Aims: Neck circumference (NC) was found to be related to the risk factors for coronary heart disease (CHD). However, the effects of NC on CHD are still controversial. To evaluate the relationship between NC and CHD, a meta-analysis of observational studies was performed. Method: Eligible studies on the association between NC and CHD were searched in Medline, Embase, Ovid, and Web of Science databases published in English from January 1980 to December 2016. Moreover, studies published in Chinese in Wanfang and China Hospital Knowledge databases were also searched. Random effects models in the metafor package in statistical analysis software R 3.3.3 were used for the meta-analysis. Heterogeneity was analyzed with Q statistics. Results: Eight studies were selected for the meta-analysis. A larger NC was associated with a higher prevalence of CHD . The eight studies were further divided into three subgroups according to the criteria for diagnosing CHD. In the subgroup of coronary angiography, NC was also found to be associated with the prevalence of CHD with low heterogeneity . However, in the subgroup of computed tomography or past history, no association between NC and CHD was found. In addition, subgroup analyses were also conducted according to the regions of the study. No association between NC and CHD was identified in either Chinese studies or Brazil studies . Conclusion: Larger NC is associated with increased risk of CHD, especially when coronary angiography was taken to diagnose CHD. Coronary heart disease (CHD) is one of the most common chronic non-communicable diseases in the world. CHD is also the leading reason of all-cause deaths in adults in many countries, accounting for 30.8%–40% of all deaths worldwide . IdentifIn recent decades, the prevalence of CHD increased dramatically, and the onset age of CHD significantly decreased with more CHD patients younger than 40 years old. Obesity has been shown to be an important risk factor for CHD, and always accompanied by multiple metabolic abnormalities, such as insulin resistance, diabetes, hypertension, dyslipidemia, and gout. Obesity now is a global health problem, not only in the general population , but alsHowever, the effects of NC on predicting CHD and CHD events are still controversial . Preis eThe controversial results from different studies on association between NC and CHD make it necessary to perform a meta-analysis to evaluate the relationship between NC and CHD by combining the data of all relevant studies. The meta-analysis follows the guideline provided in the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) .Eligible studies on the association between NC and CHD were searched in Medline, Embase, Ovid, and Web of Science databases published in English from January 1980 to December 2016. Moreover, studies published in Chinese from January 1980 to December 2016 in Wanfang and China Hospital Knowledge Database (CHKD) databases were also searched. The key words “neck circumference,” “cardiovascular disease,” “coronary heart disease,” and combinations of these were used. References cited in the retrieved articles were also examined to find relevant studies that had not been identified by database searches. The articles were first selected through title and abstract screening. The secondary abstract review was performed on the first screened articles with review of the full text. The final inclusion of articles was determined by consensus between two co-authors. r) or other forms of effect sizes. We excluded: (i) abstracts, letters, editorials, expert opinions, case reports, and reviews; (ii) studies that included people aged less than 18 years. Disagreement was solved by the discussion within the team authors.Inclusion criteria included: (i) observation or cohort studies; (ii) adult patients aged 18 years or older; (iii) articles that reported the association between NC and CHD in terms of odds ratios (ORs) or correlation coefficients .Q statistics. The heterogeneity was further quantified by inconsistency index (I2) with 25%, 50%, and 75% representing the evidence of low, moderate, and high heterogeneity, respectively  as effect sizes were converted to log odds ratios for the meta-analysis.Random effects models were utilized to obtain the summary ORs and their 95% CIs for the association between NC and CHD in all eligible studies and subgroup analysis. Statistical heterogeneity among studies was evaluated using the Cochran ectively . Accordip-values less than 0.05 were considered statistically significant for testing association between NC and CHD.The funnel plot and radial plot were generated to assess the publication bias. Egger regression asymmetry test for funnel plot asymmetry was also performed to detect publication bias. The metafor package in statistical analysis software R was used for the meta-analysis , and p-vSensitivity analyses were conducted to measure the robustness of the result. The study with the biggest contribution was removed from the meta-analysis. Then, the two studies using correlation coefficients as effect sizes were removed. Subgroup analysis was based on different nations and different diagnosis criteria for CHD were performed.The screening process for articles used for the meta-analysis is shown in A total of eight articles were included for the meta-analysis. Of them, three were conducted in China , three iI2 = 93.06%, p < 0.05). The weighted odds ratio estimated from the random-effects model indicated that a higher NC was associated with a higher prevalence of CHD , as shown in The random-effects model was selected for data analysis, as the test for heterogeneity showed a statistically significant result . The publication bias was assessed using funnel plot and radial plot because the heterogeneity existed. The funnel plot showed relative symmetry .The homogeneity was analyzed with symmetry . Egger rp = 0.0063, I2 for heterogeneity was 87.78% (p < 0.05). The funnel plot showed relative symmetry .To measure the robustness of the result, sensitivity analyses were conducted. According to the contribution plots of each study, the study with the largest contribution was done by symmetry . Egger rp = 0.0345, I2 for heterogeneity was 71.09% (p < 0.05). The funnel plot remained relative symmetry .Next, we removed the two studies using correlation coefficients as effect sizes . After rsymmetry . Egger rp = 0.113, I2 for heterogeneity was 98.7% (p = 0.001). The funnel plot remained relative symmetry .Subgroup analysis was conducted stratified by nations. Among the eight studies in the meta-analysis, three studies were conducted in China . A randosymmetry . Egger rp = 0.2604, I2 for heterogeneity was 77.11% (p = 0.0193). Egger regression test for funnel plot asymmetry model show that there were no significant bias among these studies .There were three studies conducted in Brazil . NC was p = 0.0007, I2 for heterogeneity was 17.02% (p = 0.1668). The funnel plot remained relative symmetry .There were four studies in which coronary angiography was performed to diagnose CHD . NC was symmetry . Egger rp = 0.7606, I2 for heterogeneity was 43.23% (p = 0.1844).There were two studies in which computed tomography was performed to evaluate the conditions of the coronary arteries . NC was p = 0.2721, I2 for heterogeneity was 99.16% (p < 0.001).In the two studies, CHD was diagnosed by the history of past myocardial infarction, acute coronary syndrome, and cardiac death . NC was Overweight/obesity is recognized as one of the most common risk factors for CHD . NC was There are several anthropometric indicators to evaluate overweight/obesity, such as BMI, waist circumference, waist hip ratio, and NC. Several meta-analysis studies on the association between anthropometric indicators with CHD showed that BMI, measured continuously or categorically, had deleterious effects on the risk of incident CHD in people across diverse populations . In a me2 per decade from 1980 to 2008 in the Cook and Nauru Islanders . The Pacific Islander Health Study had shown that the majority of the male Pacific Islander adult was already overweight by aged 18 years   . The assg obese) . A studyg obese) . An obseg obese) . Anotherg obese) . Due to g obese) , NC mighg obese) . Howeverg obese) .A variety of study designs were used for this meta-analysis, with various study population  and uncontrolled confounding factors . These variations may affect the heterogeneity and the results. In some studies related to NC and obesity, it was shown that NC in males was higher than that in females, so the cutoff of NC for evaluating obesity was sex-specific . HoweverIn studies used for the meta-analysis, CHD was diagnosed by using coronary angiography in four studies , by usinDue to the nature of observational studies used in the meta-analysis, the inherent biases cannot be controlled as in the randomized controlled studies, and some confounding effects are inevitable in observational studies. In addition, the study design and subject selection for each analysis are not coherent. For example, a cross-sectional study is more likely performed in a particular area with a high incidence, and is less likely in an area with a rare incidence. Therefore, the results could have bias and are difficul in generalizing the population of interest .Additional potential limitations to this meta-analysis include: (1) The limited number of studies included in the meta-analysis could affect the association between NC and CHD, for example, no statistical significant association exist between NC and CHD when the number of studies decreased to three; (2) Different diagnose criteria for CHD was used in these eight studies in the meta-analysis; (3) Participants in some studies were patients with high risk for CHD, for example, Our results indicate that NC may be associated with CHD, especially when coronary angiography was taken to diagnose CHD. Given the high incidence of CHD, further large-scale and prospective studies may be worthwhile to confirm this relationship between NC and CHD.We would like to thank the anonymous reviewers for their insightful comments and suggestions that helped to improve our manuscript.The authors declared no potential conflicts of interest concerning the research, authorship, or publication of this article.This study is supported by Capital’s Funds for Health Improvement and Research [2016-2-2054], and the Beijing Municipal Training Foundation for Highly-qualified and Technological Talents of Health System [2014-3-013]. Dr. Li’s, Dr. Zand’s, Mr. Fogg’s, and Dr. Dye’s time is supported by the University of Rochester’s Clinical and Translational Science Award (CTSA) number UL1 TR000042 and UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health. Dr. Zand is also supported by the National Institute of Allergy and Infectious Diseases and the National Institute of Immunology, grant numbers AI098112 and AI069351. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


In this study, we performed a comprehensive estimation and assessment for the clinical value of prostate health index (PHI) in diagnosing prostate cancer. Using the bivariate mixed‐effect model, we calculated the following parameters and their 95% confidence internals (CIs), including sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and symmetric receiver operator characteristic. Twenty eligible studies with a total number of 5543 subjects were included in the final analysis. The estimated sensitivity was 0.75 (95% CI: 0.70‐0.79) and the specificity was 0.69 (95% CI: 0.58‐0.83). The pooled area under the curve was 0.78 (95% CI: 0.74‐0.81). The combined positive likelihood ratio was 2.45 (95% CI: 2.19‐2.73) and the negative likelihood ratio was 0.36 (95% CI: 0.31‐0.43). The diagnostic odds ratio was 6.73 (95% CI: 5.38‐8.44). The posttest probability was 40% under the present positive likelihood ratio of 2.45. It seems there was no significant difference between Asian population and Caucasian population population in sensitivity and specificity. But the overlap of AUC 95% CI indicated that the diagnostic accuracy of PHI was slightly higher in the Asian population population setting than that in the Caucasian population population population (0.83 vs 0.76). Similarly, there was also overlap in AUC 95% CI, which suggested that sample size may be one of heterogeneity source. The PHI has a moderate diagnostic accuracy for detecting prostate cancer. The discrimination ability of PHI is slightly prior to free/total prostate‐specific antigen. It seems that ethnicity has an influence on the clinical value of PHI in the diagnostic of prostate cancer. Since the US food and drug administration approved the usage of serum prostate‐specific antigen (PSA), PSA had become a widespread practice in detecting prostate cancer.2We performed this study by following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guideline  topic about the diagnostic accuracy assessment of PHI for prostate cancer; (b) cancer diagnosis was confirmed by pathology gold criteria; (c) sufficient data  can be extracted for pooling. Exclusion criteria: (a) For republicated data and study, the latest study was used; (b) study cannot obtain effective data or other information; (c) irrelevant study and topic; (d) letter, reviews, comments, animal study were also excluded. Two investigators independently performed the screening process by scanning title, abstract and full‐test. We resolved the disagreements by consensus.2.3Two researchers independently extracted the data. Disagreements were resolved by consensus. We mainly extracted the following data for each study: the name of first author, the year of publication, country, study design (retrospective vs prospective), age (mean age or median age), gold standard, PHI cut‐off value, sample size, sensitivity, specificity, and four folds data including TP, FP, FN and TN.2.4We used the QUADAS‐2  tool to perform the quality assessment.2.5I2 statistic. I2 > 50% and/or P < 0.05 indicated significant heterogeneity.P < 0.05 indicated statistically significant.We used the bivariate mixed‐effect model to pool the following index and their 95% confidence internals (CIs)33.1We obtained 687 records from the initial search. Four hundred and seventeen records were ready for further screening after the duplicates were excluded. We further excluded 339 records via scanning the title and abstract and left 78 records for full‐text assessment. Fifty‐eight records were excluded including 42 records with unrelated topics and diagnostic values, 10 records with insufficient data, six reviews, comments, letter and meeting abstract. Finally, 20 eligible studies were included in the final analysis.3.2The Figure 3.3I2 = 79.36% and 80.39%, respectively, and P < 0.05). The results from random‐effect models indicated that the combined sensitivity was 0.75 (95% CI: 0.70‐0.79) and the combined specificity was 0.69 (95% CI: 0.58‐0.83). The pooled AUC was 0.78 , study design (prospective vs retrospective), and sample size (>250 vs ≤250). For Asian population population, the estimated sensitivity and specificity were 0.80 (95% CI: 0.75‐0.84) and 0.71, respectively (95% CI: 0.67‐0.75). The estimated AUC is 0.83 (95% CI: 0.79‐0.86). For Caucasian population population, the pooled sensitivity and specificity were 0.72 (95% CI: 0.66‐0.78) and 0.68 (95% CI: 0.62‐0.73), respectively. The estimated AUC is 0.76 (95% CI: 0.72‐0.79). It seems there were no significant differences in sensitivity and specificity. But the nonoverlap of AUC 95% CI indicated the diagnostic accuracy of PHI was slightly higher for Asian population population setting than that in the Caucasian population population population setting. For study design, no significant differences were observed in sensitivity (0.74 vs 0.78), specificity (0.69 vs 0.70), AUC (0.77 vs 0.80), PLR (2.39 vs 2.59), NLR (0.38 vs 0.31), and DOR (6.23 vs 8.25). The 95% CIs are presented in the Table 3.4t = 2.450, P = 0.025).We performed the sensitivity analysis via deleting some studies. Specifically, we performed pooled estimation via excluding age >70 or <60, sample size <100, and special cut‐off value. We also used the “modchk” method to perform the sensitivity analysis. The Figure 4The present results with 20 studies indicated that the combined sensitivity was 75% and the specificity was 69% with an AUC of 0.78. The pooled results suggested that the PHI has a moderate diagnostic ability for detecting prostate cancer. With the high heterogeneity within studies, the subgroup analysis was performed. There seemed to be no significant differences for the diagnostic ability of PHI in different population setting, study design type, age, and cut‐off values. No overlap of confidence interval indicated the and population setting and sample size may be one of the potential heterogeneity sources.The early diagnosis and screening of the prostate cancers is always a huge challenge. The diagnostic gold standard for prostate cancer is prostate biopsy. Previous studies also reported other methods such as digital rectal examination and transrectal ultrasonography. However, all these methods made patients feel embarrassed and uncomfortable because these methods were extremely invasive.I2 indicated that the heterogeneity within studies is high. The subgroup analysis indicated the sample size and ethnicity may be the sources of heterogeneity. But the changes in heterogeneity are limited. This effect may be in statistical level. Besides, larger sample size means that the results tend to be more accurate. But this result needs to be confirmed in other studies. Some other potential factors cannot be further assessed because of the data unavailability. Second, there are several studies with different cut‐off values. But the sensitivity analysis indicated no potential significant differences in the diagnostic ability. Third, some studies did not provide the qualitative data; we obtained these estimated results from receiver operating characteristic, which may affect the pooled results. Finally, the present study put some search restriction in Chinese and English, other studies published in other language and gray documents may influence the estimated results. Further research is needed.The main strength of the present is that we strictly followed PRISMA guidelines to perform this meta‐analysis and the quality of the included studies is quite high. Furthermore, the total sample size is more than 5000 patients and provides better estimations. Several study limitations need to be addressed. First, the Q test and In conclusion, the PHI has a moderate accuracy for detecting prostate cancer. The diagnostic accuracy of PHI is slightly prior to free/total PSA. The ethnicity seems to have an influence on the diagnostic ability of PHI. Based on these findings, different diagnostic threshold value should be set in different ethnicity. Studies with larger sample sizes and strict design are needed to confirm the present findings. Besides, combined diagnosis with other parameters should be recommended because combined diagnosis may improve the diagnostic accuracy. Click here for additional data file. Click here for additional data file.


TP53 is a tumor suppressor gene which is essential for regulating cell division and preventing tumor formation. Several studies have assessed the associations of TP53 single-nucleotide polymorphisms (SNP) with susceptibility of malignant bone tumors, including osteosarcoma and Ewing sarcoma, but the results are inconsistent. In the present meta-analysis, we aimed to elucidate the associations of TP53 rs1042522 genetic polymorphism with the risk of osteosarcoma or Ewing sarcoma. We systematically searched Medline, PubMed, Web of Science, Embase, and the Cochrane Library databases. Eligible studies assessing the polymorphisms in the TP53 rs1042522 gene and risk of malignant bone tumors were incorporated. The pooled odds ratio (OR) with its 95% confidence intervals (95% CIs) were used to assess these possible associations. Five studies with a total of 567 cases and 935 controls were finally included the meta-analysis. Meta-analysis of TP53 rs1042522 polymorphism was significantly associated with an increased risk of malignant bone tumors . Moreover, in a stratified analysis, a statistically significant correlation between this SNP and osteosarcoma risk was also observed. Our results suggest that there are significant associations of TP53 rs1042522 polymorphism with malignant bone tumors risk. More studies based on larger sample sizes and homogeneous samples are warranted to confirm these findings. Malignant bone tumors are rare human sarcomas and they account for no more than 0.2% of all kinds of tumors . OsteosaTP53 gene encodes a tumor suppressor protein p53, which is essential for cell cycle regulation and plays an important role in cancer prevention through regulating apoptosis, genomic stability, and inhibition of angiogenesis . Here upon, we comprehensively searched the up-to-date electronic databases and enrolled five independent case–control studies with a total of 567 cases and 935 controls into our meta-analysis to reveal the associations between TP53 genetic polymorphisms and risk of malignant bone tumors. There were four studies on the association between this SNP and risk of osteosarcoma, and two studies for the risk of Ewing sarcoma. The results of our analysis showed that TP53 rs1042522 polymorphism significantly increased the risk of malignant bone tumors.TP53 plays an important role in the carcinogenesis of various cancers including osteosarcoma or Ewing sarcoma. There were several studies assessing the effects of TP53 polymorphisms on the risk of malignant bone tumors, but there was no comprehensive assessment of the effects and the studies reported different results. For example, no associations of the TP53 Arg72Pro SNP (TP53 rs1042522) with Ewing sarcoma were found in Thurow et al.’s  article. No increased or decreased risk to develop osteosarcoma was observed in association with the Pro/Pro genotype variant in Toffoli et al.’s  researchTP53 is highly mutated in ∼50% of human cancers [TP53 gene or in genes of proteins that interact with p53 protein ablates its ability to prevent tumor formation and favors cellular proliferation and tumor initiation and progression. The TP53 gene mutation has been observed in the classic Li-Fraumeni Syndrome [The p53/MDM pathway comprises alternative reading frame (ARF), murine double minute 2 (MDM2), MDM4, and p53 proteins. Classically, MDM2 (or MDM4) could bind p53 and then promote proteasomal degradation of the tumor suppressor . The TP5 cancers ,22. The Syndrome ,24 incluSyndrome . AlthougSyndrome . AmongstIn our study, after stratifying by ethnicity, we observed in the meta-analysis that OR values amongst Caucasian populations were consistent with our total populations, suggesting that the existence of population differences could lead to the same SNP effects. Because only two studies assessed the patients amongst non-Caucasians population, we failed to perform further meta-analysis. Two major malignant bone tumors are osteosarcoma and Ewing sarcoma. In our meta-analysis, four studies with a total of 527 cases and 807 controls assessed the risk of osteosarcoma. Results showed that TP53 rs1042522 polymorphism was significantly associated with an increased risk of osteosarcoma. However, no statistically significant correlation between the TP53 rs1042522 polymorphism and Ewing sarcoma risk was observed. Thurow et al. [Taken all results into consideration, the present meta-analysis has several strengths. First, we used a comprehensive search strategy, and had a well-defined inclusion and exclusion criteria. Second, two reviewers performed the study selection and extracted data independently, and discrepancies were resolved by consensus. Third, we assessed the quality of the included studies by predefined criteria and the score of included studies was high. Finally, all genotype data extracted from the studies were reported in the study.TP53 gene with malignant bone tumors risk. Finally, the web resources such as the 1000 Genomes Browser providing the allele frequencies of TP53 rs1042522 polymorphism amongst different main populations were not considered and utilized in our research. The allele frequencies were significantly different with the values calculated from our included studies. All the limitations mentioned above might lead to false-positive findings. Therefore, additional prospective studies with larger sample sizes including participants of other ethnic populations are warranted. Besides, reasonable utilization of the web resources containing TP53 rs1042522 allele frequencies of different population might be considered to enlarge our control groups and provide us a significant extension of our finding in the future.Nevertheless, there are still some limitations. First, the number of cases is still relatively small; which may be inherent to the low incidence of osteosarcoma or Ewing sarcoma. And the small number of only five studies indicated that the statistical power to detect differences was suboptimal. However, the pooled results in our review were more reliable than the results in each of the individual studies. Second, the heterogeneity of the studies was high in the case of homozygote model (GG versus CC) and dominant model (GG/GC versus CC). We explored the sources of heterogeneity by sensitivity analysis. However, none of those studies altered the pooled OR significantly. And we further performed subgroup analysis and showed that ethnicity or disease may account for the heterogeneity. Third, only two studies assessed the patients amongst non-Caucasians. Therefore, additional studies with other ethnic populations are warranted to assess the association between TP53 rs1042522 polymorphism and the risk of malignant bone tumors. Fourth, malignant bone tumor comprises osteosarcoma, Ewing sarcoma, chondrosarcoma and synovial sarcoma, and so on. Osteosarcoma and Ewing’s sarcoma are the two most common primary malignant bone tumors in children, adolescents, and young adults, which cause serious damage to human health. In our research, considering the degree of malignancy, population of high incidence and bad prognosis of these two tumors are similar, we mainly concentrate on osteosarcoma and Ewing’s sarcoma and combine them together to be analyzed. However, there are many differences between these two types of tumors, such as histological origin, development, tumor location, and standard treatment strategy, which may cause heterogeneity and influence the reliability of our results in some degree. Fifth, the majority of the included trials mainly reported data on rs1042522 polymorphism, thus we were unable to examine the association of other SNPs of the In conclusion, the present meta-analysis indicates that TP53 rs1042522 polymorphism is the genetic risk factor for the susceptibility of malignant bone tumors. Well-designed studies with larger sample sizes and various SNPs are warranted to confirm our current findings.


The purpose of this study was to explore the association of miRNA-146 and miRNA-499 polymorphisms with inflammatory arthritis. A systematic search of studies on the association of miRNA-146 and miRNA-499 polymorphisms with inflammatory arthritis susceptibility was conducted in PubMed, Web of science, Elsevier ScienceDirect, and Cochrane Library. Eventually, 18 published studies were included. The strength of association between miRNA-146/499 polymorphisms and inflammatory arthritis susceptibility was assessed by odds ratios (ORs) with its 95% confidence intervals (CIs). P=0.001). Similar results were found in subgroup analysis by region. But we did not find association between miRNA-146 rs2910164 polymorphism and inflammatory arthritis susceptibility in overall population . A total of 18 case-control studies, consisting of 3385 inflammatory arthritis patients and 4584 controls, were included in the meta-analysis. This meta-analysis showed significant association between miRNA-499 rs3746444 polymorphism and inflammatory arthritis susceptibility in overall population (C vs T, OR: 1.422, 95% CI= 1.159-1.745, The present study indicates that miRNA-499 rs3746444 polymorphism is associated with inflammatory arthritis susceptibility. However, there is lack of association between miRNA-146 rs2910164 polymorphism and inflammatory arthritis susceptibility. But, we also find miRNA-146 rs2910164 and miRNA-499 rs3746444 polymorphism are associated with inflammatory arthritis in Middle East. Therefore, more large-scale studies are warranted to replicate our findings. Inflammatory arthritis is a group of complex diseases, including rheumatoid arthritis (RA), psoriatic arthritis (PsA), juvenile idiopathic arthritis (JIA), etc. And it is characterized by inflammatory cell infiltration into the joints , 2. TherMicroRNAs (miRNAs) are small noncoding RNAs, which are about 22 nucleotides long. They play an important role in regulating transcription and translation –6. Some MiRNA-146 is located on human chromosome 5q34 and related to immune regulation, inflammatory signaling pathway, because it can regulate the expression of IL-1 receptor-associated kinase (IRAK1) and IRAK2 and targets TNF receptor-associated factor 6 (TRAF6) . And, miConsidering the results of miRNA-146 and miRNA-499 polymorphisms with inflammatory arthritis are inconsistent –20, thisA systematic search of studies on the association of miRNA-146 and miRNA-499 polymorphisms with inflammatory arthritis susceptibility was conducted in PubMed, Web of science, Elsevier ScienceDirect, and Cochrane Library. Keywords for the search were as follows: (“microRNA” or “miRNA” or “microRNAs”) and (“Inflammatory arthritis”) and (“polymorphism” or “variant” or “genotype” or “SNP” or “mutations”). All relevant studies were retrieved. We screened those studies by reading its titles, abstracts, and contents carefully.The inclusion criteria were (i) studies involving the association between miRNA gene polymorphisms and inflammatory arthritis; (ii) case-control study; (iii) studies based on human; (iv) studies providing the detailed relevant genotype data of both case group and control group. Studies were excluded if (i) the study was a review, editorial, abstract, case report, or unpublished article; (ii) studies were nonhuman studies or animal experiments or cell experiments; (iii) studies had no controls or no detailed relevant genotype data.The data of the eligible studies were extracted by one investigator (Mr.Wang). The following information were collected: first author's name; year of publication; type of disease; country; region; genotyping methods; number of cases and controls in each study; characteristics of controls; category of polymorphisms and other additional information.The included studies in this meta-analysis were evaluated by another inspector (Pan). The quality assessment was based on the modified Newcastle-Ottawa Quality Assessment Scale (NOS). The scale consists of eight multiple-choice questions that involve subjects' selection, comparability in cases and controls, and assessment of exposure. High-quality response earns a point, totaling up to nine points (the comparability question earns up to two points). The higher score indicates better quality.I2 statistics. Heterogeneity was recognized as statistically significant when I2 > 50%. According to the value of I2, we chose fixed-effects or random-effects model. All subgroups were analyzed. Sensitivity analysis was used to evaluate the influence of individual study on the overall OR. Publication bias was assessed by Funnel's plot, Begg's test and Egger's test. An asymmetric plot and the P value of Egger's test or Begg's test less than 0.05 were considered as significant publication bias.All statistical analyses were performed by Stata 12.0  software. OR and 95% CI were used to evaluate the strength of association between miRNA-146/499 polymorphisms with inflammatory arthritis susceptibility under different genetic models. The effect of heterogeneity was evaluated using A total of 390 studies were retrieved from PubMed, Web of science, Elsevier Science Direct and Cochrane Library. Finally, 18 eligible studies were included in this meta-analysis. A flowchart of the included and excluded studies was shown in  P=0.365; GC+CC vs GG, OR: 0.919, 95% CI=0.762-1.110, P=0.381; CC vs GG+GC, OR: 0.935, 95% CI=0.821-1.065, P=0.310). After stratifying by region, it only showed the association between miRNA-146 rs2910164 polymorphism and inflammatory arthritis susceptibility in Middle East . After stratifying by region, the association between miRNA-499 rs3746444 polymorphism and inflammatory arthritis susceptibility was still extant in Middle East , the random-effects models were performed; others were analyzed by fixed-effects model. As for miRNA-499 rs3746444 polymorphism, the plot of sensitivity analysis was shown in The results of heterogeneity test were shown in Both environmental factors and genetic factors are related to the development of inflammatory arthritis. As for miRNAs, it is reported that they are involved in cancers and autoimmune diseases , 21. ButPresent meta-analysis included 18 relevant studies, 9 only about miRNA-146 rs2910164, 3 only about miRNA-499 rs3746444, and 6 about both miRNA-146 rs2910164 and miRNA-499 rs3746444. There were 5 studies not met Hardy–Weinberg equilibrium; we did not exclude those 5 studies. We conducted subgroup analysis according to HWE and found that the results are similar .Of course, there were some limitations in this meta-analysis. First, we searched literature based on English; it may cause language bias. Second, the number of included studies was not sufficient, which could not conduct comprehensive analysis. Third, our data of meta-analysis were from retrospective research, which may be related to the methodological deficiencies. Finally, our meta-analysis did not take the interactions between environmental factors and genetic factors into account. Therefore, the results should be interpreted with caution.In conclusion, the present study indicates that miRNA-499 rs3746444 polymorphism is associated with inflammatory arthritis susceptibility. However, there is lack of association between miRNA-146 rs2910164 polymorphism and inflammatory arthritis susceptibility. But, we find miRNA-146 rs2910164 and miRNA-499 rs3746444 polymorphism are associated with inflammatory arthritis in Middle East. Therefore, it is necessary to conduct more large-sample and more high-quality studies to further validate our findings.


The PubMed/Medline, Scopus, Web of Science, and Cochrane Library databases were searched up to April 2019 with no restrictions. The odds ratios (ORs) and 95% confidence intervals (CIs) in all analyses were calculated by Review Manager 5.3 software. The funnel plot analysis was carried out by the Comprehensive Meta-Analysis version 2.0 software. Subgroup analysis, meta-regression, and sensitivity analysis were performed for the pooled analyses. Thirty-one studies reviewed in this meta-analysis included 4710 NSCL/P patients and 7271 controls. There was no significant association between MTHFR C677T polymorphism and NSCL/P susceptibility related to allelic model , homozygote model , heterozygote model , dominant model , or recessive model . There was no significant association between MTHFR C677T polymorphism and NSCL/P susceptibility based on the ethnicity or the source of cases. There was a significant linear relationship between the year of publication and log ORs for the allele model. The results of the present meta-analysis failed to show an association between MTHFR C677T polymorphism and NSCL/P susceptibility. The subgroup analyses based on the ethnicity and the source of cases further confirmed this result.Both genetic and environmental factors affect the risk of orofacial clefts. The present meta-analysis aimed to evaluate the association between  In low- and middle-income countries, around 1/730 children is born with cleft lip/palate2. A multifactorial model of genetic inheritance has been recommended for NSCL/P based on the interaction of genetic and environmental factors1. Several lines of evidence have proven a significant association between polymorphism of genes connected to folate metabolism and increased risk of orofacial clefts3. Among genes related to folate metabolism, 5,10-methylenetetrahydrofolate reductase (MTHFR) reportedly has the highest association with NSCL/P. This gene is located on chromosome 1 at 1p36.3 and translates to MTHFR enzyme that catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, a cosubstrate for homocysteine remethylation to methionine5. MTHFR is a fundamental enzyme in folate metabolism and DNA synthesis, and MTHFR rs1801133 (C677T) is one of the most common polymorphisms which diminishes the enzyme activity8. Regulation of MTHFR activity is pivotal to maintain optimal cellular levels of methionine and S-adenosylmethionine5. Folate supplementation or its adequate dietary intake during pregnancy has been shown to prevent or decrease NSCL/P susceptibility9. Nutritional factors, such as the adequacy of folic acid in the mother’s diet, are clearly important, but other potential disturbances in ovulation or development of fetus may be due to the activity of key factors such as the MTHFR enzyme in folate metabolism11. In addition, the role of other polymorphisms of folate metabolism has been proven in recent meta-analyses13. There are six published meta-analyses related to our topic in the literature17. However, several other original articles have been recently published. Thus, we aimed to evaluate the association between MTHFR C677T and risk of NSCL/P in an updated meta-analysis of cases-control studies.Non-syndromic cleft lip/palate (NSCL/P) is a common birth defect worldwide18.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was applied for designing this meta-analysisMTHFR rs1801133 C > T (C677T) polymorphism and NSCL/P susceptibility in PubMed/Medline, Scopus, Web of Science, and Cochrane Library databases up to April 2019. The search keywords were:  and (“methylenetetrahydrofolate reductase” or “MTHFR”). The databases were searched without any restrictions. Manual search of all references quoted in published meta-analyses/reviews related to the topic was done by another author (M.S).One author (N.G) accomplished the initial search and another author (M.S) re-checked the retrieved articles; disagreements between the two authors were resolved by a third author (M.M.I). A comprehensive search was conducted on the association between MTHFR C677T polymorphism and the NSCL/P susceptibility; (c) studies designed as case-control studies; (d) studies providing sufficient data about the alleles and genotypes of MTHFR C677T polymorphism in case and control groups. Exclusion criteria: (a) studies not related to the relationship between MTHFR C677T polymorphism and the NSCL/P susceptibility; (b) duplicate studies/erratum; (c) review/meta-analysis, letter to editors, commentaries, and conference papers; (d) family-based studies; (e) case-parent triads studies; (f) studies inconsistent with the Hardy-Weinberg equilibrium (HWE) about the control group; (g) studies containing overlapping data.Inclusion criteria: (a) original studies; (b) studies reporting the relationship between Two authors (N.G and M.M.I) independently retrieved the data from each study included in this systematic review based on the eligibility criteria. Disagreements between the two authors were resolved through further discussion. The extracted data are presented in Tables To evaluate the study quality, the control group of each study was tested for the HWE. One author (M.S) calculated the HWE for each study.MTHFR C677T polymorphism and the risk of NSCL/P. To examine this association, we used five genetic models namely the allele (T vs. C), homozygote (TT vs. CC), heterozygote (CT vs. CC), dominant (TT + CT vs. CC), and recessive (TT vs. CC + CT) models. The Z test was used for evaluation of the significance of the pooled OR using both fixed effects (FE) (Mantel–Haenszel) and random effects (DerSimonian and Laird) models20. Heterogeneity across the studies was evaluated using both the Cochrane Q test22 and I2 metric24 ranging from 0 to 100%25. There was statistically significant heterogeneity if P-value < 0.1 and I2 > 50%; in that case, the random-effect model was used to estimate the pooled ORs and CI values. Otherwise, we used the fixed-effect model. The Chi-square test was used for calculation of the HWE for the control group of each study.One author (M.S) analyzed the data and other authors independently re-checked them; disagreements were resolved by discussion. The odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) in all analyses were calculated by Review Manager 5.3 to evaluate the strength of the association between 25. In addition, the results of Egger’s linear regression were retrieved using this software26. To estimate the consistency or stability of the results, we used sensitivity analysis namely cumulative analysis and one study was removed. P-value (2-tailed) <0.05 was statistically significant.Subgroup analysis was performed according to the ethnicity and the source of cases to explore potential heterogeneity. The meta-regression analysis is a technique used to assess heterogeneity between the studies. This statistical approach determines whether there is a significant association between the study period and number of individuals with the pooled OR. The Begg’s funnel plot was carried out by the Comprehensive Meta-Analysis version 2.0 software identifying the standard error of log (OR), and the precision of each study was plotted against its log (OR)five studies were systematic reviews/meta-analyses; one study was erratum; two studies were letter to editors/research letters; one study was editorial comment; three studies were family-based studies; five studies had irrelevant data; ten studies were case-control parents; three studies had no control group; five studies were case-parent triads; one study reported complex birth defects). On the other hand, by searching the references of meta-analyses, two other articles28 were found. Totally, 36 articles were included in this systematic review61 out of which, 5 studies61 had a deviation from the HWE for the control group and were excluded from the meta-analysis. Therefore, 31 articles were included and analyzed in this meta-analysis. In addition, one study62 was excluded for reducing bias compared with other previous meta-analyses, because it was a conference paper and therefore didn’t involve the eligibility criteria.A total of 353 records were retrieved from the databases and after removing the duplicates, 187 records were screened ], homozygote model , heterozygote model , heterozygote model , dominant model , and recessive model .The results of the pooled OR of the association between MTHFR C677T polymorphism and NSCL/P risk  or the source of cases .The subgroup analysis was performed based on the ethnicity and the source of cases for the association between sk Table . There wConsidering the year of publication and the number of individuals as independent variables and the log (OR) as the dependent variable, the fixed-effect meta-regression results are presented in Table MTHFR C677T polymorphism in a fixed-effect model. There was no publication bias between the NSCL/P risk and MTHFR C677T polymorphism in the genetic models. The P-values of Begg’s/Egger’s tests were 0.21470/0.12123, 0.95933/0.97596, 0.22753/0.29895, 0.25480/0.28137, and 0.93228/0.91342 for T vs. C, TT vs. CC, CT vs. CC, TT + CT vs. CC, and TT vs. CC + CT, respectively.Figure Two analyses  were performed and the pooled ORs did not change qualitatively. Therefore, the analyses showed that the pooled ORs under all genetic models were stable and trustworthy.63. MTHFR enzyme plays an important role in folate intake, and mutations of MTHFR gene significantly impact the stability and thus the function of the enzyme; MTHFR C677T is the most common mutation of this gene64. MTHFR C677T polymorphism is related to a reduction in MTHFR activity, raised plasma homocysteine concentration, and lower plasma level of folic acid, which consequently contribute to NSCL/P65. The present meta-analysis was performed to more precisely assess the relationship between MTHFR C677T polymorphism and NSCL/P susceptibility. In pooled analysis, the meta-analysis showed no significant association between MTHFR C677T polymorphism and NSCL/P risk.NSCL/P is one of the most common congenital anomalies with high rate of mortality. Its pathogenesis is difficult to be attributed to either environmental or genetic factors. The pathway of folate metabolism plays a significant role in the synthesis, repair, and methylation of DNA involved in NSCL/P pathogenesis52, five studies52, and four studies52 reported significantly increased risk of T allele, TT genotype, and CT genotype in NSCL/P patients compared with controls, respectively. Also, five studies57, two studies57, and four studies57 reported a significantly decreased risk of T allele, TT genotype and CT genotype in NSCL/P patients compared with controls, respectively. In addition, TT + CT genotype was reported to have a significantly increased risk in five studies52 and significantly decreased risk in four studies57 in NSCL/P patients compared with controls. Based on the recessive model, three studies54 reported significantly increased risk of TT genotype and one study57 reported its significantly decreased risk in NSCL/P patients compared with controls.Out of 31 studies included in the present meta-analysis, six studies14 investigating the relationship between NSCL/P and MTHFR C677T polymorphism showed that the TT genotype was a risk factor for NSCL/P in Asians in homozygote  and recessive  models. Also, based on mothers with NSCL/P progeny versus control mothers with healthy progeny in 10 studies, the TT genotype of Caucasian mothers may increase progeny NSCL/P morbidity. Another recent meta-analysis of 22 case-control studies15 showed that MTHFR C677T polymorphism was associated with a higher risk of NSCL/P. Both meta-analyses also included studies with a deviation of HWE. However, in the present meta-analysis, we excluded such studies from the pooled analysis and therefore, reviewed 31 case-control studies and had lower heterogeneity compared with the meta-analysis with 22 studies15. Almost similar to the findings of a meta-analysis with 24 studies14, our results showed no association between MTHFR C677T and susceptibility to NSCL/P. In one meta-analysis15, definition of ethnicity was different from that in another study14 and our meta-analysis. The meta-regression showed a linear relationship with a negative slope between the year of publication and log ORs for the allele model and therefore by increasing years of publication, the risk of T allele decreased in NSCL/P patients compared with controls. There were two other meta-analyses with eight16 and nine7 case-control studies related to our topic. One of them16 reported no association and another one on Asian ethnicity showed a significant association between MTHFR C677T and susceptibility to NSCL/P. Luo et al.17 on nine studies in a meta-analysis didn’t show any evidence for significant association between infant or maternal MTHFR C677T polymorphism and NSCL/P risk, but suggested that maternal MTHFR 677TT polymorphism could increase the risk of having a NSCL/P offspring in the white population. Pan et al.8 on seventeen studies showed that this polymorphism was a risk factor involved in the development of NSCL/P in Asians that definition of ethnicities in this meta-analysis was different from our meta-analysis. The results showed that the effect of each factor alone on the association was low, but such a high heterogeneity among the studies could be due to simultaneous effect of several factors such as differences in the ethnicity of the study populations, source of cases, and number of individuals.A recent meta-analysis with 24 case-control studiesThis study had several important limitations including high heterogeneity across studies, unadjusted ORs used in the studies, and intake of folic acid and other supplements that were not considered. Nevertheless, the present study included more studies with meta-regression without any deviation of HWE for controls in all studies compared with other meta-analyses. It did not have publication bias, and the results were stable.MTHFR C677T polymorphism is not associated with susceptibility to NSCL/P, and the subgroup analyses based on the ethnicity and the source of cases further confirmed this result. However, well-designed studies with larger sample size are required taking into account the role of micronutrients such as folic acid in NSCL/P risk.In conclusion, the result of the present meta-analysis revealed that


ACE I/D) polymorphism and psoriasis susceptibility.Psoriasis is a multifactorial disorder, impacted by both genetic and environmental factors. Herein, a meta-analysis assessed the association of angiotensin-converting enzyme gene insertion/deletion  and 95% confidence interval (CI) to investigate the association between P = 0.58], 0.99 , 0.81 , 0.91 , and 1.05 , respectively. A significant difference between ACE polymorphisms in patients with/without family history for the disease  and also in patients mild/severe psoriasis  was identified.Out of 61 studies retrieved from the databases, 16 studies were included in the meta-analysis. The pooled ORs for models of D vs. I, DD vs. II, ID vs. II, ID + DD vs. II, and DD vs. II + ID genotypes were 0.96 [95%CI: 0.82, 1.12; ACE I/D polymorphism may be associated with psoriasis susceptibility, while ID genotype seemed to have a protective role in Caucasian patients affected by psoriatic arthritis and in studies with hospital-based controls.The results of the meta-analysis showed that  ACE polymorphisms include an insertion (I)/deletion (D) within the intron 16 able to incorporate the most genetic variables responsible for the variability of ACE activity in serum , 0.99 , 0.81 , 0.91 , and 1.05 , respectively. Therefore, the presence of ID genotype had a significant slight protective effect against psoriasis development.Forest plot of the psoriasis susceptibility related to ACE I/D polymorphisms in East Asian populations, but the presence of ID genotype had a slight protective effect against the disease . Based on three models of DD versus II, ID versus II, and ID + DD versus II the pooled OR was 0.69 , 0.66 , and 0.67 , respectively. Such results showed that DD and ID genotypes had protective roles in psoriatic arthritis, unless this was not valid for psoriasis vulgaris type. The ID genotype had a significantly decreased susceptibility to psoriasis in studies conducted in hospital-based populations based on heterozygote model . Caucasian population was evaluated in three groups, Arab, European and the other group. The other group consisted of Iran, Pakistan and Turkey. With regard to the Caucasian population, the ID genotype had a significant protective role in psoriasis if compared with other genotypes in non-Arab and non-European population (the other group) .The results of psoriasis susceptibility related to ACE I/D polymorphisms on the basis of different considered variables are shown in Table P-value of HWE for controls was less than 0.05 .We checked publication bias for overall analysis using both Egger’s and Begg’s tests Fig. . The resACE I/D polymorphisms with psoriasis susceptibility and also the distribution of genotypes in psoriatic patients. The results indicated the ID genotype is significantly associated with decreased risk of psoriasis development. In addition, in a further subgroup analysis, such genotype resulted to be protective against psoriasis and psoriatic arthritis in Caucasian patients versus non-Arab and non-European population. The same genotype showed to be less represented among patients in hospital-based studies. Out of all studies included in the meta-analysis, four studies [The present meta-analysis investigated the association between  studies , 30, 34  studies –33 repor studies , 30, 34  studies –33, resp studies , 25, 30 ACE I/D polymorphisms and psoriasis susceptibility [P = 0.006).Previously, a meta-analysis conducted in ten studies and checking for the association between tibility  suggestetibility  showed ttibility  concludeThe present meta-analysis included 16 studies, and described the different genotypes and alleles’ distribution in the groups and subgroups analysis had also been performed. Furthermore, a careful quality assessment of the involved studies had been conducted, in order to consider only the high-quality studies and to provide a further strength to our results.ACE II genotype reduces ACE activity in skin and may prolong or augment activation of the kallikrein–kinin system, thereby increasing the risk for psoriasis [ACE I/D polymorphism, with DD genotypes having the highest and II genotypes having the lowest ACE activity [A possible explanation for its role in psoriasis may be related to the fact that the soriasis . The actsoriasis , 37. Sevsoriasis , 38. Plaactivity . Increasactivity , moreoveactivity . This shACE I/D polymorphism. In addition, no significant difference was found between polymorphisms and age at onset [In the previously published data, one study  reportedat onset –26, 29, at onset , 30, disat onset , 29. In at onset . Anotherat onset  showed tACE I/D polymorphisms [The differences between our results and those with other previous studies may be due to diverse factors, thus including racial/geographical difference, number of male/female patients in the considered study and also to the genetic heterogeneity and multifactorial etiology of psoriasis . Also, Eorphisms . In our Our study presents several important limitations: i) a high heterogeneity among the considered studies was identified; ii) the number of ethnic groups in the studies was limited; iii) in many studies the psoriasis subtypes and source of controls were not clearly specified. Notwithstanding, despite these limitations, there was no publication bias in the analyses.ACE I/D polymorphisms may be associated with psoriasis susceptibility and, in detail the ID genotype seemed to have a protective role, mainly in Caucasian patients, against psoriatic arthritis, and in the studies considering hospital-based controls. In addition, the DD genotype showed a protective role against psoriatic arthritis. In conclusion, the distributions of genotypes of ACE I/D polymorphism were different when the patients were compared in the terms of family history and severity of the disease.Summing up, the results of the present meta-analysis showed that


Many meta-analysis models contain multiple parameters, for example due tomultiple outcomes, multiple treatments or multiple regression coefficients. Inparticular, meta-regression models may contain multiple study-level covariates,and one-stage individual participant data meta-analysis models may containmultiple patient-level covariates and interactions. Here, we propose how toderive percentage study weights for such situations, in order to reveal the(otherwise hidden) contribution of each study toward the parameter estimates ofinterest. We assume that studies are independent, and utilise a decomposition ofFisher’s information matrix to decompose the total variance matrix of parameterestimates into study-specific contributions, from which percentage weights arederived. This approach generalises how percentage weights are calculated in atraditional, single parameter meta-analysis model. Application is made to one-and two-stage individual participant data meta-analyses, meta-regression andnetwork (multivariate) meta-analysis of multiple treatments. These revealpercentage study weights toward clinically important estimates, such as summarytreatment effects and treatment-covariate interactions, and are especiallyuseful when some studies are potential outliers or at high risk of bias. We alsoderive percentage study weights toward methodologically interesting measures,such as the magnitude of ecological bias (difference between within-study andacross-study associations) and the amount of inconsistency . In relation to forest plots, the PRISMAStatement recommends that ‘… it is preferable also to include, for each study, thenumerical group-specific summary data, the effect size and confidence interval, andthe percentage weight’.3 Percentage study weights aim to break down the summary meta-analysis resultinto the relative contribution of each individual study, and are easilyinterpretable by non-statisticians.Meta-analysis is the synthesis of quantitative information from related studies toproduce summary (pooled) results to help answer clinically relevant questions, suchas whether a treatment is effective. Statistical models for meta-analysis often useaggregate data (such as a treatment effect estimate and its variance) from eachstudy, but increasingly they utilise individual participant data (IPD).4 and one-stage IPD meta-analysis models may contain multiple patient-levelcovariates and interactions.6In this article, we propose a general approach to deriving percentage study weightsin meta-analysis models with multiple parameters, including meta-regression, network(multivariate) meta-analysis and IPD models with interactions. Our approachincludes, as a special case, the simpler, traditional situation where just a singleparameter (such as a treatment effect) is to be synthesised. However, mostmeta-analysis models contain multiple parameters, for example due to multipleoutcomes, multiple treatments or multiple regression coefficients. In particular,meta-regression models may contain multiple study-level covariates,711 (b) help understanddifferences between results from IPD meta-analyses and those based on aggregate dataobtained from published studies, which may include different studies and/orpatients;1214 (c) toascertain the contribution of IPD studies in meta-analyses that combine IPD andnon-IPD (aggregate data) studies;16 (d) understand thecontribution of small studies in meta-analyses with funnel plot asymmetry 17 or (e) to understand which studies are contributing most toward potentialbias or inconsistency terms, for example within a network meta-analysis.18Our aim is to allow researchers to produce percentage study weights for eachparameter of interest within a multi-parameter meta-analysis model. Our motivationis to increase transparency of each study’s contribution, which is especiallyimportant when some studies are potential outliers or at high risk of bias, toreveal how much their data contributed to the overall meta-analysis result.Similarly, if clinical decision makers are concerned that a meta-analysis result maybe unreliable for translation to practice due to the inclusion of a study from adifferent clinical population or setting, then knowing the study’s contribution isimportant. Percentage study weights are also helpful for methodological purposes,for example to: (a) help understand and explain differences between one- andtwo-stage IPD meta-analysis results, for which there is much interest;The article is structured as follows. Section 2 recaps percentage study weights in atraditional meta-analysis of a single parameter, and reveals how they can beexpressed on the variance scale using a decomposition of Fisher’s information. InSection 3, we extend this concept to multiple parameter models expressed within ageneral or generalised linear mixed model framework. These include one-stage IPDmodels, meta-regression and multivariate (network) meta-analysis. Section 4 providesthree applied examples, covering IPD meta-analysis, meta-regression and networkmeta-analysis, for the estimation of treatment effects and treatment-covariateinteractions. Special consideration of weights toward bias and inconsistency termsis also given. Section 5 concludes with discussion.We begin by outlining percentage study weights within a traditional meta-analysisthat uses a two-stage approach to summarise a single parameter. We focus onsummarising a treatment effect from multiple randomised controlled trials, but theconcepts are immediately applicable to a different parameter of interest, such as atreatment-covariate interaction.i = 1 to Krandomised trials for meta-analysis, each comparing a particular treatment tocontrol, and that the treatment effect is of interest. In the two-stageapproach, the first stage involves obtaining (e.g. from study publications or byusing IPD) the K treatment effect estimates ,20j in trial i, and Let us assume that there are In the second stage, traditionally only a single parameter estimate from eachstudy is of interest, such that a univariate meta-analysis is needed. In theabove example, the treatment effect estimates  to a meta-regression within study-levelcovariates will be considered in Section 3.In model (2), the var(iw)depending on the ‘known’ sampling error , it is well-known that the correspondingpercentage study weights are obtained by: iw values willhave the largest weight. Following equation  effects in themodel, u contains the study-level random effects , and econtains the participant-level residuals . A detailed example is given in thesupplementary material 1(a), for a one-stage ANCOVA meta-analysis model witha random treatment effect.20Here V be the variance of Y conditional onX, which can be expressed as:23Let V fora one-stage ANCOVA model .The generalised least squares procedure minimises 24 Note that V must also be estimatedsimultaneously alongside V is considered ‘known’ whenused within the solution for V is usually replaced with itsestimate.The supplementary material 1(a) gives the specification of V is usually replaced with itsestimate and assumed known. Each diagonal element of The estimated variance matrix of Let independent studies, and thus, although we give thegeneral form of model (8) above, we are only focusing on meta-analysis withindependent studies.We now generalise the decomposition outlined in Section 2.3. That is, wedecompose Fisher’s information matrix for ith study, whilst keeping the remaining elements ofX and V exactly as specified/estimated in thefull meta-analysis of all studies. This extends the observation noted underequation  and all associated covariance terms set tozero . Thisdata augmentation approach ensures that participants external to studyi have negligible contribution toward 24 it also conveniently invokes the fast SAS estimation procedure toderive An alternative approach is to derive iW terms7)for sequation by utili25 It therefore forms the basis of our proposal for deriving percentagestudy weights in all meta-analysis models containing independent studies. Itprovides study-specific weight matrices (Equation  uses theequation ),and for of For each parameter estimate within i may differ for each parameter in the model, and soshould be reported separately.It is important to note that the diagonal elements of each fit the meta-regression and obtain i,with within and between-study variances held fixed at their valuesfrom the full analysis,derive equation  or (10b)derive calculate percentage study weights for each parameter using equation.Meta-regression models extend model (3) by including study-level covariates, andcould be applied in the second stage of a two-stage approach. Such models arethemselves general linear models containing multiple parameters, and sopercentage study weights toward a parameter can be derived akin to the approachdescribed above. That is, one needs to: An example is given in Section 4.2.26 Typically, they assume a multivariate normal distribution within andbetween studies, and thus are a multiple outcome extension of univariate models(2) and (3). As such, they also fall within the linear mixed model framework ofequation , whilst accounting for their correlation.equation ,27 and squations  and 12)26 Typica18 This can be implemented in, for example, the Stata module ‘network’,29 and provides percentage weights according to the approach of Jackson et al.25 An alternative one-stage approach is possible,31 and rather fits theframework in Section 3.1 for a continuous outcome or Section 3.4 for a binaryoutcome.Multivariate meta-analysis is illustrated further in Section 4.3, in the contextof a network meta-analysis of multiple treatment comparisons. Like an IPDmeta-analysis, a network meta-analysis can be conducted as either a two- or aone-stage approach. The two-stage approach first derives treatment effectestimates (contrasts) between pairs of treatments in each study (together withtheir variances and correlations), and then synthesises them in a multivariatemeta-analysis model.32 Further, network meta-analysis of binary outcomes typically uses aone-stage approach.33We now extend the principles above to generalised linear mixed models, forexample to derive percentage study weights in a multi-parameter one-stage IPDmeta-analysis of binary outcomes. This is especially important when events arerare, as models utilising a more exact  within-study likelihoodare then preferred.23Y is the vector of patient responses,β is a column vector containing all the unknown fixedparameter effects in the model, X and Z the designmatrices, Y , ucontains the study-level random effects and e contains theparticipant-level residuals. As in the linear mixed model (8),u are typically assumed to follow a multivariate normaldistribution with mean vector 0 and variance matrixG. We can write R is the residual variance matrix,var(e). However, unlike the linear mixed model,V is not easily specified; the random effects and residualsare on different scales, and therefore V is no longer a simplelinear addition of the residual variance matrix plus random effects variancematrix. Brown and Prescott note that a first-order approximation toV is,23B is a matrix of variance terms, relatingto the underlying distribution of data Y. For example, forindependent responses from the Bernoulli and binomial distributions,B would be a diagonal matrix with diagonal entries ofB would simply be the number of predictedevents (counts). For further explanation about V, we refer tosupplementary material 1(b). If residuals are uncorrelated thenR = 23 where A is typically a matrix of numerical constantsthat again depends on the distribution of the data. For Bernoulli data andPoisson data with no offset term, A is the identity matrix. Forbinomial data, A is a diagonal matrix with diagonal entriesThe generalised linear mixed model can be expressed as,Y*, as proposed by Wolfinger and O-Connell.34 This transforms the response variable Y to anapproximately linear scale, Y*, and allows the generalisedleast squares solution for To acquire a decomposition of Fisher’s observed information matrix for23G, B and R are forced to havetheir estimated values from the full meta-analysis, as obtained from themodel estimation of choice . ForB, this requires the user to specify (functions of) BLUPvalues , andall covariance terms set to zero. This data augmentation approach ensuresthat all participants, except those in study i, havenegligible contribution.As equation, a potenBy obtaining quations  and  compared to placebo or no treatment. Ten trials wereultimately included, providing IPD for a total of 28,581 patients as detailedelsewhere.20Wang et al.Parameters for model (18) are as defined previously; the supplementary material1(a) also gives the matrix expression for the model. The key parameter ofinterest is the summary treatment effect, θ, defined as the mean difference inSBP at follow-up for the treatment minus the control group, after adjusting forbaseline values.A step-by-step guide to the analyses is given in Appendix 1, alongside SAS ProcMixed code. REML was used to estimate both models. After fitting each model,equation  with varequation ,and perequation .7 This is reinforced further by examining the percentage study weights likely torespond well to hypertension treatment than younger patients.j in studyi, and let i.To estimate treatment-covariate interactions, an often used two-stage approach is to fit model (1) in the first stage andthen a meta-regression in the second stage such as: Let This performs a weighted regression of the overall treatment effect estimates is extended to include an adjustment termfor age and an interaction term between age and treatment effectinteractions.This leads to the summary within-study interaction, 38 This extends model (18) byincluding an adjustment term for age and an interaction term between age andtreatment effect  to quantify the ecological bias gives  weights  show tha30; for brevity, we refer to these treatments as A to H. Of interest is acomparison of the odds of mortality by 30–35 days for each pair of treatments.ijr (the number of deaths)and ijn  for each treatmentgroup, in each study. It is also provided as a Stata dataset in supplementarymaterial 2. As there are eight treatments, there are 28 comparisons of interestoverall; however, only 13 of these comparisons are directly reported in at leastone trial. Further, the maximum number of trials providing direct evidence for aparticular comparison is only eight (C versus A). Thus, there is potentiallylarge opportunity to borrow strength in this example; in other words, eachtreatment comparison can utilise correlated and indirect information from otherpairs of treatments (contrasts) in the network, alongside any direct evidence.Our final example is a network meta-analysis of 28 trials to compare eightthrombolytic treatments after acute myocardial infarction33 For the two-stage approach, first the data  = logOR − logOR).Under an assumption of consistency in the direct and indirect evidence, thegeneral form of this network meta-analysis model can be written as a linearmixed model,29i, which has ‘known’ within-study variance matrixi to the basic parameters; and G containsthe variances and covariance of the random effects. Typically, Gcontains diagonal entries of 29 Our key focus in this article is that equation  relates to the indirect evidence toward the H versus B treatmentcomparison. Though this may be a chance finding, it is useful to examine it.Derivation of percentage study weights is helpful here to reveal which studiesare contributing most toward the apparent inconsistency. Just four studiescontribute: study 1 (17.1%), study 2 (16.8%), study 22 (39.6%) and study 23(26.5%). Studies 22 and 23 have about two thirds of the weight, and they bothcompare H and B. They represent the direct evidence, which is discrepant fromthe indirect evidence coming from studies 1 and 2, which compare A, B and D, andA, C and H, respectively. Therefore, the inconsistency is arising from theindirect comparison through A , and ourpercentage weights reveal that studies 1 and 2 have a very similar contributiontoward this indirect evidence. In situations like this, it is helpful foranalysts to compare studies that give indirect information with those that givedirect information, to see if there are any obvious differences that mightexplain the inconsistency. Nothing was identified here and, as mentioned above,there is generally little evidence to support inconsistency in this networkmeta-analysis overall.The described one- and two-stage models are known as ‘consistency’ models as theyassume direct and indirect evidence are consistent (in agreement). One canextend them to add inconsistency terms for each treatment contrast, either byincluding them as additional random effects with mean zero,In this article, we proposed how to calculate percentage study weights inmulti-parameter meta-analysis and meta-regression models. i, whilst allowing varianceterms to be fixed. However, for generalised linear models, the residual variancesare a function of the expected response values, and therefore it is harder to re-fitmodels whilst fixing variances after taking out studies (or cases45).Supplementary material 1 illustrates this with an example. Furthermore, the originalscale of the analysis is non-linear and so identifying sums of information matricesis not immediate. For this reason, we proposed utilising the pseudo-likelihoodapproach of Wolfinger and O-Connell,34 which transforms the response data to a pseudo-linearised variable, so thatthe maximum likelihood  solution for Fisher’s informationmatrix can again be utilised and decomposed.Our proposal assumes that studies are independent, and utilises a decomposition ofFisher’s observed information matrix to decompose the total variance matrix ofparameter estimates into study-specific contributions, from which percentage weightsare derived. The variance scale is a sensible one for quantifying percentageweights, as the variance of a parameter estimate summarises the total informationdata toward that estimate, which comprises both direct (from the parameter itself)and indirect information .Derivation of percentage study weights is simple for traditional (single parameter)meta-analysis models, and is straightforward to extend for one-stage (multipleparameter) general linear mixed models as the information matrix is not dependent onthe parameter estimates themselves; thus the study-specific information matrix within-study likelihood andso summary results often differ.40 Recent evidence suggestsone-stage models are increasingly being used.2In many situations, percentage study weights derived from a two-stage approach willbe a close approximation to those from a one-stage approach, as two- and one-stageanalyses often give similar meta-analysis results, as seen in examples 1 and 3.46 Crowther et al. show how to perform a one-stage fixed or random effects IPDmeta-analysis of survival data in this manner,47 which additionally provides the baseline hazard, and may be useful forprognostic modelling. Therefore, following this approach, meta-analysts can derivepercentage study weights as described above for generalised linear mixed models.However, as each patient now has multiple rows , the approachfor model estimation and subsequently derivation of percentage study weights may becomputationally intensive. To address this, one can rather use larger intervals,such as quarter year or half year, to approximate the likelihood and improvecomputation time for derivation of percentage study weights. When analysing withoutthe inclusion of continuous individual-level covariates, it may be possible tocollapse the IPD to just a few rows per study to make the computation easier. Thisapproach was used in the network meta-analysis example.For time-to-event data, it has been shown how the Cox regression model can be fittedusing a Poisson model due to the shared form of the contribution to the partiallog-likelihood, by splitting follow-up time into as many intervals as there are events.irrespective of thetype of approach to statistical analysis’.48 Percentage study weights should be routinely presented on forest plots, whichin our experience, are rarely provided for IPD meta-analyses. Most software packageslink forest plots with a two-stage meta-analysis. To our knowledge, the onlyexception is the ‘ipdforest’ module in Stata, which provides a forest plot with aone-stage summary result after either a one-stage linear or logistic (mixed) modelis fitted. However, this module computes percentage study weights as the ratio of astudy’s total participants over the total number of participants across all studies.Our first example  are applied in order toderive confidence intervals. Nevertheless, further consideration of this issue isneeded, and in particular, whether our approach should be modified to situationswhere the uncertainty in variance estimates is propagated toward the estimation of53 A further extension might consider percentage weights toward varianceestimates, but this should be considered in a standard (pair-wise) meta-analysisfirst, before extension to multi-parameter (e.g. network) meta-analysis models.Finally, our described approach utilises Fisher’s information matrix forIn summary, we have proposed how to derive percentage study weights formulti-parameter meta-analysis and meta-regression models, which extend those forsingle parameter models. We hope this encourages researchers to reveal thecontribution of each study toward meta-analysis results, and will be useful formethodologists aiming to understand and explain differences or potential biases inmeta-analysis models.


The variation of plantaris muscle  is currently not included in the classification system. The median value of ABI was 0.87 (interquartile range (IQR) = 0.6–1.0). There were no differences among types of syndrome . In conclusion, despite clinical recommendations, the ABI remains underused in PAES diagnosis. No correlation was detected between the index score and type of syndrome. The cases of PAES involving structures other than the gastrocnemius or popliteus muscle suggest the need to revisit the current clinical classification system.Similar to other peripheral artery diseases, vessel narrowing in popliteal artery entrapment syndrome (PAES) reduces the ankle brachial index (ABI). Since the PAES is related to several anatomical or functional variations, we sought to determine if the ABI was correlated with the type of syndrome. Through a systematic review of literature, we identified case reports and series in which the diagnosis of PAES was accompanied by ABI measurement. Twenty-seven studies included in the qualitative synthesis described 87 limbs. The most common types of the syndrome were those caused by an abnormal medial head of the gastrocnemius (type II,  Musculoskeletal injury is the most common cause of pain and dysfunction among sports participants, particularly in young males . VasculaThe ankle brachial index (ABI) is the first-line test for diagnosing peripheral artery disease ,6,7. TheDespite the increasing recognition of popliteal artery entrapment as the cause of lower limb ischemia, differentiation between several types of PAES remains a preoperative diagnostic challenge  due to tAs mentioned above, ABI is the simplest test, allowing the initial evaluation for peripheral artery disease. It has been suggested that the severity of lower limb arterial disease can be graded and the risk for cardiovascular events (such as cardiac ischemia or stroke) can be predicted based on the estimated ABI ,12. WhilTo this end, we examined the relationship between the anatomical variation causing PAES and the ABI score. Through a systematic review of case reports and case series, our study also sheds new light on the anatomical abnormalities causing PAES, indicating a need to revisit the current classification system.The following electronic databases were searched from inception to March 2019: PubMed, Web of Science, Scopus, Medline, and CINAHL . Based on the syntax rules of each database, the following terms were combined to retrieve all studies relevant to the main topic of analysis: popliteal, artery, entrapment, syndrome, anatomy, PAES, ABI, and ankle brachial index. The results were collected using reference manager software. After the removal of duplicates, three authors screened the articles for relevance based on the title and abstract. All inconsistencies were discussed with another author. The same two authors independently analyzed the full text of the remaining articles to determine their eligibility. Additionally, the reference lists of relevant articles were scanned to identify other published and non-indexed sources. The following inclusion criteria were applied: case report or case series, definitive diagnosis of PAES at the end of the diagnostic or therapeutic phase, measurement of the ABI score of every included patient before treatment, and full text written in English. Observational studies, cohort studies, and narrative and systematic reviews were excluded.The extracted data included the name of the first author, year of publication, country of origin, number of reported cases, number of limbs, age and sex of the patients, type of PAES, ABI score before the intervention, and technique used for the ABI measurement. The type of diagnosed PAES was reported according to the previously cited classification and was extracted from the included studies directly, or if explicit information about PAES type was missing, was based on the interpretation of the anatomical description reported within the study. Because a specific tool dedicated to the assessment and critical appraisal of case reports and case series in anatomical sciences is not available, the studies included in our analysis were assessed using the Joanna Briggs Institute (JBI) checklist for case reports .p < 0.05 was considered statistically significant.All analyses were performed using Stata software v.12 , USA)). Sex distribution and type of PAES are reported as frequencies and percentages. Age is reported as the mean with standard deviation (SD). The distribution of the ABI scores was analyzed using the Shapiro–Wilk test and is reported as the median with interquartile range (IQR). For the comparison of the ABI scores among different types of PAES, data were transformed to meet the assumption of normality and analyzed by parametric test. Squared ABI score was the mathematical transformation able to produce a near-normal distribution. Therefore, squared ABI scores were used to perform one-way ANOVA, with ABI scores as a dependent variable and type of PAES as an independent variable. Pairwise comparisons were conducted only if significant difference was evident. A value of An adapted PRISMA flowchart  summarizThrough a database search, 839 articles were identified. After removal of duplicates and title and abstract screening, 122 articles had their full text retrieved and were assessed for eligibility based on the inclusion criteria. Of these, 95 were deemed unsuitable for one of the following reasons: missing pre-treatment ABI score (83 reports), the article was not a case report (11 reports), or not in English (1 report). As a result, 27 reports were included in the qualitative analysis. The characteristics of the included case studies are summarized in The studies included in our analysis were assessed using the checklist developed by Moola et al. , and then = 50, 80.6%). The mean age was 32.63 years (SD 18.76). Entrapment was present only in the right limb in 24 patients (38.7%) and only in the left limb in 13 patients (20.97%). Both lower limbs were involved in 25 cases (40.32%), and 8% of those patients had different types of PAES in the right and left limbs. The results, as shown in n = 35, 40.22%) and an aberrant course of the popliteal artery around the normal medial head of the gastrocnemius muscle . There were no reports of popliteal vein entrapment (type V). Among cases that could not be classified  according to the current classification system, seven patients had an aberrant plantaris muscle.Overall, 62 patients and 87 limbs with PAES were described in the included case reports. The majority of patients were male . In only eight out of 87 limbs with PAES was the ABI score greater than 1, with the highest reported value equal to 1.2. Remarkably, patient had an ABI < 1 in dorsal rather than in plantar flexion. In the other eight limbs, the ABI scores were lower than 0.4, indicating a severe condition. In a single case, an ABI = 0 was reported , and thap = 0.72). Overall, no association was detected between the ABI and the type of PAES.The ABI score distribution was not normal and appeared left-skewed due to the rare recordings of the extremely low scores. When normally transformed ABI scores were used in parametric tests, there were no significant differences in the ABI scores among different types of PAES (F = 0.13, The current analysis of a series of case reports did not find any evidence that ABI score was correlated with the type of PAES. Although the ABI was abnormal in most cases, a greater or smaller decrease or increase in the index score at diagnosis was not related to a specific type of PAES. Accordingly, a clear association between the PAES type and disease severity, as expressed by the ABI score at diagnosis, could not be identified. One unanticipated finding was that the current classification system of PAES did not cover all cases, as several of them were caused by an aberrant plantaris muscle.Although PAES remains a rare cause of vascular disease, its incidence has increased in recent years . TherefoA prior study  noted thThe role of ABI measurement in the diagnosis of peripheral arterial disease is well established. The ABI is a very sensible diagnostic method, which when used properly, reduces the time to treatment initiation . AccordiRare ABI reporting in published PAES cases certainly limited the sample size in our study. With a small sample size, caution must be applied, as the findings may not be transferable to the wider population. Another limitation of the present analysis is related to the design of the included studies, as case reports and case series are intrinsically associated with publication bias. Admittedly, scientific robustness of the resulting data is far from the methodological rigor of a systematic review and meta-analysis of controlled clinical studies. However, several researchers ,51,52 deIn patients with peripheral artery disease, the ABI score can be influenced by arterial wall stiffness. While PAES is typically diagnosed in physically active men aged 20 to 40 years without peripheral artery disease, our review of case reports and case series points out that it affects also women and can cause limb ischemia at later age. It can be assumed that atherosclerosis may be present in some, especially older, patients with this diagnosis, and even in the absence of atherosclerosis, the wall damage caused by prolonged compression can result in tissue remodeling and arterial stiffness. However, it is not possible to adjust for arterial stiffness on the basis of data available in the reports included in our systematic review. Another limitation can be related to the fact that all cases involving an aberrant plantaris muscle were described in the same report, which likely represents a selection bias. Nevertheless, the results still provide useful information about the etiology of PAES.In conclusion, the ABI remains underused in PAES, despite clear clinical recommendations for the diagnosis of peripheral artery disease. Based on available data, there is no correlation between the ABI score and the type of PAES; hence, imaging techniques and surgical exploration remain essential for treatment choice and outcome. The cases of PAES involving structures other than the gastrocnemius or popliteus muscle suggest the need to revisit the current clinical classification system.


Observational studies suggested that tobacco smoking was associated with diabetic nephropathy (DN). However, the results were conflicting and inconsistent. In the study, we performed a meta-analysis to assess the relationship between tobacco smoking and the development of DN.We searched in PubMed, Embase, Web of Science and the Cochrane Library (CENTRAL) from database inception until Mar 8, 2018, and updated our search on May 1, 2018. We screened the reference lists of the retrieved articles. Only original prospective cohort studies which have investigated the association between smoking and DN incidence or its progression were included. Pooled HRs and 95% confidence intervals (CIs) were calculated using a random effects model.P = 0.05). However, compared to no smoking, smoking did not increase the risk of DN development in patients with T2DM . Univariate and multivariate meta-regression did not find any confounding factors. No publication bias was found in the meta-analysis.A total of 9 prospective cohort studies were identified, including more than 203337 participants. Compared with those of no smoking, smoking participants increased the risk of developing DN . The subgroup analysis showed that the current and total smoking may increase the risk of DN, but these results did not reach statistical significance , whereas former smoking significantly increased the risk of DN . Compared with no-smokers, smokers showed an elevated risk of developing DN . In patients with T2DM, those who smoked were at an increased risk of developing DN, as compared to those who had never smoked (HR = 1.05; 95% CI, 1.00–1.11, The present study highlighted that smoking was an independent risk factor for DN, especially in patients with T1DM. This is the first meta-analysis of prospective cohort studies to discuss the relationship between smoking and DN. Diabetes mellitus (DM) is a complex and chronic illness, which requiring continuous medical care with multifactorial risk-reduction strategies in addition to glycemic control. The incEarly identification the relevant risk factors for DN is vital to the implementation of effective preventive measures. Established risk factors for the development of DN include genetic susceptibility, advanced age, male gender, hypertension, poor glycemic control, duration of diabetes, and depressive symptoms, 6. FurtAccordingly, the study performed a systematical review to addressing the association between tobacco smoking and DN in patients with DM by included prospective cohort studies, as well as to assess whether the methodological characteristics of the studies influenced the variability between the estimates by performing subgroup analysis, sensitivity analysis and meta-regression.https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=96132, No. CRD42018096132).Ethical approval and informed consent were not necessary, because the study was not primary research. This study was conducted following the Meta-Analysis of Observational Studies in Epidemiology (MOOSE). Moreove. The searches were adapted for each database, using keywords that including a combination of terms related to the studies, which included tobacco smoking as a risk factor for DN. The search terms included the following key words: 1) smoking OR cigarette OR tobacco OR nicotine; and 2) proteinuria diabetes OR diabetic macro albuminuria OR diabetic microalbuminuria OR diabetic nephropathy OR diabetic nephropathies.  from database inception to Mar 8, 2018, and the search was updated in May 1, 2018 according to Systematic Reviews and Meta-analysis (PRISMA) guidelines Articles were eligible if they met the following criteria:The study evaluated adult participants (age≥18 years) who were free of DN, microalbuminuria, end-stage renal disease, and proteinuria diabetes at baseline.All of the patients with DM were evaluated by blood glucose (BG) or gamma-glutamyl transferase (GGT). Information on smoking status was collected in a standardized interview that asked whether the patient was a current or former smoker.The study used a prospective design with at least 1 year of follow-up. Prospective studies any shorter than this were not considered to have a sufficient time frame for risk and protective factors to exert a meaningful influence on DN.2, microalbuminuria >20 μg/min, consecutive UAER> 200 mg/l or a single UAER> 1000 mg/l, or persistent proteinuria >300 mg/l.The study evaluated the incidence of DN as the outcome, and the diagnostic criteria for DN included a urine albumin excretion rate (UAER)≥30 mg/24 h, a glomerular filtration rate (GFR)≤60 mL/min/1.73 mThe study reported an adjusted or unadjusted odds ratio, a hazard ratio, or relative risks and 95% confidence intervals, or the study reported the raw numbers of exposed and non-exposed participants who developed DN over the course of follow-up in a way that allowed for the calculation of odds ratios or relative risks. In instances when data were not available, we contacted the corresponding authors to request the data to enable inclusion of their study in our meta-analysis.Studies that met the following criteria were excluded:studies without primary data ;conference presentations without information about the methods or the outcomes;studies in languages other than English or Chinese.studies with less than 1 year of follow-up.Two investigators independently reviewed all potentially relevant articles, and any disagreements were resolved by discussion between the investigators.Two authors independently extracted the following data from each included studies: the first author’s last name, publication year, study locations, participant characteristics, smoking category, the number of outcomes, duration of follow-up and adjusted the risk estimates. From each study, we extracted the risk estimates, including the hazard ratio (HR), relative risk (RR), and odds ratio (OR), that reflected the greatest degree of control for potential confounders. In order to detect the impact of gender, if separate risk estimates for males and females were available in one study, we treated these as separate studies.The Newcastle–Ottawa quality assessment scale (NOS) was used to assess study quality, which including selection, comparability, outcome. The maximum of 9 points reflected the highest quality, and a total score ≥ 7 was used to indicate high-quality studies.2 statistic, which was defined as the variation that could be explained by heterogeneity. I 2< 50% and P > 0.1 indicated homogeneity in all studies[P value of ≤ 0.05 as statistically significant.All statistical analyses were performed by R version 3.4.0 2017-04-21) and metafor package. We comb17-04-21 l studies. We expll studies. Heterogl studies. We condl studies, 25. We Fig 1). As a result, a total of nine prospective cohorts were included in the systematic review and meta-analysis [Table 1). All articles were published between 1995 and 2016, which included more than 203337 participants. The continents or countries where the studies were conducted were as follows: The Americas (n = 3), Asia and Oceania (n = 2), and Europe (n = 13). The criteria for DN differed, including UAER≥30 mg/24 h[2[Based on the inclusion and exclusion criteria, a total of 2043 potentially relevant articles were identified from the databases . After duplicates were removed and studies that were clearly not relevant were excluded, 135 articles were reviewed by reading the abstract. After reviewing 34 full-text articles, 24 were excluded for various reasons, including 16 case-control studies, 3 cross-sectional studies and 3 cohort studies with subjects that were not relevant to the analysis  and was statistically significant  .All eligible studies reported the effect size of smoking and DN. The pooled results suggested that smoking could increase the risk of DM developing into DN. The pooled HR was 1.07 .The authors investigated the potential sources of heterogeneity between the studies through subgroup analyses and meta-regression. The subgroup analyses based on those characteristics were conducted for analytical purposes, and the basic characteristics of included studies were grouped as follows: sex ; geographic location (Americas/Europe/Asia-Oceania); follow-up period (≤5 years or >5 years), loss to follow-up (≤30% or >30%), diagnostic criteria for DN (proteinuria or GFR), smoking status , NOS score (9 or 8), adjusted data (Yes or No), effect size (HR/OR/RR), and sample size (≤500 or >500); type of samples (convenience/population-based); age (adults only/adults and elders); sex ; follow-up period (<5 years/≥5 years); and type of DM (T1DM or T2DM). The cut-off points for dichotomization of the covariates were based on the distribution of each factor across all the studies in the study. No significant differences were proven by subgroup analysis of all the covariates (Table 2).The subgroup analysis by the type of smoking demonstrated that the current and total smoking might add to the risk of DN, although these results did not reach statistical significance ; however, former smoking significantly increased the risk of DN  . Seven sub-cohorts reported smoking might be associated with an increased risk of the development of DN in patients with T2DM. Compared with never-smokers, smokers did not show an increased risk of developing DN in patients with T2DM .Eleven sub-cohorts reported smoking might be associated with an increased risk of the development of DN in patients with T1DM. Compared with never smoking, smoking was associated with an increased risk of developing DN in patients with T1DM .Furthermore, univariate and multivariate meta-regression analyses were performed by including all of study characteristics as covariates in the meta-regression model. All the covariates received both univariate and multivariate meta-regressions with a random effects model. The univariate regression indicated that NOS score (p = 0.02) and Europe (p = 0.01) might explain the residual heterogeneity; however, the multivariate meta-regression demonstrated that those confounding factors might not have led to the heterogeneity (detailed information shown in P< 0.0001).The sensitivity analysis was assessed to determine the stability of the results by removing one study at a time to examine the influence of each study on the pooled estimate. After oP = 0.38) and Egger’s publication bias test  indicated that there was no publication bias in this analysis .We conducted a general funnel plot and Begg’s rank correlation and Egger’s linear regression tests to assess publication bias. Begg’s funnel plot , but the criteria for DN differed, might impact the result. Last but not least, because of none of included original cohorts calculated clinical significance, this meta-analysis did not identify the clinical impact of smoking for progression of DN. The magnitude of association is very small in most of included studies, as a result, the magnitude of pooled RR still small, this might not be clinically significant. Although we included all of available factors to detect the confounding factor by subgroup-analysis and meta-regression, but residual confounding eg. age, the period of smoking, the type of cigarette, and so on , 33 mighBased on current evidence, the present study demonstrated that smoking was an independent risk factor for DN, especially in patients with T1DM. This is the first meta-analysis of prospective cohort studies to discuss the relationship between smoking and DN. Therefore, high-quality and adequately powered studies in the subgroup of patients are warranted.S1 File(DOC)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table10.5061/dryad.5vr45tb.All relevant data for the paper is publicly accessible in the Dryad repository. DRYAD DOI: doi:(DOC)Click here for additional data file.