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Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization
Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic p...
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AUC Knowledge Fountain
2026
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| access_status_str | Open Access |
| author | Talal, Marwa |
| author_browse | Talal, Marwa |
| author_facet | Talal, Marwa |
| author_sort | Talal, Marwa |
| collection | Thesis |
| description | Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.
Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature importance, predictive performance, and phenotypic heterogeneity. Surrogate indices of insulin resistance and visceral adiposity were also derived.
Results: MetS was associated with widespread metabolic alterations across multiple biochemical classes, with the largest effect sizes observed for lipoprotein (LP) subfraction measures, particularly very-low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) subclass concentrations and particle diameters. LP metrics consistently outperformed other metabolite classes in distinguishing MetS from nonMetS, both in univariate and multivariate models, and retained predictive value when considered independently of diagnostic criteria. LP-based models also identified metabolic disturbances in individuals not meeting conventional MetS thresholds, suggesting utility for early or subclinical risk detection. Subtype analyses revealed distinct metabolic patterns across different combinations of diagnostic criteria, highlighting phenotypic heterogeneity within MetS.
Conclusions: This large-scale integrative analysis demonstrates that detailed LP subfraction profiling provides strong, independent discriminatory power for MetS classification and may capture early metabolic risk overlooked by standard diagnostic thresholds. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3665 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:59.828Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3665 Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization Talal, Marwa Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome. Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature importance, predictive performance, and phenotypic heterogeneity. Surrogate indices of insulin resistance and visceral adiposity were also derived. Results: MetS was associated with widespread metabolic alterations across multiple biochemical classes, with the largest effect sizes observed for lipoprotein (LP) subfraction measures, particularly very-low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) subclass concentrations and particle diameters. LP metrics consistently outperformed other metabolite classes in distinguishing MetS from nonMetS, both in univariate and multivariate models, and retained predictive value when considered independently of diagnostic criteria. LP-based models also identified metabolic disturbances in individuals not meeting conventional MetS thresholds, suggesting utility for early or subclinical risk detection. Subtype analyses revealed distinct metabolic patterns across different combinations of diagnostic criteria, highlighting phenotypic heterogeneity within MetS. Conclusions: This large-scale integrative analysis demonstrates that detailed LP subfraction profiling provides strong, independent discriminatory power for MetS classification and may capture early metabolic risk overlooked by standard diagnostic thresholds. 2026-01-01T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2607 https://fount.aucegypt.edu/context/etds/article/3665/viewcontent/Metabolic_Syndrome_Beyond_Diagnostic_Criteria_Population_Scale_Integrative_Metabolomics_Characterization.pdf https://fount.aucegypt.edu/context/etds/article/3665/filename/4/type/additional/viewcontent/Turnitin_Report_Metabolic_Syndrome_Beyond_Diagnostic_Criteria__Population_Scale_Integrative_Metabolomics.pdf Theses and Dissertations AUC Knowledge Fountain Metabolomics Metabolic Syndrome Machine Learning Modeling Diagnostics Biomarkers UK Biobank Elastic Net Lipoprotein Subfractions Biochemical Phenomena, Metabolism, and Nutrition Bioinformatics Biomedical Informatics Biotechnology Cardiovascular Diseases Computational Biology Diagnosis Nutritional and Metabolic Diseases |
| spellingShingle | Metabolomics Metabolic Syndrome Machine Learning Modeling Diagnostics Biomarkers UK Biobank Elastic Net Lipoprotein Subfractions Biochemical Phenomena, Metabolism, and Nutrition Bioinformatics Biomedical Informatics Biotechnology Cardiovascular Diseases Computational Biology Diagnosis Nutritional and Metabolic Diseases Talal, Marwa Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title | Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title_full | Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title_fullStr | Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title_full_unstemmed | Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title_short | Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization |
| title_sort | metabolic syndrome beyond diagnostic criteria population scale integrative metabolomics characterization |
| topic | Metabolomics Metabolic Syndrome Machine Learning Modeling Diagnostics Biomarkers UK Biobank Elastic Net Lipoprotein Subfractions Biochemical Phenomena, Metabolism, and Nutrition Bioinformatics Biomedical Informatics Biotechnology Cardiovascular Diseases Computational Biology Diagnosis Nutritional and Metabolic Diseases |
| url | https://fount.aucegypt.edu/etds/2607 https://fount.aucegypt.edu/context/etds/article/3665/viewcontent/Metabolic_Syndrome_Beyond_Diagnostic_Criteria_Population_Scale_Integrative_Metabolomics_Characterization.pdf https://fount.aucegypt.edu/context/etds/article/3665/filename/4/type/additional/viewcontent/Turnitin_Report_Metabolic_Syndrome_Beyond_Diagnostic_Criteria__Population_Scale_Integrative_Metabolomics.pdf |
| work_keys_str_mv | AT talalmarwa metabolicsyndromebeyonddiagnosticcriteriapopulationscaleintegrativemetabolomicscharacterization |