Functional Aspects of the Obesity Paradox in Patients with Severe Coronavirus Disease-2019: A Retrospective, Multicenter Study.

BACKGROUND: Results of studies investigating the association between body mass index (BMI) and mortality in patients with coronavirus disease-2019 (COVID-19) have been conflicting. METHODS: This multicenter, retrospective observational study, conducted between January 2020 and August 2021, evaluated the impact of obesity on outcomes in patients with severe COVID-19 in a Korean national cohort. A total of 1,114 patients were enrolled from 22 tertiary referral hospitals or university-affiliated hospitals, of whom 1,099 were included in the analysis, excluding 15 with unavailable height and weight information. The effect(s) of BMI on patients with severe COVID-19 were analyzed. RESULTS: According to the World Health Organization BMI classification, 59 patients were underweight, 541 were normal, 389 were overweight, and 110 were obese. The overall 28-day mortality rate was 15.3%, and there was no significant difference according to BMI. Univariate Cox analysis revealed that BMI was associated with 28-day mortality (hazard ratio, 0.96; p=0.045), but not in the multivariate analysis. Additionally, patients were divided into two groups based on BMI ≥25 kg/m2 and underwent propensity score matching analysis, in which the two groups exhibited no significant difference in mortality at 28 days. The median (interquartile range) clinical frailty scale score at discharge was higher in nonobese patients (3 [3 to 5] vs. 4 [3 to 6], p<0.001). The proportion of frail patients at discharge was significantly higher in the nonobese group (28.1% vs. 46.8%, p<0.001). CONCLUSION: The obesity paradox was not evident in this cohort of patients with severe COVID-19. However, functional outcomes at discharge were better in the obese group.

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.

BACKGROUND: Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants. RESULTS: We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses. CONCLUSIONS: The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package 'UNISS', which can be downloaded from http://github.com/statpng/uniss.

Obesity Paradox and Functional Outcomes in Sepsis: A Multicenter Prospective Study.

OBJECTIVES: In Asian populations, the correlation between sepsis outcomes and body mass is unclear. A multicenter, prospective, observational study conducted between September 2019 and December 2020 evaluated obesity's effects on sepsis outcomes in a national cohort. SETTING: Nineteen tertiary referral hospitals or university-affiliated hospitals in South Korea. PATIENTS: Adult patients with sepsis ( n = 6,424) were classified into obese ( n = 1,335) and nonobese groups ( n = 5,089). MEASUREMENTS AND RESULTS: Obese and nonobese patients were propensity score-matched in a ratio of 1:1. Inhospital mortality was the primary outcome. After propensity score matching, the nonobese group had higher hospital mortality than the obese group (25.3% vs 36.7%; p < 0.001). The obese group had a higher home discharge rate (70.3% vs 65.2%; p < 0.001) and lower median Clinical Frailty Scale (CFS) (4 vs 5; p = 0.007) at discharge than the nonobese group, whereas the proportion of frail patients at discharge (CFS ≥ 5) was significantly higher in the nonobese group (48.7% vs 54.7%; p = 0.011). Patients were divided into four groups according to the World Health Organization body mass index (BMI) classification and performed additional analyses. The adjusted odds ratio of hospital mortality and frailty at discharge for underweight, overweight, and obese patients relative to normal BMI was 1.25 ( p = 0.004), 0.58 ( p < 0.001), and 0.70 ( p = 0.047) and 1.53 ( p < 0.001), 0.80 ( p = 0.095), and 0.60 ( p = 0.022), respectively. CONCLUSIONS: Obesity is associated with higher hospital survival and functional outcomes at discharge in Asian patients with sepsis.

The association of plasma cytokines including VEGF with recurrent wheezing in allergic patients.

BACKGROUND: Various cytokines have been studied to determine their functions in the pathogenesis of allergic diseases and their potential as therapeutic targets, but the roles and clinical applicability of many of these cytokines still remain unclear. OBJECTIVE: We aimed to measure the plasma levels of eight cytokines known to be relevant to allergic diseases, and to determine their association with the diagnostic characteristics of allergic patients. METHODS: The levels of a panel of eight cytokines (IL-5, IL-10, IL12p70, Leptin, CXCL5/ENA-78, CCL2/MCP-1, PDGFBB, and VEGF) were measured in plasma obtained from 83 allergic patients. We investigated whether the cytokine levels differed between children and adults. Statistical analyses were then performed to examine their association with the diagnostic characteristics of allergic patients. RESULTS: The levels of leptin, CCL2/MCP-1, PDGFBB, and VEGF were significantly higher in adult patients with allergic rhinitis than in children. Among patients with asthma, the levels of leptin and PDGFBB were elevated in adults. PDGFBB and VEGF levels were significantly associated with asthma. Interestingly, there was a significant association between VEGF level and recurrent wheezing regardless of the analyzed conditions. The levels of VEGF and PDGFBB or CCL2/MCP-1 showed a significant increase together in the presence of recurrent wheezing in child patients. CONCLUSIONS: The plasma levels of four cytokines, particularly VEGF, showed significant associations with some diagnostic characteristics in allergic patients. We suggested that plasma VEGF, which performs pleiotropic functions in allergic responses, could serve as a serological marker relevant to recurrent wheezing in allergic patients.

Genetic Diversity and Genome-Wide Association Study of Seed Aspect Ratio Using a High-Density SNP Array in Peanut (Arachis hypogaea L.).

Peanut (Arachis hypogaea L.) is one of the important oil crops of the world. In this study, we aimed to evaluate the genetic diversity of 384 peanut germplasms including 100 Korean germplasms and 284 core collections from the United States Department of Agriculture (USDA) using an Axiom_Arachis array with 58K single-nucleotide polymorphisms (SNPs). We evaluated the evolutionary relationships among 384 peanut germplasms using a genome-wide association study (GWAS) of seed aspect ratio data processed by ImageJ software. In total, 14,030 filtered polymorphic SNPs were identified from the peanut 58K SNP array. We identified five SNPs with significant associations to seed aspect ratio on chromosomes Aradu.A09, Aradu.A10, Araip.B08, and Araip.B09. AX-177640219 on chromosome Araip.B08 was the most significantly associated marker in GAPIT and Regularization method. Phosphoenolpyruvate carboxylase (PEPC) was found among the eleven genes within a linkage disequilibrium (LD) of the significant SNPs on Araip.B08 and could have a strong causal effect in determining seed aspect ratio. The results of the present study provide information and methods that are useful for further genetic and genomic studies as well as molecular breeding programs in peanuts.

Population Structure and Genetic Diversity in Korean Cowpea Germplasm Based on SNP Markers.

Cowpea is one of the most essential legume crops providing inexpensive dietary protein and nutrients. The aim of this study was to understand the genetic diversity and population structure of global and Korean cowpea germplasms. A total of 384 cowpea accessions from 21 countries were genotyped with the Cowpea iSelect Consortium Array containing 51,128 single-nucleotide polymorphisms (SNPs). After SNP filtering, a genetic diversity study was carried out using 35,116 SNPs within 376 cowpea accessions, including 229 Korean accessions. Based on structure and principal component analysis, a total of 376 global accessions were divided into four major populations. Accessions in group 1 were from Asia and Europe, those in groups 2 and 4 were from Korea, and those in group 3 were from West Africa. In addition, 229 Korean accessions were divided into three major populations (Q1, Jeonra province; Q2, Gangwon province; Q3, a mixture of provinces). Additionally, the neighbor-joining tree indicated similar results. Further genetic diversity analysis within the global and Korean population groups indicated low heterozygosity, a low polymorphism information content, and a high inbreeding coefficient in the Korean cowpea accessions. The population structure analysis will provide useful knowledge to support the genetic potential of the cowpea breeding program, especially in Korea.

Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data.

Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this paper, we propose a new statistical method to identify differentially co-expressed genes from microarray gene expression data. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods to detect co-expressed genes through extensive simulation studies. Also, we applied it to various microarray gene expression datasets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer.

Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data.

BACKGROUND: In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other. RESULTS: We propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. CONCLUSIONS: The proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.

New variable selection strategy for analysis of high-dimensional DNA methylation data.

In genetic association studies, regularization methods are often used due to their computational efficiency for analysis of high-dimensional genomic data. DNA methylation data generated from Infinium HumanMethylation450 BeadChip Kit have a group structure where an individual gene consists of multiple Cytosine-phosphate-Guanine (CpG) sites. Consequently, group-based regularization can precisely detect outcome-related CpG sites. Representative examples are sparse group lasso (SGL) and network-based regularization. The former is powerful when most of the CpG sites within the same gene are associated with a phenotype outcome. In contrast, the latter is preferred when only a few of the CpG sites within the same gene are related to the outcome. In this paper, we propose new variable selection strategy based on a selection probability that measures selection frequency of individual variables selected by both SGL and network-based regularization. In extensive simulation study, we demonstrated that the proposed strategy can show relatively outstanding selection performance under any situation, compared with both SGL and network-based regularization. Also, we applied the proposed strategy to identify differentially methylated CpG sites and their corresponding genes from ovarian cancer data.

Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data.

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.