Introduction
Heart failure is a major manifestation of cardiovascular disease in patients with CKD.1,2 Heart failure and kidney disease often coexist, thus magnifying their effects on morbidity and mortality.1 Previous studies demonstrate that abnormalities in myocardial structure and function, such as increased left ventricular (LV) mass and abnormal LV systolic function, typically defined by reduced ejection fraction, are independently associated with worse kidney function and poor kidney outcomes.3–5 Possible mechanisms include reduction in renal blood flow, passive renal venous congestion, neurohormonal alterations, and adaptive inflammatory changes.6 However, abnormalities detected on conventional echocardiography are often detected late in the trajectory of heart failure development, just before progression to symptomatic heart failure.
In-depth characterization of cardiac mechanics through 2-dimensional speckle-tracking echocardiography (2D-STE) allows for identification and quantification of earlier changes in myocardial function compared with traditional echocardiography.7,8 STE can provide an assessment of myocardial contractility of the LV and right ventricle (RV), LV diastolic function, and left atrial (LA) function. Given the utility of STE to detect early changes in cardiac mechanics, these methods can provide unique insights into the pathophysiology of myocardial dysfunction in individuals without clinical heart failure and may be a more sensitive marker to identify individuals at risk for kidney disease and kidney disease progression. We investigated the associations of multiple indices of cardiac mechanics assessed through 2D-STE with prevalent CKD and changes in kidney function over time in the Cardiovascular Health Study (CHS).
Methods
Study Population
CHS is an ongoing prospective, community-based cohort study designed to investigate risk factors (RFs) for cardiovascular disease in adults aged 65 years or older.9 CHS recruited White and Black adults aged 65 years or older during two periods across four communities in the United States from 1989 to 1993 (Forsyth County, NC; Sacramento County, CA; Washington County, MD; and Pittsburgh, PA). Individuals were eligible to participate if they were aged 65 years or older, noninstitutionalized, remained in the area of at least 3 years, and able to provide informed consent. Key exclusion criteria included being wheelchair-bound, receiving hospice care, or receiving cancer treatment.9 The original cohort comprised 5201 individuals recruited during 1989–1990. An additional 687 individuals were recruited during 1992–1993 as part of a supplemental Black cohort but were not included in these analyses because of timing of STE and eGFR measurements.9 Participants had annual in-person study visits and were contacted by phone every 6 months. Each respective institutional review board approved the protocols, and all participants gave written informed consent.9
In this study, we evaluated associations of cardiac mechanics with baseline and change in kidney function. Baseline analyses investigated the associations between STE indices and prevalent CKD. The primary change analyses examined the associations between STE indices and kidney function decline over 7 years. The study population for baseline analyses included 3516 of 5201 participants from the original cohort who completed a year 2 visit. Individuals were excluded if they did not have both a year 2 STE (N=985) and a year 2 measurement of kidney function (N=548). Individuals were also excluded if they had a history of heart failure (N=152). Supplemental Table 1 presents baseline characteristics of those included and excluded from the primary analyses.
The primary change analyses were restricted to 2135 participants from the original cohort with available year 2 STE and kidney function measurements at both year 2 and year 9 (Figure 1).
;)
Figure 1: Study population.
Primary Exposure
The primary exposures were 6 parameters derived from 2D-STE (LV longitudinal strain [LVLS], LV early diastolic strain rate [EDSR], LV early diastolic tissue velocity [e′], E/e′ ratio, RV free wall strain [RVFWS], and LA reservoir strain [LARS]). The clinical interpretations of cardiac mechanics indices are listed in Supplemental Table 2. LVLS is a measure of LV systolic function, EDSR is a measure of LV diastolic function, RVFWS a measure of right ventricular systolic function, and LARS a measure of LA compliance (i.e., ability of the LA to fill from the pulmonary veins during ventricular systole). We present all strain values as positive absolute percentages, with lower absolute strain and strain rate values representing worse deformation patterns. The e′ velocity is considered relatively preload independent and is a measure of early diastolic myocardial relaxation as the mitral annulus ascends during early and rapid LV filling.10 Reductions in e′ velocity represent impaired LV relaxation.10 A higher E/e′ ratio reflects higher LV filling pressures.10
2D-STE
Comprehensive 2D, M-mode, and Doppler echocardiograms were obtained at the year 2 study visit (1989–1990) for the original cohort.11 Echocardiograms were recorded onto Super Video Home System (VHS) tapes using Toshiba SSH-160A cardiac ultrasound machines. The archived CHS images were digitized from the VHS tapes at the Echocardiography Reading Center (Irvine, CA) using the TIMS 2000 DICOM system (Foresight Imaging, Chelmsford, MA) from 2016 to 2018, as previously reported.7,11 We digitized cine loops of two to four cardiac cycles at a frame rate of 30 frames per second (fps) from the parasternal short-axis and apical two-, three-, and four-chamber view.12 Images were stored offline in DICOM format. The apical four-chamber cine loops were used for this investigation, which have previously been shown to be representative of all three apical views together.7
Five experienced readers then performed STE to obtain strain analysis using TOMTEC Cardiac Performance Analysis, v4.5 software. Echocardiograms were assigned chamber-specific image quality scores by the reader who was blinded to all other data, as described previously.7,12 Speckle-tracking measurements were completed using R-R wave ECG gating to define the cardiac cycle.7,12 The readers manually traced the left and right ventricle and left atrial endocardial borders in the apical four-chamber view to derive LVLS, EDSR, e′ velocity, RVFWS, and LARS curves. LVLS and EDSR were averaged from six segments in the LV and defined as the peak LV strain during systole and early diastole, respectively. LARS was averaged from six segments and defined as the peak LA strain and corresponded to ventricular systole.12 RV free wall strain was averaged from the three RV free wall segments (base, mid, apex) and defined as the peak RV strain during systole. Reproducibility of strain measures in CHS was reported previously demonstrating high interobserver and intraobserver reproducibility and low bias.7,11,13,14 The e′ velocity was calculated as the average of the septal and lateral mitral annulus. Of note, e′ velocities are generally underestimated when measured by STE, resulting in lower e′ and higher E/e′ ratios compared with tissue Doppler imaging measurements.7
Outcomes
The outcome for the cross-sectional analyses was the presence of CKD, defined as an eGFR ≤60 ml/min per 1.73 m2 at baseline (year 2). The primary outcome for change analyses was 30% decline in eGFR between year 2 and year 9 adjusted for baseline eGFR. The outcomes for sensitivity analyses were (1) rapid decline in kidney function defined as an annualized eGFR decline >3 ml/min per 1.73 m2 per year between year 2 and year 9 adjusted for baseline eGFR and (2) change in eGFR between year 2 and year 9 adjusted for baseline eGFR.
Laboratory measurements for serum creatinine were conducted at year 2 and year 9 at the CHS Core Laboratory using standardized assays. A particle-enhanced immunonephelometric assay (N Latex Cystatin C, Dade Behring) with a nephelometer (Behring Nephelometer II Analyzer, Dade Behring) was used to measure and calibrate Cystatin C.15 eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine-cystatin C equation.16
Covariates
Similar to previous studies in CHS, echocardiography reader, CHS site, and quality were included as covariates for this investigation. Information on participant demographics, medication use, and clinical data were collected at the baseline study visit for each respective cohort. Self-reported covariates included age, sex, current smoking (yes/no), history of diabetes (yes/no), hypertension (yes/no), cardiovascular disease (yes/no), number of antihypertensive drugs, and alcohol use (yes/no). Diabetes was determined by self-report, the use of hypoglycemic agents or use of insulin, or a fasting blood glucose ≥126 mg/dl.15 History of cardiovascular disease was defined as coronary heart disease (angina pectoris, myocardial infarction, angioplasty, coronary bypass surgery) or stroke.17 Body mass index (BMI) was calculated as weight in kilograms divided by height, measured in meters, squared. Systolic blood pressure (SBP) was measured by trained personnel three times while participants were seated for 5 minutes, and the average of the last two reads was used. SBP and heart rate were used as continuous covariates. Hemoglobin was measured at local laboratories near each field center using automated instruments.18
Statistical Analysis
We used standard descriptive statistics to compare demographics and clinical characteristics of the included (N=3516) versus excluded (N=1685) individuals. We next compared demographics and clinical characteristics of the study cohort (N=3516) according to presence or absence of CKD at baseline. For the cross-sectional analyses, relative risk (RR) estimates for presence of CKD were calculated for each STE parameter using Poisson regression models. We adjusted our models for main effects of imaging (echocardiography reader, institution, and image quality), sociodemographic factors (age, Black race, and male sex), and cardiovascular RFs (physical activity, smoking, BMI, number of hypertension medications, SBP, heart rate, prevalent coronary heart disease, alcohol use, diabetes, hypertension, and hemoglobin).
In change analyses, we used Poisson regression to test the associations between baseline STE parameters and our primary outcome of 30% decline in kidney function over 7 years. We used a similar modeling approach as our cross-sectional analyses and additionally adjusted for baseline eGFR.
In sensitivity analyses, we used Poisson regression to test the association between baseline STE parameters and rapid decline in kidney function (defined as an annualized eGFR decline >3 ml/min per 1.73 m2 per year between year 2 and year 9). We also used linear regression analyses to test the association between baseline STE parameters and change in eGFR over 7 years as a continuous variable. The modeling approach used for sensitivity analyses was similar to that of the primary change analyses.
Given that the purpose of the current report was to test the hypothesis that alterations in cardiac mechanics that represent subclinical cardiac dysfunction affect kidney function over time, multiplicity testing was not performed. The results should be considered exploratory and hypothesis generating. Analyses were performed using R (R Core Team [2021], Vienna, Austria). Two-sided P values <0.05 were considered statistically significant.
Results
Supplemental Table 1 presents differences between included (N=3516) and excluded (N=1685) individuals from the original cohort for the cross-sectional analyses. Individuals included in these analyses had less cardiovascular disease and diabetes and had better 2D-STE parameters than those not included. Table 1 presents differences between individuals with and without eGFR <60 ml/min per 1.73 m2 at baseline in the study population (N=3516). Individuals with CKD had more cardiovascular disease, more hypertension, were older in age, and had a higher SBP and lower hemoglobin at baseline than those without CKD. Figure 2 demonstrates boxplots of cardiac mechanics indices by CKD status; all cardiac function measures were worse in the subset with CKD.
Table 1 -
Baseline characteristics according to kidney disease prevalence, defined as eGFR <60 ml/min per 1.73 m
2, of the original cohort
Baseline Characteristics
Total (N=3516) |
eGFR <60 ml/min per 1.73 m2 (N =1040) |
eGFR ≥60 ml/min per 1.73 m2 (N=2476) |
P value |
| Age, yr |
75±6 |
71±5 |
<0.001 |
| Female sex, % (N) |
57 (593) |
62 (1546) |
0.003 |
| Black, % (N) |
4 (36) |
4 (99) |
0.51 |
| Coronary heart disease, % (N) |
25 (263) |
16 (389) |
<0.001 |
| Diabetes mellitus, % (N) |
15 (154) |
13 (319) |
0.14 |
| Hypertension, % (N) |
67 (701) |
51 (1260) |
<0.001 |
| Antihypertensive medications, N
|
0.58±0.49 |
0.38±0.49 |
<0.001 |
| RAAS inhibitors %, (N) |
8 (81) |
4 (107) |
<0.001 |
| Current smoking, % (N) |
12 (126) |
11 (276) |
0.45 |
| Alcohol use, % (N) |
9 (88) |
13 (327) |
<0.001 |
| Heart rate (BPM) |
65±12 |
64±11 |
<0.01 |
| BMI, kg/m2
|
27±84.5 |
26±4.3 |
<0.001 |
| Systolic BP, mmHg |
138±22 |
134±21 |
<0.001 |
| Hemoglobin, g/dl |
13.9±1.4 |
14.1±1.2 |
<0.001 |
| eGFR, ml/min per 1.73 m2
|
49±9 |
76±11 |
<0.001 |
| LVLS, absolute %, N=3497 |
13.7±3.6 |
14.6±3.7 |
<0.001 |
| EDSR, 1/sec, N=3429 |
0.61±0.23 |
0.67±0.24 |
<0.001 |
| e′, cm/s, N=3370 |
2.6±0.96 |
2.8±1.0 |
<0.001 |
| E/e′, N=3279 |
31±15 |
29±14 |
<0.001 |
| LARS, absolute %, N=3420 |
35.5±15.7 |
41.7±15.2 |
<0.001 |
| RVFWS, absolute %, N=3094 |
15.1±4.7 |
15.9±4.9 |
<0.001 |
Results are reported as means±SD, proportions or medians with interquartile ranges. Number of individuals with each speckle-tracking echocardiographic parameter are listed next to each parameter. RAAS, renin-angiotensin-aldosterone system; BPM, beats per minute; BMI, body mass index; BP, blood pressure; CHD, coronary heart disease; eGFR, estimated glomerular filtration rate; LVLS, left ventricular longitudinal strain; EDSR, early diastolic strain rate; e′, left ventricular early diastolic tissue velocity; LARS, left atrial reservoir strain; RVFWS, right ventricular free wall strain.
Figure 2: Unadjusted boxplots of speckle-tracking echocardiography indices by CKD status. e′, left ventricular early diastolic tissue velocity; EDSR, early diastolic strain rate; LARS, left atrial reservoir strain; LVLS, left ventricular longitudinal strain; RVFW, right ventricular free wall strain
Baseline Analyses
In the minimally adjusted models, all six of the measures of cardiac function were associated with greater likelihood of prevalent CKD at baseline (Table 2). After multivariable adjustment, worse cardiac parameters of LV systolic dysfunction (LVLS), LV diastolic dysfunction and relaxation (EDSR and e′), and RV dysfunction (RVFWS) remained significantly associated with CKD prevalence. Of the indices of cardiac mechanics, the largest risk estimates were for subclinical LV diastolic function (RR per 1 SD decrease in EDSR 1.08; 95% confidence interval [CI], 1.02 to 1.14; P value 0.01); (RR per 1 SD decrease in e′ 1.08; 95% CI, 1.01 to 1.14; P value 0.02).
Table 2 -
Association of 2D speckle-tracking echocardiogram measures with prevalence of CKD in cross-sectional analyses
| 2D Speckle-Tracking Echocardiogram Measure |
Model 1 |
Model 2 |
| RR (95% CI) |
P value |
RR (95% CI) |
P value |
| Left ventricular longitudinal strain |
1.20 (1.14 to 1.27) |
<0.001 |
1.07 (1.01 to 1.14) |
0.02 |
| Left ventricular early diastolic strain rate |
1.21 (1.15 to 1.28) |
<0.001 |
1.08 (1.02 to 1.14) |
0.01 |
| Left ventricular early diastolic tissue velocity (e′) |
1.17 (1.11 to 1.24) |
<0.001 |
1.08 (1.01 to 1.14) |
0.02 |
| E/e′ ratioa |
1.09 (1.04 to 1.15) |
<0.001 |
1.00 (0.95 to 1.06) |
0.98 |
| Left atrial reservoir strain |
1.16 (1.10 to 1.23) |
<0.001 |
1.06 (1.00 to 1.12) |
0.05 |
| Right ventricular free wall strain |
1.13 (1.06 to 1.19) |
<0.001 |
1.07 (1.00 to 1.13) |
0.04 |
Results are reported RR with 95% CI per 1 SD decrease in speckle-tracking echocardiographic parameter. Model 1 adjusts for reader, institution, quality. Model 2 adjusts for factors in model 1 and age, Black race, male sex, heart rate, physical activity score, history of coronary heart disease, diabetes, hypertension, antihypertensive therapy, smoking, alcohol use, body mass index, systolic blood pressure, and hemoglobin. RR, relative risk; CI, confidence interval; e′, left ventricular early diastolic tissue velocity.
aPer 1 SD increase in E/e′.
Primary Change Analyses
Of the 2135 individuals with both year 2 and year 9 eGFR, 201 individuals developed the binary outcome of 30% decline in eGFR over 7 years. In minimally adjusted models, worse measures of cardiac function were significantly associated with 30% decline in eGFR except for RV dysfunction (RVFWS). After multivariable adjustment, left atrial dysfunction (RR, 1.18; 95% CI, 1.01 to 1.38 per SD lower LARS) and LV diastolic dysfunction (RR, 1.21; 95% CI, 1.04 to 1.41 per SD lower EDSR) were associated with 30% decline in eGFR (Table 3).
Table 3 -
Association of 2D speckle-tracking echocardiogram indices with 30% decline in eGFR over 7 years
| 2D Speckle-Tracking Echocardiogram Measure |
Model 1 |
Model 2 |
| RR (95% CI) |
P value |
RR (95% CI) |
P value |
| Left ventricular longitudinal strain |
1.24 (1.07 to 1.43) |
0.005 |
1.12 (0.96 to 1.30) |
0.14 |
| Left ventricular early diastolic strain rate |
1.35 (1.16 to 1.56) |
<0.001 |
1.21 (1.04 to 1.41) |
0.02 |
| Left ventricular early diastolic tissue velocity (e′) |
1.17 (1.01 to 1.35) |
0.03 |
1.10 (0.95 to 1.28) |
0.20 |
| E/e′a |
1.26 (1.11 to 1.43) |
<0.001 |
1.14 (0.995 to 1.30) |
0.06 |
| Left atrial reservoir strain |
1.22 (1.05 to 1.42) |
0.012 |
1.18 (1.01 to 1.38) |
0.04 |
| Right ventricular free wall strain |
1.11 (0.95 to 1.30) |
0.20 |
1.09 (0.93 to 1.29) |
0.28 |
Results are reported RR of 30% decline in eGFR over 7 years with 95% CI per 1 SD decrease in speckle-tracking echocardiographic parameter. Model 1 adjusts for reader, institution, and quality. Model 2 adjusts for factors in model 1 and age, Black race, male sex, heart rate, physical activity score, history of coronary heart disease, diabetes, hypertension, antihypertensive therapy, smoking, alcohol use, body mass index, systolic blood pressure, hemoglobin, and baseline eGFR. RR, relative risk; CI, confidence interval; e′, left ventricular early diastolic tissue velocity.
aRR of 30% decline in eGFR over 7 years with 95% CI per 1 SD increase in E/e′.
Sensitivity Analyses
A total of 211 of the 2135 individuals developed the binary outcome of rapid decline in kidney function. In the minimally adjusted models, LV systolic dysfunction, LA dysfunction, LV diastolic dysfunction, and elevated filling pressures were all associated with rapid decline in kidney function defined as an annualized eGFR decline >3 ml/min per 1.73 m2 per year between year 2 and year 9 (Supplemental Table 3). After multivariable adjustment, abnormal LV diastolic function (RR per 1 SD lower EDSR 1.19; 95% CI, 1.03 to 1.38) and elevated LV filling pressures (RR per 1 SD higher E/e′ 1.14; 95% CI, 1.005 to 1.30) remained significantly associated with rapid decline in kidney function (Supplemental Table 3). For the linear outcome of change in eGFR, after multivariable adjustment, no parameter remained significantly associated with change in eGFR over 7 years (data not shown).
Discussion
In a large cohort of older patients enrolled in the CHS, we comprehensively investigated associations between indices of cardiac mechanics derived from 2D-STE and prevalent CKD and change in kidney function over time. Parameters associated with LV systolic and diastolic dysfunction, LA dysfunction, and RV systolic dysfunction were all independently associated with prevalent CKD on cross-sectional analyses. Parameters associated with LV diastolic dysfunction (EDSR and LARS) were associated with decline in kidney function over time and remained significant after adjustment for numerous demographic variables, baseline kidney function, and cardiovascular RFs. These findings support the link between subclinical abnormalities in cardiac function related to abnormal LV diastolic function and progressive decline in kidney function. Importantly, our findings implicate LV diastolic dysfunction, even in individuals without a history of clinically overt heart failure, as a RF for decline in kidney function.
Patients with existing kidney disease with or without clinical heart failure often have abnormalities in cardiac structure and function.5 Abnormalities include conventional echocardiographic measures that encompass LV hypertrophy and increased LV mass, as well as abnormalities in systolic and diastolic myocardial function appreciated on 2D-STE.5,19,20 Studies also demonstrate independent associations of CKD with abnormal strain parameters and that worsening kidney function is associated with abnormalities in cardiac mechanics indices. The current results expand on prior findings and demonstrate that in individuals without a history of clinical heart failure, abnormal cardiac mechanics are associated with greater likelihood of prevalent CKD. This is important because abnormalities in myocardial dysfunction ascertained by strain measurements are often associated with normal ejection fraction and can generally be detected before more global cardiac dysfunction.
Parameters of diastolic function were associated with both a 30% decline in kidney function and a rapid decline in kidney function in sensitivity analyses. This suggests that subtle subclinical abnormalities in cardiac mechanics may be a RF for kidney function decline, even before global cardiac dysfunction and clinical heart failure occur. Studies in individuals without kidney disease demonstrated significant associations of LVM with kidney outcomes.21–24 Previous studies also demonstrated that in patients with preexisting kidney disease, increased LVM was associated with kidney disease progression to dialysis.25,26 Elevated LV filling pressures are also associated with acute kidney injury and progression to end-stage kidney disease.27 This study extends these findings and provides evidence to demonstrate that subclinical diastolic abnormalities that represent abnormal LV relaxation, in individuals free of clinical heart failure, were associated with progression of kidney dysfunction.
There are several possible mechanisms to explain our findings.28,29 Ultrastructural changes of LV remodeling that may occur in diastolic dysfunction include fibroblast proliferation, collagen deposition, and myocardial fibrosis.30 Together, these changes may lead to myocardial stiffness, alterations in LV geometry and tone, and ultimately progression to stage C heart failure.30 Worsening kidney function may directly be a result of abnormal diastolic parameters but may also be a result of shared mechanisms, such as proinflammatory changes, microvascular dysfunction, and possibly neurohormonal activation that result in both abnormal cardiac structure and kidney function. Further interventional studies may further elucidate these mechanisms. Finally, the relationship between cardiac mechanics and kidney function is bidirectional, and so our associations are likely interwoven. Numerous alterations specific to kidney disease increase the risk for the development of subclinical myocardial dysfunction, including anemia and abnormalities in the iron axis, alterations in markers of mineral bone disease,31,32 accelerated propensity for vascular calcification, volume overload, and neurohormonal activation.21,33–35 Whether the presence of underlying kidney dysfunction resulted in worse cardiac mechanics or abnormalities in myocardial strain resulted in the decline in kidney function cannot be discerned completely.
Our study has several possible implications. Concomitant cardiac and kidney disease leads to significant cardiovascular events and mortality.36–41 Elucidating pathologic factors involved in the presence and progression of kidney dysfunction remains critically important for prevention of end-stage kidney disease, cardiovascular events, and mortality. Novel echocardiographic indices such as those appreciated on 2D-STE may identify a population that may benefit from early diagnosis, monitoring, and treatment. Recently, the introduction of therapies such as sodium glucose cotransporter 2 inhibitors (SGLT2i) and mineralocorticoid receptor antagonists, which prevent both heart failure events and kidney-related outcomes, provide new avenues for pharmacologic intervention in patients affected with heart failure and CKD.42–45 These same treatments may also benefit individuals with subclinical myocardial dysfunction. Indeed, a small substudy of individuals enrolled in the EMPA-REG outcome trial demonstrated that 3–6 months of therapy with a SGLT2i was associated with a significant reduction in LVM.46 In addition, targeted use of early diuretic therapy to decrease LV filling pressures may be able to improve subclinical cardiac abnormalities. Further interventional studies are needed to determine whether these therapies can improve subclinical cardiac abnormalities ascertained on 2D-STE in individuals without overt heart failure and prevent heart failure hospitalizations and kidney function decline.
We acknowledge limitations in our study. The results of our analyses cannot demonstrate causality and indeed, the relationship between cardiac dysfunction and kidney disease is likely to be bidirectional. In addition, only two measurements of eGFR were used to define our outcomes, and we did not have measurements of albuminuria. Despite the wealth of covariate data, unmeasured confounders not accounted for, such as albuminuria, could affect our results. In addition, use of RAAS inhibition was low at the time this cohort was recruited. We also did not adjust for multiple comparisons; therefore, our findings are exploratory and require further investigation. The methods used for assessment of cardiac strain were designed to allow use of archived research videotapes obtained before contemporary digital echocardiography. Hence, the speckle-tracking strain values in this study are not comparable with values obtained in the contemporary clinical setting, which are obtained using different methodology (digital echocardiograms with prospective speckle-tracking). However, for a well-powered population-based study, the methods used for analysis of archived tapes have been well validated for research purposes.7 Finally, given that the CHS recruited an older population and patients were required to have two eGFR measurements to be included in our change analyses, individuals included in our change analyses may have been a healthier population that did not have progressive kidney function decline compared with those excluded. Owing to this, the current analysis may underestimate the true association between baseline measurements of subclinical cardiac function and progression of kidney disease.
In conclusion, abnormal indices of cardiac mechanics were associated with both prevalent CKD and decline in kidney function in a cohort of older individuals without clinical heart failure. Given the exploratory nature of our study, further studies are needed to confirm our findings, to better understand the mechanisms that may link subclinical abnormalities of cardiac mechanics to kidney dysfunction, and to test whether interventions that may improve subclinical diastolic dysfunction markers can prevent kidney disease progression.
Disclosures
N. Bansal reports the following—advisory or leadership role: Kidney360 Associate Editor. T. Isakova reports the following—consultancy: Akebia Therapeutics, Inc.; honoraria: Akebia Therapeutics, Inc.; and advisory or leadership role: Associate Editor, American Journal of Kidney Diseases. J.H. Ix reports the following—consultancy: Akebia, AstraZeneca, Bayer, Cincor, and Sanifit; research funding: Baxter International and Juvenile Diabetes Research Foundation; honoraria: Akebia, AstraZeneca, Bayer, Cincor, and Sanifit; and advisory or leadership role: AlphaYoung. J. Kizer reports the following—ownership interest: Abbott, Bristol Myers Squibb, Johnson & Johnson, Medtronic, Merck, and Pfizer. R. Mehta reports the following—consultancy: Akebia/Otsuka and AstraZeneca; ownership interest: AbbVie, Inc.; honoraria: Akebia/Otsuka and AstraZeneca; advisory or leadership role: Editorial Board, Journal of Cardiac Failure; and speakers bureau: AstraZeneca. H. Patel reports the following—ownership interest: Alux World LLC. B. Psaty reports the following—honoraria: I serve on the Steering Committee of the Yale Open Data Access project, funded by Johnson & Johnson.; advisory or leadership role: See honoraria—same as above. S.J. Shah reports the following—research grants from Actelion, AstraZeneca, Corvia, Novartis, and Pfizer and has received consulting fees from Abbott, Actelion, AstraZeneca, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardiora, Coridea, CVRx, Cyclerion, Cytokinetics, Edwards Lifesciences, Eidos, Eisai, Imara, Impulse Dynamics, Intellia, Ionis, Ironwood, Lilly, Merck, MyoKardia, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Roche Diagnostics, Sanofi, Shifamed, Tenax, Tenaya, and United Therapeutics. M.G. Shlipak reports the following—consultancy: Cricket Health, Intercept Pharmaceuticals, University of Washington-Cardiovascular Health Study, and Veterans Medical; research funding: Bayer Pharmaceuticals; honoraria: AstraZeneca, Bayer, and Boeringer Ingelheim; advisory or leadership role: American Journal of Kidney Disease, Journal of the American Society of Nephrology, and Circulation; and other interests or relationships: Board Member, Northern California Institute for Research and Education. All remaining authors have nothing to disclose.
Funding
This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants U01HL080295 and U01HL130114 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. This study was also supported by grants P30DK114857, K23HL150236 (RM), R01DK102438 (TI), U54 HL160273 (SJS), R01 HL107577 (SJS), R01 HL127028 (SJS), R01 HL140731 (S.J. Shah), and R01 HL149423 (S.J. Shah), American Heart Association #16SFRN28780016 (S.J. Shah) and #19TPA34890060 (S.S. Khan). Research reported in this publication was also supported, in part, by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number KL2TR001424.
Acknowledgments
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 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.
Author Contributions
Conceptualization: Rupal Mehta, Sanjiv J. Shah.
Data curation: Sanjiv J. Shah.
Formal analysis: Nisha Bansal, Petra Buzkova.
Funding acquisition: Sanjiv J. Shah.
Investigation: Nisha Bansal, Rupal Mehta, Sanjiv J. Shah, Michael G. Shlipak.
Methodology: Nisha Bansal, Rupal Mehta, Bruce Psaty, Sanjiv J. Shah, Michael G. Shlipak.
Resources: Sanjiv J. Shah.
Supervision: Nisha Bansal, Sanjiv J. Shah, Michael G. Shlipak.
Validation: Nisha Bansal, Petra Buzkova, Sanjiv J. Shah, Michael G. Shlipak.
Writing – original draft: Nisha Bansal, Rupal Mehta, Sanjiv J. Shah, Michael G. Shlipak.
Writing – review & editing: Nisha Bansal, Petra Buzkova, Jeanette Cheng, John S. Gottdiener, Tamara Isakova, Joachim H. Ix, Sadiya S. Khan, Jorge R. Kizer, Rupal Mehta, Harnish Patel, Bruce Psaty, Sanjiv J. Shah, Michael G. Shlipak.
Data Sharing Statement
Anonymized data created for the study are or will be available in a persistent repository on publication: Analyzable Data, CHS, https://chs-nhlbi.org/.
Supplemental Material
This article contains the following supplemental material online at https://links.lww.com/KN9/A333.
Supplemental Table 1. Baseline characteristics of the total CHS population and individuals excluded and included from the study population.
Supplemental Table 2. Clinical interpretation of 2D speckle-tracking echocardiography parameters.
Supplemental Table 3. Association of 2D speckle-tracking echocardiogram measures with rapid decline in kidney function (defined as an annualized eGFR decline >3 ml/min/1.72 m2 per year over 7 years).
References
1. House AA, Wanner C, Sarnak MJ, et al. Heart failure in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2019;95(6):1304–1317. doi:
10.1016/j.kint.2019.02.022
2. Bansal N, Zelnick L, Bhat Z, et al. Burden and outcomes of heart failure hospitalizations in adults with chronic kidney disease. J Am Coll Cardiol. 2019;73(21):2691–2700. doi:
10.1016/j.jacc.2019.02.071
3. Shi HT, Wang XJ, Li J, et al. Association of left ventricular hypertrophy with a faster rate of renal function decline in elderly patients with non-end-stage renal disease. J Am Heart Assoc. 2015;4(11):e002213. doi:
10.1161/JAHA.115.002213
4. Khan NA, Ma I, Thompson CR, et al. Kidney function and mortality among patients with left ventricular systolic dysfunction. J Am Soc Nephrol. 2006;17(1):244–253. doi:
10.1681/Asn.2005030270
5. Park M, Hsu CY, Li Y, et al. Associations between kidney function and subclinical cardiac abnormalities in CKD. J Am Soc Nephrol. 2012;23(10):1725–1734. doi:
10.1681/Asn.2012020145
6. Damman K, Testani JM. The kidney in heart failure: an update. Eur Heart J. 2015;36(23):1437–1444. doi:
10.1093/eurheartj/ehv010
7. Aguilar FG, Selvaraj S, Martinez EE, et al. Archeological echocardiography: digitization and speckle tracking analysis of archival echocardiograms in the HyperGEN study. Echocardiography. 2016;33(3):386–397. doi:
10.1111/echo.13095
8. Blessberger H, Binder T. Two dimensional speckle tracking echocardiography: basic principles. Heart. 2010;96(9):716–722. doi:
10.1136/hrt.2007.141002
9. Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1(3):263–276. doi:
10.1016/1047-2797(91)90005-w
10. Nagueh SF, Smiseth OA, Appleton CP, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2016;29(4):277–314. doi:
10.1016/j.echo.2016.01.011
11. Patel RB, Delaney JA, Hu M, et al. Characterization of cardiac mechanics and incident atrial fibrillation in participants of the Cardiovascular Health Study. JCI Insight. 2020;5(19):e141656. doi:
10.1172/jci.insight.141656
12. Tan AX, Shah SJ, Sanders JL, et al. Association between myocardial strain and frailty in CHS. Circ Cardiovasc Imaging. 2021;14(5):e012116. doi:
10.1161/circimaging.120.012116
13. Massera D, Hu M, Delaney JA, et al. Adverse cardiac mechanics and incident coronary heart disease in the Cardiovascular Health Study. Heart. 2022;108(7):529–535. doi:
10.1136/heartjnl-2021-319296
14. Kamel H, Bartz TM, Longstreth WT Jr., et al. Cardiac mechanics and incident ischemic stroke: the Cardiovascular Health Study. Sci Rep. 2021;11(1):17358. doi:
10.1038/s41598-021-96702-z
15. Smitson CC, Scherzer R, Shlipak MG, et al. Association of blood pressure trajectory with mortality, incident cardiovascular disease, and heart failure in the Cardiovascular Health Study. Am J Hypertens. 2017;30(6):587–593. doi:
10.1093/ajh/hpx028
16. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi:
10.7326/0003-4819-150-9-200905050-00006
17. Shastri S, Katz R, Rifkin DE, et al. Kidney function and mortality in octogenarians: Cardiovascular Health Study All Stars. J Am Geriatr Soc. 2012;60(7):1201-1207. doi:
10.1111/j.1532-5415.2012.04046.x
18. Zakai NA, Katz R, Hirsch C, et al. A prospective study of anemia status, hemoglobin concentration, and mortality in an elderly cohort: the Cardiovascular Health Study. Arch Intern Med. 2005;165(19):2214–2220. doi:
10.1001/archinte.165.19.2214
19. Unger ED, Dubin RF, Deo R, et al. Association of chronic kidney disease with abnormal cardiac mechanics and adverse outcomes in patients with heart failure and preserved ejection fraction. Eur J Heart Fail. 2016;18(1):103–112. doi:
10.1002/ejhf.445
20. Dubin RF, Deo R, Bansal N, et al. Associations of conventional echocardiographic measures with incident heart failure and mortality: the Chronic Renal Insufficiency Cohort. Clin J Am Soc Nephrol. 2017;12(1):60–68. doi:
10.2215/CJN.02700316
21. Tuegel C, Bansal N. Heart failure in patients with kidney disease. Heart. 2017;103(23):1848–1853. doi:
10.1136/heartjnl-2016-310794
22. Zelnick LR, Katz R, Young BA, et al. Echocardiographic measures and estimated GFR decline among African Americans: the Jackson Heart Study. Am J Kidney Dis. 2017;70(2):199–206. doi:
10.1053/j.ajkd.2016.11.022
23. Park M, Shlipak MG, Katz R, et al. Subclinical cardiac abnormalities and kidney function decline: the multi-ethnic study of atherosclerosis. Clin J Am Soc Nephrol. 2012;7(7):1137–1144. doi:
10.2215/CJN.01230212
24. Bansal N, Katz R, Dalrymple L, et al. NT-ProBNP and troponin T and risk of rapid kidney function decline and incident CKD in elderly adults. Clin J Am Soc Nephrol. 2015;10(2):205–214. doi:
10.2215/CJN.04910514
25. Paoletti E, Bellino D, Gallina AM, Amidone M, Cassottana P, Cannella G. Is left ventricular hypertrophy a powerful predictor of progression to dialysis in chronic kidney disease? Nephrol Dial Transplant. 2011;26(2):670–677. doi:
10.1093/ndt/gfq409
26. Chen SC, Su HM, Hung CC, et al. Echocardiographic parameters are independently associated with rate of renal function decline and progression to dialysis in patients with chronic kidney disease. Clin J Am Soc Nephrol. 2011;6(12):2750–2758. doi:
10.2215/CJN.04660511
27. Choi JS, Baek SH, Chin HJ, et al. Systolic and diastolic dysfunction affects kidney outcomes in hospitalized patients. BMC Nephrol. 2018;19(1):292. doi:
10.1186/s12882-018-1103-2
28. Farshid A, Pathak R, Shadbolt B, Arnolda L, Talaulikar G. Diastolic function is a strong predictor of mortality in patients with chronic kidney disease. BMC Nephrol. 2013;14(1):280. doi:
10.1186/1471-2369-14-280
29. Halley CM, Houghtaling PL, Khalil MK, Thomas JD, Jaber WA. Mortality rate in patients with diastolic dysfunction and normal systolic function. Arch Intern Med. 2011;171(12):1082–1087. doi:
10.1001/archinternmed.2011.244
30. Galderisi M. Diagnosis and management of left ventricular diastolic dysfunction in the hypertensive patient. Am J Hypertens. 2011;24(5):507–517. doi:
10.1038/ajh.2010.235
31. Bundy JD, Cai X, Mehta RC, et al. Serum calcification propensity and clinical events in CKD. Clin J Am Soc Nephrol. 2019;14(11):1562–1571. doi:
10.2215/CJN.04710419
32. Bundy JD, Cai X, Scialla JJ, et al. Serum calcification propensity and coronary artery calcification among patients with CKD: the CRIC (Chronic Renal Insufficiency Cohort) study. Am J Kidney Dis. 2019;73(6):806–814. doi:
10.1053/j.ajkd.2019.01.024
33. Faul C, Amaral AP, Oskouei B, et al. FGF23 induces left ventricular hypertrophy. J Clin Invest. 2011;121(11):4393–4408. doi:
10.1172/jci46122
34. Scialla JJ, Xie H, Rahman M, et al. Fibroblast growth factor-23 and cardiovascular events in CKD. J Am Soc Nephrol. 2014;25(2):349–360. doi:
10.1681/ASN.2013050465
35. Charytan DM, Fishbane S, Malyszko J, McCullough PA, Goldsmith D. Cardiorenal syndrome and the Role of the bone-mineral axis and anemia. Am J Kidney Dis. 2015;66(2):196–205. doi:
10.1053/j.ajkd.2014.12.016
36. Al-Ahmad A, Rand WM, Manjunath G, et al. Reduced kidney function and anemia as risk factors for mortality in patients with left ventricular dysfunction. J Am Coll Cardiol. 2001;38(4):955–962. doi:
10.1016/s0735-1097(01)01470-x
37. Xu D, Murakoshi N, Sairenchi T, et al. Anemia and reduced kidney function as risk factors for new onset of atrial fibrillation (from the Ibaraki Prefectural Health Study). Am J Cardiol. 2015;115(3):328–333. doi:
10.1016/j.amjcard.2014.10.041
38. Levin A, Thompson CR, Ethier J, et al. Left ventricular mass index increase in early renal disease: impact of decline in hemoglobin. Am J Kidney Dis. 1999;34(1):125–134. doi:
10.1016/s0272-6386(99)70118-6
39. Foley RN, Parfrey PS, Harnett JD, Kent GM, Murray DC, Barre PE. The impact of anemia on cardiomyopathy, morbidity, and mortality in end-stage renal disease. Am J Kidney Dis. 1996;28(1):53–61. doi:
10.1016/s0272-6386(96)90130-4
40. Eriksson D, Goldsmith D, Teitsson S, Jackson J, van Nooten F. Cross-sectional survey in CKD patients across Europe describing the association between quality of life and anaemia. BMC Nephrol. 2016;17(1):97. doi:
10.1186/s12882-016-0312-9
41. Alexander M, Kewalramani R, Agodoa I, Globe D. Association of anemia correction with health related quality of life in patients not on dialysis. Curr Med Res Opin. 2007;23(12):2997–3008. doi:
10.1185/030079907x242502
42. Petrie MC, Verma S, Docherty KF, et al. Effect of dapagliflozin on worsening heart failure and cardiovascular death in patients with heart failure with and without diabetes. JAMA. 2020;323(14):1353–1368. doi:
10.1001/jama.2020.1906
43. Heerspink HJL, Stefansson BV, Correa-Rotter R, et al. Dapagliflozin in patients with chronic kidney disease. N Engl J Med. 2020;383(15):1436–1446. doi:
10.1056/nejmoa2024816
44. Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295–2306. doi:
10.1056/nejmoa1811744
45. Mahaffey KW, Neal B, Perkovic V, et al. Canagliflozin for primary and secondary prevention of cardiovascular events: results from the CANVAS Program (Canagliflozin Cardiovascular Assessment Study). Circulation. 2018;137(4):323–334. doi:
10.1161/circulationaha.117.032038
46. Verma S, Garg A, Yan AT, et al. Effect of empagliflozin on left ventricular mass and diastolic function in individuals with diabetes: an important clue to the EMPA-REG OUTCOME trial? Diabetes Care. 2016;39(12):e212–e213. doi:
10.2337/dc16-1312
