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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Petrov, Megan E. Jiao, Nana Panchanathan, Sarada S. Reifsnider, Elizabeth Coonrod, Dean V. Liu, Li Krajmalnik-Brown, Rosa Gu, Haiwei Davidson, Laurie A. Chapkin, Robert S. Whisner, Corrie M. |
| Abstract | Background Overweight, obesity, and associated comorbidities are a pressing global issue among children of all ages, particularly among low-income populations. Rapid weight gain (RWG) in the first 6 months of infancy contributes to childhood obesity. Suboptimal sleep-wake patterns and gut microbiota (GM) have also been associated with childhood obesity, but little is known about their influences on early infant RWG. Sleep may alter the GM and infant metabolism, and ultimately impact obesity; however, data on the interaction between sleep-wake patterns and GM development on infant growth are scarce. In this study, we aim to investigate associations of infant sleep-wake patterns and GM development with RWG at 6 months and weight gain at 12 months. We also aim to evaluate whether temporal interactions exist between infant sleep-wake patterns and GM, and if these relations influence RWG. Methods The Snuggle Bug/ Acurrucadito study is an observational, longitudinal study investigating whether 24-h, actigraphy-assessed, sleep-wake patterns and GM development are associated with RWG among infants in their first year. Based on the Ecological Model of Growth, we propose a novel conceptual framework to incorporate sleep-wake patterns and the GM as metabolic contributors for RWG in the context of maternal-infant interactions, and familial and socio-physical environments. In total, 192 mother-infant pairs will be recruited, and sleep-wake patterns and GM development assessed at 3 and 8 weeks, and 3, 6, 9, and 12 months postpartum. Covariates including maternal and child characteristics, family and environmental factors, feeding practices and dietary intake of infants and mothers, and stool-derived metabolome and exfoliome data will be assessed. The study will apply machine learning techniques combined with logistic time-varying effect models to capture infant growth and aid in elucidating the dynamic associations between study variables and RWG. Discussion Repeated, valid, and objective assessment at clinically and developmentally meaningful intervals will provide robust measures of longitudinal sleep, GM, and growth. Project findings will provide evidence for future interventions to prevent RWG in infancy and subsequent obesity. The work also may spur the development of evidence-based guidelines to address modifiable factors that influence sleep-wake and GM development and prevent childhood obesity. |
| Related Links | https://bmcpediatr.biomedcentral.com/counter/pdf/10.1186/s12887-021-02832-8.pdf |
| Ending Page | 16 |
| Page Count | 16 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712431 |
| DOI | 10.1186/s12887-021-02832-8 |
| Journal | BMC Pediatrics |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2021-08-31 |
| Access Restriction | Open |
| Subject Keyword | Pediatrics Internal Medicine infant sleep Actigraphy circadian rhythm sleep-wake pattern Gastrointestinal microbiota Rapid weight gain child overweight time varying effect models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Pediatrics, Perinatology and Child Health |
| Journal Impact Factor | 2/2023 |
| 5-Year Journal Impact Factor | 2.4/2023 |
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