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BackgroundEnvironmental pollutant exposure may play certain roles in the pathogenesis and progression of diabetes mellitus including gestational diabetes mellitus (GDM). We hypothesize that heavy metal exposure may trigger GDM during pregnancy. The objective of this study was to investigate the possible associations between selected heavy metal exposure and GDM risk.MethodsThis investigation is a retrospective case–control study nested within a cohort of 1359 pregnant women. These participants were recruited in Xiamen Maternity and Child Care Hospital, China, during June to July, 2012. All their newborns’ meconium samples were collected. By reviewing the antenatal care records, 166 GDM mothers were screened out from the 1359 participants; 137 of 166 GDM mothers offered their newborns’ meconium samples for the metal analysis. Those 137 mothers were set as the case group. Similarly, 294 healthy mothers without any gestational complication were initially screened out from the rest 1193 non-GDM mothers. 190 of the 294 healthy mothers offered their newborns’ meconium samples for the metal analysis. Those 190 mothers were set as the control group. Arsenic (As), mercury (Hg), lead (Pb), cadmium (Cd), and chromium (Cr) levels in these case–control meconium samples were measured by inductively coupled plasma mass spectrometry. The possible association between the metal levels and maternal GDM risk of studied subjects was assessed by binary logistic regression.ResultsGDM prevalence of 12.21% was observed in the investigated 1359 participants. The concentrations of As, Hg, Cr and Cd in studied cases were significantly higher (p < 0.05) than those of controls. After adjustments for maternal age, pre-pregnant body mass index, gravidity, parity, hepatitis B virus infection, and newborn sex, As, Cd and Cr were found to be positively associated with GDM prevalence in dose-dependent manners. Among them, As was detected in all samples and its levels associated the maternal GDM with the adjusted odds ratios of 3.28 [95% CI 1.24, 8.71], 3.35 [95% CI 1.28, 8.75] and 5.25 [95% CI 1.99, 13.86] for the 2nd, 3rd and 4th quartiles, respectively.ConclusionsThe present work implies that exposure to some of the selected metals (noticeably As) may contribute to maternal GDM risk during pregnancy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-015-0004-0) contains supplementary material, which is available to authorized users.

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Exposure to airborne particulate matter (PM)2.5, a PM with aerodynamic diameter of less than 2.5 µm, is known to be associated with a variety of adverse health effects. However, the molecular mechanisms involved in fine PM toxicity are still not well characterized. The present study aims to provide new insights into the cytotoxicity of PM2.5 on human lung epithelial cells (A549) at the proteomic level. Two-dimensional difference gel electrophoresis revealed a total of 27 protein spots, whose abundance were significantly altered in A549 cells exposed to water-soluble PM2.5 extracts (WSPE). Among these, 12 spots were upregulated while 15 were downregulated. Twenty-two proteins were further identified by matrix-assisted laser desorption/ionization time-of-flight tandem mass/mass spectrometry and database search. The results revealed that oxidative stress, metabolic disturbance, dysregulation of signal transduction, aberrant protein synthesis and degradation, as well as cytoskeleton disorganization are major factors contributing to WSPE-mediated toxicity in human lung cells. It is further proposed that induction of apoptosis through p53, c-Myc and p21 pathways may be one of the key toxicological events occurred in A549 cells under WSPE stress. The data obtained here will aid our understanding of the toxic mechanisms related to PM2.5, and develop useful biomarkers indicative of inhalable PM2.5 exposure.

Abstract:The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.

Recently, the number of women suffering from gestational diabetes mellitus (GDM) has risen dramatically. GDM attracts increasing attention due to its potential harm to the heath of both the fetus and the mother. We designed this case-control study to investigate the metabolome response of newborn meconium and urine to maternal GDM. GDM mothers (n = 142) and healthy controls (n = 197) were recruited during June-July 2012 in Xiamen, China. The newborns' metabolic profiles were acquired using liquid chromatography coupled to mass spectrometry. The data showed that meconium and urine metabolome patterns clearly discriminated GDM cases from controls. Fourteen meconium metabolic biomarkers and three urinary metabolic biomarkers were tentatively identified for GDM. Altered levels of various endogenous biomarkers revealed that GDM may induce disruptions in lipid metabolism, amino acid metabolism, and purine metabolism. An unbalanced lipid pattern is suspected to be a GDM-specific feature. Furthermore, the relationships between the potential biomarkers and GDM risk were evaluated by binary logistic regression and receiver operating characteristic analysis. A combined model of nine meconium biomarkers showed a great potential in diagnosing GDM-induced disorders.

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