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Anglia Ruskin University ARU Featured PhD Programmes
Anglia Ruskin University ARU Featured PhD Programmes

Relating glucose levels in newborn babies to later developmental outcomes

Liggins Institute

Applications accepted all year round Funded PhD Project (Students Worldwide)

About the Project

Hypoglycaemia (low blood sugar) is the commonest metabolic condition of the newborn. It affects up to 15% of babies, and the incidence is increasing as risk factors such as maternal diabetes and preterm birth are becoming more common. Neonatal hypoglycaemia may cause long-term brain damage, but it is not known how low the glucose levels need to be in which babies to cause damage. Further, there is some evidence that fluctuating glucose levels, and particularly low followed by high levels, are associated with adverse later development.


We have data from several hundred children born at risk of neonatal hypoglycaemia, in whom we have detailed records about their blood sugar levels, and also details of their development at different ages. The objective of this project would be to undertake a statistical modelling approach to understand the relationships between measures of blood sugar levels in the newborn period and aspects of development at different ages. This would involve developing approaches to combining different data sets, different outcome measures at different ages, and measures of glucose variability. Statistical tools are likely to include mixed models for developmental outcomes and latent class models to characterise variation and subgroups.

Funding notes:

Scholarships and awards are available to support Honours, Masters and PhD students at the Liggins Institute, including a Liggins Institute ’Start Up’ Doctoral Scholarship for your first year’s fees. Find out more here:

There are no international fees for PhD students: as long as you live in New Zealand during your period of enrolment, and even if you initially start your PhD from overseas, you will pay the same as New Zealanders.

Funding Notes

The project requires either a degree in statistics or a degree in some area of health and biomedical sciences with a strong background in statistical modelling. Experience with longitudinal data analysis would be helpful, as would strong statistical programming skills

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