A fully green sample preparation method for synthetic antioxidants determination in edible oils based on natural feather fiber-supported liquid extraction.

The current study proposed a novel feather fiber-supported liquid extraction (FF-SLE) method for extracting analytes from oil samples. The natural feather fibers were used as the oil support material and directly loaded in the plastic tube of a disposable syringe to construct the low-cost extraction device (∼0.5 CNY). The edible oil without any pretreatment including dilution was added directly to the extraction device, followed by the addition of the green extraction solvent of ethanol. As an example, the proposed method was applied to extract nine synthetic antioxidants from edible oils. The optimized extraction conditions for processing 0.5 g of oil were obtained when the syringe dimension was 5 mL, the extraction solvent was 0.5 mL of ethanol, the amount of feather fibers was 200 mg of duck feather fibers and the static extraction time was 10 min. The applications to seven kinds of feathers and seven kinds of edible oils all indicated the excellent oil removal efficiencies (>98.0%). Combined with high-performance liquid chromatography-ultraviolet, a quantification method was validated with satisfied linearity (R2≥0.994), accuracy (95.8-114.6%) and precision (≤8.3%) with the limits of detection ranging from 50 to 100 ng/g. The proposed FF-SLE method was simple, effective, convenient, low-cost, green and environmental-friendly for the extraction of analytes from oil samples prior to instrument analysis.

Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method.

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.

Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method.

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.

Association between dyslipidemia and chronic kidney disease: a cross-sectional study in the middle-aged and elderly Chinese population.

BACKGROUND: Dyslipidemia, a well-known risk factor for cardiovascular disease, is common in patients with kidney disease. Recent studies discerned that dyslipidemias play a critical role in renal damage progression in renal diseases, but the association between dyslipidemias and chronic kidney disease (CKD) in the general population remains unknown. Thus, we assessed whether the growing prevalence of dyslipidemia could increase the risk of CKD. METHODS: A total of 4779 middle-aged and elderly participants participated in this study. Dyslipidemias were defined by the 2007 Guidelines in Chinese Adults. Incident CKD was defined as albuminuria and/or reduced estimated glomerular filtration rate (eGFR, < 60 ml×min(-1)×1.73 m(-2)). Regression analysis was used to evaluate the association between dyslipidemia and albuminuria/reduced eGFR. RESULTS: Participants with hypercholesterolemia exhibited a greater prevalence of albuminuria and reduced eGFR (10.0% vs. 6.1%, P = 0.001; 4.0% vs. 2.4%, P = 0.028, respectively). Both hypercholesterolemia and low high density lipoprotein cholesterol (HDL-C) were independently associated with albuminuria (odds ratio (OR) 1.49; 95% confidence interval (CI) 1.08 - 2.07 and OR 1.53; 95%CI 1.13 - 2.09, respectively). The multivariable adjusted OR of reduced eGFR in participants with hypercholesterolemia was 1.65 (95%CI 1.03 - 2.65). As the number of dyslipidemia components increased, so did the OR of CKD: 0.87 (95%CI 0.65 - 1.15), 1.29 (95%CI, 0.83 - 2.01), and 7.87 (95%CI, 3.75 - 16.50) for albuminuria, and 0.38 (95%CI 0.21 - 0.69), 1.92 (95%CI 1.14 - 3.25), and 5.85 (95%CI 2.36 - 14.51) for reduced eGFR, respectively. CONCLUSIONS: Our findings indicate that dyslipidemias increase the risk of CKD in the middle-aged and elderly Chinese population. Hypercholesterolemia plays an important role in reducing total eGFR. Both low HDL-C and hypercholesterolemia are associated with an increased risk for albuminuria.