The Changing Face of Drug-induced Adrenal Insufficiency in the Food and Drug Administration Adverse Event Reporting System

Context: Adrenal insufficiency (AI) is a life-threatening condition complicating heterogeneous disorders across various disciplines, with challenging diagnosis and a notable drug-induced component. Objective: This work aimed to describe the spectrum of drug-induced AI through adverse drug event reports received by the US Food and Drug Administration (FDA). Methods: A retrospective disproportionality analysis reporting trends of drug-induced AI was conducted on the FDA Adverse Event Reporting System (FAERS) (> 15 000 000 reports since 2004). AE reports were extracted from FAERS over the past 2 decades. Interventions included cases containing any of the preferred terms in the Medical Dictionary for Regulatory Activities describing AI, and signals of disproportionate reporting for drugs recorded in 10 or more cases as primary suspect. Results: We identified 8496 cases of AI: 97.5% serious, 41.1% requiring hospitalization. AI showed an exponential increase throughout the years, with 5282 (62.2%) cases in 2015 to 2020. We identified 56 compounds associated with substantial disproportionality: glucocorticoids (N = 1971), monoclonal antibodies (N = 1644, of which N = 1330 were associated with immune checkpoint inhibitors-ICIs), hormone therapy (N = 291), anti-infectives (N = 252), drugs for hypercortisolism or adrenocortical cancer diagnosis/treatment (N = 169), and protein kinase inhibitors (N = 138). Cases of AI by glucocorticoids were stable in each 5-year period (22%-27%), whereas those by monoclonal antibodies, largely ICIs, peaked from 13% in 2010 to 2015 to 33% in 2015 to 2020. Conclusion: We provide a comprehensive insight into the evolution of drug-induced AI, highlighting the heterogeneous spectrum of culprit drug classes and the emerging increased reporting of ICIs. We claim for the urgent identification of predictive factors for drug-induced AI, and the establishment of screening and educational protocols for patients and caregivers

Identifying new potential biomarkers in adrenocortical tumors based on mrna expression data using machine learning

Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC

Sleep quality and sex-related factors in adult patients with immune-mediated diabetes: a large cross-sectional study

AimTo analyze sleep quality and its relationships with clinical and biochemical features in a large cohort of adults with autoimmune diabetes.MethodsWe administered to 553 patients with autoimmune diabetes the questionnaires: Pittsburgh Sleep Quality Index (PSQI), diabetes distress scale, diabetes-related quality of life and diabetes treatment satisfaction questionnaire. We excluded patients with missing HbA1c +/- 4 months from PSQI administration or incorrect PSQI compilation (n = 110).ResultsAltered sleep quality was recorded in 142/443 subjects (32%), insufficient total sleep time in 177/443 (40%). The altered sleep quality group had higher HbA1c (median 56 mmol/mol [interquartile range-IQR 49-62] vs 59 [IQR 52-68]; P < 0.001), higher average HbA1c in the previous 36 months (59 mmol/mol [IQR 54-68] vs 56 [IQR 51-62]; P < 0.001), and more individuals with HbA1c > 53 mmol/mol (74.6% vs 62.8%; P = 0.014). Diabetes duration (P = 0.63), type of insulin delivery (P = 0.48) and glucose monitoring (P = 0.35) were uninfluential. Patients with altered sleep quality showed higher prevalence of autoimmune (42 vs 28%; P = 0.005) and mental diseases (12 vs 4%; P = 0.002); there were greater emotional distress, and lower quality of life and treatment satisfaction (P < 0.001 for all), irrespective of sex. Men with altered sleep quality had higher HbA1c and prevalence of autoimmune diseases. Women showed greater prevalence of psychiatric disorders. Average HbA1c of the previous 36 months, autoimmune or psychiatric disorders were independent predictive factors for altered sleep quality.ConclusionOne-third of the patients with autoimmune diabetes showed altered sleep quality, which associates with worse glycemic control, and autoimmune and mental disorders, with sex-specific differences

Adrenal Insufficiency with Anticancer Tyrosine Kinase Inhibitors Targeting Vascular Endothelial Growth Factor Receptor: Analysis of the FDA Adverse Event Reporting System

Background: We described clinical features of adrenal insufficiency (AI) reported with tyrosine kinase inhibitors (TKIs) targeting vascular endothelial growth factor receptor (VEGFR) in the Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: Reports of AI recorded in FAERS (January 2004–March 2022) were identified through the high-level term “adrenal cortical hypofunctions”. Demographic and clinical features were inspected, and disproportionality signals were detected through the Reporting Odds Ratio (ROR) and Information Component (IC) with relevant 95% confidence/credibility interval (CI), using different comparators and adjusting the ROR for co-reported corticosteroids and immune checkpoint inhibitors (ICIs). Results: Out of 147,153 reports with VEGFR-TKIs, 314 cases of AI were retained, mostly of which were serious (97.1%; hospitalization recorded in 44.9%). In a combination regimen with ICIs (43% of cases), VEGFR-TKIs were discontinued in 52.2% of the cases (26% as monotherapy). The median time to onset was 72 days (IQR = 14–201; calculated for 189 cases). A robust disproportionality signal emerged, also in comparison with other anticancer drugs (ROR = 2.71, 95%CI = 2.42–3.04; IC = 0.25, 95%CI = 0.07–0.39). Cabozantinib, sunitinib and axitinib generated robust disproportionality even after ROR adjustment. Conclusions: We call pharmacologists, internists, oncologists and endocrinologists to raise awareness of serious AI with VEGFR-TKIs, and to develop dedicated guidelines, especially for combination regimens with immunotherapy

Microscopic correlations of non-Hermitian Dirac operators in three-dimensional QCD

In the presence of a non-vanishing chemical potential the eigenvalues of the Dirac operator become complex. We calculate spectral correlation functions of complex eigenvalues using a random matrix model approach. Our results apply to non-Hermitian Dirac operators in three-dimensional QCD with broken flavor symmetry and in four-dimensional QCD in the bulk of the spectrum. The derivation follows earlier results of Fyodorov, Khoruzhenko and Sommers for complex spectra exploiting the existence of orthogonal polynomials in the complex plane. Explicit analytic expressions are given for all microscopic k-point correlation functions in the presence of an arbitrary even number of massive quarks, both in the limit of strong and weak non-Hermiticity. In the latter case the parameter governing the non-Hermiticity of the Dirac matrices is identified with the influence of the chemical potential

Computerized tomography texture analysis of pheochromocytoma: relationship with hormonal and histopathological data

Objectives Pheochromocytomas are rare tumors which can present with heterogeneous secretion profiles, clinical manifestations, and radiologic appearance. Under a histopathological point of view, they can be characterized as more or less aggressive with the Pheochromocytoma of the Adrenal gland Scaled Score (PASS) and the Grading system for Adrenal Pheochromocytoma and Paraganglioma (GAPP) score. The aim of this study is to analyze the texture analysis characteristics of pheochromocytoma and identify whether the texture analysis can yield information aiding in the diagnosis and the characterization of those tumors. Methods Radiological, biochemical, and histopathological data regarding 30 consecutive patients with histologically confirmed pheochromocytoma were analyzed. Images obtained in the unenhanced, late arterial, venous, and delayed phases were used for the texture analysis. Results Urinary epinephrine and metanephrine levels showed a significant correlation (R-2 = 0.946; R-2 = 699) in the multivariate linear model with texture features, as well as Ki-67 (R-2 = 0.397), PASS score (R-2 = 0.182), GAPP score (R-2 = 0.705), and cellularity showed a significant correlation (R-2 = 0.389). The cluster analysis based on radiomic features resulted in 2 clusters, with significative differences in terms of systolic and diastolic blood pressure values at the time of diagnosis (p = 0.025), GAPP score (4 vs 6, p = 0.05), histological pattern (1-2, p = 0.039), and comedonecrosis (0% vs 50%, p = 0.013). Conclusion In conclusion, our study provides the proof of concept for the use of texture analysis on contrast-enhanced CT images as a noninvasive, quantitative tool for helping in the characterization of the clinical, biochemical, and histopathological features of pheochromocytoma

Rates of glycaemic deterioration in a real-world population with type 2 diabetes

Aims/hypothesis: There is considerable variability in how diabetes progresses after diagnosis. Progression modelling has largely focused on 'time to failure' methods, yet determining a 'coefficient of failure' has many advantages. We derived a rate of glycaemic deterioration in type 2 diabetes, using a large real-world cohort, and aimed to investigate the clinical, biochemical, pharmacological and immunological variables associated with fast and slow rates of glycaemic deterioration. Methods: An observational cohort study was performed using the electronic medical records from participants in the Genetics of Diabetes Audit and Research in Tayside Study (GoDARTS). A model was derived based on an individual's observed HbA(1c) measures from the first eligible HbA(1c) after the diagnosis of diabetes through to the study end (defined as insulin initiation, death, leaving the area or end of follow-up). Each HbA(1c) measure was time-dependently adjusted for the effects of non-insulin glucose-lowering drugs, changes in BMI and corticosteroid use. GAD antibody (GADA) positivity was defined as GAD titres above the 97.5th centile of the population distribution. Results: The mean (95% CI) glycaemic deterioration for type 2 diabetes and GADA-positive individuals was 1.4 (1.3, 1.4) and 2.8 (2.4, 3.3) mmol/mol HbA(1c) per year, respectively. A younger age of diagnosis, lower HDL-cholesterol concentration, higher BMI and earlier calendar year of diabetes diagnosis were independently associated with higher rates of glycaemic deterioration in individuals with type 2 diabetes. The rate of deterioration in those diagnosed at over 70 years of age was very low, with 66% having a rate of deterioration of less than 1.1 mmol/mol HbA(1c) per year, and only 1.5% progressing more rapidly than 4.4 mmol/mol HbA(1c) per year. Conclusions/interpretation: We have developed a novel approach for modelling the progression of diabetes in observational data across multiple drug combinations. This approach highlights how glycaemic deterioration in those diagnosed at over 70 years of age is minimal, supporting a stratified approach to diabetes management

New insights into the comorbid conditions of Turner syndrome: results from a long-term monocentric cohort study

Purpose Many questions concerning Turner syndrome (TS) remain unresolved, such as the long-term complications and, therefore, the optimal care setting for adults. The primary aim of this long-term cohort study was to estimate the incidence of comorbid conditions along the life course. Methods A total of 160 Italian patients with TS diagnosed from 1967 to 2010 were regularly and structurally monitored from the diagnosis to December 2019 at the University Hospital of Bologna using a structured multidisciplinary monitoring protocol. Results The study cohort was followed up for a median of 27 years (IQR 12-42). Autoimmune diseases were the comorbid condition with the highest incidence (61.2%), followed by osteoporosis and hypertension (23.8%), type 2 diabetes (16.2%) and tumours (15.1%). Median age of onset ranged from 22 years for autoimmune diseases to 39 years for type 2 diabetes. Malignant tumours were the most prominent type of neoplasm, with a cumulative incidence of 11.9%. Papillary thyroid carcinoma was the most common form of cancer, followed by skin cancer and cancer of the central nervous system. Only one major cardiovascular event (acute aortic dissection) was observed during follow-up. No cases of ischaemic heart disease, heart failure, stroke or death were recorded. Conclusions This cohort study confirms the need for continuous, structured and multidisciplinary lifelong monitoring of TS, thus ensuring the early diagnosis of important comorbid conditions, including cancer, and their appropriate and timely treatment. In addition, these data highlight the need for the increased surveillance of specific types of cancer in TS, including thyroid carcinoma

Unquenched QCD dirac operator spectra at nonzero baryon chemical potential

The microscopic spectral density of the QCD Dirac operator at nonzero baryon chemical potential for an arbitrary number of quark flavors was derived recently from a random matrix model with the global symmetries of QCD. In this paper we show that these results and extensions thereof can be obtained from the replica limit of a Toda lattice equation. This naturally leads to a factorized form into bosonic and fermionic QCD-like partition functions. In the microscopic limit these partition functions are given by the static limit of a chiral Lagrangian that follows from the symmetry breaking pattern. In particular, we elucidate the role of the singularity of the bosonic partition function in the orthogonal polynomials approach. A detailed discussion of the spectral density for one and two flavors is given

Corticotroph tumor progression after bilateral adrenalectomy (Nelson’s syndrome):systematic review and expert consensus recommendations

Corticotroph tumor progression (CTP) leading to Nelson's syndrome (NS) is a severe and difficult-to-treat complication subsequent to bilateral adrenalectomy (BADX) for Cushing's disease. Its characteristics are not well described, and consensus recommendations for diagnosis and treatment are missing