Suchergebnisse

THE PRESENT DOCTORAL THESIS WAS FUNDED BY THE FOLLOWING INSTITUTIONS: - Institute of Health Carlos III - Personal funding: "Contratos i-PFIS: doctorados IIS-empresa en ciencias y tecnologías de la salud de la convocatoria 2017 de la Acción Estratégica en Salud 2013–2016, Project number: IFI17/00048". - Institute of Health Carlos III - Health Research Fund (ERDF FUNDS) - Projects numbers PI05/1968, PI11/01425, PI11/02042, PI11/02059, PI16/01301, PI16/01205, PI16/00871 and PI20/00711. - Redes temáticas de investigación cooperativa RETIC (Red SAMID RD12/0026/0015). - Mapfre Foundation ("Research grants by Ignacio H. de Larramendi 2017"). - ERDF/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant number P18-RT-2248). - University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES). ; Childhood obesity can develop early in life leading to the appearance of metabolic alterations such as insulin resistance (IR). If maintained during adulthood, obesity and IR usually derive into the development of more serious conditions like Type II Diabetes and cardiovascular disease, which considerably increase morbidity and mortality in affected populations. As many other complex disorders, obesity and its associated cardiometabolic comorbidities constitute a complex phenotype arising from the interaction between an at-risk molecular profile (involving genomics, transcriptomics, epigenomics and proteomics disturbances) and environmental exposures. On this sense, one of the most promising fields of research in obesity has involved the identification of early-life predictive molecular biomarkers able to stratify patients according to their risk for developing cardiometabolic complications later in life. Interestingly, the ideal and most robust biomarker discovery approach would involve the simultaneous analysis of multiple omics data layers at a once, allowing tracking a molecular disturbance from all its possible dimensions. Due to the complexity of omics data, nevertheless, new and innovative analytics approaches have been demanded. In the middle of this need, bioinformatics and artificial intelligence (AI) have experienced a remarkable boost due to their ability to automatically obtain descriptive or predictive models from massive amounts of data (Big Data). The present Doctoral Thesis gathers a series of research works in which bioinformatics and AI are conveniently applied to several obesity observational omics research projects for identifying new molecular biomarkers of IR and metabolic alterations in children and adolescents with obesity. Study populations are composed of more than 2000 Spanish children with ages ranging from 2-18 years. In summary, the results presented in the present Doctoral Thesis indicate that; 1) obesity is a complex disorder resulting from the interaction between genetic and environmental factors, 2) the creation of predictive tools based on the combination of small-risk effects genetic variants is an interesting but simple strategy for predicting future obesity, 3) multi-omics research approaches in obesity are necessary to understand the complex molecular mechanisms underlying disease, and 4) the application of eXplainable Artificial Intelligence (XAI) machine learning (ML) models can help us to unravel the complex relationships between omics molecular elements. The application of multi-omics research approaches and the use of complex analytical tools (such as bioinformatics and AI) are the correct way for approaching a true implementation of a personalized care in obesity. Further studies like those presented in the present Doctoral Thesis and as well as larger cohorts projects should be encouraged in order to validate presented findings. This will require a close collaboration between clinicians and basic researchers, and the creation of multidisciplinary teams, in which the presence of mixed bioinformatics profiles will be of great importance. ; La obesidad infantil puede desarrollarse en etapas tempranas de la vida y dar lugar a la aparición de alteraciones metabólicas como la resistencia a la insulina (RI). Si se mantienen durante la edad adulta, la obesidad y la RI suelen derivar en el desarrollo de afecciones más graves como la diabetes tipo II y las enfermedades cardiovasculares, que aumentan considerablemente la morbilidad y la mortalidad en las poblaciones afectadas. Como muchos otros trastornos complejos, la obesidad y sus comorbilidades cardiometabólicas constituyen un fenotipo complejo que surge de la interacción entre un perfil molecular de riesgo (que implica alteraciones genómicas, transcriptómicas, epigenómicas y proteómicas) y las exposiciones ambientales. En este sentido, uno de los campos de investigación más prometedores en materia de obesidad ha consistido en la identificación de biomarcadores moleculares predictivos durante las etapas tempranas de la vida, capaces de estratificar a los pacientes en función de su riesgo de desarrollar complicaciones cardiometabólicas en la edad adulta. Uno de los enfoques más interesantes y robustos para el descubrimiento de biomarcadores implicaría el análisis simultáneo de múltiples capas de datos ómicos a la vez, permitiendo el estudio de una alteración molecular desde todas sus posibles dimensiones. Sin embargo, debido a la complejidad de los datos ómicos, se requieren enfoques analíticos innovadores. En medio de esta necesidad, la bioinformática y la inteligencia artificial (IA) han experimentado un notable impulso debido a su capacidad para obtener automáticamente modelos descriptivos o predictivos a partir de cantidades masivas de datos (Big Data). La presente Tesis Doctoral recoge una serie de trabajos de investigación en los que la bioinformática y la IA se aplican convenientemente a varios estudios ómicos de obesidad para identificar nuevos biomarcadores moleculares de RI y alteraciones metabólicas en niños y adolescentes con obesidad. Las poblaciones de estudio de la presente tesis doctoral están compuestas por más de 2000 niños españoles con edades comprendidas entre los 2 y los 18 años. En resumen, los resultados recogidos en la presente Tesis Doctoral indican que 1) la obesidad es un trastorno complejo resultante de la interacción entre factores genéticos y ambientales, 2) la creación de herramientas predictivas basadas en la combinación de polimorfismos genéticos es una estrategia interesante pero sencilla para predecir el desarrollo de obesidad, 3) los enfoques de investigación multiómicos en obesidad son necesarios para comprender los complejos mecanismos moleculares subyacentes a la enfermedad, y 4) la aplicación de modelos de aprendizaje automático de Inteligencia Artificial eXplicable (XAI) puede ayudarnos a desentrañar las complejas relaciones existentes entre los elementos moleculares ómicos. La aplicación de enfoques de investigación multi-ómica y el uso de herramientas analíticas complejas (como la bioinformática y la IA) son el camino correcto hacia una verdadera implementación de una medicina personalizada en la obesidad. En el futuro, deben fomentarse más estudios como los recogidos en la presente Tesis Doctoral, así como proyectos de reclutamiento de cohortes más grandes para validar los hallazgos presentados. Esto requerirá una estrecha colaboración entre clínicos e investigadores básicos, y la creación de equipos multidisciplinares, en los que la presencia de perfiles bioinformáticos mixtos será de gran importancia. ; Tesis Univ. Granada. ; Institute of Health Carlos III IFI17/00048 ; Institute of Health Carlos III - Health Research Fund (ERDF FUNDS) - Projects numbers PI05/1968, PI11/01425, PI11/02042, PI11/02059, PI16/01301, PI16/01205, PI16/00871 and PI20/00711 ; RETIC (Red SAMID RD12/0026/0015) ; Mapfre Foundation ; ERDF/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant number P18-RT-2248) ; University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES)

The authors would like to thank the Spanish children and parents who participated in the study. ; Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8 ), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response. ; Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica (I + D + I), Instituto de Salud Carlos III-Health Research Funding (FONDOS FEDER) PI1102042 PI1102059 PI1601301 PI1600871 ; Spanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health Products EC10-243 EC10-056 EC10-281 EC10-227 ; Regional Government of Andalusia ("Plan Andaluz de investigacion, desarrollo e innovacion (2018)") P18-RT-2248 ; Mapfre Foundation ("Research grants by Ignacio H. de Larramendi 2017") ; Instituto de Salud Carlos III IFI17/00048

This study was funded by the Spanish Ministry of Health, the Institute of Health Carlos III (ISCIII), and the European Regional Development Fund (grants PS09/02272, PS09/02147, PS09/01095, PS09/00849, PS09/00461, and PI12-02755); the Andalusian Council of Health (grant PI-0569-2010); the Spanish Network of Primary Care Research, redIAPP (grant RD06/ 0018); the Aragon group (grant RD06/0018/0020); the Bizkaya group (grant RD06/0018/0018); the Castilla-Leon group (grant RD06/0018/0027); the Mental Health Barcelona Group (grant RD06/0018/0017); the Mental Health, Services and Primary Care Malaga group (grant RD06/0018/0039); and the projects "PI18/00238" and "PI18/00467" funded by the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"). This study was performed as part of a PhD thesis conducted within the Official Doctoral Programme in Biomedicine of the University of Granada, Spain. Augusto Anguita-Ruiz was supported by a Ministry of Economy and Competitiveness and Institute of Health Carlos III fellowship (IFI17/00048). Juan Antonio Zarza-Rebollo received financial support from the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Ana M. Perez-Gutierrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). Elena Lopez-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program (IJC2019040080-I), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramon y Cajal Program (RYC-2014-15774). The authors thank the Institute of Health Carlos III (ISCIII), the European Regional Development Fund (FEDER), the Andalusian Council of Health and Andalusian Health Service (SAS), the Primary Care Prevention and Health Promotion Research Network (redIAPP), the Biomedical Research Institute of Malaga (IBIMA), and the Biomedical Research Centre (CIBM) from the University of Granada for their economic and logistic support. The authors thank all the patients and General Practitioners who participated in the trial. ; Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesityassociated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals. ; Instituto de Salud Carlos III Spanish Government Institute of Health Carlos III (ISCIII) European Commission PS09/02272 PS09/02147 PS09/01095 PS09/00849 PS09/00461 PI12-02755 ; Andalusian Council of Health PI-0569-2010 ; Spanish Network of Primary Care Research, redIAPP RD06/ 0018 ; Gobierno de Aragon RD06/0018/0020 ; Bizkaya group RD06/0018/0018 ; Castilla-Leon group RD06/0018/0027 ; Mental Health Barcelona Group RD06/0018/0017 ; Mental Health, Services and Primary Care Malaga group RD06/0018/0039 ; Instituto de Salud Carlos III PI18/00238 PI18/00467 FI19/00228 ; European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future" ; Ministry of Economy and Competitiveness ; Institute of Health Carlos III fellowship IFI17/00048 ; Spanish Government BES-2017-082698 ; Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019040080-I ; Ministry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-15774 ; Andalusian Council of Health ; Andalusian Health Service (SAS) ; Primary Care Prevention and Health Promotion Research Network (redIAPP) ; Biomedical Research Institute of Malaga (IBIMA) ; Biomedical Research Centre (CIBM) from the University of Granada ; European Commission

Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response ; This research was supported by the Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I + D + I), Instituto de Salud Carlos III-Health Research Funding (FONDOS FEDER) (PI1102042, PI1102059, PI1601301 and PI1600871); by the Spanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health Products (EC10-243, EC10-056, EC10-281 and EC10-227); by the Regional Government of Andalusia ("Plan Andaluz de investigación, desarrollo e innovación (2018), P18-RT-2248") and by the ...