Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease
IntroductionSubstance use disorders (SUDs) are heterogeneous diseases with overlapping biological mechanisms and often present with co-occurring disease, such as cardiovascular disease (CVD). Gene networks associated with SUDs also implicate additional biological pathways and may be used to stratify...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1572243/full |
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| author | Everest Castaneda Everest Castaneda Elissa Chesler Erich Baker |
| author_facet | Everest Castaneda Everest Castaneda Elissa Chesler Erich Baker |
| author_sort | Everest Castaneda |
| collection | DOAJ |
| description | IntroductionSubstance use disorders (SUDs) are heterogeneous diseases with overlapping biological mechanisms and often present with co-occurring disease, such as cardiovascular disease (CVD). Gene networks associated with SUDs also implicate additional biological pathways and may be used to stratify disease subtypes. Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. Comparatively, the graph spectrum's use of comprehensive information facilitates hypothesis testing and inter-disease clustering by using a larger range of graph characteristics. By adding a connectivity-based method, network rankings of similarity and relationships are explored between classes of SUDs and CVD.MethodsGraph spectral clustering's utility is evaluated relative to commonly used network algorithms for discernment between two distinct co-occurring disorders and capacity to rank pathways based on their distinctiveness. A collection of graphs' structures and connectivity to functionally identify the relationship between CVD and each of four classes of SUDs, namely alcohol use disorder (AUD), cocaine use disorder (CUD), nicotine use disorder (NUD), and opioid use disorder (OUD) is evaluated. Moreover, a Kullback-Leibler (KL) divergence is implemented to identify maximally distinctive genes (Dg). The emphasis of genes with high Dg enables a Jaccard similarity ranking of pathway distinctiveness, creating a functional “network fingerprint”.ResultsSpectral graph outperforms other connectivity-based approaches and reveals interesting observations about the relationship among SUDs. Between CUD and CVD, the gamma-aminobutyric acidergic and arginine metabolism pathways are distinctive. The neurodegenerative prion disease and tyrosine metabolism are emphasized between OUD and CVD. The graph spectrum between AUD and NUD to CVD is not significantly divergent.ConclusionGraph spectral clustering with KL divergence illustrates differences among SUDs with respect to their relationship to CVD, suggesting that despite a high-level co-occurring diagnosis or comorbidity, the nature of the relationship between SUD and CVD varies depending on the substance involved. The graph clustering method simultaneously provides insight into the specific biological pathways underlying these distinctions and may reveal future basic and clinical research avenues into addressing the cardiovascular sequelae of SUD. |
| format | Article |
| id | doaj-art-88a1434f90f748eb9ebc49d4c3e2e620 |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-88a1434f90f748eb9ebc49d4c3e2e6202025-08-20T02:41:26ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-07-011910.3389/fnins.2025.15722431572243Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular diseaseEverest Castaneda0Everest Castaneda1Elissa Chesler2Erich Baker3Department of Biology, Baylor University, Waco, TX, United StatesSchool of Engineering and Computer Science, Baylor University, Waco, TX, United StatesThe Jackson Laboratory, Bar Harbor, ME, United StatesDepartment of Mathematics and Computer Science, Belmont University, Nashville, TN, United StatesIntroductionSubstance use disorders (SUDs) are heterogeneous diseases with overlapping biological mechanisms and often present with co-occurring disease, such as cardiovascular disease (CVD). Gene networks associated with SUDs also implicate additional biological pathways and may be used to stratify disease subtypes. Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. Comparatively, the graph spectrum's use of comprehensive information facilitates hypothesis testing and inter-disease clustering by using a larger range of graph characteristics. By adding a connectivity-based method, network rankings of similarity and relationships are explored between classes of SUDs and CVD.MethodsGraph spectral clustering's utility is evaluated relative to commonly used network algorithms for discernment between two distinct co-occurring disorders and capacity to rank pathways based on their distinctiveness. A collection of graphs' structures and connectivity to functionally identify the relationship between CVD and each of four classes of SUDs, namely alcohol use disorder (AUD), cocaine use disorder (CUD), nicotine use disorder (NUD), and opioid use disorder (OUD) is evaluated. Moreover, a Kullback-Leibler (KL) divergence is implemented to identify maximally distinctive genes (Dg). The emphasis of genes with high Dg enables a Jaccard similarity ranking of pathway distinctiveness, creating a functional “network fingerprint”.ResultsSpectral graph outperforms other connectivity-based approaches and reveals interesting observations about the relationship among SUDs. Between CUD and CVD, the gamma-aminobutyric acidergic and arginine metabolism pathways are distinctive. The neurodegenerative prion disease and tyrosine metabolism are emphasized between OUD and CVD. The graph spectrum between AUD and NUD to CVD is not significantly divergent.ConclusionGraph spectral clustering with KL divergence illustrates differences among SUDs with respect to their relationship to CVD, suggesting that despite a high-level co-occurring diagnosis or comorbidity, the nature of the relationship between SUD and CVD varies depending on the substance involved. The graph clustering method simultaneously provides insight into the specific biological pathways underlying these distinctions and may reveal future basic and clinical research avenues into addressing the cardiovascular sequelae of SUD.https://www.frontiersin.org/articles/10.3389/fnins.2025.1572243/fulldisease-associated prioritizationsubstance use disordercardiovascular diseasegraph spectrumfunctional fingerprint |
| spellingShingle | Everest Castaneda Everest Castaneda Elissa Chesler Erich Baker Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease Frontiers in Neuroscience disease-associated prioritization substance use disorder cardiovascular disease graph spectrum functional fingerprint |
| title | Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease |
| title_full | Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease |
| title_fullStr | Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease |
| title_full_unstemmed | Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease |
| title_short | Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease |
| title_sort | spectral divergence prioritizes key classes genes and pathways shared between substance use disorders and cardiovascular disease |
| topic | disease-associated prioritization substance use disorder cardiovascular disease graph spectrum functional fingerprint |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1572243/full |
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