Does being a preemie dispose to delivering a preemie?

A population-based cohort study from Canada suggests that women who were themselves born prematurely may be more likely to have a very preterm or preterm delivery, irrespective of risks associated with hypertension and diabetes.

Researchers analyzed data on all women who were born preterm (n = 51,148) and term (823,991) in Quebec, Canada, between 1976 and 1995. They frequency matched the two cohorts 1:2 preterm to term to examine the relationship of a history of preterm birth between women and their mothers.

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The study included 7405 women who were born preterm (554 were born before 32 weeks’ gestation and 6851 were born at 32 to 36 weeks’ gestation) and 16,714 women who were born at term. The women born preterm delivered 12,248 newborns and the women born at term delivered 27,879 newborns. Overall, 14.2% of the women born very preterm, 13.0% of the women born preterm, and 9.8% of the women born at term prematurely delivered at least once during the study period; 2.4%, 1.8%, and 1.2%, respectively, delivered very prematurely (both P<.001 for trend).

After adjusting for variables including own birth weight for gestational age and pregnancy complications, the odds of a preterm first live delivery associated with maternal preterm birth were elevated by 1.63-fold (95% confidence interval [CI] 1.22-2.19) for women born before 32 weeks’ gestation and 1.41-fold (95% CI 1.27-1.57) for women born between 32 and 36 weeks’ gestation, relative to those who born at term.

Even low-risk breast cancer patients worry about genetics

Women diagnosed with low-risk breast cancer may still be concerned about likelihood of developing other cancers-both for themselves and their family members. The findings, from a new study in The Journal of Clinical Oncology, suggest that clinicians should discuss genetic screening with all breast cancer patients to help reduce worry.

Researchers at the University of Michigan Medical School surveyed women in metropolitan Detroit and Los Angeles diagnosed with nonmetastatic breast cancer from 2005 to 2007 and whose disease was reported to SEER registries. The women were asked about their experiences with hereditary risk evaluation. Multivariable models were used to evaluate correlates of a strong desire for genetic testing, the unmet need for discussion with a healthcare professional, and receipt of testing.

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Of the 1536 women who completed the survey, 35% stated a strong desire to undergo genetic testing, 28% said that they’d discussed such testing with a healthcare professional, and 19% indicated that they’d received genetic testing. Younger women, Latinas, and those who had a family history of breast cancer were more likely to have a strong desire for genetic testing. Yet in the minority patients, a failure to discuss genetic testing with a healthcare professional was significantly more likely than in white patients. (Odds ratios for blacks, English-speaking Latinas, and Spanish-speaking Latinas compared with whites were 1.68, 2.44, and 7.39, respectively.) Worry during the long-term survivorship period was higher among those who hadn’t discussed testing with a healthcare professional (48.7% vs 24.9%; P <.001).

Overall patients, who underwent testing were younger and more likely to have a family history of cancer and less likely to be black. 

Genetic risk algorithm could stratify breast cancer prevention

A major study of more than 65,000 women suggests that breast cancer prevention may one day be tailored based on screening for 77 common genetic mutations associated with the disease. Published in The Journal of the National Cancer Institute, the results show that it may be possible to more accurately predict a woman’s individual risk of breast cancer by combining traditional risk assessments with stratification using breast cancer-associated single nucleotide polymorphisms (SNPs).

Led by The Institute of Cancer Research, London, and involving hundreds of research institutions, the study looked at the value of using 77 SNPs or individual mutations in DNA to predict a woman’s risk of breast cancer. The researchers tested all possible pair-wise multiplicative interactions and constructed a polygenic risk score (PRS) for breast cancer overall and by ER status. The data were from 33,673 women with breast cancer and 33,381 controls of European origin who had previously participated in 41 studies conducted by the Breast Cancer Association Consortium.

A significant link was found between the PRS and breast cancer risk. Women in the top 20% for PRS were 1.8 times more likely to develop breast cancer than the average woman. Women in the top 1% of PRS were more than 3 times more likely to develop breast cancer than average (OR 3.36 [95% CI = 2.95 to 3.83, P = 7.5 x ^10-74] versus the middle quintile). 

For women with a first-degree family history of breast cancer, lifetime risk of breast cancer in the lowest and highest quintiles of PRS was 8.6% and 24.4%, respectively, versus 5.2% and 16.6% for those with no family history of the disease. In the lowest and highest quintiles of PRS, risk of developing breast cancer by age 80 was 3.5% (95% CI = 2.6% to 4.4%) and 29.0% (95% CI = 24.9% to 33.5%), respectively. Risk of developing estrogen receptor-positive disease was 15.7% in women in the highest quintile of PRS versus 4.1% in the lowest quintile.

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Looking at the absolute risk of breast cancer at different ages, the researchers estimated the age at which women with various PRS levels would reach a threshold risk of 2.4%, or the average 10-year risk for a woman age 47. They found that women above the 99^th percentile of PRS reached it at age 32 years, whereas risk rose to that level at age 57 in women in the 20^th to 40^th percentiles, and women with the lowest PRS levels never reached a risk of 2.4%.

“The low absolute risk of breast cancer among women at the lowest end of the risk distribution,” the authors said, “raises the possibility that such women might be recommended more limited surveillance…using SNP profiles rather than age alone could lead to more effective screening programs.” They noted that because of inconsistency in the data, their model did not take into account lifestyle/environmental factors and suggested that interactions with those variables and PRS should be studied in the future.