Hui Peng

Protein-energy wasting (PEW) is a concept proposed in 2008 by the International Society of Renal Nutrition and Metabolism (ISRNM) to describe a form of malnutrition associated with chronic kidney d...

Background: Increased vascularity of the peritoneal membrane contributes to the ultrafiltration dysfunction in peritoneal dialysis (PD), which is an important subject of research, including animal studies. In this study, we have examined how animal studies assessing peritoneal vascularization in the context of PD are designed, conducted, and reported. Methods: Relevant original articles published in 1979–2024 were identified in PubMed® and assessed for completeness, transparence, and unbiased reporting. Results: Animal studies analyzed were conducted primarily in normal rodents and were based on two basic experimental models (exposure to peritoneal dialysis fluid or chlorhexidine gluconate). In both cases, increased peritoneal vascularity was reported consistently, although to a varying degree. Studies in uremic animals were infrequent (20%), but suggested strongly that uremia itself increased peritoneal vascularity and was associated with decreased peritoneal ultrafiltration and increased peritoneal solute transport rates. Use of chlorhexidine accelerated membrane injury but did not lead to significant new insights. Pharmacological attempts to limit increased peritoneal vascularization demonstrated that such interventions were feasible at multiple levels. However, all these studies were limited by the choice of animals and drug doses, as well as by omissions in reporting key information. Conclusions: This literature review found that there was considerable variability in the design, methodology, and reporting of animal studies investigating the effects of dialysis on peritoneal membrane vascularity. A framework to enhance their reporting, in line with the current ARRIVE guidelines, is needed. Abandoning the use of chlorhexidine in future animal studies on PD should be considered.

BACKGROUND: Ambulatory blood pressure monitoring is indispensable for diagnosing nocturnal hypertension (NH) among patients with chronic kidney disease, but it is costly and time-consuming. Screening tools for predicting high-risk NH are urgently needed. METHODS: A large cohort of 5769 patients with nondialysis chronic kidney disease were enrolled (hospital A: 4565; hospital B: 1204). Patients from hospital A were split 8:2 into training/internal test sets; hospital B served as the external test set. A total of 1006 patients with at least 2 valid ambulatory blood pressure monitoring recordings were used for secondary validation. We proposed a table-value diffusion model by incorporating generative modeling concepts to map distributions between clinical variables and predict NH. The predicted probabilities for NH were stratified and validated according to patients’ true adverse renal/cardiovascular prognoses. RESULTS: Through a rigorous selection process combining univariate ranking and clinical expertise, we selected 7 core predictors: age, body mass index, clinic systolic/diastolic blood pressure, estimated glomerular filtration rate, hypertension history, and use of non–renin-angiotensin-aldosterone system antihypertensive agents. In the test sets, the 7-variable diffusion model achieved a significantly higher area under the curve than the 7-variable logistic model (internal: 0.870 versus 0.807, P P 0.692, intermediate-risk 0.692–0.515, and low-risk <0.515). Patients in the high-risk group had a higher incidence of adverse renal/cardiovascular events than those in the low-risk group. CONCLUSIONS: We developed a concise 7-variable diffusion model for NH using a table-value diffusion model, supporting targeted ambulatory blood pressure monitoring screening in patients with chronic kidney disease.

Abnormal circadian pattern of urinary sodium excretion was associated with high blood pressure and target-organ injury. However, whether urinary circadian

Intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN) are frequent haemodialysis complications; each independently linked to increased cardiovascular risk and all-cause mortality. Existing predictive models often fail to capture the irregular nature of real-world haemodialysis data. The study objective was to develop an advanced machine learning architecture specifically designed for irregular time-series analysis of dialysis. . We conducted a retrospective analysis of 1 300 haemodialysis patients, encompassing 182 111 sessions and 1 201 323 timestamped observations. Patients were randomly assigned to training (80%), validation (10%), and testing (10%) cohorts. IDH and IDHTN were defined by systolic blood pressure changes from pre-dialysis baseline: IDH is defined as a systolic blood pressure lower than 90 mmHg, and IDHTN is defined as an increase of ≥10 mmHg in systolic blood pressure without the occurrence of IDH. A Transformer model with dual-attention mechanism was developed for real-time event prediction. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1 score. In the whole cohort, the incidence of IDH and IDHTN was 0.79% and 25.60%, respectively. The Transformer model demonstrated robust predictive accuracy in the test cohort, achieving AUROCs of 0.96 for IDH and 0.88 for IDHTN (all P < 0.01). The model’s high AUPRCs for both definitions confirmed its sensitivity in detecting early or moderate blood pressure fluctuations. To assess clinical utility, a complementary LightGBM model using pre-dialysis features predicted common symptoms or interventions with an accuracy of 0.926. Feature importance analyses validated established risk factors and identified key time-dependent covariates. Our dual-attention Transformer model enables accurate, real-time prediction of intradialytic blood pressure abnormalities by effectively interpreting complex, asynchronous clinical data.

Objectives: High glucose induces mitochondrial dysfunction in podocytes, contributing to the development of diabetic nephropathy (DN). There is increasing evidence that muscles play a protective role by secreting myokines into the kidneys. Here, we investigated how skeletal muscle influences podocyte health via muscle–kidney crosstalk. Methods: To increase myokine release, we overexpressed PGC‐1α specifically in skeletal muscle (mPGC‐1α) and crossed these mice with db/m mice to generate diabetic mPGC‐1α:db/db mice. In parallel, db/db mice were treated intraperitoneally with recombinant murine interleukin‐15 (IL‐15). Mechanistic studies were performed using isolated primary podocytes and cultured podocyte cell lines. Results: Compared with db/db controls, mPGC‐1α:db/db mice exhibited reduced urinary albumin excretion ( p < 0.001), mesangial matrix expansion ( p < 0.001), glomerular basement membrane thickening ( p < 0.001) and urinary podocin excretion ( p < 0.001), along with increased podocyte number ( p < 0.001). Podocytes from mPGC‐1α:db/db mice showed higher expression of Nephrin and COX IV ( p < 0.05) and upregulation of multiple mitochondrial function‐related genes, notably OPA1 ( p < 0.05). Skeletal muscle from mPGC‐1α:db/db mice displayed elevated IL‐15 mRNA ( p < 0.05) and protein ( p < 0.01) levels, accompanied by increased plasma IL‐15 concentrations ( p < 0.05). IL‐15 treatment enhanced podocyte mitochondrial respiration, including basal oxygen consumption rate (OCR, p < 0.05), ATP‐coupled respiration ( p < 0.05) and maximal respiration ( p < 0.05). IL‐15 preserved mitochondrial fusion under high‐glucose conditions by increasing OPA1 expression ( p < 0.05) and promoted OPA1 transcription via histone H3 acetylation at its promoter ( p < 0.05). Conclusions: Skeletal muscle‐derived IL‐15 mediates renal protection by maintaining mitochondrial fusion in podocytes during DN progression. Targeting this pathway may offer a therapeutic strategy to preserve kidney function and slow progression to end‐stage renal disease.

Introduction: Primary membranous nephropathy (PMN) is one of the leading causes of nephrotic syndrome in adults, but reliable prognostic assessment tools remain limited. Although traditional clinical parameters and serum anti-PLA2R antibodies constitute cornerstones of prognosis assessment, they fail to fully capture the heterogeneity of PMN treatment responses. Identification of multidimensional prognostic risk factors facilitates further guidance for treatment in PMN patients. This study aimed to identify novel urinary protein signatures predictive of clinical remission and delineate molecular subtypes associated with relapse.​ Methods: We performed quantitative proteomic profiling on extracellular vesicles (uEVs) isolated from baseline urine samples of 86 biopsy-confirmed PMN patients using nanoflow high-performance liquid chromatography-tandem mass spectrometry (nanoHPLC-MS/MS). The primary endpoints were clinical remission (complete or partial remission) and time to clinical remission. A prognostic model was developed by screening 101 machine learning algorithms, with the final risk score derived from a random survival forest and stepwise Cox regression, internally validated via bootstrap resampling. Relapse-associated molecular subtypes were identified using nonnegative matrix factorization (NMF). Results: During a median follow-up of 8 months (IQR: 3.4–18.5), 76.1% of patients achieved remission, with a median time to remission of 11.2 months (95% CI: 6.2–18.0). The four-protein risk model (PON1, ACTBL2, RDX, TPP1) effectively stratified patients into high- and low-risk groups (Harrell's C-index = 0.729). The combined model integrating this proteomic signature with clinical features (anti-PLA2R Ab, age, eGFR) demonstrated superior predictive performance (Harrell's C-index = 0.744) compared to the clinical-feature-only model (Harrell's C-index = 0.636). To improve clinical applicability, we developed a web-based interactive Shiny application for individualized risk prediction in PMN patients. Additionally, proteomic clustering identified three distinct molecular subtypes (PMN1-3), with subtype PMN2 emerging as an independent predictor of relapse (OR = 10.26, 95% CI: 1.68–81.97; p < 0.05). Conclusion: The combined model incorporating four noninvasive urinary proteomic signatures and clinical characteristics significantly improved performance of predicting clinical remission in PMN patients compared to the clinical-feature-only model. Furthermore, molecular subtyping of the urinary proteome can identify patients at high risk for relapse. These findings provide a foundation for integrating advanced proteomics into personalized prognostic assessment for PMN patients, pending external validation in larger cohorts.

Peritoneal dialysis (PD), as a renal replacement therapy, relies heavily on the structural and functional integrity of the peritoneum. In some patients, however, the peritoneum may undergo adverse remodeling and fibrotic thickening, resulting in treatment failure. Here, a previously unrecognized metabolic‐epigenetic mechanism contributing to peritoneal fibrogenesis is uncovered, wherein lactate accumulation in injured peritoneal mesothelial cells promotes histone H3K18 lactylation and transcriptional activation of macrophage‐recruiting chemokine CCL2. In a mouse model of peritoneal fibrosis induced by chlorhexidine gluconate (CG) or PD fluid, the administration of extracellular vesicles derived from human bone marrow mesenchymal stem cells (MSC‐EVs) significantly ameliorates histological and functional changes in the peritoneum. Single‐cell RNA sequencing reveals that MSC‐EVs attenuate mesothelial‐macrophage crosstalk by suppressing CCL2 signaling. Mechanistically, MSC‐EVs reprogram glycolytic metabolism in mesothelial cells, reduce lactate production, and inhibit H3K18 lactylation‐dependent transcriptional activation of CCL2. Pharmacologic blockade of lactate production recapitulates the protective effects of MSC‐EVs. These findings suggest that lactate‐induced histone lactylation is a key driver of peritoneal fibrosis, positioning MSC‐EVs as a promising cell‐free therapeutic strategy for targeting metabolic‐epigenetic inflammation in serosal injury.

Encapsulating peritoneal sclerosis (EPS) is a life-threatening fibrotic condition characterized by severe abdominal adhesions, chronic inflammation, and significant morbidity. The lack of effective treatments for EPS stems from a limited understanding of its underlying mechanisms. In this study, we developed a modified mouse model of PD-induced EPS and investigated the role of the STING signaling pathway in disease progression. Our findings reveal that STING activation in peritoneal mesothelial cells significantly increases the secretion of the macrophage chemokine CCL2, leading to enhanced macrophage infiltration and the formation of pathological adhesions. Notably, pharmacological inhibition of STING using the inhibitor H151 effectively reduced macrophage infiltration and fibrosis, demonstrating its therapeutic potential in alleviating EPS. These results identify the STING pathway as a critical mediator of EPS pathogenesis and suggest that STING inhibitors could offer a promising therapeutic strategy to prevent or reverse EPS, particularly in clinical settings such as peritoneal dialysis.

Aims: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) are established markers of glucose metabolism dysregulation. This study compared their predictive values for mortality and arrhythmic events in patients with reduced ejection fraction following acute myocardial infarction (AMI).Materials and Methods: We analyzed 1933 AMI patients with reduced ejection fraction from the MIMIC‐IV database (v3.1, 2008–2022). The primary endpoint was in‐hospital mortality, with secondary endpoints including 1‐year all‐cause mortality, ventricular tachycardia/ventricular fibrillation (VT/VF), and cardiac arrest. Multivariate logistic regression models evaluated associations with in‐hospital outcomes, while Cox proportional hazards models assessed 1‐year mortality. Restricted cubic spline models examined non‐linear relationships between SHR and outcomes, with discriminative ability compared using area under the receiver operating characteristic curve (AUC) analysis.Results: Among patients (mean age 67.3 years), 401 (20.7%) died during hospitalization and 662 (34.2%) within one year. After adjustment, SHR showed the strongest association with in‐hospital mortality (odds ratio [OR]: 1.51, 95% confidence interval [CI]: 1.24–1.82) compared to GV (OR: 1.00, 95% CI: 0.99–1.01). For 1‐year mortality, SHR maintained superior performance (hazard ratio: 1.40, 95% CI: 1.19–1.65), with the highest tertile significantly associated with increased risk. ROC analysis confirmed SHR's superior predictive capacity for both mortality endpoints and VT/VF, while none of the indices significantly predicted cardiac arrest. SHR's predictive value was more pronounced in non‐diabetic patients.Conclusions: In post‐AMI patients with reduced ejection fraction, SHR demonstrated superior predictive value for mortality compared to GV, supporting its incorporation into risk stratification models for individualized glucose management.