Objective-Anti-cyclic citrullinated peptide (CCP) antibodies are a serological marker for rheumatoid arthritis (RA); up to 10%-15% of patients with systemic lupus erythematosus (SLE) are also positive. While anti-CCP in RA is citrulline-dependent, anti-CCP in some other diseases is citrulline-independent and reacts with both CCP and the unmodified (arginine-containing) cyclic arginine peptide (CAP). We investigated the citrulline dependence of anti-CCP and its significance in the arthritis of SLE.Methods-IgG anti-CCP was compared by ELISA to anti-CAP in sera from patients with SLE (n = 335) and RA (n = 47) and healthy controls (n = 35). SLE patients were divided into 5 groups based on their joint involvement: subset I: deforming/erosive arthritis (n = 20); II: arthritis fulfilling (or likely fulfilling) American College of Rheumatology criteria for RA but without erosions (n = 18); III: joint swelling but not fulfilling RA criteria (n = 39); IV: arthritis without documented joint swelling (n = 194); and V: no arthritis (n = 58).Results-Anti-CCP (> 1.7 units) was found in 68% (32/47) of patients with RA and 17% (55/329) of those with SLE. It was more common in SLE patients with deforming/erosive arthritis (38%). High anti-CCP (> 10 units) was found in RA (26%) and deforming/erosive SLE (12%). High anti-CCP/CAP ratios (> 2, indicating a selectivity to CCP) were found in 91% of anti-CCP-positive RA and 50% of anti-CCP-positive SLE patients with deforming/erosive arthritis. Patients from subset II did not have high anti-CCP/CAP. Conclusion-Citrulline dependence or high levels (> 10) of anti-CCP were common in SLE patients with deforming/erosive arthritis, while most anti-CCP in SLE patients was citrullineindependent. This may be useful in identifying a subset of SLE patients with high risk for development of deforming/erosive arthritis.

Blood group AB was a protective factor against GDM in pregnant Chinese women.

To investigate the possible risk factors related to macrosomia. Pregnant women and their newborns (n = 1041) were recruited from a cohort study in Maternal and Child Care Center of Hefei from January 2011 to July 2012. Questionnaires were applied to collect the demographic data besides the medical records. Detailed health records of the entire pregnancy were obtained using retrospective study. Meanwhile the data of neonatal outcomes was prospectively tracked. Associations between exposure risk factors and macrosomia were analyzed using Pearson's chi squared test. Logistic regression models were used to assess the independent association between these potential predictors and macrosomia. The incidence of macrosomia of this cohort was 11.24% of which male: female = 2.55:1. Male incidence (8.07%) of macrosomia was higher than female (3.17%), p < 0.001. Body mass index (BMI) before pregnancy (pre-BMI), maternal height, parity were not independently associated with macrosomia; multiple logistic regression analysis indicated that macrosomia was mainly independently associated with weight gain in pregnancy (OR=1.14, 95% CI [1.10-1.19]), maternal age (OR = 1.09, 95% CI [1.03-1.15]) and gestational age (OR = 1.62, 95% CI [1.31-1.99]), respectively. Our findings indicate that weight gain in pregnancy, maternal age and gestational age should be considered as independent risk factors for macrosomia.

Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.