High pulse pressure is a risk factor for prodromal Alzheimer’s disease: a longitudinal study

Results

Demographic and clinical data

There were 669 people with high PP and 706 with normal PP in the ADNI. Comparisons of characteristics between groups were presented in Table 1. Participants in the high PP group were more likely to be older (P < .001) and hypertension (P < .001) than those with normal PP. But there was no group difference in gender, education, APOE Ɛ4 carrier status, and other vascular risk factors (all P > .050). Clinical profiles of patients changed drastically due to data availability issues, the new selected sub-datasets were presented in Supplementary Table 1.

Table 1. Participant demographic and clinical information.

Participant features	Normal PP (<60mmHg)	High PP (≥60mmHg)	P Value
N	706	669
Age(Mean ± SD, year)	72.31±6.91	74.73±6.86	<0.001
Gender (M/F)	396/310	366/303	0.606
Education (Mean ± SD, year)	16.19±2.76	16±2.83	0.214
APOE Ɛ4 carrier status (2/1/0)	59/248/399	47/226/396	0.500
BMI (Mean ± SD, kg/m2)	27.1±4.84	26.94±4.78	0.373
CVD(yes/no)	156/550	154/515	0.682
Hyperlipemia (yes/no)	329/377	320/349	0.647
Hypertension (yes/no)	296/410	347/322	<0.001
T2DM (yes/no)	44/662	60/609	0.055
Cognitive diagnoses
CN/MCI	255/451	259/410	0.320
Abbreviations: Normal PP=normal pulse pressure; High PP=high pulse pressure; SD=standard deviation; APOE, apolipoprotein epsilon; BMI, Body Mass Index; CVD, Cardiovascular Disease; T2DM, Type 2 diabetes mellitus; CN, cognitively normal; MCI, mild cognitive impairment.

Pulse pressure and CSF biomarkers

CSF measurements were available for 977 non-demented participants at baseline (n = 364 CN, 613 MCI), of whom 517 had normal PP. In cross-sectional analyses, after excluding extreme outlines, high PP was associated with a decrease in Aβ₄₂ (β = -.525, P = .015, Figure 1A), as well as increases in T-tau (β = .077, P = .011, Figure 1B), P-tau (β = .097, P = .003, Figure 1C), T-tau/Aβ₄₂ (β = .131, P = .004, Figure 1D) and P-tau/Aβ₄₂ (β = .157, P = .001, Figure 1E) after adjustment for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors, cognitive diagnosis and extracted CSF volume. When stratified by cognitive diagnosis, the associations between PP and CSF biomarkers still persisted within MCI group and high PP was associated with higher P-tau and P-tau/Aβ₄₂ levels in CN group (Figure 1C and 1E); when stratified by age, the associations between PP and CSF biomarkers still persisted within very old group and PP elevation was associated with increased T-tau/Aβ₄₂ in young old group (Supplementary Table 2).

Figure 1. Association between pulse pressure (PP) and AD biomarkers at baseline. (A) PP is negatively correlated with CSF Aβ₄₂ within non-dementia and MCI groups; (B) PP is positively correlated with CSF T-tau within non-dementia and MCI groups; (C) PP is positively correlated with P-tau in all diagnostic groups; (D) PP is positively correlated with CSF T-tau/ Aβ₄₂ within non-dementia and MCI groups; (E) PP is positively correlated with CSF P-tau/Aβ₄₂ in all diagnostic groups; (F) PP was positively correlated with cortical Aβ load in summary SUVR within non-dementia and MCI groups. Abbreviations: AD, Alzheimer’s disease; CSF, cerebrospinal fluid; CN, cognitively normal; MCI, mild cognitive impairment; Aβ, β-amyloid; SUVR, standardized uptake value ratio. *p<.05; **p<.01; *** p<.001.

There were 526 people who had at least one follow-up visit at baseline enrolled in the five-year longitudinal analysis. Longitudinally, we did not find any association between baseline PP and CSF biomarkers. Similarly, no associations were detected when the analyses were stratified by cognitive diagnosis (Supplementary Table 2), while baseline high PP was associated with lower T-tau/Aβ₄₂ in the young old subgroup when stratified by age (Supplementary Table 2).

Pulse pressure and AV45 PET imaging

The mean Aβ load measured by the florbetapir AV45 standardized uptake value ratio (SUVR) was available in 739 participants at baseline (n = 280 CN, 459 MCI), of whom 358 had high PP. In cross-sectional analyses, after excluding extreme outlines, we found that PP was positively correlated with cortical Aβ load in summary SUVR (β = .018, P=.011, Figure 1F) when adjusted for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors, cognitive diagnosis and florbetapir mean of composite ref region. When stratified by cognitive diagnosis and age, the association between PP and cortical Aβ load remained significant within MCI (β = .034, P = .003, Figure 1F) and the young old group (Supplementary Table 2).

There were 550 people who had at least one follow-up visit at baseline enrolled in the five-year longitudinal analysis. Longitudinally, we did not identify a statistically significant association between PP and summary SUVR (β = 1.457*e^-3, P = .144). Similarly, no associations were detected when the analyses were stratified by cognitive diagnosis or age (Supplementary Table 2).

Pulse pressure and MRI measurements

Measurements of hippocampal, entorhinal, and mid-temporal volumes were available in 1,137 participants at baseline (n = 694 MCI), of whom 554 had high PP. In cross-sectional analyses, after excluding extreme outlines, increased PP was not associated with hippocampal volume (β = -1.084*e³, P = .312), entorhinal volume (β = -2.311*e¹, P = .554), or mid-temporal volume (β = 4.364*e¹, P = .752) when adjusted for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors, cognitive diagnosis and intracranial volume. Similarly, no associations were detected between PP and MRI structure when the analyses were stratified by cognitive diagnosis and age (Supplementary Table 3).

There were 1,042 who had at least one follow-up visit at baseline enrolled in the five-year longitudinal analysis. Longitudinally, increased PP was associated with an accelerated decline in hippocampal volume (β = -16.903, P = .039, Figure 2A) and entorhinal volume (β = -20.014, P = .031, Figure 2B) over time. When stratified by cognitive diagnosis and age, the association between PP and entorhinal volume still persisted among those with MCI and the young old subgroup (Supplementary Figure 1A and Table 3).

Figure 2. Associations between baseline pulse pressure and measurements of brain aging. Data from linear mixed-effects models adjusted for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors, cognitive diagnosis, as well as intracranial volume. Increased PP level was associated with an accelerated decline in measurements of brain aging. (A–C) Increased PP level was associated with accelerated decline in hippocampal volume, entorhinal volume and episodic memory performance.

Pulse pressure and ADNI-MEM & ADNI-EF

ADNI-MEM and ADNI-EF were available in 1,375 (n = 514 CN, 861 MCI) participants at baseline, of whom 706 had normal PP. In cross-sectional analyses, no association of PP was found with memory performance (β = −.030, P = .343) or executive function performance (β = −.042, P = .298). Similarly, no association was detected when the analyses were stratified by cognitive diagnosis while high PP was associated with worse memory performance in young old group when stratified by age (Supplementary Table 3).

There were 1322 who had at least one follow-up visit at baseline enrolled in the five-year longitudinal analysis. Longitudinally, increased PP was associated with worse memory performance with a strong tendency towards statistical significance (β = -1.829*e^-2, P = .058, Figure 2C). In stratified analyses restricted to MCI participants, increased PP was associated with a greater decline in memory performance over time (β = −.034, P = .012, Supplementary Figure 1B and Table 3). We did not find any statistically significant associations in other stratified analyses (Supplementary Table 3).

Pulse pressure and clinical disease progression

Kaplan-Meier analysis revealed participants with high PP at baseline showed more rapid progression over the following five years, compared with those with normal PP (P <.001, Figure 3). In Cox regression models (adjusted for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors and cognitive diagnosis), the individuals with high PP had a higher risk of progression to AD (hazard ratio 1.216, 95% CI 1.051-1.461, P = .011).

Figure 3. Pulse pressure predicts more rapid progression to dementia. The Cox regression indicates that a higher pulse pressure was associated with more rapid progression to dementia. Visually, the survival plot displays results for high pulse pressure in comparison with normal pulse pressure.

Systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension

SBP exhibited the same pattern of associations with CSF biomarkers found in the PP analyses. Higher SBP was associated with a decrease in Aβ₄₂, as well as increases in T-tau, P-tau, T-tau/Aβ₄₂ and P-tau/Aβ₄₂ (Supplementary Table 4); in AV45 PET imaging analyses, SBP was positively correlated with cortical Aβ load in summary SUVR, and the association still persisted within MCI and the young old subgroup (Supplementary Table 4); in MRI measurements analyses, we only found that higher SBP was associated with smaller mid-temporal volume within MCI (Supplementary Table 4); in ADNI-MEM and ADNI-EF analyses, higher SBP was associated with worse memory performance and executive function performance in the young old subgroup (Supplementary Table 4).

There was no association between DBP and CSF biomarkers or AV45 PET imaging analyses (Supplementary Table 5); in MRI measurements analyses, DBP was negatively correlated with mid-temporal volume and it still persisted within MCI subgroup (Supplementary Table 5); in ADNI-MEM and ADNI-EF analyses, higher DBP was associated with worse memory performance, and when stratified by age, the association still persisted within very old subgroup while higher DBP was associated with worse executive function performance in MCI subgroup when stratified by cognitive diagnosis (Supplementary Table 5).

Hypertension exhibited the same pattern of associations with CSF biomarkers and AV45 PET imaging found in the DBP analyses (Supplementary Table 6); in MRI measurements analyses, we found hypertension was negatively correlated with hippocampal volume, entorhinal volume, and mid-temporal volume in the young old subgroup (Supplementary Table 6); in ADNI-MEM and ADNI-EF analyses, hypertension was associated with worse executive function performance, and when stratified by age and cognitive diagnosis, the association still persisted within MCI, the young old and very old subgroups while association between hypertension and memory performance was found in the young old subgroup (Supplementary Table 6).

Materials and Methods

ADNI

The data used for this analysis were downloaded from the ADNI database (adni.loni.usc.edu). The ADNI, an ongoing, multisite longitudinal, large-scale study launched in 2003, was designed to develop clinical, imaging, genetic and biochemical biomarkers for the early detection and tracking of AD [41]. Participants in the ADNI study underwent baseline and periodic physical and neurological examinations, standardized neuropsychological assessments, and biological sampling (blood, urine, and CSF) [13]. Regional ethical committees of all participating institutions approved the ADNI. All study participants provided written informed consent.

Participants

A total of 2,046 participants from ADNI1, ADNI Grand Opportunity, and ADNI 2 completed the blood pressure assessment at baseline. Among them, 335 participants without demographic and clinical information, and 336 who were classified as dementia were excluded. Finally, the remaining 1,375 non-demented participants were enrolled in this study (Figure 4). Clinical disease progression was ascertained for a large subset of participants (n = 1,338) who were followed up with serial clinical assessments at varying intervals for different length of time ranging from 0 year to 5 years.

Figure 4. Flow diagram of participant selection. Abbreviations: CSF, cerebrospinal fluid; MRI, magnetic resonance imaging.

PP measurements

Seated brachial artery SBP and DBP were obtained and PP was calculated as the difference between SBP and DBP. PP of 60 mmHg or higher was defined as high PP [42].

Covariates

Accumulating evidence supports a role of vascular risk factors in the development and etiology of AD [43]. For purposes of this study, participant medical history data of vascular risk factors was obtained until the date of baseline blood pressure. The covariates consisted of age, gender, education, APOE Ɛ4 carrier status, body mass index (BMI) which was calculated as weight in kilograms divided by the square of height in meters, and vascular risk factors, such as type 2 diabetes mellitus (T2DM), hyperlipemia, hypertension, as well as medical history of cardiovascular disease (CVD) including myocardial infarction, angina, heart failure and atrial fibrillation. We classified these diseases based on the medical history information and/or use of anti-medications.

CSF biomarker measurements

The CSF collection and procedural protocols have been described previously [44]. All participants underwent lumbar puncture which was performed with a 20- or 24-gauge spinal needle as described in the ADNI procedures manual (http://www.adni-info.org/) and AD biomarkers including Aβ_1-42, P-tau, and T-tau were measured using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) with Innogenetics (INNO-BIA AlzBio3; Ghent, Belgium; for research use–only reagents) immunoassay kit–based reagents. All tests were administered at baseline and at 12, 24, 36, 48 and 60 months.

18F florbetapir AV45 PET imaging

Preprocessed florbetapir imaging data were downloaded from the LONI ADNI site (http://adni.loni.usc.edu). The data preprocessing is accessible online (adni. loni. ucla.edu/about-data-samples/image-data/). For quantifying cerebral cortical Aβ, preprocessed florbetapir image data and co-registered structural MRI were analyzed using Freesurfer (version 4.5.0) (surfer.nmr. mgh.harvard.edu/) as described previously and online (adni.loni.ucla.edu/research/pet-post-processing/). Briefly, image data were acquired in four 5-min frames 50–70 minutes after injection of approximately 10mCi of 18F florbetapir, the four frames were co-registered to one another, averaged, interpolated to a uniform image and voxel size (160×106×96, 1.5 mm³), and smoothed to a uniform resolution (8 mm FWHM) to account for differences between scanners [45]. The mean Aβ retention, measured by the florbetapir AV45 SUVR, was normalized to the whole cerebellum as a summary measure of florbetapir retention for each participant in cross-sectional analyses; and a composite reference region, which was made up of whole cerebellum, brainstem/pons, and eroded subcortical white matter, has been evaluated for longitudinal analyses. All tests were administered at baseline and at 12, 24, 36, 48 and 60 months.

Brain structure

The process for MRI acquisition has been described elsewhere in ADNI publications [2, 46–48]. Structural brain images were acquired using 1.5T or 3T MRI systems with T1-weighted scans using a sagittal volumetric magnetization-prepared rapid acquisition gradient echo sequence. The ADNI project offers scans that have been preprocessed (gradient warping, scaling, B1 correction, and N3 inhomogeneity correction) to correct for different scanners across sites [49]. All tests were administered at baseline and at 3, 6, 12, 18, 24, 36, 48 and 60 months.

Neuropsychological composites

The ADNI neuropsychological protocol, including calculation of ADNI-MEM and ADNI-EF composite measures, has been reported previously [50, 51]. The ADNI-MEM included a composite z score based on item-level data from the Rey Auditory Verbal Learning Test, the Mini-Mental State Examination (MMSE), the AD Assessment Scale Cognitive Test, and Logical Memory I and II. The ADNI-EF included item-level data from the Trail Making Test Parts A and B, Digit Span Backward, Digit Symbol, Animal Fluency, Vegetable Fluency, and Clock Drawing Test [52]. All tests were administered at baseline and at 6, 12, 18, 24, 36, 48 and 60 months.

Clinical disease progression

CN and MCI participants were divided into group of clinical disease progression and stable, respectively. Participants were defined as having clinical disease progression if their global CDR/MMSE or clinical classification score changed (CN subjects converted to MCI or AD, or their global CDR scores rose to 0.5 or greater; MCI subjects lost more than 3 points between first and last MMSE, or converted to AD at follow-up, or got a score less than 24 on the last MMSE) [53–55]. If the above criteria have not been met at follow-up, participants were considered stable; regardless of the lost of subjects, once the progression criteria have been met during 5-year follow-up, they were deemed progressive.

Statistical analyses

Baseline demographic, clinical and diagnostic characteristics were compared between PP groups using Mann-Whitney U test for continuous variables and χ2 analyses for categorical variables, respectively. We used means and standard errors for continuous measures and proportions for categories. Multiple linear regression was used to explore the association between PP and biomarkers in cross-sectional analyses after adjusting for age, gender, education, APOE Ɛ4 carrier status, vascular risk factors and cognitive diagnosis at baseline. Before regression analyses, participants who had a value >3 or <3 SD from the mean value were regarded as extreme outlines and excluded. In case of skewed distribution (Shapiro-Wilk test > 0.05) of biomarker data, transformation was performed to approximate a normal distribution via “car” package of R software. Interaction terms for age were used to explore whether strata effect existed, in order to minimize the difference between subgroup sample sizes, we chose 75 years old as the cutoff value (<75 years old vs. ≥ 75 years old). In case of any potential interactions (P < 0.1), subgroup analyses were further performed. Mixed-model regression with time modeled as years from baseline for each participant was used to explore the longitudinal influences of PP at baseline on AD biomarkers and cognition after adjusting with age, gender, education, APOE Ɛ4 carrier status, vascular risk factors, cognitive diagnosis. The time-by-exposure interaction terms tested whether PP were associated with changes in the given outcomes (CSF biomarkers, AV45 PET imaging biomarkers, MRI structure, as well as ADNI-MEM and ADNI-EF) over the follow-up period. Kaplan-Meier survival analysis investigated the relationship between baseline PP and clinical disease progression using years to cognitive decline as the time variable. Cox proportional hazards models (adjusted for age, gender, education, APOE Ɛ4 carrier status, diagnosis, and vascular risk factors) were used to test the predictive ability of baseline PP for clinical disease progression. All tests were two-tailed. Statistical significance was set at P < .05. R version 3.5.1 and GraphPad Prism 7.00 software were used for statistical analyses and figure preparation.

Although PP was the focus of the study, primary cross-sectional analyses which were identical to those used in PP were repeated to examine SBP (< 120mmHg, ≥ 120 and < 140mmHg, ≥ 140mmHg), DBP (< 80mmHg, ≥ 80 and < 90mmHg, ≥ 90mmHg) [56], and hypertension (based on the medical history information and/or use of anti-medications) in relation to biomarkers to determine their contributions to the PP findings. We did this because it was highly correlated with SBP and disambiguated the relative contributions of systolic and diastolic pressure to results, which may have provided mechanistic insight.

Author Contributions

JTY, LT and YY conceptualized the study, analyzed and interpreted the data, and revised the manuscript. WYS analyzed and interpreted the data, drafted and revised the manuscript, and prepared the figures. ZTW and FRS did the statistical analysis, and prepared the figures. YHM, WX and XNS interpreted the data and revised the manuscript. QD and YY drafted and revised the manuscript. LT designed and conceptualized the study, and revised the manuscript. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Conflicts of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported by grants from the National Natural Science Foundation of China (91849126, 81571245, and 81771148), the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Editorial Note

These corresponding authors have a verified history of publications using their personal email addresses for correspondence.

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