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Title: Associations of childhood head growth with health and human capital in adult life and in the next generation
Author: Pandey, Shivam
ISNI:       0000 0004 7972 0966
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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Nature of the work undertaken: Most studies of the 'developmental origins of health and disease hypothesis' have related early weight and height measurements to later life outcomes, but few have considered head size. I study the association of early head size and growth with adult cognitive and cardiometabolic outcomes, and intergenerational outcomes, using the New Delhi Birth Cohort (NDBC). This was set up in 1969 in New Delhi, India and enrolled 20,755 married women in the reproductive age-group resulting in 9,169 pregnancies whose anthropometric data, including head circumference were collected from birth till early adulthood at defined time points. I develop and compare suitable statistical models and advise on the choice of method for analysis of such data. Contribution to subject knowledge in the area: Head size and disproportion of head size relative to other body measurements at birth, and childhood head growth were unrelated to either educational attainment or blood pressure, and therefore early head size is not an indicator of early life programing in this population. Improving childhood nutrition and promoting linear growth up to age 2 years may be important for higher adult cognitive development. Contrastingly, becoming a heavier adolescent is associated with an increased risk of adult hypertension. Similar associations of early life maternal and paternal head growth with next generation birth weight suggest that they result from genetic factors which are non-modifiable or persisting environment between generations. Understanding the environmental factors influencing brain growth might help increase next-generation birth weight. Conditional and spline approaches provide similar goodness of fit in my data, and associations of head growth with the different adult outcomes were similar. Conditional growth modelling is suitable for studies with a small number of body measurements per individual, while spline models might be better for datasets with a larger number of measurements.
Supervisor: Osmond, Clive ; Fall, Caroline Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available