Person: Diao, Nancy
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Diao
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Nancy
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Diao, Nancy
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Publication A prospective cohort study of the association between drinking water arsenic exposure and self-reported maternal health symptoms during pregnancy in Bangladesh(BioMed Central, 2014) Kile, Molly L; Rodrigues, Ema; Mazumdar, Maitreyi; Dobson, Christine B; Diao, Nancy; Golam, Mostofa; Quamruzzaman, Quazi; Rahman, Mahmudar; Christiani, DavidBackground: Arsenic, a common groundwater pollutant, is associated with adverse reproductive health but few studies have examined its effect on maternal health. Methods: A prospective cohort was recruited in Bangladesh from 2008–2011 (N = 1,458). At enrollment (<16 weeks gestational age [WGA]), arsenic was measured in personal drinking water using inductively-coupled plasma mass spectrometry. Questionnaires collected health data at enrollment, at 28 WGA, and within one month of delivery. Adjusted odds ratios (aORs) and 95% confidence intervals (95% CI) for self-reported health symptoms were estimated for each arsenic quartile using logistic regression. Results: Overall, the mean concentration of arsenic was 38 μg/L (Standard deviation, 92.7 μg/L). A total of 795 women reported one or more of the following symptoms during pregnancy (cold/flu/infection, nausea/vomiting, abdominal cramping, headache, vaginal bleeding, or swollen ankles). Compared to participants exposed to the lowest quartile of arsenic (≤0.9 μg/L), the aOR for reporting any symptom during pregnancy was 0.62 (95% CI = 0.44-0.88) in the second quartile, 1.83 (95% CI = 1.25-2.69) in the third quartile, and 2.11 (95% CI = 1.42-3.13) in the fourth quartile where the mean arsenic concentration in each quartile was 1.5 μg/L, 12.0 μg/L and 144.7 μg/L, respectively. Upon examining individual symptoms, only nausea/vomiting and abdominal cramping showed consistent associations with arsenic exposure. The odds of self-reported nausea/vomiting was 0.98 (95% CI: 0.68, 1.41), 1.52 (95% CI: 1.05, 2.18), and 1.81 (95% CI: 1.26, 2.60) in the second, third and fourth quartile of arsenic relative to the lowest quartile after adjusting for age, body mass index, second-hand tobacco smoke exposure, educational status, parity, anemia, ferritin, medication usage, type of sanitation at home, and household income. A positive trend was also observed for abdominal cramping (P for trend <0.0001). A marginal negative association was observed between arsenic quartiles and odds of self-reported cold/flu/infection (P for trend = 0.08). No association was observed between arsenic and self-reported headache (P for trend = 0.19). Conclusion: Moderate exposure to arsenic contaminated drinking water early in pregnancy was associated with increased odds of experiencing nausea/vomiting and abdominal cramping. Preventing exposure to arsenic contaminated drinking water during pregnancy could improve maternal health.Publication Prenatal Metals Exposure and Child Birth and Growth in Bangladesh(2015-04-24) Diao, Nancy; Christiani, David; Liang, Liming; Mazumdar, MaitreyiThe objective of this dissertation is to contribute to ongoing research on prenatal metals exposure, in terms of arsenic, lead, and manganese, and infant health and growth, and to deepen the understanding of the complexity of such problems. We seek to do so in three parts. First we examine the association between combined prenatal metals exposure and infant birth weight and head circumference. Then, we look at the effect on birth weight from the HFE gene variants and its interaction effects with arsenic. Finally, we look at the association of prenatal metals exposure and child growth up to 36 months. The study populations of all three of our studies are taken from mothers enrolled in 2 hospitals affiliated with Dhaka Community Hospital in Bangladesh. They were given self-administered questionnaires at time of enrollment and are followed after birth. Child measurements were taken at time of birth, and the biomarker for these studies are cord blood metal measurements. In the first part of this dissertation, through multivariate linear regression, including a metal interaction term, we found that prenatal arsenic and manganese exposure individually associated with lowered birth weight and birth head circumference. We also found evidence of interactions between the two metals, suggesting that joint exposure creates greater deficit in birth outcomes. In the next part, looking at gene-environment interactions, we found significant modification effects of multiple SNPs on the HFE gene that increased the association between arsenic and birth weight. We also found direct effect of less studied HFE genes to lower birth weight. Finally, we assessed the effect of prenatal metals exposure on early growth in children through longitudinal analysis. In following the weight and height of the child from birth up to 36 months of age, our results indicated adverse association between arsenic and manganese on growth.Publication Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis(Public Library of Science, 2012) Zhao, Yang; Chen, Feng; Zhai, Rihong; Lin, Xihong; Diao, Nancy; Christiani, DavidGWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.Publication Genetic Association Analysis Using Sibship Data: A Multilevel Model Approach(Public Library of Science, 2012) Zhao, Yang; Yu, Hao; Zhu, Ying; Ter-Minassian, Monica; Peng, Zhihang; Shen, Hongbing; Diao, Nancy; Chen, FFamily based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.