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      Association between chronic lead exposure and markers of kidney injury: A systematic review and meta-analysis

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          Abstract

          In view of inconsistent reports on the association between chronic lead (Pb) exposure and renal injury markers (potential site of injury), the present systematic review explored their association by reviewing studies that investigated chronic Pb-exposed and those without obvious Pb exposure. Studies reporting blood Pb levels(BLL) and biomarkers of kidney injury [i.e. N-acetyl-β-D-glucosaminidase (NAG), Micro-Globulin(μG) and others] among chronic Pb-exposed and unexposed individuals were systematically searched from digital databases available until February 26, 2024. Preferred Reporting Items of Systematic Reviews and Meta-Analysis Guidelines were adhered to during the execution. Pooled effect size and heterogeneity were estimated using the random effect model and I2Studies reporting blood Pb levels(BLL) and biomarkers of kidney injury [i.e. N-acetyl-β-D-glucosaminidase (NAG), Micro-Globulin(μG) and others] among chronic Pb-exposed and unexposed individuals were systematically searched from digital databases available until February 26, 2024. Preferred Reporting Items of Systematic Reviews and Meta-Analysis Guidelines were adhered to during the execution. Pooled effect size and heterogeneity were estimated using the random effect model and I2. Pooled quantitative analysis revealed elevated BLL [25.64 (21.59–29.70) µg/dL] Pb-exposed group. The pooled analysis confirmed significantly higher urinary NAG [0.68(0.26–1.10) units], α1μG [3.82(0.96–6.68) mg/g creatinine] β 2μG [1.5(0.86–2.14) units and serum creatinine [0.03(0.00–0.05) mg/dL] levels in Pb-exposed group, with high heterogeneity. Current observations indicate the proximal tubular injury as the early and potential site of Pb-induced renal injury. Pb-exposed individuals experience proximal tubular injury (KIM-1, NAG) and dysfunction (β2μG, α1μG, Cystatin-C) prior to obvious clinical renal failure. Present observations should caution the policymakers towards drafting regulations for periodic screening with markers of renal injury and / or dysfunction among those chronically exposed to lead despite the certainty of evidence is very low.

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          Highlights

          • Chronic Pb exposure is associated with proximal tubular injury i.e. ↑ KIM-1 & NAG, earlier to onset of clinical renal failure.

          • Pb exposure is associated with proximal tubular dysfunction i.e. ↑ β2μG, α1μG & Cystatin-C, prior to clinical renal failure.

          • Existing literature is primarily observational and highly heterogeneous carrying high risk of bias.

          • The quality of evidence from existing studies is low.

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          Most cited references70

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          Rayyan—a web and mobile app for systematic reviews

          Background Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Results Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
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            GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables.

            This article is the first of a series providing guidance for use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system of rating quality of evidence and grading strength of recommendations in systematic reviews, health technology assessments (HTAs), and clinical practice guidelines addressing alternative management options. The GRADE process begins with asking an explicit question, including specification of all important outcomes. After the evidence is collected and summarized, GRADE provides explicit criteria for rating the quality of evidence that include study design, risk of bias, imprecision, inconsistency, indirectness, and magnitude of effect. Recommendations are characterized as strong or weak (alternative terms conditional or discretionary) according to the quality of the supporting evidence and the balance between desirable and undesirable consequences of the alternative management options. GRADE suggests summarizing evidence in succinct, transparent, and informative summary of findings tables that show the quality of evidence and the magnitude of relative and absolute effects for each important outcome and/or as evidence profiles that provide, in addition, detailed information about the reason for the quality of evidence rating. Subsequent articles in this series will address GRADE's approach to formulating questions, assessing quality of evidence, and developing recommendations. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Estimating the mean and variance from the median, range, and the size of a sample

              Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.
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                Author and article information

                Contributors
                Journal
                Toxicol Rep
                Toxicol Rep
                Toxicology Reports
                Elsevier
                2214-7500
                29 November 2024
                December 2024
                29 November 2024
                : 13
                : 101837
                Affiliations
                [a ]ICMR – National Institute of Occupational Health, Ahmedabad, India
                [b ]ICMR – National Institute of Epidemiology, Chennai, India
                Author notes
                [* ]Correspondence to: Scientist, “E”, ICMR-National Institute of Occupational Health, Meghaninagar, Ahmedabad 380016, India. balachandar.rakesh@ 123456gmail.com
                [1]

                Both authors have equally contributed and would be deemed first authors

                [2]

                Both authors have equally contributed in leading the study

                Article
                S2214-7500(24)00220-8 101837
                10.1016/j.toxrep.2024.101837
                11664089
                39717854
                58bcea3d-ab98-4279-86f1-433c6c301fa1
                © 2024 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 September 2024
                : 20 November 2024
                : 25 November 2024
                Categories
                Article

                kidney injury markers,lead exposure,n-acetyl-β-d-glucosaminidase,β-2-microglobuline,kidney injury molecule-1,systematic review

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