Obesity is a well-established risk factor for atherosclerosis. However, it is unknown which measure of adiposity best relates to atherosclerosis in relatively lean maintenance hemodialysis (MHD) patients. We aimed to explore and compare the associations between different adiposity indices reflecting general, abdominal, visceral adiposity and arteriosclerosis risk with atherogenic index of plasma(AIP) in relatively lean MHD patients. We conducted a multicenter, cross-sectional study in Guizhou Province, Southwest China. General/abdominal adiposity indices like body mass index (BMI), waist circumference(WC), waist-height ratio(WHtR), conicity index (Ci) and visceral obesity indices including visceral adiposity index (VAI), lipid accumulation product (LAP) and the hypertriglyceridemic waist phenotype (HW phenotype) were recorded. Univariate and multivariate linear regression models were used. All adiposity indices correlated positively with AIP in univariate analysis both in men and women except for Ci. After adjustment for age and traditional atherosclerosis risk factors, BMI, WC, WHtR, VAI and LAP still had associations with AIP both in men (β = 0.265, 0.153, 0.16, 0.788 and 0.74, respectively, all P < 0.001) and women (β = 0.34,0.199, 0.21, 0.83 and 0.74, respectively, all P < 0.001). After further adjustment for BMI, associations between AIP and VAI, LAP remained significant, but associations between WC, WHtR and AIP disappeared. The HW phenotype, VAI, and LAP, validated and convenient markers of visceral obesity, were superior to classical anthropometric general/ abdominal adiposity indices for atherosclerosis risk assessment, especially in relatively lean MHD patients aged 40 years or older.

The mortality rates for patients undergoing hemodialysis (HD) remain unacceptably high compared to the general population, and more specific information about the causes of death is not known. The study aimed to develop and validate a risk prediction model that uses common clinical factors to predict the probability of cardiovascular events in maintenance hemodialysis (MHD) patients. The study involved 3488 adult patients who received regular scheduled hemodialysis treatment at 20 hemodialysis centers in southwest China between June 2015 and August 2020, with follow-up until August 2021. The optimal parameter set was identified by multivariable Cox regression analyses and Cross-LASSO regression analyses and was used to establish a nomogram for predicting the risk of cardiovascular events in maintenance hemodialysis patients at 3 and 5 years. The performance of the model was evaluated using the consistency index (Harrell’s C-index), the area under the receiver operating characteristic (ROC) curve, and calibration plots. The model was validated by tenfold cross-validation and bootstrapping with 1000 resamples. In the derivation cohort, the model yields an AUC of 0.764 [95% confidence interval (CI), 0.737–0.790] and 0.793 [CI, 0.757–0.829] for predicting the risk of cardiovascular events of MHD patients at 3 and 5 years. In the internal validation cohort AUC of 0.803 [95% CI, 0.756–0.849], AUC of 0.766 [95% CI, 0.686–0.846], and the external validation cohort AUC of 0.826 [95% CI, 0.765–0.888], AUC of 0.817 [95% CI, 0.745–0.889] at 3 and 5 years. The model’s calibration curve is close to the ideal diagonal. By tenfold cross-validation analyses, the 3- and 5-year risk of cardiovascular events (AUC 0.732 and 0.771, respectively). By the bootstrap resampling method, the derivation cohort and validation cohort (Harrell’s C-index 0.695 and 0.667, respectively) showed good uniformity with the model. The constructed model accurately predicted cardiovascular events of MHD patients in the 3rd and 5th years after dialysis. And the further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Kidney functions, including electrolyte and water reabsorption and secretion, could be influenced by circulating hormones. The pituitary gland produces a variety of hormones and cytokines; however, the influence of these factors on the kidney has not been well explained and explored. To provide more in-depth information and insights to support the pituitary–kidney axis connection, we used mouse pituitary and kidney single-cell transcriptomics data from the GEO database for further analysis. Based on a ligand–receptor pair analysis, cell–cell interaction patterns between the pituitary and kidney cell types were described. Key ligand–receptor pairs, such as GH-GHR, PTN-SDC2, PTN-SDC4, and DLK1-NOTCH3, were relatively active in the pituitary–kidney axis. These ligand–receptor pairs mainly target proximal tubule cells, principal cells, the loop of Henle, intercalated cells, pericytes, mesangial cells, and fibroblasts, and these cells are related to physiological processes, such as substance reabsorption, angiogenesis, and tissue repair. Our results suggested that the pituitary gland might directly regulate kidney function by secreting multiple hormones or cytokines and indicated that the above ligand–receptor pairs might represent a new research focus for studies on kidney function or kidney disease.

Immunoglobulin A nephropathy (IgAN), the most common glomerular disease, is characterized by immune complex deposition and abnormal complement system activ

Peritoneal dialysis (PD) patients have a high incidence of cardiovascular events (CVEs). Left ventricular fraction shortening (LVFS), one of the echocardiographic parameters, is an independent risk factor for mortality in previous studies. The aim of this study was to evaluate associations between LVFS and CVEs in PD patients. This was a single-center observational cohort study. Seven hundred and eighty-four PD patients were enrolled from 1 January 2012 to 1 June 2021 and followed until 1 June 2022. The primary outcome was the incidence of CVEs. PD patients were categorized into three groups according to the tertiles of LVFS levels (tertile 1-tertile 3). Kaplan-Meier method, Cox proportional hazard models and competing risk regression models were used for survival analysis. The areas under the curve (AUC) of receiver-operating characteristic analysis was used to determine the predictive values of LVFS for CVEs. A preplanned subgroup analysis was assessed according to age, gender, and the presence of hypertension and dyslipidemia, etc. During a median follow-up period of 42.3 months (interquartile range 24.0-79.0 months), 259 CVEs occurred. Compared to the other two groups respectively, patients in tertile 3 group had the lowest incidence of CVEs (24.5% vs 31.6% vs 43.0%, respectively, p < 0.05). After multiple adjustments, the tertile 3 group was associated with the 45.1% decrease in the CVEs hazard compared to that of the tertile1 group (SHR = 0.549, 95%CI: 0.395-0.762, p < 0.001). Subgroup analysis demonstrated that tertile 1 group as the reference, the association between LVFS and CVEs in tertile 3 group was robust among female patients (HR = 0.506, 95%CI: 0.309-0.829, p = 0.007), aged < 45 years (HR = 0.496, 95%CI: 0.331-0.744, p = 0.001), history of hypertension (HR = 0.586, 95%CI: 0.349-0.872, p = 0.008) and combined with dyslipidemia (HR = 0.464, 95%CI: 0.269-0.799, p = 0.006). This study suggests that LVFS is independently associated with the increased risk of CVEs in PD patients, especially those with aged < 45 years, female, with hypertension and dyslipidemia.