Predictors of proximal vs. distal colorectal cancers

Dis Colon Rectum. 2001 Feb;44(2):251-8. doi: 10.1007/BF02234301.


Background: Because proximal colorectal cancers have a tendency to present at a more advanced stage and thus have a poorer prognosis, it is important to understand the factors associated with the development of proximal colorectal cancer. We hypothesized that older age, female gender, and the presence of comorbid illness would be associated with proximal cancers.

Methods: Incident cases of colorectal cancer (n = 9,550) occurring in 1994 were identified from Florida's population-based statewide cancer registry. We categorized colorectal cancers as either proximal (cecum, ascending colon, and transverse colon) or distal (descending colon, sigmoid colon, rectosigmoid, and rectum). Multiple logistic regression analysis was used to determine the multivariable relationship between clinical characteristics and the odds of a proximal-occurring lesion.

Results: Four characteristics emerged as independent predictors of a proximal lesion. Each year of increasing age was associated with a 2.2 percent increase in the odds of a proximal lesion, whereas female gender was associated with a 38 percent increase in the odds of a proximal lesion. The presence of a comorbid condition was associated with a 28 percent greater odds of a proximal lesion, and, finally, black, non-Hispanic race was associated with a 24 percent greater odds of a proximal lesion.

Conclusions: We found that increasing age, female gender, black, non-Hispanic race, and the presence of comorbid illnesses were factors associated with a greater likelihood of developing colorectal cancer in a proximal location. Further studies will be required to confirm these findings and to establish the mechanism by which comorbidity influences the site of colorectal cancer development.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Black People
  • Colorectal Neoplasms / epidemiology*
  • Colorectal Neoplasms / mortality
  • Comorbidity
  • Female
  • Florida / epidemiology
  • Humans
  • Logistic Models
  • Male
  • Multivariate Analysis
  • Registries
  • Risk Factors
  • Sex Factors