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

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Title: Multi-metal co-exposure and type 2 diabetes: integrating biomonitoring evidence, inflammatory mechanisms, and a framework for future risk stratification.
Authors: Mishra RK; Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Vaishali, Bihar, 844102, India., Roy S; Biological Engineering, Lousiana State University, Baton Rouge, LA, USA., Mishra A; Department of Biomedical Science, Dr. B.R. Ambedkar Centre for Biomedical Research (ACBR), Delhi, India., Prakash V; Department of Endocrinology, Indira Gandhi Institute of Medical Sciences (IGIMS), Patna, Bihar, India., Shekhar R; Department of Biochemistry, Indira Gandhi Institute of Medical Sciences (IGIMS), Patna, Bihar, India., Kumar N; Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India., Murti K; Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Vaishali, Bihar, 844102, India. krishnamurti@niperhajipur.ac.in.
Source: Environmental geochemistry and health [Environ Geochem Health] 2026 Oct 04; Vol. 48 (15). Date of Electronic Publication: 2026 Oct 04.
Publication Type: Journal Article; Review
Language: English
Journal Info: Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 8903118 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2983 (Electronic) Linking ISSN: 02694042 NLM ISO Abbreviation: Environ Geochem Health Subsets: MEDLINE
Imprint Name(s): Publication: 1999- : Dordrecht : Kluwer Academic Publishers
Original Publication: Kew, Surrey : Science and Technology Letters, 1985-
MeSH Terms: Diabetes Mellitus, Type 2*/chemically induced , Diabetes Mellitus, Type 2*/epidemiology , Diabetes Mellitus, Type 2*/metabolism , Metals, Heavy*/toxicity , Metals, Heavy*/analysis , Biological Monitoring*/methods , Environmental Exposure*/adverse effects , Environmental Pollutants*/toxicity , Environmental Pollutants*/analysis, Inflammation/chemically induced ; Humans ; Risk Assessment ; Oxidative Stress
Abstract: 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.
(© 2026. The Author(s), under exclusive licence to Springer Nature B.V.)
Competing Interests: Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Use of Artificial Intelligence: The authors declare that no generative artificial intelligence (AI) tools were used to generate or write the scientific content of this manuscript. All scientific interpretation, literature synthesis, and manuscript preparation were performed by the authors. Schematic figures were created using BioRender and subsequently edited and finalized by the authors.
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Contributed Indexing: Keywords: Biomonitoring; Heavy metal mixtures; Inflammation; Mixture modelling; Oxidative stress; Risk stratification; Type 2 diabetes mellitus
Substance Nomenclature: 0 (Metals, Heavy)
0 (Environmental Pollutants)
Entry Date(s): Date Created: 20261004 Date Completed: 20261004 Latest Revision: 20261004
Update Code: 20261005
DOI: 10.1007/s10653-026-03518-9
PMID: 42829886
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Items – Name: Title
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  Data: Multi-metal co-exposure and type 2 diabetes: integrating biomonitoring evidence, inflammatory mechanisms, and a framework for future risk stratification.
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  Data: <searchLink fieldCode="AU" term="%22Mishra+RK%22">Mishra RK</searchLink>; Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Vaishali, Bihar, 844102, India.<br /><searchLink fieldCode="AU" term="%22Roy+S%22">Roy S</searchLink>; Biological Engineering, Lousiana State University, Baton Rouge, LA, USA.<br /><searchLink fieldCode="AU" term="%22Mishra+A%22">Mishra A</searchLink>; Department of Biomedical Science, Dr. B.R. Ambedkar Centre for Biomedical Research (ACBR), Delhi, India.<br /><searchLink fieldCode="AU" term="%22Prakash+V%22">Prakash V</searchLink>; Department of Endocrinology, Indira Gandhi Institute of Medical Sciences (IGIMS), Patna, Bihar, India.<br /><searchLink fieldCode="AU" term="%22Shekhar+R%22">Shekhar R</searchLink>; Department of Biochemistry, Indira Gandhi Institute of Medical Sciences (IGIMS), Patna, Bihar, India.<br /><searchLink fieldCode="AU" term="%22Kumar+N%22">Kumar N</searchLink>; Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India.<br /><searchLink fieldCode="AU" term="%22Murti+K%22">Murti K</searchLink>; Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Vaishali, Bihar, 844102, India. krishnamurti@niperhajipur.ac.in.
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  Data: <searchLink fieldCode="JN" term="%228903118%22">Environmental geochemistry and health</searchLink> [Environ Geochem Health] 2026 Oct 04; Vol. 48 (15). <i>Date of Electronic Publication: </i>2026 Oct 04.
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  Data: Journal Article; Review
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  Data: English
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Kluwer+Academic+Publishers%22">Kluwer Academic Publishers </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>8903118 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1573-2983 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2202694042%22">02694042 </searchLink><i>NLM ISO Abbreviation: </i>Environ Geochem Health <i>Subsets: </i>MEDLINE
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  Data: <i>Publication</i>: 1999- : Dordrecht : Kluwer Academic Publishers<br /><i>Original Publication</i>: Kew, Surrey : Science and Technology Letters, 1985-
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2%22">Diabetes Mellitus, Type 2*</searchLink>/<searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2+chemically+induced%22">chemically induced</searchLink> <br /><searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2%22">Diabetes Mellitus, Type 2*</searchLink>/<searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2+epidemiology%22">epidemiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2%22">Diabetes Mellitus, Type 2*</searchLink>/<searchLink fieldCode="MM" term="%22Diabetes+Mellitus%2C+Type+2+metabolism%22">metabolism</searchLink> <br /><searchLink fieldCode="MM" term="%22Metals%2C+Heavy%22">Metals, Heavy*</searchLink>/<searchLink fieldCode="MM" term="%22Metals%2C+Heavy+toxicity%22">toxicity</searchLink> <br /><searchLink fieldCode="MM" term="%22Metals%2C+Heavy%22">Metals, Heavy*</searchLink>/<searchLink fieldCode="MM" term="%22Metals%2C+Heavy+analysis%22">analysis</searchLink> <br /><searchLink fieldCode="MM" term="%22Biological+Monitoring%22">Biological Monitoring*</searchLink>/<searchLink fieldCode="MM" term="%22Biological+Monitoring+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Environmental+Exposure%22">Environmental Exposure*</searchLink>/<searchLink fieldCode="MM" term="%22Environmental+Exposure+adverse+effects%22">adverse effects</searchLink> <br /><searchLink fieldCode="MM" term="%22Environmental+Pollutants%22">Environmental Pollutants*</searchLink>/<searchLink fieldCode="MM" term="%22Environmental+Pollutants+toxicity%22">toxicity</searchLink> <br /><searchLink fieldCode="MM" term="%22Environmental+Pollutants%22">Environmental Pollutants*</searchLink>/<searchLink fieldCode="MM" term="%22Environmental+Pollutants+analysis%22">analysis</searchLink><br /><searchLink fieldCode="MH" term="%22Inflammation%22">Inflammation</searchLink>/<searchLink fieldCode="MH" term="%22Inflammation+chemically+induced%22">chemically induced</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Risk+Assessment%22">Risk Assessment</searchLink> ; <searchLink fieldCode="MH" term="%22Oxidative+Stress%22">Oxidative Stress</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.<br /> (© 2026. The Author(s), under exclusive licence to Springer Nature B.V.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Use of Artificial Intelligence: The authors declare that no generative artificial intelligence (AI) tools were used to generate or write the scientific content of this manuscript. All scientific interpretation, literature synthesis, and manuscript preparation were performed by the authors. Schematic figures were created using BioRender and subsequently edited and finalized by the authors.
– Name: Ref
  Label: References
  Group: RefInfo
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