Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy

Varghese, Bincy; Jala, Aishwarya; Meka, Soumya; Adla, Deepthi; Jangili, Shraddha; Talukdar, Ratna Kanta; Mutheneni, Srinivasa Rao; Borkar, Roshan M.; Adela, Ramu

doi:10.1016/j.ajogmf.2022.100829

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…Another study in India with 203 pregnant women used metabolomics and ML to investigate pregnancy-related hypertension, identifying 20 altered metabolic pathways. XGBoost and decision trees were highly effective in predictions, with some models reaching up to 98.6% accuracy [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

“… Summary of the applications of AI in the risk prediction, diagnosis, management, and treatment of hypertension [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. …”

Section: Figurementioning

confidence: 99%

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Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

Alam,

Gonzalez Suarez

2024

Clinics and Practice

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This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.

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Section: Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

Alam,

Gonzalez Suarez

2024

Clinics and Practice

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“…The injection volume was 5 μL. All of the samples were kept at 4 °C during analysis. , The optimization of LC parameters and mass conditions is given in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Urinary Metabolomics Identified Metabolic Perturbations Associated with Gutka, a Smokeless Form of Tobacco

Jorvekar

Jala

Rai

et al. 2023

Chem. Res. Toxicol.

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Gutka, a form of smokeless tobacco, is widely used in the Indian subcontinent and in other regions of South Asia. Smokeless tobacco exposure is most likely to increase the incidence of oral cancer in the Indian population, and metabolic changes are a hallmark of cancer. The development of biomarkers for early detection and better prevention measures for smokeless tobacco users at risk of oral cancer can be aided by studying urinary metabolomics and offering insight into altered metabolic profiles. This study aimed to investigate urine metabolic alterations among smokeless tobacco users using targeted LC-ESI-MS/MS metabolomics approaches to better understand the effects of smokeless tobacco on human metabolism. Smokeless tobacco users’ specific urinary metabolomics signatures were extracted using univariate, multivariate analysis and machine learning methods. Statistical analysis identified 30 urine metabolites significantly associated with metabolomic alterations in humans who chew smokeless tobacco. Receiver operator characteristic (ROC) curve analysis evidenced the 5 most discriminatory metabolites from each approach that could differentiate between smokeless tobacco users and controls with higher sensitivity and specificity. An analysis of multiple-metabolite machine learning models and single-metabolite ROC curves revealed discriminatory metabolites capable of distinguishing smokeless tobacco users from nonusers more effectively with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in smokeless tobacco users, including arginine biosynthesis, beta-alanine metabolism, TCA cycle, etc. This study devised a novel strategy to identify exposure biomarkers among smokeless tobacco users by combining metabolomics and machine learning algorithms.

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Enhancing clinical decision support with explainable deep learning framework for C-section forecasting

Zafar,

Javaid,

Shaheen

et al. 2024

Computing

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Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy

Cited by 11 publications

References 34 publications

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

Urinary Metabolomics Identified Metabolic Perturbations Associated with Gutka, a Smokeless Form of Tobacco

Enhancing clinical decision support with explainable deep learning framework for C-section forecasting

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