Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores.

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.

BACKGROUND: Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders. METHODS: A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease. RESULTS: For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77). CONCLUSIONS: This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.

Genome-wide association study of metabolic dysfunction-associated fatty liver disease in a Korean population.

Genome-wide association studies have identified several genetic variants associated with nonalcoholic fatty liver disease. To emphasize metabolic abnormalities in fatty liver, metabolic (dysfunction)-associated fatty liver disease (MAFLD) has been introduced; thus, we aimed to investigate single-nucleotide polymorphisms related to MAFLD and its subtypes. A genome-wide association study was performed to identify genetic factors related to MAFLD. We used a Korean population-based sample of 2282 subjects with MAFLD and a control group of 4669. We replicated the results in a validation sample which included 639 patients with MAFLD and 1578 controls. Additionally, we categorized participants into three groups, no MAFLD, metabolic dysfunction (MD)-MAFLD, and overweight/obese-MAFLD. After adjusting for age, sex, and principal component scores, rs738409 [risk allele G] and rs3810622 [risk allele T], located in the PNPLA3 gene, showed significant associations with MAFLD (P-values, discovery set = 1.60 × 10-15 and 4.84 × 10-10; odds ratios, 1.365 and 1.284, validation set = 1.39 × 10-4, and 7.15 × 10-4, odds ratios, 1.299 and 1.264, respectively). An additional SNP rs59148799 [risk allele G] located in the GATAD2A gene showed a significant association with MAFLD (P-values, discovery set = 2.08 × 10-8 and validation set = 0.034, odds ratios, 1.387 and 1.250). rs738409 was significantly associated with MAFLD subtypes ([overweight/obese-MAFLD; odds ratio (95% confidence interval), P-values, 1.515 (1.351-1.700), 1.43 × 10-12 and MD-MAFLD: 1.300 (1.191-1.416), 2.90 × 10-9]. There was a significant relationship between rs3810622 and overweight/obese-MAFLD and MD-MAFLD [odds ratios (95% confidence interval), P-values, 1.418 (1.258, 1.600), 1.21 × 10-8 and 1.225 (1.122, 1.340), 7.06 × 10-6, respectively]; the statistical significance remained in the validation set. PNPLA3 was significantly associated with MAFLD and MAFLD subtypes in the Korean population. These results indicate that genetic factors play an important role in the pathogenesis of MAFLD.

Clinical Strategies in Gene Screening Counseling for the Healthy General Population.

The burgeoning interest in precision medicine has propelled an increase in the use of genome tests for screening purposes within the healthy population. Gene screening tests aim to pre-emptively identify those individuals who may be genetically predisposed to certain diseases. However, as genetic screening becomes more commonplace, it is essential to acknowledge the unique challenges it poses. A prevalent issue in this regard is the occurrence of falsepositive results, which can lead to unnecessary additional tests or treatments, and psychological distress. Additionally, the interpretation of genomic variants is based on current research evidence, and can accordingly change as new research findings emerge, potentially altering the clinical significance of these variants. Conversely, a further prominent concern regards false assurances in genetic testing, as genetic tests can yield false-negative results, potentially posing a significant clinical risk. Moreover, the results obtained for the same disease can vary among different genetic testing services, due to differences in the types of variants assessed, the scope of tests, analytical methods, and the algorithms used for predicting diseases. Consequently, whereas genetic testing holds significant promise for the future of medicine, it poses unique challenges. If conducted without a full understanding of its implications, genetic testing may fail to achieve its purpose potentially hindering effective health management. Therefore, to ensure a comprehensive understanding of the implications of genetic testing within the general population, sufficient discussion and careful consideration should be given to counseling based on gene test results.

The association between platelet-related parameters and nonalcoholic fatty liver disease in a metabolically healthy nonobese population.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease characterized by subclinical inflammation and is related to obesity and metabolic syndrome (MS), but it is also frequently observed in nonobese populations. We aimed to evaluate the relationship between the white blood cell count-to-mean platelet volume ratio (WBC/MPV), platelet-to-lymphocyte count ratio (PLR) and lymphocyte-monocyte ratio (LMR) in association with NAFLD, considering the presence of obesity and MS. Additionally, we aimed to investigate whether these parameters exhibited similar correlations in metabolic dysfunction-associated steatotic liver disease (MASLD) as observed in NAFLD. This cross-sectional study included subjects who underwent a comprehensive health evaluation, including blood tests and abdominal ultrasonography. Subgroup analyses were conducted based on obesity and MS. Out of a total 5929 subjects (3271 males, mean age 49.7 ± 10.6 years), 2253 (38.0%) had NAFLD. WBC/MPV was significantly higher, and PLR was significantly lower in subjects with NAFLD. In the analysis restricted to the nonobese (BMI < 25 kg/m2) population without MS, both WBC/MPV and PLR were independently associated with NAFLD: WBC/MPV (adjusted OR 3.366; 95% CI 2.238-5.066) and PLR (adjusted OR 0.997; 95% CI 0.996-0.999). When assessing the risk of NAFLD based on the WBC/MPV and PLR quartiles, the adjusted OR and 95% CI for the lowest quartile compared to the highest were 2.055 (95% CI 1.626-2.602) for WBC/MPV and 0.660 (95% CI 0.523-0.832) for PLR in the nonobese, metabolically healthy group. The levels of WBC/MPV and PLR were independently associated with NAFLD. Furthermore, in MASLD, an association with WBC/MPV, PLR and LMR was identified, similar to the results observed in NAFLD, even after adjusting for confounding variables. In conclusion, the present study demonstrated a significant association between NAFLD and platelet-related parameters, especially in nonobese, metabolically healthy subjects.

A novel approach of kinship determination based on the physical length of genetically shared regions of chromosomes.

BACKGROUND: Determination of genetic relatedness between individuals plays a crucial role in resolving numerous civil cases involving familial relationships and in forensic investigation concerning missing persons. Short tandem repeats (STRs), known for their high degree of DNA polymorphism, have traditionally been the primary choice of DNA markers in genetic testing, but their application for kinships testing is limited to cases involving close kinship. SNPs have emerged as promising supplementary markers for kinship determination. Nevertheless, the challenging remains in discriminating between third-degree or more distant relatives, such as first cousins, using SNPs. OBJECTIVE: To investigate a kinship analysis method for distant degree of familial relationships using high-density SNP data. METHODS: A high-density SNP data from 337 individuals of Korean families using Affymetrix Axiom KORV1.0-96 Array was obtained for this study. SNPs were aligned by chromosomal positions, and identity-by-state (IBS) was determined, and then shared regions as consecutive SNPs with IBS of 1 or 2 were investigated. The physical lengths of these IBS segments were measured and summed them to create an Index, as a measure of kinship. RESULTS: The kinship was determined by the physical length of shared chromosomal regions that are distinguished by each kinship. Using this method, the relationship was able be distinguished up to the fourth degree of kinship, and non-relatives were clearly distinguished from true relatives. We also found a potential for this approach to be used universally, regardless of microarray platforms for SNP genotyping and populations. CONCLUSION: This method has a potential to determine the different degree of kinship between individuals and to distinguish non-relatives from true relatives, which can be of great help for practical applications in kinship determination.

Genetic and Metabolic Characteristics of Lean Nonalcoholic Fatty Liver Disease in a Korean Health Examinee Cohort.

Background/Aims: The pathophysiology of lean nonalcoholic fatty liver disease (NAFLD) is unclear but has been shown to be associated with more diverse pathogenic mechanisms than that of obese NAFLD. We investigated the characteristics of genetic or metabolic lean NAFLD in a health checkup cohort. Methods: This retrospective cross-sectional study analyzed single nucleotide polymorphism data for 6,939 health examinees. Lean individuals were categorized according to a body mass index cutoff of 23 kg/m2. Single nucleotide polymorphisms were analyzed using genotyping arrays. Results: The prevalence of lean NAFLD was 21.6% among all participants with NAFLD, and the proportion of lean NAFLD was 18.5% among lean participants. The prevalence of metabolic syndrome and diabetes among lean patients with NAFLD was 12.4% and 10.4%, respectively. Lean NAFLD appeared to be metabolic-associated in approximately 20.1% of patients. The homozygous minor allele (GG) of PNPLA3 (rs738409) and heterozygous minor alleles (CT, TT) of TM6SF2 (rs58542926) were associated with lean NAFLD. However, the prevalence of fatty liver was not associated with the genetic variants MBOAT7 (rs641738), HSD17B13 (rs72613567), MARC1 (rs2642438), or AGXT2 (rs2291702) in lean individuals. Lean NAFLD appeared to be associated with PNPLA3 or TM6SF2 genetic variation in approximately 32.1% of cases. Multivariate risk factor analysis showed that metabolic risk factors, genetic risk variants, and waist circumference were independent risk factors for lean NAFLD. Conclusions: In a considerable number of patients, lean NAFLD did not appear to be associated with known genetic or metabolic risk factors. Further studies are required to investigate additional risk factors and gain a more comprehensive understanding of lean NAFLD.

Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease.

BACKGROUND: Hypertensive disorders during pregnancy are associated with the risk of long-term cardiovascular disease after pregnancy, but it has not yet been determined whether genetic predisposition for hypertensive disorders during pregnancy can predict the risk for long-term cardiovascular disease. OBJECTIVE: This study aimed to evaluate the risk for long-term atherosclerotic cardiovascular disease according to polygenic risk scores for hypertensive disorders during pregnancy. STUDY DESIGN: Among UK Biobank participants, we included European-descent women (n=164,575) with at least 1 live birth. Participants were divided according to genetic risk categorized by polygenic risk scores for hypertensive disorders during pregnancy (low risk, score ≤25th percentile; medium risk, score 25th∼75th percentile; high risk, score >75th percentile), and were evaluated for incident atherosclerotic cardiovascular disease, defined as the new occurrence of one of the following: coronary artery disease, myocardial infarction, ischemic stroke, or peripheral artery disease. RESULTS: Among the study population, 2427 (1.5%) had a history of hypertensive disorders during pregnancy, and 8942 (5.6%) developed incident atherosclerotic cardiovascular disease after enrollment. Women with high genetic risk for hypertensive disorders during pregnancy had a higher prevalence of hypertension at enrollment. After enrollment, women with high genetic risk for hypertensive disorders during pregnancy had an increased risk for incident atherosclerotic cardiovascular disease, including coronary artery disease, myocardial infarction, and peripheral artery disease, compared with those with low genetic risk, even after adjustment for history of hypertensive disorders during pregnancy. CONCLUSION: High genetic risk for hypertensive disorders during pregnancy was associated with increased risk for atherosclerotic cardiovascular disease. This study provides evidence on the informative value of polygenic risk scores for hypertensive disorders during pregnancy in prediction of long-term cardiovascular outcomes later in life.

Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value.

Long-term cardiovascular outcomes of gestational diabetes mellitus: a prospective UK Biobank study.

BACKGROUND: Previous studies showed that gestational diabetes mellitus (GDM) can be a risk factor for subsequent atherosclerotic cardiovascular disease. However, there is a paucity of information regarding diverse cardiovascular outcomes in elderly women after GDM. In the current study, we examined whether women with a history of GDM have an increased risk for long-term overall cardiovascular outcomes. METHODS: Among the UK participants, we included 219,330 women aged 40 to 69 years who reported at least one live birth. The new incidence of diverse cardiovascular outcomes was compared according to GDM history by multivariable Cox proportional hazard models. In addition, causal mediation analysis was performed to examine the contribution of well-known risk factors to observed risk. RESULTS: After enrollment, 13,094 women (6.0%) developed new overall cardiovascular outcomes. Women with GDM history had an increased risk for overall cardiovascular outcomes [adjusted HR (aHR) 1.36 (95% CI 1.18-1.55)], including coronary artery disease [aHR 1.31 (1.08-1.59)], myocardial infarction [aHR 1.65 (1.27-2.15)], ischemic stroke [aHR 1.68 (1.18-2.39)], peripheral artery disease [aHR 1.69 (1.14-2.51)], heart failure [aHR 1.41 (1.06-1.87)], mitral regurgitation [aHR 2.25 (1.51-3.34)], and atrial fibrillation/flutter [aHR 1.47 (1.18-1.84)], after adjustment for age, race, BMI, smoking, early menopause, hysterectomy, prevalent disease, and medication. In mediation analysis, overt diabetes explained 23%, hypertension explained 11%, and dyslipidemia explained 10% of the association between GDM and overall cardiovascular outcome. CONCLUSIONS: GDM was associated with more diverse cardiovascular outcomes than previously considered, and conventional risk factors such as diabetes, hypertension, and dyslipidemia partially contributed to this relationship.

Impaired migration of autologous induced neural stem cells from patients with schizophrenia and implications for genetic risk for psychosis.

Stem cell technologies have presented explicit evidence of the neurodevelopmental hypothesis of schizophrenia. However, few studies investigated relevance of the schizophrenia genetic liability and the use of genetic reprogramming on pluripotent stem cells to the impaired neurodevelopment shown by stem cells. Therefore, this study sought to investigate the cellular phenotypes of induced neural stem cells (iNSCs) derived without genetic modification from patients with schizophrenia and from genetic high risk (GHR) individuals. Three patients with a diagnosis of schizophrenia, 3 GHR individuals who had two or more relatives with schizophrenia, and 3 healthy volunteers participated. iNSCs were derived using a small molecule-based lineage switch method, and their gene expression levels and migration capabilities were examined. Demographic characteristics were not different among the groups (age, χ2 = 5.637, P = .060; education, χ2 = 2.111, P = .348). All participants stayed well during the follow-up except one GHR individual who developed psychosis 1.5 years later. Migration capacity was impaired in iNSCs from patients with schizophrenia (SZ-iNSCs) compared to iNSCs from GHR individuals or controls (P < .001). iNSCs from a GHR individual who later developed schizophrenia showed migratory impairment that was similar to SZ-iNSCs. Gene expression levels of Sox2 in SZ-iNSCs were significantly lower than those in controls (P = .028). Defective migration in genetically unmodified SZ-iNSCs is the first direct demonstration of neurodevelopmental abnormalities in schizophrenia. Additionally, alterations in gene expression in SZ-iNSCs suggest mechanisms by which genetic liability leads to aberrant neurodevelopment.

Dissecting the clinical relevance of polygenic risk score for obesity-a cross-sectional, longitudinal analysis.

BACKGROUND: Obesity is a global pandemic disease whose prevalence is increasing worldwide. The clinical relevance of a polygenic risk score (PRS) for obesity has not been fully elucidated in Asian populations. METHOD: We utilized a comprehensive health check-up database from the Korean population in conjunction with genotyping to generate PRS for BMI (PRS-BMI). We conducted a phenome-wide association (PheWAS) analysis and observed the longitudinal association of BMI with PRS-BMI. RESULTS: PRS-BMI was generated by PRS-CS. Adding PRS-BMI to a model predicting ten-year BMI based on age, sex, and baseline BMI improved the model's accuracy (p = 0.003). In a linear mixed model of longitudinal change in BMI with aging, higher deciles of PRS were directly associated with changes in BMI. In the PheWAS, significant associations were observed for metabolic syndrome, bone density, and fatty liver. In the lean body population, those having the top 20% PRS-BMI had higher BMI and body fat mass along with better metabolic trait profiles compared to the bottom 20%. A bottom-20% PRS-BMI was a risk factor for metabolically unhealthy lean body (odds ratio 3.092, 95% confidence interval 1.707-6.018, p < 0.001), with adjustment for age, sex and BMI. CONCLUSIONS: Genetic predisposition to obesity as defined by PRS-BMI was significantly associated with obesity-related disease or trajectory of obesity. Low PRS-BMI might be a risk factor associated with a metabolically unhealthy lean body. Better understanding the mechanisms of these relationships may allow tailored intervention in obesity or early selection of populations at risk of metabolic disease.

Corrigendum: Genetic Determinants of Visit-to-Visit Lipid Variability: Genome-Wide Association Study in Statin-Naïve Korean Population.

[This corrects the article DOI: 10.3389/fcvm.2022.811657.].

Development and validation of a novel strong prognostic index for colon cancer through a robust combination of laboratory features for systemic inflammation: a prognostic immune nutritional index.

BACKGROUND: Systemic inflammation is associated with survival outcomes in colon cancer. However, it is not well-known which systemic inflammatory marker is a powerful prognostic marker in patients with colon cancer. METHODS: A total of 4535 colon cancer patients were included in this study. We developed a novel prognostic index using a robust combination of seven systemic inflammation-associated blood features of the discovery set. The predictability and generality of the novel prognostic index were evaluated in the discovery, validation and replication sets. RESULTS: Among all combinations, the combination of albumin and monocyte count was the best candidate expression. The final formula of the proposed novel index is named the Prognostic Immune and Nutritional Index (PINI). The concordance index of PINI for overall and progression-free survival was the highest in the discovery, validation and replication sets compared to existing prognostic inflammatory markers. PINI was found to be a significant independent prognostic factor for both overall and progression-free survival. CONCLUSIONS: PINI is a novel prognostic index that has improved discriminatory power in colon cancer patients and appears to be superior to existing prognostic inflammatory markers. PINI can be utilised for decision-making regarding personalised treatment as the complement of the TNM staging system.

Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits.

The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.

NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.

BACKGROUND: Disease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology-common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications. RESULTS: To improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

Genetic Determinants of Visit-to-Visit Lipid Variability: Genome-Wide Association Study in Statin-Naïve Korean Population.

BACKGROUND AND AIM: There is a growing evidence that fluctuation in lipid profiles is important in cardiovascular outcomes. We aimed to identify single nucleotide polymorphism (SNP) variants associated with low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein-cholesterol (HDL-C) variability in statin-naïve Korean subjects and evaluate their associations with coronary atherosclerosis. METHODS: In statin-naïve subjects from Gene-Environment of Interaction and phenotype cohort, we performed genome-wide association studies of lipid variability; the discovery (first) and replication (second) sets included 4,287 and 1,086 subjects, respectively. Coronary artery calcium (CAC) score and degree of coronary artery stenosis were used as outcome measures. Cholesterol variability was determined by standard deviation and average successive variability, and significant coronary atherosclerosis was defined as CAC score ≥400 or coronary stenosis ≥70%. RESULTS: Mean HDL-C and LDL-C level were 54 ± 12 and 123 ± 30 mg/dL in the first set and 53 ± 12 and 126 ± 29 mg/dL in the second set. APOA5 rs662799 and APOA5 rs2266788 were associated with LDL-C variability and PXDNL rs80056520, ALDH2 rs671, HECTD4 rs2074356, and CETP rs2303790 were SNPs associated for HDL-C variability. APOA5 rs662799 passed Bonferroni correction with p-value of 1.789 × 10-9. Among the SNPs associated with cholesterol variability, rs80056520 and rs2266788 variants were associated with CACS ≥400 and coronary stenosis ≥70% and rs662799 variant was associated with coronary stenosis ≥70%. CONCLUSION: Two SNPs associated with LDL-C variability (APOA5 rs662799 and rs2266788) and one SNP associated with HDL-C variability (PXDNL rs80056520) were significantly associated with advanced coronary artery stenosis. Combining GWAS results with imaging parameters, our study may provide a deeper understanding of underlying pathogenic basis of the link between lipid variability and coronary atherosclerosis.

Prognostic Effect of Inflammatory Genes on Stage I-III Colorectal Cancer-Integrative Analysis of TCGA Data.

Background inflammatory status indicators have been reported as prognostic biomarkers of colorectal cancer (CRC). However, since inflammatory interactions with the colon involve various modes of action, the biological mechanism linking inflammation and CRC prognosis has not been fully elucidated. We comprehensively evaluated the predictive roles of the expression and methylation levels of inflammation-related genes for CRC prognosis and their pathophysiological associations. METHOD: An integrative analysis of 247 patients with stage I-III CRC from The Cancer Genome Atlas was conducted. Lasso-penalized Cox proportional hazards regression (Lasso-Cox) and statistical Cox proportional hazard regression (CPH) were used for the analysis. RESULTS: Models to predict overall survival were designed with respective combinations of clinical variables, including age, sex, stage, gene expression, and methylation. An integrative model combining expression, methylation, and clinical features performed better (median C-index = 0.756) than the model with clinical features alone (median C-index = 0.726). Based on multivariate CPH with features from the best model, the methylation levels of CEP250, RAB21, and TNPO3 were significantly associated with overall survival. They did not share any biological process in functional networks. The 5-year survival rate was 29.8% in the low methylation group of CEP250 and 79.1% in the high methylation group (p < 0.001). CONCLUSION: Our study results implicate the importance of integrating expression and methylation information along with clinical information in the prediction of survival. CEP250, RAB21, and TNPO3 in the prediction model might have a crucial role in CRC prognosis and further improve our understanding of potential mechanisms linking inflammatory reactions and CRC progression.

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer.

MOTIVATION: To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks. RESULTS: As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. AVAILABILITY AND IMPLEMENTATION: iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.