Medically speaking, depression is a messy illness: diagnosis is often subjective, especially in its early stages, and the pharmaceuticals used to treat it – often in hit-or-miss fashion – can have bizarre side effects, including deepening the depression instead of easing it.
With a new blood test that can diagnose depression with better than 70-percent accuracy, researchers at the Indiana University School of Medicine hope to transform diagnosis and treatment for an illness that touches an estimated 300 million people worldwide and one in every ten Americans.
The researchers took blood samples from people clinically diagnosed with depression or bipolar disorder when their moods were normal, dark, or manic. Eventually, the team winnowed 26 RNA markers that reveal the presence of one of a clinical condition, how severe a person’s illness is, predict the risk of a person’s depression becoming acute, and a person’s risk of developing bipolar disorder later on.
Just as important, the biomarker assay can reveal the relative proportion of the illness’s effect on the genes involved in a specific individual.
That means studies can be carried out to determine which drugs affect which biomarkers most directly, eventually enabling physicians to personalize prescriptions to reduce trial and error.
The Indiana research also identified four existing drugs used in other illnesses that could be repurposed as antidepressants and two natural compounds that might stabilize moods.
The study found that eight of the 26 genes identified were linked to the body’s circadian rhythm, helping to explain why some people become depressed in winter and why mood disorders often disturb sleep.
TRENDPOST: The work could be key in catching and controlling depression in its early stages, which could have major implications for the economy as well as quality of life: depression is ranked as the leading cause of disability among people ages 15 to 44 – the period during which most people establish themselves in careers.
More broadly, bioscientists are learning to read the body’s subtle signals of specific diseases, a fledgling field that will lead to the data-derived personalization of medical treatments for a range of disorders now treated by doctors’ hunches or with one-size-fits-all protocols.
