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Metabolomics based biomarkers of dietary intake- new tools for nutrition research

Periodic Reporting for period 4 - A-DIET (Metabolomics based biomarkers of dietary intake- new tools for nutrition research)

Reporting period: 2020-02-01 to 2021-07-31

The current approaches for assessing dietary intake have a number of well documented limitations and can be influenced by random and systematic errors. Examples of such methods include 24-hour recalls, food records and food frequency questionnaires. The inaccuracy associated with dietary assessment is one of the main stumbling blocks in assessing the links between diet and health. These poor estimates of dietary exposure are a critical problem in the field of nutritional epidemiology. The overarching objective of A-DIET is to develop strategies for assessment of dietary intake.
A-DIET developed novel strategies, using a combined approach of metabolomic biomarker data and dietary data to enhance accuracy of dietary assessment. We demonstrated that urinary biomarkers can be used to estimate intake of foods with specific examples including citrus fruit, apple intake and chicken intake. We developed a model to allow classification of people into dietary patterns using urinary data only. Hence removing the need for self-reported dietary data. Importantly, we demonstrated that the dietary patterns determined were reproducible over time.
In addition, we developed two new software tools: Biointake (Biomarker guided intake) and multiMarker. Biointake combined dietary and biomarker data to develop calibration equations and allows the users to correct for measurement error in self reported data. mulitMarker is a new statistical approach for combining multiple biomarkers for prediction of food intake: it is accompanied by a shiny app to allow widespread use of the approach.

Overall, the outputs from A-DIET have contributed to significant advancement of the field of dietary biomarkers and the tools and concepts developed will enable a more accurate assessment of dietary intake and this in turn will allow epidemiologists to examine the relationship between diet and health.
A-DIET used existing data, collected new data and developed new software packages. Three human intervention studies were completed: (1) a Discovery study (n=20) whose objectives were to identify biomarkers of specific foods, (2) the Validation study’s (n=61) aim was to validate the putatively identified biomarkers from the Discovery study and (3) the CONFIRM study (n=175) which was used to determine dietary patterns. Analysis of the data from existing data and the new data from these studies has resulted in a number of key publications.
We demonstrated that a urinary biomarker could determine food intake in a number of examples. Here I highlight the example of citrus intake. Calibration curves were constructed with the urinary proline betaine concentration against the known orange juice intake (g/day). Excellent agreement was observed between estimated intakes and actual intakes with the agreement assessed through Bland and Altman analysis. A correlation of 0.92 was reported between actual intake and predicted intake again highlighting the high level of agreement. Further to this, the ability of the biomarker to estimate intake was tested in an independent cross sectional. Using the calibration curves determined in the controlled intervention study the intake (g/day) was estimated from the urinary concentration of proline betaine. There was excellent agreement between the self-report intake and the estimated intake from the biomarker. The significance of this lies with the fact that it clearly demonstrates how biomarkers may be used in a larger cohort/population setting to estimate food intake. Following, this we applied a similar method to other foods.
Dietary patters: we developed a model based on urinary metabolomics data that could classify individuals into four dietary patterns: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns had lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4 p < 0.001). The stability of participants’ dietary pattern membership ranged from 25.0% to 61.5%. This work is important as it demonstrates the potential of classifying individuals into dietary patterns based on urinary metabolites data only.

We developed two software tools (Shiny Apps) and associated R packages. Bio-intake; biomarker guided dietary intake allows users to upload mean daily self-reported citrus intake data (g/day) (estimated from food diaries) and computes calibrated intakes (g/day) based on a biomarker calibration equation (embedded into the package). multiMarker is a web application that infers the relationship between multiple biomarkers and food quantity data from an intervention study and allows prediction of food intake when only biomarker data are available. In addition, the framework allows quantification of the uncertainty in intake predictions.
In addition, to date results from our work have been published in 35 peer reviewed publications and presented at many international conferences and workshops.
The methods and concepts developed in ADIET move the field of dietary biomarkers beyond the state of the art. Highlights include (1) the demonstration that urinary biomarker measures can be used to estimate the quantity of food intake (2) The development of the model to classify individuals into a dietary pattern based solely on biomarker data and (3) the development of two software tools that are freely available to the community and help address measurement error in self-reported dietary data thus improving the accuracy of dietary assessment.