Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resist...

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Autores: Torres-Martos, Álvaro, Anguita Ruiz, Augusto, Bustos-Aibar, Mireia, Ramírez-Mena, Alberto, Arteaga, María, Bueno, Gloria, Leis Trabazo, María Rosaura, Aguilera, Concepción M., Alcalá, Rafael, Alcalá-Fernández, Jesús
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/45281
Acceso en línea:https://hdl.handle.net/10347/45281
Access Level:acceso abierto
Palabra clave:Pediatric obesity
Insulin resistance
Epigenomics
Multiomics
Machine Learning
Explainable Artificial Intelligence
32 Ciencias médicas
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network_name_str España
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dc.title.none.fl_str_mv Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
title Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
spellingShingle Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
Torres-Martos, Álvaro
Pediatric obesity
Insulin resistance
Epigenomics
Multiomics
Machine Learning
Explainable Artificial Intelligence
32 Ciencias médicas
title_short Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
title_full Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
title_fullStr Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
title_full_unstemmed Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
title_sort Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study
dc.creator.none.fl_str_mv Torres-Martos, Álvaro
Anguita Ruiz, Augusto
Bustos-Aibar, Mireia
Ramírez-Mena, Alberto
Arteaga, María
Bueno, Gloria
Leis Trabazo, María Rosaura
Aguilera, Concepción M.
Alcalá, Rafael
Alcalá-Fernández, Jesús
author Torres-Martos, Álvaro
author_facet Torres-Martos, Álvaro
Anguita Ruiz, Augusto
Bustos-Aibar, Mireia
Ramírez-Mena, Alberto
Arteaga, María
Bueno, Gloria
Leis Trabazo, María Rosaura
Aguilera, Concepción M.
Alcalá, Rafael
Alcalá-Fernández, Jesús
author_role author
author2 Anguita Ruiz, Augusto
Bustos-Aibar, Mireia
Ramírez-Mena, Alberto
Arteaga, María
Bueno, Gloria
Leis Trabazo, María Rosaura
Aguilera, Concepción M.
Alcalá, Rafael
Alcalá-Fernández, Jesús
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Pediatric obesity
Insulin resistance
Epigenomics
Multiomics
Machine Learning
Explainable Artificial Intelligence
32 Ciencias médicas
topic Pediatric obesity
Insulin resistance
Epigenomics
Multiomics
Machine Learning
Explainable Artificial Intelligence
32 Ciencias médicas
description Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people’s lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-20
2024
2024-08-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10347/45281
url https://hdl.handle.net/10347/45281
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00711
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00563
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00924
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00988
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00028
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00129
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F01032
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00165
Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00191
European Commission http://dx.doi.org/10.13039/501100000780 Horizon Europe Framework Programme 101080219
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
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spelling Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal studyTorres-Martos, ÁlvaroAnguita Ruiz, AugustoBustos-Aibar, MireiaRamírez-Mena, AlbertoArteaga, MaríaBueno, GloriaLeis Trabazo, María RosauraAguilera, Concepción M.Alcalá, RafaelAlcalá-Fernández, JesúsPediatric obesityInsulin resistanceEpigenomicsMultiomicsMachine LearningExplainable Artificial Intelligence32 Ciencias médicasPediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people’s lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.Elsevier20242024-08-2020242024-08-20journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10347/45281reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)InglésengInstituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00711Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00563Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00924Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII) PI20%2F00988Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00028Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00129Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F01032Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00165Instituto de Salud Carlos III http://dx.doi.org/10.13039/501100004587 Plan Estatal de Investigación Científica, Técnica y de Innovación para el periodo 2021-2023 PI23%2F00191European Commission http://dx.doi.org/10.13039/501100000780 Horizon Europe Framework Programme 101080219open accesshttp://purl.org/coar/access_right/c_abf2© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/452812026-06-15T12:47:27Z
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