Systolic blood pressure and chronic kidney disease progression in patients with primary glomerular disease

Analysis of chronic kidney disease epidemiology in Kazakhstan using nationwide data for 2014-2020 and forecasting future trends of prevalence and mortality for 2030

According to the Global Burden of Disease (GBD) study, chronic kidney disease (CKD) was prevalent in 697.5 million individuals worldwide in 2017. By 2040, it is anticipated that CKD will rank as the fifth most common cause of death. This study aims to examine the epidemiology of CKD in Kazakhstan and to project future trends in CKD prevalence and mortality by 2030. The retrospective analysis was performed on a database acquired from the Unified National Electronic Health System for 703,122 patients with CKD between 2014 and 2020. During the observation period, 444,404 women and 258,718 men were registered with CKD, 459,900 (66%) were Kazakhs and 47% were older than 50. The incidence rate notably decreased: 6365 people per million population (PMP) in 2014 and 4040 people PMP in 2020. The prevalence changed from 10,346 to 38,287 people PMP, and the mortality rate increased dramatically from 279 PMP to 916 PMP. Kazakhstan's central regions, Turkestan and Kyzylorda were identified as the most burdensome ones. The ARIMA model projected 1,504,694 expected prevalent cases in 2030. The predicted mortality climbed from 17,068 cases in 2020 to 37,305 deaths in 2030. By 2030, the prevalence and mortality of CKD will significantly increase, according to the predicted model. A thorough action plan with effective risk factor management, enhanced screening among risk populations, and prompt treatment are required to lessen the burden of disease in Kazakhstan.

Does Metabolic Syndrome and Its Components Have Prognostic Significance for Renal and Cardiovascular Outcomes in IgA Nephropathy?

BACKGROUND

Patients with IgA nephropathy (IgAN), a chronic kidney disease (CKD), are significantly more likely to have cardiovascular (CV) mortality and morbidity than the general population. The occurrence of metabolic syndrome (MetS) and metabolic risk factors are independent risk factors for CV disease and renal progression. The purpose of this study was to determine how metabolic characteristics in a homogeneous population of CKD patients relate to prognosis.

METHODS

A total of 145 patients with CKD stages 1-4 diagnosed with IgA nephropathy (92 men and 53 women, aged 54.7 ± 13 years) were examined and monitored for a median of 190 months. All-cause mortality and any CV event, such as stroke, myocardial infarction, revascularization (CV), end-stage renal disease, and renal replacement therapy (renal), have been included in the composite endpoints (CV and renal).

RESULTS

Patients with MetS had significantly more primary endpoint events (23/65 patients vs. 15/60 patients, p < 0.001) compared to the non-MetS group. The MetS group had a statistically significant increase in both primary renal and CV endpoints (18/65 vs. 10/60, p = 0.001), and in CV endpoint events (7/65 vs. 6/60, p = 0.029) among the secondary endpoints (CV and renal separately). Based on Cox regression analysis, the main endpoint independent predictors of survival were dyslipidemia, eGFR, hemoglobin, urine albuminuria, and diabetes mellitus. Independent predictors of secondary renal endpoints were dyslipidemia, hemoglobin, urine albumin, and eGFR. Predictors of secondary cardiovascular endpoints were gender, BMI, and diabetes. When Kaplan-Meier curves were analyzed at the combined endpoints (CV and renal) or each endpoint independently, significant differences were seen between MetS and non-MetS. With more MetS components, the primary endpoint rate increased significantly (MetS comp. 0 vs. MetS comp. 2+, primary endpoints, p = 0.012).

CONCLUSIONS

Our results show that the metabolic profile has a prognostic role not only for renal endpoints but also for CV endpoints in IgAN. BMI, hyperuricemia, hypertension, and diabetes have a predictive value for the prognosis of IgA nephropathy.

Intensive blood pressure control and the progression of IgA nephropathy: a cohort study using marginal structural models

BACKGROUND

In chronic kidney disease, current guidelines recommend systolic blood pressure (SBP) below 120 mmHg. However, the renoprotective effect of intensive blood-pressure (BP) lowering on immunoglobulin A nephropathy (IgAN) remains undetermined. We aimed to determine the effect of intensive BP control on the progression of IgAN.

METHODS

At Peking University First Hospital, 1530 patients with IgAN were enrolled. An examination of the relationship between baseline and time-updated BP and composite kidney outcomes, defined as development of end-stage kidney disease (ESKD) or a 30% decline in estimated glomerular filtration rate (eGFR), was conducted. Baseline and time-updated BPs were modeled using multivariate causal hazards models and marginal structural models (MSMs).

RESULTS

In a median follow-up of 43.5 (interquartile range 27.2, 72.7) months, 367 (24.0%) patients experienced the composite kidney outcomes. No significant associations were found between baseline BP and the composite outcomes. Using MSMs with time-updated SBP for analysis, a U-shaped association was found. In reference to SBP 110-119 mmHg, hazard ratios (95% confidence intervals) for the SBP categories <110, 120-129, 130-139 and ≥140 mmHg were 1.48 (1.02-2.17), 1.13 (0.80-1.60), 2.21 (1.54-3.16) and 2.91 (1.94-4.35), respectively. The trend was more prominent in patients with proteinuria ≥1 g/day and eGFR ≥60 mL/min/1.73 m2. After analyzing time-updated diastolic BP, no similar trend was observed.

CONCLUSIONS

In patients with IgAN, intensive BP control during the treatment period may retard the kidney disease progression, but the potential risk of hypotension still needs to be considered.

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

Genetic evidence supporting the causal role of gut microbiota in chronic kidney disease and chronic systemic inflammation in CKD: a bilateral two-sample Mendelian randomization study

Background

The association of gut microbiota (GM) and chronic kidney disease (CKD), and the relevancy of GM and chronic systemic inflammation in CKD, were revealed on the basis of researches on gut-kidney axis in previous studies. However, their causal relationships are still unclear.

Objective

To uncover the causal relationships between GM and CKD, as well as all known GM from eligible statistics and chronic systemic inflammation in CKD, we performed two-sample Mendelian randomization (MR) analysis.

Materials and methods

We acquired the latest and most comprehensive summary statistics of genome-wide association study (GWAS) from the published materials of GWAS involving GM, CKD, estimated glomerular filtration rate (eGFR), c-reactive protein (CRP) and urine albumin creatine ratio (UACR). Subsequently, two-sample MR analysis using the inverse-variance weighted (IVW) method was used to determine the causality of exposure and outcome. Based on it, additional analysis and sensitivity analysis verified the significant results, and the possibility of reverse causality was also assessed by reverse MR analysis during this study.

Results

At the locus-wide significance threshold, IVW method and additional analysis suggested that the protective factors for CKD included family Lachnospiraceae (P=0.049), genus Eubacterium eligens group (P=0.002), genus Intestinimonas (P=0.009), genus Streptococcu (P=0.003) and order Desulfovibrionales (P=0.001). Simultaneously, results showed that genus LachnospiraceaeUCG010 (P=0.029) was a risk factor for CKD. Higher abundance of genus Desulfovibrio (P=0.048) was correlated with higher eGFR; higher abundance of genus Parasutterella (P=0.018) was correlated with higher UACR; higher abundance of class Negativicutes (P=0.003), genus Eisenbergiella (P=0.021), order Selenomonadales (P=0.003) were correlated with higher CRP levels; higher abundance of class Mollicutes (0.024), family Prevotellaceae (P=0.030), phylum Tenericutes (P=0.024) were correlated with lower levels of CRP. No significant pleiotropy or heterogeneity was found in the results of sensitivity analysis, and no significant causality was found in reverse MR analysis.

Conclusion

This study highlighted associations within gut-kidney axis, and the causal relationships between GM and CKD, as well as GM and chronic systemic inflammation in CKD were also revealed. Meanwhile, we expanded specific causal gut microbiota through comprehensive searches. With further studies for causal gut microbiota, they may have the potential to be new biomarkers for targeted prevention of CKD and chronic systemic inflammation in CKD.

Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m² or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88-4.41) in the UK Biobank and 9.36 (5.26-16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011-0.029) in the UK Biobank and 0.024 (95% CI, 0.002-0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods.

The Effect of Metabolic Syndrome and Its Individual Components on Renal Function: A Meta-Analysis

BACKGROUND

Observational studies have reported inconsistent findings in the relationship between metabolic syndrome (MetS), its components, and loss of renal function, mainly including eGFR decline, new-onset CKD, and ESRD. This meta-analysis was performed to investigate their potential associations.

METHODS

PubMed and EMBASE were systematically searched from their inception to 21 July 2022. Observational cohort studies in English assessing the risk of renal dysfunction in individuals with MetS were identified. Risk estimates and their 95% confidence intervals (CIs) were extracted and pooled using the random-effects approach.

RESULTS

A total of 32 studies with 413,621 participants were included in the meta-analysis. MetS contributed to higher risks of renal dysfunction (RR = 1.50, 95% CI = 1.39-1.61) and, specifically, rapid decline in eGFR (RR 1.31, 95% CI 1.13-1.51), new-onset CKD (RR 1.47, 95% CI 1.37-1.58), as well as ESRD (RR 1.55, 95% CI 1.08-2.22). Moreover, all individual components of MetS were significantly associated with renal dysfunction, while elevated BP conveyed the highest risk (RR = 1.37, 95% CI = 1.29-1.46), impaired fasting glucose with the lowest and diabetic-dependent risk (RR = 1.20, 95% CI = 1.09-1.33).

CONCLUSIONS

Individuals with MetS and its components are at higher risk of renal dysfunction.

Prevalence and associated risk factors of chronic kidney disease: A case study within SIME clinics in Quito, Ecuador 2019-2021

Background

Ecuador has been experiencing an epidemiological transition due to its demographic and lifestyle changes, where non-communicable diseases are the leading cause of death, including chronic kidney disease (CKD). Quito, Ecuador's capital city, is one of the cities burdened by CKD, yet it is unknown the factors that contribute to the rising incidence of this disease. The purpose of this study was to estimate the prevalence of CKD among non-institutionalized adults in Quito between 2019 and 2021, and to examine its associations with various risk factors.

Methods

For the analysis of prevalence, the Kidney Disease: Improving Global Outcomes guidelines were used, where an estimated glomerular filtration rate (eGFR) of < 60 ml/min/1.73 m2 was counted as a presumed case of CKD. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate eGFR. Multiple linear regression models were used to determined associations between blood pressure, blood glucose, sex, and zone with eGFR. A t-test of independence was used to determine difference in means between sex and zone and eGFR.

Results

A prevalence of 7.2% was found, in which almost 45% of the participants were classified within stages 2-4 of this disease. The risk factors that were significantly associated with eGFR were systolic blood pressure (β = -0.43, p < 0.001), sex, and zone (p < 0.001).

Conclusions

Overall a high prevalence of CKD was found among adults who visited SIME clinics in Quito. Associations between main risk factors and eGFR were found, yet further research is needed to explore CKD in Ecuador and its main cities.

Genetically predicted adiponectin causally reduces the risk of chronic kidney disease, a bilateral and multivariable mendelian randomization study

Background: It is not clarified whether the elevation of adiponectin is the results of kidney damage, or the cause of kidney function injury. To explore the causal association of adiponectin on estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD), this study was performed.

Materials and methods: The genetic association of adiponectin were retrieved from one genome-wide association studies with 39,883 participants. The summary-level statistics regarding the eGFR (133,413 participants) and CKD (12,385 CKD cases and 104,780 controls) were retrieved from the CKDGen consortium in the European ancestry. Single-variable Mendelian randomization (MR), bilateral and multivariable MR analyses were used to verify the causal association between adiponectin, eGFR, and CKD.

Results: Genetically predicted adiponectin reduces the risk of CKD (OR = 0.71, 95% CI = 0.57–0.89, p = 0.002) and increases the eGFR (β = 0.014, 95% CI = 0.001–0.026, p = 0.034) by the inverse variance weighting (IVW) estimator. These findings remain consistent in the sensitivity analyses. No heterogeneity and pleiotropy were detected in this study (P for MR-Egger 0.617, P for global test &gt; 0.05, and P for Cochran’s Q statistics = 0.617). The bilateral MR identified no causal association of CKD on adiponectin (OR = 1.01, 95% CI = 0.96–1.07, p = 0.658), nor did it support the association of eGFR on adiponectin (OR = 0.86, 95% CI = 0.68–1.09, p = 0.207) by the IVW estimator. All the sensitivity analyses reported similar findings (p &gt; 0.05). Additionally, after adjusting for cigarette consumption, alcohol consumption, body mass index, low density lipoprotein, and total cholesterol, the ORs for CKD are 0.70 (95% CI = 0.55–0.90, p = 0.005), 0.75 (95% CI = 0.58–0.97, p = 0.027), 0.82 (95% CI = 0.68–0.99, p = 0.039), 0.74 (95% CI = 0.59–0.93, p = 0.011), and 0.79 (95% CI = 0.61–0.95, p = 0.018), respectively.

Conclusion: Using genetic data, this study provides novel causal evidence that adiponectin can protect the kidney function and further reduce the risk of CKD.

Prediction models used in the progression of chronic kidney disease: A scoping review

OBJECTIVE

To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).

DESIGN

Scoping review.

DATA SOURCES

Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.

STUDY SELECTION

All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.

DATA EXTRACTION

Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.

RESULTS

From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.

CONCLUSIONS

Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Autoimmune-mediated renal disease and hypertension

Hypertension is a major risk factor for cardiovascular disease, chronic kidney disease (CKD), and mortality. Troublingly, hypertension is highly prevalent in patients with autoimmune renal disease and hastens renal functional decline. Although progress has been made over the past two decades in understanding the inflammatory contributions to essential hypertension more broadly, the mechanisms active in autoimmune-mediated renal diseases remain grossly understudied. This Review provides an overview of the pathogenesis of each of the major autoimmune diseases affecting the kidney that are associated with hypertension, and describes the current state of knowledge regarding hypertension in these diseases and their management. Specifically, discussion focuses on Systemic Lupus Erythematosus (SLE) and Lupus Nephritis (LN), Immunoglobulin A (IgA) Nephropathy, Idiopathic Membranous Nephropathy (IMN), Anti-Neutrophil Cytoplasmic Antibody (ANCA)-associated glomerulonephritis, and Thrombotic Thrombocytopenic Purpura (TTP). A summary of disease-specific animal models found to exhibit hypertension is also included to highlight opportunities for much needed further investigation of underlying mechanisms and novel therapeutic approaches.