Hypersensitivity pneumonitis (HP), also referred to as exogenous allergic alveolitis, is one of the most common interstitial lung diseases (ILDs). A potential immune biomarker, Krebs von den lgen-6 (KL-6) characterizes the progression and severity of HP. The meta-analysis in this study was conducted to elucidate the variations in the concentrations of KL-6 in different types of HP.A systematic search of various databases such as EMBASE, Pubmed, CNKI, VIP, Web of Science, and WanFang was carried out to find relevant published articles between January 1980 and August 2022 that explored the relationship between KL-6 and allergic pneumonia. Standardized mean difference (SMD) and 95% confidence interval (CI) were used as effect sizes for comparison among different groups. The GSE47460 and GSE150910 datasets were downloaded to extract and validate the differences in KL-6 mRNA expression between HP lung tissue and healthy controls. Furthermore, the single-cell sequencing dataset GSE135893 was downloaded to extract KL-6 mRNA expression in type II alveolar epithelial cells to validate the differences between HP and healthy controls. Two researchers evaluated the quality of the included studies by employing Newcastle-Ottawa Scale. All the qualified studies were subjected to statistical analyses carried out utilizing RevMan 5.2, Stata 11.0, and R software 4.1.3.Twenty studies aligned perfectly with the inclusion criteria of the meta. The concentrations of KL-6 were substantially higher in the blood of HP patients as compared to the control group. Subgroup analyses were carried out in accordance with the allergen source and the results revealed that patients with different allergens had higher blood KL-6 concentrations than healthy controls. Additionally, different subgroups of subjects were created for meta-analysis as per the fibrosis status, race, measurement method, and sample type. The concentration of KL-6 in blood was much higher in all HP subgroups than in healthy control groups. Moreover, the bioinformatics analysis revealed that KL-6 mRNA expression was higher in HP lung tissue and type II alveolar epithelial cells as compared to healthy controls.The present meta-analysis and bioinformatics analysis suggested that the concentration levels of KL-6 varied between HP patients and healthy individuals, and the KL-6 concentrations may be higher in the blood samples of HP patients.https://www.crd.york.ac.uk/prospero/, CRD42022355334. Common allergens include microorganisms , animal proteins , chemical sensitizers (isocyanates), and organic dust particles . The incKrebs von den Lungen-6 (KL-6) is a glycoprotein localized on the membrane of type II alveolar epithelial cells, mainly released by propagating or damaged type II alveolar epithelial cells, and is specific for determining damage to these cells . High exTherefore, a meta-analysis was essential for the quantitative evaluation of the correlation between KL-6 concentration and the occurrence of HP by utilizing all available relevant studies. The microarray data and single-cell sequencing results were included in this study by means of biological analysis to further validate the reliability of the meta-analysis results.22.1https://www.crd.york.ac.uk/PROSPERO/, ID isCRD42022355334). One investigator comprehensively searched published research articles from January 1980 to August 2022 in Pubmed, EMBASE, CNKI, VIP, Web of Science, and WanFang databases for the search terms “allergic pneumonia” or “exogenous allergic alveolitis” or “hypersensitivity pneumonitis” or “Exogenous allergic alveolitis “ or “HP” or “EAA” and “salivary liquefied glycoconjugate antigen 6” or “Krebs von den Lungen-6” or “KL-6”.The meta-analysis conducted in this study was registered in the International Prospective Register for Systematic Evaluation : case-control studies, cross-sectional studies, or cohort studies (2); literature studies involving KL-6 level in blood samples from patients with HP, including individuals with acute hypersensitivity pneumonitis (AHP), chronic allergic pneumonia, chronic hypersensitivity pneumonitis (CHP), fibrotic hypersensitivity pneumonitis (fHP) and non-fibrotic hypersensitivity pneumonitis (nfHP); the content of the literature was not limited by age, sex, nationality or race (3); HP diagnostic criteria according to the guidelines established by the American Thoracic Society (ATS)/Japanese Respiratory Society (JRS)/Latin American Thoracic Society (ALAT) in 2020 , i.e., aExclusion criteria (1): inability to obtain sufficient data from the original research article and failure to communicate with the authors to obtain detailed data (2); case reports, review articles, meta-analyses, conference papers, and animal studies; and (3) articles with overlapping data already included in this study.2.3Based on the above search method, an initial screening of eligible articles based on article titles and abstracts was performed by two researchers. Subsequently, the re-evaluation of the selected articles was carried out by reading and reviewing the full text carefully. If there was disagreement between the two researchers, a third investigator was contacted and a final unanimous decision was made after consultation.2.4Tables were created to retrieve the following information from articles eligible for inclusion (1): basic information: first author, publication date, etc. (2); baseline characteristics of study participants: e.g., country, race, age, sample size, study design, measurement method, measurement sample type, allergens causing HP, and KL-6 concentration of the subject.2.5The literature’s quality was evaluated by two researchers following the Newcastle-Ottawa Scale (NOS) . In case2.6https://www.ncbi.nlm.nih.gov/geo/). GSE47460 (Homo sapiens). Dataset GSE135893 (Homo sapiens).Data sets  needed for bioinformatics analysis were obtained from the Gene Expression Omnibus database (GEO) . Subsequently, the manufacturer-provided annotation file was followed to change the name of the probe to a gene name, and the probes lacking gene names were removed. The “affy” software package of the R software was employed for the chip dataset’s preprocessing and normalization. The gene expression quantifications of GSE47460 were corrected for batches using ComBat from the “sva” R package. The R package “Biomart” was utilized for the filtration and validation of the GSE150910 data set by removing duplicated and missing data and transforming it by employing the statistical procedure log2 (TPM).The GSE135893 dataset’s (lung) computational analysis was carried out by employing the R package “Seurat” (4.0.3). Quality control analyses  were carried out in both datasets as per the R package “Seurat”, respectively. The Seurat RunPCA function was utilized for the calculation of the principal component analysis (PCA). The scRNA-seq data was normalized by employing Seurat NormalizeData functions, while Seurat IntegrateData and FindIntegrationAnchors functions, established on robust principal component analysis (RPCA), were utilized for the integration of multiple samples. UMAP (uniform manifold approximation and projection) was employed for dimension reduction; and Louvain clusters were analyzed utilizing the 30 primary principal components with the Seurat FindClusters function and RunUMAP function, respectively, with the resolution adjusted at 0.8. Seurat’s FindAllMarkers function was employed to locate markers of clusters, and identification of cell types was conducted based on markers of each cluster in accordance with CellMarker , Panglao2.7https://tech.cochrane.org/revman) to determine SMD and 95% CI, and the Z-test was employed to assess the impact of the combined SMD. Cochran Q and I2 indicators were employed to assess the heterogeneity in each study, with scores ranging from 0% to 100%. Additionally, substantial heterogeneity was witnessed in the illustration of P heterogeneity with Pheterogeneity < 0.1 and I2 > 50%. The combined SMD and 95% CI values were calculated by employing the fixed-effect and random-effect models. The sensitivity and meta-regression analyses were employed to calculate the heterogeneity sources, and subgroup analysis was carried out as per the type of allergen, fibrosis, race, assay approach, and sample types. The funnel diagram asymmetry was utilized to assess publication bias, and Egger’s and Begg’s test results were calculated by means of Stata 11.0 software. The values reported in terms of median and quartile in several articles were altered to mean and standard deviation (https://www.graphpad.com/scientific-software/prism/) and R software 4.1.3 were employed to carry out the visualization and various statistical analyses.The subsequent data analyses were accomplished by employing Stata 11.0  and the RevMan 5.2 software program , a random-effects model was chosen for the combined analysis. Meta-analysis results indicated that overall HP patients had substantially higher expression of KL-6 than healthy controls   Figure 23.4.2A subgroup analysis was carried out on the included literature because of the high heterogeneity of the meta-analysis of the overall population.3.4.2.12 = 61%, P < 0.0001); Two studies provided data on the concentrations of KL-6 in patients with fungi-related HP versus healthy individuals, and the results indicated that KL-6 concentrations were significantly higher compared to the healthy controls; 2 studies . Five studies  , and mushroom-related HP . Subgroup analysis was performed according to HP allergens. Seven studies , 24–28 p studies , 31 were studies –23 were  0.0002) Figure 3 0.0002)  revealed3.4.2.22 = 96%, P < 0.0001). There were three studies involving nfHP . Eight studies   and non-fibrotic hypersensitivity pneumonitis (nfHP) groups. 7 studies , 23, 27  studies , 28–31 w 0.0001) Table 3.3.4.2.32 = 84%, P < 0.0001). Seven studies reported the association between HP patients and concentrations of KL-6 in an Asian population   Table 3.3.4.2.42 = 95%, P < 0.0001)  Table 3. 0.0001)  reported3.4.2.52 = 94%, P < 0.0001). In the CLEIA group, concentrations of KL-6 were also considerably higher in HP patients as compared to healthy ones . Two studies  (Different studies used different methods to measure KL-6 concentrations. Thirteen studies used Enzyme linked-immuno-sorbent assay (ELISA) , 22–29,  studies , 30 were = 0.02) Table 3.3.53.5.1Data from two publications on KL-6 concentrations in fHP versus nfHP were analyzed to further compare KL-6 concentration levels between nfHP patients and fHP patients. Mostafa AI et al.  observed3.5.2One study on the relationship between KL-6 concentration levels in acute hypersensitivity pneumonitis (AHP) and chronic hypersensitivity pneumonitis (CHP) was included. Okamoto T et al.  found no3.6While conducting the sensitivity analysis, every one of the 20 studies was removed one after the other, and a meta-analysis of the remaining studies was carried out at each step and the results were sequentially compared with the previous elimination. If the overall impact did not change significantly, then the included studies had high stability, and the credibility of the result was verified. If the change significantly impacted the results, then the included studies had poor stability. Based on these criteria, the results suggested that the stability was good.3.7Egger’s regression test and Begg’s rank correlation were used to assess publication bias by constructing a funnel plot to show the relationship between the SMD and the SE of logarithmic SMD. The funnel plot for assessment of publication bias was asymmetrical, suggesting probable publication bias. Egger’s test and Begg’s test yielded similar results to the visual inspection for asymmetry of the funnel plot: Egger P <0.05; Begg P < 0.05. Figure 43.84This study revealed that the KL-6 concentrations in the blood of allergic pneumonia patients were higher in comparison to healthy individuals, which was verified in the meta-analysis of various subgroups. Elevated KL-6 concentrations may have a quantitative role in differentiating the severity and typing of HP. In both Caucasian and Asian populations, concentrations of KL-6 were much higher in HP-suffering patients in comparison to healthy individuals, and the increase was even more pronounced in the Asian population with HP, which may be associated with different living environments and the differences in immune responses to allergens in different ethnic groups. The results of bioinformatics analysis suggested that KL-6 concentrations in HP lung tissue and type II alveoli were higher in comparison to healthy control groups, and higher KL-6 concentrations displayed a negative correlation with DLCO% predicted and FEV1% predicted indexes in HP patients. Okamoto T’s  study reSustained high concentrations of KL-6 were closely linked to the progression of interstitial lung disease , 34. Addvia the lungs .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.


FNDC5 is a novel and important player in energy regulation related to glucose metabolism and insulin levels. Thus, it may affect the incidence of type 2 diabetes mellitus (T2DM). Nevertheless, the association between FNDC5 single nucleotide polymorphisms (SNPs) and susceptibility to T2DM remains unclear. The aim of this meta-analysis was to explore whether the SNPs, rs3480 and rs16835198, are associated with the risk of T2DM.st, 2022 were screened to identify the included studies. R software was also applied for calculation of odds ratio (OR), 95% confidence interval (95% CI), heterogeneity, and sensitivity analysis.Studies published before February 1P = 0.0002, I2 = 59%) genetic model. However, there was no statistically significant correlation between rs16835198 and susceptibility to T2DM under allelic , heterozygote , homozygote , recessive , and dominant  genetic models.Seven studies for rs3480  and five studies for rs16835198  were included in this meta-analysis. The results revealed a statistically significant association of rs3480 with T2DM under homozygote (GG vs AA: OR = 1.76, 95% CI = 1.31–2.37, The present meta-analysis revealed that rs3480 in FNDC5 is significantly associated with susceptibility to T2DM, while rs16835198 does not show such an association. The number of patients with type 2 diabetes mellitus (T2DM) is increasing worldwide, and T2DM has become one of the most serious medical and health issues worldwide  . AccordIrisin, a novel intriguing myokine, was recently reported and described by Bostrom et al. Irisin is released upon cleavage of the plasma membrane protein fibronectin type III domain containing protein 5 (FNDC5), whose gene expression is suggested to be driven by muscle-specific transgenic overexpression of the exercise-responsive transcriptional co-activator peroxisome proliferator-activated receptor (PPAR)-γ co-activator-1α (PGC-1α) . In an aThe incidence of T2DM is closely related to genetic and environmental factors , 13. SeaSeveral studies have evaluated the association of SNPs in FNDC5 with susceptibility to T2DM. However, the results are inconsistent. Therefore, the role of these FNDC5 SNPs in the risk of T2DM remains unclear. Here, we conducted a meta-analysis based on the available data to determine whether FNDC5 rs3480 (G>A) and rs16835198 (G>T) SNPs are associated with susceptibility to T2DM.In order to ensure the transparency and accuracy of the reporting medical research, the present meta-analysis was conducted following the PRISMA guidelines, as they are appropriate for systematic reviews and meta-analyses , 15.st, 2022.PubMed, Embase, Cochrane, China National Knowledge Infrastructure, and Chinese BioMedical Literature databases were used to retrieve literatures systematically. The language of the studies was limited to Chinese and English. The search strategy involved the use of the following terms: “FNDC5,” “fibronectin type III domain containing protein 5,” “type 2 Diabetes mellitus,” “T2DM,” “single nucleotide polymorphism,” and “SNP.” Systematic retrieval was conducted until February 1The inclusion criteria were as follows: (1) case-control study on the correlation between the SNPS, rs3480 and rs16835198, and T2DM risk; (2) the diagnosis of T2DM conforms to WHO diagnostic criteria; (3) the study population in the study is consistent with Hardy-Weinberg Equilibrium (HWE); (4) the literature provides genotypic and/or allelic frequencies of the rs3480 and rs16835198 SNPs.Studies were excluded if one of the following exclusion criteria was fulfilled: (1) no control group; (2) comments, review, abstracts, letters, conference presentations, and studies on animal models; (3) lack of genotypic and/or allelic frequencies of the rs3480 and rs16835198 SNPs. In case of duplicate publications, the study with the largest sample size was included.Two authors (Yang and Ni) read the titles of the articles independently and assessed the quality of the included articles. In case of any disagreement, a decision was made after discussion. The two authors extracted the following data from all included articles: first author, year of publication, country of participants, number of cases and controls, genotypic distribution in cases and controls, genotyping methods, and HWE. An external referee was invited in case of disagreements not resolved by both investigators.We applied the Newcastle Ottawa scale (NOS) to evaluate the quality of eligible studies from different aspects: (1) adequate definition of case; (2) representativeness of the cases; (3) selection of controls; (4) definition of controls; (5) comparability of cases and controls; (6) ascertainment of exposure; (7) same method of ascertainment for cases and controls; (8) non-response rate. The NOS has a score range of 0 to 9, and ≥7 was considered of high quality , 16.P < 0.05. Q test and I2 statistic were used to assess heterogeneity among the included studies. The heterogeneity was obvious if the P value of the Q test < 0.1 or I2 ≥ 50% (We employed R (version 4.0.3) software and meta package for statistical analyses. To evaluate the strength of correlation between rs3480, rs16835198, and T2DM under five genetic models, odds ratios (ORs) and 95% confidence interval (CIs) were calculated. Statistical significance was set at I2 ≥ 50% .Egger’s test , it would be more proper to use random-effects model first, even not statistical heterogeneity was observed , 19, ther’s test , 20.Literature search was carried out according to the PRISMA flow chart shown in Seven studies involving 5475 patients with T2DM and 4855 healthy controls were included in the meta-analysis to explore the potential association between rs3480 and susceptibility to T2DM.P = 0.0002, I2 = 59%) genetic model was statistically significant. In contrast, no statistical significance was found for correlations between rs3480 and T2DM susceptibility in allelic , heterozygote , recessive , and dominant  genetic models. Our results suggested that people carrying the G allele in rs3480 had higher susceptibility to T2DM. The forest plots are presented in The present meta-analysis discovered that the correlations between an FNDC5 rs3480 (G/A) and susceptibility to T2DM in homozygote  models. Sensitivity analyses were further applied for different genetic models. As shown in There was obvious heterogeneity in the allelic, heterozygote, homozygote, and recessive models; therefore, subgroup analysis was performed. As shown in P = 0.11, I2 = 84%), heterozygote , homozygote , recessive , and dominant  genetic models. A total of five studies involving 4217 patients with T2DM and 4019 healthy controls were finally included to assess the potential correlation between rs16835198 and susceptibility to T2DM. No obvious asymmetry was observed in the Egger’s test for any comparison, which suggested that the findings were unlikely to be influenced by publication bias. The results of rs3480 and rs16835198 were summarized in T2DM is a complex polygenic metabolic disease caused by the interaction of genetic and environmental factors. Unhealthy lifestyles increase the risk of T2DM, but not all individuals with unhealthy lifestyle habits develop the disease. Therefore, genetic factors play a very important role in the onset and progression of T2DM, which needs to be further studied. SNPs are polymorphisms of the DNA sequence caused by variations in a single nucleotide at the genomic level. They are commonly inherited in humans, accounting for more than 90% of all known polymorphisms.In 2007, Sladek et al. used Genome Wide Association Study (GWAS) to identify diabetes susceptibility genes in the French population. Several research groups further identified and confirmed SNPs associated with diabetes susceptibility. Thus, the association between SNPs and T2DM susceptibility is being gradually revealed –33. FNDCMultiple SNPs significantly associated with metabolic disease susceptibility in different populations have been found in the FNDC5 gene. Rs16835198 was found to be significantly associated with insulin sensitivity and obesity in the German and Egyptian populations, respectively . The G aIn addition, our meta-analysis showed that rs16835198 is not associated with susceptibility to T2DM. Rs16835198 is located on the 3’ region of the FNDC5 gene, which is unlikely to affect the amino acid sequence of the protein products . Rs16835This meta-analysis has some limitations. First, it included nine articles with large and heterogeneous populations, including three studies on Chinese Han individuals, two on Egyptian populations, and four on individuals from Southern Brazil, Saudi Arabia, Iraq, and Japan each. The differences among races may have affected the results. The best approach would have been to conduct subgroup analysis for each race, but the literature volume of the corresponding subgroups was not sufficiently large. Therefore, a comprehensive analysis can only be conducted after the inclusion of more articles. Second, only English and Chinese articles were included in this meta-analysis, and data presented in other languages were not included.In conclusion, we found that the rs3480 G allele in FNDC5 may confer moderate risk for T2DM. Further investigation of these SNPs may improve our understanding of the occurrence and progression of T2DM. We are aware that the present meta-analysis results were derived from a limited sample size. Therefore, future analyses with larger sample sizes and including more studies are required to define the associations between rs3480 and T2DM risk.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.XYa and LN searched literature and collected data. JS and XYu analyzed the data. DL supervised the project. XYa wrote the original manuscript. DL reviewed and revise the manuscript. All authors read and approved the final version of the manuscript.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.


The pooled event rates and the 95% CIs for the A, AB, B, and O-blood groups were 0.518 (95% CI: 0.411–0.622), 0.592 (95% CI: 0.495–0.683), 0.205 (95% CI: 0.041–0.612), and 0.283 (95% CI: 0.247–0.322), respectively. According to the findings, people with Factor V Leiden with blood group AB are more likely to develop venous thromboembolism than those with blood groups A, O, and B. The overall statistical significance of the ABO blood group’s susceptibility to venous thromboembolism in individuals with Factor V Leiden was <0.001 . In conclusion, the current meta-analysis provides an additional indication that blood group AB individuals with Factor V Leiden are at higher risk of developing venous thromboembolism, and blood type B is connected to a lower risk of developing venous thromboembolism.A limited number of studies investigated the association between the ABO blood groups and the incidence of venous thromboembolism in individuals with Factor V Leiden; however, discordant findings were reported. Consequently, this systematic review and meta-analysis aimed to evaluate the existing evidence on the susceptibility of the ABO blood group to venous thromboembolism in individuals with Factor V Leiden. All English-published articles on the Web of Science, Scopus, PubMed, EMBASE, and Google Scholar were comprehensively and systematically searched by the author without a time or region limit. Four studies were included in the qualitative synthesis and meta-analysis after the removal of studies that were not eligible. According to the analyses of the fixed and random effects, the point estimates of the effect size and the 95% confidence interval were 0.416 (95% CI: 0.397–0.435) and 0.392 (95% CI: 0.288–0.507), respectively. In contrast, the homogeneity test  reveals that blood group data distributions have a heterogenous structure (Q = 432.187;  One of the primary clinical symptoms of venous thromboembolism (VTE) is deep vein thrombosis (DVT) . DVT is Furthermore, there is a great deal of disagreement surrounding the best way to manage carriers in terms of both the necessity and efficacy of antithrombotic therapies . The relThe higher risk and greater severity of thrombosis in homozygous carriers, or people with combinations of several gene abnormalities, are examples of the impact of gene-gene interactions in VTE .Activated protein C (APC)-resistance prevalence or FVL carrier frequency range from around 12 to 20 percent in patients who are primarily Caucasian and have experienced a first-ever venous thromboembolism, and they are linked to an increased risk of venous thromboembolism by 2.7 to 6.6 times ,10,11. TThe link between arterial thrombotic disorders and these frequent genetic variations is quite weak as the per-allele relative risks (RR) was 1.17 for FVL and 1.31 for PT, according to a meta-analysis including 66,155 cases and 91,307 controls) . Many fiIt seems acceptable to assess the significance of gene-gene interactions on thrombotic risk within this paradigm. Unfortunately, there is not much research that has looked into this potential, especially in arterial thrombosis. Numerous investigations have conclusively shown that the ABO blood type has a clinically significant impact on in vivo hemostasis. Numerous studies have consistently shown that those with non-O blood had a much higher risk of VTE (between 1.8 and 2.5-fold) ,20,21,22Patients with bleeding disorders, however, are overrepresented in the blood group O. It is interesting to note that FVL carriers with non-O blood groups appear to have a much higher risk of VTE. Unfortunately, because of the small numbers of carriers, particularly among controls, these findings were based on research with weak statistical power ,27,28,29Since there is a better recognition of systematic review and meta-analysis findings in health policy and decision-making processes, this study was conducted to provide summary estimates on the relationship between the susceptibility of the ABO blood groups to venous thromboembolism in individuals with Factor V Leiden in order to reduce the gap in the literature and support evidence-based decision making.The protocol of the study was registered and approved by The International Prospective Register of Systematic Reviews . In addition, the PRISMA recommendations were considered in this systematic review and meta-analysis .All English-published articles on the Web of Science, Scopus, PubMed, EMBASE, and Google Scholar were comprehensively and systematically searched by the author without a time or region limit. A combination of search methods was employed to increase the search scale, namely: (1) search MESH  using the following terms: “ABO blood group”, “Venous Thromboembolism”, and “Factor V Leiden”, and (2) free-text search. The following were the inclusion requirements: Studies have described the frequency distribution of venous thromboembolism in people with factor V Leiden according to each blood group. On the other hand, the following exclusion criteria were considered: (1) Reviews; (2) Non-English language; (3) Case reports; (4) Studies where the end measure was not the frequency of venous thromboembolism in people with factor V Leiden in each blood type ; (5) Studies lacking pertinent data; and (6) Studies without Full Text.The screening procedures were managed, and duplicates were eliminated using EndNote V.X8 software. After removing duplicates, the author individually screened the titles, abstracts, and full texts to determine whether the studies were eligible. The first author’s name, publication date, study title, nation of origin, gender, mean participant age, sample size, frequency, and percentage of venous thromboembolism in individuals with factor V Leiden cases according to each blood group category , as well as other information, were all extracted and recorded independently using a standardized data collection form that was developed in accordance with the sequence of variables required from the primary source.The quality of the included studies was evaluated using the “The Quality Assessment Tool For Quantitative Studies (QATFQS),” which was developed by the “Effective Public Health Practice Project (EPHPP)” . An assep-value.All statistical analyses were done using Comprehensive Meta-Analysis Software . The number of studies that should be included in the meta-analysis to reset the effect size value obtained from the meta-analysis was estimated using the Fail-Safe N technique. The studies included in the meta-mean analysis’s effect sizes were calculated. The data is given in the forest plots. The ABO blood group susceptibilities to venous thromboembolism in people with Factor V Leiden were pooled and evaluated using a random-effect model. The retrieved data were used to calculate the event rate, their accompanying 95 percent confidence intervals, and the 2 statistic, and I2 values of 25%, 50%, and 75% are considered low, moderate, and high estimates, respectively [p-value < 0.1 or I2 < 50%, a non-significant level of statistical heterogeneity was assumed. The significant degree of variability led to the selection of a random-effect model [The heterogeneity between the included studies was determined using the Iectively . When p-ct model . A funnen = 1126), Scopus (n = 1578), PubMed (n = 1563), EMBASE (n = 1064), Google Scholar (n = 2562). The duplicates were then removed, leaving 2479 studies. We eliminated 1275 studies by title screening and then used abstract screening to eliminate 962 irrelevant studies out of 1204. Following this, we read the full texts of the remaining 242 publications and omitted 238 pieces of research since they did not meet our inclusion criteria. The qualitative synthesis and meta-analysis eventually comprised four papers in total . However, a random effect model should be utilized for the meta-analysis when the Q-value is significant (p < 0.10), indicating that there is heterogeneity in the studies [The fixed effect and random effect models are the two statistical models available for a meta-analysis. The fixed effect model presupposes that there is a single genuine effect size shared by all of the studies included in the meta-analysis and that any observed difference between studies is due to random chance or sampling mistakes . The ran studies . Both mo studies .p-value < 0.001). The Q test, however, could not be accurate if there are only a few papers included in the meta-analysis. Given that Cochran’s Q test has poor statistical strength and is insensitive, a heterogeneity p value of < 0.10 (rather than 0.05) denotes the presence of heterogeneity [According to the analyses of the fixed and random effects of the 16 subgroups within the 4 studies included, the point estimates of the effect size and the 95% confidence interval were 0.416 (95% CI: 0.397–0.435) and 0.392 (95% CI: 0.288–0.507), respectively. A point estimate should ideally be (1) Consistent. The estimate is more accurate the larger the sample size. (2) Neutral. The expectation of the average observation value, or “average observation value”, is equal to the associated population parameter. (3) The most effective or best unbiased estimate is the one with the smallest variance among all reliable, unbiased estimates (a measure of the amount of dispersion away from the estimate) . In contWe chose a random-effects model to carry out a meta-analysis in accordance with the sample’s heterogeneity. Thus, using the random-effects model, the relationship between the ABO blood group and the risk of venous thromboembolism in individuals with Factor V Leiden was investigated. The amount of real heterogeneity between the included studies  was 0.887.2), evaluates the pooled fraction of variability between the included studies in a meta-analysis explained by differences rather than by sampling error [2 values derived from the meta-analysis were lower than 99 percent (96.529%), indicating that there is a homogenous and narrow dispersion of effect sizes in the current meta-analysis  , 0.592 (95% CI: 0.495–0.683), 0.205 (95% CI: 0.041–0.612), and 0.283 (95% CI: 0.247–0.322), respectively. According to the findings, people with Factor V Leiden with blood group AB are more likely to develop venous thromboembolism than those with blood groups A, O, and B. The overall statistical significance of the ABO blood group’s susceptibility to venous thromboembolism in individuals with Factor V Leiden was <0.001  .p-values for Kendall’s tau with and without continuity and Egger’s regression intercept were 0.928, 0.964, and 0.661, respectively , 0.592 (95% CI: 0.495–0.683), 0.205 (95% CI: 0.041–0.612), and 0.283 (95% CI: 0.247–0.322), respectively. Accordingly, people with Factor V Leiden with blood group AB are more likely to develop venous thromboembolism than those with blood groups A, O, and B. However, in earlier research, it was discovered that non-O blood type increased the risk of venous thromboembolism. To answer this question, previous general population studies lacked the necessary power. The results of earlier investigations are supported by the cumulative effect of ABO blood type and factor V Leiden on the risk of venous thromboembolism ,18,46.The study conducted by Birgitte et al. revealed that prothrombin G20210A mutations and factor V Leiden R506Q accounted for 10% and 1%, respectively, of the population-attributable risk for venous thromboembolism and non-O blood type, respectively, for 20% of the population. This suggests that genetic testing for thrombophilia should include ABO blood type . Their fIn studies of thrombophilia, this prothrombotic genetic risk factor is typically not examined. However, considering that thrombosis is a multigenic and complex disease, the moderate thrombotic risk associated with the non-O blood group (OR: 1.8–2.5) and the high prevalence of prothrombotic non-O genotypes in the general population (>50 percent) make the ABO blood group of general interest in thrombosis . It is pMany studies have found a connection between various ABO blood types and a number of diverse, and often odd, phenotypic traits  ,51. ManySince higher levels of von Willebrand factor and/or factor VIII are risk factors for venous thromboembolism, this link between ABO blood type and venous thromboembolism can be partially explained by the elevated amounts of these factors in non-O people’s blood . The disAdditionally, genetics account for 66 percent of the overall variation in plasma levels of von Willebrand factor, with ABO blood type accounting for 30 percent of this genetic component . The obsOne of the factor V gene’s three activated protein C cleavage sites is eliminated by the single nucleotide mutation known as factor V Leiden R506Q. As a result, factor V is inactivated at a slower pace, increasing thrombin generation and venous thromboembolism risk . ProthroIt would be helpful to understand how the ABO blood group interacts with other venous thrombosis risk factors, including genetic  and acquired risk factors . VTE is a multicausal disease in which thrombosis must occur concurrently with a number of risk factors, both inherited and acquired. As a result, the majority of people who have a single thrombophilic risk factor, especially those who have common prothrombotic genotypes, are asymptomatic. Moreover, thrombophilia typically has no effect on the likelihood of recurrence or the length of anticoagulant medication.Therefore, genetic testing for thrombophilia raises serious ethical questions regarding insurance and social security issues in asymptomatic carriers of single thrombophilic defects even though it is of limited value to the symptomatic patient and offers little benefit over and above the family history ,59.As a result, to more accurately assess a person’s risk of thromboembolic events, it is required to identify clusters of risk variables. Our findings indicate that those with Factor V Leiden who have an AB blood type have a considerably higher risk of venous thromboembolism. This suggests that ABO phenotyping or genotyping analyses may be useful tools for estimating future thrombophilic risk profiles and may have consequences for the management of thrombosis.This systematic review and meta-analysis was the first study to examine the relationship between people with Factor V Leiden and the ABO blood group’s susceptibility to venous thromboembolism. However, the following restrictions should be taken into account while interpreting the current results: First off, only four eligible articles were included in this meta-analysis, and studies from different nations around the world were not included. As a result, we believe that those articles are still in the preliminary stages, and additional verification is required to guarantee their inherent quality. Second, despite our meta-strict analysis’s inclusion criteria, significant heterogeneity was discovered. Third, because adjusted confounders varied between trials, we were unable to do a subgroup analysis. Finally, we believe that the sample sizes used in the included studies were not accurate representations of the initial general populations.We revealed that people with Factor V Leiden and blood type AB > A > O > B had a higher rate of venous thromboembolism. This evidence-based meta-analysis study also shows that blood group A people are more prone to venous thromboembolism. Venous thromboembolism risk is reportedly decreased in people with blood type B. However, the precise molecular and clinical mechanism underlying the variable sensitivity of ABO blood groups to venous thromboembolism in people with Factor V Leiden remains largely unknown. Therefore, more research is required to better understand how the ABO blood group affects venous thromboembolism and to determine whether stepping up infection control measures according to blood category could lower the chance of developing venous thromboembolism. Additionally, it is crucial to report blood types and subtypes for every infected person in every community so that they can later be correlated with a larger data set based on their demographic data, such as gender, age, ethnicity, etc.


Objective: To elucidate the relationship between CYP17A1/CYP19A1/SHBG gene polymorphisms and PCOS susceptibility.Methods: We searched multiple databases from inception to December 2020 and meta analysis was conducted to elucidate the relationship between gene polymorphisms and PCOS risk.Results: 26 studies were included, comprising 4860 PCOS and 4043 controls. CYP17A1 rs743572 polymorphisms were found to be negatively associated with PCOS risk under dominant model  in the general population while neither CYP19A1 rs2414096 polymorphisms  nor SHBG rs6529 polymorphisms  was associated with PCOS susceptibility under dominant model in the general population.Conclusion: CYP17A1 rs7435721 polymorphisms might be protective factors against PCOS in general populations.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/#myprospero, identifier CRD4202122640. Polycystic ovary syndrome (PCOS) is a common reproductive endocrine disorder that occurs in approximately 5–10% of the women of childbearing age . PCOS isHyperandrogenemia is a key indicator of PCOS, and increased androgen concentrations including testosterone (T) and androstenedione have been observed in most patients, while increased dehydroepiandrosterone sulfate has been observed in a minority [∼25%] of the patients . AndrogePrevious genetic association studies have found that a large number of genetic variations are related to PCOS susceptibility, and genetic factors may greatly impact the occurrence of PCOS . Single This meta-analysis was registered with the PROSPERO international prospective register of systematic reviews (registration number CRD42021226402). We searched medical literature for relevant studies using PubMed, EMBASE, the Cochrane Library, Web of Science, WanFang Database, and China National Knowledge Infrastructure (CNKI) from their date of establishment to December 2020. A total of 463 records were identified using electronic search strategies; in addition, one relevant record was obtained from the reference lists of the included studies. We used the search terms: “Steroid 17-alpha-Hydroxylase or CYP17,” “aromatase or CYP19,” “sex hormone binding globulin or SHBG,” “Single nucleotide polymorphism (SNP) or polymorphisms or genotype or genetic or mutation or variant,” and “Polycystic ovary syndrome or PCOS.” We limited the publication type to case-control studies, and there were no language or location restrictions. We also tried to search in grey literature, but no new relevant cohort studies were found.To be included in this meta-analysis, the studies needed to meet the following inclusion criteria: (a) originated from a case-control study design; (b) reported the cases of PCOS patients diagnosed with one of the following three diagnostic criteria: National Institutes of Health (NIH) 1999, Rotterdam 2003, and AE-PCOS Society 2006; (c) evaluated the association between SHBG or CYP17 or CYP19 gene polymorphisms and PCOS; (d) reported odds ratios (ORs) and corresponding 95% confidence intervals (CIs) or provided the distribution of sufficient genotypic and allelic data for estimation in cases and controls.Exclusion criteria included the following: (a) insufficient data on genotyping; (b) no control population; (c) study included disorders other than PCOS; (d) case reports, case series, abstracts, reviews, meta-analysis, comments, editorial articles, or letters without original data; (e) duplicate data; (f) the SNP was reported in less than 5 case-control study.p-value of HWE in the control group. As described elsewhere, quality, internal validity, and risk of bias of the included studies were assessed using the validated quality of genetic association studies checklist (Q-Genie)  independently extracted data and assessed methodological quality. Data were extracted from the included studies to collect the following necessary information: first author, year of publication, country of origin, ethnicity, number of cases and controls, PCOS diagnostic criteria, genotype method, genotype data, and evidence of Hardy-Weinberg equilibrium (HWE). Genotype data of case and control studies were extracted to calculate the OR with 95% CI and Q-Genie) . The Q-gp < 0.1 and I2 > 50% were considered to indicate significant heterogeneity  was used to perform the statistical analysis.Pooled ORs with corresponding 95% CIs were calculated to identify the potential association between susceptibility to PCOS and gene polymorphisms for the following genotypic models: dominant model (mtmt + wtmt vs. wtwt), recessive model (mtmt vs. wtwt + wtmt), co-dominant model (mtmt vs. wtwt) or (wtmt vs. wtwt), and complete overdominant model (mtmt + wtwt vs. wtmt) for rs743572 of CYP17 gene polymorphisms, rs2414096 of CYP19 gene polymorphisms, rs6529 of SHBG gene polymorphisms. In order to synthesize the best genetic model, we refer to the method of screening the best genetic model in the study of ogeneity , using oogeneity . For allThe initial search strategy retrieved a total of 464 studies from all electronic databases. After reading the titles, abstracts, and full texts, we selected 26 studies , which iFor a quantitative analysis, at least two studies are required for each polymorphism. For there were few cohort studies on specific loci polymorphisms , to make the conclusions more reliable, we only included loci with no less than five cohort studies for quantitative analysis.p = 0.017, OR = 0.83, 95%CI 0.72–0.97, I2 = 74.80%, Pheterogeneity = 0.000) in the general population. In subgroup analyses by ethnicity, however, no significant association between the CYP17A1 rs743572 polymorphisms and the PCOS susceptibility was found in the Asian , Caucasian  and mixed  population. We evaluated the overall potential publication biases with LFK index = 0.37 and Doi plots showing no asymmetry  polymorphisms and PCOS risk. As shown in symmetry .p = 0.578, OR = 0.87, 95%CI 0.54–1.41, I2 = 95.90%, Pheterogeneity = 0.000). We evaluated the overall potential publication biases with LFK index = -0.37 and Doi plots showing no asymmetry  polymorphisms and PCOS risk. All of the five studies were conducted in the Asian population , no signsymmetry .p = 0.752, OR = 0.99, 95%CI 0.94–1.05, I2 = 60.90%, Pheterogeneity = 0.012) in the general population. In subgroup analyses by ethnicity, no significant association between the SHBG rs6529 polymorphisms and the risk of PCOS susceptibility was found in the Asian  and Caucasian  population. The overall potential publication biases were evaluated with LFK index = -0.60 and Doi plots showing no asymmetry  polymorphisms and PCOS risk. As shown in symmetry .Our meta-analysis included 4860 PCOS patients and 4043 control participants from 26 case-control studies to evaluate CYP17A1, CYP19A1, and SHBG gene polymorphisms. The results showed that CYP17A1 rs743572 polymorphisms were found to be negatively associated with PCOS risk under dominant model in the general population while neither CYP19A1 rs2414096 polymorphisms nor SHBG rs6529 polymorphisms was associated with PCOS susceptibility under dominant model in the general population.The CYP17A1 gene encodes a cytochrome P450 enzyme on chromosome 10q24.3. The enzyme converts pregnenolone and progesterone into 17-hydroxyprogesterone and 17-hydroxyprogesterone, respectively, through the activity of 17-hydroxylase. These steroids are converted to dehydroepiandrosterone (DHEA) and 4-androstenedione through the activity of 17, 20-lyase . An addiThe CYP19A1 gene is located on chromosome 15q21.2 and encodes aromatase P450, which plays an important role in the synthesis of estrogens from androgens . AromataSHBG is mainly synthesized in the liver, binding androgens in a high-fidelity manner, and thereby making it biologically inaccessible to target tissues. Several polymorphisms of the SHBG gene on chromosome 17 have been shown to alter SHBG liver biosynthesis, plasma levels, and plasma clearance efficiency, thereby regulating the distribution of androgens . In patiOur meta results confirmed that androgen synthesis related gene polymorphisms may affect ovarian function and thus affect the susceptibility of PCOS. Neuroendocrine dysfunction is a component of PCOS, mainly manifested by increased secretion of LH. LH excess associated with PCOS may be secondary to the peripheral events within the ovary, the mechanism of neuroendocrine dysfunction resulting in an elevated LH in PCOS may be an uncoupling of hypothalamic estradiol inhibition by elevated ovarian androstenedione, while elevated LH may cause hyperandrogenemia . Herein,As hyperandrogenemia is a key characteristics seen in PCOS patients, we focused on the CYP17A1 and CYP19A1 gene polymorphisms that affect androgen synthesis and the SHBG gene polymorphisms that affect SHBG synthesis to determine the relationship between gene polymorphisms and PCOS risk through a comprehensive literature search, so as to draw more comprehensive, accurate, and reliable conclusions. In addition, the studies that we included here were conducted in different countries and using different ethnic groups, making the pooled results more universal. Finally, according to the quality evaluation system, all the articles included in this study were of medium quality.The present study has several limitations. First, in this meta-analysis, the combined OR was estimated by the number of genotypes or alleles in the case and control subjects, without adjusting for other confounding factors, which might cause bias. Second, although the study group was large, some subgroup analyses were performed with quite small sample sizes. Despite the sensitivity analysis, our results might still be affected by a type II error. Thus, future research encompassing a larger and more detailed sample set is necessary. Third, we did not search for gray literature, the publication bias might still affect the results of our meta-analysis. Fourth, some polymorphic studies have significant inter-study heterogeneity, and some studies have a deviation from the genotype distribution of the HWE. Finally, this meta-analysis does not discuss gene-environment or gene-gene interactions. However, the conclusions and limitations of this study provide some direction for the design of future studies.In summary, this meta-analysis showed that CYP17A1 rs7435721 polymorphisms might serve as a protective factor against PCOS in general populations. Given that the pathogenesis of PCOS is extremely complex, the probability that specific gene polymorphisms could significantly contribute to its development is low, and we strongly recommend further studies with a larger sample size to comprehensively explore the potential roles of gene-gene and gene-environmental interactions in the development of PCOS.CYP17A1 rs743572 polymorphisms were found to be negatively associated with PCOS risk under dominant model in the general population while neither CYP19A1 rs2414096 polymorphisms nor SHBG rs6529 polymorphisms was associated with PCOS susceptibility under dominant model in the general population. CYP17A1 rs7435721 polymorphisms in general populations might be protective factors against PCOS.


Common vaccinations may have impacts on dementia risk, but current evidence is inconsistent. We therefore investigated the association between vaccinations and dementia risk by systematic review and meta-analysis approach.We conducted an extensive search of PubMed, Embase, Cochrane Library, and Web of Science to identify studies that compared the risk of dementia in vaccinated versus unvaccinated populations. The adjusted hazard ratio (HR) and corresponding 95% confidence intervals (CIs) were pooled as measures.Poverall effect < 0.001; 2 =I91.8%, Pheterogeneity<0.001). All types of vaccination were associated with a trend toward reduced dementia risk, with rabies (HR=0.43), tetanus & diphtheria & pertussis (Tdap) (HR=0.69), herpes zoster (HR=0.69), influenza (HR=0.74), hepatitis A (HR=0.78), typhoid (HR=0.80), and hepatitis B (HR=0.82) vaccinations being significant. Individuals with more full vaccination types and more annual influenza vaccinations were less likely to develop dementia. Gender and age had no effect on this association.Of the 9124 records initially retrieved, 17 studies with 1857134 participants were included in our analysis. The overall pooled results showed that vaccinations were associated with a 35% lower dementia risk (HR=0.65, 95% CI: 0.60-0.71, Routine adult vaccinations are associated with a significant reduction in dementia risk and may be an effective strategy for dementia prevention. Further research is needed to elucidate the causal effects of this association and the underlying mechanisms. Dementia is a syndrome that manifests as progressive cognitive decline and is the seventh leading cause of death among all diseases . Age is Unfortunately, to date, with the exception of aducanumab, which was approved by the FDA in 2021 for the treatment of Alzheimer’s disease even if its efficacy is highly debated , no therFor years, public health authorities have wanted to dispel the myth that vaccinated people are more likely to develop Alzheimer’s disease. This is because vaccination stimulates the immune system and thus causes inflammation, which is part of the pathology of Alzheimer’s disease. But a more important reason is that the occasional neuropsychiatric disorders occurring after vaccination are over-reported in the media as a cause-and-effect relationship to appeal to public sentiment . A previThe present systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines , and theTwo authors extensively searched the PubMed, Embase, Cochrane Library, and Web of Science for relevant studies published from database inception to November 30, 2021. Subject terms (vaccine(s), vaccination, dementia, Alzheimer disease) and corresponding synonyms were combined to form the search strategy. No filters or language restrictions were used. The specific search strategy for each database is provided in the Studies were considered for inclusion if all the following conditions were met: (a) the study subjects were humans. (b) For cohort studies, there was a vaccinated cohort and a non-vaccinated cohort; for case-control studies, the case group was patients with dementia and the control group was not. Dementia was clearly defined, and the use of diagnosis codes from the patient medical records and standard clinical criteria were acceptable. (c) Studies compared the risk of dementia in vaccinated population versus unvaccinated populations. (d) The study design was a longitudinal study, including cohort or case-control studies.Studies that met any of the following criteria would be excluded: (a) Literature that did not generate raw data such as reviews, meta-analyses, comments. (b) Non-human studies such as animal models and cells. (c) The intervention is a therapeutic vaccine specifically for dementia or Alzheimer’s disease. (d) Case reports, case series, or research which did not establish a temporal relationship between vaccinations and dementia onset, such as cross-sectional study.Two authors independently browsed the titles and abstracts of the retrieved records. Full-text reading was performed for potentially eligible papers. Excluded papers would be checked again by a third author.For eligible studies, two authors independently extracted the following information: first author, year of publication, sample size, gender, age, region, population source, study period, type of vaccine, measures of identifying dementia and vaccination, study design, effect size, adjusted confounding factors, and duration of follow-up. Any disagreements that arose during the above process were resolved by consensus among all authors.The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS) . For cohQ test and Higgins’ 2I statistics  and the corresponding 95% CI for the dementia in the vaccinated population compared to the non-vaccinated population . When boatistics . Heterogspection , 30. No l method .All data synthesis and analysis in this study was done using STATA MP/16.0 . All P values were two-tailed and less than 0.05 was considered significant.The pre-developed search strategies yielded a total of 9124 potentially relevant papers. After removing 4015 duplicates, the titles and abstracts of the remaining records were screened, and 4739 clearly non-relevant papers were excluded. Full-text reading was performed on 370 papers, and 17 studies were finally included in this systematic review. The study selection process and the reasons for exclusion after full-text reading are presented in A total of 17 studies involving 1857,134 participants assessed the association between common vaccinations and subsequent risk of dementia , 32–43. The overall level of quality of the included studies was excellent, with NOS scores ranging from 7-9 Table 2.2 =I91.8%, Pheterogeneity<0.001), so the random-effects model was used. The pooled 17 studies was 0.65 , suggesting that vaccinations overall reduced the risk of future dementia by 35% , herpes zoster , and Tdap  vaccinations and subsequent lower dementia risk were significantly associated. The greatest reduction in dementia risk was seen after BCG vaccination, but statistical significance was borderline . Pneumonia  and poliomyelitis  vaccinations also tended to be associated with a lower risk of dementia, but without statistical significance . Furthermore, Wilkinson et al. explored the association between seven other vaccinations and dementia risk using the Secure Anonymised Information Linkage databank containing the population of Wales, UK. All vaccinations were associated with a reduced risk of dementia, with reduced risk ranging from 18% to 57% among those vaccinated against hepatitis A, hepatitis B, typhoid, and rabies, whereas statistical significance was absent for yellow fever, cholera, and meningitis vaccines. Only studies on influenza vaccine explored the effects of vaccination dose on dementia risk. Individuals who received 1 or 2-3 influenza vaccines during the follow-up period had no reduced risk of dementia compared to unvaccinated individuals, whereas people who received at least 4 annual influenza vaccinations had a 49% reduced risk of dementia   Table 3.P=0.003) ; WilkinsP=0.003) . These fP-values of Begg’s and Egger’s tests were 0.6343 and 0.2127, respectively, indicating a low possibility of publication bias. However, the funnel plot exhibited a slight asymmetry, so publication bias was still suspected (P<0.001), indicating that the effect of potentially unpublished studies on the current pooled results was subtle.The We assessed the effect of individual studies on the overall result through excluding one study at a time and then combining the remaining studies. The pooled HR did not change significantly when any single study was excluded, indicating that the current overall result is robust Figure 4This systematic review and meta-analysis comprehensively investigated the current evidence regarding the effect of routine adult vaccinations on the risk of dementia, and the pooled results from 17 studies with more than 1.8 million participants showed a 35% reduction in the dementia risk after vaccinations. This reduction was observed to be significant in influenza, herpes zoster, Tdap, hepatitis A, hepatitis B, typhoid, and rabies vaccinations, with similar trends but not significant in other vaccines. The reduced risk did not vary with age, gender, or type of dementia. Compared to the previous meta-analysis on influenza vaccination conducted by Veronese et al, we added four new studies related to influenza vaccination and overall pooled results were similar. Interestingly, we found a dose-response effect of vaccination on the incidence of dementia. More full vaccination types and a greater number of annual influenza vaccinations were associated with a lower risk of dementia.The exact biological mechanism underlying the association between vaccination and reduced risk of dementia is unclear. One possible explanation is that vaccination can reduce the probability of developing infectious diseases. Studies have shown that infectious events may impair cognition and increase the risk of dementia , 45. A lIt may also be argued that the apparent protective effect of vaccination simply reflects the different health characteristics and lifestyles between vaccinated population and unvaccinated population, such as better knowledge of disease prevention, higher education and income, and greater social support . Of noteEven if the evidence from observational studies cannot definitively show that vaccinations will prevent dementia, the benefits of timely and full vaccination for adults are still substantial. As the body’s immunity declines with age, especially in people with underlying diseases such as hypertension, coronary heart disease, and diabetes, following the recommended immunization schedule not only prevents vaccine-related infectious diseases, but also prevents the exacerbation of existing chronic diseases due to infections . MoreoveSeveral limitations of this study need to be considered. The number of studies exploring the association between certain vaccine types and dementia risk is small, so it is not clear whether this association varies by region and ethnicity. The populations in some studies were limited to those with specific conditions, such as chronic kidney failure, chronic obstructive pulmonary disease, and bladder cancer. Furthermore, the predominant type of vaccine used against the same pathogens differed across time and regions, for example, there were inactivated, recombinant, and live attenuated influenza vaccines. These may have contributed to the heterogeneity of this meta-analysis. Finally, current findings may be affected by publication bias, but the conclusions remain unchanged after adding hypothetical unpublished studies.Our study shows that routine vaccinations are significantly associated with dementia risk reduction and more vaccinations have a stronger protective effect on dementia development. Vaccination may be an inexpensive and accessible intervention to prevent cognitive decline and deserve to be incorporated into the management of dementia prevention strategies. Prospective studies and basic experiments are needed to clarify the causal relationship and underlying mechanisms of this association.The original contributions presented in the study are included in the article/Conception and design: ZS and ML. Database search, study selection, and data extraction: XW and SH, Statistical analysis: TX and DC. Drafting of manuscript: XW, HY, ML, YZ, and JL. Editing and critical revision of the manuscript: ZS and ML. All authors contributed to the article and approved the submitted version.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.


JAMA, there has been an explosion of genetic candidate association studies, including GWAS. To develop an accurate test to help identify those at risk for at least Alcohol Use Disorder (AUD), Blum’s group developed the Genetic Addiction Risk Severity (GARS) test, consisting of ten genes and eleven associated risk alleles. In order to statistically validate the selection of these risk alleles measured by GARS, we applied strict analysis to studies that investigated the association of each polymorphism with AUD or AUD-related conditions published from 1990 until 2021. This analysis calculated the Hardy–Weinberg Equilibrium of each polymorphism in cases and controls. If available, the Pearson’s χ2 test or Fisher’s exact test was applied to comparisons of the gender, genotype, and allele distribution. The statistical analyses found the OR, 95% CI for OR, and a post-risk for 8% estimation of the population’s alcoholism prevalence revealed a significant detection. The OR results showed significance for DRD2, DRD3, DRD4, DAT1, COMT, OPRM1, and 5HTT at 5%. While most of the research related to GARS is derived from our laboratory, we are encouraging more independent research to confirm our findings.Since 1990, when our laboratory published the association of the DRD2 Taq A1 allele and severe alcoholism in  Substance Use Disorder (SUD) clinicians and scientists face great obstacles in the fight against the current Alcohol Use Disorder (AUD) and opioid pandemic worldwide. Although significant progress has been made, the death toll from narcotic overdoses reached 72,000 fatalities in the United States alone in 2017. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA) have not been able to adequately address the severity of the current substance abuse epidemic using novel Food and Drug Administration (FDA) approved treatments. Medication-Assisted Treatments (MAT) that have been approved by the FDA primarily work by modulating the function of the nucleus accumbens. This is accomplished by inhibiting dopamine release and function at the pre-neuron of the nucleus accumbens , and upoDrug overdoses are the most prevalent in the United States, but they are an international problem that demands urgent, innovative solutions. Short-term opioid substitution therapy can reduce harm; however, long-term patients risk being locked into a lifetime of SUD . AlternaThere is increasing movement to opt for the non-addicting narcotic antagonist naltrexone to treat AUD. Recent studies have shown that naltrexone is beneficial by attenuating craving via “psychological extinction” and reducing relapse. Buprenorphine is currently the MAT of choice, but injectable naltrexone plus an agent to improve dopaminergic function and tone may renew interest amongst addiction physicians and patients. Even with the extended injectable option, there is still poor compliance. As such, our group described an open-label investigation in humans showing improvement in naltrexone compliance and outcomes with dopamine augmentation with the pro-dopamine regulator KB220 262 days) compared to naltrexone alone (37 days) [2 days coBlum’s group first coined the term “RDS” in 1995 to identify a group of behaviors with a common neurobiological mechanism associated with a polymorphic allelic propensity for hypodopaminergia as determined by measuring a number of reward genes and associated risk alleles. Understanding this, coupled with the rising scientific consensus regarding the underlying concept of RDS, supports the hypothesis of a common mechanism that groups both chemical and behavioral addictions together . The disSubsequent large-scale genomics studies have had limited success in identifying alleles implicated in addiction and RDS. Despite the utility of Genome-Wide Association Studies (GWAS) and next-generation sequencing, some key issues exist. For example, GWAS is a useful tool for identifying new clusters of genes that may contribute to genetic antecedents related to specific RDS behaviors such as AUD. The next key step following the GWAS results is to converge the data to identify individual candidate genes. Thus, if there is indeed a blueprint or clue regarding specific known genes and associated polymorphic risk alleles linked to a specific phenotype such as AUD, it is significant even though the contribution of each gene may be small.Several neurotransmitters are involved in the processing of reward and punishment. These pathways involve at least six quintessential neurotransmitters and many second messengers linked to the mesolimbic and Pre-Frontal Cortex (PFC). One function is to regulate the final pathway of “wanting”, causing net neuronal dopamine release. In keeping with the current literature, we are cognizant of the many second messengers as well as the interrelatedness of at least seven finite neurotransmitter systems involving dopaminergic activity. We are not proposing that these genes are unimportant; rather, that they influence the final dopaminergic pathway and its function at the nucleus accumbens. We make this distinction to avoid confusion and to inform the readership that hypodopaminergia is, in fact, a significant factor that influences unwanted substance and non-substance addictive behavior seeking. In this regard, being cognizant of these difficulties and awaiting further research, the BRC was utilized as a blueprint  for a liBlum’s laboratory worked toward developing an accurate genetic test to predict the liability risk for RDS behaviors, including AUD ,13. GeneThe development of this patented GARS test involved the selection of ten reward candidate genes that reflected a hypodopaminergic trait, including the dopamine receptors ; Dopamine Transporter (DAT1); serotonin transporter, COMT, MAO, GABA, Mu opiate receptor, Single Nucleotide Polymorphisms (SNPs), and point mutations. The genes determined to negatively influence the net release of dopamine at the brain reward site were chosen from thousands of association studies providing clear evidence of specific risk for all addictions.The idea of “dopamine homeostasis” stems from the basic understanding of the importance of having a “normal” balance of not only dopamine but many well-characterized neurotransmitters and even metabolic systems. To that end, further research is necessary to understand the normative interface between hedonostatic and homeostatic mechanisms and how their collapse is manifested in the mounting allostatic load and eventual addiction. One additional thought worthwhile to consider herein is an ensuing field of addiction diabetology that could propel a unique multi-disciplinary collaboration unrestricted by unnecessary boundaries separating addiction medicine and psychiatry from diabetology and its related disciplines , thus contributing to a greater depth and sophistication of clinical formulations and therapeutic interventions.This article aimed to provide the field with statistical validation of a number of select reward-based genes and associated polymorphisms that primarily induce reduced dopamine function in the meso-limbic brain reward circuitry. While the genetics proposed herein are based on AUD cases and controls, we encourage further work to confirm this and other known “reward processing dysregulation” as an expanded phenotype.Studies investigating the association of each polymorphism with AUD or AUD-related conditions were provided for a meta-analysis. They were published from 1990 to 2021. Abstracts of studies were then examined to see whether these studies analyzed the association between the polymorphism of each candidate gene and AUD and/or AUD-related conditions. The following criteria were necessary to be included in the review: (I) a peer-reviewed publication; (II) a case–control design; (III) independent from the others ; and (IV) sufficient information to compute an odds ratio (OR) and/or likelihood ratios (LR) along with 95% confidence intervals (CI). If two studies contained overlapping samples, the study with the larger sample size was kept for this meta-analysis.χ2 test or Fisher’s exact test was used for comparing gender, genotype, and allele distribution. The age difference was evaluated with the Pearson’s χ2 test and the Wilcoxon rank-sum test. To assess the association of each polymorphism with AUD and AUD-related conditions, the summary OR and their 95% CI were estimated for individual studies using the Pearson’s χ2 test if the contingency tables were available. Considering between-study heterogeneities caused by different genders, ethnicities, nationalities, diagnostic criteria, and so on, both fixed-effects and random-effects models were applied. For the fixed-effects analyses , the effect size of each individual study was integrated using inverse variance methods to generate a pooled OR and 95% CI, assuming that the genotype effect is constant across studies and the detected variation is attributable to random variation; see Laird and Mosteller [Z-test . The Bonferroni correction was applied if multiple comparisons had to be performed. Repetition of the meta-analysis was conducted to run a sensitivity analysis, with one study being omitted at a time, and no significant impact was reported. Furthermore, based on the values of pre-test odds = pre-test risk/(1-pre-test risk), the post-test odds were calculated as a product of OR and pre-test odds. Then, the post-test risk was estimated by the value of post-test odds divided by 1 + post-test odds. For the purpose of this analysis, the pre-test risk was assumed to be 8%.We first incorporated all the data from the selected studies to implement an overall analysis and then conducted stratified analyses. All these analyses were two-sided by default and were performed with the statistical programming language R. For this analysis, the Hardy–Weinberg equilibrium of each polymorphism in cases and controls was assessed. If available, the Pearson’s osteller . For theosteller . When coQ and I2 tests were employed to evaluate the potential heterogeneity among various studies. The Cochran’s Q test was calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, weighting the contribution of each study by its inverse variance. The Q statistic follows a χ2 distribution with n − 1 degrees of freedom (df), where n is the number of studies. The magnitude of I2 = 1 − df/Q then describes the proportion of variation across studies caused by heterogeneity rather than by chance; see Higgins and Thompson [I2 < 0.4. The method introduced by Hedges and Pigott [Cochran’s Thompson . Evidencd Pigott ,26 was ud Pigott  and the d Pigott  were perd Pigott ). The fud Pigott  was atteWe are not able to identify the OR for DRD1 currently. Given this, we will continue to obtain additional case–control studies for each allele in the GARS that will allow for the obtaining of OR statistics. It is anticipated that since we are close to having the proof for each allele showing risk for AUD over controls , the incorporation of a new additional cohort of peer-reviewed and published works in the literature should enable a more robust finding.In some cases, the risk estimates for one copy of each variant  may be higher than ever for people with two copies of one variant. However, because a patient with either one or two copies is managed similarly in a clinical setting, the test report provided to the user will have the same interpretation as the test report for both genotypes. While some more work needs to be accomplished, it is important to highlight that at this stage, based on dichotomization of the GARS clinical data, any combination of these gene-associated alleles that reaches the level ≤ 4 loads onto the risk for drugs and gene-associated alleles that reaches the level ≤ 7 loads onto the risk for AUD . Once thWith the advent of GWAS, there has been an explosion of very large studies related to the genetic polymorphic antecedents of AUD. While others have found evidence for a number of novel clusters of many genes, mostly second messengers, along with the requirement for convergence of these genes to candidates, our approach focuses on finite neurotransmitter pathways. We agree that future GWAS studies seem tantamount to unlocking additional candidates for AUD risk, but we believe the present approach has current hieratic value, requiring independent confirmation. While GWAS studies utilize very large sample sizes and many SNPs, we are not convinced that controls utilized in this sophisticated research reflect RDS-free symptomatology, which may prevent true associations between disease and the controls (disease-ridden).Due to differences among studies , considerable heterogeneity was observed, potentially resulting in publication bias  and/or increased false-positive rates . Not all studies included in this analysis provided information regarding the aforementioned variables. Many of the studies did not provide such information to allow us to exclude or include patients with comorbid disorders in the reported samples; this makes it difficult to assess the effect of the above covariates inducing heterogeneity among studies. Even though the variables such as publication year, study populations, and diagnostic criteria did not seem to be potential sources of heterogeneity, other possible sources of heterogeneity, such as the onset and duration of alcohol addiction and other comorbid conditions or complications, could not be assessed in this meta-analysis. AUD is a complex addictive disorder with multifactorial pathogenesis and is often comorbid with other substance abuse or neuropsychiatric disorders, which likely share common genetic risk factors in the dopaminergic reward system. The potential interaction of candidate genes with others may also lead to heterogeneity among studies. The gene-by-gene or SNP-by-SNP interaction could not be examined due to insufficient studies on other variants that significantly contribute to the vulnerability of complex addictive phenotypes.Furthermore, in this meta-analysis, only case–control studies were considered, which are more susceptible to sampling bias resulting from the potential differences between alcoholics and control groups when compared to family-based studies; see Gamma et al. . The intOur work has been based on a number of candidate gene methods, which were first initiated by the work of Blum and Noble in 1990  as the fMoreover, a GWAS study involving a sample size of 1.2 million individuals involving both tobacco and alcoholism identified 566 polymorphisms in 406 loci associated with various stages of tobacco use  and alcohol use, with 150 loci providing evidence of a pleiotropic association . HoweverHundreds of published studies about dopamine and its role in addictive and compulsive behaviors were reviewed in search of a reliable gene-based diagnostic test to identify heritable risk factors for RDS. Our goal was to illustrate the process used to select the risk alleles of reward genes for the GARS test and to address the limitations caused by inconsistent results in many case–control behavioral association studies. Limitations are perhaps due to the failure of investigators to adequately screen controls for drugs, AUD, and RDS behaviors, including nicotine dependence, obesity, pathological gambling, and internet gaming addiction. One example of accomplishing RDS-free controls is derived from Blum et al. , which rIn conclusion, unlike one gene-one disease (OGOD), RDS is polygenetic and complex. Even though the genes evaluated are not the only ones associated with AUD, we focused on these genes and the related polymorphisms because the available GARS test only assesses genes chosen based on hypodopaminergia. Therefore, other genes such as alcohol metabolism genes  combined with GARS may provide an even stronger association in terms of risk. Certainly, utilizing genetic risk assessment in any form will help identify early on the future risk, now termed “preaddiction” , which iFrom 1999–2019, nearly 500,000 people have died from an opioid overdose, involving any opioid, including prescription and illicit opioids . While wTo that end, we suggest one important way to help reduce overdose rates would be OST incorporated into preventive and therapeutic strategies of metabolic assessments in conjunction with established genetic risk assessment testing, such as the Genetic Addiction Risk Severity (GARS) complemented by, in combination with RDS, Questionnaire RDSQ29 , or any In summary, the results of the statistical analyses displayed the OR, 95% CI for OR, and a post-risk estimated population alcoholism prevalence of 8%. Using only the OR derived from this meta-analysis of 74,566 case–control subjects, DRD2, DRD3, DRD4, DAT1, COMT, OPRM1, and 5HTT all showed significant risk favoring cases of AUD compared to non-AUD controls. While the DRD1 gene did show a significant OR, the small sample size prevented us from capturing the post-risk for 8% of the population.