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. Author manuscript; available in PMC: 2024 Aug 1.
Published in final edited form as: Am J Kidney Dis. 2023 Mar 18;82(2):225–236. doi: 10.1053/j.ajkd.2023.01.442

Cardiac Structure and Function and Subsequent Kidney Disease Progression in Adults With CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Junichi Ishigami 1,2, Mayank Kansal 3, Rupal Mehta 4, Anand Srivastava 4, Mahboob Rahman 5, Mirela Dobre 5, Sadeer G Al-Kindi 6, Alan S Go 7,8,9, Sankar D Navaneethan 10, Jing Chen 11, Jiang He 11, Zeenat Bhat 12, Bernard G Jaar 1,2,13, Lawrence J Appel 1,2, Kunihiro Matsushita 1,2, CRIC Study investigators
PMCID: PMC10440229  NIHMSID: NIHMS1884026  PMID: 36935072

Abstract

Rationale & Objective:

The heart-kidney crosstalk is recognized as the cardiorenal syndrome. We examined the association of cardiac function and structure with the risk of kidney failure with replacement therapy (KFRT) in a chronic kidney disease (CKD) population.

Study Design:

Prospective observational cohort study.

Setting & Participants:

3,027 participants from the Chronic Renal Insufficiency Cohort Study.

Exposures:

Five pre-selected variables that assess different aspects of cardiac structure and function: left ventricular mass index (LVMI), LV volume, left atrial (LA) area, peak tricuspid regurgitation (TR) velocity, and left ventricular ejection fraction (EF) as assessed by echocardiography.

Outcomes:

Incident KFRT (primary outcome), and annual eGFR slope (secondary outcome).

Analytical Approach:

Multivariable Cox models and mixed-effects models.

Results:

Mean age was 59 (SD 11) years, 54% were men, and mean eGFR was 43 (17) ml/min/1.73m2. Between 2003 and 2018 (median follow-up, 9.9 years), 883 participants developed KFRT. Higher LVMI, LV volume, LA area, peak TR velocity, and lower EF were each statistically significantly associated with an increased risk of KFRT, with corresponding HRs for the highest vs. lowest quartiles (lowest vs. highest for EF) of 1.70 (95%CI, 1.27 to 2.26), 1.50 (1.19 to 1.90), 1.43 (1.11 to 1.84), 1.45 (1.06 to 1.96), and 1.26 (1.03 to 1.56), respectively. For secondary outcome, participants in the highest vs. lowest quartiles (lowest vs. highest for EF) had a statistically significantly faster eGFR decline, except for LA area (ΔeGFR slope per year, −0.57 [95%CI, −0.68 to −0.46] mL/min/1.73m2 for LVMI, −0.25 [−0.35 to −0.15] mL/min/1.73m2 for LV volume, −0.01 [−0.12 to −0.01] mL/min/1.73m2 for LA area, −0.42 [−0.56 to −0.28] mL/min/1.73m2 for peak TR velocity, and −0.11 [−0.20 to −0.01] mL/min/1.73m2 for EF, respectively).

Limitations:

The possibility of residual confounding.

Conclusions:

Multiple aspects of cardiac structure and function were statistically significantly associated with the risk of KFRT. These findings suggest that cardiac abnormalities and incidence of KFRT are potentially on the same causal pathway related to the interaction between hypertension, heart failure, and coronary artery diseases.

Keywords: Cardiorenal, Chronic Kidney Disease, Cardiovascular Disease, Echocardiography, End-stage kidney disease, Kidney failure with replacement therapy

Plain Language Summary:

Heart disease and kidney disease are known to interact with each other. In this study, we examined whether cardiac abnormalities, as assessed by echocardiography, were linked to the subsequent progression of kidney disease among people living with chronic kidney disease. We found that people with abnormalities in heart structure and function had a greater risk of progression to advanced chronic kidney disease that required kidney replacement therapy and had a faster rate of decline in kidney function. Our study indicates the potential role of abnormal heart structure and function in the progression of kidney disease among people living with chronic kidney disease.

Introduction

The crosstalk between the heart and kidney has been recognized as the cardiorenal syndrome. The elevated risk of cardiovascular disease (CVD) in chronic kidney disease (CKD) is well-described;1, 2 conversely, clinical CVD, particularly heart failure, has been shown to be associated with the risk of kidney failure with replacement therapy (KFRT).3 Given the continuum of CVD pathophysiology,4, 5 it is plausible that subclinical changes of cardiac structure and function are associated with the risk of adverse kidney outcome, even in those without clinical manifestation of CVD.

Several previous studies have related echocardiographic findings, mostly the presence of LV hypertrophy (LVH), to the risk of adverse kidney outcomes.6–12 However, previous studies mostly examined interim kidney outcomes (e.g., incident CKD, faster estimated glomerular filtration rate [eGFR] decline) in cohorts with few or no individuals with CKD.6–9 CKD cohorts that have explored this study question also have some important caveats such as smaller sample size (n<700)10–14 and selected populations (Asians10, 11, 15 or African Americans with hypertension12).

Within the Chronic Renal Insufficiency Cohort (CRIC) Study, a large, ethnically-diverse US cohort of individuals with non-dialysis dependent CKD, we evaluated the independent association of pre-selected echocardiographic measures of left ventricular (LV) structure and function and right ventricular (RV) pressure with the risk of KFRT as well as CKD progression assessed by eGFR slope.

Methods

Study population

Details on the rationale and design for the CRIC Study were reported previously.16 In brief, the CRIC Study is a National Institute of Diabetes and Digestive and Kidney Diseases funded, prospective cohort of individuals with CKD. Between 2003 and 2008, CRIC investigators enrolled 3,939 individuals with mild to moderate CKD. Major exclusion criteria for the CRIC Study included previously received dialysis > 1-month, previous organ or bone marrow transplant, New York Heart Association class III or IV, known cirrhosis, polycystic kidney disease, and pregnant women.17 Of note, these criteria were used at the time of recruitment; and therefore, participants might have experienced progressive diseases during follow-up (i.e., advanced heart failure). Transthoracic echocardiography was performed during the second study visit that occurred approximately 1 year after the enrollment. Of 3,520 participants who completed this visit, 3,027 participants underwent an echocardiogram and were included in the analysis. Participant characteristics were overall comparable between those who did and did not undergo an echocardiogram (Table S1). This study was approved by the Institutional Review Board at each clinical center and scientific and data coordinating center, and conducted in compliance with the Declaration of Helsinki. All participants provided written informed consent.

Echocardiography

Echocardiography was performed at each clinical site according to a standard protocol provided by the core echocardiographic laboratory at University of Pennsylvania.18 Taped or digital images were sent to the core laboratory, and measurements were performed by a registered diagnostic cardiac sonographer according to a standard protocol. Data were reported as missing if the image was not available or had poor quality. Overall, the image quality was excellent in 25%, good in 58%, fair in 16%, and poor in 1%. A quantitative quality study in a random subset (n=103) showed intra-rater reliability coefficients of 0.922 for LV mass and 0.849 for ejection fraction (EF).

Exposures of interest were echocardiographic measures of LV mass index (LVMI), LV end- iastolic volume, LVEF, left atrial (LA) area, and peak tricuspid regurgitation (TR) velocity. These parameters were selected to reflect LV structure, LV systolic and diastolic function, and RV pressure. All echocardiographic measures were assessed according to the recommendations by the American Society of Echocardiography and the European Association of Cardiovascular Imaging.19–21

LV mass was calculated using the area length method from the parasternal short axis view at the papillary muscle level in 2-dimensional mode: LV mass = 1.05 [5/6 × A1 × (L + T)] – [5/6 × A2 × L], where A1 = epicardial area at end diastole (cm2), A2 = endocardial area at end diastole (cm2), L = ventricular length at end diastole (cm), and T = average wall thickness (cm). LV mass was then indexed to height in m2.7. LVH was defined as LVMI >51 g/ m2.7 for men and >47 g/m2.7 for women, and further classified into concentric (relative wall thickness [RWT] >0.42) or eccentric (RWT ≤0.42) LVH.19 RWT was measured as the ratio of twice the posterior wall thickness divided by the LV internal diameter in diastole.

EF was calculated by 100 × (LV end-diastolic volume- LV end-systolic volume / LV end-diastolic volume) using the modified Simpson’s rule method. LV end-diastolic volume and LV end systolic volume were obtained from the apical four chamber view. EF was clinically categorized as reduced (≤40%), mildly reduced (41–49%), and preserved (≥50%).22

At the time of the echocardiography study, LA volume, an echocardiographic component in assessing diastolic function, was not included in the recommendations for a standardized echocardiography report.23 Accordingly, LA length was not measured on the initial echocardiograms which is required to calculate LA volume. Thus, we used the average of two LA areas as an alternative measure for diastolic function.24

Peak TR velocity was obtained using spectral continuous-wave Doppler signal of tricuspid regurgitation in the apical 4-chamber view. Peak TR velocity was clinically diagnosed as normal <2.5 m/s, mildly elevated 2.5–2.8 m/s, and elevated >2.8 m/s.19–21

Kidney outcomes

The primary outcome of interest was incident KFRT defined as initiation of maintenance dialysis or receipt of kidney transplant. All events were ascertained through semiannual contacts to participants or their proxies or through linkage to the United States Renal Data System.25 Follow-up was continued until KFRT, death, lost-to-follow-up, or January 1, 2018.

The secondary outcome was eGFR slope using longitudinal data with repeated eGFR assessments per person. eGFR was calculated using the CRIC GFR estimating equation, which was developed using serum creatinine, cystatin C, and anthropometrics in a sub-cohort of 1,433 CRIC participants who underwent urinary (125)I-iothalamate clearance testing.26 Within CRIC participants, this equation was shown to be more accurate compared to other equations.26 Additionally, we repeated the analysis using the new CKD-EPI 2021 equation without race27 in line with the recently published recommendation,28 although our analytic data were collected prior to the development of this equation. In the CRIC Study, participants were invited to a study visit annually, where blood was collected for serum creatinine, cystatin C, and other biomarkers. For participants who initiated kidney replacement therapy, we assumed an eGFR of 10 mL/min/1.73m2 at that time.29

Covariates

All covariates were based on data at the second study visit, the point at which echocardiography was performed. Age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, and other), smoking status (current vs. non-current), alcohol use (current vs. non-current), and medical history (CVD and chronic obstructive pulmonary disease) were self-reported. Body mass index was calculated by weight in kilograms divided by height in meters squared. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or taking antihypertensive drugs. Diabetes was defined as a fasting glucose ≥126 mg/dL, a non-fasting glucose ≥200 mg/dL, or taking antidiabetic drugs. Urine albumin-to-creatinine ratio (ACR) was calculated as albumin concentration divided by creatinine concentration in a spot urine sample.

Statistical analysis

Baseline characteristics were presented for the overall population and by quartiles of each echocardiographic parameter.30 Between-group differences were compared using chi-square tests or ANOVA. Incidence rates and their 95% confidence intervals (95% CIs) were estimated according to echocardiographic parameters using Poisson regression models. We used multivariable Cox models to estimate hazard ratios (HRs) with incident KFRT. Model 1 was adjusted for age, sex, race/ethnicity, and center. Model 2 further adjusted for eGFR and ACR, given their strong association with KFRT.31 Model 3 additionally adjusted for other major confounders including body mass index, systolic blood pressure, smoking status, alcohol use, diabetes, and history of CVD and chronic obstructive pulmonary disease, use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, and hemoglobin levels. Each echocardiographic parameter was treated as a categorical variable using quartiles. The lowest quartile served as a reference except for EF, where the highest quartile was the reference. We used overall quartiles but not sex-specific quartiles, because the distribution of echocardiographic parameters were largely similar between men and women in the CRIC Study (Table S2). We also repeated the analysis using clinical diagnosis of LVH, reduced EF, and elevated peak TR velocity using the guideline recommended thresholds.19–21

Several sensitivity analyses were performed. First, we repeated the analysis using sex-specific quartiles of LVMI. Second, we performed an analysis using the CKD-EPI 2021 equation without race.27 Third, since systolic blood pressure and hemoglobin levels may be an important mediator in the association of cardiac structure and function with the risk of KFRT, we ran the models treating these variables as a time-varying covariate. Fourth, since clinical CVD is a risk factor for KFRT,32, 33 the association between echocardiographic findings and the risk of KFRT may be explained by the interim occurrence of clinical CVD. To exclude the influence of clinical CVD on the incidence of KFRT, we conducted an analysis censoring for incident heart failure, myocardial infarction, and stroke that occurred prior to incident KFRT. In the CRIC Study, all CVD events have been adjudicated by a panel of physicians according to the CRIC definitions of CVD.16 Fifth, we used Fine-Gray sub-distribution hazard models to assess sub-hazards treating death as a competing event. Sixth, since LV volume, EF, LA area, and peak TR velocity may be influenced by LVMI, we tested the models further adjusting for LVMI in addition to Model 3 covariates.

Subgroup analyses were also performed for age, sex, race, eGFR, and ACR. For subgroup analyses, each echocardiographic parameter was continuously modeled as 1-SD increment after visually checking normal distributions of echocardiographic parameters in histograms (Figure S1). Interactions were statistically assessed using the likelihood ratio tests.

For analyses of our secondary outcome, eGFR slopes were estimated according to the quartile of echocardiographic parameters using multivariable mixed-effects models accounting for within-individual correlation, with the inclusion of interaction terms between echocardiographic parameters and years since the baseline visit. When the model covariates included missing data, we used the largest sample available for each analysis. All statistical analyses were performed using Stata version 15 (StataCorp, College Station, TX). A two-sided P value less than 0.05 was considered statistical significance.

Results

Baseline characteristics

Among 3027 eligible participants, mean (SD) age was 59 (11) years, with 54% men, and 45% self-identified as non-Hispanic Black, 46% non-Hispanic White, and 9% of other race/ethnicities (Table 1). Twenty-four percent had eGFR <30 mL/min/1.73m2, and 27% had ACR ≥300 mg/g. When baseline characteristics were compared by quartiles of LVMI, participants in higher quartiles of LVMI tended to be older, men, non-Hispanic Black, have high body mass index, hypertension, diabetes, history of CVD and chronic pulmonary obstructive disease, and have lower eGFR and higher ACR. Similar demographic and clinical profiles were generally observed in higher quartiles of LV volume, LA area, and peak TR velocity, and lower quartiles of EF (Table S3–S6).

Table 1:

Baseline characteristics of CRIC participants

Characteristics Overall (n=3027) LVMI, quartile
Q1: <41.4 (n=662) Q2: 41.4–49.2 (n=662) Q3: 49.2–58.9 (n=662) Q4: ≥58.9 (n=663)
Age yr., mean (SD) 59 (11) 56 (12) 59 (11) 60 (11) 60 (9.3)
Male n (%) 1628 (54) 333 (50) 356 (54) 362 (55) 351 (53)
Race/Ethnicity, n (%)
 Non-Hispanic Black 1403 (46) 412 (62) 325 (49) 279 (42) 190 (29)
 Non-Hispanic White 1355 (45) 206 (31) 279 (42) 318 (48) 402 (61)
 Others 269 (9) 44 (7) 58 (9) 65 (10) 71 (11)
Body mass index kg/m2, mean (SD) (n=3006) 32 (7.6) 28 (5.3) 30 (6.1) 33 (6.8) 36 (8.3)
Systolic BP mmHg, mean (SD) (n=3017) 126 (21) 117 (16) 124 (19) 128 (20) 137 (25)
Diastolic BP mmHg, mean (SD) (n=3012) 70 (13) 69 (11) 70 (12) 70 (13) 71 (15)
Current smoking, n (%) 380 (13) 78 (12) 69 (10) 83 (13) 95 (14)
Alcohol use, n (%) (n=3020) 1783 (59) 465 (70) 410 (62) 376 (57) 318 (48)
Hypertension, n (%) (n=3022) 2675 (88) 498 (75) 572 (86) 618 (93) 646 (97)
Diabetes, n (%) 1394 (46) 190 (29) 255 (39) 328 (50) 431 (65)
Cardiovascular disease, n (%) 1109 (37) 128 (19) 175 (26) 256 (39) 383 (58)
COPD, n (%) (n=2985) 153 (5.1) 28 (4.2) 24 (3.6) 26 (3.9) 43 (6.5)
Use of ACEi or ARB, n (%) (n=3025) 2093 (69) 423 (64) 460 (70) 463 (70) 477 (72)
Hemoglobin level g/dL, mean (SD) (n=2980) 13 (1.8) 13 (1.6) 13 (1.7) 13 (1.8) 12 (1.8)
eGFR ml/min/1.73m2, n (%) (n=2954)
 ≥45 1262 (43) 372 (57) 319 (49) 251 (39) 171 (27)
 30–44 979 (33) 199 (30) 225 (35) 218 (34) 214 (33)
 <30 715 (24) 86 (13) 106 (16) 171 (27) 254 (40)
ACR mg/g, n (%) (n=2925)
 <30 1344 (46) 383 (59) 346 (54) 286 (45) 175 (28)
 30–299 781 (27) 160 (25) 158 (25) 177 (28) 177 (28)
 ≥300 802 (27) 104 (16) 140 (22) 172 (27) 279 (44)
Echocardiographic parameters, mean (SD)
 LVMI g/m2.7 (n=2649) 51 (14) 36 (4.0) 45 (2.2) 54 (2.8) 71 (10)
 LV volume in end-diastole ml (n=2961) 34 (9.3) 27 (5.2) 31 (5.5) 34 (6.8) 42 (11)
 LV ejection fraction % (n=2973) 54 (8.6) 56 (6.3) 56 (6.9) 55 (7.8) 51 (12)
 LA area cm2 (n=2559) 23 (5.2) 20 (4.2) 21 (3.8) 23 (4.8) 26 (5.3)
 Peak TR velocity m/sec (n=1604) 2.5 (0.35) 2.3 (0.26) 2.4 (0.30) 2.5 (0.31) 2.6 (0.43)
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Sample sizes are shown if there are missing data. Abbreviations: CRIC, Chronic Renal Insufficiency Cohort; BP, blood pressure; COPD, chronic obstructive pulmonary disease; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio; LVMI, left ventricular mass index; LA, left atrium; TR, tricuspid regurgitation.

Echocardiographic parameters and incident KFRT

During median (interquartile interval [IQI]) follow-up of 9.9 (4.2 to 11.7) years, there were 883 cases of incident KFRT. Crude incidence rate per 1,000 person-years was 36.3 (95%CI, 34.0 to 38.8). Overall, participants with higher LVMI, LV volume, LA area, and peak TR velocity and those with lower EF had a higher incidence rate of KFRT (Table 2).

Table 2:

Incidence rates and hazard ratios of KFRT by quartiles of LVMI, LV volume in end-diastole, EF, LA area, and peak TR velocity

Exposures Incidence rate per 1,000 PY Model 1 Model 2 Model 3
LVMI (g/m2.7)
N 2649 2611 2611 2510
Q1: <41.4 17.5 (14.5 to 21.0) 1 [Reference] 1 [Reference] 1 [Reference]
Q2: 41.4–49.2 29.5 (25.4 to 34.2) 1.64 (1.29 to 2.09) 1.47 (1.14 to 1.89) 1.47 (1.14 to 1.91)
Q3: 49.2–58.9 38.7 (33.6 to 44.4) 2.09 (1.65 to 2.64) 1.61 (1.26 to 2.06) 1.40 (1.07 to 1.83)
Q4: ≥59.0 73.0 (65.1 to 81.9) 3.60 (2.86 to 4.54) 2.13 (1.67 to 2.70) 1.70 (1.27 to 2.26)
LV volume in end-diastole (ml)
N 2961 2921 2921 2812
Q1: <27.7 20.9 (17.7 to 24.6) 1 [Reference] 1 [Reference] 1 [Reference]
Q2: 27.7–32.5 25.7 (22.1 to 29.9) 1.17 (0.93 to 1.46) 1.24 (0.98 to 1.55) 1.18 (0.93 to 1.49)
Q3: 32.5–38.3 39.3 (34.5 to 44.7) 1.70 (1.38 to 2.09) 1.45 (1.17 to 1.79) 1.19 (0.95 to 1.49)
Q4: ≥38.4 68.5 (61.4 to 76.5) 2.73 (2.23 to 3.34) 1.99 (1.62 to 2.45) 1.50 (1.19 to 1.90)
LV ejection fraction (%)
N 2973 2931 2931 2822
Q1: <51.2 46.7 (41.2 to 52.8) 1.53 (1.26 to 1.86) 1.34 (1.10 to 1.64) 1.26 (1.03 to 1.56)
Q2: 51.3–55.2 37.3 (32.7 to 42.5) 1.33 (1.09 to 1.62) 1.38 (1.13 to 1.69) 1.29 (1.05 to 1.60)
Q3: 55.2–59.0 35.2 (30.8 to 40.3) 1.21 (0.99 to 1.48) 1.27 (1.03 to 1.56) 1.40 (1.14 to 1.73)
Q4: ≥59.1 27.7 (24.0 to 32.1) 1 [Reference] 1 [Reference] 1 [Reference]
LA area (cm2)
N 2559 2518 2518 2425
Q1: <19.0 23.7 (20.1 to 28.0) 1 [Reference] 1 [Reference] 1 [Reference]
Q2: 19.1–22.0 28.7 (24.6 to 33.6) 1.17 (0.93 to 1.47) 1.20 (0.95 to 1.52) 1.09 (0.86 to 1.39)
Q3: 22.1–25.8 36.4 (31.6 to 42.0) 1.39 (1.12 to 1.74) 1.38 (1.10 to 1.74) 1.29 (1.01 to 1.65)
Q4: ≥25.9 60.9 (53.8 to 69.0) 2.34 (1.89 to 2.91) 1.75 (1.40 to 2.19) 1.43 (1.11 to 1.84)
Peak TR velocity (m/sec)
N 1604 1573 1573 1517
Q1: <2.22 26.4 (21.5 to 32.5) 1 [Reference] 1 [Reference] 1 [Reference]
Q2: 2.23–2.42 32.6 (27.1 to 39.4) 1.13 (0.85 to 1.50) 1.15 (0.86 to 1.55) 1.09 (0.81 to 1.49)
Q3: 2.43–2.66 29.6 (24.3 to 36.0) 1.11 (0.83 to 1.49) 1.20 (0.88 to 1.63) 1.15 (0.83 to 1.58)
Q4: ≥2.67 51.5 (43.4 to 61.1) 1.69 (1.27 to 2.25) 1.46 (1.08 to 1.96) (1.06 to 1.96)
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*

Model 1 was adjusted for center, age, sex, race/ethnicity. Model 2 additionally adjusted for eGFR and ACR. Model 3 additionally adjusted for body mass index, systolic blood pressure, current smoker, alcohol use, diabetes, history of cardiovascular disease, COPD, use of ACEi or ARB, and hemoglobin levels. Abbreviations: KFRT, kidney failure with replacement therapy; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio; BP, blood pressure; COPD, chronic obstructive pulmonary disease; ACEi, angiotensin converting enzyme inhibitors; ARB, angiotensin II receptor blockers; LVMI, left ventricular mass index; LA, left atrium; TR, tricuspid regurgitation.

In a multivariable Cox model adjusted for age, sex, race/ethnicity, and center, participants with the highest quartile of LVMI had a 3.6-fold higher risk of KFRT (HR 3.60 [95%CI, 2.86 to 4.54]) compared to those with the lowest quartile (Model 1 in Table 2). The corresponding HRs for LV volume, LA area, and peak TR velocity were 2.73 (95%CI, 2.26 to 3.34), 2.34 (1.89 to 2.91) and 1.69 (1.27 to 2.25), respectively. The lowest quartile of EF was also statistically significantly associated with the risk of KFRT as compared to the highest quartile (HR, 1.53 [95%CI, 1.26 to 1.86]). LVMI was the only parameter with all three quartiles (vs. the referent quartile) statistically significantly associated with KFRT in Model 1.

The associations were slightly to modestly attenuated with additional adjustment for eGFR and ACR (Model 2 in Table 2). When we further adjusted for other confounders, the associations were consistent and remained strong (Model 3 in Table 2). The HRs for the highest vs. lowest [lowest vs. highest for EF] quartiles were 1.70 (95% CI, 1.27 to 2.26) for LVMI, 1.50 (1.19 to 1.90) for LV volume, 1.26 (1.03 to 1.56) for EF, 1.43 (1.11 to 1.84) for LA area, and 1.45 (1.06 to 1.96) for peak TR velocity.

The associations were consistent when using sex-specific quartiles of LVMI (Table S7), using eGFR based on the CKD-EPI 2021 equation without race as a covariate (Table S8), accounting for systolic blood pressure as a time-varying covariate (Table S9), accounting for hemoglobin levels as a time-varying covariate (Table S10), and when LVH, reduced EF, and elevated peak TR velocity were analyzed using the recommended cut-offs in the guidelines (Table S11). For example, eccentric and concentric LVH were each associated with the risk of KFRT with similar HRs (1.48 [95%CI, 1.18 to 1.85] for eccentric and 1.50 [1.26 to 1.78] for concentric LVH; Model 2 in Table S11).

During follow-up, there were 603 cases of incident CVD (471 heart failures, 193 myocardial infarctions, and 87 strokes) that occurred prior to KFRT. When analyses were censored for the interim occurrence of CVD events, the HRs for incident KFRT were mostly unchanged regardless of interim CVD subtypes, including heart failure (Figure S2). The associations were consistent when we accounted for 682 deaths as a competing risk (Table S12). The associations of LV volume, EF, LA area, and peak TR velocity were all consistent and remained significant when the models were further adjusted for LVMI in addition to Model 3 covariates (Table S13). In subgroup analyses, the associations were generally consistent across demographic and clinical subgroups without significant interaction (p-for-interaction >0.05 for all comparisons) (Figure 1).

Figure 1: Subgroup analysis by age, sex, race, eGFR, and ACR.

Figure 1:

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The HRs and 95%CI were shown per +1 SD increase in each echocardiographic parameter. Model was adjusted for center, age, sex, race/ethnicity, body mass index, systolic blood pressure, current smoker, alcohol use, diabetes, history of cardiovascular disease, chronic obstructive pulmonary disease, use of ACEi or ARB, hemoglobin levels, eGFR, and ACR. Abbreviations: HR, hazard ratio; CI, confidence interval; LVMI, left ventricular mass index; LA, left atrium; TR, tricuspid regurgitation; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio.

Echocardiographic parameters and eGFR slope

During follow-up, participants had a median (IQI) of seven (4 to 12) eGFR assessments per person. Multivariable mixed-effects models revealed that participants in increasing quartiles of LVMI and peak TR velocity experienced a faster eGFR decline (Figure 2A and 2E). Compared to the lowest quartile of LVMI, the eGFR slope was greater by −0.57 (95%CI, −0.68 to −0.46) mL/min/1.73m2 per year in the highest quartile (Table 3). On a relative scale, compared to the eGFR slope in the lowest quartile, the eGFR slope was greater by −87.1% (−109.4% to −64.8%) in the highest quartile of LVMI. For TR velocity, the highest quartile was associated with −0.42 (95%CI, −0.56 to −0.28) mL/min/1.73m2 greater eGFR slope compared to those in the lowest quartile. Participants in the highest quartile of LV volume also had a faster eGFR decline compared to the lowest quartile. eGFR slopes were overall similar across quartiles of EF or LA area, although there was a statistically significant difference in the eGFR slope for the lowest quartile of EF compared to the highest (Figure 2B and 2C, Table 3). These findings were consistent when using the CKD-EPI 2021 equation without race (Table S14), and assessing the percent change in eGFR from baseline, as calculated by change in eGFR from baseline divided by eGFR at baseline (Figure S3).

Figure 2: eGFR trajectory by quartiles of A: LVMI, B: LV volume, C: EF, D: LA area, and E: peak TR velocity.

Figure 2:

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The mean change in eGFR over time was estimated from multivariable mixed-effects models, treating individuals as a random effect and incorporating multiple eGFR assessments per person. At each study visit, the mean eGFR change from baseline was calculated by subtracting the mean predicted eGFR at baseline from the mean predicted eGFR at the corresponding study visit. Model was adjusted for center, age, sex, race/ethnicity, body mass index, systolic blood pressure, current smoker, alcohol use, diabetes, and history of cardiovascular disease, COPD, use of ACEi or ARB, hemoglobin levels, and ACR. Abbreviations: COPD, chronic obstructive pulmonary disease; ACEi, angiotensin converting enzyme inhibitors; ARB, angiotensin II receptor blockers; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio; LVMI, left ventricular mass index; LA, left atrium; TR, tricuspid regurgitation.

Table 3:

Annual eGFR slope by quartiles of LVMI, LV volume, EF, LA area, and peak TR velocity

Exposures eGFR slope, ml/min/1.73m2 per year ΔeGFR slope %difference in eGFR slope
LVMI (g/m2.7)
Q1: <41.4 −0.66 (−0.72 to −0.59) 0 [Reference] 0 [Reference]
Q2: 41.4–49.2 −0.64 (−0.71 to −0.57) 0.02 (−0.07 to 0.11) 2.5% (−11.0% to 15.9%)
Q3: 49.2–58.9 −0.85 (−0.93 to −0.78) −0.20 (−0.29 to −0.10) −30.3% (−46.8% to −13.7%)
Q4: ≥59.0 −1.23 (−1.32 to −1.13) −0.57 (−0.68 to −0.46) −87.1% (−109.4% to −64.8%)
LV volume in end-diastole (ml)
Q1: <27.7 −0.72 (−0.78 to −0.66) 0 [Reference] 0 [Reference]
Q2: 27.7–32.5 −0.71 (−0.77 to −0.65) 0.01 (−0.08 to 0.09) 0.8% (−11.2% to 12.9%)
Q3: 32.5–38.3 −0.75 (−0.82 to −0.68) −0.03 (−0.12 to 0.06) −4.8% (−17.6% to 8.1%)
Q4: ≥38.4 −0.97 (−1.05 to −0.88) −0.25 (−0.35 to −0.15) −34.9% (−51.2% to −18.6%)
LV ejection fraction (%)
Q1: <51.2 −0.88 (−0.96 to −0.81) −0.11 (−0.20 to −0.01) −13.8% (−26.7% to −0.8%)
Q2: 51.3–55.2 −0.72 (−0.79 to −0.65) 0.05 (−0.04 to 0.15) 7.1% (−4.4% to 18.6%)
Q3: 55.2–59.0 −0.69 (−0.75 to −0.62) 0.09 (−0.00 to 0.18) 11.7% (0.6% to 22.8%)
Q4: ≥59.1 −0.78 (−0.84 to −0.71) 0 [Reference] 0 [Reference]
LA area (cm2)
Q1: <19.0 −0.80 (−0.87 to −0.74) 0 [Reference] 0 [Reference]
Q2: 19.1–22.0 −0.72 (−0.79 to −0.65) 0.09 (−0.01 to 0.18) 10.8% (−0.6% to 22.1%)
Q3: 22.1–25.8 −0.83 (−0.91 to −0.76) −0.03 (−0.13 to 0.06) −4.0% (−16.4% to 8.3%)
Q4: ≥25.9 −0.81 (−0.90 to −0.72) −0.01 (−0.12 to 0.10) −1.5% (−15.3% to 12.3%)
Peak TR velocity (m/sec)
Q1: <2.22 −0.57 (−0.66 to −0.49) 0 [Reference] 0 [Reference]
Q2: 2.23–2.42 −0.60 (−0.68 to −0.51) −0.02 (−0.14 to 0.10) −4.2% (−25.6% to 17.2%)
Q3: 2.43–2.66 −0.73 (−0.81 to −0.64) −0.15 (−0.27 to −0.03) −26.9% (−50.6% to −3.2%)
Q4: ≥2.67 −1.00 (−1.11 to −0.88) −0.42 (−0.56 to −0.28) −73.8% (−105.9% to −41.8%)
Open in a new tab
*

Model was adjusted for center, age, sex, race/ethnicity, body mass index, systolic blood pressure, current smoker, alcohol use, diabetes, and history of cardiovascular disease, and COPD, use of ACEi or ARB, hemoglobin levels, and ACR. Abbreviations: COPD, chronic obstructive pulmonary disease; ACEi, angiotensin converting enzyme inhibitors; ARB, angiotensin II receptor blockers; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio; LVMI, left ventricular mass index; LA, left atrium; TR, tricuspid regurgitation.

Finally, the associations were consistent when eGFR slopes were estimated across the range of each echocardiographic parameter on a continuous scale (Figure S4). Greater LVMI was almost linearly associated with faster eGFR decline. For peak TR velocity, higher peak TR velocity was linearly associated with faster eGFR decline in the range of elevated peak TR velocity (i.e., ≥2.5 m/s). We also observed modestly faster eGFR decline along with lower EF when EF was <50%. Finally, eGFR slopes were similar across the range of LA area.

Discussion

In this study of over 3,000 individuals with mild to moderate CKD, selected echocardiographic parameters of LV structure, LV systolic function, LV diastolic function, and RV pressure were each independently associated with the risk of KFRT, our primary outcome. These associations were consistent after accounting for interim CVD events or death as a competing risk, and across demographic and clinical subgroups. When eGFR slope, our secondary outcome, was analyzed, all echocardiographic parameters, except for LA area, were statistically significantly associated with faster decline in eGFR, although the associations were strongest for LVMI and peak TR velocity.

Among echocardiographic parameters assessed in our study, LVMI demonstrated the most robust associations with subsequent KFRT and eGFR decline. We also showed that LVH was associated with an increased risk of KFRT regardless of eccentric or concentric LVH, confirming a previous report.14 This observation supports the importance of LVH in the pathophysiology of kidney disease progression.10–12 There are a few potential mechanisms linking LVH to adverse kidney outcomes. For example, this association may reflect shared risk factors such as hypertension and diabetes. In addition, LVH is a risk factor for clinical CVD, which may subsequently increase the risk of adverse kidney outcomes.32, 33 However, the association remained robust even after accounting for these risk factors. Other potential mechanisms would include volume overload and pressure overload, which are primary causes of LVH.34 Also, LVH may reflect chronic pathophysiological conditions that are related to kidney disease progression; for example, neurohormonal activation and chronic inflammation are commonly seen in the development of both LVH and CKD.35, 36

Higher levels of RV pressure, as measured by peak TR velocity, also demonstrated robust associations with kidney outcomes. Our findings are consistent with a previous study showing an elevated risk of CKD progression (50% decrease in eGFR or KFRT) associated with pulmonary hypertension.37 With longer follow-up and more outcome cases, we extend this study to show the association across the range of RV pressure in both incident KFRT and slope analyses. Given the close relationship between RV and LV hemodynamics, our finding may merely mirror the association between LVH and CKD progression, as discussed above. However, the association of peak TR velocity remained mostly unchanged even after adjusting for LVMI (Table S13). Another possible mechanism is that systemic venous congestion due to increased RV pressure worsens kidney function.38 Also, pulmonary hypertension caused by lung diseases and chronic hypoxia may play some role in kidney disease progression.39

We also report significant associations of LV function (i.e., EF and LA area) with adverse kidney outcomes. However, the association was not as evident as that for LVMI or TR velocity, especially in slope analyses. These findings were generally consistent with a few studies where LV function was only modestly associated with kidney outcomes such as decline to eGFR <60 mL/min/1.73m2.7, 9, 10 Thus, our findings together with others suggest that LV function parameters may be less predictive of kidney function decline compared to LVMI (or TR velocity). Nonetheless, underlying mechanisms linking LV function to adverse kidney outcomes should be further explored using more sensitive LV function parameters relative to EF and LA area, such as LV strain and tissue Doppler echocardiography.8 A recent study suggests that the use of LA area may overestimate the prevalence of diastolic dysfunction, compared to the definition recommended in the most recent classification guideline.40 Furthermore, LA dilation may be not only caused by LV dysfunction, but also attributable to atrial fibrillation, valvular disease, and obesity.

Our findings have important clinical implications. While the risk of CVD in CKD is well-recognized, our findings suggest that specific cardiac abnormalities should also be viewed as important risk factors for CKD progression.41, 42 Some have proposed an emerging clinical concept of cardiorenal medicine to address bi-directional relationship between CVD and CKD.43, 44 In this regard, echocardiography may provide important prognostic information to guide therapy for protecting cardiorenal health. For example, echocardiographic findings may help inform the use of cardiorenal protective drugs (e.g., renin-angiotensin-system inhibitors, sodium- glucose co-transporter 2 inhibitors) when these medications are considered.45, 46 Of note, their major indications such as hypertension and diabetes are particularly common in CKD. Finally, despite the wide implementation of echocardiography in clinical care, the timing and frequency of echocardiography are not necessarily specified in the guidelines of CKD management,31,40 except for those on dialysis.47 Because clinical utility of echocardiography should be evaluated in the context of its associated costs and patient and provider burden, future studies are warranted to inform the optimal utilization of echocardiography in the care of patients with kidney disease, including its cost-effectiveness.

Our findings should be interpreted under several limitations. First, several echocardiographic parameters were not available in our study (e.g., LA volume index or myocardial strain). Second, our follow-up duration (median 9.9 years) might not allow us to characterize even longer interaction of cardiac parameters with kidney disease progression. Third, although the CRIC Study is a multi-racial/ethnic study, Black or White participants comprised nearly 90% of study participants. Thus, the generalizability of our findings to some other races/ethnicities may be limited. Fourth, survival bias is possible since participants with severe phenotype of heart diseases, such as symptomatic heart failure, might be more likely to die before the development of KFRT. Fifth, our observed associations may be partly explained by indirect effect, such as mediation by high blood pressure, although we confirmed the robust association even after accounting for systolic blood pressure as a time-varying covariate. Sixth, in the present study, echocardiographic measures were assessed once at baseline; and therefore, longitudinal change in echocardiographic parameters (e.g., LVH) and the risk of KFRT should be examined in the future research. Finally, we could not exclude the possibility of residual confounding due to the nature of observational study.

Nonetheless, our study has a number of strengths. First, our study examined adults with CKD, a clinically meaningful population who bears a high burden of both CVD and KFRT. Second, we analyzed the CRIC Study, a very well-phenotyped cohort with extensive longitudinal data among US adults with a wide spectrum of CKD. Third, we were able to uniquely assess incident KFRT and eGFR slope as the clinically relevant kidney outcomes. Fourth, we performed models accounting for interim CVD or death as a competing risk, and conducted subgroup analyses in major demographic and clinical subgroups of CKD.

In conclusion, selected echocardiographic parameters of cardiac structure and function were generally associated with increased risk of KFRT. Higher LVMI and peak TR velocity were strongly associated with faster eGFR decline in slope analyses. These findings suggest that cardiac abnormalities, as assessed by multiple aspects of cardiac structure and function, and incident KFRT are potentially on the same causal pathway related to the interaction between hypertension, heart failure, and coronary artery diseases.

Supplementary Material

1

Acknowledgements:

The authors thank the other investigators, staff, and participants of the CRIC study for their valuable contribution.

Support:

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 NIH/NCATS UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI UL1 RR-024131, Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, NM R01DK119199. JI is supported by K01DK125616. RM is supported by K23HL150236. The funders did not have any role in study design, data collection, analysis, reporting, or the decision to submit for publication.

Footnotes

CRIC Study Investigators: Debbie L Cohen, MD; Harold I. Feldman, MD, MSCE; James P. Lash, MD; Robert G. Nelson, MD, PhD, MS; Panduranga S. Rao, MD; Vallabh O. Shah, PhD, MS; and Mark L. Unruh, MD, MS.

Additional Information: Authors MR, ASG, JC, and LJA are CRIC Study Investigators

Financial Disclosure: RM has stock interest in AbbVie and is on the speakers’ bureau for AstraZeneca. The other authors declare that they have no relevant financial interests.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

Peer Review: Received September 8, 2022. Evaluated by 3 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Eduardo Lacson, Jr, MD, MPH). Accepted in revised form December 5, 2022. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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