ArticleHypertension (Dallas, Tex. : 1979)2020
Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.
Article in Hypertension (Dallas, Tex. : 1979), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 131 citations in OpenAlex.
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Metabolic syndrome and a broken heart: trust your gut or risk your heart.American journal of physiology. Heart and circulatory physiology · 2026Review
- Machine learning and experiment reveals gut microbial predictors of functional fatty acids in pork.Food chemistry: X · 2026Article
- The Microbial Mechanisms of Cardiovascular Disease: Oral Dysbiosis as a Systemic Instigator.Journal of cardiovascular translational research · 2026Review
- Deciphering gut microbial impact in coronary artery disease through multimodal computational approaches.BMC microbiology · 2025Article
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- A comprehensive narrative review on precision medicine approach to hypertension: exploring the role of genetics, epigenetics, microbiome, and artificial intelligence.Journal of health, population, and nutrition · 2025Review
- Integrated Microbiome Data Analysis Reveals Potential Pneumonia Microbial Biomarkers in ICU Patients: A Machine Learning Approach.Current microbiology · 2025Article
- Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome.BMC microbiology · 2025Article
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Gut Microbiota in Heart Failure-The Role of Inflammation.Biomedicines · 2025Review
- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
- The Gut Microbiota and Colorectal Cancer: Understanding the Link and Exploring Therapeutic Interventions.Biology · 2025Review
- Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).BMC public health · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.Computational and structural biotechnology journal · 2024Article
- Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis.JACC. Asia · 2024Article
- Gut Microbiota and Metabolic Alterations Associated with Heart Failure and Coronary Artery Disease.International journal of molecular sciences · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Methodology for biomarker discovery with reproducibility in microbiome data using machine learning.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome-based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome-based ML approach for diagnostic screening of CVD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.