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Issues in Measuring and Interpreting Diet and Its Contribution to Obesity

  • Etiology of Obesity (T Gill, Section Editor)
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Abstract

Purpose of Review

This review summarises the issues related to the measurement and interpretation of dietary intake in individuals with overweight and obesity, as well as identifies future research priorities.

Recent Findings

Some aspects of the assessment of dietary intake have improved through the application of technology-based methods and the use of dietary biomarkers. In populations with overweight and obesity, misreporting bias related to social desirability is a prominent issue. Future efforts should focus on combining technology-based dietary methods with the use of dietary biomarkers to help reduce and account for the impact of these biases.

Summary

Future research will be important in terms of strengthening methods used in the assessment and interpretation of dietary intake data in the context of overweight and obesity.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Development Initiatives. Global nutrition report 2017: nourishing the SDGs. Bristol: Development Initiatives; 2017.

    Google Scholar 

  2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9945):766–81.

    Article  Google Scholar 

  3. Dobbs R, Sawers C, Thompson F, Manyika J, Woetzel JR, Child P et al. Overcoming obesity: An initial economic analysis. London, UK: McKinsey Global Institute. 2014 p. 1–106.

  4. Huang Y, Xu M, Xie L, Wang T, Huang X, Lv X, et al. Obesity and peripheral arterial disease: a Mendelian Randomization analysis. Atherosclerosis. 2016;247:218–24.

    Article  CAS  PubMed  Google Scholar 

  5. Cronin O, Morris DR, Walker PJ, Golledge J. The association of obesity with cardiovascular events in patients with peripheral artery disease. Atherosclerosis. 2013;228(2):316–23.

    Article  CAS  PubMed  Google Scholar 

  6. Cao Q, Yu S, Xiong W, Li Y, Li H, Li J, et al. Waist-hip ratio as a predictor of myocardial infarction risk: a systematic review and meta-analysis. Medicine. 2018;97(30):e11639.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Babu GR, Murthy GVS, Ana Y, Patel P, Deepa R, Neelon SEB, et al. Association of obesity with hypertension and type 2 diabetes mellitus in India: a meta-analysis of observational studies. World journal of diabetes. 2018;9(1):40–52.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Giraldez-Garcia C, Franch-Nadal J, Sangros FJ, Ruiz A, Carraminana F, Goday A, et al. Adiposity and diabetes risk in adults with prediabetes: heterogeneity of findings depending on age and anthropometric measure. Obesity (Silver Spring, Md). 2018;26:1481–90.

    Article  CAS  Google Scholar 

  9. Son YJ, Kim J, Park HJ, Park SE, Park CY, Lee WY, et al. Association of waist-height ratio with diabetes risk: a 4-year longitudinal retrospective study. Endocrinology and metabolism (Seoul, Korea). 2016;31(1):127–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kim YJ, Hwang SD, Oh TJ, Kim KM, Jang HC, Kimm H, et al. Association between obesity and chronic kidney disease, defined by both glomerular filtration rate and albuminuria, in Korean adults. Metabolic syndrome and related disorders. 2017;15(8):416–22.

    Article  CAS  PubMed  Google Scholar 

  11. Garofalo C, Borrelli S, Minutolo R, Chiodini P, De Nicola L, Conte G. A systematic review and meta-analysis suggests obesity predicts onset of chronic kidney disease in the general population. Kidney International. 2017;91(5):1224–35.

    Article  PubMed  Google Scholar 

  12. Herrington WG, Smith M, Bankhead C, Matsushita K, Stevens S, Holt T, et al. Body-mass index and risk of advanced chronic kidney disease: prospective analyses from a primary care cohort of 1.4 million adults in England. PloS one. 2017;12(3):e0173515.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dong Y, Zhou J, Zhu Y, Luo L, He T, Hu H, et al. Abdominal obesity and colorectal cancer risk: systematic review and meta-analysis of prospective studies. Bioscience reports. 2017;37(6).

  14. Aune D, Navarro Rosenblatt DA, Chan DS, Vingeliene S, Abar L, Vieira AR, et al. Anthropometric factors and endometrial cancer risk: a systematic review and dose-response meta-analysis of prospective studies. Ann Oncol. 2015;26(8):1635–48.

    Article  CAS  PubMed  Google Scholar 

  15. Hidayat K, Du X, Chen G, Shi M, Shi B. Abdominal obesity and lung cancer risk: systematic review and meta-analysis of prospective studies. Nutrients. 2016;8(12).

  16. Feng J, Chen Q, Yu F, Wang Z, Chen S, Jin Z, et al. Body mass index and risk of rheumatoid arthritis: a meta-analysis of observational studies. Medicine. 2016;95(8):e2859.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Silverwood V, Blagojevic-Bucknall M, Jinks C, Jordan JL, Protheroe J, Jordan KP. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis and cartilage. 2015;23(4):507–15.

    Article  CAS  PubMed  Google Scholar 

  18. Reyes C, Leyland KM, Peat G, Cooper C, Arden NK, Prieto-Alhambra D. Association between overweight and obesity and risk of clinically diagnosed knee, hip, and hand osteoarthritis: a population-based cohort study. Arthritis & rheumatology (Hoboken, NJ). 2016;68(8):1869–75.

    Article  Google Scholar 

  19. Luppino FS, de Wit LM, Bouvy PF, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry. 2010;67(3):220–9.

    Article  PubMed  Google Scholar 

  20. de Wit LM, van Straten A, van Herten M, Penninx BWJH, Cuijpers P. Depression and body mass index, a u-shaped association. BMC Public Health. 2009;9(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Mulugeta A, Zhou A, Power C, Hypponen E. Obesity and depressive symptoms in mid-life: a population-based cohort study. BMC psychiatry. 2018;18(1):297.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Martin-Rodriguez E, Guillen-Grima F, Martí A, Brugos-Larumbe A. Comorbidity associated with obesity in a large population: the APNA study. Obesity Research & Clinical Practice. 2015;9(5):435–47.

    Article  Google Scholar 

  23. Kent S, Fusco F, Gray A, Jebb SA, Cairns BJ, Mihaylova B. Body mass index and healthcare costs: a systematic literature review of individual participant data studies. Obesity Reviews. 2017;18(8):869–79.

    Article  PubMed  Google Scholar 

  24. Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(10010):2287–323.

    Article  PubMed  Google Scholar 

  25. United Nations. Open Working Group proposal for sustainable development goals. New York: United Nations; 2014.

    Google Scholar 

  26. Hawkes C, Popkin BM. Can the sustainable development goals reduce the burden of nutrition-related non-communicable diseases without truly addressing major food system reforms? BMC Medicine. 2015;13:143.

    Article  PubMed  PubMed Central  Google Scholar 

  27. World Health Organization (WHO). Obesity: preventing and managing the global epidemic. Report of a World Health Organization Consultation (Technical Report Series). Geneva: Switzerland World Health Organization; 2000.

    Google Scholar 

  28. Scarborough P, Burg MR, Foster C, Swinburn B, Sacks G, Rayner M, et al. Increased energy intake entirely accounts for increase in body weight in women but not in men in the UK between 1986 and 2000. British Journal of Nutrition. 2011;105(9):1399–404.

    Article  CAS  PubMed  Google Scholar 

  29. Yancy WS, Wang C-C, Maciejewski ML. Trends in energy and macronutrient intakes by weight status over four decades. Public health nutrition. 2014;17(2):256–65.

    Article  PubMed  Google Scholar 

  30. Brown RE, Sharma AM, Ardern CI, Mirdamadi P, Mirdamadi P, Kuk JL. Secular differences in the association between caloric intake, macronutrient intake, and physical activity with obesity. Obesity Research & Clinical Practice. 2016;10(3):243–55.

    Article  Google Scholar 

  31. Scagliusi FB, Ferriolli E, Pfrimer K, Laureano C, Cunha CS, Gualano B, et al. Characteristics of women who frequently under report their energy intake: a doubly labelled water study. Eur J Clin Nutr. 2009;63(10):1192–9.

    Article  CAS  PubMed  Google Scholar 

  32. Schoeller DA. How accurate is self-reported dietary energy intake? Nutrition reviews. 1990;48(10):373–9.

    Article  CAS  PubMed  Google Scholar 

  33. Pfrimer K, Vilela M, Resende CM, Scagliusi FB, Marchini JS, Lima NK, et al. Under-reporting of food intake and body fatness in independent older people: a doubly labelled water study. Age and ageing. 2015;44(1):103–8.

    Article  PubMed  Google Scholar 

  34. Hebert JR, Ma Y, Clemow L, Ockene IS, Saperia G, Stanek IIIEJ, et al. Gender differences in social desirability and social approval bias in dietary self-report. American Journal of Epidemiology. 1997;146(12):1046–55.

    Article  CAS  PubMed  Google Scholar 

  35. • Cade JE. Measuring diet in the 21st century: use of new technologies. The Proceedings of the Nutrition Society. 2017;76(3):276–82 This review highlights the need and importance of technology-based dietary assessment including web-based tools, smartphone apps and online library tools.

    Article  PubMed  Google Scholar 

  36. Illner AK, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. International journal of epidemiology. 2012;41(4):1187–203.

    Article  PubMed  Google Scholar 

  37. •• Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the Reporting of Observational Studies in Epidemiology – nutritional epidemiology (STROBE-nut): an extension of the STROBE statement. Nutrition Bulletin. 2016;41(3):240–51 This review focuses on the methodological strengths and limitations of self-reported dietary intake methods (including traditional and technology-based methods) and dietary biomarkers. This paper also considers potential sources of errors in dietary intake and provides recommendations for minimising the risk of those errors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bingham SA. Biomarkers in nutritional epidemiology. Public Health Nutr. 2002;5(6a):821–7.

    Article  PubMed  Google Scholar 

  39. Freisling H, van Bakel MM, Biessy C, May AM, Byrnes G, Norat T, et al. Dietary reporting errors on 24 h recalls and dietary questionnaires are associated with BMI across six European countries as evaluated with recovery biomarkers for protein and potassium intake. The British Journal of Nutrition. 2012;107(6):910–20.

    Article  CAS  PubMed  Google Scholar 

  40. Hedrick VE, Dietrich AM, Estabrooks PA, Savla J, Serrano E, Davy BM. Dietary biomarkers: advances, limitations and future directions. Nutrition Journal. 2012;11:109.

    Article  PubMed  PubMed Central  Google Scholar 

  41. • Naska A, Lagiou A, Lagiou P. Dietary assessment methods in epidemiological research: current state of the art and future prospects. F1000Research. 2017;6:926 This review focuses on the methodological strengths and limitations of self-reported dietary intake methods (including traditional and technology-based methods) and dietary biomarkers. This paper also considers potential sources of errors in dietary intake and provides recommendations for minimising the risk of those errors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Collins CE, Watson J, Burrows T. Measuring dietary intake in children and adolescents in the context of overweight and obesity. Int J Obes. 2010;34(7):1103–15.

    Article  CAS  Google Scholar 

  43. Food and Agriculture Organization of the United Nations (FAO). Dietary assessment: a resource guide to method selection and application in low resource settings. Rome, Italy: FAO; 2018.

    Google Scholar 

  44. Thompson FE, Subar AF, Loria CM, Reedy JL, Baranowski T. Need for technological innovation in dietary assessment. Journal of the American Dietetic Association. 2010;110(1):48–51.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Thomson FE, Subar AF. Dietary assessment methodology. In: Coulston AM, Boushey, Carol J, Ferruzzi Mario G editors. Nutrition in the prevention and treatment of disease 3rd ed. London: Academic Press 2013. p. 5–46.

  46. Gibson RS. Principles of nutritional assessment. USA: Oxford University Press; 2005.

    Google Scholar 

  47. Rebro SM, Patterson RE, Kristal AR, Cheney CL. The effect of keeping food records on eating patterns. Journal of the Academy of Nutrition and Dietetics. 1998;98(10):1163.

    CAS  Google Scholar 

  48. Willett W. Nutritional epidemiology. 3rd ed. Oxford: Oxford University Press. 2012.

  49. Cullen KW, Watson K, Himes JH, Baranowski T, Rochon J, Waclawiw M, et al. Evaluation of quality control procedures for 24-h dietary recalls: results from the Girls Health Enrichment Multisite Studies. Preventive Medicine. 2004;38:14–23.

    Article  Google Scholar 

  50. Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiology and Health. 2014;36:e2014009.

    Article  PubMed  PubMed Central  Google Scholar 

  51. • Brouwer-Brolsma EM, Brennan L, Drevon CA, van Kranen H, Manach C, Dragsted LO, et al. Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance. Proceedings of the Nutrition Society. 2017;76(4):619–27 This review provides an overview of dietary biomarker research with a particular focus on the efforts of the Joint Programming Initiative: A Healthy Diet for a Healthy Life’ (JPI-HDHL) and Food Biomarkers Alliance (FoodBAll).

    Article  PubMed  Google Scholar 

  52. Wong JE, Parnell WR, Black KE, Skidmore PM. Reliability and relative validity of a food frequency questionnaire to assess food group intakes in New Zealand adolescents. Nutrition journal. 2012;11(1):65.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Madden JP, Goodman SJ, Guthrie HA. Validity of the 24-hr. recall. Analysis of data obtained from elderly subjects. Journal of the American Dietetic Association. 1976;68(2):143–7.

    CAS  PubMed  Google Scholar 

  54. Bingham S, Cassidy A, Cole T, Welch A, Runswick S, Black A, et al. Validation of weighed records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biological markers. British Journal of Nutrition. 1995;73(4):531–50.

    Article  CAS  PubMed  Google Scholar 

  55. Goris AH, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: selective underreporting of fat intake. The American journal of clinical nutrition. 2000;71(1):130–4.

    Article  CAS  PubMed  Google Scholar 

  56. Heitmann BL, Lissner L. Dietary underreporting by obese individuals--is it specific or non-specific? BMJ. 1995;311(7011):986–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hill RJ, Davies PS. The validity of self-reported energy intake as determined using the doubly labelled water technique. The British journal of nutrition. 2001;85(4):415–30.

    Article  CAS  PubMed  Google Scholar 

  58. Westerterp KR, Goris AH. Validity of the assessment of dietary intake: problems of misreporting. Current Opinion in Clinical Nutrition and Metabolic Care. 2002;5(5):489–93.

    Article  PubMed  Google Scholar 

  59. Svendsen M, Tonstad S. Accuracy of food intake reporting in obese subjects with metabolic risk factors. The British journal of nutrition. 2006;95(3):640–9.

    Article  CAS  PubMed  Google Scholar 

  60. O'Sullivan A, Gibney MJ, Brennan L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. The American journal of clinical nutrition. 2011;93(2):314–21.

    Article  CAS  PubMed  Google Scholar 

  61. Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. Journal of the National Cancer Institute. 2011;103(14):1086–92.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Prentice RL, Huang Y. Measurement error modeling and nutritional epidemiology association analyses. The Canadian journal of statistics = Revue canadienne de statistique. 2011;39(3):498–509.

    PubMed  PubMed Central  Google Scholar 

  63. •• Kirkpatrick SI, Collins CE, Keogh RH, Krebs-Smith SM, Neuhouser ML, Wallace A. Assessing dietary outcomes in intervention studies: pitfalls, strategies, and research needs. Nutrients. 2018;10(8). This review is drawn from a workshop held at the International Society of Behavioral Nutrition and Physical Activity 2017 Annual Meeting which highlights methodological considerations for nutritional intervention studies. This paper focuses on differential reporting of dietary intake and provides researchers with recommendations for preventing social desirability bias.

  64. Natarajan L, Pu M, Fan J, Levine RA, Patterson RE, Thomson CA, et al. Measurement error of dietary self-report in intervention trials. Am J Epidemiol. 2010;172(7):819–27.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Neuhouser ML, Tinker L, Shaw PA, Schoeller D, Bingham SA, Horn LV, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative. Am J Epidemiol. 2008;167(10):1247–59. https://doi.org/10.1093/aje/kwn026.

    Article  PubMed  Google Scholar 

  66. •• Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI, Freedman LS. Statistical issues related to dietary intake as the response variable in intervention trials. Statistics in medicine. 2016;35(25):4493–508. This review focuses on measurement errors associated with self-reported dietary intake and provides statistical methodological recommendations.

    Article  PubMed  PubMed Central  Google Scholar 

  67. • Boushey CJ, Spoden M, Zhu FM, Delp EJ, Kerr DA. New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. The Proceedings of the Nutrition Society. 2017;76(3):283–94 Review of the literature for publications from December 2013 to January 2016 related to image-assisted or image-based dietary assessment.

    Article  CAS  PubMed  Google Scholar 

  68. Rollo ME, Ash S, Lyons-Wall P, Russell AW. Evaluation of a mobile phone image-based dietary assessment method in adults with type 2 diabetes. Nutrients. 2015;7(6):4897–910.

    Article  PubMed  PubMed Central  Google Scholar 

  69. • Hassannejad H, Matrella G, Ciampolini P, De Munari I, Mordonini M, Cagnoni S. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods. International Journal of Food Sciences and Nutrition. 2017;68(6):656–70. This review discusses the methodological strengths and limitations of automatic diet monitoring including image analysis and wearable sensors.

    Article  PubMed  Google Scholar 

  70. Kerr DA, Dhaliwal SS, Pollard CM, Norman R, Wright JL, Harray AJ, et al. BMI is associated with the willingness to record diet with a mobile food record among adults participating in dietary interventions. Nutrients. 2017;9(3):244.

    Article  PubMed Central  Google Scholar 

  71. • Vu T, Lin F, Alshurafa N, Xu W. Wearable food intake monitoring technologies: a comprehensive review. Computers. 2017;6(1):4 The review discusses the methodological strengths and limitations of wearable monitoring techologies which measure ear-based chewing and swallowing, as well as image-processing techniques that identify food types.

    Article  Google Scholar 

  72. Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Acad Nutr Diet. 2016;116(10):1568–77.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Farooq M, Sazonov E. A novel wearable device for food intake and physical activity recognition. Sensors (Basel, Switzerland). 2016;16(7):1067.

    Article  PubMed Central  Google Scholar 

  74. Doulah A, Farooq M, Yang X, Parton J, McCrory MA, Higgins JA, et al. Meal microstructure characterization from sensor-based food intake detection. Frontiers in Nutrition. 2017;4(31).

  75. Biesalski HK, Dragsted LO, Elmadfa I, Grossklaus R, Muller M, Schrenk D, et al. Bioactive compounds: definition and assessment of activity. Nutrition. 2009;25(11–12):1202–5.

    Article  PubMed  Google Scholar 

  76. Burrows T, Rollo M, Ho YY. What is the validity of dietary assessment methods in adults when comparatively validated by double labelled water (DLW) as an objective reference measure for energy intake (EI) (In-press). Journal of the Academy of Nutrition and Dietetics. 2018;110:1501–10.

    Google Scholar 

  77. Palmnäs MSA, Kopciuk KA, Shaykhutdinov RA, Robson PJ, Mignault D, Rabasa-Lhoret R, et al. Serum metabolomics of activity energy expenditure and its relation to metabolic syndrome and obesity. Scientific Reports. 2018;8(1):3308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ravussin E, Bogardus C. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. The American Journal of Clinical Nutrition. 1989;49(5):968–75.

    Article  CAS  PubMed  Google Scholar 

  79. Goran MI, Beer WH, Wolfe RR, Poehlman ET, Young VR. Variation in total energy expenditure in young healthy free-living men. Metabolism: Clinical and Experimental. 1993;42(4):487–96.

    Article  CAS  Google Scholar 

  80. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 Suppl 2):S102–38.

    Article  PubMed  Google Scholar 

  81. Cheung W, Keski-Rahkonen P, Assi N, Ferrari P, Freisling H, Rinaldi S, et al. A metabolomic study of biomarkers of meat and fish intake. The American Journal of Clinical Nutrition. 2017;105(3):600–8.

    Article  CAS  PubMed  Google Scholar 

  82. National Health and Medical Research Council (NHMRC). A review of the evidence to address targeted questions to inform the revision of the Australian dietary guidelines. Canberra: ACT NHMRC; 2011.

    Google Scholar 

  83. • Rollo ME, Williams RL, Burrows T, Kirkpatrick SI, Bucher T, Collins CE. What are they really eating? A review on new approaches to dietary intake assessment and validation. Current Nutrition Reports. 2016;5(4):307–14 This review discusses dietary intake assessment, with a particular focus on technology-based methods, as well as dietary validation methods.

    Article  Google Scholar 

  84. Burrows TL, Williams R, Rollo M, Wood L, Garg ML, Jensen M, et al. Plasma carotenoid levels as biomarkers of dietary carotenoid consumption: a systematic review of the validation studies. Journal of Nutrition & Intermediary Metabolism. 2015;2(1):15–64.

    Article  Google Scholar 

  85. Burrows TL, Warren JM, Colyvas K, Garg ML, Collins CE. Validation of overweight children's fruit and vegetable intake using plasma carotenoids. Obesity (Silver Spring). 2009;17(1):162–8.

    Article  CAS  Google Scholar 

  86. Makki K, Froguel P, Wolowczuk I. Adipose tissue in obesity-related inflammation and insulin resistance: cells, cytokines, and chemokines. ISRN inflammation. 2013;2013:139239.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Pezdirc K, Hutchesson MJ, Whitehead R, Ozakinci G, Perrett D, Collins CE. Fruit, vegetable and dietary carotenoid intakes explain variation in skin-color in young Caucasian women: a cross-sectional study. Nutrients. 2015;7(7):5800–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Tilg H, Adolph TE. Influence of the human intestinal microbiome on obesity and metabolic dysfunction. Current Opinion in Pediatrics. 2015;27(4):496–501.

    Article  CAS  PubMed  Google Scholar 

  89. Landberg R, Aman P, Friberg LE, Vessby B, Adlercreutz H, Kamal-Eldin A. Dose response of wholegrain biomarkers: alkylresorcinols in human plasma and their metabolites in urine in relation to intake. The American Journal of Clinical Nutrition. 2009;89(1):290–6.

    Article  CAS  PubMed  Google Scholar 

  90. Aubertin-Leheudre M, Koskela A, Marjamaa A, Adlercreutz H. Plasma alkylresorcinols and urinary alkylresorcinol metabolites as biomarkers of cereal fiber intake in Finnish women. Cancer Epidemiol Biomarkers Prev. 2008;17(9):2244–8.

    Article  CAS  PubMed  Google Scholar 

  91. Lissner L. Measuring food intake in studies of obesity. Public Health Nutr. 2002;5(6a):889–92.

    Article  PubMed  Google Scholar 

  92. Ptomey LT, Willis EA, Honas JJ, Mayo MS, Washburn RA, Herrmann SD, et al. Validity of energy intake estimated by digital photography plus recall in overweight and obese young adults. J Acad Nutr Diet. 2015;115(9):1392–9.

    Article  PubMed  PubMed Central  Google Scholar 

  93. •• Archundia Herrera MC, Chan CB. Narrative review of new methods for assessing food and energy intake. Nutrients. 2018;10(8). This systematic review discusses the methodological strengths and limitations of technology-based dietary assessment in the context of the 4Ms of Obesity Assessment and Management (Mental, Mechanical, Metabolic and Monetary).

  94. Hutchesson MJ, Truby H, Callister R, Morgan PJ, Davies PS, Collins CE. Can a Web-based food record accurately assess energy intake in overweight and obese women? A pilot study. Journal of Human Nutrition and Dietetics. 2013;26(Suppl 1):140–4.

    Article  PubMed  Google Scholar 

  95. •• Walker JL, Ardouin S, Burrows T. The validity of dietary assessment methods to accurately measure energy intake in children and adolescents who are overweight or obese: a systematic review. Eur J Clin Nutr. 2018;72(2):185–97. This systematic review analyses the validity of dietary assessment in children and adolescents with overweight and obesity.

    Article  PubMed  Google Scholar 

  96. Hise ME, Sullivan DK, Jacobsen DJ, Johnson SL, Donnelly JE. Validation of energy intake measurements determined from observer-recorded food records and recall methods compared with the doubly labeled water method in overweight and obese individuals. The American Journal of Clinical Nutrition. 2002;75(2):263–7.

    Article  CAS  PubMed  Google Scholar 

  97. Calvert M, Blazeby J, Altman DG, Revicki DA, Moher D, Brundage MD, et al. Reporting of patient-reported outcomes in randomized trials: the CONSORT PRO extension. JAMA. 2013;309(8):814–22.

    Article  CAS  PubMed  Google Scholar 

  98. Subar AF, Kirkpatrick SI, Mittl B, Zimmerman TP, Thompson FE, Bingley C, et al. The automated self-administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet. 2012;112(8):1134–7.

    Article  PubMed  PubMed Central  Google Scholar 

  99. National Cancer Institute. The Institute for Nutrition and Physical Activity. ASA24-Australia (2016). VIC: Australia. National Cancer Institute. 2016.

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Authors and Affiliations

  1. Faculty of Health and Medicine, School of Health Sciences, University of Newcastle, Callaghan, NSW, 2308, Australia

    Rachael M. Taylor, Rebecca L. Haslam, Tracy L. Burrows, Kerith R. Duncanson, Lee M. Ashton, Megan E. Rollo, Vanessa A. Shrewsbury & Clare E. Collins

  2. Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Level 3 ATC Building, Callaghan, NSW, 2308, Australia

    Rachael M. Taylor, Rebecca L. Haslam, Tracy L. Burrows, Kerith R. Duncanson, Lee M. Ashton, Megan E. Rollo, Vanessa A. Shrewsbury, Tracy L. Schumacher & Clare E. Collins

  3. Faculty of Health and Medicine, Department of Rural Health, University of Newcastle, Tamworth, NSW, 2340, Australia

    Tracy L. Schumacher

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  1. Rachael M. Taylor
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  2. Rebecca L. Haslam
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Taylor, R.M., Haslam, R.L., Burrows, T.L. et al. Issues in Measuring and Interpreting Diet and Its Contribution to Obesity. Curr Obes Rep 8, 53–65 (2019). https://doi.org/10.1007/s13679-019-00336-2

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