WP3 - Food Intake Biomarker Classification and Validation
Led by Dr. Lars Ove Dragsted
The current pace of biomarker discovery and biomarker applications is higher than ever before due to the rapid development of ‘omics’ technologies and data collection. This rapid development may reshape future research in nutrition and health. In order to support this development there is a need to
For nine major food groups (Table 1) the FoodBAll consortium aims to:
- develop ontologies for food, nutrition, and diet-related health areas.
- develop a solid scheme for biomarker classification that will provide a well-defined ontology for the field in order to better understand the potential of biomarkers and to communicate their use and application.
- introduce a general validation system based on standardized criteria for food intake biomarkers in order to develop them into trusted research tools according to their intended use. The validation criteria will include standard analytical quality control (chemical and biological stability, analytical performance, and inter-laboratory reproducibility) along with criteria related to biomarker kinetics (dose-response, time-response from single exposures, time-response from repeated exposures), plausibility for the actual foods, as well as robustness and reliability when applied in real life exposure. The validation has a dual purpose: (1) to estimate the current level of validation of candidate food intake biomarkers based on an objective and systematic approach, and (2) to pinpoint which additional studies are needed to provide full validation of each candidate food intake biomarker.
- improve methods to systematically search both older and more recent literature for the best biomarkers for foods, food groups, and food constituents and to develop and support database systems to include updated information on the validity of biomarker measurements for different applications.
For nine major food groups (Table 1) the FoodBAll consortium aims to:
- identify and evaluate existing putative intake biomarkers based on the literature,
- validate the more promising candidates using a coherent quality assessment scheme, and
- create a database including all suggestive food intake biomarkers along with their current level of validity for assessing exposure.
Figure 1. A schematic overview of a framework supporting the development of dietary biomarkers
An ontology and a classification scheme serve as the tools to navigate the targeted class of biomarkers. For each specific class of biomarkers, a literature search would be conducted to provide reviews of the current state of knowledge on putative biomarkers. Putative biomarkers may also be identified by new explorative research. Candidate biomarkers are selected from the putative biomarkers by removing implausible entries based on literature. A validation scheme is applied on the candidate biomarkers to assess their validity by a defined set of criteria to identify the most promising candidate biomarkers as partially or fully validated for a specified use. Further validation studies may be used to systematically validate the best candidate biomarkers. All of the available information is shared in public databases to support further studies on the development of biomarkers.
An ontology and a classification scheme serve as the tools to navigate the targeted class of biomarkers. For each specific class of biomarkers, a literature search would be conducted to provide reviews of the current state of knowledge on putative biomarkers. Putative biomarkers may also be identified by new explorative research. Candidate biomarkers are selected from the putative biomarkers by removing implausible entries based on literature. A validation scheme is applied on the candidate biomarkers to assess their validity by a defined set of criteria to identify the most promising candidate biomarkers as partially or fully validated for a specified use. Further validation studies may be used to systematically validate the best candidate biomarkers. All of the available information is shared in public databases to support further studies on the development of biomarkers.
Table 1. Principle foods and food groups investigated within WP3
| Food Group and Related Foods | Food Group and Related Foods |
|---|---|
| Alcoholic Beverages (, , , , )
Alcohol as such Beer Cider Dessert wine Red (and rose) wines White wine Whisky, cognac, gin, and other distillates Food of animal origin (, , , , , , ) Dairy Products Dairy products in general Dairy fat/butter Milk Fermented non-solid dairy products Cheeses Casein and whey protein Meat Meat in general White meat Pink meat Red meat Offal meat Processed meat Cooked and grilled meat Fish and Other Marine Food Fatty fish Lean fish (from the sea or from lakes) Crustaceans and mollusks Fish oil Eggs and Processed Eggs Fruits and Vegetables Fruit (in a culinary sense) (, , , ) Berries (strawberry, blackberry, raspberry, blackcurrant, redcurrant, blueberry, cranberry, …) Pomes (apple, pear, quince) Grapes Citrus fruits (orange, lemon, lime, grapefruit, pomelo, clementine, mandarin orange) Banana Drupes (peach, apricot, nectarine, plum, cherry) Other tropical fruits (pineapple, mango, papaya, kiwi, guava, passion fruit, …) Other fruits (muskmelon, watermelon, persimmon, pomegranate, fig, …) Vegetables (, , , , , , ) Cruciferous (cabbage, kale, broccoli, cauliflower, Brussels sprouts) Potato Root vegetables (carrot, turnip, parsnip, celeriac, radish, beetroot, salsify, cassava) Leafy greens (spinach, lettuce, endive, garden rocket) Fruit vegetables (egg plant, tomato, bell pepper) Gourds (pumpkin, cucumber, squash, zucchini) Allium vegetables (onion, garlic, shallot, chive, ramson) Other vegetables (asparagus, artichoke, leek, celery, …) Cereals and Whole Grains (, , , , ) Oat and processed oat products Barley and processed barley products Wheat and processed wheat products Rye and processed rye products Other grains and grain products Rice Sorghum Mixed cereal products Other cereals and whole grains | Nuts and Vegetable Oils (, , , )
Nuts Walnuts Almonds* Hazel nuts Pistachio Macadamia nuts Peanuts* Brazil nuts Other nuts Oils Olive oil Sunflower oil Flaxseed oil Rapeseed oil Legumes (, , ) Peas Soy and misu products Lentils Chickpeas Beans Spices and Herbs (, ) Anise Basil Black pepper Caraway Chili pepper Cinnamon Clove Coriander Cumin Curcumin (Tumeric) Dill Fennel Fenugreek Ginger Lemongrass Marjoram Nutmeg Oregano Parsley Peppermint Rosemary Saffron Sage Spearmint Tarragon Thyme Non-Alcoholic Beverages (, , , ) Coffee Tea Low-calorie sweetener-containing beverages Sugar-sweetened beverages Confectionary (, ) Cocoa Chocolate Liquorice Sugar-based sweets (bonbons) Wine gums Other confectionary |
- University of Copenhagen (DK)
- University of Barcelona (ES)
- AIRC (FR)
- University of Wageningen (NL)
- CRA-NUT (IT)
- Agroscope (CH)
- University of Alberta (CA)
- Ghent University (BE)
- University College of Dublin (IE)
- University of Eastern Finland (FI)
- INRA (FR)
- Fondazione Edmund Mach (IT)
- Technical University of Munich (DE)
- Max Rubner-Institut (DE)
- Chalmers University of Technology (SE)