Classifying Childhood Obesity Using Longitudinal BMI Data
Author Information
Author(s): Thaker Vidhu, Ebrahim Nia, Khadegi Apurva, Deng Shuliang, Qian Kun, Yao Zonghui, Thaker Shaleen, May Benjamin, Patibandala Nandan, Lopez-Pintado Sara
Primary Institution: Columbia University Irving Medical Center
Hypothesis
Can a childhood obesity classification system using longitudinal clinical data better predict cardiometabolic risks than traditional cross-sectional BMI measurements?
Conclusion
The longitudinal BMI classification may better reflect long-term cardiometabolic risk in children.
Supporting Evidence
- Obesity was observed in 24.1% and severe obesity in 10.6% of the children studied.
- Individuals with early onset obesity had higher odds of remaining in the same or higher obesity class.
- Children in the high-SES group had lower odds of obesity.
Takeaway
This study shows that tracking children's weight over time can help doctors understand their health better than just looking at one weight measurement.
Methodology
This observational study used electronic health record data from a tertiary care hospital and an independent cohort, analyzing BMI measurements over time.
Potential Biases
The study may be biased due to its focus on children attending tertiary care centers, which may not represent the general population.
Limitations
The study is limited by sparse data for both BMI and cardiometabolic outcomes.
Participant Demographics
The study included children aged 2–20 years, with a sample that was approximately 50.2% female.
Statistical Information
P-Value
0.01 - < 0.001
Confidence Interval
95% CI 0.73–0.92
Statistical Significance
p < 0.001
Digital Object Identifier (DOI)
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