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461 results on '"Proitsi, Petroula"'

1. Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

Author

Gómez-Pascual, Alicia, Naccache, Talel, Xu, Jin, Hooshmand, Kourosh, Wretlind, Asger, Gabrielli, Martina, Lombardo, Marta Tiffany, Shi, Liu, Buckley, Noel J., Tijms, Betty M., Vos, Stephanie J.B., ten Kate, Mara, Engelborghs, Sebastiaan, Sleegers, Kristel, Frisoni, Giovanni B., Wallin, Anders, Lleó, Alberto, Popp, Julius, Martinez-Lage, Pablo, Streffer, Johannes, Barkhof, Frederik, Zetterberg, Henrik, Visser, Pieter Jelle, Lovestone, Simon, Bertram, Lars, Nevado-Holgado, Alejo J., Gualerzi, Alice, Picciolini, Silvia, Proitsi, Petroula, Verderio, Claudia, Botía, Juan A., and Legido-Quigley, Cristina

Published
2024
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2. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

Author

Green, Rebecca E., Lord, Jodie, Scelsi, Marzia A., Xu, Jin, Wong, Andrew, Naomi-James, Sarah, Handy, Alex, Gilchrist, Lachlan, Williams, Dylan M., Parker, Thomas D., Lane, Christopher A., Malone, Ian B., Cash, David M., Sudre, Carole H., Coath, William, Thomas, David L., Keuss, Sarah, Dobson, Richard, Legido-Quigley, Cristina, Fox, Nick C., Schott, Jonathan M., Richards, Marcus, and Proitsi, Petroula

Published
2023
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3. Circulating Metabolome and White Matter Hyperintensities in Women and Men

Author

Sliz, Eeva, Shin, Jean, Ahmad, Shahzad, Williams, Dylan M, Frenzel, Stefan, Gauß, Friederike, Harris, Sarah E, Henning, Ann-Kristin, Hernandez, Maria Valdes, Hu, Yi-Han, Jiménez, Beatriz, Sargurupremraj, Muralidharan, Sudre, Carole, Wang, Ruiqi, Wittfeld, Katharina, Yang, Qiong, Wardlaw, Joanna M, Völzke, Henry, Vernooij, Meike W, Schott, Jonathan M, Richards, Marcus, Proitsi, Petroula, Nauck, Matthias, Lewis, Matthew R, Launer, Lenore, Hosten, Norbert, Grabe, Hans J, Ghanbari, Mohsen, Deary, Ian J, Cox, Simon R, Chaturvedi, Nishi, Barnes, Josephine, Rotter, Jerome I, Debette, Stephanie, Ikram, M Arfan, Fornage, Myriam, Paus, Tomas, Seshadri, Sudha, Pausova, Zdenka, and Group, for the NeuroCHARGE Working

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Prevention, Clinical Research, Aging, Aged, Brain, Diabetes Mellitus, Type 2, Female, Humans, Magnetic Resonance Imaging, Male, Metabolome, Middle Aged, White Matter, NeuroCHARGE Working Group, brain, glucuronic acid, hydroxyphenylpyruvate, lipid ratios, lipidomics, lipids, lysophosphatidylcholines, metabolomics, sphingomyelins, white matter, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundWhite matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.MethodsWe studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)

Published
2022

4. Genetic risk for attention-deficit/hyperactivity disorder predicts cognitive decline and development of Alzheimer’s disease pathophysiology in cognitively unimpaired older adults

Author

Leffa, Douglas T., Ferrari-Souza, João Pedro, Bellaver, Bruna, Tissot, Cécile, Ferreira, Pamela C. L., Brum, Wagner S., Caye, Arthur, Lord, Jodie, Proitsi, Petroula, Martins-Silva, Thais, Tovo-Rodrigues, Luciana, Tudorascu, Dana L., Villemagne, Victor L., Cohen, Ann D., Lopez, Oscar L., Klunk, William E., Karikari, Thomas K., Rosa-Neto, Pedro, Zimmer, Eduardo R., Molina, Brooke S. G., Rohde, Luis Augusto, and Pascoal, Tharick A.

Published
2023
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7. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease

Author

Wightman, Douglas P., Jansen, Iris E., Savage, Jeanne E., Shadrin, Alexey A., Bahrami, Shahram, Holland, Dominic, Rongve, Arvid, Børte, Sigrid, Winsvold, Bendik S., Drange, Ole Kristian, Martinsen, Amy E., Skogholt, Anne Heidi, Willer, Cristen, Bråthen, Geir, Bosnes, Ingunn, Nielsen, Jonas Bille, Fritsche, Lars G., Thomas, Laurent F., Pedersen, Linda M., Gabrielsen, Maiken E., Johnsen, Marianne Bakke, Meisingset, Tore Wergeland, Zhou, Wei, Proitsi, Petroula, Hodges, Angela, Dobson, Richard, Velayudhan, Latha, Heilbron, Karl, Auton, Adam, Sealock, Julia M., Davis, Lea K., Pedersen, Nancy L., Reynolds, Chandra A., Karlsson, Ida K., Magnusson, Sigurdur, Stefansson, Hreinn, Thordardottir, Steinunn, Jonsson, Palmi V., Snaedal, Jon, Zettergren, Anna, Skoog, Ingmar, Kern, Silke, Waern, Margda, Zetterberg, Henrik, Blennow, Kaj, Stordal, Eystein, Hveem, Kristian, Zwart, John-Anker, Athanasiu, Lavinia, Selnes, Per, Saltvedt, Ingvild, Sando, Sigrid B., Ulstein, Ingun, Djurovic, Srdjan, Fladby, Tormod, Aarsland, Dag, Selbæk, Geir, Ripke, Stephan, Stefansson, Kari, Andreassen, Ole A., and Posthuma, Danielle

Published
2021
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8. Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

Author

Gómez-Pascual, A, Naccache, T, Xu, J, Hooshmand, K, Wretlind, A, Gabrielli, M, Lombardo, M, Shi, L, Buckley, N, Tijms, B, Vos, S, Ten Kate, M, Engelborghs, S, Sleegers, K, Frisoni, G, Wallin, A, Lleó, A, Popp, J, Martinez-Lage, P, Streffer, J, Barkhof, F, Zetterberg, H, Visser, P, Lovestone, S, Bertram, L, Nevado-Holgado, A, Gualerzi, A, Picciolini, S, Proitsi, P, Verderio, C, Botía, J, Legido-Quigley, C, Gómez-Pascual, Alicia, Naccache, Talel, Xu, Jin, Hooshmand, Kourosh, Wretlind, Asger, Gabrielli, Martina, Lombardo, Marta Tiffany, Shi, Liu, Buckley, Noel J, Tijms, Betty M, Vos, Stephanie J B, Ten Kate, Mara, Engelborghs, Sebastiaan, Sleegers, Kristel, Frisoni, Giovanni B, Wallin, Anders, Lleó, Alberto, Popp, Julius, Martinez-Lage, Pablo, Streffer, Johannes, Barkhof, Frederik, Zetterberg, Henrik, Visser, Pieter Jelle, Lovestone, Simon, Bertram, Lars, Nevado-Holgado, Alejo J, Gualerzi, Alice, Picciolini, Silvia, Proitsi, Petroula, Verderio, Claudia, Botía, Juan A, Legido-Quigley, Cristina, Gómez-Pascual, A, Naccache, T, Xu, J, Hooshmand, K, Wretlind, A, Gabrielli, M, Lombardo, M, Shi, L, Buckley, N, Tijms, B, Vos, S, Ten Kate, M, Engelborghs, S, Sleegers, K, Frisoni, G, Wallin, A, Lleó, A, Popp, J, Martinez-Lage, P, Streffer, J, Barkhof, F, Zetterberg, H, Visser, P, Lovestone, S, Bertram, L, Nevado-Holgado, A, Gualerzi, A, Picciolini, S, Proitsi, P, Verderio, C, Botía, J, Legido-Quigley, C, Gómez-Pascual, Alicia, Naccache, Talel, Xu, Jin, Hooshmand, Kourosh, Wretlind, Asger, Gabrielli, Martina, Lombardo, Marta Tiffany, Shi, Liu, Buckley, Noel J, Tijms, Betty M, Vos, Stephanie J B, Ten Kate, Mara, Engelborghs, Sebastiaan, Sleegers, Kristel, Frisoni, Giovanni B, Wallin, Anders, Lleó, Alberto, Popp, Julius, Martinez-Lage, Pablo, Streffer, Johannes, Barkhof, Frederik, Zetterberg, Henrik, Visser, Pieter Jelle, Lovestone, Simon, Bertram, Lars, Nevado-Holgado, Alejo J, Gualerzi, Alice, Picciolini, Silvia, Proitsi, Petroula, Verderio, Claudia, Botía, Juan A, and Legido-Quigley, Cristina

Abstract

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Published
2024

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

Author

Roubroeks, Janou A.Y., Smith, Adam R., Smith, Rebecca G., Pishva, Ehsan, Ibrahim, Zina, Sattlecker, Martina, Hannon, Eilis J., Kłoszewska, Iwona, Mecocci, Patrizia, Soininen, Hilkka, Tsolaki, Magda, Vellas, Bruno, Wahlund, Lars-Olof, Aarsland, Dag, Proitsi, Petroula, Hodges, Angela, Lovestone, Simon, Newhouse, Stephen J., Dobson, Richard J.B., Mill, Jonathan, van den Hove, Daniël L.A., and Lunnon, Katie

Published
2020
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11. The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume

Author

Lupton, Michelle K, Strike, Lachlan, Hansell, Narelle K, Wen, Wei, Mather, Karen A, Armstrong, Nicola J, Thalamuthu, Anbupalam, McMahon, Katie L, de Zubicaray, Greig I, Assareh, Amelia A, Simmons, Andrew, Proitsi, Petroula, Powell, John F, Montgomery, Grant W, Hibar, Derrek P, Westman, Eric, Tsolaki, Magda, Kloszewska, Iwona, Soininen, Hilkka, Mecocci, Patrizia, Velas, Bruno, Lovestone, Simon, Initiative, the Alzheimer's Disease Neuroimaging, Brodaty, Henry, Ames, David, Trollor, Julian N, Martin, Nicholas G, Thompson, Paul M, Sachdev, Perminder S, and Wright, Margaret J

Subjects
Biological Psychology, Psychology, Genetic Testing, Dementia, Neurosciences, Aging, Clinical Research, Alzheimer's Disease, Genetics, Brain Disorders, Neurodegenerative, Prevention, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Aetiology, Neurological, Adolescent, Adult, Aged, Aged, 80 and over, Alzheimer Disease, Amygdala, Apolipoproteins E, Case-Control Studies, Cognitive Dysfunction, Cohort Studies, Female, Genetic Association Studies, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Membrane Glycoproteins, Middle Aged, Receptors, Immunologic, Risk, Young Adult, Alzheimer's disease, Polygenic risk score, APOE, TREM2, Alzheimer's Disease Neuroimaging Initiative, Clinical Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Reduction in hippocampal and amygdala volume measured via structural magnetic resonance imaging is an early marker of Alzheimer's disease (AD). Whether genetic risk factors for AD exert an effect on these subcortical structures independent of clinical status has not been fully investigated. We examine whether increased genetic risk for AD influences hippocampal and amygdala volumes in case-control and population cohorts at different ages, in 1674 older (aged >53 years; 17% AD, 39% mild cognitive impairment [MCI]) and 467 young (16-30 years) adults. An AD polygenic risk score combining common risk variants excluding apolipoprotein E (APOE), and a single nucleotide polymorphism in TREM2, were both associated with reduced hippocampal volume in healthy older adults and those with MCI. APOE ε4 was associated with hippocampal and amygdala volume in those with AD and MCI but was not associated in healthy older adults. No associations were found in young adults. Genetic risk for AD affects the hippocampus before the clinical symptoms of AD, reflecting a neurodegenerative effect before clinical manifestations in older adults.

Published
2016

12. Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study.

Author

Østergaard, Søren D, Mukherjee, Shubhabrata, Sharp, Stephen J, Proitsi, Petroula, Lotta, Luca A, Day, Felix, Perry, John RB, Boehme, Kevin L, Walter, Stefan, Kauwe, John S, Gibbons, Laura E, Alzheimer’s Disease Genetics Consortium, GERAD1 Consortium, EPIC-InterAct Consortium, Larson, Eric B, Powell, John F, Langenberg, Claudia, Crane, Paul K, Wareham, Nicholas J, and Scott, Robert A

Subjects
Alzheimer’s Disease Genetics Consortium, GERAD1 Consortium, EPIC-InterAct Consortium, Humans, Alzheimer Disease, Genetic Predisposition to Disease, Risk Factors, Polymorphism, Single Nucleotide, Female, Male, Mendelian Randomization Analysis, Obesity, Cardiovascular, Diabetes, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Tobacco Smoke and Health, Brain Disorders, Human Genome, Aging, Clinical Research, Genetics, Tobacco, Prevention, Neurodegenerative, Alzheimer's Disease, 2.1 Biological and endogenous factors, Metabolic and endocrine, General & Internal Medicine, Medical and Health Sciences
Abstract

BackgroundPotentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR).Methods and findingsWe used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses.ConclusionsInherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk.

Published
2015

13. Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort

Author

Kim, Min, Snowden, Stuart, Suvitaival, Tommi, Ali, Ashfaq, Merkler, David J., Ahmad, Tahmina, Westwood, Sarah, Baird, Alison, Proitsi, Petroula, Nevado-Holgado, Alejo, Hye, Abdul, Bos, Isabelle, Vos, Stephanie, Vandenberghe, Rik, Teunissen, Charlotte, ten Kate, Mara, Scheltens, Philip, Gabel, Silvy, Meersmans, Karen, Blin, Olivier, Richardson, Jill, De Roeck, Ellen, Sleegers, Kristel, Bordet, Régis, Rami, Lorena, Kettunen, Petronella, Tsolaki, Magda, Verhey, Frans, Sala, Isabel, Lléo, Alberto, Peyratout, Gwendoline, Tainta, Mikel, Johannsen, Peter, Freund-Levi, Yvonne, Frölich, Lutz, Dobricic, Valerija, Engelborghs, Sebastiaan, Frisoni, Giovanni B., Molinuevo, José L., Wallin, Anders, Popp, Julius, Martinez-Lage, Pablo, Bertram, Lars, Barkhof, Frederik, Ashton, Nicholas, Blennow, Kaj, Zetterberg, Henrik, Streffer, Johannes, Visser, Pieter J., Lovestone, Simon, and Legido-Quigley, Cristina

Published
2019
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14. Author Correction: A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease

Author

Wightman, Douglas P., Jansen, Iris E., Savage, Jeanne E., Shadrin, Alexey A., Bahrami, Shahram, Holland, Dominic, Rongve, Arvid, Børte, Sigrid, Winsvold, Bendik S., Drange, Ole Kristian, Martinsen, Amy E., Skogholt, Anne Heidi, Willer, Cristen, Bråthen, Geir, Bosnes, Ingunn, Nielsen, Jonas Bille, Fritsche, Lars G., Thomas, Laurent F., Pedersen, Linda M., Gabrielsen, Maiken E., Johnsen, Marianne Bakke, Meisingset, Tore Wergeland, Zhou, Wei, Proitsi, Petroula, Hodges, Angela, Dobson, Richard, Velayudhan, Latha, Heilbron, Karl, Auton, Adam, Sealock, Julia M., Davis, Lea K., Pedersen, Nancy L., Reynolds, Chandra A., Karlsson, Ida K., Magnusson, Sigurdur, Stefansson, Hreinn, Thordardottir, Steinunn, Jonsson, Palmi V., Snaedal, Jon, Zettergren, Anna, Skoog, Ingmar, Kern, Silke, Waern, Margda, Zetterberg, Henrik, Blennow, Kaj, Stordal, Eystein, Hveem, Kristian, Zwart, John-Anker, Athanasiu, Lavinia, Selnes, Per, Saltvedt, Ingvild, Sando, Sigrid B., Ulstein, Ingun, Djurovic, Srdjan, Fladby, Tormod, Aarsland, Dag, Selbæk, Geir, Ripke, Stephan, Stefansson, Kari, Andreassen, Ole A., and Posthuma, Danielle

Published
2022
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15. Plasma proteomic profiles of UK Biobank participants with multiple sclerosis.

Author

Jacobs, Benjamin M., Vickaryous, Nicola, Giovannoni, Gavin, Proitsi, Petroula, Waters, Sheena, and Dobson, Ruth

Subjects
MULTIPLE sclerosis, GLIAL fibrillary acidic protein, BLOOD proteins, PROTEOMICS, MAGNETIC resonance imaging
Abstract

Objective: We aimed to describe plasma protein biomarkers of multiple sclerosis risk and to explore protein biomarkers of disease severity using radiological outcome measures. Methods: Multiple sclerosis cases and controls were identified in UK Biobank, a longitudinal cohort study of ~500,000 British adults. Plasma proteins were assayed in ~50,000 UK Biobank participants using the Olink proximity extension assay. We performed case–control association testing to examine the association between 2911 proteins and multiple sclerosis, using linear models adjusted for confounding covariates. Associations with radiological lesion burden and brain volume were determined in a subset of the cohort with available magnetic resonance imaging, using normalized T2‐hyperintensity volume or whole brain volume as the outcome measure. Results: In total, 407 prevalent multiple sclerosis cases and 39,979 healthy controls were included. We discovered 72 proteins associated with multiple sclerosis at a Bonferroni‐adjusted p value of 0.05, including established markers such as neurofilament light chain and glial fibrillary acidic protein. We observed a decrease in plasma Granzyme A, a marker of T cell and NK cell degranulation, which was specific to multiple sclerosis. Higher levels of plasma proteins involved in coagulation were associated with lower T2 lesion burden and preserved brain volume. Interpretation: We report the largest plasma proteomic screen of multiple sclerosis, replicating important known associations and suggesting novel markers, such as the reduction in granzyme A. While these findings require external validation, they demonstrate the power of biobank‐scale datasets for discovering new biomarkers for multiple sclerosis. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk

Author

Jansen, Iris E., Savage, Jeanne E., Watanabe, Kyoko, Bryois, Julien, Williams, Dylan M., Steinberg, Stacy, Sealock, Julia, Karlsson, Ida K., Hägg, Sara, Athanasiu, Lavinia, Voyle, Nicola, Proitsi, Petroula, Witoelar, Aree, Stringer, Sven, Aarsland, Dag, Almdahl, Ina S., Andersen, Fred, Bergh, Sverre, Bettella, Francesco, Bjornsson, Sigurbjorn, Brækhus, Anne, Bråthen, Geir, de Leeuw, Christiaan, Desikan, Rahul S., Djurovic, Srdjan, Dumitrescu, Logan, Fladby, Tormod, Hohman, Timothy J., Jonsson, Palmi V., Kiddle, Steven J., Rongve, Arvid, Saltvedt, Ingvild, Sando, Sigrid B., Selbæk, Geir, Shoai, Maryam, Skene, Nathan G., Snaedal, Jon, Stordal, Eystein, Ulstein, Ingun D., Wang, Yunpeng, White, Linda R., Hardy, John, Hjerling-Leffler, Jens, Sullivan, Patrick F., van der Flier, Wiesje M., Dobson, Richard, Davis, Lea K., Stefansson, Hreinn, Stefansson, Kari, Pedersen, Nancy L., Ripke, Stephan, Andreassen, Ole A., and Posthuma, Danielle

Published
2019
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19. Urinary metabolic phenotyping for Alzheimer’s disease

Author

Kurbatova, Natalja, Garg, Manik, Whiley, Luke, Chekmeneva, Elena, Jiménez, Beatriz, Gómez-Romero, María, Pearce, Jake, Kimhofer, Torben, D’Hondt, Ellie, Soininen, Hilkka, Kłoszewska, Iwona, Mecocci, Patrizia, Tsolaki, Magda, Vellas, Bruno, Aarsland, Dag, Nevado-Holgado, Alejo, Liu, Benjamine, Snowden, Stuart, Proitsi, Petroula, Ashton, Nicholas J., Hye, Abdul, Legido-Quigley, Cristina, Lewis, Matthew R., Nicholson, Jeremy K., Holmes, Elaine, Brazma, Alvis, and Lovestone, Simon

Published
2020
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21. Author Correction: A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease

Author

Wightman, Douglas P., Jansen, Iris E., Savage, Jeanne E., Shadrin, Alexey A., Bahrami, Shahram, Holland, Dominic, Rongve, Arvid, Børte, Sigrid, Winsvold, Bendik S., Drange, Ole Kristian, Martinsen, Amy E., Skogholt, Anne Heidi, Willer, Cristen, Bråthen, Geir, Bosnes, Ingunn, Nielsen, Jonas Bille, Fritsche, Lars G., Thomas, Laurent F., Pedersen, Linda M., Gabrielsen, Maiken E., Johnsen, Marianne Bakke, Meisingset, Tore Wergeland, Zhou, Wei, Proitsi, Petroula, Hodges, Angela, Dobson, Richard, Velayudhan, Latha, Heilbron, Karl, Auton, Adam, Sealock, Julia M., Davis, Lea K., Pedersen, Nancy L., Reynolds, Chandra A., Karlsson, Ida K., Magnusson, Sigurdur, Stefansson, Hreinn, Thordardottir, Steinunn, Jonsson, Palmi V., Snaedal, Jon, Zettergren, Anna, Skoog, Ingmar, Kern, Silke, Waern, Margda, Zetterberg, Henrik, Blennow, Kaj, Stordal, Eystein, Hveem, Kristian, Zwart, John-Anker, Athanasiu, Lavinia, Selnes, Per, Saltvedt, Ingvild, Sando, Sigrid B., Ulstein, Ingun, Djurovic, Srdjan, Fladby, Tormod, Aarsland, Dag, Selbæk, Geir, Ripke, Stephan, Stefansson, Kari, Andreassen, Ole A., and Posthuma, Danielle

Published
2021
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22. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Author

Winchester, Laura M., primary, Harshfield, Eric L., additional, Shi, Liu, additional, Badhwar, AmanPreet, additional, Khleifat, Ahmad Al, additional, Clarke, Natasha, additional, Dehsarvi, Amir, additional, Lengyel, Imre, additional, Lourida, Ilianna, additional, Madan, Christopher R., additional, Marzi, Sarah J., additional, Proitsi, Petroula, additional, Rajkumar, Anto P., additional, Rittman, Timothy, additional, Silajdžić, Edina, additional, Tamburin, Stefano, additional, Ranson, Janice M., additional, and Llewellyn, David J., additional

Published
2023
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24. Investigating the genetic relationship between depression symptoms and Alzheimer’s Disease in clinically diagnosed and proxy cases

Author

Gilchrist, Lachlan, primary, Spargo, Thomas P., additional, Green, Rebecca E., additional, Coleman, Jonathan R.I., additional, Howard, David M., additional, Thorp, Jackson G., additional, Adey, Brett, additional, Lord, Jodie, additional, Davies, Helena L., additional, Mundy, Jessica, additional, ter Kuile, Abigail, additional, Davies, Molly R., additional, Hübel, Christopher, additional, Bristow, Shannon, additional, Lee, Sang Hyuck, additional, Rogers, Henry, additional, Curtis, Charles, additional, Kalsi, Gursharan, additional, Arathimos, Ryan, additional, Corbett, Anne, additional, Ballard, Clive, additional, Brooker, Helen, additional, Creese, Byron, additional, Aarsland, Dag, additional, Hampshire, Adam, additional, Velayudhan, Latha, additional, Eley, Thalia C., additional, Breen, Gerome, additional, Iacoangeli, Alfredo, additional, Koks, Sulev, additional, Lewis, Cathryn M., additional, and Proitsi, Petroula, additional

Published
2023
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25. Variants of the elongator protein 3 ( ELP3 ) gene are associated with motor neuron degeneration

Author

Simpson, Claire L, Lemmens, Robin, Miskiewicz, Katarzyna, Broom, Wendy J, Hansen, Valerie K, van Vught, Paul WJ, Landers, John E, Sapp, Peter, Van Den Bosch, Ludo, Knight, Joanne, Neale, Benjamin M, Turner, Martin R, Veldink, Jan H, Ophoff, Roel A, Tripathi, Vineeta B, Beleza, Ana, Shah, Meera N, Proitsi, Petroula, Van Hoecke, Annelies, Carmeliet, Peter, Horvitz, H Robert, Leigh, P Nigel, Shaw, Christopher E, van den Berg, Leonard H, Sham, Pak C, Powell, John F, Verstreken, Patrik, Brown, Robert H, Robberecht, Wim, and Al-Chalabi, Ammar

Subjects
Rare Diseases, Genetics, Neurodegenerative, Biotechnology, Brain Disorders, Neurosciences, ALS, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adult, Aged, Aged, 80 and over, Amyotrophic Lateral Sclerosis, Animals, Drosophila, Female, Genetic Predisposition to Disease, Genetic Variation, Histone Acetyltransferases, Humans, Male, Mice, Mice, Transgenic, Middle Aged, Motor Neurons, Mutation, Nerve Tissue Proteins, White People, Zebrafish, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Amyotrophic lateral sclerosis (ALS) is a spontaneous, relentlessly progressive motor neuron disease, usually resulting in death from respiratory failure within 3 years. Variation in the genes SOD1 and TARDBP accounts for a small percentage of cases, and other genes have shown association in both candidate gene and genome-wide studies, but the genetic causes remain largely unknown. We have performed two independent parallel studies, both implicating the RNA polymerase II component, ELP3, in axonal biology and neuronal degeneration. In the first, an association study of 1884 microsatellite markers, allelic variants of ELP3 were associated with ALS in three human populations comprising 1483 people (P=1.96 x 10(-9)). In the second, an independent mutagenesis screen in Drosophila for genes important in neuronal communication and survival identified two different loss of function mutations, both in ELP3 (R475K and R456K). Furthermore, knock down of ELP3 protein levels using antisense morpholinos in zebrafish embryos resulted in dose-dependent motor axonal abnormalities [Pearson correlation: -0.49, P=1.83 x 10(-12) (start codon morpholino) and -0.46, P=4.05 x 10(-9) (splice-site morpholino), and in humans, risk-associated ELP3 genotypes correlated with reduced brain ELP3 expression (P=0.01). These findings add to the growing body of evidence implicating the RNA processing pathway in neurodegeneration and suggest a critical role for ELP3 in neuron biology and of ELP3 variants in ALS.

Published
2009

28. Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia

Author

Winchester, Laura M, Harshfield, Eric L, Shi, Liu, Badhwar, AmanPreet, Al Khleifat, Ahmad, Clarke, Natasha, Dehsarvi, Amir, Lengyel, Imre, Lourida, Ilianna, Madan, Christopher R, Marzi, Sarah J, Proitsi, Petroula, Rajkumar, Anto P, Rittman, Timothy, Silajdžić, Edina, Tamburin, Stefano, Ranson, Janice M, Llewellyn, David J, Harshfield, Eric L [0000-0001-8767-0928], and Apollo - University of Cambridge Repository

Subjects
machine learning, AI, biomarker discovery, multimodal, dementia
Abstract

INTRODUCTION: With the increase in large multimodal cohorts and high throughput technologies, the potential for discovering novel biomarkers is no longer limited by dataset size. METHODS: Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex datasets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. RESULTS: Remaining challenges include a lack of diversity in the datasets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. DISCUSSION: By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice.

Published
2023
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29. Multiomics machine learning identifies inflammation molecular pathways in prodromal Alzheimer’s Disease

Author

Gómez-Pascual, Alicia, primary, Naccache, Talel, additional, Xu, Jin, additional, Hooshmand, Kourosh, additional, Wretlind, Asger, additional, Gabrielli, Martina, additional, Lombardo, Marta Tiffany, additional, Shi, Liu, additional, Buckley, Noel J., additional, Tijms, Betty M., additional, Vos, Stephanie J. B., additional, ten Kate, Mara, additional, Engelborghs, Sebastiaan, additional, Sleegers, Kristel, additional, Frisoni, Giovanni B., additional, Wallin, Anders, additional, Lleó, Alberto, additional, Popp, Julius, additional, Martinez-Lage, Pablo, additional, Streffer, Johannes, additional, Barkhof, Frederik, additional, Zetterberg, Henrik, additional, Visser, Pieter Jelle, additional, Lovestone, Simon, additional, Bertram, Lars, additional, Nevado-Holgado, Alejo J., additional, Gualerzi, Alice, additional, Picciolini, Silvia, additional, Proitsi, Petroula, additional, Verderio, Claudia, additional, Botía, Juan A., additional, and Legido-Quigley, Cristina, additional

Published
2023
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30. The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume

Author

Lupton, Michelle K., Strike, Lachlan, Hansell, Narelle K., Wen, Wei, Mather, Karen A., Armstrong, Nicola J., Thalamuthu, Anbupalam, McMahon, Katie L., de Zubicaray, Greig I., Assareh, Amelia A., Simmons, Andrew, Proitsi, Petroula, Powell, John F., Montgomery, Grant W., Hibar, Derrek P., Westman, Eric, Tsolaki, Magda, Kloszewska, Iwona, Soininen, Hilkka, Mecocci, Patrizia, Velas, Bruno, Lovestone, Simon, Brodaty, Henry, Ames, David, Trollor, Julian N., Martin, Nicholas G., Thompson, Paul M., Sachdev, Perminder S., and Wright, Margaret J.

Published
2016
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31. Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN‐derived networks and highlights causal links in Alzheimer's disease

Author

Shi, Liu, primary, Xu, Jin, additional, Green, Rebecca, additional, Wretlind, Asger, additional, Homann, Jan, additional, Buckley, Noel J., additional, Tijms, Betty M., additional, Vos, Stephanie J. B., additional, Lill, Christina M., additional, Kate, Mara ten, additional, Engelborghs, Sebastiaan, additional, Sleegers, Kristel, additional, Frisoni, Giovanni B., additional, Wallin, Anders, additional, Lleó, Alberto, additional, Popp, Julius, additional, Martinez‐Lage, Pablo, additional, Streffer, Johannes, additional, Barkhof, Frederik, additional, Zetterberg, Henrik, additional, Visser, Pieter Jelle, additional, Lovestone, Simon, additional, Bertram, Lars, additional, Nevado‐Holgado, Alejo J., additional, Proitsi, Petroula, additional, and Legido‐Quigley, Cristina, additional

Published
2023
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32. The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence

Author

Ranson, Janice M, primary, Khleifat, Ahmad Al, additional, Lyall, Donald M, additional, Newby, Danielle, additional, Winchester, Laura M, additional, Proitsi, Petroula, additional, Veldsman, Michele, additional, Rittman, Timothy, additional, Marzi, Sarah, additional, Yao, Zhi, additional, Skene, Nathan, additional, Bettencourt, Conceição, additional, Kormilitzin, Andrey, additional, Foote, Isabelle F, additional, Golborne, Cecilia, additional, Lourida, Ilianna, additional, Bucholc, Magda, additional, Tang, Eugene, additional, Oxtoby, Neil P, additional, Bagshaw, Peter, additional, Walker, Zuzana, additional, Everson, Richard, additional, Ballard, Clive G, additional, van Duijn, Cornelia M, additional, Langa, Kenneth M, additional, MacLeod, Malcolm, additional, Rockwood, Kenneth, additional, and Llewellyn, David J, additional

Published
2022
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33. Genetic risk for attention‐deficit/hyperactivity disorder is associated with amyloid‐dependent cognitive decline in older adults

Author

Leffa, Douglas Teixeira, primary, Ferrari‐Souza, João Pedro, additional, Bellaver, Bruna, additional, Ferreira, Pamela C.L., additional, Tissot, Cécile, additional, Brum, Wagner S., additional, Caye, Arthur, additional, Lord, Jodie, additional, Proitsi, Petroula, additional, Tudorascu, Dana L, additional, Lopez, Oscar L., additional, Villemagne, Victor L, additional, Cohen, Ann D, additional, Klunk, William E, additional, Rosa‐Neto, Pedro, additional, Zimmer, Eduardo R., additional, Karikari, Thomas K, additional, Rohde, Luis Augusto, additional, and Pascoal, Tharick A, additional

Published
2022
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34. Additional file 7 of Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

Author

Green, Rebecca E., Lord, Jodie, Scelsi, Marzia A., Xu, Jin, Wong, Andrew, Naomi-James, Sarah, Handy, Alex, Gilchrist, Lachlan, Williams, Dylan M., Parker, Thomas D., Lane, Christopher A., Malone, Ian B., Cash, David M., Sudre, Carole H., Coath, William, Thomas, David L., Keuss, Sarah, Dobson, Richard, Legido-Quigley, Cristina, Fox, Nick C., Schott, Jonathan M., Richards, Marcus, and Proitsi, Petroula

Abstract

Additional file 7: Supplementary file 7: Supplementary Notes. Supplementary Notes (metabolomics acquisition, metabolomic and genomic quality control, polygenic risk scores, software and packages used, additional analyses, participant characteristics split by amyloid status).

Published
2023
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35. Additional file 6 of Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

Author

Green, Rebecca E., Lord, Jodie, Scelsi, Marzia A., Xu, Jin, Wong, Andrew, Naomi-James, Sarah, Handy, Alex, Gilchrist, Lachlan, Williams, Dylan M., Parker, Thomas D., Lane, Christopher A., Malone, Ian B., Cash, David M., Sudre, Carole H., Coath, William, Thomas, David L., Keuss, Sarah, Dobson, Richard, Legido-Quigley, Cristina, Fox, Nick C., Schott, Jonathan M., Richards, Marcus, and Proitsi, Petroula

Abstract

Additional file 6: Supplementary file 6: Figure 1. A. Plot showing the Zsummary preservation statistics for each module against the module size. Thresholds for moderate and strong evidence of module preservation are indicated. B. Plot showing the median rank for module preservation. Modules are ranked from most preserved (lowest number) to least preserved (highest number).

Published
2023
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36. Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk

Author

Jansen, Iris E., Savage, Jeanne E., Watanabe, Kyoko, Bryois, Julien, Williams, Dylan M., Steinberg, Stacy, Sealock, Julia, Karlsson, Ida K., Hägg, Sara, Athanasiu, Lavinia, Voyle, Nicola, Proitsi, Petroula, Witoelar, Aree, Stringer, Sven, Aarsland, Dag, Almdahl, Ina S., Andersen, Fred, Bergh, Sverre, Bettella, Francesco, Bjornsson, Sigurbjorn, Brækhus, Anne, Bråthen, Geir, de Leeuw, Christiaan, Desikan, Rahul S., Djurovic, Srdjan, Dumitrescu, Logan, Fladby, Tormod, Hohman, Timothy J., Jonsson, Palmi V., Kiddle, Steven J., Rongve, Arvid, Saltvedt, Ingvild, Sando, Sigrid B., Selbæk, Geir, Shoai, Maryam, Skene, Nathan G., Snaedal, Jon, Stordal, Eystein, Ulstein, Ingun D., Wang, Yunpeng, White, Linda R., Hardy, John, Hjerling-Leffler, Jens, Sullivan, Patrick F., van der Flier, Wiesje M., Dobson, Richard, Davis, Lea K., Stefansson, Hreinn, Stefansson, Kari, Pedersen, Nancy L., Ripke, Stephan, Andreassen, Ole A., and Posthuma, Danielle

Published
2020
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43. Multiomics profiling of human plasma and CSF reveals ATN derived networks and highlights causal links in Alzheimer’s disease

Author

Shi, Liu, primary, Xu, Jin, additional, Green, Rebecca, additional, Wretlind, Asger, additional, Homann, Jan, additional, Buckley, Noel J., additional, Tijms, Betty M., additional, Vos, Stephanie J. B., additional, Lill, Christina M., additional, Kate, Mara ten, additional, Engelborghs, Sebastiaan, additional, Sleegers, Kristel, additional, Frisoni, Giovanni B., additional, Wallin, Anders, additional, Lleó, Alberto, additional, Pop, Julius, additional, Martinez-Lage, Pablo, additional, Streffer, Johannes, additional, Barkhof, Frederik, additional, Zetterberg, Henrik, additional, Visser, Pieter Jelle, additional, Lovestone, Simon, additional, Bertram, Lars, additional, Nevado-Holgado, Alejo J., additional, Proitsi, Petroula, additional, and Legido-Quigley, Cristina, additional

Published
2022
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47. Genetic Risk for Attention-Deficit/Hyperactivity Disorder Predicts Cognitive Decline and Development of Alzheimer's Disease Pathophysiology in Cognitively Unimpaired Older Adults

Author

Leffa, Douglas Teixeira, primary, Ferrari-Souza, João Pedro, additional, Bellaver, Bruna, additional, Tissot, Cécile, additional, Ferreira, Pamela Cristina Lukasewicz, additional, Brum, Wagner S., additional, Caye, Arthur, additional, Lord, Jodie, additional, Proitsi, Petroula, additional, Martins-Silva, Thaís, additional, Tovo-Rodrigues, Luciana, additional, Tudorascu, Dana L., additional, Villemagne, Victor L., additional, Cohen, Annie, additional, Lopez, Oscar L., additional, Klunk, William E., additional, Karikari, Thomas K, additional, Rosa-Neto, Pedro, additional, Zimmer, Eduardo, additional, Molina, Brooke S.G., additional, Rohde, Luis Augusto, additional, and Pascoal, Tharick A., additional

Published
2022
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48. Circulating Metabolome and White Matter Hyperintensities in Females and Males

Author

SLIZ, Eeva, SHIN, Jean, AHMAD, Shahzad, WILLIAMS, Dylan M, FRENZEL, Stefan, GAUSS, Friederike, HARRIS, Sarah E., HENNING, Ann-Kristin, VALDES HERNANDEZ, Maria Del C., HU, Yi-Han, JIMENEZ, Beatriz, SARGURUPREMRAJ, Muralidharan, SUDRE, Carole, WANG, Ruiqi, WITTFELD, Katharina, YANG, Qiong, WARDLAW, Joanna M., VOLZKE, Henry, VERNOOIJ, Meike W., SCHOTT, Jonathan M., RICHARDS, Marcus, PROITSI, Petroula, NAUCK, Matthias, LEWIS, Matthew R., LAUNER, Lenore, HOSTEN, Norbert, GRABE, Hans J., GHANBARI, Mohsen, DEARY, Ian J., COX, Simon R., CHATURVEDI, Nishi, BARNES, Josephine, ROTTER, Jerome I., DEBETTE, Stephanie, IKRAM, M. Arfan, FORNAGE, Myriam, PAUS, Tomas, SESHADRI, Sudha, and PAUSOVA, Zdenka

Published
2022

49. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease

Author

Green, Rebecca, primary, Lord, Jodie, additional, Scelsi, Marzia A, additional, Xu, Jin, additional, Wong, Andrew, additional, Naomi-James, Sarah, additional, Handy, Alex, additional, Gilchrist, Lachlan, additional, Williams, Dylan M, additional, Parker, Thomas D, additional, Lane, Christopher A, additional, Malone, Ian B, additional, Sudre, Carole H, additional, Coath, William, additional, Thomas, David L, additional, Keuss, Sarah, additional, Dobson, Richard, additional, Legido-Quigley, Cristina, additional, Fox, Nick C, additional, Schott, Jonathan M, additional, Richards, Marcus, additional, and Proitsi, Petroula, additional

Published
2022
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