The brain is a highly dynamic organ that requires a constant energy source to function normally. This energy is mostly supplied by glucose, a simple sugar that serves as the brain’s principal fuel source. Glucose transport across the blood–brain barrier (BBB) is primarily controlled via sodium-independent facilitated glucose transport, such as by glucose transporter 1 (GLUT1) and 3 (GLUT3). However, other glucose transporters, including GLUT4 and the sodium-dependent transporters SGLT1 and SGLT6, have been reported in vitro and in vivo. When the BBB endothelial layer is crossed, neurons and astrocytes can absorb the glucose using their GLUT1 and GLUT3 transporters. Glucose then enters the glycolytic pathway and is metabolized into adenosine triphosphate (ATP), which supplies the energy to support cellular functions. The transport and metabolism of glucose in the brain are impacted by several medical conditions, which can cause neurological and neuropsychiatric symptoms. Alzheimer’s disease (AD), Parkinson’s disease (PD), epilepsy, traumatic brain injury (TBI), schizophrenia, etc., are a few of the most prevalent disorders, characterized by a decline in brain metabolism or hypometabolism early in the course of the disease. Indeed, AD is considered a metabolic disorder related to decreased brain glucose metabolism, involving brain insulin resistance and age-dependent mitochondrial dysfunction. Although the conventional view is that reduced cerebral metabolism is an effect of neuronal loss and consequent brain atrophy, a growing body of evidence points to the opposite, where hypometabolism is prodromal or at least precedes the onset of brain atrophy and the manifestation of clinical symptoms. The underlying processes responsible for these glucose transport and metabolic abnormalities are complicated and remain poorly understood. This review article provides a comprehensive overview of the current understanding of hypometabolism in AD and potential therapeutic targets.

Environmental pollutants, lifestyle factors, and intrinsic metabolism can amplify reactive oxygen and nitrogen species (ROS/RNS) generation beyond antioxidant capacity. The resulting oxidative stress damages macromolecules, perturbs redox signaling, and may accelerate biological aging. This review synthesizes evidence published mainly in 2020–2025 on how major pollutant classes (air pollutants, metals, pesticides, nanoparticles, and micro-/nanoplastics) induce ROS through shared nodes mitochondrial electron transport disruption, NADPH oxidase activation, and redox cycling/Fenton chemistry and how these signals propagate to epigenetic remodeling (DNA methylation, histone modifications, and non-coding RNAs). To move beyond descriptive cataloging, we grade the strength of evidence by study context (cell culture, animal models, human observational studies, and clinically oriented biomarker research), highlight convergent findings and unresolved controversies, and specify key methodological limits. We then compare oxidative-stress biomarker platforms by analytical specificity, pre-analytical susceptibility, and translational readiness, distinguishing validated markers from exploratory redox-epigenetic and multi-omics signatures. Finally, we discuss how exposomics and AI-assisted multi-omics integration may support biomarker discovery while emphasizing current constraints (confounding, batch effects, and limited prospective validation) that must be addressed for clinical translation.

Environmental pollutants, lifestyle factors, and intrinsic metabolic activity converge to generate an imbalance between reactive oxygen species (ROS) production and the capacity of antioxidant defense systems. This oxidative stress damages cellular macromolecules, reprograms gene expression, and accelerates aging. Here we synthesize recent evidence (2020–2025) on the mechanisms by which air pollutants, heavy metals, pesticides, nanoparticles, and microplastics generate ROS, how redox imbalance interfaces with epigenetic regulation, and how these processes drive aging and disease. Rather than condensing, we retain full mechanistic details from multiple recent reviews to provide a comprehensive resource. We also discuss emerging biomarkers, including redox‑related metabolites and epigenetic signatures, and outline future directions for integrating multi‑omics and artificial intelligence to develop personalized interventions. Citation numbering in the text follows the order of appearance.

Biosensors are evolving from isolated analytical detectors into integrated systems for molecular diagnosis, longitudinal monitoring, dynamic tissue assessment, and clinical or preclinical decision support. This critical narrative review synthesizes literature from 2018 through July 2026 on biorecognition, biointerface engineering, transduction, wearable and microneedle architectures, CRISPR diagnostics, and sensor-integrated microphysiological systems. Unlike previous work that primarily classifies biosensors by analyte, recognition chemistry, or transduction modality, this review provides a unified translational framework that shifts evaluation from analytical sensitivity alone to the ability of systems to generate reliable, longitudinal, and clinically actionable information. A central distinction is made among snapshot assays, repeated discrete measurements, and genuine molecular trajectories, which require reversible recognition, controlled sampling, calibration, drift management, and temporal fidelity. Across applications, translational maturity depends on the measurement pathway. Continuous glucose monitoring remains the clearest benchmark because it combines durable chemistry, reproducible manufacture, workflow integration, and demonstrated clinical benefit. Other platforms have reached authentic-matrix testing, feasibility, prospective validation, or regulatory clearance but remain limited by fouling, matrix effects, calibration transfer, incomplete sample-to-answer operation, device variability, and insufficient manufacturing evidence. Future progress requires durable interfaces, claim-matched validation, reproducible scale-up, interoperable data systems, and measurements that remain trustworthy across time, users, devices, and settings.

Biosensors are evolving from isolated analytical detectors into integrated systems for molecular diagnosis, longitudinal monitoring, dynamic tissue assessment, and clinical or preclinical decision support. This critical narrative review synthesizes literature from 2018 through July 2026 on biorecognition, biointerface engineering, transduction, wearable and microneedle architectures, CRISPR diagnostics, and sensor-integrated microphysiological systems. Unlike previous work that primarily classifies biosensors by analyte, recognition chemistry, or transduction modality, this review provides a unified translational framework that shifts evaluation from analytical sensitivity alone to the ability of systems to generate reliable, longitudinal, and clinically actionable information. A central distinction is made among snapshot assays, repeated discrete measurements, and genuine molecular trajectories, which require reversible recognition, controlled sampling, calibration, drift management, and temporal fidelity. Across applications, translational maturity depends on the measurement pathway. Continuous glucose monitoring remains the clearest benchmark because it combines durable chemistry, reproducible manufacture, workflow integration, and demonstrated clinical benefit. Other platforms have reached authentic-matrix testing, feasibility, prospective validation, or regulatory clearance but remain limited by fouling, matrix effects, calibration transfer, incomplete sample-to-answer operation, device variability, and insufficient manufacturing evidence. Future progress requires durable interfaces, claim-matched validation, reproducible scale-up, interoperable data systems, and measurements that remain trustworthy across time, users, devices, and settings.