Novel Molecular Diagnostic Approaches in Inherited Metabolic Disorders: A Systematic Review of Next-Generation Sequencing Applications
Keywords:
Next-generation sequencing, inherited metabolic disorders, diagnostic yield, whole exome sequencing, whole genome sequencing, cost-effectivenessAbstract
Background: Inherited metabolic disorders (IMDs) represent a diverse group of genetic conditions affecting essential metabolic pathways. Next-generation sequencing (NGS) technologies have emerged as powerful diagnostic tools for these disorders, yet their comparative effectiveness, implementation challenges, and clinical utility remain incompletely characterized.
Methods: We conducted a systematic review of studies utilizing NGS technologies (targeted panels, whole exome sequencing, whole genome sequencing) for diagnosing IMDs published between January 2010 and October 2024. Data extraction focused on diagnostic yield, technology performance metrics, novel genetic findings, clinical impact, and cost-effectiveness. Study quality was assessed using a modified QUADAS-2 tool.
Results: Eighty-seven studies comprising 4,328 patients were included. The overall diagnostic yield was 46.3% (95% CI: 42.1-50.5%), with significant variation across technologies: targeted panels (41.2%), WES (48.7%), WGS (57.9%), and combined approaches (61.0%). Yield varied by IMD category, with highest rates in amino acid metabolism disorders (58.2%) and glycogen storage disorders (56.7%). WGS demonstrated superior sensitivity for structural variants (85.2%) and non-coding regions (83.4%) compared to WES (68.3%, 7.6%) and targeted panels (42.5%, 12.3%). NGS diagnostics led to management changes in 37.2% of diagnosed cases, specific treatment initiation in 28.5%, and avoidance of unnecessary procedures in 22.3%. Cost-effectiveness analysis revealed targeted panels as most economical for well-defined disorders (ICER: $42,650/QALY), while WGS showed value in complex, previously undiagnosed cases. Meta-regression identified early age at testing (OR 1.84), consanguinity (OR 2.36), and prior biochemical evidence (OR 3.12) as predictors of diagnostic success.
Conclusions: NGS technologies significantly improve diagnostic yield in IMDs compared to conventional approaches, with substantial clinical impact. Technology selection should be guided by disorder characteristics, phenotypic specificity, and resource considerations. Implementation challenges include variant interpretation, bioinformatic standardization, and accessibility in resource-limited settings. Integration with other omics technologies and emerging sequencing methods hold promise for further enhancing diagnostic capabilities for IMDs.
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Sanderson S, Green A, Preece MA, Burton H. The incidence of inherited metabolic disorders in the West Midlands, UK. Arch Dis Child. 2006;91(11):896-899.
Ferreira CR, van Karnebeek CDM, Vockley J, Blau N. A proposed nosology of inborn errors of metabolism. Genet Med. 2019;21(1):102-106.
Lanpher B, Brunetti-Pierri N, Lee B. Inborn errors of metabolism: the flux from Mendelian to complex diseases. Nat Rev Genet. 2006;7(6):449-460.
Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet. 2013;14(10):681-691.
Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med. 2013;369(16):1502-1511.
Costain G, Jobling R, Walker S, et al. Periodic reanalysis of whole-genome sequencing data enhances the diagnostic advantage over standard clinical genetic testing. Eur J Hum Genet. 2018;26(5):740-744.
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405-424.
Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.
Wright CF, FitzPatrick DR, Firth HV. Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet. 2018;19(5):253-268.
Stark Z, Schofield D, Alam K, et al. Prospective comparison of the cost-effectiveness of clinical whole-exome sequencing with that of usual care overwhelmingly supports early use and reimbursement. Genet Med. 2017;19(8):867-874.
Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Bramer WM, Rethlefsen ML, Kleijnen J, Franco OH. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev. 2017;6(1):245.
Gopalakrishnan S, Ganeshkumar P. Systematic reviews and meta-analysis: understanding the best evidence in primary healthcare. J Family Med Prim Care. 2013;2(1):9-14.
Higgins JPT, Thomas J, Chandler J, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Chichester (UK): John Wiley & Sons; 2019.
Munn Z, Tufanaru C, Aromataris E. JBI's systematic reviews: data extraction and synthesis. Am J Nurs. 2014;114(7):49-54.
Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.
Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529-536.
Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149(12):889-897.
Hooijmans CR, Rovers MM, de Vries RB, et al. SYRCLE's risk of bias tool for animal studies. BMC Med Res Methodol. 2014;14:43.
Taylor JL, Lee LK, Scacheri PC. Benefits, limitations, and value of genomic sequencing for monogenic inborn errors of metabolism. Am J Med Genet C Semin Med Genet. 2022;190(1):38-45.
Schwarze K, Buchanan J, Taylor JC, Wordsworth S. Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genet Med. 2018;20(10):1122-1130.
Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97-111.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557-560.
Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med. 2002;21(11):1559-1573.
Roy S, Coldren C, Karunamurthy A, et al. Standards and guidelines for validating next-generation sequencing bioinformatics pipelines: A joint recommendation of the Association for Molecular Pathology and the College of American Pathologists. J Mol Diagn. 2018;20(1):4-27.
Rehm HL, Bale SJ, Bayrak-Toydemir P, et al. ACMG clinical laboratory standards for next-generation sequencing. Genet Med. 2013;15(9):733-747.
Mandelker D, Schmidt RJ, Ankala A, et al. Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing. Genet Med. 2016;18(12):1282-1289.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021. https://www.R-project.org/.
Tebani A, Afonso C, Marret S, Bekri S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations. Int J Mol Sci. 2016;17(9):1555.
Wortmann SB, Koolen DA, Smeitink JA, van den Heuvel L, Rodenburg RJ. Whole exome sequencing of suspected mitochondrial patients in clinical practice. J Inherit Metab Dis. 2015;38(3):437-443.
Clark MM, Stark Z, Farnaes L, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med. 2018;3:16.
Lionel AC, Costain G, Monfared N, et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet Med. 2018;20(4):435-443.
Yubero D, Brandi N, Ormazabal A, et al. Targeted next generation sequencing in patients with inborn errors of metabolism. PLoS One. 2016;11(5):e0156359.
Yubero D, Montero R, Armstrong J, et al. Molecular diagnosis of pediatric patients with mitochondrial respiratory chain disorders using next-generation sequencing approaches. J Pediatr. 2016;170:262-267.e6.
Tarailo-Graovac M, Shyr C, Ross CJ, et al. Exome sequencing and the management of neurometabolic disorders. N Engl J Med. 2016;374(23):2246-2255.
Kremer LS, Bader DM, Mertes C, et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun. 2017;8:15824.
Taylor JL, Clinard K, Powell CM, et al. The diagnostic utility of genome sequencing in a pediatric cohort with suspected genetic disease. Clin Transl Sci. 2022;15(1):134-143.
Meng L, Pammi M, Saronwala A, et al. Use of exome sequencing for infants in intensive care units: ascertainment of severe single-gene disorders and effect on medical management. JAMA Pediatr. 2017;171(12):e173438.
Stark Z, Tan TY, Chong B, et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet Med. 2016;18(11):1090-1096.
Schofield D, Rynehart L, Shresthra R, et al. Long-term economic impacts of exome sequencing for suspected monogenic disorders: diagnosis, management, and reproductive outcomes. Genet Med. 2019;21(11):2586-2593.
Ellingford JM, Barton S, Bhaskar S, et al. Whole genome sequencing increases molecular diagnostic yield compared with current diagnostic testing for inherited retinal disease. Ophthalmology. 2016;123(5):1143-1150.
Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell. 2013;155(1):27-38.
Retterer K, Juusola J, Cho MT, et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med. 2016;18(7):696-704.
Tan TY, Dillon OJ, Stark Z, et al. Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr. 2017;171(9):855-862.
Harrison SM, Rehm HL. Is 'likely pathogenic' really 90% likely? Reclassification data in ClinVar. Genome Med. 2019;11(1):72.
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405-424.
Boycott KM, Rath A, Chong JX, et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am J Hum Genet. 2017;100(5):695-705.
Guo L, Wang Q, Weng L, et al. Improved diagnostic strategy for metabolic diseases: a pilot study of metabolomic and genomic approaches. J Inherit Metab Dis. 2018;41(6):1107-1119.
Jacob M, Lopata AL, Dasouki M, Abdel Rahman AM. Metabolomics toward personalized medicine. Mass Spectrom Rev. 2019;38(3):221-238.
Mantere T, Kersten S, Hoischen A. Long-read sequencing emerging in medical genetics. Front Genet. 2019;10:426.
Batzir NA, Shohat M, Maya I. Chromosomal microarray analysis (CMA) a clinical diagnostic tool in the prenatal and postnatal settings. Pediatr Endocrinol Rev. 2015;13(1):448-454.
Ruiz-Mercado M, Espejo-Portero I, Rodriguez-Gomez FJ. Next-generation sequencing in the diagnosis of inborn errors of metabolism: A review. Metabolism. 2022;126:154919.
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