Detecting and Diagnosing Metabolic Disorders

Metabolic disorders occur when the body is unable to process or utilize nutrients effectively, impacting essential functions such as energy production.

They can include rare inherited disorders of metabolism, such as Gaucher disease or Wilson’s disease, which are caused by gene changes that impact healthy metabolism. Metabolic disorders can also include common disorders that occur due to a combination of genetic predisposition and lifestyle choices that lead to nutrient overconsumption.

“For example, Type 2 diabetes mellitus (T2D), affecting approximately 10% of the global population, is caused by dietary habits favoring foods rich in sugar and unhealthy fats, although variants in several genes have been associated to T2D onset and progression, and can cause other types of diabetes mellitus,” explained Dr. Irene De Biase, professor of clinical pathology at the University of Utah School of Medicine.

“Cardiovascular diseases, metabolic syndrome and non-alcoholic fatty liver disease (NAFLD) are also associated with these risk factors,” she added.

Some metabolic disorders can be caused by dietary habits favoring foods rich in sugar and unhealthy fats. Credit: iStock.

De Biase is board-certified in clinical biochemical genetics and shares the management and clinical responsibility for the Biochemical Genetics laboratory at ARUP laboratories, a national reference laboratory owned by the University of Utah. As medical director of the lab, she is dedicated to providing accurate and timely results to aid in the diagnosis and follow-up of patients with inborn errors of metabolism.

De Biase spoke with Technology Networks about the current methods for diagnosing metabolic disorders and the evolving role of biomarker discovery in improving detection and treatment.

The role of laboratory testing and biomarkers in diagnosing metabolic disorders

The diagnosis of metabolic disorders is based on laboratory testing, De Biase explained: “Laboratory testing is important for the diagnostic process of any disorder; however, it is critical to identify a disruption in metabolic processes before the onset of clinical manifestations.”

It is also essential for distinguishing between disorders with overlapping symptoms and determining appropriate treatment strategies. “Often, it also represents the first step in identifying a genetic cause of a metabolic disorder. For instance, in our biochemical genetic laboratory, we perform specific testing to diagnose inherited disorders of metabolism,” De Biase said, adding that these disorders are rare individually, but relatively common as a group, and require specific treatments. Inherited disorders of metabolism can also present as more common conditions, including sepsis, cardiovascular disease or liver disease.

“For example, patients with deficiency of lysosomal acid lipase, an enzyme involved in the metabolism of cholesterol, can present with dyslipidemia and liver disease, [which is] difficult to distinguish from NAFLD,” De Biase said. “This extremely rare disorder is treated by replacing the missing enzyme, while patients with NAFLD benefit from changes in diet, exercise levels and medications that improve insulin sensitivity, inflammation and fat metabolism.”

Beyond diagnosis, some of the changes that are detected in patients because of their disease can be used to monitor how the disease is progressing or the efficacy of a medical intervention. De Biase emphasized that such biomarkers need to be sufficiently sensitive and specific to a condition to be effectively used.

“We already utilize many biomarkers and have been accumulating evidence of their clinical utility and efficacy for decades. However, the emergence of new disorders or treatments, as well as having a better understanding of known diseases, applies a never-ending pressure to develop novel and better biomarkers,” she said.

Thankfully, De Biase feels that her team is up to the challenge, in part because of the continuous improvements made to instrumentation, such as next-generation sequencing and mass spectrometry (MS) platforms.

“The ‘omic’ technologies have increased the rate of new biomarker discovery,” De Biase said. “In the traditional approach, only a handful of markers could be analyzed, not just because of limitations in detection, but also because of the difficulty in interpreting large data set[s]. However, in the last decade, several high-throughput technologies have revolutionized the field and allowed large-scale analysis of genes and their transcripts, proteins and metabolites.  These various ‘omic’ technologies are often used together, integrating different aspects of cellular processes.”

It's important to recognize, De Biase cautioned, that a robust and easy-to-implement laboratory test for a biomarker often requires the development and validation of targeted tests. “Implementation in the lab may look very different from the discovery stage,” she said.

The future of biomarker research in metabolic disorders

At the time of interview, De Biase noted that almost 300 publications using proteomics in various cohorts of patients with diabetes mellitus have already been published.^*

“These studies aim to discover significant differences in proteins associated with developing the disease, the risk of specific clinical outcomes or the likelihood of successful treatment,” she said.

A recent study published in Diabetes Care adopted a large-scale proteomics approach to evaluate the predictive value of proteomic biomarkers in assessing 10-year T2D risk when used with the Cambridge Diabetes Risk Score, with promising results.

Another study, published in Microbiome, performed a proteomics and metaproteomics analysis of saliva from T2D patients and healthy controls. The researchers identified protein signatures that suggested altered immune-, lipid- and glucose-metabolism regulatory systems, in addition to increased oxidative stress and potential precancerous states in T2D saliva.

When asked about the future of metabolic disorder diagnosis and what this might look like, De Biase quotes the American Writer and humorist Mark Twain: “It is difficult to make predictions, particularly about the future,” she said. “However, I expect that we will continue to see an exponential growth of studies that use ‘omic’ approaches evaluating large data sets to identify correlations between biomarkers and clinical outcomes,” she said.

“These studies are necessary to translate the novel methodologies into effective clinical applications,” De Biase concluded.

March 2025*