A Low Ankle-Brachial Index and High Brachial-Ankle Pulse Wave Velocity Are Associated with Poor Cognitive Function in Patients Undergoing Hemodialysis

Wu, Ping-Hsun; Lin, Yi-Ting; Wu, Pei-Yu; Huang, Jiun-Chi; Chen, Szu-Chia; Chang, Jer-Ming; Chen, Hung-Chun

doi:https://doi.org/10.1155/2019/9421352

Disease Markers

On this page

Abstract Introduction Methods Results Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments Supplementary Materials References Copyright Related Articles

Special Issue

Novel Biomarkers of Renal Disease Progression

View this Special Issue

Research Article | Open Access

Volume 2019 | Article ID 9421352 | https://doi.org/10.1155/2019/9421352

A Low Ankle-Brachial Index and High Brachial-Ankle Pulse Wave Velocity Are Associated with Poor Cognitive Function in Patients Undergoing Hemodialysis

Ping-Hsun Wu,^1,2,3,4Yi-Ting Lin,^1,2,4,5Pei-Yu Wu,^1,2,3,6Jiun-Chi Huang,^1,2,3,6Szu-Chia Chen ,^1,2,3,6Jer-Ming Chang,^3,6,7and Hung-Chun Chen^3,6,7

Guest Editor: Yuri Battaglia

Received28 Mar 2019

Revised02 May 2019

Accepted29 Jul 2019

Published19 Aug 2019

Abstract

Patients with end-stage renal disease (ESRD) have an increased risk of both impaired cognitive function and peripheral artery disease (PAD) than the general population. The association between PAD and dementia is recognized, but there are limited studies in patients with ESRD. The aim of this study was to evaluate the relationship between ankle-brachial index (ABI) and brachial-ankle pulse wave velocity (baPWV) and cognitive impairment in patients receiving hemodialysis (HD). We enrolled 136 prevalent HD patients (mean age years, 55.9% male). Cognitive performance was measured using the Montreal Cognitive Assessment (MoCA) and Cognitive Abilities Screening Instrument (CASI) by trained psychiatrists. Associations between the cognitive function and ABI and baPWV were assessed using multiple linear regression analysis. Compared with HD patients with , patients with had lower MoCA score () and lower CASI score but did not achieve significant level (). In the multivariate stepwise linear regression analysis, ABI (per 0.1) was independently positively associated with the MoCA score (, ) and the CASI score (, ). There is a negative association between baPWV (per 100 cm/s) and CASI (, ). In conclusion, a low ABI or high baPWV was associated with a lower cognitive function in HD patients.

1. Introduction

Patients with chronic kidney disease (CKD) or end-stage renal disease (ESRD) have a higher risk of dementia than the general population [1, 2], and patients with both dementia and ESRD have been associated with disability, hospitalization, dialysis withdrawal, and mortality [3–5]. As such, cognition should be evaluated in patients with ESRD to detect vascular and nonvascular dementia and prevent cognitive decline and its consequences. Given the high prevalence of dementia in patients with ESRD, identifying clinical markers that can predict cognitive dysfunction may be beneficial for both prevention and reducing health care costs. Scuteri et al. reported an association between arterial stiffness and brain injury and related brain pathologies [6]. They concluded that increased central pulse pressure and wave reflections may influence both the brain and kidneys and that it is likely that these phenomena are emphasized in ESRD patients.

Patients with ESRD have a high prevalence of peripheral artery disease (PAD), an important manifestation of systemic atherosclerosis [7]. PAD shares similar risk factors with coronary artery disease and cerebrovascular disease [8]. The ankle-brachial index (ABI) and pulse wave velocity (PWV) are common noninvasive tools used to quantitatively assess arterial health with regard to blocked arteries and arterial stiffness, respectively. PAD has been associated with an increased risk of both cardiovascular disease [9] and cognitive dysfunction in the general population [10], and a low ABI has been reported to predict the future risk of cognitive impairment [11] and dementia [12].

However, few studies have evaluated the association between PAD and cognitive function in patients with CKD or ESRD [13, 14], despite being at high risk of atherosclerosis and dementia. Moreover, no previous study has simultaneously evaluated ABI and PWV as markers of cognitive function in ESRD patients. A better understanding of the relationship between PAD and cognitive decline in ESRD patients may provide new insights into the physiological mechanisms of the prodromal stage of dementia. Accordingly, the aim of this study was to assess the association between ABI and brachial-ankle PWV (baPWV) and cognitive function in patients receiving hemodialysis (HD).

2. Methods

2.1. Study Subjects and Design

This study was conducted at a dialysis clinic in a regional hospital in southern Taiwan from August 2016 to January 2017. All patients received regular HD three times per week with high-flux dialyzers and a blood flow rate of 250-300 mL/min, dialysate flow rate of 500 mL/min, with each HD session lasting for 3.5-4 hours. Patients years who had received maintenance dialysis for at least 90 days were recruited. After excluding the patients who refused to undergo ABI-form device () or neuropsychological () examinations, patients with atrial fibrillation (), patients with bilateral below-the-knee amputations (), and patients who had been hospitalized for 4 weeks prior to study enrollment (), the remaining 136 patients (76 men and 60 women) were included into the final analysis. The study protocol was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20160095), and written informed consent was obtained from all patients. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki.

2.2. Demographic, Medical, and Laboratory Data

Demographic and medical data including sex, age, smoking history (ever versus never), and comorbidities were obtained from interviews and the patients’ medical records. Body mass index was calculated as weight divided by height squared in kg/m². Hypertension was defined as or taking antihypertensive drugs, and diabetes was defined as fasting blood glucose level of ≥126 mg/dL or taking antidiabetic drugs. Patients with a history of cerebrovascular accidents, including cerebral bleeding and infarction, were defined as having a cerebrovascular disease, and those with a history of angina, ischemic changes in electrocardiography, old myocardial infarction, or coronary bypass surgery/angioplasty were defined as having coronary artery disease. Laboratory examinations were performed in fasting blood samples obtained ≤1 month of enrollment using an autoanalyzer (COBAS Integra 400, Roche Diagnostics GmbH, Mannheim, Germany).

2.3. Measurement of ABI and baPWV

Because ABI and baPWV might be influenced by hemodialysis [15], all the values of ABI and baPWV were measured 10–30 minutes before hemodialysis. The values of ABI and baPWV were measured by using an ABI-form device (VP1000; Colin Co. Ltd., Komaki, Japan), which automatically and simultaneously measures blood pressures in both arms and ankles using an oscillometric method [16]. Occlusion and monitoring cuffs were placed tightly around the upper arms without blood access and both sides of the lower extremities in the supine position. ABI was calculated by the ratio of the ankle systolic blood pressure divided by the arm systolic blood pressure, and the lower value of the ankle systolic blood pressure was used for the calculation. For measuring baPWV, pulse waves obtained from the brachial and tibial arteries were recorded simultaneously, and the transmission time, which was defined as the time interval between the initial increase in brachial and tibial waveforms, was determined. The transmission distance from the arm to each ankle was calculated according to body height. The baPWV value was automatically computed as the transmission distance divided by the transmission time. After obtaining bilateral baPWV values, the highest one was used as a representative for each subject. The ABI and baPWV measurements were done once in each patient. Concerning the reproducibility of ABI and baPWV, we randomly evaluated 25 patients at least 15 minutes apart for the reproducibility of ABI and baPWV by using 2 separate measurements. Mean percent error was calculated as the absolute difference divided by the average of the 2 observations.

2.4. Cognitive Function Assessment

Three cognitive function tests evaluated in this study were Montreal Cognitive Assessment (MoCA) [17, 18] and Cognitive Abilities Screening Instrument (CASI) [18, 19] (Supplementary Table 1). The MoCA is a more sensitive 30-point screening tool that includes copying a cube, verbal abstraction, serial subtraction, drawing a clock, a 5-word learning and delayed recall task, naming an animal, digit span backward and forwards, selective attention, repeating a sentence, phonemic word fluency, and spatial and temporal orientation. These tasks encompass multiple domains of cognition, including short-term memory, visuospatial ability, and executive function, language, attention, concentration and working memory, and orientation to time and place. The CASI assesses a broad range of cognitive domains using a 40-item global cognitive test, which contains nine cognitive evaluation domains, including long-term memory, short-term memory, orientation, attention, mental manipulation, list-generating fluency, language, abstraction/judgment, and drawing (Supplementary Table 1).

2.5. Statistical Analysis

Descriptive statistics were presented as percentages, , or medians (25^th-75^th percentile) for HD duration and triglycerides. Differences between groups were analyzed using the chi-square test for categorical variables and the independent -test for continuous variables with approximately normal distribution or the Mann-Whitney test for continuous variables with skewed distribution. Multiple stepwise linear regression analyses were used to identify the factors associated with cognitive function (MoCA and CASI). The adjusted multivariate variables in the models included age, sex, smoking, a history of hypertension, diabetes, cerebrovascular and coronary artery diseases, systolic and diastolic blood pressures, body mass index, log-transformed hemodialysis duration, cause of end-stage renal disease, albumin, log-transformed triglyceride, total cholesterol, hemoglobin, creatinine, calcium-phosphorus product, Kt/V, and amount of ultrafiltration. Relevant demographic parameters were also analyzed by a backward stepwise selection with values for independent variables to enter and to stay in the models set at 0.1 and subsequently a final elimination step at . All statistical analyses were performed using SPSS version 19.0 for Windows (SPSS Inc., Chicago, IL, USA) and STATA version 14 (StataCorp LP, College Station, TX, USA). A two-tailed value of <0.05 was considered to be statistically significant.

3. Results

3.1. Demographic and Clinical Characteristics

The mean age of the 136 HD patients was years, 55.9% were men, and 21.3% had an . The mean percent error for ABI and baPWV measurement was and , respectively. The patients were stratified into two groups according to or ≥0.9. Comparisons of the clinical characteristics between the two groups are shown in Table 1. Compared to the groups, the groups were older and had more diabetes-related comorbidities, lower systolic and diastolic blood pressures, and higher levels of calcium-phosphorus products. Regarding cognitive function, compared to the groups, the groups had lower MoCA () scores. The group also had a lower CASI score, but the difference was not significant ().

3.2. Associations of ABI and Cognitive Function in HD Patients

In univariate linear regression models, ABI was positively associated with MoCA (β coefficient 1.07, 95% confidence interval (CI) 0.55 to 1.59, and ) and CASI (β coefficient 2.87, 95% CI 1.45 to 4.29, and ) (Table 2). In multivariate linear regression models with stepwise backward covariate selection, ABI was persisted positively associated with MoCA (β coefficient 0.62, 95% CI 0.14 to 1.09, and ) and CASI (β coefficient 1.43, 95% CI 0.17 to 2.70, and ) (Table 2). The stepwise backward covariate selection models of ABI and cognitive function (MoCA and CASI) were demonstrated in Supplementary Tables 2-4. Since extremely high ABI (>1.3) is correlated to multiple comorbidities or arterial calcification, we reanalyzed the association between ABI and cognitive function test after excluding subjects with . Similar results found the positive association between ABI and the MoCA or CASI score in univariate linear regression analysis (β coefficient 1.08, 95% CI 0.55 to 1.61, and in MoCA; β coefficient 2.97, 95% CI 1.53 to 4.42, and in CASI) and multivariate stepwise linear regression analysis (β coefficient 0.63, 95% CI 0.15 to 1.11, and in MoCA; β coefficient 1.36, 95% CI 0.04 to 2.69, and in CASI).

3.3. Associations of baPWV and Cognitive Function in HD Patients

In univariate linear regression models, baPWV was negatively associated with MoCA (β coefficient -0.33, 95% CI -0.56 to -0.11, and ) and CASI (β coefficient -1.16, 95% CI -1.76 to -0.56, and ) (Table 3). In multivariate linear regression models with stepwise backward covariate selection, baPWV was persisted negatively associated with CASI (β coefficient -0.70, 95% CI -1.22 to -0.18, and ) but not with MoCA (β coefficient -0.075, 95% CI -0.31 to 0.16, and ) (Table 3). The stepwise backward covariate selection models of baPWV and cognitive function (MoCA and CASI) were demonstrated in Supplementary Tables 5-7. Since both sides of PAD could be found in patients with HD that influence the baPWV data, we reanalyzed the association between baPWV and cognitive function test after excluding subjects with . Similar results found the negative association between baPWV and MoCA (β coefficient -0.38, 95% CI -0.63 to -0.12, and ) or CASI score (β coefficient -1.01, 95% CI -1.65 to -0.37, and ) in the univariate linear regression analysis. However, the insignificant negative association was demonstrated in stepwise linear regression approach (β coefficient -0.20, 95% CI -0.45 to 0.06, and in MoCA; β coefficient -0.44, 95% CI -1.07 to 0.18, and in CASI).

3.4. Subgroup Analysis of ABI or baPWV and Cognitive Function in HD Patients

Subgroup analysis of gender and baseline comorbidities demonstrated that ABI was positively associated with cognitive function except for the male gender, smoking habit, and patients with hypertension and stroke comorbidities (Figure 1). The baPWV was negatively associated with cognitive function except for male, smoking habit, patients with/without diabetes comorbidity, no hypertension comorbidity, or those with a stroke history in MoCA. A similar pattern was found in the CASI test (Figure 2).

(a)

(b)

(a)

(b)

3.5. Sensitivity Analysis of ABI or baPWV and Cognitive Function in HD Patients

We evaluated the comorbidities and cognitive function test (MoCA and CASI) association. Only diabetes was negatively associated with cognitive function test score (Supplementary Table 6). Considering diabetes as an important confounder for cognitive function, sensitivity analysis of stepwise regression models with additional diabetes comorbidity adjustment in ABI or baPWV and cognitive function was analyzed. A similar result was found (Supplementary Tables 7 and 8).

4. Discussion

This study examined the relationship between PAD and cognitive performance in 136 prevalent HD patients. PAD was assessed according to ABI and baPWV, and cognitive performance was assessed according to MoCA and CASI, which collectively evaluated memory, orientation, attention, visual screening, motor speed, planning abilities, executive function, and language. A low ABI was associated with low MoCA and CASI scores, and a high baPWV was associated with low CASI scores. Since cognition was evaluated using questionnaires with different sensitivities and specificities, it would be promising results to find the association between ABI or baPWV and cognition in HD patients.

The prevalence of cognitive impairment is high in patients undergoing HD [20–22]. Considering the different definition of cognitive impairment in patients with kidney disease based on sensitivity and specificity, MoCA is a valid, more sensitive, and well-suited screening tool for cognitive impairment in patients receiving HD [23, 24]. The optimal cut-off of ≤24 points out of a 30-point maximum is lower than the cut-off value of ≤26 described in the original data collected in a population of patients with Alzheimer’s disease and mild cognitive impairment [25]. In the present HD cohort, there is a higher prevalence of cognitive impairment (84.6%; 114/136 based on MoCA points) than other HD cohorts [26]. This may be related to longer dialysis vintage and higher diabetes mellitus comorbidity in the HD cohort.

The first important finding of this study is that a low ABI was associated with poor cognitive function. The etiology for cognitive impairment in HD patients is complex, including traditional risk factors (old age, hypertension, diabetes, and smoking), nontraditional risk factors (anemia, albuminuria, inflammation, homocysteinemia, and malnutrition), and dialysis-related factors (hypotension, all of which can lead to chronic cerebral hypoxemia or edema) [27, 28]. In addition, high rates of small and large cerebral vascular injuries, including white matter lesions, subcortical atrophy, lacunar infarcts, and microbleeds, have been reported in patients with CKD in brain imaging studies [29, 30]. Vascular stiffness and abnormalities in structure and function have also been associated with cognitive decline and stroke [31]. The ABI, an easily obtained good marker of atherosclerosis [32, 33], has been shown to be a good prognostic marker of atherosclerosis for stroke. Subjects with a low ABI have been reported to have more atherosclerotic and vascular conditions, and these conditions may lead to generalized endothelial dysfunction [34], stenosis of intracranial and extracranial arteries [35], and decreased cerebral perfusion inducing oxidative stress [36]. Taken together, these findings may explain the association between low ABI and poor overall cognitive function [37]. A low ABI has also been correlated with global cortical thinning and reduced cortical thickness in the limbic, parietal, temporal, and occipital lobes [38]. A decrease in overall cerebral perfusion due to atherosclerosis may cause pathological changes such as infarcts or neurodegeneration [39]. The present study declared that a low ABI was associated with the risk of poor cognitive function in HD patients, as indicated by low MoCA and CASI scores.

The second important finding of this study is that a high baPWV was associated with poor cognitive function. Arterial stiffness is a marker of functional and structural changes in the arteries and can contribute to microvascular brain diseases, and it can be assessed noninvasively using PWV measurements. Several studies have examined the association between arterial stiffness and cognitive function in the general population [40, 41] and in patients with CKD or ESRD [13, 14]. Longitudinal studies have indicated that high values of PWV can predict lower cognitive function score in the elderly [42]. One study with 72 ESRD patients undergoing HD showed that a higher carotid-femoral PWV was associated with cognitive impairment [14]. Another study suggests an inverse association between baPWV and cognitive function among the elderly [43]. The present study demonstrated a negative association between baPWV and cognitive function score (CASI). Thus, baPWV could be considered to be a marker of cognition in ESRD patients.

In the present study, diabetes is significantly negatively associated with the cognitive function test score. However, previous cerebrovascular event history is not significantly negatively associated with the cognitive function test score because of a few patients with old stroke hospitalization records in our study. Thus, sample sizes are insufficient to found the association. Therefore, the main results were analyzed by multivariate stepwise variable selection model in this study. Stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. At each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Since diabetes is an important confounder for cognitive function, we further sensitivity analyzed the stepwise regression models with additional diabetes comorbidity adjustment, the results remain similar. In our subgroup analysis, the association between ABI/baPWV and cognitive function was still significant in diabetes mellitus strata. Thus, the relationship between ABI/baPWV and cognition was similar in patients with and without diabetes comorbidity.

Besides, a positive association was found between the duration of dialysis and cognitive function test (CASI) in the multivariate linear regression model. Although this association is counterintuitive, it could reflect a selection bias or survivor bias inherent in prevalent dialysis cohorts. Survivors of long-term dialysis often begin dialysis at a young age and have had no opportunity for renal transplantation. Young and healthy dialysis patients would actually have better cognitive function than older or more ill dialysis patients. In the stepwise selection model, the significant association between the duration of dialysis and the cognitive function test may be related to open a noncausal path because of nonconfounder (Collider variables) adjustment in the causal pathway between duration of dialysis and cognitive function. On the contrary, our study interest is the association between ABI/baPWV and cognitive function that variable selection fits the causal pathway models in our study.

The important clinical implications of our findings are that ABI and baPWV are noninvasive and easily measured parameters that can be used to help identify the HD patients at risk of cognitive dysfunction by evaluating the degree of arterial stiffness. However, there are several limitations to this study. First, a causal relationship could not be inferred due to the cross-sectional nature of the study. Nonetheless, our results may help to identify the risk of cognitive dysfunction in HD patients through regular ABI examinations. Since a long-term decline in cognitive function could not be confirmed in this study, future prospective studies are needed to address this issue. Second, this study was conducted at a single regional hospital, thereby limiting the selection and the number of patients. In addition, patients with peritoneal dialysis were not enrolled in this study; therefore, the results may not be applicable to patients undergoing peritoneal dialysis. Further studies are needed to confirm our findings. Third, ABI measurements and cognitive function tests were performed only once in each patient, which may have caused misclassification. Fourth, not all possible parameters were included in this study such as dietary habits, genetic factors, and medications. In addition, neuroimaging assessments could not be performed, so asymptomatic brain lesions could not be fully investigated. Moreover, different types of dementia and cognitive impairment etiology may have affected the results.

5. Conclusion

In this study, the HD patients with a low ABI and high baPWV were associated with a lower cognitive function, even after adjusting for age and hemodynamic and risk factors for atherosclerosis. Future studies should explore the longitudinal associations between ABI and baPWV and cognitive decline in a representative sample of HD patients. Our findings should serve to remind physicians of the importance of evaluating PAD in addition to cognitive function in HD patients.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The study was funded by grants from the Ministry of Science and Technology, Taiwan (MOST 107-2314-B-037-104 and MOST 107-2314-B-037-098-MY3), the Kaohsiung Medical University Hospital, Taiwan (KMUH106-6R17 and KMUH106-6T03), and the Kaohsiung Medical University “Aim for the Top Universities Grant,” grant nos. KMU-TP104PR24 and KMU-TP104PR26.

Supplementary Materials

Supplementary Table 1: description of the neuropsychiatric test on patients with hemodialysis in the study. Supplementary Table 2: determinants of MoCA and ABI association using multivariate stepwise linear regression analysis. Supplementary Table 3: determinants of CASI and ABI association using multivariate stepwise linear regression analysis. Supplementary Table 4: determinants of MoCA and baPWV association using multivariate stepwise linear regression analysis. Supplementary Table 5: determinants of CASI and baPWV association using multivariate stepwise linear regression analysis. Supplementary Table 6: the association between comorbidities and cognitive function (MoCA and CASI). Supplementary Table 7: the association between ABI and cognitive function test (MoCA and CASI) using multivariate stepwise linear regression analysis and further additional diabetes mellitus comorbidity adjustment. Supplementary Table 8: the association between baPWV and cognitive function test (MoCA and CASI) using multivariate stepwise linear regression analysis and further additional diabetes mellitus comorbidity adjustment. (Supplementary Materials)

References

E. O’Lone, M. Connors, P. Masson et al., “Cognition in people with end-stage kidney disease treated with hemodialysis: a systematic review and meta-analysis,” American Journal of Kidney Diseases, vol. 67, no. 6, pp. 925–935, 2016.
View at: Publisher Site | Google Scholar
A. J. Collins, B. Kasiske, C. Herzog et al., “United States renal data system 2006 annual data report abstract,” American Journal of Kidney Diseases, vol. 49, pp. A6–A7, 2007.
View at: Publisher Site | Google Scholar
M. Kurella, D. L. Mapes, F. K. Port, and G. M. Chertow, “Correlates and outcomes of dementia among dialysis patients: the Dialysis Outcomes and Practice Patterns Study,” Nephrology, Dialysis, Transplantation, vol. 21, no. 9, pp. 2543–2548, 2006.
View at: Publisher Site | Google Scholar
D. A. Rakowski, S. Caillard, L. Y. Agodoa, and K. C. Abbott, “Dementia as a predictor of mortality in dialysis patients,” Clinical Journal of the American Society of Nephrology, vol. 1, no. 5, pp. 1000–1005, 2006.
View at: Publisher Site | Google Scholar
J. M. Chillon, Z. A. Massy, and B. Stengel, “Neurological complications in chronic kidney disease patients,” Nephrology, Dialysis, Transplantation, vol. 31, no. 10, pp. 1606–1614, 2016.
View at: Publisher Site | Google Scholar
A. Scuteri, A. M. Brancati, W. Gianni, A. Assisi, and M. Volpe, “Arterial stiffness is an independent risk factor for cognitive impairment in the elderly: a pilot study,” Journal of Hypertension, vol. 23, no. 6, pp. 1211–1216, 2005.
View at: Publisher Site | Google Scholar
Y. Leskinen, J. P. Salenius, T. Lehtimaki, H. Huhtala, and H. Saha, “The prevalence of peripheral arterial disease and medial arterial calcification in patients with chronic renal failure: requirements for diagnostics,” American Journal of Kidney Diseases, vol. 40, no. 3, pp. 472–479, 2002.
View at: Publisher Site | Google Scholar
J. R. Bartholomew and J. W. Olin, “Pathophysiology of peripheral arterial disease and risk factors for its development,” Cleveland Clinic Journal of Medicine, vol. 73, Supplement 4, pp. S8–14, 2006.
View at: Publisher Site | Google Scholar
J. Blacher, M. E. Safar, A. P. Guerin, B. Pannier, S. J. Marchais, and G. M. London, “Aortic pulse wave velocity index and mortality in end-stage renal disease,” Kidney International, vol. 63, no. 5, pp. 1852–1860, 2003.
View at: Publisher Site | Google Scholar
M. Guerchet, V. Aboyans, P. Nubukpo, P. Lacroix, J. P. Clement, and P. M. Preux, “Ankle-brachial index as a marker of cognitive impairment and dementia in general population. A systematic review,” Atherosclerosis, vol. 216, no. 2, pp. 251–257, 2011.
View at: Publisher Site | Google Scholar
J. F. Price, S. McDowell, M. C. Whiteman, I. J. Deary, M. C. Stewart, and F. G. R. Fowkes, “Ankle brachial index as a predictor of cognitive impairment in the general population: ten-year follow-up of the Edinburgh Artery Study,” Journal of the American Geriatrics Society, vol. 54, no. 5, pp. 763–769, 2006.
View at: Publisher Site | Google Scholar
D. Laurin, K. H. Masaki, L. R. White, and L. J. Launer, “Ankle-to-brachial index and dementia: the Honolulu-Asia Aging Study,” Circulation, vol. 116, no. 20, pp. 2269–2274, 2007.
View at: Publisher Site | Google Scholar
D. Karasavvidou, P. Boutouyrie, R. Kalaitzidis et al., “Arterial damage and cognitive decline in chronic kidney disease patients,” Journal of Clinical Hypertension, vol. 20, no. 9, pp. 1276–1284, 2018.
View at: Publisher Site | Google Scholar
A. Tasmoc, M. D. Donciu, G. Veisa, I. Nistor, and A. Covic, “Increased arterial stiffness predicts cognitive impairment in hemodialysis patients,” Hemodialysis International, vol. 20, no. 3, pp. 463–472, 2016.
View at: Publisher Site | Google Scholar
H.-M. Su, J.-M. Chang, F.-H. Lin et al., “Influence of different measurement time points on brachial-ankle pulse wave velocity and ankle-brachial index in hemodialysis patients,” Hypertension Research, vol. 30, no. 10, pp. 965–970, 2007.
View at: Publisher Site | Google Scholar
H. Tomiyama, A. Yamashina, T. Arai et al., “Influences of age and gender on results of noninvasive brachial-ankle pulse wave velocity measurement—a survey of 12517 subjects,” Atherosclerosis, vol. 166, no. 2, pp. 303–309, 2003.
View at: Publisher Site | Google Scholar
A. S. Costa, F. E. Tiffin-Richards, B. Holschbach et al., “Clinical predictors of individual cognitive fluctuations in patients undergoing hemodialysis,” American Journal of Kidney Diseases, vol. 64, no. 3, pp. 434–442, 2014.
View at: Publisher Site | Google Scholar
Y. T. Lin, P. H. Wu, H. H. Lee et al., “Indole-3 acetic acid increased risk of impaired cognitive function in patients receiving hemodialysis,” Neurotoxicology, vol. 73, pp. 85–91, 2019.
View at: Publisher Site | Google Scholar
T. J. Hsieh, J. M. Chang, H. Y. Chuang et al., “End-stage renal disease: in vivo diffusion-tensor imaging of silent white matter damage,” Radiology, vol. 252, no. 2, pp. 518–525, 2009.
View at: Publisher Site | Google Scholar
M. K. Tamura, B. Larive, M. L. Unruh et al., “Prevalence and correlates of cognitive impairment in hemodialysis patients: the Frequent Hemodialysis Network trials,” Clinical Journal of the American Society of Nephrology, vol. 5, no. 8, pp. 1429–1438, 2010.
View at: Publisher Site | Google Scholar
J. B. Post, K. G. Morin, M. Sano, A. B. Jegede, E. Langhoff, and A. M. Spungen, “Increased presence of cognitive impairment in hemodialysis patients in the absence of neurological events,” American Journal of Nephrology, vol. 35, no. 2, pp. 120–126, 2012.
View at: Publisher Site | Google Scholar
M. J. Sarnak, H. Tighiouart, T. M. Scott et al., “Frequency of and risk factors for poor cognitive performance in hemodialysis patients,” Neurology, vol. 80, no. 5, pp. 471–480, 2013.
View at: Publisher Site | Google Scholar
F. E. Tiffin-Richards, A. S. Costa, B. Holschbach et al., “The Montreal Cognitive Assessment (MoCA) - a sensitive screening instrument for detecting cognitive impairment in chronic hemodialysis patients,” PLoS One, vol. 9, no. 10, article e106700, 2014.
View at: Publisher Site | Google Scholar
S. H. Lee, A. Cho, Y. K. Min, Y. K. Lee, and S. Jung, “Comparison of the Montreal cognitive assessment and the mini-mental state examination as screening tests in hemodialysis patients without symptoms,” Renal Failure, vol. 40, no. 1, pp. 323–330, 2018.
View at: Publisher Site | Google Scholar
Z. S. Nasreddine, N. A. Phillips, V. Bédirian et al., “The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment,” Journal of the American Geriatrics Society, vol. 53, no. 4, pp. 695–699, 2005.
View at: Publisher Site | Google Scholar
E. Erken, O. Altunoren, M. E. Senel et al., “Impaired cognition in hemodialysis patients: the Montreal Cognitive Assessment (MoCA) and important clues for testing,” Clinical Nephrology, vol. 91, no. 5, pp. 275–283, 2019.
View at: Publisher Site | Google Scholar
S. L. Seliger and M. J. Sarnak, “Subclinical vascular disease of the brain in dialysis patients,” American Journal of Kidney Diseases, vol. 50, no. 1, pp. 8–10, 2007.
View at: Publisher Site | Google Scholar
A. M. Murray, “Cognitive impairment in the aging dialysis and chronic kidney disease populations: an occult burden,” Advances in Chronic Kidney Disease, vol. 15, no. 2, pp. 123–132, 2008.
View at: Publisher Site | Google Scholar
D. G. Moodalbail, K. A. Reiser, J. A. Detre et al., “Systematic review of structural and functional neuroimaging findings in children and adults with CKD,” Clinical Journal of the American Society of Nephrology, vol. 8, no. 8, pp. 1429–1448, 2013.
View at: Publisher Site | Google Scholar
D. A. Drew, B. B. Koo, R. Bhadelia et al., “White matter damage in maintenance hemodialysis patients: a diffusion tensor imaging study,” BMC Nephrology, vol. 18, no. 1, p. 213, 2017.
View at: Publisher Site | Google Scholar
F. Forette, M. L. Seux, J. A. Staessen et al., “Prevention of dementia in randomised double-blind placebo-controlled Systolic Hypertension in Europe (Syst-Eur) trial,” The Lancet, vol. 352, no. 9137, pp. 1347–1351, 1998.
View at: Publisher Site | Google Scholar
A. Yamashina, H. Tomiyama, K. Takeda et al., “Validity, reproducibility, and clinical significance of noninvasive brachial-ankle pulse wave velocity measurement,” Hypertension Research, vol. 25, no. 3, pp. 359–364, 2002.
View at: Publisher Site | Google Scholar
S. Fishbane, S. Youn, E. J. Kowalski, and G. L. Frei, “Ankle-arm blood pressure index as a marker for atherosclerotic vascular diseases in hemodialysis patients,” American Journal of Kidney Diseases, vol. 25, no. 1, pp. 34–39, 1995.
View at: Publisher Site | Google Scholar
J. Singer, J. N. Trollor, B. T. Baune, P. S. Sachdev, and E. Smith, “Arterial stiffness, the brain and cognition: a systematic review,” Ageing Research Reviews, vol. 15, pp. 16–27, 2014.
View at: Publisher Site | Google Scholar
M. Silvestrini, I. Paolino, F. Vernieri et al., “Cerebral hemodynamics and cognitive performance in patients with asymptomatic carotid stenosis,” Neurology, vol. 72, no. 12, pp. 1062–1068, 2009.
View at: Publisher Site | Google Scholar
A. H. E. A. van Beek, J. A. H. R. Claassen, M. G. M. O. Rikkert, and R. W. M. M. Jansen, “Cerebral autoregulation: an overview of current concepts and methodology with special focus on the elderly,” Journal of Cerebral Blood Flow and Metabolism, vol. 28, no. 6, pp. 1071–1085, 2008.
View at: Publisher Site | Google Scholar
S. Hilal, M. Saini, C. S. Tan et al., “Ankle-brachial index, cognitive impairment and cerebrovascular disease in a Chinese population,” Neuroepidemiology, vol. 42, no. 2, pp. 131–138, 2014.
View at: Publisher Site | Google Scholar
M. A. Shaik, N. Venketasubramanian, C. Y. Cheng et al., “Ankle brachial index, MRI markers and cognition: the Epidemiology of Dementia in Singapore study,” Atherosclerosis, vol. 263, pp. 272–277, 2017.
View at: Publisher Site | Google Scholar
A. E. Roher, S. L. Tyas, C. L. Maarouf et al., “Intracranial atherosclerosis as a contributing factor to Alzheimer’s disease dementia,” Alzheimer's & Dementia, vol. 7, no. 4, pp. 436–444, 2011.
View at: Publisher Site | Google Scholar
T. T. van Sloten, A. D. Protogerou, R. M. A. Henry, M. T. Schram, L. J. Launer, and C. D. A. Stehouwer, “Association between arterial stiffness, cerebral small vessel disease and cognitive impairment: a systematic review and meta-analysis,” Neuroscience and Biobehavioral Reviews, vol. 53, pp. 121–130, 2015.
View at: Publisher Site | Google Scholar
A. Zeki Al Hazzouri and K. Yaffe, “Arterial stiffness and cognitive function in the elderly,” Journal of Alzheimer's Disease, vol. 42, Supplement 4, pp. S503–S514, 2014.
View at: Publisher Site | Google Scholar
A. Scuteri, M. Tesauro, S. Appolloni, F. Preziosi, A. M. Brancati, and M. Volpe, “Arterial stiffness as an independent predictor of longitudinal changes in cognitive function in the older individual,” Journal of Hypertension, vol. 25, no. 5, pp. 1035–1040, 2007.
View at: Publisher Site | Google Scholar
M. Fukuhara, K. Matsumura, T. Ansai et al., “Prediction of cognitive function by arterial stiffness in the very elderly,” Circulation Journal, vol. 70, no. 6, pp. 756–761, 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2019 Ping-Hsun Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1169

Downloads

983

Citations