The human gut microbiome can modulate metabolic health and affect insulin resistance, and it may play an important role in the etiology of gestational diabetes mellitus (GDM). Here, we compared the gut microbial composition of 43 GDM patients and 81 healthy pregnant women via whole-metagenome shotgun sequencing of their fecal samples, collected at 21–29 weeks, to explore associations between GDM and the composition of microbial taxonomic units and functional genes. A metagenome-wide association study identified 154 837 genes, which clustered into 129 metagenome linkage groups (MLGs) for species description, with significant relative abundance differences between the 2 cohorts. Parabacteroides distasonis, Klebsiella variicola, etc., were enriched in GDM patients, whereas Methanobrevibacter smithii, Alistipes spp., Bifidobacterium spp., and Eubacterium spp. were enriched in controls. The ratios of the gross abundances of GDM-enriched MLGs to control-enriched MLGs were positively correlated with blood glucose levels. A random forest model shows that fecal MLGs have excellent discriminatory power to predict GDM status. Our study discovered novel relationships between the gut microbiome and GDM status and suggests that changes in microbial composition may potentially be used to identify individuals at risk for GDM.

Few studies have explored the relationship between dietary patterns and the risk of gestational diabetes mellitus (GDM). Evidence from non-Western areas is particularly lacking. In the present study, we aimed to examine the associations between dietary patterns and the risk of GDM in a Chinese population. A total of 3063 pregnant Chinese women from an ongoing prospective cohort study were included. Data on dietary intake were collected using a FFQ at 24-27 weeks of gestation. GDM was diagnosed using a 75 g, 2 h oral glucose tolerance test. Dietary patterns were determined by principal components factor analysis. A log-binomial regression model was used to examine the associations between dietary pattern and the risk of GDM. The analysis identified four dietary patterns: vegetable pattern; protein-rich pattern; prudent pattern; sweets and seafood pattern. Multivariate analysis showed that the highest tertile of the vegetable pattern was associated with a decreased risk of GDM (relative risk (RR) 0·79, 95 % CI 0·64, 0·97), compared with the lowest tertile, whereas the highest tertile of the sweets and seafood pattern was associated with an increased risk of GDM (RR 1·23, 95 % CI 1·02, 1·49). No significant association was found for either the protein-rich or the prudent pattern. The protective effect of a high vegetable pattern score was more evident among women who had a family history of diabetes (P for interaction¼ 0·022). These findings suggest that the vegetable pattern was associated with a decreased risk of GDM, while the sweets and seafood pattern was associated with an increased risk of GDM. These findings may be useful in dietary counselling during pregnancy.

P rimary pontine hemorrhage (PPH) is the most devastating type of intracranial hemorrhage (ICH), with an acute mortality ranging from 30% to 60%. [1][2][3] Various factors including coma at admission, location, and volume of the hematoma were found to associate with these diverse outcomes. However, inconsistent predictors were reported when different parameters and populations were brought into analysis. [4][5][6][7] As a corollary, it would make cogent sense by combining significant factors into a grading scale to enhance predictive power and, meanwhile, provide a useful tool for physicians in decision-making when facing such patients. 8 Currently, there is no standard, widely accepted early prognostic model or clinical grading scale for outcome prediction in PPH patients. The ICH score, composed of age, Glasgow Coma Scale (GCS), infratentorial origin, intraventricular hemorrhage, and hemorrhage volume, has been widely validated in predicting acute mortality, as well as long-term functional outcome in spontaneous ICH.9,10 Easy to use though, the ICH score and its derivatives tended to deem infratentorial hemorrhage as an independent predictor of poor outcome. 11,12 Because of the small structure of the pons but severe manifestations caused by hemorrhagic impairment, the cutoff value Background and Purpose-We aimed to develop and validate a grading scale for predicting 30-day mortality and 90-day functional outcome in patients with primary pontine hemorrhage (PPH). Methods-We retrospectively reviewed records of consecutive patients with first-ever pontine hemorrhage from 3 teaching hospitals between 2005 and 2012. Independent factors associated with 30-day mortality were identified by logistic regression to establish a risk stratification scale, named the new PPH score. For validation of the new PPH score, we prospectively recruited subjects from 10 units between December 2014 and November 2015. The performance of the new PPH score was presented as discrimination and calibration, measured by area under the curve of the receiver operating characteristic and Hosmer-Lemeshow goodness-of-fit, respectively. Results-Data of 171 patients were available for scale development. The new PPH score consisted of 2 independent factors with individual points assigned as follows: Glasgow Coma Scale score 3 to 4 (=2 points), 5 to 7 (=1 point), and 8 to 15 (=0 point); PPH volume >10 mL (=2 points), 5 to 10 mL (=1 point), and <5 mL (=0 point). An independent cohort of 98 patients was applied as an external validation of the new PPH score. Results showed that the new PPH score was discriminative in predicting both 30-day mortality (area under the curve, 0.902) and 90-day good outcome (area under the curve, 0.927). Furthermore, the new PPH score revealed a good calibration (χ 2 =1.387; P=0.846) in 30-day mortality prediction. Conclusions-The