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Metabolomics: The Growing Potential of an Emerging Field

, by DCEG Staff

by Maura Kate Costello, M.A., and Jennifer K. Loukissas, M.P.P.

This Manhattan plot shows 67 (out of 617) metabolites significantly associated with body mass index and breast cancer. (Moore SC et al, JNCI, June 2018)

In the Division of Cancer Epidemiology and Genetics (DCEG), investigators study a range of factors that influence risk for cancer. Epidemiologists have made profound discoveries by measuring potential carcinogens and correlating them with cancer outcomes—for example, the associations of obesity and physical activity with cancer. Despite clear, causal evidence for some exposure-disease relationships, the effects they have on the human body—the carcinogenic process itself—remain largely a mystery.

All exposures we study affect our metabolism, the chemical processes that are needed to maintain life: breaking down nutrients, eliminating toxins, or building tissue. For example, protein, carbohydrates, and fat in the food we eat are metabolized into amino acids, sugars, and fatty acids, providing the building blocks of cells and tissue, compounds synthesized by our bodies for normal functioning, and the fuel to keep us going at a cellular level. When these processes work in harmony, we produce sufficient enzymes to process the “raw material.” When there is too much of a certain material, the body may take a ‘non-standard’ approach to breaking down those large molecules, producing intermediate compounds that may affect health.

These substances circulate throughout the body in a person’s blood, urine, and other biospecimens. Metabolomics—the study of these compounds and their relationships in a living organism—provides a new avenue for exploration. Cell culture or animal studies can only take us so far in our understanding of the inner workings of the body. By measuring metabolites in biospecimens, researchers can uncover clues about a person’s underlying physiologic state.

Early methods development: A foundation for success

Steven Moore and Mary Playdon

Over the past eight years, a multidisciplinary team made up of DCEG epidemiologists, biochemists, experts in nutritional studies, and biostatisticians, have been evaluating the performance of metabolomics assays and applying them to studies of cancer etiology. They have utilized metabolomics to improve measurement of key epidemiologic exposures (e.g. diet), enable assessment of mediating mechanisms (e.g. intermediaries such as estrogen that are associated with both obesity and cancer risk), and identify novel pathways of cancer initiation or progression.

Steven Moore, Ph.D., tenure-track investigator in the Metabolic Epidemiology Branch (MEB), explains: “historically, researchers could only focus on one type or class of metabolite at a time. Newer technologies allow a broader, agnostic approach to analysis that could accelerate rate of discovery.”

As of this writing, investigators have reported on 1,000 or more metabolites, often in studies of thousands of participants. The ability to do this on such a large scale—both in terms of the quantity of metabolites and the number of participants studied—depends upon sophisticated statistical tools.

Joshua Sampson and Andriy Derkach

Joshua Sampson, Ph.D., senior investigator in the Biostatistics Branch (BB), and collaborators in MEB showed that despite variation in the levels of most metabolites over time, these levels are sufficiently stable for large epidemiologic studies to identify associations between metabolites and disease risk1. Given the new application of metabolomics in epidemiology, Dr. Sampson and his post-doctoral fellow, Andriy Derkach, Ph.D., are developing novel methods to identify metabolic profiles that are either predictive of disease or that can offer insight into why known risk factors are associated with disease. Dr. Derkach adds, “Metabolites can give us a window into the underlying biological processes causing disease. Advances in statistical methodology will greatly facilitate our ability to peer through that window.”

Intriguing results begin to accumulate

Metabolomics has great value in complementing and extending exposure assessment methods in nutritional studies. Early results show reliable concordance with dietary intake, suggesting biomarker measurement can be used to validate data collected through food frequency questionnaires, and both can be used in concert to evaluate the effects of diet on health2-9.

Former MEB postdoctoral fellow and NCI K99/00 Pathway to Independence Award winner Mary Playdon, Ph.D., now at the Huntsman Cancer Institute at the University of Utah, applies metabolomics to the study of diet and health. In preliminary findings, she observed that variations in the amount or presence of these molecules directly impact human health, disease risk, and disease progression. 

“This approach can help us explore how food, vitamin supplements, and medications interact over the course of disease progression,” said Dr. Playdon. “We can evaluate how exercise and sedentary behavior influence metabolism and disease risk; and study the interaction of the human metabolome, genome, and microbiome.”

Together with Dr. Moore, she showed correlations between incomplete breakdown of branched-chain amino acids, BMI, and increased risk for breast cancer10, a pattern that ties well with emerging literature suggesting a role for perturbed metabolism of amino acids in cancer progression. According to recent work by cancer biologists, incomplete metabolism of amino acids may result in a surplus of “building-blocks” that nascent cancer cells can coopt to make more cancer cells. 

Demetrius Albanes and Rachael Stolzenberg-Solomon

Demetrius Albanes, M.D., senior investigator in MEB, has investigated the relationship between energy, glycerphospholipid, and inositol metabolism and risk of aggressive prostate cancer11, as well as arginine, antioxidant, and coffee metabolites in relation to glioma risk12. Dr. Albanes has also elucidated a metabolomic pattern for higher vitamin D status13 and examined how vitamin supplementation impacts the metabolome; for example, high doses of beta-carotene alter the metabolism of xenobiotic compounds foreign to normal human biochemistry (e.g. drugs or other synthetic chemicals)5, possibly explaining the deleterious cancer and mortality outcomes observed in controlled supplementation trials. In related work, Rachael Stolzenberg-Solomon, Ph.D., M.P.H., R.D., senior investigator in MEB, is exploring biomarkers for pancreatic cancer that may one day yield a method for early-detection of this rare and notoriously fatal malignancy.

While promising results continue to emerge, validation studies are needed within and across diverse populations, as well as explorations into specific populations to expose the biology behind established health disparities. Tracy Layne, Ph.D., M.P.H., MEB postdoctoral fellow and winner of the William G. Coleman, Jr. Minority Health and Health Disparities Research Innovation Award from the National Institute of Minority Health Disparities, is working with Dr. Albanes to evaluate metabolic patterns in African American men as part of her research to explain the higher incidence and mortality for prostate cancer in this population14.

Strength in numbers: the role of consortia

This graph illustrates the number of metabolites associated with the indicated foods at various false discovery rates. (Guertin KA et al, Am J Clin Nutr, July 2014)

To achieve sufficient statistical power, the investigators needed large numbers of carefully curated, prospective data—biospecimens, questionnaire data and disease outcomes. Drs. Moore and Stolzenberg-Solomon, have taken on leadership roles as the founding chair and secretary, respectively, of the Consortium of METabolomic Studies (COMETS), an extramural-intramural partnership that promotes collaboration among investigators leading prospective cohort studies. To date, 50 cohorts have joined COMETS, with over 150,000 participants in all. “Seventy percent have genome-wide association studies data as well,” Dr. Moore points out, “which will be critical for examining any correlations between the metabolome and the genome.”

The large number of cohorts and the population diversity within them also make replication studies possible. “Since many biospecimens have already been collected in existing cohorts,” said Dr. Moore, “and in some cases are already metabolically profiled, this research can often be pursued at no or minimal additional cost.”

Challenges and opportunities in metabolomics

As in any new field of research, there remain a number of obstacles to overcome as well as promising avenues to pursue.

"Great attention needs to be paid to standardization of handling samples,” cautioned Dr. Stolzenberg-Solomon. “Differences in the way the samples are stored, thawed, and aliquoted prior to running the assays may affect the measurement of metabolite levels.”

Methods and assays will need to be validated for the study of long-term biomarkers in hair, nails, and blood protein; food-drug interactions; and to disentangle the interplay of the gut microbiome and metabolome in human health.

In planning for future studies, Dr. Moore notes, “tools for effective data harmonization will be increasingly essential. Current assays typically yield only relative concentrations, not absolute measures; however, if results are to be useful for comparison with later studies, absolute quantitative values are needed.” MEB senior investigators, Rashmi Sinha, Ph.D., and Katherine McGlynn, Ph.D., M.P.H., and postdoctoral fellows, Erikka Loftfield, Ph.D., and Jessica Petrick, Ph.D., M.P.H., have begun this work on short-chain fatty acids and bile acids.

Meanwhile, there is much to learn about the interactions between chemical classes, as well as the role of exposures like vitamins, obesity, and smoking. Improved technology will also expand our ability to look more specifically at chemical class panels, moving from untargeted discovery to focused platforms, such as lipids and coffee metabolites.

Dr. Playdon explained, “right now we are only able to perform -omics studies on a double-level; that is, comparing two -omics profiles at a time. Eventually, we hope to achieve the ability to look at many, if not all of them at once (i.e. genomics, transcriptomics, metabolomics, including proteomics and lipidomics, and the microbiome). Doing this will enable us to answer questions more comprehensively with the full power of multiple areas of research.”


[1] Sampson JN, Boca SM, Shu XO, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22(4):631-640.

[2] Derkach A, Sampson J, Joseph J, Playdon MC, Stolzenberg-Solomon RZ. Effects of dietary sodium on metabolites: the Dietary Approaches to Stop Hypertension (DASH)-Sodium Feeding Study. Am J Clin Nutr. 2017;106(4):1131-1141.

[3] Guertin KA, Moore SC, Sampson JN, et al. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am J Clin Nutr. 2014;100(1):208-217.

[4] Guertin KA, Loftfield E, Boca SM, et al. Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. Am J Clin Nutr. 2015;101(5):1000-1011.

[5] Mondul AM, Sampson JN, Moore SC, et al. Metabolomic profile of response to supplementation with beta-carotene in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. Am J Clin Nutr. 2013;98(2):488-493.

[6] Mondul AM, Moore SC, Weinstein SJ, et al. Serum Metabolomic Response to Long-Term Supplementation with all-rac-alpha-Tocopheryl Acetate in a Randomized Controlled Trial. J Nutr Metab. 2016;2016:6158436.

[7] Playdon MC, Moore SC, Derkach A, et al. Identifying biomarkers of dietary patterns by using metabolomics. Am J Clin Nutr. 2017;105(2):450-465.

[8] Playdon MC, Sampson JN, Cross AJ, et al. Comparing metabolite profiles of habitual diet in serum and urine. Am J Clin Nutr. 2016;104(3):776-789.

[9] Playdon MC, Ziegler RG, Sampson JN, et al. Nutritional metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr. 2017;106(2):637-649.

[10] Moore SC, Playdon MC, Sampson JN, et al. A Metabolomics Analysis of Body Mass Index and Postmenopausal Breast Cancer Risk. J Natl Cancer Inst. 2018.

[11] Huang J, Mondul AM, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Prospective serum metabolomic profile of prostate cancer by size and extent of primary tumor. Oncotarget. 2017.

[12] Huang J, Weinstein SJ, Kitahara CM, Karoly ED, Sampson JN, Albanes D. A prospective study of serum metabolites and glioma risk. Oncotarget. 2017;8(41):70366-70377.

[13] Nelson SM, Panagiotou OA, Anic GM, et al. Metabolomics analysis of serum 25-hydroxy-vitamin D in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Int J Epidemiol. 2016;45(5):1458-1468.

[14] Layne TM, Graubard BI, Ma X, Mayne ST, Albanes D. Prostate cancer risk factors in black and white men in the NIH-AARP Diet and Health StudyProstate Cancer Prostatic Dis. Aug 2018.



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