Congenital disorders of glycosylation (CDG) are a group of rare, multisystemic genetic diseases caused by defects in glycan biosynthesis and protein glycosylation. Their broad clinical and genetic heterogeneity often require advanced diagnostic strategies. Clinical glycomics and glycoproteomics have emerged as powerful tools for understanding and diagnosing CDG by enabling high-resolution analysis of glycan structures and glycoproteins. Advancements in high-throughput mass spectrometry (MS) and site-specific glycoproteomics have led to the identification of disease-relevant biomarkers, providing insight into underlying glycosylation defects. These technologies enable detailed analysis of glycan structures and glycoproteins, improving early diagnosis, supporting biomarker discovery, and facilitating therapy monitoring. Integration with genomic and clinical data, including the use of dried blood spot testing and isotopic tracing, further enhances diagnostic precision and reveals the functional consequences of pathogenic variants. While challenges remain in standardizing methods, ensuring accessibility, and implementing bioinformatics tools, global collaborations and harmonized guidelines are beginning to address these gaps. Future directions include the use of artificial intelligence in data analysis, the development of comprehensive diagnostic frameworks, and international efforts to standardize glycomic methods. Collectively, these advances reinforce the growing clinical value of glycomics and glycoproteomics in the diagnosis and management of CDG.

Congenital disorders of glycosylation (CDG) are a group of rare, multisystemic genetic diseases caused by defects in glycan biosynthesis and protein glycosylation. Their broad clinical and genetic heterogeneity often require advanced diagnostic strategies. Clinical glycomics and glycoproteomics emerge as powerful tools for understanding and diagnosing CDG by enabling high-resolution analysis of glycan structures and glycoproteins. Advancements in high-throughput mass spectrometry (MS) and site-specific glycoproteomics have led to the identification of disease-relevant biomarkers, providing insight into underlying glycosylation defects. These technologies enable detailed analysis of glycan structures and glycoproteins, improving early diagnosis, supporting biomarker discovery, and facilitating therapy monitoring. Integration with genomic and clinical data, including the use of dried blood spot testing and isotopic tracing, further enhances diagnostic precision and reveals functional consequences of pathogenic variants. While challenges remain in standardizing methods, ensuring accessibility, and implementing bioinformatics tools, global collaborations and harmonized guidelines are beginning to address these gaps. Future directions include the use of artificial intelligence in data analysis, the development of comprehensive diagnostic frameworks, and international efforts to standardize glycomic methods. Collectively, these advances reinforce the growing clinical value of glycomics and glycoproteomics in the diagnosis and management of CDG.

Chronic kidney disease (CKD) is a progressive disease in which accurate estimation of glomerular filtration rate (GFR) is essential for staging and guiding therapy. Serum creatinine is widely used but influenced by non-renal factors, while cystatin C and β2-microglobulin (β2M) may provide complementary information related to filtration and tubular or inflammatory factors. This study compared the discriminatory performance of creatinine, cystatin C and β2M for separating CKD stage 3 from stage 4 within the 2021 CKD-EPI eGFR framework in 45 adults with CKD stages 3–4. CKD stage classification was defined using the 2021 CKD-EPI creatinine and creatinine–cystatin C equations (eGFRcr, eGFRcr–cys) with a threshold of 30 mL/min/1.73 m². Receiver operating characteristic (ROC) analysis evaluated each marker’s ability to distinguish moderate from severe CKD. Creatinine showed high diagnostic accuracy (AUC up to 0.98). Cystatin C achieved 100% specificity at the optimal cut-off for severe CKD and showed comparable diagnostic accuracy to creatinine under the eGFRcr–cys framework (AUC 0.978 vs. 0.957). β2M demonstrated AUCs up to 0.97, with sensitivity and specificity above 90%. These findings support a multi-marker evaluation within the 2021 CKD-EPI-based staging, rather than validation against measured GFR. Larger studies incorporating measured GFR and relevant clinical confounders are warranted.

Background/Objectives: Chronic kidney disease (CKD) is a progressive, often irreversible condition where accurate estimation of glomerular filtration rate (GFR) is essential for staging and guiding therapy. Serum creatinine is widely used but influenced by non-renal factors, while alternative markers such as cystatin C and beta-2 microglobulin (β2M) may improve accuracy. This study evaluated the diagnostic performance of creatinine, cystatin C, and β2M for GFR estimation using the 2021 CKD-EPI equations in patients with moderate to severe CKD. Methods: This exploratory study analyzed data from 45 patients with CKD stages 3–4. Serum creatinine, cystatin C, and β2M were measured, and GFR was estimated using the 2021 CKD-EPI creatinine (eGFRcr) and creatinine–cystatin C (eGFRcr-cys) equations. Receiver operating characteristic (ROC) curve analysis was performed at a predefined threshold of 30 mL/min/1.73 m² to evaluate diagnostic accuracy in distinguishing between moderate and severe CKD. Results: Serum creatinine demonstrated high diagnostic accuracy (AUC = 0.977 for eGFRcr; 0.957 for eGFRcr-cys). Cystatin C showed perfect specificity (100%) and excellent predictive accuracy (AUC = 0.908 for eGFRcr; 0.978 for eGFRcr-cys), significantly outperforming creatinine at the severe CKD threshold (p = 0.019). β2M demonstrated balanced diagnostic performance (AUC = 0.901 for eGFRcr; 0.974 for eGFRcr-cys), with sensitivity and specificity above 90% under the eGFRcr-cys equation. Conclusions: A multi-marker evaluation using creatinine, cystatin C, and β2M within the 2021 CKD-EPI equations enhances diagnostic precision for GFR estimation in CKD. These findings provide preliminary evidence and support the clinical utility of multi-marker approaches in refining CKD staging and guiding earlier therapeutic interventions.

Congenital disorders of glycosylation (CDG) are rarely, inherited metabolic diseases in which the glycosylation process is severely impaired. The prevalence has not been well determined and the aims of this study were to describe the epidemiology and calculate the birth prevalence of CDG in the Malaysian population. From 2018 to 2022, 548 patients were suspected with CDG. Sample were collected and analysed using isoelectric focusing (IEF) and capillary zone electrophoresis (CZE) methods. Qualitative measurement of sialo pattern was determined to categorised CDG Type I, II and abnormal transferrin pattern. Transferrin level was quantitated using automated BN ProSpec® System as well as demographic and clinical features were documented. The distribution of CDG was 0.4% and 2.9% was diagnosed as abnormal transferrin types. The prevalence of CDG among Malaysian was 0.22 per 100,000 live births and the combined prevalence of abnormal pattern was 0.85 per 100,000 live births. Positive cases were found in infants of Malay ethnicity. Overall, the prevalence of CDG in Malaysia was low and may be underestimated yet consistent with other reported in other countries.

Background: Congenital disorders of glycosylation (CDG) are rare inborn errors of metabolism with multisystemic manifestations. SSR4-CDG is an ultra-rare X-linked subtype caused by pathogenic variants in SSR4, a component of the translocon-associated protein (TRAP) complex essential for protein translocation and N-glycosylation. Case presentation: We report a two-year-old Malaysian male presenting with global developmental delay, central hypotonia, microcephaly with complete agenesis of the corpus callosum, recurrent infections, bilateral vesicoureteral reflux, and failure to thrive. Growth parameters (weight, length, and head circumference) were persistently below the expected percentiles, indicating postnatal growth restriction. Initial metabolic and biochemical investigations for global developmental delay were unremarkable, apart from mild hyperammonemia. Transferrin isoform analysis demonstrated a type I CDG pattern, raising suspicion of a glycosylation defect. Results: Transferrin glycopeptide LC–MS/MS showed impaired N-glycan occupancy at both glycosylation sites (Asn432 and Asn630), with reduced fully sialylated glycoforms and increased non-glycosylated peptides. Targeted metabolomics using triple quadrupole LC–MS/MS revealed systemic abnormalities, including elevated arginine and phenylalanine, reduced glutamate, increased lysophosphatidylcholine (C24:0-LPC), and generalized depletion of free and acylcarnitines. Whole-exome sequencing identified a novel hemizygous SSR4 variant (c.98del; p.Pro33LeufsTer23) on the X chromosome, predicted to produce a truncated, nonfunctional protein. Conclusions: This is the first Malaysian patient with SSR4-CDG, comprehensively characterized using a multi-omics diagnostic workflow. The integration of glycoproteomics, metabolomics, and exome sequencing provided a detailed biochemical fingerprint that expands the clinical, genetic, and metabolic spectrum of SSR4-CDG and demonstrates the diagnostic and translational value of multi-omics approaches in inborn errors of metabolism.