Sex-specific Associations Between Serum Hemoglobin Levels and the Risk of Cause-specific Death in Korea Using the National Health Insurance Service-National Health Screening Cohort (NHIS HEALS)

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network

BACKGROUND

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach.

METHODS

Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes.

RESULTS

Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively.

CONCLUSIONS

These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Binary cutpoint and the combined effect of systolic and diastolic blood pressure on cardiovascular disease mortality: A community-based cohort study

Objectives

This study aimed to examine the risk of cardiovascular disease (CVD) death according to blood pressure levels and systolic and/or diastolic hypertension.

Methods

From 20,636 cohort participants, 14,375 patients were enrolled after patients with prior hypertension on antihypertensive drugs were excluded. For the combination analysis, participants were divided into four groups (systolic/diastolic hypertension, systolic hypertension only, diastolic hypertension only, and non-hypertension). The risk of CV death was calculated using the hazard ratio (HR) and 95% confidence intervals (95% CI) in a Cox regression model.

Results

The risk of CVD death increased in systolic hypertension (HR = 1.59, 95% CI 1.26–2.00) and systolic/diastolic hypertension (HR = 1.84, 95% CI 1.51–2.25). The highest risks of hemorrhagic and ischemic stroke were observed in the diastolic hypertension (HR = 4.11, 95% CI 1.40–12.06) and systolic/diastolic hypertension groups (HR = 2.59, 95% CI 1.92–3.50), respectively. The risk of CVD death was drastically increased in those with SBP≥120 mmHg/DBP≥80 mmHg. The highest risk was observed in those with SBP of 130–131 mmHg and 134–137 mmHg.

Conclusion

The combined analysis of systolic and/or diastolic hypertension appears to be a good predictor of CVD death. The risk of CVD death in the prehypertensive group could be carefully monitored as well as in the hypertensive group, presumably due to less attention and the lack of antihypertensive treatment.

Coronary Atherosclerotic Disease and Cancer: Risk Factors and Interrelation

Background

In our clinical work, we found that cancer patients were susceptible to coronary atherosclerotic heart disease (CAD). However, less is known about the relationship between CAD and cancer. The present study aimed to identify the risk factors for CAD and cancer, as well as the relationship between CAD and cancer.

Methods

In this retrospective study, 1600 patients between January 2012 and June 2019 were enrolled and divided into groups according to whether they had CAD or cancer. Single-factor and multivariate analysis methods were applied to examine the risk factors for CAD and cancer.

Results

(1) Cancer prevalence was significantly higher in patients with CAD than in patients without CAD (47.2 vs. 20.9%). The prevalence of CAD in cancer and non-cancer patients was 78.9 and 52.4%, respectively. (2) Multivariable logistic regression showed that patients with cancer had a higher risk of developing CAD than non-cancer patients (OR: 2.024, 95% CI: 1.475 to 2.778, p < 0.001). Respiratory (OR: 1.981, 95% CI: 1.236–3.175, p = 0.005), digestive (OR: 1.899, 95% CI: 1.177–3.064, p = 0.009) and urogenital (OR: 3.595, 95% CI: 1.696–7.620, p = 0.001) cancers were significantly associated with a higher risk of CAD compared with no cancer. (3) Patients with CAD also had a higher risk of developing cancer than non-CAD patients (OR = 2.157, 95% CI: 1.603 to 2.902, p < 0.001). Patients in the Alanine aminotransferase (ALT) level ≥ 40 U/L group had a lower risk of cancer than patients in the ALT level < 20 U/L group (OR: 0.490, 95% CI: 0.333–0.722, p < 0.001). (4) An integrated variable (Y = 0.205 × 10–1 age − 0.595 × 10–2 HGB − 0.116 × 10–1 ALT + 0.135 FIB) was identified for monitoring the occurrence of cancer among CAD patients, with an AUC of 0.720 and clinical sensitivity/specificity of 0.617/0.711.

Conclusion

(1) We discovered that CAD was an independent risk factor for cancer and vice versa. (2) Digestive, respiratory and urogenital cancers were independent risk factors for CAD. (3) We created a formula for the prediction of cancer among CAD patients. (4) ALT, usually considered a risk factor, was proven to be a protective factor for cancer in this study.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

Background

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.

Methods

To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-Fold CV} = 0·90).

Findings

Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.

Interpretation

To conclude, we provide ¹H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.

Funding

BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).