E as the variance of the overall estimate from a multivariate meta‐analysis relative to the variance of the overall estimate from a univariate meta‐analysis. The extra information gained from a multivariate meta‐analysis of n studies is then similar to the extra information gained if a univariate meta‐analysis of the primary effect had a further n(1−E)/E studies. The variance contribution of a study's secondary outcomes (its borrowing of strength) can be thought of as a contrast between the variance matrix of the outcomes in that study and the set of variance matrices of all the studies in the meta‐analysis. In the bivariate case this is given a simple graphical interpretation as the borrowing‐of‐strength plot. We discuss how these findings can also be used in the context of random‐effects meta‐analysis. Our discussion is motivated by a published meta‐analysis of 10 antihypertension clinical trials.Univariate meta‐analysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate meta‐analysis allows us to take these secondary outcomes into account and can also include studies where the primary outcome is missing. We define the efficiency  Many research studies, however, report data on multiple outcomes, with the primary outcome supported by measures of one or more secondary outcomes. Multivariate meta‐analysis offers the potential for more accurate estimation by also taking the data on these secondary outcomes into account. Another advantage of the multivariate approach is the potential for increasing the number of eligible research studies, since we can also include studies where the primary outcome is missing and data are reported on only some of the secondary outcomes.et al. . To start with, we assume that all p outcomes are measured in all n studies in the meta‐analysis.We consider a multivariate meta‐analysis of Vi as known , the score function for the unknown parameter β (the derivative of the log‐likelihood) isβ isβ^ given byVis. Whether we use the actual within‐study variances Vi, or crudely approximate them all by V¯, we end up with the same variance matrix of β^.Treating each p components of y are observed, we focus interest on estimating the treatment effect for just one of these outcomes which, without loss of generality, we take to be the first. So from now on we shall describe, for each study, yi1 as the scalar treatment effect estimate for the primary outcome and the remaining components of yi as the (p−1)×1 vector of estimates for the secondary outcomes. In some cases the primary outcome may be clearly identified from the context. For example, the bivariate (p=2) example in Fibrinogen Studies Collaboration  element of V¯ in equation Even if all boration  concerneβ1 is univariate meta‐analysis, which looks only at the values of yi1 and ignores the data on the secondary outcomes. The relevant univariate model would then be1 is the unit vector 1=T. The univariate estimate isA natural comparison for the multivariate estimate of β^1 and β~1 are unbiased and normally distributed estimates of β1, and so to compare their statistical properties all we need to know is the efficiency E, which is defined byE⩽1 as the maximum likelihood estimate β^1 is fully efficient. The smaller is E, the greater is the relative contribution of the secondary outcomes, suggesting 1−E as a measure of the role of the secondary outcomes in the multivariate estimate of the primary treatment effect. This combines the information in the secondary outcomes of all the studies in the meta‐analysis and so 1−E can be thought of as a measure of total borrowing of strength, which is equivalent to BoSrRV in the notation of Jackson et al.  to match the accuracy that a fully efficient estimate (efficiency 1) can achieve with a sample size of n, the sample size would have to be increased from n to n/E. Similarly, in meta‐analysis, the extra information which the secondary outcomes of n studies give to the estimation of β1 can be thought of as like the extra information that we would obtain in univariate meta‐analysis if we could measure the primary outcomes of a further n(1−E)/E studies. For example, if there are nine studies (n=9) and E=0.9, the advantage of using multivariate instead of univariate meta‐analysis is like finding the data for one more study. This simple idea will be used several times in the analysis of the hypertension example in Section A simple example here is the familiar interpretation of efficiency in terms of sample size: for an inefficient estimate  elements of the Vis. These are the same thing if the Vis are all the same, in which case E=1. If the Vis are different then E⩽1, which suggests another interpretation of 1−E as a measure of the variation of the matrices Vi about their harmonic average V¯. This is analogous to the usual interpretation of the coefficient of variation (the ratio of standard deviation to the mean) as a simple relative measure of the variation of a univariate sample about its arithmetic mean.The last expression in equation β1 by using all n studies in the meta‐analysis. To break this down into the contributions of individual studies, define, for any study with inverse variance matrix V−1,V−1 rather than V to reflect the fact that all of the formulae for multivariate meta‐analysis that were presented earlier involve the study variances Vi only through their inverses Vi−1. As we shall see in Section V−1 and V¯, and so equation given observed meta‐analysis, in which case we can treat V¯ as if it was fixed.These calculations are comparing the relative contributions which the primary and secondary outcomes make to the estimation of T(V−1) to investigate the role of individual studies in three different ways, analogous to the definitions of influence in regression analysis.Direct interpretation: from equation T(Vi−1) is the proportional contribution of the ith study to the variance of β^1, proportional in the sense thatVi−1=σi−2 and equation T(Vi−1)=σi−2/Σiσi−2 which is just the weight of the ith study in the weighted average β^1. When p⩾2, T(Vi−1) can still be interpreted as the weight of the ith study in multivariate meta‐analysis, agreeing with the weight wir that is derived from an orthogonal decomposition of the score function in Jackson et al. (T(V−1) is not restricted to the Vs which happen to be represented in the meta‐analysis.(a)n et al. , sectionAdd‐one‐in interpretation: if n is large and the variance matrix V is of the same order of magnitude as V¯, then, under reasonable conditions on the matrices involved,n−1V¯ and using equation β^1 if we add a new study with inverse variance V−1 to the meta‐analysis. So, for large n, T(V−1) is the proportional decrease in var(β^1).(b)Leave‐one‐out interpretation: replacing V by −Vi in equation T(Vi−1) is the proportional increase in var(β^1) if the ith study is removed from the meta‐analysis.(c)We use the function n) approximations. This reflects differences in the background studies being assumed for the add‐one‐in and hold‐one‐out calculations, i.e. differences in the second argument V¯ in equation V as an existing study which we are thinking of leaving out, then the common value of T(V−1) suggests that the two effects would be the same. But one is defining this study's contribution in terms of the difference between having n+1 studies and n studies, whereas the other is comparing n−1 with n studies. If n is large there is no material difference between the two. Essentially, the add‐one‐in and hold‐one‐out approximations are ignoring the effect that adding or subtracting studies has on the value of V¯. These distinctions are analogous to the different definitions of residuals and influence in other areas of statistics. See Section p=2).The first of these properties is exact, but the second and third are only asymptotic (large E in equation var(β^1) with the value of this variance if only the primary outcomes had been measured across the whole of the meta‐analysis. Similarly, for investigating the role of individual studies, we can ask what happens to var(β^1) if we add in an extra study with variance matrix V but only take account of its primary outcome estimate y1∼N with σ2=1TV1. This will add σ−2(y1−β1) to the score function β1 but will add nothing to the score function for the secondary outcomes. Hence the contribution to the vector score function for the estimation of the complete vector β isV*−1 is defined asp×p matrix with σ−2 in the  position, and 0 everywhere else. The relative decrease in var(β^1) is therefore (approximately)borrowing of strengthB(V−1) of a study with variance matrix V to be the difference between equations var(β^1) on top of the contribution that is made by its primary outcome. If B(V−1) is 0 then nothing is gained by observing the secondary outcomes. The notation T(V−1) refers to the total contribution of a study; the notation B(V−1) refers to the borrowing of strength, i.e. how much of this proportional increase in precision is contributed by the secondary outcomes.The definition of T(V−1) is only an asymptotic approximation for the variance effect of adding a new study, as noted above we obtain exact results when adding over the existing studies. We can similarly add the univariate contributions Ω11−1 timesB(Vi−1) is equivalent to BoSirSD in the notation of Jackson et al.  and B(V−1) are linear functions in the sense that, for any positive scalar constant k,V−1 by k is like increasing the study sample size by the factor k while keeping the relative magnitudes of the elements of V−1 the same. We can think of these relative magnitudes as determined by the design of the study—characteristics of the population from which we are sampling. The actual magnitudes of the elements of V−1 are then determined by the sample size. Property B(V−1)=0, then B(kV−1)=0 for all k. So whether or not a study offers any borrowing of strength depends only on the study's design and not on its sample size.Both the functions Vis are all the same then there is no borrowing of strength, and so the secondary outcomes are then irrelevant as far as estimating β1 is concerned. This follows immediately from the above formulation, since equation B(V¯−1)=0. And so, if all the Vis are the same, Vi=V¯ and so B(Vi−1)=0 for all i; hence E=1. This also follows from a simple argument of sufficiency: if Vi=V¯ for all i then the score function V=V¯ and y=Σyi. But for any single study the estimate of βj is simply the jth treatment effect estimate yj. As Riley . Of particular interest is when only the secondary outcomes are measured, since in this case we have a study which cannot be included in a univariate analysis of the primary outcome but can be included in a multivariate analysis which can then allow information about the unobserved primary outcome to be imputed from the observed values of the secondary outcomes. In this case, J is the p×(p−1) matrix consisting of the (p−1)×(p−1) identity matrix supplemented with a row of 0s along the top.If all outcomes are measured, then V*−1. By replacing V−1 with V*−1 for studies with missing outcomes, the usual formulae for maximum likelihood estimation that was set out earlier in this section continue to apply even if some, or even all, of the studies in the meta‐analysis have one or more missing outcomes. But, despite the notation, V*−1 cannot be interpreted as a matrix inverse (it is singular), or as the known value of V−1 for an incomplete study. In reality, all the elements of V−1 are unknown parameters, but with complete data we follow the usual convention of assuming that these are known because they can be consistently estimated from the within‐study data. However, with incomplete outcomes, only the submatrix JTVJ of the full matrix V is estimable, and so we have an estimate of V*−1 but not of V−1. The rows and columns of 0s in V*−1 imply that various unidentifiable correlation parameters within V−1 are being artificially set to 0. An equation such as equation β^ is as ifV−1=V*−1. It does not mean that V−1=V*−1 in the usual sense of a mathematical equality.Some care is needed in interpreting the notation et al. T isσi,νi) are the standard errors of , ρi is the correlation between them, and the inverse of Vi isn studies, and taking the inverse, gives the harmonic means11,s22,s12) are weighted between‐studies sums of squares and products of the outcome accuracies ,ρi. As expected, each of these quantities retains the feature of a harmonic average.In the bivariate case, suppose that the variance matrix n pairs , suggesting a through‐the‐origin linear regression model in which we can examine the extent to which a study's primary accuracy σ−1 can be predicted from its secondary accuracy ν−1. Allowing for the different weights, consider predicting ui from vi, wheren observed values of ui against the corresponding values of vi, the least squares slope through the origin isu and v will be close to 0. The definitions of u and v in expression ith study is observed, then we take both νi−1 and ρi to be 0, and so ui=vi=0. If only the secondary outcome estimate is observed, we take σi−1 and ρi to be 0, leading to ui=0 and vi=νi−1.Apart from these differences in the weights, equations n values of ui against their predicted values u^i turns out to be closely related to the borrowing‐of‐strength function B(Vi−1) that was defined in Section νi and σi in terms of ui and vi, and completing the square, givesσi−2, which is proportional to the univariate variance contribution of the primary outcome, and so the borrowing of strength is just the second termB(Vi−1) is proportional to the squared residual of the point  from the diagonal prediction line u=u^. For any other study with inverse variance V−1, B(V−1) is similarly proportional to the squared residual of its point  from the line and so indicates the (approximate) decrease in var(β^1) which we would obtain if we were to add this study to the meta‐analysis. The proportionality factor isith study has missing data,  is either  when the secondary outcome estimate is missing, or ((s12/s22)νi−1),0) when the primary outcome estimate is missing. In the first case, the point is always on the line and so, as expected, there can be no borrowing of strength. In the second case, the point is down on the horizontal axis and so will generally have a non‐zero residual and so, again as expected, will contribute at least some borrowing of strength.The plot of the  plot is easier to interpret if we first scale ui and u^i by the factor Ω111/2, givingwi against w^i the borrowing‐of‐strength plot. Now the ith squared residual from the diagonal line, (wi−w^i)2, is equal to B(Vi−1). The combined variance contributions of the secondary outcomes in the meta‐analysis are indicated by the scatter of the points about the diagonal regression line. If the points all lie on the line then B(Vi−1)=0 for all i and so E=1. More generally, we can show from the earlier formulae thatE is equal to the residual sum of squares of the points in the borrowing‐of‐strength plot.The E, the root‐mean‐squared distance of the points from the diagonal line w=w^ isd¯=(10n)−1/2. This is indicated on the borrowing‐of‐strength plot by the two parallel linesn/9 studies.To aid interpretation of the borrowing‐of‐strength plot, equation  lies on the line, then ui=(s12/s22)vi and soVi (Vi12) to its primary diagonal element Vi11, whereas the right‐hand side is the ratio of the corresponding elements of Ω, or of V¯. For no borrowing of strength these are equal, and soVi=kV¯ for some scalar constant k gives no borrowing of strength. This is a sufficient but not necessary condition—all we need is that the top row (or left‐hand column) of Vi is proportional to the top row (or left‐hand column) of V¯. In particular, there is no requirement on the secondary variance νi2per se. We show in Section Equation B(Vi−1) is the proportional contribution of the ith secondary outcome estimate to var(β^1) (the direct interpretation), but, for a study outside the meta‐analysis, B(V−1) is only the approximate (large n) contribution which the secondary outcome estimate would make if this study were added to the meta‐analysis (the add‐one‐in interpretation). We see the nature of this approximation in the borrowing‐of‐strength plot. The line w=w^ is the least squares line of best fit (through the origin) for the n points . But, if we add in the new study, the value of the scale factor Ω111/2 will change, affecting the co‐ordinates for all the studies. So the residual of the new point from the line fitted by least squares to the enhanced data will not be the same as the residual from the line calculated from the original n studies alone. If n is large then adding one more study will have only a small effect on the fitted line, and so these two residuals will be similar.The borrowing‐of‐strength plot also illustrates two other aspects of borrowing of strength which were discussed in Section B(V−1) in equation V−1 by k then both w and w^ are scaled by the factor √k and so the squared residual from the diagonal line is scaled by the original factor k, which means that B(kV−1)=kB(V−1) as required. If V−1 gives no borrowing of strength then the point will simply move up or down the diagonal line according to the value of k.Secondly, we have noted the linear property of the function 3.2et al.  for stroke, y2. Values of y1 (crosses) and y2 (circles) are plotted against the within‐study correlations ρi, with the corresponding pairs of within‐study confidence intervals for β1 and β2 shown as the full and broken line segments respectively. The small numbers to the left of the confidence intervals identify the study numbers 1–10. The vertical co‐ordinates of some of the data in Fig. y1 and y2 are both well consistent with fixed effects models, leading to univariate combined confidence intervals of  for CVD log(HR), and  for stroke log(HR). It is not at all obvious from Fig. Fig. et al.  and disc . Fig. 1y1, the formulae in Section β1 and their variances asE=0.984, showing that in this example the stroke data give very little extra information for the assessment of CVD risk reduction. The last two columns of Table T(Vi−1) and the borrowing of strength B(Vi−1) for each of the 10 studies, confirming that none of these studies gives any worthwhile contribution from the secondary outcome. We can check directly that the borrowing‐of‐strength figures add up to 1−E. Fig. E=0.016. The two dotted lines are the 90% efficiency bars (27). All the points are well within these limits, confirming the high efficiency of univariate meta‐analysis and the minimal contribution of the secondary outcomes in this case.If the log‐hazard‐ratio for CVD is taken as the primary outcome, ρi and s12 but interchanging yi1 with yi2, σi with νi and s11 with s22. In terms of the original notation we are now estimating β2, givingE=0.888. The two estimates are again very similar, but the multivariate method is now noticeably more accurate. The borrowing‐of‐strength plot for estimating β2 is shown in Fig. var(β^2). This is a line plot: the upper (full) line highlighting the values of T(Vi−1) ; the lower (broken) line highlighting the corresponding values of T(Vi−1)−B(Vi−1) (univariate contributions). The distance between the two lines matches the squared residuals in Fig. β2 is about 10% lower than the variance of the univariate estimate, which is roughly what we might expect if we could increase the size of a univariate meta‐analysis from 10 to 11 studies. In this sense, the value of including data on the 10 secondary outcomes can be likened to the value of having the primary outcome estimate of one additional study.If the primary interest is to estimate log(HR) for stroke instead of CVD, then we use exactly the same formulae but with the notation reversed appropriately, retaining the same values of y1 make a modest contribution to the accuracy of β^2 but the values of y2 make almost no contribution to the accuracy of β^1. More generally, we can show that, if E=1 (no borrowing of strength) when estimating β1, then E will be strictly less than 1 (positive borrowing of strength) for estimating β2 except in the special case of all the studies having the same correlation  for studies 1–5 are the same as in Fig. β1 with complete data, none of the 10 secondary values y2 makes any useful contribution on top of the contribution of the corresponding observed values of y1. So we might expect that with these missing data all of the borrowing of strength would come from trials 5–10 since in these trials y1 is no longer available. But this is not so, as shown in the variance contributions plot in Fig. V*i−1 is the proxy matrix (9) for the ith trial: the 2×2 matrix with νi−2 as the lower diagonal element and 0s elsewhere. The sum of these two numbers in expression (31) is 0.185=1−E as expected, but the missing studies contribute only 22% of the total borrowing of strength. This illustrates one of the main points in Section β2 with the same pattern of missing data. We are again leaving trials 1–5 as before, but now trials 5–10 measure only the primary outcome. Now the borrowing of strengths B(Vi−1) for the first five trials adds up to about 1%, which is less than the sum over the same trials in the complete‐data case of about 5%  in the borrowing‐of‐strength plot is complicated by the fact that the weighted sums of squares and products in equations ui and vi in equation ρis are constant, ρi=ρ0 say, these differences in the weights can be absorbed into an overall scale factor, leading to a more transparent version of many of the formulae in Section et al., A statistical understanding of the plotting co‐ordinates  be the ordinary (unweighted) sums of squares and products of the n accuracy pairs . Then, if we imagine a scatter plot of σi−1 against νi−1, the least squares line of best fit through the origin has slope s12*/s22*. Thus, for any given value of ν−1, the least squares prediction of σ−1 isith residual in the borrowing‐of‐strength plot isLet 3.4B(V−1): the variance contribution of a study's secondary outcome relative to the overall variance Ω11. We could instead consider the ratio R(V−1)=B(V−1)/T(V−1): the contribution of the study's secondary outcome as a proportion of that study's total contribution to var(β^1). This removes the effect of any scale factor in V, so for a given meta‐analysis R(V−1) is a function of just two quantities: ρ, the correlation between the outcomes, and z, the ratio of the standard errors,ρ and z gives a complete picture of how, within a given meta‐analysis  a study's borrowing of strength, defined in this way, depends on individual study characteristics.We have measured borrowing of strength in terms of R(V−1) for s12/s22=0.660: the value of the slope parameter found in the example in Section ρ are shown on the vertical axis; values of z are shown by using a log‐scale on the horizontal axis. The contour values are labelled along the bottom and up the left‐hand side of the plot. The broken line is the zero contour when ρ=0.660z: at these values there is no borrowing of strength. The contour plot shows that R is large when either z is large (y1 less accurate than y2), or when z is small (y1 more accurate than y2) and ρ is large (outcomes highly correlated). The smaller plotting symbols 1–10 in Fig. z,ρ) for the 10 studies in the example. Most of the points are fairly close to the zero contour: for only three of these studies is R(V−1)>0.1, suggesting that E is close to 1, as found earlier. The plotting symbol X indicates the point  for a study with V=V¯ defined in equation V and V¯ can be seen on the graph as the distance between the study's  point and the harmonic mean point X, measured in the direction that is orthogonal to the contours in that region.Fig. R(V−1) becomes R(V*−1) and so the point  lies on the horizontal axis, to the extreme left if y2 is missing and to the extreme right if y1 is missing, giving R(V*−1) equal to 0 and 1 respectively, as expected.If one of the outcomes is missing, 44.1p>2. Now the outcome estimates of the ith study are yi= with yi2, the secondary outcome estimates, a (p−1)×1 vector. When p=2, all the formulae in this section reduce to the corresponding expressions that have already been seen in Section This section looks at the generalization of Section Vi=var(yi) as the partitioned matrixσi2 is the variance of yi1 as before, ρi is the (p−1)×1 vector of correlation coefficients between yi1 and yi2, Pi is the (p−1)×(p−1) correlation matrix of yi2 and Λi is the (p−1)×(p−1) diagonal matrix of the standard deviations of the components of yi2.In the multivariate case, we write ith study. The vector in the off‐diagonal partition of equation σi2(1−ai) is the residual mean square. Thus ai can be interpreted as the multiple correlation R2 of this regression: ai=0 means that the primary and secondary outcome estimates are independent; ai=1 means that they are exactly linearly related.To simplify the algebra for calculating matrix inverses, definen studies givesvar(β^1) isAdding equation −1 in equation yi: σi−1 for the primary outcome and the diagonal elements of Λi−1 for the secondary outcomes. The scalar s11 is the same as in the bivariate case, S22 is the (p−1)×(p−1) matrix of weighted sums of squares and products for the secondary outcome precisions and s12 is the corresponding (p−1)×1 vector of weighted sums of cross‐products between the primary and secondary precisions. When p=2 these formulae reduce to the corresponding quantities in Section S22 becoming the scalar s22. In the bivariate case, the weights that are involved in these sums are also the same, since when p=2 the quantities that are defined in expression ρi is now just the ordinary scalar correlation between the two outcome estimates in the ith study.As before, the components of Ωith study isfi is the (p−1)×1 vectorσi−1: the accuracy of the primary outcome. Completing the square givess12 and S22 are fixed, and so the vector fi in equation Λi−1: the accuracies of the secondary outcome estimates in the ith study.From equations ui‐ and u^i‐notation in the bivariate case tos11,s12 and S22), ui is proportional to the accuracy of the study's primary outcome and vi is a scalar linear function of the accuracies of the secondary outcomes. If we defineFor a simpler notation for equation Ω11=var(β^1), equation ith study into three non‐negative parts, analogous to the main effects and interaction of the two factors ui  and u^i (a linear function of the accuracies of the secondary outcomes). The three effects are as follows:ui2,ith study as in univariate meta‐analysis;(a)the term in (ui−u^i)2 as in bivariate meta‐analysis, measuring the difference between the actual accuracy of the primary outcome and, in some sense, what might be expected from the pattern of uis and u^is that is observed in the meta‐analysis as a whole;(b)the term in u^i2, the additional effect of the accuracies of the study's secondary outcomes. This is 0 if gi=1.(c)the term in Up to the scale factor ith study is proportional to the sum of the second and third terms of equation p=2, the quantities fi, bi and Pi are all scalars as in expression p=2, gi=1 for all i and so the third term in equation The borrowing of strength for the (ui−u^i)2‐term when p=2, the quantity u^i in equation ui−u^i by using the definition in equation ui−u^i that was defined earlier in equation gi=1, in which case the motivation of u^i as a least squares prediction of ui continues to hold in the sense that Σuiu^i/Σu^i2=1.To see the equivalence of the 4.2B(Vi−1) in equation ith study we must have two conditions: gi=1 and ui=u^i. We can exclude the trivial case ai=1 which would mean that there is an exact linear relationship between the ith study's primary and secondary outcome estimates.From gi=1 if fi=kρi for any arbitrary scalar factor k. This is also a necessary condition for gi=1, as can be verified directly by using a Lagrange multiplier calculation to find the maximum value of gi for different values of fi. From equations Vi in equation V¯).For the first condition, fi=kρi thenui=u^i then k=σi−1. This extends the proportionality between the covariance vector parts of Vi and Ω required for gi=1 to include also the  term. So the necessary and sufficient condition for no borrowing of strength is that the first row (or first column) of Vi must be a scalar multiple of the corresponding row or column of the harmonic mean matrix V¯. Thus the necessary and sufficient condition Vi12 and V¯12 are now the covariance vector components of Vi and V¯ respectively.For the second condition, if k involved, and hence no constraint on the sample size of the trial. Both small and large trials may end up giving no borrowing of strength, including studies with large correlations between the primary and secondary outcomes. Note also that condition V in equation per se.Note that condition Vi−1 in equation yi1 on yi2. We can similarly interpret equation yi2 from the primary outcome estimate yi1. The vector of regression coefficients for the ith study would then be Vi12/Vi11, which is just the left‐hand side of equation ith study to give no borrowing of strength is that the within‐study vector of regression coefficients for predicting the secondary from the primary estimates is the same as the corresponding regression vector for a study with the harmonic mean variance matrix V¯.We commented in Section 5β, has been widely discussed in the univariate meta‐analysis literature. Jackson et al.  that were discussed earlier are descriptive measures of how much the multivariate estimation of β1 has been influenced by the data on the secondary outcomes. In random‐effects models, the corresponding estimates E^ and B^i=B(V^REi−1) calculated from the fitted marginal variance matrices V^REi=Vi+Ψ^ are similarly descriptive measures of the role of the secondary outcomes within the fitted model. The definition of E in equation Ψ^ for the primary outcome must be the same as the univariate random‐effects variance estimate that we would obtain if we fitted a univariate random‐effects meta‐analysis model to the data on the primary outcome alone. Only under this condition do we retain the same interpretation of E^ and B^i as discussed earlier for fixed effects models. In practice, DL estimates are almost always used in univariate random‐effects meta‐analysis, suggesting that Ψ^ should be estimated by using a method‐of‐moments estimate which retains the univariate DL estimates as its diagonal elements. A slight modification to the truncation step in Jackson et al.  we obtain increasing between‐study heterogeneity. A small value of α means that the fixed effects model slightly underestimates the variability of the yis, and the assumed form of Ψ means that the pattern of variances remains reasonably similar to those observed in the data. We can then simulate vectors yi from equation VREi=Vi+αV¯ with the corresponding statistics calculated from the estimated marginal variances V^REi=Vi+Ψ^. For the reason discussed above, we calculate Ψ^ by using the slightly modified version of the method of Jackson et al.  to α=2 . We assume that the primary interest is the value of β2: the log‐hazard‐ratio for the risk of stroke. The second column of Table E based on ViRE. As expected, the entry 0.888 for α=0 is just the fixed effects efficiency that has already been quoted in Section Vi has the effect of reducing the relative differences between them, which explains why the values of E tend to increase as α increases. The estimated efficiencies E^ based on V^REi vary randomly between simulations, but their sample medians across the 1000 simulations, which are shown in the third column of Table Bi always add up to 1−E and so when, in the random‐effects model, E^ is different from E we cannot expect the corresponding estimated and true borrowing‐of‐strength components to be exactly comparable. However, from a practical point of view, what we would hope to see is that the studies which show the greatest (or least) borrowing of strength under the estimated model are the same, or substantially the same, as the studies which give the greatest (or least) borrowing of strength under the true model. For each simulation, the extent to which this is so is measured by the rank correlation rc. The fourth column of Table α but, as expected, tend to deteriorate slightly as the heterogeneity increases.Table 6BoSi1SD that was proposed by Jackson et al. (B(V−1) in equation In most statistical problems, taking into account data on relevant covariates or confounders leads to more accurate estimates and predictions, especially if the secondary variables are closely correlated with the main variable of interest. However, this is not necessarily so in meta‐analysis—multivariate meta‐analysis can give little or no improvement over univariate methods even if the secondary outcomes are closely correlated with the primary outcome. By writing the borrowing‐of‐strength measure n et al.  as the eV−1 by V*−1 in equation et al., et al.  are known, so that borrowing‐of‐strength measures can be evaluated explicitly. Riley  for such studies, then consistent estimates of the within‐study correlations can be calculated, but in practice this may be difficult or impossible. Various approaches to dealing with this issue have been suggested, such as sensitivity analyses that explore a variety of within‐study correlations  depend only on the Vis and not on the actual values of the yis. If each estimated Vi is consistent then so will be the estimates of the borrowing‐of‐strength quantities derived. It is important to avoid any smoothing of the Vis so that they properly reflect the characteristics of each individual study.In practice the matrix ., et al. suggeste.(et al.  suggeste


P<0.001). Subgroup analysis showed that a significantly inverse association between refrigerator use and gastric cancer risk was observed in in some Asian countries , but not in some Western countries, such as Germany, etc. Refrigerator use is significantly associated with a decreased risk of gastric cancer. Further studies are warranted to confirm whether refrigerator use could reduce the risk of gastric cancer among some Asian countries.There were many observational studies that examined the association between refrigerator use and stomach cancer. However, the results remain to be a contradiction. This study aimed to evaluate the association between refrigerator use and the risk of gastric cancer. We systematically searched the PubMed, Embase, Web of Science databases (up to 31 May 2017), and manually reviewed the references lists of retrieved articles, to identify studies that evaluated the association between refrigerator use and the risk of gastric cancer. Observational studies reporting odds ratio (OR) with 95% confidence intervals (CIs) for the relationship between refrigerator use and the risk of gastric cancer were included. Two authors independently reviewed and selected eligible studies and conducted the study quality evaluation. We included a total of twelve studies enrolling 14,361 individuals. The summarized OR the association between refrigerator use and the risk of gastric cancer was 0.70 (95% CI, 0.56–0.88;  Gastric cancer is the fourth most frequently diagnosed cancer worldwide, behind lung, breast as well as colorectal cancers, and also the globally the second common cause of cancer death . ApproxiThere are increasing number of people and surveys paying close attention to the entanglement between refrigerator use and the risk of gastric cancer, which has been of considerable interest in this field since last early periods of 1980s, when a case-control research proposed by Howson and his colleagues suggested that refrigerator use may play a protective role against gastric cancer . IncreasThe meta-analysis was reported according to the checklists of Meta-Analysis of observation in Epidemiology (MOOSE) instructions for its background, design, analysis, and interpretation .We did a thorough search the PubMed, Embase, and Web of Science databases till May 2017 for studies that investigated the association between refrigerator use and the risk of gastric cancer. The search terms were “refrigerator” or “refrigerator use” or “fridge use” or “freezer use” or “dietary intake” and “gastric” or “stomach” and “carcinoma” or “neoplasm” or “cancer” or “tumor”. The search was limited to studies in humans without any restrictions on language. Additionally, the reference lists of the retrieved articles were manually reviewed. The titles and abstracts of all identified articles were initially screened by one investigator (N.L.) for potentially relevant articles, and the eligibility of the selected full-texts articles was reviewed by two independent investigators (S.J.Y. and Y.G.).Studies that met the following criteria were included in the meta-analysis: (1) be was a case-control or cohort study design, (2) the exposure of interest was refrigerator use and the outcome of interest was gastric cancer, (3) the study reported the risk estimates with 95% confidence intervals (CIs) for the relationship between refrigerator use and the risk of gastric cancer or provided sufficient information for their calculation. Animal cases, clinical experiments, reviews, letters, and commentaries were excluded. Once the study populations were stated twice or above, we included the publication that with largest number of cases or those presented results with most complete information.We extracted the following data from the included studies: the first author’s name, year of publication, study site, study design, sample size, participants’ age range or average age at entry, follow-up (for cohort study) time period, outcome measurement, number of case and control group, number of refrigerator users, risk estimates and its 95% CI, and covariates adjusted for in statistical analysis. Two investigators (S.J.Y. and. S.C.) independently extracted the data.Two investigators (X.Y.S. and Y.Q.C.) independently performed the quality assessment via the Newcastle-Ottawa Scale (NOS) (for cohort and case-control study) .The scalWe prioritized the multivariable adjusted risk estimates at which they were reported. The odds ratios (ORs) were known to be a normally present measure for the relationship of refrigerator use with the risk of gastric cancer. We integrated the case-control and cohort research primarily in this meta-analysis, because ORs and relative risks (RRs) provide similar estimates of risk in the case of rare outcome . In studI2 statistic, where values of 25%, 50% and 75% represented cut-off points for low, moderate and high degrees of heterogeneity, respectively [P< 0.10. And all statistical analyses were conducted using STATA version 12.0 . Except where otherwise specified, P values were two-tailed significantly with level at 0.05.Statistical heterogeneity across studies was evaluated with the ectively . We usedectively . Potentiectively , with reThe results of literature retrieval and research selection are shown in Main characteristics of those included studies published between 1990 and 2011 are presented in P<0.001; I2 = 89.80%).The random-effects model results with the ORs of gastric cancer in relation to ever use of refrigerator are shown in I2 = 85.8%) did not alter the primary results. Reverse correlation has no substantial change in leave-one-out analyses omitting a study at each turn, a summarized OR ranging from 0.65  to 0.78 , which indicated that the overall results were not significantly affected by any individual study.Sensitivity analysis was used to discover the potential sources of heterogeneity between the use of refrigerators and the risk of stomach cancer, and to check the effects of various exclusions on the associated OR, and test the robustness of all of the above results. We compared the fixed and the random-effects models, but found no significant difference in the summarized OR between the two . Restricting analysis to restricting analysis to studies that reported the association between refrigerator use and the risk of gastric cancer with a reference group for never refrigerator uses .No visual asymmetry was found in the visual examination of funnel plots see . The EggRefrigerator use was inversely associated with gastric cancer based on data from 12 observations with 13 separate reports with 14,361 individuals. Compared with individuals who had never or less exposure to refrigerator use, the risk of gastric cancer was decreased by 30%.We obtained a valuable and important finding in subgroup analyses. We found that a significantly negative correlation between the use of refrigerators and the risk of gastric cancer in some Asian countries , but not in the some Western (European and American) countries, which was an interesting occurrence. This difference may in part reflect the difference in the prevalence of Helicobacter pylori (H. pylori) infection between European and Asian, which was significant risk factor for gastric cancer –25. The Certain potential biological mechanisms may explain the links between refrigerator use and the risk of gastric cancer. First, refrigerator could make foods and vegetables stay fresh for longer time, and reduce the likelihood of producing nitroso compounds , which hOur study has several notable strengths. This is the first systematic and quantified meta-analysis of studying the relationship between the use of refrigerators and the risk of gastric cancer. Because of the widespread use of refrigerator worldwide, our results are of great interests to both medical science and the public. Second, according to our subgroup analysis, there was a significantly negative relationship between the use of refrigerator and the risk of gastric cancer in some Asian countries, which provided a new insight for future research of how the biological mechanisms of refrigerator use and gastric cancer are affected by ethnicity.Some potential limitations to this study should be taken into consideration. First, most of the original studies used in our meta-analysis are of case-control study design, which is particularly vulnerable to potential biases (including selection bias and information bias). Second, the included studies were conducted in different populations, and this may confuse our analysis of the special association between the use of refrigerator and the risk of gastric cancer. Third, the different definitions for refrigerator usage across studies may bring the heterogeneity into studies’ results. Final, there was the evidence of heterogeneity across studies used for the analysis of association between refrigerator use and the risk of gastric cancer. The heterogeneity might result from the difference of participants’ characteristic, sample sizes, exposure definitions, and study designs. Thus, the results of this meta-analysis should be interpreted cautiously.More effort should be put into future research. First, the mostly research were from Asia and Europe, and only one in Venezuela. In view of the differences in potential disease impacts among different geographical locations and races, it may provide more information if there were data from more areas (such as Africa).Our meta-analysis indicates that the risk of gastric cancer is 30% lower among refrigerator users. Subgroup analyses suggest that a use of refrigerator is inversely associated with the risk of gastric cancer in some Asian countries. Studies with more samples and longer follow-up times are warranted to replicate our results.S1 Checklist(DOC)Click here for additional data file.


Accumulating evidence has demonstrated that leptin is associated to the tumorigenesis and progression of breast cancer (BC). However, these studies remain inconsistent. Thus, a meta-analysis was conducted to investigate the role of leptin in the patients with BC.A systematic search in PubMed, Embase, ISI Web of Science, and Chinese National Knowledge Infrastructure (CNKI) databases was conducted up to September 1, 2017. The standardized mean difference (SMD) with 95% confidence interval (CI) was applied to pool the effect size. A funnel plot and Egger test were used to evaluate publication bias.P <.0001). When subgroup analyses were restricted to ethnicity and menstrual status, higher serum leptin concentration was also detected in patients with BC. Moreover, BC cases with body mass index (BMI) >25 indicated significantly higher serum leptin levels . Furthermore, the BC cases with lymph node metastases showed significantly higher serum leptin concentration .Finally, 43 eligible studies were included in the current meta-analysis. Overall, serum leptin levels in BC cases were significantly higher compared with the controls (SMD = 0.61, The present meta-analysis suggests that the serum leptin may profiles as a pivotal role in the pathogenesis and metastasis of BC. In addition, leptin will provide useful information for a therapeutic target to treat BC. Growing evidence has indicated several potential predictive biomarkers and therapeutic targets for BC, such as insulin-like growth factor, intercellular adhesion molecule 1 (ICAM-1), visfatin, adiponectin, and resistin.–13Breast cancer (BC) is one of the commonest causes of cancer-related death and is the most frequently diagnosed cancer in women worldwide.–16Leptin is a circulating satiety hormone produced mainly by white adipose tissue and is expressed in normal breast epithelium and BC cell lines. Several experimental studies have indicated the crucial role played by leptin in regulating energy expenditure and metabolism and provoking proliferation. In addition, leptin can activate leptin receptor, different signaling pathways, and enzyme aromatase and exert its proliferative effects on malignant epithelial cells, which may induce carcinogenesis of breast tissue and promote the proliferation and angiogenesis of BC cells. In addition, elevated leptin expression in BC was reported to be involved in higher tumor grade and size.–20 However, other studies have reported no association between serum leptin levels and BC development.,21 Furthermore, a few studies have indicated an inverse association between circulating concentration of leptin and the risk of BC in premenopausal women.,23 Nevertheless, some authors found a negative correlation between serum leptin and BC development in the premenopausal women, but a positive correlation in postmenopausal women. The current data available investigating the association between leptin and BC remains inconsistent due to different measurement methods, inhomogeneous study designs, and small sample sizes, which result in insufficient power to detect such possible small effect.Some studies have provided strong evidence that leptin is over-expressed in the majority of BC patients and is also involved in tumorigenesis and the progression of BC.Due to the critical role of leptin in prospective molecular target for cancer prediction, prevention, and therapeutics and the inconsistency of previous studies, a comprehensive meta-analysis was conducted to evaluate the reliable association between serum leptin levels and BC risk by precise results.22.1The PRISMA protocol was prospectively conducted. Ethical approval was unnecessary in this study because it was a meta-analysis analyzing existing articles and did not need handle individual patient data. Two independent reviewers systematically searched PubMed, Embase, ISI Web of Science, and Chinese National Knowledge Infrastructure (CNKI) databases to identify relevant studies from inception to September 1, 2017, using the following search terms: “leptin” and “breast neoplasms” or “breast neoplasm” or “breast tumor” or “breast tumors” or “breast cancer” or “human mammary neoplasm” or “human mammary neoplasm” or “human mammary carcinoma”. There were no publication date or languages restrictions on trial eligibility. References from the retrieved articles were also screened for potentially relevant publications. For multiple studies based on the same case series, only the study with largest sample size was eligible.2.2The inclusion criteria were as follows:(1)a case-control study;(2)a study investigating the association between serum leptin levels and BC;(3)a study involving available data for estimating available data for calculating standardized mean difference (SMD) with 95% confidence interval (CI);(4)the participants in study should be pathological diagnosed with BC.Exclusion criteria:(1)duplicative or overlapping study;(2)the study without control subjects or other essential information;(3)abstracts, conferences, letters, or non-human studies.2.3The detail information of each included study was collected in a predesigned data extraction form independently by 2 reviewers. Items were collected as follows: first author, publication date, country, ethnicity, control source, sample size, age of participant, body mass index (BMI), BC type, serum leptin levels (mean and standard deviation), measurement method, estrogen receptor (ER) status, progesterone receptor (PR) status, lymph node invasion (LN), and treatment and menstrual status. Any discrepancy was resolved by consensus. The information was shown in Tables 2.4,25,26 The total scores ranged 0 to 9. A study with scores of ≥7 points was viewed as a high-quality study , which included the selection, the comparability of the groups, and the ascertainment of the exposure or outcome of interest with use of a “-” rating system.2.5Q test and I squared (I2) statistics test . The pooled effect size was calculated by the random-effects model (REM) if significant heterogeneity existed (I2 >50% and P <.10). Otherwise, the fixed-effects model (FEM) was applied. To investigate the potential origin of heterogeneity, stratification was employed for subgroup analyses based on ethnicity, test method, control source, study quality, menstrual status, and clinical characteristics. In addition, sensitivity analyses were also conducted by sequentially excluding individual study to assess the stability of the results.,27All of the data were calculated as SMD with 95% CI to compare the serum levels of leptin in BC cases with that in healthy controls. Heterogeneity was examined by Chi-squared-based Egger linear regression and Begg test were used to test potential publication bias. Visual inspection of asymmetry in funnel plots was carried out to further detect publication bias. All data analyses were conducted with STATA 12.0 software .33.1,29 Thus, the 2 publications can be view as 4 individual studies. Finally, 41 publications (43 studies) following our strict inclusion-exclusion criteria were eligible, which involved 14,403 subjects (6459 cases and 7944 controls),24,28–56 . However, there was a non-ignorable heterogeneity among studies (I2 = 94.9%). Thus, subgroup analyses of different specific effects were conducted to explore the origin of significant heterogeneity in our dataset  or Caucasian population   or RIA   or in Chinese . In addition, the subgroup analysis of control source indicated that there were significantly higher serum leptin levels in BC cases in hospital-based control  or population-based control    or post-menopausal BC cases  than these in controls  or Caucasian population . When the subgroup analysis by ethnicity was conducted in post-menopausal women, such significant association was identified in Asian population  or Caucasian population   or Caucasian population . When stratified by menstrual status, similar significant association was identified between the pre-menopausal BC cases  or post-menopausal BC cases  and the controls  indicated significantly higher serum leptin levels than those in controls. However, no significant association was identified regarding serum leptin levels between the BC cases and the controls with BMI <25  . In addition, the BC cases with lymph node metastases indicated significantly higher serum leptin levels than those with no lymph node metastases   Fig. . Similar15) Fig.  Table 4P = .852 3.5The Begg funnel plot and Egger regression intercept tests were used to assess publication bias. The result of Egger test indicated no significant publication bias. Moreover, the shape of the Begg funnel plot presented basically symmetric distribution Fig. .3.6Although stringent protocols were applied to carry out all studies, some of studies may affect the pooled results. Thus, sensitivity analyses were used to evaluate the stability of these results. The sensitivity analysis was conducted by sequentially excluding each study to evaluate the influence of any individual study on the pooled results. The corresponding pooled results were not significantly altered. In addition, we noticed that some publications in forest plots were far apart from the results of other publications, which may be the root cause of the moderate heterogeneity. Thus, further sensitivity analyses excluding these publications did not change our results, which indicated the robust of the analysis. Therefore, we also included these publications in our meta-analysis. Moreover, the REM was compared with the FEM, and the conclusions were not changed, which suggested the stability of our meta-analysis.P value is .819, .416, .098, and .386, respectively).Furthermore, a multivariate meta-regression analysis was conducted to assess the potential confounding factors. The results showed that the publish year, ethnicity, study quality, and test method did not have a substantial effect on heterogeneity different populations (Caucasian and Asian populations) living in different areas with different environments might have different genetic backgrounds;(2)the results from the population-based controls can represent the exposure situation of overall population;(3)the patients in these studies have different stages and types of BC, which indicated different pathways in the pathogenesis of BC;(4)these studies applied different analytic methods to measure the levels of serum leptin;(5)the quality of these eligible studies was different;(6)these patients with BC had different menstrual and treatment statuses;(7)the included cases and controls had different demographic characteristics and clinical information.,37,50,52 further sensitivity analyses were conducted, which did not change our conclusion. The between-heterogeneity decreased from 94.9% to 91.5%, which indicated that these studies might contribute to heterogeneity but not be the root cause of the moderate heterogeneity. Thus, we still included these publications in our meta-analysis. Moreover, the similar results were obtained after the REM was compared with the FEM. Furthermore, a multivariate meta-regression analysis was conducted to further evaluate, and the results indicated no substantial effect of publish year, ethnicity, study quality, and test method on heterogeneity. Therefore, we conducted subgroup analyses to investigate the origin of heterogeneity through these above-mentioned factors.First, we conducted the sensitivity analysis by sequentially excluding each study. The corresponding pooled SMDs were similar, which indicated the stability of this meta-analysis. In addition, we noticed that some publications in forest plots were far apart from the results of other publications,,58 Previous studies indicated significant association between serum leptin levels and risk of cancer in postmenopausal women but suggested discrepant results when the premenopausal women were included to analysis due to inherent biological differences.,60 However, we found a significant difference of serum leptin levels between cases and controls according to both premenopausal and postmenopausal status. Furthermore, such similar results were obtained in the subgroup analysis by menstrual status in the high-quality group.When subgroup analyses were restricted to ethnicity, measurement method, publish language, and control source, our results revealed higher serum leptin concentrations in the patients with BC. Furthermore, the subgroup analysis by ethnicity also indicated in the high-quality group that the patients with BC showed higher serum leptin levels than the healthy controls. The above-mentioned results were consistent with the previous studies that there were significant higher serum leptin levels identified in cases with colorectal and prostate cancer compared with the controls. Previous studies reported that higher circulating levels of leptin were associated to increased body fat mass and were involved in cancer development and progression. Furthermore, several authors showed serum leptin concentration was higher in obese patients with BC than that in obese patients without cancer.,50 Thus, obesity may be related to leptin production in overweight/obese person with or without BC. However, many studies did not isolate the BMI in the experimental design, which may reduce the power to reveal a reliable relationship. We conducted a meta-analysis to investigate the role of serum leptin expression in BC according to BMI. The result indicated that serum leptin levels significantly increased independently in BC cases for BMI >25. Although the mechanism remains unclear, we speculated that the elevated levels of leptin were consistent with the amount of body fat in overweight/obese groups, which could positively correlate with hyperleptinemia, insulin-sensitizing, and synthesis and activity of insulin-growth factor (IGF)-I and II. The IGF-I and II and leptin could have substantial effect on tumorigenesis through various intracellular pathways, which may synergize with other growth factors to enhance their mitogenic effects in obese hyperinsulinemic patients.,62Of the well-established risk factors for the development BC, obesity is considered as an important risk factor, especially in postmenopausal women. Leptin was one of the classical adipokines secreted by adipose tissue that was associated with insulin sensitivity, angiogenesis, and energy metabolism.Leptin is regulated by transcriptional control with sex steroid hormones such as progesterone and estrogen. Thus, amplified signals though progesterone or estrogen were associated with increased leptin levels. However, no significant differences were identified regarding serum leptin levels in BC cases with and without PR or ER. We must treat these results cautiously. First, studies in premenopausal BC cases indicated some association between serum levels of leptin and increased tissue PR concentration from BC cancer tissue. It may demonstrate that menstrual status may serve as an important factor affecting the production of leptin. Second, such association may be affected by different analytic methods, ethnicity, and influencing aspects. There were only 6 studies for analysis, which decreased the statistical power. Thus, diet and other known risk factors will remain a key area of research for understanding the BC risk and complex interactions of lifestyle. In addition, leptin was significantly higher in advanced BC with LN invasion, which demonstrated that it might play a role in the tumor metastasis. Moreover, the hyperactive leptin signaling network in central and peripheral system has a substantial effect on various steps in BC development and progression, and it can interact with breast epithelial tumor cells and with the different components within the BC microenvironment.,63,64 The higher serum leptin levels can increase the risk of BC. Therapeutic strategies to decrease leptin concentration, such as downregulation of its serum levels, decreased expression of leptin receptors, inhibition of leptin signaling by a short peptide or other leptin receptor agonists, as well as pharmacological interventions with antidiabetic drugs, should be proposed with the goal of enhancing the pharmacological effects for treating BC in the near future. In addition, all women can change their lifestyles  to achieve healthy weight for BC prevention and treatment efforts.It is known that leptin interacts with pathways in the central nervous system and direct peripheral mechanisms.We note several potential limitations in this study. First, further analyses were not performed to detect other aggressive clinicopathological features due insufficient original data, such as tumor stages, tumor differentiation , and histological grade. Second, subgroup analysis showed that disease type and ethnicity might be the source of heterogeneity. Nevertheless, there may be other inexplicable heterogeneity affecting the results. Third, a majority of studies eligible in this meta-analysis were conducted in Asian and Caucasian populations. Thus, the results are possibly mainly applicable to the Asians and Caucasians. Despite these limitations, we created a strict protocol and conducted study selection and data identification to reduce potential bias through the whole process. Thus, the objectivity and reliability of the results are guaranteed.In summary, this study suggests that the serum leptin may profiles as a pivotal role in the pathogenesis, development, and metastasis of BC. Further investigation is needed to explore a threshold of leptin which could stimulate the development of cancer. Moreover, leptin will provide useful information for a therapeutic target to treat BC. More rigorous and uniform case-control is necessary to confirm these results.Conceptualization: Li Gu, Cheng-Di Wang, Chang Cao, Lin-Rui Cai, De-Hua Li, Yuzhen Zheng.Data curation: Li Gu, Cheng-Di Wang, Chang Cao, De-Hua Li, Yuzhen Zheng.Investigation: Li Gu.Project administration: Lin-Rui Cai, Yuzhen Zheng.Software: Chang Cao, Lin-Rui Cai, De-Hua Li, Yuzhen Zheng.Supervision: Chang Cao, Lin-Rui Cai, De-Hua Li, Yuzhen Zheng.


Type 2 diabetes mellitus (T2DM) remains a public health problem in low-income countries, including African countries. Risk factors of this disease in Africa are still unclear. This study will examine the modifiable and emerging risk factors associated with T2DM in Africa.The study will include a systematic review and meta-analysis of published and unpublished empirical studies, reporting quantitative data only. We will conduct a search on scientific databases , general online search engines (e.g. Google Scholar) and key websites for grey literature using a combination of key countries/geographic terms, risk factors (e.g. overweight/obesity) and T2DM . We will use the Comprehensive Meta-Analysis Software (CMA) version 2.0 for data management and analysis. This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P).The systematic review and meta-analysis will provide a robust and reliable evidence base for policy makers and future research. This may help with identifying and implementing more cost-effective diabetes prevention strategies and improved resource allocation.CRD42016043027.This protocol has been registered with the PROSPERO international prospective register of systematic reviews. The reference number is The online version of this article (10.1186/s13643-018-0801-y) contains supplementary material, which is available to authorized users. Once described as a disease of the affluent, type 2 diabetes mellitus (T2DM) has emerged as a significant non-communicable disease (NCD) that is threatening the economic, social and cultural fabric of African populations . High diAccording to the global burden of disease study, NCDs including diabetes contribute yearly to more than 60% of the global mortality, 80% of death, as well as 54% of global disability-adjusted life years (DALY) lost , 9, 10. While knowledge of diabetes risk factors is important to positively shift population distributions, no systematic review and meta-analysis, to our knowledge, has examined the strength of associations between these risk factors and T2DM in Africa as well as the role of urban and rural areas. Identifying the most important diabetes risk factors may 1) influence policies and improve the allocation of resources and (2) substantially halt diabetes incidence, increase health gains and prevent loss of productivity due to complications and premature death  influenc. HoweverDespite a significant paucity of empirical data in Africa, some advances in diabetes risk factor studies have been made. Yet, apart from genetic factors and predispositions of diabetes in Africa , diabeteAround the globe, the epidemics of adiposity (e.g. overweight/obesity) and T2DM are mainly driven by the quality of diet and disproportionate caloric consumption . EvidencIn Africa, due to food market globalisation, multinational fast food chains have multiplied substantially, contributing to the transition from a high-fibre diet to an energy-dense westernised diet , 30. ForIn many Africa countries, fruit and vegetable availability are seasonal. As such, most African’s fruit and vegetable consumptions are not incorporated into normal daily food routines . While tNicotiana tabacum [Tobacco use (either smoked or smokeless) has a strong relationship with T2DM, either as an independent risk factor or in clusters with other risk factors such as centripetal obesity , 37. Smo tabacum , 39, whi tabacum  and used tabacum  shows th tabacum . In Afri tabacum . In SoutPrevalence of smokeless tobacco use among children aged between 13 and 15 years, in some African countries between 2007 and 2009, was 4.6%  in Tanzania , 22.2%  in rural Sierra Leone, 20.8% in Democratic Republic of Congo (Kinshasa), 21.9% in Gambia (Banjul) and 5.1%  in Cameroon (Yaounde) , 43. TheAlcohol use is a major contributor to premature death and disability . A systeogogoro in Nigeria, burukutu and pito or ginlike (mainly illicit) in Zambia and Ghana or gongo in Tanzania [Alcohol use is high in Africa, except in countries where it is prohibited. In many African countries, alcohol is central to the cultures, traditions, customs and social life, with long-standing historical significance , 57. WesTanzania . DespiteTanzania . ExacerbTanzania , includiTanzania .Physical activity is the major determinant of energy expenditure—a significant factor in energy balance and weight control . AccordiFrom a cultural perspective, structured or deliberate physical activity in most African countries is not usually viewed as a health-related risk factor but mainly through the lens of sports . AdditioFurthermore, while physical activity is obtained from its occupational  or incidental use  in most countries, the growing use of mechanisation is evident in growing urbanisation . While dHistorically, being overweight was once rare in Africa, due to food scarcity and high-energy expenditure . Howeverp = 0.05) was strongly associated with diabetes. Similar findings have been reported in populations of South Nigeria [In Africa, numerous studies presenting findings on various parameters of adiposity such as obesity, overweight, abdominal obesity, BMI and waist-to-hip ratio are marked. For example, in Motala et al.’s  study of Nigeria , South A Nigeria  and Tanz Nigeria . The ass Nigeria , South N Nigeria , South A Nigeria  and Suda Nigeria .Several studies have identified psychosocial factors  as emerging risk factors for diabetes and NCDs , 23, 84.In sum, while waiting for more empirical data and studies on diabetes risk factors to become available, existing and accessible studies may be an effective resource in understanding the contribution of these risk factors and burden of diabetes in Africa.In Africa, despite the primary epidemiological studies presented above, to date, no studies to our knowledge have systematically meta-analysed these findings. It is imperative to note, though, that ample reviews , 89, 90 The aim of this systematic review and meta-analysis is to assess the association between modifiable risk factors and T2DM in Africa.To assess the magnitudes of associations between modifiable risk factors  and T2DM in African countries)To use ranking to assess which risk factors have the strongest association with T2DM in AfricaTo examine the impact of the following moderators on associations between modifiable risk factors and diabetes: study setting  and study country language (Anglophone/Francophone)Studies on modifiable risk  and their strength of associations with T2DM as an outcome will be limited.Studies on tobacco consumption including smokeless tobacco and its association with T2DM will be the most limited.Diabetes risk factors such as obesity and physical inactivity will be the major risk factors. Obesity will be most strongly associated with T2DM compared with other modifiable risk factors.Moderators such as urban settings will have a significant influence on diabetes risk factors and T2DM.Relationships between risk factors and T2DM may differ between Africa and developed countries.The most important risk factor causing most burdens may differ between Africa and developed countries.Guided by the findings of previous meta-analysis and systematic reviews, the hypotheses of this study include:The study will comprise a systematic review and meta-analysis. The study will follow the reporting guidelines and criteria set in Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) Table .Table 1IThe literature from this area suggests that an amount of heterogeneity may exist, such as differences in methodological approaches making study comparison difficult, and some may lack clear case definitions, standard diagnostic criteria or control groups to evaluate potential study biases . Most phThe study will provide a summary of pooled measure of association between one or more risk factors and T2DM and rank them accordingly.The study will provide more reliable evidence in the understanding of the contemporary diabetes modifiable risk factor associations with T2DM in Africa.CINAHL, MEDLINE, PsycINFO, Psychology and Behavioural Science, Global Health and EmbaseDissertation of ThesisConferencesBIOSIS Previews, Embase, MEDLINE, CINAHL Plus, Grey Literature reports, PsycEXTRA, OpenGrey, WorldWideScience, World Health OrganizationInternational Diabetes Federation, African Journal Online, African Development Bank, UNICEF and USAID databaseScopus and Academic OnefileGoogle, Google Scholar, research centres in Africa and university websitesSyntax of search terms and subject headings terminologies will be adapted according to the requirements of the individual databases  with T2DM as an outcome measure. Others include study location (country/nation) and sample size, odds ratio (OR), confidence interval (CI), mean and standard deviation (SD).PRISMA, Critical Appraisal Skills Programme (CASP)  and Newc2 test. Additionally, CMA software will be used to convert standardised means. We will also report two-tailed 95% CI, p values and ORs or SDs, then use Cohen’s ‘d’ to calculate the effect sizes for each risk factor. Both bivariate and multivariate associations between risk factors and T2DM will be retained; however, only bivariate associations will be used in the meta-analysis, given the challenges to meta-analysing adjusted data [All included studies will be summarised descriptively before carrying out statistical analyses with Comprehensive Meta-Analysis (CMA) version 2 software. The analysis will synthesise the evidence to provide a summary of pooled measure of association between one or more risk factors and T2DM. The available measure of association will determine the method of collapsing and the metric that best fits. Thus, if correlation coefficients are used, other statistical measures will be converted to correlation coefficients and unadjusted odds ratios converted to correlation coefficients. Statistical measures of association between risk factors and T2DM may include regression coefficients with SD, OR, and dichotomous measures such as the χted data . Associated data ).Q and I2 statistics [Longitudinal data will be included if data are reported for multiple time points. If all included studies focus on T2DM as an outcome, heterogeneity of the effect sizes will then be assessed using the atistics . To accoatistics , i.e. enThe mixed effect models will be used for moderator analysis  as a morPublication bias will be assessed through the steps outlined below.Q and I2 statistics [I2 statistics of above 50% cut-off heterogeneity.If more than ten studies report the same risk factors, the symmetry of outcomes funnel plots will be assessed.Egger’s weighted regression method will be used .A failsafe number will be calculated to estimate the number of unlocated studies with an average zero effect size required to significantly change the results.For studies that focus on similar outcomes, heterogeneity of effect size among studies will be assessed using atistics , 104. DuIf we find that studies have not reported study designs, methods, risk factors or outcomes, corresponding authors will be contacted via email. If no further detail can be obtained, it will be narrated descriptively and identified as a study limitation.This study aims to add to the extant literature by synthesising the evidence on the association between modifiable risk factors and T2DM in Africa and to provide a reliable evidence base for policy makers and future research.This is the first known systematic review and meta-analysis of associations between TD2M and modifiable risk factors in Africa. The strengths of this study include stringent and strong adherence to the process of conducting systematic review and meta-analyses and quantitative research rigour, for example, stringent adherence to the PRISMA checklist, including a transparent approach to searching, screening, reviewing, selecting studies and data extraction . The stuThis systematic review and meta-analysis will form a part of the primary author’s PhD thesis. The findings will be disseminated through publications in peer-reviewed journals, conferences and presentations.This systematic review and meta-analysis has been registered with PROSPERO—the International Prospective Register of Systematic Reviews, registration number: CRD42016043027.Additional file 1:Search terms and strategy. (DOCX 19 kb)


MTHFR C677T (rs1801133 C>T) polymorphism has been proposed to be linked with type 2 diabetes mellitus (T2DM) susceptibility. However, the conclusions are inconsistent. Therefore, we rechecked their linkage aiming to obtain a more reliable estimation by performing an updated meta‐analysis.Methylenetetrahydrofolate reductase (MTHFR) is essential in mediating folate metabolism, and thus plays an important role in diabetes and diabetic complications. We searched electronic databases PubMed, EMBASE, CNKI, and Wanfang to obtain studies updated to October 2019.MTHFR C677T polymorphism was significantly associated with T2DM under homozygous , heterozygous , recessive , dominant , and allele  genetic models. Stratified analysis demonstrated that C677T genotype was associated with T2DM in Asian populations, but not Caucasian and African populations.After carefully screening, we finally incorporated 68 studies with 10,812 cases and 8,745 controls. The genotype frequency of C677T polymorphism was analyzed pooled to generate odds ratios (ORs) and 95% confidence intervals (CIs). Pooled results presented that MTHFR C677T polymorphism confers to T2DM, especially in Asian populations. Much more large‐scale case–control studies are needed to strengthen such conclusion in the future.Our results indicated that  MTHFR C677T polymorphism was significantly associated with type 2 diabetes mellitus (T2DM) under homozygous , heterozygous , recessive , dominant , and allele  genetic models. Stratified analysis demonstrated that C677T genotype was associated with T2DM in Asian populations, but not Caucasian and African populations. Our results indicated that MTHFR C677T polymorphism confers to T2DM, especially in Asian populations.Pooled results presented that  Of all the identified SNPs, C677T  is one of the most investigated genetic variations .We carried out a comprehensive literature search in the following databases: PubMed, EMBASE, CNKI, and Wanfang. The searching was updated to October 2019 without any language limitations. The combination of the following search terms was adopted: ‘2.2MTHFR C677T polymorphism with T2DM risk; (b) case–control design; (c) odds ratios (ORs) and their 95% confidence intervals (CIs) were able to obtain; and (d) reports Hardy–Weinberg equilibrium (HWE). Exclusion criteria were as follows: (a) reviews or meta‐analyses; (b) case‐only studies or case reports; and (c) duplicate publications.We set the following criteria when performing the selection work: (a) evaluating the association of 2.3We arranged three authors to handle data extraction: two authors to extract data independently and one author to resolve the disagreement. The following data were selectively extracted from each study: first author's surname, year of publication, country, ethnicity, genotyping methods, and genotypic distribution. The stratification analysis was conducted by ethnicity  and HWE (HWE <0.05 and HWE >0.05).2.4MTHFR C677T polymorphism and T2DM risk by calculating pooled ORs with the corresponding 95% CIs. We totally used five genetic models: homozygote model (TT vs. CC), heterozygote model (CT vs. CC), recessive model (TT vs. CT/CC), dominant model (CT/TT vs. CC), and allele model (T vs. C) to detect such relationship. Stratification analyses were also taken by ethnicity  and HWE (HWE <0.05 and HWE >0.05), aiming to detect the source of heterogeneity. We carried out Chi‐square‐based Q statistic test and inconsistency index statistics (I2) to calculate heterogeneity between study results. If the studies were homogeneous (with hetp < .10 or I2 > 50%), the random‐effects model (the DerSimonian and Laird method) was chosen. Otherwise, ORs were calculated using the fixed‐effects model (the Mantel‐Haenszel method). Sensitivity analysis was performed to assess the strength of the conclusion, by sequentially excluding each study at a time. Begg's funnel plot and Egger's linear regression test were conducted to assess publication bias. We also conducted quality assessment to detect the quality of each study using the quality assessment criteria  was adopted to conduct the current meta‐analysis. We first used Chi‐square test to check whether the genotype frequency of C677T among the controls was in HWE. After that, we determined the relationship between 33.1General process of publication selection was graphically shown in Figure 3.2The study characteristics of the final selected studies were presented in Table 3.3MTHFR C677T and T2DM: homozygote model TT versus CC, heterozygous model CT versus CC, recessive model TT versus CT/CC, dominant model CT/TT versus CC, and allele model T versus C. There was a significant association between MTHFR C677T polymorphism and T2DM under homozygous , heterozygous , recessive , dominant , and allele  genetic models in a random‐effects model.Table MTHFR C677T genotype and T2DM in Asian populations, under each genetic models homozygous , heterozygous , recessive , dominant , and allele . However, no relationship was found between MTHFR C677T genotype and T2DM in Caucasian and African, except for the recessive model in Caucasian group . When stratified by HWE, significant association was also observed in the subgroup of HWE >0.05, under homozygous , heterozygous , recessive , dominant , and allele  genetic models. Significant association was detected in heterozygous , dominant , and allele  in the subgroup of HWE < 0.05. A subgroup analysis stratified by quality score was also conducted. Significant association was detected in homozygous , recessive , dominant , and allele  genetic models, in the subgroup of quality score > 9. Significant association was also detected in homozygous , heterozygous , recessive , dominant , and allele  genetic models, in the subgroup of quality score ≤9.In the subgroup analysis based on ethnicity, we divided the included studies into three ethnicities: Asian, Caucasian, and African. We found significant association between 3.4p < .001) for the MTHFR C677T. Therefore, the random‐effect model was used to calculate the pooled ORs and 95% CIs for all the models.As shown in Table Sensitivity analysis using sequential leave‐one‐out strategy was carried out to explore the influence of a single study on the pooled ORs. The omission of each study did not impact the recalculated ORs, indicating the credibility and reliability of our results Figure .3.5Begg's funnel plot and quantitative Egger's test were adopted to test the publication bias of the current meta‐analysis. As indicated by the symmetrical shape of the Begg's funnel plots, no significant publication bias was observed Figure . Moreove4MTHFR C677T and T2DM so far. Our analysis provided strong evidence that MTHFR C677T was significantly associated with T2DM, especially in Asians. Sensitivity analysis indicated that there was no significant change in the overall results by removing one study in each turn. Publication bias analysis also showed that the results are convincible.To our knowledge, the current meta‐analysis represents the largest and most comprehensive one regarding the relationship between MTHFR C677T is a functional genetic variation that leads to amino acid substitution from alanine to valine (Ueland, Hustad, Schneede, Refsum, & Vollset, MTHFR C677T genotype has been largely investigated, contradictory conclusions still remain. In 2006, no evidence of association was found by F.I.V. Errera et al. between the 677TT genotype of MTHFR and T2DM, in Brazilian populations (Errera et al., MTHFR may influence the risk of T2DM (Wang et al., MTHFR C677T in the development of T2DM.MTHFR C677T is associated with the risk of T2DM. In 2013, Chinese academics Zhong, Rodriguez, Yang, and Li (MTHFR C677T and T2DM. Their meta‐analysis included 4,855 T2DM patients and 5,242 controls. However, they failed to obtain clear evidence of a significant association of MTHFR C677T and T2DM across all 39 studies conducted in 15 countries. They also failed to provide compelling evidence of an association specifically for African, Asian, or Caucasian populations. Interestingly, Khalid et al. (Al‐Rubeaan et al., MTHFR C677T polymorphism and T2DM in Arab population, in 2013. In 2014, Zhu et al. (MTHFR C677T polymorphism and T2DM in the Chinese Han population.Due to the divergent results among single‐country studies, several systematic meta‐analyses have been undertaken to determine conclusively whether MTHFR vary widely by ethnicity (Errera et al., MTHFR C677T was significantly associated with T2DM in Asians, but not in Caucasians or Africans. Thus, it is necessary to identify the role of C677T in different ethnicities.Genotype frequencies at the C677T locus of MTHFR was far more enough, as the development of T2DM was associated with multiple SNPs in multiple genes. Third, we also failed to determine the role of other potential influential factors in the initiation of T2DM. These potential influential factors such as life style, environment exposures, and gene–environment interactions were reported to be associated with T2DM. Fourth, it is inevitable to avoid several shortages such as misclassified genotypes, unwell‐matched sources of controls, and inconsistent qualities of the included studies, due to the retrospective nature of meta‐analysis. Finally, between‐study heterogeneity was found in all comparisons, which may compromise the reliability of conclusion.Several weaknesses should be pointed out before interpreting our conclusion. First, selection bias could not be avoided as only the articles in English and Chinese were analyzed. Other studies written in other languages were unable to include. Second, analyzing one SNP in 5MTHFR C677T polymorphism was significantly associated with T2DM, especially in Asian populations. Further well‐designed, large‐scale, and in‐depth studies are warranted to check such relationship.In all, this meta‐analysis provides a precise conclusion that The authors declare that they have no conflict of interest.Y.M. and X.L. conceived the study design and wrote the paper. K.M., L.Z., M.L., and M.Z. performed the selection, collected the data, and performed the statistical analysis. M.G. and G.Q. were responsible for the quality control of data. All authors read and approved the final manuscript.


S-transferase (GST) genotypes are associated with HCC, the conclusions are inconsistent. Therefore, we carried on a renewed meta-analysis to expound the connection between the null GSTM1, GSTT1 polymorphisms and the risk of HCC.It is well known that hepatocellular carcinoma (HCC) has been one of the most life-threatening diseases all over the world. Plenty of internal and extrinsic factors have been proven to be related to HCC, such as gene mutation, viral hepatitis, and Nitrosamines. Though previous studies demonstrated that glutathione We searched PubMed, Web of Science, Embase, and CNKI databases to select qualified researches which satisfied the inclusion criteria up to July 31, 2018. Finally, we selected 41 articles with 6124 cases and 9781 controls in this meta-analysis. We use ORs and 95% confidence interval (CI) to evaluate the correlation intension between the GSTM1 and GSTT1 null genes and the risk of HCC. All the statistical processes were executed by Stata (version 12.0).The pooled analysis showed that both GSTM1 null genotypes  and GSTT1 null genotypes  increased the risk of HCC. And GSTM1–GSTT1 dual-null genotypes also increased the risk of HCC . In the subgroup analysis, we obtained significant results among Asians when stratified by race, and the results are GSTM1 null OR = 1.44, 95% CI = (1.22–1.71), GSTT1 null OR = 1.48, 95% CI = (1.25–1.77), GSTM1–GSTT1 null OR = 1.58, 95% CI = (1.19–2.09), while we didn’t obtain significant results among Caucasians or Africans. Stratified analyses on the type of control indicated a higher risk of HCC associated with GSTM1, GSTT1 single null genotypes and GSTM1–GSTT1 dual-null genotypes in healthy people. No evidence of significant connection was discovered in chronic liver disease (CLD) except in GSTT1 single null.Our study indicated that an individual who carries the GSTM1, GSTT1 single null genotypes and GSTT1–GSTM1 dual-null genotypes is more likely to develop HCC.The online version of this article (10.1186/s12935-019-0792-3) contains supplementary material, which is available to authorized users. Hepatocellular carcinoma (HCC) has been one of the most common and terrifying cancer on account of a high mortality, which is turning into the third major cancer all over the world and is the second important factor of cancer-related deaths in China , 2. It iWhen we talk about the internal factors, it is inevitable to study the effect of changes in genetic expression of certain genes or cytokines. The change of many signaling pathways and its molecules such as extracellular regulated protein kinases, antichymotrypsin, growth factor receptor-bound protein 2 (Grb2)—associated binding protein 2 have been found to be related to the blossom of HCC . Among tAmong all the GSTs, the GSTM1 and GSTT1 have been extensively found related to various types of cancers . Though We sought several databases including but not limited to PubMed, Web of Science, Embase, and CNKI for correlative researches up to July 31, 2018 for using various combination of those keywords: GSTM1, GSTT1, hepatocellular carcinoma, polymorphism and HCC.We adopted the publications which assess the correlation of the GSTM1 and GSTT1 polymorphisms with HCC. Qualified studies must satisfy each requirement which was listed below: (1) must be a case–control study or a cohort study (2) have primitive and firsthand data and be independent from other researches, (3) have complete data to conduct statistical analysis.Exclusion criteria are: (1) primitive and firsthand data are not available; (2) we excluded the studies which were based upon the same subjects and published by the same authors and selected the biggest samples or the latest data; (3) Additionally, meeting abstract, case reports, and other meta-analysis were not adopted.Two participants independently extracted the data that we need cautiously from all qualified publications, and the related information included the first author, race of the samples, source of control, year of publication, respective number of each case and control, genotyping method, and type of control. Any potential inconsistency of the results was discussed and settled by consensus.2 statistic to measure the inconsistency [P values were two-sided, and the P < 0.05 was taken for statistically significant. In addition, for GSTT1–GSTM1 both null, we use the same method to analyze the datum.For GSTT1 and GSTM1, we respectively compared null genotype and normal genotype frequencies between cases and controls. And we calculated its ORs and 95% confidence interval (CI) to evaluate the correlation intension between the GSTM1 and GSTT1 null genes and the risk of HCC. Cochran’s Q statistic was used to assess heterogeneity and the Isistency –13. Subgsistency , 15. AllWe have selected 553 correlative papers in term of the keywords from the databases, and 512 articles were removed for not meeting all the inclusion criteria. A total of 41 qualified papers were adopted in the end and all the studies obtained high-quality score according to the Newcastle–Ottawa Scale (NOS) . When we conducted a subgroup analysis in term of race, we acquired the similar results in Asian   and African  populations , but for CLD controls, we did not find any positive results  . When stratified by race, positive results were discovered among Asians   and African  . In addition, we got similar conclusion in CLD controls  . When stratified by race, positive results were found among Asians     Fig. a while n−3) Fig. a. When s−3) Fig. b. But foP > 0.05). Sensitivity analysis was performed to measure the stability of the conclusive results, and we tell that there was no substantial transformation by removing any data set according to sensitivity analysis (Fig. We utilized Begg’s and Egger’s test to detect the possible sources of bias. The funnel plots were symmetrical as showed in Fig. sis Fig. . There wPrevious studies already showed that the null GSTM1 and GSTT1 genotypes were likely to have an association with higher risk of a different type of cancers. The study by Wang zhao-yang et al. reported the null GSTT1 and null GSTM1 might have connection with higher death risk in gastric cancer . Vesna MIn the subgroup analysis in term of ethnicity, HCC risk was increased in Asians for single null of GSTM1 and GSTT1 and GSTM1–GSTT1 double-null genotype. However, we could not get the similar conclusion in Caucasians or Africans. This may be affected by diverse living surroundings and different inherited backgrounds. There may exist many other polymorphisms with combined effects that affect cancer susceptibility. As a consequence, the effect of certain individual polymorphism on the risk of cancer could be weakened. Our data also showed that the respectively null genotypes of GSTM1 and GSTT1 genotypes stand a good chance of having a duplicate effect on the HCC. When stratified by ethnicity, significant connection was found in Asian for GSTM1, but not in African and Caucasian. This could be explained by the potential possibility that the African and Caucasian may have certain unknown repair mechanism to avoid the damage caused by deficiency of the GSTM1 gene. It is said that the infection of HBV can easily evolve to liver cirrhosis and finally to HCC . In addiDespite our great efforts to check the association between the GSTs genes and the risk of HCC, we must pay attention to some limitations. Firstly, our analysis is a kind of cursory estimate since we had no chance to analyze each individual data. Secondly, the selected trials differ in certain aspects, including differences in trial populations and other potential diseases, and it is possible that methodological differences might have confused the conclusions of subgroups of trials. Thirdly, by including studies published a long time may affect the authenticity of the result.All in all, our study showed that the single null of GSTM1, GSTT1 and GSTM1–GSTT1 double null genotypes are all connected with a higher risk of HCC. It is noteworthy that our data revealed that individuals with double-null genotypes of GSTs genes may be more likely to develop into HCC which worth further exploration. Last but not least, we need larger sample epidemiological studies of high quality to make current conclusions more reliable and stable.Additional file 1: Table S1. The evaluation of the data quality in this meta-analysis.Additional file 2: Table S2. The detailed characteristics of the included studies in this meta-analysis.