AIPmut pituitary adenomas have clinical features that may negatively impact treatment efficacy. Predisposition for aggressive disease in young patients, often in a familial setting, suggests that earlier diagnosis of AIPmut pituitary adenomas may have clinical utility.

BackgroundDisorders of sex development (DSD) are congenital conditions in which chromosomal, gonadal, or phenotypic sex is atypical. Clinical management of DSD is often difficult and currently only 13% of patients receive an accurate clinical genetic diagnosis. To address this we have developed a massively parallel sequencing targeted DSD gene panel which allows us to sequence all 64 known diagnostic DSD genes and candidate genes simultaneously.ResultsWe analyzed DNA from the largest reported international cohort of patients with DSD (278 patients with 46,XY DSD and 48 with 46,XX DSD). Our targeted gene panel compares favorably with other sequencing platforms. We found a total of 28 diagnostic genes that are implicated in DSD, highlighting the genetic spectrum of this disorder. Sequencing revealed 93 previously unreported DSD gene variants. Overall, we identified a likely genetic diagnosis in 43% of patients with 46,XY DSD. In patients with 46,XY disorders of androgen synthesis and action the genetic diagnosis rate reached 60%. Surprisingly, little difference in diagnostic rate was observed between singletons and trios. In many cases our findings are informative as to the likely cause of the DSD, which will facilitate clinical management.ConclusionsOur massively parallel sequencing targeted DSD gene panel represents an economical means of improving the genetic diagnostic capability for patients affected by DSD. Implementation of this panel in a large cohort of patients has expanded our understanding of the underlying genetic etiology of DSD. The inclusion of research candidate genes also provides an invaluable resource for future identification of novel genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1105-y) contains supplementary material, which is available to authorized users.

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We advocate an approach to the analysis of CGMS data based upon a hierarchy of relevant clinical questions alluding to the representative nature of the data, the amount of time spent in glycemic excursions, and the degree of glycemic variation. Integrated use of these algorithms distinguishes between various patterns of glycemic control in those with and without diabetes.

OBJECTIVE -To reevaluate the persistence and stability of previously observed differences between pediatric diabetes centers and to investigate the influence of demography, language communication problems, and changes in insulin regimens on metabolic outcome, hypoglycemia, and ketoacidosis. RESULTS -Mean A1C was 8.2 Ϯ 1.4%, with substantial variation between centers (mean A1C range 7.4 -9.2%; P Ͻ 0.001). There were no significant differences between centers in rates of severe hypoglycemia or diabetic ketoacidosis. Language difficulties had a significant negative impact on metabolic outcome (A1C 8.5 Ϯ 2.0% vs. 8.2 Ϯ 1.4% for those with language difficulties vs. those without, respectively; P Ͻ 0.05). After adjustement for significant confounders of age, sex, duration of diabetes, insulin regimen, insulin dose, BMI, and language difficulties, the center differences persisted, and the effect size for center was not reduced. Relative center ranking since 1998 has remained stable, with no significant change in A1C. RESEARCH DESIGN AND METHODSCONCLUSIONS -Despite many changes in diabetes management, major differences in metabolic outcome between 21 international pediatric diabetes centers persist. Different application between centers in the implementation of insulin treatment appears to be of more importance and needs further exploration.

Aims/hypothesis The objective of this study was to assess the impact of patient-led sensor-guided pump management on glycaemic control, and compare the effect with that of standard insulin pump therapy. Methods An open multicentre parallel randomised controlled trial was conducted at five tertiary diabetes centres. Participants aged 13.0-40.0 years with well-controlled type 1 diabetes were randomised 1:1 to either study group for 3 months. Randomisation was carried out using a central computer-generated schedule. Participants in the intervention group used sensor-guided pump management; no instructive guidelines in interpreting real-time data were provided ('patient-led' use). Participants in the control group continued their original insulin pump regimen. Continuous glucose monitoring (CGM) and HbA 1c level were used to assess outcomes. The primary outcome was the difference in the proportion of time in the target glycaemic range during the 3 month study period (derived from CGM, target range 4-10 mmol/l). Secondary outcomes were difference in HbA 1c , time in hypoglycaemic (≤3.9 mmol/l) and hyperglycaemic (≥10.1 mmol/l) ranges and glycaemic variability. Results Sixty-two participants were recruited and randomised; 5/31 and 2/31 withdrew from intervention and control groups, respectively, leaving 26/31 and 29/31 for the intention-to-treat analyses. When adjusted for baseline values, the mean end-of-study HbA 1c was 0.43% lower in the intervention group compared with the control group (95% CI 0.19 to 0.75%; p=0.009). No difference was observed in CGM-derived time in target (measured difference 1.72; 95% CI −5.37 to 8.81), hypoglycaemic (0.54; 95% CI −3.48 to 4.55) or hyperglycaemic (−2.18; 95% CI −10.0 to 5.69) range or in glycaemic variability (−0.29; 95% CI −0.34 to 0.28). Within the intervention group, HbA 1c was 0.51% lower in participants with sensor use ≥70% compared with participants with sensor use <70% (95% CI −0.98 to −0.04, p=0.04). Five episodes of device malfunction occurred. Conclusions/interpretation Individuals established on insulin pump therapy can employ sensor-guided pump management to improve glycaemic control. An apparent dose-dependent effect of sensor usage was noted; however, Diabetologia

OBJECTIVE -In this study, we used neurocognitive assessment and neuroimaging to examine brain function in youth with type 1 diabetes studied prospectively from diagnosis.RESEARCH DESIGN AND METHODS -We studied type 1 diabetic (n ϭ 106) and control subjects (n ϭ 75) with no significant group difference on IQ at baseline 12 years previously by using the Wechsler Abbreviated Scale of General Intelligence, magnetic resonance spectroscopy and imaging, and metabolic control data from diagnosis.RESULTS -Type 1 diabetic subjects had lower verbal and full scale IQs than control subjects (both P Ͻ 0.05). Type 1 diabetic subjects had lower N-acetylaspartate in frontal lobes and basal ganglia and higher myoinositol and choline in frontal and temporal lobes and basal ganglia than control subjects (all P Ͻ 0.05). Type 1 diabetic subjects, relative to control subjects, had decreased gray matter in bilateral thalami and right parahippocampal gyrus and insular cortex. White matter was decreased in bilateral parahippocampi, left temporal lobe, and middle frontal area (all P Ͻ 0.0005 uncorrected). T2 in type 1 diabetic subjects was increased in left superior temporal gyrus and decreased in bilateral lentiform nuclei, caudate nuclei and thalami, and right insular area (all P Ͻ 0.0005 uncorrected). Early-onset disease predicted lower performance IQ, and hypoglycemia was associated with lower verbal IQ and volume reduction in thalamus; poor metabolic control predicted elevated myoinositol and decreased T2 in thalamus; and older age predicted volume loss and T2 change in basal ganglia.CONCLUSIONS -This study documents brain effects 12 years after diagnosis in a type 1 diabetic sample whose IQ at diagnosis matched that of control subjects. Findings suggest several neuropathological processes including gliosis, demyelination, and altered osmolarity. Diabetes Care 32:445-450, 2009

Family factors, particularly dynamic and communication factors such as parental over-involvement and adolescent-parent concordance on responsibility for diabetes care appear be important determinants of metabolic outcomes in adolescents with diabetes. However, family dynamic factors do not account for the substantial differences in metabolic outcomes between centres.