Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation.

Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.

Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC-MS-based untargeted metabolomics.

INTRODUCTION: During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics. OBJECTIVES: The Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document. METHODS: Presentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance. RESULTS: Live polling results and follow-up discussions offered a broad international perspective on QA/QC practices. CONCLUSIONS: Community input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers.

Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics.

BACKGROUND: Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities. AIM OF REVIEW: A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry. KEY SCIENTIFIC CONCEPTS OF REVIEW: Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a "living guidance" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

Metabolomics 2022 workshop report: state of QA/QC best practices in LC-MS-based untargeted metabolomics, informed through mQACC community engagement initiatives.

INTRODUCTION: The Metabolomics Quality Assurance and Quality Control Consortium (mQACC) organized a workshop during the Metabolomics 2022 conference. OBJECTIVES: The goal of the workshop was to disseminate recent findings from mQACC community-engagement efforts and to solicit feedback about a living guidance document of QA/QC best practices for untargeted LC-MS metabolomics. METHODS: Four QC-related topics were presented. RESULTS: During the discussion, participants expressed the need for detailed guidance on a broad range of QA/QC-related topics accompanied by use-cases. CONCLUSIONS: Ongoing efforts will continue to identify, catalog, harmonize, and disseminate QA/QC best practices, including outreach activities, to establish and continually update QA/QC guidelines.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia.

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data.

BACKGROUND: Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. AIM OF REVIEW: To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW: We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies.

Many epidemiologic studies use metabolomics for discovery-based research. The degree to which sample handling may influence findings, however, is poorly understood. In 2016, serum samples from 13 volunteers from the US Department of Agriculture's Beltsville Human Nutrition Research Center were subjected to different clotting (30 minutes/120 minutes) and refrigeration (0 minutes/24 hours) conditions, as well as different numbers (0/1/4) and temperatures (ice/refrigerator/room temperature) of thaws. The median absolute percent difference (APD) between metabolite levels and correlations between levels across conditions were estimated for 628 metabolites. The potential for handling artifacts to induce false-positive associations was estimated using variable hypothetical scenarios in which 1%-100% of case samples had different handling than control samples. All handling conditions influenced metabolite levels. Across metabolites, the median APD when extending clotting time was 9.08%. When increasing the number of thaws from 0 to 4, the median APD was 10.05% for ice and 5.54% for room temperature. Metabolite levels were correlated highly across conditions (all r's ≥ 0.84), indicating that relative ranks were preserved. However, if handling varied even modestly by case status, our hypotheticals showed that results can be biased and can result in false-positive findings. Sample handling affects levels of metabolites, and special care should be taken to minimize effects. Shorter room-temperature thaws should be preferred over longer ice thaws, and handling should be meticulously matched by case status.

Building infrastructure at the National Cancer Institute to support metabolomic analyses in epidemiological studies.

In recent years, metabolomic analyses have been increasingly performed in studies with an epidemiological design. Since 2012, the National Cancer Institute has recognized the importance of supporting these efforts by strategically building resources and infrastructure to move this area forward. This review outlines those efforts, including building infrastructure, leveraging existing resources, establishing the COnsortium of METabolomics Studies (COMETS), and improving rigor and reproducibility.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists.

BACKGROUND: The application of metabolomics to epidemiologic studies is increasing. AIM OF REVIEW: Here, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research. KEY SCIENTIFIC CONCEPTS OF REVIEW: Many challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.

Towards quality assurance and quality control in untargeted metabolomics studies.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day 'Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies' held at the National Cancer Institute's Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

Novel colon cancer susceptibility variants identified from a genome-wide association study in African Americans.

Genome-wide association studies (GWAS) in ethnic/racial minority populations can help to fine-map previously identified risk regions or discover new risk loci because of the genetic diversity in these populations. We conducted a GWAS of colorectal cancer (CRC) in 6,597 African Americans (1,894 cases and 4,703 controls) (Stage 1) and followed up the most promising markers in a replication set of 2,041 participants of African descent (891 cases and 1,150 controls) (Stage 2). We identified a novel variant, rs56848936 in the gene SYMPK at 19q13.3, associated with colon cancer risk (odds ratio 0.61 for the risk allele G, p = 2.4 × 10-8 ). The frequency of the G allele was 0.06 in African Americans, compared to <0.01 in Europeans, Asians and Amerindians in the 1000 Genomes project. In addition, a variant previously identified through fine-mapping in this GWAS in the region 19q13.1, rs7252505, was confirmed to be more strongly associated with CRC in the African American replication set than the variant originally reported in Europeans (rs10411210). The association between rs7252505 and CRC was of borderline significance (p = 0.05) in a Hispanic population GWAS with 1,611 CRC cases and 4,330 controls. With the three datasets combined, the odds ratio was 0.84 for the risk allele A (95% confidence interval 0.79-0.89, p = 3.7 × 10-8 ). This study further highlights the importance of conducting GWAS studies in diverse ancestry populations.

Genome-wide scan of 29,141 African Americans finds no evidence of directional selection since admixture.

The extent of recent selection in admixed populations is currently an unresolved question. We scanned the genomes of 29,141 African Americans and failed to find any genome-wide-significant deviations in local ancestry, indicating no evidence of selection influencing ancestry after admixture. A recent analysis of data from 1,890 African Americans reported that there was evidence of selection in African Americans after their ancestors left Africa, both before and after admixture. Selection after admixture was reported on the basis of deviations in local ancestry, and selection before admixture was reported on the basis of allele-frequency differences between African Americans and African populations. The local-ancestry deviations reported by the previous study did not replicate in our very large sample, and we show that such deviations were expected purely by chance, given the number of hypotheses tested. We further show that the previous study's conclusion of selection in African Americans before admixture is also subject to doubt. This is because the FST statistics they used were inflated and because true signals of unusual allele-frequency differences between African Americans and African populations would be best explained by selection that occurred in Africa prior to migration to the Americas.

Germline variation in NCF4, an innate immunity gene, is associated with an increased risk of colorectal cancer.

Chronic inflammation has been implicated in the etiology of colorectal adenoma and cancer; however, few key inflammatory genes mediating this relationship have been identified. In this study, we investigated the association of germline variation in innate immunity genes in relation to the risk of colorectal neoplasia. Our study was based on the analysis of samples collected from the prostate, lung, colorectal and ovarian (PLCO) Cancer Screening Trial. We investigated the association between 196 tag single nucleotide polymorphisms (SNPs) in 20 key innate immunity genes with risk of advanced colorectal adenoma and cancer in 719 adenoma cases, 481 cancer cases and 719 controls. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). After Bonferroni correction, the AG/GG genotype of rs5995355, which is upstream of NCF4, was associated with an increased risk of colorectal cancer (OR = 2.43, 95% CI = 1.73-3.39; p < 0.0001). NCF4 is part of the NAPDH complex, a key factor in biochemical pathways and the innate immune response. While not definitive, our analyses suggest that the variant allele does not affect expression of NCF4, but rather modulates activity of the NADPH complex. Additional studies on the functional consequences of rs5995355 in NCF4 may help to clarify the mechanistic link between inflammation and colorectal cancer.

Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment.

Hepatocellular carcinoma (HCC) is an aggressive malignancy mainly due to metastases or postsurgical recurrence. We postulate that metastases are influenced by the liver microenvironment. Here, we show that a unique inflammation/immune response-related signature is associated with noncancerous hepatic tissues from metastatic HCC patients. This signature is principally different from that of the tumor. A global Th1/Th2-like cytokine shift in the venous metastasis-associated liver microenvironment coincides with elevated expression of macrophage colony-stimulating factor (CSF1). Moreover, a refined 17 gene signature was validated as a superior predictor of HCC venous metastases in an independent cohort, when compared to other clinical prognostic parameters. We suggest that a predominant humoral cytokine profile occurs in the metastatic liver milieu and that a shift toward anti-inflammatory/immune-suppressive responses may promote HCC metastases.

Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens.

Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.

Innate immunity gene polymorphisms and the risk of colorectal neoplasia.

Inherited variation in genes that regulate innate immunity and inflammation may contribute to colorectal neoplasia risk. To evaluate this association, we conducted a nested case-control study of 451 colorectal cancer cases, 694 colorectal advanced adenoma cases and 696 controls of European descent within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. A total of 935 tag single-nucleotide polymorphisms (SNPs) in 98 genes were evaluated. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association with colorectal neoplasia. Sixteen SNPs were associated with colorectal neoplasia risk at P < 0.01, but after adjustment for multiple testing, only rs2838732 (ITGB2) remained suggestively associated with colorectal neoplasia (OR(per T allele) = 0.68, 95% CI: 0.57-0.83, P = 7.7 × 10(-5), adjusted P = 0.07). ITGB2 codes for the CD18 protein in the integrin beta chain family. The ITGB2 association was stronger for colorectal cancer (OR(per T allele) = 0.41, 95% CI: 0.30-0.55, P = 2.4 × 10(-) (9)) than for adenoma (OR(per T allele) = 0.84, 95%CI: 0.69-1.03, P = 0.08), but it did not replicate in the validation study. The ITGB2 rs2838732 association was significantly modified by smoking status (P value for interaction = 0.003). Among never and former smokers, it was inversely associated with colorectal neoplasia (OR(per T allele) = 0.5, 95% CI: 0.37-0.69 and OR(per T allele) = 0.72, 95% CI: 0.54-0.95, respectively), but no association was seen among current smokers. Other notable findings were observed for SNPs in BPI/LBP and MYD88. Although the results need to be replicated, our findings suggest that genetic variation in inflammation-related genes may be related to the risk of colorectal neoplasia.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review.

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Identification of metastasis-related microRNAs in hepatocellular carcinoma.

MicroRNAs (miRNAs) have been used as cancer-related biomarkers. Hepatocellular carcinoma (HCC) is an aggressive cancer with a dismal outcome largely due to metastasis and postsurgical recurrence. We investigated whether the expression of certain miRNAs are associated with HCC metastasis. We examined the miRNA expression profiles of 482 cancerous and noncancerous specimens from radical resection of 241 patients with HCC. Using a supervised algorithm and a clinically well-defined cohort of 131 cases, we built a unique 20-miRNA metastasis signature that could significantly predict (P < 0.001) primary HCC tissues with venous metastases from metastasis-free solitary tumors with 10-fold cross-validation. However, significant miRNAs could not be identified from the corresponding noncancerous hepatic tissues. A survival risk prediction analysis revealed that a majority of the metastasis-related miRNAs were associated with survival. Furthermore, the 20-miRNA tumor signature was validated in 110 additional cases as a significant independent predictor of survival (P = 0.009) and was significantly associated with both survival and relapse in 89 cases of early stage HCC (P = 0.022 and 0.002, respectively). These 20 miRNAs may provide a simple profiling method to assist in identifying patients with HCC who are likely to develop metastases/recurrence. In addition, functional analysis of these miRNAs may enhance our biological understanding of HCC metastasis.

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review.

Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.