The efficacy of high pressure liquid chromatography (HPLC) in detecting congenital glycosylation disorders (CDG)

Ozge Ozgen; Fatma Güdek Kılıç; Asuman Gedikbaşı; Mehmet Cihan Balcı; Meryem Karaca; Aslı Durmuş; Belkıs Tutu; Hüseyin Kutay Körbeyli; Fatmahan Atalar; Gülden Fatma Gökçay

doi:10.1515/tjb-2024-0011

Article Open Access

The efficacy of high pressure liquid chromatography (HPLC) in detecting congenital glycosylation disorders (CDG)

Ozge Ozgen , Fatma Güdek Kılıç , Asuman Gedikbaşı , Mehmet Cihan Balcı , Meryem Karaca , Aslı Durmuş , Belkıs Tutu , Hüseyin Kutay Körbeyli , Fatmahan Atalar and Gülden Fatma Gökçay

Published/Copyright: November 12, 2024

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Turkish Journal of Biochemistry Volume 50 Issue 1

Abstract

Objectives

Congenital disorders of glycosylation (CDG) are a family of rare inherited metabolic disorders. This study aimed to examine the carbohydrate-deficient transferrin (CDT) screening results of 1,328 patients with suspected CDG by using transferrin-high pressure liquid chromatography (Tf-HPLC) method and to evaluate the performance of the method as a reference diagnostic tool.

Methods

Relative CDT levels (CDT concentrations expressed as percent of total transferrin) were determined in serum samples by HPLC. The method sensitivity, specificity and positive predictive value (PPV) were further calculated.

Results

Abnormal transferrin isoform profile consistent with CDG Type-I and CDG Type-II were determined in 50 cases; in 44 cases asiolo-Tf (7.63 ± 5.44 %) and disialo-Tf (36.29 ± 9.04 %), in six cases monosialo-Tf (3.95 ± 0.95 %) and trisialo-Tf (25.05 ± 4.46 %) were determined and decreased tetrasialo-Tf (49.75 ± 11.59 %) was identified in all cases. Two cases having abnormal CDT pattern were molecularly diagnosed with hereditary fructose intolerance and galactosemia and 11 cases diagnosed with CDG based on clinical and molecular analysis showed a normal pattern. The sensitivity, specificity and positive predictive values of Tf-HPLC method were 81.96 %, 99% and 96 %, respectively.

Conclusions

Tf-HPLC is a useful, highly sensitive, cost-advantageous and reliable method for the detection and preliminary diagnosis of CDG for laboratories working with large sample series.

Keywords: congenital disorders of glycosylation (CDG); serum transferrin; inborn errors of metabolism; n-glycosylation; HPLC; diagnosis

Introduction

A group of newborn hereditary diseases with multisystemic involvement caused by glycosylation disorders are defined as congenital glycosylation disorders (CDG) and include approximately over 170 rare genetic disorders since their discovery in the 1980s [1]. CDG occurs as a result of errors in the glycan part of glycoproteins or glycolipids and the binding of these glycans to proteins and lipids and disruptions in related metabolic pathways [2], 3]. Protein N-glycosylation, protein O-glycosylation, glycosphingolipid synthesis, glycosylphosphatidylinositol (GPI)-anchor synthesis, and other glycosylation pathways are evaluated for classification of the disease [1]. In general, defects that occur during the structural processes of glycoproteins or glycolipids in the endoplasmic reticulum (ER) are included in the CDG type 1 (CDG-I) classification, and molecular processing defects in the ER and Golgi are associated with CDG type 2 (CDG-II) [2], 3]. Two main types of protein glycosylation are N-glycosylation and O-glycosylation, while N-glycosylation disorders are commonly diagnosed group which involve the attachment of glycans onto the amide group of asparagine [3].

CDG are multisystem diseases affecting multiple organs. In clinical examination, patients with psychomotor retardation, hypotonicity and classical dysmorphic findings such as inverted nipples, fat pads and strabismus, as well as patients with low antithrombin three levels, are referred to further examinations to diagnose CDG. However, CDG syndrome should be suspected in the presence of unexplained nutritional problems, growth retardation, hepatic fibrosis, gastrointestinal system problems, coagulation disorders, cerebral ataxia, hypothyroidism and hypoglycemia [4], [5], [6], [7]. Despite the variety of symptoms, the most common symptom in individuals with CDG is neurological defects. Neurological symptoms are also observed in many newborn hereditary diseases. Therefore, it may resemble many genetic diseases at the diagnosis stage [8], [9], [10]. Due to the similarity of symptoms with many genetic and neurological diseases, difficulties arise in making the correct diagnosis [8]. Every patient with suspected diagnosis of hereditary metabolic diseases has to be considered for the differential diagnosis of CDG. Treatments of individuals with CDG limited to nutritional regulation and organ transplantation. Additionally, treatments may include interventions to relieve symptoms of CDG [7]. However, diagnostic and treatment options may not be available for all newborns and are not suitable for all types of CDG.

Several transferrin isoforms are present in the human serum, they can be detected and separated by high-performance liquid chromatography (HPLC) methods. Serum carbohydrate deficient transferrin (CDT) analysis is the widely accepted first-line screening test [11] for CDG suspected patients. CDT is a term for all isoforms of sialic acid-deficient tricyalo-, disialo-, monocyalo-, asialotransferrin [12]. The serum of a healthy individual contains four sialic acids in the form of tetrasialo-transferrin, while a decrease in the tetrasialo-transferrin form and an increase in CDTs are present in CDG patients. Type 1 pattern in CDG-I, characterized by an increase of di- and/or asialo-transferrin; a type 2 pattern in CDG-II, characterized by an increase of tri-, di-, mono-and/or asialo-transferrin [13], 14]. Moreover, if transferrin-HPLC (Tf-HPLC) reveals abnormal transferrin profiles, next-line testings such as genetic analysis can be performed.

The aim of this study is to evaluate the effectiveness of the Tf-HPLC method in measuring transferrin isoforms in serum for the preliminary diagnosis of CDG, especially for laboratories working with large sample series.

Materials and methods

All procedures in this study was performed in accordance with the current Helsinki Declaration.

Patients

In this study at Istanbul Medical Faculty, Division of Pediatric Nutrition and Metabolism, we evaluated the chromatography results performed with the Tf-HPLC method of patients referred due to suspicion of CDG over the period of five years between 2018 and 2022. At our center patients referred with the suspicion of CDG were evaluated with a first-line laboratory approach (Table 1) to evaluate for inherited metabolic diseases. CDT measurement results were evaluated with the Tf-HPLC method for CDG.

Table 1:

First-line routine laboratory screening for inherited metabolic diseases.

Complete blood count	Hemoglobin, hematocrit, mean erythrocyte volume, leukocyte, platelet count
Biochemical analysis	Fasting blood glucose, AST, ALT, urea, creatinine, uric acid, albumin, sodium, potassium, chloride, blood gases, ammonia, lactate
Inherited metabolic disease selective screening	Complete urine analysis, sediment ‘crystaluria’ Reductant in urine, monosaccharide thin layer chromatography with suspicious result, Urine group reactions, cyanide nitroprusside, fast blue B test, Screening for biotinidase deficiency in the DBS/serum, Amino acid and acyl carnitine in blood (tandem MS/MS)
Amino acid metabolism	Quantitative amino acid analysis, blood, urine
Organic acidemia	Ammonia, organic acid analysis and in urine (GC/MS)
Galactosemia	Beutler test, total galactose, galactose-1-phosphate (serum/DBS)
Mitochondrial disease/disorders of pyruvate metabolism	Lactate, pyruvate, lactate/pyruvate ratio
CDG	Serum transferrin HPLC

CDG, congenital disorders of glycosylation; DBS, dried blood spot testing; GC/MS, gas chromatography mass spectrometry; MS/MS, tandem mass spectrometry; HPLC, high performance liquid chromatography.

Sample collection

The venous blood of patients clinically suspected to have CDG was collected into 2 cc dry tubes without anticoagulant and kept at room temperature for 30 min. Then, the serum samples were separated by centrifugation at 3,000 rpm for 10 min. Serum samples were stored at −20 °C immediately after separation and were analyzed within 2–3 weeks by using the Tf-HPLC method for separation of transferrin isoforms.

Tf-HPLC

The serum samples of patients were analyzed by HPLC (Thermo Finnigan Spectra System) by using the RECIPE ClinRep^® Complete Kit (Recipe Chemicals & Instruments GmbH Labortechnik, Munich, Germany) as described in the kit protocol. In short, 150 µL serum sample was first mixed with 30 µL serum balancing reagent and incubated for 5 min at room temperature. Then, the precipitating reagents were added and the samples were left in the cold (4 °C) for an hour and further centrifuged at 10,000 rpm for 5 min at 4 °C. 400 µL of the clear supernatant was manually injected to HPLC system. The ClinCheck level 1–2 control serums were used to determine the accuracy and imprecision of the analysis. The same protocol was also applied for the ClinCheck level 1–2 control serums. In the first part of HPLC, serum matrix components were adsorbed on an anion-exchanger analytical column. Further the column switching, serum matrix components were washed and transferrin isoforms were separated on and eluted from the analytical column [15]. The samples were measured with a UV/VIS (ultraviolet/visible) detector. HPLC conditions: gradient elution, flow rate 1.2 mL/min, run time 12–30 min column re-equilibration time. CDT isoforms were then separated into chromatographic peaks and CDT calculation was performed.

CDT % measurement with HPLC

With the Tf-HPLC method, the relative amount of any single or combination of glycoforms to total transferrin was measured.

A peak was observed according to the retention time of CDT on the chromatogram. A shift in the retention time due to the heat would be expected. The CDT retention times for each isoform were as follows; asiolo-transferrin; 5.5 min, mono sialo; 6.5 min, disialo-transferrin; 7.2 min, trisialo-transferrin; 7.5 min, tetrasialo-transferrin; 8.5 min and pentasioalo-transferrin; 9.2 min.

The percentage of CDT was automatically calculated with the following formula; CDT% (analyzed transferrin)=(area of analyzed transferrin/area of total transferrin) × 100.

For CDT (asialo-, monosialo-, disialo-, trisialo-Tf), the method’s lower detection limit was determined to be 0.4 %, with a detectable lower limit of 0.6 %. The accuracy for measurement CV (coefficient of variation) was 2.6 %, and the inter-measurement precision CV was 2.9 %. Normal values were considered <1.75 %, while pathological values were >2.5 %. The distribution of each transferrin isoform relative to total transferrin was measured by the percentage area under the graphical curve (%AUC).

Method sensitivity, specificity and PPV calculation for Tf-HPLC

To categorize patients accurately, we estimated the sensitivity, specificity, positive predictive value (PPV) of the Tf-HPLC method as follows; sensitivity=[a/(a + c)] × 100, specificity=[d/(b + d)] × 100 and positive predictive value (PPV)=[a/(a + b)] × 100. True positive (a)=patients with the condition identified by the test. True negative (d)=patients without the condition correctly identified by the test. False negative (c)=patients with the condition missed by the test. False positive (b)=patients without the condition incorrectly identified by the test [16].

Results

The Tf-HPLC results of 1,328 patients with the suspicion of CDG were evaluated. The mean age of the study group was the range of 8.4 ± 6.9 years.

The separation of the various transferrin glycoforms was obtained by the use of the Recipe HPLC test. The graphical representation of a serum material obtained from a patient without CDG is included in Figure 1E. The peaks representing the abnormal transferrin forms (asialo-, disialo-, trisialo-, tetrasialo-, and pentasialo-Tf) were identified from their respective retention times in the chromatogram (4.04, 6.34, 7.10, 8.17, 9.16 min, respectively) (Figure 1A–D).

Figure 1:

Transferrin glycoform patterns in serum samples from CDG patients. Transferrin glycoform patterns in serum samples from CDG patients representing pathologic patterns (A–D) and normal pattern (E) analyzed by HPLC. From left to right; asialo-transferrin, monosialo-transferrin, disialo-transferrin, trisialo-transferrin, tetrasialo-transferrin and pentasialo-transferrin. The arrows indicate the increased isoforms corresponding to asialo-, monosialo-, disialo-, trisialo-, tetrasialo- transferrin.

The CDT values of patients were determined with Tf-HPLC for preliminary screening. Abnormal CDT patterns were detected in 52 cases (3.9 %) and the female/male ratio was found to be 25/27. Abnormal transferrin types compatible with CDG Type-I and CDG Type-II were identified in 44 cases (7.63 ± 5.44 % asialo and 36.29 ± 9.04 % disialo-Tf), and in six cases (3.95 ± 0.95 % monosialo- and 25.05 ± 4.46 % trisialo-Tf), respectively, and decreased of tetra-sialotransferrin isoform (49.75 ± 11.59 %) was observed in all cases. Two cases with abnormal CDT patterns were diagnosed with hereditary fructose intolerance and classical galactosemia. When the results of clinical and molecular evaluations were examined, 11 cases diagnosed with CDG had a normal CDT profile [17].

Tf-HPLC method sensitivity, specificity and positive predictive value

The number of true positives (n=50) represented the number of patients suspected with CDG and having abnormal CDT results. The number of true negatives (n=1,278) represented the number of patients suspected with CDG and having normal CDT results. The number of false negatives (n=11) represented the number of patients suspected with CDG and having normal CDT values and the number of false positives (n=2) represented the number of patients suspected CDG and having abnormal CDT results. One of these patients was diagnosed with classic galactosemia and the other patient with hereditary fructose intolerance. The Tf-HPLC method showed a sensitivity of 81.96 %, specificity of 99 % and the positive predictive value of 96 % (Table 3).

Discussion

The clinical diversity and genetic complexity of CDG require a multifaceted diagnostic approach, combining clinical evaluations, biochemical analyses, and genetic testing to accurately identify subtypes and guide interventions.

Initial CDG diagnosis typically relies on total serum N-glycan profiling, followed by transferrin isoform detection and subsequent genetic analysis for detailed diagnosis [14], 15], 18].

The Tf-HPLC method, established in our laboratory for over 20 years, serves as a reliable pre-screening tool for CDG. Its utility lies in the visualization of transferrin glycoform patterns, as well as its reproducibility and performance in measurements [19], 20]. HPLC emerges as the preferred method for CDT evaluation, recommended for its ease of pre-processing, safe analysis, and objective peak quantification. It enables accurate determination of CDT/transferrin ratios, facilitating faster evaluation of large patient populations with inherited metabolic diseases, thereby enabling timely diagnosis and appropriate therapy implementation for CDG subgroups [21], 22].

Quintana et al. compared HPLC, capillary zone electrophoresis (CZE), and isoelectric focusing (IEF) methods. Their findings suggest that HPLC and CZE are effective in separating and re-analyzing transferrin isoforms, they could detect certain CDG subtypes that IEF cannot [23]. Their other study, along with our own experience, supports the utility of the Tf-HPLC method for CDG screening, particularly in laboratories handling large sample volumes. Tf-HPLC offers easy sample processing, facilitates extended series of analyses, and allows for peak quantification, enabling researchers to make objective interpretations compared to IEF [24], [25], [26], [27].

In a study by Biffi et al., HPLC analysis was highlighted for its accurate detection of asialo and disialo transferrin concentrations [28].

In a recently overview of CDG epidemiological data [1], transferrin isoelectric focusing (Tf-IEF) emerged as the gold standard method for preliminary screening. However, despite its widespread use Tf-IEF via capillary electrophoresis is laborious, time-consuming, and unsuitable for automation or accurate quantification [24], 29], 30]. Additionally, it only yields positive results in approximately 60 % of CDG cases and may miss a significant number of CDG cases, particularly PMM2-CDG, the most common N-glycosylation defect [8], 31].

In studies worldwide, different methods are used for initial CDG detection due to disease variability (see Table 2). Instead of false positivity/negativity, most prioritize true positive data presentation. Thus, in our CDG-focused study, we can assess sensitivity, specificity, and positive predictive value for the HPLC method.

Table 2:

Example of methods utilized in studies on CDG Diagnosis across varies countries.

Country	Year	Annual incidence per 100,000	Number of screened patients/CDG positives-CDG false positive-CDG false negative	Methods	Reference
UK	2024	ND	921 individuals/8 CDG positive	pES	Allen et al. 2024 [32]
Malesia	2018–2022	0.22 per 100,000	548 CDG suspected patients/13 CDG positive	IEF and CZE	Hamzan et al. 2023 [33]
Portugal	2001–2021	ND	63 CDG positive	HPLC, TF-IEF, CZE,WES,WGS	Quelhas et al. 2021 [34]
Poland	1997–2020	0.013 per 100,000	23,183 individuals/39 CDG positive/4 false negative	TF-IEF, WES, enzyme analysis	Lipinski et al. 2021 [35]
Türkiye	2020	ND	1,331 CDG suspected patients/11 CDG positive	CZE, Sanger sequencing (PMM2 Gene)	Yıldız et al. 2020 [36]
Brazil	2008–2017	ND	1,546 CDG suspected patients/4 CDG positive/10 false positive	TF-IEF, complementary genetic analysis	Magalhaes et al. 2020 [37]
Argentina	2007–2017	ND	554 CDG suspected patients/7 CDG positive/2 false positive	TF-IEF, HPLC, WES	Asteggiano et al. 2018 [38]
Spain	1997–2017	ND	97 CDG positive	IEF, HPLC, CZE, WES	Perez-Cerda et al. 2017 [39]
Saudi Arabia	2017	ND	27 CDG positive/41 CDG positive case extracted from literature	TF-HPLC, WES, CDG Gene panel	Alsubhi et al. 2017 [40]
India	2017	ND	50 CDG suspected patients/2 CDG positive	TF-HPLC, CE	Dave et al. 2017 [11]
Portugal and Spain	2008	ND	8,000 individuals/65 altered %CDT/25 CDG positive/25 non identified/15 false positive	TIA, TF-IEF	Perez –Cerda et al. 2008 [41]
Italy	2007	ND	168 individuals/6 CDG positive	Immunoturbidimetric assay, TF-HPLC, enzyme analysis, PCR	Biffi et al. 2007 [28]
Denmark/Holland	2000	ND	1,370 individuals/101 CDG positive	ELISA	Schollen et al. 2000 [42]

ND, not defined; pES, prenatal exome sequencing; IEF, isoelectric focusing; CZE, capillary zone electrophoresis; HPLC, high performance liquid chromatography; TF-IEF, transferrin isoelectric focusing; WES, whole exome sequencing; WGS, whole genome sequencing; CE, capillary electrophoresis; TIA, % CDT turbidimetric immunoassay; CDT, serum carbohydrate deficient transferrin; ELISA, enzyme-linked immunoSorbent assay.

Table 3:

Diagnostic data for CDG suspected patients using TF-HPLC analysis.

Specificity=99 %	True negative (d)=1,278
	False positive (b)=2
Sensitivity=82 %	True positive (a)=50
	False negative (c)=11
Positive predictive value=96 %	True positive (a)=50
	False positive (b)=2

In our study of 1,328 patients with suspected CDG, the Recipe %CDT HPLC test identified abnormal CDT patterns in 50 CDG patients (3.76 %) and genetic analysis confirmed these results. We observed very low false-negative (11/1,278) and false-positive (2/1,278) rates (0.15–0.86 %, respectively), possibly attributed to genetic transferrin variants and clinical/pharmacological factors previously reported [43], 44]. Moreover, similar transferrin defects may occur in untreated disorders like galactosemia and hereditary fructose intolerance [45]. To ensure accurate diagnosis, it’s crucial to exclude secondary glycosylation abnormalities leading to false-positive profiles, such as hereditary fructose intolerance, galactosemia, and severe liver disease, etc. [13].

Conclusions

Our study using Tf-HPLC in a metabolic laboratory demonstrates improved screening for CDG, aiding clinicians in diagnosis. Taking into account the fact that CDG is a group of ultra rare disorders with multisystem involvement and heterogeneous phenotype, the screening criteria for suspected CDG should be expanded [15], 22].

Corresponding author: Gülden Fatma Gökçay, MD, Prof., Department of Pediatrics, Division of Nutrition and Metabolism, Istanbul University Faculty of Medicine, Turgut Özal Millet Cd. 34093 Fatih, Istanbul, Türkiye; and Department of Rare Diseases, Child Health Institute, Division of Pediatric Nutrition and Metabolism, Istanbul University Medical Faculty, Istanbul, Türkiye, E-mail: ghuner@istanbul.edu.tr

Research ethics: The study was conducted in accordance with the Declaration of Helsinki, as revised in 2013. Ethical approval was obtained from the Istanbul Faculty of Medicine, Clinical Research Ethics Committee, number: E-29624016-050.04-2451987, Istanbul, Türkiye.
Informed consent: Informed consent was obtained from all individuals included in this study.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. O.O. reviewed the reports and conducted the study. K.F.G. performed the laboratory analyses. M.C.B., M.K., A.D., B.A., H.K.K. did the clinical evaluation. O.O., A.G., F.A., G.F.G. contributed to the evaluation of the results and revised the manuscript.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

References

1. Piedade, A, Francisco, R, Jaeken, J, Sarkhail, P, Brasil, S, Ferreira, CR, et al.. Epidemiology of congenital disorders of glycosylation (CDG) overview and perspectives. J Rare Dis 2022;1:3. https://doi.org/10.1007/s44162-022-00003-6.Search in Google Scholar

2. Lee, HF, Chi, CS. Congenital disorders of glycosylation and infantile epilepsy. Epilepsy Behav 2023;142:109214. https://doi.org/10.1016/j.yebeh.2023.109214.Search in Google Scholar PubMed

3. Teneiji, AA, Bruun, TJ, Sidky, S, Cordeiro, D, Cohn, RD, Londono, RM, et al.. Phenotypic and genotypic spectrum of congenital disorders of glycosylation type I and type II. Mol Genet Metabol 2017;120:235–42. https://doi.org/10.1016/j.ymgme.2016.12.014.Search in Google Scholar PubMed

4. Makhamreh, MM, Cottingham, N, Ferreira, CR, Berger, S, Al-Kouatly, HB. Nonimmune hydrops fetalis and congenital disorders of glycosylation: a systematic literature review. J Inherit Metab Dis 2020;43:223–33. https://doi.org/10.1002/jimd.12162.Search in Google Scholar PubMed

5. Paprocka, J, Jezela-Stanek, A, Tylki-Szymanska, A, Grunewald, S. Congenital disorders of glycosylation from a neurological perspective. Brain Sci 2021;11:88. https://doi.org/10.3390/brainsci11010088.Search in Google Scholar PubMed PubMed Central

6. Scott, K, Gadomski, T, Kozicz, T, Morava, E. Congenital disorders of glycosylation: new defects and still counting. J Inherit Metab Dis 2014;37:609–17. https://doi.org/10.1007/s10545-014-9720-9.Search in Google Scholar PubMed PubMed Central

7. Lipiński, P, Tylki-Szymańska, A. Congenital disorders of glycosylation: what clinicians need to know? Front Pediatr 2021;9:715151. https://doi.org/10.3389/fped.2021.715151.Search in Google Scholar PubMed PubMed Central

8. Francisco, R, Marques-da-Silva, D, Brasil, S, Pascoal, C, Dos Reis Ferreira, V, Morava, E, et al.. The challenge of CDG diagnosis. Mol Genet Metabol 2019;126:1–5. https://doi.org/10.1016/j.ymgme.2018.11.003.Search in Google Scholar PubMed

9. Ferreira, CR, Altassan, R, Marques-Da-Silva, D, Francisco, R, Jaeken, J, Morava, E. Recognizable phenotypes in CDG. J Inherit Metab Dis 2018;41:541–53. https://doi.org/10.1007/s10545-018-0156-5.Search in Google Scholar PubMed PubMed Central

10. Sönmezer, GG. The role of HPLC in the evaluation of transferrin glycoforms in cases suspected of congenital glycosylation disorders [Master Thesis]. Istanbul, Türkiye: Istanbul University, Istanbul Medical Faculty; 2006.Search in Google Scholar

11. Dave, MB, Dherai, AJ, Udani, VP, Hegde, AU, Desai, NA, Ashavaid, TF. Comparison of transferrin isoform analysis by capillary electrophoresis and HPLC for screening congenital disorders of glycosylation. J Clin Lab Anal 2018;32:e22167. https://doi.org/10.1002/jcla.22167.Search in Google Scholar PubMed PubMed Central

12. Bortolotti, F, Sorio, D, Bertaso, A, Tagliaro, F. Analytical and diagnostic aspects of carbohydrate deficient transferrin (CDT): a critical review over years 2007–2017. J Pharmaceut Biomed Anal 2018;147:2–12. https://doi.org/10.1016/j.jpba.2017.09.006.Search in Google Scholar PubMed

13. Lefeber, DJ, Morava, E, Jaeken, J. How to find and diagnose a CDG due to defective N-glycosylation. J Inherit Metab Dis 2011;34:849–52. https://doi.org/10.1007/s10545-011-9370-0.Search in Google Scholar PubMed PubMed Central

14. Hennet, T. Diseases of glycosylation beyond classical congenital disorders of glycosylation. Biochim Biophys Acta Gen Subj 2012;1820:1306–17. https://doi.org/10.1016/j.bbagen.2012.02.001.Search in Google Scholar PubMed

15. Scherpenzeel, M, Willems, E, Lefeber, DJ. Clinical diagnostics and therapy monitoring in the congenital disorders of glycosylation. Glycoconj J 2016;33:345–58.10.1007/s10719-015-9639-xSearch in Google Scholar PubMed PubMed Central

16. Trevethan, R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health 2017;20:307. https://doi.org/10.3389/fpubh.2017.00307.Search in Google Scholar PubMed PubMed Central

17. Ozgen, O, Kilic, FG, Gedikbasi, A, Balci, MC, Karaca, M, Durmus, A. Retrospective analysis of carbohydrate-deficient transferrin for CDG screening: a single center study. Abstracts. J Inherit Metab Dis 2023;46:257. https://doi.org/10.1002/jimd.12668.Search in Google Scholar

18. Caslavska, J, Thormann, W. Monitoring of transferrin isoforms in biological samples by capillary electrophoresis. J Separ Sci 2018;41:303–22. https://doi.org/10.1002/jssc.201700914.Search in Google Scholar PubMed

19. Jeppsson, JO, Arndt, T, Schellenberg, F, Wielders, JP, Anton, RF, Whitfield, JB, et al.. Toward standardization of carbohydrate-deficient transferrin (CDT) measurements: I. Analyte definition and proposal of a candidate reference method. Clin Chem Lab Med 2007;45:558–62. https://doi.org/10.1515/cclm.2007.107.Search in Google Scholar

20. Bianchi, V, Raspagni, A, Arfini, C, Vidali, M. High performance liquid chromatography evaluation of serum carbohydrate-deficient transferrin and more sialylated transferrin glycoforms in children. Scand J Clin Lab Investig 2012;72:274–80. https://doi.org/10.3109/00365513.2012.660537.Search in Google Scholar PubMed

21. Helander, A, Bergström, J, Freeze, HH. Testing for congenital disorders of glycosylation by HPLC measurement of serum transferrin glycoforms. Clin Chem 2004;50:954–8. https://doi.org/10.1373/clinchem.2003.029629.Search in Google Scholar PubMed

22. Francisco, R, Brasil, S, Poejo, J, Jaeken, J, Pascoal, C, Videira, PA, et al.. Congenital disorders of glycosylation (CDG): state of the art in 2022. Orphanet J Rare Dis 2023;18:329. https://doi.org/10.1186/s13023-023-02879-z.Search in Google Scholar PubMed PubMed Central

23. Quintana, E, Montero, R, Casado, M, Navarro-Sastre, A, Vilaseca, MA, Briones, P, et al.. Comparison between high performance liquid chromatography and capillary zone electrophoresis for the diagnosis of congenital disorders of glycosylation. J Chromatogr, B: Anal Technol Biomed Life Sci 2009;877:2513–18. https://doi.org/10.1016/j.jchromb.2009.06.031.Search in Google Scholar PubMed

24. Helander, A, Bergstrom, JP. Determination of carbohydrate-deficient transferrin in human serum using the Bio-Rad % CDT by HPLC test. Clin Chim Acta 2006;371:187–90. https://doi.org/10.1016/j.cca.2006.03.010.Search in Google Scholar PubMed

25. Helander, A, Husa, A, Jeppsson, JO. Improved HPLC method for carbohydrate-deficient transferrin in serum. Clin Chem 2003;49:1881–90. https://doi.org/10.1373/clinchem.2003.023341.Search in Google Scholar PubMed

26. Lacey, JM, Bergen, HR, Magera, MJ, Naylor, S, O’Brien, JF. Rapid determination of transferrin isoforms by immunoaffinity liquid chromatography and electrospray mass spectrometry. Clin Chem 2001;47:513–18. https://doi.org/10.1093/clinchem/47.3.513.Search in Google Scholar

27. Quintana, E, Navarro-Sastre, A, Hernández-Pérez, JM, García-Villoria, J, Montero, R, Artuch, R, et al.. Screening for congenital disorders of glycosylation (CDG): transferrin HPLC versus isoelectric focusing (IEF). Clin Biochem 2009;42:408–15. https://doi.org/10.1016/j.clinbiochem.2008.12.013.Search in Google Scholar PubMed

28. Biffi, S, Tamaro, G, Bortot, B, Zamberlan, S, Severini, GM, Carrozzi, M. Carbohydrate-deficient transferrin (CDT) as a biochemical tool for the screening of congenital disorders of glycosylation (CDGs). Clin Biochem 2007;40:1431–4. https://doi.org/10.1016/j.clinbiochem.2007.08.015.Search in Google Scholar PubMed

29. Kingma, HA, Van Der Sluijs, FH, Heiner-Fokkema, MR. Fast screening of N-glycosylation disorders by sialotransferrin profiling with capillary zone electrophoresis. Ann Clin Biochem 2018;55:693–701. https://doi.org/10.1177/0004563218779609.Search in Google Scholar PubMed PubMed Central

30. Jaeken, J, Hennet, T, Matthijs, G, Freeze, HH. CDG nomenclature: time for a change. Biochim Biophys Acta (BBA) – Mol Basis Dis 2009;1792:825–6. https://doi.org/10.1016/j.bbadis.2009.08.005.Search in Google Scholar PubMed PubMed Central

31. Altassan, R, Peanne, R, Jaeken, J, Barone, R, Bidet, M, Borgel, D, et al.. International clinical guidelines for the management of phosphomannomutase 2-congenital disorders of glycosylation: diagnosis, treatment and follow up. J Inherit Metab Dis 2019;42:5–28. https://doi.org/10.1002/jimd.12024.Search in Google Scholar PubMed

32. Allen, SK, Chandler, NJ, Kinning, E, Harrison, V, Brothwell, SLC, Vijay, S, et al.. Diagnosis of inborn errors of metabolism through prenatal exome sequencing with targeted analysis for fetal structural anomalies. Prenat Diagn 2024;44:432–42. https://doi.org/10.1002/pd.6476.Search in Google Scholar PubMed

33. Hamzan, NI, Ismail, IK, Vun, J, Jolin, CS, Mohammad, FA, Ridzuan, SNA, et al.. Epidemiology and prevalence of patients with congenital disorders of glycosylation in Malaysia. Asian J Med Biomed 2023;7:56–64. https://doi.org/10.37231/ajmb.2023.7.1.601.Search in Google Scholar

34. Quelhas, D, Martins, E, Azevedo, L, Bandeira, A, Diogo, L, Garcia, P, et al.. Congenital disorders of glycosylation in Portugal-two decades of experience. J Pediatr 2021;231:148–56. https://doi.org/10.1016/j.jpeds.2020.12.026.Search in Google Scholar PubMed

35. Lipiński, P, Bogdańska, A, Tylki-Szymańska, A. Congenital disorders of glycosylation: prevalence, incidence and mutational spectrum in the Polish population. Mol Genet Metab Rep 2021;11:100726. https://doi.org/10.1016/j.ymgmr.2021.100726.Search in Google Scholar PubMed PubMed Central

36. Yıldız, Y, Arslan, M, Çelik, G, Kasapkara, ÇS, Ceylaner, S, Dursun, A, et al.. Genotypes and estimated prevalence of phosphomannomutase 2 deficiency in Turkey differ significantly from those in Europe. Am J Med Genet A 2020;182:705–12. https://doi.org/10.1002/ajmg.a.61488.Search in Google Scholar PubMed

37. Magalhães, APPS, Burin, MG, Souza, CFM, de Bitencourt, FH, Sebastião, FM, Silva, TO, et al.. Transferrin isoelectric focusing for the investigation of congenital disorders of glycosylation: analysis of a ten-year experience in a Brazilian center. J Pediatr 2020;96:710–16. https://doi.org/10.1016/j.jped.2019.05.008.Search in Google Scholar PubMed PubMed Central

38. Asteggiano, CG, Papazoglu, M, Bistué Millón, MB, Peralta, MF, Azar, NB, Spécola, NS, et al.. Ten years of screening for congenital disorders of glycosylation in Argentina: case studies and pitfalls. Pediatr Res 2018;84:837–41. https://doi.org/10.1038/s41390-018-0206-6.Search in Google Scholar PubMed

39. Pérez-Cerdá, C, Girós, ML, Serrano, M, Ecay, MJ, Gort, L, Pérez Dueñas, B, et al.. A population-based study on congenital disorders of protein N- and combined with O-glycosylation experience in clinical and genetic diagnosis. J Pediatr 2017;183:170–7. https://doi.org/10.1016/j.jpeds.2016.12.060.Search in Google Scholar PubMed

40. Alsubhi, S, Alhashem, A, Faqeih, E, Alfadhel, M, Alfaifi, A, Altuwaijri, W, et al.. Congenital disorders of glycosylation: the Saudi experience. Am J Med Genet A 2017;173:2614–21. https://doi.org/10.1002/ajmg.a.38358.Search in Google Scholar PubMed

41. Pérez-Cerdá, C, Quelhas, D, Vega, AI, Ecay, J, Vilarinho, L, Ugarte, M. Screening using serum percentage of carbohydrate-deficient transferrin for congenital disorders of glycosylation in children with suspected metabolic disease. Clin Chem 2008;54:93–100. https://doi.org/10.1373/clinchem.2007.093450.Search in Google Scholar PubMed

42. Schollen, E, Kjaergaard, S, Legius, E, Schwartz, M, Matthijs, G. Lack of Hardy-Weinberg equilibrium for the most prevalent PMM2 mutation in CDG-Ia (congenital disorders of glycosylation type Ia). Eur J Hum Genet 2000;8:367–71. https://doi.org/10.1038/sj.ejhg.5200470.Search in Google Scholar PubMed

43. Helander, A, Eriksson, G, Stibler, H, Jeppsson, JO. Interference of transferrin isoform types with carbohydrate-deficient transferrin quantification in the identification of alcohol abuse. Clin Chem 2001;47:1225–33. https://doi.org/10.1093/clinchem/47.7.1225.Search in Google Scholar

44. Bergström, JP, Helander, A. HPLC evaluation of clinical and pharmacological factors reported to cause false-positive carbohydrate-deficient transferrin (CDT) levels. Clin Chim Acta 2008;389:164–6. https://doi.org/10.1016/j.cca.2007.11.020.Search in Google Scholar PubMed

45. Sturiale, L, Barone, R, Fiumara, A, Perez, M, Zaffanello, M, Sorge, G, et al.. Hypoglycosylation with increased fucosylation and branching of serum transferrin N-glycans in untreated galactosemia. Glycobiology 2005;15:1268–76. https://doi.org/10.1093/glycob/cwj021.Search in Google Scholar PubMed

Received: 2024-01-27

Accepted: 2024-08-20

Published Online: 2024-11-12

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/tjb-2024-0011

Keywords for this article

congenital disorders of glycosylation (CDG); serum transferrin; inborn errors of metabolism; n-glycosylation; HPLC; diagnosis

Creative Commons

BY 4.0