Health is increasingly recognized as a dynamic state of physiological equilibrium rather than the mere absence of disease. Traditional clinical biomarkers capture only limited aspects of physiology and fail to reflect the multidimensional and dynamic nature of human homeostasis. Metabolomics, by comprehensively profiling small-molecule metabolites downstream of genetic, proteomic, environmental, and lifestyle influences, offers a sensitive and functional readout of an individual’s physiological state. This review catalogues current advances in applying metabolomics to characterize metabolic features of health, focusing on the influence of age, sex, body mass index, physical activity, diet, lifestyle behaviors, microbiome composition, and population heterogeneity. Numerous cohort studies have shown that substantial metabolic variability exists amongst individuals within apparent healthy populations, underscoring the need for stratified and contextual reference frameworks. We further discuss major challenges in defining a standardized metabolic baseline, including analytical platform heterogeneity, biofluid specificity, population diversity, and the predominance of cross-sectional study designs. Finally, we highlight the role of large-scale longitudinal cohorts, biobanks, multi-omics integration, and artificial intelligence–driven tools in overcoming these barriers. Establishing robust, dynamic, and personalized metabolic baselines will be critical for redefining health, enabling early intervention, and supporting predictive and preventive medicine.

Observational studies of the ongoing coronavirus disease 2019 (COVID-19) outbreak suggest that a cytokine storm is involved in the pathogenesis of severe illness. However, the molecular mechanisms underlying the altered pathological inflammation in COVID-19 are largely unknown. We report here that toll-like receptor (TLR) 4-mediated inflammatory signaling molecules are upregulated in peripheral blood mononuclear cells (PBMCs) from COVID-19 patients, compared with healthy controls. Among the most highly increased inflammatory mediators in severe/critically ill patients, S100A9, an alarmin and TLR4 ligand, was found as a noteworthy biomarker, because it inversely correlated with the serum albumin levels. These data support a link between TLR4 signaling and pathological inflammation during COVID-19 and contribute to develop therapeutic approaches through targeting TLR4-mediated inflammation.

Introduction: The mutated in colorectal cancer (MCC) gene was initially identified as a candidate tumor suppressor gene in colorectal cancer, acting as a negative regulator of cell cycle progression. However, its functional roles in brain tumors, particularly glioblastoma, remain largely unexplored. This study reveals a significant association between MCC status and glioblastoma.Methods: We explored MCC expression in the glioblastoma database, patient samples, and cell lines. We investigated the proliferation and migration of the cell lines in MCC gene knockdown using small interfering RNA.Results: In vitro analyses revealed elevated protein and mRNA levels of MCC in several glioblastoma cell lines (U118MG and T98G). Silencing MCC expression via siRNA-mediated knockdown resulted in increased proliferation and migration of these cell lines. Supporting these findings, analyses of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases confirmed higher MCC expression in glioblastoma tumors than in normal brain tissue. Importantly, we observed that high MCC expression was associated with poor prognosis in glioblastoma patients, highlighting its potential role in disease progression. Additionally, this study identifies a nuclear localization of MCC in the glioblastoma cell line.Discussion: These findings indicate that MCC expression is significantly upregulated in glioblastoma and may play a role in its pathophysiology, warranting further investigation.