{"id":{"repo_id":"dialnet","oai_identifier":"oai:dialnet.unirioja.es:TES0000023089"},"canonical_url":"https://search.dev.ndltd.org/etd/dialnet/oai:dialnet.unirioja.es:TES0000023089","repository":{"repo_id":"dialnet","name":"Dialnet","base_url":"https://dialnet.unirioja.es/oaites/OAIHandler"},"display":{"title":"Multiplatform metabolome profiling to identify specific signatures and biomarkers in blood samples: untargeted approach","abstract":"One of the significant challenges in identifying effective therapy in many chronic and neurodegenerative diseases is the need for reliable biomarkers. Thus, new point-of-care diagnostics tools are essential for unambiguously distinguishing diseased patients from healthy ones providing results in rapid time. In this doctoral thesis, an untargeted metabolomics approach based on high-throughput analytical techniques such as vibrational spectroscopy and liquid chromatography-mass spectrometry (LC-MS) was evaluated in different studies related to the field of health and disease. Thus, this doctoral thesis's main objective is to provide an objective diagnosis of disorders such as Parkinson’s, Alzheimer’s, Amyotrophic lateral sclerosis and Metabolic Syndrome. Different studies were performed to obtain a metabolic profile of healthy and diseased patients. Thus, to obtain specific metabolomic fingerprinting, multiple analytical and multivariate strategies were tested and combined to exploit their respective strengths and drawbacks. Therefore, distinct mid-infrared metabolic fingerprints in the abovementioned diseases were investigated for patient stratification and to guide an accurate and early differential diagnosis. In addition, UPLC-MS analysis successfully complemented vibrational spectroscopy, providing excellent patient discrimination based on specific blood biomarkers. The obtained results are auspicious, giving place to the new hypothesis about disease pathogenesis and possible involved metabolic pathways that should be validated by a further targeted and multidisciplinary approach.","abstract_html":"One of the significant challenges in identifying effective therapy in many chronic and neurodegenerative diseases is the need for reliable biomarkers. Thus, new point-of-care diagnostics tools are essential for unambiguously distinguishing diseased patients from healthy ones providing results in rapid time. In this doctoral thesis, an untargeted metabolomics approach based on high-throughput analytical techniques such as vibrational spectroscopy and liquid chromatography-mass spectrometry (LC-MS) was evaluated in different studies related to the field of health and disease. Thus, this doctoral thesis's main objective is to provide an objective diagnosis of disorders such as Parkinson’s, Alzheimer’s, Amyotrophic lateral sclerosis and Metabolic Syndrome. Different studies were performed to obtain a metabolic profile of healthy and diseased patients. Thus, to obtain specific metabolomic fingerprinting, multiple analytical and multivariate strategies were tested and combined to exploit their respective strengths and drawbacks. Therefore, distinct mid-infrared metabolic fingerprints in the abovementioned diseases were investigated for patient stratification and to guide an accurate and early differential diagnosis. In addition, UPLC-MS analysis successfully complemented vibrational spectroscopy, providing excellent patient discrimination based on specific blood biomarkers. The obtained results are auspicious, giving place to the new hypothesis about disease pathogenesis and possible involved metabolic pathways that should be validated by a further targeted and multidisciplinary approach.","abstract_has_math":false,"creators":["Tkachenko, Kateryna"],"institution":"Universidad de La Rioja (España)","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Pizarro Millán, Consuelo (Universidad de La Rioja)","González Sáiz, José María (Universidad de La Rioja)"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T06:27:03Z","subjects":[],"languages":["eng"],"rights":["LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dialnet.unirioja.es/servlet/oaites?codigo=315717","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Pizarro Millán, Consuelo (Universidad de La Rioja)","González Sáiz, José María (Universidad de La Rioja)"]},{"key":"dc:creator","label":"Author","values":["Tkachenko, Kateryna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["Universidad de La Rioja (España)"]},{"key":"dc:type","label":"Dc Type","values":["text (thesis)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dialnet.unirioja.es/servlet/oaites?codigo=315717"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["One of the significant challenges in identifying effective therapy in many chronic and neurodegenerative diseases is the need for reliable biomarkers. Thus, new point-of-care diagnostics tools are essential for unambiguously distinguishing diseased patients from healthy ones providing results in rapid time. In this doctoral thesis, an untargeted metabolomics approach based on high-throughput analytical techniques such as vibrational spectroscopy and liquid chromatography-mass spectrometry (LC-MS) was evaluated in different studies related to the field of health and disease. Thus, this doctoral thesis's main objective is to provide an objective diagnosis of disorders such as Parkinson’s, Alzheimer’s, Amyotrophic lateral sclerosis and Metabolic Syndrome. Different studies were performed to obtain a metabolic profile of healthy and diseased patients. Thus, to obtain specific metabolomic fingerprinting, multiple analytical and multivariate strategies were tested and combined to exploit their respective strengths and drawbacks. Therefore, distinct mid-infrared metabolic fingerprints in the abovementioned diseases were investigated for patient stratification and to guide an accurate and early differential diagnosis. In addition, UPLC-MS analysis successfully complemented vibrational spectroscopy, providing excellent patient discrimination based on specific blood biomarkers. The obtained results are auspicious, giving place to the new hypothesis about disease pathogenesis and possible involved metabolic pathways that should be validated by a further targeted and multidisciplinary approach.","Uno de los desafíos significativos en la identificación de terapias efectivas en muchas enfermedades crónicas y neurodegenerativas es la necesidad de biomarcadores confiables. Por lo tanto, nuevas herramientas de diagnóstico en los puntos de atención son esenciales para distinguir sin ambigüedades a los pacientes enfermos de los sanos, proporcionando resultados rápidos. En esta tesis doctoral, se evaluó un enfoque de metabolómica no dirigida basado en técnicas analíticas de alto rendimiento, como la espectroscopia vibracional y la cromatografía líquida-espectrometría de masas (LC-MS), en diferentes estudios relacionados con el campo de la salud y la enfermedad. Por lo tanto, el objetivo principal de esta tesis doctoral es desarrollar estrategias para el diagnóstico objetivo de trastornos como la enfermedad de Parkinson, la enfermedad de Alzheimer, la Esclerosis lateral amiotrófica y el Síndrome metabólico. Se llevaron a cabo diferentes estudios para obtener un perfil metabólico de pacientes sanos y enfermos. De este modo, para obtener un perfil metabólico específico, se probaron y combinaron múltiples estrategias analíticas acopladas con estrategias multivariadas con el fin de explotar sus respectivas fortalezas y debilidades. Por lo tanto, se investigó la existencia de huellas metabólicas distintas en el infrarrojo medio en las enfermedades mencionadas anteriormente, para la estratificación de pacientes y guiar un diagnóstico diferencial preciso y temprano. Además, el análisis UPLC-MS complementó con éxito la espectroscopía vibracional, proporcionando una excelente discriminación de pacientes basada en biomarcadores específicos de la sangre. Los resultados obtenidos son muy prometedores, dando lugar a nuevas hipótesis sobre la patogénesis de las enfermedades y las posibles vías metabólicas involucradas, que deberían ser validadas mediante un enfoque multidisciplinario y dirigido."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multiplatform metabolome profiling to identify specific signatures and biomarkers in blood samples: untargeted approach"]}]}],"canonical_facts":{"dc:contributor":["Pizarro Millán, Consuelo (Universidad de La Rioja)","González Sáiz, José María (Universidad de La Rioja)"],"dc:creator":["Tkachenko, Kateryna"],"dc:date":["2023"],"dc:description":["One of the significant challenges in identifying effective therapy in many chronic and neurodegenerative diseases is the need for reliable biomarkers. Thus, new point-of-care diagnostics tools are essential for unambiguously distinguishing diseased patients from healthy ones providing results in rapid time. In this doctoral thesis, an untargeted metabolomics approach based on high-throughput analytical techniques such as vibrational spectroscopy and liquid chromatography-mass spectrometry (LC-MS) was evaluated in different studies related to the field of health and disease. Thus, this doctoral thesis's main objective is to provide an objective diagnosis of disorders such as Parkinson’s, Alzheimer’s, Amyotrophic lateral sclerosis and Metabolic Syndrome. Different studies were performed to obtain a metabolic profile of healthy and diseased patients. Thus, to obtain specific metabolomic fingerprinting, multiple analytical and multivariate strategies were tested and combined to exploit their respective strengths and drawbacks. Therefore, distinct mid-infrared metabolic fingerprints in the abovementioned diseases were investigated for patient stratification and to guide an accurate and early differential diagnosis. In addition, UPLC-MS analysis successfully complemented vibrational spectroscopy, providing excellent patient discrimination based on specific blood biomarkers. The obtained results are auspicious, giving place to the new hypothesis about disease pathogenesis and possible involved metabolic pathways that should be validated by a further targeted and multidisciplinary approach.","Uno de los desafíos significativos en la identificación de terapias efectivas en muchas enfermedades crónicas y neurodegenerativas es la necesidad de biomarcadores confiables. Por lo tanto, nuevas herramientas de diagnóstico en los puntos de atención son esenciales para distinguir sin ambigüedades a los pacientes enfermos de los sanos, proporcionando resultados rápidos. En esta tesis doctoral, se evaluó un enfoque de metabolómica no dirigida basado en técnicas analíticas de alto rendimiento, como la espectroscopia vibracional y la cromatografía líquida-espectrometría de masas (LC-MS), en diferentes estudios relacionados con el campo de la salud y la enfermedad. Por lo tanto, el objetivo principal de esta tesis doctoral es desarrollar estrategias para el diagnóstico objetivo de trastornos como la enfermedad de Parkinson, la enfermedad de Alzheimer, la Esclerosis lateral amiotrófica y el Síndrome metabólico. Se llevaron a cabo diferentes estudios para obtener un perfil metabólico de pacientes sanos y enfermos. De este modo, para obtener un perfil metabólico específico, se probaron y combinaron múltiples estrategias analíticas acopladas con estrategias multivariadas con el fin de explotar sus respectivas fortalezas y debilidades. Por lo tanto, se investigó la existencia de huellas metabólicas distintas en el infrarrojo medio en las enfermedades mencionadas anteriormente, para la estratificación de pacientes y guiar un diagnóstico diferencial preciso y temprano. Además, el análisis UPLC-MS complementó con éxito la espectroscopía vibracional, proporcionando una excelente discriminación de pacientes basada en biomarcadores específicos de la sangre. Los resultados obtenidos son muy prometedores, dando lugar a nuevas hipótesis sobre la patogénesis de las enfermedades y las posibles vías metabólicas involucradas, que deberían ser validadas mediante un enfoque multidisciplinario y dirigido."],"dc:format":["application/pdf"],"dc:identifier":["https://dialnet.unirioja.es/servlet/oaites?codigo=315717"],"dc:language":["eng"],"dc:publisher":["Universidad de La Rioja (España)"],"dc:rights":["LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI"],"dc:title":["Multiplatform metabolome profiling to identify specific signatures and biomarkers in blood samples: untargeted approach"],"dc:type":["text (thesis)"]},"updated_at":"2026-07-24T06:27:03Z"}