Diet/sugar-free soft drinks are considered to be healthier than regular soft drinks. However, few studies have examined the relationship between the types of soft drinks (regular and diet/sugar-free) and lung cancer (LC)/all-cancer (AC) risk. In this study, we comprehensively assessed the influence of the type of soft drink consumption on LC/AC risk based on the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Multivariable Cox proportional hazards and competing risks Fine-Gray regression models adjusted for relevant confounders were used to estimate hazard ratios (HRs) and subdistribution HRs for different types of soft drink consumption. In the PLCO population, female subgroup, and the ever/current smoker subgroup, consumption of both regular and diet soft drinks was associated with a significantly reduced risk of LC compared with no soft drinks at all. For the non-lung cancer (NLC) risk, consumption of only diet soft drinks had a significant positive association for the total population and female subgroup. Based on our findings, it was suggested that partial replacement of regular soft drinks with diet soft drinks might be beneficial to LC prevention, especially for females and ever/current smokers. Additionally, completely replacing regular soft drinks with diet soft drinks might be detrimental to NLC prevention, especially for females. It was estimated that LC has caused 131 880 deaths in 2021 in the United States and cancer became the world's second-leading cause of diet-related death in 2017. Risk factors of LC are mainly smoking, genetic, and other environmental factors. Some studies have also reported an association between dietary factors and cancer risk. Plant-based foods, including fruits and vegetables, are categorized as protective factors for cancer, while red meat, processed meat, and alcoholic drinks are categorized as risk factors. Coffee, tea, and milk consumption are also associated with LC–8. However, few studies have examined the relationship between the types of soft drinks (regular and diet/sugar-free) and LC/all-cancer (AC) risk.Cancer is the leading cause of death, and lung cancer (LC) is the leading cause of cancer deaths globally. Previously published systematic reviews, meta-analyses, and cohort studies showed that the consumption of soft drinks (regular and diet) affected the morbidity and mortality of several diseases–17, including type 2 diabetes, metabolic syndrome, and coronary heart disease. Although less tasty than regular soft drinks, diet soft drinks are preferred by consumers because they contain less sugar and are considered healthier than regular soft drinks. However, some studies have suggested that consumption of diet/sugar-free soft drinks or artificially sweetened beverage is a risk factor for all-cause and cardiovascular disease (CVD) death,16–18. It would therefore be interesting to study the relationship between the types of soft drink consumption and the incidence of LC and AC.Soft drinks mainly include soda, pop, syrup, or other sugary carbonated or non-carbonated non-alcoholic beveragesIn the present study, we performed a comprehensive analysis of prospectively collected data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to investigate the role of soft drink consumption type in LC/AC risk. In the PLCO population, consumption of both regular and diet soft drinks was associated with a significantly reduced risk of LC compared with no soft drinks at all. For non-lung cancer (NLC) risk, consumption of only diet soft drinks had a significant positive association. According to our findings, it was suggested that partial replacement of regular soft drinks with diet soft drinks might be beneficial to LC prevention, and completely replacing regular soft drinks with diet soft drinks might be detrimental to NLC prevention.–20. PLCO trial was designed to evaluate the efficacy of selected cancer screening methods in reducing mortality from PLCO cancers. From 1993 to 2001, the PLCO trial enrolled 154 897 eligible participants aged 55 to 74 years from 10 study centers in the United States. The PLCO trial had two randomized arms, including an intervention arm and a control arm. Participants in the control arm received usual care, while those in the intervention arm were screened for PLCO cancers during the first 3–4 years. All participants were followed up until they died or until December 31, 2009, whichever came first. Both arms received more than 10 years of follow-up after enrollment (median follow-up time 11.3 years). The primary recruitment strategy was mass mailing to the general population living near the screening center. Patients who had a history of some type of cancer within the PLCO and were currently undergoing cancer treatment or post-pneumonectomy were excluded, and all other responders were included in the study.The PLCO trial is a randomized, controlled, two-armed trial conducted by the National Cancer Institute–20. Data from the above questionnaires were merged based on the PLCO ID numbers for subsequent analyses. Totally, there were 154 897 observations in the PLCO dataset. During quality control, observations of any pre-baseline history of cancer and diabetes mellitus, invalid data of DHQ, absence from follow-up, and extreme energy consumption (top 1% or bottom 1%) were excluded. A total of 2190 observations without valid follow-up time for LC were also excluded. The final remaining data set for subsequent analyses with LC outcomes consisted of 97 133 observations. The same quality control process was also conducted for AC outcomes, and the final data set consisted of 92 997 observations. The flow charts of this study are shown in andSupplementary Fig. 1 (available online) for LC and AC outcomes, respectively. The median follow-up time was 8.9 years for both LC and AC outcomes.The self-administered baseline questionnaire (BQ), dietary history questionnaire (DHQ), and supplementary questionnaire (SQX) completed by participants provided information on related factors for this studye.g., age, sex, education, ethnicity), lifestyle , family history of cancers, personal health history, and past screenings. All participants received an annual study update questionnaire asking if any cancers had been diagnosed in the past year–23. DHQ was a food frequency questionnaire with 156 questions, providing a variety of dietary nutrition data on consumption of various foods, drinks, and supplements. SQX provided additional background information, including factors related to physical activity.Upon entering the PLCO trial, participants completed the BQ about demographics , and the categorical data such as sex, smoking status, and family history of LC/AC were expressed as numbers and percentages. Kruskal Wallis test was used for continuous variables and Chi-square test was used for categorical variables to compare the demographic characteristics of the four soft drink consumption types and LC/AC strata.,10,22.Multivariable Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for LC or AC. Potential confounders were adjusted in the models, including age, sex , race, arm, center, family history of LC/AC, the education level , body mass index (BMI) types at baseline, smoking status (not for never and ever/current smokers subgroups), physical activity status, daily energy consumption, red meat, fruits, vegetables and coffee, alcohol consumption, and estrogen use The AC outcomes were further subdivided into LC and NLC outcomes. Cause-specific Cox and competing risks Fine-Gray regression models were performed accordingly. Potential confounders shown above were adjusted in the models.Cumulative incidences of LC/AC outcomes stratified by soft drink consumption types were estimated and log-rank tests were performed. Cause-specific and competing risks cumulative incidences of LC/NLC outcomes by soft drink consumption types were also estimated. The log-rank test and Gray's test were performed for LC and NLC, respectively., version 4.2.0 with R packages "cmprsk", "survival"–28, and "tableone".P<0.05 was considered statistically significant.All the statistical analyses were performed using R software, and complete descriptive characteristics of the participants with LC and AC outcomes are presented inSupplementary Table 1–4 (available online). All the demographic characteristics showed significant association with soft drink consumption types (P<0.001). All the demographic characteristics, including soft drink consumption types, showed significant association with LC/AC outcomes (P<0.001) except for the "Arm" (P=0.868) and "Alcohol consumption" (P=0.569) with LC outcome.Simplified descriptive characteristics of eligible PLCO participants with LC outcomes according to the types of soft drink consumption are presented in. Significant differences in the estimated cumulative incidence were demonstrated since theP-value of the log-rank test is less than 0.001 for LC risk.The cumulative incidence of LC stratified by soft drink consumption types is shown in andSupplementary Table 5 (available online). For the total population, soft drink consumption had a protective effect with statistically significant adjusted HRs for the "Both" type with the "None" type as reference . Both regular and diet/sugar-free soft drink consumption were associated with a significant reduction in LC risk. A decreasing trend of HRs for different soft drink consumption types was identified with the HRs for "Diet only", "Regular only" and "Both" types sequentially.Results of adjusted HRs and corresponding CIs for soft drink consumption types estimated by main Multivariable Cox regression models for LC outcomes are shown inP=0.043; "Both", HR=0.62, 95% CI: 0.45–0.87,P=0.005). For the ever/current smoking subgroup, "Diet only" and "Both" types had statistically significant adjusted HRs . Diet/sugar-free soft drink consumption was associated with a significant reduction in LC risk and drinking both regular and diet soft drinks had the lowest risk. For male and never-smoking status subgroups, no statistically significant results were found for soft drink consumption.Similar results were shown in the female and ever/current smoking subgroups. Soft drink consumption had protective effects. A decreasing trend of HRs for different soft drink consumption types was identified with the HRs for "Regular only", "Diet only", and "Both" types sequentially. For the female subgroup, "Diet only" and "Both" types had statistically significant adjusted HRs with the "None" type as reference . Similar to the LC outcomes, there were significant differences in the estimated cumulative incidence since theP-value of the log-rank test was less than 0.001 for AC.The cumulative incidence of AC stratified by soft drink consumption types is shown inSupplementary Fig. 3 (available online) andSupplementary Table 6 (available online). Although there were as similar declining trends of HRs for different soft drink consumption types as the previous primary analysis results of LC outcomes, no statistically significant results were found in the total population and subgroups of males, females, never, and ever/current smokers.Multivariable Cox regression models for the AC outcomes were also conducted to discover the association between soft drink consumption and the AC outcomes. The results of estimated HRs for soft drink consumption types are demonstrated in andSupplementary Fig. 4 and5 (available online). The cause-specific and competing risks cumulative incidences were similar. Similar to the previous results of LC and AC outcomes, there were significant differences in the estimated cause-specific and competing risks cumulative incidence since theP-values of the log-rank and Gray's tests were less than 0.001 for both LC and NLC.The AC outcomes were subdivided into LC and NLC outcomes. The cause-specific and competing risks cumulative incidences of LC and NLC stratified by soft drink consumption types are shown in and,Supplementary Fig. 6 and7 (available online) andSupplementary Table 7 and8 (available online). Results of corresponding CHRs and SHRs were similar. CHRs and SHRs for LC were consistent with the results of HR for LC outcomes in the primary analysis shown before.Cause-specific multivariable Cox regression models and competing risks Fine-Gray models were conducted to discover the association between soft drink consumption and the LC and NLC outcomes. The results of estimated cause-specific hazard ratios (CHRs) and competing risks subdistribution hazard ratios (SHRs) for soft drink consumption types are demonstrated inP=0.004; SHR=0.72, 95% CI: 0.57–0.91,P=0.005). Both regular and diet/sugar–free soft drink consumptions were associated with a significant reduction in LC risk. For NLC, soft drink consumption had a harmful effect with statistically significant adjusted CHR and SHR for the "Diet only" type with the "None" type as reference . Decreasing trends of CHRs and SHRs for different soft drink consumption types for both LC and NLC outcomes were identified for "Diet only", "Regular only" and "Both" types sequentially. These trends were consistent with the results of the previous primary analysis of LC outcomes and the secondary analysis of AC outcomes.For the total population, soft drink consumption had a protective effect with statistically significant adjusted CHR and SHR for the "Both" type with the "None" type as reference for LC . For the ever/current smoking subgroup, "Diet only" type had statistically significant adjusted CHR, and "Both" type had significant adjusted CHR and SHR . Diet/sugar–free soft drink consumption was associated with a significant reduction in LC risk and drinking both regular and diet soft drinks had the lowest risk. For male and never–smoking status subgroups, no statistically significant results were found for soft drink consumption.Similar results were shown in the female and ever/current smoking subgroups for LC. Soft drink consumption had protective effects. A decreasing trend of HRs for different soft drink consumption types was identified with the HRs for "Regular only", "Diet only", and "Both" types sequentially. For the female subgroup, "Diet only" and "Both" types had statistically significant adjusted HRs with the "None" type as reference . For male, ever/current smoking, and never smoking subgroups, no statistically significant results were found for soft drink consumption.For NLC, similar results were shown in the female subgroup. Soft drink consumption had a harmful effect with statistically significant adjusted CHR and SHR for the "Diet only" type with the "None" type as reference , which were similar to the results obtained from the overall LC outcomes. A decreasing trend of HRs for different soft drink consumption types was shown. Soft drink consumption had a protective effect. For NSCLC outcomes, the "Both" types had statistically significant adjusted HRs with "None" type as a reference in the female subgroup  and ever/current smoking subgroup . However, for SCLC outcomes, no statistically significant results of soft drink consumption were investigated due to the limited cases.Sensitivity analyses of small/non-small cell LC (SCLC/NSCLC) types were performed by using the main multivariable Cox models with adjusted variables. Results are shown inThe protective effects of soft drink consumption types were demonstrated. Compared with no soft drink consumption, people having both regular and diet soft drinks were inversely associated with LC risk for females and ever/current smoking subgroups. These findings were not only consistent with the primary analysis of the LC outcomes, but also suggested that soft drink consumption types might have different protective effects on LC types.In this large prospective study of the association between types of soft drink consumption and LC/AC risks, diet/sugar-free soft drink consumption was found to be negatively associated with LC risk, particularly in females and ever/current smokers. People who consumed both regular and diet soft drinks had the lowest risk of LC compared with those not drinking any soft drinks, especially females and ever/current smokers. These findings suggested that taking diet soft drinks as a partial alternative to regular soft drinks might be helpful in preventing LC. Although the overall trend was similar, there were no similarly significant results for AC risk. The consumption of only diet soft drinks was positively associated with the NLC risk, particularly in females. In sensitivity analyses, the overall trend was similar across different LC types although there was a lack of statistical significance in some subgroups.–17. There were no significant associations for either regular or diet soft drinks with the cancer mortality–17. In our secondary analysis of AC outcomes, there was no significant association between the incidence of AC and the type of soft drink consumption. Our result was consistent with previous studies. However, in our analysis of subdivided LC and NLC outcomes, significant associations were found. This suggested that the type of soft drink consumption might have different associations with different cancer incidences. However, this was not sufficient for causal inference, and the association needed to be further validated. Therefore, our findings should be interpreted with caution. If these associations were confirmed, it would be meaningful to investigate the mechanisms behind them.Previous studies have discussed the relationship between the cancer mortality and soft drink consumption. Therefore, it is necessary to continue advocating strict restrictions on the consumption of sugar-sweetened beverages. A partial replacement of regular soft drinks with diet soft drinks might be beneficial to overall health and have the potential to be useful in LC prevention. The preference for soft drinks indicates that soft drinks might have some positive effects on people, potentially emotionally or mentally,15,30. However, regular soft drinks are sugar-sweetened beverages, which are not good for one's health. Switching to diet soft drinks might retain the positive effects of regular soft drinks on people to some extent, while reducing the negative effects of sugar intake on health. Since there was a difference in taste between diet and regular soft drinks, completely replacing regular soft drinks with diet soft drinks might not retain the positive effects of regular soft drinks and might be detrimental to NLC prevention. Different results of the subgroup analyses suggested different effects on the types of soft drink consumption in different populations. Women and ever/current smokers were more likely to prevent LC by partially replacing regular soft drinks with diet soft drinks. For NLC risk, women might have an increased risk due to completely replacing regular soft drinks with diet soft drinks. Overall, this study was only an exploratory analysis of the effect of soft drink consumption types on LC and AC risks. Thus, our findings should be interpreted with caution. Prospective cohort studies with large samples of follow-up and more comprehensive inclusion of factors related to soft drink consumption are still needed to confirm the results of this study.Refined carbohydrates from sugar-sweetened beverages are associated with an increased risk of LC. Therefore, the main strengths of our study included its prospective design, large-sample size, and long-term follow-up. It provided more convincing evidence for causal inference than case-control studies. The abundant data from the PLCO trial enabled us to adjust for a broad range of potential confounders (including diet-related variables) and examine the potential influence of reverse causality. Although other prospective cohort studies, such as the UK Biobank,31, had larger total sample sizes, the proportions of lung cancer cases were much smaller, so PLCO had significant strength. Moreover, this study was the first to comprehensively evaluate the association between soft drink consumption types  and LC/AC incidence with significant HR results of Cox and competing risks Fine-Gray models with adjusted confounders. The mechanisms behind these significant associations would be meaningful to study if they were confirmed.The present study was an analysis of prospective cohort data from the advantageous PLCO cancer screening trial because participants might feel stressed. For example, smoking status was obtained by self-reporting, so the accuracy was uncertain, which might affect the results of Cox and Fine-Gray models with adjusted confounders. Thirdly, the number of cancer cases might be insufficient to obtain reliable results by various subgroup analyses. Fourthly, despite adjustment for a range of potential confounders, this study could not eliminate residual confounders and thus verify the cause of the observed associations. Finally, our results were not sufficient for causal inference, and the findings needed to be further validated. In conclusion, this prospective analysis of the PLCO trial showed that after adjusting for relevant confounders, people who consumed both regular and diet soft drinks had a reduced risk of LC compared with non-drinkers, suggesting that partial replacement of regular soft drinks with diet soft drinks might be beneficial to LC prevention, especially for females and ever/current smokers. On the other hand, those who consumed only diet soft drinks had an increased risk of NLC, suggesting that completely replacing regular soft drinks with diet soft drinks might be detrimental to NLC prevention, especially for females.Nevertheless, this study had some limitations, so our findings should be interpreted with caution. Firstly, Dietary information was collected in the form of cross-sectional questionnaires in the prospective PLCO trial. Long-term dietary habits might not be correctly reflected. Secondly, self-reported information by the baseline and food frequency questionnaires were usually prone to bias


ORs) and 95% confidence intervals (CIs) of the association between dietary salt intake and risk of gastric cancer. Subgroup analyses were used to identify possible sources of heterogeneity. Results: Thirty-eight case-control studies were included in this meta-analysis . The pooled ORs showed a significantly positive association between high salt intake and gastric cancer compared with low salt intake ; p < 0.001). In subgroup meta-analysis for geographic region, estimation method for dietary salt intake and the source of controls, this association was not changed. Conclusion: Higher dietary salt intake increased the risk of gastric cancer. This study has implications for the prevention of gastric cancer.Aim: The effect of dietary salt intake on the risk of gastric cancer is not clear. A meta-analysis was performed to estimate the association between dietary salt intake and the risk of gastric cancer. Methods: Three major databases were searched to retrieve case-control studies published in English before 1 July 2022. Random effects model analysis was used to obtain the pooled odds ratios ( Gastric cancer has long been a major public health issue . AlthougHelicobacter pylori (H. pylori) colonization in the stomach, which is a known risk factor for gastric cancer  OR ‘gastric neoplasms’ OR ‘stomach cancer’ OR ‘gastric cancer’) AND . The searches were unlimited by time up to 1 July 2022, but were limited to human studies.OR) estimates, including 95% confidence intervals (CIs), for different salt intake categories. The studies were excluded if they met any of the following criteria: (1) being duplicate publications; (2) not being relevant; (3) being systematic reviews, meta-analyses, meeting abstracts, letters, and dissertations without the relevant information; (4) not being case-control studies; (5) OR and 95% CI not be reported. Studies with larger sample sizes was chosen among duplicate publications from the same case-control study. Exclusion criteria were applied sequentially by first screening the titles and abstracts, and then the full text. Duplicate records were excluded before screening began. The flow chart of the selection of studies is shown in The studies were selected if they met all of the following criteria: (1) being a case-control study; (2) total sample size over 100; (3) assessment of salty food intake, preference of salty food, use of table salt and relevant indexes as exposure; (4) the authors reported odds ratio (Two investigators (Xiaomin Wu and Liling Chen) independently conducted the literature search, reviewed the retrieved articles, and extracted detailed information from included articles.OR estimates with the greatest adjustment.Any disagreement about whether a study met the inclusion criteria was resolved by group discussions with the third investigator (Junxia Cheng). The following characteristics of the identified studies and respective populations were recorded: first author, year of publication, country, region, gender, age (years) (mean/range), sample size of participants, match or not, the source of controls, estimation methods for dietary salt intake, comparisons, and adjustment variables for each study. The estimation methods for dietary salt intake in the different studies were provided in terms of total dietary salt intake or in terms of preference for salty food, or both. For our analysis, we used the outcome provided for total dietary salt intake whenever possible. Furthermore, we extracted Quality assessment was performed according to the Newcastle–Ottawa scale for observational studies . This scORs and 95% CIs were considered as the effect size for all studies in this meta-analysis. The value from each study and the corresponding standard error were transformed into their natural logarithms to stabilize the variances and normalize their distribution. The pooled OR with corresponding 95% CI was estimated using a random effect model, weighting for the inverse of the variance. Heterogeneity among the studies was estimated using the 2I statistic, with values of 25%, 50%, and 75% representing low, moderate, and high degrees of heterogeneity, respectively, with a p value < 0.10 deemed to be significant. A forest plot was used to visualize the ORs and 95% CIs of the included studies. A funnel plot was used to visualize a potential publication bias and Egger’s linear regression test was used to measure the asymmetry of the funnel plot, with a p value < 0.10 deemed to be significant. The influence of a single study was examined by sensitivity analysis. Subgroup analyses were used to identify associations between the risk of gastric cancer and relevant study characteristics  as possible sources of heterogeneity. All tests were two-tailed and statistical significance was defined as p < 0.05.STATA/SE 16 for Windows was used to analyze the data. The The study selection process and results from the literature search are shown in OR: 1.55, 95% CI: 1.45–1.64). There was significant heterogeneity between studies . There was publication bias detected in the meta-analysis (p < 0.001)   for studies conducted in Europe, 1.48  for studies conducted in Asia, and 1.65  for studies conducted in America; there was statistically significant heterogeneity among studies of salt intake in Europe (p < 0.001 and 2I = 77.2%), Asia (p < 0.001 and 2I = 86.3%), and America (p = 0.006 and 2I = 72.1%)  for studies that estimated salt addition and 2.03  for studies that estimated consumption of salty foods or salt preference; there was statistically significant heterogeneity among studies that estimated salt addition (p < 0.001 and 2I = 87.2%) and there was statistically medium heterogeneity among studies that estimated consumption of salty foods or salt preference (p < 0.001 and 2I = 66.0%)  for studies with controls from the community and 2.19  for studies with controls from hospitals; there was statistically significant heterogeneity in studies  . (2) In subgroup analyses by geographical region and estimation method for salt intake, the significantly positive association was not changed.In this meta-analysis, it was found that (1) compared with low dietary salt intake, high dietary salt intake could increase the gastric cancer risk  The gastric mucosa could be damaged by high salt concentration directly, which leads to hyperplasia of the gastric pit epithelium and increases the probability of endogenous mutations ,14. Addic cancer ,14. The cer risk . More secer risk . Elevatecer risk ,55. Thisnfection ,56,57. Tnfection ,14,56,58There was significant heterogeneity among the included studies. This situation was also observed in other comparable studies ,14. The Publication bias existed in our meta-analysis. Negative results were not be reported, especially in studies published in the 1990s, which is the main source of publication bias.H. pylori, smoking, and other relevant risk factors were not able to be considered in this meta-analysis. Third, given the observational nature of the included studies, our study lacked evidence to clarify causation. Fourth, the estimation methods for dietary salt intake, which contributed to the heterogeneity of this study, were not classified in more detail.There exist several potential limitations in this study. First, we only included studies published in English and we did not search grey literatures. The actual total number of eligible studies may be larger than the currently included studies. Second, confounding risk factors such as In conclusion, it was indicated that higher dietary salt intake increased the risk of gastric cancer. Participants who prefer salty foods need to receive dietary education and diet management for the prevention of gastric cancer. This finding has important public health implications. Societies and individuals may succeed in lowering their risk for gastric cancer by reducing dietary salt intake. An additional meta-analysis that includes more cohort studies is needed.


Previous systematic reviews reported that serum vitamin D deficiency was associated with risk of hypertension. The aim was to conduct a meta-epidemiological analysis for evaluating the potential effects of publication bias.The selection criterion was defined as a follow-up study for evaluating the association between circulating 25-hydroxyvitam D level and hypertension risk in adults. A funnel plot and Egger’s test were used to detect a publication bias. If a publication bias was identified, trim-and-fill analysis (TFA) with linear estimator was performed to estimate a summary relative risk (sRR).P-value of Egger’s test (=0.015) and asymmetry of the funnel plot showed that there was a publication bias. TFA resulted in that statistical significance disappeared in the association between vitamin D level and hypertension risk in total cohorts  as well as men and women cohorts.The meta-analysis of 13 cohorts resulted in the lower the vitamin D, the higher the risk of hypertension statistically significant . But The The publication bias-adjusted results by TFA had no statistically significant association between vitamin D levels and the risk of hypertension. The significant results in previous systematic reviews might be interpreted as due to publication bias. Vitamin D is associated with the renin-angiotensin system, vascular inflammation, and calcification –4. BasedFour systematic reviews evaluating the hypothesis were published –9. They Meanwhile, vitamin D deficiency is more common in women . TherefoA total of 11 studies selected for meta-analysis by four systematic reviews in Table Then, each article was evaluated whether it satisfies the selection criterion such as a follow-up study for evaluating the association between circulating 25(OH)D level and hypertension risk in adults. After selecting the articles that satisfy the selection criterion, it was checked whether the participants duplicate among the selected studies. If there were overlaps in the study participants, a study with a larger number of hypertensive patients was selected.The sRR adjusted for the most confounders in each study were extracted. The lowest to highest sRR values were unified to determine the risk of hypertension due to vitamin D deficiency. If the sRR was presented with the lowest circulating 25(OH)D level as the reference, the inverse was used such that the highest level as the reference.Heterogeneity of studies was assessed with I-squared value (%). Random effects model meta-analysis was performed . PublicaAmong 11 articles selected by 4 systematic reviews in Table P-value of Egger’s test (=0.015) and asymmetry of funnel plot   D was associated with a significant, 1.22-fold  increase in hypertension risk in adults while publication bias exists. The above results obtained from 12 studies were like the results of Qi et al. [P-value of Egger’s test was 0.38, the hypertension risk per 25 nmol/L increments in 25(OH)D levels did not have statistical significance in men  and in women .However, the TFA considering publication bias resulted in that there was no association between vitamin D level and hypertension risk regardless of sex groups Table . These rP-value of Egger’s test (=0.08). Thus, there is necessary to adjust the positivity criterion of presenting a publication bias in Egger’s test as P-value < 0.1 [P-value of Egger’s test in Burgaz et al. [P-value of Egger’s test decreased as the increased number of selected studies for meta-analysis.The funnel plot to check a publication bias showed asymmetry in this study. Only Kunutsor et al.  in Tableue < 0.1 . In addiz et al.  containiIn addition to publication bias, causes of funnel plot asymmetry involve chance, choice of effect measure, choice of precision measurement, and heterogeneity . The selThe major strength of this study was secured two additional studies , 30 so tThe main limitation was related to the limitations of TFA. The effect size adjusted for publication bias provided by TFA should meet with the assumption that asymmetry in the funnel plot was only caused by publication bias . BecauseIn conclusion, the bias-adjusted results by TFA had no statistically significant association between vitamin D levels and the risk of hypertension. The significant results in previous systematic reviews could be interpreted as due to publication bias.


Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as The online version contains supplementary material available at 10.1186/s13059-022-02753-4. Meta-analysis for molecular epidemiology in large populations has seen great success in linking high-dimensional ‘omic features to complex health-related phenotypes. One example of this is in genome-wide association studies (GWAS ), where Meta-analysis of microbial community profiles presents unique quantitative challenges relative to other types of ‘omics data such as GWAS  or gene IBD represents one of the best-studied, microbiome-linked inflammatory phenotypes to date which thus stands to benefit from such approaches , 21. AmoIn this work, we introduce and validate a statistical framework for population-scale meta-analysis of microbiome data, and apply it to the largest collection to date of ten published 16S rRNA gene sequencing-based IBD studies , our methods consist of three main components: batch and study effect correction (MMUPHin_Correct), meta-analyzed differential abundance testing (MMUPHin_MetaDA), and population structure discovery (MMUPHin_Discrete and MMUPHin_Continuous). First, MMUPHin_Correct performs batch correction of microbial abundance data, by extending methods from the gene expression literature  to zero , and depleting the gut’s typical fastidious anaerobe population as a result  . B. B35]. BIn particular, while the second continuous population structure captured the Firmicutes-Bacteroidetes tradeoff present in most gut microbiome studies  while uncovering new associations  not confidently associated with inflammation by other populations or data types. Almost all effects were exhibited similarly using either stool or mucosal profiling, with a small number of exceptions showing significant differentiation . Novel disease-treatment response interactions were observed . The study also showed no evidence of additional population structure, such as microbiome-driven discrete disease subtypes, within CD or UC. The meta-analysis framework developed for the study, MMUPHin, has been extensively evaluated and its performance for batch effect removal, supervised meta-analysis of exposures and covariates, and unsupervised population structure discovery validated on a variety of simulated microbial community types. It is extensible to integration of microbial community taxonomic or functional profiles from other data types , environments, or health conditions.We provide a novel framework for microbial community meta-analysis and apply it to the first large-scale integration of over 5100 amplicon profiles of the stool and mucosal microbiomes in IBD. This identified a significantly reproducible gradient in the gut microbiome indicative of increasing dysbiosis in subsets of patients. Our results supported many of the taxonomic associations previously ascribed to IBD  as a framework for meta-analysis of microbial community studies using taxonomic, functional, or other abundance profiles. It includes components for batch effect adjustment, differential abundance testing, and unsupervised discrete and continuous population structure discovery.Y was modelled with respect to both batch variable and biologically relevant covariate(s) X:i indicates batch/study, j indicates sample, and p indicates feature. γip and δip are batch-specific location and scale parameters. σp is a feature-specific standardization factor. βp are covariate-specific coefficients, and ϵijp is an independent error term following a standard normal distribution. Iijp is a binary  zero-count indicator, to allow for zero inflation of features. As in ComBat, γip and δip are modelled with normal and inverse-gamma priors, respectively. Hyperparameters are estimated with empirical Bayes estimators as in ComBat [For microbial community batch correction, we extended the batch correction method developed for gene expression data in ComBat  with an n ComBat . The posY), batch/study information, and optionally any other covariates X that are potentially confounded with batch but encode important biological information. MMUPHin outputs an adjusted profile X (if provided).Per-sample feature counts are then re-normalized to keep sample read depth unchanged post-correction. In practice, the user provides sample microbial abundance table . This y package :\documenβp is the overall differential abundance effect of feature p. ϵip is per-study measurement error, and eip is study-specific random effects term (not present in fixed-effect models).p.Overall, for running MMUPHin_MetaDA, the user provides a microbial community profile, study design (batch) information, the main exposure variable of interest, and optional additional covariates. If any meta-analyzed studies include repeated measures , then random covariates can also be provided and will be modelled for such studies. MMUPHin_MetaDA then performs MaAsLin2 regression modelling within each study and aggregates effect sizes of the exposure variable We note that MMUPHin_MetaDA always accounts for the batch variable in its supervised differential abundance testing. This agrees with the field’s consensus on the most appropriate way to address batch effects during supervised testing , 58. Thrk, yielding (for a specific k) training sample clusters Ak1, Ak2, …, Akk. Note that Ak1, Ak2, …, Akk jointly forms a partition of the testing sample indices. The same clustering analysis is then performed on the validation samples, and the resulting partition of sample space provides classification membership potentially different from clustering memberships Ak1, Ak2, …, Akk. Prediction strength for kclusters is defined asnkl =  ∣ Akl∣, or the number of test samples in the lth cluster.For unsupervised discrete  structure discovery of a single study, again after batch correction, MMUPHin_Discrete uses average prediction strength , an estak is appropriately describing the true number of discrete clusters in the dataset, then average prediction strength should be close to one .Average prediction strength is the average of prediction strengths across randomization iterations. Intuitively, it characterizes the degree of agreement between the clustering structures in randomly partitioned validation and training subsets; if k in study i with validation study i′ isAkil indicates the lth cluster membership in study i, when cluster number is specified as k; nkil =  ∣ Akil∣. The average generalized prediction in study i for cluster number k is then defined as the average of gpsii′(k) across all i′ ≠ i, i.e., all validation studies (instead of iterations of randomized partitions). Similar to the single study prediction strength, it describes the generalizability of clustering structure in study i in external validation studies.We additionally generalized this metric to meta-analysis settings, where we aimed to quantify the agreement of clustering structures between studies. In the meta-analytical setting, generalized prediction strength for cluster number i, PCA is performed on normalized and arcsin square root-transformed microbial abundance data. Given a user-specified threshold on variance explained, we record its top PC loading vectors, Ji is the smallest number of top loading vectors that jointly explain percentage of variability in the dataset past a customizable threshold 0 < thresholdv < 1 (default to 80%).For each dataset wij and wi ′ j′, similarity is quantified with the absolute value of cosine coefficient [cos  < wij, wi ′ j′ > ∣. This yields a network of PC loading vectors associated by weighted edges wij and wi ′ j′, retaining edges only if their weight surpasses a customizable similarity threshold .This threshold is default to 0.7, which is close to the theoretical guarantee that all size-three clusters will by definition have positive cosine coefficients between all PC pairs. In practice, we recommend the user to vary this parameter as needed to evaluate robustness and interpretability.For two PC loadings from different datasets fficient  ∣ cos  <Clustering by modularity score avoids large clusters with few intracluster edges and prioritizes smaller clusters that are more densely connected , but also be smaller and thus are less interpretable in terms of consistent biological signals across studies.For a module Wk represent strong, mutually independent, and reproducible covariation signals across the microbial datasets; they are used to identify continuously varying gradients in microbial abundance profiles that represent reproducible population structures. Specifically, given a sample with normalized and transformed microbial abundance measurements x, its continuous score for module k is defined as x′Wk, as in regular PCA.The module-wide consensus vectors Wk can be further examined by correlating Wk with the top PC loadings in each such validation study. For each additional study, Wk is considered to be validated in that dataset if its absolute cosine coefficient with at least one of the dataset’s top PCs surpasses the coefficient similarity cutoff thresholds; the number of top PCs to consider in the validation dataset loadings is determined with the same cutoff thresholdv.If additional studies are available, the reproducibility of each We extended our previous work in cancer gene expression subtyping  to perfohttp://huttenhower.sph.harvard.edu/sparsedossa). This is a model of microbial community structure using a set of zero-inflated log-normal distributions fit to selected training data, in this case drawn from the IBD gut microbiome [We performed extensive simulation studies  used. This is to ensure the counts of the modified feature are not dominated by the values of the target covariate, but instead distributed similarly to real data. The SparseDOSSA association model thus differs from MMUPHin’s model in two substantial ways: (i) MMUPHin’s associations are defined within the exponentiated component and are thus better described as a multiplicative effect, whereas SparseDOSSA’s effects are directly applied on untransformed data, and (ii) SparseDOSSA additionally ensures realistic data generation with the re-standardization procedure.This is the same initial distributional assumption as the MMUPHin batch correction model, when there are no batch or covariates effects. However, for spiked-in associations with metadata , SparseDOSSA uses a different model. Given a simulated, pre-spiking-in feature count vector ϕ parameter), number of batches, sample sizes, and dimensionality . For each combination of simulation parameters, we performed 20 random replications . Additional file Thus, the only component of the SparseDOSSA model that requires fitting to training data is the aforementioned zero-inflated log-normal null distribution. In our analysis, this was always PRISM , while on=200 and 1000), as well as the percentage of features associated with metadata  , a binary positive control , continuous positive control , and negative control  variables. Microbial abundance data was simulated to be associated with the batch and the two positive control variables at varying effect sizes , but not with the negative control variable. We additionally varied the number of samples per batch , total number of microbial features (Performance of batch correction methods was quantified by omnibus associations (PERMANOVA R2) between the simulated microbial abundance data with the batch and positive control variables, before and after batch correction. For ComBat  and our We evaluated false positive rates (FPR) for meta-analytic feature association testing, specifically the null case in which there are no associations between microbial features and covariates, but false associations can arise in the presence of batch effects with unbalanced distribution of covariate values across studies Fig. b. For sip-values < 0.05 for associations with the exposure variable. Four data normalization and analysis regimes were evaluated , number of clusters , and effect size of association . For the evaluation of MMUPHin’s unsupervised methods (both here for MMUPHin_Discrete and during continuous population structure discovery below for MMUPHin_Continuous), we fixed the number of samples per batch at 500, the number of total features at 1000, and the percent of associated features at 20%. These were guided by the fact that the underlying unsupervised methods  require larger sample sizes for good performance even without batch confounding, and are generally only practical with higher feature dimensions  and counted as a success only if the two agreed. The percentage of success for a given parameter combination across the 20 random replications was used as the evaluation metric for model performance. We compared the performance of clustering before and after MMUPHin_Correct  and a continuously varying gradient (uniformly distributed between −1 and 1), at varying number of batches and effect size of both associations (as above). The number of samples per batch, total number of microbial features, and the percentage of features associated were fixed at the same values as above . We again compared the performance of continuous score discovery on the batch confounded and batch-corrected data  gut microbiome studies of IBD for meta-analysis , universally available.Type of controls . Control information was available directly for CS-PRISM, Jansson-Lamendella, and Pouchitis, inferred from study design described in manuscript for Herfarth, HMP2, MucosalIBD, and RISK , and not applicable for BIDMC-FMT, LSS-PRISM, and PROTECT (only has IBD subjects).Sample type , universally available.° CS-PRISM: terminal ileum, neo-ileum, pouch are aggregated as ileum; cecum, ascending/left-sided colon, transverse colon, descending/right-sided colon, and sigmoid colon were aggregated as colon; rectum classification was kept unchanged.° HMP2: ileum classification kept unchanged; cecum, ascending/right-sided colon, transverse colon, descending/left-sided colon, and sigmoid colon were aggregated as colon.° MucosalIBD: all terminal ileum samples, aggregated to ileum.° Pouchitis: terminal ileum, pouch, pre-pouch ileum aggregated as ileum; sigmoid colon aggregated to colon.° PROTECT: all rectum samples, classification kept unchanged.° RISK: terminal ileum was aggregated to ileum; rectum kept unchanged.Body site of biopsy sample collection , with more detailed classifications recorded separately in case of need. Mappings for the relevant datasets are:° Location for CDs , available for BIDMC-FMT, CS-PRISM, Herfarth, Jansson-Lamendella, LSS-PRISM, and Pouchitis.° Behavior for CDs , available for CS-PRISM, Herfath, Jansson-Lamendella, LSS-PRISM, Pouchitis, and RISK.° Extent for UCs , available for CS-PRISM, Jansson-Lamendella, LSS-PRISM, Pouchitis, and PROTECT.Montreal classifications:Age at sample collection (in years), available for BIDMC-FMT, CS-PRISM, Herfarth, HMP2, LSS-PRISM, MucosalIBD, Pouchitis, PROTECT, RISK.Age at diagnosis (in years). Directly available for CS-PRISM, HMP2, LSS-PRISM, and Pouchitis, inferred as baseline age for PROTECT and RISK as these were new-onset cohorts.Race . Directly available for CS-PRISM, Herfarth, HMP2, PROTECT, and RISK, inferred from manuscript cohort description for Jansson-Lamendella .Gender . Available for BIDMC-FMT, CS-PRISM, Herfarth, HMP2, Jansson-Lamendella, LSS-PRISM, MucosalIBD, Pouchitis, PROTECT,Treatment variables, including antibiotics, immunosuppressants, steroids, and 5-ASA. These variables were encoded as yes/no to indicate, approximately, currently receiving them at the time of sampling. Additional information such as specific medication or delivery method was recorded separately if available in case of need. We note the potentially confounding difference in studies’ definitions of treatment: for Pouchitis and PROTECT authors defined antibiotics as receiving the treatment within the past month , whereas for CS-PRISM, HMP2, LSS-PRISM, and RISK such determination was not possible (antibiotics “yes” was defined as “currently taking”). Likewise, we had no additional information to determine the time extent for the other three treatments, beyond that according to metadata/publication, patients were “currently taking” the treatment at sample collection.We manually curated subject- and sample-specific metadata across studies to ensure consistency. Variables collected and curated include:https://github.com/biobakery/ibd_meta_analysis.For a comprehensive list of curation mapping schema, please refer to our metadata curation repository: Sequences were processed, per cohort, with the published, standardized bioBakery workflow  using thAcross samples, a median of 81.51% reads / sample passed quality control filtering and were successfully assigned to OTUs with Greengenes identifiers .Quality-controlled (truncated and filtered) sequences, Greengenes mapped OTU count profiles, and curated sample metadata are available at the Human Microbial Bioactives Resource Portal  effect correction was first performed within individual studies . Microbial abundance profiles across all studies were then jointly corrected for study effects, while modelling disease status (IBD or control), disease (CD or UC), and sample type (biopsy or stool) as covariates. Reduction of batch and study effects was evaluated by PERMANOVA R2 Fig. a.n=16, two technical batches) and RISK  with the dada2 method [We additionally evaluated the impact of OTU- versus ASV-based bioinformatics pipelines on our method and meta-analysis results  as implemented in the R package vegan  using BrP-values were FDR adjusted across features for each variable. For the full list of models adopted as well as cohort stratification strategy, please refer to Additional file To identify microbial features individually significantly associated with one or more covariates, we applied MMUPHin’s differential abundance testing model (MMUPHin_MetaDA) as described above. Cohorts were first stratified by sample type (biopsy or stool) and, where appropriate, diseases (CD or UC) prior to model fitting. Arcsin square root-transformed genus level taxon abundances were tested for covariate associations in individual cohort strata with multivariate linear modelling . Covariates used for adjustment include age, gender, and race for disease variables, and additionally disease status for treatment variables. Effect sizes across cohort strata were aggregated with a random effects model with restricted maximum likelihood estimation . P-valueTo relate these real data results to our simulation evaluation, the real-world data characteristics best correspond with the simulated scenario with eight batches and four thousand samples in total , 200 microbial features , and 10% spiked features at batch effect size 10, which yielded ~10% PERMANOVA R2 for batch effect and 3% R2 for binary exposure  .The two β2 in this model corresponds to the effect of B3 in addition to the overall contrasts between B23 versus B1. The meta-analysis aggregated p-values of these effects were reported as the differentiation between the most severe and “medium” severity phenotypes  with the moderator variable. Figure Dehalobacterium and Enterobacteriacea; all significant interactions as well as individual study effects are in Additional file The moderator effects We performed discrete subtype discovery  in IBD,We additionally extended our clustering evaluation analysis to other dissimilarity metrics  and clustering strength measurements , which were also explored in previous efforts . Importav) was set to default (80%), but we varied the PC similarity  cutoff thresholds between 0.5 and 0.8 to assess the sensitivity of the two identified PC clusters in Fig. s = 0.65). As we show in Additional file s(0.5) PC networks become denser, with the two PC clusters in Fig. s is large (0.8), the network is sparser, with only the most highly similar nodes of the two clusters forming smaller communities. We thus concluded that the two identified clusters in Fig. Continuous structure discovery was performed with MMUPHin’s corresponding component (MMUPHin_Continuous). The four largest studies  were subsetted to baseline samples (only relevant for PROTECT), stratified by CD/UC and biopsy/stool sample type, and used as the training sets for MMUPHin_Continuous. The minimum variance explained threshold . The reproducibility of each continuous score within a study was defined as the maximum absolute cosine coefficient between the score’s consensus loading (as provided by MMUPHin_Continuous) and the top three principal component loadings discovered independently within that study. Note that the number of top principal components considered here was set to a fixed value (three) instead of based on a percent variance cutoff as in MMUPHin_Continuous. This is because in the two identified clusters in Fig. As we show in Additional file Assignment of continuous scores was straightforward given the two consensus loading vectors provided by MMUPHin. Within each study, arcsin square root-transformed relative abundances were centered per-feature, the transformed abundance matrix was then multiplied by each consensus loading via dot product to generate per-sample continuous scores. These scores were used for visualization as in Fig. Additional file 1: Supplemental Figures.Additional file 2: Supplemental Notes.Additional file 3: Supplemental Table 1. Additional demographic, clinical, and bioinformatics characteristics of included studies.Additional file 4: Supplemental Table 2. Full set of varying simulation parameters evaluated for MMUPHin validation.Additional file 5: Supplemental Table 3. Detailed specification for omnibus and per-feature differential abundance testing models.Additional file 6: Supplemental Table 4. Per-study and meta-analytically aggregated effect sizes of per-feature differential abundance tests performed.Additional file 7: Supplemental Table 5. Per-study and meta-analytically aggregated effect sizes of tests on per-feature abundance associated with disease phenotypic severity.Additional file 8: Supplemental Table 6. Per-study and meta-analytically aggregated effect sizes of tests on the interaction between disease/treatment and sample type/disease on per-feature abundances.Additional file 9: Supplemental Table 7. Consensus loadings for the two identified continuous scores characterizing the IBD gut microbiome population structure.Additional file 10: Review history.


However, the results remain ambiguous and indefinite.Over the past two decades, several studies have focused on the association between a common polymorphism , and GWAS Catalog databases, covering all publications until February 10, 2022. Odds ratios (OR) with 95% confidence intervals (CI) were used to evaluate the strength of the association. Publication bias was assessed using both Begg and Egger tests.rs1800795 polymorphism and T2DM risk in Asians, mixed population, and hospital-based (HB) subgroups . However, increased associations were found from total, mixed population, and HB subgroups between rs1800795 polymorphism and T1DM susceptibility .Overall, 34 case–control studies with 7257 T2DM patients and 15,598 controls, and 12 case–control studies  were included in the analysis. A significantly lower association was observed between the IL-6 rs1800795 polymorphism is associated with susceptibility to decreased T2DM and increased T1DM.In summary, there is definite evidence to confirm that The online version contains supplementary material available at 10.1186/s13098-022-00851-8. Diabetes mellitus (DM) is a chronic medical condition in which the body either produces too little insulin from pancreatic islets or lacks effective access to insulin . Type 1 Interleukin-6 (IL-6), a classic proinflammatory cytokine, plays a prominent role in the inflammatory response and is associated with insulin resistance and T2DM . In addirs1800795  [rs1800795 polymorphism functionally affects IL-6 promoter activity, indicating that the carried CC genotype individual is associated with lower plasma levels of IL-6 compared with individuals with the GG genotype [The IL-6 gene is located on chromosome 7p21. The gene, which includes seven exons, covers approximately 12.8 kb of genomic DNA . A commogenotype . In addigenotype , 15, demrs1800795 polymorphism showed a highly significant association with T2DM [rs1800795 polymorphism and T2DM. For T1DM, an increased association was observed between T1DM and the polymorphism by Cooper et al. [rs1800795 is not associated with T1DM risk [rs1800795 polymorphism and T1DM/T2DM [Several epidemiological studies have observed associations between genetic variants of IL-6 and the risk of DM. For instance, Saxena et al. observed that the ith T2DM . In contith T2DM . In addir et al. . Howeverr et al. . Two met1DM risk , 22. On 1DM risk , 24. In 1DM/T2DM , 25–60.https://www.ebi.ac.uk/gwas/) until on Feb 10, 2022, with keywords including ‘Interleukin-6/IL-6’, ‘polymorphism/variant’, and ‘Diabetes Mellitus/DM/TIDM/T2DM’. Two researchers  evaluated the articles to identify the stages through the abstract and then the full article. Systematic analysis/meta-analysis, case studies, other polymorphisms, insufficient data for each genotype, and duplications were identified and removed from further analysis. In addition, our meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . Eligible studies were selected based on the following criteria: @) studies assessing the association between TIDM or T2DMAdditional file: As per journal requirements, every additional file must have a corresponding caption. In this regard, please be informed that the caption was taken from the Additional file rs1800795 variants; @) case/control studies; and @) age-and sex-matched control subjects. The exclusion criteria were: @) not case/control studies; @) insufficient genotype frequency; @) duplicate studies; and @) significantly biased articles. Information including the name of the first author, year of publication, origin, race, DM type, genotype methods, and Hardy–Weinberg equilibrium (HWE) was collected.We used online databases, including PubMed, Embase, CNKI, Wanfang, and GWAS Catalog  for cross-sectional study quality assessment. The methodological quality of each study , comparability, and outcomes were assessed using the NOS tool. Studies with a score of more than 7 out of 10 were considered suitable. This cutoff point was determined after reviewing relevant meta-analyses from the literature –63.IL-6 rs1800795 polymorphism and the risk of TIDM/T2DM was measured using 95% confidence interval (CI) and odds-ratio (OR) according to the genotype frequencies of the case and control groups. Ethnic groups were divided into African, mixed, Caucasian, and Asian groups. Population-based (PB) and hospital-based (HB) control subgroups were also identified.The correlation between Z-test. In these studies, the heterogeneity hypothesis was assessed using the Q-test based on the chi-squared test [IL-6 rs1800795, we studied the relationship between variation and the risk of T2DM in the C-allele vs. G-allele, CG vs. GG, and CC + CG vs. GG models; and C-allele vs. G-allele, CC vs. GG, CC vs. CG + GG, CG vs. GG, and CC + CG vs. GG models for T1DM risk. The asymmetry of the funnel plot was evaluated using Begg’s test, and publication bias was evaluated using Egger’s test. Statistical significance was set at P < 0.05 [P < 0.05), and the χ2 test was used to evaluate the deviation of rs1800795 polymorphism from the expected frequency of HWE [http://www.power-analysis.com/.The statistical significance of the results was calculated using the red test . If signred test , 66. ForP < 0.05 . Pearsony of HWE . All stahttp://string-db.org/) to construct an IL-6 gene–gene interaction network.To fully understand the role of IL-6 and its potential functional partners in DM, we used the STRING online server (https://www.ncbi.nlm.nih.gov/snp/rs1800795#frequency_tab) . The distribution of genotypes in controls was not consistent with the HWE in T2DM (9 case–control studies) [rs1800795 polymorphism was conducted using the genotyping methods listed in Table A total of 1356 articles were identified from the four main databases . 1260 papers were excluded after reading the abstract, and 96 articles were used for a complete evaluation. Among them, 50 articles were excluded for the following reasons: systematic analysis/meta-analysis (10), only case studies (9), other polymorphisms in the IL-6 gene 15), insufficient data for each genotype (8), and duplication (8)  , 53, 60 studies) , 48  ligand family. For example, the first related gene is IL-6R, which is the receptor of the IL-6 gene. Qi et al. reported that the IL6R rs8192284 variant was significantly associated with plasma CRP level and could predict diabetes risk . Jiao etAlthough we performed a comprehensive meta-analysis, this study has several limitations. First, studies from mixed populations and Africans are limited, which leads to missing or insufficient results and may influence the conclusion. Second, one single gene or one polymorphism may not have the power to result in the development of DM, which is a complex process including gene–gene or gene-environment interactions, and further studies should pay close attention to the same. Third, four databases were included, and some valuable studies from other databases or languages could not be identified, which should have an impact on the current conclusions. Finally, most of the studies were selected using the PCR–RFLP technique in current publications, and the authors may apply to duplicate selected samples for the second time at least 10% of the total samples to confirm the genotypes detected by PCR–RFLP, as real-time PCR is a reference method which can verify the genotyping in PCR–RFLP technique to avoid false positives.IL-6 rs1800795 polymorphism was associated with significantly increased T1DM risk in a mixed population. In contrast, a decreased association was found in T2DM susceptibility in Asians. Consequently, further well-designed large-scale studies, particularly those related to gene–gene and gene-environment interactions, are warranted.In summary, our meta-analysis provided evidence that the Additional file 1: PRISMA 2019 checklist.


RET polymorphisms and susceptibility to Hirschsprung disease (HSCR). However, some of the results are inconsistent and the studies were conducted with small sample sizes. Therefore, we performed a meta-analysis to clarify the relationship.There are numerous published studies on the association between Relevant data were retrieved from PubMed, Web of Science, Cochrane Library, EMBASE, CNKI, and Google Scholar according to PRISMA guidelines. Odds ratios (OR) were calculated to assess susceptibility to HSCR. Meanwhile, heterogeneity and publication bias were also calculated by R software package (version 4.2.1). The protocol was published in PROSPERO (CRD42022348940).RET polymorphism rs2435357  and 7 studies on the RET polymorphism rs2506030 . The analysis revealed that rs2435357  and rs2506030  were significantly associated with susceptibility to HSCR.A total of 12 studies were included in the meta-analysis and comprised 12 studies on the RET may be related to susceptibility to HSCR, of which rs2435357 (T > C) is the causal locus and rs2506030 (A > G) is the protective locus.The polymorphisms rs2435357 and rs2506030 in the https://www.crd.york.ac.uk/prospero/, identifier:CRD42022348940. Hirschsprung disease (HSCR) is a very common developmental deformity of the digestive tract with a global incidence of approximately 1 case per 5,000 live births . HSCR isThere is increasing evidence that genetic factors play an important role in the pathogenesis of HSCR , 7, 8. WRET is the main susceptible gene in HSCR  family and participates in the proliferation, migration and differentiation of enteric neural crest cells (ENCCs)  . Abnorma of HSCR .RET gene-related mutations are associated with the pathogenesis of HSCR  and HSCR susceptibility –16. Howe of HSCR . Rs24353 of HSCR . Rs24353https://www.crd.york.ac.uk/prospero/, CRD42022348940).This systematic review and meta-analysis study was performed in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The protocol was published in PROSPERO , and Google Scholar. Search terms were [(HSCR) or (Hirschsprung) or (HD) or ] and [(ret) or (rs2435357) or (rs2506030)], and relevant data were manually searched according to specified standards. All studies considered potentially relevant were read in their entirety and subsequently selected for inclusion in the current study. Any differences arising therein were resolved after discussion with a third researcher. Finally, the selected studies were summarized.P < 0.01), 2) review articles, (3) animal experiments, (4) studies with duplicate data, and (5) severe data loss.Criteria were developed for the screening process to ensure the stability of the included data. Inclusion criteria: (1) analyzing the relationship between rs2435357 or rs2506030 and HSCR susceptibility, (2) cohort or case-control study, and (3) data for each genotype could be collected. Exclusion criteria: (1) the genotype frequency of the control group in the study did not meet the Hardy–Weinberg equilibrium , sample size, genotypic data from the cases and controls, genotypic classification, and sample source.Data were extracted from articles that met the inclusion criteria for analysis. This included the year of publication, region, author, HWE  . The NOSP value >0.1 indicated that the data included in the study were quite heterogeneous and suitable for the use of a random effects model; otherwise, a fixed effects model was used  and corresponding confidence intervals (CIs) were analyzed using the data collected to quantify the relationship between the two SNPs (rs2435357 and rs2506030) and susceptibility to HSCR. During analysis, the uppercase letters “A” and “T” were identified as the mutant alleles for rs2435357 and rs2506030, respectively, while the uppercase letters “G” and “C” were defined as wild-type alleles. The HWE for each study was assessed by Chi-square test . Additiowas used . Becausewas used –24, the A total of 452 studies were retrieved from PubMed, EMBASE, Web of Science and Cochrane Library, Google Scholar, and CNKI. Among them, 107 repetitive articles were excluded and 294 articles that did not meet the inclusion criteria were excluded according to the title and abstract. Subsequently, a further 39 articles were excluded after analysis of the full text of the remaining 51 articles. Ultimately, 12 articles were included in the meta-analysis. Of the 12 selected studies, 7 investigated both rs2435357 and rs2506030. A PRISMA flowchart of the literature retrieval strategy is shown in There were 12 studies on rs2435357 , 26–35 aAll studies included in the meta-analysis were of high quality with NOS scores between 6 and 8 . FurtherRET and the susceptibility risk of HSCR are presented in P = 3.72e-36, I2 = 79.8%, P = 0.000). In addition, the following allelic models were also significantly associated with HSCR susceptibility: homozygous ; dominant ; and recessive .The meta-analysis results of the association between the rs2435357 polymorphism of the P = 7.43e-42, I2 = 80.2%, P = 0.000); homozygous model ; heterozygous model ; dominant model ; recessive model . Subgroup analysis by genotyping method and source of controls also revealed there was association between the rs2435357 polymorphism and HSCR risk in TaqMan, PCR, and Hospital-Based (H-B) subgroups. Results for other subgroups are presented in Stratified analysis by ethnicity revealed that the rs2435357 polymorphism was significantly associated with HSCR risk in Asia: allele mode . There was also sufficient evidence that the following allelic models were associated with HSCR susceptibility: homozygous ; dominant ; and recessive .Data were collected from seven studies, comprising 1,849 cases with HSCR and 3,054 controls. The meta-analysis results  revealedRET gene polymorphism and HSCR susceptibility was significant in the TaqMan subgroup . In addition, there was an association between rs2506030 polymorphism and HSCR risk in the Hospital-Based (H-B) subgroup. Results of the subgroup analysis of sample source and genotyping method are shown in Subgroup analyses were performed by ethnicity, source of controls, and genotyping method. The association between Sensitivity analysis calculates the overall correlation coefficient obtained by removing a group of studied data respectively, and then quantifies the difference between each correlation coefficient to obtain the stability of the whole data and the model. The robustness of the meta-analysis was assessed by comparing the results of analyses before and after the removal of one study. The processed results were not significantly different from the previous results, thus the data and model have high robustness .P = 0.395); homozygous model (TT vs. CC: P = 0.216); heterozygous model ; recessive model (TT + CT + CC: P = 0.287). In addition, in the meta-analysis of rs2506030, no publication bias found: allele: A vs. G: P = 0.225); homozygous (AA vs. GG: P = 0.470); heterozygous model (AA vs. AG: P = 0.172); dominant model .Harbord's test was performed to assess the possible publication bias of included studies. The results showed that in the meta-analysis of rs2435357, five genetic models showed no publication bias: allele mode  in a single GWAS using cases of HSCR of European origin (EA) from five different countries . Three SNPs—rs2435357, rs2506030, and rs2506004—were previously reported to be potentially unrelated to HSCR susceptibility  is 3.842, suggesting that rs2435357 promotes the incidence of HSCR; while the OR value of rs2506030 (A > G) is 0.519, indicating that rs2506030 (A > G) has a positive effect on HSCR. Previous studies have found that rs2435357 and rs2506030 do not have linkage disequilibrium (Of the two SNPs included in our meta-analysis, rs2435357 is currently the most studied and the most significantly associated with HSCR susceptibility. rs2506030 is an association point for a ∼125-kb upstream of tibility . The resilibrium , implyinHowever, the study also has defects. The first is that the sample size of the study is still small, which may affect the final results. Second, the results of this study may not be applicable to cases from other regions since the cases included in this study were only from Asia and Europe. Third, only published study data were included in the meta-analysis, which could cause publication bias. In addition, most of the articles included in the study lacked data on gender, age, and pathological type of HSCR of the subjects, therefore hierarchical analysis was performed on these covariates. Finally, HSCR is a combination of environmental and genetic aspects that cannot be comprehensively analyzed in a meta-analysis due to limitations in the study data.RET—rs2435357 and rs2506030 and HSCR susceptibility, with rs2506030 exhibiting the most significant association with HSCR susceptibility. In the future, larger samples and more comprehensive analysis will more fully determine their association. Moreover, the related findings of the association between RET gene polymorphisms and HSCR susceptibility provide novel suggestions for HSCR pathogenesis research.This study revealed the relationship between two polymorphisms of


As a natural antioxidant, uric acid plays a protective role against neurodegenerative disorders, including Parkinson’s disease (PD). Therefore, the risk of PD has been found to be lower in people with hyperuricemia. In this article, we conducted a systematic review and meta-analysis to investigate whether gout affects the future risk of developing PD.We searched PubMed, Scopus, the Web of Science, and Google Scholar to find relevant studies, up to March 16, 2022. Studies investigating the risk of PD, following a gout diagnosis, were included if they were cross-sectional, case–control or cohort studies. The Newcastle Ottawa Scale (NOS) checklist was used to assess the quality of all included studies. The meta-analysis was performed using STATA 17.0.Ten studies were included, which were comprised of three case-controls, six cohort studies and one nested case–control study. We found no significant association between gout and the risk of PD among both sexes , although the association was significant for females . Subgroup analysis also showed no significant findings by age group, whether they were receiving treatment for gout, study design, quality assessment score, and method of gout ascertainment. In contrast, the studies that defined PD according to the use of drugs showed significant results . There was a significant publication bias on the association between gout and PD.The presence of gout had no significant effect on the risk of subsequently developing PD. Further analyses are recommended to investigate the effects of demographic and behavioral risk factors.The online version contains supplementary material available at 10.1186/s12883-022-02874-0. Parkinson’s disease (PD) is a chronic, progressive disease primarily characterized by the degeneration of dopaminergic nigrostriatal neurons –3. PD isThe intracellular metabolism of dopamine is prone to the production of free radicals, therefore exposing dopaminergic cells to a greater risk of oxidative stress toxicity . Urate oSince gout is a chronic form of hyperuricemia, there is a possibility that gout may have a protective effect on the development of PD. Therefore, the present systematic review and meta-analysis aimed to evaluate whether an association exists between gout and the subsequent onset of PD.We performed this systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .In the present study, two authors independently searched the electronic databases, including PubMed, Scopus and the Web of Science to find relevant studies from the inception of the databases up to March 16, 2022. Furthermore, we manually searched the first 100 pages of the Google Scholar search engine to identify any eligible studies. There were no limitations or restrictions in the search fields, such as study type, date, or language. We also used backward and forward citation searches of the included studies, in order to find as many relevant articles as possible. Our search strategy included all terms related to Gout and PD, which is described in detail in Supplementary Table Two researchers independently screened the title and abstract of all identified articles, based on the eligibility criteria. Following this, the same researchers reviewed the full texts of all selected articles. The inclusion criteria were as follows: 1) Cross-sectional studies investigating the development of PD in subjects with gout; 2) Case–control or cohort studies comparing the risk of PD in participants with and without gout; 3) odd ratio (OR), hazard ratio (HR), or relative risk (RR) with 95% confidence interval (CI) were reported or could be calculated. We excluded those studies which included patients diagnosed with PD before being diagnosed with gout. In addition, case reports, case series, editorials, commentaries, letters, review articles, notes, news, book chapters, meta-analyses, and the re-analysis of previously published articles were excluded. Any disagreements between the two researchers were resolved by discussion or consultation with a third reviewer.We conducted the data extraction process using predefined Microsoft Office Excel forms. Two authors independently collected the following information from each eligible study: first author, study title, year of publication, country of study, sample size, study population, age range of participants, follow-up duration, smoking status, status of anti-gout therapy, using caffeine or diuretic agents or non-steroidal anti-inflammatory drugs (NSAIDs) or other drugs that can play a role in the treatment or augment the risk of gout disease , comorbidities, diet status, Charlson-Romano comorbidity score, gout diagnosis method, PD ascertainment, and the reported effect sizes , along with their 95% CIs that controlled for any potential confounders. Any discrepancies between the two researchers were resolved by discussion or consulting another reviewer.To assess the risk of bias and study eligibility of the selected articles, two authors independently used the Newcastle Ottawa Scale (NOS) to evaluate and score each included study according to the different parameters . In brieI2 statistics. I-square values above 50% represent significant heterogeneity. In the case of significant heterogeneity, we performed a random effect analysis, otherwise a fixed effect model was used [p-value of less than 0.5 was treated as statistically significant.The meta-analysis was performed using Stata 17.0 . We assessed statistical heterogeneity using the Q test and was used , 30. Thewas used . ORs werwas used . We aimewas used . Subgrouwas used . A p-valn = 899), Scopus (n = 730), the Web of Science (n = 503), and Google Scholar (n = 1). Following the removal of 492 duplicate articles, the remaining 1,641 studies were screened and 75 records were selected for full text review. Sixty four publications were excluded because they did not report the outcome of interest. We could not access the full text of one article and thus it was also excluded [A total of 2,133 articles were identified from PubMed  Fig. A. MoreovSubgroup analysis was undertaken comparing age group, whether patients were receiving treatment for gout, study design, quality assessment scores, and type of gout and PD diagnoses. The pooled RRs for the age groups < 75 years old and ≥ 75 years of age were 0.95 (95% CI: 0.68 to 1.35) and 0.86 (95% CI: 0.67 to 1.09), respectively , which indicates there was significant publication bias on the association between gout and PD. The funnel plot also shows an asymmetrical distribution of the included articles Fig. .Fig. 4FuThe present systematic review and meta-analysis was conducted with the aim of examining whether an association exists between gout and the subsequent development of PD. The ten publications included approximately 800 thousand gout patients and nearly 14.5 million non-gout cases. The study did not find an inverse association between a history of gout and PD in either sex, which is in contrast with previous evidence showing a lower risk of developing PD in males with gout , or in bA previous meta-analysis, conducted in 2015, included three case–control and two cohort studies and aimed to investigate the possible association between gout and PD . This reAnother controversial issue is the predictive value of uric acid levels in the subsequent risk of PD development. A meta-analysis on nearly five thousand participants was performed with the objective of comparing the levels of serum uric acid in PD patients with those of a control group. The study came to the conclusion that serum uric acid levels were substantially lower in patients with PD, compared to the control group , for both male  and female  cases . FurtherThe following points may explain this inconsistency. Firstly, it could be hypothesized that the protective role of uric acid on the development of PD follows a dose-gradient, in which gradually increasing the level of serum uric acid up to the upper limit of the normal range represents an inverse association with PD onset, while above that level the association disappears overall and a positive association appears among female participants. Secondly, given that the majority of patients with hyperuricemia never develop gout, there may be a genetic predisposing factor in which uric acid might lose the neuroprotective properties when a patient presents with symptoms of gout. Nevertheless, it could be argued that there may be more than one genetic factor, either leading to hyperuricemia (without gout) and lowering the risk of PD, or causing hyperuricemia with gout and not protecting from PD. Another possibility is that there is one genetic factor that reduces the risk of developing PD and is collaterally associated with hyperuricemia, but that hyperuricemia is an epiphenomenon that has no direct protective effect on dopaminergic neurons. Thirdly, the condition of pro-inflammation and oxidative stress, due to the deposit of monosodium urate in gout attacks , might oApart from acting as an antioxidant molecule, a paradoxical pro-oxidative role has also been linked to uric acid . By incrUnder normal conditions, the leakage of uric acid molecules through the blood brain barrier is low. However, it has been shown that the concentration of uric acid is higher in the cerebrospinal fluid of males, than among females . In addi7The present meta-analysis has several advantages over the earlier attempt . FirstlyIn contrast to several previous studies, which have found hyperuricemia to have a protective effect on the subsequent development of PD, our meta-analysis provides high-quality evidence that there is no overall association between gout and the risk of developing PD. In contrast, we found that females suffering from gout were more vulnerable to the subsequent onset of PD. Further observational investigations and meta-analyses, as well as more subgroup analyses of the demographic and behavioral risk factors , are recommended in order to more clearly understand the association between gout and PD. In addition, the mechanism by which the sex differences occur, regarding the antioxidative and pro-oxidative features of uric acid, needs to be further investigated.  Additional file 1:Supplementary Table 1. Search strategies for PubMed, Scopus, Web of Science, and Google scholar. Supplementary Table 2. Comorbid conditions of individuals who participated in the studies included in the meta-analysis. Supplementary Table 3. Risk of bias assessment for the included cohort studies. Supplementary Table4. Risk of bias assessments for the included case-control studies.


PPARγ) and PPARγ coactivator1 family (PPARGC1A and PPARGC1B) gene single nucleotide variants (SNVs)were strongly associated with cancer susceptibility. The purpose of this study was to investigate the association of PPARγ, PPARGC1A, and PPARGC1B variants with the risk of gastric cancer (GC).Previous studies showed that peroxisome proliferator-activated receptor gamma  = 0.62, 95% confidence interval (95%CI) = 0.42–0.93, adjusted P = 0.019; GC + GG vs. GG: OR = 0.63 95%CI = 0.42–0.93, adjusted P = 0.019; respectively). In addition, stratified analysis revealed that the PPARγ rs1801282 SNV was correlated with the risk of GC in subgroups of age ≥ 61, no smoking, and no alcohol consuming. We also confirmed that the PPARγ rs3856806 C > T SNV promoted the risk of GC in women. The PPARGC1A rs8192678 TT genotype decreased the susceptibility of GC in men. The PPARGC1A rs2970847 C > T SNV decreased the susceptibility of GC in the subgroup of BMI ≥ 24 kg/m2. The PPARGC1B rs7732671 G > C and rs17572019 G > A SNVs promoted the risk of GC in the subgroup of BMI ≥ 24 kg/m2.The PPARγ, PPARGC1A, and PPARGC1B SNVs may be associated with the susceptibility of GC in eastern Chinese population. Future studies with larger populations, detailed H. pylori infection status for subgroup analysis, and functional study are needed to further clarify the relationship between these SNVs and GC risk.This study indicates that the The online version contains supplementary material available at 10.1186/s12920-022-01428-0. Gastric cancer (GC) accounts for more than 5% of all new cancer cases worldwide, making it the fifth most common cancer and the third leading cause of cancer-related deaths . China iPeroxisome activated receptor gamma (PPARγ), a type II nuclear receptor gene, located on chromosome 3p25, is a member of the peroxisome activated receptor (PPAR) superfamily involved in adipogenesis, lipid metabolism, cell proliferation, chronic inflammation, and insulin sensitivity [PPARγ signaling pathway was associated with the development of obesity, diabetes, and cancers [PPARγ expression was associated with the prognosis of various tumors, including cancers of the breast, pancreas, and colorectum [ cancers . PPAR is cancers . Severallorectum –10. Whenlorectum , 12. PPAlorectum –15. PPARlorectum –18. Theylorectum , 20. Thelorectum –23. Incrlorectum , 24. Howlorectum . PPARGC1lorectum , 28. Estlorectum . PPARGC1PPARγ, PPARGC1A, and PPARGC1B SNVs with susceptibility of esophageal, colorectal, and hepatocellular carcinomas [PPARγ, PPARGC1A, and PPARGC1B SNVs might be effective susceptibility markers of GC. According to the previous studies [PPARγ rs1801282 C > G, rs3856806 C > T, PPARGC1A rs8192678 C > T rs2970847 C > T, and PPARGC1B rs7732671 G > C, rs17572019 G > A SNVs are chosen to investigate the association between these SNVs and GC risk in a Chinese cohort.Single nucleotide variant (SNV) is the most common type of genetic variant that affects gene expression through different mechanisms and is associated with genetic susceptibility to cancer . In the rcinomas –33. Ther studies –35, PPARThis cohort is part of a previous study . GC grouIn this study, 2mlEDTA anticoagulant fasting peripheral venous blood was donated by each participant and stored at -70℃ until DNA extraction was performed. Genomic DNA extraction was conducted by using a commercial blood DNA extraction kit  following the procedure according to the manufacturer’s instructions.Simply put, cryopreserved blood samples were water bath thawing. After erythrocyte lysis and removal, nuclear release, protein precipitation, and removal, genomic DNA precipitation, and re-dissolve, genomic DNA was obtained. The concentration and purity of DNA were assayed for quality control by a microspectrophotometer.tm Kit  [As with the prior, the genotypes of the six SNVs were assessed by multiplex fluorescent PCR using a custom-by-design 48-Plex SNPscan, China) . For quahttp://ihg.gsf.de/cgi-bin/hw/hwa1.pl). Genotype frequencies of the SNVs variants were compared using a Chi-square test (χ2) or Fisher’s exact test among GC cases and controls. The relationship of the SNVs with susceptibility to GC was assessed by Multivariate logistic regression analysis to estimate by odds ratios (ORs) and 95% confidence intervals (CIs). The student’s t-test and chi-square (χ2) test were used to compare continuous variables and discrete variables between CG patients and healthy controls, respectively. A P < 0.05 (two-tailed) was considered statistically significant. All statistical analyses performed in the present study were conducted using the SPSS software package .The genotype frequency of each SNV in the control group was tested for deviation from Hardy-Weinberg equilibrium (HWE) by Pearson’s goodness-of-fit chi-square using online Chi-square software . Thus, GC cases and controls were well matched. However, there was a significant higher in tobacco smoking, alcohol consumption, and body mass index (BMI) < 24 (kg/m2) in GC cases than in healthy controls .The Demographic variables and risk factors of the cohort are summarized in Table As summarized in Table P values of the SNVs rs1801282, rs3856806, rs8192678, rs2970847, rs7732671, and rs17572019) from Hardy–Weinberg equilibrium (HWE) test were more than 0.05 . However, the genotype distribution of rs3856806 variants did not reach HWE (P = 0.026).Five Overall, all genotyping successful rates for the SNVs were > 95%, suggesting that the study met the requirements of molecular epidemiology.PPARγ rs1801282 SNV was associated with a decreased risk for GC . After adjusting for age, gender, BMI, smoking status, and alcohol consumption, the results remained statistically significant . However, no significant relationship was found between GC risk and other genotypes assayed in genetic models .The distribution of genotypes and genotype frequencies are summarized in Table PPARγ rs1801282 C > G SNV, stratified analysis revealed that this variant constituted a GC protective factor in subgroups of age ≥ 61 years, no smoking, and no alcohol consuming (age ≥ 61years: additive model: GC vs. CC: adjusted P = 0.043; no smoking: additive model: GC vs. CC: adjusted P = 0.033, dominant model: GC + GG vs. CC: adjusted P = 0.032; no alcohol consuming: additive model: GC vs. CC: adjusted P = 0.006, dominant model: GC + GG vs. CC: adjusted P = 0.008; respectively, Table To determine whether the effects of these SNVs were modified by factors such as smoking, age, sex, alcohol consumption, and BMI, a stratified analysis was performed(Table PPARγ rs3856806 C > T SNV, stratified analysis revealed that this variant constituted a GC risk factor in females (additive model: CT vs. CC: adjusted P = 0.037; dominant model: CT + TT vs. CC: adjusted P = 0.038, Additional file For PPARGC1A rs8192678 TT genotype, stratified analysis revealed that this variant constituted a GC protective factor in males. (homozygote model: TT vs. CC: adjusted P = 0.045, Additional file For the PPARGC1Ars2970847 C > T SNV, stratified analysis revealed that this variant constituted a GC protective factor in subgroups of BMI ≥ 24 kg/m2. (additive model: CT vs. CC: adjusted P = 0.028, Additional file For PPARGC1B rs7732671 G > C SNV, stratified analysis revealed that this variant constituted a GC risk factor in subgroups of BMI ≥ 24 kg/m2. (additive model: GC vs. GG: adjusted P = 0.027; dominant model: GC + CC vs. GG: adjusted P = 0.033, Additional file For PPARGC1B rs17572019 G > A SNV, stratified analysis revealed that this variant constituted a GC risk factor in subgroups of BMI ≥ 24 kg/m2. (additive model: GA vs. GG: adjusted P = 0.028; dominant model: GA + AA vs. GG: adjusted P = 0.034, Additional file For PPARγ rs1801282 C > G SNV was associated with a decreased risk for GC. In addition, PPARγ rs1801282 C > G SNV conferred decreased risk for GC patients among subgroups of age ≥ 61 years, no smoking and no alcohol consuming; PPARγ rs3856806 C > T SNV conferred risk for GC patients in females; PPARGC1A rs8192678 TT genotype conferred decreased risk for GC patients in male; PPARGC1A rs2970847 C > T conferred decreased risk for GC patients in a subgroup of BMI ≥ 24 kg/m2; both PPARGC1B rs7732671 G > C and PPARGC1B rs17572019 G > A conferred risk for GC patients in a subgroup of BMI ≥ 24 kg/m2.This study revealed that H. pylori infection [The rs1801282 SNV has been reported to be associated with susceptibility to a variety of tumors. However, in some studies such as on breast and colorectal cancers, the correlation tended to vary by race and/or cancer type , 36–42. nfection . A Turkinfection . Our stuThe impact of PPARγ on cancers is complex and bidirectional, PPARγ acts as a tumor promoter by induction of lipogenic gene ACLY, MIG12, FASN, and NR1F1, stem cell-related gene KLF4, ALDH and upregulation Nox1 ROS, and VEGF. PPARγ act as a tumor suppressor by inducing apoptosis through the upregulation of PTEN, suppression of NF-κB, and many other signaling pathways , 45, 46.PPARγ may vary by tumor type and race. Further functional studies are needed to clarify the impact of rs1801282 SNV on GC.Variants in the PPARγ affect gene transcription and expression, which has been intensively studied in metabolic diseases. An in vitro experiment demonstrated that rs1801282 SNV reduced the binding affinity of the receptor to the reactant and induced a reduction in transcriptional activity and was associated with lower BMI and improved insulin sensitivity , 51. HowPPARγ rs3856806 C > T variant increased overall cancer susceptibility [PPARγ rs3856806 C > T constituted a risk factor for GC in women.Rs3856806 C > T variant, in exon 6, is a synonymous variant. It is an exon splice enhancement site that may reduce transcription of PPARγ . There itibility , 61. In tibility . Our resPPARC1A, which substitutes glycine with serine at amino acid position 482 in exon 8. Research has also shown that the expression of PPARGC1A is significantly lower in those carrying the minor allele [In GC tissues and GC cell lines, PPARGC1A expression was upregulated and associated with metastasis, invasion, and induce apoptosis of GC cells .  Rs8192r allele . A studyr allele . In prevr allele , 33. Ther allele . Since mPPARGC1A rs2970847 is a synonymous variant, which is reported associated with the risk of type 2 diabetes, obesity, and insulin resistance [PPARGC1A rs2970847 SNV and susceptibility of esophageal, colorectal, and hepatocellular carcinomas [2 in an Additive model. A study of an Iranian population revealed that rs2970847 SNV downregulates insulin signaling pathways and is associated with insulin resistance. Carriers of the T allele of rs2970847 had decreased performance of PPARGC1A and higher risk for obesity [sistance , 68. In rcinomas –33, howe obesity . The corPPARGC1B is located on chromosome 5 and consists of 1023 amino acids. PPARGC1B is highly similar to PPARGC1A in terms of structure, function, and mechanism [PPARGC1B rs7732671 variant increases breast cancer risk and affects cancer progression [PPARGC1B rs17572019 G > A SNV [PPARGC1B rs7732671 G > C and rs17572019 G > A SNVs were found to be a risk factor for GC in the additive and dominant models in the group with BMI ≥ 24 kg/m2. A study on breast cancer found that rs7732671 G > C enhances ERRα and ERRγ signaling and modulates aerobic glycolysis [echanism . Studiesgression . The ris > A SNV . In the ycolysis , furtherPPARγ, PPARGC1A, PPARGC1B variants, and GC susceptibility. This study demonstrated that the PPARγ, PPARGC1A, and PPARGC1B SNVs were associated with genetic susceptibility to GC. It could be a potential biomarker in the prevention and screening of GC in the Chinese population. However, there were several limitations in this study. First, H. pylori infection status was inaccessible, which affect the further subgroup analysis. Second, since both the GC cases and controls were hospital-based, the potential selection bias might have occurred. Third, many other environmental and personal factors might be associated with the etiology of GC, such as socioeconomic status, literacy, and diet, but were not collected and analyzed adequately in this study.To our knowledge, the present study is the largest sample size study to date exploring the relationship between PPARγ rs1801282 C > G SNV was associated with a decreased risk for GC in eastern Chinese population. Future studies with larger populations, detailed H. pylori infection status, and functional studies are needed to further clarify the relationship between these SNVs and GC risk.In conclusion, this study indicates that the Additional file 1. Supplementary Table S1.Additional file 2. Supplementary Table S2.Additional file 3. Supplementary Table S3.Additional file 4. Supplementary Table S4.Additional file 5. Supplementary Table S5.Additional file 6. Supplementary Primers.Additional file 7. Raw_data.