ReviewExpert review of molecular diagnostics2026
Integrated proteomics and metabolomics for predicting cardiometabolic risk in pregnant women living with HIV: current evidence and future directions.
Review in Expert review of molecular diagnostics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
introductionPregnant women living with HIV (PWLWH) experience cardiometabolic risk across pregnancy and postpartum, including hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), dyslipidemia, obesity, and later metabolic disease. This heterogenous biology reflects gestational adaptation, HIV-associated immune adaptation, antiretroviral therapy (ART), placental function, and underlying cardiometabolic susceptibility. Integrated proteomic-metabolomic prediction studies in PWLWH remain scarce, positioning current evidence as a foundation for biomarker discovery and prospective validation. AREAS COVERED: This review evaluates molecular and biomarker evidence from PWLWH alongside broader pregnancy literature informing proteomic and metabolomic discovery. Approaches include mass-spectrometry-based proteomics and metabolomics, affinity-based platforms including Olink and SomaScan, nuclear magnetic resonance, targeted assays, and multi-omics integration using regression, network, machine learning, and latent-factor methods. Emphasis is placed on inflammatory, endothelial, angiogenic, lipid, amino-acid, mitochondrial, and immune-metabolic pathways relevant to HDP, GDM, dyslipidemia, and postpartum cardiometabolic risk. EXPERT OPINION: Integrated proteomics and metabolomics offer near-term value for mechanistic discovery and reproducible molecular risk signatures. Translation requires longitudinal cohorts, measured ART exposure, standardized pre-analytics, independent validation, and improvement beyond clinical risk factors. High burden settings additionally require resource-appropriate sampling, simplified targeted assays, local laboratory capacity, sustainable bioinformatics, demonstrable clinical utility, and scalable translational strategies.
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