Several studies have explored the effect of anthropometric risk factors on metabolic syndrome. However, no systematic effort has explored the effect of overweight and obesity on the prevalence of metabolic syndrome in India. Thus, we undertook a meta-analysis to estimate the effect of anthropometric risk factors on the prevalence of metabolic syndrome.We searched databases PubMed Central, EMBASE, MEDLINE, and Cochrane library and search engines ScienceDirect and Google Scholar, from January 1964 through March 2021. We used the Newcastle–Ottawa scale to assess the quality of published studies, conducted a meta-analysis with a random-effects model, and reported pooled odds ratios (OR) with 95% CIs.We analyzed 26 studies with a total of 37,965 participants. Most studies had good to satisfactory quality on the Newcastle–Ottawa scale. Participants who were overweight  or obese  had higher odds of having metabolic syndrome than those of normal or low body weight. Sensitivity analysis showed no significant variation in the magnitude or direction of outcome, indicating the lack of influence of a single study on the overall pooled estimate.Overweight and obesity are significantly associated with metabolic syndrome. On the basis of evidence, clinicians and policy makers should implement weight reduction strategies among patients and the general population. Researchers have tested various treatment options to reduce the prevalence of metabolic syndrome, such as lifestyle and diet modifications, pharmacologic therapy, weight reduction, behavioral therapy, and bariatric surgery.Overweight and obese adults have about 5 times higher odds of having metabolic syndrome than adults with normal or low body weight.Given the evidence linking obesity to metabolic syndrome, clinicians and policy makers should recommend weight management measures to patients and the general population. Interventions promoting physical activity should be developed and implemented in phases in high-prevalence settings.Metabolic syndrome encompasses a spectrum of disorders, including central obesity, atherogenic dyslipidemia , elevated blood pressure, elevated blood glucose, and prothrombotic and proinflammatory states . MetabolMetabolic syndrome is characterized by chronic low-grade inflammation, which is caused by complex interactions between genetic and environmental factors . PrevaleSeveral studies have explored the reasons why obesity and physical inactivity affect metabolic syndrome. Adipocyte hypertrophy and hyperplasia, enhanced by obesity and overweight, influence the overproduction of biologically active metabolites, known as adipocytokines, such as free fatty acids, tumor necrosis factor-α, interleukin-6, and plasminogen activator inhibitor-1 . These mExisting evidence on anthropometric factors related to metabolic syndrome is not country-specific; however, it is essential to know whether the influence of these factors differs from country to country. Although India has almost one-third of the world’s adult population with metabolic syndrome, no systematic effort has been made to explore the effect of overweight and obesity on the syndrome’s prevalence in India. To develop effective strategies and implement relevant policies or programs to address the prevalence of metabolic syndrome, policy makers must have information on its contributing factors. However, we found no systematic review to date that examined the association worldwide between anthropometric factors and metabolic syndrome. Hence, we undertook our meta-analysis to estimate the effect of anthropometric risk factors on the prevalence of metabolic syndrome to inform researchers in India and worldwide.We conducted a systematic review and meta-analysis of observational studies. The protocol was registered in PROSPERO under the registration number CRD42019147277. The “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement 2020” was used to report this systematic review incorporating the meta-analyses . The insWe included any observational study irrespective of its design  that reported the relevant exposure (anthropometric risk factors) and outcome (metabolic syndrome) in India. We did not restrict studies by geographic region  or study setting . Only full-text publications were included, and we excluded studies published as conference abstracts, case reports, or case series and unpublished data.The studies included were conducted among adults in India aged 18 years or older and assessed the association of anthropometric risk factors (overweight/obesity) with metabolic syndrome. We excluded disease-specific  studies. Studies reporting prevalence of metabolic syndrome in relation to different anthropometric factors were included irrespective of the type of definition or criteria used for diagnosis .We conducted a systematic search of literature in electronic databases  and by using search engines (ScienceDirect and Google Scholar). Both medical subject headings (MeSH) and free-text words were used for all searches . We usedOur study selection process involved 3 stages:Primary screening: Two independent investigators (Y.K. and S.R.) performed primary screening of title, abstract, and keywords by executing the literature search. Full-text articles were retrieved for the studies shortlisted on the basis of eligibility criteria.Secondary screening: Full text of these retrieved studies was screened by Y.K. and S.R. and assessed against our eligibility criteria. We included studies that satisfied all eligibility criteria with respect to design, participants, exposure, and outcome.Finalizing the study selection: Disagreements among investigators during the screening process were resolved, and final consensus on inclusion of studies was reached with the help of another investigator (S.M.). Where the required information was missing, we contacted the corresponding author of the respective study and obtained the information. If we received no response from the author, the study was excluded..We manually extracted data by using a predefined, structured data extraction form that included general information about the article, such as author and year of publication, and information related to the methods section, including study design, setting, sample size, sampling strategy, study participants, inclusion and exclusion criteria, exposure and outcome assessment method, quality-related information, the number of participants in exposed and nonexposed groups, and the number of exposed and nonexposed participants with metabolic syndrome. Data were entered by S.R., and entries were double-checked by Y.K. and S.M. for accuracy. We based our criteria for a diagnosis of metabolic syndrome on those of the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) , the Int.Box. Criteria for Diagnosis of Metabolic Syndrome, by OrganizationThree or more of the following:Waist circumference: ≥102 cm in men; ≥88 cm in womenTriglyceride: ≥1.7 mmol/L or treatment for elevated trigylcerideHigh-density lipoprotein cholesterol (HDL-C): men, <1.03 mmol/L; women, <1.29 mmol/L; or undergoing treatment for low HDL-CBlood pressure: systolic ≥130 mmHg or diastolic ≥85 mm Hg or hypertension treatment or previously diagnosed hypertensionFasting blood glucose: ≥5.6 mmol/L, treatment for elevated blood glucose, or previously diagnosed type 2 diabetesIncreased waist circumference  plus 2 or more of the following:Triglyceride: ≥1.7 mmol/L or treatment for elevated triglycerideHDL-C: men,  <1.03 mmol/L; women,  <1.29 mmol/L; or treatment for low HDL-CBlood pressure: systolic,  ≥130 mm Hg; diastolic,  ≥85 mm Hg; hypertension treatment; or previously diagnosed hypertensionFasting blood glucose: ≥5.6 mmol/L, treatment for elevated blood glucose, or previously diagnosed type 2 diabetesThree or more of the following:Waist circumference: men, ≥40 inches (≥102 cm); women, ≥35 inches (≥89 cm), measured at the top of the iliac crest at the end of a normal expirationTriglyceride: ≥150 mg per dL (≥1.70 mmol per L) or receiving pharmacologic therapy for elevated triglyceride levelsHDL-C: men, <40 mg/per dL (<1.05 mmol per L); women, <50 mg per dL (<1.30 mmol per L); or receiving pharmacologic therapy for low HDL-CBlood pressure: systolic, ≥130 mm Hg; diastolic, ≥85 mm Hg, or receiving pharmacologic therapy for hypertensionFasting blood glucose: ≥100 mg per dL (≥5.6 mmol per L) or receiving pharmacologic therapy for elevated fasting blood glucose levelThree or more of the following 5 risk factors:Increased waist circumference: men, ≥90 cm; women, ≥80 cmTriglyceride level of  ≥1.7 mmol/LHDL-C: men,  <1.0 mmol/L; women,  <1.3 mmol/LBlood pressure: systolic, ≥130 mm Hg; diastolic, ≥ 85 mm Hg; or current use of antihypertensive medicationsFasting blood sugar:  ≥5.6 mmol/L or currently using antidiabetes medicationsTwo independent investigators, S.R. and S.M., used the Newcastle–Ottawa Quality Assessment Scale to perform risk-of-bias assessment for observational studies . The sca2 test of heterogeneity and by using I2 statistics to quantify the inconsistency. An I2 less than 25% indicates mild heterogeneity; 25%–75%, moderate; and more than 75%, substantial (We used STATA version 14.2 (StataCorp) to perform our meta-analysis. Because all outcomes were dichotomous, the number of events and participants in each group were entered to obtain the pooled effect estimate in terms of odds ratio (OR). We used the random effects model with the inverse variance method to calculate the weight of individual studies. Evidence of between-study variance resulting from heterogeneity was assessed through a χstantial . Study-sP value less than .20 were used to perform multivariable meta-regression for identifying the source of heterogeneity between the studies. We assessed publication bias for each of the outcomes by using a funnel plot and a Doi plot , minor asymmetry , and major asymmetry   .We performed sensitivity analysis to assess the robustness of results by removing the studies one by one and checking for any significant variation in results. We also performed random-effects cumulative meta-analysis to delineate temporal changes in the magnitude and direction of the pooled association estimate because the evidence accumulates over time. First, we sorted the studies by publication year and then added them sequentially to analysis in chronological order, recalculating the pooled estimates with each added study .We found 3,321 studies through our systematic literature search. We also retrieved full texts for 4 articles obtained through manual searching of the bibliographies in the retrieved studies. After removing duplicates, we screened 2,786 articles. Of these, we excluded 2,659 because the title and abstract indicated that they did not have relevant study participants or exposure or outcomes. We assessed 127 for eligibility and excluded 101 . A total of 26 studies with a total of 37,965 participants were included in our final review for qualitative and quantitative  synthesis and met our eligibility criteria –41. We uHalf of the included studies 13 of 26) were conducted in the southern states of Tamil Nadu, Kerala, Andhra Pradesh, and Telangana ,34,36–40 were conUnder the selection bias domain, most studies 17 of 26) had high bias risk related to representativeness of the sample  of 26 ha–37,39,40I2 = 94.5%; P < .001), indicating that overweight adults had 5.47 times higher odds of having metabolic syndrome than adults of normal and below-normal body mass index (BMI) (weight in kg/height in m2) (I2 = 76%).Fifteen studies –37,39,40t in m2) . We alsoSubgroup analysis based on geographical region showed that overweight adults in the southern region had the highest estimated magnitude of association with metabolic syndrome . Analysis based on study setting showed that community-based studies yielded the highest estimated magnitude of association between overweight and metabolic syndrome . Although subgroup analysis based on the metabolic syndrome definition showed high variation in the estimated magnitude of association, that analysis was based on a single study’s estimate, not a pooled estimate. Subgroup analysis based on the quality of studies did not show any variation in the magnitude or direction of the association. Univariable meta-regression showed that none of these factors were significantly associated with the size of the pooled effect and cannot explain the substantial heterogeneity in the results.P = .06), whereas the Doi plot showed signs of major asymmetry with an LFK index of 4.02. Sensitivity analysis showed no significant variation in the magnitude or direction of outcome, indicating a single study’s lack of influence on the overall pooled estimate. The cumulative random-effects meta-analysis also did not show any significant difference in the magnitude and direction of the pooled estimate over the range of years.The funnel plot showed signs of asymmetry and was also statistically proved by Egger test , indicating that obese adults have 5 times higher odds of having metabolic syndrome than nonobese adults (I2 = 90.4%).Twenty-one studies ,38,40,41e adults . CompariSubgroup analysis based on the geographic region showed that obese adults in the northern region had the highest estimated magnitude of association with metabolic syndrome . Analysis based on study setting showed that community-based studies had the highest estimated magnitude of association between obesity and metabolic syndrome . Subgroup analysis based on the metabolic syndrome definition showed that studies using International Diabetes Foundation criteria reported maximum estimated magnitude of association between obesity and metabolic syndrome . Analysis based on the quality of studies showed no variation in the magnitude or direction of the association. Univariable meta-regression revealed that none of the factors were significantly associated with the pooled effect size and cannot explain the substantial heterogeneity in the results.P = .03), whereas the Doi plot showed signs of major asymmetry with an LFK index of 2.96. Sensitivity analysis showed no significant variation in the magnitude or direction of outcome, indicating the lack of influence of a single study on the overall pooled estimate. Cumulative random-effects meta-analysis showed that the magnitude of this association has decreased in the past decade (2011–2020) compared with studies published in the previous decade (2001–2010).Publication bias was graphically checked by funnel plot and Doi plot. The funnel plot showed signs of asymmetry and was also statistically proved by Egger test  ,46. The In addition to studying mechanisms related to the sympathetic nervous system, we need a detailed understanding of how BMI modifies the cardiometabolic processes in humans, because the combination of obesity and metabolic syndrome has been found to be much more deadly than either disorder alone ,53. UndeThe management of people who are overweight and obese with metabolic syndrome mainly focuses on lifestyle changes rather than medical management. Physical activity is an important intervention because of its role in insulin signaling, independent of the phosphatidylinositol-3-kinase pathway, and in the enhancement of glucose transporter type 4 translocation and glucose transport during the stimulation of skeletal muscles by contraction . This meAnalysis based on geographic region showed that adults in the northern and southern regions of India had a higher estimated magnitude of association between obesity and metabolic syndrome than other regions. A possible reason for this finding could be the higher magnitude of risk factors for noncommunicable disease among adults residing in northern and southern states compared with that of other regions. The latest report of India’s National Nutritional Monitoring Bureau showed that the northern and southern states have a higher degree of risk factors, such as poor dietary habits, diabetes, hypertension, and hypercholesterolemia, than other regions . Social The major strength of our review was the rigorous literature search and methodology followed to provide reliable estimates. In addition, this was the first review providing the association between anthropometric risk factors and metabolic syndrome among adults in India. We also found that most of the 26 studies included in our review were of high quality and had standard criteria for defining overweight and obesity, which might make our study findings generalizable. We also found no significant change in the magnitude and direction of the association between metabolic syndrome and BMI with respect to the exposure–outcome relationship in sensitivity analysis.2 test for heterogeneity and I2 statistics were significant). We explored the reason for such high heterogeneity by using meta-regression analysis. However, we found no factors attributable to the differences among the included studies. In addition, we found the possibility of publication bias with both overweight and obesity, limiting the credibility of the generated evidence. Most of our included studies were cross-sectional in nature, which made it difficult to establish causation between the exposure and disease. Hence, more longitudinal studies are needed in India to identify accurate and reliable effect estimates and make evidence-based recommendations for reducing the BMI level among the general population.Our study also had limitations. Our results should be interpreted with caution and inferred accordingly, considering the difference in methods and quality across the included studies. In our analysis, we found significant between-study variability (χWe found overweight and obesity to be significantly associated with metabolic syndrome. Given the evidence, clinicians and policy makers alike should implement weight reduction strategies among their patients and the general population. Though our results provide some crucial information for a better understanding of the association of anthropometric risk factors and metabolic syndrome, longitudinal studies are still needed to establish the temporality of the association and the causal link. Understanding this causal link with special focus on the dose–response relationship will overcome a crucial barrier in the management of patients with metabolic syndrome and help prevent many cardiovascular diseases and deaths worldwide.


This meta-analysis demonstrated that ITPA 94C>A polymorphism could affect 6-MP-induced toxicities. Our findings suggested that ITPA genotyping might help predict 6-MP-induced myelosuppression and hepatotoxicity.6-Mercaptopurine (6-MP) is a cornerstone of the maintenance regimen for pediatric acute lymphoblastic leukemia (ALL). Inosine triphosphate pyrophosphatase (ITPA) is considered a candidate pharmacogenetic marker that may affect metabolism and 6-MP-induced toxicities; however, the findings are inconsistent. Therefore, we attempted to evaluate the effect of ITPA 94C>A polymorphism on 6-MP-induced hematological toxicity and hepatotoxicity through a systematic review and meta-analysis. A literature search for qualifying studies was conducted using the PubMed, Web of Science, and Embase databases until October 2021. Overall, 10 eligible studies with 1072 pediatric ALL patients were included in this meta-analysis. The results indicated that ITPA 94C>A was significantly associated with 6-MP-induced neutropenia (OR 2.38, 95% CI: 1.56–3.62;  Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, accounting for approximately 25% of all cancers among children and 75–80% of childhood leukemias ,2. The sA combination of daily 6-mercaptopurine (6-MP) and weekly methotrexate for two to three years is the standard maintenance therapy for childhood ALL ,6. The iInosine triphosphate pyrophosphatase (ITPA), another enzyme involved in purine metabolism, catalyzes the pyrophosphohydrolysis of inosine triphosphate (ITP) to inosine monophosphate (IMP). ITPA 94C>A (rs1127354) is one of the most well-known polymorphisms associated with ITPA deficiency, which traps purines in the form of ITP, resulting in thiopurine toxicities, including myelosuppression and hepatotoxicity. As ITPA plays a role in protecting cells from the accumulation of toxic metabolites, such as ITP, it has been considered as a possible candidate gene that may affect metabolism and 6-MP-induced toxicities with inter-individual variability ,15,16.Although several studies reported a clinical association between ITPA polymorphism and toxicities related to 6-MP treatment, the results were inconsistent. Therefore, we conducted a comprehensive systematic review and meta-analysis to determine the association between 94C>A polymorphism and 6-MP-induced toxicities in pediatric ALL. This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines . The regStudies were included if they met the following criteria: (1) patients diagnosed with pediatric ALL received 6-MP-based maintenance therapy; (2) evaluated the association between the toxicity of 6-MP and ITPA 94C>A polymorphism; (3) provided sufficient data to calculate the odds ratio (OR) and 95% confidence interval (CI). Studies were excluded due to the following reasons: (1) conference abstracts, summaries, and reviews; (2) unable to extract the data; (3) not written in English. Only the most recent and comprehensive data were included in this study if overlapping data were identified.Two researchers independently extracted all data, and inconsistencies were discussed and resolved by consensus. The following information was collected from each eligible study: first author’s name, publication year, country, number of patients receiving 6-MP , mean age, 6-MP dose, definition of outcomes , and genotyping method. In addition, the number of patients with and without leukopenia, neutropenia, and hepatotoxicity were recorded for each study. Among the outcomes, data on febrile neutropenia were included in neutropenia. We requested some data from the corresponding authors when the data were not extractable from the published paper.Two researchers conducted a quality assessment independently according to the Newcastle-Ottawa Scale (NOS) for cohort studies . The scop value < 0.05 was considered statistically significant. Heterogeneity between studies was estimated with the chi-square test and I2 statistic. I² > 50% was regarded as statistically significant heterogeneity. The selection of the proper effects model was based on the analysis results. In the absence of any statistical evidence of heterogeneity, the fixed-effects model (Mantel-Haenszel method) was used; otherwise, the random-effects model (DerSimonian-Laird method) was used to calculate pooled estimates [Meta-analysis was performed using Review Manager (RevMan) version 5.4 . The OR and 95% CI were used to determine the association between ITPA 94C>A polymorphism and risk of 6-MP-induced toxicities. A stimates ,20. Sensstimates ,22. n = 25), not 6-MP study (n = 1), no pediatric patients (n = 1), data on other polymorphisms (n = 3), no ITPA 94C>A outcome (n = 9), outcomes other than toxicity (n = 8), toxicity outcomes with other parameters (n = 8), and not having sufficient data to calculate the OR (n = 6). An additional two studies were added through manual search. One study [A detailed flow chart of the study selection process is shown in ne study  involvedne study ,30,31,32p = 0.005). As there was heterogeneity among these studies , a random-effects model was used (p = 0.20) using a random-effects model  (p = 0.0009) using a fixed-effects model .The meta-analysis results comparing the toxicities of 6-MP between the ITPA 94C>A variant (CA or AA) and wild-type homozygote (CC) groups are shown in was used A. For le = 0.01) B. For heI2 range: 46–63%). However, sensitivity analysis of leukopenia indicated that the ITPA 94C>A variant had significantly increased toxicity risk  with the omission of the Tanaka et al. study [I2 range of 0–49%. When the Azimi et al. study was excluded, heterogeneity was greatly reduced  [Sensitivity analysis was performed to assess the stability of the results by sequential omission of each study . Accordil. study . In addi = 0.50) . p > 0.05). As the number of studies included in each analysis was limited, some results of subgroup analysis did not achieve statistical significance. For hepatotoxicity, 94C>A variant significantly increased the risk in Asians  and Middle Eastern .Subgroup analysis by ethnicity was also performed . There wp = 0.2931 and p = 0.2415, respectively), leukopenia , and hepatotoxicity .The funnel plots for outcomes are shown in This meta-analysis evaluated the association between ITPA gene polymorphism (94C>A) and 6-MP-induced toxicities in pediatric patients with ALL. Our results indicated that the 94C>A variant was significantly associated with an increased risk of neutropenia and hepatotoxicity. Sensitivity analysis demonstrated consistent results. Maintenance therapy is required to prevent relapse for patients with ALL, and prolonged exposure to 6-MP is an important part of the maintenance regimen . 6-MP reITPA catalyzes the hydrolysis of ITP to IMP. IMP is a key metabolite in purine metabolism, which is converted to adenosine triphosphate (ATP)/guanosine triphosphate (GTP) via adenosine monophosphate (AMP)/guanosine monophosphate (GMP) . ITPA isSeveral studies have reported that the ITPA 94C>A variant could increase the risk of thiopurine-related hematological toxicity ,41,42,43Nevertheless, it was reported that the ITPA 94C>A variant has a protective mechanism against ribavirin (RBV) toxicity. RBV is a purine nucleoside analog that mimics inosine, guanosine, or adenosine  and exhiThe allele frequencies of the ITPA 94C>A variant indicated inter-ethnic variability . In comparison with TPMP, the ITPA 94C>A allele shows an almost complete reversal in allele frequencies in each population ,53. TherPreviously, two meta-analyses published in 2007 and 2022 investigated the correlation between ITPA and the adverse effects of azathioprine (AZA)/6-MP. van Dieren et al.  did not Barba et al.  conducteSeveral limitations should be considered in this study. First, differences in study characteristics, including ethnicity and definition of toxicities, may lead to heterogeneity. Second, some confounding factors that could affect the risk of 6-MP-induced toxicities, such as maintenance regimens , types of remission therapy, baseline lab values , and comorbidities, could not be adjusted. Despite the inconsistencies in individual results, we found that ITPA 94C>A polymorphism may be associated with an increased risk of 6-MP-induced neutropenia and hepatotoxicity. Therefore, our findings suggest that ITPA genotyping may help predict 6-MP-induced toxicities in patients with pediatric ALL. Nevertheless, the results should be confirmed with a larger population.


To determine the incidence of pneumoconiosis worldwide and its influencing factors.Systematic review and meta-analysis.Cohort studies on occupational pneumoconiosis.PubMed, Embase, the Cochrane Library and Web of Science were searched until November 2021. Studies were selected for meta-analysis if they involved at least one variable investigated as an influencing factor for the incidence of pneumoconiosis and reported either the parameters and 95% CIs of the risk fit to the data, or sufficient information to allow for the calculation of those values.The pooled incidence of pneumoconiosis and risk ratio (RR) and 95% CIs of influencing factors.Our meta-analysis included 19 studies with a total of 335 424 participants, of whom 29 972 developed pneumoconiosis. The pooled incidence of pneumoconiosis was 0.093 (95% CI 0.085 to 0.135). We identified the following influencing factors: (1) male , (2) smoking , (3) tunnelling category , (4) helping category , (5) age (the highest incidence occurs between the ages of 50 and 60), (6) duration of dust exposure  and (7) cumulative total dust exposure (CTD) . A dose–response analysis revealed a significant positive linear dose–response association between the risk of pneumoconiosis and duration of exposure and CTD . The Pearson correlation analysis revealed that silicosis incidence was highly correlated with cumulative silica exposure .The incidence of pneumoconiosis in occupational workers was 0.093 and seven factors were found to be associated with the incidence, providing some insight into the prevention of pneumoconiosis.CRD42022323233. To our knowledge, this is the first systematic review and meta-analysis to estimate the incidence of pneumoconiosis in dust-exposed workers by pooling data from only cohort studies.Our study provided quantitative relationship between influencing factors and pneumoconiosis with a larger sample size.Due to the limited literature available, some potentially relevant risk factors, such as alcohol consumption and tuberculosis, were excluded from this meta-analysis.Significant heterogeneity in the pooled results for a variety of reasons and the small number of included studies may limit the dependability of our results for certain risk factors.Pneumoconiosis is inclusive of a group of serious pulmonary diseases associated with the inhalation of mineral dust and corresponding reactions of lung tissues, including diffuse fibrosis and progressive lung dysfunction. Although pneumoconiosis among dust-exposed workers has received considerable worldwide attention in terms of incidence, risk factors, early diagnosis, prevention and intervention, there are currently few established therapeutic options for the disease, highlighting the importance of precautionary measures.1et alMethods of assessing exposure, controlling dust concentration, using tools to prevent exposure, undergoing regular physical examination and preserving pathological records have been implemented to protect workers from dust inhalation, and eventually, some studies have reported a downward trend since 2015Targeted prevention requires a deeper comprehension of the influencing factors. Even though previous studies have demonstrated that the incidence rate of pneumoconiosis is affected by a variety of factors, including advanced age, male gender, smoking and dust exposure,We conducted this systematic review and meta-analysis to provide some clues for the targeted prevention of pneumoconiosis in light of the paucity and controversy of evidence currently available. With a larger sample size, this meta-analysis sought to address and explore the incidence and the impact of different influencing factors on the morbidity of pneumoconiosis, as well as the shape of the relation between them.This meta-analysis was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statementBy using search terms related to “pneumoconiosis” and “cohort studies”, two reviewers independently and systematically searched through online databases, including PubMed, EMBASE, the Cochrane Library and Web of Science, before November 2021, without regard to the language of publication. Through a thorough discussion with a third reviewer, any discrepancies in the literature search process were resolved. Each electronic database’s search terms are detailed in 10.1136/bmjopen-2022-065114.supp8Supplementary dataTwo reviewers exported all studies identified by the search strategy to Endnote V.X9 independently . The titles and abstracts of all search results were screened first, followed by an independent examination of the full texts of all eligible literature. This meta-analysis required the following inclusion criteria for studies: (1) all involving occupational dust exposure workers  are divided into with-pneumoconiosis group and without-pneumoconiosis group according to the national diagnostic criteria of pneumoconiosis published by the International Labour Organization  or Ministry of Health of the People’s Republic of China The quality of the included studies was assessed independently by two authors using the Newcastle-Ottawa Scale (NOS). The NOS contains eight categories relating to methodological quality and each study was given an eventual score out of a maximum of 9 points. A score of 0–6 indicated a low-quality study, whereas a score of 7–9 indicated a high-quality study. Any disagreements about quality assessment were resolved through a full discussion with a third reviewer.Two reviewers independently conducted data extraction and any disagreements about data extraction were resolved through a full discussion with a third reviewer. The following information was extracted: (1) study characteristics: first author name, publication year, country, duration of follow‐up, sample size; (2) type of pneumoconiosis included; (3) participant numbers (separating those who developed pneumoconiosis and those who did not); (4) characteristics of these groups (such as gender); (5) exposure of interest (duration or dust concentrations); (6) information about the outcome of interest (incidence of pneumoconiosis) and (7) risk estimates with 95% CIs from any statistic model.Statistical analyses were conducted by using Statistical Package for the Social Sciences , Stata V.16.1 software (StataCorp) and R V.4.1.3 statistical software . First, to investigate the incidence of pneumoconiosis in dust-exposed workers, the pooled incidence and its 95% CIs were calculated using a random-effects model. Second, we chose the influencing factors that were mentioned in at least two studies to investigate the relationship between those and the incidence of pneumoconiosis. A dichotomous variable was reported as frequency and proportion, and the risk ratios (RRs) and their 95% CIs were summarised to assess the outcome using a random-effects model. Third, we compared the highest categories to the lowest categories and conducted dose-response meta-analyses to investigate the association between the risk of pneumoconiosis and dust exposure including two aspects: duration of dust exposure and cumulative total dust exposure concentration (CTD). A p<0.05 was considered significant for all tests.et al,First, we examined the association between exposure and pneumoconiosis risk using a random-effects model to summarise RRs and 95% CIs from each study obtained by comparing the highest versus the lowest exposure categories. The dose–response analysis was then conducted using the method described by Greenland and Longnecker3-years of CTD. First, study-specific slope lines were estimated, and then, using a random-effects model, these lines were combined to obtain an overall average slope. Finally, obtained regression values of the dose–response relationship in the spline and line models at the chosen reference level  and the incidence of silicosis was estimated using the Pearson correlation. Due to the incidence rate of silicosis being non-normally distributed data , the natural logarithm of the incidence rate was used as the normal distribution variable.2 were calculated as indicators of heterogeneity . Subgroup analyses were performed to investigate possible reasons for the significant heterogeneity and address the effects of important factors. We divided the data into subgroups based on the type of pneumoconiosis , occupational category for CWP, and study quality (NOS<7 vs NOS≥7), and then compared across subgroups.Both the Q statistic and IEliminating individual studies one by one, a sensitivity analysis was conducted to determine the effect of each study on the pooled estimate. Using the funnel plots and Egger’s tests, we evaluated the potential risk of publication bias in the included studies.Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.Among these studies, there were six prospective studies, nine retrospective studies and four retrospective-prospective cohort studies. The range of NOS scores was 5–9, and publish years ranged from 1981 to 2021. This meta-analysis involved 335 424 participants, including 29 972 pneumoconiosis patients and 305 452 non-pneumoconiosis workers. We analysed seven factors influencing the morbidity of pneumoconiosis, including gender, smoking, coal workers’ occupational categories, duration of dust exposure, age, CTD and CSE .2=99.93 %, p<0.0001) , with a high-level heterogeneity between studies (I<0.0001) , table 2<0.0001) . Egger’s10.1136/bmjopen-2022-065114.supp1Supplementary data10.1136/bmjopen-2022-065114.supp2Supplementary dataWe found seven factors influencing the incidence of pneumoconiosis, including gender, smoking, age, coal workers’ occupational categories, duration of dust exposure, CTD and CSE.2=93%). Subgroup analysis for only high-quality studies  with non-significant heterogeneity  revealed the possible cause of significant heterogeneity  with heterogeneity  by using a random-effects model. No data pooling was possible for any identified subgroup  with heterogeneity  (2=99%)  and lowest among helping workers , according to the pooled results from various occupational categories. Thus, we compared tunnelling workers to other categories  and helping workers to workers in other categories . Using a random-effects model, the pooled results of six studies indicated that tunnelling workers had a significantly higher risk than other workers  with heterogeneity  . And hel (2=99%) . Signifi2=91.49%, p<0.01). In dose–response analysis, there was no indication of a nonlinear association between the duration of dust exposure and risk of pneumoconiosis , regarding 2.5 years as a reference. No data pooling was possible for dose-response meta-analysis of any identified subgroup.Seven studies, including six high-quality studies and one low-quality study, reported the relationship between the duration of dust exposure and the incidence of pneumoconiosis. The summary effect size for pneumoconiosis morbidity comparing the longest and shortest duration of dust exposure was calculated by using a random-effects model . After pooling six study-specific estimates using the restricted maximum likelihood method in a random-effects model, the result of the formal statistical hypothesis test indicated that there was no indication of a nonlinear association between the duration of dust exposure and pneumoconiosis risk , regarding 50 mg/m3 years as a reference.Six studies involving a total of 189 037 participants and 10 271 patients reported the association between CTD and pneumoconiosis incidence. The summary effect size for pneumoconiosis morbidity comparing the highest and lowest CTD was calculated by using a random-effects model . And the effect sizes of influencing factors, including gender, smoking, age, occupational categories, duration of dust exposure, CTD and CSE, was investigated using a larger sample size and a more comprehensive methodology than ever before. Further, in terms of the quantified relationship between risk factors and the incidence, we found a linear dose–response relationship between CTD and exposure duration and the pneumoconiosis risk, and a linear correlation between CSE and the incidence of silicosis.The pooled incidence estimated by this meta-analysis was higher than the Global Burden of Disease Study 2017 analysis’s reported incidence rate of 7.5% (95% CI 6.6% to 8.4%).In addition, our results demonstrated that the pooled incidence of silicosis is higher than that of CWP, which is consistent with the findings of numerous previous studies. Numerous pathological processes, including macrophage autophagy, autoimmune, apoptosis and proptosis, have indicated that crystalline silica or quartz, the most prevalent occupational dust, is the most significant cause of silicosis.Consistent with many previous studies,et alet alConcerning the relationship between smoking and pneumoconiosis, the current studies primarily focus on the morbidity and mortality of pneumoconiosis,et alThere are occupational categories for any worker who is exposed to dust on the job, but the vast majority of occupational studies on this topic have focused on coal miners.The observed positive association is consistent with results from previous clinical studies on the duration and incidence of pneumoconiosis. In 11 state-owned coal mines, for instance, a retrospective cohort study found that the rate of morbidity increased sharply with the length of employment.3 years in this meta-analysis, due to a reduction in sample size, the relationship between a higher concentration of CTD and the risk of pneumoconiosis is less reliable. The association between CTD and the incidence of pneumoconiosis should be further investigated and analysed, and the Occupational Safety and Health Administration should eventually impose permissible limits for CTD.Although there is no comparable meta-analysis for CTD, our results are consistent with previous clinical research that revealed a positive association between the incidence of pneumoconiosis and CTD.et al found that the incidence ratio of silicosis increased by 4% for each additional year of silica exposure.3.In the absence of the effect size of silica on the risk of silicosis, we estimated the association between CSE and the incidence of silicosis using Pearson correlation analysis. Previous studies have identified a positive correlation between the incidence of silicosis and silica.There are some inevitable limitations in our meta-analysis. First, significant heterogeneity in the pooled results for a variety of reasons and the small number of included studies may reduce the validity of our results for certain risk factors. Second, given the limited literature available, some potentially relevant risk factors, such as alcohol consumption and tuberculosis, were not included in this meta-analysis.In conclusion, this meta-analysis revealed that the incidence of pneumoconiosis among occupational workers was 0.093 and that there were seven factors associated with this incidence, including male, smoking, age, coal workers’ occupational categories, duration of dust exposure, CTD and CSE. In addition, we discovered a linear dose–response relationship between the risk of pneumoconiosis and the duration of dust exposure and CTD. Our findings will provide hints for pneumoconiosis prevention strategies.


R 4.0.1.  The Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards were followed, and the protocol was registered in PROSPERO (CRD42020200077). Five electronic databases were searched to identify eligible studies published between 1990 and 2022. Search terms included anti-TB treatment and drug-induced liver injury. Studies that reported the incidence of ATLI or provided sufficient data to calculate the incidence of ATLI were included, and duplicate studies were excluded. Meta-analysis was conducted on the basis of logit-transformed metrics for the incidence of ATLI with 95% confidence intervals (CIs), followed by a predefined subgroup meta-analysis. Temporal trend analyses were performed to describe the change in pooled incidence over time. A random effects metaregression was conducted to explore the source of heterogeneity. All statistical analyses were carried out using P < 0.001). Patients who received first-line anti-TB drugs, patients in South America, and patients with hepatitis B and C virus coinfection had a higher incidence of ATLI . Sensitivity analyses also confirmed this robust incidence after the exclusion of some studies. The metaregression showed that different anti-TB regimens and geographical regions were important explanatory factors of the heterogeneity between studies.  A total of 160 studies from 156 records with 116147 patients were included in the meta-analysis. Based on the random effects model, the pooled incidence of ATLI was 11.50% (95% CI: 10.10%–12.97%) and showed an upward trend over time ( The present systematic review provided a basis for estimating the incidence of ATLI worldwide, which varied among patients with different anti-TB regimens in different geographical regions and with different coinfections and had an upward trend. Regular liver function monitoring is imperative for patient safety during the anti-TB treatment course. Mycobacterium tuberculosis, is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent, ranking above HIV/AIDS [Tuberculosis (TB), an ancient disease caused by HIV/AIDS . Drug trHIV/AIDS . Of the HIV/AIDS . AlthougHIV/AIDS . One of HIV/AIDS . ATLI caHIV/AIDS .The incidence of ATLI varies widely depending upon the characteristics of the particular cohort, drug regimens involved, a threshold used to define DILI, monitoring, and reporting practices . Based oThe incidence of ATLI can be used to estimate the magnitude of the harm to the population and the disease burden of the population, and it is also the basis for conducting epidemiological studies and exploring the risk factors for ATLI, especially the calculation of sample size . The inchttps://www.crd.york.ac.uk/PROSPERO), and the registration number is CRD42020200077.This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement . This stLongitudinal studies reporting the incidence of ATLI in patients with anti-TB treatment were systematically searched in electronic databases, including PubMed, MEDLINE, EMBASE, ClinicalTrials.gov, and the Cochrane Library , from Jan 1, 1990, to Apr 25, 2022. The search terms fell into two categories: anti-TB treatment and drug-induced liver injury. The computer-based searches combined free terms, MeSH terms, and keywords related to anti-TB treatment and adverse drug reactions with a focus on liver injury. Detailed retrieval strategies for each database are listed in the Supplementary Material , and duplicate records were deleted. Two reviewers independently screened each record by titles, keywords, and abstracts against the eligibility criteria. Full texts were referred to when information in the records was inadequate for evaluation. Any disagreement between the two groups of reviewers was resolved by an additional reviewer.Mycobacterium tuberculosis; (2) study patients were treated with specific anti-TB drugs; (3) studies reported the incidence of ATLI or provided sufficient data to calculate the incidence of ATLI; (4) data were collected in a prospective manner with a longitudinal study design, including case series, retrospective or prospective cohort studies, or randomized controlled trials; (5) studies were published in English. Additionally, in the case of multiple publications, the study with the most up-to-date or comprehensive information was included. Duplicate studies were excluded from the present study.Studies were included if they met all of the following criteria: (1) study patients had any type of tuberculosis infection caused by Data extraction was conducted by two independent reviewers with a standardized predesigned data collection form. Data were abstracted, where available, on the study, publication date, geographical location, sample size, number of participants lost to follow-up, age and sex, anti-TB regimens used, diagnostic criteria of ATLI, causality assessment of ATLI, and usage of hepatoprotective drugs. All data were directly taken from the included studies, and no further information was obtained by consulting the authors.Mycobacterium tuberculosis and are available only with a physician's prescription and come in tablet, capsule, liquid, and injectable forms [DILI is caused by medications, herbal and dietary supplements, or other xenobiotics that result in abnormalities in liver tests or in hepatic dysfunction that cannot be explained by other causes . Anti-TBle forms . Hepatople forms . Geograple forms .The risk of bias in individual studies was assessed by two reviewers using the modified Quality in Prognosis Studies (QUIPS)  tool. ThR 4.0.1 , and all P values were two-tailed. The statistical analysis strategies referred to previous literature [Q test and I2 statistics. P < 0.10 or I2 >50% indicated substantial heterogeneity [P < 0.10 [Q test for heterogeneity was used to compare the incidence across subgroups, and 0.05 was defined as the threshold of the P value for statistical significance. When the incidence was reported for a multiyear period, the midpoint of the time interval was regarded as the year of the study. A line chart was created to graphically demonstrate the temporal trend of the ATLI incidence, and the trend test of time was carried out using the multivariate Mann-Kendall trend test. Sensitivity analyses were also conducted by omitting studies with a high risk of bias and those without descriptions of ATLI diagnostic criteria, anti-TB drugs, patient age information, patient sex information, and recruitment period. In addition, a multivariate metaregression was conducted to explore the potential source of heterogeneity, and multicategorical variables were defined as dummy variables.All statistical analyses were carried out using terature . Heterogogeneity . The pooP < 0.10 . FurtherA total of 4700 records were identified from five electronic databases, and 160 studies from 156 records with 116147 patients were finally included in the meta-analysis. The flow chart of the included and excluded records is shown in I2 = 98.2%) from 1990 to 2022 based on the random effects model. However, Egger's test indicated potential publication bias (P < 0.10).As the forest plot  shows, tThe results of the subgroup analysis are summarized in Z = 5.236, P < 0.001)  and 2001  and a higher incidence in 2016  and 2020 . An upward trend was observed in the pooled incidence of ATLI from 1999 to 2020 (< 0.001) .n = 31) and those without a description of ATLI diagnostic criteria (n = 18), anti-TB drugs (n = 19), patient age information (n = 28), patient sex information (n = 17), and patient recruitment period (n = 12), the pooled incidences of ATLI were 11.52% (95% CI: 9.98%–13.15%), 11.82% (95% CI: 10.44%–13.27%), 11.41% (95% CI: 9.91%–13.01%), 11.84% (95% CI: 10.32%–13.45%), 11.60% (95% CI: 10.18%–13.09%), and 11.34% (95% CI: 9.90%–12.87%), respectively, and the incidence of ATLI remained stable (After excluding studies with a high risk of bias (d stable .P < 0.05), which together explained 21.04% of the total variance between studies.To the best of our knowledge, this is the first systematic review and meta-analysis on the incidence of ATLI in the world, and the pooled incidence of ATLI in the past 30 years was robust. Furthermore, patients who received first-line anti-TB drugs, patients in South America, and patients with HBV + HCV coinfection had a higher incidence of ATLI than other patients, while sex and the hepatoprotective effect did not display a significant difference in the pooled incidence of ATLI. Although data from only 36 countries were available in the present study, the statistical map also suggested differences in the incidence of ATLI between countries. This study has positive significance for understanding the current global status of ATLI, assessing its disease burden, and implementing ATLI prevention and control measures.Identification of the risk factors for ATLI is essential to prevent ATLI and improve patient outcomes. Conclusions from different studies were inconsistent regarding whether sex was related to the occurrence of ATLI, namely, whether male sex was a risk factor  or femalBecause the mechanism underlying DILI is not well understood, there is still no single biomarker or diagnostic tool to unequivocally diagnose DILI . Many paIn the present study, the pooled incidence curve for ATLI showed a clear upward trend worldwide, which is consistent with other research findings , 45. In I2 was very high in the present study. After metaregression was performed in this systematic review, only 21.04% of the total variance could be explained. Indeed, there are arguments against pooling estimates in the presence of extensive heterogeneity. However, this systematic review included studies attempting to measure the same outcome, and a high I2 may be driven by the substantial number of large, precise studies [There are several potential limitations of the present systematic review. First, 60.6% of the included studies were of moderate methodological quality. However, study quality did not appear to be the main source of heterogeneity according to the subgroup analyses and metaregression. Sensitivity analyses that were carried out by omitting studies with a high risk of bias produced robust results as well. Second, possible publication bias should be considered when evaluating the results. Limiting the study to English-language articles may have potentially led to a language bias. Third, the heterogeneity statistic  studies ; therefoIn conclusion, the present systematic review provided a basis for estimating the incidence of ATLI worldwide, which varied among patients with different anti-TB regimens, in different geographical regions, and with different coinfections and had an upward trend. Risk assessment and regular liver function monitoring are imperative for patient safety during the anti-TB treatment course.


GSTM1, GSTP1, GSTT1, HLA-C, HLA-DQB1, HLA-DR1, HLA-DR3, HLA-DR4, HLA-DR7, and HLA-DR8. These genes code for proteins that are involved in cell-cell adhesions (CTNNA3), glutathione conjugation for xenobiotic metabolism (GST gene family), and immune system response (HLA gene family). The most compelling pooled PORs were for two studies on CTNNA3  and three studies on HLA-DR1 . Bioinformatics of the predicted protein pathways for DA shows overlap with biomarker-associated pathways in workers before development of asthma, suggesting overlap in toxicokinetic and toxicodynamic pathways of diisocyanates. The control groups were also compared against each other and differences were negligible. Suggestions for improving future research are also presented. Of the highest importance, the literature was found to be profoundly publication-biased, in which researchers need to report the data for all studied markers regardless of the statistical significance level. We demonstrate the utility of evaluating the overlap in predicted protein pathway functions for identifying more consistency across the reported literature including for asthma research, biomarker research, and in vitro studies. This will serve as an important resource for researchers to use when generating new hypothesis-driven research about diisocyanate toxicology.Diisocyanates are widely used compounds that pose a safety concern for workers in occupations within the spray-paint, spray-foam insulation, and furniture varnish industries. Epidemiological studies show that only a subset of workers exposed to diisocyanates develop diisocyanate-induced occupational asthma , indicating that genetic susceptibility may play a role. The purpose of this systematic literature review was to compile and meta-analyze the reported data on genetic susceptibility markers for DA. Three databases  were searched and 169 non-duplicate publications were identified, of which 22 relevant occupational studies were included in this review. Researchers reported prevalence odds ratios (PORs) for 943 comparisons in 82 different genes/serotypes. Protein network functions for the DA-associated genes from this review include: antigen processing, lymphocyte activation, cytokine production regulation, and response to oxidative stress. Meta-analysis of comparisons between workers with DA and controls was conducted for 23 genetic markers within: CTNNA3,  Diisocyanates are a commonly used class of chemicals that are reacted with polyols to produce polyurethane and are one of the most common causes of occupational asthma . The mosEvery case of DA is very costly. An assessment performed in the United Kingdom (UK) for occupational asthma cases from 2003 showed that occupational isocyanate-asthma cases result in millions of Great Britain Pounds (GBP) in direct and indirect costs from healthcare and lost work and pay . Over thN-acetyltransferase (NAT) genotype (HLA) serotypes, which are involved in antigen presentation . An early investigation in the field identified that slow acetylators were disproportionately present among ten of eleven (91%) DA cases . The stugenotype . Other eentation , and manThe objective for this systematic literature review with meta-analysis was to identify all studies in which associations between genetic markers and the development of DA were investigated in diisocyanate-exposed worker populations see . Gaininghttps://www.zotero.org; Librarians Mary White and Jennifer Walker at the University of North Carolina at Chapel Hill were consulted for guidance to conduct the literature search on human genetic susceptibility markers for the development of DA from occupational isocyanate exposure. The electronic databases Embase, Pubmed, and Scopus were queried on 27 February 2019 (without publication date restrictions on the results) and again on 30 January 2020. Synonyms of genetics, diisocyanates, and occupational asthma were used as the search terms see . Medicalin vivo studies, and in vitro studies were also excluded. The remaining papers were independently screened for details about the medical DA diagnosis and for descriptions of genetic susceptibility polymorphisms for DA as compared to at least one control group without DA. The most common control groups consisted of exposed workers without symptoms , exposed workers with asthma symptoms for whom DA was ruled out via a negative specific inhalation challenge , and non-exposed healthy individuals . All data for DA-, AW, and NC were extracted in the systematic review.All original research study designs that reported associations between genetic markers and susceptibility for development of DA were included, e.g., case-control candidate gene studies and GWAS. Letters to the editor, abstracts, reviews, and books were excluded. Off-topic studies, murine model 1. Peer-reviewed research2. Subjects are adult workers who were medically diagnosed with occupational DA3. Includes and describes at least one control group without DA4. Genetic susceptibility markers for DA are described compared to the control group(s)1. Animal model study2. Cell culture studyIndividual studies were assessed for their quality using a list of questions we generated that are particularly relevant to these studies. We used the Newcastle-Ottawa scale for case-control studies as a starting point to generate the list of study characteristics that we evaluated . For eacThe principal measures collected were genetic polymorphisms and their associated genes and prevalence odds ratios (POR) for susceptibility to developing occupational DA. Information was also collected on: study design, study populations’ demographic characteristics, number of participants, type and duration of isocyanate exposure, health and exposure status of the controls, testing for HWE in controls, information on whether corrections for multiple comparisons were made, and which potential confounding factors were adjusted for in logistic regression models. All extracted information is in the All research groups that had reported findings on the impact of genetics on the prevalence of DA in workers were contacted in an attempt to gather missing information that was not reported but no one was able to supply additional information on their studies. This was stated to be due to records disposition or the record keeper becoming unreachable after leaving the position.https://www.genemania.org; To learn more about which protein networks might be involved in the development of DA, an online tool called GeneMANIA , heterozygous genotype vs. homozygous major allele and homozygous minor allele, and homozygous minor allele vs. homozygous major allele and heterozygous genotype (recessive model) for single nucleotide polymorphisms (SNPs).The authors reported odds ratios that were for prevalence of DA amongst the people in their studies and thus are actually PORs. The POR assessments that we meta-analyzed included several types of comparisons. These included PORs for null Meta-analyzed genetic markers were not categorized as being statistically significant due to the small number of studies and the need for further research before any conclusions are drawn about the associations between the genetic markers and DA. Instead, genetic markers were arranged by the precision of the estimates. Precision was determined by calculating ratios of the pooled upper 95% confidence interval (UCI) divided by the lower 95% confidence interval (LCI), UCI/LCI, in which ratios that are closer to 1 demonstrate that the gathered data is more precise. It is important to note that the strength of the genetic markers’ association with DA may change with further research.Sensitivity analysis was used to investigate whether the type of controls used for analysis altered the meta-analysis findings. Many of the studies had used two separate control groups, either a combination of DA- and AW or both AW and NC. In this situation, we had to decide which data to use in the main meta-analysis. We decided to use AW controls if available and NC data if AW controls were not a part of the study. Thus, sensitivity analysis was needed to evaluate the impact of that decision on the analysis results. The sensitivity analysis consisted of using biologically-relevant ranked lists of preference to decide which control group data to include in each analysis when multiple control groups were used in a single study. When authors reported PORs adjusted for potential confounders, we also ran separate analyses for each combination of control group rank ordering using the non-adjusted and the author-adjusted PORs when available. There were 23 genetic markers that were studied by two or more research groups and we were able to evaluate with meta-analysis and this sensitivity analysis. The rank ordering of which control group was used for the analyses were: (1) AW,NC, (no DA-) using unadjusted PORs [primary analysis], (2) AW,NC, (no DA-) using author adjusted PORs, (3) NC,AW, (no DA-) using unadjusted PORs, (4) NC,AW, (no DA-) using author adjusted PORs, (5) DA-,AW, NC using unadjusted PORs, (6) DA-,AW, NC using author adjusted ORs, and (7) combined control data when a study had more than one control group. The variables that the researchers adjusted for included: age, atopy, duration of exposure, sex, smoking status, and/or type of exposure. Another component of the sensitivity analysis included calculating PORs using data from the 1,000 Genomes project   as an al. NC controls were calculated across all markers in studies with two control groups in order to evaluate whether there were appreciable differences in the genetics between the control groups. This can provide more information on how using different control groups might impact results when studying the relationship between genetics and DA risk. For this evaluation, the ORs of controls with and without the genetic marker genotype were compared directly to each other for each reported genetic marker and then meta-analyzed. For example, for AW vs. NC, the OR for each genetic marker was calculated using the equation:When a study included two control groups, the control groups were also compared directly to each other. Pooled ORs for DA- vs. AW and AW vsThis review complies with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) recommendations .in vitro studies, and/or were not original research (letters to editors and reviews). The full text was then retrieved for the remaining 35 publications after which five publications lacking a control group and eight abstracts with later corresponding full-study publications were excluded, leaving 22 publications that were included in the systematic literature review. See The database searches yielded 334 results, which were reduced to 169 after duplicate publications were removed. Title and abstract screening eliminated another 134 publications that did not pertain to the research question, were animal model or No studies were excluded based on their method of DA diagnosis. Nearly all studies included workers who were diagnosed with DA based on a specific inhalation challenge (SIC) causing forced expiratory volume in one second (FEV1) to decrease by ≥ 20%. Three studies did not specify their definition for a positive test but did perform SIC testing . BernsteThe 22 included publications reported data for 943 genetic marker comparisons  within 82 different genes for DA versus DA-, AW, and/or NC controls. Information about the study designs and the full list of extracted PORs for all 22 publications are available in the In the candidate-gene studies, genes whose tested genetic markers were reported to include significant associations within a study population were: HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-DR, GSTP1, GSTM1, NAT1, NAT2, HLA-A, IL4RA, HLA-C, HLA-Cw, SOD2 aka MnSOD, HOA1, SERPIN1, SERPINB3, EPHX1, HLA-B, HLA-E, HLA-DOA, HLA-DQA2, HLA-DPB1, MBL2, PTGS1, PTGS2, TGF-B, and TNF-a .DA-prevalence associated genes from the GWAS studies were: CTNNA1, CTNNA3, GADL1, PFKFB3, PCNX, UGT2B4, CRTAC1, C11orf74 or RAG2, ALK, TUSC3, MLLT3 or SLC24A2, TRPM8, IBTK, KCNIP4, PDGFD, LHPP, DOCK2, PCTK2, ASTN2, C7orf13, SAMD12, IGF2BP1 or B4GALNT2, AHNAK, MAGI3 or LRIG2, ACMSD, CDH17, HERC2, NPAS3, ODZ3, PITPNC1, PRKCA, SLC6A12, TACR1, ZBTB16, ATF3, and FAM71A .On the other hand, genes whose tested genetic markers were all insignificant within a given candidate-gene study population included: HLA-A, HLA-B, HLA-Cw, HLA-DRB1, HLA-DPB1, HLA-DQ, HLA-DQA1, HLA-DQB1, HLA-DR, GSTP1, GSTT1, IL13, CD14, TNF-a, NAT1, NAT2, NK2R, ADRB2, IL4RA, GSTM1, EPHX, ADAM33, ALOX5, GDF15, IL10, IL1a, IL1B, and IL1RN . There aMost of the time, research groups studied different genetic markers within the genes listed above. For a subset of those genes, in some studies genetic markers with significant associations were observed while in others no markers were identified within those genes that were associated with differences in the prevalence of DA: GSTP1, HLA-A, HLA-B, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DR, HLA-DRB1, NAT1, NAT2, and TNF-a.All genes with significant markers identified by multiple studies  were inpThe meta-analyses included 23 genetic markers within 11 genes/serotypes: CTNNA3, GSTM1, GSTP1, GSTT1, HLA-Cw, HLA-DQB1, HLA-DR1, HLA-DR3, HLA-DR4, HLA-DR7, and HLA-DR8 see , and tauSensitivity analysis was performed to evaluate the impact of our decision to use unadjusted PORs and to prioritize using AW controls followed by using NC controls and not using DA-control data in the meta-analysis see . The senThe sensitivity analysis for the 33 genotypes for 23 genetic markers involved analyzing the data seven different ways described in the methods section and then evaluating how consistent the findings were across the analyses. Three genes or serotypes had markers with stronger evidence of their association with DA that was mostly consistent across half or more of the sensitivity analyses: HLA DR1, CTNNA3, and GSTT1 present/null. Genes or serotypes with evidence that had a directional POR in only one or two of the seven analyses and therefore were more sensitive to analysis parameters  were HLA-C, HLA-DQB1, and GSTM1. Genes or serotypes with consistently very little evidence for their association with DA prevalence were GSTP1 Val105/Val105, HLA-DR3, HLA-DR4, HLA-DR7, and HLA-DR8, which had PORs that straddled one (in which a POR of 1 indicates no difference between cases and controls) and were consistently negative across all seven ways of analyzing the data for their association with DA. For the four CTNNA3 SNPs that we were able to analyze using data from the 1,000 genomes project  serotypes (involved in immune system response), as well as glutathione-S-transferase (GST) and N-acetyltransferase (NAT) genes (both involved in metabolism) . Three eGST and NAT gene families), immune system function (HLA gene family), regulation of cytokine production , and response to oxidative stress   might conjugate diisocyanates with glutathione and they observed that GSTM1(null) increased the risk of DA  in the Finnish population  but was to the left of the null for DA vs. AW  (The first candidate gene study for GST was conducted by Piirilä and colleagues in 2001 with the hypothesis that glutathione pulation . More repulation . However0.5–1.3) . This seGST and HLA genes can be found in the original publications  SNPs and they had more precise confidence intervals than most of the other genetic markers studied. CTNNA3 is a gene that codes for a protein that is important for stretch-resistant cell-cell adhesions and was studied by two groups . InitialWhen the NC and AW results from Kim and Bernstein’s studies  were metThe other genetic marker that showed association with DA prevalence after meta-analysis was HLA-DR1 serotype, which was protective against DA with a pooled POR of 0.24. By taking the inverse of the POR, this means that workers without HLA-DR1 serotype were more than four times more likely to have DA than controls. HLA-DR1 is one of more than 16 specificities of HLA-DRB1  and was It is notable, however, that only 2 or 3 studies reported findings on each of these genetic markers for meta-analysis. Additionally, the confidence intervals for the meta-analyzed markers spanned a substantial range (the average UCL/LCI was 6 ± 3), demonstrating that there is still a lot of uncertainty about the true PORs for these markers. Thus, more research needs to be conducted in order to better understand the potential role of these genetic markers in affecting DA prevalence.Predicted protein network functions for the genetic markers associated with prevalence of DA showed a lot of overlap with diisocyanate research investigating genes associated with variation in biomarker levels before asthma develops. Bioinformatics analysis revealed that inflammatory pathways appear to be important in both diisocyanate biomarker and asthma research. Protein pathways for genetic and epigenetic markers that are associated with differences in biomarker levels and/or asthma risk have functions including but not limited to: immune function , cell-adhesions , metabolism (NAT and GST gene families), and inflammation  see . Regulatin vitro, in vivo, human studies, genetic studies, epigenetic studies, asthma research, and biomarker research for diisocyanates , they are also prone to several forms of bias that can impact the results. Some of the biases that could be impacting the findings of the included studies are publication bias, selection bias, and ascertainment bias. These are our main validity concerns, but the small numbers of published results per genetic variable (in part because of the publication bias) prevented us from conducting the stratified and meta-regression analyses of validity characteristics that we would ordinarily conduct in a review of a more fully reported literature.There was substantial publication bias from researchers who only published their statistically significant findings and did not report the data for all of the insignificant markers that were studied too. We contacted all of the research groups for missing information but none of them were able to provide us with additional data or other pertinent information. Unreported data is a very concerning problem that could result in researchers placing too much focus on genetic markers that appear to have an association with DA. For example, if one study found a significant DA association but a majority of studies observed that the genetic marker is insignificant, then it could indicate that the marker is not actually of interest when the results are meta-analyzed. Instead, the genetic marker may have been a false positive result in the one study. This cannot be determined though if the statistically insignificant results are never published. Thus, it is critical for researchers to publish all of their findings, including by uploading the full results data into a public repository when performing a GWAS.Selection bias can occur when the included subjects are unrepresentative of the population from which the cases arise. This is a problem for the NC group who were unexposed and are therefore not representative of the population from which DA cases arise. Furthermore, unfortunately, the extent to which selection bias might be influencing the current body of research for the DA- and AW comparisons is not possible to evaluate because not enough information was provided about the recruitment of the cases and controls. Sometimes information about which industry the workers were in when they were exposed to diisocyanates and/or which clinic the cases were recruited from was reported, but often no other recruitment details were included. Because of this, selection bias cannot be sufficiently compared between studies. For example, volunteer bias could not be compared across studies because no details were provided about how cases and controls were contacted about recruitment for the studies . Most researchers also did not report whether the cases and DA- or AW controls were matched in any way, such as by recruiting controls from the same workplaces where the cases had worked. If controls were recruited from different workplaces, communities, industries, countries, etc. then they may not be a representative control group for the worker populations from which the cases arose.There may also be some ascertainment bias of the cases and controls if there were false-positive DA or false-negative DA classifications of workers. False-positive DA classifications are rather unlikely, but false negatives may be of concern because DA can take up to ten or more years to develop . This meThe main limitations of this meta-analysis are (1) that there were few studies that investigated overlapping genetic markers and (2) that the published literature is profoundly affected by publication bias from insignificant results not consistently being reported. Additionally, the industrial use or occupational exposure settings  were often not reported and the studies either included only TDI-exposed workers or a mix of workers who were exposed to HDI, MDI, TDI, or a combination of diisocyanates. Thus, we were unable to analyze whether the type of diisocyanate or the main exposure route influence which genes are associated with DA. Another limitation is that only a limited number of research groups have studied genetic associations with DA and there was limited diversity across the populations that were studied.As such, this work should be used for generating new hypothesis-driven research and to better understand the predicted protein functions that may be impacting DA etiology. More studies are required to gather further evidence for whether any of these specific genetic markers increase or decrease worker risk of developing DA. Additionally, When selecting a control group, it is important to consider the pros and cons of each choice. For our main analysis we used data for AW when available, or NC when AWs were not studied . AW controls in the included studies had more than 10 years of exposure to diisocyanates on average and had remained asymptomatic, making them the most reliable true negative controls who are the least likely to later develop DA. On the other hand, we believe that some of the DA-controls may have had false negative results for their medical testing because those workers had already developed respiratory symptoms and might actually belong in the DA group . It is also possible that DA-workers are more likely to develop DA later if they continued to be exposed to diisocyanates; this could particularly pertain to the workers who had less than 10 years of occupational exposure. Sometimes DA takes many years to develop; Malo and colleagues reported that 40% of workers in their study developed DA after more than 5 years of work . Additiovs. NC). Additionally, there were only a few genes for which this direct comparison between the control group genotypes could be performed , the specific markers that were studied  were almost always different and therefore most of the findings could not be meta-analyzed. Over eighty genes/serotypes and nearly one thousand POR assessments of specific markers were reported in studies on DA but at most only two or three publications had data reported for the same markers. Also important to recognize is that more markers have been tested than that, but the GWAS studies did not report results for all of the SNPs they tested, and sometimes they only reported p-values and not a POR for SNPs. However, PORs are needed to better understand the effect size of the genotype on the prevalence of DA. As a result, only 33 POR assessments for markers within 11 genes/serotypes could be meta-analyzed, which is only 3.5% of all reported data for 943 genetic marker POR comparisons. The lack of overlap for meta-analyzing results across research groups could become more infrequent as GWAS replace candidate gene studies, but only if the researchers report POR findings for all SNPs in the GWAS .In future research on the impact of genetics on DA prevalence, it would be beneficial for investigators to study genetic markers that overlap with previous research. Even though many of the genetic markers that were reported in the literature were often within the same genes as reported in the previous research , the duration of diisocyanate exposure, the industry the workers worked in, the type of diisocyanate to which the workers were exposed, whether any of the cases or controls overlapped with another published study, how workers were recruited, and what the non-response rates were for cases and controls. Researchers should be careful to include more information about the history and recruitment of cases and controls when they publish studies in the future and must begin to report data for all of their findings .GST, CTNNA3, and HLA gene families were protective in the meta-analysis, suggesting that GST genes might be involved in the metabolism of diisocyanates, CTNNA3 may impact tissue permeability and affect exposure uptake, and the HLA gene family might influence immune system response in a way that alters worker risk of developing DA. More research needs to be conducted to better understand the relationship between various genetic markers and DA risk.This systematic literature review presents protein network functions for genes that have had markers reported to be associated with DA, and the meta-analyses provide new insights into which genetic markers have the most compelling evidence within the present literature. Specifically, genetic markers within the In this review, we found that the main challenge for meta-analysis is that the literature is profoundly publication biased. We discuss how study design and reporting can be improved in future studies on the genetics of DA, or any other disease with a genetic component. When deciding which genetic markers to study in future candidate gene studies, researchers can refer to the


We studied the article written by Morteza Motedayen et al. (1) that was published in the International Journal of Reproductive Biomedicine in July 2019. The objective of this study was to determine the relationship between body mass index and preeclampsia. The results of this meta-analysis showed that there is a significant relationship between BMI and the risk of preeclampsia, so it can be said that BMI may be one of the ways to diagnose preeclampsia. Although these results were very interesting, some methodological issues should be considered:Authors for assessing risk of bias and quality of included studies used the Newcastle-Ottawa checklist, but the authors have not reported results of this scale in tables and results.In the method section, the authors used funnel plot and Beggs test for reporting publication bias, but results related to Beggs test, for example, Beta, p-value, and 95% CI were not reported in the manuscript. Also, for assessing of publication bias in the meta-analysis, it is better to use the Egger test for publication bias, because this test use regression for detecting the correlation between variables and its SD (2-4).Search strategy is not complete, because this step in meta-analysis should be done independently by 2 researchers. In meta-analysis studies search strategy, quality assessment or risk of bias, and data extraction should be done independently by 2 researchers. On the other hand, gray's literature is not done by authors. The database was incomplete, Web of sciences was not mentioned or searched. Therefore, it seems that in this meta-analysis, the search strategy was not sensitive.One of the most important aspects of meta-analysis is to determine whether heterogeneity exists in the studies, and investigate the source of such heterogeneity. In this meta-analysis, this section is ambiguous (5). Results showed that heterogeneity is higher and authors have not conducted any subgroup analysis for detecting heterogeneity sources.In the analysis section, the authors should be calculated the weight or standard mean difference in 2 groups of case-control studies and estimated this standard mean difference in meta-analysis . For each study, authors should calculate the standardized mean difference (difference in mean outcomes between groups / standard deviation of outcome among participate). After that, authors should combine these effects and report the pooled estimates .


The NAFLD risk was greater in the studies with higher quality  or using the equation‐based methods for NAFLD ascertainment  or urinary sodium collection as a sodium intake assessment . The overall certainty of the evidence was very low. In conclusion, high sodium intake seems to be related to increased NAFLD risk. Further well‐designed studies are needed to clarify this association and shed light on the underlying mechanisms.Findings on the association of sodium with nonalcoholic fatty liver disease (NAFLD) are conflicting. The present systematic review and meta‐analysis study aimed to assess the association between salt or sodium intake or serum sodium levels and NAFLD risk. Relevant articles were identified by searching PubMed, Web of Knowledge, Scopus, Proquest, and Embase databases through May 1, 2021, without language restriction. The pooled odds ratio (OR) and 95% confidence interval (CI) were estimated using Der‐Simonian and Laird method and random‐effects meta‐analysis. The certainty of the evidence was rated using the GRADE method. Out of 6470 documents, 7 epidemiological/observational  studies on the relationship between dietary salt/sodium intakes and NAFLD risk met our inclusion criteria. The meta‐analysis of all studies showed a significant positive association between the highest salt/sodium intake and NALFD risk  with a meaningful heterogeneity among studies (I High sodium intake was related to increased NAFLD risk. The NAFLD risk was greater in the studies with higher quality or using the equation‐based methods for NAFLD ascertainment or urinary sodium collection as a sodium intake assessment. The overall certainty of the evidence was very low. All original observational articles, including prospective and retrospective cohort, case–control, and cross‐sectional studies (Study Design), in a population of all races and ages and both genders (Population), without language restriction, assessing the association between dietary salt/sodium intake or serum levels of sodium (Exposure), and risk of NAFLD/NASH  were eligible. Studies were included if the comparator was healthy participants (not NAFLD cases) or patients who suffered from low‐grade NAFLD with lower dietary salt/sodium intake (Comparison). The exclusion criteria were in vitro and in vivo investigations, clinical trials, literature reviews, newspapers, books, notes, surveys, letters, editorials, case reports, and clinical trial registration. Moreover, studies on the high‐salt dietary patterns were ineligible. The searching, screening, and selection process were conducted by S.S.Z. and A.R.S. independently. Discrepancies between the two authors were resolved through discussion or by other reviewers.2.3The two authors independently extracted data, including the last name of the first author, year of publication, country, study design, age, gender, number of participants, dietary assessment tool, sodium intake measurement method, outcome assessment method, follow‐up duration (for cohort studies), levels of salt or sodium, and the number of events in the highest and lowest categories. The data extraction sheet is summarized in Table The risk of bias was assessed by the two authors independently, using the Newcastle–Ottawa Scale (NOS)  with a 95% confidence interval (CI) were calculated for the included studies. When the data needed to estimate these values were not available in the article, a request for further information was sent to the authors by email. The summary ORs and 95% CI were also estimated for the highest vs. the lowest categories of salt or sodium using the Der‐Simonian and Laird method. A random‐effects model also was selected to compensate for methodological between‐study heterogeneities, that is, population and methodological diversities. Subgroup analysis was performed based on the region, study design, risk of bias, methods of NAFLD ascertainment, and sodium intake measurement tools to assess the source of heterogeneity across studies. Besides, sensitivity analysis was executed to evaluate the impact of removing each investigation on the pooled results. The presence of possible publication bias was not assessed in the current meta‐analysis due to the small number of the included studies (<10)  approach with the ROBINS‐I tool was used to determine the certainty of evidence. This tool grades the evidence as high, moderate, low, or very low strength. Studies can be downgraded according to five domains  or upgraded based on three criteria    +0.139×body mass index (BMI)+0.718×loge(gamma‐glutamyltransferase(GGT))+0.053×waist circumference–15.745)/(1+ e0.953×loge(triglycerides)+0.139×BMI+0.718×loge(GGT)+0.053×waist circumference–15.745)×100 +BMI  .The result of the meta‐analysis of seven identified epidemiological studies on the association between sodium and NAFLD risk is presented in Figure p‐value =.597, Figure p‐value =.285, Figure 2 = 96.30%) compared to the High/Very high risk of bias  than the ones applying fibroscan or ultrasonography diagnostic markers  was significantly a better predictor of the NAFLD risk compared to the dietary assessment questionnaires   or using equation‐based method for NAFLD ascertainment  or urinary sodium collection as a sodium intake assessment   Table . Due to 5) Table . However5) Table . These cLiver biopsy is the gold standard, although invasive, diagnostic tool for NAFLD. This disease usually is screened and diagnosed with blood‐based biomarkers , imaging‐based technology , or estimation equations  , NICE Guideline, No. Obesity, type 2 diabetes, dyslipidemia, hypertension, and metabolic syndrome are known risk factors of NAFLD , NICE Guideline, No. There are few proposed mechanisms regarding the effect of sodium on NAFLD development. Uetake et al. reported aggravations of steatosis and NASH in high‐fat/high‐salt diet‐fed LOX‐1 transgenic/apoE knockout mice because of oxidative stress and inflammation induction  for salt or sodium intake assessment were utilized in other studies, which made the potential of measurement error. Fifth, the cut points of exposure classifications varied among studies that might represent different sodium consumption levels between communities. Sixth, the low number of included studies prevented the publication bias tests and a firm conclusion; thus, more research is needed to confirm the present findings. Moreover, the difference in exposure assessments prohibited dose–response analysis. Finally, the limitations resulted in very low certainty of the evidence.6In conclusion, the present meta‐analysis revealed that higher sodium intake is associated with a higher NAFLD risk. This association tended to be increased in the studies with a lower risk of bias or using the predictive equation for NAFLD ascertainment or urinary sodium excretion for a sodium intake assessment. These findings provide evidence for another health hazard of excessive salt intake and further rationale for salt restriction. It is worth mentioning that our estimated pooled effect size is unadjusted and should be interpreted with caution. Because of high heterogeneity and significant subgroup differences, further large sample and long‐term prospective cohort investigations using accurate methods are warranted to confirm the independent association between high salt intake and NAFLD risk. Besides, the effects of a low‐salt diet on NAFLD management and the potential underlying mechanisms should be investigated in future studies.The authors report no conflict of interest.The lead author affirms that this manuscript is an honest, accurate, and transparent account of the reported study. The reporting of this work is compliant with PRISMA guidelines. The lead author affirms that no important aspects of the study have been omitted and that any discrepancies from the study as planned (PROSPERO registration number: CRD42021232065) have been explained.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Tab S1‐S4Click here for additional data file.