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Andy R Ness

Researcher at University Hospitals Bristol NHS Foundation Trust

Publications -  425
Citations -  38919

Andy R Ness is an academic researcher from University Hospitals Bristol NHS Foundation Trust. The author has contributed to research in topics: Body mass index & Population. The author has an hindex of 87, co-authored 415 publications receiving 35275 citations. Previous affiliations of Andy R Ness include National Institute for Health Research & University of Bristol.

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Interactions of genetic and environmental risk factors with respect to body fat mass in children: Results from the ALSPAC study

TL;DR: To evaluate if percentile‐specific effects of genetic, environmental and lifestyle obesity risk factors on body mass index (BMI) might reflect gene‐environment interactions with respect to the development of overweight, a large number of participants were found to be overweight or obese.
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Folate, vitamin B12, ferritin and haemoglobin levels among women of childbearing age from a rural district in South India.

TL;DR: Folic acid and vitamin B12 deficiencies co-exist and should be supplemented together and folate levels were lower in older and breastfeeding women, but not associated with parity and were higher among pregnant women.
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Behavioural early-life exposures and body composition at age 15 years.

TL;DR: This study does not provide compelling evidence for associations between most early-life factors and body composition in adolescence, however, possible associations with dietary patterns and physical inactivity in early childhood require further investigation in other cohorts that have direct measurements of adolescent body composition.
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Associations between sex-typed behaviour at age 3½ and levels and patterns of physical activity at age 12: the Avon Longitudinal Study of Parents and Children

TL;DR: Higher male-typical behaviour in early childhood is associated with higher physical activity in early adolescence, particularly in boys.
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Drawing causal inferences in epidemiologic studies of early life influences

TL;DR: An approach is required that combines traditional epidemiologic and statistical principles with the use of novel and sophisticated analytic methods to address the issue of confounding.