孕期全血金属暴露与妊娠期糖尿病风险的关联

Association between maternal blood metal exposure during pregnancy and the risk of gestational diabetes mellitus

摘要

目的 采用单一金属与混合暴露分析方法,评估孕早期全血中铅(Pb)、铜(Cu)、锌(Zn)、钙(Ca)、镁(Mg)与铁(Fe)六种金属与妊娠期糖尿病(GDM)风险的关联。方法 于2023年6月至10月在深圳市龙华区妇幼保健院纳入191例妊娠10~12⁺⁶周的孕妇。妊娠10~12⁺⁶周天采集全血样本,采用原子吸收光谱仪测定金属水平。24~28周进行75 g口服葡萄糖耐量试验(OGTT)诊断GDM。采用多变量逻辑回归评估单一金属与GDM的关联,运用贝叶斯核机器回归(BKMR)分析金属混合暴露效应。结果 191例孕妇中79例(41.4%)确诊GDM,且GDM组全血Zn水平显著高于非GDM组(Z=-2.086,P=0.038)。多变量调整后,Zn水平升高与GDM发生风险呈正相关(OR=1.975,95%CI:1.036~3.766)。BKMR分析提示金属混合暴露与GDM风险呈正向关联,Zn与GDM呈稳健正相关,而Cu表现为负相关。结论 孕早期全血金属混合暴露增加GDM发生风险,其中Zn在多模型分析中表现出稳定的正相关,而Cu可能具有一定的保护作用。

Abstract

Objective To evaluate the associations between early-pregnancy whole blood concentrations of lead (Pb),copper (Cu),zinc (Zn),calcium (Ca),magnesium (Mg),and iron (Fe) and the risk of gestational diabetes mellitus (GDM) using both single-metal and mixture-based analytical approaches. Methods A total of 191 pregnant women at 10-12⁺⁶ weeks of gestation were recruited from the Longhua District Maternal and Child Health Hospital in Shenzhen between June and October 2023.Blood samples were collected at 10-12⁺⁶ weeks of gestation,and metal concentrations were measured using atomic absorption spectrometry.GDM was diagnosed by a 75 g oral glucose tolerance test (OGTT) at 24-28 weeks of gestation.Multivariable logistic regression was applied to assess associations between individual metals and GDM risk,and Bayesian kernel machine regression (BKMR) was used to evaluate the effects of metal mixture exposure. Results Among the 191 participants,79 (41.4%) were diagnosed with GDM.Blood Zn concentrations were significantly higher in the GDM group than in the non-GDM group (Z=-2.086,P=0.038).After multivariable adjustment,higher Zn levels were positively associated with GDM risk (OR=1.975,95%CI:1.036-3.766).BKMR analysis suggested an overall positive association between metal mixture exposure and GDM risk,with Zn showing a robust positive association and Cu exhibiting an inverse association. Conclusion Early pregnancy exposure to whole-blood metal mixture exposure was associated with GDM risk.Zn demonstrated a consistent positive association across multiple models,whereas Cu may have a protective effect.

关键词

孕早期 / 妊娠期糖尿病 / 金属暴露 / 锌 / BKMR

Key words

early pregnancy / gestational diabetes mellitus / metal exposure / zinc / BKMR

参考文献

[1] Sweeting A, Wong J, Murphy H R, et al. A clinical update on gestational diabetes mellitus[J]. Endocr Rev, 2022, 43(5): 763-793.

[2] Wang H, Li N, Chivese T, et al. IDF diabetes atlas:estimation of global and regional gestational diabetes mellitus prevalence for 2021 by international association of diabetes in pregnancy study group's criteria[J]. Diabetes Res Clin Pract, 2022, 183: 109050.

[3] Storey K M, Shay L, Poteat T, et al. Pregnancy disorders and maternal consequences:maternal pre-pregnancy risks and postpartum consequences of gestational diabetes[J]. Reproduction, 2025, 169(6): e250050.

[4] Billionnet C, Mitanchez D, Weill A, et al. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012[J]. Diabetologia, 2017, 60(4): 636-644.

[5] Ye W, Luo C, Huang J, et al. Gestational diabetes mellitus and adverse pregnancy outcomes:systematic review and meta-analysis[J]. BMJ, 2022, 377: e067946.

[6] Valero P, Fuentes G, Cornejo M, et al. Exposome and foetoplacental vascular dysfunction in gestational diabetes mellitus[J]. Mol Aspects Med, 2022, 87: 101019.

[7] Wu W, Ren J, Wang J, et al. Metalloestrogens exposure and risk of gestational diabetes mellitus:evidence emerging from the systematic review and meta-analysis[J]. Environ Res, 2024, 248: 118321.

[8] Jia X, Zhang L, Zhao J, et al. Associations between endocrine-disrupting heavy metals in maternal hair and gestational diabetes mellitus:a nested case-control study in China[J]. Environ Int, 2021, 157: 106770.

[9] 向郑婷彦, 王苏苏, 颜崇淮. 环境低水平镉暴露与母婴健康危害流行病学研究进展[J]. 中国妇幼健康研究, 2024, 35(7): 61-66.

[10] Yilmaz B, Terekeci H, Sandal S, et al. Endocrine disrupting chemicals:exposure,effects on human health,mechanism of action,models for testing and strategies for prevention[J]. Rev Endocr Metab Disord, 2020, 21(1): 127-147.

[11] Predieri B, Iughetti L, Bernasconi S, et al. Endocrine disrupting chemicals' effects in children:what we know and what we need to learn?[J]. Int J Mol Sci, 2022, 23(19): 11899.

[12] Guarnotta V, Amodei R, Frasca F, et al. Impact of chemical endocrine disruptors and hormone modulators on the endocrine system[J]. Int J Mol Sci, 2022, 23(10): 5710.

[13] Eckers A, Klotz L O. Heavy metal ion-induced insulin-mimetic signaling[J]. Redox Rep, 2009, 14(4): 141-146.

[14] Tyrrell J B, Hafida S, Stemmer P, et al. Lead (Pb) exposure promotes diabetes in obese rodents[J]. J Trace Elem Med Biol, 2017, 39: 221-226.

[15] Wang B, Zhang W, Chen C, et al. Lead exposure and impaired glucose homeostasis in Chinese adults:a repeated measures study with 5 years of follow-up[J]. Ecotoxicol Environ Saf, 2022, 243: 113953.

[16] Hong H, Xu Y, Xu J, et al. Cadmium exposure impairs pancreatic beta-cell function and exaggerates diabetes by disrupting lipid metabolism[J]. Environ Int, 2021, 149: 106406.

[17] Buha A, Dukic-Cosic D, Curcic M, et al. Emerging links between cadmium exposure and insulin resistance:human,animal,and cell study data[J]. Toxics, 2020, 8(3): 63.

[18] Rahaman M S, Rahman M M, Mise N, et al. Environmental arsenic exposure and its contribution to human diseases,toxicity mechanism and management[J]. Environ Pollut, 2021, 289: 117940.

[19] Wei S, Qiu T, Yao X, et al. Arsenic induces pancreatic dysfunction and ferroptosis via mitochondrial ROS-autophagy-lysosomal pathway[J]. J Hazard Mater, 2020, 384: 121390.

[20] Behar A, Dennouni-Medjati N, Harek Y, et al. Selenium overexposure induces insulin resistance:in silico study[J]. Diabetes Metab Syndr, 2020, 14(6): 1651-1657.

[21] Steinbrenner H. Interference of selenium and selenoproteins with the insulin-regulated carbohydrate and lipid metabolism[J]. Free Radic Biol Med, 2013, 65: 1538-1547.

[22] Zhang Q, Li X, Liu X, et al. Association between maternal antimony exposure and risk of gestational diabetes mellitus:a birth cohort study[J]. Chemosphere, 2020, 246: 125732.

[23] Wang X, Gao D, Zhang G, et al. Exposure to multiple metals in early pregnancy and gestational diabetes mellitus:a prospective cohort study[J]. Environ Int, 2020, 135: 105370.

[24] He S, Jiang T, Zhang D, et al. Association of exposure to multiple heavy metals during pregnancy with the risk of gestational diabetes mellitus and insulin secretion phase after glucose stimulation[J]. Environ Res, 2024, 248: 118237.

[25] Liu P J, Ma L, Li R, et al. The association between the imbalance of single-carbon nutrients in early pregnancy and gestational diabetes mellitus risk is influenced by serum selenium status:a cohort study[J]. Diabetes Metab Syndr Obes, 2023, 16: 3275-3283.

[26] Bobb J F, Valeri L, Claus Henn B, et al. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures[J]. Biostatistics, 2015, 16(3): 493-508.

[27] Zhu G, Zheng T, Xia C, et al. Plasma levels of trace element status in early pregnancy and the risk of gestational diabetes mellitus:a nested case-control study[J]. J Trace Elem Med Biol, 2021, 68: 126829.

[28] Zhao D, Chen J, Li X, et al. A prospective study of early pregnancy metal concentrations and gestational diabetes mellitus based on a birth cohort in Northwest China[J]. BMC Pregnancy and Childbirth, 2025, 25(1): 387.

[29] Deng G, Chen H, Liu Y, et al. Combined exposure to multiple essential elements and cadmium at early pregnancy on gestational diabetes mellitus:a prospective cohort study[J]. Front Nutr, 2023, 10: 1278617.

[30] Fan J, Zhang T, Yu Y, et al. Is serum zinc status related to gestational diabetes mellitus?a meta-analysis[J]. Matern Child Nutr, 2021, 17(4): e13239.

[31] Valko M, Morris H, Cronin M T. Metals,toxicity and oxidative stress[J]. Curr Med Chem, 2005, 12(10): 1161-1208.

[32] Li L, Xu J, Zhang W, et al. Associations between multiple metals during early pregnancy and gestational diabetes mellitus under four statistical models[J]. Environ Sci Pollut Res Int, 2023, 30(43): 96689-96700.

基金资助

国家自然科学基金(82103857)

出生人口健康教育部重点实验室“本科生开放课题”(JKBK20246)

安徽省环境与人口健康重点实验室开放课题(JKYS20254)

省级大学生创新创业训练计划项目(S202410366063)

广州市南沙区民生科技项目(2024MS003)