Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models.
Nida ZiauddeenPaul J RoderickGillian SantorelliJohn WrightNisreen A AlwanPublished in: PLOS global public health (2022)
Tackling the childhood obesity epidemic can potentially be facilitated by risk-stratifying families at an early-stage to receive prevention interventions and extra support. Using data from the Born in Bradford (BiB) cohort, this analysis aimed to externally validate prediction models for childhood overweight and obesity developed as part of the Studying Lifecourse Obesity PrEdictors (SLOPE) study in Hampshire. BiB is a longitudinal multi-ethnic birth cohort study which recruited women at around 28 weeks gestation between 2007 and 2010 in Bradford. The outcome was body mass index (BMI) ≥91st centile for overweight/obesity at 4-5 years. Discrimination was assessed using the area under the receiver operating curve (AUC). Calibration was assessed for each tenth of predicted risk by calculating the ratio of predicted to observed risk and plotting observed proportions versus predicted probabilities. Data were available for 8003 children. The AUC on external validation was comparable to that on development at all stages (early pregnancy, birth, ~1 year and ~2 years). The AUC on external validation ranged between 0.64 (95% confidence interval (CI) 0.62 to 0.66) at early pregnancy and 0.82 (95% CI 0.81 to 0.84) at ~2 years compared to 0.66 (95% CI 0.65 to 0.67) and 0.83 (95% CI 0.82 to 0.84) on model development in SLOPE. Calibration was better in the later model stages (early life ~1 year and ~2 years). The SLOPE models developed for predicting childhood overweight and obesity risk performed well on external validation in a UK birth cohort with a different geographical location and ethnic composition.
Keyphrases
- early life
- body mass index
- weight gain
- early stage
- gestational age
- physical activity
- weight loss
- metabolic syndrome
- type diabetes
- insulin resistance
- young adults
- squamous cell carcinoma
- machine learning
- mental health
- pregnancy outcomes
- adipose tissue
- rectal cancer
- big data
- lymph node
- radiation therapy
- artificial intelligence
- low cost
- cervical cancer screening