Identification of novel hypertension biomarkers using explainable AI and metabolomics
<h3>Background</h3><p dir="ltr">The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization’s Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513540398776320 |
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| author | Karthik Sekaran (16845959) |
| author2 | Hatem Zayed (835448) |
| author2_role | author |
| author_facet | Karthik Sekaran (16845959) Hatem Zayed (835448) |
| author_role | author |
| dc.creator.none.fl_str_mv | Karthik Sekaran (16845959) Hatem Zayed (835448) |
| dc.date.none.fl_str_mv | 2024-11-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11306-024-02182-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Identification_of_novel_hypertension_biomarkers_using_explainable_AI_and_metabolomics/30094309 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Medical biochemistry and metabolomics Health sciences Health services and systems Biomarkers Explainable artificial intelligence Hypertension Metabolomics Qatar Precision Health Institute-Qatar Biobank Shapley additive explanations Vanillylmandelic acid |
| dc.title.none.fl_str_mv | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization’s Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is a significant public health concern that can lead to serious health complications if left untreated. Metabolic dysfunction is a primary cause of hypertension. By studying key biomarkers, we can discover new treatments to improve the lives of those with high blood pressure.</p><h3>Aims</h3><p dir="ltr">This study aims to use explainable artificial intelligence (XAI) to interpret novel metabolite biosignatures linked to hypertension in Qatari Population.</p><h3>Methods</h3><p dir="ltr">The study utilized liquid chromatography-mass spectrometry (LC/MS) method to profile metabolites from biosamples of Qatari nationals diagnosed with stage 1 hypertension (n = 224) and controls (n = 554). Metabolon platform was used for the annotation of raw metabolite data generated during the process. A comprehensive series of analytical procedures, including data trimming, imputation, undersampling, feature selection, and biomarker discovery through explainable AI (XAI) models, were meticulously executed to ensure the accuracy and reliability of the results.</p><h3>Results</h3><p dir="ltr">Elevated Vanillylmandelic acid (VMA) levels are markedly associated with stage 1 hypertension compared to controls. Glycerophosphorylcholine (GPC), N-Stearoylsphingosine (d18:1/18:0)*, and glycine are critical metabolites for accurate hypertension prediction. The light gradient boosting model yielded superior results, underscoring the potential of our research in enhancing hypertension diagnosis and treatment. The model’s classification metrics: accuracy (78.13%), precision (78.13%), recall (78.13%), F1-score (78.13%), and AUROC (83.88%) affirm its efficacy. SHapley Additive exPlanations (SHAP) further elucidate the metabolite markers, providing a deeper understanding of the disease’s pathology.</p><h3>Conclusion</h3><p dir="ltr">This study identified novel metabolite biomarkers for precise hypertension diagnosis using XAI, enhancing early detection and intervention in the Qatari population.</p><h2>Other Information</h2><p dir="ltr">Published in: Metabolomics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11306-024-02182-3" target="_blank">https://dx.doi.org/10.1007/s11306-024-02182-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_828d7b44c7eaa6ecf139790f9845cd99 |
| identifier_str_mv | 10.1007/s11306-024-02182-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30094309 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Identification of novel hypertension biomarkers using explainable AI and metabolomicsKarthik Sekaran (16845959)Hatem Zayed (835448)Biomedical and clinical sciencesCardiovascular medicine and haematologyMedical biochemistry and metabolomicsHealth sciencesHealth services and systemsBiomarkersExplainable artificial intelligenceHypertensionMetabolomicsQatar Precision Health Institute-Qatar BiobankShapley additive explanationsVanillylmandelic acid<h3>Background</h3><p dir="ltr">The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization’s Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is a significant public health concern that can lead to serious health complications if left untreated. Metabolic dysfunction is a primary cause of hypertension. By studying key biomarkers, we can discover new treatments to improve the lives of those with high blood pressure.</p><h3>Aims</h3><p dir="ltr">This study aims to use explainable artificial intelligence (XAI) to interpret novel metabolite biosignatures linked to hypertension in Qatari Population.</p><h3>Methods</h3><p dir="ltr">The study utilized liquid chromatography-mass spectrometry (LC/MS) method to profile metabolites from biosamples of Qatari nationals diagnosed with stage 1 hypertension (n = 224) and controls (n = 554). Metabolon platform was used for the annotation of raw metabolite data generated during the process. A comprehensive series of analytical procedures, including data trimming, imputation, undersampling, feature selection, and biomarker discovery through explainable AI (XAI) models, were meticulously executed to ensure the accuracy and reliability of the results.</p><h3>Results</h3><p dir="ltr">Elevated Vanillylmandelic acid (VMA) levels are markedly associated with stage 1 hypertension compared to controls. Glycerophosphorylcholine (GPC), N-Stearoylsphingosine (d18:1/18:0)*, and glycine are critical metabolites for accurate hypertension prediction. The light gradient boosting model yielded superior results, underscoring the potential of our research in enhancing hypertension diagnosis and treatment. The model’s classification metrics: accuracy (78.13%), precision (78.13%), recall (78.13%), F1-score (78.13%), and AUROC (83.88%) affirm its efficacy. SHapley Additive exPlanations (SHAP) further elucidate the metabolite markers, providing a deeper understanding of the disease’s pathology.</p><h3>Conclusion</h3><p dir="ltr">This study identified novel metabolite biomarkers for precise hypertension diagnosis using XAI, enhancing early detection and intervention in the Qatari population.</p><h2>Other Information</h2><p dir="ltr">Published in: Metabolomics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11306-024-02182-3" target="_blank">https://dx.doi.org/10.1007/s11306-024-02182-3</a></p>2024-11-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11306-024-02182-3https://figshare.com/articles/journal_contribution/Identification_of_novel_hypertension_biomarkers_using_explainable_AI_and_metabolomics/30094309CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300943092024-11-03T03:00:00Z |
| spellingShingle | Identification of novel hypertension biomarkers using explainable AI and metabolomics Karthik Sekaran (16845959) Biomedical and clinical sciences Cardiovascular medicine and haematology Medical biochemistry and metabolomics Health sciences Health services and systems Biomarkers Explainable artificial intelligence Hypertension Metabolomics Qatar Precision Health Institute-Qatar Biobank Shapley additive explanations Vanillylmandelic acid |
| status_str | publishedVersion |
| title | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| title_full | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| title_fullStr | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| title_full_unstemmed | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| title_short | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| title_sort | Identification of novel hypertension biomarkers using explainable AI and metabolomics |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Medical biochemistry and metabolomics Health sciences Health services and systems Biomarkers Explainable artificial intelligence Hypertension Metabolomics Qatar Precision Health Institute-Qatar Biobank Shapley additive explanations Vanillylmandelic acid |