Evaluation of growth and early infant feeding: a challenge for scientists, industry and regulatory bodies

Ferdinand Haschke1, Philipp Steenhout, Dominik Grathwohl

  • 1Nestle Nutrition Institute, Vevey, Switzerland. ferdinand.haschke@nestle.com

Insights

Infant formula trials require growth studies for safety and efficacy. Using the WHO growth standard is crucial, especially for at-risk populations, to accurately assess infant development.

Area of Science:

  • Pediatrics
  • Nutrition Science
  • Clinical Trials

Background:

  • Growth studies are essential for evaluating infant formula safety and efficacy.
  • Randomized clinical trials following healthy infants up to 4-6 months are standard practice.
  • Breastfed reference groups are necessary as formula-fed infant growth can differ.

Purpose of the Study:

  • To highlight the necessity of growth studies for infant formula evaluation.
  • To discuss the appropriate use and limitations of the World Health Organization (WHO) growth standard.
  • To emphasize the importance of using the WHO standard in trials involving at-risk infant populations.

Main Methods:

  • Review of existing meta-analyses and growth references.
  • Comparison of infant growth data between formula-fed and breastfed groups.
  • Consideration of specific populations, such as infants of overweight mothers.

Main Results:

  • The WHO growth standard, based on breastfed infants, shows higher weight-for-age than other references up to 6 months.
  • Meta-analyses suggest breastfed reference groups and infants on low-protein formula may show lower weight and BMI compared to the WHO standard.
  • Infants of overweight/obese mothers or those at risk for malnutrition require careful monitoring.

Conclusions:

  • The WHO growth standard is a key reference for infant formula trials.
  • Understanding the limitations of the WHO standard is vital for accurate interpretation of growth data.
  • The WHO standard is recommended for documenting the safety of infant formulas, particularly in at-risk populations.

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