title: Association between mammographic density and age-related lobular involution of the breast.
abstract: OBJECTIVE
  Mammographic density and lobular involution are both significant risk factors for breast cancer, but whether these reflect the same biology is unknown. We examined the involution and density association in a large benign breast disease (BBD) cohort.
  
  
  METHODS
  Women in the Mayo Clinic BBD cohort who had a mammogram within 6 months of BBD diagnosis were eligible. The proportion of normal lobules that were involuted was categorized by an expert pathologist as no (0%), partial (1% to 74%), or complete involution (>or= 75%). Mammographic density was estimated as the four-category parenchymal pattern. Statistical analyses adjusted for potential confounders and evaluated modification by parity and age. We corroborated findings in a sample of women with BBD from the Mayo Mammography Health Study (MMHS) with quantitative percent density (PD) and absolute dense and nondense area estimates.
  
  
  RESULTS
  Women in the Mayo BBD cohort (n = 2,667) with no (odds ratio, 1.7; 95% CI, 1.2 to 2.3) or partial (odds ratio, 1.3; 95% CI, 1.0 to 1.6) involution had greater odds of high density (DY pattern) than those with complete involution (P trend < .01). There was no evidence for effect modification by age or parity. Among 317 women with BBD in the MMHS study, there was an inverse association between involution and PD (mean PD, 22.4%, 21.6%, 17.2%, for no, partial, and complete, respectively; P trend = .04) and a strong positive association of involution with nondense area (P trend < .01). No association was seen between involution and dense area (P trend = .56).
  
  
  CONCLUSIONS
  We present evidence of an inverse association between involution and mammographic density.
authors.affiliation: Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
authors.forename: Karthik
authors.initials: K
authors.lastname: Ghosh
authors.forename: Lynn C
authors.initials: LC
authors.lastname: Hartmann
authors.forename: Carol
authors.initials: C
authors.lastname: Reynolds
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Visscher
authors.forename: Kathleen R
authors.initials: KR
authors.lastname: Brandt
authors.forename: Robert A
authors.initials: RA
authors.lastname: Vierkant
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Scott
authors.forename: Derek C
authors.initials: DC
authors.lastname: Radisky
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Sellers
authors.forename: V Shane
authors.initials: VS
authors.lastname: Pankratz
authors.forename: Celine M
authors.initials: CM
authors.lastname: Vachon
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000375:Aging; D001706:Biopsy; D001941:Breast Diseases / Q000150:complications / Q000000981:diagnostic imaging* / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000209:etiology* / Q000473:pathology; D016009:Chi-Square Distribution; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D008875:Middle Aged; D016017:Odds Ratio; D010298:Parity; D011237:Predictive Value of Tests; D011247:Pregnancy; D018570:Risk Assessment; D012307:Risk Factors