Cardiovascular checks show link to depression

Methods used for analysing a person’s risk of cardiovascular disease are shedding new light on assessing and modifying a person’s risk of depression, according to a study from the Australian National University (ANU).

The study, led by Philip Batterham from the Centre for Mental Health Research at ANU, used data from Australia’s only longitudinal lifespan study – the PATH Through Life study – to identify factors that may increase the risk of depression.

Tracking results across four years, the researchers found that the factors that were associated with depression varied across different groups, as Mr Batterham explained.

“Those who started with many depression symptoms were more likely to be depressed after four years, particularly if they were under financial pressure, participating less in employment or experiencing poor physical health,” he said.

“However, the risk of depression for those who started with few symptoms was determined more by other lifestyle factors such as substance use, or background characteristics such as gender and life events.”

The researchers gathered the data from the PATH Through Life project, which tracks health and welfare of more than 7,000 people from the Australian Capital Territory and Queanbeyan region. They then used statistical methods taken from cardiovascular research to examine ways to assess and modify a person’s risk of depression.

“Cardiovascular risk can be assessed based on a number of behavioural factors such as age, diet and exercise, along with background characteristics such as age and gender. Applying a similar categorisation to assessing a person’s risk of depression performed favourably compared to conventional methods used to examine individual risk factors,” he said.

The researchers hope that the results of the study will enable the creation of an online tool that will help individuals and health professionals identify someone’s risk of developing depression.

“We’re hoping that we can use this approach to develop a predictive tool for use in e-health websites, so that we can have some estimation – based on characteristics and behaviours – to identify areas were people might be at risk, and give them guidance on areas of their lifestyle that they could modify to decrease that risk.”