***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Type 2 diabetes mellitus shares risk factors for and has shown a positive association with colorectal cancer .  Anthropometric measures ( height , weight , and body mass index ( weight (kg)/height (m)(2 ) ) and metabolic abnormalities associated with insulin resistance syndrome ( IRS ) ( abnormalities in measured blood pressure , high density lipoprotein ( HDL ) cholesterol , and total cholesterol ) were prospect ively evaluated for associations with incident colon ( n = 227 ) , rectal ( n = 183 ) , and colorectal ( n = 410 ) cancers diagnosed between 1985 and 2002 in 28,983 Finnish male smokers from the Alpha-Tocopherol , Beta-Carotene Cancer Prevention Study . Cox proportional hazards models were used to calculate hazard ratios and 95 % confidence intervals . In comparison with the lowest quintile , the highest quintile of body mass index was significantly associated with colorectal cancer ( hazard ratio ( HR ) = 1.70 , 95 % confidence interval ( CI ) : 1.01 , 2.85 ; p-trend = 0.01 ) , particularly colon cancer . Subjects with a cluster of three IRS-related conditions ( hypertension , body mass index > /=25 kg/m(2 ) , and HDL cholesterol level < 40 mg/dl ( < 1.55 mmol/liter ) ) , compared with those with fewer conditions , had a significantly increased risk of colorectal cancer ( HR = 1.40 , 95 % CI : 1.12 , 1.74 ) , particularly colon cancer ( HR = 1.58 , 95 % CI : 1.18 , 2.10 ) , but not rectal cancer . These results support the hypothesis that the significant association observed between IRS-defining metabolic abnormalities and colorectal cancer is determined primarily by adiposity BACKGROUND AND AIM Obesity as a risk factor for colorectal neoplasm ( CRN ) is controversial . In the present study , we evaluated visceral obesity as a risk factor for CRN . METHODS We prospect ively enrolled 200 consecutive , asymptomatic adults ( male : female = 133:67 , mean age , 50.9 + /- 8.5 years ) undergoing both colonoscopy and abdominopelvic computed tomography ( CT ) scan for routine health evaluations . The presence or absence and the characteristics of CRN were determined during colonoscopy . The amount of  visceral adipose tissue ( VAT ) and subcutaneous adipose tissue was measured by an abdominopelvic CT scan .  Body mass index , waist circumference , and percentage of body fat were measured . Blood pressure and other blood markers for assessing the metabolic syndrome were also investigated . RESULTS Of the 200 patients , 53 ( 26.5 % ) had CRN . Old age , smoking , metabolic syndrome , and a high fasting plasma glucose level were associated with an increased risk of CRN . VAT ( P < 0.01 ) and waist circumference ( P = 0.01 ) were significantly higher in those with CRN . A multivariate analysis of the risks of CRN showed an odds ratio of 4.07 ( 95 % confidence interval : 1.01 - 16.43 , P = 0.03 ) for those with VAT over 136.61 cm(2 ) relative to those with VAT under 67.23 cm(2 ) . Waist circumference , metabolic syndrome , and fasting plasma glucose levels were not independent risk factors for CRN in the multivariate analysis . CONCLUSION Increased VAT is an independent risk factor for CRN . Further large scale studies are needed to clarify the causal relationship between VAT and CRN 
    Output:
    
Insulin may act directly on tissue as a mitogenic and antiapoptotic growth factor.2,8 Insulin can also increase insulin-like growth factor-1 bioactivity.2,9,10 The shared metabolic factors underlying both type 2 diabetes and cancer include visceral adiposity , inflammation , hyperglycemia , and hyperinsulinemia , all of which lead to increased insulin receptor substrate ( IRS ) , and IRS potentially increases tumor cell growth and proliferation .
This study described the existing research in the area of MetS and CRC and analyzed its characteristics including study design , characteristics of study subjects , type of neoplasm , potential confounding factors , and definitions of MetS. It also identified heterogeneity in effects among multiple studies and had increased statistical power to detect an association compared with individual studies .