Revista de nefrología y terapéutica

Diabetic nephropathy is a prevalent and debilitating complication of diabetes mellitus that significantly contributes to the global burden of chronic kidney disease. While it is well-established that diabetes is a major risk factor for the development of renal impairment, the precise mechanisms underlying this complex interplay between metabolic factors and renal dysfunction remain a subject of ongoing research. This article reviews the multifaceted relationship between diabetes and renal impairment, exploring the intricate web of metabolic factors and their influence on renal health. Understanding these mechanisms is critical for the development of effective prevention and treatment strategies for diabetic nephropathy.

Renal impairment, characterized by a decline in kidney function, represents a significant health challenge affecting millions of individuals worldwide. The kidneys play a vital role in maintaining homeostasis by filtering waste products and regulating fluid and electrolyte balance. Impairment of renal function can lead to a cascade of complications, including chronic kidney disease and end-stage renal disease, necessitating renal replacement therapy such as dialysis or transplantation. Pharmacological interventions play a critical role in managing renal impairment, and this article provides an overview of current trends and future prospects in this field.

Renal impairment, including chronic kidney disease, represents a significant global health challenge with a growing prevalence. Timely and accurate prediction of renal impairment progression is crucial for effective patient management, resource allocation, and the development of personalized treatment plans. In recent years, artificial intelligence and machine learning have emerged as powerful tools for enhancing our ability to predict and manage renal impairment progression. This research article explores the applications of AI and ML in predicting renal impairment progression, discusses their benefits, challenges, and the future outlook for this transformative field.

Objective: There are clinical conditions with histological evidence of non-ischemic myocardial necrosis that are associated with elevated troponin levels, such as the structural and functional alterations of the Left Ventricle (LV) that occur in Left Ventricular Diastolic Dysfunction (LVDD). We analyzed the relationship between the ultrasensitive Troponin I biomarker (TnI-US) and LVDD in a cohort of asymptomatic Hemodialysis (HD) patients.

Methods: Descriptive cross-sectional study including 80 patients. Categorical variables were compared using Chi2 test, and quantitative variables were compared with Student's t-test or Mann-Whitney U-test. ROC curve to determine the predictive value of TnI-US levels for LVDD. Logistic regression analysis to determine the factors independently associated with LVDD.

Results: The mean TnI-US was 31.2 ± 59.3 pg/ml, and 40% of patients had TnI-US >20 pg/ml. These patients had higher frequency of LVDD (56.3% vs. 25%, p=0.005). 37.5% of patients had LVDD and higher proportion of moderate/severe Left Ventricular Hypertrophy (LVH) (63.3% vs. 36.7%, p=0.02), lower heart rate at the start of HD (66.9 ± 8.6 bpm vs. 77.2 ± 43.6 bpm, p=0.03), and higher TnI-US (47.4 ± 81.9 pg/ml vs. 21.5 ± 38.1 pg/ml, p=0.005). Logistic regression analysis showed that TnI-US >20 pg/ml [OR: 4.1 (95% CI 1.3-12.1), p=0.01] and moderate/severe LVH [OR: 5.1 (95% CI 1.7-15.2), p=0.003] were independently associated with LVDD, while an increase in heart rate [OR: 0.9 (95% CI 0.8-0.9); p=0.02] was independently associated with a lower risk of LVDD.

Conclusion: TnI-US can be used as a biomarker for LVDD in asymptomatic patients on HD.