Cadmium exposure and the risk of GDM: evidence emerging from the systematic review and meta-analysis

Metalloestrogens exposure and risk of gestational diabetes mellitus: Evidence emerging from the systematic review and meta-analysis

BACKGROUND

Metalloestrogens are metals and metalloid elements with estrogenic activity found everywhere. Their impact on human health is becoming more apparent as human activities increase.

OBJECTIVE

Our aim is to conduct a comprehensive systematic review and meta-analysis of observational studies exploring the correlation between metalloestrogens (specifically As, Sb, Cr, Cd, Cu, Se, Hg) and Gestational Diabetes Mellitus (GDM).

METHODS

PubMed, Web of Science, and Embase were searched to examine the link between metalloestrogens (As, Sb, Cr, Cd, Cu, Se, and Hg) and GDM until December 2023. Risk estimates were derived using random effects models. Subgroup analyses were conducted based on study countries, exposure sample, exposure assessment method, and detection methods. Sensitivity analyses and adjustments for publication bias were carried out to assess the strength of the findings.

RESULTS

Out of the 389 articles identified initially, 350 met our criteria and 33 were included in the meta-analysis, involving 141,175 subjects (9450 cases, 131,725 controls). Arsenic, antimony, and copper exposure exhibited a potential increase in GDM risk to some extent (As: OR = 1.28, 95 % CI [1.08, 1.52]; Sb: OR = 1.73, 95 % CI [1.13, 2.65]; Cu: OR = 1.29, 95 % CI [1.02, 1.63]), although there is a high degree of heterogeneity (As: Q = 52.93, p < 0.05, I2 = 64.1 %; Sb: Q = 31.40, p < 0.05, I2 = 80.9 %; Cu: Q = 21.14, p < 0.05, I2 = 71.6 %). Conversely, selenium, cadmium, chromium, and mercury exposure did not exhibit any association with the risk of GDM in our study.

DISCUSSION

Our research indicates that the existence of harmful metalloestrogens in the surroundings has a notable effect on the likelihood of GDM. Hence, we stress the significance of environmental elements in the development of GDM and the pressing need for relevant policies and measures.

Correlation between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus in different geographical regions: a meta-analysis

BACKGROUND

The association between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus (GDM) has been explored in diverse populations across different geographical regions. Yet, most of these studies have been confined to a limited number of loci, resulting in inconsistent findings. In this study, we conducted a comprehensive review of published literature to identify studies examining the relationship between TCF7L2 and CAPN10 gene polymorphisms and the incidence of GDM in various populations. We specifically focused on five loci that were extensively reported in a large number of publications and performed a meta-analysis.

METHODS

We prioritized the selection of SNPs with well-documented correlations established in existing literature on GDM. We searched eight Chinese and English databases: Cochrane, Elton B. Stephens. Company (EBSCO), Embase, Scopus, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database and retrieved all relevant articles published between the inception of the database and July 2022. The Newcastle Ottawa Scale (NOS) was used to evaluate the selected articles, and the odds ratio (OR) was used as the combined effect size index to determine the association between genotypes, alleles, and GDM using different genetic models. Heterogeneity between the studies was quantified and the I2 value calculated. Due to large heterogeneities between different ethnic groups, subgroup analysis was used to explore the correlation between genetic polymorphisms and the incidence of GDM in the different populations. The stability of the results was assessed using sensitivity analysis. Begg's and Egger's tests were used to assess publication bias.

RESULTS

A total of 39 articles reporting data on 8,795 cases and 16,290 controls were included in the analysis. The frequency of the rs7901695 genotype was statistically significant between cases and controls in the European population (OR = 0.72, 95% CI: 0.65-0.86) and the American population (OR = 0.61, 95% CI: 0.48-0.77). The frequencies of rs12255372, rs7901695, rs290487, and rs2975760 alleles were also considerably different between the cases and controls in the populations analyzed.

CONCLUSIONS

rs7903146, rs12255372, rs7901695, rs290487, and rs2975760 were associated with the incidence of GDM in different populations.

Recent advances in the application of ionomics in metabolic diseases

Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.