, Blair IP, Kwok JB, Mather KA, Mellick GD, Sachdev PS, Wallace L, Henders AK, Zwamborn RAJ, Hop PJ, Lunnon K, Pishva E, Roubroeks JAY, Soininen H, Tsolaki M, Mecocci P, Lovestone S, Kłoszewska I, Vellas B, Furlong S, Garton FC, Henderson RD, Mathers S, McCombe PA, Needham M, Ngo ST, Nicholson G, Pamphlett R, Rowe DB, Steyn FJ, Williams KL, Anderson TJ, Bentley SR, Dalrymple-Alford J, Fowder J, Gratten J, Halliday G, Hickie IB, Kennedy M, Lewis SJG, Montgomery GW, Pearson J, Pitcher TL, Silburn P, Zhang F, Visscher PM, Yang J, Stevenson AJ, Hillary RF, Marioni RE, Harris SE, Deary IJ, Jones AR, Shatunov A, Iacoangeli A, van Rheenen W, van den Berg LH, Shaw PJ, Shaw CE, Morrison KE, Al-Chalabi A, Veldink JH, Hannon E, Mill J, Wray NR, and McRae AF

Subjects
Alleles, Biomarkers, Blood Cells metabolism, Case-Control Studies, Disease Susceptibility, Gene Expression Profiling, Genetic Loci, Genetic Predisposition to Disease, Humans, Neurodegenerative Diseases metabolism, DNA Methylation, Epigenesis, Genetic, Genome-Wide Association Study, Neurodegenerative Diseases etiology
Abstract

Background: People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease., Results: We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights., Conclusions: We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Published
2021
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41. Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer's disease.

Author

Whiley L, Chappell KE, D'Hondt E, Lewis MR, Jiménez B, Snowden SG, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Swann JR, Hye A, Lovestone S, Legido-Quigley C, and Holmes E

Subjects
Biological Availability, Humans, Serotonin, Tryptophan metabolism, Alzheimer Disease, Kynurenine metabolism
Abstract

Background: Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer's disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls., Methods: Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations., Results: Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine., Conclusions: Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.

Published
2021
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42. Urinary metabolic phenotyping for Alzheimer's disease.

Author

Kurbatova N, Garg M, Whiley L, Chekmeneva E, Jiménez B, Gómez-Romero M, Pearce J, Kimhofer T, D'Hondt E, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Aarsland D, Nevado-Holgado A, Liu B, Snowden S, Proitsi P, Ashton NJ, Hye A, Legido-Quigley C, Lewis MR, Nicholson JK, Holmes E, Brazma A, and Lovestone S

Subjects
Aged, Aged, 80 and over, Alzheimer Disease urine, Biomarkers urine, Cognitive Dysfunction genetics, Cognitive Dysfunction metabolism, Cognitive Dysfunction urine, Female, Humans, Male, Metabolomics methods, Quantitative Trait Loci, Alzheimer Disease genetics, Alzheimer Disease metabolism, Phenotype
Abstract

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.

Published
2020
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43. The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Author

Mårtensson G, Ferreira D, Granberg T, Cavallin L, Oppedal K, Padovani A, Rektorova I, Bonanni L, Pardini M, Kramberger MG, Taylor JP, Hort J, Snædal J, Kulisevsky J, Blanc F, Antonini A, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Lovestone S, Simmons A, Aarsland D, and Westman E

Subjects
Brain diagnostic imaging, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Reproducibility of Results, Deep Learning
Abstract

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Published
2020
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44. An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene.

Author

Roubroeks JAY, Smith AR, Smith RG, Pishva E, Ibrahim Z, Sattlecker M, Hannon EJ, Kłoszewska I, Mecocci P, Soininen H, Tsolaki M, Vellas B, Wahlund LO, Aarsland D, Proitsi P, Hodges A, Lovestone S, Newhouse SJ, Dobson RJB, Mill J, van den Hove DLA, and Lunnon K

Subjects
Aged, Aged, 80 and over, Alzheimer Disease diagnosis, Apolipoproteins E genetics, Brain metabolism, Cognitive Dysfunction blood, Cognitive Dysfunction diagnosis, Cognitive Dysfunction genetics, Female, Genotype, Humans, Male, Alzheimer Disease blood, Alzheimer Disease genetics, DNA Methylation genetics, Genome-Wide Association Study methods, Homeodomain Proteins genetics
Abstract

A growing number of epigenome-wide association studies have demonstrated a role for DNA methylation in the brain in Alzheimer's disease. With the aim of exploring peripheral biomarker potential, we have examined DNA methylation patterns in whole blood collected from 284 individuals in the AddNeuroMed study, which included 89 nondemented controls, 86 patients with Alzheimer's disease, and 109 individuals with mild cognitive impairment, including 38 individuals who progressed to Alzheimer's disease within 1 year. We identified significant differentially methylated regions, including 12 adjacent hypermethylated probes in the HOXB6 gene in Alzheimer's disease, which we validated using pyrosequencing. Using weighted gene correlation network analysis, we identified comethylated modules of genes that were associated with key variables such as APOE genotype and diagnosis. In summary, this study represents the first large-scale epigenome-wide association study of Alzheimer's disease and mild cognitive impairment using blood. We highlight the differences in various loci and pathways in early disease, suggesting that these patterns relate to cognitive decline at an early stage., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2020
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45. The genetic architecture of human brainstem structures and their involvement in common brain disorders.

Author

Elvsåshagen T, Bahrami S, van der Meer D, Agartz I, Alnæs D, Barch DM, Baur-Streubel R, Bertolino A, Beyer MK, Blasi G, Borgwardt S, Boye B, Buitelaar J, Bøen E, Celius EG, Cervenka S, Conzelmann A, Coynel D, Di Carlo P, Djurovic S, Eisenacher S, Espeseth T, Fatouros-Bergman H, Flyckt L, Franke B, Frei O, Gelao B, Harbo HF, Hartman CA, Håberg A, Heslenfeld D, Hoekstra PJ, Høgestøl EA, Jonassen R, Jönsson EG, Kirsch P, Kłoszewska I, Lagerberg TV, Landrø NI, Le Hellard S, Lesch KP, Maglanoc LA, Malt UF, Mecocci P, Melle I, Meyer-Lindenberg A, Moberget T, Nordvik JE, Nyberg L, Connell KSO, Oosterlaan J, Papalino M, Papassotiropoulos A, Pauli P, Pergola G, Persson K, de Quervain D, Reif A, Rokicki J, van Rooij D, Shadrin AA, Schmidt A, Schwarz E, Selbæk G, Soininen H, Sowa P, Steen VM, Tsolaki M, Vellas B, Wang L, Westman E, Ziegler GC, Zink M, Andreassen OA, Westlye LT, and Kaufmann T

Subjects
Brain Diseases diagnostic imaging, Brain Diseases metabolism, Brain Stem diagnostic imaging, Brain Stem metabolism, Brain Stem pathology, Genes, Overlapping, Genetic Loci, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Multifactorial Inheritance, Organ Size genetics, Brain Diseases genetics, Brain Diseases pathology, Brain Stem anatomy & histology
Abstract

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

Published
2020
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46. Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain.

Author

Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, Alnæs D, Barch DM, Baur-Streubel R, Bertolino A, Bettella F, Beyer MK, Bøen E, Borgwardt S, Brandt CL, Buitelaar J, Celius EG, Cervenka S, Conzelmann A, Córdova-Palomera A, Dale AM, de Quervain DJF, Di Carlo P, Djurovic S, Dørum ES, Eisenacher S, Elvsåshagen T, Espeseth T, Fatouros-Bergman H, Flyckt L, Franke B, Frei O, Haatveit B, Håberg AK, Harbo HF, Hartman CA, Heslenfeld D, Hoekstra PJ, Høgestøl EA, Jernigan TL, Jonassen R, Jönsson EG, Kirsch P, Kłoszewska I, Kolskår KK, Landrø NI, Le Hellard S, Lesch KP, Lovestone S, Lundervold A, Lundervold AJ, Maglanoc LA, Malt UF, Mecocci P, Melle I, Meyer-Lindenberg A, Moberget T, Norbom LB, Nordvik JE, Nyberg L, Oosterlaan J, Papalino M, Papassotiropoulos A, Pauli P, Pergola G, Persson K, Richard G, Rokicki J, Sanders AM, Selbæk G, Shadrin AA, Smeland OB, Soininen H, Sowa P, Steen VM, Tsolaki M, Ulrichsen KM, Vellas B, Wang L, Westman E, Ziegler GC, Zink M, Andreassen OA, and Westlye LT

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020
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47. Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study.

Author

van Maurik IS, Vos SJ, Bos I, Bouwman FH, Teunissen CE, Scheltens P, Barkhof F, Frolich L, Kornhuber J, Wiltfang J, Maier W, Peters O, Rüther E, Nobili F, Frisoni GB, Spiru L, Freund-Levi Y, Wallin AK, Hampel H, Soininen H, Tsolaki M, Verhey F, Kłoszewska I, Mecocci P, Vellas B, Lovestone S, Galluzzi S, Herukka SK, Santana I, Baldeiras I, de Mendonça A, Silva D, Chetelat G, Egret S, Palmqvist S, Hansson O, Visser PJ, Berkhof J, and van der Flier WM

Subjects
Aged, Aged, 80 and over, Alzheimer Disease cerebrospinal fluid, Alzheimer Disease epidemiology, Alzheimer Disease pathology, Cognitive Dysfunction pathology, Disease Progression, Europe epidemiology, Female, Follow-Up Studies, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Multicenter Studies as Topic statistics & numerical data, Nerve Degeneration, Neuroimaging, North America epidemiology, Organ Size, Phosphorylation, Prognosis, Protein Processing, Post-Translational, tau Proteins chemistry, Amyloid beta-Peptides cerebrospinal fluid, Biomarkers cerebrospinal fluid, Cognitive Dysfunction cerebrospinal fluid, Hippocampus pathology, Magnetic Resonance Imaging, Peptide Fragments cerebrospinal fluid, Proportional Hazards Models, tau Proteins cerebrospinal fluid
Abstract

Background: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia., Methods: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework., Findings: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76)., Interpretation: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour., Funding: ZonMW-Memorabel., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Published
2019
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48. Common brain disorders are associated with heritable patterns of apparent aging of the brain.

Author

Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, Alnæs D, Barch DM, Baur-Streubel R, Bertolino A, Bettella F, Beyer MK, Bøen E, Borgwardt S, Brandt CL, Buitelaar J, Celius EG, Cervenka S, Conzelmann A, Córdova-Palomera A, Dale AM, de Quervain DJF, Di Carlo P, Djurovic S, Dørum ES, Eisenacher S, Elvsåshagen T, Espeseth T, Fatouros-Bergman H, Flyckt L, Franke B, Frei O, Haatveit B, Håberg AK, Harbo HF, Hartman CA, Heslenfeld D, Hoekstra PJ, Høgestøl EA, Jernigan TL, Jonassen R, Jönsson EG, Kirsch P, Kłoszewska I, Kolskår KK, Landrø NI, Le Hellard S, Lesch KP, Lovestone S, Lundervold A, Lundervold AJ, Maglanoc LA, Malt UF, Mecocci P, Melle I, Meyer-Lindenberg A, Moberget T, Norbom LB, Nordvik JE, Nyberg L, Oosterlaan J, Papalino M, Papassotiropoulos A, Pauli P, Pergola G, Persson K, Richard G, Rokicki J, Sanders AM, Selbæk G, Shadrin AA, Smeland OB, Soininen H, Sowa P, Steen VM, Tsolaki M, Ulrichsen KM, Vellas B, Wang L, Westman E, Ziegler GC, Zink M, Andreassen OA, and Westlye LT

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Brain diagnostic imaging, Child, Child, Preschool, Female, Genome-Wide Association Study, Humans, Infant, Magnetic Resonance Imaging, Male, Mental Disorders diagnostic imaging, Mental Disorders genetics, Middle Aged, Neuropsychological Tests, Schizophrenia genetics, Schizophrenia pathology, Sex Characteristics, Young Adult, Aging genetics, Aging pathology, Brain growth & development, Brain Diseases diagnostic imaging, Brain Diseases genetics
Abstract

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.

Published
2019
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49. Inflammatory biomarkers in Alzheimer's disease plasma.

Author

Morgan AR, Touchard S, Leckey C, O'Hagan C, Nevado-Holgado AJ, Barkhof F, Bertram L, Blin O, Bos I, Dobricic V, Engelborghs S, Frisoni G, Frölich L, Gabel S, Johannsen P, Kettunen P, Kłoszewska I, Legido-Quigley C, Lleó A, Martinez-Lage P, Mecocci P, Meersmans K, Molinuevo JL, Peyratout G, Popp J, Richardson J, Sala I, Scheltens P, Streffer J, Soininen H, Tainta-Cuezva M, Teunissen C, Tsolaki M, Vandenberghe R, Visser PJ, Vos S, Wahlund LO, Wallin A, Westwood S, Zetterberg H, Lovestone S, and Morgan BP

Subjects
Aged, Amyloid beta-Peptides blood, Cohort Studies, Complement Factor B, Complement Factor H, Humans, Internationality, Prognosis, Alzheimer Disease blood, Alzheimer Disease diagnosis, Biomarkers blood, Cognitive Dysfunction blood, Cognitive Dysfunction diagnosis, Inflammation
Abstract

Introduction: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers., Methods: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed., Results: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71)., Discussion: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2019
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50. Stability of graph theoretical measures in structural brain networks in Alzheimer's disease.

Author

Mårtensson G, Pereira JB, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Lovestone S, Simmons A, Volpe G, and Westman E

Subjects
Aged, Female, Humans, Male, Alzheimer Disease diagnostic imaging, Alzheimer Disease physiopathology, Cerebral Cortex diagnostic imaging, Cerebral Cortex physiopathology, Models, Neurological, Nerve Net diagnostic imaging, Nerve Net physiopathology
Abstract

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.

Published
2018
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