Background

Epidemiologic studies on associations between metals and insulin resistance and β-cell dysfunction have been cross-sectional and focused on individual metals.

Objective

We assessed the association of exposure to metal mixtures, based on assessment of 15 urinary metals, with both baseline levels and longitudinal changes in homeostatic model assessments for insulin resistance (HOMA-IR) and β-cell function (HOMA-β).

Methods

We examined 1262 women, aged 45–56 years at baseline (1999–2000), who were followed through 2015–2016, from the Study of Women’s Health Across the Nation. Urinary concentrations of 15 metals (arsenic, barium, cadmium, cobalt, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, and zinc) were determined at baseline. HOMA-IR and HOMA-β were repeatedly measured over 16 years of follow-up. A two-stage modeling was used to account for correlations in dependent and independent variables: In stage-1, linear mixed effects models were used to estimate the participant-specific baseline HOMA levels from random intercepts and participant-specific rates of changes from random slopes. In stage-2, adaptive elastic-net (AENET) models were fit to identify components of metal mixtures associated with participant-specific baseline levels and rates of changes in HOMA-IR and HOMA-β, respectively. An environmental risk score (ERS) was used to integrate metal mixture effects from AENET results.

Results

In multivariable adjusted AENET models, urinary zinc was associated with higher HOMA-IR at baseline, whereas molybdenum was associated with lower HOMA-IR at baseline. The estimated changes in baseline HOMA-IR for one standard deviation increase in log-transformed urinary metal concentrations were 5.76% (3.05%, 8.55%) for zinc and −3.25% (−5.45%, −1.00%) for molybdenum, respectively. Urinary zinc was also associated with lower HOMA- β at baseline. Arsenic was associated with a slightly faster rate of decline in HOMA-β in the AENET model evaluating associations between metals and rate of changes. Significant associations of ERS with both HOMA-IR and HOMA-β at baseline were observed. ERS for the rate of changes was not calculated and examined in relation to rates of changes in HOMA-IR and HOMA-β because only a single metal was selected by AENET.

Conclusion

Exposure to metal mixtures may be exerting effects on insulin resistance and β-cell dysfunction, which might be mechanisms by which metal exposures lead to elevated diabetes risks.

中文翻译：

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尿液金属混合物和葡萄糖稳态的纵向变化：全国妇女健康研究 (SWAN)。

背景

关于金属与胰岛素抵抗和 β 细胞功能障碍之间关联的流行病学研究是横断面的，并集中在单个金属上。

客观的

我们基于对 15 种尿金属的评估，评估了暴露于金属混合物与胰岛素抵抗 (HOMA-IR) 和 β 细胞功能 (HOMA-β) 稳态模型评估的基线水平和纵向变化的关联。

方法

我们检查了来自全国妇女健康研究的 1262 名女性，基线年龄为 45-56 岁（1999-2000 年），这些女性在 2015-2016 年期间被跟踪。在基线测定了 15 种金属（砷、钡、镉、钴、铯、铜、汞、锰、钼、镍、铅、锑、锡、铊和锌）的尿液浓度。在 16 年的随访中反复测量 HOMA-IR 和 HOMA-β。使用两阶段建模来解释因变量和自变量的相关性：在第一阶段，线性混合效应模型用于从随机截距估计参与者特定的基线 HOMA 水平和随机斜率的参与者特定变化率. 在第二阶段，自适应弹性网 (AENET) 模型适用于识别与参与者特定基线水平和 HOMA-IR 和 HOMA-β 变化率相关的金属混合物成分。环境风险评分 (ERS) 用于整合来自 AENET 结果的金属混合物效应。

结果

在多变量调整的 AENET 模型中，尿锌与基线时较高的 HOMA-IR 相关，而钼与基线时较低的 HOMA-IR 相关。对于对数转换的尿金属浓度增加一个标准差，基线 HOMA-IR 的估计变化分别为锌的 5.76%（3.05%、8.55%）和钼的 -3.25%（-5.45%、-1.00%）。尿锌也与基线时较低的 HOMA-β 相关。在评估金属与变化率之间关联的 AENET 模型中，砷与 HOMA-β 下降速度稍快有关。观察到 ERS ​​与基线时的 HOMA-IR 和 HOMA-β 显着相关。因为 AENET 只选择了一种金属，所以没有计算和检查与 HOMA-IR 和 HOMA-β 的变化率相关的变化率的 ERS。

结论

接触金属混合物可能会对胰岛素抵抗和β细胞功能障碍产生影响，这可能是金属接触导致糖尿病风险升高的机制。