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Interpreting alignment to the EAT-Lancet diet using dietary intakes of lactating mothers in rural Western Kenya.

Wilhemina QuarpongSamson WakoliShadrack OiyeAnne M Williams
Published in: Maternal & child nutrition (2023)
The EAT-Lancet reference diet intends to be good for planetary and human health. We compared single multiple pass method 24-h dietary intake of mothers (n = 242) from a cross-sectional study in Western Kenya to the recommended range of intake of 11 EAT-Lancet food groups (e.g., 0-100 g/day legumes; maximum score 11), defining alignment two ways: daily intake among food groups where a minimum intake of 0 g was either acceptable or unacceptable. Ordinal logistic regression models assessed associations between alignment and body mass index (BMI). Cost of mothers' diets and hypothetical diets within recommended ranges (lower bounds >0 g) were estimated using food price data from markets within the mothers' locality. Mean energy intake was 1827 (95% confidence interval [CI]: 1731-1924) kcal/day. Relative to the EAT-Lancet diet, mothers' diets were on average higher for grains; within recommendations for tubers, fish, beef and dairy; closer to lower bounds for chicken, eggs, legumes and nuts; and lower for fruits and vegetables. Mean (95% CI) alignment scores were 8.2 (8.0-8.3) when 0 g intakes were acceptable and 1.7 (1.6-1.9) otherwise. No significant associations were found between alignment and BMI. Mothers' diets and hypothetical diets within recommended ranges averaged 184.6 KES (1.6 USD) and 357.5 KES (3.0 USD)/person/day, respectively. Lactating mothers' diets were not diverse and diverged from the reference diet when an intake of 0 g was considered unacceptable. Lower bound intakes of 0 g for micronutrient-dense food groups are inappropriate in food-insecure populations. It would likely cost more than mothers currently spend to tailor their diets to the EAT-Lancet reference diet.
Keyphrases
  • weight loss
  • human health
  • weight gain
  • physical activity
  • body mass index
  • risk assessment
  • climate change
  • south africa
  • electronic health record
  • machine learning
  • big data