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Current perspectives on global sugar consumption: definitions, recommendations, population intakes, challenges and future direction.

Janette WaltonHaley BellRoberta ReAnne P Nugent
Published in: Nutrition research reviews (2021)
Currently, there is considerable emphasis on the relationship between dietary sugar consumption and various health outcomes, with some countries and regions implementing national sugar reduction campaigns. This has resulted in significant efforts to quantify dietary sugar intakes, to agree on terms to describe dietary sugars and to establish associated recommendations. However, this information is infrequently collated on a global basis and in a regularised manner. The present review provides context regarding sugar definitions and recommendations. It provides a global review of the available data regarding dietary sugar intake, considering forms such as total, free and added sugars. A comprehensive breakdown of intakes is provided by age group, country and sugar form. This analysis shows that free sugar intakes as a percentage of total energy (%E) are the highest for children and adolescents (12-14%E) and the lowest for older adults (8%E). This trend across lifecycle stages has also been observed for added sugars. The available data also suggest that, while some reductions in sugar intake are observed in a few individual studies, overall intakes of free/added sugars remain above recommendations. However, any wider conclusions are hampered by a lack of detailed high-quality data on sugar intake, especially in developing countries. Furthermore, there is a need for harmonisation of terms describing sugars (ideally driven by public health objectives) and for collaborative efforts to ensure that the most up-to-date food composition data are used to underpin recommendations and any estimates of intake or modelling scenarios.
Keyphrases
  • public health
  • clinical practice
  • electronic health record
  • quality improvement
  • big data
  • physical activity
  • weight gain
  • body mass index
  • machine learning
  • social media