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Ultra-processed foods and recommended intake levels of nutrients linked to non-communicable diseases in Australia: evidence from a nationally representative cross-sectional study
  1. Priscila P Machado1,2,
  2. Euridice M Steele2,
  3. Renata B Levy2,3,
  4. Zhixian Sui4,
  5. Anna Rangan5,
  6. Julie Woods6,
  7. Tim Gill7,
  8. Gyorgy Scrinis8,
  9. Carlos A Monteiro2,9
  1. 1Graduate Program in Nutrition in Public Health, School of Public Health, University of Sao Paulo, Sao Paulo, Brazil
  2. 2Center for Epidemiological Research in Nutrition and Health, University of Sao Paulo, Sao Paulo, Brazil
  3. 3Department of Preventive Medicine, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
  4. 4School of Public Health and Community Medicine, University of New South Wales, Sydney, New South Wales, Australia
  5. 5School of Life and Environmental Sciences, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
  6. 6Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia
  7. 7Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
  8. 8School of Agriculture and Food, The University of Melbourne, Melbourne, Victoria, Australia
  9. 9Department of Nutrition, School of Public Health, University of Sao Paulo, Sao Paulo, Brazil
  1. Correspondence to Dr Carlos A Monteiro; carlosam{at}


Objective This study aimed to describe the consumption of ultra-processed foods in Australia and its association with the intake of nutrients linked to non-communicable diseases (NCDs).

Design Cross-sectional study.

Setting National Nutrition and Physical Activity Survey (2011-2012).

Participants 12,153 participants aged 2+ years.

Main outcome measures Average dietary content of nutrients linked to NCDs and the prevalence of intake outside levels recommended for the prevention of NCDs.

Data analysis Food items were classified according to the NOVA system, a classification based on the nature, extent and purpose of industrial food processing. The contribution of each NOVA food group and their subgroups to total energy intake was calculated. Mean nutrient content of ultra-processed food and non-ultra-processed food fractions of the diet were compared. Across quintiles of the energy contribution of ultra-processed foods, differences in the intake of nutrients linked to NCDs as well as in the prevalence of intakes outside levels recommended for the prevention of NCDs were examined.

Results Ultra-processed foods had the highest dietary contribution (42.0% of energy intake), followed by unprocessed or minimally processed foods (35.4%), processed foods (15.8%) and processed culinary ingredients (6.8%). A positive and statistically significant linear trend was found between quintiles of ultra-processed food consumption and intake levels of free sugars (standardised β 0.43, p<0.001); total (β 0.08, p<0.001), saturated (β 0.18, p<0.001) and trans fats (β 0.10, p<0.001); sodium (β 0.21, p<0.001) and diet energy density (β 0.41, p<0.001), while an inverse relationship was observed for dietary fibre (β -0.21, p<0.001) and potassium (β -0.27, p<0.001). The prevalence of non-recommended intake levels of all studied nutrients increased linearly across quintiles of ultra-processed food intake, notably from 22% to 82% for free sugars, from 6% to 11% for trans fat and from 2% to 25% for dietary energy density, from the lowest to the highest ultra-processed food quintile.

Conclusion The high energy contribution of ultra-processed foods impacted negatively on the intake of non-ultra-processed foods and on all nutrients linked to NCDs in Australia. Decreasing the dietary share of ultra-processed foods would substantially improve the diet quality in the country and help the population achieve recommendations on critical nutrients linked to NCDs.

  • food processing
  • diet
  • quality of diet
  • food consumption
  • Australia

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  • Contributors CAM, EMS, PPM and RBL designed the research; EMS, JW, PPM and ZS took care of data management and analyses; AR, CAM, EMS, GS, JW, PPM, RBL, TG and ZS interpreted the data; PPM wrote the first draft of the manuscript; AR, CAM, EMS, GS, JW, PPM, RBL, TG and ZS revised each draft for important intellectual content. All authors read and approved the final manuscript.

  • Funding This work was supported by the Sao Paulo Research Foundation (FAPESP), grant #2015/14900-9 and grant #2016/13168-5 (PPM is a beneficiary of a doctoral scholarship). PPM received a doctoral scholarship from the Coordination for the Improvement of Higher Education Personnel (CAPES) from October 2016 to February 2017. FAPESP and CAPES had no role in the design, analysis or writing of this manuscript.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval This study was a secondary analysis using deidentified data from the ABS Basic Confidentialised Unit Record Files, and permission to use the data was obtained. Ethics approval for the survey was granted by the Australian Government Department of Health and Ageing Departmental Ethnics Committee in 2011.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available.

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