Four methods to identify sensitive periods of infant weight gain associated with adolescent BMI in a Chinese birth cohort
Abstract Infant weight gain is strongly linked to a higher risk of childhood and adolescent obesity. However, limited growth assessments and inconsistent weight gain measures hinder the identification of sensitive early-life periods. This study aims to use various methods to quantify weight gain acr...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10366-7 |
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| Summary: | Abstract Infant weight gain is strongly linked to a higher risk of childhood and adolescent obesity. However, limited growth assessments and inconsistent weight gain measures hinder the identification of sensitive early-life periods. This study aims to use various methods to quantify weight gain across different periods during the first two years of life and further identify sensitive periods of infant growth. We conducted a prospective birth cohort in rural western China. Infant weight was collected at birth, 1, 3, 6, 9, 12, 18, 24 months of age and early adolescence. We used four methods to quantify weight gain across four periods (0–6, 6–12, 12–18, and 12–24 months of age): weight gain velocity z-score (WGV-Z), change in weight-for- age and sex z-score (CWAZ), conditional weight standardized residuals (CWR), and area under the growth prediction curve of each individual (AUC). Generalized estimating equation was used to examine the associations between infant weight gain and adolescent zBMI, underweight and overweight/obesity. Among 1388 infants enrolled, 735 adolescents (61.0% male) were assessed, with a mean age of 11.74 (SD,0.49) years old. The results of the AUC method indicate that the period from birth to 6 months of age is a sensitive period for infant weight gain. A higher AUC during birth to 6 months of age is associated with a higher adolescent zBMI (beta = 0.152, 95%CI 0.119, 0.186), an increased risk of overweight/obese (OR = 1.159, 95%CI 1.066, 1.261) and a decreased risk of underweight (OR = 0.653,95% CI 0.571, 0.747). Additionally, the AUC method showed the highest predictive power, although the observed difference did not reach statistical significance. AUC method based on multiple anthropometric measurements is able to quantify weight gain and consequently identify a sensitive period that has long-lasting influences on life-course health outcomes. |
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| ISSN: | 2045-2322 |