Chronic Kidney Disease Measures For Cardiovascular Risk Prediction
Mar 15, 2022
Contact: Audrey Hu Whatsapp/hp: 0086 13880143964 Email: audrey.hu@wecistanche.com
Yejin Mok 1, Shoshana H. Ballew 1, Kunihiro Matsushita *
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Welch Center for Prevention, Epidemiology, and Clinical Research, USA
ABSTRACT
Chronic kidney disease (CKD) affects 15–20% of adults globally and causes various complications, one of the most important being cardiovascular disease (CVD). CKD has been associated with many CVD subtypes, especially severe ones like heart failure, independent of potential confounders such as diabetes and hypertension. There is no consensus in major clinical guidelines as to how to incorporate the two key measures of CKD (glomerular filtration rate and albuminuria) for CVD risk prediction. This is a critical missed opportunity to appropriately refine predicted risk and personalize prevention therapies according to CKD status, particularly since these measures are often already evaluated in clinical care. In this review, we provide an overview of CKD definition and staging, the subtypes of CVD most associated with CKD (chronic kidney disease), major pathophysiological mechanisms, and the current state of CKD as a predictor of CVD in major clinical guidelines. We will introduce the novel concept of a “CKD Add-on”, which allows the incorporation of CKD (chronic kidney disease) measures in existing risk prediction models, and the implications of taking into account CKD in the management of CVD risk.
Keywords:
Chronic kidney disease, Cardiovascular disease, Risk prediction
Benefit of cistanche: Treating cardiovascular and cerebrovascular diseases
1. Introduction
Chronic kidney disease (CKD) affects 15–20% of adults in many countries around the world and causes various complications [1]. As a consequence, the 2019 Global Burden of Disease study ranked CKD as the 12th leading risk factor impacting disability-adjusted life-years [2]. Cardiovascular disease (CVD) is recognized as one of the most important complications of CKD (chronic kidney disease), as well as a leading cause of death in patients with CKD. Of note, CKD has been associated with many CVD subtypes, especially severe ones like heart failure, independent of potential confounders such as diabetes and hypertension.
Despite a huge body of evidence demonstrating the excess risk of CVD in patients with CKD, there is no consensus in major clinical guidelines as to how to incorporate CKD for the risk prediction of CVD. This can result in inconsistent practice among healthcare providers and suboptimal management of CVD risk in persons with CKD. Importantly, unlike some novel biomarkers, measures of CKD (e.g., serum creatinine) are often evaluated in clinical practice. Thus, this is a critical missed opportunity to appropriately refine predicted risk with readily available information and personalize prevention therapies according to CKD status.
In this review, we provide an overview of the CVD subtypes that have been linked to CKD (chronic kidney disease), major pathophysiological mechanisms, and the current situation regarding CKD as a predictor of CVD in major clinical guidelines. Then, we will introduce the novel concept of a “CKD Add-on”, which allows the incorporation of CKD into existing risk prediction models, and conclude with the implications of taking into account CKD in the management of CVD risk.
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2. Definition and staging of CKD
Current guidelines define CKD as an abnormality detected through pathology, imaging, or assessment of blood and urine tests. Glomerular filtration rate (GFR) and albuminuria are often the two key components to define and stage CKD. Indeed, the Kidney Disease Improving Global Outcomes (KDIGO) guideline classifies CKD (chronic kidney disease) by level of GFR (stage G1- G5) and level of albuminuria (stage A1-A3) (Fig. 1) [3]. This classification is based on graded risk increases in each of these categories for major outcomes such as all-cause and cardiovascular mortality and kidney failure [4]. The associations of lower GFR and higher albuminuria with adverse outcomes were largely consistent across age groups, race, sex, and the presence of hypertension and diabetes [5–9]. GFR is cumbersome to measure directly, so clinical providers or researchers usually use estimated GFR (eGFR), calculated from the serum
concentration of a kidney filtration marker. Serum creatinine is recommended as the primary filtration marker by the current KDIGO guideline [3]; however, this marker may be impacted by non-kidney determinants such as muscle mass, raising a concern of misclassification in older or frail individuals [10]. There has also been recent controversy over the inclusion of race in the current equations for eGFR with serum creatinine [11–13]. In this context, another major filtration marker, cystatin C, is not affected by muscle mass (but by other non-kidney determinants like inflammation [14,15]), and its equation for GFR does not include a term for the race, potentially accelerating the utilization as a filtration marker in clinical practice and research. Importantly, eGFR based on cystatin C vs. serum creatinine has been more strongly associated with adverse outcomes [16,17].
Fig. 1. 2012 KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease, Stages and Prognosis of CKD (chronic kidney disease). Green: low risk (if no other markers of kidney disease, no CKD); yellow: moderately increased risk; orange: high risk; red: very high risk. Adapted from Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group [124].
Urine albumin-to-creatinine ratio (ACR) is the guideline preferred measure for albuminuria [3]. Albumin is the most common protein type in the urine and a marker of kidney damage. ACR can be measured from spot urine samples, instead of 24-h urine collection, and elevated levels are a strong predictor for cardiovascular outcomes, even when still in the normal range [18,19]. However, ACR is not measured as regularly due to either cost restraints or differences in practice patterns, so urinary protein may instead be assessed using the total protein in urine protein-to-creatinine ratio (PCR) or using a semiquantitative urine dipstick for protein. These other measures may not be as standardized or sensitive as ACR [20], but the guideline does allow for substitution with these measures should ACR be unavailable [3]. Conveniently, a tool to convert PCR and urine dipstick protein to ACR has been recently released [21].
3. CKD as a risk factor of CVD: epidemiological perspectives
As expressed by the term “cardiorenal syndrome,” a close link between kidney and heart has been long recognized. However, the impact of CKD on the cardiovascular system is far beyond heart disease. Indeed, a number of CVD outcomes have been linked to CKD (chronic kidney disease): coronary heart disease, heart failure, atrial fibrillation, sudden cardiac death, stroke, abdominal aortic aneurysm, peripheral artery disease, and venous thrombosis (Fig. 2) [1,18,19,22–27]. This multifaceted impact of CKD is likely to reflect a number of pathophysiological pathways provoked by CKD, as summarized in the next section.
Since no studies have explored all of these CVD outcomes in a single population using the same analytic approach, it is not easy to compare the strength of the associations with CKD. However, a series of individual-participant level meta-analysis studies from the CKD Prognosis Consortium (CKD-PC) shed some light on this issue. For example, CKD-PC has compared the associations of CKD (chronic kidney disease) with a few CVD outcomes and found that the association of CKD was stronger with heart failure and cardiovascular mortality than coronary heart disease and stroke (Fig. 3) [18]. CKD-PC extended the analysis to peripheral artery disease and observed that albuminuria was particularly strongly associated with the risk of lower extremity amputation (Fig. 3) [19]. Thus, CKD has demonstrated particularly strong associations with severe types of CVD outcomes.
4. Mechanisms behind the increased risk of CVD in CKD
Although pathophysiological pathways linking CKD (chronic kidney disease) to CVD risk have not been fully understood, several mechanisms such as shared risk factors between CKD and CVD, volume overload, bone-mineral metabolism disorder, uremic toxins, anemia, inflammation, and oxidative stress play a role. The key mechanisms may vary according to CKD (chronic kidney disease) stages (e.g., shared risk factors playing a major role at milder stages and uremic toxins at more severe stages). There are several previous review articles providing an in-depth discussion of these pathophysiological pathways [28,29], and thus we provide a high-level summary here.
Fig. 2. Impact of CKD on the cardiovascular system. CKD is associated with a number of CVD outcomes such as coronary heart disease, heart failure, atrial fibrillation, sudden cardiac death, stroke, abdominal aortic aneurysm, peripheral artery disease, and venous thrombosis.
Fig. 3. Adjusted hazard ratios and 95% CIs (shaded areas) of coronary heart disease (A and B), stroke (C and D), heart failure (E and F), cardiovascular mortality (G and H), and peripheral artery disease (PAD) defined by leg amputation (I and J) according to eGFR (left column) and ACR (right column). The reference is eGFR 95 ml/min/1.73 m2 and ACR 5 mg/g (diamond). Dots represent statistical significance (p < 0.05). *Adjustments were for age, sex, race/ethnicity, smoking, systolic blood pressure, antihypertensive drugs, diabetes, total and high-density lipoprotein cholesterol concentrations, and albuminuria (ACR or dipstick) or eGFR, as appropriate. Adapted from Matsushita et al. [18, 19].
4.1. Shared risk factors between CKD and CVD
Major shared risk factors between CKD (chronic kidney disease) and CVD include diabetes and hypertension. Diabetes is the leading cause of CKD and dialysis in many countries [30] and considerably increases the risk of various CVD subtypes such as myocardial infarction, stroke, and heart failure [31]. Hypertension is another major cause of CKD and is the leading cause of CVD, especially stroke [32,33]. From a slightly different perspective, CKD can cause lipid disorders, such as reduced high-density lipoprotein cholesterol and elevated oxidized low-density lipoprotein cholesterol, which enhances CVD risk [34].
4.2. Volume/pressure overload
Kidney dysfunction, typically at advanced stages, results in volume overload [35], which can cause abnormalities of cardiac structures and functions [36,37]. Volume overload is the consequence of excessive sodium loading by reduced glomerular filtration. Hypertension in CKD (chronic kidney disease) is often driven by sodium dysregulation and alterations in renin-angiotensin-aldosterone system activity and can result in cardiac pressure overload and, thus, left ventricular hypertrophy [38–40]. Indeed, a large community-based study demonstrated increased left ventricular mass tightly associated with both lower eGFR and higher ACR [37]. This study also found a link between CKD and left ventricular dilation and diastolic dysfunction, which could partly explain why CKD patients have a high risk of heart failure [37,41,42].
4.3. Bone-mineral metabolism
The relationship between CKD (chronic kidney disease) and disorders of bone-mineral metabolism is well-known. Abnormality of blood calcium and phosphorus levels can be seen in advanced CKD [43], and these electrolytes can accelerate vascular calcification [44,45] and increase CVD risk in patients with CKD [46]. Elevated phosphorus has direct effects on vascular smooth muscle cells and causes vascular calcification [47]. In general, calcium and phosphorus concentrations are tightly regulated by hormones such as parathyroid hormone, vitamin D, and fibroblast growth factor 23. However, some of these hormones may increase the risk of CVD [48–50]. For example, several studies have shown that fibroblast growth factor 23 levels promote cardiomyocyte growth by binding to fibroblast growth factor receptors [51], which may induce left ventricular hypertrophy [51,52], an important precursor of heart failure.
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4.4. Dyskalemia
Potassium abnormality is a common condition in CKD (chronic kidney disease) [53]. The prevalence of hypokalemia (≤4 mEq/L) and hyperkalemia (≥5 mEq/L) are similar in CKD (12%–18%), but severe hyperkalemia (≥5.5 mEq/L) is more common than severe hypokalemia (≤3.5 mEq/L) (3%–8% vs. 1%–3%, respectively) [54]. Dyslexia occurs due to comorbidities and medication use commonly associated with CKD. Diabetes, heart failure, and renin-angiotensin-aldosterone system inhibitors increase the risk of hyperkalemia, whereas the use of some diuretics increases the risk of hypokalemia [54,55]. Reduced urine potassium excretion in CKD is also a contributor to hyperkalemia [56]. Both hypo- and hyper-kalemia are associated with the risk of arrhythmias that could partly explain why CKD patients have a high risk of sudden cardiac death [57,58].
4.5. Uremic toxins
A number of solutes can accumulate in patients with CKD (chronic kidney disease) due to their reduced clearance by the kidney. Among uremic retention solutes, indoxyl sulfate and p-cresyl sulfate have attracted attention [59]. Both of these uremic solutes are generated in the process of protein fermentation by microbiota in the gut and are shown to cause endothelial dysfunction [60]. In addition, both uremic toxins have been suggested to induce vascular stiffness and vascular calcification in patients with CKD [61, 62]. Moreover, a few observational studies have shown their independent associations with CVD events in patients with CKD [63,64]. In addition, asymmetric dimethylarginine, a uremic toxin generated from endogenous metabolism, promotes endothelial dysfunction by inhibiting the production of nitric oxide [65]. In observational studies, a high level of asymmetric dimethylarginine was independently associated with cardiovascular outcomes in patients with CKD [66,67].
4.6. Anemia
Since the kidney produces erythropoietin, anemia is a common complication of reduced kidney function [68] and may contribute to developing CVD. Anemia and consequential impaired oxygen delivery increase cardiac workload [69]. Indeed, a few observational studies have shown that anemia in CKD (chronic kidney disease) was associated with adverse CVD outcomes, such as coronary heart disease, stroke, and heart failure [70–72].
4.7. Inflammation and oxidative stress
CKD is considered a chronic proinflammatory state. For example, impaired kidney function prolongs the plasma half-life of several proinflammatory cytokines, such as interleukin-6 [73]. Also, increased translocation of intestinal bacterial products has been reported in CKD (chronic kidney disease) and can induce systemic inflammation [74,75]. Several studies have reported increased levels of inflammatory markers such as C-reactive protein, interleukin-6, and tumor necrosis factor-α in patients with CKD [76,77]. These markers have been associated with adverse CVD phenotypes, including atherosclerosis, atrial fibrillation, arterial stiffness, and left ventricular hypertrophy, in patients with CKD [78–84].
CKD is also associated with oxidative stress [85–87]. In CKD, antioxidant capacity can be reduced, leading to increased oxidation of proteins, carbohydrates, and lipids which can cause cardiovascular damage [88]. Also, accumulated uremic toxins can increase oxidative stress, which can cause endothelial dysfunction and thus increase the risk of CVD [75,87]. Elevated oxidative stress has been shown to induce calcium deposition in vascular smooth muscle cells and promote vascular calcification in CKD (chronic kidney disease) patients [89].
5. Incorporating CKD in CVD risk prediction
5.1. Inconsistency in major clinical guidelines for primary prevention
Guidelines are inconsistent in terms of incorporation of CKD (chronic kidney disease) measures into risk prediction. The 2018 American Heart Association/ American College of Cardiology (AHA/ACC) Cholesterol Guideline recommends the use of the Pooled Cohort Equation (PCE) for prediction of the 10-year risk of developing atherosclerotic CVD (ASCVD) [90]. This equation uses traditional cardiovascular risk factors but does not include CKD measures. Nonetheless, this guideline acknowledges GFR 15–59 ml/min/1.73 m2 as a risk-enhancing factor. However, it does not specify how to incorporate into risk prediction and does not include albuminuria as a risk enhancer.
The 2016 European Society of Cardiology (ESC) Guideline on Cardiovascular Disease Prevention in Clinical Practice is developed by a joint task force of the ESC as well as representatives from other societies, including the European Atherosclerosis Society (EAS) [91], and uses a risk prediction tool, Systematic Coronary Risk Evaluation (SCORE), for 10-year cardiovascular mortality based on traditional risk factors [92]. Regarding the incorporation of CKD measures, the ESC guideline categorizes eGFR <30 ml/min/1.73 m2 or diabetes plus albuminuria as “very high” risk and eGFR 30–59 ml/min/1.73 m2 as “high” risk, regardless of traditional risk factors. Similar to the AHA/ACC guideline, the 2016 ESC guideline puts more emphasis on eGFR over albuminuria. While this may reflect greater awareness and availability of information on kidney function (e.g., serum creatinine) in clinical practice as compared to albuminuria [93], this is an important caveat given growing evidence demonstrating albuminuria as a stronger predictor of CVD than eGFR [18,94].
The new 2021 ESC guideline has been just released and to some extent overcomes this caveat by recognizing albuminuria as a key measure to define CKD, in addition to eGFR [95]. More specifically, this new guideline categorizes severe CKD (i.e., red zone in Fig. 1) and moderate CKD (i.e., orange zone in Fig. 1), according to the KDIGO CKD (chronic kidney disease) guideline, as “very high” and “high” CVD risk. However, this approach does not take into account other major risk factors and thus may misclassify the CVD risk [96]. This new guideline recommends using a new risk prediction equation, SCORE2, for estimating 10-year total CVD risk, but unfortunately, this equation does not incorporate the two key CKD measures, eGFR and albuminuria [95]. The approach of the 2021 ESC guideline seems quite similar to that in the 2021 Canadian Cardiovascular Society Guidelines and the 2013 KDIGO Lipids Guideline, which also recognize albuminuria (ACR ≥30 mg/g) as a key measure to define CKD [97,98] and recommend statins in all patients with CKD aged ≥50 years, regardless of other risk factors. For CKD patients younger than 50 years of age, these two guidelines recommend predicting coronary disease risk but do not specify a risk calculator to be used.
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5.2. "CKD Add-on" as a new approach to incorporate CKD on top of PCE and SCORE
Even if the aforementioned clinical guidelines want to incorporate CKD measures into their own risk prediction models, they cannot simply do so since most studies deriving those risk prediction models (i.e., PCE and SCORE) did not fully evaluate the two key CKD (chronic kidney disease) measures, eGFR, and albuminuria. To overcome this caveat, we have recently proposed a new approach of a “CKD Add-on” (originally called “CKD Patch”) [99]. This approach allows us to calibrate the predicted risk based on the difference between an individual’s actual levels of CKD measures and their “expected” values from traditional CVD risk factors like age and diabetes (e. g., an individual with actual eGFR 45 ml/min/1.73 m2 and expected eGFR 60 ml/min/1.73 m2 had eGFR 15 ml/min/1.73 m2 lower than expected and thus the predicted risk of this individual should be higher than originally predicted without taking eGFR into account) and the strength of associations between the CKD measures with cardiovascular outcomes of interest (i.e., hazard ratios adjusted for traditional CVD risk factors). The general concept of “CKD Add-on” is visually presented in Fig. 4. We recently validated that the addition of CKD measures with this “CKD Add-on” approach improved the risk prediction of CVD outcomes on top of two major risk prediction models used in clinical guidelines, the PCE and SCORE [100].
For example, a Caucasian managed 55 years old is considered in the intermediate-risk category with a 10-year predicted risk of ASCVD of 9.7% according to PCE if he has the following risk factor profile: current smoking, no diabetes, systolic blood pressure 130 mmHg (no treatment), high-density lipoprotein cholesterol 55 mg/dL, and total cholesterol 200 mg/dL. If he has eGFR 35 ml/min/1.73 m2 and ACR 200 mg/g, based on our CKD (chronic kidney disease) Add-on for PCE, the newly calibrated predicted risk should be 15.7%. If eGFR is 20 ml/min/1.73 m2 and ACR is 600 mg/g, the predicted risk exceeds 20%, the threshold of high-risk, and this individual would be a candidate to receive intensive primary prevention therapies. The risk calculators with “CKD Add-on” are available online for both PCE (http://ckdpcrisk.org/ckdpatchpce/) and SCORE (HTTP:// ckdpcrisk.org/ckdpatchscore/). The development and validation of the “CKD Add-on” for the new SCORE2 in the new 2021 ESC guideline are underway.
5.3. Secondary prevention settings
Risk stratification systems for secondary prevention among patients with prior CVD have historically focused on short-term risk (e.g., in-hospital mortality) to guide acute care [101–104]. However, given the availability of some novel and effective medications (e.g., PCSK9 [proprotein convertase subtilisin/Kexin type 9] inhibitors), the need for longer-term risk stratification for secondary prevention has become important. In this context, the 2018 AHA/ACC Cholesterol Guideline introduced the new dichotomous classification of very high-risk vs. high-risk ASCVD patients to guide extensive lipid therapy [90]. This guideline defines very high-risk ASCVD as 2 or more major ASCVD events (i.e., acute coronary syndrome within 12 months, history of myocardial infarction, ischemic stroke, and symptomatic peripheral artery disease) or 1 major ASCVD event plus multiple high-risk conditions (otherwise defined as high-risk ASCVD). This classification acknowledges reduced GFR, but not elevated albuminuria, as a high-risk condition. However, the 2019 ESC/EAS Guideline for the management of dyslipidaemias does not necessarily subdivide the risk of adverse outcomes in patients with CVD [105].
Despite the body of evidence indicating a stronger association of albuminuria with CVD outcomes than GFR, as mentioned above, albuminuria is underappreciated in secondary prevention as well. Importantly, a few studies demonstrated the prognostic value of albuminuria in patients with prior CVD [106–109]. For example, in the US community-based cohort study, high ACR was associated with adverse outcomes in patients with ASCVD, independent of high-risk conditions recognized in the 2018 AHA/ACC Cholesterol Guideline [106]. ACR ≥30 mg/g classified 4% of high-risk to very high-risk and 11% of very high-risk to extremely very high-risk group with good calibration (Fig. 5). Of note, ACR was more predictive than some high-risk conditions (e.g., hypertension, current smoking, and diabetes) or reduced GFR.
Fig. 4. Conceptual scheme of CKD (chronic kidney disease) Add-on for calibrating the risk based on 1. The difference between observed and expected values of CKD measures and 2. The relative risk according to CKD measures. Adapted from Matsushita et al. [99].
6. Clinical implications of including CKD for CVD risk prediction
As mentioned above, risk prediction is central for personalized and targeted preventive therapy. Indeed, CVD risk prediction is recommended for guiding lipid and blood pressure control [90,110]. The need for optimal risk prediction for both primary and secondary prevention of CVD is higher than ever, given the availability of new effective but expensive medications [111–113]. Of those, SGLT2 (sodium-glucose contra porter type 2) inhibitors are particularly relevant to patients with CKD since this class of medication has been shown to reduce the risk of both kidney outcomes (e.g., kidney failure requiring renal replacement therapy) and CVD outcomes (especially heart failure) [112,114,115].
Another important aspect is the assessment of the two key CKD (chronic kidney disease) measures in clinical practice. Serum creatinine is widely measured in clinical practice. For example, in the US, this test is performed over 280 million times a year [116]. However, although albuminuria assessment is recommended in some clinical conditions such as diabetes, hypertension, and CKD [117,118], the assessment of albuminuria is done much less frequently than that of eGFR, even among patients with diabetes, probably due to logistical challenges of collecting urine. For example, a new US study has demonstrated the 1-year median testing rates of 52.9% for ACR and 89.5% for eGFR [119]. On the other hand, the non-invasiveness of urine testing is an important advantage. Therefore, increasing awareness of the clinical importance of albuminuria in primary care, cardiology, and any other relevant specialties is necessary. Notably, the new UK guideline for CKD has extended the recommendation of assessing albuminuria to patients with CVD [120].
Some other approaches for implementing CKD (chronic kidney disease) measures in clinical practice are worth mentioning. A clinical decision-making support system has been shown as a potential tool to improve the identification of CKD, monitoring disease progression, adherence to appropriate treatment targets, and follow-up assessment of albuminuria [121]. Most electronic health records already have tools for care, including alerts and reminders. Thus, supportive technology can help manage CKD in clinical practice and integrate collected information on CKD into risk assessment. For example, the Kidney Failure Risk Equation (KFRE), a tool to predict the risk of kidney failure requiring renal replacement therapy based on routinely collected clinical variables like age, sex, eGFR, and ACR [122], is being implemented in the nephrology community [123]. A similar approach can be taken for CVD prediction in persons with CKD.
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7. Summary
CKD affects a substantial proportion of the adult population worldwide and there is growing evidence, from observational studies as well as examinations of pathological mechanisms, of the increased CVD risk in persons with CKD. While major guidelines recognize this excess risk, the incorporation of CKD (chronic kidney disease) measures into recommended risk pre-diction tools is not yet established. Markers of CKD are collected in routine clinical care, although it is notable that there is less awareness of the importance of collecting albuminuria measures in other clinical specialties outside of nephrology. This is an area for improvement and action, which could be helped by recognition from guidelines of the importance of albuminuria in risk assessment for CVD. Once eGFR and/ or albuminuria are measured, the “CKD Add-on” method can incorporate these CKD measures into existing CVD risk prediction tools and improve the care of patients with even better targeted preventive therapies.
Fig. 5. Cumulative incidence of the composite outcome in risk categories with and without incorporating elevated urine ACR ≥30 mg/g. Elevated ACR is added on top of the original high-risk conditions without accounting for albuminuria to classify extremely very high-risk, very high-risk, and high-risk ASCVD. Thus, there is no downward risk classification. Striped bars represent participants who were reclassified to a higher risk category by elevated ACR. ACR = urine albumin-to-creatinine ratio; ASCVD = atherosclerotic cardiovascular disease. Adapted from in press article: Mok et al. [125].
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: K. M. received funding and personal fee from Kyowa Kirin and personal fee from Akebia and Fukuda Denshi outside of the submitted work.
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