Ethics statement
The present study complied with the Declaration of Helsinki, and was approved by the Local Ethics Committee of People’s Hospital of Xinjiang (Xinjiang, China). Written informed consent was obtained from all subjects prior to study participation.
Subjects
Subjects referred for polysomnography (PSG) to the Hypertension Center of People's Hospital of Xinjiang for the initial investigation of OSA were recruited consecutively, from April to December 2016 (previously described in [15]). Subjects with non-OSA and severe OSA (exhibiting extreme phenotype based on AHI), were selected for an additional genomic study. In this study, we chose AHI (either very low or very high) as the extreme phenotype. Extreme phenotype sampling which selecting subjects from the extremes of trait distribution was applied, and it can increase the ability and statistical power to detect rare variants[16]. Following the inclusion and exclusion criteria as detailed in our previous study[15], while excluding smokers, a total of 100 subjects (50 non-OSA and 50 severe OSA) were enrolled in the study.
Interventions
Overnight polysomnography (PSG) monitoring, clinical data acquisition, blood sample collection and genomic DNA extraction of all subjects followed the standard techniques as previously described[15,17]. In brief, all subjects underwent overnight PSG monitoring (Compumedics E series, Australia), and evaluated by a registered polysomnographic technologist according to the American Academy of Sleep Medicine (AASM) criteria for scoring [18]. Non-OSA was defined as an AHI < 5 events/h, and severe OSA with an AHI ≥ 30 events/h. The general demographic and clinical data were collected, mainly including age, gender, body mass index (BMI), neck circumference and abdominal circumference, as well as personal/family medical history and lifestyle habits (alcohol consumption, smoking status). Two equal samples of fasting venous blood (fasting ≥ 10 hours, cubital vein blood sample) were collected from each subject in the morning after PSG. Fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high density lipoprotein-cholesterol (HDL-C), and low density lipoprotein-cholesterol (LDL-C) levels were determined with an automatic biochemical analyzer (Beckman, CA, USA) at the central laboratory of People’s Hospital of Xinjiang using standard techniques. 3ml venous blood from another sample was collected in EDTA anticoagulant tubes for all subjects. Genomic DNA was extracted from whole blood using PAXgene Blood DNA kit (Qiagen, Germany), and the purity of DNA was measured by a spectrophotometer (NanoDrop2000, MA, USA). For all samples, the total amount of DNA required was at least 2μg with the concentration of DNA ≥ 50ng/μL. Afterwards, the extracted DNA was preserved at -80℃ and sent to Genesky Biotechnologies Inc. (Shanghai, China) for targeted capture sequencing.
Targeted capture sequencing
Twelve putative genes, EDN1, APOE, LEP, LEPR, IRS1, UCP1, ADIPOQ, PEMT, PPARG, SLC2A4, FABP2 and ADRA2A, were selected from available literature[19,20] and Public Health Genomics Knowledge Base (https://phgkb.cdc.gov/PHGKB/hNHome.action) as the metabolic syndrome-related genes for the following targeted sequencing.
100 DNA samples were analyzed for targeted capture sequencing of the above genes with Agilent sureselectXT custom Kit (Agilent Technologies, CA, USA) on an Illumina HiSeq platform (Illumina, CA, USA). Sequencing reads were aligned to the human reference genome (UCSC hg38) with BWA algorithm and variant calling was carried out using GATK HaplotypeCaller. Single-nucleotide variant annotation was performed with ANNOVAR (http://annovar.openbioinformatics.org/en/latest/). Frequency of variants was evaluated based on publically available databases (1000Genomes, ESP6500, ExAC03). A combination of pathogenicity prediction softwares (SIFT[21], Polyphen V2[22], Mutation Taster[23], CADD[24], DANN[25]) was used to predict the potential impact of each genetic variant on gene function. Requiring at least two of the softwares to support the variant may be damaging.
Statistical Analyses
Continuous data were expressed as means±standard deviations or medians (interquartile range), while categorical data were expressed as n (%). Independent Student’s t-test or Mann-Whitney U-test was used to analyze continuous variables according to the normality of data distribution. Chi-squared test or Fisher’s exact test was used for categorical variables as appropriate. Logistic regression analysis was performed to explore the association between OSA and gene variants. Hardy-Weinberg equilibrium, single-nucleotide polymorphism (SNP) association analyses and multiple comparison correction were performed using PLINK (version 1.0.7; http://pngu.mgh.harvard.edu/purcell/plink/). P-value < 0.05 was considered statistically significant.