Multi-metal co-exposure and type 2 diabetes: integrating biomonitoring evidence, inflammatory mechanisms, and a framework for future risk stratification.

Diabetes Mellitus Type 2 prevalence is still increasing around the world, and there is growing evidence of the role of environmental co-exposures to heavy metals-cadmium, lead, arsenic, mercury, and chromium-in glycaemia impairment. Contrarily to studies on single metal toxicology, real-life situations consist in exposure to mixtures of heavy metals through environmental, occupational and dietary pathways, especially in places like the Indo-Gangetic plain of India, characterized by the presence of geological arsenic pollution in association with metals exposure. This paper provides a narrative review on the current state of art in the domain of three key issues connected to risk stratification in Type 2 Diabetes Mellitus: methods for biomonitoring metals, inflammatory and molecular pathways involved in diabetes onset and progression related to metal exposures and statistical approaches for modeling joint effects of mixtures. Firstly, discuss on the biomonitoring methods, with particular emphasis to blood, urine and hair and nail as classical matrices and new emerging matrices (saliva, buccal cells). The analytical technique (AAS vs ICP-MS) is considered as well. It will focus on the heterogeneity between different methodologies, including matrix-specific exposure window and inter-laboratory variability. Regarding mechanistic aspects, there is an agreement in scientific literature on several molecular pathways involved in glycaemia alterations, including oxidative stress, NF-κB inflammation pathway, mitochondria dysfunction, epigenetics alteration and insulin signaling impairment, with possible synergistic rather than additive action of heavy metals in the impairment of beta-cell function and insulin resistance. Finally, it discusses some recent advances in quantifying joint metal effects on diabetes through specific statistics as WQS regression and Bayesian Kernel Machine regression (BKMR) and quantile g-computation. Even though important methodological advances have been made, a translational gap remains: no validated procedure currently exists that integrates exposure metrics, inflammatory biomarkers and clinical variables for individual risk stratification. We therefore outline a research-stage framework that could guide future development of such tools.