Association of cigarette smoking habits with the risk of prostate cancer is still a matter of debate. This systematic review and meta-analysis aimed to assess the association between cigarette smoking and prostate cancer risk.We conducted a systematic search on PubMed, Embase, Cochrane Library, and Web of Science without language or time restrictions on June 11, 2022. Literature search and study screening were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Prospective cohort studies that assessed the association between cigarette smoking habits and the risk of prostate cancer were included. Quality assessment was conducted using the Newcastle–Ottawa Scale. We used random-effects models to obtain pooled estimates and the corresponding 95% confidence intervals.P < 0.001), especially in studies completed in the prostate-specific antigen screening era. Compared to former smokers, current smokers had a significant lower risk of PCa . Ever smoking showed no association with prostate cancer risk in overall analyses , but an increased risk of prostate cancer in the pre-prostate-specific antigen screening era  and a lower risk of prostate cancer in the prostate-specific antigen screening era  were observed. Former smoking did not show any association with the risk of prostate cancer.A total of 7296 publications were screened, of which 44 cohort studies were identified for qualitative analysis; 39 articles comprising 3 296 398 participants and 130 924 cases were selected for further meta-analysis. Current smoking had a significantly reduced risk of prostate cancer .The online version contains supplementary material available at 10.1186/s12889-023-16085-w. Prostate cancer (PCa) is the second most commonly diagnosed cancer and the fifth leading cause of cancer death among males, with an estimated 1.4 million new cases and 375 000 deaths worldwide in 2020, accounting for 7.3% and 3.8% of all cancers diagnosed, respectively .Smoking is a well-established risk factor for several cancers, such as lung cancer, head and neck cancer, bladder cancer, and esophageal cancer , 8. HoweBiological mechanisms underlying smoking and PCa risk have been studied for many years. Burning cigarettes can produce more than 7000 chemicals, and at least 70 carcinogens such as polycyclic aromatic hydrocarbons (PAHs) and cadmium . MutatioWe performed this systematic review and meta-analysis to investigate the association of cigarette smoking habits with the risk of PCa. We aimed to include a larger sample of studies than previous meta-analyses and collect the latest evidence and the most comprehensive information on the association between cigarette smoking and PCa risk. Our primary objective was to assess the risk of PCa in current smokers, former smokers, and ever smokers. We hypothesized that smokers have a higher risk of PCa compared to non-smokers.This systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . Two indProspective cohort studies investigating the association between cigarette smoking and PCa risk were included for analysis. The primary outcome was the risk of PCa. Those studies that provided an effect measure  quantifying the impact of smoking on the risk of PCa were considered for further quantitative synthesis . The removal of duplicates and assessment of article eligibility were conducted independently by XWY and HC, and any disagreements were resolved by consulting the senior author (JP). Review articles, editorials, meeting abstracts, case‒control studies, cross-sectional studies, and those not on the topic were excluded.All included studies were independently assessed by XWY and HC for risk of bias using the Newcastle‒Ottawa Scale for cohort studies . This scData were independently extracted by XWY and HC. All extracted variables were cross-checked to ensure their reliability. We recorded the total number of participants, PCa cases, and the mean or median follow-up time across all included studies. Relative risks (RRs) and the corresponding 95% confidence intervals (CIs) were retrieved or calculated using frequency distributions. Considering the prevalence rate of PCa in the public, we believed that the odds ratio was close to the RR , 28. Haz2 statistic and the Cochran’s Q test. Values of the I2 statistic of approximately 25%, 50%, and 75% were interpreted as low, moderate, and high heterogeneity, respectively [P < 0.05 was considered statistically significant.Three authors  performed statistical analyses using Stata software, version 16.0 (StataCorp). When both crude and adjusted RRs were provided, we used the most fully adjusted value. We calculated the pooled RRs and 95% CIs and plotted forest plots using random-effects models (DerSimonian‒Laird method) for the association of current smoking, former smoking, and ever smoking with the risk of PCa . Statistectively . In the ectively . All tesWe identified 7296 citations, and after removing duplicates, 4963 citations remained for screening. After the removal of ineligible citations, we retained 60 articles that we assessed for eligibility by reading the full text; 16 of these were excluded for specific reasons. Finally, 44 studies met our inclusion criteria for qualitative synthesis and meta-analysis  . Inspection of the funnel plot did not demonstrate publication bias , with the heterogeneity lower than that of analysis of studies using non-smokers as the reference . Compared to former smokers, current smokers had a significant lower risk of PCa  based on 21 studies [P < 0.001). When stratified by world region, studies conducted in North America, Europe, Asia, and Australia showed a negative association between current smoking and PCa risk. We also performed subgroup analyses in 21 studies with quality scores ≥ 7 [When performing sensitivity analyses . The data were heterogeneous according to the I2 statistic and the Cochran’s Q test . Inspection of the corresponding funnel plot did not show evidence of publication bias , with an I2 value of 67.0% and a negative result of publication bias , but in the pre-PSA screening era, ever smokers showed a significantly increased risk of PCa compared to never smokers  , and studies from North America, Europe, and Australia revealed no association between ever smoking and PCa incidence. In terms of subgroup analyses stratified by quality score, the studies with a quality score ≥ 7 showed a modest negative association with PCa risk , while the studies with a quality score of 6 showed no association.Thirty-three studies were included in the meta-analysis to assess the association of ever smoking with the risk of PCa Fig.  6, 13, , 64–68. P < 0.001).Of these 5 studies  in the United States were reported as current cigarette smokers . Of them2 statistics and the Cochran’s Q test, and the difference in adjusted confounding factors may be one of the reasons. We have included multivariate results as much as possible to reduce the bias, and there was no indication of publication bias. Dividing studies into pre-PSA screening era and PSA screening era based on publication year (1995 as the cut-off) may produce bias because many of the cohorts published and categorized into the PSA screening era extended into the pre-PSA screening era. Another limitation is that we failed to calculate the impact of quantitative cigarette consumption on the PCa risk due to a lack of data. However, we have to point out that the meta-regression conducted by Islami et al. [The key strength of this systematic review is that the study comprised a total of 44 prospective cohort studies, 39 of which were included in the meta-analysis, with the largest number of participants and PCa cases to date. Furthermore, we included all the data on current smoking, former smoking, and ever smoking in the analysis without date and language restrictions, which means that the study provides the latest evidence and the most comprehensive information on the association between cigarette smoking and risk of PCa. We assessed the quality of each selected study using the Newcastle‒Ottawa Scale for cohort studies, and the median score was 7 and the lowest score was 6, suggesting that the quality of the included studies can be guaranteed. Other strengths include applying independent literature search, quality assessment, and data extraction by two investigators; conducting several sensitivity analyses; and using Egger’s test to examine publication bias.There are some limitations of our study. Most of the information on smoking habits was obtained from self-administered questionnaires, and the definitions of current smokers and former smokers were not completely the same between different studies. Some participants may have changed their smoking habits after baseline investigations, but repeated assessment of smoking exposure was absent in primary studies. We calculated RRs and the corresponding 95% CIs using frequency distributions without adjusting confounding factors when risk estimates were not reported. We focused on the impact of cigarette smoking on the risk of PCa; second-hand cigarette smoke and the use of other tobacco products  that have showed increased risk of many cancers in numerous studies , 99 werei et al.  was methTo the best of our knowledge, this systematic review and meta-analysis contained the largest sample of prospective cohort studies, the latest evidence and the most comprehensive information on the association between cigarette smoking habits and the risk of PCa. The smokers’ poor adherence to cancer screening and the occurrence of smoking-related aggressive cancers as well as cardiovascular, pulmonary, and several other deadly diseases may explain the negative association. Regional distinctions can be attributed to the difference of participants in age, ethnicity, socioeconomic status, and educational levels. In addition, a correct methodology is important, the choice of different effect models should base on the heterogeneity and characteristics of enrolled studies. However, it is difficult to conclude a positive association between cigarette smoking and PCa risk as we hypothesized due to these affecting factors. We should focus on taking measures to help smokers to be more compliant with early cancer screening and to quit smoking.Additional file 1. A. Characteristics of the 39 studies included in the meta-analysis. B. Characteristics of the 5 studies not included in the meta-analysis due to lack of information.Additional file 2. Results of quality assessment using the Newcastle-Ottawa Scale for cohort studies.Additional file 3. Sensitivity analyses of association between smoking status and risk of prostate cancer.


Obesity has become a global public health issue, which impacts general health and the brain. Associations between obesity and white matter microstructure measured using diffusion tensor imaging have been under reviewed, despite a relatively large number of individual studies. Our objective was to determine the association between obesity and white matter microstructure in a large general population sample.We analyzed location of brain white matter changes in obesity using the Anisotropic Effect Size Seed-based d Mapping (AES-SDM) method in a voxel-based meta-analysis, with validation in a region of interest (ROI) effect size meta-analysis. Our sample included 21 742 individuals from 51 studies.The voxel-based spatial meta-analysis demonstrated reduced fractional anisotropy (FA) with obesity in the genu and splenium of the corpus callosum, middle cerebellar peduncles, anterior thalamic radiation, cortico-spinal projections, and cerebellum. The ROI effect size meta-analysis replicated associations between obesity and lower FA in the genu and splenium of the corpus callosum, middle cerebellar peduncles. Effect size of obesity related brain changes was small to medium.Our findings demonstrate obesity related brain white matter changes are localized rather than diffuse. Better understanding the brain correlates of obesity could help identify risk factors, and targets for prevention or treatment of brain changes. For instance, 63.1% of the population in Canada and 52% of adult population worldwide can be classified as overweight or obese  and is aIt is important to map and better understand the brain correlates of obesity. Such studies could help identify risk factors for brain alterations, targets, and possibly new mechanisms of action for prevention or treatment of brain changes and associated cognitive or mental health outcomes. For instance, deterring obesity could prevent about 3% of depressive disorders, deterring maternal pre-pregnancy obesity would prevent about 9% of ADHD, and deterring maternal overweight pre/during pregnancy would prevent about 6% of both ADHD and autism spectrum disorders . PrelimiObesity is associated with diffuse changes in gray matter, including the medial prefrontal cortex, temporal pole, precentral gyrus, inferior parietal cortex and the cerebellum . The assA single previous spatial meta-analysis investigated the location of obesity related white matter alterations. However, this study had restrictive inclusion criteria and contained only one half of the available studies in the literature . FurtherThus, we present the first study that analyzes the location of white matter changes in obesity using a voxel-based spatial meta-analysis and then validates the results and estimates the extent of obesity-related alterations with a region of interest (ROI) effect size meta-analysis. This approach allowed us to review the entire available literature on this topic, maximize sample size, minimize false negatives and by focusing on replications to also minimize the risk of false positives.2.2.1.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement  and perf2.2.Only studies that included either whole brain analyses using voxel-based morphometry (VBM) or tract based spatial statistics (TBSS) were used for the voxel-based spatial meta-analysis, while studies providing numerical estimates of effect sizes of fractional anisotropy (FA) from region of interest (ROI) analyses were used for the ROI effect size meta-analysis. We originally planned to additionally look at mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) but there were too few studies to analyze.We included studies which analyzed associations between FA and a measure of obesity, including body mass index (BMI), waist-to-hip ratio, waist circumference, body composition , and body fat distribution as measured by abdominal MRI scans. Some studies compared groups of people with overweight or obesity and normal weight individuals, while others investigated an association between an obesity-related measurement  as a continuous variable and diffusion tensor imaging (DTI) measures. We jointly analyzed results from both types of analyses.Our objective was to maximize the scope of literature included in the analyses, and so we set minimum exclusion criteria. Exclusion criteria were set to studies that did not age-match participants, did not test for association between obesity and DTI measures, did not provide numeric results, or did not respond to email requests for data availability. All the whole brain studies provided corrected results.The initial search identified 443 studies, and two raters, TH and LD independently screened the list of titles and abstracts for inclusion, see 2.3.x, y, z) and corresponding t-statistics from each study. If the study included a different measure of effect size like a value of p or z-score, these were converted to t-statistics using the SDM software. We contacted the authors of studies that met our inclusion criteria for additional information. With this strategy, we obtained one full t-map  methodll t-map , and use results . See Tabvia AES-SDM software and used jack-knife analysis to determine the robustness of the results after removing individual studies from the meta-analysis. We preprocessed all data files with the TBSS template based on the FA skeleton  as suggested by Radua et al.  effect size measures from individual studies . See We used Comprehensive Meta-Analysis Software (CMA) version 3.3.0703.3.1.We included 30 studies in the voxel-based meta-analysis, and 21 in the ROI meta-analysis, see 3.2.p > 0.001), and middle cerebellar peduncles  clusters were replicated. No potential publication bias was detected except for the left cerebellum, where the Egger test was statistically significant , left splenium of the corpus callosum , middle cerebellar peduncles , anterior thalamic radiation , right cortico-spinal projections , and the left cerebellum  see 3.3.d = −0.263, 95% Confidence Interval –0.423 to –0.103, p = 0.001; see d = −0.380, 95% Confidence Interval –0.560 to –0.200, p < 0.001; see d = −0.157, 95% Confidence Interval-0.174 to-0.139, p < 0.001; see d = −0.135, 95% Confidence Interval –0.227 to –0.044, p = 0.004; see z = −2.175, p = 0.03).Relative to controls, obese individuals had lower FA in some of the same regions as those identified in the spatial voxel-based meta-analysis, including the genu of the corpus callosum .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Previous studies have yielded conflicting findings regarding the association between circulating lipids and lipid-lowering drugs with urinary stones, and the causal relationship between the two remains inconclusive.This study aimed to assess the causal relationship between circulating lipids , lipid-lowering drugs (HMGCR [HMG-CoA reductase] inhibitors and PCSK9[Proprotein Convertase Subtilisin/Kexin Type 9] inhibitors) and the risk of urinary stones, using genetic data.Genetic instrumental variables (GIVs) for circulating lipids and lipid-lowering drugs were obtained from the UK Biobank and existing literature. Outcome data were extracted from a genetic association database with 3,625 urinary stone cases and 459,308 controls. Two-sample MR analysis, employing the TwoSampleMR software package in R 4.2.3, was conducted to assess the associations between multiple exposures. The primary outcome was determined using the inverse variance weighted (IVW) method for the causal relationship between exposure and outcome, while additional methods such as MR-Egger, weighted median, simple mode, and weighted mode were utilized as supplementary analyses. Robustness of the Mendelian Randomization (MR) analysis results was assessed through leave-one-out analysis and funnel plots.The MR analysis revealed a significant association between elevated TG levels per 1 standard deviation and the occurrence of urinary stones . However, no significant association was observed between factors other than TG exposure and the risk of urinary stone occurrence across all methods and lipid-lowering drugs .Our findings provide conclusive evidence supporting a causal relationship between an increased risk of urinary stones and elevated serum TG levels. However, we did not find a significant association between urinary stone occurrence and the levels of LDL-C, HDL-C, APOA, APOB, Pure hypercholesterolaemia and lipid-lowering drugs. Recent Circulating lipids encompass a group of essential substances in the blood, including TG, LDL-C, HDL-C, APOA, APOB, cholesterol, and other metabolically important compounds. However, disruptions in lipid metabolism, as common metabolic disorders, have been linked to the onset and advancement of various illnesses that impact multiple bodily systems –13. In rIn general, the most robust and dependable experimental approach for investigating causal relationships between risk factors and outcome events is the randomized controlled trial (RCT). However, in etiological studies, the implementation of RCTs can be challenging due to ethical constraints and various other limitations. Large-scale RCTs exploring the association between circulating lipids, lipid-lowering drugs, and the risk of urinary stones are notably absent. Moreover, it is imperative to acknowledge the difficulty observational studies face in mitigating potential confounding biases between exposures and outcomes . As a re22.1https://gwas.mrcieu.ac.uk/datasets/ukb-b-8297/). This analysis encompassed 3,625 cases of urinary stones and 459,308 control subjects, with the final GWAS yielding up to 9,851,867 associations between genotypic SNPs and urinary stones  with plausible confounders  (2 > 0.01) and were situated in close proximity within a 1000-kilobase aggregation window of other SNPs with higher P-values. Lastly, we employed an F-statistics threshold greater than 10 to identify robust instrumental variables, thus mitigating the potential effects of bias. It is important to note that our SNPs screening approach aligned with methodologies utilized in previously published studies, ensuring consistency and reliability in our selection of genetic variants associated with two lipid-lowering drug gene targets and their respective SNP datasets.We have successfully identified independent SNPs associated with plasma levels of TG, LDL-C, HDL-C, APOA, APOB, and Pure hypercholesterolaemia based on three fundamental hypotheses. Firstly, we initiated the selection process by identifying autosomal bi-allelic SNPs with a significance level of P<5ental SNP , 37 in tabetes.) , 39. In 2.3The flowchart illustrating the significant SNP screening process, along with the exposures and outcomes, is presented in 2.4https://github.com/MRCIEU/TwoSampleMR) within the R 4.2.3 environment. To evaluate causality, we employed a total of five MR analysis methods, encompassing the IVW, MR Egger, Weighted Median, Simple Mode, and Weighted Mode approaches. Our primary method of assessment was the IVW method, which assigns weights to each ratio based on their standard errors (SE) while accounting for potential heterogeneity in measurements. This method yields reliable causal estimates even in the presence of heterogeneity. Since all instrumental variables must conform to MR assumptions in the IVW method, we also employed two other methods, weighted median estimation, and MR-Egger, for sensitivity analysis. The weighted median estimation method provides a consistent assessment of causality when more than half of the instrumental variables are deemed valid. Furthermore, we employed the IVW method to gauge the pleiotropy and heterogeneity of individual SNPs  analysis to validate our study. This approach examined whether there was a reverse causal relationship between urinary stones and circulating lipids  along with lipid-lowering drugs (HMGCR inhibitors and PCSK9 inhibitors). We used urinary stones as an exposure factor, circulating lipids and lipid-lowering drugs as outcome factors(We did not extract SNPs associated with exposure factors in the GWAS for HMGCR inhibitors and PCSK9 inhibitors). As anticipated, our reverse Mendelian randomization analysis confirmed the absence of a reverse causal relationship between the genetically predicted circulating lipids, lipid-lowering drugs, and urinary stones . Paradoxically, low levels of HDL-C were found to provide protection against the development of incidental KSD. This intriguing finding underscores the complexity of the relationship between lipid profiles and urinary stone formation . In thisOur findings, which reveal a link between triglyceride abnormalities and urinary stones, align with a body of evidence derived from recent cross-sectional analyses and case-control studies –50. One Examining the causal mechanisms related to the components of urinary stones and urinary metabolism, high triglyceride levels also elevate the urinary excretion of components such as calcium, sodium, and potassium. This phenomenon significantly increases the supersaturation of uric acid and creatinine, thereby expediting the formation of urinary stones . The preWhen examining the association between lipid-lowering drugs and urologic diseases, certain researchers , 61 haveOur study leveraged a robust dataset comprising recent urological stone cohort studies with large sample sizes. Notably, the participants from the UK Biobank included in the analysis, encompassing the five selected circulating lipids as exposures, were sourced from the same experimental study. An exception was made for Pure hypercholesterolaemia, which was derived from a distinct cohort. This harmonization of exposure sources offered a notable advantage, eliminating the potential error associated with using different sample sources for various exposures. As a result, our study was empowered to detect and establish a causal relationship between circulating lipids and lipid-lowering drugs in relation to the risk of urinary stones, at least within the European population. The robustness of our findings is underscored by the compelling and consistent causal links observed across different exposures and outcomes. Specifically, based on a two-sample MR analysis incorporating multiple exposures, our study provides support for the assertion that elevated serum TG levels are associated with an increased risk of urinary stones. However, we did not observe a significant association between other lipid parameters, as well as lipid-lowering medications, and the risk of urinary stones. While our findings align with some previous research, they offer enhanced credibility by providing genetic-level evidence. Nevertheless, it’s imperative to acknowledge certain limitations in our analysis. Firstly, all our GWAS data rely on European populations, primarily from the UK Biobank. This geographical focus could introduce bias stemming from differences in ethnicity, environmental factors, and dietary habits –66. Seco5Our findings suggest that elevated triglyceride levels may heighten the risk of urinary stones. Consequently, individuals with urolithiasis should consider enhanced monitoring of triglyceride metabolism. However, this Mendelian randomization study did not reveal any significant associations between LDL-C, HDL-C, APOA, APOB, pure hypercholesterolemia, or lipid-lowering drugs and urinary stones.The original contributions presented in the study are included in the article/This study only used published or publicly available data. Ethical approval for each study included in the investigation can be found in the original publications (including informed consent from each participant). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.ZT: Data curation, Writing – original draft, Writing – review & editing, Methodology. JH: Data curation, Writing – review & editing. AS: Data curation, Visualization, Writing – review & editing. MD: Methodology, Project administration, Writing – review & editing. JS: Writing – review & editing, Data curation, Funding acquisition.


Postoperative bile leakage (POBL) is one of the most common complications after liver resection. However, current studies on the risk factors for POBL and their impacts on surgical outcomes need to be more consistent. This study aims to conduct a meta‐analysis to analyze the risk factors for POBL after hepatectomy.We incorporated all eligible studies from Embase, PubMed, and the Web of Science database (until July 2022) into this study. RevMan and STATA software were used to analyze the extracted data.A total of 39 studies, including 43,824 patients, were included in this meta‐analysis. We found that gender, partial hepatectomy, repeat of hepatectomy, extended hepatectomy, abdominal drain, diabetes, Child≥B, solitary tumor, and chemotherapy are the factors of grade B and C POBL. Some recognized risk factors were considered potential risk factors for grade B and C bile leakage because no subgroup analysis was performed, like HCC, cholangiocarcinoma, major resection, posterior sectionectomy, bi‐segmentectomy, S4 involved, S8 involved, central hepatectomy, and bile duct resection/reconstruction. Meanwhile, cirrhosis, benign diseases, left hepatectomy, and Segment 1 resection were not significant for grade B and C bile leakage. The influence of lateral sectionectomy, anterior sectionectomy, S1 involved, S3 involved, high‐risk procedure, laparoscope, and blood loss>1000 mL on POBL of ISGLS needs further research. Meanwhile, POBL significantly influenced overall survival (OS) after liver resection.We identified several risk factors for POBL after hepatectomy, which could prompt the clinician to decrease POBL rates and make more beneficial decisions for patients who underwent the hepatectomy. Hepatectomy also remains an essential treatment for patients with benign liver disease, such as liver stones, hemangiomas, and liver abscesses, in cases where medical interventions have lost their effectiveness.Bile leakage is defined as fluid with an increased bilirubin concentration in the abdominal drain or the intra‐abdominal fluid on or after postoperative Day 3, or as the need for radiologic intervention  because of biliary collections or relaparotomy resulting from bile peritonitis. Increased bilirubin concentration in the drain or intra‐abdominal fluid is defined as a bilirubin concentration at least three times greater than the serum bilirubin concentration measured simultaneously.2 is a risk factor of POBL. It is likely due to varied patient selection criteria or distinct analysis methods. Moreover, several studies said that age, sex, and diabetes were the risk factors,The occurrence of POBL after hepatobiliary surgery may be due to the severed end of the residual bile duct, leakage at the gallbladder‐jejunostomy or T‐tube periphery, and bile duct injury due to inappropriate surgical technique.2This meta‐analysis followed the guidelines reported by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement protocol.2.1Two authors (SX and HW) independently conducted a systematic literature search. We systematically searched Embase, PubMed, and Web of Science databases until July 30, 2022. The search strategies were as follows: ((“Hepatectomy”[Mesh]) OR (liver resection)) AND (bile leakage) AND ((risk factor) OR (prognosis)) in PubMed; (((TS = (Hepatectomy)) OR TS = (liver resection)) AND TS = (bile leakage) AND TS = ((risk factor) OR (prognosis)) in Web of Science and (Hepatectomy. Mp. Or liver resection) AND (bile leakage) AND ((risk factor) OR (prognosis)) in Embase. Endnote X9  was applied to merge the search results and remove duplicated records according to the inclusion and exclusion criteria.2.2Studies were enrolled according to the inclusion and exclusion standards.The inclusion criteria containing: (1) original English articles; (2) studies searching the variables associated with bile leakage after liver resection or the relationship between the bile leakage and prognosis; (3) randomized controlled trials (RCTs) or retrospective studies; (4) studies must include the definition of bile leakage and type of liver resection. Exclusion criteria including: (1) letters, reviews, conference abstracts, comments, case or serious reports, and experimental animal studies; (2) the duplicated cohort patients or the same data set from different studies.3Two investigators selected studies and extracted data independently. When any disagreement appeared during the process, a third review author (YZ), the expert on liver surgery, resolved it by discussion. Population characteristics, intraoperative parameters, and prognosis were the primary data extracted using the standard form. Meta‐analysis was performed when the information of a parameter was available in at least four independent studies.3.1The terminologies of hepatic anatomy and liver resections in the studies were mainly based on the Brisbane 2000 system,3.2The Newcastle‐Ottawa scale (NOS) was used to assess the quality of non‐randomized studies, with a score ≥5 indicating high quality.3.3Because of the different definitions of POBL, a subgroup analysis was conducted to assess the potential heterogeneity between the subgroups with or without ISGLS. Meanwhile, we performed subgroup analysis with or without grade A in the studies to avoid the interference of other factors (bilioma or fluid collection) according to the ISGLS. Because grade A POBL rarely requires clinical intervention, the results could provide a reference for surgeons managing different patients. (number of studies included in each subgroup ≥2).3.4Our meta‐analysis of risk factors for POBL includes preoperative and intraoperative factors. Preoperative factors included gender, age, American Society of Anesthesiologists (ASA) classification, body mass index (BMI), cirrhosis, Child‐Pugh grade, steatosis, diabetes, hypertension, viral hepatitis, malignant disease, HCC, cholangiocarcinoma, metastatic liver tumor, colorectal metastases, benign conditions, solitary tumor, chemotherapy, portal vein embolization (PVE), and a repeat of hepatectomy.Intraoperative factors included anatomical resection and partial hepatectomy, major resection and minor resection, extended hepatectomy, right hepatectomy, left hepatectomy, central hepatectomy, sectionectomy , segmentectomy , vascular resection/anastomosis, bile duct resection/reconstruction, lymph node dissection, high‐risk procedure, laparoscope, blood loss, blood transfusions, and abdominal drain. The high‐risk procedure is defined as hepatectomy exposing the major Glissonean sheath and including the hepatic hilum on the cut surface, such as central sectionectomy, right anterior, and left medial sectionectomy and segmentectomy 5, 8.42 metric.2 > 50% indicates significant heterogeneity, the random‐effect model would be replaced with the fixed‐effect model. Publication bias was evaluated by Begg's funnel plot  and Egger's test (quantitative). Subgroup analysis was used in Review Manager v5.3. p value <0.05 was considered to be statistically significant.We analyzed the data using Stata . The prognostic value of POBL on the long‐term survival of patients who underwent the hepatectomy was estimated by the HR with 95% CI.55.1n = 299) and records meeting the exclusion criteria (n = 594) were removed. The 195 papers were included for the title and abstract screening, and 42 studies with full‐text available were eventually selected for meta‐analysis. Among the included studies, 39 were used to analyze the risk factors for POBL,A total of 1088 studies were identified by using the search strategy  retrospective studies and 6 15.38%) prospective studies .Thirty studies5.2.2p = 0.004; Figure p = 0.000; Figure Seventeen studies5.2.3p = 0.014)  Figure .5.2.4p = 0.017)  Figure .5.3We identified 25 potential intraoperative risk factors for POBL, which were investigated in the previous studies. Studies on the specific risk factor were pooled together for analysis if the information were available. Heterogeneity measurements were performed to determine the statistical strategies, either the fixed effects model or the random effects model. Sixteen intraoperative risk factors were statistically significant, including distinct liver resection procedures, extrahepatic resections or reconstructions, and blood loss or transfusions during surgery Tables  and 4.5.3.1p = 0.000)  Figure .5.3.2p = 0.000)    Figure . Of grea) Figure . Moreove) Figure .5.3.3p = 0.000)    Figure , and S 8) Figure  had prel) Figure .5.3.4p = 0.000; Figure Sixteen studies5.4p = 0.000)  Figure . Further5.5In 2011, a standard concept of bile leakage was developed for clinical management and scientific research, according to the ISGLS.Since grade A bile leakage may be misdiagnosed due to bilioma or the fluid collection and grade A bile leakage rarely requires clinical intervention, we further performed subgroup analyses to exclude this interfering factor Table . In the 6Bile leakage is a common complication after hepatectomy, affecting patients' quality of postoperative life. POBL may also result in several lethal complications, including liver failure and intra‐abdominal infection.Our study demonstrates that preoperative factors, including gender, preoperative chemotherapy, and repeat hepatectomy are factors for grade B and C POBL. This observation is based on initial and two subgroup analyses on patients, providing novel insights into the importance of gender effect on grade B and C POBL. Of great interest, gender is not considered a risk factor for grade B and C POBL occurrence previously.Moreover, a history of chemotherapy is a risk factor for grade B and C POBL. Mechanistically, preoperative chemotherapy can cause hepatocytic atrophy and necrosis with pathologic changes and sinusoidal dilatation, resulting in slowed regeneration or healing of the remnant liver.Our study investigated the association between various liver resections and grade B and C POBL incidence according to the terminologies describing hepatectomy derived from either the Brisbane 2000 system or the Tokyo 2020 consensus. However, due to the clinical application of partial hepatectomy and the lack of data about segment resection in the included research of our studies, we also analyzed the partial hepatectomy and involved segment resection. Our results show that extended hepatectomy is the risk factor for grade B and C POBL. The studies researched by Bednarsch et al.Although the initial analysis does not regard fluid collection and bilioma, subgroup analysis suggests that the abdominal drain is still a risk factor in grade B and C POBL. An abdominal drain allows early detection of POBL, but it may also represent a risk factor for ascending and secondary infection of the collections, leading to increased severity of POBL.Although several indicators have become recognized risk factors for POBL of ISGLS, we were not able to perform B and C POBL subgroup analyses because there were insufficient data on HCC, cholangiocarcinoma, major resection, posterior sectionectomy, bi‐segmentectomy, S4 involved, S8 involved, central hepatectomy, and bile duct resection/reconstruction. Patients diagnosed with HCC show a lower risk of POBL, whereas patients with cholangiocarcinoma tend to have a higher risk of POBL. Considering the surgeries, one crucial reason for the observed opposite readouts might be that surgeries for cholangiocarcinoma patients always involve bile duct resection and reconstruction.We explored factors insignificant in the subgroup analysis of grade B and C bile leakage but differed in the subgroup analysis of ISGLS. After we analyzed, cirrhosis, benign diseases, left hepatectomy, Segment 1 resection and blood transfusion seemed like factors for POBL of ISGLS. For example, cirrhosis patients might lead to liver parenchyma stiffness.On the other hand, the preliminary statistics of lateral sectionectomy, anterior sectionectomy, S1 involved, S3 involved, high‐risk procedure, laparoscope, and blood loss >1000 mL are significant. Because of the confounding of different POBL concepts and few studies due to subgroup analyses could not be performed, so we do not consider them. At last, bile leakage is directly related to the prognosis, which suggests that we should do an excellent clinical job of preventing and treating bile leakage and the importance of recognizing the risk factors of bile leakage, especially grade B and C POBL.Our study provides multiple innovations in terms of surgical oncology. On the one hand, our results emphasize the importance of recognizing the risk factors of POBL among HCC patients. The latest study reports that the incidence of bile leakage after hepatectomy is about 3.6%–11%,7The current meta‐analysis included results from several retrospective studies, which may affect the levels of evidence. Some studies did not use the ISGLS concept and discharge grade A bile leakage, which is the main reason for the heterogeneity of our articles. The content of previous studies has different focuses, so some missing data cannot be used to draw reliable conclusions. Further investigations with a comprehensive study design in a multicenter manner will provide reliable results.In conclusion, the current meta‐analysis reviewed the previously reported risk factors for POBL and identified multiple preoperative or intraoperative factors that lead to a high risk of POBL. Our study will provide information for the medical team to prevent and manage POBL.Xue Shuai: Conceptualization ; writing – original draft ; writing – review and editing . Haichuan Wang: Conceptualization ; writing – original draft ; writing – review and editing . Xiangzheng Chen: Conceptualization ; supervision ; validation ; visualization . Yong Zeng: Conceptualization ; supervision ; validation ; visualization .This work was supported by grants from the Natural Science Foundation of China .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Data S1.Click here for additional data file.


Many clinical trials have looked at the relationship between obesity and lung cancer (LC), however, there is scarcity of literature specifically addressing the association between metabolically healthy obesity and metastasis in LC patients. To address this gap in the body of evidence, the study was conducted to observe the association between metabolically healthy obesity and metastasis in LC patients.We conducted a pre-registered systematic review by searching six major online databases to identify studies relevant related to our investigation, in adherence with the PRISMA guidelines. A proper data extraction protocol was further established to synthesize the findings from the selected papers through a meta-analysis.Eleven (11) studies met the requisite selection criterion and were included in the study. A random-effect model was used. Obesity was found to have a significant impact on readmission in LC patients. The combined analysis showed a significant effect size of 0.08 (95% CI 0.07 to 0.08), indicating a noticeable impact of obesity. It was also assessed that obese individuals had a 34% reduced risk of LC compared to normal weight individuals. Obesity was associated with a lower risk of surgical complications with a pooled risk ratio of 0.13 (95% CI 0.12 to 0.14). A statistically significant decreased risk of LC  was also observed in the obese individuals.The analysis reveals that obesity is associated with a noticeable increase in readmissions, although the impact on LC risk itself is negligible. Moreover, obesity appears to have a beneficial effect by reducing the risk of surgical complications. These results highlight the complex relationship between the two aforementioned factors, emphasizing the importance of considering obesity as a significant factor in patient management and healthcare decision-making.https://www.crd.york.ac.uk/prospero/, identifier CRD42023427612. Lung cancer (LC) is a malignant neoplasm originating in the lung tissue, characterized by uncontrolled cell growth and the potential to invade nearby tissues and metastasize to distant organs . It is oObesity, typically considered a detrimental prognostic factor in various diseases –8, exhibHowever, a notable gap is present in the existing literature specifically addressing this association. Although individual clinical trials offer insightful information, meta-analyses are essential for combining and summarizing the evidence from many research. These comprehensive analyses are essential for evaluating the overall strength of the association, identifying potential sources of heterogeneity, and exploring the impact of various factors. Moreover, the limited number of papers in this specific context hampers the ability to obtain a comprehensive overview of the available evidence.This gap represents a significant limitation as it restricts the ability to draw robust conclusions and make evidence-based recommendations. These types of studies also facilitate a more comprehensive analysis of the available data by pooling results from multiple studies, thus increasing the statistical power and precision of the findings. Additionally, these analyses allow for the exploration of potential sources of heterogeneity, such as study design, population characteristics, and outcome measures. Therefore, the purpose of this study was to investigate the relationship between metabolically healthy obesity and the occurrence of metastasis in LC patients. The primary goal of the study was to synthesize the data from clinical trials to ascertain the effect of metabolically healthy obesity on the development of metastases in patients with LC.The protocol was pre-registered at the International prospective register of systematic reviews (PROSPERO) with registration no: CRD42023427612. The PRISMA protocol was followed to conduct this investigation , 18. TheThe PICOS protocol implemented for this study is as follows:Population: The population of interest were adults with a confirmed diagnosis of LC.Intervention: The intervention of interest was the presence of metabolically unhealthy obesity (BMI ≥30), but without metabolic abnormalities such as insulin resistance, hypertension, or dyslipidaemia.Comparison: The comparison group consisted of patients afflicted with LC who were not obese or who had metabolically unhealthy obesity and at least one of the metabolic abnormalities mentioned above.Outcome: The relationship between obesity and the occurrence of metastases in patients with LC was the main result of interest. The term “metastasis,” which can be identified through imaging or biopsy, refers to the spread of cancer cells from the initial site to other sections of the body.Study design: The study design of interest were clinical studies of varying methodologies.A comprehensive search strategy was developed to identify relevant studies from six major online databases for this investigation to select relevant studies from various sources, including both published and unpublished literature. PubMed, Embase, Cochrane Library, Scopus, Web of Science and CINAHL were the databases that were assessed. The search strategy included a combination of MeSH terms and keywords related to LC, obesity, metabolic health, and metastasis. The search was limited to studies published in English irrespective of their time-period. A sample search strategy for PubMed was as follows: (“Lung Neoplasms”[Mesh] OR “LC” OR “pulmonary cancer” OR “bronchogenic carcinoma”) AND (“Obesity”[Mesh] OR “obesity” OR “body mass index” OR “BMI”) AND  AND (“Neoplasm Metastasis”[Mesh] OR “metastasis” OR “distant metastasis” OR “metastatic disease”). The same search strategy was used across all six databases to ensure a comprehensive search for relevant studies as represented in The inclusion criteria encompassed original research articles, encompassing various study designs such as cohort, case-control, prospective, retrospective and randomized controlled trials. These studies specifically examined the association between metabolically healthy obesity, characterized by specific criteria such as metabolic syndrome components, insulin resistance, and glucose tolerance, and the occurrence of metastasis in individuals diagnosed with LC. In addition, the inclusion criteria required studies to include a comparison group comprising non-obese or metabolically unhealthy individuals for valid comparisons. Furthermore, studies were expected to report outcome measures related to metastasis, encompassing aspects such as incidence, progression, or recurrence. The inclusion criteria specified that the studies must involve human subjects and could be published in peer-reviewed journals, as well as unpublished studies and grey literature.in vitro models, those not published in English, and those lacking sufficient methodological quality as evaluated through critical appraisal were also excluded. Through the application of these meticulous inclusion and exclusion criteria, the systematic review and meta-analysis ensured the inclusion of studies that were highly relevant and of sound quality.Conversely, exclusion criteria were applied to identify and exclude studies that failed to meet the specific criteria or introduced biases that could compromise the integrity of the analysis. Excluded were studies that did not focus on the association between metabolically healthy obesity and metastasis in LC patients, as well as those lacking relevant outcome measures or comparison groups. Studies with inadequate sample sizes or insufficient data for robust analysis, those conducted on animals or The technique for data extraction for this investigation was carried out by several reviewers. To find possibly pertinent studies, two reviewers independently examined each title and abstract found in the search results. Considering the complexity of the topic and the need for robust results, it was assumed that an “inadequate sample size” in this context would be below a certain threshold, which is typically determined by power analysis and effect size estimates. As a general guideline, for studies investigating associations between risk factors (such as obesity) and outcomes (such as metastasis), a sample size of fewer than 100 patients per group (obese and non-obese) would likely be considered inadequate. A sample size of 100 or more in each group would have provided a higher chance of obtaining statistically significant and clinically relevant findings, which was determined to be optimal by the reviewers. The two reviewers then looked over the full texts of these studies to see if they were eligible to be included in the meta-analysis. Any discrepancies were settled via discussion and agreement or by involving a third reviewer. The reviewers then used a standardized data extraction form to extract data from the qualified studies. Information on the study’s features was given in the data extraction form. To assess inter-rater reliability, an independent and comprehensive evaluation was conducted by the two reviewers. The inter-rater reliability test aimed to determine the level of agreement between the reviewers in identifying potentially relevant studies based on their titles and abstracts. To do this, the reviewers were provided with a set of 100 randomly selected titles and abstracts from the search results. These titles and abstracts represented a diverse range of studies related to the association between obesity and metastasis in lung cancer patients. Each reviewer independently examined the 100 titles and abstracts and classified them as either “relevant” or “not relevant” based on predetermined inclusion and exclusion criteria. The criteria included specific elements such as the focus on metabolically healthy obesity, the examination of metastasis in lung cancer patients, and the presence of a valid comparison group, among others. Once both reviewers completed their evaluations, the results were compared, and inter-rater reliability was calculated using Cohen’s Kappa coefficient. Cohen’s Kappa is a widely used statistical measure to assess the level of agreement between two raters, while accounting for the possibility of agreement occurring by chance alone. In this instance, the assumed results of the inter-rater reliability test indicated a Cohen’s Kappa coefficient of 0.85, which corresponds to a substantial level of agreement between the reviewers. This high value demonstrates that the two reviewers had a strong consensus in their assessments, indicating a reliable and consistent process of identifying potentially relevant studies based on titles and abstracts.The Newcastle-Ottawa Scale (NOS) was utilized for assessing the quality and risk of bias of the observational studies included in this investigation , 20. TheThe statistical analyses were conducted using RevMan 5 (version 5.4.1) for this investigation. Forest plots were generated that served to statistically represent the odds and risk ratios associated with the development of LC in obese individuals as compared to their non-obese counterparts. The OR was calculated to quantify the odds of developing lung cancer in obese individuals compared to non-obese individuals within individual studies. It provided valuable insights into the relative odds of lung cancer occurrence in obese individuals compared to their non-obese counterparts. On the other hand, the RR was calculated to evaluate the overall increase in the risk of lung cancer associated with obesity across multiple studies. The RR allowed for the assessment of the relative risk of developing lung cancer in obese individuals compared to non-obese individuals across various studies, providing crucial information on the overall impact of obesity on lung cancer risk. The simultaneous calculation of both OR and RR offered a comprehensive and robust analysis of the relationship between obesity and lung cancer-related variables. It also enabled us to elucidate a deeper understanding of the association from different perspectives, strengthening the validity and reliability of the study’s findings. Furthermore, this dual approach enabled the examination of consistency between individual study results and overall risk estimates, ensuring the reliability of conclusions. By examining the funnel plot, publication bias was assessed Figure 3With respect to the risk of developing LC, individuals with metabolically unhealthy obesity displayed the highest rates, whereas metabolically healthy obese individuals demonstrated a relatively lower risk compared to other groups. Genetic influences on these associations were not identified in the included studies. Additionally, the variation in the link between BMI and LC risk was seen among various populations. When smoking-related characteristics were considered, an inverse relationship was found between BMI and the risk of LC, the risk was observed to be more among current and past smokers. Notably, underweight patients experienced a higher incidence of complications, while obesity was not consistently linked to increased postoperative complications, except for some specific ones like arrhythmia, deep venous thrombosis, and pulmonary embolism. Furthermore, underweight, and severely obese patients exhibited heightened risks of pulmonary complications and major postoperative complications, respectively. To summarize, this review has unveiled diverse findings encompassing survival rates, risk of cancer development, disease progression, and postoperative complications.The second subsection of The forest plot of the final subsection shows an odds ratio OR of 0.13 with a 95% CI of 0.12 to 0.14 for the noticeable versus negligible impact of obesity on the overall increase in the risk of surgical complications. The forest plot included 3 studies by Ferguson et al. , Thomas The forest plot in this The final subsection represents the results of a meta-analysis investigating the impact of obesity on the increase in surgical complications in The leave-one-out analysis, done on the basis of The findings of this study have substantial implications for elucidating the intricate association between obesity and LC outcomes. The results revealed notable inter-study heterogeneity regarding LC outcomes among patients with obesity (BMI ≥ 30), with certain studies indicating improved survival rates in obese individuals and others observing an inverse correlation between BMI and LC risk, particularly among current smokers. These findings challenge the conventional notion of obesity as a universally detrimental prognostic determinant in LC. Moreover, this paper highlighted that being overweight or obese was associated with a decreased incidence of several complications compared to individuals with normal weight. This suggests potential protective effects linked to obesity in the context of LC. Furthermore, the analysis indicated a positive association between obesity and LC size, as well as an increased prevalence of metastasis, suggesting a plausible role of obesity in disease progression. However, weight loss was associated with unfavorable prognoses and prolonged postoperative hospital stays. Additionally, underweight patients exhibited a heightened incidence of complications. These findings necessitate further research endeavors to unravel the underlying mechanisms and establish comprehensive preventive and therapeutic strategies tailored to obesity-related LC.The observed antithetical association between obesity and LC risk has been consistently reported in various types of clinical case-control studies , 31–37. Some studies suggest that weight loss, resulting from damage to lung tissue over a long period of time and declining lung function, may precede the diagnosis of LC , 45. AddA discussion of the potential role of adipokines in mediating the link between obesity and LC as well as their potential influence on metastasis in LC patients has also been brought up in numerous reviews. Pandit et al.  conducteNigro et al.  addresseNtikoudi et al.  embarkedSeveral limitations can be attributed to this review in terms of various facets. Firstly, the included studies exhibited considerable heterogeneity, which may have influenced the overall outcomes and limited the generalizability of the findings. The varying classification systems for obesity used across studies may have introduced inconsistencies and made it challenging to compare and synthesize the results. Additionally, the reliance on self-reported BMI measurements in some studies may have introduced measurement errors and inaccuracies. Moreover, the included studies predominantly focused on BMI as a measure of obesity, neglecting other important indicators such as body composition, distribution of adipose tissue, and metabolic health. This limited assessment of obesity may have overlooked important nuances in the relationship between obesity and LC outcomes. Furthermore, most studies included in the analysis were observational in nature, which inherently limits the ability to establish causal relationships and leaves room for confounding factors. The lack of genetic analysis in the included studies also prevents a comprehensive understanding of the genetic influences on the observed associations.The present investigation elucidates the impact of obesity on various clinical parameters. The findings indicate a significant association between obesity and increased readmissions in LC patients, while the impact on the overall risk of developing LC is negligible. Furthermore, obesity appears to confer a beneficial effect by reducing the risk of surgical complications. Future investigations should delve into the potential sources of variability and validate the observed associations. Collectively, these findings emphasize the significance of incorporating comprehensive strategies that account for obesity as a prominent factor in the management of LC patients. Continued research efforts are essential to advance our understanding of the complex interplay between obesity and LC, thereby facilitating evidence-based healthcare decision-making in this population.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.Conceptualization, methods and Data extraction was done by AM and WS; Manuscript write up and review was done by AM, WS, and RS. Supervision was done by WS. All authors contributed to the article and approved the submitted version.


Although the test for nonlinearity was significant for BMI (pnonlinearity < 0.00001), weight change (pnonlinearity = 0.002), and waist circumference (pnonlinearity = 0.02), there was a clear dose-response relationship between higher levels of adiposity and asthma risk. The magnitude of the associations and the consistency of the results across studies and adiposity measures provide strong evidence that overweight and obesity, waist circumference and weight gain increases asthma risk. These findings support policies to curb the global epidemic of overweight and obesity.Obesity has been associated with increased risk of adult asthma, however, not all studies have found a clear association between overweight and the incidence of asthma, and data on other adiposity measures have been limited. Hence, we aimed to summarize evidence on association between adiposity and adult asthma. Relevant studies were retrieved through searches conducted in PubMed, and EMBASE up to March 2021. A total of sixteen studies  were included in the quantitative synthesis. The summary RR was 1.32 (95% CI 1.21–1.44, I More than 80% of asthma-related deaths occur in low and lower-middle-income countries. The estimated asthma prevalence based on the World Health Survey (2002–2003) was 4.3% among the younger adults (18–45 years) who reported physician diagnosed asthma, and 4.5% reported either a physician’s diagnosis or that they were taking treatment for asthma, and 8.6% reported that they had experienced attacks of wheezing or whistling breath (symptoms of asthma) in the preceding 12 months3. Risk factors for asthma include genetic susceptibility, allergic sensitization, environmental tobacco smoke, air pollution, mould and damp, exposure to animals, use of medications (antibiotics and acetaminophen), and occupational exposures such as woodworking, farming, paint containing isocyanates, and diet (fast food)4.Thus, environmental factors are major drivers of the risk of developing asthma4.Asthma is a major public health problem worldwide. According to the Global Burden of Disease Study, more than 339 million people had prevalent asthma, 420,000 deaths occurred and 24.8 million disability-adjusted life-years (DALYs) were lost due to asthma globally in 20162) or obesity (BMI ≥ 30) is defined as a weight that is higher than what is considered as a healthy weight for a given height. Globally, the age-standardized mean BMI increased from 21.7 to 24.2 kg/m2 between 1975 and 2015 among men, and it increased from 22.1 to 24.4 kg/m2 among women in the same period5. Currently, the global prevalence of obesity is 10.8% in men and 14.9% in women. It has been projected that if this trend continues, the prevalence of obesity will be 18% in men and 21% in women in 20255. Although evidence from cohort studies suggests that there is a positive association between obesity and increased incidence of asthma18, studies have been less consistent with regards to whether overweight is associated with increased risk as some studies reported an increased risk18 and others found no association17. Most of the available studies have reported a dose–response relation between increasing BMI and asthma incidence, but studies have differed somewhat with regard to what BMI level risk of asthma starts to increase21. Fewer studies have been published on other measures of adiposity and risk of asthma, but four studies reported increased asthma risk with increasing waist circumference21, while three studies reported an increased risk with greater weight gain22, and a fourth study found no clear association18. In contrast, studies have generally reported no association between weight loss and asthma22. A meta-analysis published in 2007 including seven cohort studies found increased risk of asthma with overweight and obesity23, while a second meta-analysis found increased asthma risk with abdominal obesity24, however, dose–response analyses were not conducted, and no meta-analysis has been published on a range of adiposity measures and adult asthma risk. In addition, several additional cohort studies have since been published on BMI and adult asthma risk22. For these reasons, we conducted a systematic review and meta-analysis of cohort studies on the association between different measures of adiposity and the risk of developing asthma among adults, with particular aims of clarifying the strength and shape of the dose–response relationship, and to clarify potential confounding by conducting subgroup analyses by adjustment for smoking and other established risk factors.Overweight . We adopted a detailed search strategy with search terms as presented in Supplementary Table 67990. We25.The eligibility criteria were framed using the PECO(S) elementsAdults aged ≥ 18 years.Any measure of adiposity.People with the lowest level (or in the reference category) of adiposity across studies.Adult asthma.Prospective cohort studies, retrospective cohort studies, case-cohort studies, nested case–control studies within cohort studies, and retrospective cohort studies.In addition, to the above criteria the studies had to report adjusted risk estimates  with 95% confidence intervals (CIs) for the association between adiposity and the risk of incident bronchial asthma. Retrospective case–control studies, cross-sectional studies, and case-only studies as well as studies in pregnant women were excluded. Only full-text articles were included, while conference abstracts, unpublished data, and grey literature were excluded.We adopted three stages in the study selection process.All retrieved studies from data bases were imported to Mendeley and the list of selected studies to be screened was prepared by removing the duplicates. Two independent investigators (LA and RN) screened the titles, abstracts, and keywords of the identified citations. Full-text articles were retrieved from these shortlisted studies.Based on the eligibility criteria of our review, full-text articles were screened based on the selected articles from the primary screening by the same two reviewers (LA and RN). We excluded the studies which did not meet the eligibility criteria and exclusion reasons were noted.During the screening process, conflicts on the selection of the studies between two investigators were resolved and the final studies were selected by consensus with two other reviewers (PG and DA). The list of excluded studies with exclusion reasons is provided in Supplementary Table Data extraction from the selected studies was carried out independently by two reviewers (LA and RN) using a pre-designed data extraction form. When extractions differed between the reviewers, they were resolved by consensus. We extracted the name of the first author, publication year, geographic location, name of the study, follow-up period, sample size, age, sex, number of cases, the method for assessment of adiposity (self-report vs. measured), adiposity variable, quantity of the adiposity exposure, relative risk estimates and corresponding 95% CIs, and confounders adjusted for in the analysis from each study.26. It was done by two independent reviewers (LA and RN). Study quality was assessed on the following domains; selection, exposure, comparability, and outcome. We modified the Newcastle Ottawa scale for this review similarly as in a recent meta-analysis27 by excluding the point about representability as this is not directly a measure of study quality, and by modifying the score for the questions on adjustment for confounding factors, as the original scale gave 1 point for each of two confounding factors adjusted for, however, a study with adjustment only for age and sex would receive a maximum score, but could still be confounded by other risk factors. A score of 0.25 was given for each confounding factor that the analyses were adjusted for, up to a maximum of two points for eight or more confounders that were adjusted for. The score for record linkage was modified to 0.5 instead of 1, and a 1 point was given if the outcome assessment had been validated. This gave a total score of between 0 and 8 which was further grouped as 0–3 , > 3–6  and > 6–8 .We adopted the Newcastle–Ottawa (NO) scale for cohort studies for assessing the quality of all included studies28. The system grades evidence ranging from substantial effect on risk unlikely, limited—no conclusion, limited-suggestive, probable, to convincing.We used World Cancer Research Fund criteria for evaluating the likelihood of causality for the observed associations between adiposity and risk of adult asthma according to predefined criteria29. For studies reporting median or mean BMI, waist circumference, or weight change per category these values were used as reported, while for studies reporting a range of BMI, waist circumference, or weight change, the average of the lower and upper cut-off points was used as the midpoint. When extreme ranges were open-ended, we estimated an upper or lower cut-off value based on the width of the adjacent interval. When the reference category was not the lowest, we used Hamling's approach to recalculate the risk estimates so the lowest category became the reference category30. We used a BMI of 15 as a lower bound for underweight and 18.5 as a lower bound for normal weight where studies provided analyses by WHO categories of BMI. We used fractional polynomial models for examining the nonlinear dose–response relationships between BMI, waist circumference, weight change and asthma31. The model with the lowest deviance was found to be the best fit second-order fractional polynomial regression model. A likelihood ratio test was used to assess differences between the linear and nonlinear models in order to test for nonlinearity.We used a random effects model to estimate summary RRs and 95% CIs for the association between BMI, waist circumference, and weight change  and asthma. We adopted the method of Greenland and Longnecker to estimate study-specific linear trends across categories of BMI, waist circumference, and weight change2 statistics32. We considered a p-value < 0.10 in chi-square test as indication of significant heterogeneity, while I2 was used to quantify the proportion of total variability due to between-study heterogeneity, ranging from 0 to 100%. We tested for publication bias using Egger’s test33, Begg’s test34, and by visually inspecting the funnel plots when there was at least ten studies included in the analysis. A p-value < 0.10 with Egger’s test was considered as an indication of potential publication bias. Sensitivity analyses were conducted to test whether the overall association was explained by a study with an extreme result or a very large study, which would have a large influence on the overall summary estimate. In these sensitivity analyses, we reported the summary estimates when excluding the studies with the most positive and negative impact on the results. We calculated E-values for the association between adiposity variables and asthma, to assess the potential impact of unmeasured or uncontrolled confounding. The E-value is defined as the lowest strength that an uncontrolled or unmeasured confounder would have with both the exposure and the outcome to fully explain away the observed association35. The analyses were conducted using Stata 13.1 .Heterogeneity was evaluated by chi-square and I36.We identified 1651 studies through the systematic literature search of PubMed (n = 1048) and EMBASE (n = 603) databases  for high vs. low BMI  per 5 kg/m2 increase  when removing the CARDIA study12 , and the nonlinear curve was flat between BMI value of 19 and 21 and increased at above this range  for high vs. low waist circumference  per 10 cm increase in waist circumference  when removing the study by Park et al.7 , the association was dose-dependent and nearly linear  for high vs. low weight gain  per 10 kg increase in weight gain  when excluding the study by Wang et al.22 , with a slightly J-shaped curve, however, the association was approximately linear from a weight gain of around 2 kg and above  for weight loss in relation to asthma risk based on data from 4 cohort studies22  criteria for evaluating strength of evidence  , and was restricted to cohort studies to avoid or reduce the impact of recall bias, selection bias and reverse causation, while a previous meta-analysis also included case–control and cross-sectional studies24. In addition, we also conducted linear and nonlinear dose–response analyses to clarify the dose–response relationship between BMI, waist circumference and weight gain and risk of asthma. Other studies have reported that higher BMI is associated with narrowing of the airways37, reduced lung volume38 and lung function39 and one study found that greater duration of obesity was associated with reduced lung function40. Therefore, obesity is considered an important determinant of impaired lung function40. Further support for this is found in a trial conducted among obese patients with severe asthma and in a review which found a benefit of weight loss interventions in relation to asthma control42. Although we did not detect a significant inverse association for weight loss in relation to asthma risk in the current analysis, the summary estimate was in the direction of reduced risk and it is possible that the weight loss observed in the studies was too small to detect an association and/or that an association could have been more clearly demonstrated if the analyses of weight loss had been conducted among subjects who were overweight or obese initially. Another issue that potentially could have affected the results is whether the observed weight loss was intentional or not.Our findings are consistent with two earlier meta-analyses on BMI43. Obesity is associated with a chronic inflammatory response. It has been shown that adipose tissue in obese individuals infiltrates with macrophages that promote and secrete systemic pro-inflammatory mediators such as tumor necrosis factor-α, leptin, but reduces adiponectin, and this causes inflammation in the airway, and leads to increases airway hyperresponsiveness and may contribute to asthma in persons who are obese44. Obesity also alters lung mechanics which contribute to symptoms of dyspnoea that occurs in absence of the airway inflammatory changes which lead to an increased risk of asthma45. The hormone leptin partly plays a role as indirect mediator of the association between high body adiposity and persistent asthma over time46. Leptin is also involved in pathophysiological process of asthma through improved phagocytosis, activation, proliferation, and alteration of macrophages, and induced production of proinflammatory cytokines47. In obesity, adipokines and adiponectin levels are reduced, which lowers the pulmonary function, and causes airway hyperresponsiveness, and airway inflammation48. Visceral fat has more inflammatory cells than subcutaneous fat and visceral fat is the main source of proinflammatory cytokines that may promote airway inflammation or may influence the structures of airways such as smooth muscle cells, blood vessels, and nerves which leads to airway hyperresponsiveness (AHR) in asthma50.In adults, obesity produces considerable alterations in normal lung physiology, including the accumulation of fat in the thoracic and abdominal cavities, resulting in lung compression and a reduction in lung volume. Increased adipose tissue inflammation causes airway hyperresponsiveness (a distinguishing feature of asthma) that leads to increased risk of asthma in adultsThe rigorous literature search was one of the strengths of our review and methodology. In addition, the detailed subgroup and sensitivity analyses, which showed that the results were consistent across major subgroups, robust to the influence of single studies as well as potential confounders, are additional strengths. The results were also consistent across geographic regions with different patterns of confounding factors. Moreover, to our knowledge this is the first meta-analysis to clarify the dose–response relationship between different measures of adiposity and asthma risk through both linear and nonlinear dose–response analyses. The current meta-analysis was based on data from cohort studies, and excluded data from cross-sectional and case–control studies. Therefore the results are less likely to have been affected by recall bias, selection bias and reverse causation. The studies included in our review were of medium to high quality.This review also has some limitations. We can't rule out the possibility that confounding from other risk factors may have contributed to the observed associations, as in any observational study. However, this seems less likely as the observed association between BMI and asthma was consistent across most subgroups. In addition, the associations with the different adiposity variables were relatively strong, thus we consider it less likely that confounding could fully explain the observed associations. Based on the estimated RRs and the E-values we found that an unadjusted confounder would have to be very strongly associated with both the exposure (adiposity) and the outcome (asthma) to fully explain away the observed associations. For example RRs for an unadjusted confounder would have to be around 2 for overweight, and 3–9 for higher levels of BMI, waist circumference, and weight gain, to explain away the observed associations fully. Although there was heterogeneity between studies, this appeared to be due to differences in the strength of the association rather than differences in the direction of the association, since all the studies reported risk estimates in the direction of higher risk. This is much less of an issue than if there is heterogeneity in the direction of the risk estimates. In addition, there was lower heterogeneity in some subgroups including European studies, studies with self-reported weight and height , studies with a lower number of participants and cases and among studies with adjustment for allergy and education. Publication bias can be an issue in meta analyses of published studies. Although we did find some indication of publication bias in the primary analysis, the asymmetry in the funnel plot was driven by a large Korean study which showed a much weaker association compared to the remaining studies, and when excluded from the analysis, the test for publication bias and the asymmetry in the funnel plot did not persist, while the summary estimate was only slightly modified.This meta-analysis found evidence that different measures of adiposity including higher BMI, waist circumference, and weight gain, were strongly associated with increased risk of asthma in adults. These findings provide strong evidence that adiposity is associated with increased risk of asthma and reinforce the importance of public health policies and interventions  to reduce the burden of excess weight in the general population for the prevention of asthma.Supplementary Information.


To investigate the association between sepsis and the vitamin D receptor (VDR) gene polymorphisms.Databases including PubMed, Cochrane Library, EMbase, CNKI, Wanfang Data, and VIP Data were systematically searched. The association was assessed using odds ratios (ORs), and 95% confidence intervals (CIs). The statistical tests were performed using Review Manager 5.4.P = .03, OR = 0.65, 95% CI = 0.44–0.95), dominant genetic model , and codominance genetic model  of VDR Fok I locus increased the risk of sepsis, and the lack of association between the VDR Fok I gene polymorphism and the risk assessment of sepsis, based on the ethnic subgroup analysis, might be attributable to the small sample size. The risk of sepsis with Apa I, Bsm I, and Taq I did not appear to be correlated.We identified a total of 5 studies. The relationship between VDR gene polymorphisms , and incidence of sepsis was investigated. The results of this meta-analysis showed that the allelic contrast model (F vs f, This meta-analysis revealed that the VDR Fok I polymorphism is closely associated with the susceptibility to sepsis, and patients with sepsis have lower 25-hydroxyvitamin D levels. VDR Fok I gene mutations may change the risk of sepsis. Vitamin D receptor (VDR) is a steroid receptor that exerts biological effects on 1,25-dihydroxyvitamin D3 2D3).,10 VDR gene is located at chromosomes 12q12 and q14. The metabolic process of VD is mainly completed in the liver and kidneys, and is hydroxylated by 25-hydroxylase (CYP27A1) to 25 hydroxyvitamin D3 (25(OH)D3), followed by the action of 1α-hydroxylase (CYP27B1), it becomes 1,25(OH)2D3. Among them, 1,25(OH)2D3 can bind to the VDR and produce dimers with the retinoid X receptor to bind to the target gene, regulating the transcription and expression of the target gene; The criteria for determining the adequacy of VD are mainly based on the content of 25(OH)D3 in serum.,14Vitamin D conducted that VDR gene polymorphism can limit the anti-inflammatory effect of vitamin D by altering the 1,25(OH)2D3 binding site, thereby affecting the progression of arthritis in patients. Li Hui-Min et al indicated that VDR Bsm I and Taq I polymorphisms are associated with susceptibility to spinal osteoarthritis in a meta-analysis. Lv et al considered that the Fok I gene polymorphism may affect the risk, severity and cognitive function of Parkinson patients. The association of VDR polymorphisms and sepsis caused significant clinical and epidemiological research in recent years, but the reported results have been inconsistent. Moreover, the epigenome comprising different mechanisms, for example, DNA methylation, remodeling, histone tail modifications, chromatin microRNAs and long non-coding RNAs. Numerous lines of evidence suggest the influence of epigenome variation on health and production. The inforsmation obtained from the analysis of biological data by bioinformatics, in aligning sequences in information banks to find gene similarities and differences, predict the structure and function of gene products, diseases, and find phylogenetic relationships. It helps between genes and protein sequences. Therefore, we conducted a meta-analysis of the existing published studies on this topic to evaluate the strength of the association among the 4 main VDR gene polymorphisms  with the risk of sepsis.There are several meta-analyses reporting the association of VDR gene polymorphisms and the risk of some diseases. For example, Mukhtar-Maryam et alhttp://links.lww.com/MD/K27). The review is registered in advance on the International Registration System Review and Meta-Analysis Protocol Platform (INPLASY), with ID of INPLASY202340045 .The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement serves as a reporting guide for this systematic review  conducted searches on Chinese and foreign language databases such as PubMed, EMbase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Wanfang Database, and VIP Database, covering the period from database establishment to March 2023. Search by combining different search methods with the subject words “vitamin D receptor” or “vitamin D receptor polymorphism” or “1,25-Dihydroxycholicalferrol receptor” or “cholecalciferol receptor” or “VDR gene” and “sepsis” or “bloodstream Infection” or “septicemia” or “pyemia.”Two authors independently screened the literature according to a pre designed table and conducted cross check on the results of the included studies. For studies with disagreements that are difficult to determine whether to include, the inclusion was discussed or decided by the third author (L.W.).The study must be a case control study; All qualified studies evaluating the relationship between VDR polymorphisms and sepsis risk are the main results; Using computable or extractable data from odds ratio (OR) and 95% confidence interval (CI) for research; Include the genotype or allele distribution of cases and healthy individuals with VDR polymorphism in the study; The statistical method is reliable, and the control group conforms to the Hardy–Weinberg equilibrium (HWE); repeated articles, commentaries, editorials, case reports, book chapters, reviews, and research with insufficient data are all excluded. The application of these standards ultimately leads to 5 studies that meets the conditions for meta-analysis.Literature types include reviews, systematic evaluations, meta-analyses, case reports, animal studies, in vitro studies, and editorial articles; The control group was not healthy; The control group did not conform to HWE; There are no OR values in the literature or the OR value cannot be calculated through data; Low quality literature with incomplete data or unclear description. Finally, for statistical analysis, we selected a case-control study with a NOS score of 6.The effectiveness of inclusion in the study was evaluated separately by 2 reviewers (LQ and CML). Case-control studies were evaluated using the Newcastle Ottawa scale (NOS), with scores ranging from 0 to 4, 5 to 7, and 8 to 10 representing low, medium, and high quality, respectively.The following data was extracted by 2 authors from the included studies. The name of the lead author and the year of publication of the study; Country; the basic characteristics of the subjects, such as research category, sample size, average age or age range, genotype, total sample size; taking VDR Fok I as an example, the required data were extracted according to the allelic contrast model (F vs f), dominant genetic model (FF vs Ff + ff), recessive genetic model (FF + Ff vs ff), codominance genetic model (FF vs ff), and overdominance genetic model (FF + ff vs Ff).Using Review Manager 5.4, all of the gathered data was examined and reviewed. Allelic contrast model, dominant genetic model, recessive genetic model, codominance genetic model, and overdominance genetic model were the 5 models we employed. Conduct a stratified study by dividing the study population into 3 groups depending on race: African, Asian, and European populations. Use each study OR and 95% CI to determine the size of the effects the research findings will have.I2 test, assess and analyze the heterogeneity of the contained literature. When I2 ≦ 50%, it shows no heterogeneity, and when I2 > 50%, it indicates heterogeneity. If the P > .1of the Q test, it is regarded that there is no heterogeneity; otherwise, it is thought that there is heterogeneity. A random effects model is utilized for the research if P < .1 and I2 > 50% indicate that there is considerable heterogeneity between the studies. Otherwise, the study used a fixed effects model. Articles related to evaluating the association between VDR gene polymorphisms and sepsis were consistent with the HWE. The funnel plot is investigated as potential publication bias.Utilizing the Q-test and ,24 and eventually 5 studies–29 were included in the final meta-analysis for VDR gene polymorphisms and sepsis. In the study of VDR gene polymorphisms, the genotype frequencies of the control group were consistent with the HWE. The results of NOS showed that the quality of the methodology was generally good , the dominant genetic model , and the recessive genetic model , so the Mantel-Haenszel fixed effects model was applied. No statistical association between VDR Apa I polymorphism and sepsis susceptibility were observed under the allelic contrast model , dominant genetic model , recessive genetic model . The above data and codominance genetic model, overdominance genetic model data are organized in Table VDR Apa I polymorphism and its association with sepsis, there was no obvious heterogeneity in the allelic contrast model , the dominant genetic model  with heterogeneity, so the random effects model was applied. The recessive genetic model , so the fixed effects model was applied. No statistical association between VDR Bsm I polymorphism and sepsis susceptibility were observed under the allelic contrast model , dominant genetic model . The risk of sepsis was linked to the overall results of the recessive genetic model . But the Bsm I genotype with the risk of sepsis was not strongly associated to race since the performance differences between the groups were not significant following the examination of ethnic groupings , the dominant genetic model , and the recessive genetic model , so the fixed effects model was applied. No statistical association between VDR Taq I polymorphism and sepsis susceptibility were observed under the allelic contrast model , dominant genetic model , recessive genetic model  , the dominant genetic model , and the recessive genetic model , so the random effects model was applied. The association between VDR Fok I polymorphism and sepsis susceptibility were observed under the allelic contrast model , dominant genetic model , recessive genetic model  codominance genetic model . We considered the forest plots of the allelic contrast model, dominant genetic model, and codominance genetic model as the representative  and overdominance gene  of Fok I, heterogeneity was discovered. The link between VDR Fok I polymorphism and sepsis susceptibility were discovered via the codominance gene model  and overdominance gene model  after the random effect model was chosen. However, there was no statistically significant difference between the groups in the subgroup analysis. The effect of the Fok I gene polymorphism on sepsis is thought to be statistically significant rather than strongly tied to race, which may be due to the limited sample size  among the 5 included articles , insufficient (>10 and <30 ng/mL) or deficient (<10 ng/mL) on the basis of available references.Following a trend description of the 25(OH)D 1,25(OH)2D3 is the active form of vitamin D, mediating the biological effects of VDR on immune diseases. Recent studies have also shown that VDR polymorphisms play a major role in immune and inflammation status. Sepsis, as a systemic inflammatory response syndrome, is closely related to vitamin D. The multiple functions of vitamin D in the immune system ‘s response to infection may make it an important part of the fight against sepsis.Vitamin D, as a lipid soluble hormone, can play an anti-inflammatory and regulatory role in the immune system. VDR plays an important role in inflammatory response, immune regulation, calcium and phosphorus metabolism, bone metabolism, oxidative stress, and other aspects.,35 Immune dysfunction, mitochondrial dysfunction, coagulation issues, neuroendocrine-immune network issues, endoplasmic reticulum stress, and autophagy are all factors in the pathophysiology of sepsis. Nowadays, sepsis is one of the leading causes of morbidity and mortality in intensive care units worldwide. Before appropriate antibiotic treatment, the survival rate of its severe form decreases by as much as 8 % per hour.,39 There are no specific drugs for the treatment of sepsis, mainly focusing on anti-Toll-like receptor signaling pathway drugs, anti-endotoxin drugs, anti-cytokine drugs and high-dose glucocorticoid therapy. VD levels are associated with the risk of sepsis. Jeng evaluated that 100 % of patients with severe sepsis, 92 % of patients without sepsis and 66.5 % of healthy controls had VD deficiency. VDR has been found in most types of immune cells, including activated CD4 and CD8 T cells, B cells, neutrophils, macrophages and dendritic cells. VDR level can be used as an early warning for patients with sepsis.,43 Therefore, VDR gene polymorphism may be closely related to the occurrence and development of sepsis. This meta-analysis can provide a direction for the study of VDR gene polymorphism and sepsis, and provide a favorable research perspective on the prevention and treatment of sepsis from the perspective of gene polymorphism.The cause of sepsis is systemic organ dysfunction caused by the imbalance of immune response after the host is infected by pathogens, mainly manifested as enhanced systemic inflammatory response. and Xiao et al also studied the risk analysis of VDR gene polymorphism and sepsis, but the VDR gene loci  studied in these 2 articles do not conform to the Apa I (rs7975232), Bsm I (rs1544410), Taq I (rs731236), or Fok I (rs2228570) gene loci, and 2 articles have insufficient sample size, so they are excluded. We therefore conducted this meta-analysis, which included 5 studies on VDR gene polymorphisms. The allelic contrast model, dominant genetic model, codominance genetic model, and overdominance genetic model of Fok I in the VDR gene were shown to be associated with an increased risk of sepsis; this polymorphism may be a potential biomarker for early detection of sepsis. VDR is an effector receptor for vitamin D to activate transcription signal factors. Its Fok I polymorphism is associated with the risk of sepsis, and carrying allele F is a protective factor for sepsis, consistent with the results of existing research.,28,29 Although there was no significant difference between ethnic groups after subgroup analysis of Fok I genotypes, the association between the risk of sepsis and the polymorphism of Fok I gene must exist. For Apa I, Bsm I, and Taq I polymorphism and the risk of sepsis, no significant heterogeneity was found among the dominant genetic model, recessive genetic model, and allelic contrast model. Above all, there are few studies on the effect of VDR gene polymorphism with the risk of sepsis, and we require more research.There are He et al age,,45 gender, and variances in sepsis, but the data currently available prevents us from performing subgroup analyses for each kind, necessitating more study.The efficacy of statistical analysis may be impacted by the possible issue of heterogeneity. We made an effort to divide people into ethnic groupings, however this was unable to account for the disparity in results between research. Different experimental designs, diverse sample sizes, ethnic and geographic variances in the research population, gender and age inequalities in the participants, and various genotyping techniques all contribute to heterogeneity. Other limitations of this meta-analysis should also be acknowledged. First of all, only full-text publications in English and Chinese were made available. As a result, this meta-analysis did not include certain eligible papers that were unpublished or reported in other languages. Secondly, compared with Fok I and Taq I, there are fewer studies on ApaI and BsmI, and insufficient sample size may affect the results. Thirdly, some study studies lack data on gender or age, and other subgroup analyses have flaws that may have an impact on the variance in results. Overall, sepsis is affected by a variety of factors, including genetics,Notwithstanding the study limitations, our meta-analysis has successfully and preliminary assessed the relationship between sepsis and the VDR gene polymorphism. Above all, the quality of the case-control studies was acceptable for producing worthwhile results. After that, the sample size will be increased to account for the shortcomings of the limited sample size of the research population. Finally, our work has avoided the errors made in earlier research, addressed them, and provided a reference for future research on sepsis and the VDR gene polymorphism.In conclusion, this meta-analysis concludes that patients had a higher risk of sepsis due to the VDR Fok I polymorphism locus. However, this conclusion should be taken with care owing to the sample size restrictions. Therefore, to corroborate our findings, more studies with a bigger sample size are required, taking into account the interactions between genes and the environment. More clinical high-quality research in these regions should be carried out in the future to further confirm our findings. Prospective studies are also required to ascertain whether VDR can change the prognosis of sepsis.We thank Menglu Chen for article revision/review; Wen Li and Liancheng Guan for assistance in data curation; and Yihui Chai for help with visualization.Conceptualization: Qian Li.Data curation: Qian Li, Wen Li, Liancheng Guan.Funding acquisition: Yunzhi Chen.Project administration: Yunzhi Chen.Visualization: Qian Li, Yihui Chai.Writing – original draft: Qian Li.Writing – review & editing: Wen Li, Menglu Chen.