Amiodarone-induced thyroid dysfunction in children: insights from the THYRAMIO study.

BACKGROUND: Amiodarone treatment is effective against various types of arrhythmias but is associated with adverse effects affecting, among other organs, thyroid function. Amiodarone-induced thyroid dysfunction was not thoroughly evaluated in children as it was in adults, yet this affection may lead to irreversible neurodevelopmental complications. Our study aimed to define the incidence and risk factors of amiodarone-induced thyroid dysfunction in children. METHODS: The study was designed as an observational study with a retrospective clinical series of 152 children treated by amiodarone in the Pediatric Cardiology Unit of our center from 1990 to 2019. All patients were divided into three groups according to their thyroid status: euthyroid, AIH (amiodarone-induced hypothyroidism) or AIT (amiodarone-induced thyrotoxicosis). Patients from these three groups were compared in terms of key clinical and therapeutic features. RESULTS: Amiodarone-induced thyroid dysfunction was present in 23% of patients. AIT (5.3%) was three times less common than AIH (17.7%), and its occurrence increased with older age (p < 0.05), treatment dosage (p < 0.05), treatment duration (p < 0.05) and the number of loading doses administered (p < 0.05). There were no distinctive clinical features between euthyroid and AIH groups. A multivariable prediction model of AIT was built, with a yield of 66.7% as positive predictive value and 96.7% as negative predictive value. CONCLUSION: We observed that one in five children developed amiodarone-induced thyroid dysfunction. Special attention is required for older children with a high dosage and long-term therapy and who received a large number of loading doses, since these children are at risk to develop AIT, which is more delicate to manage than AIH.

KATP channel blockers control glucagon secretion by distinct mechanisms: A direct stimulation of α-cells involving a [Ca2+]c rise and an indirect inhibition mediated by somatostatin.

OBJECTIVE: Glucagon is secreted by pancreatic α-cells in response to hypoglycemia and its hyperglycemic effect helps to restore normal blood glucose. Insulin and somatostatin (SST) secretions from ß- and δ-cells, respectively, are stimulated by glucose by mechanisms involving an inhibition of their ATP-sensitive K+ (KATP) channels, leading to an increase in [Ca2+]c that triggers exocytosis. Drugs that close KATP channels, such as sulfonylureas, are used to stimulate insulin release in type 2 diabetic patients. α-cells also express KATP channels. However, the mechanisms by which sulfonylureas control glucagon secretion are still largely debated and were addressed in the present study. In particular, we studied the effects of KATP channel blockers on α-cell [Ca2+]c and glucagon secretion in the presence of a low (1 mM) or a high (15 mM) glucose concentration and evaluated the role of SST in these effects. METHODS: Using a transgenic mouse model expressing the Ca2+-sensitive fluorescent protein, GCaMP6f, specifically in α-cells, we measured [Ca2+]c in α-cells either dispersed or within whole islets (by confocal microscopy). By measuring [Ca2+]c in α-cells within islets and glucagon secretion using the same perifusion protocols, we tested whether glucagon secretion correlated with changes in [Ca2+]c in response to sulfonylureas. We studied the role of SST in the effects of sulfonylureas using multiple approaches including genetic ablation of SST, or application of SST-14 and SST receptor antagonists. RESULTS: Application of the sulfonylureas, tolbutamide, or gliclazide, to a medium containing 1 mM or 15 mM glucose increased [Ca2+]c in α-cells by a direct effect as in ß-cells. At low glucose, sulfonylureas inhibited glucagon secretion of islets despite the rise in α-cell [Ca2+]c that they triggered. This glucagonostatic effect was indirect and attributed to SST because, in the islets of SST-knockout mice, sulfonylureas induced a stimulation of glucagon secretion which correlated with an increase in α-cell [Ca2+]c. Experiments with exogenous SST-14 and SST receptor antagonists indicated that the glucagonostatic effect of sulfonylureas mainly resulted from an inhibition of the efficacy of cytosolic Ca2+ on exocytosis. Although SST-14 was also able to inhibit glucagon secretion by decreasing α-cell [Ca2+]c, no decrease in [Ca2+]c occurred during sulfonylurea application because it was largely counterbalanced by the direct stimulatory effect of these drugs on α-cell [Ca2+]c. At high glucose, i.e., in conditions where glucagon release was already low, sulfonylureas stimulated glucagon secretion because their direct stimulatory effect on α-cells exceeded the indirect effect by SST. Our results also indicated that, unexpectedly, SST-14 poorly decreased the efficacy of Ca2+ on exocytosis in ß-cells. CONCLUSIONS: Sulfonylureas exert two opposite actions on α-cells: a direct stimulation as in ß-cells and an indirect inhibition by SST. This suggests that any alteration of SST paracrine influence, as described in diabetes, will modify the effect of sulfonylureas on glucagon release. In addition, we suggest that δ-cells inhibit α-cells more efficiently than ß-cells.

Tumor apelin and obesity are associated with reduced neoadjuvant chemotherapy response in a cohort of breast cancer patients.

Obesity is a known factor increasing the risk of developing breast cancer and reducing disease free survival. In addition to these well-documented effects, recent studies have shown that obesity is also affecting response to chemotherapy. Among the multiple dysregulations associated with obesity, increased level of the apelin adipokine has been recently shown to be directly involved in the association between obesity and increased breast cancer progression. In this study, we analyzed in a retrospective cohort of 62 breast cancer patients the impact of obesity and tumoral apelin expression on response to neoadjuvant chemotherapy. In the multivariate logistic regression, obesity and high tumoral apelin expression were associated with a reduced response to NAC in our cohort. However, obesity and high tumoral apelin expression were not correlated, suggesting that those two parameters could be independently associated with reduced NAC response. These findings should be confirmed in independent cohorts.

Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring.

BACKGROUND/AIMS: A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers. METHODS: We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden's J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods. RESULTS: The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity. CONCLUSIONS: The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial.

INTRODUCTION: Data quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials. METHODS: CSM with SMART™ uses advanced statistical tools that help identify centers with atypical data patterns which might be the sign of an underlying quality issue. This approach was used to assess the quality and consistency of the data collected in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, involving 1495 patients across 232 centers in Japan. RESULTS: In the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, very few atypical data patterns were found among the participating centers, and none of these patterns were deemed to be related to a quality issue that could significantly affect the outcome of the trial. DISCUSSION: CSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.