Neutrophil to Lymphocyte Ratio Decrease and Its Association with Clinical Changes in Late-Life Depression Treated with Vortioxetine (Lu AA21004)

Depression affects up to 35% of the elderly population, and is often called late-life depression. It significantly burdens life quality in old age and public health. Researchers are paying more attention to blood biomarkers when studying depression because these markers are easy and cheap to collect. Previous studies have shown that people with depression tend to have a higher ratio of neutrophils (i.e. a type of white blood cell that helps your body fight off infections by attacking and destroying harmful bacteria and other invaders) to lymphocytes (NLR) in their blood compared to healthy people, but no such difference was noted in the ratios of platelets (i.e. small, colorless cell fragments in your blood that help stop bleeding by clumping together and forming clots) to lymphocytes (PLR) or monocytes (i.e a type of white blood cell that helps clean your blood by finding and destroying germs and infected cells) to lymphocytes (MLR). Our study will look at how these biomarker levels change after an eight-week trial of a novel type of antidepressant medication. We want to see if these changes can tell us whether the medication helps improve depression and cognitive function. Previous research by Katona and Hansen found that vortioxetine (Lu AA21004) was more effective than a placebo in improving symptoms of depression and cognition. We aim to see if these biomarkers, especially NLR, decrease after treatment and whether the medication has a better effect than a placebo. We will also explore how these biomarker levels relate to clinical improvements in patients. Our study will include patients who completed necessary blood tests before and after the antidepressant trial. We will use paired statistical tests (i.e. a method used to compare two sets of data and determine if their differences are significant, such as comparing before and after results in the same group of people) and regression (i.e. a statistical technique that helps understand how a dependent variable (like a health outcome) is related to one or more independent variables (like age or treatment type)) as analytical models. Ultimately, we hope to find out if changes in these biomarkers can help predict how well antidepressants work, which could lead to better treatment plans for older adults with depression.