Associations of metals and metal mixtures with glucose homeostasis : A combined bibliometric and epidemiological study
Copyright © 2024 Elsevier B.V. All rights reserved..
This study employs a combination of bibliometric and epidemiological methodologies to investigate the relationship between metal exposure and glucose homeostasis. The bibliometric analysis quantitatively assessed this field, focusing on study design, predominant metals, analytical techniques, and citation trends. Furthermore, we analyzed cross-sectional data from Beijing, examining the associations between 14 blood metals and 6 glucose homeostasis markers using generalized linear models (GLM). Key metals were identified using LASSO-PIPs criteria, and Bayesian kernel machine regression (BKMR) was applied to assess metal mixtures, introducing an "Overall Positive/Negative Effect" concept for deeper analysis. Our findings reveal an increasing research interest, particularly in selenium, zinc, cadmium, lead, and manganese. Urine (27.6%), serum (19.0%), and whole blood (19.0%) were the primary sample types, with cross-sectional studies (49.5%) as the dominant design. Epidemiologically, significant associations were found between 9 metals-cobalt, copper, lithium, manganese, nickel, lead, selenium, vanadium, zinc-and glucose homeostasis. Notably, positive-metal mixtures exhibited a significant overall positive effect on insulin levels, and notable interactions involving nickel were identified. These finding not only map the knowledge landscape of research in this domain but also introduces a novel perspective on the analysis strategies for metal mixtures.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:470 |
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| Enthalten in: |
Journal of hazardous materials - 470(2024) vom: 15. Apr., Seite 134224 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Kai [VerfasserIn] |
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| Links: |
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| Themen: |
Bibliometric |
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| Anmerkungen: |
Date Completed 24.04.2024 Date Revised 24.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jhazmat.2024.134224 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370726049 |
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| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a This study employs a combination of bibliometric and epidemiological methodologies to investigate the relationship between metal exposure and glucose homeostasis. The bibliometric analysis quantitatively assessed this field, focusing on study design, predominant metals, analytical techniques, and citation trends. Furthermore, we analyzed cross-sectional data from Beijing, examining the associations between 14 blood metals and 6 glucose homeostasis markers using generalized linear models (GLM). Key metals were identified using LASSO-PIPs criteria, and Bayesian kernel machine regression (BKMR) was applied to assess metal mixtures, introducing an "Overall Positive/Negative Effect" concept for deeper analysis. Our findings reveal an increasing research interest, particularly in selenium, zinc, cadmium, lead, and manganese. Urine (27.6%), serum (19.0%), and whole blood (19.0%) were the primary sample types, with cross-sectional studies (49.5%) as the dominant design. Epidemiologically, significant associations were found between 9 metals-cobalt, copper, lithium, manganese, nickel, lead, selenium, vanadium, zinc-and glucose homeostasis. Notably, positive-metal mixtures exhibited a significant overall positive effect on insulin levels, and notable interactions involving nickel were identified. These finding not only map the knowledge landscape of research in this domain but also introduces a novel perspective on the analysis strategies for metal mixtures | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Bibliometric | |
| 650 | 4 | |a Cross-sectional | |
| 650 | 4 | |a Glucose homeostasis | |
| 650 | 4 | |a Metal | |
| 650 | 4 | |a Metal mixture | |
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| 650 | 7 | |a Metals |2 NLM | |
| 700 | 1 | |a Yang, Yisen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Jiaxin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Quan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yanbing |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yaoyu |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Meiduo |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Qun |e verfasserin |4 aut | |
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