Nonclassical congenital adrenal hyperplasia: Metabolic and hormonal profile.

OBJECTIVE: To investigate both metabolic and hormonal profiles of untreated women with nonclassical congenital adrenal hyperplasia (NCCAH). The secondary objective was to compare above profiles with polycystic ovary syndrome (PCOS) women and healthy controls. DESIGN: Retrospective, case-control study. PATIENTS: Women assigned to one of the groups: (1) NCCAH (n = 216), (2) PCOS (n = 221), (3) regularly menstruating (n = 216). MEASUREMENTS: Lipid profile including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol along with both fasting glucose (Glu) and insulin (Ins) levels and hormonal parameters were determined among all participants. RESULTS: Both NCCAH and PCOS women had higher body mass index in comparison to the controls (+7% and 18.9%, respectively). NCCAH women exhibited higher TC (+34.1%) and fasting glucose levels (+18.9%) together with elevated testosterone (60.2%), dehydroepiandrosterone sulphate (28.1%), free androgen index (91.9%) and antimüllerian hormone (58%) in comparison to healthy controls. PCOS group showed unfavourably altered metabolic profile reflected by higher TC (+35.4%), TG (+25%), fasting Glu (+22%), fasting Ins (+34.4%) along with homoeostatic model assessment for insulin resistance (HOMA-IR; 36.2%) in comparison to the controls. NCCAH women showed both lower insulin (-28.5%) and HOMA-IR (-31.8%) levels when compared to the PCOS. CONCLUSIONS: NCCAH women showed less adversely altered metabolic profile than PCOS women, but not as favourable as in the healthy controls. Optimisation of screening for metabolic and reproductive health may help to initiate the treatment and improve treatment outcomes.

Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort.

BACKGROUND: To date, there has been no reliable in vitro test to either diagnose or differentiate nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD). The aim of the present study was to develop and validate an artificial neural network (ANN) for the prediction of N-ERD in patients with asthma. METHODS: This study used a prospective database of patients with N-ERD (n = 121) and aspirin-tolerant (n = 82) who underwent aspirin challenge from May 2014 to May 2018. Eighteen parameters, including clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant (ISS) and urine were extracted for the ANN. RESULTS: The validation sensitivity of ANN was 94.12% (80.32%-99.28%), specificity was 73.08% (52.21%-88.43%), and accuracy was 85.00% (77.43%-92.90%) for the prediction of N-ERD. The area under the receiver operating curve was 0.83 (0.71-0.90). CONCLUSIONS: The designed ANN model seems to have powerful prediction capabilities to provide diagnosis of N-ERD. Although it cannot replace the gold-standard aspirin challenge test, the implementation of the ANN might provide an added value for identification of patients with N-ERD. External validation in a large cohort is needed to confirm our results.