Pathophysiology of Diabetic Kidney Disease

Qaz, Moarij; Sawaf, Hanny; Ismail, Jeeda; Qazi, Huma; Vachharajani, Tushar J.

doi:10.33590/emjnephrol/22-00060

Cited by 4 publications

References 70 publications

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Role of Epigenetic Changes in the Pathophysiology of Diabetic Kidney Disease

Liebisch,

Wolf

2024

Glomerular Dis

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Background: Diabetic kidney disease (DKD) is a global health issue. Epigenetic changes play an important role in the pathogenesis of this disease. Summary: DKD is currently the leading cause of kidney failure worldwide. Although much is known about the pathophysiology of DKD, the research field of epigenetics is relatively new. Several recent studies have demonstrated that diabetes-induced dysregulation of epigenetic mechanisms alters the expression of pathological genes in kidney cells. If these changes persist for a long time, the so-called “metabolic memory” could be established. In this review, we highlight diabetes-induced epigenetic modifications associated with DKD. While there is a substantial amount of literature on epigenetic changes, only a few studies describe the underlying molecular mechanisms. Detailed analyses have shown that epigenetic changes play an important role in known pathological features of DKD, such as podocyte injury, fibrosis, accumulation of extracellular matrix, or oxidative injury, all of which contribute to the pathophysiology of disease. The transforming growth factor-b plays a key role, as it is involved in all mentioned epigenetic types of regulation. Key messages: Epigenetic is crucial for the development and progression of diabetic kidney disease, but the detailed molecular mechanisms have to be further analyzed more in detail.

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Role of Epigenetic Changes in the Pathophysiology of Diabetic Kidney Disease

Liebisch,

Wolf

2024

Glomerular Dis

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Homeostasis: understanding the effects of impaired mechanisms

Leontino,

Duval-Pazzaglia

2024

Br J Nurs

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This article focuses on homeostasis and offers a pathophysiological perspective. The dynamic mechanisms responsible for maintaining internal balance and disruptive processes will be analysed through the lens of key systems including the nervous, endocrine and renal systems. The environmental factors and their potential impact on homeostasis have been considered. A clinical case study will contextualise homeostasis in clinical practice.

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Chronic Kidney Disease Early Prediction Using Machine Learning

Alnokta,

Alkuddsi,

Aleinikovas

et al. 2024

Preprint

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In the human body, the kidneys, play the important role of filtering wastes and toxic bodies from the blood. Chronic kidney disease (CKD) is a condition in which the human kidneys are damaged and unable to filter the blood in a proper way. It is a nontransmissible disease that causes mortality of large numbers worldwide and is very expensive to properly detect and diagnose, therefore, CKD patients often reach its chronic stages, especially in countries with limited resources. Furthermore, CKD is a silent killer due to the lack of physical symptoms at the initial stage, but a steady loss of glomerular filtration rate (GFR) occurs over a period longer than three months. CKD is a fatal disease if left undetected as it leads to renal failure, in the worst cases. However, the early diagnosis of CDK can significantly reduce the mortality rate. Moreover, if CKD is predicted early and correctly, it results in an increased probability of successful treatment and prolongs the patient’s life. The advances in ML, in addition to predictive analytics, provide promising results which in turn prove the capability of prediction in CKD and beyond. The utilization of ML methods in nephrology enables the building of ML models to better detect the at-risk patients of CKD especially in primary care settings. The current study carries out a prediction-based method that helps in early detecting of CKD patients at the early stage. In this study, we utilize on of the boosting method, XGBoost to achieve a higher prediction accuracy for CKD. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of the model was evaluated with accuracy. It attained 98% accuracy for training and testing sets. The way the research was done leads to the conclusion that recent improvements in machine learning, along with the help of predictive modeling, make for an interesting way to find new solutions that can then be used to test the accuracy of prediction in the field of kidney disease and beyond.

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Pathophysiology of Diabetic Kidney Disease

Cited by 4 publications

References 70 publications

Role of Epigenetic Changes in the Pathophysiology of Diabetic Kidney Disease

Role of Epigenetic Changes in the Pathophysiology of Diabetic Kidney Disease

Homeostasis: understanding the effects of impaired mechanisms

Chronic Kidney Disease Early Prediction Using Machine Learning

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