Proteomic cardiovascular risk assessment in chronic kidney disease

Machine learning in cardiovascular risk assessment: Towards a precision medicine approach

Cardiovascular diseases remain the leading cause of global morbidity and mortality. Validated risk scores are the basis of guideline-recommended care, but most scores lack the capacity to integrate complex and multidimensional data. Limitations inherent to traditional risk prediction models and the growing burden of residual cardiovascular risk highlight the need for refined strategies that go beyond conventional paradigms. Artificial intelligence and machine learning (ML) provide unique opportunities to refine cardiovascular risk assessment and surveillance through the integration of diverse data types and sources, including clinical, electrocardiographic, imaging and multi-omics derived data. In fact, ML models, such as deep neural networks, can handle high-dimensional data through which phenotyping and cardiovascular risk assessment across diverse patient populations become much more precise, fostering a paradigm shift towards more personalized care. Here, we review the role of ML in advancing cardiovascular risk assessment and discuss its potential to identify novel therapeutic targets and to improve prevention strategies. We also discuss key challenges inherent to ML, such as data quality, standardized reporting, model transparency and validation, and discuss barriers in its clinical translation. We highlight the transformative potential of ML in precision cardiology and advocate for more personalized cardiovascular prevention strategies that go beyond previous notions.

Dried Blood Spots to Assess Cardiovascular-Kidney-Metabolic Health

Dried blood spot sampling offers a scalable strategy to close diagnostic gaps and improve global surveillance for cardiovascular-kidney-metabolic syndrome. However, assay performance and the extent of validity vary widely between biomarkers used in cardiovascular-kidney-metabolic health assessment under different settings and have not been well described. To fill this gap, we conducted a systematic search of the literature and a narrative synthesis through April 2024 and included reports with laboratory or field validation measuring biomarkers that can be used in cardiovascular-kidney-metabolic health assessment. We categorized assays into categories based on laboratory validation: excellent performance (r>0.95 with gold standard methods and coefficients of variation <5%), very good performance (r>0.90 and coefficients of variation <10%), reasonable performance (r>0.80 and coefficients of variation <15%), and poor performance (r<0.80 or coefficients of variation >15%). The extent of validation was determined by the total number of field validation studies with strong agreement. Hemoglobin A1c has strong laboratory and field validation and should be considered for expansion into clinical testing in low-resource settings. Traditional lipid biomarkers showed poor performance in field validation studies, but apoB (apolipoprotein B), creatinine, cystatin C, and NT-proBNP (N-terminal prohormone of brain natriuretic peptide) showed promising initial laboratory validation results and deserve greater attention in field validation studies. High-sensitivity C-reactive protein has strong laboratory and field validation but has limited clinical utility. Dried blood spot assays have been developed for biomarkers that offer mechanistic insights including inflammatory and vascular injury markers, fatty acids, malondialdehyde, asymmetric dimethylarginine, trimethylamine N-oxide, carnitines, and omics.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Proteomic Assessment of the Risk of Secondary Cardiovascular Events among Individuals with CKD

Key Points

Machine learning and large-scale proteomics led to a 16-protein secondary cardiovascular risk model in patients with CKD. Hepatic fibrosis and liver X receptor activation represented biologic pathways that link kidney disease and risk of secondary cardiovascular events. An understanding of the circulating proteins associated with secondary cardiovascular events may help to identify novel therapeutic targets.

Background

Cardiovascular risk models have been developed primarily for incident events. Well-performing models are lacking to predict secondary cardiovascular events among people with a history of coronary heart disease, stroke, or heart failure who also have CKD. We sought to develop a proteomic risk score for cardiovascular events in individuals with CKD and a history of cardiovascular disease.

Methods

We measured 4638 plasma proteins among 1067 participants from the Chronic Renal Insufficiency Cohort (CRIC) and 536 individuals from the Atherosclerosis Risk in Communities (ARIC) Cohort. All had non–dialysis-dependent CKD and coronary heart disease, heart failure, or stroke at study baseline. A proteomic risk model for secondary cardiovascular events was derived by elastic net regression in CRIC, validated in ARIC, and compared with clinical models. Biologic mechanisms of secondary events were characterized through proteomic pathway analysis.

Results

A 16-protein risk model was superior to the Framingham Risk Score for secondary events, including a modified score that included eGFR. In CRIC, the annualized area under the receiver operating characteristic curve (area under the curve) within 1–5 years ranged between 0.77 and 0.80 for the protein model and 0.57 and 0.72 for the clinical models. These findings were replicated in the ARIC validation cohort. Biologic pathway analysis identified pathways and proteins for cardiac remodeling and fibrosis, vascular disease, and thrombosis.

Conclusions

The proteomic risk model for secondary cardiovascular events outperformed clinical models on the basis of traditional risk factors and eGFR.

Integrative proteomic analyses across common cardiac diseases yield mechanistic insights and enhanced prediction

Cardiac diseases represent common highly morbid conditions for which molecular mechanisms remain incompletely understood. Here we report the analysis of 1,459 protein measurements in 44,313 UK Biobank participants to characterize the circulating proteome associated with incident coronary artery disease, heart failure, atrial fibrillation and aortic stenosis. Multivariable-adjusted Cox regression identified 820 protein-disease associations-including 441 proteins-at Bonferroni-adjusted P < 8.6 × 10-6. Cis-Mendelian randomization suggested causal roles aligning with epidemiological findings for 4% of proteins identified in primary analyses, prioritizing therapeutic targets across cardiac diseases (for example, spondin-1 for atrial fibrillation and the Kunitz-type protease inhibitor 1 for coronary artery disease). Interaction analyses identified seven protein-disease associations that differed Bonferroni-significantly by sex. Models incorporating proteomic data (versus clinical risk factors alone) improved prediction for coronary artery disease, heart failure and atrial fibrillation. These results lay a foundation for future investigations to uncover disease mechanisms and assess the utility of protein-based prevention strategies for cardiac diseases.

Application of proteomics for novel drug discovery and risk prediction optimisation in stroke and myocardial infarction: a review of in-human studies

The use of proteomics in human studies investigating stroke and myocardial infarction (MI) has been increasing, prompting a review of the literature. This revealed proteinaceous biomarkers of stroke from thrombi, brain tissue, cells, and particles, some of which cross the blood-brain barrier (BBB). Several proteins were also implicated in coronary artery disease (CAD), which often underlies MI, cholesterol transportation, and inflammation. Furthermore, the platelet proteome revealed itself as a potential therapeutic target, along with differentially expressed proteins associated with MI progression. Moreover, proteomic data enhanced the performance of conventional risk scores and causal protein discovery has improved interventions and drug development for patients with MI and other conditions. These findings suggest that proteomics holds much promise for future stroke and MI research.

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841-0.868] vs. 0.845 [0.829-0.860] vs 0.553 [0.528-0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843-0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854-0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.

Enhanced prediction of atrial fibrillation risk using proteomic markers: a comparative analysis with clinical and polygenic risk scores

BACKGROUND

Proteomic biomarkers have shown promise in predicting various cardiovascular conditions, but their utility in assessing the risk of atrial fibrillation (AF) remains unclear. This study aimed to develop and validate a protein-based risk score for predicting incident AF and to compare its predictive performance with traditional clinical risk factors and polygenic risk scores in a large cohort from the UK Biobank.

METHODS

We analysed data from 36 129 white British individuals without prior AF, assessing 2923 plasma proteins using the Olink Explore 3072 assay. The cohort was divided into a training set (70%) and a test set (30%) to develop and validate a protein risk score for AF. We compared the predictive performance of this score with the HARMS2-AF risk model and a polygenic risk score.

RESULTS

Over an average follow-up of 11.8 years, 2450 incident AF cases were identified. A 47-protein risk score was developed, with N-terminal prohormone of brain natriuretic peptide (NT-proBNP) being the most significant predictor. In the test set, the protein risk score (per SD increment, HR 1.94; 95% CI 1.83 to 2.05) and NT-proBNP alone (HR 1.80; 95% CI 1.70 to 1.91) demonstrated superior predictive performance (C-statistic: 0.802 and 0.785, respectively) compared with HARMS2-AF and polygenic risk scores (C-statistic: 0.751 and 0.748, respectively).

CONCLUSIONS

A protein-based risk score, particularly incorporating NT-proBNP, offers superior predictive value for AF risk over traditional clinical and polygenic risk scores, highlighting the potential for proteomic data in AF risk stratification.

Large-Scale Proteomics Improve Prediction of Chronic Kidney Disease in People With Diabetes

OBJECTIVE

To develop and validate a protein risk score for predicting chronic kidney disease (CKD) in patients with diabetes and compare its predictive performance with a validated clinical risk model (CKD Prediction Consortium [CKD-PC]) and CKD polygenic risk score.

RESEARCH DESIGN AND METHODS

This cohort study included 2,094 patients with diabetes who had proteomics and genetic information and no history of CKD at baseline from the UK Biobank Pharma Proteomics Project. Based on nearly 3,000 plasma proteins, a CKD protein risk score including 11 proteins was constructed in the training set (including 1,047 participants; 117 CKD events).

RESULTS

The median follow-up duration was 12.1 years. In the test set (including 1,047 participants; 112 CKD events), the CKD protein risk score was positively associated with incident CKD (per SD increment; hazard ratio 1.78; 95% CI 1.44, 2.20). Compared with the basic model (age + sex + race, C-index, 0.627; 95% CI 0.578, 0.675), the CKD protein risk score (C-index increase 0.122; 95% CI 0.071, 0.177), and the CKD-PC risk factors (C-index increase 0.175; 95% CI 0.126, 0.217) significantly improved the prediction performance of incident CKD, but the CKD polygenic risk score (C-index increase 0.007; 95% CI -0.016, 0.025) had no significant improvement. Adding the CKD protein risk score into the CKD-PC risk factors had the largest C-index of 0.825 (C-index from 0.802 to 0.825; difference 0.023; 95% CI 0.006, 0.044), and significantly improved the continuous 10-year net reclassification (0.199; 95% CI 0.059, 0.299) and 10-year integrated discrimination index (0.041; 95% CI 0.007, 0.083).

CONCLUSIONS

Adding the CKD protein risk score to a validated clinical risk model significantly improved the discrimination and reclassification of CKD risk in patients with diabetes.

Research hotspots and future trends in lipid metabolism in chronic kidney disease: a bibliometric and visualization analysis from 2004 to 2023

Background

Disorders of lipid metabolism play a key role in the initiation and progression of chronic kidney disease (CKD). Recently, research on lipid metabolism in CKD has rapidly increased worldwide. However, comprehensive bibliometric analyses in this field are lacking. Therefore, this study aimed to evaluate publications in the field of lipid metabolism in CKD over the past 20 years based on bibliometric analysis methods to understand the important achievements, popular research topics, and emerging thematic trends.

Methods

Literature on lipid metabolism in CKD, published between 2004 and 2023, was retrieved from the Web of Science Core Collection. The VOSviewer (v.1.6.19), CiteSpace (v.6.3 R1), R language (v.4.3.2), and Bibliometrix (v.4.1.4) packages (https://www.bibliometrix.org) were used for the bibliometric analysis and visualization. Annual output, author, country, institution, journal, cited literature, co-cited literature, and keywords were also included. The citation frequency and H-index were used to evaluate quality and influence.

Results

In total, 1,285 publications in the field of lipid metabolism in CKD were identified in this study. A total of 7,615 authors from 1,885 institutions in 69 countries and regions published articles in 466 journals. Among them, China was the most productive (368 articles), and the United States had the most citations (17,880 times) and the highest H-index (75). Vaziri Nosratola D, Levi Moshe, Fornoni Alessia, Zhao Yingyong, and Merscher Sandra emerged as core authors. Levi Moshe (2,247 times) and Vaziri Nosratola D (1,969 times) were also authors of the top two most cited publications. The International Journal of Molecular Sciences and Kidney International are the most published and cited journals in this field, respectively. Cardiovascular disease (CVD) and diabetic kidney disease (DKD) have attracted significant attention in the field of lipid metabolism. Oxidative stress, inflammation, insulin resistance, autophagy, and cell death are the key research topics in this field.

Conclusion

Through bibliometric analysis, the current status and global trends in lipid metabolism in CKD were demonstrated. CVD and DKD are closely associated with the lipid metabolism of patients with CKD. Future studies should focus on effective CKD treatments using lipid-lowering targets.

The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects

Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.

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.

Gualou xiebai decoction ameliorates cardiorenal syndrome type II by regulation of PI3K/AKT/NF-κB signalling pathway

BACKGROUND

Cardiorenal syndromes type II (CRS2) is a multi-organ ailment that manifests as a combination of cardiac and renal dysfunction, resulting in chronic kidney disease due to chronic cardiac insufficiency. It affects at least 26 million people worldwide, and its prevalence is increasing. Gualou Xiebai Decoction (GXD), a traditional Chinese medicine (TCM) with a rich history of application in the management of coronary artery disease, has been explored for its potential therapeutic benefits in CRS2. Nevertheless, the mechanism by which GXD alleviates CRS2 remains obscure, necessitating further investigation.

PURPOSE

The aim of this study was to assess the effects of the ethanolic extract of GXD on CRS2 and to elucidate the underlying mechanism in a rat model of myocardial infarction, offering a potential target for clinical treatment for CRS2.

STUDY DESIGN AND METHODS

A rat model of CRS2 was induced by surgical myocardial infarction and treated with GXD for 10 weeks. Cardiac function was assessed using echocardiography, while serum and urine biochemistry were analyzed to evaluate potential cardiac and renal damage. Furthermore, tissue samples were obtained for histological, protein, and genetic investigations. In addition, network pharmacology analysis and molecular docking were utilized to predict the primary active compounds, potential therapeutic targets, and interventional pathways through which GXD could potentially exert its effects on CRS2. Subsequently, these predictions were confirmed in vivo and vitro through various analyses.

RESULTS

The current investigation employed echocardiography to exhibit the apparent cardiac remodeling following the induction of myocardial infarction. Damage to the heart and kidneys of CRS2 rats was effectively ameliorated by administration of GXD. The outcomes derived from the analyses of HE and Masson staining indicated that the pathological damage to the heart and kidney tissues of rats in the GXD groups was considerably alleviated. Using network pharmacology analysis, AKT1, IL-6, and TNF-α were identified as plausible therapeutic targets for the treatment of CRS with GXD. Subsequent functional and pathway enrichment analysis of the underlying targets disclosed that the PI3K/AKT/NF-κB signaling pathway may be involved in the mechanism of GXD in the treatment of CRS2. Immunohistochemical, western blot, RT-PCR and immunofluorescence staining were employed to demonstrate that GXD can regulate the PI3K/AKT/NF-κB signaling pathway in the CRS2 rat model. Ultimately, administration of the PI3K/AKT agonist 740Y-P counteracted the effect of diosmetin, which was one of the potential active components of GXD analysed by compound-target-disease network, on p-PI3K and p-AKT in vitro.

CONCLUSIONS

The findings of this study suggest that GXD improves cardiac and renal function in CRS2 rats and that the underlying mechanism involves inhibition of the PI3K/AKT/NF-κB pathway.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

The emerging Janus face of SVEP1 in development and disease

Sushi, von Willebrand factor type A, EGF, and pentraxin domain containing 1 (SVEP1) is a large extracellular matrix protein that is also detected in circulation. Recent plasma proteomic and genomic studies have revealed a large number of associations between SVEP1 and human traits, particularly chronic disease. These include associations with cardiac death and disease, diabetes, platelet traits, glaucoma, dementia, and aging; many of these are causal. Animal models demonstrate that SVEP1 is critical in vascular development and disease, but its molecular and cellular mechanisms remain poorly defined. Future studies should aim to characterize these mechanisms and determine the diagnostic, prognostic, and therapeutic value of measuring or intervening on this enigmatic protein.

Proteomics of CKD progression in the chronic renal insufficiency cohort

Progression of chronic kidney disease (CKD) portends myriad complications, including kidney failure. In this study, we analyze associations of 4638 plasma proteins among 3235 participants of the Chronic Renal Insufficiency Cohort Study with the primary outcome of 50% decline in estimated glomerular filtration rate or kidney failure over 10 years. We validate key findings in the Atherosclerosis Risk in the Communities study. We identify 100 circulating proteins that are associated with the primary outcome after multivariable adjustment, using a Bonferroni statistical threshold of significance. Individual protein associations and biological pathway analyses highlight the roles of bone morphogenetic proteins, ephrin signaling, and prothrombin activation. A 65-protein risk model for the primary outcome has excellent discrimination (C-statistic[95%CI] 0.862 [0.835, 0.889]), and 14/65 proteins are druggable targets. Potentially causal associations for five proteins, to our knowledge not previously reported, are supported by Mendelian randomization: EGFL9, LRP-11, MXRA7, IL-1 sRII and ILT-2. Modifiable protein risk markers can guide therapeutic drug development aimed at slowing CKD progression.

Life's Essential 8 and Mortality in US Adults with Chronic Kidney Disease

INTRODUCTION

The current prevalence of chronic kidney disease (CKD) is substantial, and CKD individuals face a heightened risk of mortality, encompassing both all-cause and cause-specific outcomes. The current study aims to investigate the potential impact of adhering to Life's Essential 8 (LE8) on reducing mortality among CKD individuals.

METHODS

Using the National Health and Nutrition Survey (NHANES) data from 2005 to 2018, we analyzed 22,420 US adults (≥20 years old). CKD is defined by urinary albumin-to-creatinine ratio (≥30 mg/g or 3 mg/mmol) and estimated glomerular filtration rate (<60 mL/min/1.73 m2). The components of LE8, including diet, physical activity (PA), nicotine exposure, sleep, body mass index, blood lipids, blood glucose, and blood pressure (BP), were measured and given a score of 0-100. The total LE8 score was the unweighted average of all components and was divided into low cardiovascular health (CVH) (0-49), moderate CVH (50-79), and high CVH (80-100). A Cox proportional hazards regression model was used to explore the associations of LE8 with all-cause, cardiovascular disease (CVD), and cancer mortality, which were followed prospectively by the National Center for Health Statistics until December 31, 2019.

RESULTS

In the overall population, individuals with moderate CVH had a 47% lower risk of CKD, while high CVH was linked to a 55% lower risk compared to low CVH. During a median follow-up of 7.58 years, CKD individuals had a 93% higher all-cause mortality rate and a 149% higher CVD mortality rate compared to those without CKD. Among the CKD individuals, every 10-point increase in the LE8 score was associated with reduced risks of 17% for all-cause mortality (especially PA, nicotine exposure, blood glucose, and BP), 18% for CVD mortality (especially PA), and 12% for cancer mortality (especially PA and sleep health). In additional and sensitivity analysis, the results remained significant after further consideration of potential confounding of renal function. Additionally, LE8 demonstrated superior risk stratification for CVD mortality among CKD patients compared with LS7. Interaction was observed between LE8 and age, education level, marital status, and drinking status.

CONCLUSION

The current study demonstrates that adherence to higher LE8 levels within CKD individuals is associated with a reduced risk of both all-cause and cause-specific mortality.