Gestational diabetes mellitus (GDM) is associated with increased maternal and fetal health risks, yet reliable predictive biomarkers remain poorly established. Acylcarnitines (ACs), key metabolites involved in fatty acid oxidation and energy metabolism, have been identified as potential early biomarkers for type 2 diabetes (T2D). However, their role in the metabolic alterations of GDM and pre-gestational diabetes mellitus (PGDM) during pregnancy remains largely unexplored.

This was a case-control study involving 75 pregnant women categorized into three groups: normal pregnancy (n = 25), gestational diabetes mellitus (GDM, n = 28), and pre-gestational diabetes mellitus (PGDM, n = 22). Plasma samples were collected during the second trimester for PGDM and third trimester for GDM and normal pregnancies. A targeted metabolomic approach using ultra-high-pressure liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-MS/MS) was employed to quantify 30 acylcarnitine species. Univariate analyses were performed using ANOVA or Kruskal-Wallis tests, and principal component analysis (PCA) was applied to identify underlying metabolic patterns while controlling for collinearity.

Women with GDM showed significantly higher median levels of C5 (0.105 vs. 0.075 µmol/L, P < 0.001), C16-OH (0.005 vs. 0.004 µmol/L, P = 0.040), and C18:1-OH (0.009 vs. 0.007 µmol/L, P = 0.012) compared to controls. In PGDM, C3 (0.441 vs. 0.279 µmol/L, P = 0.018) and C5 (0.142 vs. 0.075 µmol/L, P < 0.001) were markedly elevated. Notably, C0, C3DC, C5, C10:1, and C14 differed significantly between GDM and PGDM (P < 0.05 for all). Principal component analysis revealed that PC2 (driven by short-chain ACs) was strongly associated with PGDM (adjusted OR = 1.48, 95% CI: 1.14-1.91, P = 0.006), while PC3 (hydroxylated long-chain ACs) was linked to GDM (adjusted OR = 1.21, 95% CI: 1.01-1.44, P = 0.046).

Our findings reveal specific alterations in circulating acylcarnitine profiles in GDM and PGDM, highlighting their potential as novel metabolic indicators for screening, diagnosis, and management of diabetic pregnancies. Further studies are warranted to validate these metabolites as clinical biomarkers in larger cohorts.

The online version contains supplementary material available at 10.1007/s40200-026-01935-2.

Wolfram syndrome type 1 (WS1) is a rare autosomal recessive disorder involving diabetes mellitus, optic atrophy, and neurodegeneration, caused by biallelic WFS1 mutations. Though typically diagnosed in childhood, adult-onset cases may be missed due to variable symptom onset. We describe a 38-year-old woman with early-onset insulin-requiring, autoantibody-negative diabetes, progressive visual loss due to optic atrophy, bilateral sensorineural hearing loss, secondary amenorrhea with hyperprolactinemia, and arginine-vasopressin (AVP) deficiency. Magnetic resonance imaging (MRI) demonstrated atrophy of the optic nerves/chiasm and cerebellum. Genetic testing revealed a novel homozygous exon 8 WFS1 loss-of-function variant, which was classified as pathogenic. This case underscores the need for heightened clinical suspicion, imaging-based markers, and timely genetic testing to diagnose Wolfram syndrome in adult patients and to guide their management and eligibility for emerging trials.

The stress hyperglycemia ratio (SHR) and glycemic variability (GV) both reflect acute glycemic fluctuations, with established roles in cardiovascular and stroke diseases. However, their prognostic value in acute hyperlipidemic pancreatitis (HTG-AP) remains underexplored. Traditional assessments based on single measurements fail to capture the dynamic evolution. Therefore, this study aims to evaluate the early predictive value of Cumulative stress hyperglycemia ratio (CumSHR) and trajectory combined with GV in patients with hyperlipidemic pancreatitis.

This study collected data from 959 patients with HTG-AP. SHR and GV were calculated using standardized formulas; CumSHR was derived from the area under the curve (AUC) based on SHR data from the 7 days prior to admission. Multivariable analyses and RCS analysis assessed whether CumSHR predicted severe hyperlipidemic acute pancreatitis (HTG-SAP). Subgroup stratification was performed based on clinically distinct glucose metabolic states, while Latent class growth mixture model (LCGMM) identified dynamic SHR trajectory sub-phenotypes. Kaplan-Meier (K-M) curves compared survival rates across different risk trajectory groups. Finally, machine learning models predicted HTG-SAP risk, and SHapley Additive exPlanations (SHAP) identified key predictors.

In the Jiangxi cohort, 162 cases (16.9%) of HTG-AP developed HTG-SAP. RCS analysis demonstrated a U-shaped association between CumSHR and HTG-SAP and persistent organ failure (POF) (P < 0.001). The LCGMM identified three dynamic trajectories: the sustained high-value group (SHG-T3) exhibited the highest risk of HTG-SAP (55.6%, compared with 10.6% in the low-value gradually decreasing group [LDG-T1]). Subsequently, stratified into normal glucose regulation (NGR), pre-diabetes Mellitus (Pre-DM), and diabetes mellitus (DM), high CumSHR + high GV showed increased risk compared to low CumSHR + low LGV (P < 0.05). Among machine learning models predicting HTG-SAP risk, the Naive-Bayes model demonstrated the highest predictive accuracy. SHAP analysis identified CumSHR as one of the most important predictors.

This study demonstrates that CumSHR exhibits significant association with early assessment of severe conditions in patients with HTG-AP, with its trajectory effectively capturing dynamic changes to compensate for the limitations of static prediction.

The Endothelial Activation and Stress Index (EASIX) serves as a biomarker of endothelial dysfunction. Although EASIX is prognostic in critical illnesses such as sepsis, its specific association with mortality in critically ill patients with diabetes mellitus (DM) is undetermined. This study was designed to evaluate this relationship.

This retrospective study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. EASIX was calculated as lactate dehydrogenase (U/L) × creatinine (mg/dL) / platelet count (10⁹/L) and analyzed as log2-transformed values and by tertiles. The primary outcome was 30-day mortality, with 365-day mortality as a secondary outcome. Cox proportional hazards regression, Kaplan-Meier analysis, restricted cubic splines, subgroup analyses, and mediation analysis with lactate as a mediator were employed to evaluate the prognostic association. Sensitivity analyses, including propensity score matching, were performed to assess the robustness of the findings.

This study included 4,175 critically ill patients with DM. After full adjustment, each log2‑unit increase in the EASIX score was associated with a significantly increased risk of both 30‑day mortality (HR 1.20, 95% CI 1.16-1.24) and 365‑day mortality (HR 1.14, 95% CI 1.11-1.17). Mortality risk demonstrated a graded increase across ascending EASIX tertiles. A linear dose‑response relationship was confirmed, and this association remained consistent across most predefined subgroups. Propensity score matching analysis further supported this association. Mediation analysis indicated that lactate levels partially mediated the observed relationship between EASIX and mortality.

EASIX is independently associated with increased 30‑day and 365‑day mortality in critically ill patients with DM, an effect partially mediated by lactate.

Not applicable.

Gestational diabetes mellitus (GDM) is a common pregnancy complication linked to adverse outcomes for both mother and infant. Research on drug-related GDM remains limited.

This study aims to explore the risk factors and potential mechanisms of drug-related GDM using the FDA Adverse Event Reporting System (FAERS).

We analyzed FAERS data from the past, focusing on drugs potentially associated with GDM. Significant drug signals were identified using statistical reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayesian geometric mean (EBGM), bayesian confidence propagation neural network (BCPNN), and univariate logistic regression analysis. We used multivariate logistic regression to analyze independent risk factors, built a multivariate logistic regression model to predict GDM, and used the generalized variance inflation factor (GVIF) to evaluate the multicollinearity of the predictive factors in the model. Furthermore, we performed gene target prediction, functional enrichment analysis, and protein-protein interaction (PPI) analysis to explore the mechanisms involved, and assessed drug-genes interactions through molecular docking simulations.

By analyzing 1137 cases of potentially drug-related GDM, we identified 12 drugs as potential independent risk factors for GDM. We constructed a multivariate logistic regression model incorporating drug and patient characteristics to predict GDM, with an area under the curve (AUC) of 0.847. GVIF analysis confirmed that there was no multicollinearity among the predictors. Potential target genes of antipsychotic drugs are mainly enriched in cellular response to dopamine, phospholipase C-activating G protein-coupled receptor signaling pathway, and blood circulation. After integrating the target gene group and the insulin receptor signaling pathway gene set, the potential target genes have extensive connections with the insulin receptor signaling pathway, with DRD2 and KCNH2 being hub genes in the PPI network.

This study identified 12 drugs that may be independent risk factors for GDM, including quetiapine, aripiprazole, olanzapine, prednisolone, venlafaxine, risperidone, escitalopram, clozapine, mirtazapine, ziprasidone, rosuvastatin, and trazodone. The established predictive model has certain clinical value, and the potential mechanisms by which antipsychotic drugs may lead to GDM were further analyzed. These results provide potential clues for the early identification of GDM and early warning of suspected drugs, and offer a reference for further exploration of its pathogenesis.

X-linked immune dysregulation, polyendocrinopathy, and enteropathy (IPEX) syndrome is a rare X-linked disorder caused by mutations in the forkhead box P3 (FOXP3) gene. It typically presents very early in life with the classic triad of intractable diarrhea, type 1 diabetes mellitus, and dermatitis. Kidney involvement has been reported in a substantial minority of patients with IPEX syndrome, with membranous nephropathy being the most frequent biopsy pattern.

In this case report, we describe a 15-year-old boy who developed type 1 diabetes mellitus at age one year and later developed difficult-to-control asthma, eczema-like skin rashes, and food allergies. At nine years of age, he was noted to have increasing creatinine levels and proteinuria. Kidney biopsy revealed overlapping features of membranous and immunoglobulin A (IgA) nephropathy. Genetic testing identified a hemizygous pathogenic variant in FOXP3 (c·1040 G > A, p.Arg347His), establishing a diagnosis of IPEX syndrome. Despite supportive management, his kidney function continued to decline, progressing to stage V chronic kidney disease, and he is now awaiting hematopoietic stem cell transplantation. This case highlights an unusual kidney presentation of IPEX syndrome.

The coexistence of membranous and IgA nephropathy may have contributed to the rapid progression of the disease. Clinicians should consider IPEX syndrome in children with kidney disease accompanied by autoimmune endocrinopathies or allergic features, even if the classic gastrointestinal involvement is missing.

Previous studies have found that the relationship between changes in BMI and renal function outcomes remains unclear, with both increases and decreases in BMI being associated with a decline in eGFR, especially in the elderly hip fracture population. Therefore, this study aimed to investigate the association between BMI and eGFR upon hospital admission in elderly patients with hip fractures, to better understand the underlying pathophysiological connections and provide a basis for clinical patient management.

We conducted a retrospective cross-sectional study involving 1,417 elderly patients with hip fractures, collecting clinical data such as BMI and eGFR at admission. Generalized additive models, smooth curve fitting, and two-piecewise linear regression models were used to explore the linear and non-linear relationships between BMI and eGFR.

A total of 1,417 patients with hip fractures were included, with females accounting for 73.04%. The mean age of the patients was 77.57 ± 8.73 years. The mean eGFR was 80.23 ± 21.76 mL/min/1.73 m², and the mean BMI was 22.04 ± 3.25 kg/m². After adjusting for age, sex, fracture classification, number of comorbidities, smoking, hypertension, coronary heart disease, diabetes mellitus, white blood cell, lymphocyte, neutrophil, hemoglobin, red cell distribution width, albumin and globulin, a significant correlation between BMI and eGFR was still observed (β: -0.72, 95% CI: -1.04 to -0.40). Trend analysis after categorizing BMI into four groups showed that the decrease in eGFR increased progressively with increasing BMI. Non-linear analysis revealed an inflection point at a BMI of 27.5 kg/m². To the left of the inflection point, for every 1 kg/m² increase in BMI, eGFR decreased by 0.91 mL/min/1.73 m², while no correlation was found to the right of the inflection point.

There is a non-linear relationship between BMI and eGFR at admission in elderly patients with hip fractures, with a clear inflection point. This suggests that BMI should be considered an indicator for renal function risk assessment when managing these patients, and that targeted renal protective measures should be considered for patients in different BMI ranges.

Artemisia monosperma Delile (Asteraceae) is traditionally used for gastrointestinal issues and diabetes. This study aimed to characterize its phenolic profile and evaluate the molecular basis for its antioxidant, anti-inflammatory, and antidiabetic properties. The defatted 80% aqueous methanol extract (DAME) of A. monosperma aerial parts was profiled by HPLC-HRMS to identify phenolic compounds. Antihyperglycemic activity was tested via the Sucrose Loading Model (SLM) and a Type 2 diabetes model. Anti-inflammatory effects were measured in LPS-stimulated RAW264.7 cells by TNF-α and CRP gene expression. Wound-healing potential was evaluated in BJ cells, and antioxidant activity was assessed against superoxide, DPPH, and NO radicals. Molecular docking was used to examine the binding of the identified metabolites to targets, including inducible nitric oxide synthase (iNOS) and sulfonylurea receptor 1 (SUR1). The HRHPLC/MS analysis of the DAME of A. monosperma tentatively identified 25 secondary metabolites in the negative mode, including twelve phenolic acid derivatives, eight flavonoid compounds, and five miscellaneous terpenes. The DAME exhibits potent antihyperglycemic activity, with a 300 mg% dose reducing blood glucose levels by approximately 26% in a Type 2 diabetes model, comparable to diamicron. It also shows strong anti-inflammatory effects, reducing TNF-α and CRP gene expression by 66% and 82%, respectively, in LPS-stimulated RAW264.7 cells. Furthermore, it enhances wound healing, achieving approximately 79% closure in treated BJ cells compared to 58% in controls, and exhibits antioxidant activity. The identified library of compounds 1-25 was virtually screened against (iNOS) and (SUR1) to assess their binding affinities for each target. The binding modes of the compounds with the lowest energy scores for each were investigated. Different compounds demonstrated stronger affinities to each target than the co-crystallized ligand, among which compounds 4, 9, 11, and 14 possessed the lowest binding energies to both. A. monosperma DAME contains a variety of phenolic compounds and shows a potent multi-functional agent possessing anti-diabetic, anti-inflammatory, wound healing, and antioxidant activities, suggesting its therapeutic potential. However, more research is necessary to confirm its safety for clinical application.

Melatonin (MT) can regulate the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), but its effect on the osteogenic differentiation of BMSCs under high glucose (HG) conditions is unclear. Therefore, in this study, the effect of MT on the osteogenic differentiation of BMSCs under HG conditions was investigated.

A mouse model of diabetic osteoporosis (DOP) was induced by the intraperitoneal injection of streptozotocin (STZ), and macrophages or BMSCs were cultured with 25 mM glucose to construct an in vitro cell model. Different doses of MT were used to treat the mice or cells. Genes and proteins were assessed through RT‒qPCR and Western blotting. ALP staining, alizarin red staining, and HE staining were used to assess the osteogenic differentiation of BMSCs and the advancement of DOP in mice.

Under normal conditions, MT could increase the expression of osteogenic differentiation-related proteins RUNX2, OCN, and OPN, and enhance differentiation and mineralization levels in BMSCs; however, MT failed to stimulate osteogenic differentiation in BMSCs under HG conditions. Furthermore, regardless of whether under HG conditions, in macrophages, MT suppressed the expression of the M1 phenotype markers CD86, iNOS, and CCR7 while increasing the expression of the M2 phenotype markers CD206, Arg1, and Ym1. Subsequent experiments revealed that under HG conditions, MT indirectly promoted the osteogenic differentiation of BMSCs through the enhancement of the M2 polarization of macrophages; however, MT was unable to directly influence the osteogenic differentiation of BMSCs. Additionally, in mouse experiments, administering high doses of MT effectively mitigated DOP by lowering blood glucose levels, ameliorating pathological damage in femoral tissues, and enhancing collagen accumulation and osteogenic markers expression. From a mechanistic standpoint, MT triggered autophagy by counteracting the suppressive effect of HG on NRF2, thus reducing HG-triggered ROS generation and inflammation in macrophage, promoting the M2 polarization of macrophages, and mitigating the suppressive effect of HG on the osteogenic differentiation of BMSCs.

Our study indicates that under HG conditions, MT improves osteogenic differentiation by regulating the crosstalk between M2 macrophages and BMSCs.

The Chinese translation of "carbohydrate" has long been a topic of considerable debate in chemistry, biomedicine, and nutrition-related disciplines. This issue is not merely linguistic. In Chinese-language contexts, inconsistency among carbohydrate-related expressions may create ambiguity in nutrition education and public understanding, and may introduce practical challenges for literature retrieval and interdisciplinary collaboration, especially in fields such as type 2 diabetes mellitus (T2DM), where distinctions among dietary carbohydrates, sugars, and glucose could be crucial. This article traces the historical evolution of the Chinese translation of "carbohydrate" to clarify its historical trajectory and scientific implications. Historical evidence demonstrates that the term "carbohydrate" did not appear in dictionaries or chemistry books published prior to 1900. However, at the turn of the 20th century, multiple translations emerged, most of which were influenced by the Japanese term "tansuikabutsu/." The earliest recorded Chinese translation appeared in Huaxue Yuanliu Lun. During the early Republic of China, "tanshui huawu/" became the most commonly used term, which was later revised around 1920 with the addition of a semantic radical to the character "tan." In 1932, the National Institute for Compilation and Translation introduced the term "tang/," which gained popularity alongside "tanshui hua(he) wu." However, "tang" was officially abolished in the mid-to-late 1950s and gradually phased out in subsequent decades. By 1980, "tanshui huahe wu/)" and "tang lei/" were officially established as equivalent translations. Currently, "tang lei" is preferred in some disciplinary standards, although "tanshui huahe wu" remains widely used by convention. By reviewing this history, the present work highlights three key principles for addressing terminological ambiguity in nutrition communication. While this historical narrative is anchored in the Chinese context, the communication risks and mitigation strategies discussed might be relevant to other cross-lingual or cross-disciplinary setting, where everyday dietary language interfaces with technical biomedical terminology.