Dynamic Methods for Childhood Hypoglycemia Phenotyping: A Narrative Review

Hypoglycemia results from an imbalance between glucose entering the blood compartment and glucose demand, caused by a defect in the mechanisms regulating postprandial glucose homeostasis. Hypoglycemia represents one of the most common metabolic emergencies in childhood, potentially leading to serious neurologic sequelae, including death. Therefore, appropriate investigation of its specific etiology is paramount to provide adequate diagnosis, specific therapy and prevent its recurrence. In the absence of critical samples for biochemical studies, etiological assessment of children with hypoglycemia may include dynamic methods, such as in vivo functional tests, and continuous glucose monitoring. By providing detailed information on actual glucose fluxes in vivo, proof-of-concept studies have illustrated the potential (clinical) application of dynamic stable isotope techniques to define biochemical and clinical phenotypes of inherited metabolic diseases associated with hypoglycemia. According to the textbooks, individuals with glycogen storage disease type I (GSD I) display the most severe hypoglycemia/fasting intolerance. In this review, three dynamic methods are discussed which may be considered during both diagnostic work-up and monitoring of children with hypoglycemia: 1) functional in vivo tests; 2) in vivo metabolic profiling by continuous glucose monitoring (CGM); 3) stable isotope techniques. Future applications and benefits of dynamic methods in children with hypoglycemia are also discussed.

Genotypic and clinical analysis of 49 Chinese children with hepatic glycogen storage diseases

BACKGROUND

Glycogen storage disease (GSD) is a relatively rare inborn metabolic disorder, our study aims to investigate the genotypic and clinical feature of hepatic GSDs in China.

METHODS

The clinical and genotypic data of 49 patients with hepatic GSDs were collected retrospectively and analyzed.

RESULTS

After gene sequencing, 49 patients were diagnosed as GSDs, including GSD Ia (24 cases), GSD IIIa (11 cases), GSD IXa (8 cases), GSD VI (3 cases) and GSD Ib (3 cases). About 45 gene variants of G6PC, AGL, PHKA2, PYGL, and SLC37A4 were detected; among which, 22 variants were unreported previously. c.648G>T (p. Leu216Leu) of G6PC exon 5 is the most common variant for GSD Ia patients (20/24,83.33%）, splice variant c.1735+1G>T of AGL exon 13 is relatively common among GSD IIIa, while novel variant accounts for the majority of GSD IXa and GSD VI patients. As for clinical features, there was no significant difference in the onset age among group GSD Ia, GSD IIIa, and GSD IXa, but the age at diagnosis and average disease duration from diagnosis of GSD Ia were significantly higher than GSD IIIa and GSD IXa. Body weight of GSD patients was basically normal, but growth retardation was relatively common among them, especially for GSD Ia patients; and renomegaly was only found in GSD Ia. Besides, serum cholesterol, triglyceride, lactic acid, and uric acid in GSD Ia were significantly higher than those with GSD IIIa and IXa (p < 0.05); but ALT, AST, CK, and LDH of GSD III and GSD IXa were significantly higher when compared to GSD Ia (p < 0.05).

CONCLUSIONS

All hepatic GSDs patients share similarity in clinical and biochemical spectrum, but delayed diagnosis and biochemical metabolic abnormalities were common in GSD Ia. For family with GSD proband, pedigree analysis and genetic testing is strongly recommended.