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1. Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

Author

Klee, Matthias, Aho, V. T. E., May, P., Heintz-Buschart, A., Landoulsi, Z., Jónsdóttir, S. R., Pauly, C., Pavelka, L., Delacour, L., Kaysen, A., Krüger, R., Wilmes, P., Leist, A. K., Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Bassis, Michele, Batutu, Roxane, Beaumont, Katy, Béchet, Sibylle, Berchem, Guy, Bisdorff, Alexandre, Boussaad, Ibrahim, Bouvier, David, Castillo, Lorieza, Contesotto, Gessica, De Bremaeker, Nancy, Dewitt, Brian, Diederich, Nico, Dondelinger, Rene, Ramia, Nancy E, Ferrari, Angelo, Frauenknecht, Katrin, Fritz, Joëlle, Gamio, Carlos, Gantenbein, Manon, Gawron, Piotr, Georges, Laura, Ghosh, Soumyabrata, Giraitis, Marijus, Glaab, Enrico, Goergen, Martine, Gómez De Lope, Elisa, Graas, Jérôme, Graziano, Mariella, Groues, Valentin, Grünewald, Anne, Hammot, Gaël, Hanff, Anne-Marie, Hansen, Linda, Heneka, Michael, Henry, Estelle, Henry, Margaux, Herbrink, Sylvia, Herzinger, Sascha, Hundt, Alexander, Jacoby, Nadine, Jónsdóttir, Sonja, Klucken, Jochen, Kofanova, Olga, Krüger, Rejko, Lambert, Pauline, Landoulsi, Zied, Lentz, Roseline, Longhino, Laura, Lopes, Ana Festas, Lorentz, Victoria, Marques, Tainá M., Marques, Guilherme, Martins Conde, Patricia, May, Patrick, Mcintyre, Deborah, Mediouni, Chouaib, Meisch, Francoise, Mendibide, Alexia, Menster, Myriam, Minelli, Maura, Mittelbronn, Michel, Mtimet, Saïda, Munsch, Maeva, Nati, Romain, Nehrbass, Ulf, Nickels, Sarah, Nicolai, Beatrice, Nicolay, Jean-Paul, Noor, Fozia, Gomes, Clarissa P. C., Pachchek, Sinthuja, Pauly, Claire, Pauly, Laure, Pavelka, Lukas, Perquin, Magali, Pexaras, Achilleas, Rauschenberger, Armin, Rawal, Rajesh, Reddy Bobbili, Dheeraj, Remark, Lucie, Richard, Ilsé, Roland, Olivia, Roomp, Kirsten, Rosales, Eduardo, Sapienza, Stefano, Satagopam, Venkata, Schmitz, Sabine, Schneider, Reinhard, Schwamborn, Jens, Severino, Raquel, Sharify, Amir, Soare, Ruxandra, Soboleva, Ekaterina, Sokolowska, Kate, Theresine, Maud, Thien, Hermann, Thiry, Elodie, Ting Jiin Loo, Rebecca, Trouet, Johanna, Tsurkalenko, Olena, Vaillant, Michel, Vega, Carlos, Vilas Boas, Liliana, Wilmes, Paul, Wollscheid-Lengeling, Evi, and Zelimkhanov, Gelani

Published
2024
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2. Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.

Author

Brzenczek, Cyril, Klopfenstein, Quentin, Hähnel, Tom, Fröhlich, Holger, Glaab, Enrico, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Bassis, Michele, Batutu, Roxane, Beaumont, Katy, Béchet, Sibylle, Berchem, Guy, Bisdorff, Alexandre, Boussaad, Ibrahim, Bouvier, David, and Castillo, Lorieza

Subjects
DIGITAL technology, CROSS-sectional method, RESEARCH funding, PREDICTION models, QUESTIONNAIRES, DIAGNOSIS, GAIT in humans, WEARABLE technology, PARKINSON'S disease, MOVEMENT disorders, GAIT disorders, NEUROLOGICAL disorders, HALLUCINATIONS, METABOLOMICS, MACHINE learning, BIOMARKERS, COMORBIDITY
Abstract

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson's disease over seven years in a cohort: a comparative analysis.

Author

Hanff, Anne-Marie, Krüger, Rejko, McCrum, Christopher, Ley, Christophe, NCER-PD, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Bassis, Michele, Batutu, Roxane, Beaumont, Katy, Béchet, Sibylle, Berchem, Guy, Bisdorff, Alexandre, Boussaad, Ibrahim, Bouvier, David, and Castillo, Lorieza

Subjects
PARKINSON'S disease, CHRONIC diseases, STATISTICAL models, APATHY, RESEARCH personnel
Abstract

Introduction: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. Methods: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. Results: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001). Conclusion: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Comprehensive blood metabolomics profiling of Parkinson's disease reveals coordinated alterations in xanthine metabolism.

Author

de Lope, Elisa Gómez, Loo, Rebecca Ting Jiin, Rauschenberger, Armin, Ali, Muhammad, Pavelka, Lukas, Marques, Tainá M., Gomes, Clarissa P. C., Krüger, Rejko, Glaab, Enrico, On behalf of the NCER-PD Consortium, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ammerlann, Wim, Arena, Giuseppe, Balling, Rudi, Bassis, Michele, Batutu, Roxane, Beaumont, Katy, and Becker, Regina

Published
2024
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6. Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson's study.

Author

Pachchek, Sinthuja, Landoulsi, Zied, Pavelka, Lukas, Schulte, Claudia, Buena-Atienza, Elena, Gross, Caspar, Hauser, Ann-Kathrin, Reddy Bobbili, Dheeraj, Casadei, Nicolas, May, Patrick, Krüger, Rejko, on behalf of the NCER-PD Consortium, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Balling, Rudi, and Bassis, Michele

Published
2023
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7. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions

Author

Baldini, Federico, Hertel, Johannes, Sandt, Estelle, Thinnes, Cyrille C., Neuberger-Castillo, Lorieza, Pavelka, Lukas, Betsou, Fay, Krüger, Rejko, Thiele, Ines, Aguayo, Gloria, Allen, Dominic, Ammerlann, Wim, Aurich, Maike, Balling, Rudi, Banda, Peter, Beaumont, Katy, Becker, Regina, Berg, Daniela, Binck, Sylvia, Bisdorff, Alexandre, Bobbili, Dheeraj, Brockmann, Kathrin, Calmes, Jessica, Castillo, Lorieza, Diederich, Nico, Dondelinger, Rene, Esteves, Daniela, Ferrand, Jean-Yves, Fleming, Ronan, Gantenbein, Manon, Gasser, Thomas, Gawron, Piotr, Geffers, Lars, Giarmana, Virginie, Glaab, Enrico, Gomes, Clarissa P. C., Goncharenko, Nikolai, Graas, Jérôme, Graziano, Mariela, Groues, Valentin, Grünewald, Anne, Gu, Wei, Hammot, Gaël, Hanff, Anne-Marie, Hansen, Linda, Hansen, Maxime, Haraldsdöttir, Hulda, Heirendt, Laurent, Herbrink, Sylvia, Herzinger, Sascha, Heymann, Michael, Hiller, Karsten, Hipp, Geraldine, Hu, Michele, Huiart, Laetitia, Hundt, Alexander, Jacoby, Nadine, Jarosław, Jacek, Jaroz, Yohan, Kolber, Pierre, Kutzera, Joachim, Landoulsi, Zied, Larue, Catherine, Lentz, Roseline, Liepelt, Inga, Liszka, Robert, Longhino, Laura, Lorentz, Victoria, Mackay, Clare, Maetzler, Walter, Marcus, Katrin, Marques, Guilherme, Martens, Jan, Mathay, Conny, Matyjaszczyk, Piotr, May, Patrick, Meisch, Francoise, Menster, Myriam, Minelli, Maura, Mittelbronn, Michel, Mollenhauer, Brit, Mommaerts, Kathleen, Moreno, Carlos, Mühlschlegel, Friedrich, Nati, Romain, Nehrbass, Ulf, Nickels, Sarah, Nicolai, Beatrice, Nicolay, Jean-Paul, Noronha, Alberto, Oertel, Wolfgang, Ostaszewski, Marek, Pachchek, Sinthuja, Pauly, Claire, Perquin, Magali, Reiter, Dorothea, Rosety, Isabel, Rump, Kirsten, Satagopam, Venkata, Schlesser, Marc, Schmitz, Sabine, Schmitz, Susanne, Schneider, Reinhard, Schwamborn, Jens, Schweicher, Alexandra, Simons, Janine, Stute, Lara, Trefois, Christophe, Trezzi, Jean-Pierre, Vaillant, Michel, Vasco, Daniel, Vyas, Maharshi, Wade-Martins, Richard, and Wilmes, Paul

Subjects
Male, Systemic disease, Parkinson's disease, Physiology, Luxembourg, Plant Science, Disease, Transsulfuration pathway, Biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Structural Biology, RNA, Ribosomal, 16S, Pantothenic acid, medicine, Humans, Microbiome, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Aged, Metabolic modelling, 0303 health sciences, Gut microbiome, Methionine, Dopaminergic, Parkinson Disease, Cell Biology, Middle Aged, medicine.disease, 3. Good health, Gastrointestinal Microbiome, RNA, Bacterial, chemistry, Computational modelling, lcsh:Biology (General), Case-Control Studies, Parkinson’s disease, Female, General Agricultural and Biological Sciences, 030217 neurology & neurosurgery, Developmental Biology, Biotechnology, Research Article
Abstract

Background Parkinson’s disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson’s Study (n = 147 typical PD cases, n = 162 controls). Results All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms. Conclusion Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

Published
2020
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8. Age at onset as stratifier in idiopathic Parkinson's disease – effect of ageing and polygenic risk score on clinical phenotypes.

Author

Pavelka, L., Rauschenberger, A., Landoulsi, Z., Pachchek, S., May, P., Glaab, E., Krüger, R., on behalf of the NCER-PD Consortium, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Allen, Dominic, Ammerlann, Wim, Balling, Rudi, Bassis, Michele, Beaumont, Katy, Becker, Regina, Bellora, Camille, and Berchem, Guy

Published
2022
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9. Author Correction: Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson's study.

Author

Pachchek, Sinthuja, Landoulsi, Zied, Pavelka, Lukas, Schulte, Claudia, Buena-Atienza, Elena, Gross, Caspar, Hauser, Ann-Kathrin, Reddy Bobbili, Dheeraj, Casadei, Nicolas, May, Patrick, Krüger, Rejko, on behalf of the NCER-PD Consortium, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Ammerlann, Wim, Arena, Giuseppe, Balling, Rudi, and Bassis, Michele

Published
2023
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10. Publisher Correction: Age at onset as stratifier in idiopathic Parkinson's disease – effect of ageing and polygenic risk score on clinical phenotypes.

Author

Pavelka, L., Rauschenberger, A., Landoulsi, Z., Pachchek, S., May, P., Glaab, E., Krüger, R., on behalf of the NCER-PD Consortium, Acharya, Geeta, Aguayo, Gloria, Alexandre, Myriam, Ali, Muhammad, Allen, Dominic, Ammerlann, Wim, Balling, Rudi, Bassis, Michele, Beaumont, Katy, Becker, Regina, Bellora, Camille, and Berchem, Guy

Published
2022
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