we followed the prisma ( preferred reporting items for systematic reviews and meta - analyses ) checklist for reporting systematic reviews and meta - analyses ( 6 ) . we systematically searched medline , embase , central , cinahl , and web of science through october 2011 for studies in humans of the association between metabolic syndrome and cancer . our core search consisted of the terms metabolic syndrome , insulin resistance syndrome , and syndrome x , combined with specific terms for each cancer site : colorectal ( colon and rectum ) , gastric , esophageal , hepatobiliary ( liver and gallbladder ) , pancreas , lung , bladder , thyroid , renal , leukemia , malignant melanoma , multiple myeloma , and non - hodgkin lymphoma for both sexes and prostate , breast , ovary , and endometrium for single sex . relevant journals , bibliographies , reviews , and personal files were hand searched for additional articles . we included studies if 1 ) their aim was to assess the effect of metabolic syndrome on risk of cancer or association with cancer , 2 ) they reported the definition of metabolic syndrome according to criteria of national or international scientific associations , federations , or organizations ( traditional definitions ) or if they used proxy indicators in the absence of the original data ( nontraditional definitions ) , and 3 ) they included at least three factors , even in the absence of others . we included cohort studies , nested case - control studies , control arms from clinical trials , case - control studies , patient series , and mortality studies . we specified that every study must either report risk estimates ( relative risks [ rrs ] , odds ratios , hazard ratios , and standardized incidence ratio ) with 95% cis separately for men , women , or both or must report sufficient data to estimate these . if a site - specific dataset had been published more than once , we used the most recent publication . we included a specific cancer site in the analysis if there were at least two cohort datasets . we excluded studies that were not published as full reports , such as conference abstracts and letters to editors , and studies of cancer precursors ( e.g. , colorectal adenoma ) . from each retrieved article , we extracted the following data : name of the first author , year of publication , country where the study was performed , specific outcomes , follow - up time , proportion of men and women , total number of individuals , number of cases , and risk estimates and their 95% cis ( presence versus absence of metabolic syndrome ) . , k.e . , and p.c . ) until interrater reliability (   0.60 ) was established . methodological quality of each study was assessed according to three study components that might affect the strength of the association between metabolic syndrome and cancer risk : length of follow - up for cohort studies , whether metabolic syndrome definition was traditional or nontraditional , and the extent of adjustments for potential confounding factors . we also collected , where available , risk estimates of the association with cancer for each single factor of the syndrome taken at its highest level . the primary end point was to assess the association between metabolic syndrome and cancer risk in cohort studies . for the main outcome at each cancer site , we graded the evidence for study quality and for the risk of bias : study quality was based on the number of datasets , number of events , width of cis , and heterogeneity ; risk of bias was mainly based on type of study and adjustment for confounders . heterogeneity of the effect across studies was assessed by q statistics , which is distributed as  statistics ( 7 ) . a value of p < 0.10 was used to indicate lack of homogeneity ( heterogeneity ) among effects . i statistics were provided to quantify the percentage of total variation across studies that was attributable to heterogeneity rather than to chance . i values of 25 , 50 , and 75% correspond to cutoff points for low , moderate , and high degrees of heterogeneity . we used a fixed - effects model if i value significance was > 0.1 ; otherwise , we used a random - effect model . we did subgroup analyses for each site to identify study - level factors that modify the association between the presence of metabolic syndrome and cancer risk : these factors include sex , subsite ( e.g. , colon and rectum ) , definition of metabolic syndrome ( traditional versus nontraditional ) , and design ; for incidence of cancer , we considered cohort studies , nested case - control studies , and control arms of clinical trials . sensitivity analyses evaluated whether the results could have been affected markedly by a single study and were repeated using a fixed - effects model . publication bias was examined in funnel plots and with a regression asymmetry test : the egger test is best for cancer sites with 10 or more datasets . we used stata , version 9.0 ( stata , college station , tx ) , to analyze data . we followed the prisma ( preferred reporting items for systematic reviews and meta - analyses ) checklist for reporting systematic reviews and meta - analyses ( 6 ) . we systematically searched medline , embase , central , cinahl , and web of science through october 2011 for studies in humans of the association between metabolic syndrome and cancer . our core search consisted of the terms metabolic syndrome , insulin resistance syndrome , and syndrome x , combined with specific terms for each cancer site : colorectal ( colon and rectum ) , gastric , esophageal , hepatobiliary ( liver and gallbladder ) , pancreas , lung , bladder , thyroid , renal , leukemia , malignant melanoma , multiple myeloma , and non - hodgkin lymphoma for both sexes and prostate , breast , ovary , and endometrium for single sex . relevant journals , bibliographies , reviews , and personal files were hand searched for additional articles . we included studies if 1 ) their aim was to assess the effect of metabolic syndrome on risk of cancer or association with cancer , 2 ) they reported the definition of metabolic syndrome according to criteria of national or international scientific associations , federations , or organizations ( traditional definitions ) or if they used proxy indicators in the absence of the original data ( nontraditional definitions ) , and 3 ) they included at least three factors , even in the absence of others . we included cohort studies , nested case - control studies , control arms from clinical trials , case - control studies , patient series , and mortality studies . we specified that every study must either report risk estimates ( relative risks [ rrs ] , odds ratios , hazard ratios , and standardized incidence ratio ) with 95% cis separately for men , women , or both or must report sufficient data to estimate these . if a site - specific dataset had been published more than once , we used the most recent publication . we included a specific cancer site in the analysis if there were at least two cohort datasets . we excluded studies that were not published as full reports , such as conference abstracts and letters to editors , and studies of cancer precursors ( e.g. , colorectal adenoma ) . from each retrieved article , we extracted the following data : name of the first author , year of publication , country where the study was performed , specific outcomes , follow - up time , proportion of men and women , total number of individuals , number of cases , and risk estimates and their 95% cis ( presence versus absence of metabolic syndrome ) . , k.e . , and p.c . ) until interrater reliability (   0.60 ) was established . methodological quality of each study was assessed according to three study components that might affect the strength of the association between metabolic syndrome and cancer risk : length of follow - up for cohort studies , whether metabolic syndrome definition was traditional or nontraditional , and the extent of adjustments for potential confounding factors . we also collected , where available , risk estimates of the association with cancer for each single factor of the syndrome taken at its highest level . the primary end point was to assess the association between metabolic syndrome and cancer risk in cohort studies . for the main outcome at each cancer site , we graded the evidence for study quality and for the risk of bias : study quality was based on the number of datasets , number of events , width of cis , and heterogeneity ; risk of bias was mainly based on type of study and adjustment for confounders . unless otherwise stated , we used the most adjusted risk estimate from each study . heterogeneity of the effect across studies was assessed by q statistics , which is distributed as  statistics ( 7 ) . a value of p < 0.10 was used to indicate lack of homogeneity ( heterogeneity ) among effects . i statistics were provided to quantify the percentage of total variation across studies that was attributable to heterogeneity rather than to chance . i values of 25 , 50 , and 75% correspond to cutoff points for low , moderate , and high degrees of heterogeneity . we used a fixed - effects model if i value significance was > 0.1 ; otherwise , we used a random - effect model . we did subgroup analyses for each site to identify study - level factors that modify the association between the presence of metabolic syndrome and cancer risk : these factors include sex , subsite ( e.g. , colon and rectum ) , definition of metabolic syndrome ( traditional versus nontraditional ) , and design ; for incidence of cancer , we considered cohort studies , nested case - control studies , and control arms of clinical trials . sensitivity analyses evaluated whether the results could have been affected markedly by a single study and were repeated using a fixed - effects model . publication bias was examined in funnel plots and with a regression asymmetry test : the egger test is best for cancer sites with 10 or more datasets . we used stata , version 9.0 ( stata , college station , tx ) , to analyze data . we screened 2,628 potentially relevant , nonduplicate articles . the  score for concordance between reviewers rating the articles was 0.620.77 the final number of articles ( 850 ) included in the meta - analysis was 43 ( supplementary fig . 1 ) , which reported on 116 datasets ( supplementary table 2 ) . all articles were published in english . the analysis included 38,940 cancer cases ( 18,180 men and 20,201 women , plus 559 cases for gallbladder cancer not divided by sex ) . the median follow - up per cohort studies and per cancer site varied from 3 years ( endometrium ) to 12.2 years ( prostate ) . notably , no north american population data contributed to the summaries for gallbladder , ovary , thyroid , and bladder cancers . the proportion of studies in which the definition of metabolic syndrome was traditional varied by cancer sites : higher for colorectal ( 14 vs. 8 nontraditional ) ; approximately equal for breast , hepatobiliary and prostate ; and lower for pancreas . no further differentiation was made when the number of datasets for cancer site was four or fewer . the number of potential confounding factors ( cancer - site  specific risk factors ) included in the adjusted analyses also varied ( supplementary table 3 ) . 1a and b shows the results of meta - analyses of rr ( for presence of metabolic syndrome ) in men and in women , respectively , for cohort studies only . separate meta - analyses for some relevant sites and for sex are given in supplementary figs . 2a ; 1.25 , p < 0.001 ) cancers and weakly associated with bladder cancer ( 1.10 , p = 0.013 ) . between - study heterogeneity was low or moderate for liver , colorectal , and bladder cancer ( i = 0.0 , 35 , and 0.0% , respectively ) ( fig . the quality of the evidence was high for the association with colorectal cancer ( high number of datasets and events , narrow cis , and low heterogeneity ) , moderate for liver cancer , and low for bladder cancer . the overall risk of bias was low , as all studies were prospective cohort studies and most adjusted for many confounders . summary risk estimates by cancer sites in men ( a ) and in women ( b ) . meta - analyses for some common cancer sites in both sexes : colorectal and liver cancer in men ( a ) and colorectal , breast postmenopausal , endometrial , and pancreatic cancer in women ( b ) . 2b ; 1.58 , p < 0.0001 ) , breast postmenopausal ( fig . 2b ; 1.56 , p = 0.017 ) , rectal ( 1.52 , p = 0.005 ) , and colorectal ( fig . 2b ; 1.34 , p = 0.006 ) cancers ; the association with ovary cancer ( 1.26 ) was of borderline significance ( p = 0.054 ) . between - study heterogeneity was high for endometrial , breast postmenopausal , and colorectal cancers and moderate or low for rectal ( i = 35% ) , pancreas ( 0.0% ) , and ovary cancers ( 8.8% ) ( fig . the quality of the evidence was moderate for the association with colorectal and pancreas cancers and low for endometrium and breast postmenopausal cancers . the overall risk of bias was low , as all studies were prospective cohort studies and most adjusted for many confounders . associations with metabolic syndrome were stronger in women than in men for pancreas ( p = 0.01 ) , rectal ( p = 0.01 ) , and bladder ( p = 0.01 ) cancers . we also examined whether estimates varied between populations in cancer sites for which we had at least two datasets from the main geographical regions ( table 2 ) . for colorectal cancer , for example , we recorded a positive association in u.s . and europe populations for men and in europe populations for women ; for postmenopausal breast cancer , the positive association was lost in europe populations ; for liver cancer , the association remained significant in europe and asia populations for men only ; and for prostate cancer the association became negative in u.s . populations ( almost exclusively whites , rr 0.79 , p = 0.001 , i = 9% ) . this last figure was also significant if a mortality study ( dataset n = 69 ) was excluded ( rr 0.75 [ 95% ci 0.600.94 ] , main analyses and prespecified subgroup analyses for cancer sites we also examined mortality from cancer in the available studies for which we had at least two datasets ( table 2 ) . ( two for both sexes , one for men only ) and a case - control study from china ( both sexes ) for colorectal cancer only ( 8,9,16,20 ) . risk estimate for cancer mortality was 1.61 ( p < 0.0001 ) , with no heterogeneity ( i = 0.0% ) . we also examined whether results for cancer association differed according to whether studies of different design ( case - control and patient series ) were included in the full analysis ( table 2 ) . for breast cancer , the inclusion of two case - control studies ( 26,27 ) with 3,950 cases produced a significant overall association ( 11 datasets , 9,643 cases ) of 1.23 ( p = 0.009 ) with high heterogeneity ( 88% ) . for liver cancer , the inclusion of two large case - control studies ( 29,30 ) , with an additional 4,951 cases , produced a significant association for women ( rr 1.62 , p < 0.0001 ) . the definitions used for diagnosis of metabolic syndrome affected estimates of the association between metabolic syndrome and cancer risk ( table 2 ) . for both sexes , the estimates remained similar for colorectal cancer ( rr 1.33 and 1.22 for traditional versus nontraditional definitions ) ; for liver cancer , both definitions achieved significant associations ( 1.88 and 1.51 ) ; for pancreas cancer , there was no association with traditional definitions ( rr 1.13 , p = 0.745 ) ; for prostate cancer , both definitions gave similar results ; and for breast cancer , there was an association with traditional definitions only ( 1.45 , p = 0.025 ) . to summarize the results for cohort studies with available data ( 32 cohorts ) , risk estimates for single factors were equal to metabolic syndrome in 15 cohorts , higher in 11 cohorts , and lower in 6 cohorts . for colorectal cancer , for example , all increased cancer risk was explained by diabetes alone ( 9,10 ) , diabetes and waist ( 10 ) , diabetes and bmi ( 12 ) , and triglycerides > 150 mg / dl ( 15 ) ; other single or combined factors explained part ( from 30 to 50% ) of the increased risk conveyed by metabolic syndrome : bmi ( 11,21 ) , waist ( 16,19 ) , bmi and lipid ( 17 ) , bmi and dysglycemia ( 14 ) , and hypertension ( 21 ) . influence analysis showed that no single study affected the sex - specific summary estimates for most sites . moreover , we did not note funnel plot asymmetry for cancer sites where a sufficient number of datasets exited to run the egger test ( colorectal cohorts men , p = 0.912 ; colorectal cohorts women , p = 0.201 ; and prostate cancer cohorts , p = 0.085 ) . our results from meta - analyses of prospective cohort studies indicate that metabolic syndrome is consistently associated with an increased risk of several cancers in adults . however , many of the reported associations are small ( rr between 1.1 and 1.6 ) and might differ between sexes for some sites and also across populations . in particular , the associations were stronger in women for some cancers ( pancreas and rectal ) , and the magnitude of the associations was highest for sex - specific cancers ( endometrial and breast postmenopausal ) . moreover , from analyses in which sufficient datasets existed , the association was stronger for colorectal cancer in female european populations ( rr 1.64 [ 95% ci 1.172.28 ] ; five datasets with 2,665 incident cancers ) and became protective for prostate cancer in the white u.s . populations , which needs confirmation from future studies . given the widespread diffusion of metabolic syndrome ( 1 ) and the increased cancer mortality associated with metabolic syndrome ( 2 ) , the findings of the present meta - analysis may have a clinical significance . at least for some common cancer sites ( colorectal cancer in both sexes , liver cancer in men , and pancreas cancer in women ) , we are confident that the results are real , as the grading for study quality was moderate to high and overall risk of bias was low . moreover , the inclusion of the few case - control studies did not change the overall estimates significantly . in general , the most robust association seems to be with colorectal cancer in both sexes and liver cancer in men . however , part of the association may be explained by the presence of obesity and overt hyperglycemia . metabolic syndrome may be a surrogate marker for other cancer risk factors , such as decreased physical activity , consumption of high  calorie dense foods , high dietary fat intake , low fiber intake , and oxidative stress ( 1 ) . excess adiposity , in particular visceral obesity , results in a state of chronic systemic low - grade inflammation , attributed to production of inflammatory cytokines by both adipocytes and infiltrating immune cells creating a protumorigenic environment ( 51 ) . by contrast , adiponectin levels are inversely associated with risk of some cancer , and some polymorphisms of adiponectin and its receptor genes are associated with multiple cancer risk ( 52 ) . the altered balance between proinflammatory and antiinflammatory cytokines driven by central obesity might contribute to insulin resistance , a core component of the metabolic syndrome . the igf-1 axis has also been implicated in the progression of breast , pancreatic , and esophageal cancer ( 53 ) : levels of igf are influenced by circulating insulin levels , with increasing insulin leading to decreased levels of igf - binding proteins 1 and 2 , thus increasing the bioavailability of igf . although not suggested by the formal statistical tests that we undertook , there is still a possibility of publication bias considering that the tests were likely to be underpowered . moreover , we can not exclude the possibility of residual confounding and bias because of misclassification . although the included studies attempted to control for various known risk factors , the possibility of residual or unmeasured confounding can not be ruled out . single - point measurement increases the chance of random measurement error , which may underestimate the reported associations . studies on the association between metabolic syndrome and cancer risk used different factors and cutoff points , which complicate comparisons between studies . additionally , metabolic factors were not directly measured in some cohorts but replaced either by proxy indicators of the factor ( i.e. , hypercholesterolemia as a proxy indicator of high triglyceride and/or low hdl cholesterol levels ) , self - reported diagnosis of diseases ( i.e. , diabetes , hypertension ) , or specific drug use ( antidiabetic , antihypertensive , and antidyslipidemic ) . however , there was a consistent positive association between studies , despite the use of different definitions . our pooled estimates for the primary end point were based on prospective analyses with detailed adjustment for a wide range of variables . we used uniform methods and subgroup analyses to better define associations across cancer types between sexes , populations , cancer subsites , and definitions of metabolic syndrome . moreover , this is the first meta - analysis that entailed a comprehensive search for all studies that assessed association between metabolic syndrome and cancer risk . findings from this meta - analysis , which includes many recently published studies , suggest that metabolic syndrome is associated with increased risk of common cancers . the excess risk of cancer conferred by metabolic syndrome is low to moderate and in part explained by accompanying obesity of hyperglycemia . nevertheless , the increasing prevalence of metabolic syndrome worldwide and the high incidence of some malignancies , particularly colorectal and breast cancers , imply that every year many cases of cancer are attributable to metabolic syndrome . preventive strategies ( primary prevention and early detection of cancer ) are urgently needed , as has been suggested for patients affected by fully developed diseases , such as diabetes ( 54 ) . moreover , patients with the metabolic syndrome , even in the absence of obesity or diabetes , should be encouraged to undergo appropriate cancer screenings , at least for some more frequently involved sites , as recommended for all people of their age and sex . more importantly , we need evidence of whether effective interventions to reduce the prevalence of metabolic syndrome in adult populations ( 55 ) will reduce cancer risk . the formulation of public health strategies based on sustained and bearable lifestyle changes can hopefully obtain significant results in the fight against cancer at the population level .