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Our study aimed to assess the distribution of copper (Cu) in umbilical cord serum and estimated the association between umbilical serum Cu status and neonatal birth outcomes in a Chinese population. Through the Ma'anShan Birth Cohort Study, 2689 maternal-singleton pairs with detailed birth records and available serum samples were identified. The tertile levels of ln-transformed Cu were used to define low, medium, and high levels for serum Cu. The median for umbilical cord serum Cu was 298.2 μg/L with a range of 123.1-699.6 μg/L in this study population. Our study found a positive association between the concentration of serum Cu in the umbilical cord and the duration of gestation. Compared with medium Cu levels, we found that infants with low Cu levels had a significant higher risk of preterm birth (OR = 5.06, 95% CI 2.74, 9.34) and early-term birth (OR = 1.36, 95% CI 1.10, 1.69) in the crude model. We also found that infants with high Cu levels had a significant higher risk of late- or post-term birth (OR = 1.47, 95% CI 1.11, 1.95). A significant higher risk of preterm, early-term, and late- or post-term birth still remained, even after adjustment for potential confounding factors. Our findings suggested that both Cu deficiency and Cu overload had an adverse effect on neonatal birth outcomes.

Genome-wide association studies (GWAS) have been successfully applied in identifying single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and coronary heart disease (CAD). However, the SNPs to date can only explain a small percentage of the genetic variances of traits. Here, we applied a genetic pleiotropic conditional false discovery rate (cFDR) method that combines summary statistic p values from different multi-center GWAS datasets, to detect common genetic variants associated with these two traits. The enrichment of SNPs associated with BMI and CAD was assessed by conditional Q-Q plots and the common variants were identified by the cFDR method. By applying the cFDR level of 0.05, 7 variants were identified to be associated with CAD (2 variants being novel), 34 variants associated with BMI (11 variants being novel), and 3 variants associated with both BMI and CAD (2 variants being novel). The SNP rs653178 (ATXN2) is noteworthy as this variant was replicated in an independent analysis. SNP rs12411886 (CNNM2) and rs794356 (HIP1) were of note as the annotated genes may be associated with processes that are functionally important in lipid metabolism. In conclusion, the cFDR method identified novel variants associated with BMI and/or CAD by effectively incorporating different GWAS datasets.