Comparison of Untargeted Metabolomic Profiling vs

Original Investigation | Pediatrics

Comparison of Untargeted Metabolomic Profiling vs Traditional MetabolicScreening to Identify Inborn Errors of MetabolismNing Liu, PhD; Jing Xiao, PhD; Charul Gijavanekar, PhD; Kirk L. Pappan, PhD; Kevin E. Glinton, MD, PhD; Brian J. Shayota, MD; Adam D. Kennedy, PhD; Qin Sun, PhD;V. Reid Sutton, MD; Sarah H. Elsea, PhD

Abstract

IMPORTANCE Recent advances in newborn screening (NBS) have improved the diagnosis of inbornerrors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBSpanels, nor are they covered in standard, first-line biochemical testing.

OBJECTIVE To examine the utility of untargeted metabolomics as a primary screening tool for IEMsby comparing the diagnostic rate of clinical metabolomics with the recommended traditionalmetabolic screening approach.

DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study compares data from 4464clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids,plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received forclinical metabolomic screening at a College of American Pathologists and Clinical LaboratoryImprovement Amendments–certified biochemical genetics laboratory. Data analysis was performedfrom September 2019 to August 2020.

EXPOSURES Metabolic and molecular tests performed at a genetic testing reference laboratory inthe US and available clinical information for each patient were assessed to determine diagnostic rate.

MAIN OUTCOMES AND MEASURES The diagnostic rate of traditional metabolic screeningcompared with clinical metabolomic profiling was assessed in the context of expanded NBS.

RESULTS Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 familieswere identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected,including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male,and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screeningidentified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 differentmetabolic conditions were identified, including 49 conditions not presently included on theRecommended Uniform Screening Panel for NBS.

CONCLUSIONS AND RELEVANCE These findings suggest that untargeted metabolomics provideda 6-fold higher diagnostic yield compared with the conventional screening approach and identifieda broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolictesting approaches identify few disorders beyond those covered on the NBS. These data support the

(continued)

Key PointsQuestion Is untargeted metabolomic

profiling associated with a significant

increase in the diagnostic rate of

screening for inborn errors of

metabolism (IEMs) compared with the

traditional metabolic screening

approach?

Findings This cross-sectional analysis

of 4464 traditional metabolic screening

samples and 2000 plasma metabolomic

screening samples received at a clinical

biochemical laboratory between July

2014 and February 2019 found a 1.3%

diagnostic rate for traditional metabolic

screening, whereas clinical

metabolomics supported diagnosis

in 7.1% of cases, providing an

approximately 6-fold higher diagnostic

rate in screening for IEMs and

identifying more disorders and more

disease types compared with the

traditional screening approach.

Meaning With expanded newborn

screening available in all states in the US,

a broader approach to primary

screening for IEMs is needed, and these

data support the application of

untargeted clinical metabolomics to

serve as a primary screening approach.

+ Supplemental content

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Open Access. This is an open access article distributed under the terms of the CC-BY License.

JAMA Network Open. 2021;4(7):e2114155. doi:10.1001/jamanetworkopen.2021.14155 (Reprinted) July 12, 2021 1/14

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Abstract (continued)

capability of clinical untargeted metabolomics in screening for IEMs and suggest that broaderscreening approaches should be considered in the initial evaluation for metabolic disorders.

JAMA Network Open. 2021;4(7):e2114155. doi:10.1001/jamanetworkopen.2021.14155

Introduction

Early detection and diagnosis of inborn errors of metabolism (IEMs) are imperative because manyconditions are amenable to treatment, and sequelae that develop before therapy is initiated areoften irreversible.1-3 Newborn screening (NBS) is the first-tier assessment of IEMs and aims toidentify apparently healthy newborns with serious conditions to improve neonatal and lifelonghealth outcomes. As a result of advances in tandem mass spectrometry technology, NBS hasexpanded, and 49 metabolic conditions, including 25 core conditions and 24 secondary conditions,are currently listed on the Recommended Uniform Screening Panel (RUSP) with variations indifferent states. However, many treatable IEMs are not included on the RUSP; thus, individuals withsigns and symptoms of an IEM still require further testing, even when the NBS findings are normal.

The standard diagnostic approach for IEMs involves the recognition of symptoms and routinelaboratory abnormalities followed by biochemical and/or genetic testing. Clinically, IEMs have a widerange of presentations, from acute to chronic, involving virtually any tissue or organ of the humanbody, and clinical manifestations may arise at different periods, ranging from neonatal to lateadulthood.4,5 Therefore, the nonspecific nature of these sequelae often presents diagnosticchallenges. Laboratory assessments of metabolic disorders may be prolonged because the standardapproach involves multiple, and often sequential, targeted biochemical tests.1,6 Optimal evaluationof patients presenting with nonspecific neurological findings, including intellectual disability, globaldevelopmental delays, or autism spectrum disorder, follows recommendations provided by theAmerican Academy of Neurology, the American Academy of Pediatrics, and the National Academy ofClinical Biochemistry, stating that first-line screening for IEMs should be considered in the initialevaluation.7-9 The traditional metabolic screening approach includes a trio of biochemical analyses:plasma amino acids (PAA), plasma acylcarnitine profile (ACP), and urine organic acids (UOA). NBStargets 43 of 49 disorders diagnosable by this trio of tests, and, to our knowledge, no recent studieshave assessed the current diagnostic rate for this trio of tests in the era of expanded NBS (eTable 1in the Supplement).

Clinical metabolomics, a broad, comprehensive analysis of small molecules in body fluids, usesliquid chromatography–coupled mass spectrometry and is sensitive, with diverse coverage.10,11 Thismethod is capable of detecting multiple metabolites with varying chemical properties in a single testfor the global analysis of perturbations in biochemical pathways that would otherwise requiremultiple targeted tests. The potential application of untargeted metabolomics to clinical diagnosticscreening for IEMs in the precision medicine era is supported by multiple published studies.12-15 Totest our hypothesis that untargeted metabolomics could function as a primary initial screen for IEMs,we assessed data from a large-scale clinical analysis of 2000 clinical metabolomic samples todetermine the diagnostic rate compared with that of traditional metabolic screening (PAA, ACP,and UOA).

Methods

This study was approved by the Baylor College of Medicine institutional review board with a waiverof informed consent because this study was performed to assess effectiveness of clinical laboratorytesting, involved no more than minimal risk to the participants, could not be performed practicablywithout the waiver, and would not adversely affect the rights and welfare of the participants. All

JAMA Network Open | Pediatrics Untargeted Metabolomic Profiling vs Traditional Screening to Identify Inborn Errors of Metabolism





laboratory testing reported in this study was performed at the request of the referring clinician andin the process of clinical care of the patient. This study follows the Strengthening the Reporting ofObservational Studies in Epidemiology (STROBE) reporting guideline.

Patient PopulationThis cross-sectional study compared data from 2 different cohorts: a traditional metabolic screeningcohort and a metabolomic screening cohort defined by the tests ordered in our clinical laboratory.The traditional screening cohort included samples from 1488 consecutive patients from 1483unrelated families (4464 clinical samples collected in total, 1 sample each for 3 tests) referred forclinical biochemical testing from June 2014 through October 2018. All individuals underwent PAA,ACP, and UOA analyses from samples collected on the same day. Most samples received were notsubmitted with a clinical indication or phenotyping information; however, it is likely that most wereobtained to screen for IEMs or follow-up of equivocal results from other testing, including NBS. Theclinical metabolomic screening cohort consisted of plasma samples from 1807 unrelated families(2000 clinical samples collected in total) assessed by untargeted metabolomic profiling betweenJuly 2014 and February 2019, including 158 families that were also analyzed by traditional screeningapproach. Only the initial plasma metabolomic samples collected from the probands were included inour analysis pipeline, as the metabolites of the repeated specimens may not accurately reflect thereal disease states because of dietary or medication management. Most of the clinical metabolomicsamples were submitted with detailed descriptions of the patient’s clinical phenotype.

A request for traditional targeted biochemical testing and/or metabolomic testing was madesolely at the discretion of the referring health care practitioner with no required or potential criteriaand no filtering by the laboratory reflecting the unbiased comparison of these approaches. All testingwas performed in the clinical biochemical genetics laboratory at Baylor Genetics. Untargeted clinicalmetabolomics was performed in collaboration with Metabolon, Inc. Both laboratories are certified bythe College of American Pathologists and in compliance with the Clinical Laboratory ImprovementAmendments.

ProceduresTargeted clinical biochemical tests (PAA, ACP, and UOA) were performed as described previously(eAppendix 1 in the Supplement).13 Clinical metabolomic profiling of plasma was developed andperformed through a collaboration between Baylor College of Medicine, Baylor Genetics, andMetabolon, Inc, and was performed as previously described (eAppendix 2 in theSupplement).10,11,13,16-20

Analysis Pipeline and Diagnostic CriteriaTesting results from PAA, ACP, and UOA analyses were reviewed, analyzed, and interpreted by board-certified clinical biochemical geneticists through a standard data analysis pipeline (Figure 1A). A casewas classified as abnormal when substantial abnormal results were detected in a single sample, suchas the presence, reduction, or elevation of specific diagnostic markers for known metabolicdisorders, or when combined results of PAA, ACP, and/or UOA analyses suggested a patternindicating a possible IEM. When no substantial abnormalities were detected, the patient was notfurther considered in the analysis pipeline. An abnormal case was classified as diagnosed afterin-depth analysis considering all other data collected from the patient. To facilitate this analysis, allavailable clinical and genetic details were considered, including clinical phenotype, metabolomicprofiling analysis, targeted biochemical diagnostic testing results, and molecular testing results,which may include exome sequencing, chromosome microarray, cytogenetic analysis, fluorescent insitu hybridization, targeted gene sequencing panels, mitochondrial DNA sequencing, and/ormitochondrial DNA copy number analysis. Data were integrated, reviewed, and interpreted by aninterdisciplinary analysis team according to current American College of Medical Genetics andGenomics and Association for Molecular Pathology guidelines. A case was considered confirmed only




http://www.equator-network.org/reporting-guidelines/strobe/



when available clinical presentation, genetic testing results, and follow-up evaluations wereconsistent with the biochemical findings for a diagnosis of IEMs. Cases with a definitive diagnosiswere pooled, and the positive diagnostic rate was determined.

To assess clinical metabolomic screening outcomes, a similar global meta-analysis pipeline wasapplied to provide an in-depth analysis for every clinical sample. The cases in the metabolomic cohortwith substantial biochemical abnormalities were divided into different categories according towhether the biochemical profiles were (1) specific for, (2) indicative of, or (3) nonspecific for an IEM(Figure 1B). Further in-depth analysis of these categories by integrating other findings were appliedto confirm or exclude the diagnosis for each case (the same pipeline as described already for thetraditional screening approach). Of note, some cases were not classified as positive in this analysiseven though metabolomic profiling data were consistent with a disease pattern; this underestimateof final positive diagnostic rate was due either to metabolomic data not being definitively diagnosticfor the specific condition and/or the absence of other testing to confirm the diagnosis. As a referencelaboratory, follow-up and/or comprehensive clinical data were not available for all patients tested.

Figure 1. Assessment of Clinical Samples Referred for Metabolic Screening

1483 Traditional screeningapproach cohort

159 Biochemical abnormalitiesindicate suspicion ofdiagnosis of IEM

19 Confirmed by integrationof all available geneticinformation from patients

71 Ruled out by integratedother biochemical andsequencing testing

69 No follow-up data available

1324 Normal or no substantialmetabolic abnormalities

Analysis of clinical samples received for traditionalbiochemical screening approach

A

1807 Metabolomic screening cohort (plasma)

912 Metabolic abnormalities identified

895 Normal or no substantialmetabolomic abnormalities

128 Abnormalities werediagnostic for IEM

128 Confirmed diagnostic cases in total67 Metabolomics findings confirmed

by targeted biochemical tests

655 Abnormalities weresuggestive of a specificcategory of IEM

129 Abnormalities werenonspecific for an IEM

84 Confirmed by integration of allavailable genetic informationfrom patients56 Metabolomics, sequencing

data, and clinicalphenotypes wereconsistent for IEM28 Confirmed by further

targeted biochemicaltests

28 Metabolomics and clinicalfeatures were consistentfor IEM16 Confirmed by further

targeted biochemicaltests


40 Confirmed by integrationof all available geneticinformation from patients

53 Ruled out by integratedtargeted biochemical andsequencing testing


36 Metabolomics, sequencingdata, and clinicalphenotypes wereconsistent for IEM

4 Metabolomics and clinicalfeatures were consistentfor IEM

18 Confirmed by furthertargeted biochemicaltests

2 Confirmed by furthertargeted biochemicaltests

3 Confirmed by integration ofmetabolomic, biochemicaland sequencing data, andclinical phenotypes 3 Confirmed by further

targeted biochemical tests1 Metabolomics and clinical

phenotypes were consistentfor non-IEM condition

5 Ruled out by integratedtargeted biochemical andsequencing testing


Analysis of clinical samples received for plasma clinical metabolomic profilingB

A, Analysis of clinical samples received for traditional biochemical screening. Patientsreferred for targeted traditional screening approaches including plasma amino acids,urine organic acids, and plasma acylcarnitine profile between June 2014 and October2018 were reviewed and all associated patient data evaluated. Numbers representunrelated families. B, Analysis of clinical samples received for plasma untargeted clinical

metabolomics. Patients referred for plasma metabolomic profiling between July 2014and February 2019 were evaluated to provide a comprehensive and in-depth analysis.Numbers represent unique, unrelated families. IEMs indicates inborn errors ofmetabolism.




Statistical AnalysisData were collected from samples received between July 2014 and February 2019. Descriptivestatistics were used to characterize the clinical data using Excel for Mac spreadsheet software version16.30 (Microsoft). Available current clinical data from all referred cases were gathered and analyzedfrom September 2019 to August 2020.

Results

Traditional Metabolic Screening for IEMsDemographic Characteristics of Clinical CasesTraditional metabolic screening performed on samples from 1483 families submitted to thebiochemical genetics laboratory for traditional metabolic screening was primarily pediatric (1465children [98.8%]; mean [SD] age, 4.1 [6.0] years; range, 0-65 years), including 912 (61.5%) malepatients. The cohort included 1016 children younger than 5 years (68.5%), 449 children andadolescents aged 5 to 21 years (30.3%), and 18 individuals older than 21 years (1.2%) (Table 1).Limited clinical information was provided for each patient; however, most samples were submittedbecause of nonspecific neurological presentations, whereas others were submitted for the follow-upof abnormal NBS results.

Traditional Metabolic Screening FindingsBy following a multistep workflow pipeline for data integration and analysis (Figure 1A and asdescribed in the Methods), 159 of 1483 families (10.7%) with biochemical abnormalities wereidentified. Further integrated analysis revealed that 19 of 159 cases (11.9%) were considereddiagnostic for an IEM, giving a final positive diagnostic rate of 1.3% (19 of 1483 cases), whereas an IEMwas ruled out for 71 cases (44.7%). The remaining 69 cases (43.4%) lacked other available testinginformation to confirm the diagnosis. In total, 14 IEMs were identified (Table 1 and eTable 2 in theSupplement). With the exception of L-2/D-2-hydroxyglutaric aciduria, homocystinuria megaloblasticanemia, cblG type, and ethylmalonic encephalopathy, 11 of 14 conditions (78.6%) are included onthe RUSP, including 7 conditions covered by the core conditions and 4 disorders covered by RUSPsecondary conditions, corresponding to a diagnostic rate of 0.2% for non-RUSP IEMs (3 of1483 IEMs).

Metabolomic Screening for IEMsDemographic Characteristics of Clinical CasesA global meta-analysis process was applied to delineate the findings from plasma metabolomicprofiling of 2000 consecutive clinical samples from 1807 unrelated families tested in a similar periodas traditional metabolic screening (Table 1). Laboratory sample submission requirements included adetailed clinical note and laboratory test history; however, this guideline was followed onlyapproximately 75% of the time, limiting the analysis of data. In this cohort, most patients werepediatric (1665 patients [92.1%]; mean [SD] age, 8.1 [10.4] years; range, 0-80 years) and male (1059

Table 1. Cohort Demographic Characteristics and Comparison of Diagnostic Rate Between Clinical Metabolomics and Traditional Metabolic Screening Approaches

Screening methodsScreening cohort,No. of samples

Patients, No. (%) Overall screening resultsa

Male Age <21 y Neurological Positive cases, No. Identified IEMs, No.Positive diagnosticrate, %

Traditional approach(plasma, urine)(n = 1483 families)

4464 912 (61.5) 1465 (98.8) NA 19 14 (11 covered by RUSP) 1.3

Metabolomics (plasma)(n = 1807 families)

2000 1059 (58.6) 1665 (92.1) 1464 (81) 128 70 (21 covered by RUSP) 7.1

Abbreviations: IEMs, inborn errors of metabolism; NA, not available; RUSP, Recommended Uniform Screening Panel.a A total of 158 families appeared in both cohorts with 20 of 158 (12.7%) receiving a diagnosis; however, 7 of 158 (4.4%) were positively screened by both testing approaches, whereas

13 of 158 (8.2%) were identified only by metabolomics.





patients [58.6%]), including 894 children younger than 5 years (49.5%), 771 children andadolescents aged 5 to 21 years (42.6%), and 142 individuals older than 21 years (7.9%). In this cohort,1464 patients (81.0%) presented with autism spectrum disorder, speech delay, hypotonia, and/orseizures, whereas 219 (12.1%) had nonneurological presentations. Clinical data were not provided forthe remaining 124 (6.9%) patients.

Overview of Metabolomic ScreeningIn each individual plasma sample, a mean (SD) of 776 (130) metabolites were detected, and 620 (78)metabolites were z-scored. An integrated analysis of metabolomic data considered all availableclinical and genetic information for every patient and followed a step-by-step filtering workflowpipeline (Figure 1B). A total of 1807 unique families were stratified according to the metabolicabnormalities revealed by plasma metabolomic profiling into 2 major categories: 912 families(50.5%) with substantial alterations of disease-associated analytes and 895 families (49.5%) withnormal or not substantially abnormal profiles.

Among the 912 families with abnormal results, biochemical patterns were categorized asfollows: (1) specific and diagnostic for an IEM (128 of 912 families [14.0%]); (2) suggestive of a singleIEM or single category of IEM but not definitive (655 of 912 families [71.8%]); or (3) abnormal butnonspecific for a known disease of category (129 of 912 families [14.1%]). Among the 128 families inthe diagnostic category, 84 (65.6%) were further confirmed as positive according to additionaltesting, and 39 different metabolic conditions were identified. The remaining 44 families (34.4%) didnot have additional testing available for absolute confirmation of diagnosis and were considereddiagnostic on the basis of the metabolomic profile; however, according to our strict assessmentcriteria, these cases were not included in the final number of confirmed cases reported here.

Details of positive diagnosed cases and known and novel disease-related secondary analytesrevealed by metabolomics21-35 are reported in Table 2 and eTable 3 in the Supplement. Among the655 families in the suggestive category (Figure 1B), 40 families (6.1%) were confirmed by furthertesting, including 36 families (90.0%) confirmed via molecular testing. In 53 families (8.1%), asuspected diagnosis was ruled out, and the remaining 562 families (85.8%) were not analyzedbecause of the lack of available information. This analysis identified 36 different conditions in thiscategory (eTable 3 in the Supplement).

For the remaining 129 families in the nonspecific category, the inclusion of sequencing data, aswell as other genetic information, led to confirmed diagnoses in 3 families (2.3%) with identificationof 3 different conditions and 1 non-IEM condition in 1 family (0.78%), whereas suspected diagnoseswere ruled out for 5 families (3.9%) (Table 2 and eTable 3 in the Supplement). The remaining 120families (93.0%) were not further analyzed because of a lack of information.

In total, our analysis confirmed 128 of 912 cases (14.0%) as positive, including 95 cases thatwere confirmed by molecular testing and 67 cases that had confirmation with additional targetedquantitative metabolic testing, further supporting the reliability of metabolomic profiling. Aconservative overall diagnostic rate of 7.1% (approximately 6-fold higher than that for traditionalscreening) was determined in our cohort of 1807 families.

Metabolomic Profiling Expands Diagnostic Screening for IEMsThrough this comprehensive, integrated analysis, we were able to successfully identify either keydiagnostic metabolites or related secondary metabolites for 70 distinct IEMs in 128 unique families(Table 2 and eTable 3 in the Supplement). Although 21 of these conditions are on the RUSP, including13 disorders covered as core conditions and 8 as secondary conditions, 49 identified IEMs (70.0%)are not presently included on the RUSP and represent a wide variety of metabolic disorders,including amino acidopathies, organic acidemias, fatty acid oxidation disorders, vitamin or cofactordeficiencies, carbohydrate metabolism disorders, mitochondrial respiratory chain defects,peroxisomal disorders, nucleic acid metabolism disorders, and neurotransmitter abnormalities; 26 of








Table 2. IEMs Identified by Plasma Clinical Metabolomic Screening

RUSP categoryand IEM No. IEM name OMIM No.a Family No. Key disease-related analytes detected in plasmab

Conditions not on RUSP

1 Adenylosuccinase deficiency (ADSLD)23 103050 28, 85, 390, 499,584, 1749

N6-succinyladenosine

2 AICA-ribosiduria due to ATIC deficiency 608688 1797 N6-succinyladenosine

3 α-methylacyl-CoA racemase deficiency (AMACRD)c 614307 847 Phytanate, 7-α-hydroxy-3-oxo-4-cholestenoated

4 Aromatic L-amino acid decarboxylase deficiency(AADCD)18,24

608643 180, 812 3-Methoxytyrosine, 3-methoxytyramine sulfate ↓,d vanillylmandelate ↓,dopamine 3-O-sulfate ↓d

5 Autism, susceptibility to, X-linked 6 (AUTSX6) 300872 576, 814, 1461 N6,N6,N6-trimethyllysine, acetylcarnitine ↓, propionylcarnitine ↓,carnitine ↓, deoxycarnitine ↓

6 β-ureidopropionase deficiency (UPB1D) 613161 1258 3-Ureidopropionate, 5,6-dihydrothymine, 5,6-dihydrouracil,3-aminoisobutyrate ↓

7 Brown-Vialetto-Van Laere syndrome 2 (BVVLS2)21 614707 1757 Riboflavin (due to initiation of supplement), pyridoxine and pyridoxate(normalized in response to riboflavin), medium chain (C6, C8, C10:1)and very long chain acylcarnitines (C24), 2-hydroxyglutarate,methylsuccinate, ethylmalonate, kynurenine,d N-formylanthranilicacid,d kynurenate ↓,d picolinate ↓,d methionine sulfone ↓d

8 Carbamoyl phosphate synthetase I deficiency,hyperammonemia due to

237300 184 Pyroglutamine,d citrulline ↓

9 Cerebral creatine deficiency syndrome 2 (CCDS2) 612736 57, 234, 347,1550

Guanidinoacetate, creatine ↓

10 D-bifunctional protein deficiency33 261515 958, 1486 1-Lignoceroyl-GPC (24:0), docosadienoate, multiple sphingomyelins ↓,phosphatidylcholines ↓

11 Developmental and epileptic encephalopathy 25,with amelogenesis imperfecta (DEE25)26

615905 39, 41, 52 Citrate

12 Dihydrolipoamide dehydrogenase deficiency (DLDD) 246900 137 Lactate, pyruvate, 3-methyl-2-oxobutyrate,d 3-methyl-2-oxovalerate,d

4-methyl-2-oxopentanoate,d 2-hydroxyglutarate

13 Encephalopathy, ethylmalonic (EE)25 602473 430, 1246, 1470,1603

Ethylmalonate, butyrylcarnitine, isobutyrylcarnitine, isovalerylcarnitine,glutarylcarnitine, 2-methylbutyrylcarnitine, methylsuccinate, phenolsulfate ↓, 3-indoxyl sulfate ↓, glycolithocholate sulfate ↓

14 Epilepsy, pyridoxine-dependent (EPD) 266100 102, 933 Pipecolate, 6-oxopiperidine-2-carboxylate

15 Fructose intolerance, hereditary (HFI) 229600 902 Fructose

16 GABA-transaminase deficiency20 613163 27, 292, 387,1333

2-Pyrrolidinone,d succinimide,d succinamic acidd

17 Glutaric aciduria III (GA3) 231690 207 Glutarate, arachidate (20:0),d octadecadienedioate (C18:2-DC),d

hexadecanedioate,d octadecenedioate (C18:1-DC),d ximenoylcarnitine(C26:1)d

18 Glycerol kinase deficiency (GKD)27 307030 562, 987 Glycerol, long chain and very long chain monoacylglycerolsd

19 Glycine encephalopathy (GCE) 605899 514 Glycine

20 Glycogen storage disease 1A (GSD1A) 232200 1347 Lactate, urate, pyruvate, palmitoyl-linoleoyl-glycerol,d glucose ↓

21 Homocystinuria due to deficiency of n(5,10)-methylenetetrahydrofolate reductase activity

236250 156 Methionine sulfoxide ↓,d betaine ↓, methionine ↓, methionine sulfone↓d

22 Homocystinuria-megaloblastic anemia, cblGcomplementation type (HMAG)

250940 923 S-adenosylhomocysteine,d methionine ↓

23 Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (HHHS)

238970 557 Ornithine, homocitrulline, N-δ-acetylornithined

24 Isopentenyl-diphosphate delta isomerase 1 (IDI1)a 604055 1733 Deoxycholate,d 1-(1-enyl-stearoyl)-2-docosahexaenoyl-GPE(P-18:0/22:6),d octadecadienedioate (C18:2-DC),d dodecadienoate(12:2),d 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (P-16:0/18:2),d 1-(1-enyl-stearoyl)-2-linoleoyl-GPE (P-18:0/18:2),d 1,2-dilinoleoyl-GPC(18:2/18:2),d multiple sphingomyelins ↓

25 L-2-hydroxyglutaric aciduria (L2HGA) 236792 469 2-Hydroxyglutaric acid

26 Lesch-Nyhan syndrome (LNS) 300322 358 Inosine, uracil

27 Lipoyltransferase 1 deficiency (LIPT1D)28 616299 731 Leucine, isoleucine, valine, 2-hydroxyadipate, isovalerylcarnitine,glycine isovalerylglycine, isovalerylcarnitine (C5), 1-stearoyl-2-arachidonoyl-GPI (18:0/20:4), 3-hydroxy-3-methylglutarate,2-aminoadipate, tyrosine, arginine, serine oxalate (ethanedioate) ↓,malonate ↓

28 Liver failure, infantile, transient (LFIT) 613070 595 3-(4-Hydroxyphenyl) lactate,d phenyllactate,d

4-hydroxyphenylpyruvate,d N-acetylphenylalanine,d fumarate,d

alanine,d lactate, pyruvate, glycochenodeoxycholate,d glycohycholate,d

taurocholate,d bilirubin

29 Lysinuric protein intolerance (LPI) 222700 1302 Ornithine ↓, urea ↓, arginine ↓, lysine ↓, dimethylarginine ↓d

30 Mitochondrial complex V (ATP synthase) deficiency,nuclear type 2 (MC5DN2)

614052 34, 1556 3-Methylglutaconate, 3-methylglutarylcarnitine, alanine, lactate

(continued)




Table 2. IEMs Identified by Plasma Clinical Metabolomic Screening (continued)


31 Mitochondrial DNA depletion syndrome 1(mitochondrial neurogastrointestinalencephalomyopathy type) (MTDPS1)29

603041 1120 Thymidine, thymine, 5,6-dihydrothymine ↓

32 Mitochondrial DNA depletion syndrome 9(encephalomyopathic type with methylmalonicaciduria) (MTDPS9)

245400 50 Succinylcarnitine, propionylcarnitine, methylmalonate,hexanoylcarnitine, butyrylcarnitine, malate ↓

33 Mitochondrial short-chain enoyl-CoA hydratase 1deficiency (ECHS1D)30

616277 1261 β-hydroxyisovalerate, 1-lignoceroyl-GPC (24:0),d 3-hydroxy-3-methylglutarate, laurate (12:0)d

34 Multiple mitochondrial dysfunctions syndrome 1(MMDS1)31

605711 1469 Glycinee

35 Neurodegeneration, infantile-onset, biotin-responsive (NERIB)

618973 171 Pantothenate (vitamin B5) ↓,d carnitine ↓,d multiple carnitinederivatives ↓d

36 Ornithine transcarbamylase deficiency,hyperammonemia due to32

311250 177, 819, 1094 Orotate, ornithine, N-carbamoylaspartate,d uridine, uracil, alanine,aspartate, glutamine, citrulline ↓, arginine ↓

37 Peroxisome biogenesis disorder 1A (Zellweger)(PDB1A)33

214100 361 Pipecolate, docosadioate,d multiple sphingomyelins ↓

38 Peroxisome biogenesis disorder 4A (Zellweger)(PDB4A)

614862 440 Pipecolate, 1-lignoceryl-GPC,d 7-HOCA,d hexadecanedioate,d

octadecanedioate,d eicosanodioate,d docosadioate,d phytanate, multiplesphingomyelins ↓,d plasmalogens ↓, phosphatidylcholines ↓,d

phosphatidylethanolamines ↓d

39 Peroxisome biogenesis disorder 8A (Zellweger)(PDB8A)

614876 252 Pipecolate, phytanate, long chain fatty acids, 1-lignoceryl-GPC,d

7-HOCA,d hexadecanedioate,d octadecanedioate,d eicosanodioate,d

docosadioate,d phytanate, multiple sphingomyelins ↓,d plasmalogens ↓,phosphatidylcholines ↓,d phosphatidylethanolamines ↓d

40 Phosphoglycerate dehydrogenase deficiency(PSATD)19

610992 1253 Serine ↓, glycine ↓, glycerophosphocholine ↓,d

glycerophosphoethanolamine ↓,d multiple lipids ↓d

41 Phosphoserine aminotransferase deficiency(PHGDHD)19

601815 863, 1063 Serine ↓, glycine ↓, glycerophosphocholine ↓,d

glycerophosphoethanolamine ↓,d multiple lipids ↓d

42 Pyruvate dehydrogenase, α-1 (PDHA1) 300502 393 Lactate, pyruvate, alanine

43 Short stature, developmental delay, and congenitalheart defects (SDDHD; TKT)34

617044 1034 Ribitol, arabitol/xylitol, ribonate, erythronate, arabonate/xylonate,d

erythritol, ribose

44 Smith-Lemli-Opitz syndrome (SLOS) 270400 373, 647 7-Dehydrocholesterol, cholesterol ↓

45 Spastic paraplegia 9B, autosomal recessive (SPG9B) 616586 60, 1449 Proline ↓, ornithine ↓, citrulline ↓

46 Spondyloepimetaphyseal dysplasia, Genevieve type(SEMDG)

610442 1483 N-acetylglucosamine/N-acetylgalactosamine,d N-acetylneuraminate ↓

47 Succinic semialdehyde dehydrogenase deficiency(SSADHD)

271980 721 2-Pyrrolidinone,d 4-guanidinobutanoated

48 Transaldolase deficiency (TALDOD)34 606003 420, 1028 Ribitol, ribonate, erythronate, arabitol/xylitol, erythritol, sedoheptulose

49 Urocanase deficiency (UROCD)35 276880 606, 1411 Trans-urocanate, imidazole propionate,d cis-urocanate

RUSP core conditions

50 Acyl-CoA dehydrogenase, medium-chain, deficiencyof (ACADMD)

201450 226, 1441, 1602,1800

Hexanoylcarnitine, octanoylcarnitine, hexanoylglycine, cis-4-decenoylcarnitine

51 Adrenoleukodystrophy (ALD) 300100 341 Numerous significant abnormalities in bile acid, fatty acid, and lipidmetabolism

52 Carnitine deficiency, systemic primary (CDSP) 212140 704, 1222 Carnitine ↓, multiple acylcarnitines ↓

53 Citrullinemia, classic32 215700 1006 Citrulline, N-acetylcitrullined

54 Galactosemia I (GALAC1) 230400 1489, 1519 Galactitol, galactonate

55 Glutaric acidemia I (GA1) 231670 916, 1091 Glutarylcarnitine, glutarate

56 HSD10 mitochondrial disease (HSD10MD) 300438 854, 1072, 1450 Tiglylcarnitine, tiglylglycine (rare), 2-hydroxy-3-methylvalerate,β-hydroxyisovalerate, isoleucine, 3-hydroxyisobutyrate,3-hydroxy-2-ethylpropionate,d 2-methylbutyroylcarnitined

57 Isovaleric acidemia (IVA) 243500 1035 Isovalerylcarnitine, 2-methylbutyroylcarnitine, isovalerylglycine,isovalerate

58 Maple syrup urine disease (MSUD) 248600 86, 1648 Leucine, valine, isoleucine, allo-isoleucine, 2-hydroxy-3-methylvalerate, 3-methyl-2-oxovalerate, isovalerate,β-hydroxyisovalerate, N-acetylisoleucine,d N-acetylleucine,d

3-methyl-2-oxobutyrated

59 Methylmalonic aciduria due to methylmalonyl-CoAmutase deficiency

251000 62, 792, 971,1121, 1178

Propionylcarnitine, 2-methylcitrate, methylmalonate

60 Phenylketonuria (PKU) 261600 482, 672, 1191,1299

Phenylalanine, phenylpyruvate, phenyllactate, phenylacetate,N-acetylphenylalanine,d N-formylphenylalanine,d 3-(4-hydroxyphenyl)lactated

(continued)




Figure 2. Comparison of Metabolic Conditions Screened by Plasma Clinical Metabolomics, Traditional Metabolic Screening, and Newborn Screening (NBS)

0

16

8

10

12

14

No.

of d

isor

ders

6

4

2

Category of inborn error of metabolismCarbohydrates Glycosylation Lipids Mitochondrial Nitrogen Peroxisomes Vitamins

Metabolomics only, not covered by RUSP

Metabolomics only, covered by RUSP

Metabolomics and traditional, not covered by RUSP

Metabolomics and traditional, covered by RUSP

In this cohort analysis, clinical metabolomics (plasma) identified 70 metabolic conditions that were categorized into 7 disease groups according to their respective biochemicalpathways or disease groups, as well as common pathophysiological mechanisms. The Recommended Uniform Screening Panel (RUSP) was applied as the criterion for determining theconditions screened by NBS. IEMbase version 2.0.0 was applied as the criterion for categorizing the conditions screened by clinical metabolomics and the RUSP. Carbohydrates refersto disorders of carbohydrates, glycosylation refers to congenital disorders of glycosylation, lipids refers to disorders of lipids, mitochondrial refers to mitochondrial disorders of energymetabolism, nitrogen refers to disorders of nitrogen-containing compounds, peroxisomal refers to disorders of peroxisomes and oxalate, and vitamins refers to disorders of vitamins,cofactors, and minerals.

Table 2. IEMs Identified by Plasma Clinical Metabolomic Screening (continued)


61 Propionic acidemia 606054 51, 127, 223,1223

Propionylcarnitine, propionylglycine, 2-methylcitrate, glycine

62 Tyrosinemia, type I (TYRSN1) 276700 791 3-(4-Hydroxyphenyl) lactate,d 4-hydroxyphenylpyruvate, phenyllactate,tyrosine, N-acetyltyrosine

RUSP secondary conditions

63 2-Methylbutyryl-CoA dehydrogenase deficiency 610006 1274 2-Methylbutyroylcarnitine, 2-methylbutyrylglycine, 3-hydroxy-2-ethylpropionate, tiglylcarnitine, isobutyrylglycine

64 3-Hydroxyisobutyryl-CoA hydrolase deficiency(HIBCHD)

250620 70 3-Hydroxyisobutyrate ↓

65 Acyl-CoA dehydrogenase, short-chain, deficiency of(ACADSD)

201470 253, 833 Ethylmalonate, butyrylcarnitine, methylsuccinate

66 Argininemia32 207800 33, 254, 539,650, 829, 1683

Arginine, argininate, N-acetylarginine, 2-oxoarginine, dimethylarginine,4-guanidinobutanoate, homoarginine, orotate, uracil, urea ↓, ornithine↓

67 Citrullinemia, type II, neonatal onset 605814 1753 Citrulline, homocitrulline,d arginine, argininate,d homoarginine,d

methionine, phenyllactate, 4-hydroxyphenylpyruvate,N-acetylphenylalanine,d argininosuccinate, bilirubin, galactonate,galactitol, cholesterol

68 Hyperphenylalaninemia, nonphenylketonuria mild,included

261600 201 Phenylalanine, phenylpyruvate, phenyllactate

69 Methylmalonic aciduria and homocystinuria, cblCtype (MAHCC)

277400 315 Methylmalonate, 3-hydroxy-3-methylglutaric acid, propionylcarnitine,methionine ↓

70 Multiple acyl-CoA dehydrogenase deficiency(MADD)

231680 522, 1156, 1532 α-hydroxyisovalerate,d 2-hydroxy-3-methylvalerate,2-hydroxyglutarate, 2-methylbutyrylglycine, glutarate,glutarylcarnitine, ethylmalonate, methylsuccinate,d butyrylcarnitineand a wide variety of various acylcarnitines

Abbreviations: ↓, Decrease in analyte; IEMs, inborn errors of metabolism; OMIM, Online Mendelian Inheritance in Man; RUSP, Recommended Uniform Screening Panel.a Mendelian Inheritance in Man numbers are shown for all disorders except isopentenyl-diphosphate-δ-isomerase 1 deficiency, for which the Mendelian Inheritance in Man gene

number is provided (https://www.omim.org).b Analytes listed achieved a z score less than or equal to −2 or greater than or equal to 2 compared with reference control population or were identified as a rare molecule and were not

z scored. All analytes listed represent elevations unless indicated with a downward arrow (↓).c Pristanate was not identified in this patient sample because of the absence of pristanate in the compound library at the early time of testing; the current platform identifies this

metabolite as a rare compound.d Indicates analytes that were not previously reported for the indicated IEMs and may represent new biomarkers.e Elevated glycine and lactate were found in cerebrospinal fluid metabolomics.




https://www.omim.org

these 49 conditions are not covered by the traditional screening approach, and 7 of these 26disorders are currently treatable (Figure 2, Table 2, and eTable 4 in the Supplement).

Discussion

In this cross-sectional study, we conducted a comprehensive comparison of metabolomic screeningwith traditional metabolic screening to provide insight into the utility and efficacy of bothapproaches in the identification and diagnosis of inherited metabolic disorders. Traditional combinedmetabolic screening assays (PAA, ACP, and UOA) had a positive diagnostic rate of 1.3% and identified14 IEMs, 11 of which (78.6%) are included on the RUSP, identifying only 3 conditions not on therecommended NBS panel. In comparison, the diagnostic rate for clinical metabolomic screening was7.1%, and the platform successfully identified 21 conditions included in the RUSP, as well as 49conditions not included in the RUSP (Table 2). Importantly, 26 of these 49 conditions are not coveredby the traditional screening approach, and 7 of these 26 disorders are currently treatable, indicatingthat early screening of these conditions by clinical metabolomics may prevent severe clinicalconsequences caused by these disorders (eTable 4 in the Supplement). These findings suggest thatuntargeted metabolomic profiling has a higher diagnostic rate (approximately 6-fold higher) inscreening for IEMs and, more importantly, screens for more metabolic diseases compared with thetraditional screening approach, supporting the utility of clinical metabolomics as a first-line screeningtool for IEMs (Figure 2 and eTable 4 in the Supplement).7-9,36

We observed that 84 of 128 cases in the diagnostic category were confirmed as positive byintegrated analysis with additional genomic and/or biochemical testing, confirming the diagnosis of39 different metabolic conditions in this category (eTable 3 in the Supplement). These findingssuggest that in addition to broadly screening for IEMs, metabolomics alone has the ability to be usedas a diagnostic tool for these disorders. For many cases in the suggestive category, althoughmetabolomic profiling indicated abnormalities in specific metabolic pathways, the biochemicalprofile alone could not distinguish between related disorders. However, subsequent testing, such asDNA sequencing and enzyme analysis, indeed confirmed a specific IEM. In these cases, although theassociated metabolomic patterns were not pathognomonic, screening results provided furtherguidance for follow-up testing, thus preventing incorrect and/or delayed diagnoses in these cases. Ofnote, a large number of cases lacked further testing results for us to consider in our diagnosticpipeline; therefore, the 7.1% diagnostic rate for clinical metabolomics may be an underestimate.

A major advantage of untargeted metabolomic analysis over targeted metabolic testing is thewide range of metabolites and classes of metabolites that may be assayed in a single biologicalsample. Similar metabolite coverage by targeted approaches would otherwise require multipleindividual clinical tests, which may still focus on only a limited set of analytes from a singlebiochemical class. Moreover, because most of the compounds in the reference standard library arenot clinically available as targeted assays performed in clinical biochemical genetics laboratories inthe US, clinical metabolomics has a greater capacity to find novel and specific biomarkers for knownIEMs, as well as discovering new metabolic disorders.

In addition, metabolomics also allows for the detection of pathway-related secondarymetabolites often not present in targeted assays. These biochemicals may serve as novel andconfirmative biomarkers.20,37 For example, in GABA-transaminase deficiency (cases 27, 292, 387, and1333 in Table 2; eTable 3 in the Supplement), metabolomics identified significantly elevated2-pyrrolidinone (a lactam cyclization form of GABA), succinimide, and succinamic acid, which aremore readily detected than labile GABA, and identified as novel biomarkers for this disorder.20 Arecent publication38 has asserted that classic biomarkers for some disorders could be missed byuntargeted metabolomics; however, detection of secondary metabolites may still provide sufficientclues to facilitate the follow-up testing of these IEMs.18

In this study, we describe a novel clinical metabolomics method that was applied in the clinicalscreening and diagnosis of IEMs. Disease identification by metabolomic profiling is based on the









substantial alterations of key biomarkers and is buttressed by the pattern of findings within knownbiochemical pathways. This comprehensive approach is especially helpful when patients presentwith a nonspecific neurologic phenotype or in cases where prior metabolic testing has beenunrevealing. With current technologies, the diagnostic yield of the traditional screening approach islimited beyond the identification of disorders already contained in the RUSP and screened for atbirth. However, our results and those of others demonstrate that metabolomics can detect manydisorders not included in most NBS panels.12-14,21,39 Therefore, plasma metabolomic analysis shouldbe considered as an initial screening approach when screening for IEMs, and it may also be useful toprovide functional evidence for genomic variants of uncertain significance.22

LimitationsThis untargeted metabolomic analysis platform has several limitations. First, the test is designed todetect small molecules and will not detect larger metabolites such as complex oligosaccharides andlipids, potentially limiting the applicability for lysosomal disorders or congenital disorders ofglycosylation. Second, some clinically relevant molecules that require special extraction methods (eg,homocysteine) are not identified in this assay. Third, semiquantitative z scores, not absolute values,for measured compounds are provided in this screening assay. Fourth, the current turnaround timefor this assay is 14 to 21 days, limiting its use for acute metabolic interventions; however, statisticalanalysis with reporting within 3 of 5 days of sample receipt is possible. Fifth, as with any metabolictesting, diet, medication, or clinical status at the time of sample collection may mask or otherwisealter metabolic abnormalities; thus, a nondiagnostic metabolic assessment does not completely ruleout the possibility of underlying metabolic disease.

Conclusions

In this cross-sectional study, we presented a clinical untargeted metabolomic profiling method witha higher diagnostic screening rate for IEMs (approximately 6-fold higher) that detected more IEMsthan the traditional metabolic screening approach, identifying many disorders not included in NBS.With the implementation of expanded NBS, our results support the need to use a broader screeningapproach, such as untargeted clinical metabolomics or a large panel of targeted analytes, to morecomprehensively assess for metabolic disorders in the initial assessment of patients for IEMs andespecially for the initial evaluation of neurological phenotypes that include intellectual disability,seizures, global developmental delays, or autism spectrum disorder. Furthermore, in considerationof the increasing use of genome first approaches to diagnosis, including exome or whole genomesequencing, clinical metabolomics offers a broad spectrum functional screen for IEMs that cansupport a comprehensive approach to genetic diagnosis.

ARTICLE INFORMATIONAccepted for Publication: April 16, 2021.

Published: July 12, 2021. doi:10.1001/jamanetworkopen.2021.14155

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Liu N et al.JAMA Network Open.

Corresponding Author: Sarah H. Elsea, PhD, Department of Molecular and Human Genetics, Baylor College ofMedicine, One Baylor Plaza, R717, Houston, TX 77030 ([email protected]).

Author Affiliations: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas(Liu, Xiao, Glinton, Shayota, Sun, Sutton, Elsea); Baylor Genetics, Houston, Texas (Liu, Gijavanekar, Sun, Sutton,Elsea); Metabolon, Inc, Durham, North Carolina (Pappan, Kennedy); Now with Owlstone Medical, Inc, ResearchTriangle Park, North Carolina (Pappan); Now with Division of Medical Genetics, Department of Pediatrics,University of Utah, Salt Lake City (Shayota).





https://jamanetwork.com/pages/cc-by-license-permissions/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamanetworkopen.2021.14155

mailto:[email protected]

Author Contributions: Drs Liu and Elsea had full access to all of the data in the study and take responsibility for theintegrity of the data and the accuracy of the data analysis.

Concept and design: Sutton, Elsea.

Acquisition, analysis, or interpretation of data: Liu, Xiao, Gijavanekar, Pappan, Glinton, Shayota, Kennedy,Sun, Elsea.

Drafting of the manuscript: Liu, Xiao, Pappan, Shayota, Sutton, Elsea.

Critical revision of the manuscript for important intellectual content: Gijavanekar, Pappan, Glinton, Shayota,Kennedy, Sun, Elsea.

Statistical analysis: Liu, Xiao, Shayota, Kennedy, Elsea.

Obtained funding: Elsea.

Administrative, technical, or material support: Xiao, Shayota, Sun, Elsea.

Supervision: Sun, Sutton, Elsea.

Conflict of Interest Disclosures: Baylor College of Medicine derives revenue for genetic testing in a joint venturewith Baylor Genetics. Baylor Genetics has an ongoing business collaboration with Metabolon, Inc, to support thedevelopment of the testing described in this study. Dr Kennedy reported owning stock options in Metabolon, Inc,outside the submitted work; in addition, Dr Kennedy reported having a patent for Metabolomics issued. No otherdisclosures were reported.

Funding/Support: This work was supported, in part, by the National Institutes of Health (grant T32 GM007526 toDrs Liu and Shayota), the Takeda American College of Medical Genetics and Genomics Foundation Next GenerationMedical Biochemical Subspecialty Fellowship (grant to Dr Shatoya), and the Urea Cycle Disorders Consortium(grant to Dr Glinton).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection,management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; anddecision to submit the manuscript for publication.

Additional Contributions: We thank the Baylor Genetics IT group for their assistance with access to moleculartesting results and Susan Toon, BS, in particular for her excellent IT support. We thank all the members of theBaylor Genetics Biochemical Genetics Laboratory for their technical assistance. Gerard Berry, MD (BostonChildren’s Hospital), and colleagues from Baylor College of Medicine and Texas Children’s Hospital, includingMichael F. Wangler, MD, Lisa Emrick, MD, William Craigen, MD, PhD, Seema Lalani, MD, Fernando Scaglia, MD,Lindsay C. Burrage, MD, PhD, Brendan Lee, MD, PhD, Thomas C. Caskey, MD, and Arthur Beaudet, MD, providedencouragement, suggestions, and feedback. None of these individuals was compensated for their contributionsbeyond their normal salary.

Additional Information: The RUSP can be found at https://www.hrsa.gov/advisory-committees/heritable-disorders/rusp/index.html and https://www.babysfirsttest.org. A list of treatable IEMs can be found at https://www.treatable-id.org/about.html. IEMbase 7 version 2.0.0 (http://www.iembase.org) was applied as the criterion8 for categorizing conditions screened by clinical metabolomics and the RUSP.

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39. Haijes HA, Willemsen M, Van der Ham M, et al. Direct infusion based metabolomics identifies metabolicdisease in patients’ dried blood spots and plasma. Metabolites. 2019;9(1):E12. doi:10.3390/metabo9010012

SUPPLEMENT.eAppendix 1. Traditional Biochemical ScreeningeAppendix 2. Metabolomic AnalyseseReferenceseTable 1. RUSP Conditions Covered by Traditional Screening and Clinical MetabolomicseTable 2. Disorders Identified by Traditional ScreeningeTable 3. Clinical Description of Positively Diagnosed Metabolomic CohorteTable 4. Categorized Metabolic Conditions Screened by Clinical Plasma Metabolomics and Covered by TraditionalScreening and/or the RUSP









https://dx.doi.org/10.1038/s41436-019-0442-0

https://dx.doi.org/10.1038/gim.2017.262



https://www.ncbi.nlm.nih.gov/pubmed/30600976

https://www.ncbi.nlm.nih.gov/pubmed/30600976

https://dx.doi.org/10.1371/journal.pone.0184022

https://dx.doi.org/10.1038/s41598-020-66401-2

https://dx.doi.org/10.3390/metabo9010012

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Comparison of Untargeted Metabolomic Profiling vs