Gastric cancer is the third leading cause of cancer mortality worldwide. Studies investigating the effect of salt on gastric cancer have mainly used self-reported measures, which are not as accurate as sodium/creatinine ratios because individuals may not know the amount of salt in their food. Using data from a prospective cohort study, we investigated the effect of salt intake on progression to gastric precancerous lesions. Salt intake was estimated by urinary sodium/creatinine ratios, self-reported frequencies of adding salt to food, and total added table salt. We repeated the analyses among groups with and without Helicobacter pylori infection. We did not observe a positive association between salt intake, measured by urinary sodium/creatinine ratio, and overall progression in the gastric precancerous process (adjusted risk ratio (RR): 0.94; 95% confidence interval (CI) 0.76–1.15). We did observe an association between salt intake and increased risk for progression to dysplasia or gastric cancer overall (RR: 1.32; 95% CI: 0.96–1.81), especially among those who continued to have H. pylori infection at the five-month follow-up (adjusted RR: 1.53; 95% CI: 1.12–2.09), and among those who had persistent H. pylori infection over 12 years (adjusted RR: 1.49; 95% CI: 1.09–2.05). Salt intake may increase the risk of gastric dysplasia or gastric cancer in individuals with H. pylori infection.

Background: The process by which salt affects the gastric precancerous process has not been adequately studied in humans. Methods: We investigated the effects of salt on gastric inflammation, epithelial damage, the density of Helicobacter pylori infection, and gastric epithelial cell proliferation, all of which may be mediators between salt and gastric precancerous/cancerous lesions. These potential mediators were measured using gastric biopsies as: (a) the density of polymorphonuclear and mononuclear cells (gastric inflammation), (b) mucus depletion (gastric epithelial damage), and (c) the severity of H. pylori infection. Salt intake was measured with spot urine samples (using urinary sodium/creatinine ratios), self-reported frequency of adding salt to food, and as total added salt. Results: The average sodium/creatinine ratio (at baseline and post-treatment at five months) was associated with increased epithelial damage over the 12-year follow-up period among those with a greater severity of chronic inflammation and among those with continued H. pylori infection after treatment at five months. This association was stronger when both severe gastric inflammation and H. pylori infection were present at five months (ß: 1.112, 95% CI: 0.377, 1.848). Conclusion: In humans, salt was associated with an increase in epithelial damage in stomachs with more severe previous H. pylori-induced chronic inflammation.

A power calculation for a study with a quantitative outcome requires information on the outcome distribution under the alternative hypothesis. Researchers face challenges when they concisely specify alternative distributions in genetic studies because power depends on genotype frequencies and the average effect of each genotype. In GWAS, investigators evaluate hundreds of thousands of associations; therefore it is unrealistic to specify gene frequencies and gene effects for each test and some simplification is needed. Software packages are available to calculate power, but many of them have limited flexibility and / or may have a steep learning curve. In this review, we describe to researchers and graduate students the essentials of a power calculation for testing for an association between a quantitative trait and genotypes. In addition, we provide them with the codes of the different available software packages—free and commercial—to calculate this power. The calculations can be carried out using virtually any computer language that computes the cumulative distribution function of a non-central F-distribution.