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.
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.
Case presentation: A 33-year-old female presented with recurrent hypoglycemia. Endogenous hyperinsulinemia was confirmed by a prolonged fast, however serial imaging was negative. Incidental finding of an ovarian mass gave rise to the suspicion of an insulin-producing ovarian tumor. Subsequent multimodality pancreatic imaging remained negative, requiring more invasive investigations. The tumor was localized by specialized arteriography using calcium stimulation to support the diagnosis of an insulinoma. However, repeated negative imaging led to further delays in definitive management, with worsening hypoglycemia. The surgery was finally performed three years after the initial presentation with successful removal of the tumor using intra-operative ultrasound.
Clinical discussion: It is important to emphasize that preoperative radiological imaging is useful to localize pancreatic lesions. However, most insulinomas could only be detected intraoperatively. The absence of suggestive radiological evidence should not deter surgeons from proceeding with definitive surgical intervention.
Conclusion: The case highlights the importance of a multidisciplinary approach in the management of a complicated case.
OBJECTIVE: This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.
METHODS: Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.
RESULTS: Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.
CONCLUSIONS: Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.