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Biochemical evaluation of an infant with hypoglycemia resulting from a novel de novo mutation of the GLUD1 gene and hyperinsulinism-hyperammonemia syndrome

Balasubramaniam S, Kapoor R, Yeow JH, Lim PG, Flanagan S, Ellard S, et al.

J Pediatr Endocrinol Metab, 2011;24(7-8):573-7.
PMID: 21932603

Hyperinsulinism-hyperammonemia syndrome (HI/HA) (OMIM 606762), the second most common form of congenital hyperinsulinism (CHI) is associated with activating missense mutations in the GLUD1 gene, which encodes the mitochondrial matrix enzyme, glutamate dehydrogenase (GDH). Patients present with recurrent symptomatic postprandial hypoglycemia following protein-rich meals (leucine-sensitive hypoglycemia) as well as fasting hypoglycemia accompanied by asymptomatic elevations of plasma ammonia. In contrast to other forms of CHI, the phenotype is reported to be milder thus escaping recognition for the first few months of life. Early diagnosis and appropriate management are essential to avoid the neurodevelopmental consequences including epilepsy and learning disabilities which are prevalent in this disorder. We report an infant presenting with afebrile seizures secondary to hyperinsulinemic hypoglycemia resulting from a novel de novo mutation of the GLUD1 gene.

Matched MeSH terms: Hyperinsulinism/diagnosis*
Fulltext Determinants of High Fasting Insulin and Insulin Resistance Among Overweight/Obese Adolescents

Ling JC, Mohamed MN, Jalaludin MY, Rampal S, Zaharan NL, Mohamed Z

Sci Rep, 2016 11 08;6:36270.
PMID: 27824069 DOI: 10.1038/srep36270

Hyperinsulinaemia is the earliest subclinical metabolic abnormality, which precedes insulin resistance in obese children. An investigation was conducted on the potential predictors of fasting insulin and insulin resistance among overweight/obese adolescents in a developing Asian country. A total of 173 overweight/obese (BMI > 85th percentile) multi-ethnic Malaysian adolescents aged 13 were recruited from 23 randomly selected schools in this cross-sectional study. Waist circumference (WC), body fat percentage (BF%), physical fitness score (PFS), fasting glucose and fasting insulin were measured. Insulin resistance was calculated using homeostasis model assessment of insulin resistance (HOMA-IR). Adjusted stepwise multiple regression analysis was performed to predict fasting insulin and HOMA-IR. Covariates included pubertal stage, socioeconomic status, nutritional and physical activity scores. One-third of our adolescents were insulin resistant, with girls having significantly higher fasting insulin and HOMA-IR than boys. Gender, pubertal stage, BMI, WC and BF% had significant, positive moderate correlations with fasting insulin and HOMA-IR while PFS was inversely correlated (p

Matched MeSH terms: Hyperinsulinism/diagnosis*
Fulltext Comprehensive screening shows that mutations in the known syndromic genes are rare in infants presenting with hyperinsulinaemic hypoglycaemia

Laver TW, Wakeling MN, Hua JHY, Houghton JAL, Hussain K, Ellard S, et al.

Clin Endocrinol (Oxf), 2018 Nov;89(5):621-627.
PMID: 30238501 DOI: 10.1111/cen.13841

OBJECTIVE: Hyperinsulinaemic hypoglycaemia (HH) can occur in isolation or more rarely feature as part of a syndrome. Screening for mutations in the "syndromic" HH genes is guided by phenotype with genetic testing used to confirm the clinical diagnosis. As HH can be the presenting feature of a syndrome, it is possible that mutations will be missed as these genes are not routinely screened in all newly diagnosed individuals. We investigated the frequency of pathogenic variants in syndromic genes in infants with HH who had not been clinically diagnosed with a syndromic disorder at referral for genetic testing.
DESIGN: We used genome sequencing data to assess the prevalence of mutations in syndromic HH genes in an international cohort of patients with HH of unknown genetic cause.
PATIENTS: We undertook genome sequencing in 82 infants with HH without a clinical diagnosis of a known syndrome at referral for genetic testing.
MEASUREMENTS: Within this cohort, we searched for the genetic aetiologies causing 20 different syndromes where HH had been reported as a feature.
RESULTS: We identified a pathogenic KMT2D variant in a patient with HH diagnosed at birth, confirming a genetic diagnosis of Kabuki syndrome. Clinical data received following the identification of the mutation highlighted additional features consistent with the genetic diagnosis. Pathogenic variants were not identified in the remainder of the cohort.
CONCLUSIONS: Pathogenic variants in the syndromic HH genes are rare; thus, routine testing of these genes by molecular genetics laboratories is unlikely to be justified in patients without syndromic phenotypes.

Matched MeSH terms: Congenital Hyperinsulinism/diagnosis*
Fulltext A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Murphy N, Cross AJ, Abubakar M, Jenab M, Aleksandrova K, Boutron-Ruault MC, et al.

PLoS Med, 2016 Apr;13(4):e1001988.
PMID: 27046222 DOI: 10.1371/journal.pmed.1001988

BACKGROUND: Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.

Matched MeSH terms: Hyperinsulinism/diagnosis