Diabetic nephropathy (DN) is one of the most common microvascular complications in diabetes mellitus patients and is characterized by thickened glomerular basement membrane, increased extracellular matrix formation, and podocyte loss. These phenomena lead to proteinuria and altered glomerular filtration rate, that is, the rate initially increases but progressively decreases. DN has become the leading cause of end-stage renal disease. Its prevalence shows a rapid growth trend and causes heavy social and economic burden in many countries. However, this disease is multifactorial, and its mechanism is poorly understood due to the complex pathogenesis of DN. In this review, we highlight the new molecular insights about the pathogenesis of DN from the aspects of immune inflammation response, epithelial-mesenchymal transition, apoptosis and mitochondrial damage, epigenetics, and podocyte-endothelial communication. This work offers groundwork for understanding the initiation and progression of DN, as well as provides ideas for developing new prevention and treatment measures.

Hyperuricemia is an important potential pathogenic factor for hypertension, cardiovascular disease and stroke. The current study aimed to investigate the prevalence of hyperuricemia and its relationship to lifestyle characteristics and dietary habits in centenarians and near-centenarians. Patients and methods: In total, 966 centenarians and 788 near-centenarians were included. Community-based surveys were conducted to collect information about lifestyle. Blood examinations were performed using enzymatic assays. T-tests and χ2 tests were used to investigate significant indicators of hyperuricemia, and multivariate logistic regression was used to analyze the related risk factors. A comprehensive analysis of nineteen modifiable factors, including lifestyle characteristics, dietary habits, general characteristics and blood test indexes, was conducted. Results: The prevalence of hyperuricemia was 29.02%. The percentage of men, waist circumference (WC), waist-hip ratio, estimated glomerular filtration rate (eGFR), levels of total protein (TP), alanine aminotransferase, aspartate aminotransferase, triglycerides, highdensity lipoprotein cholesterol, serum homocysteine, serum uric acid, serum urea and serum creatinine, passive smoking, alcohol consumption, snoring, preference for fried flavors, and meat, seafood and vegetable consumption were significantly different between the hyperuricemia group and the normouricemia group (p<0.05). Multivariate logistic regression analysis showed that WC (OR=1.020), eGFR (OR=0.960), TP level (OR=1.038), serum urea level (OR=1.154), passive smoking (OR=2.589), snoring (OR=2.003), meat consumption (OR=2.506), seafood consumption (OR=1.422) and vegetable consumption (OR=0.521) were significantly associated with the risk of hyperuricemia (p<0.05). Conclusion: Low eGFR and vegetable consumption, high WC, TP, and serum urea levels, passive smoking, snoring, and high meat and seafood consumption were independent risk factors for hyperuricemia. It is recommended that people at high risk for hyperuricemia should actively limit their intake of fried food, alcohol and purine-rich food, increase their intake of fresh vegetables, actively treat sleep apnea syndrome, avoid passive smoking, maintain a healthy WC and seek to improve their kidney and liver function.

Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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