PURPOSE.To examine whether patients with age-related macular degeneration (AMD), glaucoma, or Fuchs corneal dystrophy report limiting their activity due to a fear of falling as compared with a control group of older adults with good vision. METHODS.We recruited 345 patients (93 with AMD, 57 with Fuchs, 98 with glaucoma, and 97 controls) from the ophthalmology clinics of Maisonneuve-Rosemont Hospital (Montreal, Canada) to participate in a cross-sectional study from September 2009 until July 2012. Control patients who had normal visual acuity and visual field were recruited from the same clinics. Participants were asked if they limited their activity due to a fear of falling. Visual acuity, contrast sensitivity, and visual field were measured and the medical record was reviewed.RESULTS. Between 40% and 50% of patients with eye disease reported activity limitation due to a fear of falling compared with only 16% of controls with normal vision. After adjustment for age, sex, race, number of comorbidities, cognition, and lens opacity, the Fuchs groups was most likely to report activity limitation due to a fear of falling (odds ratio [OR] ¼ 3.07; 95% confidence interval [CI], 1.33-7.06) followed by the glaucoma group (OR ¼ 2.84; 95% CI, 1.36-5.96) and the AMD group (OR ¼ 2.42; 95% CI, 1.09-5.35). Contrast sensitivity best explained these associations.CONCLUSIONS. Activity limitation due to a fear of falling is very common in older adults with visually impairing eye disease. Although this compensatory strategy may protect against falls, it may also put people at risk for social isolation and disability. (Invest Ophthalmol Vis Sci. 2012;53:7967-7972)

Rationale: Albuminuria is an early clinical feature in the progression of diabetic nephropathy (DN). Podocyte insulin resistance is a main cause of podocyte injury, playing crucial roles by contributing to albuminuria in early DN. G protein-coupled receptor 43 (GPR43) is a metabolite sensor modulating the cell signalling pathways to maintain metabolic homeostasis. However, the roles of GPR43 in podocyte insulin resistance and its potential mechanisms in the development of DN are unclear. Methods: The experiments were conducted by using kidney tissues from biopsied DN patients, streptozotocin (STZ) induced diabetic mice with or without global GPR43 gene knockout, diabetic rats treated with broad-spectrum oral antibiotics or fecal microbiota transplantation, and cell culture model of podocytes. Renal pathological injuries were evaluated by periodic acid-schiff staining and transmission electron microscopy. The expression of GPR43 with other podocyte insulin resistance related molecules was checked by immunofluorescent staining, real-time PCR, and Western blotting. Serum acetate level was examined by gas chromatographic analysis. The distribution of gut microbiota was measured by 16S ribosomal DNA sequencing with faeces. Results: Our results demonstrated that GPR43 expression was increased in kidney samples of DN patients, diabetic animal models, and high glucose-stimulated podocytes. Interestingly, deletion of GPR43 alleviated albuminuria and renal injury in diabetic mice. Pharmacological inhibition and knockdown of GPR43 expression in podocytes increased insulin-induced Akt phosphorylation through the restoration of adenosine 5'-monophosphate-activated protein kinase α (AMPKα) activity. This effect was associated with the suppression of AMPKα activity through post-transcriptional phosphorylation via the protein kinase C-phospholipase C (PKC-PLC) pathway. Antibiotic treatment-mediated gut microbiota depletion, and faecal microbiota transplantation from the healthy donor controls substantially improved podocyte insulin sensitivity and attenuated glomerular injury in diabetic rats accompanied by the downregulation of the GPR43 expression and a decrease in the level of serum acetate. Conclusion: These findings suggested that dysbiosis of gut microbiota-modulated GPR43 activation contributed to albuminuria in DN, which could be mediated by podocyte insulin resistance through the inhibition of AMPKα activity.

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BackgroundHeart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients.Materials and methodsThe data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms.ResultsA total of 2,678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO2), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95.ConclusionUsing the ML model could accurately predict the development of AKI in HF patients.

BackgroundLipid metabolism disorder, as one major complication in patients with chronic kidney disease (CKD), is tied to an increased risk for cardiovascular disease (CVD). Traditional lipid-lowering statins have been found to have limited benefit for the final CVD outcome of CKD patients. Therefore, the purpose of this study was to investigate the effect of microinflammation on CVD in statin-treated CKD patients.MethodsWe retrospectively analysed statin-treated CKD patients from January 2013 to September 2020. Machine learning algorithms were employed to develop models of low-density lipoprotein (LDL) levels and CVD indices. A fivefold cross-validation method was employed against the problem of overfitting. The accuracy and area under the receiver operating characteristic (ROC) curve (AUC) were acquired for evaluation. The Gini impurity index of the predictors for the random forest (RF) model was ranked to perform an analysis of importance.ResultsThe RF algorithm performed best for both the LDL and CVD models, with accuracies of 82.27% and 74.15%, respectively, and is therefore the most suitable method for clinical data processing. The Gini impurity ranking of the LDL model revealed that hypersensitive C-reactive protein (hs-CRP) was highly relevant, whereas statin use and sex had the least important effects on the outcomes of both the LDL and CVD models. hs-CRP was the strongest predictor of CVD events.ConclusionMicroinflammation is closely associated with potential CVD events in CKD patients, suggesting that therapeutic strategies against microinflammation should be implemented to prevent CVD events in CKD patients treated by statin.