Predictors Of Response To Antidepressants: Utility Of Behavioural, Neuroimaging And Genetics Data
Funder
National Health and Medical Research Council
Funding Amount
$310,071.00
Summary
Major depressive disorder (MDD) is projected to cause the second greatest global burden of disease by 2020, highlighting the urgent need for valid predictors of effective treatment response. Currently, there are no accurate predictors of response to antidepressants in MDD, and successful treatment relies greatly on 'trial and error'. This process is demanding on health resources, and may be a factor in the high suicide rates in depressed patients. Previous research on treatment response has been ....Major depressive disorder (MDD) is projected to cause the second greatest global burden of disease by 2020, highlighting the urgent need for valid predictors of effective treatment response. Currently, there are no accurate predictors of response to antidepressants in MDD, and successful treatment relies greatly on 'trial and error'. This process is demanding on health resources, and may be a factor in the high suicide rates in depressed patients. Previous research on treatment response has been limited by recruitment of small, heterogeneous patient samples, lack of placebo control, and a failure to examine task related activity in brain imaging studies. Perhaps one of the more troubling aspects of research that aims to predict treatment response to antidepressant medications is the use of commonly used outcome measures such as the Hamilton Rating Depression Scale (HAM-D), which were developed long before current classification systems of depression came into use. The US Federal Drug Administration has recently identified what they call a translational gap such that behavioural and biological measures are the most robust for detection of disorders such as depression, yet these measures remain to be translated into clinical tools that can be used to evaluate treatment. The aim of the current study therefore is to determine whether genetic variability is related to treatment outcome as defined by a more objective outcome measure (facial expression perception) using a randomised controlled design. The study will also determine whether brain measures (fMRI, EEG) enhance the prediction of SSRI response to both clinical and behavioural measures, over and above the genetic contribution.Read moreRead less
Molecular dissection of the functional regions of genes that encode actinins (ACTN2 and ACTN3) and their contribution to normal variation in skeletal muscle function. The project has discovered a common genetic variant that affects skeletal muscle structure, function and metabolism and influences athletic ability, and response to diet and exercise. The project will study how this gene influences muscle bulk and strength, the metabolic efficiency of muscle and the risk of obesity in the general ....Molecular dissection of the functional regions of genes that encode actinins (ACTN2 and ACTN3) and their contribution to normal variation in skeletal muscle function. The project has discovered a common genetic variant that affects skeletal muscle structure, function and metabolism and influences athletic ability, and response to diet and exercise. The project will study how this gene influences muscle bulk and strength, the metabolic efficiency of muscle and the risk of obesity in the general population.Read moreRead less