To develop and temporally validate a nationwide prediction model for cesarean delivery following induction or augmentation of labor in Japan.

We conducted a retrospective cohort study using the Japan Society of Obstetrics and Gynecology Perinatal Database. Women with singleton pregnancies who underwent induction or augmentation of labor at ≥37 weeks of gestation between 2020 and 2022 were included. Cases from 2020 to 2021 (n = 113 572) formed the development cohort, and cases from 2022 (n = 59 045) served as the temporal validation cohort. Predictors were selected based on clinical relevance. Variable selection used least absolute shrinkage and selection operator logistic regression with the one standard error rule, followed by multivariable logistic regression. Model performance was assessed with the area under the receiver operating characteristic curve (AUROC) and calibration plots.

A total of 172 617 women were included in the study. Thirteen predictors were selected: maternal age, height, pre-pregnancy body mass index (BMI), gestational BMI gain, multiparity, gestational age, assisted reproductive technology pregnancy, mechanical cervical ripening, pregestational diabetes mellitus, hypertensive disorders of pregnancy, epidural analgesia, birthweight, and neonatal sex. Discrimination was good in the development cohort (AUROC 0.757, 95% confidence interval [CI] 0.754-0.761) and temporal validation cohort (AUROC 0.767, 95% CI 0.762-0.772). Multiparity and epidural analgesia were associated with lower risk, whereas all other predictors increased cesarean risk (all P < 0.001).

This nationwide prediction model demonstrated robust performance and might support individualized counseling, risk assessment, and perinatal care planning.

Achieving rapid glycemic stability with intensive insulin therapy in hospitalized type 2 diabetes patients remains unpredictable. We hypothesized that underlying insulin resistance is a key determinant of this response. This exploratory study aimed to test this hypothesis and develop a predictive nomogram.

We retrospectively analyzed 393 hospitalized patients initiating insulin pump therapy with continuous glucose monitoring. Stability was defined as Time in Range >70% for 24 consecutive hours. Collected data included body mass index, hemoglobin A1c (HbA1c), fasting glucose, fasting insulin, lipids, and calculated insulin resistance indices. Cox regression identified predictors of time to stability.

Analysis confirmed insulin resistance's significant role. Multivariate Cox regression identified older age, higher fasting insulin, elevated HbA1c, and a higher triglyceride-glucose (TyG) index as independent risk factors for prolonged time to stability (all P<0.05), with the TyG index interpreted as a marker of lipotoxic metabolic inflexibility rather than merely a surrogate of insulin resistance. A nomogram integrating these factors demonstrated good predictive accuracy, with a C-index of 0.801 (95% CI: 0.77-0.83). Calibration and decision curve analysis supported its clinical utility.

Insulin resistance significantly influences time to glycemic stabilization. The developed nomogram, incorporating the TyG index, fasting insulin, age, and HbA1c, provides a practical tool for early risk stratification, potentially guiding more personalized inpatient diabetes management.

Diabetic retinopathy (DR) can significantly impair vision and lead to blindness. Vitamin D (VD) has been shown to enhance the production of anti-inflammatory factors, alleviating the effects of hyperglycemia. However, downstream genes and molecular networks associated with VD signaling in DR remain unidentified. This study aimed to employ a systems biology approach to nominate high-priority candidate genes and cellular contexts as a hypothesis-generating effort to facilitate future functional studies on the role of VD in DR.

DR-related datasets were obtained from public databases to identify differentially expressed genes (DEGs). Seven canonical VD metabolism-related genes (VDRGs) were subjected to weighted gene co-expression network analysis (WGCNA) to identify VD signaling-associated model genes. Candidate genes were selected based on the intersection of DEGs and model genes. "Boruta" and support vector machine-recursive feature elimination (SVM-RFE), along with expression validation, were used to screen for biomarkers. Further analyses included immune infiltration, gene set enrichment analysis (GSEA), regulatory network construction, and drug prediction. Single-cell RNA sequencing (scRNA-seq) was utilized to assess cellular heterogeneity, identifying distinct cell clusters and key cells based on gene expression profiles. Cell-cell communication within immune cells was also examined. Biomarker expression levels in clinical samples were validated through real-time reverse transcription polymerase chain reaction (RT-qPCR).

The biomarkers SLC36A1 and RAB23 were identified as VD signaling-associated downstream candidates and validated. GSEA revealed their primary association with glucose metabolism. B cells and CD4 T cells were identified as differentially expressed immune cells. Both biomarkers were regulated by a competing endogenous RNA (ceRNA) network, and the drug "methyl methanesulfonate" targeted both biomarkers simultaneously. Single-cell analysis identified 11 distinct cell types, including classical monocytes, B cells, and T cells. B cells and classical monocytes were identified as key cells due to the differential expression of biomarkers. The cell-cell communication network highlighted interactions, particularly between classical monocytes, B cells, and T cells. The differentiation of key cells and the stage of biomarker expression were also uncovered. RT-qPCR analysis revealed a significant upregulation of SLC36A1 and RAB23 in the DR group compared to controls (F = 5.184 p = 0.027 < 0.05; F = 4.147 p = 0.047 < 0.05).

SLC36A1 and RAB23 were identified as VD signaling-associated downstream biomarkers in DR, providing a framework for exploring the potential link between VD signaling and DR pathogenesis through these candidate genes.

Adrenal insufficiency is a life-threatening condition that often presents with non-specific symptoms, complicating diagnosis in elderly patients.

We report a case of a 77-year-old man with diabetes mellitus, hypertension and a history of cerebrovascular accident who presented with nausea, vomiting, weight loss and persistent giddiness. Laboratory tests revealed hyponatraemia and low serum osmolality. Further endocrine evaluation showed low morning cortisol, a suboptimal response to the short Synacthen test and suppressed adrenocorticotropic hormone levels confirming secondary adrenal insufficiency. The patient later disclosed recent use of a traditional Chinese medicine suggesting possible hidden glucocorticoid exposure and suppression of the hypothalamic-pituitary-adrenal axis. He improved after initiation of hydrocortisone replacement and discontinuation of the suspected products.

This case emphasises the need for greater awareness of the potential adulteration of traditional Chinese medicines with glucocorticoids. It also highlights the critical role of laboratory testing in diagnosing adrenal insufficiency, detecting hidden adulterants and recognising the limitations of immunoassays in interpreting adrenal function tests.

To evaluate the effects of multidisciplinary management in the Diabetes Reversal Clinic on the remission of type 2 diabetes mellitus (T2DM), and explore the predictive factors for remission of diabetes.

This was a real-world, single-arm observational study. Patients with T2DM who received remission-oriented treatment at the Diabetes Reversal Clinic and followed up regularly for more than 24 weeks were included. The primary outcome was the remission rate of T2DM, and the secondary outcomes were changes in fasting blood glucose (FBG), 2-hour postprandial blood glucose, HbA1c, blood lipids, and body composition. Differences in clinical characteristics between the remission and non-remission groups were analyzed. The multivariate logistic regression analysis was performed to screen predictive factors.

The remission rate was 29.24% (50/171) after multidisciplinary management in the Diabetes Reversal Clinic. The remission group was younger, had a shorter duration of diabetes, used fewer types of medications at baseline, had a lower FBG and HbA1c, a higher β-cell function index HOMA-β, and a lower triglyceride level. The multivariate logistic regression analysis revealed that age (OR = 0.93, 95% CI: 0.89-0.97, P = 0.002), type of medications at baseline (OR = 0.45, 95% CI: 0.26-0.77, P = 0.004), baseline FBG (OR = 0.64, 95% CI: 0.46-0.89, P = 0.008), and weight loss magnitude (OR = 1.15, 95% CI: 1.01-1.31, P = 0.038) were independent predictors of T2DM remission.

In this real-world study, the remission rate of T2DM patients who visited the Diabetes Reversal Clinic for more than 24 weeks was 29.24% (50/171). Younger age, shorter disease duration, fewer baseline medications, lower FBG and HbA1c, higher HOMA-β, lower triglycerides, and greater weight loss were associated with remission of T2DM. Among them, age, baseline medication type, baseline FBG, and weight loss were identified as factors independently associated with remission. However, due to the single-arm observational design, causality cannot be established, and further prospective controlled trials are required to confirm these findings.

Immune checkpoint inhibitors (ICIs) significantly improve prognosis and survival outcomes in cancer patients by enhancing immune function, thereby providing new therapeutic hope for cancer patients. However, with the widespread clinical application of ICIs, an increasing number of immune-related adverse events (irAEs) have been reported. Immune checkpoint inhibitor-induced type 1diabetes mellitus (ICI-T1DM) is a rare but potentially life-threatening irAE, usually presenting as acute onset and easily progressing to diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS), which poses a serious threat to patients' safety. This study reports a case of DKA in an 81-year-old female patient diagnosed with cervical squamous cell carcinoma without history of diabetes mellitus, which developed after multiple cycles of Cadonilimab. The patient's blood glucose levels were effectively controlled via insulin therapy and fluid resuscitation, and a definitive diagnosis of ICI-T1DM was confirmed. Taking this case as a starting point, this article reviews the epidemiology, clinical characteristics, pathogenesis, and clinical management strategies of ICI-T1DM, aiming to enhance clinicians' awareness of ICI-T1DM, especially the endocrine toxicity of dual-target ICIs such as cadonilimab, and provide practical reference for ensuring the safety of ICI therapy in cancer patients.

Hemoglobin A1c (HbA1c) is a critical biomarker used for the diagnosis and management of diabetes. However, nonglycemic genetic variations may affect the accuracy of HbA1c measurements.

We presented a clinical evaluation of a type 2 diabetic patient with an SLC4A1 (solute carrier family 4 member 1) gene mutation, characterized by high blood glucose and low HbA1c, and estimated the carrier frequency of SLC4A1 variants in Chinese population.

A 56-year-old patient with type 2 diabetes presented with a low HbA1c level, an elevated glycated albumin percentage (GA), normal hemoglobin and albumin levels, hemolysis, and increased red blood cell osmotic fragility. Exome sequencing revealed a heterozygous mutation in SLC4A1 gene (c.1239_1241del), which is associated with hereditary spherocytosis. Further research indicates that around 0.756% of individuals in China carry pathogenic or likely pathogenic SLC4A1 variants.

We report the SLC4A1 c.1239_1241del variant, which perturbs HbA1c via nonglycemic mechanisms, likely through a reduction in the erythrocyte lifespan, and similar variants may not be rare in Chinese population.

At the start of the COVID-19 pandemic, there were concerns that some antidiabetic medications might worsen outcomes, though anti-inflammatory properties suggested possible benefits. Many observational studies examined antidiabetic medications use and COVID-19 outcomes. Meta-analyses showed that insulin was linked to worse outcomes, while metformin, sodium-glucose cotransporter 2 (SGLT-2) inhibitors, and glucagon-like peptide-1 (GLP-1) agonists were associated with better outcomes. Findings on dipeptidyl peptidase-4 (DPP-4) inhibitors, pioglitazone, and sulfonylureas were mixed-showing neutral, beneficial, or negative effects. However, randomized controlled trials (RCTs) testing these medications after SARS-CoV-2 infection found no effect on COVID-19 outcomes, implying that their anti-inflammatory effects do not translate into meaningful clinical benefits during acute infection. This discrepancy prompts questioning what observational studies actually measured. Given that many studies applied robust statistical methods, their results are unlikely solely due to confounding or indication bias. We hypothesize that these studies reveal broader cardiovascular effects and illuminate diabetes management more than they inform COVID-19 pathology. Their findings align with current 2022 American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines for the management of type 2 diabetes mellitus endorsing metformin, SGLT-2 inhibitors, and GLP-1 agonists as first-line therapies, recommending cautious early insulin use, and reserving DPP-4 inhibitors, sulfonylureas, and pioglitazone for selective cases. This is applicable regardless of COVID-19 status. Further research should determine whether infection-related clinical endpoints, such as mortality or hospitalization from COVID-19 or other infections, might serve as valid surrogate markers for cardiovascular outcomes.

This study aimed to evaluate the clinical characteristics, symptom presentation, and management outcomes of patients diagnosed with paroxysmal supraventricular tachycardia (PSVT) at King Hamad University Hospital (KHUH), a tertiary care center. It examined the influence of demographics, comorbidities, and sex on treatment responses to pharmacologic and non-pharmacologic interventions, including cardioversion.

A retrospective observational study was conducted on 427 adult patients with PSVT (ICD-10: I47.1, confirmed by chart review) between January 2018 and early 2024. Data were extracted from the KHUH HOPE electronic medical records system. Descriptive statistics summarized baseline characteristics; chi-square tests examined categorical associations. Kaplan-Meier analyses for the cardioversion subgroup (n = 18) are presented as exploratory and descriptive only.

Females comprised 56.9% of the cohort (mean age 52.8 ± 15.2 years). Palpitations were the most common symptom (94.4%). Of 219 patients who received adenosine, 177 (80.8%) achieved cardioversion with the first dose, 25 (11.4%) with the second, and 17 (7.8%) with the third. Vagal maneuvers showed reduced success in patients with structural heart disease and diabetes mellitus. Electrical cardioversion was required in 18 patients (4.2%). Pacemaker implantation was documented in 3 patients (0.7%) for co-existing conduction disease, not as a PSVT treatment. Comorbidities significantly influenced outcomes: hypertension was associated with favorable vagal maneuver response, while heart failure and ischemic heart disease correlated with poor response across all modalities.

PSVT generally responds well to first-line treatments. However, individualized strategies are warranted for patients with structural heart disease or significant comorbidities. Observed sex-based and age-related response differences are descriptive and require multivariable validation.