Patients with CKD are at greater risk for atherosclerotic disease compared with their non-CKD counterparts, and are at higher risk of adverse outcomes from atherosclerosis (1–4). CKD has been associated with greater risk of amputation, multivessel coronary artery disease (CAD), and cardiac and all-cause mortality (1–8).
Potential contributors to atherosclerotic risk may include fibrosis, inflammation, cardiac stress, and subclinical ischemia. Traditional circulating biomarkers (N-terminal pro b-type natriuretic peptide [NT-proBNP] and high-sensitivity troponin T [hsTnT]) and those that are more novel (growth differentiation factor-15 [GDF-15] and soluble ST2 [sST2]) may reflect early alterations in these pathways that may contribute to atherosclerosis in patients with CKD. For example, NT-proBNP is secreted by myocytes and is used as a marker of cardiac stress that may occur in the setting of early ischemia (9–11). HsTnT levels tend to increase in the setting of myocardial ischemia, and may be a marker of myocardial injury and subclinical ischemia (12–14). GDF-15 is a part of the TGF-β family and may reflect myocardial ischemia, stretch, and inflammation (15–17). sST2 is a member of the IL-1 receptor family that may be associated with cardiomyocyte fibrosis in response to subclinical cardiac ischemia (18,19).
These biomarkers have been studied extensively in the general population. Elevated hsTnT and NT-proBNP levels may be associated with atherosclerotic disease (20–24). Higher levels of sST2 may be associated with greater severity of CAD, long-term mortality, and diastolic load (25–28). GDF-15 may be associated with subclinical atherosclerosis, progression of atherosclerosis, and mortality in patients with CAD (26,29–31). In the CKD population specifically, there are promising data on the associations of these cardiac biomarkers with some clinical outcomes (32–37).
However, the associations between these biomarkers and atherosclerotic events in the CKD population have not been well established, and greater understanding of these associations may help augment our understanding of the biologic insults involved in atherosclerotic cardiovascular disease, compared with other subtypes of cardiovascular disease. In this paper, we study a panel of circulating cardiac biomarkers (namely NT-proBNP, hsTnT, GDF-15, and sST2) and their associations with atherosclerotic events in a large cohort of participants with CKD.
Materials and Methods
We studied adults with mild to moderate CKD in the Chronic Renal Insufficiency Cohort (CRIC) study. A total of 3939 participants were enrolled in the CRIC study between June 2003 and August 2008 at seven clinical centers across the United States (Ann Arbor/Detroit, MI; Baltimore, MD; Chicago, IL; Cleveland, OH; New Orleans, LA; Philadelphia, PA; and Oakland, CA) (38,39). Details on study design, baseline characteristics, and inclusion and exclusion criteria have been previously published (38). Participants on maintenance dialysis or with prior kidney transplant were not included at cohort entry. CRIC also excluded participants with advanced heart failure, defined as New York Heart Association Class III or IV, on cohort entry. All study participants had annual in-person study visits where detailed interviews were conducted, brief physical examination performed, laboratory measures obtained, and cardiovascular testing performed. All study participants provided written informed consent, and the study protocol was approved by institutional review boards at each of the participating sites.
For this analysis, we excluded participants with known atherosclerotic disease at study entry, which was determined by self-report of prevalent coronary artery disease, peripheral vascular disease, or prior stroke (n=1200). We further excluded participants who did not have stored blood available for measurement of the cardiac biomarkers of interest (n=179). After applying these exclusions, 2560 participants were analyzed (Supplemental Figure 1).
Our exposures included four cardiac biomarkers: NT-proBNP, hsTnT, GDF-15, and sST2. All four cardiac biomarkers were measured from stored samples at baseline. NT-proBNP and hsTnT were measured at the University of Maryland at baseline in 2008 from EDTA plasma stored at −70°C using a chemiluminescent microparticle immunoassay (www.roche-diagnostics.us, Basel, Switzerland) on the ElecSys 2010. The range of values for NT-proBNP was from 5 to 35,000 pg/ml, and the coefficient of variation was 9% at a level of 126 pg/ml and 6% at 4319 pg/ml. HsTnT was measured using the highly sensitive assay with a range of values from 3 to 10,000 pg/ml (40). Any values below the lower limit of blank were characterized as “undetectable.” The coefficient of variation was 6% at a level of 26 pg/ml and 5% at 2140 pg/ml. The value of the 99th percentile cutoff from a healthy reference population was 13 pg/ml for hsTnT with a 10% coefficient of variation (40). GDF-15 and sST2 were measured from EDTA plasma stored at −70°C from samples at baseline in batch at the University of Pennsylvania Laboratory. GDF‐15 and sST2 were measured using ELISA (R&D Systems, Minneapolis, MN) and had intra‐assay coefficients of variation of 2% and 3%, respectively.
Incident Atherosclerotic Disease
Our primary outcome was incident atherosclerotic disease, defined as the composite of new myocardial infarction (MI), stroke, or peripheral vascular disease. Every 6 months at each study visit, participants were asked if they had visited an emergency department or had been hospitalized. Medical records from corresponding hospitals or health care systems were queried for qualifying encounters. We censored participants at death, which was identified by reports from next of kin, retrieval of death certificates, and public records, if available.
MI was defined as a typical rise and either slow or rapid fall in cardiac enzymes along with either typical symptoms of a MI, or ECG changes compatible with this diagnosis. MI could also be new perfusion abnormalities with a corresponding wall motion abnormality or ECG changes. Patient-reported hospitalizations triggered retrieval of medical records as above, which were reviewed and adjudicated by two physicians (38).
Stroke was defined as any new neurologic deficit of at least 24 hours duration. A report of such from a patient resulted in the retrieval of medical records, which were reviewed and adjudicated by two physicians. They classified each event as either definite/probable stroke, or no stroke.
Peripheral vascular disease was defined as either amputation due to peripheral vascular disease, or revascularization (either surgical or percutaneous) (38). In a manner similar to the above, patient reports of hospitalization or emergency department visit prompted retrieval of medical records, which were reviewed and adjudicated.
Symptoms of Atherosclerotic Disease
We evaluated symptoms of atherosclerotic disease at baseline as a secondary outcome in the study. Data regarding symptoms of atherosclerotic disease was obtained at study entry from participants using the Kidney Disease Quality of Life 36-question screening, which surveyed participants on physical and mental functioning, burden of disease, symptoms, and effects of kidney disease on daily life (41). Symptoms that were considered for possible atherosclerotic disease included chest pain, shortness of breath, and inability to climb stairs.
Inability to climb stairs was graded as “limited a lot,” “limited a little,” or “not limited at all.” We dichotomized any limitation as positive symptoms, combining those who answered “limited a lot” or “limited a little” versus those with no limitations as absence of this symptom. Screening questions for chest pain and shortness of breath were assessed on a scale of “not bothered at all” to “extremely bothered.” We took “not bothered at all” to be the absence of symptoms, and any degree of “bothered” to be the presence of symptoms.
Coronary Artery Calcium Scores
Coronary artery calcium (CAC) scores, a marker of subclinical atherosclerotic disease, were secondary outcomes in the analyses (42). A subset of participants (n=1054) underwent electron-beam or multidetector computed tomography scan of the coronary arteries within 6 months of study enrollment. A cardiologist read all scans (at Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center) to assess calcification via the total Agatston score (43). This method requires 20 levels of proximal coronary artery imaging. Areas of calcification ≥130 Hounsfield Units (HU) are identified, and the area (in square millimeters) of each plaque is multiplied by a factor related to the maximal plaque attenuation (factor 1 for 130–199 HU, factor 2 for 200–299 HU, factor 3 for 300–399 HU, and factor 4 for ≥400 HU). The total Agatston score is the sum of all areas of interest (43).
At the baseline visit, participants provided information on their sociodemographic characteristics, medical history, medication usage, and lifestyle behaviors. Race/ethnicity was by self-report, and was categorized as non-Hispanic White, non-Hispanic Black, Hispanic, and other. Baseline cardiovascular disease status was determined by self-report and was defined as history of coronary artery disease, heart failure, or stroke. BP and body mass index were assessed using standard protocols (44). Diabetes mellitus was defined as a fasting glucose >126 mg/dl, a nonfasting glucose >200 mg/dl, or use of medications for diabetes mellitus, including insulin. Alcohol use was dichotomized as none versus any in the past 12 months. Tobacco use was dichotomized as current tobacco use versus no tobacco use at time of cohort entry.
Serum creatinine was measured using an enzymatic method on an Ortho Vitros 950 (Raritan, NJ) at the CRIC Central Laboratory, using a standardized enzymatic method (45). eGFR was calculated from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration equation (46). Additional measurements included 24-hour urine protein, 24-hour urine sodium, LDL cholesterol, HDL cholesterol, hemoglobin, and C-reactive protein. Framingham Risk Scores were calculated at baseline using the “General Cardiovascular Risk Profile” method (47). The risk factors used to calculate these scores are: age, sex, natural logarithm of total cholesterol, natural logarithm of HDL cholesterol, natural logarithm of systolic blood pressure, treatment for hypertension, smoking status, and presence of diabetes.
Summary statistics and distributions of NT-proBNP, hsTnT, GDF-15, and sST2 were generated. Study variables were described overall and across quartiles of NT-proBNP, GDF-15, and sST2, and categories of hsTnT. For hsTnT, participants with concentrations <10 pg/ml were designated as the referent group to ensure stability in the statistical models, given the small number of participants in the <3 and 3–9.9 pg/ml range. For participants who had an hsTnT value <3, we set their hsTnT value to 1.5 pg/ml, then calculated the SD as if the variable were continuous, assuming 1.5 pg/ml was their “true” value. Participants with hsTnT >10 pg/ml were divided into tertiles (for a total of four categories of hsTnT). Each biomarker was modeled continuously (per SD of the natural log[biomarker]) for testing associations.
Crude rates of atherosclerotic events were calculated across categories of each biomarker as specified above. Adjusted incidence rates of atherosclerotic events were calculated across biomarker categories using Poisson regression with adjustment for age, self-reported race and ethnicity, eGFR, 24-hour urine total protein excretion, diabetes mellitus, congestive heart failure, systolic and diastolic BP, body mass index, HDL, LDL, smoking status, hemoglobin, diuretic and beta blocker use, and 10-year Framingham atherosclerotic cardiovascular disease (ASCVD) risk scores. For each incidence rate, a 95% confidence interval (95% CI) as calculated via a nonparametric bootstrap approach with 2000 replicates (48). Cox proportional hazards models were fit for the composite outcome (MI, stroke, or peripheral vascular disease [PVD]) and for each individual outcome, and follow-up was censored at the end of administrative follow-up, loss to follow-up, or death, whichever occurred first. If a participant had more than one atherosclerotic event at the time of hospitalization, the event was counted as one occurrence in the composite outcome. We performed a series of nested Cox proportional hazard models with sequential adjustment for potential confounders. Model 1 adjusted for demographic factors including age, sex, and self-reported race and ethnicity. Model 2 adjusted for the covariates included in Model 1 and eGFR, 24-hour urine total protein excretion, diabetes mellitus, congestive heart failure, systolic and diastolic BP, body mass index, HDL, LDL, smoking status, hemoglobin, diuretic and beta blocker use, and 10-year Framingham atherosclerotic cardiovascular disease (ASCVD) risk scores.
We performed four additional sensitivity analyses. First, to determine whether the observed associations were independent of treatment practices, we adjusted for the covariates of Model 2 stated above and cardioprotective medications (i.e., aspirin, statins, angiotensin converting enzyme inhibitors, and angiotensin receptor blockers). Second, we adjusted for high-sensitivity C-reactive protein in addition to the covariates of Model 2 to test whether the observed associations were independent of other inflammatory markers. Third, we adjusted for markers of bone mineral metabolism (calcium, phosphate, fibroblast growth factor-23, and total parathyroid hormone) in addition to the covariates of Model 2 because these factors have been associated with atherosclerotic disease in patients with CKD (49). Finally, because decreased kidney clearance may contribute to elevations in serum concentrations of NT-proBNP and hsTnT, we tested for multiplicative interaction by eGFR, modeled continuously.
In secondary analyses, after dichotomizing our symptom outcomes as described above to either the presence or absence of symptoms, we used logistic regression to estimate the odds ratio of the presence of symptoms per SD increment in the log-transformed biomarker. We performed sequential adjustments for the covariates of Models 1 and 2 listed above.
As an additional secondary analysis, we investigated the associations between baseline cardiac biomarker levels and CAC scores as a marker of subclinical atherosclerotic disease (42). We modeled our exposure as per SD increase in natural log-adjusted biomarker levels, and our outcome as categorial Agatston score (0, >0 to <100, and ≥100 given prior work in this cohort) (50). Associations were assessed via multimodal logistic regression, using Agatston score of 0 as the referent group, with adjustment for the covariates of Model 2 listed above.
In all analyses, missing covariates were multiply imputed using chained equations via the mice package in R (51). The multiple analyses over imputations were combined using standard Rubin’s rules to account for the variability in the imputation procedure (52). All analyses were performed using R 4.0.2 (R Foundation for Computing, Vienna, Austria).
Characteristics of the Study Population
Among 2560 eligible participants, the mean age was 56 years old, and 51% of the study population were males. Non-Hispanic White participants made up 43% of the population, 39% of participants were non-Hispanic Black, and 14% were Hispanic (Table 1). The median 24-hour urine protein level was 0.2 g per day across the population, with an interquartile range (IQR) of 0.1–0.8 g per day. The mean eGFR was 45.7 ml/min per 1.73 m2. A total of 43% of the population had diabetes; 4% had a history of heart failure, and 12% had atrial fibrillation. Participants with higher NT-proBNP levels had higher 24-hour proteinuria, lower eGFR, higher prevalence of diabetes, and were more likely to be Hispanic. These patterns were similar for hsTnT, GDF-15, and sST2 (Supplemental Tables 1–4).
Table 1. -
Baseline characteristics of study participants (n
Race and ethnicity
| Non-Hispanic White
| Non-Hispanic Black
|eGFR, ml/min per 1.73 m2
|24-h urine protein, g/day, median
|24-h urine sodium, mg/day, median
|History of heart failure
|History of atrial fibrillation
|Systolic BP, mm Hg
|Diastolic BP, mm Hg
|Body mass index, kg/m2
|LDL cholesterol, mg/dl
|HDL cholesterol, mg/dl
|C-reactive protein, mg/L, median
|Fibroblast growth factor-23 (RU/ml), median
|Serum phosphate, mg/dl
|Total parathyroid hormone (pg/ml), median
|Level of NT-proBNP, median, pg/ml
|Level of hsTnT, median, pg/ml
|Level of GDF-15, median, pg/ml
|Level of sST2, median, ng/ml
|Framingham 10-year CVD risk (%), median
Entries are mean (SD) for continuous variables and n (%) for categorical variables, except as noted. ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; NT-proBNP, N-terminal pro B-type natriuretic peptide; hsTnT, high-sensitivity troponin T; GDF-15, growth differentiation factor 15; sST2, soluble ST2; CVD, cardiovascular disease.
Association of NT-proBNP with Risk of Incident Atherosclerotic Disease
We observed a total of 270 atherosclerotic events over a median follow-up of 8.13 (IQR, 5.72–9.46) years. This included 163 MI events, 70 patients with incident PVD, and 70 stroke events. As demonstrated in Figure 1 and Supplemental Table 5, the incidence of events was higher with higher categories of baseline biomarker levels. This trend remained after adjusting for multiple possible covariates (Supplemental Figure 2).
When modeled continuously as standard deviations of natural-log transformed levels of NT-proBNP, NT-proBNP was associated with a 1.5-fold higher risk of atherosclerotic CVD events (hazard ratio [HR], 1.51; 95% CI, 1.27 to 1.81) after adjusting for traditional atherosclerotic risk factors (Table 2). When we evaluated each individual CVD event type, we observed significant associations between higher NT-proBNP and risk of MI, PVD, and stroke events in unadjusted and adjusted models (Table 2). When NT-proBNP was modeled categorically, we observed a graded increase in incidence of composite atherosclerotic events, strokes, and MIs across higher quartiles of NT-proBNP (Supplemental Table 6).
Table 2. -
Associations of baseline biomarkers and incident atherosclerotic disease (n
||Myocardial Infarction Hazard Ratio (95% Confidence Interval)
||Peripheral Vascular Disease Hazard Ratio (95% Confidence Interval)
||Stroke Hazard Ratio (95% Confidence Interval)
||Composite Hazard Ratio (95% Confidence Interval)
|Number of events
NT-proBNP (per SD of ln[NT-proBNP])
||1.97 (1.67 to 2.32)
||2.38 (1.84 to 3.07)
||1.83 (1.41 to 2.36)
||1.98 (1.74 to 2.26)
| Model 1
||1.89 (1.60 to 2.24)
||2.34 (1.82 to 3.01)
||1.79 (1.39 to 2.3)
||1.95 (1.71 to 2.22)
| Model 2
||1.52 (1.21 to 1.91)
||1.55 (1.09 to 2.22)
||1.54 (1.10 to 2.14)
||1.51 (1.27 to 1.80)
hsTnT (per SD of ln[hsTnT])
||1.96 (1.71 to 2.25)
||2.63 (2.17 to 3.17)
||1.68 (1.36 to 2.07)
||2.06 (1.85 to 2.29)
| Model 1
||1.96 (1.68 to 2.29)
||2.64 (2.15 to 3.25)
||1.59 (1.25 to 2.02)
||2.05 (1.82 to 2.31)
| Model 2
||1.60 (1.31 to 1.97)
||1.75 (1.32 to 2.32)
||1.31 (0.95 to 1.79)
||1.61 (1.38 to 1.89)
GDF-15 (per SD of ln[GDF-15])
||1.88 (1.60 to 2.21)
||2.69 (2.08 to 3.47)
||1.79 (1.40 to 2.29)
||2.00 (1.76 to 2.27)
| Model 1
||1.77 (1.49 to 2.11)
||2.72 (2.09 to 3.55)
||1.70 (1.31 to 2.20)
||1.94 (1.70 to 2.22)
| Model 2
||1.36 (1.07 to 1.73)
||1.56 (1.07 to 2.25)
||1.35 (0.94 to 1.94)
||1.44 (1.19 to 1.73)
sST-2 (per SD of ln[sST2])
||1.49 (1.27 to 1.75)
||1.53 (1.20 to 1.95)
||1.29 (1.00 to 1.66)
||1.43 (1.26 to 1.63)
| Model 1
||1.46 (1.24 to 1.73)
||1.41 (1.09 to 1.82)
||1.25 (0.96 to 1.62)
||1.39 (1.22 to 1.58)
| Model 2
||1.28 (1.07 to 1.54)
||1.12 (0.86 to 1.45)
||1.11 (0.86 to 1.44)
||1.19 (1.04 to 1.36)
Hazard ratios are per standard deviation of the log-transformed biomarker. Model 1: adjusted for age, sex, and self-reported race and ethnicity. Model 2: adjusted for age, sex, self-reported race and ethnicity, eGFR, log-transformed 24-hour urine protein, DM, heart failure, SBP, DBP, BMI, HDL, LDL, tobacco use, hemoglobin, beta blocker use, diuretic use, and Framingham risk score. NT-proBNP, N-terminal pro B-type natriuretic peptide; hsTnT, high-sensitivity troponin T; GDF-15, growth differentiation factor 15; sST2: soluble ST2; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure.
Association of hs-TnT with Risk of Incident Atherosclerotic Disease
HsTnT was also significantly associated with incident atherosclerotic disease when modeled continuously as SD of natural-log adjusted hsTnT. After adjusting for traditional risk factors for atherosclerotic disease (Model 2), the HR for hsTnT was 1.61 (95% CI, 1.38 to 1.89). Greater levels of hsTnT were significantly associated with MI and PVD events after adjusting for traditional atherosclerosis risk factors (Model 2), but not with stroke events (Table 2). As demonstrated in Supplemental Table 6, when modeled categorically we observed a graded increase in incidence of MI, PVD, strokes, and composite events across higher categories of hsTnT.
Association of GDF-15 with Risk of Incident Atherosclerotic Disease
GDF-15 levels were similarly modeled continuously as natural-log adjusted levels. After adjusting for traditional atherosclerosis risk factors (Model 2) the association between GDF-15 and atherosclerotic events was statistically significant (HR, 1.44; 95% CI, 1.19 to 1.73) (Table 2). As shown in Table 2, higher levels of GDF-15 were associated with MI and PVD events after adjusting for traditional atherosclerosis risk factors (Model 2). When modeled categorically, any level of GDF-15 above the referent quartile 1 conferred a statistically significant increased risk of composite event (Supplemental Table 6).
Association of sST2 with Risk of Incident Atherosclerotic Disease
SST2 levels were modeled continuously as natural-log adjusted levels. Using SD of this exposure as our predictor, sST2 was associated with a 1.19-fold risk of atherosclerotic CVD events (HR, 1.19; 95% CI, 1.04 to 1.36) after adjusting for traditional atherosclerotic risk factors. As shown in Table 2, higher levels of sST2 were associated with MI events after adjusting for traditional atherosclerosis risk factors (Model 2). When modeled categorically, the highest category of sST2 conferred a statistically significant increased risk of composite event versus the referent category (Supplemental Table 6).
Sensitivity Analyses: Adjustment for Cardiovascular Medications, Inflammatory Markers, and Markers of Bone Mineral Metabolism
We evaluated whether the observed associations would be attenuated with adjustment for potential mediators of atherosclerosis, including the use of cardiovascular medications (aspirin, statins, angiotensin converting enzyme [ACE]-inhibitors, and angiotensin receptor blockers [ARBs]), high-sensitivity C-reactive protein (as a marker of inflammation), and markers of bone mineral metabolism (calcium, phosphate, fibroblast growth factor-23, and total parathyroid hormone). As demonstrated in Supplemental Table 7, adjusting for these additional data only mildly attenuated the associations noted in our adjusted primary analyses.
Sensitivity Analysis: Testing for Multiplicative Interaction by eGFR
When testing associations between cardiac risk markers and the composite outcome, the P value for interaction by continuous eGFR was 0.07 for NT-proBNP, 0.14 for hsTnT, 0.49 for GDF-15, and 0.88 for sST2.
Secondary Analysis: Association of Cardiac Biomarkers with Symptoms of Atherosclerotic Disease
After adjustment for traditional risk factors for atherosclerotic disease (Model 2), NT-proBNP was associated with inability to climb stairs (odds ratio [OR], 1.04; 95% CI, 1.02 to 1.07), hsTnT was associated with shortness of breath (OR, 1.04; 95% CI, 1.01 to 1.06) and inability to climb stairs (OR, 1.07; 95% CI, 1.05 to 1.10), and GDF-15 was associated with all symptoms (OR for chest pain, 1.04; 95% CI, 1.01 to 1.06; OR for shortness of breath 1.05; 95% CI, 1.02 to 1.08; OR for inability to climb stairs, 1.09; 95% CI, 1.06 to 1.12) (Table 3).
Table 3. -
Cross-sectional associations of cardiac biomarkers (N-terminal pro B-type natriuretic peptide, high-sensitivity troponin T, growth differentiation factor 15, and soluble ST2) and symptoms of atherosclerotic disease at baseline (n
||Chest Pain Odds Ratio (95% Confidence Interval)
||Shortness of Breath Odds Ratio (95% Confidence Interval)
||Inability to Climb Stairs Odds Ratio (95% Confidence Interval)
|Number of positive responses
NT-proBNP (per SD of ln[NT-proBNP])
||1.12 (1.00 to 1.25)
||1.27 (1.17 to 1.38)
||1.61 (1.48 to 1.75)
| Model 1
||1.12 (1.00 to 1.26)
||1.25 (1.14 to 1.36)
||1.45 (1.33 to 1.59)
| Model 2
||1.01 (0.99 to 1.03)
||1.03 (1.00 to 1.05)
||1.04 (1.02 to 1.07)
hsTnT (per SD of ln[hsTnT])
||1.01 (0.91 to 1.13)
||1.23 (1.13 to 1.33)
||1.54 (1.41 to 1.68)
| Model 1
||1.04 (0.93 to 1.17)
||1.30 (1.18 to 1.42)
||1.75 (1.58 to 1.94)
| Model 2
||1.00 (0.98 to 1.02)
||1.04 (1.01 to 1.06)
||1.07 (1.05 to 1.10)
GDF-15 (per SD of ln[GDF-15])
||1.15 (1.03 to 1.29)
||1.30 (1.20 to 1.41)
||1.81 (1.66 to 1.98)
| Model 1
||1.20 (1.07 to 1.36)
||1.30 (1.19 to 1.42)
||1.70 (1.55 to 1.87)
| Model 2
||1.04 (1.01 to 1.06)
||1.05 (1.02 to 1.08)
||1.09 (1.06 to 1.12)
sST-2 (per SD of ln[sST2])
||1.01 (0.91 to 1.13)
||1.08 (0.99 to 1.18)
||1.09 (1.01 to 1.18)
| Model 1
||1.03 (0.92 to 1.15)
||1.11 (1.02 to 1.22)
||1.16 (1.06 to 1.26)
| Model 2
||1.00 (0.99 to 1.02)
||1.02 (1.00 to 1.04)
||1.01 (0.99 to 1.03)
Odds ratios are per standard deviation increment in the log-transformed biomarker. Model 1: adjusted for age, sex, and self-reported race and ethnicity. Model 2: adjusted for age, sex, self-reported race and ethnicity, eGFR, log-transformed 24-hour urine protein, DM, heart failure, SBP, DBP, BMI, HDL, LDL, tobacco use, hemoglobin, beta blocker use, diuretic use. NT-proBNP, N-terminal pro B-type natriuretic peptide; hsTnT, high-sensitivity troponin T; GDF-15, growth differentiation factor 15; sST2, soluble ST2; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index.
Table 4. -
Associations between baseline cardiac risk markers and categorical coronary artery calcium scores relative to coronary artery calcium score of 0
||Coronary Artery Calcium >0 to <100
||Coronary Artery Calcium ≥100
|Odds Ratio (95% Confidence Interval)
||Odds Ratio (95% Confidence Interval)
NT-proBNP (per SD of ln[NT-proBNP])
||1.33 (1.14 to 1.55)
||1.57 (1.35 to 1.83)
||1.23 (0.98 to 1.53)
||1.42 (1.11 to 1.81)
HsTnT (per SD of ln[hsTnT])
||1.62 (1.38 to 1.92)
||2.1 (1.78 to 2.48)
||1.27 (0.98 to 1.63)
||1.22 (0.93 to 1.62)
GDF-15 (per SD of ln[GDF-15])
||1.67 (1.42 to 1.96)
||2.29 (1.93 to 2.72)
||1.75 (1.44 to 2.11)
||2.03 (1.68 to 2.46)
SST2 (per SD of ln[sST2])
||1.23 (1.06 to 1.44)
||1.40 (1.19 to 1.63)
||1.17 (0.98 to 1.41)
||1.09 (0.91 to 1.32)
Adjusted for age, sex, self-reported race and ethnicity, eGFR, log-transformed 24-hour urine protein, DM, heart failure, SBP, DBP, BMI, HDL, LDL, tobacco use, hemoglobin, beta blocker use, diuretic use, and Framingham risk score. NT-proBNP, N-terminal pro B-type natriuretic peptide; HsTnT, high-sensitivity troponin T; GDF-15: growth differentiation factor 15; sST2, soluble ST2; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index.
Secondary Analysis: Associations with CAC Scores
In unadjusted models, higher levels of cardiac biomarkers were significantly associated with non-zero CAC scores. After adjustment for the covariates of Model 2 as above, NT-proBNP, hsTnT, and GDF-15 remained associated with non-zero CAC scores (Table 4).
In summary, in a large population of participants with CKD without a known history of atherosclerosis, we found significant associations between NT-proBNP, hsTnT, GDF-15, and sST2 and the development of incident atherosclerotic disease, even when adjusting for multiple potential confounders, including measures of kidney function. Of all the cardiac biomarkers, hsTnT demonstrated the strongest association with atherosclerotic outcomes. These four cardiac biomarkers were associated with symptoms suggestive of subclinical atherosclerotic CVD; additionally, NT-proBNP, hsTnT, and GDF-15 were associated with higher baseline CAC scores in a subset of participants. This adds to the growing literature describing the associations between these cardiac biomarkers and cardiovascular disease, and expands their potential use into the realm of CKD patients.
NT-proBNP and hsTnT may be associated with atherosclerotic disease, incident coronary heart disease, and all-cause and cardiovascular morality in the general population (20–24,53). NT-proBNP is thought to be a marker of myocardial stress, which may also increase in ischemic states (9–11,54,55). HsTnT has been suggested as a marker of myocardial ischemia, which may increase with severity of ischemia (12–14,56,57). It is plausible that elevations in these biomarkers may be present in patients with clinically undetected atherosclerosis. Previous studies have not included patients with a wide range of CKD in their analyses. Our work expands the use of these biomarkers for atherosclerosis to patients with CKD, a population in which mechanisms contributing to atherosclerotic disease remain incompletely understood. On the basis of these findings, subclinical myocardial stress and ischemia may be important in the pathogenesis of atherosclerosis in CKD.
Studies in the general population have suggested associations between GDF-15 and carotid artery plaque (29,58), risk of recurrent MI (59,60), and risk of mortality in patients with known atherosclerosis (26,31,). In patients with CKD, GDF-15 has been associated with higher risk of mortality and incident heart failure (37). GDF-15 may increase in response myocardial stretch, ischemia, and inflammation; it may promote cardioprotection via the inhibition of the c-Jun N-terminal kinase, epidermal growth factor receptor, and Bcl-2–associated death promoter pathways, and activation of protective signaling pathways (15–17,). Our findings corroborate the above studies in the general population in demonstrating associations between GDF-15 and atherosclerotic events in the CKD population. These findings may also serve to highlight the possible mechanisms of early atherosclerotic disease, including subclinical ischemia and inflammation, which may be targets for future therapies.
Circulating levels of sST2 are higher in patients with ST-elevation myocardial infarction as compared with non-ST-elevation myocardial infarction and stable angina (27); higher sST2 levels have also been associated with higher CAC scores in the general population (69). In contrast, higher sST2 was not significantly associated with CAC scores in our analyses, possibly suggesting differing pathophysiology in patients with versus without CKD. Studies examining the associations between sST2 and future cardiovascular events have demonstrated very mixed findings. A study of 391 patients who underwent carotid endarterectomy did not demonstrate an association between sST2 levels and secondary atherosclerotic events (70). Soluble ST2 is a byproduct of the ST2L/IL-33 pathway, which is activated in response to myocardial injury and has cardioprotective properties; it is thought that sST2 acts on the inflammatory pathway as a decoy receptor for IL-33, thus attenuating its protective properties (71–73). Our findings suggest inflammatory pathways may be important in the pathogenesis of atherosclerotic disease in patients with CKD.
It is noteworthy that our observed values for NT-proBNP, hsTnT, and GDF-15 were higher than those typically noted in the general population. In the Framingham Heart Study, the median values of NT-proBNP were 16.5 pg/ml in men, and 44.0 pg/ml in women, compared with a median of 99.2 (IQR, 36.6–255.5) pg/ml in our population (74). In 19,501 participants in the Generation Scotland Scottish Health Survey, the median value of hsTnT was 3.3 (IQR, 1.5–6.0) pg/ml, compared with a median value of 12.8 (IQR, 7.7–22.3) pg/ml in our population (75). Similarly, the median level of GDF-15 in the Framingham Offspring Study was 1020 (IQR, 803–1362) pg/ml in men, and 1017 (IQR, 809–1297) pg/ml in women, versus 1331 (IQR, 904–1995) pg/ml in our study (76). Decreased kidney clearance partially explains higher levels of NT-proBNP and hsTnT (77–80). GDF-15 may also be cleared by the kidneys, at least in part (molecular mass of mature GDF-15 approximately 30 kDa), and several studies have noted an inverse correlation between eGFR and GDF-15 levels (81,82). Conversely, levels of sST2 in our population (median, 14.9; IQR 11.0–20.1 ng/ml) were comparable to or lower than those reported in the Framingham Heart Study (median, 23.5 ng/ml in men, 19.5 ng/ml in women) (83). Although it is possible sST2 is partially cleared by the kidneys given its molecular mass of 37 kDa (84), several studies have not demonstrated associations with decreasing eGFR (85,86). Despite the possible association of higher levels of these circulating biomarkers with decreased kidney clearance, we did not find that eGFR was an effect modifier in our analyses. On the basis of our data and previous literature, it is likely that decreased kidney clearance alone cannot explain the observed elevations.
Some previous studies have investigated the associations between NT-proBNP, hsTnT, GDF-15, and sST2 with symptoms of atherosclerosis, which may precede the acute presentation (58,). NT-proBNP may be associated with cardiac, but not pulmonary, dyspnea (91). Higher levels of hsTnT may be associated with longer duration of chest pain and dyspnea (90), and worsening of heart failure symptoms in patients with CKD (92). Higher levels of GDF-15 have been positively associated with angina (58), baseline heart failure symptoms in CKD (92), and worsening of heart failure symptoms in CKD (92). We observed significant associations between several of our cardiac biomarkers and the presence of chest pain, shortness of breath, and inability to climb stairs, although these symptoms may be nonspecific to cardiovascular disease in patients with CKD (90,93).
CAC scores may be associated with risk of MI and all-cause mortality in patients with CKD (50). In this study, we found that NT-proBNP, hsTnT, and GDF-15 were significantly associated with non-zero CAC scores. However, due to the unique physiology of CKD, CAC scores in this population may reflect both intimal and medial calcification, and may lack specificity as a result (94,95). Further work on how best to identify patients with CKD at high risk for atherosclerotic CVD is needed as diagnoses of subclinical disease remains a challenge in this high-risk population.
We have shown in this study that NT-proBNP, hsTnT, GDF-15, and sST2 are all associated with the development of atherosclerotic disease in the CKD population. It is possible these data may identify mechanisms that could be the target for therapies to prevent and treat atherosclerotic disease in CKD. Future studies could assess the use of these cardiac biomarkers (of which two are widely clinically available) as part of a clinical risk score. Better identification of patients with CKD at risk for atherosclerotic disease may lead to more interventions for early treatment.
Our study has several key strengths: first, it was performed in a large cohort of participants with a wide range of CKD severity. We also controlled for a large set of possible confounders, including traditional risk factors for atherosclerosis. We examined for possible mediation by inflammatory markers and the use of cardioprotective medications and found that these factors did not meaningfully alter our reported associations. Our study does have some limitations, however. First, all baseline atherosclerotic disease history was obtained on the basis of self-report, and we were unable to exclude individuals who may have had clinically silent or early-stage atherosclerotic disease at the onset of the study. With regards to symptoms of atherosclerosis, chest pain, shortness of breath, and an inability to climb stairs may not be specific symptoms for atherosclerosis in the CKD population. It is possible that participants without atherosclerosis, but with CKD, may have these symptoms because of volume overload related to kidney dysfunction. Finally, the cohort was composed of research volunteers who were closely followed in clinic, which may limit the generalizability to other CKD populations.
In conclusion, this study demonstrated that higher levels of NT-proBNP, hsTnT, GDF-15, and sST2 were significantly associated with increased risk for development of atherosclerotic disease in a large cohort of participants with CKD. Further work is warranted to determine the potential use for these cardiac biomarkers in clinical contexts, and better understand the biologic mechanisms underpinning these associations.
A. Go reports receiving research funding from Amarin Pharmaceuticals, Bristol Meyers-Squibb, CSL Behring, iRhythm Technologies, Janssen Research and Development, and Novartis. H. Feldman reports being a consultant for InMed, Inc., Kyowa Hakko Kirin Co, Ltd. (ongoing), and the National Kidney Foundation (ongoing); and reports having an advisory or leadership role as the American Journal of Kidney Disease, Editor in Chief, Steering Committee Chair of the CRIC Study, and Member of Advisory Board of the National Kidney Foundation. L. Zelnick reports having consultancy agreements with Veterans Medical Research Foundation; and reports being a scientific advisor or member as a Statistical Editor for the Clinical Journal of the American Society of Nephrology. M. Kansal reports receiving research funding from AstraZeneca; and reports other interests or relationships as an American College of Cardiology Member. M. Shlipak reports having consultancy agreements with Cricket Health, Intercept Pharmaceuticals, University of Washington Cardiovascular Health Study, and Veterans Medical; reports receiving research funding from Bayer Pharmaceuticals; reports receiving honoraria from AstraZeneca, Bayer, and Boehringer Ingelheim; reports being a scientific advisor or member of the American Journal of Kidney Disease, Circulation, and the Journal of the American Society of Nephrology; and reports having other interests or relationships as a Board Member of the Northern California Institute for Research and Education. M. Weir reports being a consultant for, receiving honoraria (modest, <US$10,000) from, and an advisory or leadership role with, AstraZeneca, Boehringer-Ingelheim, Bayer, CareDx, Janssen, Merck, NovoNordisk, and Vifor Pharma. N. Bansal reports having an advisory or leadership role as Kidney360 Associate Editor. R. Mehta reports having consultancy agreements with and receiving honoraria from Akebia/Otsuka and AstraZeneca; reports having an ownership interest in AbbVie, Inc.; reports being a scientific advisor or member of the Editorial Board of Journal of Cardiac Failure; and reports receiving speakers bureau from AstraZeneca. All remaining authors have nothing to disclose.
This study was supported by the National Institutes of Health grants R01 DK103612 (to N. Bansal) and T32 DK007467 (to B. Lidgard).
Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902, and U24DK060990). In addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland General Clinical Research Center M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the NCATS component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research UL1TR000433, University of Illinois at Chicago Center for Clinical and Translational Science UL1RR029879, Tulane Center of Biomedical Research Excellence for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/National Center for Research Resources University of California San Francisco Clinical and Translational Science Institute UL1 RR-024131, Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, NM R01DK119199. Roche Diagnostics provided partial funding for the NT-proBNP and hsTnT assays.
N. Bansal and B. Lidgard conceptualized the study; N. Bansal and L. Zelnick were responsible for the data curation; B. Lidgard and L. Zelnick were responsible for the formal analysis; N. Bansal was responsible for the funding acquisition; N. Bansal, B. Lidgard, and L. Zelnick were responsible for the investigation and the methodology; N. Bansal and B. Lidgard were responsible for the project administration and visualization; N. Bansal and L. Zelnick were responsible for the resources and the software; N. Bansal provided supervision; B. Lidgard wrote the original draft; A. Anderson, N. Bansal, H. Feldman, A. Go, J. He, M. Kansal, B. Lidgard, R. Mehta, M. Mohanty, M. Shlipak, E. Soliman, M. Weir, and L. Zelnick reviewed and edited the manuscript; L. Zelnick was responsible for the validation.
This article contains the following supplemental material online at http://kidney360.asnjournals.org/lookup/suppl/doi:10.34067/KID.0006222021/-/DCSupplemental.
Supplemental Table 1. Baseline characteristics by quartile of N-terminal pro B-type natriuretic peptide (NT-proBNP).
Supplemental Table 2. Baseline characteristics by category of high-sensitivity troponin T (hsTnT).
Supplemental Table 3. Baseline characteristics by quartile of growth differentiation factor 15 (GDF-15).
Supplemental Table 4. Baseline characteristics by quartile of soluble ST2 (sST2).
Supplemental Table 5. Number and incidence rate of atherosclerotic disease by biomarker categories.
Supplemental Table 6. Associations of categorical biomarkers with incident atherosclerotic disease.
Supplemental Table 7. Further adjusting for cardioprotective medications, high-sensitivity C-reactive protein, and markers of bone mineral metabolism.
Supplemental Figure 1. CONSORT flow diagram for study size.
Supplemental Figure 2. Incidence rates of atherosclerotic events by biomarker category, adjusted for demographic data, kidney function, comorbidities, and medication use.
1. Arroyo D, Betriu A, Valls J, Gorriz JL, Pallares V, Abajo M, Gracia M, Valdivielso JM, Fernandez E; on behalf of the investigators from the NEFRONA study: Factors influencing pathological ankle-brachial index values along the chronic kidney disease spectrum: The NEFRONA study. NDT 32: 513–520, 2016 https://doi.org/10.1093/ndt/gfw039
2. Dumaine RL, Montalescot G, Steg PG, Ohman EM, Eagle K, Bhatt DL; REACH Registry Investigators: Renal function, atherothrombosis extent, and outcomes in high-risk patients. Am Heart J 158: 141–148.e1, 2009 https://doi.org/10.1016/j.ahj.2009.05.011
3. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu C-Y: Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351: 1296–1305, 2004 https://doi.org/10.1056/NEJMoa041031
4. Valdivielso JM, Rodríguez-Puyol D, Pascual J, Barrios C, Bermúdez-López M, Sánchez-Niño MD, Pérez-Fernández M, Ortiz A: Atherosclerosis in chronic kidney disease: More, less, or just different? Arterioscler Thromb Vasc Biol 39: 1938–1966, 2019 https://doi.org/10.1161/ATVBAHA.119.312705
5. Arroyo D, Betriu A, Martinez-Alonso M, Vidal T, Valdivielso JM, Fernández E; investigators from the NEFRONA study: Observational multicenter study to evaluate the prevalence and prognosis of subclinical atheromatosis in a Spanish chronic kidney disease cohort: Baseline data from the NEFRONA study. BMC Nephrol 15: 168, 2014 https://doi.org/10.1186/1471-2369-15-168
6. Gross M-L, Meyer H-P, Ziebart H, Rieger P, Wenzel U, Amann K, Berger I, Adamczak M, Schirmacher P, Ritz E: Calcification of coronary intima and media: Immunohistochemistry, backscatter imaging, and x-ray analysis in renal and nonrenal patients. Clin J Am Soc Nephrol 2: 121–134, 2007 https://doi.org/10.2215/CJN.01760506
7. Schwarz U, Buzello M, Ritz E, Stein G, Raabe G, Wiest G, Mall G, Amann K: Morphology of coronary atherosclerotic lesions in patients with end-stage renal failure. Nephrol Dial Transplant 15: 218–223, 2000 https://doi.org/10.1093/ndt/15.2.218
8. Sedlis SP, Jurkovitz CT, Hartigan PM, Kolm P, Goldfarb DS, Lorin JD, Dada M, Maron DJ, Spertus JA, Mancini GB, Teo KK, Boden WE, Weintraub WS; COURAGE Study Investigators: Health status and quality of life in patients with stable coronary artery disease and chronic kidney disease treated with optimal medical therapy or percutaneous coronary intervention (post hoc findings from the COURAGE trial). Am J Cardiol 112: 1703–1708, 2013 https://doi.org/10.1016/j.amjcard.2013.07.034
9. Yasue H, Yoshimura M, Sumida H, Kikuta K, Kugiyama K, Jougasaki M, Ogawa H, Okumura K, Mukoyama M, Nakao K: Localization and mechanism of secretion of B-type natriuretic peptide in comparison with those of A-type natriuretic peptide in normal subjects and patients with heart failure. Circulation 90: 195–203, 1994 https://doi.org/10.1161/01.CIR.90.1.195
10. Brown TM, Bittner V: Biomarkers of atherosclerosis: clinical applications. Curr Cardiol Rep 10: 497–504, 2008 https://doi.org/10.1007/s11886-008-0078-1
11. Olsen MH, Hansen TW, Christensen MK, Gustafsson F, Rasmussen S, Wachtell K, Ibsen H, Torp-Pedersen C, Hildebrandt PR: N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population. Eur Heart J 28: 1374–1381, 2007 https://doi.org/10.1093/eurheartj/ehl448
12. de Lemos JA, Drazner MH, Omland T, Ayers CR, Khera A, Rohatgi A, Hashim I, Berry JD, Das SR, Morrow DA, McGuire DK: Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population. JAMA 304: 2503–2512, 2010 https://doi.org/10.1001/jama.2010.1768
13. Mishra RK, Li Y, DeFilippi C, Fischer MJ, Yang W, Keane M, Chen J, He J, Kallem R, Horwitz EJ, Rafey M, Raj DS, Go AS, Shlipak MG; CRIC Study Investigators: Association of cardiac troponin T with left ventricular structure and function in CKD. Am J Kidney Dis 61: 701–709, 2013 https://doi.org/10.1053/j.ajkd.2012.11.034
14. Wang J, Tan GJ, Han LN, Bai YY, He M, Liu HB: Novel biomarkers for cardiovascular risk prediction. J Geriatr Cardiol 14: 135–150, 2017
15. Bonaterra GA, Zügel S, Thogersen J, Walter SA, Haberkorn U, Strelau J, Kinscherf R: Growth differentiation factor-15 deficiency inhibits atherosclerosis progression by regulating interleukin-6-dependent inflammatory response to vascular injury. J Am Heart Assoc 1: e002550, 2012 https://doi.org/10.1161/JAHA.112.002550
16. Kempf T, Eden M, Strelau J, Naguib M, Willenbockel C, Tongers J, Heineke J, Kotlarz D, Xu J, Molkentin JD, Niessen HW, Drexler H, Wollert KC: The transforming growth factor-beta superfamily member growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res 98: 351–360, 2006 https://doi.org/10.1161/01.RES.0000202805.73038.48
17. Preusch MR, Baeuerle M, Albrecht C, Blessing E, Bischof M, Katus HA, Bea F: GDF-15 protects from macrophage accumulation in a mouse model of advanced atherosclerosis. Eur J Med Res 18: 19, 2013 https://doi.org/10.1186/2047-783X-18-19
18. Weinberg EO, Shimpo M, Hurwitz S, Tominaga S, Rouleau J-L, Lee RT: Identification of serum soluble ST2 receptor as a novel heart failure biomarker. Circulation 107: 721–726, 2003 https://doi.org/10.1161/01.CIR.0000047274.66749.FE
19. Weinberg EO, Shimpo M, De Keulenaer GW, MacGillivray C, Tominaga S, Solomon SD, Rouleau JL, Lee RT: Expression and regulation of ST2, an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation 106: 2961–2966, 2002 https://doi.org/10.1161/01.CIR.0000038705.69871.D9
20. Danek J, Hnatek T, Maly M, Taborsky M, Belacek J, Skvaril J, Pospisilova L, Cernohous M, Sedlon P, Hajsl M, Zavoral M: Troponin levels in patients with stable CAD. Cor Vasa 59: e229–e234, 2017 https://doi.org/10.1016/j.crvasa.2016.12.001
21. Laufer EM, Mingels AMA, Winkens MHM, Joosen IA, Schellings MW, Leiner T, Wildberger JE, Narula J, Van Dieijen-Visser MP, Hofstra L: The extent of coronary atherosclerosis is associated with increasing circulating levels of high sensitive cardiac troponin T. Arterioscler Thromb Vasc Biol 30: 1269–1275, 2010 https://doi.org/10.1161/ATVBAHA.109.200394
22. Caselli C, Prontera C, Liga R, De Graaf MA, Gaemperli O, Lorenzoni V, Ragusa R, Marinelli M, Del Ry S, Rovai D, Giannessi D, Aguade-Bruix S, Clemente A, Bax JJ, Lombardi M, Sicari R, Zamorano J, Scholte AJ, Kaufmann PA, Knuuti J, Underwood SR, Clerico A, Neglia D: Effect of coronary atherosclerosis and myocardial ischemia on plasma levels of high-sensitivity troponin T and NT-proBNP in patients with stable angina. Arterioscler Thromb Vasc Biol 36: 757–764, 2016 https://doi.org/10.1161/ATVBAHA.115.306818
23. Abdullah SM, Khera A, Das SR, Stanek HG, Canham RM, Chung AK, Morrow DA, Drazner MH, McGuire DK, de Lemos JA: Relation of coronary atherosclerosis determined by electron beam computed tomography and plasma levels of n-terminal pro-brain natriuretic peptide in a multiethnic population-based sample (the Dallas Heart Study). Am J Cardiol 96: 1284–1289, 2005 https://doi.org/10.1016/j.amjcard.2005.06.073
24. Daniels LB, Clopton P, deFilippi CR, Sanchez OA, Bahrami H, Lima JA, Tracy RP, Siscovick D, Bertoni AG, Greenland P, Cushman M, Maisel AS, Criqui MH: Serial measurement of N-terminal pro-B-type natriuretic peptide and cardiac troponin T for cardiovascular disease risk assessment in the Multi-Ethnic Study of Atherosclerosis (MESA). Am Heart J 170: 1170–1183, 2015 https://doi.org/10.1016/j.ahj.2015.09.010
25. Bartunek J, Delrue L, Van Durme F, Muller O, Casselman F, De Wiest B, Croes R, Verstreken S, Goethals M, de Raedt H, Sarma J, Joseph L, Vanderheyden M, Weinberg EO: Nonmyocardial production of ST2 protein in human hypertrophy and failure is related to diastolic load. J Am Coll Cardiol 52: 2166–2174, 2008 https://doi.org/10.1016/j.jacc.2008.09.027
26. Wang TJ, Wollert KC, Larson MG, Coglianese E, McCabe EL, Cheng S, Ho JE, Fradley MG, Ghorbani A, Xanthakis V, Kempf T, Benjamin EJ, Levy D, Vasan RS, Januzzi JL: Prognostic utility of novel biomarkers of cardiovascular stress: The Framingham Heart Study. Circulation 126: 1596–1604, 2012 https://doi.org/10.1161/CIRCULATIONAHA.112.129437
27. Demyanets S, Speidl WS, Tentzeris I, Jarai R, Katsaros KM, Farhan S, Krychtiuk KA, Wonnerth A, Weiss TW, Huber K, Wojta J: Soluble ST2 and interleukin-33 levels in coronary artery disease: Relation to disease activity and adverse outcome. PLoS One 9: e95055, 2014 https://doi.org/10.1371/journal.pone.0095055
28. Dieplinger B, Egger M, Haltmayer M, Kleber ME, Scharnagl H, Silbernagel G, de Boer RA, Maerz W, Mueller T: Increased soluble ST2 predicts long-term mortality in patients with stable coronary artery disease: Results from the Ludwigshafen risk and cardiovascular health study. Clin Chem 60: 530–540, 2014 https://doi.org/10.1373/clinchem.2013.209858
29. Gopal DM, Larson MG, Januzzi JL, Cheng S, Ghorbani A, Wollert KC, Kempf T, D'Agostino RB, Polak JF, Ramachandran VS, Wang TJ, Ho JE: Biomarkers of Cardiovascular Stress and Subclinical Atherosclerosis in the Community. 60: 1402–1408, 2014
30. Wang J, Wei L, Yang X, Zhong J: Roles of growth differentiation factor 15 in atherosclerosis and coronary artery disease. J Am Heart Assoc 8: e012826, 2019 https://doi.org/10.1161/JAHA.119.012826
31. Kempf T, Sinning JM, Quint A, Bickel C, Sinning C, Wild PS, Schnabel R, Lubos E, Rupprecht HJ, Münzel T, Drexler H, Blankenberg S, Wollert KC: Growth-differentiation factor-15 for risk stratification in patients with stable and unstable coronary heart disease: Results from the AtheroGene study. Circ Cardiovasc Genet 2: 286–292, 2009 https://doi.org/10.1161/CIRCGENETICS.108.824870
32. Bansal N, Hyre Anderson A, Yang W, Christenson RH, deFilippi CR, Deo R, Dries DL, Go AS, He J, Kusek JW, Lash JP, Raj D, Rosas S, Wolf M, Zhang X, Shlipak MG, Feldman HI: High-sensitivity troponin T and N-terminal pro-B-type natriuretic peptide (NT-proBNP) and risk of incident heart failure in patients with CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study. J Am Soc Nephrol 26: 946–956, 2015 https://doi.org/10.1681/ASN.2014010108
33. Bansal N, Zelnick L, Go A, Anderson A, Christenson R, Deo R, Defilippi C, Lash J, He J, Ky B, Seliger S, Soliman E, Shlipak M; CRIC Study Investigators: Cardiac biomarkers and risk of incident heart failure in chronic kidney disease: The CRIC (Chronic Renal Insufficiency Cohort) Study. J Am Heart Assoc 8: e012336, 2019 https://doi.org/10.1161/JAHA.119.012336
34. Bansal N, Zelnick L, Shlipak MG, Anderson A, Christenson R, Deo R, deFilippi C, Feldman H, Lash J, He J, Kusek J, Ky B, Seliger S, Soliman EZ, Go AS; CRIC Study Investigators: Cardiac and stress biomarkers and chronic kidney disease progression: The CRIC Study. Clin Chem 65: 1448–1457, 2019 https://doi.org/10.1373/clinchem.2019.305797
35. Lamprea-Montealegre JA, Zelnick LR, Shlipak MG, Floyd JS, Anderson AH, He J, Christenson R, Seliger SL, Soliman EZ, Deo R, Ky B, Feldman HI, Kusek JW, deFilippi CR, Wolf MS, Shafi T, Go AS, Bansal N; CRIC Study Investigators: Cardiac biomarkers and risk of atrial fibrillation in chronic kidney disease: The CRIC Study. J Am Heart Assoc 8: e012200, 2019 https://doi.org/10.1161/JAHA.119.012200
36. Nair V, Robinson-Cohen C, Smith MR, Bellovich KA, Bhat ZY, Bobadilla M, Brosius F, de Boer IH, Essioux L, Formentini I, Gadegbeku CA, Gipson D, Hawkins J, Himmelfarb J, Kestenbaum B, Kretzler M, Magnone MC, Perumal K, Steigerwalt S, Ju W, Bansal N: Growth differentiation factor-15 and risk of CKD progression. J Am Soc Nephrol 28: 2233–2240, 2017 https://doi.org/10.1681/ASN.2016080919
37. Tuegel C, Katz R, Alam M, Bhat Z, Bellovich K, de Boer I, Brosius F, Gadegbeku C, Gipson D, Hawkins J, Himmelfarb J, Ju W, Kestenbaum B, Kretzler M, Robinson-Cohen C, Steigerwalt S, Bansal N: GDF-15, galectin 3, soluble ST2, and risk of mortality and cardiovascular events in CKD. Am J Kidney Dis 72: 519–528, 2018 https://doi.org/10.1053/j.ajkd.2018.03.025
38. Feldman HI, Appel LJ, Chertow GM, Cifelli D, Cizman B, Daugirdas J, Fink JC, Franklin-Becker ED, Go AS, Hamm LL, He J, Hostetter T, Hsu CY, Jamerson K, Joffe M, Kusek JW, Landis JR, Lash JP, Miller ER, Mohler ER 3rd, Muntner P, Ojo AO, Rahman M, Townsend RR, Wright JT; Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: The Chronic Renal Insufficiency Cohort (CRIC) Study: Design and methods. J Am Soc Nephrol 14[Suppl 2]: S148–S153, 2003 https://doi.org/10.1097/01.ASN.0000070149.78399.CE
39. Lash JP, Go AS, Appel LJ, He J, Ojo A, Rahman M, Townsend RR, Xie D, Cifelli D, Cohan J, Fink JC, Fischer MJ, Gadegbeku C, Hamm LL, Kusek JW, Landis JR, Narva A, Robinson N, Teal V, Feldman HI; Chronic Renal Insufficiency Cohort (CRIC) Study Group: Chronic Renal Insufficiency Cohort (CRIC) Study: Baseline characteristics and associations with kidney function. Clin J Am Soc Nephrol 4: 1302–1311, 2009 https://doi.org/10.2215/CJN.00070109
40. Giannitsis E, Kurz K, Hallermayer K, Jarausch J, Jaffe AS, Katus HA: Analytical validation of a high-sensitivity cardiac troponin T assay. Clin Chem 56: 254–261, 2010 https://doi.org/10.1373/clinchem.2009.132654
41. Hays RD, Kallich JD, Mapes DL, Coons SJ, Carter WB: Development of the kidney disease quality of life (KDQOL) instrument. Qual Life Res 3: 329–338, 1994 https://doi.org/10.1007/BF00451725
42. Bundy JD, Chen J, Yang W, Budoff M, Go AS, Grunwald JE, Kallem RR, Post WS, Reilly MP, Ricardo AC, Rosas SE, Zhang X, He J; CRIC Study Investigators: Risk factors for progression of coronary artery calcification in patients with chronic kidney disease: The CRIC study. Atherosclerosis 271: 53–60, 2018 https://doi.org/10.1016/j.atherosclerosis.2018.02.009
43. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R: Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15: 827–832, 1990 https://doi.org/10.1016/0735-1097(90)90282-T
44. NCfHS: National Health and Nutrition Examination Survey Anthropometry Procedures Manual, Centers for Disease Control and Prevention, 2000. Available at: https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf
. Accessed October 7, 2020
45. Joffe M, Hsu CY, Feldman HI, Weir M, Landis JR, Hamm LL; Chronic Renal Insufficiency Cohort (CRIC) Study Group: Variability of creatinine measurements in clinical laboratories: Results from the CRIC study. Am J Nephrol 31: 426–434, 2010 https://doi.org/10.1159/000296250
46. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS; CKD-EPI Investigators: Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367: 20–29, 2012 https://doi.org/10.1056/NEJMoa1114248
47. D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB: General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 117: 743–753, 2008 https://doi.org/10.1161/CIRCULATIONAHA.107.699579
48. Efron RJT: An Introduction to the Bootstrap, New York, Chapman and Hall, 1994 https://doi.org/10.1201/9780429246593
49. Scialla JJ, Xie H, Rahman M, Anderson AH, Isakova T, Ojo A, Zhang X, Nessel L, Hamano T, Grunwald JE, Raj DS, Yang W, He J, Lash JP, Go AS, Kusek JW, Feldman H, Wolf M; Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: Fibroblast growth factor-23 and cardiovascular events in CKD. J Am Soc Nephrol 25: 349–360, 2014 https://doi.org/10.1681/ASN.2013050465
50. Chen J, Budoff MJ, Reilly MP, Yang W, Rosas SE, Rahman M, Zhang X, Roy JA, Lustigova E, Nessel L, Ford V, Raj D, Porter AC, Soliman EZ, Wright JT Jr, Wolf M, He J; CRIC Investigators: Coronary artery calcification and risk of cardiovascular disease and death among patients with chronic kidney disease. JAMA Cardiol 2: 635–643, 2017 https://doi.org/10.1001/jamacardio.2017.0363
51. Royston P: Multiple imputation of missing values. Stata J 4: 227–241, 2004 https://doi.org/10.1177/1536867X0400400301
52. Rubin DB: Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons, Inc., Hoboken, NJ, 1987 https://doi.org/10.1002/9780470316696
53. Daniels LB, Laughlin GA, Clopton P, Maisel AS, Barrett-Connor E: Minimally elevated cardiac troponin T and elevated N-terminal pro-B-type natriuretic peptide predict mortality in older adults: results from the Rancho Bernardo Study. J Am Coll Cardiol 52: 450–459, 2008 https://doi.org/10.1016/j.jacc.2008.04.033
54. Bibbins-Domingo K, Ansari M, Schiller NB, Massie B, Whooley MA: B-type natriuretic peptide and ischemia in patients with stable coronary disease: Data from the Heart and Soul study. Circulation 108: 2987–2992, 2003 https://doi.org/10.1161/01.CIR.0000103681.04726.9C
55. Marumoto K, Hamada M, Hiwada K: Increased secretion of atrial and brain natriuretic peptides during acute myocardial ischaemia induced by dynamic exercise in patients with angina pectoris. Clin Sci (Lond) 88: 551–556, 1995 https://doi.org/10.1042/cs0880551
56. Januzzi JL Jr, Bamberg F, Lee H, Truong QA, Nichols JH, Karakas M, Mohammed AA, Schlett CL, Nagurney JT, Hoffmann U, Koenig W: High-sensitivity troponin T concentrations in acute chest pain patients evaluated with cardiac computed tomography. Circulation 121: 1227–1234, 2010 https://doi.org/10.1161/CIRCULATIONAHA.109.893826
57. Sabatine MS, Morrow DA, de Lemos JA, Jarolim P, Braunwald E: Detection of acute changes in circulating troponin in the setting of transient stress test-induced myocardial ischaemia using an ultrasensitive assay: Results from TIMI 35. Eur Heart J 30: 162–169, 2009 https://doi.org/10.1093/eurheartj/ehn504
58. Lind L, Wallentin L, Kempf T, Tapken H, Quint A, Lindahl B, Olofsson S, Venge P, Larsson A, Hulthe J, Elmgren A, Wollert KC: Growth-differentiation factor-15 is an independent marker of cardiovascular dysfunction and disease in the elderly: Results from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. Eur Heart J 30: 2346–2353, 2009 https://doi.org/10.1093/eurheartj/ehp261
59. Wollert KC, Kempf T, Wallentin L: Growth differentiation factor 15 as a biomarker in cardiovascular disease. Clin Chem 63: 140–151, 2017 https://doi.org/10.1373/clinchem.2016.255174
60. Dallmeier D, Brenner H, Mons U, Rottbauer W, Koenig W, Rothenbacher D: Growth differentiation factor 15, its 12-month relative change, and risk of cardiovascular events and total mortality in patients with stable coronary heart disease: 10-year follow-up of the KAROLA Study. Clin Chem 62: 982–992, 2016 https://doi.org/10.1373/clinchem.2016.254755
61. Wollert KC, Kempf T, Peter T, Olofsson S, James S, Johnston N, Lindahl B, Horn-Wichmann R, Brabant G, Simoons ML, Armstrong PW, Califf RM, Drexler H, Wallentin L: Prognostic value of growth-differentiation factor-15 in patients with non-ST-elevation acute coronary syndrome. Circulation 115: 962–971, 2007 https://doi.org/10.1161/CIRCULATIONAHA.106.650846
62. Hagström E, James SK, Bertilsson M, Becker RC, Himmelmann A, Husted S, Katus HA, Steg PG, Storey RF, Siegbahn A, Wallentin L; PLATO Investigators: Growth differentiation factor-15 level predicts major bleeding and cardiovascular events in patients with acute coronary syndromes: Results from the PLATO study. Eur Heart J 37: 1325–1333, 2016 https://doi.org/10.1093/eurheartj/ehv491
63. Fuernau G, Poenisch C, Eitel I, de Waha S, Desch S, Schuler G, Adams V, Werdan K, Zeymer U, Thiele H: Growth-differentiation factor 15 and osteoprotegerin in acute myocardial infarction complicated by cardiogenic shock: A biomarker substudy of the IABP-SHOCK II-trial. Eur J Heart Fail 16: 880–887, 2014 https://doi.org/10.1002/ejhf.117
64. Andersson J, Fall T, Delicano R, Wennberg P, Jansson J-H: GDF-15 is associated with sudden cardiac death due to incident myocardial infarction. Resuscitation 152: 165–169, 2020 https://doi.org/10.1016/j.resuscitation.2020.05.001
65. Adela R, Banerjee SK: GDF-15 as a target and biomarker for diabetes and cardiovascular diseases: A translational prospective. J Diabetes Res 2015: 490842, 2015 https://doi.org/10.1155/2015/490842
66. Schlittenhardt D, Schober A, Strelau J, Bonaterra GA, Schmiedt W, Unsicker K, Metz J, Kinscherf R: Involvement of growth differentiation factor-15/macrophage inhibitory cytokine-1 (GDF-15/MIC-1) in oxLDL-induced apoptosis of human macrophages in vitro and in arteriosclerotic lesions. Cell Tissue Res 318: 325–333, 2004 https://doi.org/10.1007/s00441-004-0986-3
67. Ago T, Sadoshima J: GDF15, a cardioprotective TGF-beta superfamily protein. Circ Res 98: 294–297, 2006 https://doi.org/10.1161/01.RES.0000207919.83894.9d
68. Ackermann K, Bonaterra GA, Kinscherf R, Schwarz A: Growth differentiation factor-15 regulates oxLDL-induced lipid homeostasis and autophagy in human macrophages. bioRxiv. 2018: 354043 10.1101/354043.
69. Oh J, Park S, Yu HT, Chang HJ, Lee SH, Kang SM, Choi D: Lack of superiority for soluble ST2 over high sensitive c-reactive protein in predicting high risk coronary artery calcium score in a community cohort. Yonsei Med J 57: 1347–1353, 2016 https://doi.org/10.3349/ymj.2016.57.6.1347
70. Willems S, Quax PHA, de Borst GJ, de Vries JP, Moll FL, de Kleijn DP, Hoefer IE, Pasterkamp G: Soluble ST2 levels are not associated with secondary cardiovascular events and vulnerable plaque phenotype in patients with carotid artery stenosis. Atherosclerosis 231: 48–53, 2013 https://doi.org/10.1016/j.atherosclerosis.2013.08.024
71. Aimo A, Migliorini P, Vergaro G, Franzini M, Passino C, Maisel A, Emdin M: The IL-33/ST2 pathway, inflammation and atherosclerosis: Trigger and target? Int J Cardiol 267: 188–192, 2018 https://doi.org/10.1016/j.ijcard.2018.05.056
72. Lott JM, Sumpter TL, Turnquist HR: New dog and new tricks: Evolving roles for IL-33 in type 2 immunity. J Leukoc Biol 97: 1037–1048, 2015 https://doi.org/10.1189/jlb.3RI1214-595R
73. Pascual-Figal DA, Januzzi JL: The biology of ST2: The International ST2 Consensus Panel. Am J Cardiol 115[Suppl]: 3B–7B, 2015 https://doi.org/10.1016/j.amjcard.2015.01.034
74. Fradley MG, Larson MG, Cheng S, McCabe E, Coglianese E, Shah RV, Levy D, Vasan RS, Wang TJ: Reference limits for N-terminal-pro-B-type natriuretic peptide in healthy individuals (from the Framingham Heart Study). Am J Cardiol 108: 1341–1345, 2011 https://doi.org/10.1016/j.amjcard.2011.06.057
75. Welsh P, Preiss D, Shah ASV, McAllister D, Briggs A, Boachie C, McConnachie A, Hayward C, Padmanabhan S, Welsh C, Woodward M, Campbell A, Porteous D, Mills NL, Sattar N: Comparison between high-sensitivity cardiac troponin T and cardiac troponin I in a large general population cohort. Clin Chem 64: 1607–1616, 2018 https://doi.org/10.1373/clinchem.2018.292086
76. Ho JE, Mahajan A, Chen MH, Larson MG, McCabe EL, Ghorbani A, Cheng S, Johnson AD, Lindgren CM, Kempf T, Lind L, Ingelsson E, Vasan RS, Januzzi J, Wollert KC, Morris AP, Wang TJ: Clinical and genetic correlates of growth differentiation factor 15 in the community. Clin Chem 58: 1582–1591, 2012 https://doi.org/10.1373/clinchem.2012.190322
77. Diris JH, Hackeng CM, Kooman JP, Pinto YM, Hermens WT, van Dieijen-Visser MP: Impaired renal clearance explains elevated troponin T fragments in hemodialysis patients. Circulation 109: 23–25, 2004 https://doi.org/10.1161/01.CIR.0000109483.45211.8F
78. Srisawasdi P, Vanavanan S, Charoenpanichkit C, Kroll MH: The effect of renal dysfunction on BNP, NT-proBNP, and their ratio. Am J Clin Pathol 133: 14–23, 2010 https://doi.org/10.1309/AJCP60HTPGIGFCNK
79. Chesnaye NC, Szummer K, Bárány P, Heimbürger O, Magin H, Almquist T, Uhlin F, Dekker FW, Wanner C, Jager KJ, Evans MEQUAL Study Investigators: Association between renal function and troponin T over time in stable chronic kidney disease patients. J Am Heart Assoc 8: e013091, 2019 https://doi.org/10.1161/JAHA.119.013091
80. Tsutamoto T, Sakai H, Yamamoto T, Nakagawa Y: Renal clearance of N-Terminal pro-brain natriuretic peptide is markedly decreased in chronic kidney disease. Circ Rep 1: 326–332, 2019 https://doi.org/10.1253/circrep.CR-19-0063
81. Kim JS, Kim S, Won CW, Jeong KH: Association between plasma levels of growth differentiation factor-15 and renal function in the elderly: Korean Frailty and Aging Cohort Study. Kidney Blood Press Res 44: 405–414, 2019 https://doi.org/10.1159/000498959
82. Thorsteinsdottir H, Salvador CL, Mjøen G, Lie A, Sugulle M, Tøndel C, Brun A, Almaas R, Bjerre A: Growth differentiation factor 15 in children with chronic kidney disease and after renal transplantation. Dis Markers 2020: 6162892, 2020 https://doi.org/10.1155/2020/6162892
83. Coglianese EE, Larson MG, Vasan RS, Ho JE, Ghorbani A, McCabe EL, Cheng S, Fradley MG, Kretschman D, Gao W, O’Connor G, Wang TJ, Januzzi JL: Distribution and clinical correlates of the interleukin receptor family member soluble ST2 in the Framingham Heart Study. Clin Chem 58: 1673–1681, 2012 https://doi.org/10.1373/clinchem.2012.192153
84. Mueller T, Dieplinger B: Soluble ST2 and galectin-3: What we know and don’t know analytically. EJIFCC 27: 224–237, 2016
85. Bayes-Genis A, Zamora E, de Antonio M, Galán A, Vila J, Urrutia A, Díez C, Coll R, Altimir S, Lupón J: Soluble ST2 serum concentration and renal function in heart failure. J Card Fail 19: 768–775, 2013 https://doi.org/10.1016/j.cardfail.2013.09.005
86. Kim MS, Jeong TD, Han SB, Min WK, Kim JJ: Role of soluble ST2 as a prognostic marker in patients with acute heart failure and renal insufficiency. J Korean Med Sci 30: 569–575, 2015 https://doi.org/10.3346/jkms.2015.30.5.569
87. Harper RW, Kennedy G, DeSanctis RW, Hutter AM Jr: The incidence and pattern of angina prior to acute myocardial infarction: A study of 577 cases. Am Heart J 97: 178–183, 1979 https://doi.org/10.1016/0002-8703(79)90353-3
88. Kouvaras G, Bacoulas G: Unstable angina pectoris as a warning symptom before acute myocardial infarction. Q J Med 64: 679–684, 1987
89. Salama RH, El-Moniem AE, El-Hefney N, Samor T: N-TerminaL PRO-BNP in acute coronary syndrome patients with ST elevation versus non ST elevation in Qassim region of Saudi Arabia. Int J Health Sci (Qassim) 5: 136–145, 2011
90. Salerno FR, Parraga G, McIntyre CW: Why is your patient still short of breath? Understanding the complex pathophysiology of dyspnea in chronic kidney disease. Semin Dial 30: 50–57, 2017 https://doi.org/10.1111/sdi.12548
91. Su Q, Liu H, Zhang X, Dang W, Liu R, Zhao X, Yuan X, Qin Y, Zhang J, Chen C, Xia Y: Diagnostic values of NT-proBNP in acute dyspnea among elderly patients. Int J Clin Exp Pathol 8: 13471–13476, 2015
92. Tummalapalli SL, Zelnick LR, Andersen AH, Christenson RH, deFilippi CR, Deo R, Go AS, He J, Ky B, Lash JP, Seliger SL, Soliman EZ, Shlipak MG, Bansal N; CRIC Study Investigators: Association of cardiac biomarkers with the Kansas City cardiomyopathy questionnaire in patients with chronic kidney disease without heart failure. J Am Heart Assoc 9: e014385, 2020 https://doi.org/10.1161/JAHA.119.014385
93. Leikis MJ, McKenna MJ, Petersen AC, Kent AB, Murphy KT, Leppik JA, Gong X, McMahon LP: Exercise performance falls over time in patients with chronic kidney disease despite maintenance of hemoglobin concentration. Clin J Am Soc Nephrol 1: 488–495, 2006 https://doi.org/10.2215/CJN.01501005
94. Cheng XS, Mohanty S, Turner V, Mastrodicasa D, Winther S, Fleischmann D, Tan JC, Fearon WF: Coronary computed tomography angiography in diagnosing obstructive coronary artery disease in patients with advanced chronic kidney disease: A systematic review and meta-analysis. Cardiorenal Med 11: 44–51, 2021
95. Bashir A, Moody WE, Edwards NC, Ferro CJ, Townend JN, Steeds RP: Coronary artery calcium assessment in CKD: Utility in cardiovascular disease risk assessment and treatment? Am J Kidney Dis 65: 937–948, 2015 https://doi.org/10.1053/j.ajkd.2015.01.012