TABLE 1.

Strategies and approaches for advancing dietary biomarker development¹

Challenges	Recommendations/resources needed
Define dietary biomarkers and their utility in nutrition research
Multiple dietary biomarker definitions in use	Adopt a universally accepted biomarker classification scheme with a well-developed ontology for use by the nutritional epidemiology and dietary biomarker community
Lack of publicly available comprehensive databases on dietary biomarkers	Develop or expand well-curated, publicly available international databases on dietary biomarkers such as Exposome-Explorer and Phenol-Explorer for prioritization of candidate biomarkers
Lack of comprehensive food composition databases	Develop and maintain comprehensive food composition databases
Approaches to studying biomarkers
Studies are often conducted with no clear regard for human heterogeneity	Capture information on host factors (e.g., genetics, gut microbiome, behavioral and cultural practices) that may help to explain heterogeneity in dietary biomarker measures
Current feeding studies are “siloed” and often single studies conducted for a shorter duration, involving smaller sample sizes	Conduct larger CFSs, testing a variety of foods and dietary patterns across diverse populations to identify universal candidate biomarkers
Shortage of appropriately collected specimen repositories for dietary biomarker development	Collect a variety of biospecimens (e.g., fecal samples, blood cells, saliva, toenails, hair) as part of feeding studies to discover and validate both short- and long-term dietary biomarkers
	Leverage existing biospecimen repositories from feeding studies and prospective cohorts to validate dietary biomarkers
	Encourage long-term storage of biospecimens from completed feeding studies for dietary biomarker development studies
Lack of standardized specimen collection and processing protocols for omics analysis	Implement well-standardized specimen collection and processing protocols to ensure reproducibility, comparability, and generalizability across studies
Cumbersome sampling procedures and lack of integration of advanced devices for sample collection	Develop new sampling techniques for efficient collection and wider acceptance and improved adherence in large studies (e.g., dried blood spots) and adopt wearables and smartphone devices that allow for continuous metabolite monitoring
Analytical and statistical considerations of biomarker development
Metabolite coverage and reproducibility	Encourage sharing of spectral data and chemical databases of biologically feasible structures of metabolites
	Support internationally co-ordinated efforts for providing resources on food constituent libraries and biomarker data from various laboratories
	Facilitate distribution of relevant metabolite standards (e.g., Food Compound Exchange)
Shortage of strategies to evaluate variation within and between laboratories	Develop standardized approaches for evaluating laboratory variation and normalizing for drift and differences across laboratories
Shortage of statistical methods for handling measurement error and applying to dietary exposure assessment	Conduct methodologic work on statistical procedures for intake biomarker discovery and disease application
Sharing sensitive metadata across laboratories is difficult	Establish secure portals accessible via cloud computing and portability environments for sharing metadata
Lack of minimum reporting standards for statistical analytic pipeline/workflow for nutritional metabolomics studies	Establish minimum reporting standards to support study replication
Dietary biomarker discovery and validation
Dietary biomarker development is lengthy with no clear validation criteria	Adopt a universal dietary biomarker validation strategy that is accepted by the nutrition research community
Untargeted metabolomics produces multiple metabolites with no quantitative measures	Develop targeted and quantitative assays for validation studies after initial biomarker identification
Areas where more data are needed
Lack of comprehensive food composition databases	Create and maintain truly comprehensive food composition databases by expanding existing databases, such as FooDB, in terms of chemical coverage and breadth of human food intake
	Integrate more fully the various food composition databases using shared links, common identifiers, and common ontologies
	Extend food composition databases to archive experimentally acquired or accurately predicted referential MS/MS and/or NMR spectra data to facilitate food or dietary biomarker identification
Lack of concerted efforts and community resources necessary for dietary biomarker development	Support international efforts to prepare, acquire, or synthesize authentic food-specific compounds and their MS/MS and/or NMR spectra and enable access via open-source databases (e.g., GNPS, MoNA, FooDB, HMDB, the Metabolomics Workbench, and MetaboLights)
	Support international efforts to prepare, acquire, or synthesize authentic gut-derived, liver-derived, or similarly biotransformed food compounds and their MS/MS and/or NMR spectra. Facilitate access, via open-source databases such as GNPS, MoNA, FooDB, HMDB, the Metabolomics Workbench, and MetaboLights
	Improve algorithms and open-access software to more accurately predict metabolic biotransformation products (mimicking liver, microbial, or promiscuous biotransformations) to facilitate in silico metabolomics
	Improve algorithms and open-source software to more accurately predict MS/MS spectra (at multiple collision energies and on different platforms), NMR spectra, collisional cross-section data (for IMS data), and GC or HPLC retention times of small molecules
Specificity is a challenge for dietary biomarker development	Use combinations of biomarkers from either a single study or pooled data from several feeding studies to increase marker specificity
	Develop reference ranges for biomarkers across different populations and age ranges (children compared with adults)
Integration of dietary biomarkers with other omics techniques
Neither genomics nor metabolomics tools alone provide complete understanding of how dietary components are metabolized	Integrate other omics methods in dietary biomarker analysis with a view to understanding the impact of individual variation and personalized responses
	Identify and further explore the effect of SNPs on dietary biomarker measures
	Improve tools (databases, software, statistical methods) to facilitate the integration of genomics, metagenomics, proteomics, and metabolomics data in nutritional studies
Lack of systematically collected catalogs of SNPs	Continuously update databases or catalogs of SNPs, genes, and gene signatures that alter the metabolism, presence, or abundance of known and potential dietary biomarkers
Other critical elements
Lack of concerted efforts for biomarker development	Foster collaboration among multidisciplinary researchers
	Encourage public–private partnerships for collecting and sharing the data on dietary biomarkers that would not be otherwise freely available
	Train early career scientists in dietary biomarker development
Lack of common ontology for dietary biomarker literature	Support standard ontology efforts through development of newer and broader algorithms for electronically mining the literature
	Convene taskforces for developing common data elements for dietary biomarker research

¹CFS, controlled feeding study; GNPS, Global Natural Products Social Molecular Networking; HMDB, Human Metabolome Database; IMS, Ion Mobility Separation; MoNA, MassBank of North America; SNP, single-nucleotide polymorphism.