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Results for “Systems-level health data integration”

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PubMed2026

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗

PubMed2025

AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical symptoms. By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct a unique connectivity map spanning the microbiome, immune system and plasma metabolome in health and ME/CFS adjusted for age, gender and additional clinical factors. This map uncovers altered associations between microbial metabolism (for example, short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFN-γ and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing unique mechanisms-specifically, how multi-omics dynamics are associated to the disease's heterogeneous symptoms.

Humans↗

PubMed2026

Arrhythmia and cardiomyopathy risk in Taiwan with complementary biobank evidence on thyroid genetic susceptibility: an integrative population-based framework.

BACKGROUND: Arrhythmia-induced cardiomyopathy (AiCM) is a potentially reversible cause of ventricular dysfunction; however, only a subset of patients with arrhythmia develop cardiomyopathy. Emerging evidence suggests that endocrine factors, particularly thyroid dysfunction with genetic susceptibility, may contribute to inter-individual variability in arrhythmia-related myocardial outcomes. METHODS: We performed a dual-cohort population-based study using the National Health Insurance Research Database (NHIRD, 2000-2015) and the Taiwan Biobank (TWB). In NHIRD, we examined the association between newly diagnosed arrhythmia and incident cardiomyopathy using Cox proportional hazards models. In TWB, genome-wide data, thyroid-stimulating hormone (TSH), polygenic risk scores (PRSs), lifestyle factors, and metabolic comorbidities were analyzed using multivariable regression and interaction models to assess determinants of thyroid dysfunction. RESULTS: In the NHIRD cohort, arrhythmia was associated with a significantly increased risk of incident cardiomyopathy (adjusted hazard ratio (aHR): 2.49, 95% CI: 1.94-2.96), with atrial fibrillation showing the strongest association among arrhythmia subtypes. In the TWB cohort, a higher thyroid polygenic risk score was strongly associated with thyroid dysfunction (adjusted odds ratio (aOR): 6.64, 95% CI: 5.86-7.52). The association between genetic susceptibility and thyroid dysfunction was further modified by metabolic and lifestyle factors, including diabetes, hyperlipidemia, and dietary patterns. Genome-wide analysis identified multiple loci associated with thyroid-stimulating hormone regulation, consistent with a polygenic architecture of thyroid endocrine traits. CONCLUSION: Arrhythmia was associated with an increased risk of cardiomyopathy in a nationwide cohort, while thyroid genetic susceptibility was strongly associated with thyroid dysfunction in a biobank cohort and modified by metabolic and lifestyle factors. These findings provide complementary population-level evidence of parallel cardiovascular and endocrine-genetic associations. Because the two cohorts were not individually linked, causal inference cannot be established. The results support a systems-level framework of endocrine-cardiac interaction and suggest that integrated clinical and genetic risk assessment may help identify individuals who warrant closer monitoring.

arrhythmia↗