Cardiac autonomic neuropathy (CAN) has been suggested to be associated with hypoglycemia and impaired hypoglycemia unawareness. We have assessed the relationship between CAN and extensive measures of glucose variability (GV) in patients with type 1 and type 2 diabetes. Participants with diabetes underwent continuous glucose monitoring (CGM) to obtain measures of GV and the extent of hyperglycemia and hypoglycemia and cardiovascular autonomic reflex testing. Of the 40 participants (20 T1DM and 20 T2DM) (aged 40.70 ± 13.73 years, diabetes duration 14.43 ± 7.35 years, HbA1c 8.85 ± 1.70%), 23 (57.5%) had CAN. Despite a lower coefficient of variation (CV) (31.26 ± 11.87 vs. 40.33 ± 11.03, P = 0.018), they had a higher CONGA (8.42 ± 2.58 vs. 6.68 ± 1.88, P = 0.024) with a lower median LBGI (1.60 (range: 0.20-3.50) vs. 4.90 (range: 3.20-7.40), P = 0.010) and percentage median time spent in hypoglycemia (4 (range:4-13) vs. 1 (range:0-5), P = 0.008), compared to those without CAN. The percentage GRADE_Euglycemia (3.30 ± 2.78 vs. 5.69 ± 3.09, P = 0.017) and GRADE_Hypoglycemia (0.3 (range: 0 - 3.80) vs. 1.8 (range: 0.9-6.5), P = 0.036) were significantly lower, while the percentage median GRADE_{Hyperglycemia} (95.45 (range:93-98) vs. 91.6 (82.8-95.1), P = 0.013) was significantly higher in participants with CAN compared to those without CAN. CAN was associated with increased glycemic variability with less time in euglycemia attributed to a greater time in hyperglycemia but not hypoglycemia.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

A 35-year-old Qatari man presented to our hospital with a 4-month history of mild abdominal pain, weight loss, and jaundice. He was found to have central intra-abdominal mass and a single testis in the scrotum. His investigations showed cholestatic jaundice and very high level ofβ-HCG (1131379 IU/L). CT scans of the chest and abdomen showed a huge pelvic-abdominal mass with extensive retroperitoneal lymphadenopathy, in addition to liver and lung metastases. CT-guided Tru-Cut biopsy of the mass showed mixed germ cell tumor. Chemotherapy was refused by the patient and his family. In the following days, the patient bled from his liver metastases leading to hemorrhagic shock, hemorrhage from metastatic sites of choriocarcinoma containing tumors is named choriocarcinoma syndrome. He was transferred to the medical intensive care unit, where he was intubated and resuscitated. Embolization of the right hepatic artery was done, but failed to control the bleeding, which continued with development of disseminated intravascular coagulopathy and a severe abdominal compartment syndrome, and eventually the patient died.

Graves’ disease (GD) may display uncommon manifestations. We report a patient with rare complications of GD and present a comprehensive literature review. A 35-year-old woman presented with a two-week history of dyspnea, palpitations, and edema. She had a raised jugular venous pressure, goiter, and exophthalmos. Laboratory tests showed pancytopenia, a raised alkaline phosphatase level, hyperbilirubinemia (mainly direct bilirubin), and hyperthyroidism [TSH: 46.08 pmol/L (reference values: 2.6–5.7)]. Her thyroid uptake scan indicated GD. Echocardiography showed a high right ventricular systolic pressure: 60.16 mmHg. Lugol’s iodine, propranolol, cholestyramine, and dexamethasone were initiated. Hematologic investigations uncovered no reason for the pancytopenia; therefore, carbimazole was started. Workup for hepatic impairment and pulmonary hypertension (PH) was negative. The patient became euthyroid after 3 months. Leukocyte and platelet counts and bilirubin levels normalized, and her hemoglobin and alkaline phosphatase levels and right ventricular systolic pressure (52.64 mmHg) improved. This is the first reported single case of GD with the following three rare manifestations: pancytopenia, cholestatic liver injury, and PH with right-sided heart failure. With antithyroid drugs treatment, pancytopenia should resolve with euthyroidism, but PH and liver injury may take several months to resolve.

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment.