Why do some people carry a disease like COVID-19 and remain asymptomatic, while others who have no underlying conditions may die from it? Genetics may be key—and an especially promising approach is the polygenic risk score, using data from biobank-linked electronic health records. But there are challenges.

Some of our major health risks were actually predicted by environmental factors our mothers experienced while we were still in the womb—and some, such as temperature and humidity, can even affect the number of grandchildren we will have. This bigger picture is known as the “birth environment”—and its influence on health has long fascinated Mary Regina Boland, PhD.

Given the fundamental role that proteins play in the processes that underlie various diseases, including cancer, it's important to identify relevant proteomic biomarkers—for early detection, new drug target identification and precision medicine strategies. But standard methods fail to detect some useful ones.

A new statistical method identifies bacteria that grow at different rates in disease, such as inflammatory bowel disease, vs. in normal individuals. The potential: develop treatments by inhibiting or promoting bacterial growth.

Does exposure to air pollution during early gestation increase our risk of heart disease? The answer appears to be yes—both for humans and for some of our closest animal companions—according to several studies led by Mary Regina Boland, PhD.

Pregnant women take some medications whose effects on the fetus are largely unknown. The authors sought to design a machine-learning method that uses big data housed in clinical records, coupled with information on the chemical properties of the drug, to determine if these "Category C" drugs were likely to be harmful or safe to the fetus.

Each year in the U.S., bipolar disorder affects about six million adults, and about 16 million adults experience at least one major depressive episode,  according to the National Institute of Mental Health. This study is the largest to date that examined motor-activity patterns in people who represent the full range of mood-disorder subgroups--(Bipolar I (BPI), Bipolar II (BPII) and major depression (MDD)--in a non-clinical population sample. Its findings are thus are more likely to apply to the general community and to offer usable clues about biomarkers for mood disorders.