Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson

Vista sencilla de ítem
dc.contributor.advisorPinzón Velasco, Andrés Mauriciospa
dc.contributor.authorForero Rodriguez, Lady Johannaspa
dc.contributor.researchgatehttps://www.researchgate.net/profile/Lady-Forero-Rodriguezspa
dc.contributor.researchgroupGrupo de Investigación en Bioinformática y Biología de Sistemasspa
dc.date.accessioned2023-08-02T02:31:27Z
dc.date.available2023-08-02T02:31:27Z
dc.date.issued2023
dc.descriptionilustraciones, gráficas, tablasspa
dc.description.abstractLa Enfermedad de Parkinson (EP) es una enfermedad neurodegenerativa de carácter progresivo y crónico, principal causa de pérdida de coordinación de los movimientos (alteraciones motoras) y de alteraciones no motoras, entre las que se incluyen los síntomas gastrointestinales. Clínicamente es definida como la pérdida de neuronas dopaminérgicas en el mesencéfalo. Alteraciones características en la composición de la microbiota intestinal han sido reportadas en pacientes con EP vs controles sanos, sin embargo no se ha caracterizado la composición en pacientes vs controles en latinoamérica y su función aún no es clara en el contexto de esta enfermedad. Para abordar dicha problemática se obtuvo materia fecal de participantes Colombianos (n =25 EP casos idiopáticos, n =25 controles) para el análisis de información de 16S rRNA y su posterior modelado computacional. Los participantes con EP fueron evaluados por un neurólogo experto en desórdenes del movimiento y todos los individuos respondieron a cuestionarios de consumo. Las muestras de heces de estos individuos fueron analizadas a través de la secuenciación del gen 16S rRNA. Se hizo análisis de diversidad, composición diferencial y modelamiento computacional individualizado teniendo en cuenta la dieta y composición bacteriana fecal de cada participante. Nuestros resultados más importantes incluyen lo siguiente: tres metabolitos de la dieta fueron diferentes entre pacientes con PD y controles, tales como Ácidos grasos trans, Carbohidratos y Potasio. Seis zOTUs cambiaron significativamente en sus abundancias relativas entre pacientes con EP ycontroles sanos. Familias Verrucomicrobioaceae, Lachnospiraceae, Peptostreptococcaceae, Lactobacilliaceae, y Streptococcaceae. Además, el modelado metabólico personalizado de los microbiomas intestinales reveló patrones metabólicos que pueden asociarse a la enfermedad, particularmente la particularmente la producción de cinco metabolitos (Ácido fenilacético , Indol , L-triptófano, D-Fructosa y Ácido Mirístico), relacionados al metabolismo de aminoácidos aromáticos y la dieta. Por tanto, los resultados sugieren que la dieta y la composición bacteriana intestinal podrían afectar el metabolismo del huésped y así mismo relacionarse con la enfermedad. (Texto tomado de la fuente).spa
dc.description.abstractParkinson's Disease (PD) is a progressive and chronic neurodegenerative disease that is the main cause of loss of coordination of movements (motor disorders) and non-motor disorders, including gastrointestinal symptoms. Clinically it is defined as the loss of dopaminergic neurons in the midbrain. Alterations in the composition of the intestinal microbiota have been reported in patients with PD vs healthy controls, however the composition in patients vs. controls in Latin America has not been characterized and its function is still unclear in the context of this disease. To address this problem, fecal matter was obtained from Colombian participants (n = 25 PD idiopathic cases, n = 25 controls) for the analysis of rRNA16S information. Participants with PD were evaluated by a neurologist with expertise in movement disorders, and all individuals answered consumer questionnaires. All this formation was therefore used for further personalized computational analysis. Stool samples from these individuals were analyzed through 16S rRNA gene sequencing. An analysis of diversity, differential composition and individualized computational modeling was carried out, taking into account the diet and fecal bacterial composition of each participant. Our most important results include the following: three dietary metabolites were different between PD patients and controls such as Trans Fatty Acids, Carbohydrates and Potassium. Six zOTUs changed significantly in their relative abundances between PD patients and healthy controls. Families Verrucomicrobioaceae, Lachnospiraceae, Peptostreptococcaceae, Lactobacilliaceae, and Streptococcaceae. Furthermore, individualized metabolic modeling of gut microbiomes revealed metabolic patterns that may be associated with disease, production of five metabolites (Phenylacetic Acid, Indole, L-Tryptophan, D-Fructose, and Myristic Acid), related to the metabolism of aromatic amino acids, and the diet. Therefore, the results suggest that the diet and the intestinal bacterial composition could affect the metabolism of the host and therefore relate to the disease.eng
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ciencias Biomédicasspa
dc.description.methodsSujetos de estudio Originalmente se reclutaron 56 sujetos de edad (con una diferencia máxima de + 2 años) y sexo emparejados (31 pacientes con EP, 25 sujetos de control) para participar en el estudio. Se excluyeron 6 participantes en base a los criterios de inclusión/exclusión, con lo que el número total de sujetos fue de 50 (25 pacientes con EP, 25 sujetos de control). El estudio fue aprobado por el comité de ética de la Universidad Nacional de Colombia y todos los participantes dieron su consentimiento informado (Anexo A). El diagnóstico de los pacientes fue realizado por un neurólogo especialista con amplia experiencia en trastornos del movimiento, utilizando los datos obtenidos de historias clínicas y la escala de deterioro de Webster para la enfermedad de Parkinson, la cual cuantifica el grado de inhabilidad de un paciente, determinando así la severidad de la enfermedad. Los criterios de exclusión utilizados fueron: 1) uso regular de probióticos o antibióticos los últimos 3 meses antes de la toma de la muestra. 2) Parkinsonismo secundario, 3) Parkinson familiar, 3) Parkinson familiar, 3) enfermedades primarias gastrointestinales, 4) otras alteraciones neurológicas o psiquiátricas, 5) cambios en los hábitos dietarios. Para los controles los criterios de exclusión fueron 1) uso regular de probióticos o antibióticos los últimos 3 meses antes de la toma de muestras. 2)50 enfermedades primarias gastrointestinales, 3) alteraciones neurológicas o psiquiátricas, 4) cambios en los hábitos dietarios. Toma de muestras y extracción de ADN Las muestras fecales se recogieron en casa con las indicaciones del médico en recipientes estériles desechables. Las muestras fecales se recogieron en horas de la mañana, se congelaron y se almacenaron a -20°C para su posterior procesamiento (máximo 3 días después). Se utilizó el ZymoBIOMICS DNA Miniprep Kit (Zymo Research) según las instrucciones del fabricante y ciertos perfeccionamientos del protocolo en casa (Anexo 2). Tras la extracción del ADN (3 réplicas técnicas por muestra), se cuantificó mediante Nanodrop ND-1000 (Thermo Fisher Scientific, Wilmington, DE), Qubit Invitrogen (Life Technologies, CA) y se evaluó la integridad del ADN mediante electroforesis en gel de agarosa al 0,8%. Amplicón del gen 16S rRNA y secuenciación Los extractos de ADN se transfirieron a placas de 96 pocillos. Los servicios de secuenciación de la Universidad de Iowa (Iowa State University DNA Facility, Estados Unidos) se encargaron de la preparación y secuenciación del ADN. Las regiones hipervariables del gen 16S rRNA bacteriano V4-V5 se amplificaron utilizando el cebador universal 16S forward (515F: GTGYCAGCMGCCGCGTAA) y el cebador reverse (926R:CCGYCAATTYMTTTRAGTTT). De este modo, las bibliotecas de ADN se multiplexaron para su secuenciación en la plataforma Illumina MiSeq (2 × 250 fines pareados) en un solo carril de la celda de flujo. Bioinformática y análisis estadístico Se obtuvieron los datos ya demultiplexados, las réplicas fueron concatenadas, se filtraron con el programa prinseq-lite-0.20.4. Se cortaron las bases por debajo de Q-score 24 al principio y al final de las lecturas (Secuencias almacenadas en la base de datos SRA del geneBank con el número de acceso BioProject ; PRJNA975118, disponibles a partir del51 24 de Mayo de 2024). Tras el procesamiento, se obtuvieron las secuencias únicas para la eliminación de quimeras y la agrupación en unidades taxonómicas operativas de radio cero (zOTUs) utilizando el algoritmo unoise3 encontrado en Usearch [336–340]. Las secuencias con menos de 400 pb se eliminaron de los datos brutos. Las asignaciones taxonómicas se obtuvieron utilizando el conjunto de secuencias de entrenamiento de la base de datos RDP v16 con un nivel de confianza del 80%. Las comparaciones estadísticas y la visualización de los datos se realizaron mediante R (versión 4.1.0). Para considerar las comparaciones significativas se tuvieron en cuenta los valores p < 0,05, o los valores p ajustados < 0,05. En las comparaciones, para evaluar variables clínicas potencialmente confusas, se utilizó la prueba t de Student, la prueba de rangos con signo de Wilcoxon o la prueba exacta de Fisher, dependiendo del tipo y la distribución de cada variable. Las figuras se trazaron con ggplot2 (v. 2_3.3.5) principalmente. El análisis de la estructura y composición de la comunidad bacteriana se realizó mediante el paquete R phyloseq [349] (v. 1.36.0). El tamaño de la biblioteca de cada muestra se normalizó utilizando la rarefacción para igualar la profundidad de la secuencia y excluir los taxones presentes en menos del 20% de todas las muestras. La diversidad y la riqueza bacteriana se analizaron utilizando Phyloseq con los índices de diversidad alfa (riqueza observada, índice de Shannon e índice de Simpson inverso). Se utilizó la prueba de suma de rangos de Wilcoxon para evaluar las diferencias de diversidad alfa. Para evaluar la diversidad beta, se utilizó phyloseq (v. 1.36.0). Se utilizaron medidas de distancia como la métrica de distancia ponderada y no ponderada de UniFrac para la ordenación sin restricciones de las proporciones de géneros entre los grupos de EP y controles. El análisis multivariado permutado de la varianza PERMANOVA, se ejecutó con el comando ANOSIM (Análisis de similitudes) y Adonis (Permutational Multivariate Analysis of variance using Distance Matrices) parámetro perm = 9999 implementado en vegan (v. 2.5-7) [341]. Estos fueron trazados por el escalamiento multidimensional no métrico (NMDS) utilizando ggplot2 (v. 2_3.3.5) paquetes en el software R.52 Evaluamos la abundancia diferencial entre los grupos de EP y controles mediante la prueba de expresión diferencial basada en un modelo que utiliza la distribución binomial negativa DESeq2 (v. 1.32.0) utilizando el paquete Phyloseq. Este análisis realiza una normalización de las bibliotecas, tiene en cuenta sólo las zOTUs comunes de los dos grupos y obtiene un valor corregido de diferencias significativas [342]. Para la elección de los géneros se toman aquellas zOTUs que se encuentran entre las 100 primeras más abundantes, es decir, se tomaron aquellas zOTUs que están por encima de una abundancia relativa de 0.1. Y realizamos comparaciones en tres niveles taxonómicos (zOTU, género y familia), entre los grupos de EP y controles. Obtención de datos nutricionales y de la enfermedad Con base en los mismos sujetos de estudio y toma de muestras detallada en el capítulo 1 una nutricionista diplomada desarrolló una evaluación nutricional completa adaptada a los fines de la investigación en los 50 sujetos (25 casos y 25 controles) mediante una visita domiciliaria entre junio de 2018 y febrero de 2019. Se obtuvo información sobre diferentes datos clínicos entre los que se incluyen Deposiciones por semana, Escala de Bristol (evaluar de forma descriptiva y gráfica siete tipos de heces, según su forma y consistencia [408]), Escala de Webster, Perímetro de pantorrilla, Escala de gravedad de la enfermedad, la cual es basada en la Escala de Hoehn y Yahr que es utilizada para medir la progresión de los síntomas de la enfermedad y el nivel de discapacidad [409], medicamentos consumidos, otras patologías, entre otras.76 Obtención de componentes de los alimentos La evaluación nutricional incluyó un test de screening de desnutrición con la herramienta de screening de Ferguson [410], la historia nutricional, la evaluación antropométrica, el patrón alimentario y la ingesta nutricional. Se evaluó la ingesta de alimentos con dos recordatorios de 24 horas de los Pasos Múltiples diseñados por el Departamento de Agricultura de los Estados Unidos (USDA). El patrón alimentario de los últimos seis meses se identificó mediante un Cuestionario de Frecuencia Alimentaria cualitativo modificado según la Encuesta Nacional de Situación Nutricional de 2005 y 2010 [411], las Guías Alimentarias para los Estadounidenses y los nutrientes trazadores de riesgo relativos a la composición de la microbiota y la enfermedad de Parkinson. Cada alimento fue codificado y analizado utilizando tanto la Tabla Colombiana de Composición de Alimentos - 2015 [412], como la Lista de Composición de Alimentos del USDA [413], Finlandia [414] y Alemania [415], que comprende 60 nutrientes. Adicionalmente, se evaluó el cuestionario de frecuencia de alimentos según la periodicidad en una escala de 10 puntos avalada por el USDA [416]. Evaluación de datos nutricionales En la evaluación antropométrica se utilizó el peso, la estatura o la estimación de la altura del talón de la rodilla con la ecuación desarrollada para Chumlea [417] . Se evaluó la masa muscular con las circunferencias del brazo y la pantorrilla y la masa grasa con la circunferencia del pliegue cutáneo del tríceps. En el análisis se añadió el IMC estratificado por el punto de corte para adultos [418] y adultos mayores [419]. Estimación de la masa muscular y grasa según los puntos de corte establecidos en la Tercera Encuesta Nacional de Salud y Nutrición (NHANES) [420] y Frisancho [421] para la circunferencia muscular del brazo.77 Adicionalmente, se contrastó la ingesta de nutrientes siguiendo las recomendaciones dietéticas establecidas en la Resolución colombiana 3803 de 2016. Para ello se tomaron las Recomendaciones de Ingesta de Energía y Nutrientes (RIEN), que es la adaptación de las Ingestas Dietéticas de Referencia (DRI). El valor de la ingesta de cada nutriente se comparó con los siguientes valores: Rango de Distribución de Macronutrientes Adecuado (RDA), EAR (Requerimiento Medio Estimado), Cantidades Dietéticas Recomendadas (RDA), AI (Ingesta Adecuada) o UL (Nivel de Ingesta Superior Tolerable) para establecer si la ingesta de cada nutriente era deficit o supera el valor recomendado. Sólo se tomaron para el análisis los nutrientes indicados en las recomendaciones. Análisis estadístico Las comparaciones estadísticas y la visualización de los datos se realizaron mediante R (versión 4.1.0). Para considerar las comparaciones significativas se tuvieron en cuenta los valores p < 0,05, o los valores p ajustados < 0,05. En las comparaciones para evaluar variables clínicas potencialmente confusas, se utilizó la prueba t de Student, la prueba de rangos con signo de Wilcoxon o la prueba exacta de Fisher, dependiendo del tipo y la distribución de cada variable. Las figuras se trazaron con ggplot2 (v. 2_3.3.5). Las variables de nutrientes (metabolitos o elementos alimentarios) de la dieta de los pacientes y los controles se ajustaron por la ingesta energética (dividida por la ingesta energética total en kilocalorías y multiplicada por la media de kilocalorías o dividida por la ingesta energética total en kilocalorías y multiplicada por 1000 ). A partir de los cuestionarios de consumo de 24 H se obtuvo la información correspondiente de cada participante, la cantidad de cada alimento y nutrientes pertenecientes a cada alimento (metabolitos de la dieta). Con el fin de explorar posibles patrones dietarios, se utilizó el Análisis de Componentes Principales (PCA) de la energía ajustada. La correlación entre las variables se calculó utilizando la correlación de rangos de Spearman y la prueba de rangos con signo de Wilcoxon.78 Reconstrucción de la comunidad bacteriana personalizada Para predecir los procesos metabólicos de la comunidad microbiana, integramos la información de abundancia de cada individuo basada en la colección AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) (Versión 1.02). La colección AGORA está compuesta por 818 reconstrucciones metabólicas de especies microbianas comunes del intestino humano. Estos modelos se utilizan para el análisis del flujo metabólico como el FBA (Flux Balance Analysis). Así, tomamos las secuencias filtradas del gen 16S rRNA pertenecientes a las zOTUs obtenidas y las mapeamos a los modelos correspondientes contenidos en una colección refinada de los modelos publicados originalmente en la colección AGORA usando el paquete MicrobiomeAGORA [477,478,479]. Para ello tomamos el 97% como identidad de secuencia mínima y así encontramos el vecino más cercano basado en las secuencias 16S. Análisis individualizado de reconstrucción de la comunidad bacteriana Para predecir la composición funcional del microbioma intestinal a partir de los datos del 16S, utilizamos PICRUSt2 (v 2-2.3.0-6) entre los grupos de EP y de control.108 Como se ha dicho antes, para modelar el comportamiento de la comunidad de la composición del microbioma previamente identificada, utilizamos el paquete de R llamado BacArena [314], aquí las bacterias se representan como individuos situados en rejillas, un lugar donde pueden moverse aleatoriamente y son capaces de tomar e intercambiar metabolitos que se producen/consumen o se encuentran en el entorno. El entorno se compone de 3600 celdas de rejilla donde los modelos de microbios pueden crecer y cerca de 300-500 reconstrucciones de microbios añadidos inicialmente en función de las abundancias relativas. Con un total de 12H pasos de tiempo de crecimiento de cada uno de los modelos microbianos, esto teniendo en cuenta que existe una aproximación del ritmo de alimentación y ayuno durante el día de 12 h. Realizamos el modelado basado en restricciones utilizando el paquete de R Sybil [323] donde la biomasa es el objetivo de la optimización en el FBA [318] y ILOG CPLEX [484] como solucionador de programación lineal. Esta simulación se realizó utilizando 10 réplicas en paralelo (Anexo P). En estos modelos de comunidad intestinal bacteriana específica para cada individuo, obtuvimos la concentración de productos metabólicos finales como los AGCCs al final de la simulación y las diferencias significativas entre el control sano y el modelado personalizado de los pacientes con EP fueron probadas usando el modelo lineal generalizado. Software Las simulaciones y su análisis se realizaron en el entorno R (4.1.0 y 3.6.1) [485]. Se utilizaron los paquetes BacArena [486] y sybil (versión 2.2.0 ) [323]. Además se utilizaron los paquetes: sybilSBML (versión 3.1.2) [487], el solucionador de programación lineal CPLEX (versión 12.7.1) [488], el paquete R cplexAPI (versión 1.4.0) [489]. Para la computación en paralelo, se utilizó el software parallel, con el paquete foreach (versión 1.5.2 ), doParallel (versión 1.0.17) [490–492].spa
dc.description.researchareaReconstrucción y análisis de redes biológicas Investigaciónspa
dc.format.extent283 páginas +1 anexospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84412
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Medicinaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Medicina - Doctorado en Ciencias Biomédicasspa
dc.relation.indexedBiremespa
dc.relation.references1. Lebouvier, T.; Chaumette, T.; Paillusson, S.; Duyckaerts, C.; Bruley des Varannes, S.; Neunlist, M.; Derkinderen, P. The Second Brain and Parkinson’s Disease. Eur J Neurosci 2009, 30, 735–741, doi:10.1111/j.1460-9568.2009.06873.x.spa
dc.relation.references2. Orozco, J.L.; Valderrama-Chaparro, J.A.; Pinilla-Monsalve, G.D.; Molina-Echeverry, M.I.; Castaño, A.M.P.; Ariza-Araújo, Y.; Prada, S.I.; Takeuchi, Y. Parkinson’s Disease Prevalence, Colombiaage Distribution and Staging In. Neurol. Int. 2020, doi:10.4081/ni.2020.8401.spa
dc.relation.references3. Dorsey, E.R.; Constantinescu, R.; Thompson, J.P.; Biglan, K.M.; Holloway, R.G.; Kieburtz, K.; Marshall, F.J.; Ravina, B.M.; Schifitto, G.; Siderowf, A.; et al. Projected Number of People with Parkinson Disease in the Most Populous Nations, 2005 through 2030. Neurology 2007, doi:10.1212/01.wnl.0000247740.47667.03.spa
dc.relation.references4. Tran, J.; Anastacio, H.; Bardy, C. Genetic Predispositions of Parkinson’s Disease Revealed in Patient-Derived Brain Cells. Npj Park. Dis. 2020, 6, 1–18, doi:10.1038/s41531-020-0110-8.spa
dc.relation.references5. Ball, N.; Teo, W.-P.; Chandra, S.; Chapman, J. Parkinson’s Disease and the Environment. Front. Neurol. 2019, doi:10.3389/fneur.2019.00218.spa
dc.relation.references6. Maiti, P.; Manna, J.; Dunbar, G.L. Current Understanding of the Molecular Mechanisms in Parkinson’s Disease: Targets for Potential Treatments. Transl. Neurodegener. 2017, 6, 28, doi:10.1186/s40035-017-0099-z.spa
dc.relation.references7. Surmeier, D.J. Determinants of Dopaminergic Neuron Loss in Parkinson’s Disease. FEBS J. 2018, 285, 3657–3668, doi:10.1111/febs.14607.spa
dc.relation.references8. Munoz-Pinto, M.F.; Empadinhas, N.; Cardoso, S.M. The Neuromicrobiology of Parkinson’s Disease: A Unifying Theory. Ageing Res. Rev. 2021, doi:10.1016/j.arr.2021.101396.spa
dc.relation.references9. Alexander, G.E. Biology of Parkinson’s Disease: Pathogenesis and Pathophysiology of a Multisystem Neurodegenerative Disorder. Dialogues Clin. Neurosci. 2004, 6, 259–280.spa
dc.relation.references10. Pellicano, C.; Benincasa, D.; Pisani, V.; Buttarelli, F.R.; Giovannelli, M.; Pontieri, F.E. Prodromal Non-Motor Symptoms of Parkinson’s Disease. Neuropsychiatr. Dis. Treat. 2007, 3, 145–152.spa
dc.relation.references11. Braak, H.; de Vos, R.A.; Bohl, J.; Del Tredici, K. Gastric Alpha-Synuclein Immunoreactive Inclusions in Meissner’s and Auerbach’s Plexuses in Cases Staged for Parkinson’s Disease-Related Brain Pathology. Neurosci Lett 2006, 396, 67–72, doi:10.1016/j.neulet.2005.11.012.spa
dc.relation.references12. Braak, H.; Del Tredici, K.; Rüb, U.; De Vos, R.A.I.; Jansen Steur, E.N.H.; Braak, E. Staging of Brain Pathology Related to Sporadic Parkinson’s Disease. Neurobiol. Aging 2003, doi:10.1016/S0197-4580(02)00065-9.spa
dc.relation.references13. Schaeffer, E.; Kluge, A.; Böttner, M.; Zunke, F.; Cossais, F.; Berg, D.; Arnold, P.182 Alpha Synuclein Connects the Gut-Brain Axis in Parkinson’s Disease Patients – A View on Clinical Aspects, Cellular Pathology and Analytical Methodology. Front. Cell Dev. Biol. 2020, 8, 573696, doi:10.3389/fcell.2020.573696.spa
dc.relation.references14. Klann, E.M.; Dissanayake, U.; Gurrala, A.; Farrer, M.; Shukla, A.W.; Ramirez-Zamora, A.; Mai, V.; Vedam-Mai, V. The Gut–Brain Axis and Its Relation to Parkinson’s Disease: A Review. Front. Aging Neurosci. 2022, 13, doi:10.3389/fnagi.2021.782082.spa
dc.relation.references15. Romano, S.; Savva, G.M.; Bedarf, J.R.; Charles, I.G.; Hildebrand, F.; Narbad, A. Meta-Analysis of the Parkinson’s Disease Gut Microbiome Suggests Alterations Linked to Intestinal Inflammation. Npj Park. Dis. 2021, 7, 1–13, doi:10.1038/s41531-021-00156-z.spa
dc.relation.references16. Toledo, A.R.L.; Monroy, G.R.; Salazar, F.E.; Lee, J.-Y.; Jain, S.; Yadav, H.; Borlongan, C.V. Gut-Brain Axis as a Pathological and Therapeutic Target for Neurodegenerative Disorders. Int. J. Mol. Sci. 2022, 23, doi:10.3390/ijms23031184.spa
dc.relation.references17. De Angelis, M.; Ferrocino, I.; Calabrese, F.M.; De Filippis, F.; Cavallo, N.; Siragusa, S.; Rampelli, S.; Di Cagno, R.; Rantsiou, K.; Vannini, L.; et al. Diet Influences the Functions of the Human Intestinal Microbiome. Sci. Rep. 2020, 10, 4247, doi:10.1038/s41598-020-61192-y.spa
dc.relation.references18. Baert, F.; Matthys, C.; Mellaerts, R.; Lemaître, D.; Vlaemynck, G.; Foulon, V. Dietary Intake of Parkinson’s Disease Patients. Front. Nutr. 2020, doi:10.3389/fnut.2020.00105.spa
dc.relation.references19. Unger, M.M.; Spiegel, J.; Dillmann, K.U.; Grundmann, D.; Philippeit, H.; Burmann, J.; Fassbender, K.; Schwiertz, A.; Schafer, K.H. Short Chain Fatty Acids and Gut Microbiota Differ between Patients with Parkinson’s Disease and Age-Matched Controls. Park. Relat Disord 2016, 32, 66–72, doi:10.1016/j.parkreldis.2016.08.019.spa
dc.relation.references20. Bedarf, J.R.; Hildebrand, F.; Coelho, L.P.; Sunagawa, S.; Bahram, M.; Goeser, F.; Bork, P.; Wüllner, U. Functional Implications of Microbial and Viral Gut Metagenome Changes in Early Stage L-DOPA-Naïve Parkinson’s Disease Patients. Genome Med. 2017, doi:10.1186/s13073-017-0428-y.spa
dc.relation.references21. Keshavarzian, A.; Green, S.J.; Engen, P.A.; Voigt, R.M.; Naqib, A.; Forsyth, C.B.; Mutlu, E.; Shannon, K.M. Colonic Bacterial Composition in Parkinson’s Disease. Mov. Disord. 2015, 30, 1351–1360, doi:10.1002/mds.26307spa
dc.relation.references22. Hill-Burns, E.M.; Debelius, J.W.; Morton, J.T.; Wissemann, W.T.; Lewis, M.R.; Wallen, Z.D.; Peddada, S.D.; Factor, S.A.; Molho, E.; Zabetian, C.P.; et al. Parkinson’s Disease and Parkinson’s Disease Medications Have Distinct Signatures of the Gut Microbiome. Mov. Disord. 2017, 32, 739–749, doi:10.1002/mds.26942.spa
dc.relation.references23. Heintz-Buschart, A.; Pandey, U.; Wicke, T.; Sixel-Döring, F.; Janzen, A.; Sittig-Wiegand, E.; Trenkwalder, C.; Oertel, W.H.; Mollenhauer, B.; Wilmes, P. The Nasal and Gut Microbiome in Parkinson’s Disease and Idiopathic Rapid Eye Movement Sleep Behavior Disorder. Mov. Disord. 2018, doi:10.1002/mds.27105.spa
dc.relation.references24. Hopfner, F.; Künstner, A.; Müller, S.H.; Künzel, S.; Zeuner, K.E.; Margraf, N.G.; Deuschl, G.; Baines, J.F.; Kuhlenbäumer, G. Gut Microbiota in Parkinson Diseasein a Northern German Cohort. Brain Res. 2017, 1667, 41–45, doi:10.1016/j.brainres.2017.04.019.spa
dc.relation.references25. Scheperjans, F.; Aho, V.; Pereira, P.A.; Koskinen, K.; Paulin, L.; Pekkonen, E.; Haapaniemi, E.; Kaakkola, S.; Eerola-Rautio, J.; Pohja, M.; et al. Gut Microbiota Are Related to Parkinson’s Disease and Clinical Phenotype. Mov Disord 2015, 30, 350–358, doi:10.1002/mds.26069.spa
dc.relation.references26. Aho, V.T.E.; Pereira, P.A.B.; Voutilainen, S.; Paulin, L.; Pekkonen, E.; Auvinen, P.; Scheperjans, F. Gut Microbiota in Parkinson’s Disease: Temporal Stability and Relations to Disease Progression. EBioMedicine 2019, 44, 691–707, doi:10.1016/j.ebiom.2019.05.064.spa
dc.relation.references27. Petrov, V.A.; Saltykova, I.V.; Zhukova, I.A.; Alifirova, V.M.; Zhukova, N.G.; Dorofeeva, Yu.B.; Tyakht, A.V.; Kovarsky, B.A.; Alekseev, D.G.; Kostryukova, E.S.; et al. Analysis of Gut Microbiota in Patients with Parkinson’s Disease. Bull. Exp. Biol. Med. 2017, 162, 734–737, doi:10.1007/s10517-017-3700-7.spa
dc.relation.references28. Qian, Y.; Yang, X.; Xu, S.; Wu, C.; Song, Y.; Qin, N.; Chen, S.-D.; Xiao, Q. Alteration of the Fecal Microbiota in Chinese Patients with Parkinson’s Disease. Brain. Behav. Immun. 2018, 70, 194–202, doi:10.1016/j.bbi.2018.02.016.spa
dc.relation.references29. Pietrucci, D.; Cerroni, R.; Unida, V.; Farcomeni, A.; Pierantozzi, M.; Mercuri, N.B.; Biocca, S.; Stefani, A.; Desideri, A. Dysbiosis of Gut Microbiota in a Selected Population of Parkinson’s Patients. Parkinsonism Relat. Disord. 2019, 65, 124–130, doi:10.1016/j.parkreldis.2019.06.003.spa
dc.relation.references30. Jin, M.; Li, J.; Liu, F.; Lyu, N.; Wang, K.; Wang, L.; Liang, S.; Tao, H.; Zhu, B.; Alkasir, R. Analysis of the Gut Microflora in Patients With Parkinson’s Disease. Front. Neurosci. 2019, 13.spa
dc.relation.references31. Hasegawa, S.; Goto, S.; Tsuji, H.; Okuno, T.; Asahara, T.; Nomoto, K.; Shibata, A.; Fujisawa, Y.; Minato, T.; Okamoto, A.; et al. Intestinal Dysbiosis and Lowered Serum Lipopolysaccharide-Binding Protein in Parkinson’s Disease. PLOS ONE 2015, 10, e0142164, doi:10.1371/journal.pone.0142164.spa
dc.relation.references32. Li, C.; Cui, L.; Yang, Y.; Miao, J.; Zhao, X.; Zhang, J.; Cui, G.; Zhang, Y. Gut Microbiota Differs Between Parkinson’s Disease Patients and Healthy Controls in Northeast China. Front. Mol. Neurosci. 2019, 12, 171, doi:10.3389/fnmol.2019.00171.spa
dc.relation.references33. Li, F.; Wang, P.; Chen, Z.; Sui, X.; Xie, X.; Zhang, J. Alteration of the Fecal Microbiota in North-Eastern Han Chinese Population with Sporadic Parkinson’s Disease. Neurosci. Lett. 2019, 707, 134297, doi:10.1016/j.neulet.2019.134297.spa
dc.relation.references34. Lin, C.H.; Chen, C.C.; Chiang, H.L.; Liou, J.M.; Chang, C.M.; Lu, T.P.; Chuang, E.Y.; Tai, Y.C.; Cheng, C.; Lin, H.Y.; et al. Altered Gut Microbiota and Inflammatory Cytokine Responses in Patients with Parkinson’s Disease. J. Neuroinflammation 2019, 16, 1–9, doi:10.1186/s12974-019-1528-y.spa
dc.relation.references35. Li, W.; Wu, X.; Hu, X.; Wang, T.; Liang, S.; Duan, Y.; Jin, F.; Qin, B. Structural Changes of Gut Microbiota in Parkinson’s Disease and Its Correlation with Clinical Features. Sci. China Life Sci. 2017, doi:10.1007/s11427-016-9001-4.spa
dc.relation.references36. Baldini, F.; Hertel, J.; Sandt, E.; Thinnes, C.C.; Neuberger-Castillo, L.; Pavelka, L.; Betsou, F.; Krüger, R.; Thiele, I. Parkinson’s Disease-Associated Alterations of the Gut Microbiome Predict Disease-Relevant Changes in Metabolic Functions. BMC Biol. 2020, 18, 62, doi:10.1186/s12915-020-00775-7.spa
dc.relation.references37. Thiele, I.; Palsson, B.Ø. A Protocol for Generating a High-Quality Genome-Scale Metabolic Reconstruction. Nat. Protoc. 2010, 5, 93–121, doi:10.1038/nprot.2009.203.spa
dc.relation.references38. Magnúsdóttir, S.; Heinken, A.; Kutt, L.; Ravcheev, D.A.; Bauer, E.; Noronha, A.; Greenhalgh, K.; Jäger, C.; Baginska, J.; Wilmes, P.; et al. Generation of Genome-Scale Metabolic Reconstructions for 773 Members of the Human Gut Microbiota. Nat. Biotechnol. 2016, 35, 81–89, doi:10.1038/nbt.3703.spa
dc.relation.references39. Heinken, A.; Basile, A.; Thiele, I. Advances in Constraint-Based Modelling of Microbial Communities. Curr. Opin. Syst. Biol. 2021, 27, 100346, doi:10.1016/j.coisb.2021.05.007.spa
dc.relation.references40. Bauer, E.; Thiele, I. From Metagenomic Data to Personalized in Silico Microbiotas: Predicting Dietary Supplements for Crohn’s Disease. Npj Syst. Biol. Appl. 2018, 4, doi:10.1038/s41540-018-0063-2.spa
dc.relation.references41. Heinken, A.; Hertel, J.; Thiele, I. Metabolic Modelling Reveals Broad Changes in Gut Microbial Metabolism in Inflammatory Bowel Disease Patients with Dysbiosis. Npj Syst. Biol. Appl. 2021, 7, 1–11, doi:10.1038/s41540-021-00178-6.spa
dc.relation.references42. de Lau, L.M.; Breteler, M.M. Epidemiology of Parkinson’s Disease. Lancet Neurol. 2006, 5, 525–535, doi:10.1016/S1474-4422(06)70471-9.spa
dc.relation.references43. Lebouvier, T.; Chaumette, T.; Damier, P.; Coron, E.; Touchefeu, Y.; Vrignaud, S.; Naveilhan, P.; Galmiche, J.P.; Bruley des Varannes, S.; Derkinderen, P.; et al. Pathological Lesions in Colonic Biopsies during Parkinson’s Disease. Gut 2008, 57, 1741–1743, doi:10.1136/gut.2008.162503.spa
dc.relation.references44. Mulak, A.; Bonaz, B. Brain-Gut-Microbiota Axis in Parkinson’s Disease. World J Gastroenterol 2015, 21, 10609–10620, doi:10.3748/wjg.v21.i37.10609.spa
dc.relation.references45. Braak, H.; Del Tredici, K. Neuropathological Staging of Brain Pathology in Sporadic Parkinson’s Disease: Separating the Wheat from the Chaff. J. Park. Dis. 2017, doi:10.3233/JPD-179001.spa
dc.relation.references46. Hertel, J.; Harms, A.C.; Heinken, A.; Baldini, F.; Thinnes, C.C.; Glaab, E.; Vasco, D.A.; Pietzner, M.; Stewart, I.D.; Wareham, N.J.; et al. Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson’s Disease. Cell Rep. 2019, doi:10.1016/j.celrep.2019.10.035.spa
dc.relation.references47. Marinos, G.; Kaleta, C.; Waschina, S. Defining the Nutritional Input for Genome-Scale Metabolic Models: A Roadmap. PLOS ONE 2020, 15, e0236890, doi:10.1371/journal.pone.0236890.spa
dc.relation.references48. Raman, K.; Chandra, N. Flux Balance Analysis of Biological Systems: Applications and Challenges. Brief. Bioinform. 2009, 10, 435–449, doi:10.1093/bib/bbp011.spa
dc.relation.references49. Heinken, A.; Ravcheev, D.A.; Baldini, F.; Heirendt, L.; Fleming, R.M.T.; Thiele, I. Personalized Modeling of the Human Gut Microbiome Reveals Distinct Bile Acid Deconjugation and Biotransformation Potential in Healthy and IBD Individuals. bioRxiv 2017, doi:10.1101/229138.spa
dc.relation.references50. Heinken, A.; Thiele, I. Systems Biology of Host-Microbe Metabolomics. Wiley Interdiscip. Rev. Syst. Biol. Med. 2015, 7, 195–219, doi:10.1002/wsbm.1301.spa
dc.relation.references51. Gupta, V.K.; Paul, S.; Dutta, C. Geography, Ethnicity or Subsistence-Specific Variations in Human Microbiome Composition and Diversity. Front. Microbiol. 2017, 8.spa
dc.relation.references52. Porras, A.M.; Shi, Q.; Zhou, H.; Callahan, R.; Montenegro-Bethancourt, G.; Solomons, N.; Brito, I.L. Geographic Differences in Gut Microbiota Composition Impact Susceptibility to Enteric Infection. Cell Rep. 2021, 36, 109457, doi:10.1016/j.celrep.2021.109457.spa
dc.relation.references53. Suzuki, T.A.; Worobey, M. Geographical Variation of Human Gut Microbial Composition. Biol. Lett. 2014, 10, 20131037, doi:10.1098/rsbl.2013.1037.spa
dc.relation.references54. Forero-Rodríguez, L.J.; Josephs-Spaulding, J.; Flor, S.; Pinzón, A.; Kaleta, C. Parkinson’s Disease and the Metal–Microbiome–Gut–Brain Axis: A Systems Toxicology Approach. Antioxidants 2022, 11, 71, doi:10.3390/antiox11010071.spa
dc.relation.references55. Tysnes, O.B.; Storstein, A. Epidemiology of Parkinson’s Disease. J. Neural Transm. 2017, 124, 901–905, doi:10.1007/s00702-017-1686-y.spa
dc.relation.references56. Avendaño-Avendaño, S.B.; Bernal-Pacheco, O.; Esquivia-Pájaro, C.T. Caracterización funcional y calidad de vida en pacientes con enfermedad de Parkinson. Rev. Colomb. Med. Física Rehabil. 2019, 29, 93–102, doi:10.28957/rcmfr.v29n2a3.spa
dc.relation.references57. Qamar, M.A.; Sauerbier, A.; Politis, M.; Carr, H.; Loehrer, P.; Chaudhuri, K.R. Presynaptic Dopaminergic Terminal Imaging & Non-Motor Symptoms Assessment of Parkinson’s Disease: Evidence for Dopaminergic Basis? Park. Dis. 2017, doi:10.1038/s41531-016-0006-9.spa
dc.relation.references58. Kouli, A.; Torsney, K.M.; Kuan, W.-L. Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis. In Parkinson’s Disease: Pathogenesis and Clinical Aspects; 2018 ISBN 978-0-9944381-6-4.spa
dc.relation.references59. Pfeiffer, R.F. Non-Motor Symptoms in Parkinson’s Disease. Parkinsonism Relat. Disord. 2016, doi:10.1016/j.parkreldis.2015.09.004.spa
dc.relation.references60. Brudek, T. Inflammatory Bowel Diseases and Parkinson’s Disease. J. Park. Dis. 2019, doi:10.3233/JPD-191729.spa
dc.relation.references61. Bellou, V.; Belbasis, L.; Tzoulaki, I.; Evangelou, E.; Ioannidis, J.P.A. Environmental Risk Factors and Parkinson’s Disease: An Umbrella Review of Meta-Analyses. Parkinsonism Relat. Disord. 2016, doi:10.1016/j.parkreldis.2015.12.008.spa
dc.relation.references62. Chen, H.; Ritz, B. The Search for Environmental Causes of Parkinson’s Disease: Moving Forward. J. Park. Dis. 2018, doi:10.3233/JPD-181493.spa
dc.relation.references63. Pang, S.Y.-Y.; Ho, P.W.-L.; Liu, H.-F.; Leung, C.-T.; Li, L.; Chang, E.E.S.; Ramsden, D.B.; Ho, S.-L. The Interplay of Aging, Genetics and Environmental Factors in the Pathogenesis of Parkinson’s Disease. Transl. Neurodegener. 2019, 8, 23, doi:10.1186/s40035-019-0165-9.spa
dc.relation.references64. Calabrese, V.; Santoro, A.; Monti, D.; Crupi, R.; Di Paola, R.; Latteri, S.; Cuzzocrea, S.; Zappia, M.; Giordano, J.; Calabrese, E.J.; et al. Aging and Parkinson’s Disease: Inflammaging, Neuroinflammation and Biological Remodeling as Key Factors in Pathogenesis. Free Radic. Biol. Med. 2018, 115, 80–91, doi:10.1016/j.freeradbiomed.2017.10.379.spa
dc.relation.references65. Hindle, J.V. Ageing, Neurodegeneration and Parkinson’s Disease. Age Ageing 2010, 39, 156–161, doi:10.1093/ageing/afp223.spa
dc.relation.references66. Reeve, A.; Simcox, E.; Turnbull, D. Ageing and Parkinson’s Disease: Why Is Advancing Age the Biggest Risk Factor? Ageing Res. Rev. 2014, 14, 19–30, doi:10.1016/j.arr.2014.01.004.spa
dc.relation.references67. Liu, Y.; Yao, Z.; Zhang, L.; Zhu, H.; Deng, W.; Qin, C. Insulin Induces Neurite Outgrowth via SIRT1 in SH-SY5Y Cells. Neuroscience 2013, 238, 371–380, doi:10.1016/j.neuroscience.2013.01.034.spa
dc.relation.references68. de Mello, N.P.; Orellana, A.M.; Mazucanti, C.H.; de Morais Lima, G.; Scavone, C.; Kawamoto, E.M. Insulin and Autophagy in Neurodegeneration. Front. Neurosci. 2019, 13.spa
dc.relation.references69. Sekar, S.; Taghibiglou, C. Elevated Nuclear Phosphatase and Tensin Homolog (PTEN) and Altered Insulin Signaling in Substantia Nigral Region of Patients with Parkinson’s Disease. Neurosci. Lett. 2018, 666, 139–143, doi:10.1016/j.neulet.2017.12.049.spa
dc.relation.references70. Yang, L.; Wang, H.; Liu, L.; Xie, A. The Role of Insulin/IGF-1/PI3K/Akt/GSK3β Signaling in Parkinson’s Disease Dementia. Front. Neurosci. 2018, 12, 73, doi:10.3389/fnins.2018.00073.spa
dc.relation.references71. Salvatore, M.F.; McInnis, T.R.; Cantu, M.A.; Apple, D.M.; Pruett, B.S. Tyrosine Hydroxylase Inhibition in Substantia Nigra Decreases Movement Frequency. Mol. Neurobiol. 2019, 56, 2728–2740, doi:10.1007/s12035-018-1256-9.spa
dc.relation.references72. Vila, M. Neuromelanin, Aging, and Neuronal Vulnerability in Parkinson’s Disease. Mov. Disord. 2019, 34, 1440–1451, doi:10.1002/mds.27776.spa
dc.relation.references73. Wong, Y.; Luk, K.; Purtell, K.; Nanni, S.B.; Stoessl, A.J.; Trudeau, L.-E.; Yue, Z.; Krainc, D.; Oertel, W.; Obeso, J.A.; et al. Neuronal Vulnerability in Parkinson Disease and Putative Therapeutics: Should the Focus Be on Axonal and Synaptic Terminals? Mov. Disord. Off. J. Mov. Disord. Soc. 2019, 34, 1406–1422, doi:10.1002/mds.27823.spa
dc.relation.references74. Diederich, N.J.; Surmeier, D.J.; Uchihara, T.; Grillner, S.; Goetz, C.G. Parkinson’s Disease: Is It a Consequence of Human Brain Evolution? Mov. Disord. 2019, 34, 453–459, doi:10.1002/mds.27628.spa
dc.relation.references75. Gonzalez-Rodriguez, P.; Zampese, E.; Surmeier, D.J. Chapter 3 - Selective Neuronal Vulnerability in Parkinson’s Disease. In Progress in Brain Research; Björklund, A., Cenci, M.A., Eds.; Recent Advances in Parkinson’s Disease; Elsevier, 2020; Vol. 252, pp. 61–89.spa
dc.relation.references76. Surmeier, D.J.; Obeso, J.A.; Halliday, G.M. Selective Neuronal Vulnerability in Parkinson Disease. Nat. Rev. Neurosci. 2017, 18, 101–113, doi:10.1038/nrn.2016.178.spa
dc.relation.references77. Matuz-Mares, D.; González-Andrade, M.; Araiza-Villanueva, M.G.; Vilchis-Landeros, M.M.; Vázquez-Meza, H. Mitochondrial Calcium: Effects of Its Imbalance in Disease. Antioxid. Basel Switz. 2022, 11, 801, doi:10.3390/antiox11050801.spa
dc.relation.references78. Xu, J.; Minobe, E.; Kameyama, M. Ca2+ Dyshomeostasis Links Risk Factors to Neurodegeneration in Parkinson’s Disease. Front. Cell. Neurosci. 2022, 16.spa
dc.relation.references79. Wacquier, B.; Combettes, L.; Dupont, G. Cytoplasmic and Mitochondrial Calcium Signaling: A Two-Way Relationship. Cold Spring Harb. Perspect. Biol. 2019, 11, a035139, doi:10.1101/cshperspect.a035139.spa
dc.relation.references80. Santulli, G.; Xie, W.; Reiken, S.R.; Marks, A.R. Mitochondrial Calcium Overload Is a Key Determinant in Heart Failure. Proc. Natl. Acad. Sci. 2015, 112, 11389–11394, doi:10.1073/pnas.1513047112.spa
dc.relation.references81. González-Rodríguez, P.; Zampese, E.; Stout, K.A.; Guzman, J.N.; Ilijic, E.; Yang, B.; Tkatch, T.; Stavarache, M.A.; Wokosin, D.L.; Gao, L.; et al. Disruption of Mitochondrial Complex I Induces Progressive Parkinsonism. Nature 2021, 599, 650–656, doi:10.1038/s41586-021-04059-0.spa
dc.relation.references82. Betzer, C.; Jensen, P.H. Reduced Cytosolic Calcium as an Early Decisive Cellular State in Parkinson’s Disease and Synucleinopathies. Front. Neurosci. 2018, 12.spa
dc.relation.references83. Better to Keep in Touch: Investigating Inter‐organelle Cross‐talk - Rossini - 2021 - The FEBS Journal - Wiley Online Library Available online: https://febs.onlinelibrary.wiley.com/doi/full/10.1111/febs.15451 (accessed on 4 August 2022).spa
dc.relation.references84. Ghemrawi, R.; Khair, M. Endoplasmic Reticulum Stress and Unfolded Protein Response in Neurodegenerative Diseases. Int. J. Mol. Sci. 2020, 21, E6127, doi:10.3390/ijms21176127.spa
dc.relation.references85. Cai, Y.; Arikkath, J.; Yang, L.; Guo, M.L.; Periyasamy, P.; Buch, S. Interplay of Endoplasmic Reticulum Stress and Autophagy in Neurodegenerative Disorders. Autophagy 2016, 12, 225–244, doi:10.1080/15548627.2015.1121360.spa
dc.relation.references86. Kinghorn, K.J.; Asghari, A.M.; Castillo-Quan, J.I. The Emerging Role of Autophagic-Lysosomal Dysfunction in Gaucher Disease and Parkinson’s Disease. Neural Regen. Res. 2017, 12, 380–384, doi:10.4103/1673-5374.202934.spa
dc.relation.references87. Lin, K.-J.; Lin, K.-L.; Chen, S.-D.; Liou, C.-W.; Chuang, Y.-C.; Lin, H.-Y.; Lin, T.-K. The Overcrowded Crossroads: Mitochondria, Alpha-Synuclein, and the Endo-Lysosomal System Interaction in Parkinson’s Disease. Int. J. Mol. Sci. 2019, 20, E5312, doi:10.3390/ijms20215312.spa
dc.relation.references88. Ray, B.; Bhat, A.; Mahalakshmi, A.M.; Tuladhar, S.; Bishir, M.; Mohan, S.K.; Veeraraghavan, V.P.; Chandra, R.; Essa, M.M.; Chidambaram, S.B.; et al. Mitochondrial and Organellar Crosstalk in Parkinson’s Disease. ASN Neuro 2021, 13, 17590914211028364, doi:10.1177/17590914211028364.spa
dc.relation.references89. Shulman, J.M.; De Jager, P.L.; Feany, M.B. Parkinson’s Disease: Genetics and Pathogenesis. Annu. Rev. Pathol. Mech. Dis. 2011, 6, 193–224, doi:10.1146/annurev-pathol-011110-130242.spa
dc.relation.references90. Paganini-Hill, A. Risk Factors for Parkinson’s Disease: The Leisure World Cohort Study. Neuroepidemiology 2001, 20, 118–124, doi:10.1159/000054770.spa
dc.relation.references91. Hernán, M.A.; Zhang, S.M.; Rueda-deCastro, A.M.; Colditz, G.A.; Speizer, F.E.; Ascherio, A. Cigarette Smoking and the Incidence of Parkinson’s Disease in Two Prospective Studies. Ann. Neurol. 2001, 50, 780–786, doi:10.1002/ana.10028.spa
dc.relation.references92. Hernán, M.A.; Takkouche, B.; Caamaño-Isorna, F.; Gestal-Otero, J.J. A Meta-Analysis of Coffee Drinking, Cigarette Smoking, and the Risk of Parkinson’s Disease. Ann. Neurol. 2002, 52, 276–284, doi:10.1002/ana.10277.spa
dc.relation.references93. Ritz, B.; Ascherio, A.; Checkoway, H.; Marder, K.S.; Nelson, L.M.; Rocca, W.A.; Ross, G.W.; Strickland, D.; Van Den Eeden, S.K.; Gorell, J. Pooled Analysis of Tobacco Use and Risk of Parkinson Disease. Arch. Neurol. 2007, 64, 990–997, doi:10.1001/archneur.64.7.990.spa
dc.relation.references94. Breckenridge, C.B.; Berry, C.; Chang, E.T.; Sielken, R.L.; Mandel, J.S. Association between Parkinson’s Disease and Cigarette Smoking, Rural Living, Well-Water Consumption, Farming and Pesticide Use: Systematic Review and Meta-Analysis. PloS One 2016, 11, e0151841, doi:10.1371/journal.pone.0151841.spa
dc.relation.references95. Lewis, A.; Miller, J.H.; Lea, R.A. Monoamine Oxidase and Tobacco Dependence. Neurotoxicology 2007, 28, 182–195, doi:10.1016/j.neuro.2006.05.019.spa
dc.relation.references96. Krause, K.-H.; Dresel, S.H.; Krause, J.; Kung, H.F.; Tatsch, K.; Ackenheil, M. Stimulant-like Action of Nicotine on Striatal Dopamine Transporter in the Brain of Adults with Attention Deficit Hyperactivity Disorder. Int. J. Neuropsychopharmacol. 2002, 5, 111–113, doi:10.1017/S1461145702002821.spa
dc.relation.references97. Wang, C.; Zhou, C.; Guo, T.; Huang, P.; Xu, X.; Zhang, M. Association between Cigarette Smoking and Parkinson’s Disease: A Neuroimaging Study. Ther. Adv. Neurol. Disord. 2022, 15, 17562864221092566, doi:10.1177/17562864221092566.spa
dc.relation.references98. Rodak, K.; Kokot, I.; Kratz, E.M. Caffeine as a Factor Influencing the Functioning of the Human Body—Friend or Foe? Nutrients 2021, 13, 3088, doi:10.3390/nu13093088.spa
dc.relation.references99. Ren, X.; Chen, J.-F. Caffeine and Parkinson’s Disease: Multiple Benefits and Emerging Mechanisms. Front. Neurosci. 2020, 14.spa
dc.relation.references100. Ross, G.W.; Abbott, R.D.; Petrovitch, H.; Morens, D.M.; Grandinetti, A.; Tung, K.H.; Tanner, C.M.; Masaki, K.H.; Blanchette, P.L.; Curb, J.D.; et al. Association of Coffee and Caffeine Intake with the Risk of Parkinson Disease. JAMA 2000, 283, 2674–2679, doi:10.1001/jama.283.20.2674.spa
dc.relation.references101. Chen, J.F.; Xu, K.; Petzer, J.P.; Staal, R.; Xu, Y.H.; Beilstein, M.; Sonsalla, P.K.; Castagnoli, K.; Castagnoli, N.; Schwarzschild, M.A. Neuroprotection by Caffeine and A(2A) Adenosine Receptor Inactivation in a Model of Parkinson’s Disease. J. Neurosci. Off. J. Soc. Neurosci. 2001, 21, RC143.spa
dc.relation.references102. Ascherio, A.; Zhang, S.M.; Hernán, M.A.; Kawachi, I.; Colditz, G.A.; Speizer, F.E.; Willett, W.C. Prospective Study of Caffeine Consumption and Risk of Parkinson’s Disease in Men and Women. Ann. Neurol. 2001, 50, 56–63, doi:10.1002/ana.1052.spa
dc.relation.references103. Benedetti, M.D.; Bower, J.H.; Maraganore, D.M.; McDonnell, S.K.; Peterson, B.J.; Ahlskog, J.E.; Schaid, D.J.; Rocca, W.A. Smoking, Alcohol, and Coffee Consumption Preceding Parkinson’s Disease: A Case-Control Study. Neurology 2000, 55, 1350–1358, doi:10.1212/wnl.55.9.1350.spa
dc.relation.references104. Ribeiro, J.A.; Sebastião, A.M. Caffeine and Adenosine. J. Alzheimers Dis. JAD 2010, 20 Suppl 1, S3-15, doi:10.3233/JAD-2010-1379.spa
dc.relation.references105. Kong, H.; Jones, P.P.; Koop, A.; Zhang, L.; Duff, H.J.; Chen, S.R.W. Caffeine Induces Ca2+ Release by Reducing the Threshold for Luminal Ca2+ Activation of the Ryanodine Receptor. Biochem. J. 2008, 414, 441–452, doi:10.1042/BJ20080489.spa
dc.relation.references106. Yang, J.Y.; Yang, G.; Ren, J.; Zhao, J.; Li, Sh. Caffeine Suppresses GABA Receptor-Mediated Current in Rat Primary Sensory Neurons via Inhibition of Intracellular Phosphodiesterase. Neurophysiology 2015, 47, 108–114, doi:10.1007/s11062-015-9506-1.spa
dc.relation.references107. Madeira, M.H.; Boia, R.; Ambrósio, A.F.; Santiago, A.R. Having a Coffee Break: The Impact of Caffeine Consumption on Microglia-Mediated Inflammation in Neurodegenerative Diseases. Mediators Inflamm. 2017, 2017, 4761081, doi:10.1155/2017/4761081.spa
dc.relation.references108. Zhou, S.-J.; Zhu, M.-E.; Shu, D.; Du, X.-P.; Song, X.-H.; Wang, X.-T.; Zheng, R.-Y.; Cai, X.-H.; Chen, J.-F.; He, J.-C. Preferential Enhancement of Working Memory in Mice Lacking Adenosine A(2A) Receptors. Brain Res. 2009, 1303, 74–83, doi:10.1016/j.brainres.2009.09.082.spa
dc.relation.references109. Sinha, R.A.; Farah, B.L.; Singh, B.K.; Siddique, M.M.; Li, Y.; Wu, Y.; Ilkayeva, O.R.; Gooding, J.; Ching, J.; Zhou, J.; et al. Caffeine Stimulates Hepatic Lipid Metabolism by the Autophagy-Lysosomal Pathway in Mice. Hepatol. Baltim. Md 2014, 59, 1366–1380, doi:10.1002/hep.26667.spa
dc.relation.references110. Liu, Y.-W.; Yang, T.; Zhao, L.; Ni, Z.; Yang, N.; He, F.; Dai, S.-S. Activation of Adenosine 2A Receptor Inhibits Neutrophil Apoptosis in an Autophagy-Dependent Manner in Mice with Systemic Inflammatory Response Syndrome. Sci. Rep. 2016, 6, 33614, doi:10.1038/srep33614.spa
dc.relation.references111. Scheperjans, F.; Pekkonen, E.; Kaakkola, S.; Auvinen, P. Linking Smoking, Coffee, Urate, and Parkinson’s Disease-a Role for Gut Microbiota? J. Park. Dis. 2015, 5, 255–262, doi:10.3233/JPD-150557.spa
dc.relation.references112. Sian-Hülsmann, J.; Mandel, S.; Youdim, M.B.H.; Riederer, P. The Relevance of Iron in the Pathogenesis of Parkinson’s Disease. J. Neurochem. 2011, doi:10.1111/j.1471-4159.2010.07132.x.spa
dc.relation.references113. Muñoz, P.; Humeres, A. Iron Deficiency on Neuronal Function. BioMetals 2012, doi:10.1007/s10534-012-9550-x.spa
dc.relation.references114. Savica, R.; Grossardt, B.R.; Carlin, J.M.; Icen, M.; Bower, J.H.; Ahlskog, J.E.; Maraganore, D.M.; Steensma, D.P.; Rocca, W.A. Anemia or Low Hemoglobin Levels Preceding Parkinson Disease: A Case-Control Study. Neurology 2009, doi:10.1212/WNL.0b013e3181bd80c1.spa
dc.relation.references115. Dev, S.; Babitt, J.L. Overview of Iron Metabolism in Health and Disease. Hemodial. Int. 2017, doi:10.1111/hdi.12542.spa
dc.relation.references116. Hare, D.J.; Arora, M.; Jenkins, N.L.; Finkelstein, D.I.; Doble, P.A.; Bush, A.I. Is Early-Life Iron Exposure Critical in Neurodegeneration? Nat. Rev. Neurol. 2015, doi:10.1038/nrneurol.2015.100.spa
dc.relation.references117. Hare, D.J.; Cardoso, B.R.; Raven, E.P.; Double, K.L.; Finkelstein, D.I.; Szymlek-Gay, E.A.; Biggs, B.A. Excessive Early-Life Dietary Exposure: A Potential Source of Elevated Brain Iron and a Risk Factor for Parkinson’s Disease. Npj Park. Dis. 2017, doi:10.1038/s41531-016-0004-y.spa
dc.relation.references118. Anzai, Y.; Gatenby, C.; Friend, S.; Maravilla, K.R.; Hu, S.C.; Cain, K.C.; Agarwal, P. Brain Iron Concentrations in Regions of Interest and Relation with Serum Iron Levels in Parkinson Disease. J. Neurol. Sci. 2017, doi:10.1016/j.jns.2017.04.035.spa
dc.relation.references119. Logroscino, G.; Marder, K.; Graziano, J.; Freyer, G.; Slavkovich, V.; LoIacono, N.; Cote, L.; Mayeux, R. Altered Systemic Iron Metabolism in Parkinson’s Disease. Neurology 1997, doi:10.1212/WNL.49.3.714.spa
dc.relation.references120. Fasano, M.; Bergamasco, B.; Lopiano, L. Modifications of the Iron-Neuromelanin System in Parkinson’s Disease. J. Neurochem. 2006, doi:10.1111/j.1471-4159.2005.03638.x.spa
dc.relation.references121. Gerlach, M.; Double, K.L.; Youdim, M.B.H.; Riederer, P. Potential Sources of Increased Iron in the Substantia Nigra of Parkinsonian Patients. In Proceedings of the Journal of Neural Transmission, Supplement; 2006.spa
dc.relation.references122. Lotfipour, A.K.; Wharton, S.; Schwarz, S.T.; Gontu, V.; Schäfer, A.; Peters, A.M.; Bowtell, R.W.; Auer, D.P.; Gowland, P.A.; Bajaj, N.P.S. High Resolution Magnetic Susceptibility Mapping of the Substantia Nigra in Parkinson’s Disease. J. Magn. Reson. Imaging 2012, doi:10.1002/jmri.22752.spa
dc.relation.references123. Levi, S.; Finazzi, D. Neurodegeneration with Brain Iron Accumulation: Update on Pathogenic Mechanisms. Front. Pharmacol. 2014, doi:10.3389/fphar.2014.00099.spa
dc.relation.references124. Xuan, M.; Guan, X.; Gu, Q.; Shen, Z.; Yu, X.; Qiu, T.; Luo, X.; Song, R.; Jiaerken, Y.; Xu, X.; et al. Different Iron Deposition Patterns in Early- and Middle-Late-Onset Parkinson’s Disease. Parkinsonism Relat. Disord. 2017, doi:10.1016/j.parkreldis.2017.08.013.spa
dc.relation.references125. Dexter, D.T.; Wells, F.R.; Lee, A.J.; Agid, F.; Agid, Y.; Jenner, P.; Marsden, C.D. Increased Nigral Iron Content and Alterations in Other Metal Ions Occurring in Brain in Parkinson’s Disease. J. Neurochem. 1989, doi:10.1111/j.1471-4159.1989.tb07264.x.spa
dc.relation.references126. Ottman, N.; Reunanen, J.; Meijerink, M.; Pietila, T.E.; Kainulainen, V.; Klievink, J.; Huuskonen, L.; Aalvink, S.; Skurnik, M.; Boeren, S.; et al. Pili-like Proteins of Akkermansia Muciniphila Modulate Host Immune Responses and Gut Barrier Function. PLoS ONE 2017, doi:10.1371/journal.pone.0173004.spa
dc.relation.references127. Ulla, M.; Bonny, J.M.; Ouchchane, L.; Rieu, I.; Claise, B.; Durif, F. Is R2* a New MRI Biomarker for the Progression of Parkinson’s Disease? A Longitudinal Follow-Up. PLoS ONE 2013, doi:10.1371/journal.pone.0057904.spa
dc.relation.references128. Yu, X.; Du, T.; Song, N.; He, Q.; Shen, Y.; Jiang, H.; Xie, J. Decreased Iron Levels in the Temporal Cortex in Postmortem Human Brains with Parkinson Disease. Neurology 2013, doi:10.1212/WNL.0b013e31827f0ebb.spa
dc.relation.references129. Bonetto, J.; Villaamil-Lepori, E. Update on the Oxidative Stress Associated with Arsenic Exposure. Curr. Top. Toxicol. 2014.spa
dc.relation.references130. Branca, J.J.V.; Morucci, G.; Pacini, A. Cadmium-Induced Neurotoxicity: Still Much Ado. Neural Regen. Res. 2018, doi:10.4103/1673-5374.239434.spa
dc.relation.references131. Baranowska-Bosiacka, I.; Gutowska, I.; Rybicka, M.; Nowacki, P.; Chlubek, D. Neurotoxicity of Lead. Hypothetical Molecular Mechanisms of Synaptic Function Disorders. Neurol. Neurochir. Pol. 2012, doi:10.5114/ninp.2012.31607.spa
dc.relation.references132. Aschner, M.; Erikson, K.M.; Hernández, E.H.; Tjalkens, R. Manganese and Its Role in Parkinson’s Disease: From Transport to Neuropathology. NeuroMolecular Med. 2009, 11, doi:10.1007/s12017-009-8083-0.spa
dc.relation.references133. Kozlowski, H.; Luczkowski, M.; Remelli, M.; Valensin, D. Copper, Zinc and Iron in Neurodegenerative Diseases (Alzheimer’s, Parkinson’s and Prion Diseases). Coord. Chem. Rev. 2012, doi:10.1016/j.ccr.2012.03.013.spa
dc.relation.references134. Martí, Y.; Matthaeus, F.; Lau, T.; Schloss, P. Methyl-4-Phenylpyridinium (MPP+) Differentially Affects Monoamine Release and Re-Uptake in Murine Embryonic Stem Cell-Derived Dopaminergic and Serotonergic Neurons. Mol. Cell. Neurosci. 2017, 83, 37–45, doi:10.1016/j.mcn.2017.06.009.spa
dc.relation.references135. Tanner, C.M.; Kamel, F.; Ross, G.W.; Hoppin, J.A.; Goldman, S.M.; Korell, M.; Marras, C.; Bhudhikanok, G.S.; Kasten, M.; Chade, A.R.; et al. Rotenone, Paraquat, and Parkinson’s Disease. Environ. Health Perspect. 2011, 119, 866–872, doi:10.1289/ehp.1002839.spa
dc.relation.references136. Henchcliffe, C.; Beal, M.F. Mitochondrial Biology and Oxidative Stress in Parkinson Disease Pathogenesis. Nat. Clin. Pract. Neurol. 2008, 4, 600–609, doi:10.1038/ncpneuro0924.spa
dc.relation.references137. See, W.Z.C.; Naidu, R.; Tang, K.S. Cellular and Molecular Events Leading to Paraquat-Induced Apoptosis: Mechanistic Insights into Parkinson’s Disease Pathophysiology. Mol. Neurobiol. 2022, 59, 3353–3369, doi:10.1007/s12035-022-02799-2.spa
dc.relation.references138. Dinis-Oliveira, R.J.; Remião, F.; Carmo, H.; Duarte, J.A.; Navarro, A.S.; Bastos, M.L.; Carvalho, F. Paraquat Exposure as an Etiological Factor of Parkinson’s Disease. Neurotoxicology 2006, 27, 1110–1122, doi:10.1016/j.neuro.2006.05.012.spa
dc.relation.references139. Kuter, K.; Nowak, P.; Gołembiowska, K.; Ossowska, K. Increased Reactive Oxygen Species Production in the Brain after Repeated Low-Dose Pesticide Paraquat Exposure in Rats. A Comparison with Peripheral Tissues. Neurochem. Res. 2010, 35, 1121–1130, doi:10.1007/s11064-010-0163-x.spa
dc.relation.references140. McCormack, A.L.; Thiruchelvam, M.; Manning-Bog, A.B.; Thiffault, C.; Langston, J.W.; Cory-Slechta, D.A.; Di Monte, D.A. Environmental Risk Factors and Parkinson’s Disease: Selective Degeneration of Nigral Dopaminergic Neurons Caused by the Herbicide Paraquat. Neurobiol. Dis. 2002, 10, 119–127, doi:10.1006/nbdi.2002.0507.spa
dc.relation.references141. Wang, G.; Fan, X.-N.; Tan, Y.-Y.; Cheng, Q.; Chen, S.-D. Parkinsonism after Chronic Occupational Exposure to Glyphosate. Parkinsonism Relat. Disord. 2011, 17, 486–487, doi:10.1016/j.parkreldis.2011.02.003.spa
dc.relation.references142. Kim, Y.; Kim, I.; Sung, J.-M.; Song, J. Parkinson’s Disease in a Worker Exposed to Insecticides at a Greenhouse. Ann. Occup. Environ. Med. 2021, 33, e6, doi:10.35371/aoem.2021.33.e6.spa
dc.relation.references143. Cattani, D.; de Liz Oliveira Cavalli, V.L.; Heinz Rieg, C.E.; Domingues, J.T.; Dal-Cim, T.; Tasca, C.I.; Mena Barreto Silva, F.R.; Zamoner, A. Mechanisms Underlying the Neurotoxicity Induced by Glyphosate-Based Herbicide in Immature Rat Hippocampus: Involvement of Glutamate Excitotoxicity. Toxicology 2014, 320, 34–45, doi:10.1016/j.tox.2014.03.001.spa
dc.relation.references144. Caioni, G.; Cimini, A.; Benedetti, E. Food Contamination: An Unexplored Possible Link between Dietary Habits and Parkinson’s Disease. Nutrients 2022, 14, 1467, doi:10.3390/nu14071467.spa
dc.relation.references145. Deng, H.; Wang, P.; Jankovic, J. The Genetics of Parkinson Disease. Ageing Res. Rev. 2018, 42, 72–85, doi:10.1016/j.arr.2017.12.007.spa
dc.relation.references146. Polymeropoulos, M.H.; Lavedan, C.; Leroy, E.; Ide, S.E.; Dehejia, A.; Dutra, A.; Pike, B.; Root, H.; Rubenstein, J.; Boyer, R.; et al. Mutation in the Alpha-Synuclein Gene Identified in Families with Parkinson’s Disease. Science 1997, 276, 2045–2047, doi:10.1126/science.276.5321.2045.spa
dc.relation.references147. Satake, W.; Nakabayashi, Y.; Mizuta, I.; Hirota, Y.; Ito, C.; Kubo, M.; Kawaguchi, T.; Tsunoda, T.; Watanabe, M.; Takeda, A.; et al. Genome-Wide Association Study Identifies Common Variants at Four Loci as Genetic Risk Factors for Parkinson’s Disease. Nat. Genet. 2009, 41, 1303–1307, doi:10.1038/ng.485.spa
dc.relation.references148. Foo, J.N.; Tan, L.C.; Irwan, I.D.; Au, W.-L.; Low, H.Q.; Prakash, K.-M.; Ahmad-Annuar, A.; Bei, J.; Chan, A.Y.; Chen, C.M.; et al. Genome-Wide Association Study of Parkinson’s Disease in East Asians. Hum. Mol. Genet. 2017, 26, 226–232, doi:10.1093/hmg/ddw379.spa
dc.relation.references149. Imputation of Sequence Variants for Identification of Genetic Risks for Parkinson’s Disease: A Meta-Analysis of Genome-Wide Association Studies. The Lancet 2011, 377, 641–649, doi:10.1016/S0140-6736(10)62345-8.spa
dc.relation.references150. Simón-Sánchez, J.; Schulte, C.; Bras, J.M.; Sharma, M.; Gibbs, J.R.; Berg, D.; Paisan-Ruiz, C.; Lichtner, P.; Scholz, S.W.; Hernandez, D.G.; et al. Genome-Wide Association Study Reveals Genetic Risk Underlying Parkinson’s Disease. Nat. Genet. 2009, 41, 1308–1312, doi:10.1038/ng.487.spa
dc.relation.references151. Nalls, M.A.; Pankratz, N.; Lill, C.M.; Do, C.B.; Hernandez, D.G.; Saad, M.; DeStefano, A.L.; Kara, E.; Bras, J.; Sharma, M.; et al. Large-Scale Meta-Analysis of Genome-Wide Association Data Identifies Six New Risk Loci for Parkinson’s Disease. Nat. Genet. 2014, 46, 989–993, doi:10.1038/ng.3043.spa
dc.relation.references152. Chang, D.; Nalls, M.A.; Hallgrímsdóttir, I.B.; Hunkapiller, J.; van der Brug, M.; Cai, F.; International Parkinson’s Disease Genomics Consortium; 23andMe Research Team; Kerchner, G.A.; Ayalon, G.; et al. A Meta-Analysis of Genome-Wide Association Studies Identifies 17 New Parkinson’s Disease Risk Loci. Nat. Genet. 2017, 49, 1511–1516, doi:10.1038/ng.3955.spa
dc.relation.references153. Nalls, M.A.; Blauwendraat, C.; Vallerga, C.L.; Heilbron, K.; Bandres-Ciga, S.; Chang, D.; Tan, M.; Kia, D.A.; Noyce, A.J.; Xue, A.; et al. Identification of Novel Risk Loci, Causal Insights, and Heritable Risk for Parkinson’s Disease: A Meta-Analysis of Genome-Wide Association Studies. Lancet Neurol. 2019, 18, 1091–1102, doi:10.1016/S1474-4422(19)30320-5.spa
dc.relation.references154. Li, W.; Fu, Y.; Halliday, G.M.; Sue, C.M. PARK Genes Link Mitochondrial Dysfunction and Alpha-Synuclein Pathology in Sporadic Parkinson’s Disease. Front. Cell Dev. Biol. 2021, 9.spa
dc.relation.references155. Schnorr, S.L.; Candela, M.; Rampelli, S.; Centanni, M.; Consolandi, C.; Basaglia, G.; Turroni, S.; Biagi, E.; Peano, C.; Severgnini, M.; et al. Gut Microbiome of the Hadza Hunter-Gatherers. Nat. Commun. 2014, 5, doi:10.1038/ncomms4654.spa
dc.relation.references156. De Filippo, C.; Cavalieri, D.; Di Paola, M.; Ramazzotti, M.; Poullet, J.B.; Massart, S.; Collini, S.; Pieraccini, G.; Lionetti, P. Impact of Diet in Shaping Gut Microbiota Revealed by a Comparative Study in Children from Europe and Rural Africa. Proc. Natl. Acad. Sci. 2010, 107, 14691–14696, doi:10.1073/pnas.1005963107.spa
dc.relation.references157. Fragiadakis, G.K.; Smits, S.A.; Sonnenburg, E.D.; Van Treuren, W.; Reid, G.; Knight, R.; Manjurano, A.; Changalucha, J.; Dominguez-Bello, M.G.; Leach, J.; et al. Links between Environment, Diet, and the Hunter-Gatherer Microbiome. Gut Microbes 2019, 10, 216–227, doi:10.1080/19490976.2018.1494103.spa
dc.relation.references415. Home - Souci • Fachmann • Kraut Datenbank Available online: https://www.sfk.online/#/home (accessed on 4 September 2022).spa
dc.relation.references416. Diet History Questionnaire II (DHQ II) Paper Forms | EGRP/DCCPS/NCI/NIH Available online: https://epi.grants.cancer.gov/dhq2/forms/ (accessed on 4 September 2022).spa
dc.relation.references417. Chumlea, W.C.; Roche, A.F.; Steinbaugh, M.L. Estimating Stature from Knee Height for Persons 60 to 90 Years of Age. J. Am. Geriatr. Soc. 1985, 33, 116–120, doi:10.1111/j.1532-5415.1985.tb02276.x.spa
dc.relation.references418. CDC Defining Adult Overweight and Obesity Available online: https://www.cdc.gov/obesity/basics/adult-defining.html (accessed on 4 September 2022).spa
dc.relation.references419. Dodd, K. BMI in the Elderly: What You Need to Know. Geriatr. Dietit. 2020.spa
dc.relation.references420. NHANES III (1988-1994) Available online: https://wwwn.cdc.gov/nchs/nhanes/nhanes3/default.aspx (accessed on 4 September 2022).spa
dc.relation.references421. Frisancho, A.R. New Norms of Upper Limb Fat and Muscle Areas for Assessment of Nutritional Status. Am. J. Clin. Nutr. 1981, 34, 2540–2545, doi:10.1093/ajcn/34.11.2540.spa
dc.relation.references422. ATNF-AZA/Microbial_community_modellings at Master · Mucosimmunol/ATNF-AZA Available online: https://github.com/mucosimmunol/aTNF-AZA (accessed on 12 April 2023).spa
dc.relation.references423. Effenberger, M.; Reider, S.; Waschina, S.; Bronowski, C.; Enrich, B.; Adolph, T.E.; Koch, R.; Moschen, A.R.; Rosenstiel, P.; Aden, K.; et al. Microbial Butyrate Synthesis Indicates Therapeutic Efficacy of Azathioprine in IBD Patients. J. Crohns Colitis 2021, 15, 88–98, doi:10.1093/ecco-jcc/jjaa152.spa
dc.relation.references424. Graspeuntner, S.; Waschina, S.; Künzel, S.; Twisselmann, N.; Rausch, T.K.; Cloppenborg-Schmidt, K.; Zimmermann, J.; Viemann, D.; Herting, E.; Göpel, W.; et al. Gut Dysbiosis with Bacilli Dominance and Accumulation of Fermentation Products Precedes Late-Onset Sepsis in Preterm Infants. Clin. Infect. Dis. 2019, doi:10.1093/cid/ciy882.spa
dc.relation.references425. Azzouz, L.L.; Sharma, S. Physiology, Large Intestine. In StatPearls; StatPearls Publishing: Treasure Island (FL), 2023.spa
dc.relation.references426. Islam, M.A.; Amin, M.N.; Siddiqui, S.A.; Hossain, M.P.; Sultana, F.; Kabir, M.R. Trans Fatty Acids and Lipid Profile: A Serious Risk Factor to Cardiovascular Disease, Cancer and Diabetes. Diabetes Metab. Syndr. Clin. Res. Rev. 2019, doi:10.1016/j.dsx.2019.03.033.spa
dc.relation.references427. Advanced Nutrition and Human Metabolism by Sareen Stepnick Gropper, Jack Smith | 9781133104070 | Get Textbooks | New Textbooks | Used Textbooks | College Textbooks - GetTextbooks.Com Available online: https://www.gettextbooks.com/isbn/9781133104070/ (accessed on 11 April 2023).spa
dc.relation.references428. Ernährung des Menschen - Elmadfa, Ibrahim; Leitzmann, Claus: 9783825285524 - IberLibro Available online: https://www.iberlibro.com/9783825285524/Ern%C3%A4hrung-Menschen-Elmadfa-I brahim-Leitzmann-3825285529/plp (accessed on 11 April 2023).spa
dc.relation.references429. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids - PubMed Available online: https://pubmed.ncbi.nlm.nih.gov/25077263/ (accessed on 11 April 2023).spa
dc.relation.references430. Akbulut, A.C.; Pavlic, A.; Petsophonsakul, P.; Halder, M.; Maresz, K.; Kramann, R.; Schurgers, L. Vitamin K2 Needs an RDI Separate from Vitamin K1. Nutrients 2020, 12, 1852, doi:10.3390/nu12061852.spa
dc.relation.references431. Rakhra, V.; Galappaththy, S.L.; Bulchandani, S.; Cabandugama, P.K. Obesity and the Western Diet: How We Got Here. Mo. Med. 2020, 117, 536–538.spa
dc.relation.references432. Yeung, S.S.Y.; Kwan, M.; Woo, J. Healthy Diet for Healthy Aging. Nutrients 2021, 13, 4310, doi:10.3390/nu13124310.spa
dc.relation.references433. Mischley, L.K.; Lau, R.C.; Bennett, R.D. Role of Diet and Nutritional Supplements in Parkinson’s Disease Progression. Oxid. Med. Cell. Longev. 2017, 2017, 6405278, doi:10.1155/2017/6405278.spa
dc.relation.references434. Filippis, F.D.; Pellegrini, N.; Vannini, L.; Jeffery, I.B.; Storia, A.L.; Laghi, L.; Serrazanetti, D.I.; Cagno, R.D.; Ferrocino, I.; Lazzi, C.; et al. High-Level Adherence to a Mediterranean Diet Beneficially Impacts the Gut Microbiota and Associated Metabolome. Gut 2016, 65, 1812–1821, doi:10.1136/gutjnl-2015-309957.spa
dc.relation.references435. Neyrinck, A.M.; Possemiers, S.; Verstraete, W.; De Backer, F.; Cani, P.D.; Delzenne, N.M. Dietary Modulation of Clostridial Cluster XIVa Gut Bacteria (Roseburia Spp.) by Chitin-Glucan Fiber Improves Host Metabolic Alterations Induced by High-Fat Diet in Mice. J. Nutr. Biochem. 2012, 23, 51–59, doi:10.1016/j.jnutbio.2010.10.008.spa
dc.relation.references436. Albani, G.; Albani, S.; Keshavarzian, A. Role of Diet, Physical Activity and Immune System in Parkinson’s Disease; Frontiers Media SA, 2021; ISBN 978-2-88966-441-2.spa
dc.relation.references437. Zhang, H.; Wielen, N. van der; Hee, B. van der; Wang, J.; Hendriks, W.; Gilbert, M. Impact of Fermentable Protein, by Feeding High Protein Diets, on Microbial Composition, Microbial Catabolic Activity, Gut Health and beyond in Pigs. Microorganisms 2020, 8, 1735, doi:10.3390/microorganisms8111735.spa
dc.relation.references438. Zhang, C.; Yu, M.; Yang, Y.; Mu, C.; Su, Y.; Zhu, W. Effect of Early Antibiotic Administration on Cecal Bacterial Communities and Their Metabolic Profiles in Pigs Fed Diets with Different Protein Levels. Anaerobe 2016, 42, 188–196, doi:10.1016/j.anaerobe.2016.10.016.spa
dc.relation.references439. Ma, D.; Wang, A.C.; Parikh, I.; Green, S.J.; Hoffman, J.D.; Chlipala, G.; Murphy, M.P.; Sokola, B.S.; Bauer, B.; Hartz, A.M.S.; et al. Ketogenic Diet Enhances Neurovascular Function with Altered Gut Microbiome in Young Healthy Mice. Sci. Rep. 2018, 8, 6670, doi:10.1038/s41598-018-25190-5.spa
dc.relation.references440. Yu, H.; Qiu, N.; Meng, Y.; Keast, R. A Comparative Study of the Modulation of the Gut Microbiota in Rats by Dietary Intervention with Different Sources of Egg-White Proteins. J. Sci. Food Agric. 2020, 100, 3622–3629, doi:10.1002/jsfa.10387.spa
dc.relation.references441. Kisuse, J.; La-ongkham, O.; Nakphaichit, M.; Therdtatha, P.; Momoda, R.; Tanaka, M.; Fukuda, S.; Popluechai, S.; Kespechara, K.; Sonomoto, K.; et al. Urban Diets Linked to Gut Microbiome and Metabolome Alterations in Children: A Comparative Cross-Sectional Study in Thailand. Front. Microbiol. 2018, 9, 1345, doi:10.3389/fmicb.2018.01345.spa
dc.relation.references442. Lee, C.B.; Chae, S.U.; Jo, S.J.; Jerng, U.M.; Bae, S.K. The Relationship between the Gut Microbiome and Metformin as a Key for Treating Type 2 Diabetes Mellitus. Int. J. Mol. Sci. 2021, 22, 3566, doi:10.3390/ijms22073566.spa
dc.relation.references443. Oteng, A.-B.; Kersten, S. Mechanisms of Action of Trans Fatty Acids. Adv. Nutr. Bethesda Md 2020, 11, 697–708, doi:10.1093/advances/nmz125.spa
dc.relation.references444. Uauy, R.; Aro, A.; Clarke, R.; Ghafoorunissa; L’Abbé, M.R.; Mozaffarian, D.; Skeaff, C.M.; Stender, S.; Tavella, M. Who Scientific Update on Trans Fatty Acids: Summary and Conclusions. Eur. J. Clin. Nutr. 2009, doi:10.1038/ejcn.2009.15.spa
dc.relation.references445. Shannon, J.; King, I.B.; Moshofsky, R.; Lampe, J.W.; Li Gao, D.; Ray, R.M.; Thomas, D.B. Erythrocyte Fatty Acids and Breast Cancer Risk: A Case-Control Study in Shanghai, China. Am. J. Clin. Nutr. 2007, 85, 1090–1097, doi:10.1093/ajcn/85.4.1090.spa
dc.relation.references446. Larsson, S.C.; Bergkvist, L.; Wolk, A. High-Fat Dairy Food and Conjugated Linoleic Acid Intakes in Relation to Colorectal Cancer Incidence in the Swedish Mammography Cohort. Am. J. Clin. Nutr. 2005, 82, 894–900, doi:10.1093/ajcn/82.4.894.spa
dc.relation.references447. van Dam, R.M.; Rimm, E.B.; Willett, W.C.; Stampfer, M.J.; Hu, F.B. Dietary Patterns and Risk for Type 2 Diabetes Mellitus in U.S. Men. Ann. Intern. Med. 2002, 136, 201–209, doi:10.7326/0003-4819-136-3-200202050-00008.spa
dc.relation.references448. Okamura, T.; Hashimoto, Y.; Majima, S.; Senmaru, T.; Ushigome, E.; Nakanishi, N.; Asano, M.; Yamazaki, M.; Takakuwa, H.; Hamaguchi, M.; et al. Trans Fatty Acid Intake Induces Intestinal Inflammation and Impaired Glucose Tolerance. Front. Immunol. 2021, 12.spa
dc.relation.references449. Ma, W.-W.; Zhao, L.; Yuan, L.-H.; Yu, H.-L.; Wang, H.; Gong, X.-Y.; Wei, F.; Xiao, R. Elaidic Acid Induces Cell Apoptosis through Induction of ROS Accumulation and Endoplasmic Reticulum Stress in SH‑SY5Y Cells. Mol. Med. Rep. 2017, 16, 9337–9346, doi:10.3892/mmr.2017.7830.spa
dc.relation.references450. Artis, D.; Spits, H. The Biology of Innate Lymphoid Cells. Nature 2015, 517, 293–301, doi:10.1038/nature14189.spa
dc.relation.references451. Chen, H.; Zhang, S.M.; Hernán, M.A.; Willett, W.C.; Ascherio, A. Dietary Intakes of Fat and Risk of Parkinson’s Disease. Am. J. Epidemiol. 2003, 157, 1007–1014, doi:10.1093/aje/kwg073.spa
dc.relation.references452. Morris, M.C.; Evans, D.A.; Bienias, J.L.; Tangney, C.C.; Bennett, D.A.; Aggarwal, N.; Schneider, J.; Wilson, R.S. Dietary Fats and the Risk of Incident Alzheimer Disease. Arch. Neurol. 2003, 60, 194–200, doi:10.1001/archneur.60.2.194.spa
dc.relation.references453. Rowland, I.; Gibson, G.; Heinken, A.; Scott, K.; Swann, J.; Thiele, I.; Tuohy, K. Gut Microbiota Functions: Metabolism of Nutrients and Other Food Components. Eur. J. Nutr. 2018, doi:10.1007/s00394-017-1445-8.spa
dc.relation.references454. Blaak, E.E.; Riccardi, G.; Cho, L. Carbohydrates: Separating Fact from Fiction. Atherosclerosis 2021, doi:10.1016/j.atherosclerosis.2021.03.025.spa
dc.relation.references455. Thompson, M.E.; Noel, M.B. Issues in Nutrition: Carbohydrates. FP Essent. 2017, 452, 26–30.spa
dc.relation.references456. Conlon, M.A.; Bird, A.R. The Impact of Diet and Lifestyle on Gut Microbiota and Human Health. Nutrients 2014, 7, 17–44, doi:10.3390/nu7010017.spa
dc.relation.references457. Deehan, E.C.; Duar, R.M.; Armet, A.M.; Perez-Muñoz, M.E.; Jin, M.; Walter, J. Modulation of the Gastrointestinal Microbiome with Nondigestible Fermentable Carbohydrates To Improve Human Health. Microbiol. Spectr. 2017, doi:10.1128/microbiolspec.bad-0019-2017.spa
dc.relation.references458. Ota, M.; Matsuo, J.; Ishida, I.; Takano, H.; Yokoi, Y.; Hori, H.; Yoshida, S.; Ashida, K.; Nakamura, K.; Takahashi, T.; et al. Effects of a Medium-Chain Triglyceride-Based Ketogenic Formula on Cognitive Function in Patients with Mild-to-Moderate Alzheimer’s Disease. Neurosci. Lett. 2019, 690, 232–236, doi:10.1016/j.neulet.2018.10.048.spa
dc.relation.references459. Barichella, M.; Cereda, E.; Cassani, E.; Pinelli, G.; Iorio, L.; Ferri, V.; Privitera, G.; Pasqua, M.; Valentino, A.; Monajemi, F.; et al. Dietary Habits and Neurological Features of Parkinson’s Disease Patients: Implications for Practice. Clin. Nutr. Edinb. Scotl. 2017, 36, 1054–1061, doi:10.1016/j.clnu.2016.06.020.spa
dc.relation.references460. Wurtman, R.J.; Wurtman, J.J.; Regan, M.M.; McDermott, J.M.; Tsay, R.H.; Breu, J.J. Effects of Normal Meals Rich in Carbohydrates or Proteins on Plasma Tryptophan and Tyrosine Ratios. Am. J. Clin. Nutr. 2003, 77, 128–132, doi:10.1093/ajcn/77.1.128.spa
dc.relation.references461. Murakami, K.; Miyake, Y.; Sasaki, S.; Tanaka, K.; Fukushima, W.; Kiyohara, C.; Tsuboi, Y.; Yamada, T.; Oeda, T.; Miki, T.; et al. Dietary Glycemic Index Is Inversely Associated with the Risk of Parkinson’s Disease: A Case-Control Study in Japan. Nutr. Burbank Los Angel. Cty. Calif 2010, 26, 515–521, doi:10.1016/j.nut.2009.05.021.spa
dc.relation.references462. Schapira, A.H.; Cooper, J.M.; Dexter, D.; Clark, J.B.; Jenner, P.; Marsden, C.D. Mitochondrial Complex I Deficiency in Parkinson’s Disease. J. Neurochem. 1990, 54, 823–827, doi:10.1111/j.1471-4159.1990.tb02325.x.spa
dc.relation.references463. Parker, W.D.; Parks, J.K.; Swerdlow, R.H. Complex I Deficiency in Parkinson’s Disease Frontal Cortex. Brain Res. 2008, 1189, 215–218, doi:10.1016/j.brainres.2007.10.061.spa
dc.relation.references464. Tieu, K.; Perier, C.; Caspersen, C.; Teismann, P.; Wu, D.-C.; Yan, S.-D.; Naini, A.; Vila, M.; Jackson-Lewis, V.; Ramasamy, R.; et al. D-Beta-Hydroxybutyrate Rescues Mitochondrial Respiration and Mitigates Features of Parkinson Disease. J. Clin. Invest. 2003, 112, 892–901, doi:10.1172/JCI18797.spa
dc.relation.references465. Bough, K.J.; Wetherington, J.; Hassel, B.; Pare, J.F.; Gawryluk, J.W.; Greene, J.G.; Shaw, R.; Smith, Y.; Geiger, J.D.; Dingledine, R.J. Mitochondrial Biogenesis in the Anticonvulsant Mechanism of the Ketogenic Diet. Ann. Neurol. 2006, 60, 223–235, doi:10.1002/ana.20899.spa
dc.relation.references466. Dreher, M.L. Whole Fruits and Fruit Fiber Emerging Health Effects. Nutrients 2018, doi:10.3390/nu10121833.spa
dc.relation.references467. Yang, J.; Wang, H.P.; Zhou, L.; Xu, C.F. Effect of Dietary Fiber on Constipation: A Meta Analysis. World J. Gastroenterol. 2012, doi:10.3748/wjg.v18.i48.7378.spa
dc.relation.references468. Schulz, R.; Slavin, J. Perspective: Defining Carbohydrate Quality for Human Health and Environmental Sustainability. Adv. Nutr. 2021, doi:10.1093/advances/nmab050.spa
dc.relation.references469. Koh, A.; De Vadder, F.; Kovatcheva-Datchary, P.; Bäckhed, F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell 2016, doi:10.1016/j.cell.2016.05.041.spa
dc.relation.references470. Holscher, H.D. Dietary Fiber and Prebiotics and the Gastrointestinal Microbiota. Gut Microbes 2017, doi:10.1080/19490976.2017.1290756.spa
dc.relation.references471. Naismith, D.J.; Braschi, A. An Investigation into the Bioaccessibility of Potassium in Unprocessed Fruits and Vegetables. Int. J. Food Sci. Nutr. 2008, doi:10.1080/09637480701690519.spa
dc.relation.references472. Melse-Boonstra, A. Bioavailability of Micronutrients From Nutrient-Dense Whole Foods: Zooming in on Dairy, Vegetables, and Fruits. Front. Nutr. 2020, doi:10.3389/fnut.2020.00101.spa
dc.relation.references473. Afridi, H.I.; Rajput, K.; Kazi, T.G.; Talpur, F.N.; Baig, J.A. Evaluate the Correlation of Electrolytes with Biochemical Parameters in Biological Samples of Parkinson’s Disease Patients at Different Stages. 2020, doi:10.21203/rs.3.rs-22404/v1.spa
dc.relation.references474. Wu, H.; Huang, R.; Fan, J.; Luo, N.; Yang, X. Low Potassium Disrupt Intestinal Barrier and Result in Bacterial Translocation. J. Transl. Med. 2022, 20, 309, doi:10.1186/s12967-022-03499-0.spa
dc.relation.references475. Follett, J.; Darlow, B.; Wong, M.B.; Goodwin, J.; Pountney, D.L. Potassium Depolarization and Raised Calcium Induces α-Synuclein Aggregates. Neurotox. Res. 2013, doi:10.1007/s12640-012-9366-z.spa
dc.relation.references476. Ha, Y.; Jeong, J.A.; Kim, Y.; Churchill, D.G. Sodium and Potassium Relating to Parkinson’s Disease and Traumatic Brain Injury. In Metal Ions in Life Sciences; 2016.spa
dc.relation.references477. Ahmed, S.S.S.J.; Santosh, W. Metallomic Profiling and Linkage Map Analysis of Early Parkinson’s Disease: A New Insight to Aluminum Marker for the Possible Diagnosis. PLoS ONE 2010, doi:10.1371/journal.pone.0011252.spa
dc.relation.references478. Aronson, P.S.; Giebisch, G. Effects of PH on Potassium: New Explanations for Old Observations. J. Am. Soc. Nephrol. 2011, doi:10.1681/ASN.2011040414.spa
dc.relation.references479. Redondo-Useros, N.; Nova, E.; González-Zancada, N.; Díaz, L.E.; Gómez-Martínez, S.; Marcos, A. Microbiota and Lifestyle: A Special Focus on Diet. Nutrients 2020, 12, 1776, doi:10.3390/nu12061776.spa
dc.relation.references480. Yu, J.S.L.; Correia-Melo, C.; Zorrilla, F.; Herrera-Dominguez, L.; Wu, M.Y.; Hartl, J.; Campbell, K.; Blasche, S.; Kreidl, M.; Egger, A.-S.; et al. Microbial Communities Form Rich Extracellular Metabolomes That Foster Metabolic Interactions and Promote Drug Tolerance. Nat. Microbiol. 2022, 7, 542–555, doi:10.1038/s41564-022-01072-5.spa
dc.relation.references481. Blasche, S.; Kim, Y.; Mars, R.; Machado, D.; Maansson, M.; Kafkia, E.; Milanese, A.; Zeller, G.; Teusink, B.; Nielsen, J.; et al. Metabolic Cooperation and Spatiotemporal Niche Partitioning in a Kefir Microbial Community. Nat. Microbiol. 2021, 6, 196–208, doi:10.1038/s41564-020-00816-5.spa
dc.relation.references482. Keely, S.J. Decoding Host–Microbiota Communication in the Gut – Now We’re Flying! J. Physiol. 2017, 595, 417–418, doi:10.1113/JP272980.spa
dc.relation.references483. Price, N.D.; Reed, J.L.; Palsson, B.Ø. Genome-Scale Models of Microbial Cells: Evaluating the Consequences of Constraints. Nat. Rev. Microbiol. 2004, 2, 886–897, doi:10.1038/nrmicro1023.spa
dc.relation.references484. ILOG CPLEX Optimization Studio - Get Started Available online: https://www.ibm.com/products/ilog-cplex-optimization-studio/get-started (accessed on 11 May 2022).spa
dc.relation.references485. R: The R Project for Statistical Computing Available online: https://www.r-project.org/ (accessed on 11 May 2022).spa
dc.relation.references486. Bauer, Eugen. Package BacArena 2016.spa
dc.relation.references487. SybilSBML Source: R/SybilSBML.R Available online: https://rdrr.io/cran/sybilSBML/src/R/sybilSBML.R (accessed on 11 May 2022).spa
dc.relation.references488. CPLEX Optimizer | IBM Available online: https://www.ibm.com/analytics/cplex-optimizer (accessed on 11 May 2022).spa
dc.relation.references489. CRAN Package Check Results for Package CplexAPI Available online: https://cran-archive.r-project.org/web/checks/2021/2021-11-05_check_results_cple xAPI.html (accessed on 11 May 2022).spa
dc.relation.references490. Daniel, F.; Analytics, R.; Weston, S. DoMC: Foreach Parallel Adaptor for “Parallel” 2022.spa
dc.relation.references491. Daniel, F.; Corporation, M.; Weston, S. DoSNOW: Foreach Parallel Adaptor for the “snow” Package 2022.spa
dc.relation.references492. Daniel, F.; Ooi, H.; Calaway, R.; Microsoft; Weston, S. Foreach: Provides Foreach Looping Construct 2022.spa
dc.relation.references493. Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for Prediction of Metagenome Functions. Nat. Biotechnol. 2020, 38, 685–688, doi:10.1038/s41587-020-0548-6.spa
dc.relation.references494. Rivera-Chávez, F.; Lopez, C.A.; Bäumler, A.J. Oxygen as a Driver of Gut Dysbiosis. Free Radic. Biol. Med. 2017, 105, 93–101, doi:10.1016/j.freeradbiomed.2016.09.022.spa
dc.relation.references495. Summary of MetaCyc, Version 26.1 Available online: https://biocyc.org/META/organism-summary (accessed on 21 September 2022).spa
dc.relation.references496. Yu, D.; Yang, Y.; Long, J.; Xu, W.; Cai, Q.; Wu, J.; Cai, H.; Zheng, W.; Shu, X.O. Long-Term Diet Quality and Gut Microbiome Functionality: A Prospective, Shotgun Metagenomic Study among Urban Chinese Adults. Curr. Dev. Nutr. 2021, doi:10.1093/cdn/nzab026.spa
dc.relation.references497. Zheng, Y.; Bek, M.K.; Prince, N.Z.; Peralta Marzal, L.N.; Garssen, J.; Perez Pardo, P.; Kraneveld, A.D. The Role of Bacterial-Derived Aromatic Amino Acids Metabolites Relevant in Autism Spectrum Disorders: A Comprehensive Review. Front. Neurosci. 2021, 15.spa
dc.relation.references498. Persico, A.M.; Napolioni, V. Urinary P-Cresol in Autism Spectrum Disorder. Neurotoxicol. Teratol. 2013, 36, 82–90, doi:10.1016/j.ntt.2012.09.002.spa
dc.relation.references499. Pedersen, G.; Brynskov, J.; Saermark, T. Phenol Toxicity and Conjugation in Human Colonic Epithelial Cells. Scand. J. Gastroenterol. 2002, 37, 74–79, doi:10.1080/003655202753387392.spa
dc.relation.references500. McCall, I.C.; Betanzos, A.; Weber, D.A.; Nava, P.; Miller, G.W.; Parkos, C.A. Effects of Phenol on Barrier Function of a Human Intestinal Epithelial Cell Line Correlate with Altered Tight Junction Protein Localization. Toxicol. Appl. Pharmacol. 2009, 241, 61–70, doi:10.1016/j.taap.2009.08.002.spa
dc.relation.references501. Russell, W.R.; Duncan, S.H.; Scobbie, L.; Duncan, G.; Cantlay, L.; Calder, A.G.; Anderson, S.E.; Flint, H.J. Major Phenylpropanoid-Derived Metabolites in the Human Gut Can Arise from Microbial Fermentation of Protein. Mol. Nutr. Food Res. 2013, doi:10.1002/mnfr.201200594.spa
dc.relation.references502. Rekdal, V.M.; Bess, E.N.; Bisanz, J.E.; Turnbaugh, P.J.; Balskus, E.P. Discovery and Inhibition of an Interspecies Gut Bacterial Pathway for Levodopa Metabolism. Science 2019, 364, doi:10.1126/science.aau6323.spa
dc.relation.references503. van Kessel, S.P.; Auvinen, P.; Scheperjans, F.; El Aidy, S. Gut Bacterial Tyrosine Decarboxylase Associates with Clinical Variables in a Longitudinal Cohort Study of Parkinsons Disease. Npj Park. Dis. 2021, 7, 1–8, doi:10.1038/s41531-021-00260-0.spa
dc.relation.references504. van Kessel, S.P.; de Jong, H.R.; Winkel, S.L.; van Leeuwen, S.S.; Nelemans, S.A.; Permentier, H.; Keshavarzian, A.; El Aidy, S. Gut Bacterial Deamination of Residual Levodopa Medication for Parkinson’s Disease. BMC Biol. 2020, doi:10.1186/s12915-020-00876-3.spa
dc.relation.references505. Rekdal, V.M.; Bernadino, P.N.; Luescher, M.U.; Kiamehr, S.; Le, C.; Bisanz, J.E.; Turnbaugh, P.J.; Bess, E.N.; Balskus, E.P. A Widely Distributed Metalloenzyme Class Enables Gut Microbial Metabolism of Host-and Diet-Derived Catechols. eLife 2020, doi:10.7554/eLife.50845.spa
dc.relation.references506. Li, X.; Zhang, B.; Hu, Y.; Zhao, Y. New Insights Into Gut-Bacteria-Derived Indole and Its Derivatives in Intestinal and Liver Diseases. Front. Pharmacol. 2021, 12, 769501–769501, doi:10.3389/fphar.2021.769501.spa
dc.relation.references507. Pappolla, M.A.; Perry, G.; Fang, X.; Zagorski, M.; Sambamurti, K.; Poeggeler, B. Indoles as Essential Mediators in the Gut-Brain Axis. Their Role in Alzheimer’s Disease. Neurobiol. Dis. 2021, 156, 105403, doi:10.1016/j.nbd.2021.105403.spa
dc.relation.references508. Sonowal Robert; Swimm Alyson; Sahoo Anusmita; Luo Liping; Matsunaga Yohei; Wu Ziqi; Bhingarde Jui A.; Ejzak Elizabeth A.; Ranawade Ayush; Qadota Hiroshi; et al. Indoles from Commensal Bacteria Extend Healthspan. Proc. Natl. Acad. Sci. 2017, 114, E7506–E7515, doi:10.1073/pnas.1706464114.spa
dc.relation.references509. Powell, D.N.; Swimm, A.; Sonowal, R.; Bretin, A.; Gewirtz, A.T.; Jones, R.M.; Kalman, D. Indoles from the Commensal Microbiota Act via the AHR and IL-10 to Tune the Cellular Composition of the Colonic Epithelium during Aging. Proc. Natl. Acad. Sci. 2020, 117, 21519–21526, doi:10.1073/pnas.2003004117.spa
dc.relation.references510. Yang, W.; Yu, T.; Huang, X.; Bilotta, A.J.; Xu, L.; Lu, Y.; Sun, J.; Pan, F.; Zhou, J.; Zhang, W.; et al. Intestinal Microbiota-Derived Short-Chain Fatty Acids Regulation of Immune Cell IL-22 Production and Gut Immunity. Nat. Commun. 2020, 11, 4457–4457, doi:10.1038/s41467-020-18262-6.spa
dc.relation.references511. Delzenne, N.M.; Knudsen, C.; Beaumont, M.; Rodriguez, J.; Neyrinck, A.M.; Bindels, L.B. Contribution of the Gut Microbiota to the Regulation of Host Metabolism and Energy Balance: A Focus on the Gut-Liver Axis. In Proceedings of the Proceedings of the Nutrition Society; 2019.spa
dc.relation.references512. Caussy, C.; Loomba, R. Gut Microbiome, Microbial Metabolites and the Development of NAFLD. Nat. Rev. Gastroenterol. Hepatol. 2018, doi:10.1038/s41575-018-0058-x.spa
dc.relation.references513. He, L.H.; Yao, D.H.; Wang, L.Y.; Zhang, L.; Bai, X.L. Gut Microbiome-Mediated Alteration of Immunity, Inflammation, and Metabolism Involved in the Regulation of Non-Alcoholic Fatty Liver Disease. Front. Microbiol. 2021, doi:10.3389/fmicb.2021.761836.spa
dc.relation.references514. Laughlin, M.R. Normal Roles for Dietary Fructose in Carbohydrate Metabolism. Nutrients 2014, doi:10.3390/nu6083117.spa
dc.relation.references515. Meyers, A.M.; Mourra, D.; Beeler, J.A. High Fructose Corn Syrup Induces Metabolic Dysregulation and Altered Dopamine Signaling in the Absence of Obesity. PLoS ONE 2017, doi:10.1371/journal.pone.0190206.spa
dc.relation.references516. Ueno, M.; Bezerra, R.M.N.; Silva, M.S.; Tavares, D.Q.; Carvalho, C.R.; Saad, M.J.A. A High-Fructose Diet Induces Changes in Pp185 Phosphorylation in Muscle and Liver of Rats. Braz. J. Med. Biol. Res. 2000, 33, 1421–1427, doi:10.1590/S0100-879X2000001200004.spa
dc.relation.references517. Montrose, D.C.; Nishiguchi, R.; Basu, S.; Staab, H.A.; Zhou, X.K.; Wang, H.; Meng, L.; Johncilla, M.; Cubillos-Ruiz, J.R.; Morales, D.K.; et al. Dietary Fructose Alters the Composition, Localization, and Metabolism of Gut Microbiota in Association With Worsening Colitis. CMGH 2021, doi:10.1016/j.jcmgh.2020.09.008.spa
dc.relation.references518. Beisner, J.; Gonzalez-Granda, A.; Basrai, M.; Damms-Machado, A.; Bischoff, S.C. Fructose-Induced Intestinal Microbiota Shift Following Two Types of Short-Term High-Fructose Dietary Phases. Nutrients 2020, doi:10.3390/nu12113444.spa
dc.relation.references519. Benatar, J.R.; Stewart, R.A. The Effects of Changing Dairy Intake on Trans and Saturated Fatty Acid Levels- Results from a Randomized Controlled Study. Nutr. J. 2014, 13, 32, doi:10.1186/1475-2891-13-32.spa
dc.relation.references520. Dabadie, H.; Motta, C.; Peuchant, E.; LeRuyet, P.; Mendy, F. Variations in Daily Intakes of Myristic and α-Linolenic Acids in Sn-2 Position Modify Lipid Profile and Red Blood Cell Membrane Fluidity. Br. J. Nutr. 2006, doi:10.1079/bjn20061813.spa
dc.relation.references521. Russo, S.B.; Baicu, C.F.; Van Laer, A.; Geng, T.; Kasiganesan, H.; Zile, M.R.; Cowart, L.A. Ceramide Synthase 5 Mediates Lipid-Induced Autophagy and Hypertrophy in Cardiomyocytes. J. Clin. Invest. 2012, 122, 3919–3930, doi:10.1172/JCI63888.spa
dc.relation.references522. Martínez, L.; Torres, S.; Baulies, A.; Alarcón-Vila, C.; Elena, M.; Fabriàs, G.; Casas, J.; Caballeria, J.; Fernandez-Checa, J.C.; García-Ruiz, C. Myristic Acid Potentiates Palmitic Acid-Induced Lipotoxicity and Steatohepatitis Associated with Lipodystrophy by Sustaning de Novo Ceramide Synthesis. Oncotarget 2015, 6, 41479–41496.spa
dc.relation.references523. Vos, M.; Dulovic-Mahlow, M.; Mandik, F.; Frese, L.; Kanana, Y.; Haissatou Diaw, S.; Depperschmidt, J.; Böhm, C.; Rohr, J.; Lohnau, T.; et al. Ceramide Accumulation Induces Mitophagy and Impairs β-Oxidation in PINK1 Deficiency. Proc. Natl. Acad. Sci. 2021, 118, e2025347118, doi:10.1073/pnas.2025347118.spa
dc.relation.references524. Plotegher, N.; Bubacco, L.; Greggio, E.; Civiero, L. Ceramides in Parkinson’s Disease: From Recent Evidence to New Hypotheses. Front. Neurosci. 2019, 13.spa
dc.relation.references525. Walton, C.; Fowler, D.P.; Turner, C.; Jia, W.; Whitehead, R.N.; Griffiths, L.; Dawson, C.; Waring, R.H.; Ramsden, D.B.; Cole, J.A.; et al. Analysis of Volatile Organic Compounds of Bacterial Origin in Chronic Gastrointestinal Diseases. Inflamm. Bowel Dis. 2013, 19, 2069–2078, doi:10.1097/MIB.0b013e31829a91f6.spa
dc.relation.references526. Walker, A.; Schmitt-Kopplin, P. The Role of Fecal Sulfur Metabolome in Inflammatory Bowel Diseases. Int. J. Med. Microbiol. 2021, 311, 151513, doi:10.1016/j.ijmm.2021.151513.spa
dc.relation.references527. Levitt, M.D.; Furne, J.; Springfield, J.; Suarez, F.; DeMaster, E. Detoxification of Hydrogen Sulfide and Methanethiol in the Cecal Mucosa. J. Clin. Invest. 1999, 104, 1107–1114.spa
dc.relation.references528. Lozupone, C.A.; Stombaugh, J.I.; Gordon, J.I.; Jansson, J.K.; Knight, R. Diversity, Stability and Resilience of the Human Gut Microbiota. Nature 2012, 489, 220–230, doi:10.1038/nature11550.spa
dc.relation.references529. Novotný, M.; Klimova, B.; Valis, M. Microbiome and Cognitive Impairment: Can Any Diets Influence Learning Processes in a Positive Way? Front. Aging Neurosci. 2019, 11.spa
dc.relation.references530. Liu, H.; Wang, J.; He, T.; Becker, S.; Zhang, G.; Li, D.; Ma, X. Butyrate: A Double-Edged Sword for Health? Adv. Nutr. 2018, 9, 21–29, doi:10.1093/advances/nmx009.spa
dc.relation.references531. Louis, P.; Young, P.; Holtrop, G.; Flint, H.J. Diversity of Human Colonic Butyrate-Producing Bacteria Revealed by Analysis of the Butyryl-CoA:Acetate CoA-Transferase Gene. Environ. Microbiol. 2010, 12, 304–314, doi:10.1111/j.1462-2920.2009.02066.x.spa
dc.relation.references532. Jeffery, I.B.; O’Toole, P.W. Diet-Microbiota Interactions and Their Implications for Healthy Living. Nutrients 2013, 5, 234–252, doi:10.3390/nu5010234.spa
dc.relation.references533. Tan, R.; Jin, M.; Shao, Y.; Yin, J.; Li, H.; Chen, T.; Shi, D.; Zhou, S.; Li, J.; Yang, D. High-Sugar, High-Fat, and High-Protein Diets Promote Antibiotic Resistance Gene Spreading in the Mouse Intestinal Microbiota. Gut Microbes 14, 2022442, doi:10.1080/19490976.2021.2022442.spa
dc.relation.references534. Volynets, V.; Louis, S.; Pretz, D.; Lang, L.; Ostaff, M.J.; Wehkamp, J.; Bischoff, S.C. Intestinal Barrier Function and the Gut Microbiome Are Differentially Affected in Mice Fed a Western-Style Diet or Drinking Water Supplemented with Fructose. J. Nutr. 2017, 147, 770–780, doi:10.3945/jn.116.242859.spa
dc.relation.references535. Singh, R.K.; Chang, H.-W.; Yan, D.; Lee, K.M.; Ucmak, D.; Wong, K.; Abrouk, M.; Farahnik, B.; Nakamura, M.; Zhu, T.H.; et al. Influence of Diet on the Gut Microbiome and Implications for Human Health. J. Transl. Med. 2017, 15, 73, doi:10.1186/s12967-017-1175-y.spa
dc.relation.references536. Cassani, E.; Privitera, G.; Pezzoli, G.; Pusani, C.; Madio, C.; Iorio, L.; Barichella, M. Use of Probiotics for the Treatment of Constipation in Parkinson’s Disease Patients. Minerva Gastroenterol. Dietol. 2011.spa
dc.relation.references537. Gao, K.; Mu, C.; Farzi, A.; Zhu, W. Tryptophan Metabolism: A Link Between the Gut Microbiota and Brain. Adv. Nutr. 2020, 11, 709–723, doi:10.1093/advances/nmz127.spa
dc.relation.references538. Jenkins, T.A.; Nguyen, J.C.D.; Polglaze, K.E.; Bertrand, P.P. Influence of Tryptophan and Serotonin on Mood and Cognition with a Possible Role of the Gut-Brain Axis. Nutrients 2016, 8, 56, doi:10.3390/nu8010056.spa
dc.relation.references539. Hubbard, T.D.; Murray, I.A.; Perdew, G.H. Indole and Tryptophan Metabolism: Endogenous and Dietary Routes to Ah Receptor Activation. Drug Metab. Dispos. 2015, 43, 1522–1535, doi:10.1124/dmd.115.064246.spa
dc.relation.references540. Jacob, M.; Lopata, A.L.; Dasouki, M.; Abdel Rahman, A.M. Metabolomics toward Personalized Medicine. Mass Spectrom. Rev. 2019, 38, 221–238, doi:10.1002/mas.21548.spa
dc.relation.references541. Cassani, E.; Barichella, M.; Ferri, V.; Pinelli, G.; Iorio, L.; Bolliri, C.; Caronni, S.; Faierman, S.A.; Mottolese, A.; Pusani, C.; et al. Dietary Habits in Parkinson’s Disease: Adherence to Mediterranean Diet. Parkinsonism Relat. Disord. 2017, 42, 40–46, doi:10.1016/j.parkreldis.2017.06.007.spa
dc.relation.references542. Sun, J.; Li, H.; Jin, Y.; Yu, J.; Mao, S.; Su, K.P.; Ling, Z.; Liu, J. Probiotic Clostridium Butyricum Ameliorated Motor Deficits in a Mouse Model of Parkinson’s Disease via Gut Microbiota-GLP-1 Pathway. Brain. Behav. Immun. 2021, 91, 703–715, doi:10.1016/j.bbi.2020.10.014.spa
dc.relation.references543. Zacharias, H.U.; Kaleta, C.; Cossais, F.; Schaeffer, E.; Berndt, H.; Best, L.; Dost, T.; Glüsing, S.; Groussin, M.; Poyet, M.; et al. Microbiome and Metabolome Insights into the Role of the Gastrointestinal–Brain Axis in Parkinson’s and Alzheimer’s Disease: Unveiling Potential Therapeutic Targets. Metabolites 2022, 12, 1222, doi:10.3390/metabo12121222.spa
dc.relation.references544. Gough, E.; Shaikh, H.; Manges, A.R. Systematic Review of Intestinal Microbiota Transplantation (Fecal Bacteriotherapy) for Recurrent Clostridium Difficile Infection. Clin. Infect. Dis. 2011, 53, 994–1002, doi:10.1093/cid/cir632.spa
dc.relation.references545. Xue, L.J.; Yang, X.Z.; Tong, Q.; Shen, P.; Ma, S.J.; Wu, S.N.; Zheng, J.L.; Wang, H.G. Fecal Microbiota Transplantation Therapy for Parkinson’s Disease: A Preliminary Study. Medicine (Baltimore) 2020, 99, e22035–e22035, doi:10.1097/MD.0000000000022035.spa
dc.relation.references546. Klann, E.M.; Dissanayake, U.; Gurrala, A.; Farrer, M.; Shukla, A.W.; Ramirez-Zamora, A.; Mai, V.; Vedam-Mai, V. The Gut–Brain Axis and Its Relation to Parkinson’s Disease: A Review. Front. Aging Neurosci. 2022, 13, 782082, doi:10.3389/fnagi.2021.782082.spa
dc.relation.references547. Nganga, R.; Oleinik, N.; Ogretmen, B. Mechanisms of Ceramide-Dependent Cancer Cell Death. Adv. Cancer Res. 2018, 140, 1–25, doi:10.1016/bs.acr.2018.04.007.spa
dc.relation.references548. Field, B.C.; Gordillo, R.; Scherer, P.E. The Role of Ceramides in Diabetes and Cardiovascular Disease Regulation of Ceramides by Adipokines. Front. Endocrinol. 2020, 11.spa
dc.relation.references549. Lyn-Cook, L.E.; Lawton, M.; Tong, M.; Silbermann, E.; Longato, L.; Jiao, P.; Mark, P.; Wands, J.R.; Xu, H.; de la Monte, S.M. Hepatic Ceramide May Mediate Brain Insulin Resistance and Neurodegeneration in Type 2 Diabetes and Non-Alcoholic Steatohepatitis. J. Alzheimers Dis. JAD 2009, 16, 715–729, doi:10.3233/JAD-2009-0984.spa
dc.relation.references550. Custodia, A.; Aramburu-Núñez, M.; Correa-Paz, C.; Posado-Fernández, A.; Gómez-Larrauri, A.; Castillo, J.; Gómez-Muñoz, A.; Sobrino, T.; Ouro, A. Ceramide Metabolism and Parkinson’s Disease-Therapeutic Targets. Biomolecules 2021, 11, 945, doi:10.3390/biom11070945.spa
dc.relation.references551. Xing, Y.; Tang, Y.; Zhao, L.; Wang, Q.; Qin, W.; Ji, X.; Zhang, J.; Jia, J. Associations between Plasma Ceramides and Cognitive and Neuropsychiatric Manifestations in Parkinson’s Disease Dementia. J. Neurol. Sci. 2016, 370, 82–87, doi:10.1016/j.jns.2016.09.028.spa
dc.relation.references552. Riboldi, G.M.; Di Fonzo, A.B. GBA, Gaucher Disease, and Parkinson’s Disease: From Genetic to Clinic to New Therapeutic Approaches. Cells 2019, 8, 364, doi:10.3390/cells8040364.spa
dc.relation.references553. de Wit, N.M.; den Hoedt, S.; Martinez-Martinez, P.; Rozemuller, A.J.; Mulder, M.T.; de Vries, H.E. Astrocytic Ceramide as Possible Indicator of Neuroinflammation. J. Neuroinflammation 2019, 16, 48, doi:10.1186/s12974-019-1436-1.spa
dc.relation.references554. Hebbar, S.; Sahoo, I.; Matysik, A.; Argudo Garcia, I.; Osborne, K.A.; Papan, C.; Torta, F.; Narayanaswamy, P.; Fun, X.H.; Wenk, M.R.; et al. Ceramides And Stress Signalling Intersect With Autophagic Defects In Neurodegenerative Drosophila Blue Cheese (Bchs) Mutants. Sci. Rep. 2015, 5, 15926, doi:10.1038/srep15926.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc610 - Medicina y salud::616 - Enfermedadesspa
dc.subject.ddc600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosspa
dc.subject.decsParkinson Disease/pathologyeng
dc.subject.decsContenido Digestivospa
dc.subject.decsGastrointestinal Contentseng
dc.subject.decsEnfermedad de Parkinson/patologíaspa
dc.subject.decsModelos Teóricosspa
dc.subject.decsModels, Theoreticaleng
dc.subject.proposalEnfermedad de Parkinsonspa
dc.subject.proposalParkinson's diseaseeng
dc.subject.proposalMicrobioma intestinalspa
dc.subject.proposalModelamiento computacionalspa
dc.subject.proposalDieteng
dc.subject.proposalModelamiento metabólicospa
dc.subject.proposalDietaspa
dc.subject.proposalGut microbiomeeng
dc.subject.proposalComputational modelingeng
dc.subject.proposalMetabolic modelingeng
dc.titleModelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinsonspa
dc.title.translatedComputational modeling of intestinal bacterial composition in the context of Parkinson's diseaseeng
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TDspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentMedios de comunicaciónspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.fundernameUniversidad Javerianaspa
oaire.fundernameUniversidad de Kiel Alemaniaspa

Archivos

Bloque original

Mostrando 1 - 2 de 2
Miniatura
Nombre:
1018441690.2023.pdf
Tamaño:
17.82 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Doctorado en Ciencias Biomédicas
Descargar
No hay miniatura disponible
Nombre:
1018441690.2023.Anexos_L_M_N_O.xlsx
Tamaño:
315.21 KB
Formato:
Microsoft Excel XML
Descripción:
Anexos M, N, O
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Doctorado en Ciencias Biomédicas