Differentiating diabetes subtypes is essential for long-term management; however, overlapping features among type 1 diabetes (T1D), type 2 diabetes (T2D), and maturity-onset diabetes of the young (MODY) complicate diagnosis. Clinical characteristics and temporal trends according to childhood-onset diabetes subtype were investigated over a 20-year period.

We retrospectively reviewed 662 patients diagnosed with T1D, T2D, or MODY at Seoul National University Children's Hospital between 2003 and 2022 at ages ≥6 months and <19 years, excluding secondary and other specific forms. Subtypes were classified based on autoantibody status, insulin dependence, adiposity, family history, and genetic testing.

Of patients, 477 (72.1%) had T1D, 143 (21.6%) had T2D, and 39 (5.9%) had MODY; 3 had coexisting T1D and MODY. Ten patients underwent diabetes subtype reclassification during follow-up. T1D was associated with more frequent diabetic ketoacidosis (DKA) and less family history than T2D or MODY (both P < 0.001). T2D showed higher BMI z-scores and a greater prevalence of overweight and obesity (both P < 0.001). The proportion of T2D increased over time (P for trend < 0.001), whereas MODY remained stable (P for trend = 0.116). Among 21 genetically confirmed MODY cases, GCK variants were the most common, followed by HNF1A variants.

Over the 20-year study period, the proportion of T2D increased among children and adolescents. Expanded insurance coverage for autoantibody testing, along with more affordable access to next-generation sequencing (NGS) panels when clinically indicated, may facilitate earlier and more accurate diagnosis and enable individualized treatment.

The Diabetes Remission Clinical Trial (DiRECT) demonstrated that remission of type 2 diabetes can be achieved through substantial weight loss using a total diet replacement (TDR) intervention combined with structured support. Whether this approach can be delivered exclusively via eHealth has not been well studied.

This pilot study aimed to evaluate whether a TDR intervention delivered solely via eHealth could induce clinically meaningful weight loss and achieve type 2 diabetes remission.

This was a single-arm, nonblinded, single-center pilot study conducted at the clinical research facilities of Umeå University Hospital. Individuals with type 2 diabetes diagnosed within the past 6 years were included. Participants followed a TDR (~850 kcal/d) for 12 weeks, and then gradually reintroduced food, with a total study duration of 6 months. Diabetes medications were discontinued at the start of the study. Daily body weight and fasting glucose were measured at home and transmitted to the study team via connected devices. All clinical support was delivered through video calls and chat. The 2 prespecified coprimary outcomes were a reduction in weight of 15 kg or more and remission of diabetes, defined as glycated hemoglobin below 6.5% without glucose-lowering medication, from baseline to 6 months. A 75 g oral glucose tolerance test and indirect calorimetry were performed at baseline and at 6 months to assess resting energy expenditure (REE) and respiratory quotient (RQ).

Ten participants (mean age 58.5, SD 7.4 y; mean BMI 35.3, SD 5.9 kg/m²; n=9 men and n=1 woman) were enrolled. After 6 months, 6 of 10 participants achieved the prespecified coprimary outcome of ≥15 kg weight loss, and all 10 participants achieved the prespecified coprimary outcome of diabetes remission. Mean weight loss was 16.9 (SD 5.4; P=.005) kg, and glycated hemoglobin decreased by 1.0% (SD 0.8%; P=.008). Daily home measurements showed a marked reduction in fasting glucose within the first week of the intervention. Median REE decreased by 19% (IQR 17%-22%), while median fasting RQ increased by 15% (IQR 5%-27%) and median post-glucose-load RQ increased by 12% (IQR 9%-18%) at 6 months.

In this pilot study, an eHealth-delivered TDR intervention was associated with substantial weight loss and diabetes remission at 6 months without the use of glucose-lowering medication. These findings support further evaluation of fully eHealth-delivered dietary interventions in larger and longer-term randomized controlled trials.

The mechanisms underlying type 2 diabetes-associated cognitive dysfunction (DACD) remain poorly understood, hindering therapeutic progress. Here, we integrated spatial transcriptomics and single-nucleus RNA sequencing to delineate the spatiotemporal molecular and cellular landscape of DACD in brain tissues from 3- and 6-mo-old mouse models. Our findings revealed region- and cell-type-specific transcriptomic alterations, with excitatory neurons in the hippocampus and isocortex emerging as the most severely affected populations with pronounced synaptic dysfunction. Further analysis of these two regions identified disease-associated transcription factors, such as Rfx3 and Mef2c. In parallel, we uncovered multiple ligand-receptor pairs, including Hsp90b1-Lrp6 and S100a1-Ryr2, whose downstream signaling networks converged on lactate dehydrogenase B (Ldhb) as a shared effector, thereby prompting functional validation. Notably, brain-wide and excitatory neuron-specific Ldhb overexpression alleviated DACD-induced mitochondrial dysfunction, oxidative stress, neuronal apoptosis, and cognitive impairment. Collectively, our study delineates the spatiotemporal transcriptomic landscape of DACD, offers a valuable resource for mechanistic exploration, and highlights Ldhb as a potential therapeutic target in DACD.

Polycythemia vera (PV) and secondary polycythemia are clinical conditions characterized by erythrocytosis, but their biological basis and clinical course differ. Whether this difference is reflected in long-term kidney function is not well defined. We aimed to compare longitudinal estimated glomerular filtration rate (eGFR) trajectories and kidney function loss in patients with PV and secondary polycythemia.

In this retrospective, single-center cohort, adult patients with PV or secondary polycythemia were evaluated from baseline through 4 years. Repeated eGFR measurements were examined using a linear mixed-effects model; the group-time interaction was the primary inferential test, with adjustment for age, sex, diabetes mellitus, hypertension, coronary artery disease, and baseline chronic kidney disease.

The cohort included 1,616 patients: 258 with PV and 1,358 with secondary polycythemia. The global group-time interaction indicated different eGFR trajectories (p = 0.049). The adjusted PV-secondary polycythemia eGFR contrast was small at baseline but indicated lower eGFR in PV by the fourth year (- 3.86 mL/min/1.73 m²; 95% CI, - 6.39 to - 1.33; Holm-corrected p = 0.014). The fourth-year contrast remained in the same direction in most sensitivity analyses.

PV was associated with a late and modest eGFR separation compared with secondary polycythemia. These findings support consideration of kidney function monitoring in selected patients with PV and prospective assessment of factors influencing renal progression.

Chiglitazar was approved by the China National Medical Products Administration (NMPA) in 2021 for the treatment of patients with type 2 diabetes mellitus, and may also benefit from metabolic dysfunction-associated steatohepatitis (MASH).

To characterise the role of Chiglitazar in MASH, in vitro experiments examining monocyte activation and migration were conducted, alongside liver and stellate cell proliferation assays. Three distinct MASH animal models (db/db mouse model, MCD model, CCl4 model) for conducting in vivo studies were established.

The results indicated that Chiglitazar reduced MCP-1-induced THP-1 migration and down-regulated TNF-α and MCP-1 expression. It also blocked TGF-β-driven proliferation in dermal fibroblasts and hepatic stellate cells while suppressing α-SMA and CTGF expression. In vivo, Chiglitazar alleviates hepatic steatosis and inflammation in the MCD model, whilst improving hepatic inflammation and fibrosis in both the CCL4 and HFD+CCL4 models.

In the study, it has been demonstrated that chiglitazar suppresses monocyte activation and chemotaxis, thereby attenuating chronic inflammation-elicited vascular and tissue damage. It also blocks fibroblast proliferation and differentiation, curbing toxin- or metabolite- induced inflammation and fibrosis. Although CCl4, MCD, and HFD+CCl4 models were used, they only partially reproduce human MASH pathology. Future work should employ more physiologically faithful systems.

As the world's first pan-PPAR agonist approved for the treatment of Type 2 Diabetes Mellitus (T2DM), Chiglitazar has also demonstrated potential for treating MASH by modulating steatosis, inflammation, and fibrosis. This potential may be translatable into clinical treatments in the foreseeable future.

Diabetes mellitus is a metabolic disease that requires continuous glucose monitoring to achieve glycemic control. However, current glucose monitoring technologies have many limitations and are often associated with adherence issues. Therefore, in this review, we considered some recent developments in biosensors using microneedles due to their non-invasive character and interstitial fluid measurement for real-time monitoring. The review highlights the use of nanomaterials in nanotechnology, the microfabrication methods, enzyme immobilization methods, and surface modification methods that lead to increased sensitivity and biocompatibility of the sensors. The review also emphasized the advances in clinical studies and the artifacts related to microneedle sensors, such as biofouling and signal drift. Moreover, the review specifically targeted the use of emerging technologies such as non-enzymatic sensing, multiplex biomarker detection, AI-based analysis, health monitoring, and a closed-loop insulin release system. The current review includes more than 100 peer-reviewed articles. It provides a thorough analysis of how microneedle-based biosensors can be considered a new approach for glucose detection that improves the quality of patients' lives. Collectively, all the recent advancements in microneedle-based biosensors have made them a new emerging platform for next-generation personalized medicine, along with a digital healthcare system.

Diabetic kidney disease (DKD) is a major cause of chronic kidney diseases. Panoptosis has emerged as a key contributor to renal injury. However, the specific panoptosis-related genes (PRGs) that drive diabetic tubular injury remain unknown. TNFSF10, a known regulator of apoptosis and inflammation, has been linked to podocyte injury in DKD, but its role in diabetic tubular injury remains unknown. To address this, we integrated weighted gene co-expression network analysis, machine learning algorithms, and clinical data from the Nephroseq database to identify PRGs associated with DKD. The biological function of the identified hub gene was further characterized by integrating single-cell and single-nucleus RNA sequencing data with analytical frameworks for cellular communication, trajectory inference, and regulatory network. In this study, we identified TNFSF10 as a conserved and robust hub PRG for DKD. TNFSF10 expression was significantly upregulated in DKD kidneys and inversely correlated with glomerular filtration rate. TNFSF10^high kidneys exhibited elevated fibrosis and inflammation scores. scRNA/snRNA analysis revealed its predominant expression in injured proximal tubular cells. Importantly, TNFSF10⁺ proximal tubular cells showed transcriptomic signatures consistent with panoptosis activation and inflammatory signaling, along with enhanced ligand-receptor interactions with fibroblasts and immune cells. Consistently, TNFSF10 treatment induced cell death and increased the expression of multiple inflammatory genes in vitro. Collectively, our results suggest that TNFSF10 is associated with tubular injury and kidney fibroinflammation in DKD. These exploratory findings suggest it may represent a promising candidate for DKD, and further in vivo functional validation is required to confirm its diagnostic and therapeutic potential.

Diabetes mellitus (DM), a chronic metabolic disorder characterized by persistent hyperglycemia, poses a significant global health burden. Berberine (BBR), a natural isoquinoline alkaloid derived from medicinal plants such as Coptidis Rhizoma, has shown therapeutic promise for DM and its complications. Preclinical and clinical studies have revealed that BBR exerts multi-target effects, including remodeling the gut microbiome, promoting insulin secretion, ameliorating insulin resistance, and inhibiting hepatic gluconeogenesis. Its protective mechanisms against diabetic complications, such as nephropathy, cardiomyopathy, and retinopathy, involve modulating key pathways like AMPK/SIRT1, PI3K/Akt, and TGF-[Formula: see text]/Smad, which attenuate oxidative stress, inflammation, and fibrosis. Despite its low oral bioavailability, a wide array of formulation strategies, including polymer nanocarriers, lipid-based nanocarriers, mixed micelles, and absorption enhancer, have improved systemic exposure in preclinical models, although clinical translation remains limited. This review synthesizes evidence from the past decade spanning preclinical mechanisms, randomized controlled trials, pharmacokinetics and nanoformulation strategies, concluding that BBR is a promising candidate for complementary therapy, although the supporting clinical evidence remains limited. Further studies are warranted to clarify its mechanisms, and optimize its delivery. In particular, larger and longer-term trials, along with systematic pharmacokinetic-pharmacodynamic studies of BBR and its metabolites, are needed to confirm its efficacy and long-term safety.

Patients undergoing pancreatectomy for pancreatic cancer may experience gastrointestinal symptoms that impair health-related quality of life (HRQoL) before and after pancreatic surgery. The longitudinal course of these symptoms and their association with HRQoL remains unclear as large prospective studies are lacking.

Patients who underwent pancreatectomy for pancreatic cancer and filled out questionnaires of the Dutch Pancreatic Cancer Project were included in this cohort study. Gastrointestinal symptoms and HRQoL were assessed before surgery and 0-3, 3-6, 6-9, and 9-12 months after surgery. Multivariable generalized linear mixed models were applied to evaluate longitudinal changes in gastrointestinal symptoms and their association with HRQoL.

After pancreatectomy for pancreatic cancer, overall, 1009 patients were included and 2995 questionnaires were completed. Before surgery, gastrointestinal symptoms were present in 95% of patients; commonly flatulence, dry mouth, problems with taste, and altered bowel habits. Gastrointestinal symptom scores peaked at 0-3 months after surgery and generally returned to preoperative or lower levels at 9-12 months after surgery. Severe gastrointestinal symptoms (defined as 'quite a bit' or 'very much') decreased from 61% of patients before surgery to 52% at 9-12 months after surgery. Patients with two or more severe symptoms had moderate (10-20 points) to large (>20 points) clinically relevant lower scores for HRQoL.

Gastrointestinal symptoms in patients undergoing pancreatectomy for pancreatic cancer were highly prevalent and peaked within 3 months after surgery. Severe gastrointestinal symptoms affected more than half of all patients after surgery, which led to substantial declines in HRQoL.

To determine whether a CT radiomics-defined poor-prognosis phenotype in NSCLC is associated with reproducible transcriptomic programs and distinct transcription factor (TF) activity patterns.

This retrospective radiogenomic study used public datasets from The Cancer Imaging Archive. A prognostic radiomics model was developed from pretreatment CT in 402 patients from the NSCLC-Radiomics collection and evaluated in an independent cohort of 110 patients from the NSCLC Radiogenomics collection. Seven tumor-centered intra- and peritumoral regions were analyzed. Features were extracted from original, wavelet-, and Laplacian-of-Gaussian-filtered CT images. After preprocessing and training-cohort standardization, a LASSO-penalized Cox model was developed. The locked model retained eight features, all from the GTV+10-mm region, and was applied to the internal and external validation cohorts without refitting. The training-cohort median RadScore was used unchanged for Kaplan-Meier visualization. Matched transcriptomic data were analyzed using preranked gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), target-gene-based TF activity inference, TF co-activity networks, exploratory regulator prioritization, unsupervised TF-state clustering, and LINCS-based perturbational analysis.

The radiomics model achieved Harrell concordance indices of 0.785 (95% confidence interval [CI], 0.767-0.793), 0.819 (95% CI, 0.788-0.833), and 0.785 (95% CI, 0.752-0.802) in the training, internal validation, and external validation cohorts, respectively. The high-risk phenotype showed exploratory enrichment of proliferative and stress-adaptive programs, led by HALLMARK_E2F_TARGETS (normalized enrichment score [NES] = 1.696, false discovery rate [FDR] = 0.045), with additional enrichment of MYC targets and inflammatory response. HALLMARK_HYPOXIA showed a positive directional trend but did not meet the prespecified exploratory threshold. GSVA provided complementary sample-level evidence for MYC targets, hypoxia, E2F targets, DNA repair, and cell-cycle activity. Eleven of 13 evaluated TFs met the prespecified FDR threshold, including YAP1, MYC, HIF1A, E2F3, STAT3, KLF4, E2F1, and TP53. Integrated analysis prioritized HIF1A, STAT3, TP53, YAP1, and E2F1/E2F3 as candidate regulatory nodes. Unsupervised clustering of all 110 radiogenomic patients, performed independently of radiomics risk labels, identified three exploratory TF-defined states: Hypoxia_Metabolic (n = 69), Cell_Cycle_E2F (n = 31), and Hippo_Mechanosensing (n = 10). LINCS-based analysis generated regulatory-state-associated compound hypotheses.

A CT radiomics-defined poor-prognosis phenotype in NSCLC is associated with coherent transcriptomic programs and an interpretable TF regulatory architecture. This framework links routine imaging phenotypes to latent tumor-control programs and supports regulatory-state-informed therapeutic hypothesis generation.