Do extremely low birth weight babies age prematurely, and can preterm births be forecast through statistical modeling?
These are two studies that have received one-year funding from the Canadian Institutes of Health Research (CIHR).
Ryan J. Van Lieshout, assistant professor of psychiatry and behavioural neurosciences, and Louis A. Schmidt, a professor of psychology, neuroscience and behaviour, are focused on aging in adults born at extremely low birth weight (ELBW).
Preliminary work by the team suggests that these adults may be aging at a faster rate than those born at normal birth weight (NBW).
The study accesses the McMaster ELBW cohort, which includes a group of 179 ELBW survivors and 145 NBW controls born between 1977 and 1982, with more than 35 years' worth of data.
"It became apparent that some of them (ELBW survivors) looked a little older than others the same age, so we started to wonder if they might be experiencing accelerated aging," said Van Lieshout, who also holds the Albert Einstein/Irving Zucker Chair in Neuroscience.
"That would be important for them – as well as their health care providers, their families and the health care system – to know."
CIHR's Institute of Human Development, Child and Youth Health awarded a one-year grant of $75,000 for the study.
Van Lieshout says the first phase of research is centred on whether the ELBW group is, in fact, aging prematurely. If premature aging is confirmed in ELBW survivors, the team will next hone in on possible contributing factors.
"If they are prematurely aging, the hope is we can identify things that are contributing, develop interventions to prevent that from happening and help optimize their health as they age," said Van Lieshout.
Sarah McDonald, professor of obstetrics and gynecology, also received a similar grant from the same CIHR institute for a one-year study on risk assessment prediction models for preterm birth.
McDonald and her team are focused on improving the prediction of preterm birth and other pregnancy outcomes, such Caesarean sections, by using an innovative approach called algorithmic statistical modeling.
This will be the first study that combines algorithmic and conventional approaches using a full spectrum of predictor variables in data from the whole population of Ontario, according to McDonald.
"We are trying to find which women are at risk and if we can understand that, down the road we can better target care," said McDonald, who has the Tier 2 Canada Research Chair in Maternal and Child Obesity Prevention and Intervention.
"This grant enables us to work on the prediction models following our foundational work."
The long-term goal of McDonald's research is to develop an interactive risk assessment tool for clinical use.
"Preterm birth is the leading cause of death in infants and long-term disability, and it affects 1 in 12 births in Canada, so it is a big problem," said McDonald.
"This is a very exciting time for researchers and more importantly, for women, infants and families in Canada, because there are a number of different initiatives funded by CIHR that have targeted preterm births and we have more treatment options available than before."