Research Roundup
Pasteurization reduces bioactive component of breast milk
Providing human breast milk to preterm infants is a strategy for preventing complications including necrotizing enterocolitis. Because a mother’s own milk (considered the gold standard) is not always available, it is important to determine whether alternate breast milk products confer equal nutritional and bioactive value.
Danyvid Olivares-Villagómez, PhD, graduate student Kathleen McClanahan and colleagues determined how common milk pasteurization and storage techniques affect the concentration of osteopontin, a bioactive protein in breast milk that plays roles in intestinal, immunological and brain development. They measured osteopontin concentrations in human breast milk from multiple sources, including fresh and frozen single-donor samples, pooled donor breast milk (Holder-pasteurized), and a shelf-stable breast milk product (retort-pasteurized).
They found that Holder pasteurization reduced osteopontin concentration by about 50%, and that the shelf-stable product, which had a harsher (retort) pasteurization, had lower levels of osteopontin than the Holder-pasteurized pooled donor breast milk. Interestingly, freezing breast milk prior to Holder pasteurization resulted in less osteopontin degradation than Holder pasteurization prior to freezing.
The findings were reported in the journal Pediatric Research. To offset the loss of osteopontin in pasteurized breast milk, the authors suggest considering supplementation of this bioactive protein. They note that bovine osteopontin has been approved for formula supplementation in Europe and is well tolerated by infants.
Study examines social factors and cardiovascular disease risk
A study reported in PLOS ONE examined whether including social determinants of health improves accuracy in predicting who will develop cardiovascular disease (CVD). Matthew Morris, PhD, Hamidreza Moradi, PhD, and study partners analyzed data from 3,980 Black participants in the Jackson Heart Study with no CVD history at baseline. They modeled 10-year CVD risk using machine learning algorithms and evaluated the predictive value of biological, psychosocial, socioeconomic and environmental factors.
Adding social determinants of health did not improve overall predictive accuracy beyond traditional CVD risk factors such as gender, nutrition, blood pressure, smoking and cholesterol. However, analyses of relative predictive importance found that eight social determinants were among the top 15 markers of CVD risk, including insurance status, experiences with discrimination and environmental factors reflecting access to physical activity resources and healthy foods.
While traditional factors may better indicate which individuals currently face higher CVD risk, the authors note that social determinants could help guide community prevention efforts by pinpointing where societal barriers exist to individuals adopting healthy lifestyle behaviors.