Background:Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes mellitus (T2DM) post partum, with up to half of affected women progressing within a decade. Early identification of high-risk individuals is critical for implementing preventive interventions. Artificial intelligence (AI) offers enhanced predictive capabilities that can substantially enhance the prevention of postpartum diabetes. Objective:This systematic review and meta-analysis aimed to evaluate the performance of AI models in predicting the progression from GDM to T2DM or prediabetes. Methods:A total of 7 databases (MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) were systematically searched from inception through September 12, 2025, supplemented by backward and forward reference screening and biweekly alerts to capture newly published studies. This review included peer-reviewed English-language studies that applied AI algorithms to predict T2DM or prediabetes among women with previous GDM. Eligible studies focused on human participants; reported performance metrics (eg, accuracy, sensitivity, and specificity); and excluded non-AI models, animal studies, reviews, protocols, abstracts, and non-English publications. Moreover, 2 reviewers independently conducted study selection, data extraction, and risk of bias assessment using the PROBAST (Prediction Model Risk of Bias Assessment Tool)+AI tool. Pooled estimates were computed using random-effects meta-analysis models. Results:In total, 10 studies met the inclusion criteria, of which 8 were eligible for meta-analysis. The reviewed studies spanned from 2011 to 2025 and were conducted across 7 countries, predominantly in the United States (3/10, 30%). Most publications were journal articles (9/10, 90%), and retrospective designs (6/10, 60%) were slightly more common than prospective designs (4/10, 40%). AI models demonstrated high predictive performance for T2DM, with pooled accuracy of 0.85 (95% CI 0.79-0.90; prediction interval [PI] 0.64-0.98), sensitivity of 0.89 (95% CI 0.81-0.95; PI 0.63-1.00), specificity of 0.88 (95% CI 0.81-0.93; PI 0.67-0.99), F1-score of 0.80 (95% CI 0.75-0.85; PI 0.68-0.93), and area under the curve of 0.86 (95% CI 0.77-0.91; PI 0.54-0.97). However, AI performance for prediabetes prediction was modest (area under the curve=0.69, 95% CI 0.60-0.77). Subgroup analyses showed that random forest, decision tree, logistic regression, and naïve Bayes models performed comparably. Fasting plasma glucose and BMI were the most identified significant predictors in the included studies. Conclusions:AI models show potential in predicting T2DM after GDM. However, evidence remains limited by small sample sizes, high heterogeneity, lack of external validation, and high risk of bias. Our findings have important implications for digital health, supporting the integration of AI-driven risk prediction into electronic health record systems and postpartum care pathways to enable early identification, targeted prevention, and improved long-term outcomes. Future research should use large, diverse cohorts, integrate multidimensional data, adopt standardized reporting frameworks, and encourage open-access data sharing.

Background: Gestational diabetes mellitus (GDM), defined as hyperglycaemia first identified during pregnancy, is associated with increased risks of adverse maternal and neonatal outcomes. Standard screening is typically performed at 24–28 weeks’ gestation; however, earlier screening before 20 weeks has been proposed to improve outcomes. Evidence supporting early screening or diagnosis remains inconsistent. We aimed to evaluate whether early screening or early diagnosis of GDM (before 20 weeks) influences maternal and neonatal outcomes compared with standard screening or diagnosis at 24–28 weeks. Methods: We conducted a systematic review and meta-analysis of analytical studies comparing early versus standard timing of GDM screening or diagnosis. A systematic search of PubMed, Embase, Web of Science, and Cochrane CENTRAL was performed from inception to 2025. Eligible studies included those comparing early versus usual screening in unselected pregnant populations and/or early versus usual diagnosis among women with GDM; these comparisons were carefully predefined to minimize potential bias. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the MASTER scale. Pooled effects were generated using the quality-effects model. Publication bias was assessed using Doi plots. For binary outcomes, odds ratios were calculated and converted to absolute measures (risk differences and numbers needed to treat or harm) across a range of baseline risks to enhance interpretability. Outcomes assessed included gestational weight gain, preterm delivery, preeclampsia or eclampsia, macrosomia, neonatal hypoglycemia, neonatal intensive care unit admission, large-for-gestational-age infants, and caesarean delivery. Finding:14 studies met the inclusion criteria. Early diagnosis was not associated with a statistically divergent difference from usual diagnosis in terms of pregnancy-weight gain (WMD -1.08 kg), preterm delivery (OR 1.09), preeclampsia/eclampsia (OR 1.81), macrosomia (OR 0.97), PIH (OR 1.06), neonatal hypoglycemia (OR 1.61), NICU admission (OR 1.27), LGA (OR 0.98) and cesarean section (OR 1.18). Across the general obstetric population, early screening did not demonstrate a statistically divergent difference in any maternal or neonatal outcome including preterm delivery (OR 1.54), preeclampsia/eclampsia (OR 1.24), neonatal hypoglycemia (OR 1.19), Cesarean section (OR 0.87), macrosomia (OR 0.96) and LGA (OR 1.15). Translation of relative effects into absolute measures indicated no absolute clinical benefit for either early diagnosis or early screening. For early diagnosis, absolute risk differences were generally small and baseline-dependent, with directionally harmful effects for only few outcomes, particularly preeclampsia/eclampsia and neonatal hypoglycemia (NNH = 7 to 20), whereas most other outcomes demonstrated risk differences close to zero. Additionally, for early screening, absolute risk differences were largely neutral, with only preterm birth showing a small increase in absolute risk and all other outcomes indicating negligible differences. Interpretation: Early diagnosis of GDM does not identify a group with substantially different maternal and neonatal risks, likely reflecting a population for which intervention is unnecessary. Early screening, when applied to the general population of pregnant women, also does not improve outcomes and offers no absolute interventional benefit. Current evidence does

Objectives We explored the clinical practice of screening and managing hyperthyroidism and hypothyroidism during pregnancy in the Middle East and North Africa. Methods We used an online questionnaire based on clinical case scenarios to a regional physician database and invited those managing pregnant women with thyroid disease to respond. Results We analyzed 136 eligible responses. For a woman with newly diagnosed Graves' disease (GD) and wishing to conceive, 77.5% of the respondents would initiate antithyroid drugs (ATDs), while 20.3% would recommend definitive treatment with radioiodine or surgery. In the case of a relapsed GD before pregnancy, 84.3% preferred definitive treatment. For a woman with newly diagnosed GD during pregnancy, 39.4% will start propylthiouracil (PTU), 8.5% with methimazole/carbimazole, while 50.0% will start with PTU and then switch to methimazole after the first trimester. Respondents used several combinations of tests to monitor the dose of ATDs, and the thyroid test results they targeted were inconsistent, though nearly half of the respondents targeted achieving low serum thyroid-stimulating hormone (TSH) with free thyroxine (or total T4) in the upper end of the normal range. For a lactating woman with GD, 80.3% would give ATDs without stopping lactation. For the management of gestational thyrotoxicosis, 45.1% chose to follow-up, and 40.8% treated patients with PTU. Although the timing of TSH receptor antibody measurement in pregnant hyperthyroid patients was variable, 53% of respondents would check it at least once during pregnancy. The starting dose of L-thyroxine for a woman diagnosed with overt hypothyroidism in pregnancy, preconception management of euthyroid women with known thyroid autoimmunity, and approach related to ovarian hyperstimulation in women with thyroid peroxidase antibodies were widely variable. For women with known hypothyroidism, 34.6% of respondents would increase the L-thyroxine dose by 30 to 50% as soon as pregnancy is confirmed. Concerning screening, 42.7% of respondents perform universal evaluation and 70% recommend TSH < 2.5 mUI/L in the first trimester and TSH < 3 mUI/L in the second and third trimester as target results in known hypothyroid women. Conclusion Physicians' clinical practices regarding thyroid disorders in pregnant women vary. This highlights the need for focused training and quality assurance to achieve more consistent care.

The epidemiology and pathophysiology of AFLP, HELLP syndrome and infectious viral hepatitis in late pregnancy. Clinical presentation, diagnosis and management of these conditions. Complications (maternal and fetal) and how these can be managed. The role of a multidisciplinary team in management. To understand the pathophysiology of acute liver disorders in the third trimester, including the role of genetics. To understand the differences between AFLP, HELLP syndrome and other causes of acute liver disorders in late pregnancy and how their management differs. To understand the Swansea criteria for diagnosis of AFLP and how management within a multidisciplinary team can make a difference between survival and mortality. To provide management guidance to reduce the perinatal morbidity and mortality associated with acute liver disorders, including the need for liver transplantation. To understand the different types of acute viral hepatitis in late pregnancy, the differences between them and how they are managed, including the prevention of maternal‐to‐child transmission. Despite the extensive literature on liver disorders, are obstetricians well trained in recognising these in the third trimester, differentiating between the various types and providing adequate care for affected patients? Where is the ideal place to manage the pregnant woman with a liver disorder that may progress to hepatic failure? Should liver transplantation be an option for women with AFLP or HELLP syndrome who do not meet the transplantation criteria?

Context:In women with type 1 diabetes (T1D), the effects of glycaemic control on neonatal weight and the role of the placenta are not fully understood. Objective:This study explores the relationship between glycaemic control, neonatal weight, and placental weight. Design:A retrospective observational longitudinal study of pregnant women with T1DM. Setting:The study included 265 women with T1D. The target for the first A1c was set at ≤ 7.0%, while the target for the last A1c was ≤ 6.5%. The cohort was divided into four groups based on whether they achieved their target A1c (T) or had levels higher than the target (H) at each end. These groups were classified as Target-Target (T-T), Target-High (T-H), High-Target (H-T), and High-High (H-H). For the secondary objective, we included 154 women for whom placental weight data were available. Main outcome:We assessed the association between firstA1c, lastA1c, and neonatal weight, examining the mediation effect of the placenta. Results:The mean age of the participants was 29.4 years (SD 4.6), and the mean T1DM duration was 14.1 years (SD 7.1). The median neonatal weight was highest in the T-H group (3.56 kg) and lowest in the H-T group (3.20 kg) (p=0.009). FirstA1c was negatively correlated with neonatal weight (β-coefficient -150.9, p < 0.01), whereas lastA1c positively correlated (β-coefficient 162.5, p < 0.01). The association with firstA1c disappeared when correcting for placental weight, while lastA1c remained significant. The placenta mediates 65% of the impact of firstA1c on neonatal weight. Conclusions:Poor glycaemic control early in pregnancy is linked to lower neonatal birth weight, while poor control in the third trimester is associated with higher birth weight. These findings emphasize the importance of maintaining adequate glycaemic control before and during early pregnancy for better health outcomes.

The guidelines for managing prolactinomas during pregnancy are primarily based on retrospective evidence or expert opinion. A case-based questionnaire was sent to a convenience sample of endocrinologists (N = 116) and internists (N = 13) in the Middle East (N = 147) and North Africa (N = 33). Three cases of varying severity were presented, ranging from microprolactinoma to large macroprolactinoma compressing the optic chiasm. In the case of microprolactinoma, 86.7% of respondents would discontinue dopamine agonist (DA) medications when pregnancy is confirmed, 66.1% would discontinue serum prolactin measurement during pregnancy, and 95.4% would not request pituitary imaging routinely if no new symptoms developed. In contrast, only 20.0% would perform regular formal visual field (VF) testing throughout pregnancy. In the case of macroprolactinoma with no VF defect, 38.9% chose to discontinue DA therapy upon confirmation of pregnancy, 20.0% would either perform regular magnetic resonance imaging (MRI) during pregnancy or if serum prolactin were thought to be elevated out of proportion by clinical judgment, and 36.7% would not perform regular formal VF monitoring during pregnancy. In the management of macroprolactinoma with VF defect, 61.1% elected to continue DA therapy, whereas 33.9% considered referral for surgical excision as the treatment of choice. Note that 42.8% would perform regular MRIs during pregnancy, and 90.0% would perform regular formal VF monitoring. A survey of physicians revealed a diversity in managing prolactinomas during pregnancy, better education and regional adoption of the guidelines.

Aims:It is unknown if dysglycemia at 24-28 weeks of pregnancy is preceded by glycemic changes earlier in pregnancy. This study therefore examines the association between glucose excursion in the first 20 gestational weeks and the onset of gestational diabetes mellitus (GDM) in the early third trimester. Methods:A cohort study was conducted using data from the electronic medical record of the public health system in Qatar, and women with glycemic assessments done before 20 weeks and again in the early third trimester were assessed. The main outcome of the study was to examine glucose excursion (using Doi's weighted average glucose; dwAG) in early pregnancy to see if it was indicative of GDM diagnosis at the usual time. Results:At the upper normal cutoff for dwAG (6 mmol/L), the sensitivity and specificity were 71.5% and 54.1%, respectively, and the diagnostic odds ratio was ∼3, meaning that for women beyond this threshold before 20 weeks gestation, they had, on average, a 3-fold increase in odds of developing uGDM compared to women not meeting this threshold. Conclusions:It is concluded that early pregnancy glucose excursion remains in the normal range in women destined for third trimester GDM but is higher than that in those who do not develop GDM at this time and is a predictor of women at high risk early in pregnancy.

BACKGROUND:Gestational diabetes mellitus (GDM) is a common metabolic disorder characterized by hyperglycemia that is first detected during pregnancy, which is not overt diabetes. GDM poses a substantial risk for prenatal and postnatal adverse outcomes affecting both the mother and the offspring. These complications include, but are not limited to, fetal macrosomia, shoulder dystocia, respiratory distress, neonatal hypoglycemia, type 2 diabetes (T2D), and cardiovascular diseases. Screening for GDM typically occurs between 24 and 28 weeks of gestation, a timing that is considered late and may increase the risk of all the adverse outcomes associated with GDM. Treatment and prevention strategies are not standardized globally, may be suboptimal, and are often initiated after a diagnosis has been made. Therefore, our primary goal was to identify DNA methylation signatures specific to GDM to understand its underlying mechanisms. METHODS:We conducted genome-wide DNA methylation profiling for normal and GDM pregnant women across the three trimesters of pregnancy in the discovery cohort. DNA methylation levels were measured using the Infinium MethylationEPIC v2.0 BeadChip. Subsequently, our differentially methylated sites were validated in a second cohort. Furthermore, we performed downstream analyses, including KEGG pathway and Gene Ontology enrichment analysis, trait enrichment analysis, and gene expression regulation analysis for the validated differentially methylated sites identified in the second and third trimesters. RESULTS:In this study, we uncovered and validated new DNA methylation signatures that may significantly influence the expression of genes associated with GDM. Furthermore, we discovered new genes (RSL1D1, HOXD4, and MROH6) that may play a role in GDM and might be related to the risk of developing T2D and cardiovascular disease later in life. Trait analysis of the differentially methylated probes revealed that lifestyle and environmental factors are associated with the observed DNA methylation signatures in GDM. CONCLUSIONS:We conclude that DNA methylation changes during pregnancy might not fully explain GDM pathogenesis but can reflect population-specific environmental and behavioral factors before and during pregnancy. Some of these discovered CpG sites might regulate previously reported genes linked to GDM and diabetes, highlighting shared and distinct epigenetic mechanisms across populations.

Background:The physiological changes during Ramadan in people with type 2 diabetes (T2D) are not well described in the literature. However, advances in technology have created new frontiers to understand these changes. This study aims to understand the impact of Ramadan fasting on blood glucose excursion, vital signs, and physical activities in people with T2D who are on three or more antidiabetic medications. Methods:This prospective observational study was conducted at Hamad General Hospital, National Diabetes Centre, between February 1, 2020 and May 30, 2020 (covering three months before and including the month of Ramadan). We included people with T2D who were on three or more antidiabetic medications. Medications were adjusted during Ramadan based on international guidelines. Flash glucose monitoring and Fitbit devices were used to monitor glucose levels and physical activity. The primary outcomes were changes in time in range (TIR), time above range (TAR), and time below range (TBR) before and during Ramadan. Results:We included 18 patients with T2D, of whom 13 were males (72.2%). The mean age was 51.2 years (SD 7.4), the mean HBA1c was 7.8% (SD 1.0), and the mean duration of T2D was 12.5 years (SD 3.1). There were no significant changes in TIR, TAR, and TBR before and after Ramadan. There was no statistically significant difference in the TIR, TAR, and TBR during fasting hours and after iftar. However, the ambulatory glucose profile shows a reduction in glucose levels during fasting hours, reaching a nadir just before iftar, followed by a prolonged period of hyperglycemia post iftar. Physical activity levels decreased during fasting hours but increased approximately one hour before iftar. Multilinear regression analysis showed a positive correlation between engaging in vigorous physical activity and the TBR during fasting hours [β-coefficient (95% CI): 0.26 (0.07-0.45), p < 0.05]. Conclusion:Our findings show no significant changes in the overall glucose profile, except for prolonged post-iftar hyperglycemia. Intensive physical activity during fasting hours can increase the risk of hypoglycemia. This studyhighlights the need for further in-depth research to better understand the impact of lifestyle changes on blood glucose excursion during Ramadan.

Background Electrocardiography is one of the most valuable noninvasive diagnostic tools in determining the presence of many cardiovascular diseases. Genetic factors are important in determining ECG abnormalities and their link to cardiovascular diseases. Genome‐wide association studies and polygenic risk scores (PRSs) have been conducted for various ECG traits such as QT interval and QRS duration. However, these studies mainly focused on cohorts of European descent. Methods In this cohort study, genome‐wide association studies for 6 ECG traits (RR, PR, corrected QT interval [QTc], QRS, JT, and P wave duration) were conducted in a Middle Eastern cohort from the Qatar Precision Health Institute, comprising 13 827 subjects with whole‐genome sequence data. Middle Eastern PRSs were developed using clumping and thresholding, and their performance was compared with 26 published PRSs. Genetic predisposition to long QT syndrome was explored using rare variant analysis. Results Seventy‐four independent loci were obtained with genome‐wide significance across the 6 traits (P<5×10−8). Of the 74 loci, 67 (90.5%) were previously reported, and 7 loci (9.5%) were novel and contained 6 genes: STAC and CSMD1 for PR, ANK1 and NCOA2 for QRS, LSP1 for QTc, and MKLN1 for P wave duration. All 26 published PRSs showed good performance in our cohort. PGS002276 showed the best performance for QTc (R2=0.059, P=4.83×10−185), PGS002166 showed the best performance for QRS (R2=0.024, P=1.53×10−75), and PGS000905 showed the best performance for PR (R2=0.053, P=2.57×10−165). Some of these PRSs were associated with cardiovascular diseases. For example, PGS003500, a QTc PRS, was significantly associated with cardiomyopathy (odds ratio per 1 SD=1.58 [95% CI, 1.23–2.01]; P=2.42×10−4). Middle Eastern PRSs substantially outperformed published PRSs and did not perform well in the UK Biobank data. Ten pathogenic variants, including 3 that are specific to Qatari individuals, were observed in 17 long QT syndrome genes and were carried by 19 individuals. The QTc average was larger for mutation carriers (415.6±23.5 versus 402.3±18.5 in noncarriers). Five‐year follow‐up data did not show a significant change in ECG patterns, regardless of mutation status and PRS values. Four of 2302 individuals had prolonged QTc intervals over the 2 time points. Conclusions In this first genome‐wide association study for ECG traits in the Middle East using whole‐genome sequence data, 7 novel loci (6 genes) were identified. Published PRSs performed well, but newly developed Middle Eastern–specific PRSs performed the best. Novel variants in long QT syndrome genes were observed for the first time in Qatari individuals. Follow‐up data did not show significant changes in ECG patterns.

Background Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence of GDM is higher than in other parts of the world. Thus, there is a need for the early detection of GDM to avoid critical health conditions in newborns and post-pregnancy complexities of mothers. Methods In this article, we propose a machine learning (ML)-based techniques for early detection of GDM. For this purpose, we considered clinical measurements taken during the first trimester to predict the onset of GDM in the second trimester. Results The proposed ensemble-based model achieved high accuracy in predicting the onset of GDM with around 89% accuracy using only the first trimester data. We confirmed biomarkers, i.e., a history of high glucose level/diabetes, insulin and cholesterol, which align with the previous studies. Moreover, we proposed potential novel biomarkers such as HbA1C %, Glucose, MCH, NT pro-BNP, HOMA-IR- (22.5 Scale), HOMA-IR- (405 Scale), Magnesium, Uric Acid. C-Peptide, Triglyceride, Urea, Chloride, Fibrinogen, MCHC, ALT, family history of Diabetes, Vit B12, TSH, Potassium, Alk Phos, FT4, Homocysteine Plasma LC-MSMS, Monocyte Auto. Conclusion We believe our findings will complement the current clinical practice of GDM diagnosis at an early stage of pregnancy, leading toward minimizing its burden on the healthcare system.Source code is available in GitHub at: https://github.com/H-Zaky/GD.git

Objective: There are two types of criteria for diagnosing gestational diabetes mellitus (GDM). The first is based on measurement of three values on the glucose tolerance test (GTT) and making a diagnosis when any value is abnormal (individual time-point criterion). The second is based on creating a weighted average of the three values and using the average to split glycemic status into normal gestational glycemia (NGG), impaired gestational glycemia (IGG), gestational diabetes (GDM), or high-risk gestational diabetes (hGDM) (unified criterion). There is no information currently regarding how these two criteria relate to each other in the diagnosis of GDM. This study aimed to make this comparison.Design: Cross-sectional study.Setting: Publicly available data on a cohort of women in pregnancy.Participants: Pregnant women from the cohort.Methods: The cross-classification of diagnosis by two criteria was evaluated. The individual time-point criterion had a binary outcome (GDM yes/no), while the unified criterion had the four aforementioned outcomes.Results: Within the low risk (non-GDM) category by the individual time-point criterion, 1 in 85 women would have been deemed at high risk by the unified criterion. More importantly, within the high risk (GDM) category by the individual time-point criterion, 1 in 2 women would have been deemed at low risk by the unified criterion.Conclusion: The standard criterion is not equivalent to the unified criterion in terms of risk estimation. This is important as the unified criterion correlates with area under the GTT curve known to be associated with glucose excursion and is predictive of the net effect of insulin resistance and beta-cell function.

BACKGROUND:Artificial intelligence (AI) has emerged as a transformative tool for advancing gestational diabetes mellitus (GDM) care, offering dynamic, data-driven methods for early detection, management, and personalized intervention. OBJECTIVE:This systematic review aims to comprehensively explore and synthesize the use of AI models in GDM care, including screening, diagnosis, management, and prediction of maternal and neonatal outcomes. Specifically, we examine (1) study designs and population characteristics; (2) the use of AI across different aspects of GDM care; (3) types of input data used for AI modeling; and (4) AI model types, validation strategies, and performance metrics. METHODS:A systematic search was conducted across six electronic databases, identifying 126 eligible studies published up to February 2025. Data extraction and quality appraisal were independently conducted by six reviewers, using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for risk of bias assessment. RESULTS:Among 126 studies, 75% employed retrospective designs, with sample sizes ranging from 17 to over 100 000 participants. Most AI applications (85%) focused on early GDM prediction, while fewer addressed management, outcomes, or monitoring. Classical machine learning dominated (84%), with logistic regression and random forest frequently used. Internal validation was common (68%), but external validation was rare (6%). Our risk of bias appraisal indicated an overall moderate-to-good methodological quality, with notable deficiencies in analysis reporting. CONCLUSIONS:AI demonstrates strong potential to improve GDM prediction, screening, and management. Nonetheless, broader validation, enhanced model interpretability, and prospective studies in diverse populations are needed to translate these technologies into clinical practice.

ABSTRACT Introduction Many patients with Acute Coronary Syndrome (ACS) are newly diagnosed with Type 2 Diabetes Mellitus (T2DM) with very high hbA1c levels (> 10%). Early achievement of glycaemic control is of prime importance in such cases, and many guidelines recommend starting insulin together with oral anti‐diabetic drugs (OAD) as part of discharge medications. However, large numbers of treatment‐naïve patients are hesitant to use insulin due to various factors. Methods In this retrospective, single‐centre, observational study, we compared the hbA1c at 1‐year follow‐up between newly diagnosed DM patients with initial hbA1c > 10% who were discharged on insulin plus OAD versus those only on OAD after admission with ACS. Pairwise comparisons between continuous and categorical study variables were performed using t‐test, Mann–Whitney test, and chi‐square. We used STATA 18 for analysis. Baseline characteristics have been described for all the patients included in the study. In the analysis of outcomes at follow‐up, only patients who had follow‐up at 1‐year were included. Results Of 149 patients eligible for inclusion, the majority were males (97.3%). The mean age was 47 ± 8.3 years. The baseline hbA1c at diagnosis was 11.2 (10.5–12.3) %. 38 (25.5%) Were Discharged on insulin + OADs, whereas 111 (75.5%) Were Discharged Only on OADs. There was no statistically significant difference in change in hbA1c from baseline between the two groups (Mean (SD) 4.4% ± 1.8% vs. 4% ± 1.5%, p = 0.07). None of the patients had any hyperglycaemic emergency, and there were no differences in recurrent admissions due to cardiac indications (p = 0.5). Conclusion An anti‐DM regimen consisting of multiple oral agents is a safe and effective alternative to insulin plus OAD and can lead to a comparable reduction in hbA1c at 1‐year in patients who are not willing to use insulin early after diagnosis of T2DM.

Background Diabetes in pregnancy (DIP) is associated with adverse fetal and maternal outcomes. DIP is classified as either pre-existing or new-onset diabetes mellitus (DM), which is classified into gestational DM (GDM) and newly detected type 2 (N-T2D). All pregnant women in Qatar who are not known to have pre-existing DM are offered screening for DIP during the first antenatal care visit and after 24 weeks gestation. The study aims to report the DIP screening rates, the prevalence of DIP, and the impact of the universal screening program on adverse pregnancy outcomes. Methods This retrospective study included all women who gave birth in Hamad Medical Corporation (HMC) hospitals between 2019 and 2022. New-onset DIP was defined using the WHO-2013 criteria. The primary outcomes were the screening rates and the prevalence of DIP in Qatar. The secondary outcomes were the difference in preterm delivery, C-section, macrosomia, large for gestational age (LGA), small for gestational age (SGA), and intra-uterine fetal death (IUFD) between women with or without GDM. Findings We included 94,422 women who gave birth to 96,017 neonates (85.7%) out of 112,080 neonates born nationwide. The number of women with pre-existing diabetes was 2496 women. Of 91,926 eligible women, 77,372 (84.2%) were screened for DIP. The prevalence of GDM is 31.6% (95% CI: 31.3 - 32.0%); N-T2D is 2.2% (95% CI: 2.1 - 2.3%), and pre-existing Type 2 DM and Type 1 DM was 2.6% (95% CI: 0.8 - 3.0%) and 0.2% (0.19 - 0.25), respectively. Compared to the non-GDM group, women with GDM were older (30.8 +/- 5.3 versus 29.7 +/- 5.2 years, p < 0.001). After adjusting for age, women with GDM had lower risk of IUFD and SGA (0.63 [95% CI 0.50 - 0.80, p < 0.001], 0.88 [95% CI 0.84 - 0.92, p < 0.001] respectively) but higher risk of C-section and LFD (1.07 [95% CI 1.04 - 1.10, p < 0.001], 1.09 [95% CI 1.01 - 1.15, p = 0.01], respectively, compared to women with no-GDM. Interpretation Of the women eligible for screening, 84.2% were screened by the DIP program in Qatar. The prevalence of DIP in Qatar is 36.9%. Integrated care is critical for the screening and management of diabetes during pregnancy.

Rates of obesity are increasing world-wide with an estimated 1billion people projected to be obese by 2030 if current trends remain unchanged. Obesity currently considered one of the most significant associated factors of non-communicable diseases poses the greatest threat to health. Diabetes mellitus is an important metabolic disorder closely associated with obesity. It is therefore expected that with the increasing rates of obesity, the rates of diabetes in pregnancy will also be rising. This disorder may pre-date pregnancy (diagnosed or undiagnosed and diagnosed for the first time in pregnancy) or may be of onset in pregnancy. Irrespective of the timing of onset, diabetes in pregnancy is associated with both fetal and maternal complications. Outcomes are much better if control is maximised. Early diagnosis, multidisciplinary care and tailored management with optimum glycaemic control is associated with a significant reduction in not only pregnancy complications but long-term consequences on both the mother and offspring. This review brings together the current understanding of the pathogenesis of the endocrine derangements that are associated with diabetes in pregnancy how screening should be offered and management including pre-pregnancy care and the role of newer agents in management.

Over the years, several international guidelines have been developed by specialist organizations for the diagnosis of gestational diabetes mellitus (GDM). However, these guidelines vary and lack consensus on what level of glycemia defines GDM and worryingly, there is now evidence of over- or- under-diagnosis of women with GDM by current criteria. Towards this end, the National Priorities Research Program (NPRP) funded a program of research aimed at elucidating the problem with GDM diagnosis. It was determined, on completion of the project, that the solution required diagnosis of graded levels of dysglycemia in pregnancy and not just a diagnosis of presence or absence of GDM. A new diagnostic criterion (called the NPRP criterion) was created based on a single numerical summary of the three readings from the oral glucose tolerance test (GTT) that diagnosed women in pregnancy into four levels: normal, impaired, GDM and high risk GDM. This paper now examines existing GDM criteria vis-à-vis the NPRP criterion. It is noted that no significant change has happened over the years for existing criteria except for a gradual reduction in the threshold values of individual time-points or the number of time points, bringing us towards over-diagnosis of GDM in pregnancy. The new criterion unifies all readings from the GTT into one numerical value and, because it results in four levels of glycemia, represents a new way forwards for GDM diagnosis and can potentially reduce the rates of under diagnosis and over diagnosis of GDM.

ABSTRACTBackgroundAchieving and maintaining adequate glycaemic control is critical to reduce diabetes‐related complications. Therapeutic inertia is one of the leading causes of suboptimal glycaemic control.AimTo assess the degree of inertia in insulin initiation and intensification in people with Type 2 diabetes mellitus (DM‐2).MethodsWe performed a retrospective longitudinal cohort study and followed DM‐2 2 years before and 2 years after the start of insulin. The primary outcome was the proportion of patients who achieved glycaemic targets (HBA1c ≤ 7.5%) at 6th month, 1st year and 2nd year.ResultsWe included 374 predominantly male subjects (62%). The mean age was 55.3 ± 11.3 years, the mean duration of DM‐2 was 12.0 ± 7.3 years, 64.4% were obese, 47.6% had a microvascular disease, and 24.3% had a macrovascular disease. The mean HBA1c at −2nd year and −1st year was 9.2 ± 2.1% and 9.3 ± 2.0%, respectively. The mean HbA1C at the time of insulin initiation was 10.4 ± 2.1%. The mean HBA1c at 6th month, 12th month and 2nd year was 8.5 ± 1.8%, 8.4 ± 1.8% and 8.5 ± 1.7%, respectively. The proportion of subjects who achieved HBA1c targets at 6th month, 12th month and 2nd year was 32.9%, 31.0% and 32.9%, respectively. Multivariate logistic regression analysis showed that achieving HBA1c targets at 6th month and 1st year increases the odds of achieving HBA1c targets at 2nd year (OR 4.87 [2.4–9.6] p < 0.001) and (OR 6.2 [3.2–12.0], p < 0.001), respectively.ConclusionIn people with DM‐2, there was an alarming delay in starting and titrating insulin. The reduction in HBA1c plateaued at 6th month. Earlier initiation and intensification of insulin therapy are critical to achieving glycaemic targets. More studies are needed to examine the causes of therapeutic inertia from physicians', patients' and systems' points of view.

Abstract Purpose To analyze the prevalence and progression of fulminant type 1 diabetes (FT1D) in Qatar. Methods This retrospective study analyzed consecutive index- diabetic ketoacidosis (DKA) admissions (2015–2020) among patients with new-onset T1D (NT1D) in Qatar. Results Of the 242 patients, 2.5% fulfilled the FT1D diagnostic criteria. FT1D patients were younger (median-age 4-years vs.15-years in classic-T1D). Gender distribution in FT1D was equal, whereas the classic-T1D group showed a female predominance at 57.6% (n = 136). FT1D patients had a mean C-peptide of 0.11 ± 0.09 ng/ml, compared to 0.53 ± 0.45 ng/ml in classic-T1D. FT1D patients had a median length of stay (LOS) of 1 day (1-2.2) and a DKA duration of 11.25 h (11–15). The median (length of stay) LOS and DKA duration in classic-T1D patients were 2.5 days (1-3.9) and 15.4 h (11–23), respectively. The FT1D subset primarily consisted of moderate (83.3%) and severe 916.7%) DKA, whereas classic T1D had 25.4% mild, 60.6% moderate, and 14% severe DKA cases. FT1D was associated with a higher median white cell count (22.3 × 103/uL) at admission compared to classic T1D (10.6 × 103/uL). ICU admission was needed for 66.6% of FT1D patients, compared to 38.1% of classic-T1D patients. None of the patients in the FT1D group had mortality, while two died in the classic-T1D group. Conclusion This is the first study establishing the existence of FT1D in ME, which presented distinctively from classic-T1D, exhibiting earlier age onset and higher critical care requirements. However, the clinical outcomes in patients with FT1D seem similar to classic T1D.