Moving toward Objective Biomarkers of Dietary Intake

Moving toward Objective Biomarkers of Dietary Intake In the past 3 y we have witnessed a rapid growth in the number of publications in the field of biomarkers of dietary intake using metabolomics (1). There is great expectation for the role of dietary biomarkers in the assessment of dietary intake and, in particular, habitual dietary intake. However, we must be cognizant that the field is in its infancy and a significant amount of work is needed before we have validated objective biomarkers that could be used in nutritional epidemiology or in nutritional intervention studies. In order to make progress in the field a number of different study designs are needed, and all bring their own advantages and disadvantages. Study designs include, for example, intervention studies with specific foods or meals and observational studies that use biobanked samples. Unfortunately, in many recent publications there have been criticisms associated with the discovery of biomarkers using intervention studies, while applauding the use of observational studies. This is on the whole unhelpful for the field because both approaches are warranted in order to make the significant progress that is promised. In this issue of the Journal, Wang et al. (2) elegantly used an observational approach to identify novel potential serum biomarkers of habitual intake while also highlighting the limitations of the approach. They identified 42 food groups that were correlated with 199 serum metabolites. The article describes these correlations in detailed tables in the supplementary material and performs a detailed comparison with previous literature to confirm that 63 metabolite-food correlations were previously reported. Confirmation of previous findings is important for the development of an evidence base. In addition, they identified several new associations that were biologically plausible and these warrant further investigation. Although these new associations are useful, the authors also describe the limitations in terms of further work that is needed, including confirmation in intervention studies and examination of whether these biomarkers respond to varying quantities of intake of the specific foods. In this context, confirmation in intervention studies is essential to move the biomarkers forward. Furthermore, much of the previous work has been performed in urine samples, so it is timely that Wang et al. examined serum and found so many correlations with food intake (2). A key issue that needs to be addressed with serum-based biomarkers is the fact that we need to decipher whether the biomarkers are markers of intake or markers of altered metabolism as a result of the food intake. This will be difficult to examine with the use of observational studies only; however, by clever combination with intervention studies it may be possible to disentangle the metabolites that are truly representative of intake. Although this and other studies have added significantly to the field, it is also important to recognize the challenges to enable us to move forward (3). Many metabolites have a short half-life and may not be representative of habitual intake, potentially limiting the use of biomarkers. Due to prohibitive costs, very few studies perform repeated measures of the biomarkers and such analysis is essential to establish the repeatability of the biomarkers and their intraclass correlations (4). Furthermore, it is probable that multiple measurements are essential to capture habitual dietary intake. We can also learn from the classical biomarkers, such as urinary nitrogen for protein intake, where quantitative measurements are essential and reference values play a key role. However, the reality is that many metabolomic platforms are not measuring metabolites in a quantitative fashion and we have no reference values for many of the putative biomarkers of dietary intake. Future work needs to address these issues to enable the field to reach its full potential. Although challenges exist, there is no doubt that the future is bright for this area of research. Recent studies have shown dose-response relations between urinary biomarker concentrations and food intake (5, 6). Furthermore, the urinary biomarker proline betaine was used to estimate citrus intake in a cross-sectional study that used calibration curves developed in a controlled intervention study. Importantly, the estimated intake agreed well with the reported intake from a 4-d food diary, indicating the potential of these biomarkers for use in dietary assessment (5). Capturing dietary intake is extremely complex, and although exciting new results are emerging from the application of metabolomics, it is important to be cognizant of the challenges raised above. Through international collaboration, the use of multiple study designs, and multiple biofluid types it will be possible to make progress. However, we should also be realistic and acknowledge that biomarker approaches will complement rather than replace traditional dietary assessment tools. Acknowledgments The sole author had responsibility for all parts of the manuscript. Notes The author reported no funding received for this commentary. Author disclosures: LB, no conflicts of interest. References 1. Guasch-Ferre M , Bhupathiraju SN , Hu FB . Use of metabolomics in improving assessment of dietary intake . Clin Chem 2018 ; 64 : 82 – 98 . Google Scholar CrossRef Search ADS PubMed 2. Wang Y , Gapstur SM , Carter BD , Hartman TJ , Stevens VL , Gaudet MM , McCullough ML . Untargeted metabolomics identifies novel potential biomarkers of habitual food intake in a cross-sectional study of postmenopausal women . J Nutr 2018 ; 148 : 932 – 943 . 3. Lampe JW , Huang Y , Neuhouser ML , Tinker LF , Song X , Schoeller DA , Kim S , Raftery D , Di C , Zheng C et al. Diet ary biomarker evaluation in a controlled feeding study in women from the Women's Health Initiative cohort . Am J Clin Nutr 2017 ; 105 : 466 – 75 . Google Scholar CrossRef Search ADS PubMed 4. Sun Q , Bertrand KA , Franke AA , Rosner B , Curhan GC , Willett WC . Reproducibility of urinary biomarkers in multiple 24-h urine samples . Am J Clin Nutr 2017 ; 105 : 159 – 68 . Google Scholar CrossRef Search ADS PubMed 5. Gibbons H , Michielsen CJR , Rundle M , Frost G , McNulty BA , Nugent AP , Walton J , Flynn A , Gibney MJ , Brennan L . Demonstration of the utility of biomarkers for dietary intake assessment: proline betaine as an example . Mol Nutr Food Res 20 17 ; 61 : doi: 10.1002/mnfr.201700037. Epub 2017 Jul 20 . 6. Garcia-Perez I , Posma JM , Chambers ES , Nicholson JK , Mathers JC , Beckmann M , Draper J , Holmes E , Frost G . An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake . J Agric Food Chem 2016 ; 64 : 2423 – 31 . Google Scholar CrossRef Search ADS PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Nutrition Oxford University Press

Moving toward Objective Biomarkers of Dietary Intake

Journal of Nutrition , Volume Advance Article (6) – May 23, 2018

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Publisher
American Society for Nutrition
Copyright
© 2018 American Society for Nutrition.
ISSN
0022-3166
eISSN
1541-6100
D.O.I.
10.1093/jn/nxy067
Publisher site
See Article on Publisher Site

Abstract

In the past 3 y we have witnessed a rapid growth in the number of publications in the field of biomarkers of dietary intake using metabolomics (1). There is great expectation for the role of dietary biomarkers in the assessment of dietary intake and, in particular, habitual dietary intake. However, we must be cognizant that the field is in its infancy and a significant amount of work is needed before we have validated objective biomarkers that could be used in nutritional epidemiology or in nutritional intervention studies. In order to make progress in the field a number of different study designs are needed, and all bring their own advantages and disadvantages. Study designs include, for example, intervention studies with specific foods or meals and observational studies that use biobanked samples. Unfortunately, in many recent publications there have been criticisms associated with the discovery of biomarkers using intervention studies, while applauding the use of observational studies. This is on the whole unhelpful for the field because both approaches are warranted in order to make the significant progress that is promised. In this issue of the Journal, Wang et al. (2) elegantly used an observational approach to identify novel potential serum biomarkers of habitual intake while also highlighting the limitations of the approach. They identified 42 food groups that were correlated with 199 serum metabolites. The article describes these correlations in detailed tables in the supplementary material and performs a detailed comparison with previous literature to confirm that 63 metabolite-food correlations were previously reported. Confirmation of previous findings is important for the development of an evidence base. In addition, they identified several new associations that were biologically plausible and these warrant further investigation. Although these new associations are useful, the authors also describe the limitations in terms of further work that is needed, including confirmation in intervention studies and examination of whether these biomarkers respond to varying quantities of intake of the specific foods. In this context, confirmation in intervention studies is essential to move the biomarkers forward. Furthermore, much of the previous work has been performed in urine samples, so it is timely that Wang et al. examined serum and found so many correlations with food intake (2). A key issue that needs to be addressed with serum-based biomarkers is the fact that we need to decipher whether the biomarkers are markers of intake or markers of altered metabolism as a result of the food intake. This will be difficult to examine with the use of observational studies only; however, by clever combination with intervention studies it may be possible to disentangle the metabolites that are truly representative of intake. Although this and other studies have added significantly to the field, it is also important to recognize the challenges to enable us to move forward (3). Many metabolites have a short half-life and may not be representative of habitual intake, potentially limiting the use of biomarkers. Due to prohibitive costs, very few studies perform repeated measures of the biomarkers and such analysis is essential to establish the repeatability of the biomarkers and their intraclass correlations (4). Furthermore, it is probable that multiple measurements are essential to capture habitual dietary intake. We can also learn from the classical biomarkers, such as urinary nitrogen for protein intake, where quantitative measurements are essential and reference values play a key role. However, the reality is that many metabolomic platforms are not measuring metabolites in a quantitative fashion and we have no reference values for many of the putative biomarkers of dietary intake. Future work needs to address these issues to enable the field to reach its full potential. Although challenges exist, there is no doubt that the future is bright for this area of research. Recent studies have shown dose-response relations between urinary biomarker concentrations and food intake (5, 6). Furthermore, the urinary biomarker proline betaine was used to estimate citrus intake in a cross-sectional study that used calibration curves developed in a controlled intervention study. Importantly, the estimated intake agreed well with the reported intake from a 4-d food diary, indicating the potential of these biomarkers for use in dietary assessment (5). Capturing dietary intake is extremely complex, and although exciting new results are emerging from the application of metabolomics, it is important to be cognizant of the challenges raised above. Through international collaboration, the use of multiple study designs, and multiple biofluid types it will be possible to make progress. However, we should also be realistic and acknowledge that biomarker approaches will complement rather than replace traditional dietary assessment tools. Acknowledgments The sole author had responsibility for all parts of the manuscript. Notes The author reported no funding received for this commentary. Author disclosures: LB, no conflicts of interest. References 1. Guasch-Ferre M , Bhupathiraju SN , Hu FB . Use of metabolomics in improving assessment of dietary intake . Clin Chem 2018 ; 64 : 82 – 98 . Google Scholar CrossRef Search ADS PubMed 2. Wang Y , Gapstur SM , Carter BD , Hartman TJ , Stevens VL , Gaudet MM , McCullough ML . Untargeted metabolomics identifies novel potential biomarkers of habitual food intake in a cross-sectional study of postmenopausal women . J Nutr 2018 ; 148 : 932 – 943 . 3. Lampe JW , Huang Y , Neuhouser ML , Tinker LF , Song X , Schoeller DA , Kim S , Raftery D , Di C , Zheng C et al. Diet ary biomarker evaluation in a controlled feeding study in women from the Women's Health Initiative cohort . Am J Clin Nutr 2017 ; 105 : 466 – 75 . Google Scholar CrossRef Search ADS PubMed 4. Sun Q , Bertrand KA , Franke AA , Rosner B , Curhan GC , Willett WC . Reproducibility of urinary biomarkers in multiple 24-h urine samples . Am J Clin Nutr 2017 ; 105 : 159 – 68 . Google Scholar CrossRef Search ADS PubMed 5. Gibbons H , Michielsen CJR , Rundle M , Frost G , McNulty BA , Nugent AP , Walton J , Flynn A , Gibney MJ , Brennan L . Demonstration of the utility of biomarkers for dietary intake assessment: proline betaine as an example . Mol Nutr Food Res 20 17 ; 61 : doi: 10.1002/mnfr.201700037. Epub 2017 Jul 20 . 6. Garcia-Perez I , Posma JM , Chambers ES , Nicholson JK , Mathers JC , Beckmann M , Draper J , Holmes E , Frost G . An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake . J Agric Food Chem 2016 ; 64 : 2423 – 31 . Google Scholar CrossRef Search ADS PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of NutritionOxford University Press

Published: May 23, 2018

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