Narrative Review

An Overview of the Inborn Errors of Metabolism, Its Diagnosis, and Management

Introduction

Genetic disorders that impair normal metabolism with detectable biochemical anomalies arising from autosomal defects are referred to as inborn errors of metabolism (IEM). The term was first coined by a British scientist, Archibald Edward Garrod, and describes genetic deficiency or alteration in enzyme function. There are more than 500 types of inborn metabolic disorders in humans (Figure 1) of which 100 cases are related to amino acid metabolism (1). The figure itself can give an idea of how complex IEM can be, and how their diagnosis usually becomes difficult. Common metabolic defects are that of carbohydrates, lipids, fatty acid oxidation, amino acids, mucopolysaccharides, organic acids, urea cycle defects, lysosomal storage, and peroxisomal defects.

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Figure 1. Pathways and Enzymes Leading to IEM. Note. IEM: Inborn errors of metabolism. The picture is designed by Donald E. Nicholson, The University of Leeds, England – and Sigma-Aldrich in collaboration with Mick Henderson, St. James’ Hospital, Leeds – and the UK MetBioNet Training Group. Reproduced with permission from the International Union of Biochemistry and Molecular Biolog

Mutations in the genes that encode the enzymes of metabolic pathways are the major cause of disorders. The metabolites are either absent or present in extremely small concentrations, leading to such defects (2,3). Patients with IEM are unable to synthesize or use amino acids, fatty acids, organic acids, and other macromolecules because of the defects in these enzymes of the metabolic pathways. The enzyme defects also lead to substrate accumulation in the body, leading to mild to severe symptoms. The disease symptoms may appear two to three days after the baby is born and started to feed or may get delayed and appear in adulthood. IEM is a serious cause of mortality and morbidity in neonates, and a delay in their diagnosis leads to several symptoms. The common symptoms in babies include mental retardation, hyperactivity, delay in speech, lethargy, dystonia, psychomotor delay, ataxia, seizures, encephalitis, and even coma. In addition to these neurological symptoms, other symptoms are skin rashes, organomegaly, vomiting, hypoglycemia, hyperammonemia, metabolic acidosis, lactic acidosis, and ketonuria. Blood and urine samples are generally collected for the detection of such disorders (Figure 2).

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Figure 2. Detection of IEM From Blood and Urine Samples. Note. IEM: Inborn errors of metabolism

Methods

For the review, no particular structure was followed for searching research articles. The articles were mainly retrieved from PubMed and ScienceDirect databases, and the manual search of full-text papers was performed using words such as the inborn errors of metabolism, the prevalence of inborn error, genetic mutations, diagnosis of inborn error, classification of disease types, metabolomics approach toward inborn errors of metabolism, and et al.

Studies reporting the types of disease prevalence, diagnosis of the disease, and treatment of IEM were included in the study, and relevant information was summarized and integrated into this review. The review included no exclusion criteria, and the literature search was not limited to any study design, time, region, or language.

Epidemiology and Etiology of the Disease

The prevalence of IEM differs according to geographical regions and among different ethnic groups. IEM is a rare disease with an incidence of less than 1 per 100 000 births when considered individually and can generally range from 1 in every 800-2500 cases (4,5). The overall percentage related to the prevalence of each of the IEM categories, as demonstrated by Ferreira et al for generating a nosology of IEM, is shown in Figure 2 (6). Whatever the numbers, it should always be kept in mind that the number of IEM cases is always underestimated as many of them remain undiagnosed and not reported.

A systematic search of the published data (1980-2017) using databases such as EMBASE, Global Health, and Medline estimated the global birth prevalence of IEM to be 50.9 per 100 000 live births, and a pooled birth prevalence rate of eight classes of IEM was 64.6 per 100 000 live births. All global IEMs estimated 500 IEM cases for every 1 million live births, giving a number of 70 587 new IEM cases per year projected from 2015 to 2020 (7). The study on the global prevalence was based on the 49 studies selected from 25036 records after the exclusion of duplicate entries and based on the selection criteria. The study provided details of various classes of IEMs (amino acids, organic acids, fatty acids, lysosomal, carbohydrate metabolism, urea cycle, peroxisomal, and mitochondrial disorders) such as the year of detection, location, setting, period of study, total live births, cases in the cohort, birth prevalence per 100 000 live births, and number of deaths in a birth cohort. The estimated global death rate due to all-cause IEM was 3.2 per 100 000 live births and 23 529 deaths per year (0.4% child death globally); although extensive early childhood death occurs due to IEM, it is not diagnosed as a factor of death (8).

In another study performed from October 2012 to November 2015, the prevalence of IEM in India was 2.9 % (2053 cases were found positive out of 70 590 screened cases) on the screening of 119 disorders, and G6PD deficiency (44 %) and hemoglobinopathies (17.5 %) were the most prevalent cases, respectively (9). The distribution of different types of IEM is illustrated in Figure 3.

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Figure 3. Prevalence of Different Types of the Inborn Error of Metabolism

Most of the IEMs are inherited disorders and autosomal recessive in nature (10,11). The phenotype only gets expressed if the offspring inherit the mutated allele from both of their parents. Such mutated homozygotes can occur from consanguineous marriages (12) or by random mutation in the parents’ allele. The IEMs that are inherited as X-linked alleles have higher chances of prevalence in males and the random inactivation of X-chromosomes in females. The manifestations of X-linked IEMs vary from one female to another and from one tissue to another. Very few IEMs are autosomal dominant in nature and very few are linked to mitochondrial DNA (the maternal origin) (13,14). Most IEM disorders are due to a defect in an enzyme that leads to the accumulation of upstream metabolites that can have toxic effects and a reduction in essential downstream products (Figure 4).

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Figure 4. Mechanism of the Error. Note. Enzyme ‘A’ converts a substrate/precursor to an intermediate substrate at a normal rate, but an error in the production of enzyme ‘B’ leads to blockage in the conversion of the intermediate substrate to product, leading to the accumulation of an intermediate substrate or an alternative substrate/metabolite which can have a toxic effect on the body

Pathophysiology of the Disease

According to the pathophysiology of the disease, which is characterized by the alterations of multiple metabolic pathways, the pathophysiological classification of IEM can be group I diseases affecting a single function, gene, or organ, including the immune and endocrine system disorder, and group II diseases affecting biochemical pathways in different organs and systems. This condition can be further divided into three groups:

Group 1: Disorders of intermediary metabolism: This is a vast group involving different mechanisms. This group includes the IEMs that occur due to acute or progressive intoxication from compounds accumulated in the steps before the metabolic blockage. This group consists of the inborn errors of amino acid catabolism and synthesis, urea cycle defects, organic acidurias, metal disorders from intoxication due to accumulation and deficiencies, and errors in neurotransmitter catabolism, synthesis, and transport. The clinical expression is late in onset and intermittent. The diagnosis of this group is straightforward, and it is detected from plasma and urine samples by amino acid, acylcarnitine, and organic acid chromatography. The disorders are treatable with the immediate removal of toxins by drugs, special diets, and the like.

Group 2: IEMs that involve energy metabolism: The IEMs are due to deficiency in the production or the utilization of energy within the muscle, brain, liver, and myocardium. The mitochondrial defects are more severe, including the Kerbs cycle defects, disorders of mitochondrial respiratory chains, and the less severe cytoplasmic energy defects such as glycolysis and gluconeogenesis. The diagnosis of this group of disorders is complex and relies on the measurement of energetic molecules both in fasted and fed conditions in cycles, molecular analyses, and enzymatic analyses require biopsies and cell culture. Very few of these disorders are treatable.

Group 3: This group involves disorders due to the synthesis, catabolism, processing, and quality control of complex molecules. The organelles where these complex processes occur are the lysosomes, Golgi apparatus, mitochondria, peroxisomes, and endoplasmic reticulum. The symptoms of these disorders are permanent and sometimes progressive, not related to the diet, and not dependent on the intercurrent events.

Clinical Presentation of the Disease and its Outcomes

The clinical presentation of the disease varies with age and presents differently. The clinical presentation of the disease can occur in the mother’s womb, at birth, or after delivery during the initial days which is presented as deterioration after normal birth and delivery (15). Most term IEM babies seem to be well at birth but deteriorate extremely fast, even if the babies received no oral feeds. The changes in catabolism occurring in the first days of life lead to the accumulation of toxic metabolites, causing this phenomenon. The rate at which the deterioration occurs depends on the type and extent of the disease, and the most affected organ. The most common clinical presentation of IEM is cardiomyopathy, sudden death, neurological symptoms, unexplained hypoglycemia, hepatic failure or deterioration, and acid-base disbalance (16,17). Apart from the metabolic disorders that are recognized from newborn screening, the clinical presentation of the disorders can be broadly divided into four groups (Figure 5).

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Figure 5. Classification of the Clinical Presentation of IEM. Note. IEM: Inborn errors of metabolism

Some IEMs remain asymptomatic until late in infancy and present rapid and acute deterioration, including facial abnormalities such as a flattened nasal bridge, large fontanelle, epicanthal folds, and prominent forehead in the case of Zellweger syndrome (18). In addition, they present lipodystrophy, and inverted nipples in cases of congenital glycosylation disorders, genital abnormalities (19), flat nasal bridge, and cataracts in cases of Smith-Lemli-Opitz syndrome (20). Some IEMs also lead to abnormal odors such as sulfur in the case of cystinuria and tyrosinemia type I, maple syrup in the case of Maple syrup urine disease, mousy in the case of phenylketonuria, cat’s urine in the case of multiple carboxylase deficiency, and the like (21).

Evaluation and Diagnosis

Clinical approaches for the diagnosis of IEM are highly difficult because of their overlapping manifestations, especially the rare variants of some diseases. The presentation of a clinical symptom can have various reasons. A universal clinical protocol for the diagnosis of all IEMs is therefore impossible. IEMs cannot be diagnosed based on clinical data, and rather laboratory data are the primary source of diagnosis (16,22). A study reported that out of 144 clinically diagnosed IEM infants, only 12 infants were confirmed of IEM using laboratory diagnosis (16).

The IEM is analyzed from blood and urine samples using one/more of the techniques such as enzyme-linked immunosorbent assay, tandem mass spectrometry, high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), and enzyme assays (23,24). The diagnosis of IEM can be broadly divided into the following approaches:

Screening Tests

These tests are developed so that abnormal levels of metabolic biomarkers can be detected in the blood and urine sample before the manifestation of the disease. Thus, screening tests are important tools for the primary prevention of IEM. The diseases are detected in their latent asymptomatic stages, facilitating intervention and leading to improved outcomes (25). Medical intervention at an asymptomatic stage may lead to the slow progression of the disease (26) and prevent long-term or life-threatening consequences (27). Therefore, the screening tests of newborns should be made an absolute necessity with the same reliable and valid reports (28). Newborn screening programs have shown dramatic improvements in IEM outcomes. It has improved the mortality rates in cases of primary immune deficiencies (29), medium-chain acyl-CoA dehydrogenase deficiency (30), and cases of cystic fibrosis (31). The newborn screening sometimes gives wrong reports, especially in the cases of premature birth and babies who have a low birth weight (32,33).

Diagnostic Investigations

The biochemical diagnosis depends on identifying abnormally high levels of substrates or by-products arising from pathways upstream of the enzymatic blockage. The lower levels of products or the downstream metabolites of the enzymes can be detected as well (34). Proper diagnosis relies on the performed metabolic investigations that can lead to proper treatment of the disease (35,36). The severity and the symptomatic nature determine the applied diagnostic investigation. The investigations to detect IEM should include tests that detect the level and type of toxic substances, and the degree and nature of the affected system. This is generally decided by targeted mass spectrometry (MS)-based metabolite assays (34,37,38).

Metabolomics Approach for the Diagnosis of IEMs

The metabolomics approach reflects the biochemical status and thus the current phenotype of living organisms (39,40). The diagnosed compounds are catabolic and anabolic pathway metabolites, environmental factors, small molecules that act as regulators, microbial metabolites, and epimetabolites (41-43). Every change in a genetic level (e.g., mutations, single nucleotide polymorphism, and changes in gene expressions) is reflected in metabolism, and the true cellular phenotype is reflected accordingly. Metabolomics thus gives a clear picture of the insights of diseases and identifies novel biomarkers and the impact of the metabolism of drugs and its effects in vivo (44,45).

Metabolomics is mainly analyzed using MS and nuclear magnetic resonance (NMR) spectroscopy (45). Screening by mass spectroscopy is performed either by direct sample injection to the ionization source of the mass spectrometer or by gas chromatography (GC) or liquid chromatography (LC) before MS. The prior application of GC or LC reduces the complexity of the mixtures before MS (46). GC-MS and LC-MS are well suited to separate isomers, and detect and quantify the compounds compared to NMR or direct MS (47). Chromatographic separation helps in the identification of the compounds and yields additional information about the metabolites. The most common method is LC-MS, which is used for both polar and non-polar compounds. GC-MS is employed for the analysis of volatile metabolites. The analytical sensitivity and sample throughput of the metabolites are further improved by the introduction of tandem MS (MS/MS) and ultrahigh-performance LC. High-resolution MS (HRMS) has a mass sensitivity and accuracy of less than ng/L (48). Samples such as dried blood spots can be generated as thousands of detected metabolic signals in HRMS. In metabolomics, MS/MS is the principle approach of IEM screening because of its low sample volume requirement, high sensitivity and specificity, and rapid turnover. The magnetic property of the atomic nuclei is measured by NMR spectroscopy. NMR measures far few metabolites per sample than MS, is less sensitive than MS, and is less widely used accordingly. However, NMR is non-invasive in nature, fast, and reproducible. The structural and stereochemical insights of the isolated compounds can be obtained from NMR (49).

The analyses of metabolomics are divided based on two approaches (targeted and untargeted metabolomics). Targeted metabolomics looks at specific groups of compounds and analyzes them using internal standards for the reference value, while untargeted metabolomics deals with all known and unknown compounds and analyzes as many samples as possible (50-52). Considering a specific disease, targeted metabolomics detects the compounds that are directly linked to the disrupted enzyme, whereas untargeted metabolomics detects all the possible metabolites that are altered directly or in some other pathways due to the disrupted enzyme (53,54).

In specific targeted metabolomics, 10-100 metabolites are selected as targets in LC-MS/MS or direct MS/MS for the diagnosis of a specific disease. The fragment ions of the metabolites are detected and quantified using internal standards. GC-MS is employed in single-ion monitoring, and LC-triple quadrupole MS and LC-quadrupole linear ion trap MS are utilized in multi-reaction monitoring. The disadvantages of targeted metabolomics are as follows:

• The cost of internal standards increases with targeted metabolomics.

• Complexity in the analysis of data increases with targeted metabolomics.

• The risk of overlooking metabolic responses in other pathways is increased.

• The selective isolation of a group of metabolites may itself be wrong.

• Lack of specificity to classify the IEM varieties.

On the other hand, the advantages of targeted metabolomics are:

• Targeted metabolomics with pre-specified mass spectrometer conditions and internal standards helps in better quantification.

• The baseline levels of metabolites for defining healthy versus altered states can be established, and interlaboratory comparison becomes easier.

• Metabolites are identified from the applied internal standards and the specificity of MS/MS.

In untargeted metabolomics, all the ions within a certain mass range are detected in LC-MS/MS, and all possible metabolites are identified as well. The disease phenotypes are characterized based on the signal intensities of both known and unknown metabolites. The internal standards, normalizations, and quality controls help in the quantification of the metabolites. GC-MS (full scan), LC-orbital ion trap MS, and LC-quadrupole time-of-flight MS are used for quantification. The disadvantages of untargeted metabolomics include:

• The definition of “normal metabolite levels” does not stand at the level of a population as the absolute quantification of the metabolites could not be performed.

• The normalized signal intensities are not robust enough.

• Comparison can only be performed based on different groups within the study.

• There is no fixed and valid data processing parameter and keeps changing with different software taken into consideration.

• There is no standard protocol or parameter for the identification of compounds.

On the other hand, the advantages of diagnosis using untargeted metabolomics are as follows:

• Novel metabolites can be discovered from the broader provided coverage.

• All metabolites in the samples detectable using the analytical techniques are covered.

• Signals for more than 1000 metabolites can be obtained.

• The overall genomic and environmental interaction leading to particular phenotypes can be identified.

• Cost does not increase with the increase in the number of metabolites.

Several studies have compared targeted and untargeted metabolomics approaches. Compounds such as 3-mesulfateamine sulfate, dopamine 3-O-sulfate, and vanillyl mandelate are not detected in the targeted approach, while the untargeted approach can detect and analyze differences between the deficiencies of aromatic amino acid decarboxylase from the elevation of drug-induced metabolites (55). Targeted metabolomics often provides uncertain results in cases of borderline disease categories (56). The targeted approach also gives much more false positives due to the low cut-off values kept in the screening to avoid missing cases (57-59). The untargeted metabolomics approach identified novel compounds that are altered in urea cycle disorders (UCD). The compounds are responsible for X-linked UCD and ornithine transcarbamylase deficiency in females. Thus, the untargeted approach helped in identifying metabolites that are responsible for long-term UCD complications in addition to known UCD biomarkers. The specificity was also extremely high as many metabolites were not detected in normal cases but in diseased conditions (60). However, a study on urine and cerebrospinal fluid IEM demonstrated a good correlation of amino acid measurement between targeted and untargeted metabolomics approaches, and the only difference was tryptophan degraded in the targeted assay while quantified accurately in the untargeted assay (61). The untargeted metabolomics approaches could reveal 7 novel isoforms of urinary lyso-Gb3 in patients with Fabry disease using globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) as target biomarkers in the urine and plasma of Fabry. These isoforms were not detected in healthy individuals. The untargeted metabolomics represented 100 % specificity, and no false positive cases were identified (62-65). Moreover, targeted metabolite screening sometimes leads to false diagnostic reports and thus inappropriate treatment, as in the case of arginase deficiency (66). The untargeted approach revealed that more than 30 pathways are altered due to arginase deficiency which is linked to guanidino compounds (67).

Treatment and Management of the Disease

DDIEM is a drug database for IEM. This database contains a complete list of treatment options against each of the IEMs with the mode of action of the drugs, the phenotypes being corrected, along with evidence type and references for the same (68). The drugs used for specific cases of IEM and their potential can be easily studied from the database in cases of further research and advancement toward treating a particular case of IEM.

Patients with IEM may appear normal but can deteriorate to life-threatening conditions. Specific therapies and illness protocols need to be followed for the patients’ disease management. The primary protocols to be followed for the management of IEM are as follows:

Hydration Maintenance

Keeping hemodynamic stability is highly important for neonates, infants, or children with IEM. Patients are treated with the administration of intravenous fluid containing 10% dextrose solution with 75 mmol/L of sodium. In addition, + /- 20 mmol/L of potassium can be started to reduce exposure to toxic nutrients or the catabolic state. Cardiac overflow is avoided by the careful monitoring of fluid replacement for the first 48 hours. Electrolyte imbalance, metabolic acidosis, and anabolic state promotion also need to be performed gradually based on the therapeutic requirements of the disease condition of IEM.

Nutrition

The nutritional management of the patient is extremely vital in the cases of IEM. Patients who are in sepsis or are hemodynamically unstable cannot be fed by mouth in the initial one or two days to avoid further exposure to toxic metabolites. Total parenteral nutrition is preferred due to intestinal intolerance (69). The use of invasive techniques or high glucose/energy is required for immediate detoxification. On being stable, oral food intake is preferred, and the diet should be high-glucose, low-protein, carbohydrate-restricted, and with/without lipid restriction.

Removal of Toxin

Patients with acute metabolic toxins circulating in their bloodstream, including UCD or branched chain organic acidurias, may require to remove the toxin by extracorporeal measures. The procedures for toxin removal are venovenous hemodiafiltration or hemodialysis and peritoneal dialysis.

Enzyme Therapy

Enzyme-focused treatment to treat IEMs that occur due to the deficiency of enzymes has been practiced for decades for the management of IEM. The two strategies of enzyme-based treatment are enzyme replacement therapy and enzyme substitution therapy. In replacement therapy, the deficiency is directly replaced to improve the metabolic process of the body. In substitution therapy, a new enzyme is introduced so that the physiological defect is bypassed, and the accumulated substrate is converted to a harmless product using an alternative process of metabolization (70,71).

Table 1 lists enzymes that are used for enzyme replacement therapy, the year of approval, their generic names, and the IEM for which they have been approved in the last two decades.

Substrate Reduction and Product Supplementation Therapy

Substrate reduction therapy ( uses a small molecule as a drug that can decrease the accumulation of toxic substances that generally occur due to defects in substrate degradation. Substrate reduction therapy targets the pathway upstream of the IEM defect. It helps in maintaining the balance between the production and degradation of specific substrates in a metabolic pathway (82). Some other pathogenesis of IEM also occurs due to insufficient production or insufficiency in the recycling of essential substances. Thus, in such cases, the supplementation of the products is essential to avoid the deficiency of the compound. One of the most common examples is phenylketonuria where tyrosine cannot be synthesized from phenylalanine due to a deficiency of phenylalaninehydroxylase. The supplementation of tyrosine is recommended in such a case (83).

Gene Therapy

As IEMs are single-gene disorders, gene therapy for the replacement of mutated genes should reverse the pathogenesis of IEM. However, gene therapy has its disadvantages of cellular toxicity, affected immune response, and oncogenesis. Extensive clinical development has been performed in gene replacement therapies, of which the most successful has been the CRISPR/Cas9 system of editing, which is the most potent method of gene therapy (84).

Chaperone Therapy

Chaperones are small molecules that bind to the misfolded proteins to stabilize them. Chaperone proteins also help in the proper trafficking of the misfolded protein from the endoplasmic reticulum to the lysosome where the accumulated toxic substrates are metabolized. The chaperones used in the management of IEM have some advantages. They are non-immunogenic and their small size helps in easier diffusion and more bioavailability. In addition, oral medication instead of intravenous infusion and larger tissue penetration are possible in this therapy (85).

Cell or Organ Transplantation

The principal target of the therapeutic strategy of IEM is liver transplantation; as it is the main site of intermediary metabolism (86), hematopoietic stem cell, and bone marrow; as it allows healthy cells to colonize the bone marrow where the enzyme is deficient and helps enzyme replenishment (87).

Conclusion

The understanding of the pathophysiology of IEM has increased in the last decade, making the early detection and treatment of IEM highly easier than imagined. Screening helps in the treatment of patients before disease manifestation, improving the outcomes. Combination therapy can further improve IEM disease management. More multi-centered clinical trials are necessary for the development of evidence-based treatment strategies.

Authors’ Contribution

Conceptualization: Avijit Saha, Shritama Aich, Tapas Kumar Sur.

Data curation: Shritama Aich, Avijit Saha, Kheya Mukherjee.

Formal analysis: Shritama Aich, Tapas Kumar Sur.

Investigation: Shritama Aich, Avijit Saha.

Methodology: Shritama Aich.

Project administration: Avijit Saha.

Resources: Sanjay Ghosh, Shritama Aich.

Supervision: Avijit Saha, Sandip Ghosh, Kheya Mukherjee, Sanjay Vashisth.

Validation: Avijit Saha, Kheya Mukherjee, Sandip Ghosh, Sanjay Vashisth.

Visualization: Shritama Aich, Tapas Kumar Sur.

Writing–original draft: Shritama Aich.

Writing–review & editing: Shritama Aich.

Competing Interests

The authors declared that there is no conflict of interests.

Ethical Approval

Not applicable.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Informed Consent

The study was a review and thus required no informed consent from the participants.

References

1. Sharma S, Kumar P, Agarwal R, Kabra M, Deorari AK, Paul VK. Approach to inborn errors of metabolism presenting in the neonate. Indian J Pediatr 2008; 75(3):271-6. doi: 10.1007/s12098-008-0058-x [Crossref] [ Google Scholar]
2. Kopple JD. Abnormal amino acid and protein metabolism in uremia. Kidney Int 1978; 14(4):340-8. doi: 10.1038/ki.1978.134 [Crossref] [ Google Scholar]
3. Bodamer OA, Scott CR, Giugliani R. Newborn screening for Pompe disease. Pediatrics 2017; 140(Suppl 1):S4-S13. doi: 10.1542/peds.2016-0280C [Crossref] [ Google Scholar]
4. Ghosh A, Schlecht H, Heptinstall LE, Bassett JK, Cartwright E, Bhaskar SS. Diagnosing childhood-onset inborn errors of metabolism by next-generation sequencing. Arch Dis Child 2017; 102(11):1019-29. doi: 10.1136/archdischild-2017-312738 [Crossref] [ Google Scholar]
5. Mussap M, Zaffanello M, Fanos V. Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism. Ann Transl Med 2018; 6(17):338. doi: 10.21037/atm.2018.09.18 [Crossref] [ Google Scholar]
6. Ferreira CR, van Karnebeek CDM, Vockley J, Blau N. A proposed nosology of inborn errors of metabolism. Genet Med 2019; 21(1):102-6. doi: 10.1038/s41436-018-0022-8 [Crossref] [ Google Scholar]
7. Waters D, Adeloye D, Woolham D, Wastnedge E, Patel S, Rudan I. Global birth prevalence and mortality from inborn errors of metabolism: a systematic analysis of the evidence. J Glob Health 2018; 8(2):021102. doi: 10.7189/jogh.08.021102 [Crossref] [ Google Scholar]
8. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J. Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals. Lancet 2016; 388(10063):3027-35. doi: 10.1016/s0140-6736(16)31593-8 [Crossref] [ Google Scholar]
9. Sharma P, Kumar P, Tyagi MS, Sharma R, Dhot PS. Prevalence of inborn errors of metabolism in neonates. J Clin Diagn Res 2018; 12(5):7-13. doi: 10.7860/jcdr/2018/30035.11515 [Crossref] [ Google Scholar]
10. Ben-Rebeh I, Hertecant JL, Al-Jasmi FA, Aburawi HE, Al-Yahyaee SA, Al-Gazali L. Identification of mutations underlying 20 inborn errors of metabolism in the United Arab Emirates population. Genet Test Mol Biomarkers 2012; 16(5):366-71. doi: 10.1089/gtmb.2011.0175 [Crossref] [ Google Scholar]
11. Barness LA. The metabolic and molecular bases of inherited disease, 7th ed. C.R. Scriver, AL Beaudet, WS Sly, D Valle, Vol 3, McGraw Hill, New York, 1995, pp 4605. Am J Med Genet 1996; 66(1):87. doi: 10.1002/(sici)1096-8628(19961202)66:1<87::aid-ajmg20>3.0.co;2-l [Crossref] [ Google Scholar]
12. Afzal RM, Lund AM, Skovby F. The impact of consanguinity on the frequency of inborn errors of metabolism. Mol Genet Metab Rep 2018; 15:6-10. doi: 10.1016/j.ymgmr.2017.11.004 [Crossref] [ Google Scholar]
13. Kanungo S, Morton J, Neelakantan M, Ching K, Saeedian J, Goldstein A. Mitochondrial disorders. Ann Transl Med 2018; 6(24):475. doi: 10.21037/atm.2018.12.13 [Crossref] [ Google Scholar]
14. Cox TM, Watts RWE. The inborn errors of metabolism: general aspects. In: Cox TM, Firth J, Conlon C, Cox T, eds. Oxford Textbook of Medicine. 6th ed. Oxford: Oxford University Press; 2020. p. C12.1-1941. Available from: https://academic.oup.com/book/41095/chapter/351083121. Accessed February 20, 2023.
15. Saudubray JM, Martin D, de Lonlay P, Touati G, Poggi-Travert F, Bonnet D. Recognition and management of fatty acid oxidation defects: a series of 107 patients. J Inherit Metab Dis 1999; 22(4):488-502. doi: 10.1023/a:1005556207210 [Crossref] [ Google Scholar]
16. Romão A, Simon PEA, Góes JEC, Pinto LLC, Giugliani R, de Luca GR. Initial clinical presentation in cases of inborn errors of metabolism in a reference children’s hospital: still a diagnostic challenge. Rev Paul Pediatr 2017; 35(3):258-64. doi: 10.1590/1984-0462/;2017;35;3;00012 [Crossref] [ Google Scholar]
17. Agana M, Frueh J, Kamboj M, Patel DR, Kanungo S. Common metabolic disorder (inborn errors of metabolism) concerns in primary care practice. Ann Transl Med 2018; 6(24):469. doi: 10.21037/atm.2018.12.34 [Crossref] [ Google Scholar]
18. Klouwer FC, Berendse K, Ferdinandusse S, Wanders RJ, Engelen M, Poll-The BT. Zellweger spectrum disorders: clinical overview and management approach. Orphanet J Rare Dis 2015; 10:151. doi: 10.1186/s13023-015-0368-9 [Crossref] [ Google Scholar]
19. Chang IJ, He M, Lam CT. Congenital disorders of glycosylation. Ann Transl Med 2018; 6(24):477. doi: 10.21037/atm.2018.10.45 [Crossref] [ Google Scholar]
20. Kelley RI, Hennekam RC. The Smith-Lemli-Opitz syndrome. J Med Genet 2000; 37(5):321-35. doi: 10.1136/jmg.37.5.321 [Crossref] [ Google Scholar]
21. El-Hattab AW. Inborn Errors of Metabolism. Clinics in Perinatology. 2015 Jun;42(2):413–39. 10.1016/j.clp.2015.02.010.
22. Mak CM, Law EC, Lee HH, Siu WK, Chow KM, Au Yeung SK. The first pilot study of expanded newborn screening for inborn errors of metabolism and survey of related knowledge and opinions of health care professionals in Hong Kong. Hong Kong Med J 2018; 24(3):226-37. doi: 10.12809/hkmj176939 [Crossref] [ Google Scholar]
23. Medical Advisory Secretariat. Neonatal screening of inborn errors of metabolism using tandem mass spectrometry: an evidence-based analysis. Ont Health Technol Assess Ser 2003; 3(3):1-36. [ Google Scholar]
24. Rao AN, Kavitha J, Koch M, Suresh Kumar V. Inborn errors of metabolism: review and data from a tertiary care center. Indian J Clin Biochem 2009; 24(3):215-22. doi: 10.1007/s12291-009-0041-y [Crossref] [ Google Scholar]
25. Yuan Y, Su W, Zhu M. Threshold-free measures for assessing the performance of medical screening tests. Front Public Health 2015; 3:57. doi: 10.3389/fpubh.2015.00057 [Crossref] [ Google Scholar]
26. Sharma S, Prasad A. Inborn Errors of Metabolism and Epilepsy: Current Understanding, Diagnosis, and Treatment Approaches IJMS. 2017 J ul 2; 18(7):1384. doi: 10.3390/ijms18071384 [Crossref] [ Google Scholar]
27. Almannai M, Marom R, Sutton VR. Newborn screening: a review of history, recent advancements, and future perspectives in the era of next generation sequencing. Curr Opin Pediatr 2016; 28(6):694-9. doi: 10.1097/mop.0000000000000414 [Crossref] [ Google Scholar]
28. Wilson JM, Jungner YG. [Principles and practice of mass screening for disease]. Bol Oficina Sanit Panam 1968;65(4):281-393. [Spanish].
29. Kwan A, Abraham RS, Currier R, Brower A, Andruszewski K, Abbott JK. Newborn screening for severe combined immunodeficiency in 11 screening programs in the United States. JAMA 2014; 312(7):729-38. doi: 10.1001/jama.2014.9132 [Crossref] [ Google Scholar]
30. Rhead WJ. Newborn screening for medium-chain acyl-CoA dehydrogenase deficiency: a global perspective. J Inherit Metab Dis 2006; 29(2-3):370-7. doi: 10.1007/s10545-006-0292-1 [Crossref] [ Google Scholar]
31. Mak DY, Sykes J, Stephenson AL, Lands LC. The benefits of newborn screening for cystic fibrosis: the Canadian experience. J Cyst Fibros 2016; 15(3):302-8. doi: 10.1016/j.jcf.2016.04.001 [Crossref] [ Google Scholar]
32. Dion-Berboso AG, Cabic AG, Carluen-Nario I, Valeza G, Alcausin MML. Newborn screening in preterm babies at the Newborn Screening Center-National Institutes of Health, Manila: impact, implications, and outcomes on its first year of implementation. Ann Transl Med 2015; 3(Suppl 2):AB058. doi: 10.3978/j.issn.2305-5839.2015.AB058 [Crossref] [ Google Scholar]
33. Knapkova M, Hall K, Loeber G. Reliability of neonatal screening results. Int J Neonatal Screen 2018; 4(3):28. doi: 10.3390/ijns4030028 [Crossref] [ Google Scholar]
34. Vernon HJ. Inborn errors of metabolism: advances in diagnosis and therapy. JAMA Pediatr 2015; 169(8):778-82. doi: 10.1001/jamapediatrics.2015.0754 [Crossref] [ Google Scholar]
35. van Karnebeek CD, Shevell M, Zschocke J, Moeschler JB, Stockler S. The metabolic evaluation of the child with an intellectual developmental disorder: diagnostic algorithm for identification of treatable causes and new digital resource. Mol Genet Metab 2014; 111(4):428-38. doi: 10.1016/j.ymgme.2014.01.011 [Crossref] [ Google Scholar]
36. McDonald L, Rennie A, Tolmie J, Galloway P, McWilliam R. Investigation of global developmental delay. Arch Dis Child 2006; 91(8):701-5. doi: 10.1136/adc.2005.078147 [Crossref] [ Google Scholar]
37. Christopher R, Sankaran BP. An insight into the biochemistry of inborn errors of metabolism for a clinical neurologist. Ann Indian Acad Neurol 2008; 11(2):68-81. doi: 10.4103/0972-2327.41873 [Crossref] [ Google Scholar]
38. Chakrapani A, Cleary MA, Wraith JE. Detection of inborn errors of metabolism in the newborn. Arch Dis Child Fetal Neonatal Ed 2001; 84(3):F205-10. doi: 10.1136/fn.84.3.f205 [Crossref] [ Google Scholar]
39. Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature 2008; 455(7216):1054-6. doi: 10.1038/4551054a [Crossref] [ Google Scholar]
40. Fiehn O. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol 2002; 48(1-2):155-71. doi: 10.1023/a:1013713905833 [Crossref] [ Google Scholar]
41. Argmann CA, Houten SM, Zhu J, Schadt EE. A next generation multiscale view of inborn errors of metabolism. Cell Metab 2016; 23(1):13-26. doi: 10.1016/j.cmet.2015.11.012 [Crossref] [ Google Scholar]
42. Showalter MR, Cajka T, Fiehn O. Epimetabolites: discovering metabolism beyond building and burning. Curr Opin Chem Biol 2017; 36:70-6. doi: 10.1016/j.cbpa.2017.01.012 [Crossref] [ Google Scholar]
43. Linster CL, Van Schaftingen E, Hanson AD. Metabolite damage and its repair or pre-emption. Nat Chem Biol 2013; 9(2):72-80. doi: 10.1038/nchembio.1141 [Crossref] [ Google Scholar]
44. Jacob M, Malkawi A, Albast N, Al Bougha S, Lopata A, Dasouki M. A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism. Anal Chim Acta 2018; 1025:141-53. doi: 10.1016/j.aca.2018.03.058 [Crossref] [ Google Scholar]
45. Li B, He X, Jia W, Li H. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22(7):1173. doi: 10.3390/molecules22071173 [Crossref] [ Google Scholar]
46. Pitt JJ. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clin Biochem Rev 2009; 30(1):19-34. [ Google Scholar]
47. Perez ER, Knapp JA, Horn CK, Stillman SL, Evans JE, Arfsten DP. Comparison of LC-MS-MS and GC-MS analysis of benzodiazepine compounds included in the drug demand reduction urinalysis program. J Anal Toxicol 2016; 40(3):201-7. doi: 10.1093/jat/bkv140 [Crossref] [ Google Scholar]
48. Vereyken L, Dillen L, Vreeken RJ, Cuyckens F. High-resolution mass spectrometry quantification: impact of differences in data processing of centroid and continuum data. J Am Soc Mass Spectrom 2019; 30(2):203-12. doi: 10.1007/s13361-018-2101-0 [Crossref] [ Google Scholar]
49. Speyer CB, Baleja JD. Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism. Emerg Top Life Sci 2021; 5(1):39-48. doi: 10.1042/etls20200259 [Crossref] [ Google Scholar]
50. Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem 2016; 88(1):524-45. doi: 10.1021/acs.analchem.5b04491 [Crossref] [ Google Scholar]
51. Gertsman I, Barshop BA. Promises and pitfalls of untargeted metabolomics. J Inherit Metab Dis 2018; 41(3):355-66. doi: 10.1007/s10545-017-0130-7 [Crossref] [ Google Scholar]
52. Coene KLM, Kluijtmans LAJ, van der Heeft E, Engelke UFH, de Boer S, Hoegen B. Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients. J Inherit Metab Dis 2018; 41(3):337-53. doi: 10.1007/s10545-017-0131-6 [Crossref] [ Google Scholar]
53. Ribbenstedt A, Ziarrusta H, Benskin JP. Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLoS One 2018; 13(11):e0207082. doi: 10.1371/journal.pone.0207082 [Crossref] [ Google Scholar]
54. Almontashiri NAM, Zha L, Young K, Law T, Kellogg MD, Bodamer OA. Clinical validation of targeted and untargeted metabolomics testing for genetic disorders: a 3 year comparative study. Sci Rep 2020; 10(1):9382. doi: 10.1038/s41598-020-66401-2 [Crossref] [ Google Scholar]
55. Pappan KL, Kennedy AD, Magoulas PL, Hanchard NA, Sun Q, Elsea SH. Clinical metabolomics to segregate aromatic amino acid decarboxylase deficiency from drug-induced metabolite elevations. Pediatr Neurol 2017; 75:66-72. doi: 10.1016/j.pediatrneurol.2017.06.014 [Crossref] [ Google Scholar]
56. Percenti L, Vickery G. Newborn screening follow-up. N C Med J 2019; 80(1):37-41. doi: 10.18043/ncm.80.1.37 [Crossref] [ Google Scholar]
57. Kwon C, Farrell PM. The magnitude and challenge of false-positive newborn screening test results. Arch Pediatr Adolesc Med 2000; 154(7):714-8. doi: 10.1001/archpedi.154.7.714 [Crossref] [ Google Scholar]
58. Schmidt JL, Castellanos-Brown K, Childress S, Bonhomme N, Oktay JS, Terry SF. The impact of false-positive newborn screening results on families: a qualitative study. Genet Med 2012; 14(1):76-80. doi: 10.1038/gim.2011.5 [Crossref] [ Google Scholar]
59. Rock MJ, Levy H, Zaleski C, Farrell PM. Factors accounting for a missed diagnosis of cystic fibrosis after newborn screening. Pediatr Pulmonol 2011; 46(12):1166-74. doi: 10.1002/ppul.21509 [Crossref] [ Google Scholar]
60. Burrage LC, Thistlethwaite L, Stroup BM, Sun Q, Miller MJ, Nagamani SCS. Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders. Genet Med 2019; 21(9):1977-86. doi: 10.1038/s41436-019-0442-0 [Crossref] [ Google Scholar]
61. Kennedy AD, Pappan KL, Donti TR, Evans AM, Wulff JE, Miller LAD. Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay. Mol Genet Metab 2017; 121(2):83-90. doi: 10.1016/j.ymgme.2017.04.005 [Crossref] [ Google Scholar]
62. Dupont FO, Gagnon R, Boutin M, Auray-Blais C. A metabolomic study reveals novel plasma lyso-Gb3 analogs as Fabry disease biomarkers. Curr Med Chem 2013; 20(2):280-8. doi: 10.2174/092986713804806685 [Crossref] [ Google Scholar]
63. Lavoie P, Boutin M, Auray-Blais C. Multiplex analysis of novel urinary lyso-Gb3-related biomarkers for Fabry disease by tandem mass spectrometry. Anal Chem 2013; 85(3):1743-52. doi: 10.1021/ac303033v [Crossref] [ Google Scholar]
64. Auray-Blais C, Boutin M, Gagnon R, Dupont FO, Lavoie P, Clarke JT. Urinary globotriaosylsphingosine-related biomarkers for Fabry disease targeted by metabolomics. Anal Chem 2012; 84(6):2745-53. doi: 10.1021/ac203433e [Crossref] [ Google Scholar]
65. Manwaring V, Boutin M, Auray-Blais C. A metabolomic study to identify new globotriaosylceramide-related biomarkers in the plasma of Fabry disease patients. Anal Chem 2013; 85(19):9039-48. doi: 10.1021/ac401542k [Crossref] [ Google Scholar]
66. Li H, Zhao L, Singh R, Ham JN, Fadoju DO, Bean LJH. The first pediatric case of glucagon receptor defect due to biallelic mutations in GCGR is identified by newborn screening of elevated arginine. Mol Genet Metab Rep 2018; 17:46-52. doi: 10.1016/j.ymgmr.2018.09.006 [Crossref] [ Google Scholar]
67. Burrage LC, Thistlethwaite L, Stroup BM, Sun Q, Miller MJ, Nagamani SCS. Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders. Genet Med 2019; 21(9):1977-86. doi: 10.1038/s41436-019-0442-0 [Crossref] [ Google Scholar]
68. Abdelhakim M, McMurray E, Syed AR, Kafkas S, Kamau AA, Schofield PN. DDIEM: drug database for inborn errors of metabolism. Orphanet J Rare Dis 2020; 15(1):146. doi: 10.1186/s13023-020-01428-2 [Crossref] [ Google Scholar]
69. Moore FA, Feliciano DV, Andrassy RJ, McArdle AH, Booth FV, Morgenstein-Wagner TB. Early enteral feeding, compared with parenteral, reduces postoperative septic complications The results of a meta-analysis. Ann Surg 1992; 216(2):172-83. doi: 10.1097/00000658-199208000-00008 [Crossref] [ Google Scholar]
70. Brady RO, Schiffmann R. Enzyme-replacement therapy for metabolic storage disorders. Lancet Neurol 2004; 3(12):752-6. doi: 10.1016/s1474-4422(04)00938-x [Crossref] [ Google Scholar]
71. Brady RO. Enzyme replacement therapy: conception, chaos and culmination. Philos Trans R Soc Lond B Biol Sci 2003; 358(1433):915-9. doi: 10.1098/rstb.2003.1269 [Crossref] [ Google Scholar]
72. Chan B, Wara D, Bastian J, Hershfield MS, Bohnsack J, Azen CG. Long-term efficacy of enzyme replacement therapy for adenosine deaminase (ADA)-deficient severe combined immunodeficiency (SCID). Clin Immunol 2005; 117(2):133-43. doi: 10.1016/j.clim.2005.07.006 [Crossref] [ Google Scholar]
73. Schiffmann R, Kopp JB, Austin HA 3rd, Sabnis S, Moore DF, Weibel T. Enzyme replacement therapy in Fabry disease: a randomized controlled trial. Jama 2001; 285(21):2743-9. doi: 10.1001/jama.285.21.2743 [Crossref] [ Google Scholar]
74. Tolar J, Orchard PJ. alpha-L-iduronidase therapy for mucopolysaccharidosis type I. Biologics 2008; 2(4):743-51. doi: 10.2147/btt.s3180 [Crossref] [ Google Scholar]
75. Harmatz P, Whitley CB, Waber L, Pais R, Steiner R, Plecko B. Enzyme replacement therapy in mucopolysaccharidosis VI (Maroteaux-Lamy syndrome). J Pediatr 2004; 144(5):574-80. doi: 10.1016/j.jpeds.2004.03.018 [Crossref] [ Google Scholar]
76. da Silva EM, Strufaldi MW, Andriolo RB, Silva LA. Enzyme replacement therapy with idursulfase for mucopolysaccharidosis type II (Hunter syndrome). Cochrane Database Syst Rev 2016; 2(2):CD008185. doi: 10.1002/14651858.CD008185.pub4 [Crossref] [ Google Scholar]
77. Brantly ML, Lascano JE, Shahmohammadi A. Intravenous alpha-1 antitrypsin therapy for alpha-1 antitrypsin deficiency: the current state of the evidence. Chronic Obstr Pulm Dis 2018; 6(1):100-14. doi: 10.15326/jcopdf.6.1.2017.0185 [Crossref] [ Google Scholar]
78. Beutler E. Enzyme replacement in Gaucher disease. PLoS Med 2004; 1(2):e21. doi: 10.1371/journal.pmed.0010021 [Crossref] [ Google Scholar]
79. Lim JA, Li L, Raben N. Pompe disease: from pathophysiology to therapy and back again. Front Aging Neurosci 2014; 6:177. doi: 10.3389/fnagi.2014.00177 [Crossref] [ Google Scholar]
80. Magner M, Almássy Z, Gucev Z, Kieć-Wilk B, Plaiasu V, Tylki-Szymańska A. Consensus statement on enzyme replacement therapy for mucopolysaccharidosis IVA in Central and South-Eastern European countries. Orphanet J Rare Dis 2022; 17(1):190. doi: 10.1186/s13023-022-02332-7 [Crossref] [ Google Scholar]
81. Demaret T, Lacaille F, Wicker C, Arnoux JB, Bouchereau J, Belloche C. Sebelipase alfa enzyme replacement therapy in Wolman disease: a nationwide cohort with up to ten years of follow-up. Orphanet J Rare Dis 2021; 16(1):507. doi: 10.1186/s13023-021-02134-3 [Crossref] [ Google Scholar]
82. Yue WW, Mackinnon S, Bezerra GA. Substrate reduction therapy for inborn errors of metabolism. Emerg Top Life Sci 2019; 3(1):63-73. doi: 10.1042/etls20180058 [Crossref] [ Google Scholar]
83. Singh RH, Cunningham AC, Mofidi S, Douglas TD, Frazier DM, Hook DG. Updated, web-based nutrition management guideline for PKU: an evidence and consensus based approach. Mol Genet Metab 2016; 118(2):72-83. doi: 10.1016/j.ymgme.2016.04.008 [Crossref] [ Google Scholar]
84. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 2012; 337(6096):816-21. doi: 10.1126/science.1225829 [Crossref] [ Google Scholar]
85. Parenti G, Andria G, Valenzano KJ. Pharmacological chaperone therapy: preclinical development, clinical translation, and prospects for the treatment of lysosomal storage disorders. Mol Ther 2015; 23(7):1138-48. doi: 10.1038/mt.2015.62 [Crossref] [ Google Scholar]
86. Oishi K, Arnon R, Wasserstein MP, Diaz GA. Liver transplantation for pediatric inherited metabolic disorders: Considerations for indications, complications, and perioperative management. Pediatr Transplant 2016; 20(6):756-69. doi: 10.1111/petr.12741 [Crossref] [ Google Scholar]
87. Boelens JJ. Trends in haematopoietic cell transplantation for inborn errors of metabolism. J Inherit Metab Dis 2006; 29(2-3):413-20. doi: 10.1007/s10545-005-0258-8 [Crossref] [ Google Scholar]