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Journal of General Internal Medicine logoLink to Journal of General Internal Medicine
. 2018 Jul 20;33(10):1721–1728. doi: 10.1007/s11606-018-4556-x

Heart Failure and Cognitive Impairment in the Atherosclerosis Risk in Communities (ARIC) Study

Lucy S Witt 1,, Jason Rotter 2, Sally C Stearns 3, Rebecca F Gottesman 4, Anna M Kucharska-Newton 5, A Richey Sharrett 6, Lisa M Wruck 7, Jan Bressler 8, Carla A Sueta 9, Patricia P Chang 9
PMCID: PMC6153245  PMID: 30030736

Abstract

Background

Previous studies suggest that heart failure (HF) is an independent risk factor for cognitive decline. A better understanding of the relationship between HF, cognitive status, and cognitive decline in a community-based sample may help clinicians understand disease risk.

Objective

To examine whether persons with HF have a higher prevalence of cognitive impairment and whether persons developing HF have more rapid cognitive decline.

Design

This observational cohort study of American adults in the Atherosclerosis Risk in Communities (ARIC) study has two components: cross-sectional analysis examining the association between prevalent HF and cognition using multinomial logistic regression, and change over time analysis detailing the association between incident HF and change in cognition over 15 years.

Participants

Among visit 5 (2011–2013) participants (median age 75 years), 6495 had neurocognitive information available for cross-sectional analysis. Change over time analysis examined the 5414 participants who had cognitive scores and no prevalent HF at visit 4 (1996–1998).

Measurements

The primary outcome was cognitive status, classified as normal, mild cognitive impairment [MCI], and dementia on the basis of standardized cognitive tests (delayed word recall, word fluency, and digit symbol substitution). Cognitive change was examined over a 15-year period. Control variables included socio-demographic, vascular, and smoking/drinking measures.

Results

At visit 5, participants with HF had a higher prevalence of dementia (adjusted relative risk ratio [RRR] = 1.60 [95% CI 1.13, 2.25]) and MCI (RRR = 1.36 [1.12, 1.64]) than those without HF. A decline in cognition between visits 4 and 5 was − 0.07 standard deviation units [− 0.13, − 0.01] greater among persons who developed HF compared to those who did not. Results did not differ by ejection fraction.

Conclusion

HF is associated with neurocognitive dysfunction and decline independent of other co-morbid conditions. Further study is needed to determine the underlying pathophysiology.

Electronic supplementary material

The online version of this article (10.1007/s11606-018-4556-x) contains supplementary material, which is available to authorized users.

KEY WORDS: heart failure, cognitive impairment, dementia, cognitive decline

INTRODUCTION

Over five million Americans suffer from heart failure (HF).1 Approximately 25–50% of patients with HF are estimated to have cognitive impairment, commonly reporting deficits in attention, reduced executive function, slowed processing speed, and memory loss.24 Mild deficits can be classified as mild cognitive impairment (MCI), while the American Psychiatric Association defines dementia as a multidomain impairment in cognitive ability that interferes with everyday activities.5 Cerebral hypo-perfusion due to decreased cardiac output and HF-related cardioembolic stroke may be mechanisms for the reported association of HF with cognitive impairment.6, 7 Atherosclerotic vascular disease, whether from the cumulative exposure to cardiovascular risk factors (elevated glucose and blood pressure) or subclinical atherosclerosis, affects cognitive function and is often related to HF.815 Furthermore, alterations in circulatory hormones due to a failing heart or medication side effects may also lead to HF-related cognitive impairment.1618 Given that the risk factors for atherosclerosis, HF, and stroke (such as hypertension, diabetes, and smoking) are intertwined and overlapping, it is difficult to analyze the root cause of this HF-cognition association. Compounding this low left ventricular ejection fraction (EF) may be associated with worse cognitive performance, particularly in the presence of lower mean arterial pressure and the decompensated HF state.1921

The Atherosclerosis Risk in Communities (ARIC) study cohort provides an opportunity to elucidate the association between HF and cognitive dysfunction. Our study examines whether those with HF have a higher prevalence of dementia or MCI compared to those without HF. To bolster our assessment and identify more subtle changes in mental status, we also investigate the association between incident HF and change in cognitive function over time.

METHODS

This study uses multivariate regression to assess the relationship between HF and neurocognitive status in older adults through two analyses: (1) a cross-sectional analysis examining the association of HF with a categorical measure of cognition at a single point in time and (2) the association between onset of HF and the change in a continuous measure of cognition over a 15-year period.

Study Population: The ARIC Study

The ARIC study is a biracial cohort of 15,792 men and women, 45–64 years of age in 1987–1989, selected through population sampling from: Jackson, Mississippi; Forsyth County, North Carolina; suburbs of Minneapolis, Minnesota; and Washington County, Maryland. The ARIC study provides longitudinal data on risk factors and outcomes associated with atherosclerosis. Incident HF, coronary heart disease, and stroke were determined from annual follow-up phone interviews with cohort participants and review of hospitalization records. Risk factors were measured at in-person examinations. Neurocognitive information from the two most recent in-person examinations, visit 5 (2011–2013) and visit 4 (1996–1998), was used for this analysis. Only blacks were enrolled in Jackson, and extremely few blacks were enrolled in Minneapolis and Washington County. We therefore included only the majority race at each site except for Forsyth County, which included both White and Black participants. This represents standard protocol for the ARIC study.22

Study Sample

Of the estimated 10,036 cohort members alive in 2011, 6538 individuals participated in visit 5 (2011–2013). Information on neurocognitive status was available for 6495 participants, who comprise the study sample for the cross-sectional analysis. For the analysis of change in neurocognitive status over time, 5414 participants who had cognition scores from both visit 4 and visit 5 and no indication of prevalent HF at visit 4 (1996–1998) were examined.

Outcomes

In our cross-sectional analysis, visit 5 participants were classified as having normal cognition, MCI, or dementia. Classification was based on a computer-derived algorithm with physician review. The computer algorithm combined data from multiple domains including scoring on standardized assessments of delayed word recall (DWR), word fluency test (WFT), and digit symbol substitution test (DSST). Memory, executive functioning, and attention were similarly analyzed, as were data reflecting orientation, judgment, and personal care. A final component assessed functioning in everyday life as reported by participant and, when indicated, a knowledgeable informant using the Clinical Dementia Rating interview. Physician reviewers adjudicated the computer-derived algorithm to ensure appropriate neurocognitive designation. This evaluation has been described elsewhere.23, 24

The assessment of change in cognition over time examined only the subset of participants without prevalent HF at visit 4 (Fig. 1). Scores on three neurocognitive tests (DWR, WFT, DSST) were combined and standardized to visit 2 scores (1996–1998).15 Performance on these tests was assessed at visit 4 and again at visit 5 (approximately 15 years apart).25, 26 Cognitive change for those who developed HF between visit 4 and visit 5 was compared to those who did not. A separate analysis was conducted to observe cognitive change over time involving only those participants with normal cognition at visit 4. Details regarding cognitive status assessments in ARIC have been published elsewhere.27

Fig. 1.

Fig. 1

Comparing the change in neurocognitive ability over time between those who developed heart failure and those who did not. This figure graphically describes the participants analyzed in the change over time analysis. Only those without HF at visit 4 were examined. Those who subsequently developed HF within the 15-year period were compared to those who did not develop HF.

Exposure and Covariates

Prevalent HF at visit 5 was classified by having at least one of the following: an adjudicated diagnosis of HF, International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) discharge code of 428.X in first position not overruled by a physician, self-reported HF or self-report of HF medication with pro-BNP greater than 125 pg/mL, or subsequent self-report of HF or HF medication (defined as medications participants reported taking for the treatment of HF). Prevalent HF at visit 4, which eliminated participants from the longitudinal analysis, was identified by at least one of the following: ICD-9 discharge code 428 from prior hospitalizations; self-report of HF at visit 4; or self-reported medication for HF at visit 4. HF with preserved EF (HFpEF) was defined as EF ≥ 50%, with reduced EF (HFrEF) defined as EF < 50% based on echocardiogram at visit 5. These definitions have been validated previously.2830 Incident HF between visits 4 and 5, used in the longitudinal analysis, was identified by hospital heart failure-specific discharge diagnosis ICD-9 codes during that follow-up interval.

Covariates included information on demographics (age, gender, study site, education) as well as marital status, individual socioeconomic status, behaviors (drinking and smoking), and selected diagnoses (depression, diabetes, hypertension, myocardial infarction, atrial fibrillation, and stroke). A Charlson co-morbidity score was included to account for debility from other diseases.31 Hypertension was defined as systolic blood pressure (SBP) greater than 140 mmHg, a diastolic blood pressure (DBP) greater than 90 mmHg, or if the participant reported taking anti-hypertensive medication. Participants were considered diabetic if they were taking any diabetes medication, self-reported physician diagnosis of diabetes, or had a hemoglobin A1c greater than 6.5% at a study visit. Diagnoses of prevalent stroke (CVA), coronary heart disease (CHD), atrial fibrillation (AF), and myocardial infarction (MI) were ascertained from hospital records, physicians, participants, and their families throughout follow-up using standardized interviews, questionnaires, and surveillance methods previously described elsewhere.3234 All covariates were ascertained at visit 5, except education, occupation, race, and gender, which were reported at visit 1. The MacArthur Scale of Subjective Social Status assessed social standing relative to peers on a scale from 1 to 10.35 A continuous measure of depression was constructed from 12 questions representing a shorter but validated form of the CES-D depression symptoms index.36, 37

Statistical Analysis

To assess the cross-sectional association between HF and neurocognitive status at visit 5, multinomial logistic models estimated relative risk ratios (RRR) for the three-category neurocognitive outcome variable (normal, MCI, and dementia). To assess the association between onset of HF and the change in neurocognitive status between visits 4 and 5, ordinary least squares models were used. For both the cross-sectional and change over time models, dichotomous measures of HF and HF ejection fraction type (reduced versus preserved EF) were used. All models included an interaction of HF with age and controlled for age, gender, ARIC site/race, education, marital status, the McArthur scale, hypertension, diabetes, stroke, AF, CES-D score, Charlson co-morbidity score, smoking status, and drinking status.

To account for non-participation in visit 5 (estimated at nearly 40%), inverse probability of attrition weighting (IPAW) was used as a sub-analysis (online appendix). For this, the probability of non-participation was predicted as a function of complete case variables (age, gender, race, study site, education, smoking status, and occupation). Multiple imputation was used in all models to allow for a complete analysis when covariate information was not directly available. All data analyses were performed using Stata v14 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP).

RESULTS

Table 1 presents clinical characteristics for participants who attended visit 5. Included cohort members had a mean age of 76 years (SD 5.3) and most were female (59%). A total of 953 (15%) individuals had prevalent HF at visit 5, of whom 38% were classified as having dementia or MCI (compared to 24% of those without HF). The sample included only 105 cases of HFrEF, 11% of those with HF, while 184 (19%) individuals with HF were missing ejection fraction data. Compared to those with HF, those without HF (n = 5542) were more likely to be female and better educated. They were also more likely to be current drinkers. Among those with HFrEF, the average EF was 40% (SD 6.5) and only 33 had EF ≤ 35%. Those with HF had worse scores on standardized tests (DWR, WFT, DSST) at visit 5 compared to those without HF even when comparing only those with a diagnosis of dementia or MCI to each other (online appendix).

Table 1.

Clinical Characteristics by Heart Failure Type: the Atherosclerosis Risk in Communities (ARIC) Study, Visit 5 (2011–2013)

Total (N = 6495) No HF (N = 5542) HF (N = 953) p
Visit 5 age, mean(SD) 76.3 (5.3) 76.1 (5.2) 77.8 (5.5) < 0.001
Gender, N(%) < 0.001
Female 3823 (58.9%) 3325 (60.0%) 498 (52.3%)
Site/race, N(%) < 0.001
Minneapolis (White) 1325 (20.4%) 1182 (21.3%) 143 (15.0%)
Washington (White) 1416 (21.8%) 1082 (19.5%) 334 (35.0%)
Forsyth (White) 1901 (29.3%) 1686 (30.4%) 215 (22.6%)
Forsyth (Black) 1750 (26.9%) 1504 (27.1%) 246 (25.8%)
Jackson (Black) 103 (1.6%) 88 (1.6%) 15 (1.6%)
MacArthur scale of subjective social status, mean (SD)* 5.8 (1.7) 5.9 (1.7) 5.4 (1.9) <0.001
Education level, N(%)* <0.001
 Less than high school (HS) 982 (15.1%) 716 (12.9%) 266 (27.9%)
 At least high school 2696 (41.5%) 2324 (41.9%) 372 (39.0%)
 At least some post HS 2806 (43.2%) 2493 (45.0%) 313 (32.8%)
Visit 5 marital status, N(%) <0.001
 Married 4147 (63.8%) 3615 (65.2%) 532 (55.8%)
 Formerly married 2226 (34.3%) 1818 (32.8%) 408 (42.8%)
 Never married 122 (1.9%) 109 (2.0%) 13 (1.4%)
Prevalent co-morbidities at visit 5, N(%)
Hypertension* 4793 (73.8%) 3979 (71.8%) 814 (85.4%)
CHD* 966 (14.9%) 554 (10.0%) 412 (43.2%)
Diabetes 2229 (34.3%) 1725 (31.1%) 504 (52.9%)
Atrial fibrillation* 595 (9.2%) 350 (6.3%) 245 (25.7%)
Stroke* 266 (4.1%) 159 (2.9%) 107 (11.2%)
CES-depression scale, mean (SD)* 3.2 (3.1) 3.0 (3.0) 4.1 (3.5) <0.001
Charlson co-morbidity score (SD) 0.8 (1.8) 0.7 (1.5) 1.6 (2.8) <0.001
Visit 5 smoking status, N(%)* 0.66
 Current smoker 323 (5.0%) 278 (5.0%) 45 (4.7%)
 Never smoker 1794 (27.6%) 1520 (27.4%) 274 (28.8%)
 Former smoker 4364 (67.2%) 3733 (67.4%) 631 (66.2%)
Visit 5 drinking status, N(%) <0.001
 Current drinker 2966 (45.7%) 2665 (48.1%) 301 (31.6%)
 Former drinker 1800 (27.7%) 1463 (26.4%) 337 (35.4%)
 Never drinker 1302 (20.0%) 1088 (19.6%) 214 (22.5%)
Visit 5 neurocognitive status, N(%)* < 0.001
 Dementia 341 (5.3%) 241 (4.3%) 100 (10.5%)
 Mild cognitive impairment 1366 (21.0%) 1106 (20.0%) 260 (27.3%)
 Normal 4724 (72.7%) 4143 (74.8%) 581 (61.0%)
Visit 5 cognitive score, mean(SD)* − 0.57 (1.01) − 0.50 (0.99) − 1.02 (1.01) < 0.001
Visit 4 cognitive score, mean(SD)* 0.18 (0.92) 0.24 (0.90) − 0.20 (0.95) < 0.001

*Missing: MacArthur SES (259), education,11 hypertension (92), CHD (110), Atrial fibrillation (455), stroke,10 CES-D (138), smoking,14 drinking (427), neurocog status (64), V5 cognitive score(627), V4 cognitive score (644)

p values are calculated via t test and chi-squared tests for continuous and categorical covariates, respectively

Heart Failure and Cognitive Impairment at Visit 5

Table 2 provides unadjusted and multivariate-adjusted results of our analysis. With adjustment for co-morbidities, individuals with prevalent HF were more likely to have dementia (RRR = 1.60 [95% CI 1.13, 2.25]) and MCI (RRR = 1.36 [1.12, 1.64]) compared to those without HF. When separating HF by ejection fraction type, we observed a similar effect size but with limited ability to detect a statistically significant difference in cognition in the HFrEF group, potentially due to small sample size (online appendix).

Table 2.

Relative Risk Ratios of Dementia and Mild Cognitive Impairment for Those with Heart Failure Compared to Those Without

RRR of dementia (vs. normal cognition)
Model 1 (unadjusted) Model 2* (limited controls) Model 3(full controls)
HF vs. No HF 2.96 [2.31, 3.79] 2.57 [1.89, 3.47] 1.6 [1.13, 2.25]
RRR of MCI (vs. normal cognition)
Model 1 (unadjusted) Model 2* (limited controls) Model 3(full controls)
HF vs. No HF 1.68 [1.43, 1.97] 1.54 [1.3, 1.83] 1.36 [1.12, 1.64]

This table shows the relative risk ratio of those with HF to have dementia or MCI compared to those without. Three models are presented; the first is unadjusted, second adjusted for limited controls, and the third adjusted for all controls

RRR relative risk ratio, HF heart failure, MCI mild cognitive impairment

*Controlled for age, gender, site/race

Controlled for age, gender, site/race, 3-category smoking status, 3-category drinking status, 3-level education, 3-level marital status, McArthur SES scale, Charlson co-morbidity, hypertension, CHD, diabetes, atrial fibrillation, stroke

Figure 2 shows the multivariate-adjusted prevalence of dementia, MCI, and normal cognition for those with and without HF. Error bars display 95% confidence intervals for the differences compared to persons without HF. Although small sample size limits the analysis in those with HFrEF, cognition was nominally worse for participants with both HFpEF and HFrEF as compared to those without HF (online appendix).

graphic file with name 11606_2018_4556_Fig2_HTML.jpg

Fig. 2

Predicted Proportion of those with and without heart failure to have dementia, mild cognitive impairment, or normal cognition. This figure describes the predicted prevalence (PP) of dementia, mild cognitive impairment (MCI), or normal cognition at visit 5 for those participants who developed heart failure compared to those who did not. Error bars represent the 95% confidence interval in reference to those without heart failure. *p < 0.05 compared to no HF. HF heart failure, MCI mild cognitive impairment. All models control for age, gender, ARIC site/race, education, marital status, McArthur Financial SES ladder, hypertension, diabetes, stroke, atrial fibrillation, CES-D score Charlson co-morbidity score, smoking status, and drinking status. Models imputed missing covariates using multiple imputation (see Table 1) (M = 50).

Incident Heart Failure and Change in Cognition Over Time

Table 3 displays the difference in the adjusted change in neurocognitive scores between visit 4 and visit 5 for those who developed HF (n = 796) during that period. We observed a significantly greater cognitive decline among persons who developed HF over the 15-year period (mean standardized z score of − 0.50 at visit 4 to − 1.02 at visit 5) compared to those who did not develop HF (mean score of 0.24 at visit 4 to − 0.20 at visit 5), with an adjusted difference of − 0.07, (95% CI − 0.13, − 0.01). This difference is interpreted as a 15-year change in standard deviation units (SDU) for those with HF as compared to those without. This change, when compared to normal aging, represents an estimated additional 1.5 years of cognitive decline.38, 39 When analyzing change by HF type, the magnitude and direction of neurocognitive decline was similar with limited power to detect differences among those with HFrEF due to small sample size (online appendix). A post hoc analysis examining only those participants with normal cognition at visit 4 revealed that participants who developed incident HF between visits 4 and 5 had a greater decline in cognition (mean standardized z score of − 0.06) compared to those who did not develop HF (online appendix).

Table 3.

Change in Cognition by HF Type Compared to Persons Without Heart Failure Between Visit 4 (1996–1998) and Visit 5 (2011–2013): The Atherosclerosis Risk in Communities Study

Change in global z score (V4 to V5)
Model 1 (unadjusted) Model 2* (limited controls) Model 3 (full controls)
HF vs. No HF − 0.13 [− 0.19, − 0.07] − 0.11 [− 0.17, − 0.05] − 0.07 [− 0.13, − 0.01]

This table shows the adjusted change in mean standardized z score on three well-studied neurocognitive tests (delayed word recall, digit symbol substitution, and word fluency). Scores were combined and standardized to visit 2 scores (1990–1992). This difference is interpreted as a 15-year change in standard deviation units (SDU) for those with HF as compared to those without

N = 5414; All models centered at mean age. Models imputed missing covariates using multiple imputation (see Table 1)

HF heart failure

*Controlled for age, gender, site/race

Controlled for age, gender, site/race, 3-category smoking status, 3-category drinking status, 3-level education, 3-level marital status, McArthur SES scale, Charlson co-morbidity, hypertension, CHD, diabetes, atrial fibrillation, stroke

Additional analyses were conducted to examine potential effect measure modification of selected diseases, including AF, CHD, hypertension, and stroke, on the association of HF with change in cognitive status. No significant interaction was identified between these diagnoses and cognitive impairment in any HF group.

DISCUSSION

Heart failure is associated with an increased prevalence of dementia and mild cognitive impairment independent of conventional risk factors for either diagnosis. Similarly, those participants who developed incident HF showed greater cognitive decline over the same period as compared to those who did not develop HF. Given that this study controls for disease states proposed to contribute to cognitive dysfunction in the HF population (such as stroke, hypertension, diabetes, and depression), these findings suggest that HF itself may directly predispose individuals to a range of cognitive impairments.

The mechanism of this association is currently unknown and cannot be explained purely on the basis of decreased EF, as those with HFrEF showed similar results to those with HFpEF in both the cross-sectional and the change over time analyses. The relationship between EF and cognitive function has been described as nonlinear, with a steeper association between EF and decreased cognition at lower levels of EF than at higher levels;20, 40 however, our small sample of HFrEF participants limits our analysis of this relationship, especially since only 3.5% had LVEF ≤ 35%. Proposed pathways connecting HF to cognitive decline include cerebral atrophy from hypo-perfusion, infarction due to decreased blood flow, subclinical cardioembolic phenomena, and an association between natriuretic peptides and the deposition of amyloid plaques in the brain.16, 17, 41, 42 Other possible causes include the interplay between decreased cardiac output and previously described co-morbidities including microvascular damage from diabetes, vascular damage from hypertension, and arterial disease.2, 43, 44 Finally, the association might be non-causal, e.g., the result of confounding by the use of certain medications. The relationship between HF and cognitive decline is possibly bidirectional as those with HF may suffer cognitive decline as shown in our study; however, baseline poor cognition may lead to poor medication adherence for chronic diseases such as HTN and CHD further pre-disposing them to HF.45

Previous studies have described a similar relationship between HF and cognitive impairment. Jefferson et al. examined a small cohort of elderly individuals (n = 72) and found that those with worse executive functioning were more likely to have low cardiac output.19 When examining a cohort of decompensated HF patients (n = 20) compared to healthy controls, Kindermann and colleagues noted worse cognition in those with HF.21 Gottesman and colleagues examined 234 individuals with coronary arterial disease and found that those with lower EF had lower global functioning scores and worsened motor function.20 A systematic review published in 2007 by Vogels et al. pooled analyses of case-control studies examining almost 3000 patients with HF. This analysis showed that patients with HF had increased prevalence of cognitive impairment (odds ratio 1.62, 95% CI 1.48–1.79) than those without HF.17 All these studies suggest a strong cross-sectional relationship between HF and cognitive impairment, though the studies varied in their control for cardiovascular risk factors and other potential confounders.

Although prior longitudinal studies have demonstrated the relationship between more pronounced cognitive decline and heart failure, our multi-center ARIC cohort study is unique in its combination of size, detailing of cognitive status, and its lengthy follow-up of over 15 years.46 All elderly participants in this large cohort were originally recruited at home and assessed in the ARIC field centers. Thus, this analysis refers to a group of individuals most of whom were healthy enough to attend nearly daylong in-person visits. Also, because of our unique outpatient setting, the observed association between HF and cognitive impairment is less likely to be confounded by delirium or situational depression, which was noted in studies consisting primarily of hospitalized patients.17 Additionally, our study’s neuropsychological assessment included a wide range of cognitive abilities: executive functioning, spatial reasoning, and ability to perform activities of daily living. The extensive neuropsychological evaluations undertaken in this analysis and examination of an elderly population from the community is only matched by European studies including the Rotterdam Study and Three-City Study. Furthermore, ARIC includes a more racially diverse population than these other analyses.47, 48 Our study details a clear association between HF and MCI as well as dementia, which is less well described in current literature.

Several limitations pertain to this study. Importantly, although we examine a wider range of health and disease than is possible in hospital-based studies, due to the physical and temporal requirements of the comprehensive in-person examination, ARIC participants who completed visit 5 were inherently healthier than those who refused or were unable to participate. This healthy-participant effect likely contributes to the low observed prevalence of dementia and limits our ability to examine the very sickest HF patients or those with severe dementia. Additionally, our analysis examines the relationship of the change in cognition over time and concurrent development of HF. While this design works to avoid confounding by factors which are stable over time in participants (such as educational or intellectual background), the data did not allow us to determine which occurred first (cognitive decline or development of HF), so causal interpretations are not possible. Prior studies have demonstrated a predisposition to develop heart failure as cognitive function declines, and it is possible that this relationship is bidirectional which may falsely strengthen the observed relationship.27 HF medications have also been proposed to interact with or contribute to cognitive decline. Only limited information was available regarding medication usage, adherence, and temporality between visits 4 and 5, limiting inclusion of potential medication effects in our analysis. Finally, the observational nature of the study means that we cannot be sure of freedom from residual unmeasured confounding.

Despite these limitations, this analysis highlights associations between HF and neurocognitive decline that cannot be explained by known and observed risk factors. These findings can aid clinicians when caring for HF patients and ensure they are attuned to the risk of cognitive decline associated with this disease. It also provides potential areas for further analysis and therefore intervention to prevent cognitive decline, whether by improved HF treatment, increasing cardiac output, or adjusting medications.

Future studies should aim to observe changes in cognitive function occurring after HF develops, with possible neurologic imaging and more detailed examinations of disease states that may contribute to cognitive decline. Similarly, potential differences in cognitive status among those with HFrEF as compared to those with HFpEF warrant additional investigation, given a potential relationship between cardiac output and cognitive decline.

Electronic supplementary material

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Acknowledgments

The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data is collected by U01 HL096812, HL096814, HL096899, HL096902, HL096917 with previous brain MRI examinations funded by R01-HL70825. Dr. Gottesman is supported by NIH/NIA grant K24 AG052573. We also thank Jo Ellen Rodgers, Pharm D for her insight and thoughtfulness. The authors thank the staff and participants of the ARIC study for their important contributions.

Compliance with Ethical Standards

Conflict of Interest

Rebecca Gottesman is an Associate Editor for the journal Neurology.

References

  • 1.Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Executive summary: heart disease and stroke statistics--2016 update: a report from the American Heart Association. Circulation. 2016;133(4):447–54. doi: 10.1161/CIR.0000000000000366. [DOI] [PubMed] [Google Scholar]
  • 2.Pressler SJ. Cognitive functioning and chronic heart failure: a review of the literature (2002-July 2007) J Cardiovasc Nurs. 2008;23(3):239–49. doi: 10.1097/01.JCN.0000305096.09710.ec. [DOI] [PubMed] [Google Scholar]
  • 3.Bennett SJ, Sauve MJ. Cognitive deficits in patients with heart failure: a review of the literature. J Cardiovasc Nurs. 2003;18(3):219–42. doi: 10.1097/00005082-200307000-00007. [DOI] [PubMed] [Google Scholar]
  • 4.Petrucci RJ, Truesdell KC, Carter A, Goldstein NE, Russell MM, Dilkes D, et al. Cognitive dysfunction in advanced heart failure and prospective cardiac assist device patients. Ann Thorac Surg. 2006;81(5):1738–44. doi: 10.1016/j.athoracsur.2005.12.010. [DOI] [PubMed] [Google Scholar]
  • 5.Mungas D, Reed BR, Kramer JH. Psychometrically matched measures of global cognition, memory, and executive function for assessment of cognitive decline in older persons. Neuropsychology. 2003;17(3):380–92. doi: 10.1037/0894-4105.17.3.380. [DOI] [PubMed] [Google Scholar]
  • 6.Vogels RL, Oosterman JM, van Harten B, Scheltens P, van der Flier WM, Schroeder-Tanka JM, et al. Profile of cognitive impairment in chronic heart failure. J Am Geriatr Soc. 2007;55(11):1764–70. doi: 10.1111/j.1532-5415.2007.01395.x. [DOI] [PubMed] [Google Scholar]
  • 7.Wolters FJ, Zonneveld HI, Hofman A, van der Lugt A, Koudstaal PJ, Vernooij MW, et al. Cerebral Perfusion and the Risk of Dementia: A Population-Based Study. Circulation. 2017;136(8):719–28. doi: 10.1161/CIRCULATIONAHA.117.027448. [DOI] [PubMed] [Google Scholar]
  • 8.Arntzen KA, Schirmer H, Johnsen SH, Wilsgaard T, Mathiesen EB. Carotid atherosclerosis predicts lower cognitive test results: a 7-year follow-up study of 4,371 stroke-free subjects - the Tromso study. Cerebrovasc Dis. 2012;33(2):159–65. doi: 10.1159/000334182. [DOI] [PubMed] [Google Scholar]
  • 9.Yaffe K, Vittinghoff E, Pletcher MJ, Hoang TD, Launer LJ, Whitmer R, et al. Early adult to midlife cardiovascular risk factors and cognitive function. Circulation. 2014;129(15):1560–7. doi: 10.1161/CIRCULATIONAHA.113.004798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Reis JP, Launer LJ, Terry JG, Loria CM, Zeki Al Hazzouri A, Sidney S, et al. Subclinical atherosclerotic calcification and cognitive functioning in middle-aged adults: the CARDIA study. Atherosclerosis. 2013;231(1):72–7. doi: 10.1016/j.atherosclerosis.2013.08.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Knopman D, Boland LL, Mosley T, Howard G, Liao D, Szklo M, et al. Cardiovascular risk factors and cognitive decline in middle-aged adults. Neurology. 2001;56(1):42–8. doi: 10.1212/WNL.56.1.42. [DOI] [PubMed] [Google Scholar]
  • 12.Alves de Moraes S, Szklo M, Knopman D, Sato R. The relationship between temporal changes in blood pressure and changes in cognitive function: atherosclerosis risk in communities (ARIC) study. Prev Med. 2002;35(3):258–63. doi: 10.1006/pmed.2002.1077. [DOI] [PubMed] [Google Scholar]
  • 13.Gottesman RF, Albert MS, Alonso A, Coker LH, Coresh J, Davis SM, et al. Associations Between Midlife Vascular Risk Factors and 25-Year Incident Dementia in the Atherosclerosis Risk in Communities (ARIC) Cohort. JAMA Neurol. 2017;74(10):1246–54. doi: 10.1001/jamaneurol.2017.1658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Qiao Y, Suri FK, Zhang Y, Liu L, Gottesman R, Alonso A, et al. Racial differences in prevalence and risk for intracranial atherosclerosis in a US community-based population. JAMA Cardiol. 2017;2(12):1341–8. doi: 10.1001/jamacardio.2017.4041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Gottesman RF, Schneider AL, Albert M, Alonso A, Bandeen-Roche K, Coker L, et al. Midlife hypertension and 20-year cognitive change: the atherosclerosis risk in communities neurocognitive study. JAMA Neurol. 2014;71(10):1218–27. doi: 10.1001/jamaneurol.2014.1646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Feldman AM, Haller JA, DeKosky ST. Valsartan/sacubitril for heart failure: Reconciling disparities between preclinical and clinical investigations. JAMA. 2016;315(1):25–6. doi: 10.1001/jama.2015.17632. [DOI] [PubMed] [Google Scholar]
  • 17.Vogels RL, Scheltens P, Schroeder-Tanka JM, Weinstein HC. Cognitive impairment in heart failure: a systematic review of the literature. Eur J Heart Fail. 2007;9(5):440–9. doi: 10.1016/j.ejheart.2006.11.001. [DOI] [PubMed] [Google Scholar]
  • 18.Mirza SS, de Bruijn RF, Koudstaal PJ, van den Meiracker AH, Franco OH, Hofman A, et al. The N-terminal pro B-type natriuretic peptide, and risk of dementia and cognitive decline: a 10-year follow-up study in the general population. J Neurol Neurosurg Psychiatry. 2016;87(4):356–62. doi: 10.1136/jnnp-2014-309968. [DOI] [PubMed] [Google Scholar]
  • 19.Jefferson AL, Poppas A, Paul RH, Cohen RA. Systemic hypoperfusion is associated with executive dysfunction in geriatric cardiac patients. Neurobiol Aging. 2007;28(3):477–83. doi: 10.1016/j.neurobiolaging.2006.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gottesman RF, Grega MA, Bailey MM, Zeger SL, Baumgartner WA, McKhann GM, et al. Association between hypotension, low ejection fraction and cognitive performance in cardiac patients. Behav Neurol. 2010;22(1–2):63–71. doi: 10.1155/2010/725353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kindermann I, Fischer D, Karbach J, Link A, Walenta K, Barth C, et al. Cognitive function in patients with decompensated heart failure: the Cognitive Impairment in Heart Failure (CogImpair-HF) study. Eur J Heart Fail. 2012;14(4):404–13. doi: 10.1093/eurjhf/hfs015. [DOI] [PubMed] [Google Scholar]
  • 22.The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129(4):687–702. [PubMed]
  • 23.Knopman DS, Gottesman RF, Sharrett AR, Wruck LM, Windham BG, Coker L, et al. Mild cognitive impairment and dementia prevalence: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS) Alzheimers Dement (Amst). 2016;2:1–11. doi: 10.1016/j.dadm.2015.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Schneider AL, Sharrett AR, Gottesman RF, Coresh J, Coker L, Wruck L, et al. Normative data for 8 neuropsychological tests in older blacks and whites from the atherosclerosis risk in communities (ARIC) study. Alzheimer Dis Assoc Disord. 2015;29(1):32–44. doi: 10.1097/WAD.0000000000000042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Gross AL, Power MC, Albert MS, Deal JA, Gottesman RF, Griswold M, et al. Application of latent variable methods to the study of cognitive decline when tests change over time. Epidemiology. 2015;26(6):878–87. doi: 10.1097/EDE.0000000000000379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Deal JA, Sharrett AR, Albert MS, Coresh J, Mosley TH, Knopman D, et al. Hearing impairment and cognitive decline: a pilot study conducted within the atherosclerosis risk in communities neurocognitive study. Am J Epidemiol. 2015;181(9):680–90. doi: 10.1093/aje/kwu333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bressler J, Knopman DS, Sharrett AR, Gottesman RF, Penman A, Chang PP, et al. Incident heart failure and cognitive decline: the Atherosclerosis Risk in Communities Study. J Card Fail. 2016. [DOI] [PMC free article] [PubMed]
  • 28.Shah AM, Cheng S, Skali H, Wu J, Mangion JR, Kitzman D, et al. Rationale and design of a multicenter echocardiographic study to assess the relationship between cardiac structure and function and heart failure risk in a biracial cohort of community-dwelling elderly persons: the Atherosclerosis Risk in Communities study. Circ Cardiovasc Imaging. 2014;7(1):173–81. doi: 10.1161/CIRCIMAGING.113.000736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Shah AM, Claggett B, Loehr LR, Chang PP, Matsushita K, Kitzman D, et al. Heart failure stages among older adults in the community: the Atherosclerosis Risk in Communities Study. Circulation. 2017;135(3):224–40. doi: 10.1161/CIRCULATIONAHA.116.023361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Rosamond WD, Chang PP, Baggett C, Johnson A, Bertoni AG, Shahar E, et al. Classification of heart failure in the atherosclerosis risk in communities (ARIC) study: a comparison of diagnostic criteria. Circ Heart Fail. 2012;5(2):152–9. doi: 10.1161/CIRCHEARTFAILURE.111.963199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Austin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the Charlson Comorbidity Index and Elixhauser Score work. Med Care. 2015;53(9):e65–72. doi: 10.1097/MLR.0b013e318297429c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Rosamond WD, Folsom AR, Chambless LE, Wang CH, McGovern PG, Howard G, et al. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke. 1999;30(4):736–43. doi: 10.1161/01.STR.30.4.736. [DOI] [PubMed] [Google Scholar]
  • 33.White AD, Folsom AR, Chambless LE, Sharret AR, Yang K, Conwill D, et al. Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: methods and initial two years’ experience. J Clin Epidemiol. 1996;49(2):223–33. doi: 10.1016/0895-4356(95)00041-0. [DOI] [PubMed] [Google Scholar]
  • 34.Norby FL, Soliman EZ, Chen LY, Bengtson LG, Loehr LR, Agarwal SK, et al. Trajectories of cardiovascular risk factors and incidence of atrial fibrillation over a 25-year follow-up: the ARIC Study (Atherosclerosis Risk in Communities) Circulation. 2016;134(8):599–610. doi: 10.1161/CIRCULATIONAHA.115.020090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Cundiff JM, Smith TW, Uchino BN, Berg CA. Subjective social status: construct validity and associations with psychosocial vulnerability and self-rated health. Int J Behav Med. 2013;20(1):148–58. doi: 10.1007/s12529-011-9206-1. [DOI] [PubMed] [Google Scholar]
  • 36.Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. J Aging Health. 1993;5(2):179–93. doi: 10.1177/089826439300500202. [DOI] [PubMed] [Google Scholar]
  • 37.Schein RL, Koenig HG. The Center for Epidemiological Studies-Depression (CES-D) Scale: assessment of depression in the medically ill elderly. Int J Geriatr Psychiatry. 1997;12(4):436–46. doi: 10.1002/(SICI)1099-1166(199704)12:4&#x0003c;436::AID-GPS499&#x0003e;3.0.CO;2-M. [DOI] [PubMed] [Google Scholar]
  • 38.Salthouse T. Major issues in cognitive aging. Oxford University Press; 2010.
  • 39.Hayden KM, Reed BR, Manly JJ, Tommet D, Pietrzak RH, Chelune GJ, et al. Cognitive decline in the elderly: an analysis of population heterogeneity. Age and Ageing. 2011;40(6):684–9. doi: 10.1093/ageing/afr101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zuccala G, Cattel C, Manes-Gravina E, Di Niro MG, Cocchi A, Bernabei R. Left ventricular dysfunction: a clue to cognitive impairment in older patients with heart failure. J Neurol Neurosurg Psychiatry. 1997;63(4):509–12. doi: 10.1136/jnnp.63.4.509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Sabayan B, van Buchem MA, Sigurdsson S, Zhang Q, Meirelles O, Harris TB, et al. Cardiac and carotid markers link with accelerated brain atrophy: the AGES-Reykjavik Study (age, gene/environment susceptibility-Reykjavik) Arterioscler Thromb Vasc Biol. 2016;36(11):2246–51. doi: 10.1161/ATVBAHA.116.308018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Zonneveld HI, Ikram MA, Hofman A, Niessen WJ, van der Lugt A, Krestin GP, et al. N-terminal pro-B-type natriuretic peptide and subclinical brain damage in the general population. Radiology. 2017;283(1):205–14. doi: 10.1148/radiol.2016160548. [DOI] [PubMed] [Google Scholar]
  • 43.de Roos A, van der Grond J, Mitchell G, Westenberg J. Magnetic resonance imaging of cardiovascular function and the brain: is dementia a cardiovascular-driven disease? Circulation. 2017;135(22):2178–95. doi: 10.1161/CIRCULATIONAHA.116.021978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Sabayan B, van Buchem MA, Sigurdsson S, Zhang Q, Harris TB, Gudnason V, et al. Cardiac hemodynamics are linked with structural and functional features of brain aging: the age, gene/environment susceptibility (AGES)-Reykjavik Study. J Am Heart Assoc. 2015;4(1):e001294. doi: 10.1161/JAHA.114.001294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zuccala G, Marzetti E, Cesari M, Lo Monaco MR, Antonica L, Cocchi A, et al. Correlates of cognitive impairment among patients with heart failure: results of a multicenter survey. Am J Med. 2005;118(5):496–502. doi: 10.1016/j.amjmed.2005.01.030. [DOI] [PubMed] [Google Scholar]
  • 46.Hajduk A. M., Kiefe C. I., Person S. D., Gore J. G., Saczynski J. S. Cognitive Change in Heart Failure: A Systematic Review. Circulation: Cardiovascular Quality and Outcomes. 2013;6(4):451–460. doi: 10.1161/CIRCOUTCOMES.113.000121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Mirza SS, Ikram MA, Bos D, Mihaescu R, Hofman A, Tiemeier H. Mild cognitive impairment and risk of depression and anxiety: a population-based study. Alzheimers Dement. 2017;13(2):130–9. doi: 10.1016/j.jalz.2016.06.2361. [DOI] [PubMed] [Google Scholar]
  • 48.Tabue-Teguo M, Grasset L, Avila-Funes JA, Genuer R, Proust-Lima C, Peres K, et al. Prevalence and co-occurrence of geriatric syndromes in people aged 75 years and older in France: results from the Bordeaux Three-city Study. J Gerontol A Biol Sci Med Sci. 2017;73(1):109–16. doi: 10.1093/gerona/glx068. [DOI] [PubMed] [Google Scholar]

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