When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.

 

For African American patients undergoing mastectomy, the risk of complications and the postoperative appearance of the breast are among the important drivers of preferences about breast reconstruction, reports a study in the September issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS). The journal is published in the Lippincott portfolio by Wolters Kluwer.

Metabolic health before and during pregnancy may have a bigger influence on risks for mother and baby than simply controlling weight gain. Data from a recent paper by Pennington Biomedical researchers indicates that pregnant women with metabolically unhealthy obesity were more likely to develop gestational diabetes than those who were metabolically healthy. The paper, “Metabolic Health and Heterogenous Outcomes of Prenatal Interventions: A Secondary Analysis of a Randomized Clinical Trial,” was published in the Journal of American Medical Association.