Corrigendum to "Development and validation of a multiplexed LC-MS/MS ketone body assay for clinical diagnostics" [J. Mass Spectrom. Adv. Clin. Lab 31 (2024) 49-58].

[This corrects the article DOI: 10.1016/j.jmsacl.2024.01.004.].

Development and validation of a multiplexed LC-MS/MS ketone body assay for clinical diagnostics.

Objectives: Ketone bodies (KBs) serve as important energy sources that spare glucose, providing the primary energy for cardiac muscle, skeletal muscle during aerobic exercise, and the brain during periods of catabolism. The levels and relationships between the KBs are critical indicators of metabolic health and disease. However, challenges in separating isomeric KBs and concerns about sample stability have previously limited their clinical measurement. Methods: A novel 6.5-minute liquid chromatography-mass spectrometry-based assay was developed, enabling the precise measurement of alpha-, beta- and gamma-hydroxybutyrate, beta-hydroxyisobutyrate, and acetoacetate. This method was fully validated for human serum and plasma samples by investigating extraction efficiency, matrix effects, accuracy, recovery, intra- and inter-precision, linearity, lower limit of quantitation (LLOQ), carryover, specificity, stability, and more. From 107 normal samples, reference ranges were established for all analytes and the beta-hydroxybutyrate/acetoacetate ratio. Results: All five analytes were adequately separated chromatographically. An extraction efficiency between 80 and 120 % was observed for all KBs. Accuracy was evaluated through spike and recovery using 10 random patient samples, with an average recovery of 85-115 % for all KBs and a coefficient of variation of ≤ 3 %. Coefficients of variation for intra- and inter-day imprecision were < 5 %, and the total imprecision was < 10 %. No significant interferences were observed. Specimens remained stable for up to 6 h on ice or 2 h at room temperature. Conclusions: The developed method is highly sensitive and robust. It has been validated for use with human serum and plasma, overcoming stability concerns and providing a reliable and efficient quantitative estimation of ketone bodies.

Enterobacteriaceae Growth Promotion by Intestinal Acylcarnitines, a Biomarker of Dysbiosis in Inflammatory Bowel Disease.

BACKGROUND & AIMS: Altered plasma acylcarnitine levels are well-known biomarkers for a variety of mitochondrial fatty acid oxidation disorders and can be used as an alternative energy source for the intestinal epithelium when short-chain fatty acids are low. These membrane-permeable fatty acid intermediates are excreted into the gut lumen via bile and are increased in the feces of patients with inflammatory bowel disease (IBD). METHODS: Herein, based on studies in human subjects, animal models, and bacterial cultures, we show a strong positive correlation between fecal carnitine and acylcarnitines and the abundance of Enterobacteriaceae in IBD where they can be consumed by bacteria both in vitro and in vivo. RESULTS: Carnitine metabolism promotes the growth of Escherichia coli via anaerobic respiration dependent on the cai operon, and acetylcarnitine dietary supplementation increases fecal carnitine levels with enhanced intestinal colonization of the enteric pathogen Citrobacter rodentium. CONCLUSIONS: In total, these results indicate that the increased luminal concentrations of carnitine and acylcarnitines in patients with IBD may promote the expansion of pathobionts belonging to the Enterobacteriaceae family, thereby contributing to disease pathogenesis.

Biochemical characterization of patients with dihydrolipoamide dehydrogenase deficiency.

Dihydrolipoamide dehydrogenase (DLD; E3) oxidizes lipoic acid. Restoring the oxidized state allows lipoic acid to act as a necessary electron sink for the four mitochondrial keto-acid dehydrogenases: pyruvate dehydrogenase, alpha-ketoglutarate dehydrogenase, branched-chain α-keto-acid dehydrogenase, and 2-oxoadipate dehydrogenase. DLD deficiency (DLDD) is caused by biallelic pathogenic variants in DLD. Three major forms have been described: encephalopathic, hepatic, and myopathic, although DLDD patients exhibit overlapping phenotypes. Hyperlactatemia, hyperexcretion of tricarboxylic acid cycle (TCA) metabolites and branched-chain keto acids, increased plasma branched-chain amino acids and allo-isoleucine are intermittent metabolic abnormalities reported in patients with DLDD. However, the diagnostic performance of these metabolites has never been studied. Therefore, we sought to systematically evaluate the diagnostic utility of these biomarkers for DLDD. We retrospectively analyzed the results of biochemical testing of six unrelated DLDD patients, including values obtained during both well visits and acute decompensation episodes. Elevation of branched-chain amino acid concentrations was not consistently observed. We found that five of six patients in our cohort had a maximum lifetime value of allo-isoleucine of 6 µmol/L, showing that alloisoleucine elevations even during illness may be subtle. Urine organic acid analysis (UOA) during acute decompensation episodes was abnormal in all cases; however, the pattern of abnormalities had high intersubject variability. No single biomarker was universally present, even in patients experiencing metabolic decompensation. We also observed novel biochemical associations: three patients had hyperexcretion of TCA cycle metabolites during crisis; in two patients, 2-ketoadipic and 2-hydroxyadipic acids, by products of lysine degradation, were detected. We propose that these result from 2-oxoadipate dehydrogenase deficiency, an underappreciated biochemical abnormality in DLD. Given the diversity of biochemical profiles among the patients with DLDD, we conclude that accurate biochemical diagnosis relies on a high index of suspicion and multipronged biochemical analysis, including both plasma amino acid and urine organic acid quantitation during decompensation. Biochemical diagnosis during the well state is challenging. We emphasize the critical importance of multiple simultaneous biochemical tests for diagnosis and monitoring of DLDD. We also highlight the under-recognized role of DLD in the lysine degradation pathway. Larger cohorts of patients are needed to establish a correlation between the biochemical pattern and clinical outcomes, as well as a genotype-phenotype correlation.

Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group.

BACKGROUND: Machine learning (ML) has been applied to an increasing number of predictive problems in laboratory medicine, and published work to date suggests that it has tremendous potential for clinical applications. However, a number of groups have noted the potential pitfalls associated with this work, particularly if certain details of the development and validation pipelines are not carefully controlled. METHODS: To address these pitfalls and other specific challenges when applying machine learning in a laboratory medicine setting, a working group of the International Federation for Clinical Chemistry and Laboratory Medicine was convened to provide a guidance document for this domain. RESULTS: This manuscript represents consensus recommendations for best practices from that committee, with the goal of improving the quality of developed and published ML models designed for use in clinical laboratories. CONCLUSIONS: The committee believes that implementation of these best practices will improve the quality and reproducibility of machine learning utilized in laboratory medicine. SUMMARY: We have provided our consensus assessment of a number of important practices that are required to ensure that valid, reproducible machine learning (ML) models can be applied to address operational and diagnostic questions in the clinical laboratory. These practices span all phases of model development, from problem formulation through predictive implementation. Although it is not possible to exhaustively discuss every potential pitfall in ML workflows, we believe that our current guidelines capture best practices for avoiding the most common and potentially dangerous errors in this important emerging field.

Methodologies in Mitochondrial Testing: Diagnosing a Primary Mitochondrial Respiratory Chain Disorder.

BACKGROUND: Mitochondria are cytosolic organelles within most eukaryotic cells. Mitochondria generate the majority of cellular energy in the form of adenosine triphosphate (ATP) through oxidative phosphorylation (OxPhos). Pathogenic variants in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) lead to defects in OxPhos and physiological malfunctions (Nat Rev Dis Primer 2016;2:16080.). Patients with primary mitochondrial disorders (PMD) experience heterogeneous symptoms, typically in multiple organ systems, depending on the tissues affected by mitochondrial dysfunction. Because of this heterogeneity, clinical diagnosis is challenging (Annu Rev Genomics Hum Genet 2017;18:257-75.). Laboratory diagnosis of mitochondrial disease depends on a multipronged analysis that can include biochemical, histopathologic, and genetic testing. Each of these modalities has complementary strengths and limitations in diagnostic utility. CONTENT: The primary focus of this review is on diagnosis and testing strategies for primary mitochondrial diseases. We review tissue samples utilized for testing, metabolic signatures, histologic findings, and molecular testing approaches. We conclude with future perspectives on mitochondrial testing. SUMMARY: This review offers an overview of the current biochemical, histologic, and genetic approaches available for mitochondrial testing. For each we review their diagnostic utility including complementary strengths and weaknesses. We identify gaps in current testing and possible future avenues for test development.

2-Methylglutaconic acid as a biomarker in routine urine organic acids leading to the diagnosis of glutaric acidemia type I in a low excretor.

GA1 (OMIM# 231670) is an organic aciduria caused by defective Glutaryl-CoA dehydrogenase (GCDH), encoded by GCDH. Early detection of GA1 is crucial to prevent patients from developing acute encephalopathic crisis and subsequent neurologic sequelae. Diagnosis of GA1 relies on elevated glutarylcarnitine (C5DC) in plasma acylcarnitine analysis and hyperexcretion of glutaric acid (GA) and 3-hydroxyglutaric acid (3HG) in urine organic acid (UOA) analysis. Low excretors (LE), however, exhibit subtly elevated or even normal plasma C5DC and urinary GA levels, leading to screening and diagnostic challenges. The measurement of 3HG in UOA is thus often used as the 1st tier test for GA1. We described a case of LE detected via newborn screen with normal excretion of GA, absent of 3HG and increased 2-methylglutaconic acid (2MGA), which was detected at 3 mg/g creatinine (reference interval <1 mg/g creatinine) without appreciable ketones. We retrospectively examined UOA of 8 other GA1 patients and the 2MGA level ranged from 2.5 to 27.39 mg/g creatinine, which is significantly higher than normal controls (0.05-1.61 mg/g creatinine). Although the underlying mechanism of 2MGA formation in GA1 is unclear, our study suggests 2MGA is a biomarker for GA1 and should be monitored by routine UOA to evaluate its diagnostic and prognostic value.

XPO1 inhibition with selinexor synergizes with proteasome inhibition in neuroblastoma by targeting nuclear export of IkB.

Across many cancer types in adults, upregulation of the nuclear-to-cytoplasmic transport protein Exportin-1 (XPO1) correlates with poor outcome and responsiveness to selinexor, an FDA-approved XPO1 inhibitor. Similar data are emerging in childhood cancers, for which selinexor is being evaluated in early phase clinical studies. Using proteomic profiling of primary tumor material from patients with high-risk neuroblastoma, as well as gene expression profiling from independent cohorts, we have demonstrated that XPO1 overexpression correlates with poor patient prognosis. Neuroblastoma cell lines are also sensitive to selinexor in the low nanomolar range. Based on these findings and knowledge that bortezomib, a proteasome inhibitor, blocks degradation of XPO1 cargo proteins, we hypothesized that combination treatment with selinexor and bortezomib would synergistically inhibit neuroblastoma cellular proliferation. We observed that selinexor promoted nuclear retention of IkB and that bortezomib augmented the ability of selinexor to induce cell-cycle arrest and cell death by apoptosis. This synergy was abrogated through siRNA knockdown of IkB. The synergistic effect of combining selinexor and bortezomib in vitro provides rationale for further investigation of this combination treatment for patients with high-risk neuroblastoma.

Facilitating Test Status Information Communication in the Time of COVID-19.

The Coronavirus 2019 pandemic has strained nearly every aspect of pathology practice, including preanalytic, analytic, and postanalytic processes. Much of the challenges result from high demand for limited severe acute respiratory syndrome coronavirus 2 testing capacity, a resource required to facilitate patient flow throughout the hospital system and society at large. At our institution, this led to unprecedented increases in inquiries from providers to laboratory staff relating to the expected time to result for their patients. The demand was great enough to require redeployment of staff to handle the laboratory call volume. Although these data are available in our laboratory information system, the data do not interface to our electronic health record system. We developed systems using the R statistical programming language that abstract the necessary data regarding severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing from our lab system in real time, store it, and present it to clinicians for on demand querying. These data have been accessed over 2500 times by over 100 distinct users. Median length of each user session is approximately 4.9 minutes. Because our lab information system does not persistently store tracking information while our system does, we have been able to iteratively recalculate time to result values for each tracking stop as workflows have changed over time. Facility with informatics and programming concepts coupled with clinical understanding have allowed us to swiftly develop and iterate on applications which provide efficiency gains, allowing laboratory resources to focus on generating test results for our patients.

Mitochondrial dysfunction in inflammatory bowel disease alters intestinal epithelial metabolism of hepatic acylcarnitines.

As the interface between the gut microbiota and the mucosal immune system, there has been great interest in the maintenance of colonic epithelial integrity through mitochondrial oxidation of butyrate, a short-chain fatty acid produced by the gut microbiota. Herein, we showed that the intestinal epithelium could also oxidize long-chain fatty acids, and that luminally delivered acylcarnitines in bile could be consumed via apical absorption by the intestinal epithelium, resulting in mitochondrial oxidation. Finally, intestinal inflammation led to mitochondrial dysfunction in the apical domain of the surface epithelium that may reduce the consumption of fatty acids, contributing to higher concentrations of fecal acylcarnitines in murine Citrobacter rodentium-induced colitis and human inflammatory bowel disease. These results emphasized the importance of both the gut microbiota and the liver in the delivery of energy substrates for mitochondrial metabolism by the intestinal epithelium.

Derivation of a metabolic signature associated with bacterial meningitis in infants.

BACKGROUND: Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM. METHODS: In a nested case-control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection. RESULTS: Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis. CONCLUSIONS: We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants. IMPACT: In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants.We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis.Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.

Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model.

The recent increase in accessible medical and clinical laboratory "Big Data" has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine-learning data analytics for DTC data are suggested.