The roles of maternal serum lncRNA H19, miR-675, and PPARα protein levels before mid-pregnancy in predicting macrosomia remain unclear. This study aimed to investigate whether the expression of these serum molecules is associated with the risk of macrosomia in singleton pregnancies without gestational diabetes mellitus.

A nested case-control study was conducted within a prospective cohort study of 898 women with singleton pregnancies. Mothers of liveborn macrosomic newborns constituted the case group, and a random sample of mothers of the normal-birthweight newborns, matched on gestational age at blood collection and delivery date, served as controls. Serum levels of lncRNA H19, miR-675, PPARα protein, and serum lipids were measured before 20 weeks' gestation. Logistic regression, restricted cubic spline analysis, and stratified analysis were used to assess the associations. Predictive performance was explored using area under the receiver operating characteristic curve, net reclassification index (NRI), and integrated discrimination improvement (IDI).

No significant differences were observed in lncRNA H19 (Z =  - 0.344, P = 0.731), miR-675 (Z =  - 1.376, P = 0.169), or PPARα protein levels (Z < 0, P = 0.999) between macrosomia and control groups. However, in women with pre-pregnancy BMI < 24 kg/m², lower PPARα protein levels (tertile 2 vs. tertile 3) were associated with a 70% reduced risk of macrosomia (OR = 0.30, 95% CI [0.09-0.99], P = 0.049). The NRI and IDI of the combined model incorporating serum lncRNA H19, miR-675, and PPARα protein levels were statistically superior to lipid-based models (P < 0.05).

Serum lncRNA H19 and miR-675 were not associated with macrosomia risk. Lower serum PPARα protein levels in early pregnancy may be associated with a reduced risk of macrosomia, particularly in non-obese women. The combined biomarkers demonstrated preliminary predictive potential in exploratory analysis, but validation in larger cohorts is required.

Social drivers of health significantly influence diabetes and hypertension outcomes. By taking into account patients' social and economic circumstances, healthcare systems can enhance both the quality and efficiency of care delivery, leading to improved health outcomes. This study aims to assess the concordance between patient-level social drivers data gathered from a patient-reported, health-related social needs survey and the data documented in electronic health records. A comparative analysis was conducted among 165 adults diagnosed with coexisting hypertension and uncontrolled diabetes from a singular academic health system. Each participant engaged in a standardized assessment of health-related social needs survey, and the corresponding electronic health record-based social drivers of health data were extracted. Concordance at the patient level for social drivers of health was assessed using Cohen's Kappa and percent agreement. Overall, agreement between the patient-reported social needs survey and electronic health records data was low, indicating only slight alignment across various social drivers of health domains. These findings suggest that relying solely on electronic health records data may underestimate the true prevalence of patient-reported social needs in this high-risk cohort with diabetes and hypertension. To ensure high-quality care delivery, there is a critical need for healthcare systems to develop more effective and sustainable methods for capturing social drivers of health data.

Delayed insulin administration can lead to poor glycemic outcomes in patients with diabetes. Using EHR and BCMA data, we examined insulin administration patterns across different shifts and types of insulin, and the association between nurse staffing and delayed administration. We analyzed a total of 650 subcutaneous insulin administration events from 96 patients. We found that 42.0% (n=397) of the insulins had delayed administration during 7a-3p shift. Long-acting insulin (Lantus) (64.6%) had more delays than other types of insulin, suggesting that the pharmacokinetics properties of these insulins may influence how nurses prioritized their insulin administrations. We also found that higher patient-to-nurse ratio was associated with delayed insulin administration; however, we did not find nursing skill mix was associated with delays. Lastly, we found patients with delayed insulin administration had poorer glycemic control. Our study demonstrates the need for evidence-based staffing that enables nurses to deliver timely insulin administration during high-demands periods.

Hepatic fibrosis poses a significant health risk for young adults with type 2 diabetes (T2D). We propose FCFNets, a novel factual and counterfactual learning framework to predict hepatic fibrosis in young adults with T2D that can address class imbalance issue and increase interpretability leveraging electronic health records (EHRs). We designed a hybrid UNDO oversampling strategy, combining random and dissimilar oversampling that improves dataset diversity and model robustness. FCFNets also integrates SHAP-based global and instance-level explanations, alongside feature interaction analysis, providing insights into critical risk factors associated with hepatic fibrosis. The results show our proposed model outperforms various baseline methods with high sensitivity (0.846) and accuracy (0.768), while delivering counterfactual explanations. Hyperparameter tuning and dropout analysis further refine the model, ensuring optimal performance. This study demonstrates FCFNets's potential for early detection and personalized management of hepatic fibrosis, paving the way for interpretable AI applications in precision medicine.

In December 2022, the Washington Healthcare Forum Board and Washington Department of Health (DOH) senior leadership agreed to develop a plan for improving public health chronic disease surveillance in Washington state. Through a joint planning committee process, we created a plan for a cooperatively governed, technologically flexible, secure platform for sharing data. The approach, known as TRAX (Transformational Repository & Analytics eXchange), recognizes the importance of effective governance alongside health information exchange (HIE). TRAX governance partners identified priority conditions, diabetes and hypertension, to scope early projects (using anonymous patient-level longitudinal data). Leveraging public health HIE advances, like the eCR Now FHIR App, and the national Trusted Exchange Framework and Common Agreement (TEFCA) infrastructure, is reducing development time and administrative burden¹. Using nationally available standards and infrastructure, TRAX is an adaptable approach; a reproducible data sharing solution for cross-jurisdictional healthcare systems; and a potential national model for chronic disease surveillance.

Diabetes distress is prevalent among youth with type 1 diabetes mellitus (T1DM) and can negatively impact their quality of life and metabolic control. Identifying modifiable factors to reduce this distress is crucial. This study investigates the interplay between diabetes distress, self-management, and quality of life in school-aged children with T1DM amidst the COVID-19 pandemic. Its primary objective is to identify modifiable factors that can assist these children as they navigate the challenges associated with transitioning into adolescence.

A cross-sectional study with data from 341 Chinese school-age children aged 8-12 was conducted. Data were collected through an online self-report survey during the COVID-19 pandemic (June-December 2022). The data included sociodemographic and clinical characteristics, diabetes distress, diabetes care activities and diabetes problem solving of diabetes self-management and quality of life. Structural equation modeling assessed relationships and mediation effects.

All four domains of diabetes distress exhibited negative associations with quality of life (r = -0.74 to -0.77, p < 0.01). Care activities and problem-solving related to diabetes self-management mediated the associations of emotional burden and regimen-related distress with quality of life (both p < 0.05). Conversely, neither diabetes care activities nor diabetes problem-solving mediated the relationship between physician-related distress and quality of life.

Our findings indicate that problem-solving techniques related to diabetes self-management might be more effective at alleviating various aspects of diabetes distress-such as emotional burden, regimen-related distress, and interpersonal distress-compared to diabetes care activities. Interventions that teach structured problem-solving strategies could be beneficial. Given the ongoing pandemic, these findings could also serve as useful guidance for developing support strategies for school-age children with chronic conditions during future public health emergencies.

Cystic fibrosis-related diabetes (CFRD) is the most common comorbidity in cystic fibrosis (CF). After the introduction of modulator therapy, extended life expectancy and altered nutritional status may have changed the landscape of CFRD. This study aimed to evaluate the current CFRD management in Denmark.

In a nationwide, cross-sectional study, we included all individuals in Denmark diagnosed with CFRD, defined by insulin use, ≥2 abnormal OGTTs (2-h glucose ≥11.1 mmol/L), or elevated HbA1c (>48 mmol/mol). The cohort was identified using the Danish CF Registry, including all CF cases in Denmark. Health records were reviewed to validate diagnoses and extract data on treatment regimens and glycemic control.

We identified 151 people with CFRD, with a prevalence of 40% in adults with CF and 1% in adolescents. Among them, 7% used insulin pumps, 41% used basal-bolus treatment, 11% used only basal or mixed insulin, 6% used only bolus insulin, 33% did not use insulin or oral antidiabetics, and 2% used combinations or had missing data on treatment regimen. Median HbA1c was 48 mmol/mol, with 10% having HbA1c ≥ 70 mmol/mol. Continuous glucose monitoring (CGM) users (44%) had a median time in range of 70% and median time below range of 1%. No cases of severe hypoglycemia were recorded in the year before data collection.

In the Danish CFRD cohort glycemic control was generally good, although some remained severely dysregulated. While insulin remains the only antidiabetic treatment, the role of oral antidiabetics is likely to expand in the post-modulator era.

Sodium glucose co-transporter type 2 inhibitors (SGLT2i) were initially developed as glucose-lowering drugs for diabetic patients. A few years after their market authorization in Europe, their indications were expanded to include first, heart failure (HF) and, subsequently, chronic kidney disease (CKD). These expansions led to a rapid increase in the use of this drug class and a diversification of the treated patient profile in the "real-life."

Describe in-hospital SGLT2i user profiles and evaluate compliance with guidelines.

A descriptive cross-sectional study was conducted using the Bordeaux University Hospital's clinical data warehouse. It included a random sample of 250 hospital stays of different patients with at least one administration of SGLT2i between February 1, 2022, and January 31, 2023. SGLT2i user profiles were described in terms of indications. Drug co-prescriptions were also described to evaluate compliance with guidelines.

The majority of patients were aged 60-79 (59.6%), and were men (75.2%). HF was found in 87.2% of the patients treated with SGLT2i, followed by T2DM (45.2%) and CKD (21.2%). The most frequent indication profiles were HF without type II diabetes or CKD (42.0%) followed by HF and diabetes without CKD (26.0%). No patient had CKD as the sole indication. Prescriptions were considered compliant with guidelines for 76.4% of patients. Suboptimal prescriptions were mainly due to absence of another recommended drug without justification.

SGLT2i are now primarily used to treat HF. Their therapeutic potential in CKD appears to be still underestimated. Overall, compliance with guidelines appears satisfactory.

Sodium glucose co-transporter 2 (SGLT2) inhibitors were originally developed for the treatment of diabetes mellitus, however their approved use has expanded over recent years to include the treatment of heart failure and chronic kidney disease. This case series illustrates a spectrum of perioperative SGLT2 inhibitor related diabetic ketoacidosis (DKA), both euglycaemic and hyperglycaemic. We hope to highlight the challenges of both diagnosis and management of this condition.

Case 1: Euglycaemic DKA in a 54-year-old post-emergency laparotomy, which recurred following resumption of her SGLT2 inhibitor. Case 2: Euglycaemic DKA in an 82-year-old post-emergency laparotomy. Case 3: Hypergylcaemic DKA in a 52-year-old patient who underwent arthroscopic shoulder surgery as a day case. Case 4: Euglycaemic DKA in a 78-year-old post-emergency lower limb revascularisation.

These cases highlight that the use of SLGT2 inhibitors carries the risk of perioperative DKA which can be difficult to diagnose and requires prompt management.

As the indications for the use of these medications expand, we must maintain a high clinical suspicion for DKA with their perioperative use.

Synthetic data generation is a strategy used to address the lack and complex process to acquire clinical data and information, in particular in type 2 diabetes mellitus (T2DM) research. T2DM is characterized by chronic hyperglycemia with macrovascular and microvascular complications. Nevertheless, despite the importance of data to improve diagnostic accuracy, better treatments, and personalized patient care, medical datasets are often restricted by ethical and privacy constraints. In this sense, this chapter evaluates four synthetic data generation techniques, Gaussian Mixture Models (GMM), Generative Adversarial Networks (GAN), Wasserstein GAN (WGAN), and Variational Autoencoders (VAE). The quality of the generated data was assessed through statistical divergence metrics-specifically Jensen-Shannon (JSD) and Kullback-Leibler (KLD)and by analyzing their impact on classification performance. The results indicate that GMM achieved the lowest JSD, showing the best overall distributional similarity, while WGAN obtained the lowest KLD, suggesting a closer alignment in information content with real data. Additionally, GAN and WGAN demonstrated the highest predictive performance in classification tasks, indicating that they better preserved essential relationships within the data. These findings confirm that generative strategies of using synthetic data to improve T2DM research are feasible, offering an alternative to develop diagnosis tools without compromising patient confidentiality. It is possible to conclude that the generation method selection depends on the type of data and research objective, maximizing statistical similarity, optimizing performance, or balancing both aims. Synthetic data generation approaches represent a feasible approach to expand balanced and quality datasets to advance in personalized healthcare for diabetes patients.