What if there was a way to improve clinical diagnostics using data from patients’ metabolic profiles? Believe it or not, this is now a reality with the help of metabolomics; a rapidly growing field that is providing new insights into the biochemical processes that occur in our bodies. By understanding the metabolites present in a patient’s sample, we can gain valuable insights into their health status. This could provide valuable insight into disease states, allowing clinicians to identify potential biomarkers for early diagnosis.

While genomic sequencing may reveal disease risk, clinical metabolomics provides an in vivo snapshot of a patient’s current health. This can uncover information that can be used to advance clinical decision-making, including tracking a patient’s health over time to assess treatment response.³

What Are the Limitations of Metabolomics?

There are still hurdles to overcome before this technology can be adopted more widely across all fields of medicine. Historically, poor study design and inadequate sample sizes resulted in studies that could not be reproduced. Additionally, not only can metabolomics be performed using different instruments and chromatographic methods, but also libraries of molecules used as references differ between institutions making comparisons between studies difficult.¹

Yet, despite these challenges, metabolomics holds great promise for the future of clinical diagnostics. Now, one question remains: is it ready for the clinic?

As a pioneer in using metabolomics for improving human health, we believe the answer is yes.

Metabolon’s Clinical Metabolomics Approach Aids Identification of Biomarkers

Our proprietary clinical metabolomics approach has been shown to be a proven method for screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform.³

This application of clinical metabolomics has proven exceptionally significant for Inborn Errors of Metabolism (IEM) and rare disease detection. Today there are more than 7,000 rare and undiagnosed diseases. Often the clinical manifestations of these diseases are vague, or the metabolic pathway that is disturbed may not be immediately apparent. Applying global, or untargeted, analytical approaches, where prior knowledge of the affected metabolic pathway is not required, provides advantages over targeted analytical approaches by casting a wide net to measure many biomarkers, which is invaluable in a clinical setting.³

Metabolon is certified by the ISO 9001:2015 Quality Management System (QMS) standard, a standard designed to help organizations ensure they address the needs of customers and stakeholders while meeting statutory and regulatory requirements related to a product or service. Our validated Metabolon Discovery: Global Panel, held to this standard, uses a reference library of over 5400 metabolites and can identify up to 1,000 biochemicals in plasma, urine, and cerebrospinal fluid (CSF). Previous studies have shown that these biochemicals change before phenotypic changes in an individual, providing the earliest indicators of disease progression and treatment response.⁴ Diagnostic tests at Metabolon are CLIA certified and accredited by the College of American Pathologists (CAP), which are regulations and quality standards necessary for performing clinical diagnostic testing.

Metabolomics Reveals Biomarkers for Metabolic Disease

In a Journal of the American Medical Association publication, Metabolon’s platform was used to screen over 2000 samples from patients suspected of having metabolic disease. The platform correctly identified biomarker signatures in 128 cases and 70 different metabolic conditions. In many cases, multiple biomarkers were identified for several of the diseases, increasing the strength of the clinical utility of the platform by increasing the ability of Metabolon’s platform to identify specific disease signatures. Our technology identified biomarker signatures for these diseases in plasma, making it a less invasive approach than obtaining tissue biopsies. In fact, the diagnostic rate of the platform was a several-fold increase over traditional diagnostics.⁴

By presenting a comprehensive assessment of the metabolome for these patients, our technology offered in-depth coverage of multiple pathways that can be altered in patients with metabolic disease. Rather than targeting these pathways and enzymes with single tests and collecting multiple samples, our analysis gave clinicians critical information they could use to make quicker diagnoses.⁴

Metabolomics Offers Unique Hope in Battle Against Parkinson’s Disease

Though it is the second most common neurodegenerative disease globally, Parkinson’s disease is notoriously hard to diagnose. Fortunately, recent evidence suggests that sebum biomarkers could provide a non-invasive solution for early detection.

A 2019 study reported that the sebum-rich skin of Parkinson’s subjects can capture and retain volatile hydrophobic metabolites that contribute to the musky odor exuding from Parkinson’s patients. Such alterations in sebum composition may not only reflect a change in skin physiology, but also an altered skin microflora and microbial activity in Parkinson’s patients.⁵

A more recent study from the same research group, published in Nature Communications, reported that sebum obtained from a simple skin swab can be used to help diagnose Parkinson’s based on the patient’s pattern of skin lipids and related metabolites.⁵

Researchers are hoping to use sebum as a diagnostic biofluid for Parkinson’s disease to develop an inexpensive and non-invasive test for early detection. Metabolon may help pave the way towards diagnosis, thanks to our proven set of solutions to discover and validate biomarkers present in sebum, including a global metabolomics assay and a specific sebum lipid panel that captures the unique complex lipid composition and fatty acid constituents of sebum.

Conclusion

Metabolon is committed to helping realize the potential of metabolomics and is breaking ground for using it as a clinical tool. The potential of this powerful tool is limitless, and we are proud to be one of the few companies that have the expertise and certifications necessary to make this happen.

References

1. https://doi.org/10.1093/clinchem/hvab184

2. https://www.insideprecisionmedicine.com/topics/translational-research/proteomics/omics-in-translation/

3. https://www.metabolon.com/solutions/precision-medicine/

4. https://www.metabolon.com/metabolomics-drives-identification-of-rare-disease-signatures-and-diagnoses/

5. https://www.metabolon.com/can-sebum-be-used-as-a-diagnostic-biofluid-for-parkinsons-disease/

Introduction—The Global State of Research into Alzheimer’s Disease

There are currently an estimated 55 million people living with dementia in the world and every 3 seconds another person in the world develops it. As the world population is forecast to age, the numbers are predicted to almost triple by 2050. The most common form of dementia is Alzheimer’s disease (AD) which accounts for 60 to 70% of dementia (See Dementia Facts and Figures). In the US, AD is officially listed as the 6th leading cause of death. It is also an important cause of disability.¹

“The number of people living with dementia is predicted to almost triple by 2050.”

The awareness and knowledge of AD have greatly increased since it was originally described in 1906 by the German physician, Alois Alzheimer. In 2019, AD research funding in the US reached an all-time high of $2.8 billion, and in 2021, aducanumab, a monoclonal antibody that targets the amyloid plaque present in the brains of people with the disease, received accelerated approval by the FDA (See Milestones). As of August 2022, 394 interventional studies registered at ClinicalTrials.gov are actively recruiting patients. Tested therapies include anti-plaque agents, neurotransmitter modification, anti-neuroinflammation, neuroprotection interventions, cognitive enhancement, and behavioral psychological symptoms relief.² However, one aspect that remains understudied is the basis of racial and ethnic disparities in the diagnosis of AD. To this end, researchers were awarded a $45 million grant from the National Institute on Aging to study the biological differences in multi-ethnic populations with AD.

Alzheimer’s and the Genotype—An Overview into the Biomarkers and Findings at the Genomic Level for Alzheimer’s Disease

AD can be described as a non-linear, genetics-driven pathophysiological process underlain by highly heterogeneous biological and clinical alterations and temporal disease progression.³ From the genetic point of view, although most AD forms have no apparent familial aggregation, AD has a heritability level of ~70%. In addition to mutations in APP, PSEN1, and PSEN2 genes, responsible for almost all cases of early-onset dominantly-inherited AD, more than 600 genes have been implicated as susceptibility factors for AD with APOE being the most important risk factor for the late-onset AD. Carriers of the APOEe4 allele in hetero-/homozygosis are at a 3 to 4/12 to 15 times higher risk of developing AD than individuals carrying APOEe3.⁴

Alzheimer’s and the Phenotype—An Overview into the Application of Metabolomics to Uncover Insights into Alzheimer’s Disease

The genetic heterogeneity of AD causes clinical phenotype heterogeneity regarding cognitive, neurological, and behavioral symptoms. To untangle such complexity, holistic approaches are needed such as the Omics sciences. Metabolomics, the newest omics platform, possesses great potential for the diagnosis and prognosis of AD as an individual’s metabolome reflects all genetic, transcriptional, and protein alterations and includes influence from the environment. Metabolomic research so far has confirmed the intricacy of dynamic changes associated with AD progression.⁵ Further definition of metabolite-level alterations in AD should provide insights into disease mechanisms, reveal sex-specific and ethnicity-related changes, advance the development of biomarker panels, aid the choice of individualized therapies, and monitor therapy efficacy.

“The definition of metabolite-level alterations in AD should provide insights into disease mechanisms, reveal sex-specific and ethnicity-related changes, advance the development of biomarker panels, and aid the choice of individualized therapies and monitoring their efficacy.”

Metabolon’s Contribution to Alzheimer’s Disease Research

Metabolon’s pipeline deciphers thousands of discrete chemical signals from genetic and non-genetic factors to reveal metabolic networks underlying disease. One of our strongest clinical applications is the study of AD. The company has been cited in 85 publications containing the keyword “Alzheimer’s disease” from 49 organizations. For example, researchers⁶ explored the potential usefulness of rapamycin as a pharmacological intervention for extending longevity through a comprehensive approach that included metabolomics. Could rapamycin prevent AD in E4FAD mice that express human APOe4 allele and overexpress amyloid beta? The results indicate that rapamycin can restore brain functions and reduce AD risk in young, asymptomatic mice suggesting the possibility that rapamycin could be used to prevent AD in asymptomatic APOe4 carriers.⁶ In another study, researchers⁷ determined the neuroprotective and pharmacological properties of CAD-31, a curcumin-derived AD drug candidate, and assayed its therapeutic efficacy in the APPswe/PS1ΔE9 mouse model of AD. CAD-31, endowed with neuroprotective properties, was able to prevent toxic events associated with age-related neurodegeneration.⁷

Taken together, research into AD is a thriving field of biomedical science. Future work should include metabolomic approaches, which have the potential to bring new clues into the biology and provide new targets in the treatment of AD.

References

1. Alzheimer’s Association. 2022 Alzheimer’s Disease Facts and Figures; 2022.

2. Huang LK, Chao SP, Hu CJ. Clinical trials of new drugs for Alzheimer disease. J Biomed Sci. 2020;27(1):18.

3. Hampel H, Nistico R, Seyfried NT, et al. Omics sciences for systems biology in Alzheimer’s disease: State-of-the-art of the evidence. Ageing Res Rev. 2021;69:101346.

4. Knopman DS, Amieva H, Petersen RC, et al. Alzheimer disease. Nat Rev Dis Primers. 2021;7(1):33.

5. Wilkins JM, Trushina E. Application of Metabolomics in Alzheimer’s Disease. Front Neurol. 2017;8:719.

6. Lin AL, Parikh I, Yanckello LM, et al. APOE genotype-dependent pharmacogenetic responses to rapamycin for preventing Alzheimer’s disease. Neurobiol Dis. 2020;139:104834.

7. Daugherty D, Goldberg J, Fischer W, Dargusch R, Maher P, Schubert D. A novel Alzheimer’s disease drug candidate targeting inflammation and fatty acid metabolism. Alzheimers Res Ther. 2017;9(1):50.

Our bodies are a complex interplay of genes, transcripts, proteins, metabolites, and other macro and micro molecules. Understanding the multi-layered relationships between all of these molecular parts is critical for a comprehensive understanding of human health and disease. If our own biology is so complex and interwoven, doesn’t it make sense that our research and analysis be similarly layered in order to make sense of it all? This is precisely the concept behind multi-omics analysis, the combination of two or more ‘omics datasets to gain a more holistic picture of biology. In this article, we’ll explore two powerful ‘omics technologies and how combining them can revolutionize your research and advance our understanding of human biology.

The what, how, and when of transcriptomics

The field of human genomics is built on DNA sequences and the impact that aberrations in those sequences have on human health. And while advances in genome sequencing technology have contributed countless important discoveries related to human health and disease, the DNA sequence alone can’t explain the intricate biological mechanisms that contribute to many genetic diseases. Transcriptomics provides what genomics can’t—an unbiased view of all of the coding and non-coding RNAs in a biological system, and, in the context of disease research, how those transcripts change in health and disease.

By studying the transcriptome, researchers can see not only genes that are expressed, but also long noncoding RNAs (lncRNAs) and microRNAs (miRNAS) that impact protein expression. In oncology, for example, several key genes have been associated with disease onset, severity, and phenotype. Exploring the transcriptome has revealed in many cases key RNA regulators of those critical genes which can dictate whether or not cancer develops. Often, these transcriptomic signatures can be observed prior to disease onset, so transcriptomic clues represent an area ripe for research and discovery into early diagnostics and novel therapeutics.

Going beyond genomic potential

While transcriptomics is a powerful tool, there is a limit to depth of knowledge and understanding that can be gained from it. While transcriptomics provides insights into the potential molecular events that could happen inside of a cell or organism, metabolomics, by measuring the small molecules (ie, metabolites) present in a system demonstrates what is actually happening. Metabolomics is a very powerful tool for demystifying the sometimes very convoluted upstream signals (bidirectional and multi-faceted interactions between DNA and RNA elements) and providing actionable insights for what is going on in a biological system.

With the metabolome, researchers can interpret, phenotypically, a number of perturbations, whether they be changes in mutational rates, splicing events, miRNA-mediated changes in transcripts, or even post-translational modification of proteins. Metabolomics datasets can also reveal environmental inputs, including metabolites produced by the microbiome, that critically impact health and disease. With metabolomics, a holistic picture of the biochemical processes behind any biological event—genetic and non-genetic—that impact the development of disease can be revealed.

The true power of metabolomics is really only realized when it is combined with other ‘omics datasets. As discussed by a publication in Bioinformatics and Biology Insights, integrated approaches assess the flow of information from one ‘omics level to the another, bridging the gap from genotype to phenotype. Because ‘omic approaches provide a holistic view of disease processes, multi-omics datasets have a unique potential for improving prognostics and the predictive accuracy of disease phenotypes, aiding in better treatment and prevention. Because of the phenotypic information it provides, metabolomics should be a key component of any multi-omics study.

Synergy between transcriptomics and metabolomics

The combination of transcriptomics and metabolomics datasets is a synergistic one and has shown early promise in unveiling critical disease mechanisms. For example, in prostate cancer, combining transcriptomics and metabolomics data has revealed that impaired sphingosine-1-phosphate receptor 2 signaling is the mechanism behind the specificity and sensitivity of sphingosine for distinguishing between prostate cancer and benign hyperplasia. Combining transcriptomics and metabolomics can also address a significant knowledge gap in oncology research around our understanding of inflammation, providing a type of dictionary where researchers can “look up” what changes at the transcriptomic level mean phenotypically.

As an increasing number of researchers view multi-omics research, particularly the combination of transcriptomics and metabolomics, as a new standard for human disease research, the demand for multi-omics tools capable of collecting, analyzing, and visualizing data has also increased. These tools address the challenges faced by researchers integrating ‘omics datasets and bring the power of multi-omics analysis to life. With such tools available, a future where multi-omics research is the norm, rather than an advanced modality available only to a select few, is at hand.

As you consider embarking on your own metabolomics (and, hopefully, multi-omics) study, Metabolon is uniquely positioned to support your research efforts. We work with you, helping you decipher thousands of discrete chemical signals from both genetic and non-genetic factors to unravel biological pathways and discover novel biomarkers, making connections where other ‘omics cannot, and providing the definitive representation of phenotype. We’ll guide you every step of the way, from study design to data interpretation, so you can discover more with metabolomics. If you’re ready to start on your metabolomics journey, contact us today.