ObjectiveTo compare pregnancy outcomes in patients with early versus usual gestational diabetes mellitus (GDM).DesignA retrospective cohort study.SettingsThe Women’s Hospital, Hamad Medical Corporation, Qatar.ParticipantsGDM women who attended and delivered in the Women’s Hospital, between January and December 2016. GDM was diagnosed based on the 2013-WHO criteria. The study included 801 patients; of which, 273 E-GDM and 528 U-GDM. Early GDM (E-GDM) and usual GDM (U-GDM) were defined as GDM detected before and after 24 weeks’ gestation, respectively.OutcomesMaternal and neonatal outcomes and the impact of timing of GDM-diagnosis on pregnancy outcomes.ResultsAt conception, E-GDM women were older (mean age 33.5±5.4 vs 32.0±5.4 years, p<0.001) and had higher body mass index (33.0±6.3 vs 31.7±6.1 kg/m2, p=0.0059) compared with U-GDM. The mean fasting, and 1-hour blood glucose levels were significantly higher in E-GDM vs U-GDM, respectively (5.3±0.7 vs 4.0±0.7 mmol/L, p<0.001 and 10.6±1.7 vs 10.3±1.6 mmol/L, p<0.001). More patients in the U-GDM were managed on diet alone compared with E-GDM (53.6% vs 27.5%, p<0.001). E-GDM subjects gained less weight per week compared with U-GDM (0.02±0.03 vs 0.12±0.03 kg/week, p=0.0274). Maternal outcomes were similar between the two groups apart from a higher incidence of preterm labour (25.5% vs 14.4%; p<0.001) and caesarean section (52.4% vs 42.8%; p=0.01) in E-GDM vs U-GDM, respectively. After correction for covariates; gestational age at which GDM was diagnosed was associated with increased risk of macrosomia (OR 1.06, 95% CI 1.00 to 1.11; p<0.05) and neonatal hypoglycaemia (OR 1.05, 95% CI 1.00 to 1.11; p<0.05).ConclusionOur data support the concept of early screening and treatment of GDM in high-risk patients. More data are needed to examine the optimal time for screening.

Psoriasis is characterized by hyperproliferation and defective differentiation of keratinocytes (KCs). Patients with psoriasis are at a high risk of developing diabetes and cardiovascular diseases. The debate on the genetic origin of psoriasis pathogenesis remains unresolved due to lack of suitable in vitro human models mimicking the disease phenotypes. In this study, we provide the first human induced pluripotent stem cell (iPSC) model for psoriasis carrying the genetic signature of the patients. iPSCs were generated from patients with psoriasis (PsO-iPSCs) and healthy donors (Ctr-iPSCs) and were efficiently differentiated into mature KCs. RNA sequencing of KCs derived from Ctr-iPSCs and PsO-iPSCs identified 361 commonly upregulated and 412 commonly downregulated genes. KCs derived from PsO-iPSCs showed dysregulated transcripts associated with psoriasis and KC differentiation, such as HLA-C, KLF4, chemokines, type I interferon-inducible genes, solute carrier family, IVL, DSG1, and HLA-DQA1, as well as transcripts associated with insulin resistance, such as IRS2, GDF15, GLUT10, and GLUT14. Our data suggest that the KC abnormalities are the main driver triggering psoriasis pathology and highlights the substantial contribution of genetic predisposition in the development of psoriasis and insulin resistance.

Background/Aims: Urinary biomarkers can identify damage to specific parts of the nephron. We performed a cross-sectional study to characterise the pattern of diabetic nephropathy using urinary biomarkers of glomerular fibrosis (collagen IV), proximal tubular damage (α-glutathione-S-transferase, GST) and distal tubular damage (πGST). Methods: Clinical data from 457 unselected patients attending a hospital diabetes clinic were collected. Spot urine samples were analysed for albumin and creatinine. Biomarkers were measured by enzyme-linked immunosorbent assay, and corrected to urinary creatinine. Results: All 3 biomarkers correlated weakly with albumin/creatinine ratios (Pearson correlation <0.2, p values <0.001). The most common abnormality was elevated urinary collagen IV (glomerular, 35%) compared to αGST (proximal tubule, 18%) or πGST (distal tubule, 15%). The proportion of patients with abnormal biomarker results increased across the normo-, micro- and macroalbuminuria groups, with collagen IV (26, 58, 65%) and πGST (11, 25, 35%) but not αGST. Conclusion: In patients with diabetes, these urinary biomarkers appear to identify renal damage that is related to, but distinct from, urine albumin/creatinine ratios. The markers of glomerular fibrosis and distal tubular damage related most closely to the degree of albuminuria. Longitudinal studies are now required to assess whether these biomarkers can detect early renal disease with greater specificity and sensitivity than the albumin/creatinine ratio.

BackgroundDiabetes first detected during pregnancy is currently divided into gestational diabetes mellitus (GDM) and diabetes mellitus (DM)- most of which are type 2 DM (T2DM). This study aims to define the prevalence and outcomes of diabetes first detected in pregnancy based on 75-gram oral glucose tolerance test (OGTT)using the recent WHO/IADPSG guidelines in a high-risk population.MethodsThis is a retrospective study that included all patients who underwent a 75 g (OGTT) between Jan 2016 and Apr 2016 and excluded patients with known pre-conception diabetes.ResultsThe overall prevalence of newly detected diabetes in pregnancy among the 2000 patients who fulfilled the inclusion/exclusion criteria was 24.0% (95% CI 22.1–25.9) of which T2DM was 2.5% (95% CI 1.9–3.3), and GDM was 21.5% (95% CI 19.7–23.3). The prevalence of newly detected diabetes in pregnancy was similar among the different ethnic groups.The T2DM group was older (mean age in years was 34 ±5.7 vs 31.7±5.7 vs 29.7 ±5.7, p<0.001); and has a higher mean BMI (32.4±6.4 kg/m2 vs 31.7±6.2 kg/m2 vs 29.7± 6.2 kg/m2, p< 0.01) than the GDM and the non-DM groups, respectively. The frequency of pre-eclampsia, pre-term delivery, Caesarean-section, macrosomia, LGA and neonatal ICU admissions were significantly higher in the T2DM group compared to GDM and non-DM groups.ConclusionDiabetes first detected in pregnancy is equally prevalent among the various ethnic groups residing in Qatar. Newly detected T2DM carries a higher risk of poor pregnancy outcomes; stressing the importance of proper classification of cases of newly detected diabetes in pregnancy.

gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i. Conclusion:XGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.