[Risk factors and sonographic findings associated with the type of placenta accreta spectrum disorders].

Objective: To evaluate the risk factors and sonographic findings of pregnancies complicated by placenta increta or placenta percreta. Methods: Totally, 2 219 cases were retrospectively analyzed from 20 tertiary hospitals in China from January 2011 to December 2015. The data were collected based on the original case records. All cases were divided into two groups, the placenta increta (PI) group (79.1%, 1 755/2 219) and the placenta percreta (PP) group (20.9%, 464/2 219) , according to the degree of placental implantation. The risk factors and sonographic findings of placenta increta or percreta were analyzed by uni-factor and logistic regression statistic methods. Results: The risk factors associated with the degree of placental implantation were age, gravida, previous abortion or miscarriage, previous cesarean sections, and placenta previa (all P<0.05), especially, previous cesarean sections (χ(2)=157.961) and placenta previa (χ(2)=91.759). Sonographic findings could be used to predict the degree of placental invasion especially the boundaries between placenta and uterine serosa, the boundary between placenta and myometrium, the disruption of the placental-uterine wall interface and loss of the normal retroplacental hypoechoic zone(all P<0.01). Conclusions: Previous cesarean sections and placenta previa are the main independent risk factors associated with the degree of placenta implantation. Ultrasound could be used to make a prenatal suggestive diagnosis of placenta accreta spectrum disorders.

Postprandial total and HMW adiponectin following a high-fat meal in lean, obese and diabetic men.

BACKGROUND/OBJECTIVES: Recent work suggests that macronutrients are pro-inflammatory and promote oxidative stress. Reports of postprandial regulation of total adiponectin have been mixed, and there is limited information regarding postprandial changes in high molecular weight (HMW) adiponectin. The aim of this study was to assess the effect of a standardised high-fat meal on metabolic variables, adiponectin (total and HMW), and markers of inflammation and oxidative stress in: (i) lean, (ii) obese non-diabetic and (iii) men with type 2 diabetes mellitus (T2DM). SUBJECTS/METHODS: Male subjects: lean (n=10), obese (n=10) and T2DM (n=10) were studied for 6 h following both a high-fat meal and water control. Metabolic variables (glucose, insulin, triglycerides), inflammatory markers (interleukin-6 (IL6), tumour necrosis factor (TNF)α, high-sensitivity C-reactive protein (hsCRP), nuclear factor (NF)κB expression in peripheral blood mononuclear cells (p65)), indicators of oxidative stress (oxidised low density lipoprotein (oxLDL), protein carbonyl) and adiponectin (total and HMW) were measured. RESULTS: No significant changes in TNFα, p65, oxLDL or protein carbonyl concentrations were observed. Overall, postprandial IL6 decreased in subjects with T2DM but increased in lean subjects, whereas hsCRP decreased in the lean cohort and increased in obese subjects. There was no overall postprandial change in total or HMW adiponectin in any group. Total adiponectin concentrations changed over time following the water control, and the response was significantly different in lean subjects compared with subjects with T2DM (P=0.04). CONCLUSIONS: No consistent significant postprandial inflammation, oxidative stress or regulation of adiponectin was observed in this study. Findings from the water control suggest differential basal regulation of total adiponectin in T2DM compared with lean controls.

The effect of a high-fat meal on postprandial arterial stiffness in men with obesity and type 2 diabetes.

CONTEXT: Postprandial dysmetabolism is emerging as an important cardiovascular risk factor. Augmentation index (AIx) is a measure of systemic arterial stiffness and independently predicts cardiovascular outcome. OBJECTIVE: The objective of this study was to assess the effect of a standardized high-fat meal on metabolic parameters and AIx in 1) lean, 2) obese nondiabetic, and 3) subjects with type 2 diabetes mellitus (T2DM). DESIGN AND SETTING: Male subjects (lean, n = 8; obese, n = 10; and T2DM, n = 10) were studied for 6 h after a high-fat meal and water control. Glucose, insulin, triglycerides, and AIx (radial applanation tonometry) were measured serially to determine the incremental area under the curve (iAUC). RESULTS: AIx decreased in all three groups after a high-fat meal. A greater overall postprandial reduction in AIx was seen in lean and T2DM compared with obese subjects (iAUC, 2251 +/- 1204, 2764 +/- 1102, and 1187 +/- 429% . min, respectively; P < 0.05). The time to return to baseline AIx was significantly delayed in subjects with T2DM (297 +/- 68 min) compared with lean subjects (161 +/- 88 min; P < 0.05). There was a significant correlation between iAUC AIx and iAUC triglycerides (r = 0.50; P < 0.05). CONCLUSIONS: Obesity is associated with an attenuated overall postprandial decrease in AIx. Subjects with T2DM have a preserved, but significantly prolonged, reduction in AIx after a high-fat meal. The correlation between AIx and triglycerides suggests that postprandial dysmetabolism may impact on vascular dynamics. The markedly different response observed in the obese subjects compared with those with T2DM was unexpected and warrants additional evaluation.

Simple and efficient analysis of disease association with missing genotype data.

Missing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotyping platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.

Detecting haplotype effects in genomewide association studies.

The analysis of genomewide association studies requires methods that are both computationally feasible and statistically powerful. Given the large-scale collection of single nucleotide polymorphisms (SNPs), it is desirable to explore the information contained in their interrelationships. In particular, utilizing haplotypes rather than individual SNPs and accounting for correlations of polymorphisms in adjustment for multiple testing can lead to increased power. We present a statistically powerful and numerically efficient method based on sliding windows of adjacent SNPs to detect haplotype-disease association in genomewide studies. This method consists of an efficient algorithm to calculate a proper likelihood-ratio statistic for any given window of SNPs, along with an accurate and efficient Monte Carlo procedure to adjust for multiple testing. Simulation studies using the HapMap data showed that the proposed method performs well in realistic situations. We applied the new method to a case-control study on rheumatoid arthritis and identified several loci worthy of further investigations.

Efficient association mapping of quantitative trait loci with selective genotyping.

Selective genotyping (i.e., genotyping only those individuals with extreme phenotypes) can greatly improve the power to detect and map quantitative trait loci in genetic association studies. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under selective-genotyping designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants and are substantially more powerful than existing methods.