Body mass index and cancer risk: An umbrella review of meta-analyses of observational studies

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We aimed to clarify the evidence for association between body mass index (BMI) and cancer risk based on existing systematic review and meta-analyses. Methods: PubMed, Embase and Web of science were systematically searched to obtain systematic review and meta-analyses reporting association between BMI and cancer incidence. The methodological quality and strength of evidence of each meta-analysis were assessed by AMSTAR and GRADE respectively. We also assessed the heterogeneity and publication bias of all included meta-analyses. Results: Finally, 43 meta-analyses with 19 cancers were identified by this umbrella review. The result revealed that underweight was inversely associated with the incidence of brain tumors while was positively related to the risk of esophageal and lung cancer. Overweight would enhance the incidence of brain tumors, kidney cancer, endometrial cancer, ovarian cancer, multiple myeloma, bladder cancer and liver cancer. Obesity was related to the increased incidence of brain tumors, cervical cancer, kidney cancer, endometrial cancer, esophageal cancer, gastric cancer, ovarian cancer, multiple myeloma, gallbladder cancer, bladder cancer, colorectal cancer, liver cancer, thyroid cancer and Hodgkin’s lymphoma. What’s more, dose-response analysis was conducted by 10 studies and the results demonstrated that per 5Kg/m 2 increment of BMI was associated with 1.01 to 1.13 fold increased risk of general brain tumors, multiple myeloma, bladder cancer, pancreatic cancer, breast cancer, Non-Hodgkin’s lymphoma. And every 1Kg/m 2 increment of BMI was linked to 6% and 4% increment in the risk of kidney cancer and gallbladder cancer respectively. Conclusions: The evidence presented in this umbrella review showed that overweight and obesity were associated with increased risk of most cancers. We recommend that it is better to maintain BMI within the normal range to prevent the occurrence of tumors. However, prospective studies with high quality are needed to further investigate the association between BMI and various cancer risk. Body Mass Index Cancer Umbrella review Meta-analysis Systematic review Figures Figure 1 Figure 2 Figure 3 1. Introduction Excess body fatness, a growing public health problem worldwide, is most commonly measured by body mass index (BMI), which is used to classify overweight (BMI ≥ 25kg/m 2 ) and obesity (BMI ≥ 30kg/m 2 ) in adults. According to the data giving by WHO, more than 1.9 billion people who were 18 years and older were overweight in 2016, which accounted for 39% of adults. What’s more, over 650 million adults of these people were obese, which accounted for 13% of adults. Overweight and Obesity have been regarded to be related to many chronic diseases including hypertension, hypercholesterolemia, insulin resistance, type 2 diabetes, cardiovascular disease, osteoarthritis, kidney failure and liver disease (1-3). Furthermore, overweight and obesity were also reported to be risk factors of many cancers (4, 5). Cancer is a group of diseases which caused by tumorlike transformation of normal cells under selective stress. During this process, any change has the potentiality to promote the conversion of normal cells to tumor cells (6). Cell growth and proliferation must be coordinated with the presence of sufficient nutrients to support macromolecular synthesis. Obesity, a state of overnutrition, broke that balance, resulting the cellular growth factor signaling pathways to be activated for a long time and increasing the risk of tumor conversion (7). Renehan et al. performed a meta-analysis about BMI and incidence of cancer, which included 221 datasets from 141 articles and involved 282137 incident cases. The result revealed that increment of BMI was strongly related to incidence of many cancers in males and females respectively (8). The WCRF (World Cancer Research Fund) has also presented several common cancers related to obesity including esophageal adenocarcinoma and gallbladder, liver, pancreatic, renal, colorectal, advanced prostate, ovarian, endometrial, post-menopausal breast cancers. The factors caused tumors are diverse. Among these factors, lifestyle and environment were associated with 90% to 95% of all cancers, of which 14% to 20% were caused by obesity (9). Previous study has reported that 3.6% of all new tumors in the world are due to obesity, and during these new cancer cases, colon cancer, postmenopausal breast cancer as well as uterine cancer might account for more than 60% (10). Nowadays, a lot of systematic reviews and meta-analyses have reported the association between BMI and various cancers. However, to our knowledge, there has been no article extracting data from published systematic reviews and meta-analyses related to BMI and incidence of various cancers to be reported until now. Therefore, we performed an umbrella review to assess the quality of evidence and the extent of possible bias as well as systematically evaluate the associations between BMI and incidence of multiple cancers, which will help us better understand the influence of BMI on risk of different cancers. 2. Methods 2.1 Search strategy The umbrella review was designed and conducted in strict accordance with the guidelines (11). Three databases including PubMed, Embase, Web of science were systematically searched. The search algorithm used following terms: (BMI OR Body Mass Index OR underweight OR overweight OR obese OR obesity) And (cancer OR tumor OR carcinoma OR neoplasm OR malignancy) And (systematic review OR meta-analysis). We also conducted a manual screen of reference lists cited in all included articles. 2.2 Selection criteria BMI is calculated by dividing weight by the square of height. Based on the World Health Organization classification, BMI is divided into underweight (<18.5 kg/m 2 ), normal weight (18.5-24.9 kg/m 2 ), overweight (25-29.9 kg/m 2 ), and obese (≥30 kg/m 2 ). Systematic review and meta-analysis about BMI and various cancer risk were included regardless of the gender, race and region of participants. If more than one study reported the association between BMI and one kind of cancer risk, we will include the most recent one that having more participants. If a subgroup analysis was conducted by a meta-analysis based on the study design (case-control and cohort studies), we will include the results of cohort studies when the number of included literatures is more than three, otherwise, we would include the results of case-control studies. However, studies reporting BMI and other cancer outcomes including survival, mortality, prognosis, recurrence and so on were excluded. Systematic review without meta-analysis were also excluded. 2.3 Data extraction Data extraction was conducted independently by two authors (HJC and MKK). If there is a discrepancy, a third author (QD) will make the final decision. Data extracted from eligible articles include: 1) cancer outcomes, 2) category of exposure, 3) first author’s name, 4) publication year, 5) number of case and total participants, 6) meta-analysis metric, 7) summary effect size (OR, odd ratio; RR, relative risk; HR, hazard ratio) and 95% confidence intervals, 8) number of included studies, 9) study design (cohort, case-control), 10) fixed or random effect model, 11) heterogeneity, 12) publication bias (Egger’s test), 13) statistical significance. 2.4 Methodological and evidence quality assessment The methodological quality of included systematic review and meta-analysis was evaluated by the AMSTAR, a reliable, valid and widely used measurement tool based on 11 questions (12, 13). The strength of evidence was assessed by using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation), which classified the evidence as “very low”, “low”, “moderate” and “high” quality based on the assessment of risk of bias, inconsistence, indirectness, imprecision, publication bias and so on (14). 2.5 Data analysis We estimated the summary effect size and its 95% confidence interval (95% CI) using fixed-effects or random-effects models. To assess the heterogeneity among studies, we used the I 2 statistic and Cochran’s Q test. The heterogeneity will be considered to be substantial or considerable if I 2 was more than 50% or 75% respectively (15, 16). The publication bias was assessed by calculating an estimate through Egger’s regression test (17). P value < 0.10 was considered to be significant for Egger’s test. The outcome of dose-response meta-analysis was also extracted if data was available in the included articles. 3. Results 3.1 Literature review First, we retrieved 643 articles through searching database. Second, 358 studies relevant to BMI and cancer were remained after deduplicating. Then after browsing titles and abstracts, 265 articles were excluded. And next, 75 studies were also excluded after screening full text. The selection process and reasons for exclusion were presented in flow diagram (Figure 1). Finally, a total of 18 studies with 19 cancer outcomes and 43 meta-analyses were regarded as eligible for the umbrella review (Figure 2). 3.2 Characteristics of included meta-analyses The characteristic of 43 meta-analyses about 19 types of tumors are presented in the Table 1. Among these, 3 meta-analyses were about the relationship between underweight and cancer risk, and 14 meta-analyses reported the relationship between overweight and cancer risk. The association between obesity and cancer risk was investigated by 16 meta-analyses. What’ more, dose-response meta-analysis was conducted by 10 studies. According to statistical significance, we concluded that the results of 37 meta-analyses were significant association, while the results of 8 meta-analyses didn’t have significant association. 3.3 Association between BMI and various cancer risk Zhang et al. reported that underweight could decrease the occurrence risk of brain tumor (RR: 0.77, 95%CI: 0.64-0.93). However, overweight and obesity were associated with 12% (RR: 1.12, 95%CI: 1.05-1.19) and 34% (RR: 1.34, 95%CI: 1.15-1.56) higher brain tumor risk respectively. What’s more, the dose-response meta-analysis detected that every 5kg/m 2 increment of BMI was related to 13% (RR: 1.13, 95%CI: 1.07-1.20) a higher risk of overall brain tumors, however, the association was detected in meningiomas (RR: 1.19, 95%CI: 1.14-1.25) but not in glioma (RR: 1.07, 95%CI: 0.97-1.19) according to subgroup analysis (18). Based on the pooled results of 7 case-control studies, there was no significant association between overweight and cervical cancer (HR: 1.03, 95%CI: 0.81-1.25), but obesity could increase the risk by 40% (HR: 1.40, 95%CI: 1.08-1.71) when compared with normal BMI (19). The increment of weight was also linked to a higher risk of kidney cancer. Compared with normal weight, kidney cancer risk was increased by 35% (RR: 1.35, 95%CI: 1.27-1.43) for overweight people, and 76% (RR: 1.76, 95%CI: 1.61-1.91) for obese people. Moreover, the dose-response meta-analysis revealed that the kidney cancer risk increased by 6% (RR: 1.06, 95%CI: 1.05-1.06) for each 1kg/m 2 increment of BMI (20). A meta-analysis including 20 cohort studies reported a higher risk of endometrial cancer for overweight versus normal weight (RR: 1.34, 95%CI: 1.20-1.48) and obese versus normal weight (RR: 2.54, 95%CI: 2.27-2.81) (21). The results of another meta-analysis showed that underweight versus normal weight was related to a higher risk of esophageal cancer (RR: 1.78, 95%CI: 1.48-2.14). what’s more, obesity was linked to 51% (RR: 1.51, 95%CI: 1.21-1.89) higher occurrence risk of esophageal cancer. While no significant association was found between overweight and esophageal cancer (RR: 1.14, 95%CI: 0.98-1.30). However, it’s interesting that both overweight and obesity could increase risk of esophageal adenocarcinoma and decrease risk of esophageal squamous cell carcinoma according to subgroup analysis (22). Compared with normal weight, overweight and obesity were demonstrated to be related with a 4% (OR: 1.04, 95%CI: 0.96-1.12) and 13% (OR: 1.13, 95%CI: 1.03-1.24) increased risk of gastric cancer respectively. However, there was no significant statistical significance for the outcome of overweight versus normal weight. Moreover, the association between obesity and gastric cancer existed for males (OR: 1.27, 95%CI: 1.09-1.48), but not for females (OR: 1.04, 95%CI: 0.79-1.39) (23). For lung cancer, there was inverse association between BMI and occurrence risk. Being underweight could increase the risk of lung cancer (RR: 1.24, 95%CI: 1.20-1.27). On the contrary, overweight and obese could decrease the risk by 18% (RR: 0.82, 95%CI: 0.77-0.86) and 22% (RR: 0.78, 95%CI: 0.74-0.83) respectively compared with normal weight individuals. Furthermore, the dose-response meta-analysis showed nonlinear relationship, which revealed that per 5kg/m 2 increment of BMI was related to a 3% decrease in risk of overall lung cancer. All of these results demonstrated that increasing BMI might be a protective factor against lung cancer (24). Another meta-analysis demonstrated that the risk of ovarian cancer increased by 7% (RR: 1.07, 95%CI: 1.02-1.12) for overweight and 28% (RR: 1.28, 95%CI: 1.16-1.41) for obesity when compared with normal weight. When the analysis was stratified by menopausal status, it was observed that significant association between overweight/obesity and increased risk of ovarian cancer existed in premenopausal period (overweight: RR=1.31, 95%CI: 1.04-1.65; obesity: RR=1.50, 95%CI: 1.12-2.00) but not in postmenopausal status (overweight: RR=0.97, 95%CI: 0.76-1.24; obesity: RR=0.93, 95%CI: 0.61-1.42) (25). Similarly, increase of BMI was also associated with a higher risk of multiple myeloma with overweight (RR: 1.12, 95%CI: 1.07-1.18) and obesity (RR: 1.21, 95%CI: 1.08-1.35) versus normal weight. And dose-response meta-analysis reported that every 5kg/m 2 increment of BMI would increase the risk of multiple myeloma by 12% (RR: 1.12, 95%CI: 1.08-1.16) (26). In addition, overweight proved to be related to higher incidence of gallbladder cancer when compared with normal weight (RR: 1.10, 95%CI: 0.98-1.23), though this did not reach significance. While the risk of gallbladder cancer for obesity people will increase about 1.58 fold compared with normal-weight people (RR: 1.58, 95%CI:1.43-1.75). According to dose-response analysis, per 1kg/m 2 increment of BMI was related to 4% increased risk of gallbladder cancer (RR: 1.04, 95%CI:1.02-1.06) (27). Compared with subjects in the normal weight category, the risk of bladder cancer was statistically significantly elevated among people categorized as overweight (RR: 1.07, 95%CI: 1.01-1.14) or obese (RR: 1.10, 95%CI: 1.06-1.14). In the dose-response meta-analysis, BMI was related to bladder cancer risk in a linear relationship and the risk increased 4% for each 5kg/m 2 increment (RR: 1.04, 95%CI: 1.01-1.07) (28). A meta-analysis discovered an increase of 33% (RR: 1.33, 95%CI: 1.25-1.42) risk of colorectal cancer in obese participants compared with the normal weight participants. When the analysis was stratified by cancer type, the result revealed that obesity was linked to 47% increment in the risk of colon cancer and 15% increment in the risk of rectal cancer (29). The pooled results combined for overweight or obesity versus normal categories of BMI indicated that increase in body weight was associated with a significantly increased risk of liver cancer and the risk of liver cancer increase about 1.36 fold in overweight (RR: 1.36, 95%CI: 1.02-1.81) and 1.77 fold in obese people (RR: 1.77, 95%CI: 1.56-2.01) (30). The study including 9504 pancreatic cancer cases among 5037555 participants showed a potential non-liner association between BMI and pancreatic cancer risk by dose-response meta-analysis. And an increased risk of 10% (RR: 1.10, 95%CI: 1.07-1.14) was aroused by every 5kg/m 2 increment in BMI (31). However, no significant association was detected between BMI and risk of prostate cancer (overweight: HR=1.02, 95%CI: 0.98-1.05; obese: HR=0.97, 95%CI: 0.93-1.01; 5kg/m 2 increment: HR=1.01, 95%CI: 0.99-1.04) (32). Association between obesity and thyroid cancer were examined in 32 studies, and meta-analysis of included studies demonstrated that obese could increase the risk of thyroid cancer by 33% (RR: 1.33, 95%CI: 1.24-1.42) compared to people with normal categories of BMI. What’s more, obese men and women were both significantly at risk of thyroid cancer according to subgroup analysis by sex (33). When the risk estimates from 12 cohort studies of BMI and breast cancer incidence were combined, a 5kg/m 2 increment in BMI was associated with a 2% (RR: 1.02, 95%CI: 1.01-1.04) increased risk of breast cancer (34). Based on random effect model, compared to normal weight people, the estimated RR of Hodgkin’s lymphoma was 0.97 for overweight (RR: 0.97, 95%CI: 0.85-1.12) and 1.41 for obese (RR: 1.41, 95%CI: 1.14-1.75), which indicated that the risk of Hodgkin’s lymphoma increased by 41% for obesity, while no significant statistical significance was detected between overweight and risk of Hodgkin’s lymphoma (35). The dose-response meta-analysis of 16 prospective cohort studies showed that every 5kg/m 2 increase of BMI corresponded to a 7% (RR: 1.07, 95%CI: 1.04-1.10) increased risk of in Non-Hodgkin’s lymphoma (35). 3.4 Heterogeneity of included meta-analyses Three meta-analyses reporting relationship between underweight and cancer risk presented low levels heterogeneity (I 2 < 25%). The association between overweight and cancer risk was reported by 14 meta-analyses. Low levels heterogeneity (I 2 75%) was reported by 2 meta-analyses. Among 15 meta-analyses related to obesity, low levels heterogeneity (I 2 75%). About 10 dose-response meta-analyses, 2 dose-response meta-analyses (I 2 < 25%) showed low levels heterogeneity, and moderate-to-high levels heterogeneity was presented in 4 dose-response meta-analyses (I 2 25%-75%), while high levels heterogeneity was only reported in one dose-response meta-analysis (I 2 > 75%). However, the I 2 statistic could not be found in 3 dose-response meta-analyses. 3.5 Publication bias of included meta-analyses In three meta-analyses about underweight, all detected a significant publication bias. Among 14 meta-analyses about overweight, two meta-analyses reporting kidney cancer and gastric cancer detected a significant publication bias and 8 meta-analyses did not have, while the data of publication bias were not available for 4 meta-analyses. Regarding to 15 meta-analyses about obesity, 2 meta-analyses involving gallbladder cancer and thyroid cancer had significant publication bias and 10 meta-analyses did not have, however, the data of publication bias were not available for 3 meta-analyses. About 10 dose-response meta-analyses, significant publication bias was found only in one dose-response meta-analysis and not found in 6 dose-response meta-analyses, while the data of publication bias were not available for 3 dose-response meta-analyses. 3.6 AMSTAR assessment and GRADE classification The AMSTAR was used to assess the methodological quality of all included studies (supplementary Table S1), and the result showed that median AMSTAR score was 7 (range 5-9; IQR 7-8) (Table 2). The AMSTAR score of twenty meta-analyses (47%) was more than 7 and it was less than 7 in twenty-three meta-analyses (53%). The GRADE system was used to assess the quality of evidence of all outcomes (supplementary Table S2). The quality of most evidence evaluated by GRADE score was classified as low or very low quality for the fact that the studies included in our umbrella review were mainly cohort and case-control studies (Table 2), which resulted in serious risk of bias. 4. Discussion 4.1. Principal findings and interpretation A total of 43 meta-analyses with 19 cancers were included in this umbrella review. The result revealed that underweight was inversely associated with the incidence of general brain tumors while was positively related to the risk of esophageal and lung cancer. Overweight would enhance the incidence of brain tumors, kidney cancer, endometrial cancer, ovarian cancer, multiple myeloma, bladder cancer and liver cancer. Obesity was related to the increased incidence of brain tumors, cervical cancer, kidney cancer, endometrial cancer, esophageal cancer, gastric cancer, ovarian cancer, multiple myeloma, gallbladder cancer, bladder cancer, colorectal cancer, liver cancer, thyroid cancer and Hodgkin’s lymphoma. What’s more, dose-response analysis was conducted by 10 studies and the results demonstrated that per 5Kg/m 2 increment of BMI was associated with 1.01 to 1.13 fold increased risk of general brain tumors, multiple myeloma, bladder cancer, pancreatic cancer, breast cancer, Non-Hodgkin’s lymphoma (Figure 3). And every 1Kg/m 2 increment of BMI was linked to 6% increment in the risk of kidney cancer and 4% increment in the risk of gallbladder cancer. There exist a lot of potential mechanisms to explain the relationship between increment of BMI and risk of various cancers. First of all, inflammation might be a bridge between obesity and tumors. Obesity itself is a chronic inflammatory condition (36). The initiation of obesity-associated inflammation is associated with metabolic process and excessive nutrient consumption may be the major contributor (37). This kind of inflammation is mainly existed in the white adipose tissue, a special metabolic tissue made up of lipocyte (37). Adipose tissues have been regarded as one of endocrine organs, which can secrete a class of substances called adipokines including growth factors, hormones, cytokines and inflammatory factors. Adipokines could take part in a variety of biological processes in the body involving inflammation reaction, immune response, glucose metabolism and insulin sensitivity (38). Adipokines involved in the inflammation response process include interleukin 1β, 6 and 8, tumor necrosis factor α (TNFα), C-reactive protein (CRP), monocyte chemotactic protein 1 (MCP-1) and transforming growth factor β (TGFβ) and so on (38, 39). Excessive adipose tissues will also cause ischemia and hypoxia in tissues as well as cell death and the formation of crown-like structures (CLS), which is the biomarker of inflammation (40). Furthermore, too much nutrients and obesity could activate the metabolic signaling pathways mediated by nuclear factor κ B (NFκB), protein kinase R and c-Jun N-terminal kinase (JNK), which could result in a low-level inflammatory response in the body (41, 42). Epidemiological study and experimental data have demonstrated the association between chronic inflammation and cancer (43). And the fact that anti-inflammatory therapies play a role in tumor treatment is further evidence of this relationship (44). As we all know, the chronic infections of helicobacter pylori, human papillomavirus and hepatitis virus were associated with gastric cancer, cervical cancer and liver cancer respectively (40). What’s more, people with inflammatory bowel diseases are more likely to develop colon cancer (45). Chronic inflammatory responses are involved in the initiation, proliferation and progression of cancer. On the one hand, a sustained inflammatory response can leave cells in oxidative stress for long periods of time, resulting in DNA and protein damage, inhibition of apoptosis and activation of proto-oncogenes, which are related to the occurrence and development of tumors (46). On the other hand, some inflammatory factors not only play a key role in the inflammatory responses but also have a effect of promoting cancer occurrence. An animal experiment has showed that overexpression of IL-1β could result in gastric carcinoma in mice without infection of helicobacter (47). IL-6 has also been reported to promote tumorigenesis by meditating the signaling pathway of gp130/JAK/STAT3 (48). Secondly, the relationship between being overweight and obesity and increasing cancer risk could be explained by an imbalance between leptin and adiponectin. Produced by white adipose tissue， adiponectin was reported to play a significant protective role in carcinogenesis (49). On the one hand, adiponectin can activate the receptor-meditated signal path to have a direct effect on tumor cells. And it works mainly through three known receptors including adipoR1, adipoR2 and T-cadherin (50, 51). On the other hand, it has the properties of anti-inflammatory, regulating insulin sensitivity, influencing angiogenesis and inhibiting the growth, proliferation, invasion, metastasis of tumor cells (52). In addition to being influenced by genetic factors, diet and physical activity, the level of adiponectin was also affected by obesity. In obese people, circulatory adiponectin level will decrease and this may result in the systematic chronic inflammatory and increase the risk of various cancers. Numerous studies have demonstrated that low adiponectin levels were related to increasing risk of endometrial cancer, postmenopausal breast cancer and colon cancer (53-55). Leptin is also one of adipokines, whose level in the circulation system is positively associated with the total body fat (56). Being opposite to adiponectin, leptin has been demonstrated to have the effect of promoting inflammatory response and angiogenesis, inhibiting apoptosis and stimulating cell growth, migration, invasion, which might play a significant role in the development and risk of cancers (57-59). Previous studies have shown that elevated serum leptin was correlated to the increased risk of endometrial cancer and breast cancer (60, 61). Thirdly, insulin resistance/hyperinsulinemia is one of the characteristics of obesity (62). The level of adiponectin will decrease in obesity, which could result in insulin resistance, because adiponectin could stimulate AMPK (AMP-activated protein kinase) phosphorylation and activation to enhance insulin sensitivity (63). Increased inflammatory factors such as IL-6 and TNF-a in obese people could also lead to insulin resistance by activating the JNK (c-jun amino-terminal kinase) and IKK-β (IκB kinase-β)/NF-κB (nuclear factor-κB) pathways (64). Insulin is not only a metabolic hormone, but also a growth factor that can promote mitosis especially on malignant cells with overexpressed insulin receptor (IR) (65). The biological effect of insulin is achieved by binding to IRs including IR-A and IR-B subtypes and the activation of former subtype will make a stronger mitogenic effect (66). Previous studies have reported that people with diabetes, obesity and other characteristics of insulin resistance/hyperinsulinemia have a higher risk of endometrial cancer, early gastric cancer and breast cancer (67-69). Increased insulin could also decrease the level of IGF-1-binding proteins synthesized by liver, therefore the free IGF-1 will increase (65, 70). IGF-1 has been reported to be a significant mediator for realizing the effect of growth hormone, which could promote proliferation and differentiation of cells as well as inhibit cell apoptosis. What’s more the activation of IGF-1R can induce malignant transformation and the transformation will stop when suppresses the expression of IGF-1R by disrupting the IGF-1R gene targetedly (71, 72). Many prospective studies have shown possible association between high level of IGF-1 in circulation and increased risk of multiple cancers including prostate cancer, colorectal cancer and breast cancer (73-75). In addition, the potential biological mechanisms linking overweight/obesity and cancer also comprise the level of sex hormones, ectopic fat deposition as well as changes of tumor microenvironment (76, 77). 4.2. Strengths and weaknesses of the study Umbrella review has become increasing popular especially in the last 2 years, which has been regarded as one of highest levels of evidence synthesis for the fact that it includes meta-analysis with a relatively large number of subjects. As we all know, this study is the first umbrella review investigating BMI and cancer risk. However, there also existed several limitations. First of all, the studies included in the meta-analyses of our umbrella review were mainly cohort and case-control studies while lack experimental studies, which will have an impact on the overall quality of the study. Secondly, this umbrella review was a comprehensive evaluation of existing systematic reviews and meta-analyses about BMI and cancer risk. Consequently, some related studies that have not been published before our searching will not been included in this study. Thirdly, among the 42 meta-analysis, 5 meta-analysis have reported significant publication bias and the data about publication bias were not available for 10 meta-analysis. Last but not least, the risk of tumor occurrence is the result of many factors, which might exert impact on associations between BMI and cancer incidence. 5. Conclusion In conclusion, the evidence presented in this umbrella review showed that overweight and obesity were associated with increased risk of most cancer outcomes. We recommend that it is better to maintain BMI within the normal range to prevent the occurrence of tumors. However, prospective studies with high quality are needed to further investigate the association between BMI and various cancer risk. Abbreviations BMI, Body mass index; WCRF, World Cancer Research Fund; AMSTAR, a measurement tool to assess systematic review; GRADE, Grading of Recommendations, Assessment, Development and Evaluation; CI, confidence interval; HR, hazard ratio; RR, relative risk; OR, odds ratio; IL-1β, interleukin 1β; IL-6, interleukin 6; IL-8, interleukin 8; TNFα, tumor necrosis factor α; CRP, C-reactive protein; MCP-1,monocyte chemotactic protein 1; TGFβ, transforming growth factor β; CLS, crown-like structures; NFκB, nuclear factor κ B; JNK, c-Jun N-terminal kinase; AMPK, AMP-activated protein kinase; IR, insulin receptor Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interesting The authors declare that they have no competing interesting. Funding This work was supported by the Project of Science and Technology Department of Sichuan Province (Grant ID: 2021YFS0117). Author contributions Junhao Chen, Kaimin Ke and Zhenghuan Liu contributed equally in this study and were responsible for conception, methodology and drafting of the manuscript. Zhufeng Peng and Luchen Yang contributed to the data extraction, analysis and interpretation. Software operation and drawing graph were conducted by Linchun Wang and Jing Zhou. Qiang Dong was responsible for revising the draft and supervision. Acknowledgements We would like to thank all authors of original studies that were included in this study. References Martin-Rodriguez E, Guillen-Grima F, Martí A, Brugos-Larumbe A. Comorbidity associated with obesity in a large population: The APNA study. Obesity research & clinical practice. 2015;9(5):435–47. Kolb R, Sutterwala FS, Zhang W. Obesity and cancer: inflammation bridges the two. Current opinion in pharmacology. 2016;29:77–89. 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Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism: clinical and experimental. 2019;92:121–35. Tables Table 1: Associations between BMI and cancer outcomes. Outcome Category Study No. of cases/total MA metric Estimates 95%CI No. of studies Cohort Case- control Effects model I 2 Egger test P value Significant association Brain tumors Cervical cancer kidney cancer endometrial cancer esophageal cancer gastric cancer lung cancer underweight vs normal overweight vs normal obese vs normal 5kg/m 2 increment overweight vs normal obese vs normal overweight vs normal obese vs normal 1kg/m 2 increment overweight vs normal obese vs normal underweight vs normal overweight vs normal obese vs normal overweight vs normal obese vs normal underweight vs normal overweight vs normal obese vs normal 5kg/m 2 increment Zhang 2016 Poorolajal 2016 Liu 2018 Jenabi 2015 Tian 2020 Lin 2014 Duan 2015 2629/1509381 8221/3731438 8565/3853617 11395/3886677 NA/79873 NA/79873 15535/8953478 NA/NA 15535/8953478 NA/32210437 NA/32210437 >4586/>1288013 >10188/>1916105 >7480/>1711564 25686/4692721 26508/4983784 NA/NA 56189/7253941 56189/7253941 43393/NA RR RR RR RR HR HR RR RR RR RR RR RR RR RR OR OR RR RR RR RR 0.77 1.12 1.34 1.13 1.03 1.40 1.35 1.76 1.06 1.34 2.54 1.78 1.14 1.51 1.04 1.13 1.24 0.82 0.78 0.97 0.64-0.93 1.05-1.19 1.15-1.56 1.07-1.20 0.81-1.25 1.08-1.71 1.27-1.43 1.61-1.91 1.05-1.06 1.20-1.48 2.27-2.81 1.48-2.14 0.98-1.30 1.21-1.89 0.96-1.12 1.03-1.24 1.20-1.27 0.77-0.86 0.74-0.83 0.96-0.98 6 13 15 15 7 7 24 20 24 20 20 13 25 23 15 14 14 29 29 26 4 8 9 9 0 0 24 20 24 20 20 9 16 14 12 11 14 29 29 26 2 5 6 6 7 7 0 0 0 0 0 4 9 9 3 3 0 0 0 0 random random random random random random random random random random random random random random random random fixed random random random 1.7% 3.4% 71.6% 69.7% 21.2% 13.7% 39.4% 43.3% no 83.2% 72.4% 22.6% 77.2% 88.1% 45.8% 7.7% 24.3% 35.7% 40.3% 60.5% 0.096 0.374 0.349 0.006 0.945 0.169 0.031 0.671 0.265 0.253 0.160 0.019 >0.1 >0.1 0.08 0.52 0.09 0.70 0.23 NA yes yes yes yes no yes yes yes yes yes yes yes no yes no yes yes yes yes yes Table 1 (continued) Outcome Category Study No. of cases/total MA metric Estimates 95%CI No. of studies Cohort Case- control Effects model I 2 Egger test P value Significant association ovarian cancer multiple myeloma gallbladder cancer bladder cancer colorectal cancer liver cancer pancreatic cancer prostate cancer overweight vs normal obese vs normal overweight vs normal obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 1Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment obese vs normal overweight vs normal obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment Liu 2015 Wallin 2011 Li 2016 Sun 2015 Ma 2013 Sohn 2021 Aune 2012 Harrison 2020 29559/2145013 29631/2149923 8982/5708495 8879/4927212 8982/5708495 5505/9164172 5902/9236604 5558/4659063 NA/NA 38072/14201500 NA/NA 85935/8115689 NA/103972 NA/5900864 9504/5037555 NA/NA 32277/252771 157990/9351795 RR RR RR RR RR RR RR RR RR RR RR RR HR HR RR HR HR HR 1.07 1.28 1.12 1.21 1.12 1.10 1.58 1.04 1.07 1.10 1.04 1.33 1.36 1.77 1.10 1.02 0.97 1.01 1.02-1.12 1.16-1.41 1.07-1.18 1.08-1.35 1.08-1.16 0.98-1.23 1.43-1.75 1.02-1.06 1.01-1.14 1.06-1.14 1.01-1.07 1.25-1.42 1.02-1.81 1.56-2.01 1.07-1.14 0.98-1.05 0.93-1.01 0.99-1.04 25 25 15 14 15 12 15 7 14 15 7 41 5 16 23 10 13 30 12 12 15 14 15 7 10 7 14 15 7 41 5 16 23 NA NA NA 13 13 0 0 0 5 5 0 0 0 0 0 0 0 0 NA NA NA fixed random random random random random random random random random random random random random random random random random 11.3% 54.2% 0.0% 34.1% NA 31.6% 1.9% NA 37.6% 15.5% 32.1% 68.9% 56% 51% 19% 0.0% 0.0% 79.9% 0.31 0.37 NA NA 0.77 0.398 0.008 0.769 NA 0.712 NA 0.166 NA NA 0.36 NA NA NA yes yes yes yes yes no yes yes yes yes yes yes yes yes yes no no no Table 1 (continued) Outcome Category Study No. of cases/total MA metric Estimates 95%CI No. of studies Cohort Case- control Effects model I 2 Egger test P value Significant association thyroid cancer breast cancer Hodgkin's lymphoma Non-Hodgkin's lymphoma obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment Ma 2015 Liu 2018 Larsson 2011 Larsson 2011 10881/12620676 19480/22728674 1557/3679738 1492/2367388 17291/6035915 RR RR RR RR RR 1.33 1.02 0.97 1.41 1.07 1.24-1.42 1.01-1.04 0.85-1.12 1.14-1.75 1.04-1.10 32 12 5 3 16 24 12 5 3 16 8 0 0 0 0 random random random random random 24.9% 74.2% 16.7% 21..6% 9.6% <0.01 0.74 0.10 0.74 0.27 yes yes no yes yes MA, meta-analysis; CI, confidence interval; RR, relative risk; NA, not available; HR, hazard ratio; OR, odds ratio. Table 2: Assessments of AMSTAR score and GRADE classification Outcome Category Study AMSTAR GRADE Brain tumors Cervical cancer Kidney cancer Endometrial cancer Esophageal cancer Gastric cancer Lung cancer Ovarian cancer underweight vs normal overweight vs normal obese vs normal 5kg/m 2 increment overweight vs normal obese vs normal overweight vs normal obese vs normal 1kg/m 2 increment overweight vs normal obese vs normal underweight vs normal overweight vs normal obese vs normal overweight vs normal obese vs normal underweight vs normal overweight vs normal obese vs normal 5kg/m 2 increment overweight vs normal obese vs normal Zhang 2016 Poorolajal 2016 Liu 2018 Jenabi 2015 Tian 2020 Lin 2014 Duan 2015 Liu 2015 8 8 8 8 8 8 7 7 7 8 8 9 9 9 8 8 7 7 7 6 6 6 very low low very low very low very low very low very low low low very low very low very low very low very low very low low very low low low very low low very low Table 2 (continued) Outcome Category Study AMSTAR GRADE Multiple myeloma Gallbladder cancer Bladder cancer Colorectal cancer Liver cancer Pancreatic cancer Prostate cancer Thyroid cancer Breast cancer Hodgkin's lymphoma Non-Hodgkin's lymphoma overweight vs normal obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 1Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment obese vs normal overweight vs normal obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment obese vs normal 5Kg/m 2 increment overweight vs normal obese vs normal 5Kg/m 2 increment Wallin 2011 Li 2016 Sun 2015 Ma 2013 Sohn 2021 Aune 2012 Harrison 2020 Ma 2015 Liu 2018 Larsson 2011 Larsson 2011 7 7 8 8 8 8 5 6 5 8 7 7 9 6 6 6 8 7 7 7 7 very low very low very low very low very low very low very low low very low very low very low very low moderate very low very low very low very low very low very low very low low AMSTAR, a measurement tool to assess systematic review; GRADE, Grading of Recommendations, Assessment, Development and Evaluation Additional Declarations No competing interests reported. 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Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDCCA4wNQNJGjp+9sfHhBxK0pBlL9hxuNpYgTguYPJRoMCO9TYCHGB18x5MbPxf8OpBgIPmwjUGCwU5Ot4GAFskzD5ulZ/bdyTOXTmx7UMCQbGx2gIAWgxuJDdK8Pc+KLWcnthtIMBxI3EaElubfvD2HEzfcPNgmwUOkljZpnh9ALTcYidQC9EubNW8DKJATgYFsQIRf+I6nP77N8wcUlccfPvxQYSdHUAsDQwIDA2Mb3J0ElUO1MPwhSuUoGAWjYBSMVAAAL+1MW9jyLU0AAAAASUVORK5CYII=","orcid":"","institution":"Department of Urology, Institute of urology, West China Hospital, Sichuan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2022-07-25 13:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1894100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1894100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24741988,"identity":"8201e2ff-ab79-4230-9c80-4ed901061a1e","added_by":"auto","created_at":"2022-08-03 18:20:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114910,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the selection process\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/e709a0bd790155e1fde5f685.png"},{"id":24741468,"identity":"ea93da88-b722-41fb-afe8-7b8d5d5f841f","added_by":"auto","created_at":"2022-08-03 18:15:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56705,"visible":true,"origin":"","legend":"\u003cp\u003eMap of cancer outcomes related to Body mass index\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/888d76ad630240ffe61510a5.png"},{"id":24741467,"identity":"627d4507-b748-46df-8927-d16635b7174d","added_by":"auto","created_at":"2022-08-03 18:15:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48624,"visible":true,"origin":"","legend":"\u003cp\u003ePercent increase of various cancers risk for per 5Kg/m\u003csup\u003e2\u003c/sup\u003e increment of Body mass index\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/5dee0bf0ce09842dd3a63a1d.png"},{"id":25298225,"identity":"b29306b5-b47a-4c8b-a5da-9479474c8490","added_by":"auto","created_at":"2022-08-17 04:59:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":599871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/5c5b6995-cc4c-46f9-ad12-55cd02272190.pdf"},{"id":24741987,"identity":"b1cdea5d-2315-46dc-91a9-28c31312e5cc","added_by":"auto","created_at":"2022-08-03 18:20:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21014,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/1c0b294148f169eacc6e94fa.docx"},{"id":24741471,"identity":"0487223d-0124-44cb-aa2f-d40c246e86b9","added_by":"auto","created_at":"2022-08-03 18:15:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22260,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1894100/v1/e757985c75d84e28e2025354.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body mass index and cancer risk: An umbrella review of meta-analyses of observational studies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExcess body fatness, a growing public health problem worldwide, is most commonly measured by body mass index\u0026nbsp;(BMI), which is used to classify overweight (BMI \u0026ge; 25kg/m\u003csup\u003e2\u003c/sup\u003e) and obesity (BMI \u0026ge; 30kg/m\u003csup\u003e2\u003c/sup\u003e) in adults. According to the data giving by WHO, more than 1.9 billion people who were 18 years and older were overweight in 2016,\u0026nbsp;which accounted for 39% of adults. What\u0026rsquo;s more, over 650 million adults of these people were obese, which accounted for 13% of adults.\u0026nbsp;Overweight and Obesity have been regarded to be related to many chronic diseases including hypertension, hypercholesterolemia, insulin resistance, type 2 diabetes, cardiovascular disease, osteoarthritis, kidney failure and liver disease\u0026nbsp;(1-3). Furthermore, overweight and obesity were also reported to be risk factors of many cancers\u0026nbsp;(4, 5). Cancer is a group of diseases which caused by tumorlike transformation of normal cells under selective stress. During this process, any change has the potentiality to promote the conversion of normal cells to tumor cells\u0026nbsp;(6). Cell growth and proliferation must be coordinated with the presence of sufficient nutrients to support macromolecular synthesis. Obesity, a state of overnutrition, broke that balance, resulting the cellular growth factor signaling pathways to be activated for a long time and increasing the risk of tumor conversion\u0026nbsp;(7). Renehan et al. performed a meta-analysis about BMI and incidence of cancer, which included 221 datasets from 141 articles and involved 282137 incident cases. The result revealed that increment of BMI was strongly related to incidence of many cancers in males and females respectively\u0026nbsp;(8). The WCRF (World Cancer Research Fund) has also presented several common cancers related to obesity including esophageal adenocarcinoma and gallbladder, liver, pancreatic, renal, colorectal, advanced prostate, ovarian, endometrial, post-menopausal breast cancers. The factors caused tumors are diverse. Among these factors, lifestyle and environment were associated with 90% to 95% of all cancers, of which 14% to 20% were caused by obesity\u0026nbsp;(9). Previous study has reported that 3.6% of all new tumors in the world are due to obesity, and during these new cancer cases, colon cancer, postmenopausal breast cancer as well as uterine cancer might account for more than 60%\u0026nbsp;(10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNowadays, a lot of systematic reviews and meta-analyses have reported the association between BMI and various cancers. However, to our knowledge, there has been no article extracting data from published systematic reviews and meta-analyses related to BMI and incidence of various cancers to be reported until now. Therefore, we performed an umbrella review to assess the quality of evidence and the extent of possible bias as well as systematically evaluate the associations between BMI and incidence of multiple cancers, which will help us better understand the influence of BMI on risk of different cancers.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1 Search strategy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe umbrella review was designed and conducted in strict accordance with the guidelines\u0026nbsp;(11). Three databases including PubMed, Embase, Web of science were systematically searched. The search algorithm used following terms: (BMI\u0026nbsp;OR Body Mass Index OR underweight OR overweight OR obese\u0026nbsp;OR obesity) And (cancer OR tumor OR carcinoma\u0026nbsp;OR neoplasm\u0026nbsp;OR malignancy) And (systematic review\u0026nbsp;OR meta-analysis). We also conducted a manual screen of reference lists cited in all included articles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 Selection criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI is calculated by dividing weight by the square of height. Based on the World Health Organization classification, BMI is divided into underweight (\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026ge;30 kg/m\u003csup\u003e2\u003c/sup\u003e). Systematic review and meta-analysis about BMI and various cancer risk were included regardless of the gender, race and region of participants. If more than one study reported the association between BMI and one kind of cancer risk, we will include the most recent one that having more participants. If a subgroup analysis was conducted by a meta-analysis based on the study design (case-control and cohort studies), we will include the results of cohort studies when the number of included literatures is more than three, otherwise, we would include the results of case-control studies. However, studies reporting BMI and other cancer outcomes including survival, mortality, prognosis, recurrence and so on were excluded. Systematic review without meta-analysis were also excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 Data extraction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extraction was conducted independently by two authors (HJC and MKK). If there is a discrepancy, a third author (QD) will make the final decision. Data extracted from eligible articles include: 1) cancer outcomes, 2) category of exposure, 3) first author\u0026rsquo;s name, 4) publication year, 5) number of case and total participants, 6) meta-analysis metric, 7) summary effect size (OR, odd ratio; RR, relative risk; HR, hazard ratio) and 95% confidence intervals, 8) number of included studies, 9) study design (cohort, case-control), 10) fixed or random effect model, 11) heterogeneity, 12) publication bias (Egger\u0026rsquo;s test), 13) statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4 Methodological and evidence quality assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methodological quality\u0026nbsp;of included systematic review and meta-analysis was evaluated by the\u0026nbsp;AMSTAR, a reliable, valid and widely used measurement tool based on 11 questions\u0026nbsp;(12, 13). The strength of evidence was assessed by using the GRADE\u0026nbsp;(Grading of Recommendations, Assessment, Development and Evaluation), which classified the evidence as \u0026ldquo;very low\u0026rdquo;, \u0026ldquo;low\u0026rdquo;, \u0026ldquo;moderate\u0026rdquo; and \u0026ldquo;high\u0026rdquo; quality based on the assessment of risk of bias, inconsistence, indirectness, imprecision, publication bias and so on\u0026nbsp;(14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5 Data analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated the summary effect size and its 95% confidence interval (95% CI) using fixed-effects or random-effects models. To assess the heterogeneity among studies, we used the I\u003csup\u003e2\u003c/sup\u003e statistic and Cochran\u0026rsquo;s Q test. The heterogeneity will be considered to be substantial or considerable if I\u003csup\u003e2\u003c/sup\u003e was more than 50% or 75% respectively (15, 16). The publication bias was assessed by calculating an estimate through Egger\u0026rsquo;s regression test (17). P value \u0026lt; 0.10 was considered to be significant for Egger\u0026rsquo;s test. The outcome of dose-response meta-analysis was also extracted if data was available in the included articles.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Literature review\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we retrieved 643 articles through searching database. Second, 358 studies relevant to BMI and cancer were remained after deduplicating. Then after browsing titles and abstracts, 265 articles were excluded. And next, 75 studies were also excluded after screening full text. The selection process and reasons for exclusion were presented in flow diagram (Figure 1). Finally, a total of 18 studies with 19 cancer outcomes and 43 meta-analyses were regarded as eligible for the umbrella review (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Characteristics of included meta-analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristic of 43 meta-analyses about 19 types of tumors are presented in the Table 1. Among these, 3 meta-analyses were about the relationship between underweight and cancer risk, and 14 meta-analyses reported the relationship between overweight and cancer risk. The association between obesity and cancer risk was investigated by 16 meta-analyses. What\u0026rsquo; more, dose-response meta-analysis was conducted by 10 studies. According to statistical significance, we concluded that the results of 37 meta-analyses were significant association, while the results of 8 meta-analyses didn\u0026rsquo;t have significant association.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Association between BMI and various cancer risk\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhang et al. reported that underweight could decrease the occurrence risk of brain tumor (RR: 0.77, 95%CI: 0.64-0.93). However, overweight and obesity were associated with 12% (RR: 1.12, 95%CI: 1.05-1.19) and 34% (RR: 1.34, 95%CI: 1.15-1.56) higher brain tumor risk respectively. What\u0026rsquo;s more, the dose-response meta-analysis detected that every 5kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was related to 13% (RR: 1.13, 95%CI: 1.07-1.20) a higher risk of overall brain tumors, however, the association was detected in meningiomas\u0026nbsp;(RR: 1.19, 95%CI: 1.14-1.25)\u0026nbsp;but not in glioma\u0026nbsp;(RR: 1.07, 95%CI: 0.97-1.19) according to subgroup analysis\u0026nbsp;(18). Based on the pooled results of 7 case-control studies, there was no significant association between overweight and cervical cancer (HR: 1.03, 95%CI: 0.81-1.25), but obesity could increase the risk by 40% (HR: 1.40, 95%CI: 1.08-1.71) when compared with normal BMI\u0026nbsp;(19). The increment of weight was also linked to a higher risk of kidney cancer. Compared with normal weight, kidney cancer risk was increased by 35% (RR: 1.35, 95%CI: 1.27-1.43) for overweight people, and 76% (RR: 1.76, 95%CI: 1.61-1.91) for obese people. Moreover, the dose-response meta-analysis revealed that the kidney cancer risk increased by 6% (RR: 1.06, 95%CI: 1.05-1.06) for each 1kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI\u0026nbsp;(20). A\u0026nbsp;meta-analysis including 20 cohort studies reported a higher risk of endometrial cancer for overweight versus normal weight (RR: 1.34, 95%CI: 1.20-1.48) and obese versus normal weight (RR: 2.54, 95%CI: 2.27-2.81)\u0026nbsp;(21).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of another meta-analysis showed that underweight versus normal weight was related to a higher risk of esophageal cancer (RR: 1.78, 95%CI: 1.48-2.14). what\u0026rsquo;s more, obesity was linked to 51% (RR: 1.51, 95%CI: 1.21-1.89) higher occurrence risk of esophageal cancer. While no significant association was found between overweight and esophageal cancer (RR: 1.14, 95%CI: 0.98-1.30). However, it\u0026rsquo;s interesting that both overweight and obesity could increase risk of esophageal adenocarcinoma and decrease risk of esophageal squamous cell carcinoma according to subgroup analysis\u0026nbsp;(22). Compared with normal weight, overweight and obesity were demonstrated to be related with a 4% (OR: 1.04, 95%CI: 0.96-1.12) and 13% (OR: 1.13, 95%CI: 1.03-1.24) increased risk of gastric cancer respectively. However, there was no significant statistical significance for the outcome of overweight versus normal weight. Moreover, the association between obesity and gastric cancer existed for males (OR: 1.27, 95%CI: 1.09-1.48), but not for females (OR: 1.04, 95%CI: 0.79-1.39)\u0026nbsp;(23). For lung cancer, there was inverse association between BMI and occurrence risk. Being underweight could increase the risk of lung cancer\u0026nbsp;(RR: 1.24, 95%CI: 1.20-1.27). On the contrary, overweight and obese could decrease the risk by 18% (RR: 0.82, 95%CI: 0.77-0.86) and 22% (RR: 0.78, 95%CI: 0.74-0.83) respectively compared with normal weight individuals. Furthermore, the dose-response meta-analysis showed nonlinear relationship, which revealed that per 5kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was related to a 3% decrease in risk of overall lung cancer. All of these results demonstrated that increasing BMI might be a protective factor against lung cancer\u0026nbsp;(24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother meta-analysis demonstrated that\u0026nbsp;the risk of\u0026nbsp;ovarian cancer\u0026nbsp;increased by 7% (RR: 1.07, 95%CI: 1.02-1.12) for overweight and 28% (RR: 1.28, 95%CI: 1.16-1.41) for obesity when compared with normal weight. When the analysis was stratified by menopausal status, it was observed that significant association between overweight/obesity and increased risk of ovarian cancer existed in premenopausal period\u0026nbsp;(overweight: RR=1.31, 95%CI: 1.04-1.65; obesity: RR=1.50, 95%CI: 1.12-2.00) but not in postmenopausal status (overweight: RR=0.97, 95%CI: 0.76-1.24; obesity: RR=0.93, 95%CI: 0.61-1.42)\u0026nbsp;(25). Similarly, increase of BMI was also associated with a higher risk of multiple myeloma with overweight (RR: 1.12, 95%CI: 1.07-1.18) and obesity (RR: 1.21, 95%CI: 1.08-1.35) versus normal weight. And dose-response meta-analysis reported that every 5kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI would increase the risk of multiple myeloma by 12% (RR: 1.12, 95%CI: 1.08-1.16)\u0026nbsp;(26). In addition, overweight proved to be related to higher incidence of gallbladder cancer when compared with normal weight (RR: 1.10,\u0026nbsp;95%CI:\u0026nbsp;0.98-1.23), though this did not reach significance. While the risk of gallbladder cancer for obesity people will increase about 1.58 fold compared with normal-weight people (RR: 1.58, 95%CI:1.43-1.75). According to dose-response analysis, per 1kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was related to 4% increased risk of gallbladder cancer (RR: 1.04, 95%CI:1.02-1.06)\u0026nbsp;(27). Compared with subjects in the normal weight category, the risk of bladder cancer was statistically significantly elevated among people categorized as overweight (RR: 1.07, 95%CI: 1.01-1.14) or obese (RR: 1.10, 95%CI: 1.06-1.14). In the dose-response meta-analysis, BMI was related to bladder cancer risk in a linear relationship and the risk increased 4% for each 5kg/m\u003csup\u003e2\u003c/sup\u003e increment (RR: 1.04, 95%CI: 1.01-1.07)\u0026nbsp;(28).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA meta-analysis discovered an increase of 33% (RR: 1.33, 95%CI: 1.25-1.42) risk of colorectal cancer in obese participants compared with the normal weight participants. When the analysis was stratified by cancer type, the result revealed that obesity was linked to 47% increment in the risk of colon cancer and 15% increment in the risk of rectal cancer\u0026nbsp;(29). The pooled results combined for overweight or obesity versus normal categories of BMI indicated that increase in body weight was associated with a significantly increased risk of liver cancer and the risk of liver cancer increase about 1.36 fold in overweight (RR: 1.36, 95%CI: 1.02-1.81) and 1.77 fold in obese people (RR: 1.77, 95%CI: 1.56-2.01)\u0026nbsp;(30). The study including 9504 pancreatic cancer cases among 5037555 participants showed a potential non-liner association between BMI and pancreatic cancer risk by dose-response meta-analysis. And an increased risk of 10% (RR: 1.10, 95%CI: 1.07-1.14) was aroused by every 5kg/m\u003csup\u003e2\u003c/sup\u003e increment in BMI\u0026nbsp;(31). However, no significant association was detected between BMI and risk of prostate cancer (overweight: HR=1.02,\u0026nbsp;95%CI: 0.98-1.05; obese: HR=0.97, 95%CI: 0.93-1.01; 5kg/m\u003csup\u003e2\u003c/sup\u003e increment: HR=1.01, 95%CI: 0.99-1.04)\u0026nbsp;(32).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociation between obesity and thyroid cancer were examined in 32 studies, and meta-analysis of included studies demonstrated that obese could increase the risk of thyroid cancer by 33% (RR: 1.33, 95%CI: 1.24-1.42) compared to people with normal categories of BMI. What\u0026rsquo;s more, obese men and women were both significantly at risk of thyroid cancer according to subgroup analysis by sex\u0026nbsp;(33). When the risk estimates from 12 cohort studies of BMI and breast cancer\u0026nbsp;incidence were combined, a 5kg/m\u003csup\u003e2\u003c/sup\u003e increment in BMI was associated with a 2% (RR: 1.02, 95%CI: 1.01-1.04) increased risk of breast cancer\u0026nbsp;(34). Based on random effect model, compared to normal weight people, the estimated RR of Hodgkin\u0026rsquo;s lymphoma was 0.97 for overweight (RR: 0.97, 95%CI: 0.85-1.12) and 1.41 for obese (RR: 1.41, 95%CI: 1.14-1.75), which indicated that the risk of Hodgkin\u0026rsquo;s lymphoma increased by 41% for obesity, while no significant statistical significance was detected between overweight and risk of Hodgkin\u0026rsquo;s lymphoma\u0026nbsp;(35). The dose-response meta-analysis of 16 prospective cohort studies showed that every 5kg/m\u003csup\u003e2\u003c/sup\u003e increase of BMI corresponded to a 7% (RR: 1.07, 95%CI: 1.04-1.10) increased risk of in Non-Hodgkin\u0026rsquo;s lymphoma\u0026nbsp;(35).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 Heterogeneity of included meta-analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree meta-analyses reporting relationship between underweight and cancer risk presented low levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e \u0026lt; 25%). The association between overweight and cancer risk was reported by 14 meta-analyses. Low\u0026nbsp;levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e \u0026lt; 25%) and moderate-to-high levels\u0026nbsp;heterogeneity (I\u003csup\u003e2\u003c/sup\u003e 25%-75%) were reported by 6 meta-analyses respectively, while high levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e \u0026gt; 75%) was reported by 2 meta-analyses. Among 15 meta-analyses related to obesity, low levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e \u0026lt; 25%) was showed by 7 meta-analyses and moderate-to-high levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e 25%-75%) was showed by 7 meta-analyses, while only one presented high levels heterogeneity (I\u003csup\u003e2\u003c/sup\u003e \u0026gt; 75%). About 10\u0026nbsp;dose-response meta-analyses, 2 dose-response meta-analyses (I\u003csup\u003e2\u003c/sup\u003e \u0026lt; 25%) showed low levels heterogeneity, and moderate-to-high levels heterogeneity was presented in 4 dose-response meta-analyses (I\u003csup\u003e2\u003c/sup\u003e 25%-75%), while high levels heterogeneity was only reported in one dose-response meta-analysis (I\u003csup\u003e2\u003c/sup\u003e \u0026gt; 75%). However, the I\u003csup\u003e2\u003c/sup\u003e statistic could not be found in 3 dose-response meta-analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5 Publication bias of included meta-analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn three meta-analyses about underweight, all detected\u0026nbsp;a\u0026nbsp;significant publication bias. Among 14 meta-analyses about overweight, two meta-analyses reporting kidney cancer and gastric cancer detected a\u0026nbsp;significant\u0026nbsp;publication bias and 8 meta-analyses did not have, while the data of publication bias were not available for 4 meta-analyses. Regarding to 15 meta-analyses about obesity, 2 meta-analyses involving gallbladder cancer and thyroid cancer had significant publication bias and 10\u0026nbsp;meta-analyses did not\u0026nbsp;have, however, the data of publication bias were not available for 3 meta-analyses. About 10 dose-response meta-analyses, significant publication bias was found only in one dose-response meta-analysis and not found in 6 dose-response meta-analyses, while the data of publication bias were not available\u0026nbsp;for 3 dose-response meta-analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.6 AMSTAR assessment and GRADE classification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AMSTAR was used to assess the methodological quality of all included studies (supplementary Table S1), and the result showed that median AMSTAR score was 7 (range 5-9; IQR 7-8) (Table 2). The AMSTAR score of twenty meta-analyses (47%) was more than 7 and it was less than 7 in twenty-three meta-analyses (53%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GRADE system was used to assess the quality of evidence of all outcomes (supplementary Table S2). The quality of most evidence evaluated by GRADE score was classified as low or very low quality for the fact that the studies included in our umbrella review were mainly cohort and case-control studies (Table 2), which resulted in serious risk of bias.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1. Principal findings and interpretation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 43 meta-analyses with 19 cancers were included in this umbrella review. The result revealed that underweight was inversely associated with the incidence of general brain tumors while was positively related to the risk of esophageal and lung cancer. Overweight would enhance the incidence of brain tumors, kidney cancer, endometrial cancer, ovarian cancer, multiple myeloma, bladder cancer and liver cancer. Obesity was related to the increased incidence of brain tumors, cervical cancer, kidney cancer, endometrial cancer, esophageal cancer, gastric cancer, ovarian cancer, multiple myeloma, gallbladder cancer, bladder cancer, colorectal cancer, liver cancer, thyroid cancer and Hodgkin\u0026rsquo;s lymphoma. What\u0026rsquo;s more, dose-response analysis was conducted by 10 studies and the results demonstrated that per\u0026nbsp;5Kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was associated with 1.01 to 1.13 fold increased risk of general brain tumors, multiple myeloma, bladder cancer, pancreatic cancer, breast cancer, Non-Hodgkin\u0026rsquo;s lymphoma (Figure 3). And every 1Kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was linked to 6% increment in the risk of\u0026nbsp;kidney cancer and 4% increment in the risk of gallbladder cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere exist a lot of potential mechanisms to explain the relationship between increment of BMI and risk of various cancers. First of all, inflammation might be a bridge between obesity and tumors. Obesity itself is a chronic inflammatory condition\u0026nbsp;(36). The initiation of obesity-associated inflammation is associated with metabolic process and excessive nutrient consumption may be the major contributor\u0026nbsp;(37). This kind of inflammation is mainly existed in the white adipose tissue, a special metabolic tissue made up of lipocyte\u0026nbsp;(37). Adipose tissues have been regarded as one of endocrine organs, which can secrete a class of substances called adipokines including growth factors, hormones, cytokines and inflammatory factors. Adipokines could take part in a variety of biological processes in the body involving\u0026nbsp;inflammation reaction, immune response, glucose metabolism and insulin sensitivity\u0026nbsp;(38). Adipokines involved in the inflammation response process include interleukin\u0026nbsp;1\u0026beta;,\u0026nbsp;6 and 8, tumor necrosis factor \u0026alpha;\u0026nbsp;(TNF\u0026alpha;), C-reactive protein (CRP), monocyte chemotactic protein 1 (MCP-1) and transforming growth factor \u0026beta; (TGF\u0026beta;) and so on\u0026nbsp;(38, 39). Excessive adipose tissues will also cause ischemia and hypoxia in tissues as well as cell death and the formation of crown-like structures (CLS), which is the biomarker of inflammation\u0026nbsp;(40). Furthermore, too much nutrients and obesity could activate the metabolic signaling pathways mediated by nuclear factor \u0026kappa; B (NF\u0026kappa;B), protein kinase R and c-Jun N-terminal kinase (JNK), which could result in a low-level inflammatory response in the body\u0026nbsp;(41, 42).\u0026nbsp;Epidemiological study and experimental data have demonstrated the association between chronic inflammation and cancer\u0026nbsp;(43). And the fact that anti-inflammatory therapies play a role in tumor treatment is further evidence of this relationship\u0026nbsp;(44). As we all know, the chronic infections of helicobacter pylori, human papillomavirus and hepatitis virus were associated with gastric cancer, cervical cancer and liver cancer respectively\u0026nbsp;(40). What\u0026rsquo;s more, people with inflammatory bowel diseases are more likely to develop colon cancer\u0026nbsp;(45). Chronic inflammatory responses are involved in the initiation, proliferation and progression of cancer. On the one hand, a sustained inflammatory response can leave cells in oxidative stress for long periods of time, resulting in DNA and protein damage, inhibition of apoptosis and activation of proto-oncogenes, which are related to the occurrence and development of tumors\u0026nbsp;(46). On the other hand, some inflammatory factors not only play a key role in the inflammatory responses but also have a effect of promoting cancer occurrence. An animal experiment has showed that overexpression of IL-1\u0026beta; could result in gastric carcinoma in mice without infection of helicobacter\u0026nbsp;(47). IL-6 has also been reported to promote tumorigenesis by meditating the signaling pathway of gp130/JAK/STAT3\u0026nbsp;(48).\u003c/p\u003e\n\u003cp\u003eSecondly, the relationship between being overweight and obesity and increasing cancer risk could be explained by an imbalance between leptin and adiponectin. Produced by white adipose tissue，\u0026nbsp;adiponectin\u0026nbsp;was reported to play a significant protective role in carcinogenesis\u0026nbsp;(49). On the one hand, adiponectin can activate the receptor-meditated signal path to have a direct effect on tumor cells. And it works mainly through three known receptors including adipoR1, adipoR2 and T-cadherin\u0026nbsp;(50, 51). On the other hand, it has the properties of anti-inflammatory, regulating insulin sensitivity, influencing angiogenesis and inhibiting the growth, proliferation, invasion, metastasis of tumor cells\u0026nbsp;(52). In addition to being influenced by genetic factors, diet and physical activity, the level of adiponectin was also affected by obesity. In obese people, circulatory adiponectin level will decrease and this may result in the systematic chronic inflammatory and increase the risk of various cancers. Numerous studies have demonstrated that low adiponectin levels were related to increasing risk of endometrial cancer, postmenopausal breast cancer and colon cancer\u0026nbsp;(53-55). Leptin is also one of adipokines, whose level in the circulation system is positively associated with the total body fat\u0026nbsp;(56). Being opposite to adiponectin, leptin has been demonstrated to have the effect of promoting inflammatory response and angiogenesis, inhibiting apoptosis and stimulating cell growth, migration, invasion, which might play a significant role in the development and risk of cancers\u0026nbsp;(57-59). Previous studies have shown that elevated serum leptin was correlated to the increased risk of endometrial cancer and breast cancer\u0026nbsp;(60, 61).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThirdly,\u0026nbsp;insulin resistance/hyperinsulinemia is one of the characteristics of obesity\u0026nbsp;(62). The level of adiponectin will decrease in obesity, which could result in insulin resistance, because adiponectin could stimulate AMPK (AMP-activated protein kinase) phosphorylation and activation to enhance insulin sensitivity\u0026nbsp;(63). Increased inflammatory factors such as IL-6 and TNF-a in obese people could also lead to insulin resistance by activating the JNK (c-jun amino-terminal kinase) and IKK-\u0026beta; (I\u0026kappa;B kinase-\u0026beta;)/NF-\u0026kappa;B (nuclear factor-\u0026kappa;B) pathways\u0026nbsp;(64).\u0026nbsp;Insulin is not only a metabolic hormone, but also a growth factor that can promote mitosis especially on malignant cells with overexpressed insulin receptor (IR)\u0026nbsp;(65). The biological effect of insulin is achieved by binding to IRs including IR-A and IR-B subtypes and the activation of former subtype will make a stronger mitogenic effect\u0026nbsp;(66). Previous studies have reported that people with diabetes, obesity and other characteristics of insulin resistance/hyperinsulinemia have a higher risk of endometrial cancer, early gastric cancer and breast cancer\u0026nbsp;(67-69). Increased insulin could also decrease the level of IGF-1-binding proteins synthesized by liver, therefore the free IGF-1 will increase\u0026nbsp;(65, 70). IGF-1\u0026nbsp;has been reported to be a significant mediator for realizing the effect of growth hormone, which could promote proliferation and differentiation of cells as well as inhibit cell apoptosis. What\u0026rsquo;s more the activation of\u0026nbsp;IGF-1R can induce malignant transformation and the transformation will stop when suppresses the expression of IGF-1R by disrupting the IGF-1R gene targetedly\u0026nbsp;(71, 72). Many prospective studies have shown possible association between high level of IGF-1 in circulation and increased risk of multiple cancers including prostate cancer, colorectal cancer and breast cancer\u0026nbsp;(73-75).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the potential biological mechanisms linking overweight/obesity and cancer also comprise the level of sex hormones, ectopic fat deposition as well as changes of tumor microenvironment\u0026nbsp;(76, 77).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2. Strengths and weaknesses of the study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUmbrella review has become increasing popular especially in the last 2 years, which has been regarded as one of highest levels of evidence synthesis for the fact that it includes meta-analysis with a relatively large number of subjects. As we all know, this study is the first umbrella review investigating BMI and cancer risk. However, there also existed several limitations. First of all, the studies included in the meta-analyses of our umbrella review were mainly cohort and case-control studies while lack experimental studies, which will have an impact on the overall quality of the study. Secondly, this umbrella review was a comprehensive evaluation of existing systematic reviews and meta-analyses about BMI and cancer risk. Consequently, some related studies that have not been published before our searching will not been included in this study. Thirdly, among the 42 meta-analysis, 5 meta-analysis have reported significant publication bias and the data about publication bias were not available for 10 meta-analysis. Last but not least, the risk of tumor occurrence is the result of many factors, which might exert impact on associations between BMI and cancer incidence.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the evidence presented in this umbrella review showed that overweight and obesity were associated with increased risk of most cancer outcomes. We recommend that it is better to maintain BMI within the normal range to prevent the occurrence of tumors. However, prospective studies with high quality are needed to further investigate the association between BMI and various cancer risk. \u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, Body mass index; WCRF, World Cancer Research Fund; AMSTAR, a measurement tool to assess systematic review; GRADE, Grading of Recommendations, Assessment, Development and Evaluation; CI, confidence interval; HR, hazard ratio; RR, relative risk; OR, odds ratio; IL-1\u0026beta;, interleukin 1\u0026beta;; IL-6, interleukin 6; IL-8, interleukin 8; TNF\u0026alpha;, tumor necrosis factor \u0026alpha;; CRP, C-reactive protein; MCP-1,monocyte chemotactic protein 1; TGF\u0026beta;, transforming growth factor \u0026beta;; CLS, crown-like structures; NF\u0026kappa;B, nuclear factor \u0026kappa; B; JNK, c-Jun N-terminal kinase; AMPK, AMP-activated protein kinase; IR, insulin receptor\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interesting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interesting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Project of Science and Technology Department of Sichuan Province (Grant ID: 2021YFS0117).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunhao Chen, Kaimin Ke and Zhenghuan Liu contributed equally in this study and were responsible for conception, methodology and drafting of the manuscript. Zhufeng Peng and Luchen Yang contributed to the data extraction, analysis and interpretation. Software operation and drawing graph were conducted by Linchun Wang and Jing Zhou. Qiang Dong was responsible for revising the draft and supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all authors of original studies that were included in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eMartin-Rodriguez E, Guillen-Grima F, Mart\u0026iacute; A, Brugos-Larumbe A. Comorbidity associated with obesity in a large population: The APNA study. Obesity research \u0026amp; clinical practice. 2015;9(5):435\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKolb R, Sutterwala FS, Zhang W. Obesity and cancer: inflammation bridges the two. 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Metabolism: clinical and experimental. 2019;92:121\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1:\u0026nbsp;Associations between BMI and cancer outcomes.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.654563297350344%\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.640824337585869%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.911678115799804%\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.089303238469087%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases/total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.808635917566241%\"\u003e\n \u003cp\u003eMA\u003c/p\u003e\n \u003cp\u003emetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.4769381746810595%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.065750736015701%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.20117762512267%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003estudies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.004906771344455%\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.4955839057899905%\"\u003e\n \u003cp\u003eCase-\u003c/p\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.280667320902846%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.397448478900883%\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.612365063788028%\"\u003e\n \u003cp\u003eEgger\u003c/p\u003e\n \u003cp\u003etest P\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.360157016683022%\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003cp\u003eassociation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.654563297350344%\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003cp\u003etumors\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ekidney\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eendometrial\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eesophageal\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003egastric\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003elung\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.640824337585869%\"\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e1kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eincrement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eincrement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.911678115799804%\"\u003e\n \u003cp\u003eZhang 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePoorolajal 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiu 2018\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJenabi 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTian 2020\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLin 2014\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDuan 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.089303238469087%\"\u003e\n \u003cp\u003e2629/1509381\u003c/p\u003e\n \u003cp\u003e8221/3731438\u003c/p\u003e\n \u003cp\u003e8565/3853617\u003c/p\u003e\n \u003cp\u003e11395/3886677\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/79873\u003c/p\u003e\n \u003cp\u003eNA/79873\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15535/8953478\u003c/p\u003e\n \u003cp\u003eNA/NA\u003c/p\u003e\n \u003cp\u003e15535/8953478\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/32210437\u003c/p\u003e\n \u003cp\u003eNA/32210437\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026gt;4586/\u0026gt;1288013\u003c/p\u003e\n \u003cp\u003e\u0026gt;10188/\u0026gt;1916105\u003c/p\u003e\n \u003cp\u003e\u0026gt;7480/\u0026gt;1711564\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25686/4692721\u003c/p\u003e\n \u003cp\u003e26508/4983784\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/NA\u003c/p\u003e\n \u003cp\u003e56189/7253941\u003c/p\u003e\n \u003cp\u003e56189/7253941\u003c/p\u003e\n \u003cp\u003e43393/NA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.808635917566241%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.4769381746810595%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.065750736015701%\"\u003e\n \u003cp\u003e0.64-0.93\u003c/p\u003e\n \u003cp\u003e1.05-1.19\u003c/p\u003e\n \u003cp\u003e1.15-1.56\u003c/p\u003e\n \u003cp\u003e1.07-1.20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.81-1.25\u003c/p\u003e\n \u003cp\u003e1.08-1.71\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.27-1.43\u003c/p\u003e\n \u003cp\u003e1.61-1.91\u003c/p\u003e\n \u003cp\u003e1.05-1.06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.20-1.48\u003c/p\u003e\n \u003cp\u003e2.27-2.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.48-2.14\u003c/p\u003e\n \u003cp\u003e0.98-1.30\u003c/p\u003e\n \u003cp\u003e1.21-1.89\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.96-1.12\u003c/p\u003e\n \u003cp\u003e1.03-1.24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.20-1.27\u003c/p\u003e\n \u003cp\u003e0.77-0.86\u003c/p\u003e\n \u003cp\u003e0.74-0.83\u003c/p\u003e\n \u003cp\u003e0.96-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.20117762512267%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.004906771344455%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.4955839057899905%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.280667320902846%\"\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efixed\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.397448478900883%\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003cp\u003e71.6%\u003c/p\u003e\n \u003cp\u003e69.7%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39.4%\u003c/p\u003e\n \u003cp\u003e43.3%\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.2%\u003c/p\u003e\n \u003cp\u003e72.4%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.6%\u003c/p\u003e\n \u003cp\u003e77.2%\u003c/p\u003e\n \u003cp\u003e88.1%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24.3%\u003c/p\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003cp\u003e40.3%\u003c/p\u003e\n \u003cp\u003e60.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.612365063788028%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003cp\u003e\u0026gt;0.1\u003c/p\u003e\n \u003cp\u003e\u0026gt;0.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.360157016683022%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1 (continued)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.813160987074031%\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.1680376028202115%\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.338425381903642%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases/total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.7579318448883665%\"\u003e\n \u003cp\u003eMA\u003c/p\u003e\n \u003cp\u003emetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.755581668625147%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.9353701527614575%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.110458284371328%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003estudies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.99294947121034%\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.110458284371328%\"\u003e\n \u003cp\u003eCase-\u003c/p\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.462984723854289%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.7579318448883665%\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.405405405405405%\"\u003e\n \u003cp\u003eEgger\u003c/p\u003e\n \u003cp\u003etest P\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003cp\u003eassociation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.813160987074031%\"\u003e\n \u003cp\u003eovarian\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003cp\u003emyeloma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003egallbladder\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ebladder\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ecolorectal\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eliver\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epancreatic\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eprostate\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e1Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.1680376028202115%\"\u003e\n \u003cp\u003eLiu 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWallin 2011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLi 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSun 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMa 2013\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSohn 2021\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAune 2012\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHarrison 2020\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.338425381903642%\"\u003e\n \u003cp\u003e29559/2145013\u003c/p\u003e\n \u003cp\u003e29631/2149923\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8982/5708495\u003c/p\u003e\n \u003cp\u003e8879/4927212\u003c/p\u003e\n \u003cp\u003e8982/5708495\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5505/9164172\u003c/p\u003e\n \u003cp\u003e5902/9236604\u003c/p\u003e\n \u003cp\u003e5558/4659063\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/NA\u003c/p\u003e\n \u003cp\u003e38072/14201500\u003c/p\u003e\n \u003cp\u003eNA/NA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85935/8115689\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/103972\u003c/p\u003e\n \u003cp\u003eNA/5900864\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9504/5037555\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA/NA\u003c/p\u003e\n \u003cp\u003e32277/252771\u003c/p\u003e\n \u003cp\u003e157990/9351795\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.7579318448883665%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003eHR\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.755581668625147%\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.9353701527614575%\"\u003e\n \u003cp\u003e1.02-1.12\u003c/p\u003e\n \u003cp\u003e1.16-1.41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.07-1.18\u003c/p\u003e\n \u003cp\u003e1.08-1.35\u003c/p\u003e\n \u003cp\u003e1.08-1.16\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98-1.23\u003c/p\u003e\n \u003cp\u003e1.43-1.75\u003c/p\u003e\n \u003cp\u003e1.02-1.06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.01-1.14\u003c/p\u003e\n \u003cp\u003e1.06-1.14\u003c/p\u003e\n \u003cp\u003e1.01-1.07\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.25-1.42\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02-1.81\u003c/p\u003e\n \u003cp\u003e1.56-2.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.07-1.14\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98-1.05\u003c/p\u003e\n \u003cp\u003e0.93-1.01\u003c/p\u003e\n \u003cp\u003e0.99-1.04\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.110458284371328%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e30\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.99294947121034%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.110458284371328%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.462984723854289%\"\u003e\n \u003cp\u003efixed\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.7579318448883665%\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003cp\u003e34.1%\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.6%\u003c/p\u003e\n \u003cp\u003e15.5%\u003c/p\u003e\n \u003cp\u003e32.1%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68.9%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003cp\u003e51%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003cp\u003e79.9%\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.405405405405405%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 (continued)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.500590318772137%\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.736717827626919%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.7296340023612755%\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.396694214876034%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003ecases/total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.785123966942149%\"\u003e\n \u003cp\u003eMA\u003c/p\u003e\n \u003cp\u003emetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.792207792207792%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.958677685950414%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003estudies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.02125147579693%\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003eCase-\u003c/p\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.4935064935064934%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.430932703659977%\"\u003e\n \u003cp\u003eEgger\u003c/p\u003e\n \u003cp\u003etest P\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.736717827626919%\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003cp\u003eassociation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.500590318772137%\"\u003e\n \u003cp\u003ethyroid\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ebreast\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHodgkin\u0026apos;s lymphoma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNon-Hodgkin\u0026apos;s lymphoma\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.736717827626919%\"\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.7296340023612755%\"\u003e\n \u003cp\u003eMa 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiu 2018\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLarsson\u003c/p\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLarsson\u003c/p\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.396694214876034%\"\u003e\n \u003cp\u003e10881/12620676\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19480/22728674\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1557/3679738\u003c/p\u003e\n \u003cp\u003e1492/2367388\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17291/6035915\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.785123966942149%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRR\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.792207792207792%\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.958677685950414%\"\u003e\n \u003cp\u003e1.24-1.42\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.01-1.04\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.85-1.12\u003c/p\u003e\n \u003cp\u003e1.14-1.75\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04-1.10\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.02125147579693%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.4935064935064934%\"\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erandom\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.139315230224321%\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003cp\u003e21..6%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.6%\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.430932703659977%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.27\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.736717827626919%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eyes \u0026nbsp;\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMA, meta-analysis; CI, confidence interval; RR, relative risk; NA, not available; HR, hazard ratio; OR, odds ratio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Assessments of AMSTAR score and GRADE classification\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.76271186440678%\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.76271186440678%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.774011299435028%\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.774011299435028%\"\u003e\n \u003cp\u003eAMSTAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.926553672316384%\"\u003e\n \u003cp\u003eGRADE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.76271186440678%\"\u003e\n \u003cp\u003eBrain tumors\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCervical cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eKidney cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEndometrial\u0026nbsp;cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.76271186440678%\"\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e1kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eincrement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eunderweight vs normal\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eincrement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.774011299435028%\"\u003e\n \u003cp\u003eZhang 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePoorolajal 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiu 2018\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJenabi 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTian 2020\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLin 2014\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDuan 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiu 2015\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.774011299435028%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.926553672316384%\"\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 (continued)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.73342736248237%\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.451339915373765%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.322990126939352%\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.88716502115656%\"\u003e\n \u003cp\u003eAMSTAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.605077574047954%\"\u003e\n \u003cp\u003eGRADE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.73342736248237%\"\u003e\n \u003cp\u003eMultiple myeloma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGallbladder cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiver cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThyroid cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHodgkin\u0026apos;s lymphoma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNon-Hodgkin\u0026apos;s lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.451339915373765%\"\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e1Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eoverweight vs normal\u003c/p\u003e\n \u003cp\u003eobese vs normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5Kg/m\u003csup\u003e2\u003c/sup\u003e increment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.322990126939352%\"\u003e\n \u003cp\u003eWallin 2011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLi 2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSun 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMa 2013\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSohn 2021\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAune 2012\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHarrison 2020\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMa 2015\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLiu 2018\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLarsson 2011\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLarsson 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.88716502115656%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.605077574047954%\"\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003every low\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAMSTAR, a measurement tool to assess systematic review; GRADE, Grading of Recommendations, Assessment, Development and Evaluation\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Body Mass Index, Cancer, Umbrella review, Meta-analysis, Systematic review","lastPublishedDoi":"10.21203/rs.3.rs-1894100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1894100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Increasing evidence indicates that obesity is a risk factor for various tumors. We aimed to clarify the evidence for association between body mass index (BMI) and cancer risk based on existing systematic review and meta-analyses.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e PubMed, Embase and Web of science were systematically searched to obtain systematic review and meta-analyses reporting association between BMI and cancer incidence. The methodological quality and strength of evidence of each meta-analysis were assessed by AMSTAR and GRADE respectively. We also assessed the heterogeneity and publication bias of all included meta-analyses.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFinally, 43 meta-analyses with 19 cancers were identified by this umbrella review. The result revealed that underweight was inversely associated with the incidence of brain tumors while was positively related to the risk of esophageal and lung cancer. Overweight would enhance the incidence of brain tumors, kidney cancer, endometrial cancer, ovarian cancer, multiple myeloma, bladder cancer and liver cancer. Obesity was related to the increased incidence of brain tumors, cervical cancer, kidney cancer, endometrial cancer, esophageal cancer, gastric cancer, ovarian cancer, multiple myeloma, gallbladder cancer, bladder cancer, colorectal cancer, liver cancer, thyroid cancer and Hodgkin’s lymphoma. What’s more, dose-response analysis was conducted by 10 studies and the results demonstrated that per 5Kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was associated with 1.01 to 1.13 fold increased risk of general brain tumors, multiple myeloma, bladder cancer, pancreatic cancer, breast cancer, Non-Hodgkin’s lymphoma. And every 1Kg/m\u003csup\u003e2\u003c/sup\u003e increment of BMI was linked to 6% and 4% increment in the risk of kidney cancer and gallbladder cancer respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe evidence presented in this umbrella review showed that overweight and obesity were associated with increased risk of most cancers. We recommend that it is better to maintain BMI within the normal range to prevent the occurrence of tumors. However, prospective studies with high quality are needed to further investigate the association between BMI and various cancer risk.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Body mass index and cancer risk: An umbrella review of meta-analyses of observational studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-03 18:15:29","doi":"10.21203/rs.3.rs-1894100/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"710a3545-447a-49b7-88b9-6f44a4150cc0","owner":[],"postedDate":"August 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-17T04:59:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-03 18:15:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1894100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1894100","identity":"rs-1894100","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}