An increased risk of gastric cancer for pickled vegetable and salted fish intake has been suggested, yet the lack of a dose-response association warrants a quantitative analysis. We conducted a meta-analysis, combining results from our analysis of two large Korean cohort studies and those from previous prospective cohort studies. We investigated the association of pickled vegetable and salted fish intake with gastric cancer in the Korean Genome Epidemiology Study and the Korean Multi-center Cancer Cohort Study using Cox proportional hazard models. We then searched for observational studies published until November 2019 and conducted both dose-response and categorical meta-analyses. The pooled relative risk (RR) of gastric cancer incidence was 1.15 (95% Confidence Interval (CI), 1.07–1.23) for 40 g/day increment in pickled vegetable intake in a dose-response manner (P for nonlinearity = 0.11). As for salted fish intake, the pooled risk of gastric cancer incidence was 1.17 (95% CI, 0.99–1.38) times higher, comparing the highest to the lowest intake. Our findings supported the evidence that high intake of pickled vegetable and salted fish is associated with elevated risk of gastric cancer incidence.

Background: This study was performed to investigate the association between body mass index (BMI) and gastric cancer in East and Southeast Asia where most of gastric cancer is non-cardia gastric cancer. Methods: On the basis of 8,997 gastric cancer cases among the Asia Cohort Consortium participants from China, Japan, Korea, and Singapore (N = 538,835), we assessed gastric cancer risk according to BMI by calculating hazard ratios (HR) and 95% confidence intervals (CI) using the Cox proportional hazard regression model. Results: A U-shaped associations between BMI and gastric cancer risk were observed. Gastric cancer risks in underweight group (<18.5 kg/m2) and in obesity group (≥27.5 kg/m2) were higher than reference BMI group (23–24.9 kg/m2; HR, 1.15; 95% CI, 1.05–1.25 for underweight; HR, 1.12; 95% CI, 1.03–1.22 for obesity, respectively). The associations of underweight and obesity with gastric cancer risk were consistent in the analyses for non-cardia gastric cancer, intestinal-type gastric cancer, and late-onset gastric cancer. No significant association of underweight and obesity with the risk of cardia gastric cancer, diffuse-type gastric cancer, and early-onset gastric cancer was observed. In addition, we found that the U-shaped association between BMI and gastric cancer risk remained in nonsmokers, while only underweight was related to increased gastric cancer risk in smokers. Conclusions: BMI has a U-shaped association with gastric cancer risk in East and Southeast Asian population, especially for the non-cardia gastric cancer, intestinal-type gastric cancer, and late-onset gastric cancer. Impact: Future studies with consideration of anatomic location and histology of gastric cancer are needed to establish the association of underweight as well as obesity with gastric cancer risk.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations.Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations.Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs.Conclusions: This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Min JY, et al. Allergy Asthma Immunol Res. 2023 Jul;15(4):437-450. https://doi.org/10.4168/aair.2023.15.4.437

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

The purpose of this study was to determine the associations between blood hemoglobin (Hgb) levels and the risk of death by specific causes. Using the National Health Insurance Services-National Health Screening Cohort (n=487 643), we classified serum Hgb levels into 6 sex-specific groups. Cox regression analysis was used to analyze the associations between Hgb levels and the risk of cause-specific death. Hgb levels in male population showed a U-shaped, J-shaped, or inverse J-shaped association with the risk of death from ischemic heart disease, acute myocardial infarction, liver cancer, cirrhosis and chronic obstructive pulmonary disease (COPD) (all non-linear p<0.05; hazard ratio [HR]; 95% confidence interval [CI]) for the lowest and the highest Hgb levels for the risk of each cause of death in male population: HR, 1.14; 95% CI, 0.98 to 1.34; HR, 2.87; 95% CI, 1.48 to 5.57; HR, 1.16; 95% CI, 0.96 to 1.40; HR, 3.05; 95% CI, 1.44 to 6.48; HR, 1.36; 95% CI, 1.18 to 1.56; HR, 2.11; 95% CI, 1.05 to 4.26; HR, 3.64; 95% CI, 2.49 to 5.33; HR, 5.97; 95% CI, 1.44 to 24.82; HR, 1.62; 95% CI, 1.14 to 2.30; HR, 3.84; 95% CI, 1.22 to 12.13, respectively), while in female population, high Hgb levels were associated with a lower risk of death from hypertension and a higher risk of death from COPD (overall p<0.05; HR, 1.86; 95% CI, 1.29 to 2.67 for the lowest Hgb levels for hypertension; overall p<0.01, HR, 6.60; 95% CI, 2.37 to 18.14 for the highest Hgb levels for COPD). For the risk of lung cancer death by Hgb levels, a linear negative association was found in male population (overall p<0.01; the lowest Hgb levels, HR, 1.17; 95% CI, 1.05 to 1.33) but an inverse J-shaped association was found in female population (non-linear p=0.01; HR, 1.25; 95% CI, 0.96 to 1.63; HR, 2.58; 95% CI, 1.21 to 5.50). Both low and high Hgb levels were associated with an increased risk of death from various causes, and some diseases showed different patterns according to sex.