Details

Autor(en) / Beteiligte

• Aagaard, Kjersti M.
• Barkin, Shari L.
• Burant, Charles F.
• Carnell, Susan
• Demerath, Ellen
• Donovan, Sharon M.
• Eneli, Ihuoma
• Francis, Lori A.
• Gilbert‐Diamond, Diane
• Hivert, Marie‐France
• LeBourgeois, Monique K.
• Loos, Ruth J. F.
• Lumeng, Julie C.
• Miller, Alison L.
• Okely, Anthony D.
• Osganian, Stavroula K.
• Ramirez, Amelie G.
• Trasande, Leonardo
• Van Horn, Linda V.
• Wake, Melissa
• Wright, Rosalind J.
• Yanovski, Susan Z.

Titel

Understanding risk and causal mechanisms for developing obesity in infants and young children: A National Institutes of Health workshop

Ist Teil von

• Obesity reviews, 2024-04, Vol.25 (4), p.e13690-n/a

Ort / Verlag

England: Wiley Subscription Services, Inc

Erscheinungsjahr

2024

Quelle

Wiley HSS Collection

Beschreibungen/Notizen

• Summary Obesity in children remains a major public health problem, with the current prevalence in youth ages 2–19 years estimated to be 19.7%. Despite progress in identifying risk factors, current models do not accurately predict development of obesity in early childhood. There is also substantial individual variability in response to a given intervention that is not well understood. On April 29–30, 2021, the National Institutes of Health convened a virtual workshop on “Understanding Risk and Causal Mechanisms for Developing Obesity in Infants and Young Children.” The workshop brought together scientists from diverse disciplines to discuss (1) what is known regarding epidemiology and underlying biological and behavioral mechanisms for rapid weight gain and development of obesity and (2) what new approaches can improve risk prediction and gain novel insights into causes of obesity in early life. Participants identified gaps and opportunities for future research to advance understanding of risk and underlying mechanisms for development of obesity in early life. It was emphasized that future studies will require multi‐disciplinary efforts across basic, behavioral, and clinical sciences. An exposome framework is needed to elucidate how behavioral, biological, and environmental risk factors interact. Use of novel statistical methods may provide greater insights into causal mechanisms.