The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease

Association of retinal age gap with chronic kidney disease and subsequent cardiovascular disease sequelae: a cross-sectional and longitudinal study from the UK Biobank

Background

Chronic kidney disease (CKD) increases the risk of cardiovascular disease (CVD) and is more prevalent in older adults. Retinal age gap, a biomarker of aging based on fundus images, has been previously developed and validated. This study aimed to investigate the association of retinal age gap with CKD and subsequent CVD complications.

Methods

A deep learning model was trained to predict the retinal age using 19 200 fundus images of 11 052 participants without any medical history at baseline. Retinal age gap, calculated as retinal age predicted minus chronological age, was calculated for the remaining 35 906 participants. Logistic regression models and Cox proportional hazards regression models were used for the association analysis.

Results

A total of 35 906 participants (56.75 ± 8.04 years, 55.68% female) were included in this study. In the cross-sectional analysis, each 1-year increase in retinal age gap was associated with a 2% increase in the risk of CKD prevalence [odds ratio 1.02, 95% confidence interval (CI) 1.01-1.04, P = .012]. A longitudinal analysis of 35 039 participants demonstrated that 2.87% of them developed CKD in follow-up, and each 1-year increase in retinal age gap was associated with a 3% increase in the risk of CKD incidence (hazard ratio 1.03, 95% CI 1.01-1.05, P = .004). In addition, a total of 111 CKD patients (15.81%) developed CVD in follow-up, and each 1-year increase in retinal age gap was associated with a 10% increase in the risk of incident CVD (hazard ratio 1.10, 95% CI 1.03-1.17, P = .005).

Conclusions

We found that retinal age gap was independently associated with the prevalence and incidence of CKD, and also associated with CVD complications in CKD patients. This supports the use of this novel biomarker in identifying individuals at high risk of CKD and CKD patients with increased risk of CVD.

A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

Introduction

This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD).

Methods

Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics.

Results

The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897-0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633-0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho.

Conclusion

We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

Trajectories of atherosclerotic cardiovascular disease risk scores as a predictor for incident chronic kidney disease

BACKGROUND

The relationship between atherosclerosis and renal function is well established. Atherosclerotic cardiovascular disease (ASCVD) risk scores reflect atherosclerotic burden, which changes over time. We investigated the association between ASCVD risk trajectories and incident chronic kidney disease (CKD) using data from a large community-based Korean cohort with up to 16 years of follow-up.

METHODS

We analyzed data from 5032 participants without CKD from the baseline survey of the Korean Genome and Epidemiology Study Ansan-Ansung cohort. Participants were categorized into stable or increasing ASCVD risk groups based on the revised ASCVD risk pooled cohort equation over a median period of exposure of 5.8 years. Incident CKD was defined as two consecutive events of an estimated glomerular filtration rate < 60 mL/min/1.73 m2.

RESULTS

During a median 9.9 years of event accrual period, 449 (8.92%) new-onset CKD cases were identified. Multiple Cox proportional regression analyses showed that the hazard ratio (95% confidence interval) for incident CKD in the increasing group, compared to the stable group, was 2.13 (1.74-2.62) in the unadjusted model and 1.35 (1.02-1.78) in the fully-adjusted model. Significant relationships were maintained in subgroups of individuals in their 50s, without diabetes mellitus or hypertension. The prevalence of proteinuria was consistently higher in the increasing group than that in the stable group.

CONCLUSIONS

An increasing trend in ASCVD risk scores independently predicted adverse renal outcomes in patients without diabetes mellitus or hypertension. Continuous monitoring of ASCVD risk is not only important for predicting cardiovascular disease but also for predicting CKD.

Cardiovascular mortality in patients with advanced chronic kidney disease with and without diabetes: a nationwide cohort study

BACKGROUND

Cardiovascular mortality and the impact of cardiac risk factors in advanced chronic kidney disease (CKD) remain poorly investigated. We examined the risk of cardiovascular mortality in patients with advanced CKD with and without diabetes as well as the impact of albuminuria, plasma hemoglobin, and plasma low-density lipoprotein (LDL) cholesterol levels.

METHODS

In a Danish nationwide registry-based cohort study, we identified persons aged ≥ 18 years with an estimated glomerular filtration rate < 30 mL/min/1.73m2 between 2002 and 2018. Patients with advanced CKD were age- and sex-matched with four individuals from the general Danish population. Cause-specific Cox regression models were used to estimate the 1-year risk of cardiovascular mortality standardized to the distribution of risk factors in the cohort.

RESULTS

We included 138,583 patients with advanced CKD of whom 32,698 had diabetes. The standardized 1-year risk of cardiovascular mortality was 9.8% (95% CI 9.6-10.0) and 7.4% (95% CI 7.3-7.5) for patients with and without diabetes, respectively, versus 3.1% (95% CI 3.1-3.1) in the matched cohort. 1-year cardiovascular mortality risks were 1.1- to 2.8-fold higher for patients with diabetes compared with those without diabetes across the range of advanced CKD stages and age groups. Albuminuria and anemia were associated with increased cardiovascular mortality risk regardless of diabetes status. LDL-cholesterol was inversely associated with cardiovascular mortality risk in patients without diabetes, while there was no clear association in patients with diabetes.

CONCLUSIONS

Diabetes, albuminuria, and anemia remained important risk factors of cardiovascular mortality whereas our data suggest a limitation of LDL-cholesterol as a predictor of cardiovascular mortality in advanced CKD.

Correlation of Serum FGF23 and Chronic Kidney Disease-Mineral and Bone Abnormality Markers With Cardiac Structure Changes in Maintenance Hemodialysis Patients

Background

CKD-MBD is a mineral and bone metabolism syndrome caused by chronic kidney disease. FGF23 is an important factor regulating phosphorus and is the main influencer in the CKD-MBD process. In this study, we observed the correlation among serum FGF23 and calcium, phosphorus and parathyroid hormone, and the correlation between FGF23 levels and cardiac structural changes in MHD patients.

Methods

We examined serum FGF23 concentrations in 107 cases of MHD patients using the ELISA method, recorded demographic information and biochemical data, and analyzed the correlation between serum FGF23 levels and blood calcium and blood phosphorus and PTH levels. All patients were evaluated by cardiac color ultrasound, and we finally analyzed the association between the FGF23 level and cardiac structural changes.

Results

In 107 cases of MHD patients, serum FGF23 levels were linearly associated with serum calcium (r = 0.27 P < 0.01) and parathyroid hormone levels (r = 0.25, P < 0.05). FGF 23 was negatively correlated with age (r = -0.44, P < 0.01).Serum FGF23 levels were correlated with right atrial hypertrophy in HD patients (P < 0.05). No correlation was found among FGF23, left ventricular hypertrophy/enlargement, and valve calcification stenosis (P > 0.05).

Conclusion

Serum FGF23 showed a positive correlation among blood calcium levels and PTH levels in hemodialysis patients, and FGF23 levels can affect the incidence of right atrial hypertrophy in MHD patients.

The metabolic signature of cardiovascular disease and arterial calcification in patients with chronic kidney disease

BACKGROUND AND AIMS

The relationship between chronic kidney disease (CKD) and cardiovascular events is well-established. Clinically recognised risk factors of cardiovascular disease cannot fully explain this association. The objective of the present cross-sectional study was to investigate associations between serum metabolites and prevalent cardiovascular disease, as well as subclinical cardiovascular disease measured as coronary artery calcium score (CACS) in patients with CKD.

METHODS

More than 200 preselected metabolites were quantified using nuclear magnetic resonance spectroscopy in 725 patients and 174 controls from the Copenhagen CKD Cohort. CACS was determined by computed tomography.

RESULTS

Mean age of patients was 57.8 years, and 444 (61.3%) were men. Most of patients had hypercholesterolemia, and 133 (18.3%) had type 2 diabetes. Overall, 85 metabolites were significantly associated with prevalent cardiovascular disease in a model adjusted for eGFR, age, and sex, as well as Bonferroni correction for multiple testing (p < 0.001). After further adjusting for diabetes, BMI, smoking, and cholesterol-lowering medication, the significance was lost for all but six metabolites (concentration of ApoA-1, cholesterol in total HDL and HDL2, total lipids and phospholipids in large HDL particles, and the ratio of phospholipids to total lipids in smaller VLDL particles). Of the 85 metabolites associated with prevalent cardiovascular disease, 71 were also associated with CACS in a similar pattern. Yet, in the model adjusted for all seven cardiovascular risk factors, only serum glucose levels and the ratio of triglycerides to total lipids in larger LDL particles remained significant.

CONCLUSIONS

In patients with CKD, associations with prevalent cardiovascular disease were mainly found for HDL-related metabolites, while CACS was associated with glucose levels and increased triglycerides to total lipids ratio in LDL particles.

Kynurenine pathway in kidney diseases

Kidney diseases have become one of the most common health care problems. Due to a growing number of advanced aged patients with concomitant disorders the prevalence of these diseases will increase over the coming decades. Despite available laboratory tests, accurate and rapid diagnosis of renal dysfunction has yet to be realized, and prognosis is uncertain. Moreover, data on diagnostic and prognostic markers in kidney diseases are lacking. The kynurenine (KYN) pathway is one of the routes of tryptophan (Trp) degradation, with biologically active substances presenting ambiguous properties. The KYN pathway is known to be highly dependent on immunological system activity. As the kidneys are one of the main organs involved in the formation, degradation and excretion of Trp end products, pathologies involving the kidneys result in KYN pathway activity disturbances. This review aims to summarize changes in the KYN pathway observed in the most common kidney disease, chronic kidney disease (CKD), with a special focus on diabetic kidney disease, acute kidney injury (AKI), glomerulonephritis and kidney graft function monitoring. Additionally, the importance of KYN pathway activity in kidney cancer pathogenesis is discussed, as are available pharmacological agents affecting KYN pathway activity in the kidney. Despite limited clinical data, the KYN pathway appears to be a promising target in the diagnosis and prognosis of kidney diseases. Modulation of KYN pathway activity by pharmacological agents should be considered in the treatment of kidney diseases.

Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review

Background and objectives: cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. Materials and methods: the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). Results: sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. Conclusions: the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.