Research-based versus clinical serum creatinine measurements and the association of acute kidney injury with subsequent kidney function: findings from the Chronic Renal Insufficiency Cohort study.

Background:Observational studies relying on clinically obtained data have shown that acute kidney injury (AKI) is linked to accelerated chronic kidney disease (CKD) progression. However, prior reports lacked uniform collection of important confounders such as proteinuria and pre-AKI kidney function trajectory, and may be susceptible to ascertainment bias, as patients may be more likely to undergo kidney function testing after AKI. Methods:We studied 444 adults with CKD who participated in the prospective Chronic Renal Insufficiency Cohort (CRIC) Study and were concurrent members of a large integrated healthcare delivery system. We estimated glomerular filtration rate (eGFR) trajectories using serum creatinine measurements from (i) the CRIC research protocol (yearly) and (ii) routine clinical care. We used linear mixed effects models to evaluate the associations of AKI with acute absolute change in eGFR and post-AKI eGFR slope, and explored whether these varied by source of creatinine results. Models were adjusted for demographic characteristics, diabetes status and albuminuria. Results:During median follow-up of 8.5 years, mean rate of eGFR loss was -0.31 mL/min/1.73 m2/year overall, and 73 individuals experienced AKI (55% Stage 1). A significant interaction existed between AKI and source of serum creatinine for acute absolute change in eGFR level after discharge; in contrast, AKI was independently associated with a faster rate of eGFR decline (mean additional loss of -0.67 mL/min/1.73 m2/year), which was not impacted by source of serum creatinine. Conclusions:AKI is independently associated with subsequent steeper eGFR decline regardless of the serum creatinine source used, but the strength of association is smaller than observed in prior studies after taking into account key confounders such as pre-AKI eGFR slope and albuminuria

Extracellular vesicles, ageing, and therapeutic interventions

A more comprehensive understanding of the human ageing process is required to help mitigate the increasing burden of age-related morbidities in a rapidly growing global demographic of elderly individuals. One exciting novel strategy that has emerged to intervene involves the use of extracellular vesicles to engender tissue regeneration. Specifically, this employs their molecular payloads to confer changes in the epigenetic landscape of ageing cells and ameliorate the loss of functional capacity. Understanding the biology of extracellular vesicles and the specific roles they play during normative ageing will allow for the development of novel cell-free therapeutic interventions. Hence, the purpose of this review is to summarise the current understanding of the mechanisms that drive ageing, critically explore how extracellular vesicles affect ageing processes and discuss their therapeutic potential to mitigate the effects of age-associated morbidities and improve the human health span

Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury, and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that Wnt-mediated memory of prior injury contributes to fibrosis progression, and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries

Modelling the trajectories of disease accumulation in multimorbidity

Multimorbidity is defined as the co-occurrence of two or more chronic conditions. The prevalence of multimorbidity is closely related to age, and due to population ageing, multimorbidity has become a major burden for healthcare systems. The biggest challenge when modelling multimorbidity is patient heterogeneity since the patients can suffer from a wide variety of disease combinations. Previous work has shown how age, sex, and socioeconomic status are key determinants of multimorbidity prevalence and multimorbidity disease clusters. However, little is known about the order in which patients acquire multiple chronic conditions. This thesis aims to study the trajectories of disease accumulation that multimorbid patients follow. To address this challenge, we present four models that focus on the different aspects of the problem and apply them to an Electronic Health Record (EHR) dataset. First, we group chronic conditions into concordant clinical clusters and use a Multi-state Markov model to micro-simulate patient cohorts. This approach allows us to estimate how sex, socioeconomic status, and different disease clusters affect the trajectories and Life Expectancy. Second, we adapt a previously proposed method to identify the networks of chronic diseases that condense the most significant trajectories observed in the data. In this model, we avoid grouping or clustering diseases, and the resulting networks describe specific disease accumulation sequences. Third, we use a greedy structure learning algorithm to find the Bayesian Networks that better fit our EHR dataset. The results of this model help better understand the conditional dependencies between chronic conditions. Fourth, we present a Bernoulli mixture model to study multimorbid patient subtypes. The models presented in this thesis characterize the temporal patterns that multimorbid patients follow from multiple perspectives and could be used to inform where to focus treatment to prevent or delay the multimorbidity progression

An ensemble multi-model technique for predicting chronic kidney disease

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier