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Lifestyle Factors and Health01:20

Lifestyle Factors and Health

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Lifestyle factors play a critical role in maintaining overall health and preventing chronic diseases. Key elements, such as regular physical activity, a nutritious diet, and abstinence from smoking, can significantly enhance physical, mental, and emotional well-being while reducing the risk of several life-threatening conditions.
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Trends in ritodrine prescriptions among pregnant women in Japan: a nationwide descriptive analysis of an administrative hospital database from 2015 to 2024.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2026
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Genome-wide association studies of infant and toddler temperament in European and multi-ancestry populations.

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Related Experiment Video

Updated: Mar 29, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
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Routine Life-Course Health Records in Infancy Predict Being Overweight in Childhood and Adolescence: The TMM BirThree

Genki Shinoda1,2, Mami Ishikuro1,2, Taeka Matsubara3

  • 1Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba, Sendai 980-8573, Japan.

Children (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

Early identification of childhood overweight risk is possible using health records from 18-23 months. This model predicts overweight status through adolescence, aiding early intervention strategies.

Keywords:
body mass indexchildhood overweightlife-course dataobesitypredictive model

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Area of Science:

  • Pediatrics
  • Public Health
  • Biostatistics

Background:

  • Childhood overweight predicts adult obesity and related health issues.
  • Early identification of at-risk children is crucial for intervention.
  • Predicting overweight status requires analyzing longitudinal health data.

Purpose of the Study:

  • To develop a predictive model for childhood and adolescent overweight.
  • To utilize routine health records from 18-23 months of age for prediction.
  • To assess the model's performance across different age groups.

Main Methods:

  • Analysis of 1581 participants from the Tohoku Medical Megabank Cohort Study.
  • Multivariable logistic regression models predicting overweight status at various ages.
  • Evaluation using Area Under the Curve (AUC), calibration, Brier scores, and cross-validation.

Main Results:

  • Overweight status at 18-23 months strongly predicted later overweight.
  • Model discrimination was moderate to high in childhood (AUC 0.873-0.772) and modest in adolescence (AUC 0.720-0.692).
  • Cross-validation confirmed stable predictive performance and acceptable accuracy across all ages.

Conclusions:

  • Routine early life-course health records can moderately predict overweight risk.
  • The developed model shows practical potential for early preventive interventions.
  • Early identification facilitates timely interventions to mitigate long-term health consequences.