Home blood pressure is associated with cognitive impairment among elderly patients with type 2 diabetes: KAMOGAWA-HBP study

Abstract

Background:

This cross-sectional study was designed to examine the association between home blood pressure and cognitive impairment in elderly patients with type 2 diabetes.

Methods:

Home blood pressure was measured in the morning and evening for 14 consecutive days in 749 patients with type 2 diabetes. A total of 231 patients were included in the study population. Cognitive function was evaluated using the Mini-Cog test, which is used as a screening for cognitive impairment in elderly patients. We performed a logistic regression analysis and measured the area under the receiver operating characteristic curve and estimated home blood pressure as a marker of cognitive impairment.

Results:

The adjusted odds ratio (95% confidence interval) of evening systolic blood pressure for cognitive impairment was 1.24 (1.02–1.53). Evening systolic blood pressure showed the highest area under the receiver operating characteristic curve for cognitive impairment in both unadjusted and adjusted models. In all subgroup analyses except gender, home blood pressure showed higher area under the receiver operating characteristic curve than clinic blood pressure.

Conclusion:

Home blood pressure was associated with cognitive impairment in elderly patients with type 2 diabetes.

Keywords

Cross-sectional multicentre study cognitive impairment home blood pressure type 2 diabetes

Introduction

Both type 2 diabetes and cognitive impairment are disorders with high morbidity in the elderly and are major social problems. Several large population-based studies have suggested an association between type 2 diabetes and cognitive impairment,^1,2 and it has been reported that diabetes almost doubles the risk of cognitive impairment.² The morbidity of hypertensive patients with type 2 diabetes is also higher than that in the general population.³ Home blood pressure (HBP) has been found to have a stronger relation with target organ damage than clinic blood pressure (BP) in several population-based studies and prospective clinical trials.^4,5 However, there have been no studies to investigate the relationship between HBP and cognitive impairment in patients with diabetes. The aim of this study was to evaluate whether HBP is associated with cognitive impairment in elderly patients with type 2 diabetes.

Methods

Study design

This cross-sectional study is based on data from the HBP cohort of type 2 diabetes (KAMOGAWA-HBP study). The KAMOGAWA-HBP cohort included patients who had regularly attended the diabetes outpatient clinic at the Kyoto Prefectural University of Medicine or one of four other hospitals. In this study, we evaluated the association between cognitive impairment and HBP in patients who had attended the Kyoto Prefectural University of Medicine. All procedures were approved by the local Research Ethics Committee and were conducted in accordance with the Declaration of Helsinki; informed consent was obtained from all patients.

Study population

HBP measurements were performed in 749 patients with type 2 diabetes who had regularly visited the diabetes outpatient clinic at the Hospital of Kyoto Prefectural University of Medicine between 2008 and 2012. Of the 749 patients, we excluded those with inappropriate BP recordings (HBP measured by patient’s family, measured after breakfast or no measurement) (n = 10), who had progressive renal dysfunction (Cr ⩾2.0 mg/dL)⁶ (n = 4), who were younger than 65 years (n = 323), who declined to have cognitive function evaluated and whom the physician recognized as inappropriate patients for this research (patients who cannot understand the intention of the study due to serious dementia, psychiatric disorders or underlying diseases, those with visual impairment, with hearing impairment or had cardiovascular events within half a year) (n = 181). Finally, we included a total of 231 patients for the study population (122 males and 109 females; Figure 1). The diagnosis of type 2 diabetes was based on the American Diabetes Association criteria.⁷

Figure 1.

Flow diagram for the KAMOGAWA-HBP cohort.

BP measurements

HBP was self-measured using a validated, automatic device (HEM-7080IC; Omron Healthcare Co. Ltd, Kyoto, Japan)⁸ equipped with memory to store the date, time and readings. We used the electronic readouts from the device for our study analyses. Patients were instructed to perform triplicate morning and evening BP with at least 1 min between recordings for 14 consecutive days. We calculated the mean of three measurements per morning and per evening each day, and the level of HBP was computed from those 14 mean values. Morning BP measurements were made within 1 h of waking, before breakfast or taking any medicines, with the patient seated and rested for at least 5 min.⁹ Evening BP measurements were obtained in a similar manner just prior to bedtime. The cuff was placed directly around the non-dominant arm, and the position of the cuff was maintained at the level of the heart. Clinic BP was calculated as the mean value of triplicate measurements using the HEM-70801C device, at the time of study entry.

Assessment of cognitive function outcome

Cognitive function was evaluated by the physician using the Mini-Cog test, which is a practical screening tool for cognitive impairment in primary care.^10,11 The Mini-Cog can be used effectively after brief training in medical settings. It consists of two components, a three-item recall test for memory (0–3 points; each correctly recalled word = 1 point) and a simply scored clock drawing test (abnormal clock = 0 points and normal clock = 2 points).¹² In this study, cognitive impairment was defined as Mini-Cog score equal to or less than 2 points, which is the cut-off value to screen for cognitive impairment.

Physical and biochemical examinations

Information, including age, duration of diabetes, microvascular complications, macrovascular complications, smoking habit and alcohol intake, hypoglycemic medication and antihypertensive medication, were obtained at the time of study entry. Retinopathy was graded as follows: no diabetic retinopathy (NDR), simple diabetic retinopathy (SDR) and proliferative diabetic retinopathy (PDR). Neuropathy was defined by the diagnostic criteria for diabetic neuropathy proposed by the Diagnostic Neuropathy Study Group.¹³ Nephropathy was defined as urinary albumin excretion (UAE) equal to or more than 30 mg/g Cr. A macrovascular complication was defined as the presence of previous cerebrovascular disease, cardiovascular disease and/or arteriosclerosis obliterans based on the clinical history or physical examination. Smoking status (current, past or never smokers) and alcohol drinking status (everyday, social and never) were assessed by interview. Blood samples were collected for biochemical measurements. Haemoglobin A1c, triglyceride, cholesterol and other biochemical data were determined using standard laboratory assays. UAE was measured with an immunoturbidimetric assay. A mean value for UAE was determined from triplicate urine collections.

Statistical analysis

Baseline characteristics were summarized by median with interquartile range (IQR) or numbers. We performed a logistic regression to analyse the relationship between HBP and presence of cognitive impairment. We established a multivariate logistic model adjusted by age, sex, body mass index, haemoglobin A1c, cholesterol, use of antihypertensive medications, use of insulin, history of cerebral infarction and alcohol consumption status. Unadjusted and adjusted odds ratios and 95% confidence intervals (CIs) were estimated. We computed day-by-day variability of BP as within-patient coefficient of variation (CV) of measurements. CV was determined as within-patient standard deviation divided by within-patient mean. In addition, we compared the area under the receiver operating characteristic curve (AUC) of HBP and clinic BP for cognitive impairment as Mini-Cog score ⩽ 2.

We performed subgroup analyses by gender, duration of diabetes (10 years as a cut-off value), insulin use, history of cerebral infarction and age (equal to or more than 75 years and less than 75 years).

The JMP version 10.0.2 software (SAS Institute Inc., Cary, NC, USA) was used for statistical analyses, and p-values < 0.05 (two-sided) were considered statistically significant.

Results

The clinical characteristics of the patients are shown in Table 1. Median (IQR) age, duration of diabetes, body mass index and haemoglobin A1c were 72.5 (69.0–76.0) years, 14.5 (7.0–20.0) years, 23.2 (21.1–24.9) kg/m² and 6.9 (6.2–7.4)%, respectively. Of the 231 patients, 67 (29.0%) had a Mini-Cog score equal to or less than 2 points and 126 (54.5%) were treated with antihypertensive drugs.

Table 1.

Clinical characteristics of patients.

Variables	n = 231	CI (–)n = 164	CI (+)n = 67	p-value
Male	122 (52.8)	80 (48.8)	42 (62.7)	0.055
Age (years)	72.5 (69.0–76.0)	72.1 (68.0–76.0)	73.5 (70.0–77.0)	0.038
Duration of diabetes mellitus (years)	14.5 (7.0–22.0)	13.3 (6.0–18.0)	17.5 (7.0–26.5)	0.010
Body mass index (kg/m²)	23.2 (21.1–24.9)	23.0 (21.1–24.8)	23.6 (21.5–25.1)	0.214
Haemoglobin A1c (%)	6.9 (6.2–7.4)	6.8 (6.2–7.4)	7.0 (6.4–7.5)	0.184
Total cholesterol (mg/dL)	181.7 (161.0–198.8)	181.7 (161.0–198.0)	181.9 (160–203)	0.979
HDL cholesterol (mg/dL)	57.9 (46.8–68.3)	58.7 (47.0–71.0)	55.8 (45–64)	0.196
Triglycerides (mg/dL)	123.2 (78.0–152.0)	121.2 (76.3–151.8)	128.0 (86.0–152.0)	0.469
eGFR (mL/min/1.73 m²)	71.9 (58.7–84.5)	72.1 (58.6–84.4)	71.3 (59.9–85.0)	0.782
Smoking status
Current smoker	58 (25.1)	44 (26.8)	14 (20.9)	0.303
Past smoker	82 (35.5)	55 (33.5)	27 (40.3)	0.368
Alcohol consumption status
Daily	57 (24.7)	35 (21.3)	22 (32.3)	0.058
Social	32 (13.9)	28 (17.0)	4 (6.0)	0.022
Diabetes complication
Nephropathy (microalbuminuria)	110 (47.7)	75 (45.7)	35 (52.2)	0.369
Retinopathy	53 (22.9)	33 (20.1)	20 (29.9)	0.099
Neuropathy	70 (30.3)	47 (28.7)	23 (34.3)	0.411
Macrovascular complication	68 (29.4)	44 (26.8)	24 (35.8)	0.152
Use of antihypertensive medications	126 (54.5)	83 (50.6)	43 (64.2)	0.060
Use of insulin	60 (26.0)	45 (27.4)	52 (77.6)	0.427
Morning SBP	135 (124–146)	134 (122–144)	139 (127–150)	0.050
Evening SBP	131 (119–142)	130 (118–141)	135 (120–145)	0.034
Morning DBP	71 (65–77)	71 (64–76)	72 (65–77)	0.391
Evening DBP	67 (61–72)	66 (60–71)	67 (61–74)	0.354
Clinic SBP	139 (126–150)	138 (125–150)	141 (129–139)	0.257
Clinic DBP	74 (67–82)	74 (66–82)	74 (67–81)	0.982
CV of morning SBP	7.4 (6.2–8.9)	7.4 (6.3–9.0)	7.6 (6.0–8.7)	0.568
CV of evening SBP	7.8 (6.2–9.5)	7.5 (6.1–9.3)	8.4 (6.5–9.8)	0.052
CV of morning DBP	6.9 (5.7–8.8)	6.8 (5.6–8.9)	7.3 (6.0–6.8)	0.740
CV of evening DBP	8.2 (6.3–10.8)	7.9 (6.2–10.3)	8.7 (6.7–11.6)	0.185

CI: cognitive impairment; HDL: high-density lipoprotein; eGFR: estimated glomerular filtration rate; SBP: systolic blood pressure; DBP: diastolic blood pressure; CV: coefficient of variation.

For categorical variables, n (%) is presented. For continuous variables, median (interquartile range) is presented.

The unadjusted odds ratio (95% CI) of morning, evening and clinic systolic blood pressure (SBP) for cognitive impairment were 1.18 (1.00–1.40; p = 0.051), 1.20 (1.01–1.43; p = 0.036) and 1.09 (0.94–1.26; p = 0.257), respectively. The adjusted odds ratio (95% CI) of morning, evening and clinic SBP for cognitive impairment were 1.19 (0.98–1.45; p = 0.080), 1.24 (1.02–1.53; p = 0.037) and 1.12 (0.94–1.34; p = 0.192), respectively. Evening SBP showed the highest AUC in both unadjusted and adjusted models (Table 2). The unadjusted odds ratio (95% CI) of CV of morning SBP and evening SBP were 1.03 (0.92–1.16; p = 0.566) and 1.09 (1.00–1.19; p = 0.057), respectively. The adjusted odds ratio (95% CI) of CV of morning SBP and evening SBP were 1.04 (0.90–1.19; p = 0.590) and 1.11 (1.00–1.22; p = 0.049), respectively. We have evaluated the relation between cognitive impairment and the transition of BP as a graph of percentage of cognitive impairment and BP distribution (Figure 2). In all subgroup analyses except gender, morning or evening SBP showed the highest AUC, while in female, clinic SBP showed the highest AUC (Supplementary Tables 1–5).

Table 2.

Unadjusted and adjusted odds ratios (ORs) for cognitive impairment.

	Model 1			Model 2
	Unadjusted OR (95% CI)	p-value	AUC	Adjusted OR (95% CI)	p-value	AUC
Morning SBP (per 10 mm Hg increment)	1.18 (1.00–1.40)	0.051	0.580	1.19 (0.98–1.45)	0.080	0.692
Evening SBP (per 10 mm Hg increment)	1.20 (1.01–1.43)	0.036	0.582	1.24 (1.02–1.54)	0.037	0.700
Clinic SBP (per 10 mm Hg increment)	1.09 (0.94–1.26)	0.257	0.552	1.12 (0.94–1.34)	0.192	0.688
Morning DBP (per 10 mm Hg increment)	1.15 (0.84–1.57)	0.389	0.537	1.04 (0.72–1.50)	0.813	0.680
Evening DBP (per 10 mm Hg increment)	1.17 (0.84–1.62)	0.353	0.547	1.13 (0.77–1.66)	0.530	0.679
Clinic DBP (per 10 mm Hg increment)	1.00 (0.76–1.30)	0.982	0.501	0.97 (0.69–1.37)	0.870	0.682
CV of morning SBP	1.03 (0.92–1.16)	0.566	0.500	1.04 (0.90–1.19)	0.590	0.693
CV of evening SBP	1.09 (1.00–1.19)	0.057	0.581	1.11 (1.00–1.22)	0.049	0.708
CV of morning DBP	1.02 (0.92–1.11)	0.739	0.512	1.03 (0.92–1.14)	0.594	0.691
CV of evening DBP	1.06 (0.97–1.15)	0.187	0.570	1.02 (0.92–1.12)	0.651	0.693

CI: confidence interval; AUC: area under the receiver operating characteristic curve; SBP: systolic blood pressure; DBP: diastolic blood pressure; CV: coefficient of variation.

Model 2: ORs were adjusted for age, sex, body mass index, haemoglobin A1c, total cholesterol, creatinine, use of antihypertensive medications, use of insulin, history of macroangiopathy and alcohol consumption status.

Figure 2.

Relationship between cognitive impairment and (a) morning SBP, (b) morning DBP, (c) evening SBP and (d) evening DBP.

Discussion

Principal findings

This study indicated that increased HBP is associated with poorer cognitive performance than clinic BP in patients with diabetes. Evening SBP showed a positive relationship with cognitive impairment independent of age, sex, body mass index, haemoglobin A1c, total cholesterol, creatinine, use of antihypertensive medications, use of insulin, history of macrovascular complications and alcohol consumption status. The results of this study are consistent with those in the previous report,¹⁴ in which Wu et al. have showed in a cross-sectional study in elderlies with hypertension that clinic BP control correlated with cognitive function evaluated by Mini-Mental State Examination.

Interpretations

In regard to associations of HBP and cognitive function, Yeung and Loken Thornton¹⁵ reported HBP as a predictor of neuropsychological function in older adults beyond cognitive screening. They showed that higher home systolic BP predicted worse processing speed, executive function and everyday cognitive function. The possible reasons can be explained by the fact that HBP measurements provide more reproducible information on BP, exclude the white coat effect and have more prognostic significance¹⁶ than conventional BP measurements.

Another study reported that increased day-to-day BP variability is, independent of average HBP, a significant risk factor for the development of dementia in the general elderly Japanese population.¹⁷ Day-to-day BP variability and short-term BP variability can be related to autonomic dysfunction,¹⁸ and it has been reported that orthostatic hypotension, one of the symptoms of autonomic dysfunction, can cause cerebral ischaemia and associate with dementia. The results of this study, which showed the association of day-by-day variability and cognitive impairment, were also consistent with the previous reports.^18,19 In diabetes patients, autonomic dysfunction frequently occurs which may influence BP variability and may be associated with the risk of cognitive impairment, equal to or higher than that of the general elderly Japanese population.

In this study, evening SBP showed the highest AUC for cognitive impairment. Ribeiro et al.²⁰ reported that the BP circadian profiles showed a lower variability, with the absence of a nocturnal fall, especially in diabetes patients with chronic complications such as autonomic neuropathy and macroangiopathy. In diabetes patients with cardiovascular autonomic dysfunction, the relative risk of mortality is high due to the high incidence of painless myocardial infarction and severe arrhythmia.²¹ In this study, we did not measure the nocturnal BP, but in patients with high evening BP, there might be an absence of a nocturnal fall²² related to the onset of cardiovascular events.

Lv et al.²³ reported a U-shaped association between BP values and prevalence of cognitive impairment, but there was no such relationship found in this study.

In all subgroup analyses except gender, morning or evening SBP showed the highest AUC for cognitive impairment, while in female, clinic SBP showed the highest AUC of all (Supplementary Tables 1–5).

A previous study has demonstrated that female gender is positively associated with HBP variability.²⁴ As a mechanism for increasing female BP variability, intrinsic factors such as instability of the autonomic nervous system and more obvious emotional fluctuation during female BP measurement can be considered. These findings may well explain why females have a higher incidence of white coat hypertension and, in this study, why clinic SBP showed the highest AUC.

This study has several limitations. First, since this is a cross-sectional study, the causal relationship is not clear. For a useful evaluation of the relationship between HBP measurements and the cognitive impairment among patients with type 2 diabetes, a randomized controlled trial with larger sample sizes and a long-term follow-up period should be conducted. Second, the evaluation of cognitive impairment is performed only for a single Mini-Cog test, and the evaluation (Alzheimer’s type dementia, vascular dementia, Lewy body disease, etc.) or classification of dementia such as image evaluation is insufficient. Third, it has been reported that there was not enough evidence to support the routine use of the Mini-Cog test as a screening test for dementia in primary care.¹¹ However, we prioritized and selected the benefits of the Mini-Cog test, which can be done in a short time during the busy outpatient clinic in this study. Finally, this study lacks data for daytime BP and nocturnal BP measurements.

Conclusion

Cognitive impairment, including mild cognitive impairment, significantly reduces quality of life and shortens life expectancy. Considering both the social burden of cognitive impairment and the higher risk for cognitive impairment in diabetic patients, it is as important to prevent cognitive impairment as to treat diabetes. Our findings implicate the possibility that measuring not only clinic BP but also HBP, especially evening SBP, may lead to prediction of progress of cognitive impairment. Appropriate control for HBP and blood glucose level might be important to prevent cognitive impairment as well as cardiovascular diseases in clinical practice.

Supplemental Material

R_Supplementaries – Supplemental material for Home blood pressure is associated with cognitive impairment among elderly patients with type 2 diabetes: KAMOGAWA-HBP study

Supplemental material, R_Supplementaries for Home blood pressure is associated with cognitive impairment among elderly patients with type 2 diabetes: KAMOGAWA-HBP study by Keiko Iwai, Emi Ushigome, Shinobu Matsumoto, Nobuko Kitagawa, Hidetaka Ushigome, Isao Yokota, Mai Asano, Masahide Hamaguchi, Masahiro Yamazaki and Michiaki Fukui in Diabetes & Vascular Disease Research

Footnotes

Acknowledgements

The authors acknowledge Naoko Higo R.N., Machiko Hasegawa R.N. and Terumi Kaneko R.N. of the Kyoto Prefectural University of Medicine for teaching patients how to measure their blood pressure and also Sayoko Horibe of the Kyoto Prefectural University of Medicine for their secretarial assistance.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: K.I., E.U., S.M., N.K., M.A., M.H., M.Y. and M.F. have received grants, honoraria and research supports from AstraZeneca plc., Astellas Pharma Inc., Nippon Boehringer Ingelheim Co., Ltd., Daiichi Sankyo Co., Ltd., Eli Lilly Japan K.K., Kyowa Hakko Kirin Company Ltd., Kissei Pharmaceutical Co., Ltd., MSD K.K., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho Co., Ltd., Sanofi K.K., Ono Pharmaceutical Co., Ltd. and Takeda Pharmaceutical Co., Ltd. The sponsors were not involved in the study design; in the collection, analysis and interpretation of data; in the writing of this manuscript; or in the decision to submit the article for publication. The authors, their immediate families and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article. The authors declare that although they are affiliated with a department that is supported financially by pharmaceutical company, the authors received no current funding for this study and this does not alter their adherence to all the journal policies on sharing data and materials. The other authors have nothing to disclose.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Japanese Study Group for Physiology and Management of Blood Pressure and the Astellas Foundation for Research on Metabolic Disorders (grant number 4024).

Supplemental material

Supplemental material for this article is available online.

ORCID iD

Keiko Iwai

References

Allen

Frier

Strachan

MW.

The relationship between type 2 diabetes and cognitive dysfunction: longitudinal studies and their methodological limitations. Eur J Pharmacol 2004; 490: 169–175.

Stewart

Liolitsa

Type 2 diabetes mellitus, cognitive impairment and dementia. Diabet Med 1999; 16: 93–112.

Iimura

Insulin resistance and hypertension in Japanese. Hypertens Res 1996; 19: S1–S8.

Tomiyama

Horio

Yoshii

, et al. Masked hypertension and target organ damage in treated hypertensive patients. Am J Hypertens 2006; 19: 880–886.

Bobrie

Chatellier

Genes

, et al. Cardiovascular prognosis of ‘masked hypertension’ detected by blood pressure self-measurement in elderly treated hypertensive patients. JAMA 2004; 291: 1342–1349.

Jönsson

Rydberg

Sterner

, et al. Pharmacokinetics of glibenclamide and its metabolites in diabetic patients with impaired renal function. Eur J Clin Pharmacol 1998; 53: 429–435.

Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 2003; 26: S5–S20.

Coleman

Freeman

Steel

, et al. Validation of the Omron 705IT (HEM-759-E) oscillometric blood pressure monitoring device according to the British Hypertension Society protocol. Blood Press Monit 2006; 11: 27–32.

Imai

Otsuka

Kawano

, et al; Japanese Society of Hypertension. Japanese society of hypertension (JSH) guidelines for self-monitoring of blood pressure at home. Hypertens Res 2003; 26: 771–782.

10.

Sinclair

Gadsby

Hillson

, et al. Brief report: use of the Mini-Cog as a screening tool for cognitive impairment in diabetes in primary care. Diabetes Res Clin Pract 2013; 100: e23–e25.

11.

Seitz

Chan

Newton

, et al. Mini-Cog for the diagnosis of Alzheimer’s disease dementia and other dementias within a primary care setting. Cochrane Database Syst Rev 2018; 2: CD011415.

12.

Borson

Scanlan

Watanabe

, et al. Simplifying detection of cognitive impairment: comparison of the Mini-Cog and Mini-Mental State Examination in a multiethnic sample. J Am Geriatr Soc 2005; 53: 871–874.

13.

Yasuda

Sanada

Kitada

, et al. Rationale and usefulness of newly devised abbreviated diagnostic criteria and staging for diabetic polyneuropathy. Diabetes Res Clin Pract 2007; 77: S178–S183.

14.

Jiang

, et al. The association between the prevalence, treatment and control of hypertension and the risk of mild cognitive impairment in an elderly urban population in China. Hypertens Res 2016; 39: 367–375.

15.

Yeung

Loken Thornton

‘Do it-yourself’: home blood pressure as a predictor of traditional and everyday cognition in older adults. PLoS ONE 2017; 12: e0177424.

16.

Imai

Kario

Shimada

, et al; Japanese Society of Hypertension Committee for Guidelines for Self-monitoring of Blood Pressure at Home. The Japanese Society of Hypertension guidelines for self-monitoring of blood pressure at home (second edition). Hypertens Res 2012; 35: 777–795.

17.

Oishi

Ohara

Sakata

, et al. Day-to-day blood pressure variability and risk of dementia in a general Japanese elderly population: the Hisayama study. Circulation 2017; 136: 516–525.

18.

Zhang

Agnoletti

Blacher

, et al. Blood pressure variability in relation to autonomic nervous system dysregulation: the X-CELLENT study. Hypertens Res 2012; 35: 399–403.

19.

Matsumoto

Satoh

Kikuya

, et al. Day-to-day variability in home blood pressure is associated with cognitive decline: the Ohasama study. Hypertension 2014; 63: 1333–1338.

20.

Ribeiro

Geraldes

Rodrigues

, et al. 24-hour arterial pressure study and diabetic neuropathy and angiopathy of the lower limbs. Acta Med Port 1997; 10: 33–38.

21.

Asayama

Ohkubo

Hara

, et al. Repeated evening home blood pressure measurement improves prognostic significance for stroke: a 12-yearfollow-up of the Ohasama study. Blood Press Monit 2009; 14: 93–98.

22.

Yano

Inokuchi

Hoshide

, et al. Association of poor physical function and cognitive dysfunction with high nocturnal blood pressure level in treated elderly hypertensive patients. Am J Hypertens 2011; 24: 285–291.

23.

Zhu

Yin

, et al. A U-shaped association between blood pressure and cognitive impairment in Chinese elderly. J Am Med Dir Assoc 2017; 18: 193.e7–193.e13.

24.

Ishikura

Obara

Kato

, et al. Associations between day-by-day variability in blood pressure measured at home and antihypertensive drugs: the J-HOME-Morning study. Clin Exp Hypertens 2012; 34: 297–304.

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