Abstract
Background
The association between subclinical cardiovascular disease (CVD) and cognitive decline in hypertensive adults and the underlying brain pathologies remain unclear. It is also undetermined whether intensifying blood pressure (BP) treatment slows down cognitive decline associated with subclinical CVD.
Methods
We conducted a post hoc analysis of the Systolic Blood Pressure Intervention Trial. Subclinical CVD at baseline was identified by elevated levels of high-sensitivity cardiac troponin T (hs-cTnT ≥ 14 ng/L) and N-terminal pro-B-type natriuretic peptide (NT-proBNP ≥ 125 pg/mL). Global cognitive function and domain-specific measures (memory, processing speed, language, and executive function) were assessed at baseline and follow-up (years 2, 4, and 6) in 2 733 participants. White matter lesions, cerebral blood flow, and brain tissue volume were assessed by MRI at baseline and year 4 in a subset of 639 participants.
Results
Both elevated hs-cTnT and NT-proBNP levels at baseline were associated with accelerated cognitive decline across all domains after adjusting for potential confounding factors. The group with elevated levels of both cardiac biomarkers showed the fastest decline, with a larger annual decline rate of 0.033 (95% CI: 0.024–0.041) in the z-score of global cognitive function compared with the group with normal levels. Elevated levels of both biomarkers were also associated with a faster progression in white matter lesions, but not with changes in total brain tissue volume or cerebral blood flow. Intensive BP treatment did not attenuate these associations compared with standard treatment.
Conclusions
Subclinical CVD may contribute to faster white matter lesion progression and accelerated cognitive decline in patients with hypertension, regardless of intensive BP treatment.
Keywords: Blood pressure treatment, Brain pathological changes, Cognitive decline, Hypertension, Subclinical cardiovascular disease
Hypertension, affecting more than 60% of older adults (1), is a key modifiable risk factor contributing to subclinical and clinical cardiovascular disease (CVD) as well as faster cognitive decline and dementia (2,3). Subclinical CVD, assessed by abnormal cardiac biomarkers, is more prevalent in older adults with hypertension and was associated with subsequent cerebral small vessel disease (4), cognitive impairment (5,6), and dementia (7). Several cohort studies have linked elevated high-sensitivity cardiac troponin T (hs-cTnT, a marker of myocardial injury) and N-terminal pro-B-type natriuretic peptide (NT-proBNP, a marker of neurohormonal stress) to cognitive decline in the general population (8–13). However, the extent to which cardiac biomarkers could serve as modifiable risk factors for cognitive decline in treated hypertensive adults remains unknown. Addressing this question is crucial for preventing cognitive decline and dementia in the high-risk population with hypertension.
Moreover, the mechanisms by which subclinical cardiac injury leads to cognitive decline is not fully understood. Subclinical cardiac impairment could potentially lead to cerebral hypoperfusion, ischemic brain lesions, and brain atrophy, thereby increasing the risk of cognitive decline and dementia (14–17). Several cross-sectional studies have linked cardiac biomarkers to subclinical brain changes, such as ischemic lesions and atrophy, and cognitive impairment (9,18–20). However, longitudinal data on the association of cardiac biomarkers with subsequent changes in cognitive function and brain vascular pathologies, essential for assessing the temporal relationship, are limited.
Additionally, the Systolic Blood Pressure Intervention Trial (SPRINT) has demonstrated the benefits of intensive blood pressure (BP) treatment in preventing mild cognitive impairment (21). Intensive BP treatment has been shown to modify the associations of baseline cardiac biomarkers with risk of serious adverse events (22), heart failure, and mortality in the SPRINT trial (23). However, whether intensive BP treatment modifies the association of cardiac biomarkers with cognitive decline and brain vascular pathologies is yet to be determined. To elucidate the epidemiologic and mechanistic connections between cardiac biomarkers and cognitive decline in hypertensive adults, and their potential modification by intensive BP treatment, we investigated the associations of baseline hs-cTnT and NT-proBNP levels with subsequent changes in neuroimaging markers of brain vascular pathologies and cognitive decline among SPRINT participants.
Method
Data Source
We obtained data from the SPRINT trial through an approved application at the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center (https://biolincc.nhlbi.nih.gov/home/).
Study Design and Population
SPRINT was an NIH-funded, multicenter randomized clinical trial conducted among 9 361 participants in the United States and Puerto Rico. The design and protocol of SPRINT have been published previously (24,25). Briefly, participants were invited to participate if aged 50 years or older with a screening systolic blood pressure (SBP) between 130 and 180 mmHg, and had increased cardiovascular risk (25). Participants with diabetes or a history of stroke were excluded. Participants were then randomized to either intensive SBP lowering (SBP target < 120 mmHg) or standard SBP lowering (SBP target < 140 mmHg), with individual patient management at the discretion of the trial investigators.
SPRINT-MIND, a preplanned substudy of SPRINT trial (21,26,27), included global and domain-specific cognitive and neuroimaging assessments in a subset of SPRINT participants at baseline and during follow-up. Specifically, in the cognitive substudy, a subset of 2 921 participants from both treatment groups was selected (with 2 897 completing the assessment) for a comprehensive cognitive battery administered at baseline and years 2, 4, and 6 of follow-up to assess cognitive function across multiple domains (memory, processing speed, language, and executive function) (27). Among these participants, 673 participants further underwent brain MRI scans in a substudy. Among participants in these 2 substudies, 2 733 (94%) and 639 (95%) participants had both hs-cTnT and NT-proBNP measurement at baseline, respectively, and were included in the current study (see selection flowchart in Supplementary Figure S1).
The trial protocol was approved by the institutional review board at each participating site. All participants provided written informed consent in this study.
Cardiac Biomarker Measurement
The methods of laboratory assays of cardiac biomarkers in SPRINT had been reported previously (23). In brief, serum samples were obtained at the time of study entry and stored at −80 °C until biomarker measurements. Both hs-cTnT and NT-proBNP were measured from freshly thawed serum samples using an electrochemiluminescence immunoassay on the Roche CBAS 6000 platform (Roche Diagnostics, Indianapolis, IN, USA) (23). The hs-cTnT assay (fifth Generation) has an imprecision of 3.4% at 28.3 ng/L and 2.3% at 2076 ng/L, with a lower limit of quantitation of 6 ng/L. The NT-proBNP assay has an imprecision of 2.9% at 140.3 pg/mL and 2.7% at 4563 pg/mL, with a lower limit of detection of 5 pg/mL.
Cognitive Assessment
Cognitive assessment in SPRINT-MIND had been described previously (27). Details of each test were described in the Supplementary Material (Supplementary Method S1). Briefly, for all participants, in-person cognitive screening assessments at baseline and during follow-up included global cognitive function assessment with the Montreal Cognitive Assessment (MoCA); learning and memory with the Logical Memory form I (LM I) and II (LM II) subtests of the Wechsler Memory scale; and processing speed with the Digit Symbol Coding (DSC) test of the Wechsler Adult Intelligence scale. An extended cognitive assessment battery included the Hopkins Verbal Learning Test-Revised (HVLT-R), the Trail Making Test (Parts A and B), the Digit Span Test (DST), the Boston Naming Test (BNT-15), the Modified Rey-Osterrieth Complex Figure (mROCF), and Category Fluence-Animals (CF-A) among a subset of randomly selected participants (n = 2 921). Five composite scores, including global cognitive scores and scores in domains of memory, processing speed, language, and executive function, were derived by averaging the standardized score of component tests and further standardization (Supplementary Method S1). The primary cognitive outcome in this study was the annual change in global cognitive function. The secondary cognitive outcomes were cognitive decline in the following domains: memory, processing speed, language, and executive function.
Brain MRI Assessment
Brain MRIs were obtained at baseline and 4 years in 639 participants after randomization at 7 MRI sites using 3T scanners. Each participant was scanned with the same scanner at different time points. Fluid-attenuated inversion recovery T2-weighted MRI was used to measure white matter lesion volumes, T1-weighted structural MRI for brain volume and segment regions of interest, and arterial spin labeling (ASL) was used to measure cerebral blood flow (26,28,29). The primary neuroimaging outcomes for this analysis were changes in total white matter lesions, total brain volume, and total cerebral blood flow in this study. The secondary outcomes were changes in regional white matter lesions, white matter, and gray matter volumes, and cerebral blood flow in white matter and gray matter.
Statistical Analysis
Biomarkers were modeled as continuous, log-linear predictors, and as categorical variables using clinical cutoff points of hs-cTnT ≥ 14 ng/L and NT-proBNP ≥ 125 pg/mL (23). Biomarkers were evaluated individually and in combination according to the following 3 groups: (a) nonelevated (hs-cTnT < 14 ng/L and NT-proBNP < 125 pg/mL); (b) one biomarker elevated (either hs-cTnT ≥ 14 ng/L or NT-proBNP ≥ 125 pg/mL); and (c) both biomarkers elevated (hs-cTnT ≥ 14 ng/L and NT-proBNP ≥ 125 pg/mL).
We used linear mixed models to estimate the associations of baseline cardiac biomarkers with subsequent changes in cognitive function and neuroimaging markers. For outcomes of cognitive function, models included random effects for participant and clinic site and were adjusted for age (continuous), sex, race/ethnicity, treatment assignment, education level, smoking status, history of depression, history of CVD, baseline SBP (continuous), glucose level (continuous), body mass index (BMI; continuous), and chronic kidney disease (CKD) status (baseline eGFR < 60 mL/min/1.73m2). For outcomes of neuroimaging markers, models additionally included random effects for participant and MRI facility and adjustment for intracranial volume. We estimated the effect of annual changes in outcomes by incorporating the interaction term between time scale (years since randomization) and cardiac biomarker category (years × biomarker category) in models. To examine the potential effect modification of intensive SBP treatment on the associations of cardiac biomarkers with cognitive and neuroimaging outcomes, we stratified the analyses by treatment assignment and included the interaction term (treatment × years × biomarker category) to assess the effect modification by treatment groups. In this study, more than 90% of participants had completed cognitive function assessments at least twice, and missing data on continuous outcomes were handled using linear mixed models (30).
In secondary analyses, we examined the associations stratified by baseline age (≥ 75 vs < 75 years) and sex. To validate the results, we also analyzed the associations of baseline cardiac biomarkers and cognitive decline in all SPRINT participants who had available cardiac biomarkers measurements at baseline and at least one cognitive assessment (MoCA, LM I, LM II, or DSC) from baseline to follow-up (N = 8816). To assess the robustness of our findings against potential confounding by comorbidity, we further performed sensitivity analyses in which we excluded participants with prevalent atrial fibrillation, CVD, or CKD at baseline in separate models.
Data analyses were performed with R software (Version 4.3.1; R Core Team; R Institute for Statistical Computing, Vienna, Austria). A 2-sided p value lower than .05 was considered statistically significant.
Results
Study Population and Baseline Characteristics
Baseline characteristics of study participants are shown in Table 1. The mean age was 68.5 (SD, 8.6) years; 38% were women, and 9% were Hispanic. The baseline demographics of the current study participants were similar and comparable to the overall SPRINT population. (Supplementary Table S1).
Table 1.
Baseline Characteristics of SPRINT Domain-Specific Cognitive Function Substudy and MRI Substudy Participants
| Characteristic | Cognitive Domain Substudy (n = 2733) | MRI Substudy (n = 639) |
|---|---|---|
| Age, mean (SD), y | 68.5 (8.6) | 67.5 (8.2) |
| Age ≥ 75 y | 741 (27.11%) | 146 (22.85%) |
| Female | 1045 (38.24%) | 263 (41.16%) |
| Race/ethnicity | ||
| Non-Hispanic White | 1647 (60.26%) | 402 (62.91%) |
| Non-Hispanic Black | 795 (29.09%) | 195 (30.52%) |
| Hispanic | 234 (8.56%) | 33 (5.16%) |
| Othera | 57 (2.09%) | 9 (1.41%) |
| Intensive blood pressure control group | 1362 (49.84%) | 340 (53.21%) |
| Baseline systolic blood pressure, mean (SD), mm Hg | 138.9 (16.1) | 138.3 (16.8) |
| Baseline diastolic blood pressure, mean (SD), mmHg | 77.3 (11.7) | 78.0 (11.5) |
| Education (less than college degree) | 1603 (58.65%) | 367 (57.43%) |
| BMI, mean (SD), kg/m2 | 29.81 (5.66) | 29.79 (5.34) |
| Smoking status | ||
| Never | 1213/2730 (44.43%) | 290 (45.38%) |
| Former | 1192/2730 (43.66%) | 270 (42.25%) |
| Current | 325/2730 (11.90%) | 79 (12.36%) |
| History of cardiovascular disease | 544 (19.90%) | 86 (13.46%) |
| History of depression | 495/2729 (18.14%) | 120 (18.78%) |
| Atrial fibrillation | 42/2722 (1.54%) | 11/637 (1.73%) |
| Estimated GFR, mean (SD), mL/min/1.73 m2 | 70.59 (20.76) | 72.17 (20.38) |
| Estimated GFR < 60 mL/min/1.73 m2 | 846/2730 (30.99%) | 177/638 (27.74%) |
| Fasting plasma glucose, mean (SD), mg/dL | 99.33 (13.91) | 98.64 (13.33) |
| Montreal cognitive assessment, median (IQR)b | 23.0 (21.0, 26.0) | 24.0 (21.0, 26.0) |
| Cardiac biomarker | ||
| hs-cTnT, median (IQR), ng/L | 9.5 (6.6, 14.2) | 8.8 (3.0, 12.8) |
| hs-cTnT ≥ 14 ng/L | 711 (26.02%) | 126 (19.72%) |
| NT-proBNP, median (IQR), pg/mL | 90.0 (39.0, 195.0) | 80.0 (32.0, 171.5) |
| NT-proBNP ≥ 125 pg/mL | 1064 (38.93%) | 224 (35.05%) |
| Combining groupsc | ||
| Both not elevated | 1393 (50.97%) | 363 (56.81%) |
| One elevated | 905 (33.11%) | 202 (31.61%) |
| Both elevated | 435 (15.92%) | 74 (11.58%) |
Notes: BMI = body mass index (calculated as weight in kilograms divided by height in meters squared); BP = blood pressure; GFR = glomerular filtration rate; hs-cTnT = high-sensitivity cardiac troponin T; IQR = interquartile range; NT-proBNP = N-terminal pro-B-type natriuretic peptide; SPRINT = systolic blood pressure intervention trial.
aMixed race, American Indian, Asian, Hawaiian, and other unspecified.
bScores range from 0 to 30, with higher scores denoting better cognitive function.
cBoth elevated biomarker levels defined as hs-cTnT ≥ 14 ng/L and NT-proBNP ≥ 125 pg/mL.
Cardiac Biomarkers and Changes in Cognitive Function
Figure 1 shows the associations of cardiac biomarkers with global and domain-specific cognitive decline. Compared with participants who had normal hs-cTnT and NT-proBNP levels at baseline, participants with elevated levels of both biomarkers had significantly faster declines in global cognitive function and in cognitive function across all 4 domains in multivariable-adjusted models, with the most pronounced association in magnitude observed for the processing speed domain (Figure 1). Specifically, compared with participants with normal hs-cTnT and NT-proBNP levels, participants with elevated levels in both biomarkers had a greater annual decline in global cognitive function (difference in annual change of standardized z-score, −0.033; 95% CI, −0.041, −0.024; p < .001). The corresponding difference in the processing speed domain was −0.040 (95% CI, −0.050, −0.029; p < .001). Figure 2 shows the trajectories of global cognitive function change during follow-up by categories of cardiac biomarkers. Participants with both hs-cTnT and NT-proBNP elevated exhibited both lower cognitive function at baseline and faster decline during follow-up compared with those who had one biomarker elevated or neither biomarker elevated (p < .001). The trajectories were similar across both intensive and standard SBP treatment groups (p for interaction = 0.559, Figure 2). These associations between baseline cardiac biomarkers and accelerated cognitive decline were further verified by analysis of a single cognitive assessment (MoCA, LM I, LM II, or DSC) in all SPRINT participants (Supplementary Figure S2).
Figure 1.
Associations between baseline cardiac biomarkers and annual changes in global and domain-specific cognitive function among 2 733 SPRINT participants. Estimates based on linear mixed models adjusted for treatment assignment, age, sex, race/ethnicity, education, history of depression, history of CVD, baseline SBP, glucose level, BMI, smoking, and CKD status (baseline eGFR < 60 mL/min/1.73m2), with random effects for participant and site. Each unit change in the standardized z-score equals a 5-point change in the MoCA score. Notes: BMI = body mass index; CKD = chronic kidney disease; CVD = cardiovascular disease; eGFR = estimated glomerular filtration rate; hs-cTnT = high-sensitivity cardiac troponin T; NT-proBNP = N-terminal pro-B-type natriuretic peptide; SBP = systolic blood pressure; SPRINT = systolic blood pressure intervention trial.
Figure 2.
Standardized global cognitive score changes over follow-up according to categories of cardiac biomarkers. Green line denotes the estimate in group of hs-cTnT and NT-proBNP both not elevated, orange line is the group of one elevated, and red line is the group both elevated. Global cognitive function scores were predicted from linear mixed models adjusted for treatment assignment, age, sex, race/ethnicity, education, history of depression, history of CVD, baseline SBP, glucose level, BMI, smoking, and CKD status (baseline eGFR < 60 mL/min/1.73m2), with random effects for participant and site. p for interaction was 0.559 between categories of cardiac biomarkers and intensive SBP treatment assignment. The y-axis includes both the standardized cognitive score and the score equivalent to the MoCA scale (in brackets, ranging from 0 to 30) to facilitate clinical interpretation. Notes: BMI = body mass index; CKD = chronic kidney disease; CVD = cardiovascular disease; eGFR = estimated glomerular filtration rate; hs-cTnT = high-sensitivity cardiac troponin T; NT-proBNP = N-terminal pro-B-type natriuretic peptide; SBP = systolic blood pressure; SPRINT = systolic blood pressure intervention trial.
Cardiac Biomarkers and Changes in Neuroimaging Makers
Table 2 shows the associations between cardiac biomarkers and annual changes in white matter lesions, brain volume, and cerebral blood flow. Participants with elevated hs-cTnT levels had faster increases in total white matter lesion volume compared with participants with nonelevated hs-cTnT levels in multivariable analyses (adjusted difference in annual change, 0.37 cm3; 95% CI, 0.19, 0.56). Region-specific analyses showed a greater increase in white matter lesions was mainly in frontal, parietal, and temporal regions (Table 2). Participants with elevated NT-proBNP also appeared to have a larger increase in total white matter lesions but the association was not statistically significant (difference in annual change, 0.11 cm3; 95% CI, −0.05, 0.26). Continuous baseline NT-proBNP in log scale showed a significant linear trend association with a faster increase in white matter lesion (p < .05, Supplementary Table S2). Neither cardiac biomarker was associated with changes in total brain tissue volume or with changes in cerebral blood flow (Table 2).
Table 2.
Associations Between Baseline Cardiac Biomarkers and Annual Changes in Brain MRI Markers.*
| Brain MRI Markers | hs-cTnT (≥ 14 vs < 14 ng/L) | NT-proBNP (≥ 125 vs < 125 pg/mL) | Combining hs-cTnT and NT-proBNP (both elevated vs both not elevated) | |||
| Difference in Annual Change (95% CI) | p Value | Difference in Annual Change (95% CI) | p Value | Difference in Annual Change (95% CI) | p Value | |
| White matter lesion volume, cm3 (N = 633)a | ||||||
| Total | 0.37 (0.19, 0.56) | <.001 | 0.11 (−0.05, 0.26) | .18 | 0.38 (0.14, 0.63) | .002 |
| Frontal | 0.18 (0.09, 0.27) | <.001 | 0.05 (−0.02, 0.12) | .19 | 0.18 (0.07, 0.30) | .002 |
| Occipital | 0.01 (−0.01, 0.03) | .21 | 0.01 (−0.01, 0.02) | .23 | 0.02 (0.00, 0.05) | .05 |
| Parietal | 0.09 (0.04, 0.15) | <.001 | 0.02 (−0.02, 0.06) | .34 | 0.09 (0.02, 0.15) | .01 |
| Temporal | 0.08 (0.04, 0.13) | <.001 | 0.02 (−0.02, 0.06) | .29 | 0.08 (0.02, 0.14) | .009 |
| Brain tissue volume, cm3 (N = 639)b | ||||||
| Total | 0.03 (−0.85, 0.92) | .94 | 0.15 (−0.59, 0.88) | .70 | 0.08 (−1.10, 1.25) | .90 |
| Gray matter | −0.18 (−0.91, 0.56) | .64 | 0.04 (−0.57, 0.66) | .89 | −0.01 (−0.99, 0.97) | .98 |
| White matter | 0.22 (−0.27, 0.71) | .38 | 0.11 (−0.30, 0.51) | .62 | 0.10 (−0.55, 0.75) | .77 |
| Cerebral blood flow, mL/100 g/min (N = 625)c | ||||||
| Total | 0.14 (−0.53, 0.81) | .68 | −0.15 (−0.72, 0.42) | .60 | −0.03 (−0.94, 0.87) | .94 |
| Gray matter | 0.27 (−0.58, 1.11) | .54 | −0.23 (−0.95, 0.50) | .54 | −0.01 (−1.16, 1.14) | .99 |
| White matter | 0.07 (−0.38, 0.51) | .78 | −0.07 (−0.45, 0.32) | .73 | 0.08 (−0.53, 0.69) | .80 |
Notes: BMI = body mass index; CKD = chronic kidney disease; CVD = cardiovascular disease; eGFR = estimated glomerular filtration rate; hs-cTnT = high-sensitivity cardiac troponin T; NT-proBNP = N-terminal pro-B-type natriuretic peptide; MRI = Magnetic Resonance Imaging; SBP = systolic blood pressure; SPRINT = systolic blood pressure intervention trial.
*Estimates based on a linear mixed model, adjusting for intracranial volume, follow-up MRI scan days since randomization, treatment assignment, age, sex, race/ethnicity, education level, smoking status, history of CVD, baseline SBP, BMI, and CKD status (baseline eGFR < 60 mL/min/1.73m2), with random effects for participant and MRI facility.
aThe median (interquartile range) of total white matter lesion volume was 3.24 (1.58–6.20) cm3 at baseline, and 3.96 (1.82–8.81) cm3 at the year 4 follow-up.
bThe median (interquartile range) of total brain tissue volume was 1132.22 (1056.29–1203.82) cm3 at baseline, and 1108.60 (1041.45–1188.72) cm3 at the year 4 follow-up.
cThe median (interquartile range) of total cerebral blood flow was 35.79 (28.77–42.67) mL/100 g/min at baseline, and 36.75 (30.43–43.36) mL/100 g/min at the year 4 follow-up.
Effect Modification of SBP Treatment
As shown in Table 3, baseline cardiac biomarkers were significantly associated with accelerated cognitive decline in both intensive and standard SBP treatment groups (p < .001), and the effect modification by BP treatment was not statistically significant (all p for interaction > .05). The associations of elevated cardiac biomarkers with faster progression in white matter lesions were only observed in the intensive treatment group (p < .05), though the interaction was not statistically significant (all p for interaction > .05).
Table 3.
Associations of Baseline Cardiac Biomarkers With Annual Changes in Global Cognitive Function and Total White Matter Lesions, Stratified by Treatment Assignment.
| Treatment Assignment | hs-cTnT (≥ 14 vs < 14 ng/L) | NT-proBNP (≥ 125 vs < 125 pg/mL) | Combining hs-cTnT and NT-proBNP (both elevated vs both not elevated) | ||||||
| Difference in Annual Change (95% CI) | p Value | p for Interaction | Difference in Annual Change (95% CI) | p Value | p for Interaction | Difference in Annual Change (95% CI) | p Value | p for Interaction | |
| Global cognitive function, standardized z-score (N = 2 733)* | 0.52 | 0.83 | .18 | ||||||
| Intensive treatment | −0.016 (−0.026, –0.007) | .001 | −0.016 (−0.024, −0.008) | <.001 | −0.027 (−0.039, −0.015) | <.001 | |||
| Standard treatment | −0.021 (−0.030, −0.011) | <.001 | −0.017 (−0.026, −0.009) | <.001 | −0.039 (−0.051, −0.027) | <.001 | |||
| White matter lesion volume, cm3 (N = 633)† | 0.18 | 0.10 | .29 | ||||||
| Intensive treatment | 0.489 (0.246, 0.731) | <.001 | 0.224 (0.016, 0.432) | .04 | 0.509 (0.194, 0.825) | .002 | |||
| Standard treatment | 0.221 (–0.068, 0.511) | .14 | −0.039 (−0.275, 0.198) | .75 | 0.236 (−0.155, 0.626) | .24 | |||
Notes: BMI = body mass index; CVD = cardiovascular disease; CVD = cardiovascular disease; eGFR = estimated glomerular filtration rate; hs-cTnT = high-sensitivity cardiac troponin T; NT-proBNP = N-terminal pro-B-type natriuretic peptide; MRI = Magnetic Resonance Imaging; SBP = systolic blood pressure; SPRINT = systolic blood pressure intervention trial.
*Estimates based on linear mixed model, adjusting for age, sex, race/ethnicity, treatment assignment, education level, smoking status, history of depression, history of CVD, baseline SBP, glucose level, BMI, and CKD status (baseline eGFR < 60 ml/min/1.73m2), with random effects for participant and site.
†Estimates based on a linear mixed model, adjusting for intracranial volume, follow-up MRI scan days since randomization, treatment assignment, age, sex, race/ethnicity, education level, smoking status, history of CVD, baseline SBP, BMI, and CKD status (baseline eGFR < 60 ml/min/1.73m2), with random effects for participant and MRI facility.
Other Secondary and Sensitivity Analyses
The associations between elevated cardiac biomarkers and accelerated global cognitive decline did not differ statistically significantly by age (≥75 vs <75 years), whereas the associations between elevated hs-cTnT and global cognition appeared to be more pronounced in women (p for interaction = .043; Supplementary Table S3). In our secondary analyses assessing the effects of intensive BP treatment stratified by baseline cardiac biomarker levels, the effect of intensive BP treatment on slowing white matter lesion progression was only statistically significant in the group with nonelevated hs-cTnT and NT-proBNP, though the interaction was not statistically significant (p for interaction = .146, Supplementary Figure S3).
In sensitivity analyses excluding participants with atrial fibrillation, cardiovascular disease, or chronic kidney disease at baseline, the results were similar to those from the primary analyses.
Discussion
In this post hoc analysis of the SPRINT trial, we found that elevated baseline levels of hs-cTnT and NT-proBNP were associated with accelerated cognitive decline and faster progression in brain white matter lesions regardless of the intensity of BP control among older adults with hypertension. These findings emphasize the important contribution of subclinical CVD to subclinical brain vascular injury and cognitive impairment in older adults with hypertension. This study also informs the potential of utilizing cardiac biomarkers to detect hypertensive individuals at risk of accelerated cognitive decline and subsequent dementia. Intervention strategies to prevent cardiac diseases at subclinical status may help slow down cognitive decline and prevent dementia in adults with hypertension.
Several epidemiological studies have reported the associations between cardiac biomarkers and cognitive decline (8–13,31–34), showing inconclusive findings (35). For example, one report in the Atherosclerosis Risk in Communities cohort reported no associations of higher hs-cTnT and NT-proBNP with cognitive decline between 2 visits across 15 years (35). The more frequent repeated measurements and high follow-up rate in the SPRINT study make our study more robust to survival bias and measurement error. Our study extended the observation to older adults with hypertension and further demonstrated the potential of cardiac biomarkers for the early detection of accelerated cognitive decline in high-risk populations. Our study also provides new mechanistic insights into a recent report on the associations between elevated cardiac biomarkers and increased risk of adverse cognitive outcomes in SPRINT (36).
Our study found that participants with elevated levels of both hs-cTnT and NT-proBNP exhibited the fastest cognitive decline, with a larger annual decline rate of 0.033 in standardized z-score of global cognitive function compared with those with normal levels. This is equivalent to the cognitive decline associated with aging 10 years in SPRINT (27), indicating that cognitive decline associated with elevated cardiac biomarkers is potentially clinically significant. Moreover, our study assessing domain-specific cognitive function with comprehensive neuropsychological tests identified the processing speed domain as the most sensitive cognition associated with both elevated hs-cTnT and NT-proBNP levels, in line with a previous cross-sectional study in community-based older adults (13). Processing speed is a domain at an upper level of cognitive functioning conceptualization potentially involving the frontal lobe (37), and has been suggested to have the highest ischemic vulnerability to cerebral hypoperfusion and ischemia (38). Prior studies show a decline in processing speed occurs earlier in the aging process compared with other cognitive domains (39). Taken together, a decline in processing speed may serve as an early and sensitive marker for cognitive impairment linked to subclinical cardiovascular diseases. This observation of processing speed in our study provides new mechanistic insights into the longitudinal association across multiple cognitive domains and suggests processing speed may be more vulnerable to subclinical cardiac injury.
In this study, intensive BP treatment did not further slow cognitive decline associated with subclinical CVD compared with standard BP treatment. A previous study of NT-proBNP and cognitive decline in the oldest old showed that the combination of high NT-proBNP levels and low systolic BP (ranging from 110 to 147 mmHg) is associated with the steepest cognitive decline (40). In SPRINT, all participants were under treatment to achieve a target systolic BP of lower than 140 mmHg, which may have obscured the interaction between BP control and cardiac biomarkers in their effect on cognitive decline. In stratified analyses, we observed that the association between elevated hs-cTnT and global cognition was more pronounced in women, whereas the association with NT-proBNP was more pronounced in men, although the interaction terms with sex were not significant for NT-proBNP. It has been reported that women generally had lower hs-cTnT levels but higher NT-proBNP levels compared with men (41). Consequently, when using the fixed cutoff points to categorize the population, the group with elevated hs-cTnT levels may include women with more severe subclinical cardiac conditions, whereas the group with elevated NT-proBNP may include men with more severe conditions.
We also found that elevated cardiac biomarkers were associated with a faster progression in brain white matter lesions, aligning with our findings on accelerated cognitive decline and a previous report (42). In our study, the white matter lesion burden is comparable to the normative data for 70-year-old adults (43). White matter lesion is an established vascular pathology contributing to cognitive impairment and incident dementia (44–46). Our findings with both brain vascular changes and cognitive decline in the same study provide evidence of the potential mechanistic link between cardiac biomarkers and cognitive decline, extending previous observations in a cross-sectional study (19) and among memory-clinic patients with cognitive impairment or dementia (10). We did not observe any associations of hs-cTnT and NT-proBNP with changes in cerebral blood flow and brain parenchymal volume. This may be partially explained by insufficient statistical power and limited follow-up duration to observe detectable changes. Moreover, cerebral blood flow at resting may not adequately capture the impairment in cerebral hemodynamics. These results together suggest that white matter lesion progression may be a more sensitive marker for brain vascular damage and accelerated cognitive decline associated with subclinical cardiovascular diseases.
The mechanisms by which subclinical cardiac disease leads to accelerated cognitive decline are not fully understood. Elevated cardiac biomarkers reflect the underlying myocardial injury and stress and are associated with subclinical atherosclerosis and microangiopathy (47,48), which may cause cardiac remodeling and myocardial hypoperfusion, resulting in reduced cardiac output, thus leading to cerebral hypoperfusion and ischemic damage such as white matter lesions (9). Endothelial dysfunction and vascular damages could be the underlying mechanisms for both subclinical cardiac and cerebrovascular injuries. These mechanisms suggest that vascular brain changes may be the key mediators linking elevated cardiac biomarkers and cognitive impairment (9). Our additional analyses suggest that the associations between elevated cardiac biomarkers and accelerated cognitive decline might be partially mediated by progression in cerebral white matter lesions. The most pronounced effect on cognitive decline was observed in the processing speed domain, which is typically a manifestation of cerebrovascular injury, such as the white matter lesions (49). Although we did not observe an association between cardiac biomarkers and cerebral blood flow as discussed previously, cerebral hypoperfusion remains a possible pathophysiological pathway linking subclinical cardiac disease to faster progression of brain white matter lesions and accelerated cognitive decline. Additionally, blood-brain barrier disruption and other ischemic brain lesions such as cortical microinfarcts, which are frequently observed in hypertension (50), may also contribute to the observed association.
Building upon the evidence from previous studies (11–13,32), our study further suggests that hs-cTnT and NT-proBNP, 2 widely available blood-based markers of CVD, may serve as early predictors of accelerated cognitive decline and help identify individuals at high risk of developing dementia in older adults with hypertension. The association of cardiac biomarkers with accelerated cognitive decline was observed after adjusting for conventional risk factors of dementia, such as older age, obesity, smoking, lower education, depression, and history of CVD, suggesting its potential novel predictive value for cognitive impairment and dementia risk.
Several limitations of our study should also be noted. First, as a post hoc analysis, the measurement of both biomarkers was not prespecified. Second, the relatively small sample size in neuroimaging markers analyses at follow-up may result in inadequate statistical power and preclude the performance of further mediation analyses to better understand the causal relationship and the extent to which these effects are mediated by brain vascular changes. Third, though our study was based on a trial design, the association analyses were observational and there may be potential residual confounding factors, such as inflammation or lifestyle factors. In addition, endothelial dysfunction and other upper-stream factors may cause both cardiac and cerebrovascular diseases, potentially confounding our results. Finally, the participants in our study were older hypertensive patients with a higher risk of cardiovascular disease and free of diabetes, potentially limiting the generalizability of our results to other patient populations and younger populations.
Strengths of our study include comprehensive assessments of both global and domain-specific cognitive function at baseline and multiple timepoints over 6 years of follow-up, as well as the repeated measurements of neuroimaging markers, allowing us to characterize the association across various cognitive domains and examine the temporal relationship of cardiac biomarkers with annual cognitive decline and brain vascular changes. The analyses based on a randomized trial of intensive BP control also leveraged the design strengths of the original trial, such as high completion rate and comprehensive covariate assessment, and allowed us to address whether intensive BP control may modify the relationship between cardiac biomarkers and cognitive outcomes.
Conclusion
Subclinical cardiovascular disease, assessed by elevated cardiac biomarker levels of hs-cTnT and NT-proBNP, was associated with accelerated cognitive decline and faster progression in brain white matter lesions in older adults with hypertension, regardless of SBP treatment assignment. These findings suggest that subclinical CVD significantly contributes to subclinical brain vascular injury and cognitive impairment in older adults with hypertension, informing the potential of utilizing cardiac biomarkers to detect hypertensive individuals at risk of accelerated cognitive decline and subsequent dementia. Intervention strategies to prevent cardiovascular diseases at subclinical stages may help slow down cognitive decline and prevent dementia in adults with hypertension.
Supplementary Material
Acknowledgments
The authors thank the participants and staff members of the Systolic Blood Pressure Intervention Trial, which was sponsored by the National Institutes of Health (NIH), including the National Heart, Lung, and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute on Aging, and the National Institute of Neurological Disorders and Stroke, under Contract Numbers HHSN268200900040C, HHSN268200900046C, HHSN268200900047C, HHSN268200900048C, HHSN268200900049C, and Inter-Agency Agreement Number A-HL-13–002-001. It was also supported in part with resources and use of facilities through the Department of Veterans Affairs. The SPRINT investigators acknowledge the contribution of study medications (azilsartan and azilsartan combined with chlorthalidone) from Takeda Pharmaceuticals International, Inc. All components of the SPRINT study protocol were designed and implemented by the investigators. The investigative team collected, analyzed, and interpreted the data. All aspects of manuscript writing and revision were carried out by the coauthors. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the U.S. Department of Veterans Affairs, or the United States Government. A full list of contributors to SPRINT is available in the supplementary acknowledgment list at https://www.sprinttrial.org/public/dspScience.cfm. We also acknowledge the support from the following Clinical and Translational Science Awards funded by the National Center for Advancing Translational Sciences: Case Western Reserve University: UL1TR000439; Ohio State University: UL1RR025755; University of Pennsylvania: UL1RR024134 and UL1TR000003; Boston University: UL1RR025771; Stanford University: UL1TR000093; Tufts University: UL1RR025752, UL1TR000073, and UL1TR001064; University of Illinois: UL1TR000050; University of Pittsburgh: UL1TR000005; UT Southwestern: 9U54TR000017-06; University of Utah: UL1TR000105-05; Vanderbilt University: UL1TR000445; George Washington University: UL1TR000075; University of California, Davis: UL1TR000002; University of Florida: UL1TR000064; University of Michigan: UL1TR000433; Tulane University: P30GM103337 Centers of Biomedical Research Excellence Award, National Institute of General Medical Sciences; Wake Forest University: UL1TR001420. The SPRINT cardiac biomarker ancillary study was supported by the National Heart, Lung, and Blood Institute (1R01HL144112-01 and 1K24HL166681-01 for J.D. Berry), the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1 TR001860 and linked award KL2 TR001859 (S.B. Ascher), the American Heart Association (CDA 936281 for S.B. Ascher), the Center for Clinical and Translational Sciences (UL1TR003167 to J.D. Berry), and the National Institute on Aging (R01AG055606 for N.M. Pajewski).
Contributor Information
Wenxin Zhang, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Simon B Ascher, Kidney Health Research Collaborative, Department of Medicine, San Francisco Veterans Affairs Health Care System and University of California San Francisco, San Francisco, California, USA; Division of Hospital Medicine, University of California Davis, Sacramento, California, USA.
Sudipto Dolui, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ilya M Nasrallah, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Yuan Lu, Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Julia Neitzel, Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands.
Estefania Toledo, Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
Lidia Glodzik, Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, New York, USA.
Hossam A Shaltout, Hypertension and Vascular Research Center, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA; Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Timothy M Hughes, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA; Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Jarett D Berry, Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical School, Dallas, Texas, USA (Medical Sciences Section).
Yuan Ma, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Funding
This study was supported by grant R00AG071742 to Y. Ma from the National Institute on Aging.
Conflict of Interest
None.
Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Author Contributions
All authors contributed to the study design, interpretation of data, and critical review of the manuscript, and approved the final version. W.Z. and Y.M. developed the study concept and primary design; W.Z., Y.M., J.B., and S.A. conducted the analysis and played a major role in the interpretation of data; W.Z. drafted the original manuscript; W.Z. and Y.M. revised the manuscript with input from other coauthors.
References
- 1. Chow CK, Teo KK, Rangarajan S, et al. ; PURE (Prospective Urban Rural Epidemiology) Study investigators. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013;310:959–968. https://doi.org/ 10.1001/jama.2013.184182 [DOI] [PubMed] [Google Scholar]
- 2. Neirmeyer M, Eastvold A, Suchy Y. Small vessel ischemic disease. In: Kreutzer J, DeLuca J, Caplan B, eds. Encyclopedia of Clinical Neuropsychology. Cham: Springer International Publishing; 2017:1–4. https://doi.org/ 10.1007/978-3-319-56782-2_497-2. Accessed February 19, 2024. [DOI] [Google Scholar]
- 3. Brain J, Greene L, Tang EYH, et al. Cardiovascular disease, associated risk factors, and risk of dementia: an umbrella review of meta-analyses. Front Epidemiol. 2023;3:1095236. https://doi.org/ 10.3389/fepid.2023.1095236. https://www.frontiersin.org/articles/10.3389/fepid.2023.1095236. Accessed February 19, 2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Hilal S, Chai YL, van Veluw S, et al. Association between subclinical cardiac biomarkers and clinically manifest cardiac diseases with cortical cerebral microinfarcts. JAMA Neurol. 2017;74:403–410. https://doi.org/ 10.1001/jamaneurol.2016.5335 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Zhao B, Li T, Fan Z, et al. Heart-brain connections: phenotypic and genetic insights from magnetic resonance images. Science. 2023;380:abn6598. https://doi.org/ 10.1126/science.abn6598 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. van der Velpen IF, Feleus S, Bertens AS, Sabayan B. Hemodynamic and serum cardiac markers and risk of cognitive impairment and dementia. Alzheimers Dement. 2017;13:441–453. https://doi.org/ 10.1016/j.jalz.2016.09.004 [DOI] [PubMed] [Google Scholar]
- 7. Tynkkynen J, Hernesniemi JA, Laatikainen T, et al. High-sensitivity cardiac troponin I and NT-proBNP as predictors of incident dementia and Alzheimer’s disease: the FINRISK Study. J Neurol. 2017;264:503–511. https://doi.org/ 10.1007/s00415-016-8378-7 [DOI] [PubMed] [Google Scholar]
- 8. Feinkohl I, Keller M, Robertson CM, et al. ; Edinburgh Type 2 Diabetes Study (ET2DS) Investigators. Clinical and subclinical macrovascular disease as predictors of cognitive decline in older patients with type 2 diabetes. Diabetes Care. 2013;36:2779–2786. https://doi.org/ 10.2337/dc12-2241 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Jensen M, Zeller T, Twerenbold R, Thomalla G. Circulating cardiac biomarkers, structural brain changes, and dementia: emerging insights and perspectives. Alzheimers Dement. 2023;19:1529–1548. https://doi.org/ 10.1002/alz.12926 [DOI] [PubMed] [Google Scholar]
- 10. Gyanwali B, Lai MKP, Lui B, et al. Blood-based cardiac biomarkers and the risk of cognitive decline, cerebrovascular disease, and clinical events. Stroke. 2021;52:2275–2283. https://doi.org/ 10.1161/STROKEAHA.120.032571 [DOI] [PubMed] [Google Scholar]
- 11. Wijsman LW, de Craen AJ, Trompet S, et al. High-sensitivity cardiac troponin T is associated with cognitive decline in older adults at high cardiovascular risk. Eur J Prev Cardiol. 2016;23:1383–1392. https://doi.org/ 10.1177/2047487316632364 [DOI] [PubMed] [Google Scholar]
- 12. Wijsman LW, Sabayan B, van Vliet P, et al. N-terminal pro-brain natriuretic peptide and cognitive decline in older adults at high cardiovascular risk. Ann Neurol. 2014;76:213–222. https://doi.org/ 10.1002/ana.24203 [DOI] [PubMed] [Google Scholar]
- 13. Sebastiani P, Andersen SL, Sweigart B, et al. Patterns of multi-domain cognitive aging in participants of the long life family study. Geroscience. 2020;42:1335–1350. https://doi.org/ 10.1007/s11357-020-00202-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Benedictus MR, Leeuwis AE, Binnewijzend MAA, et al. Lower cerebral blood flow is associated with faster cognitive decline in Alzheimer’s disease. Eur Radiol. 2017;27:1169–1175. https://doi.org/ 10.1007/s00330-016-4450-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Abraham HMA, Wolfson L, Moscufo N, Guttmann CRG, Kaplan RF, White WB. Cardiovascular risk factors and small vessel disease of the brain: blood pressure, white matter lesions, and functional decline in older persons. J Cereb Blood Flow Metab. 2016;36:132–142. https://doi.org/ 10.1038/jcbfm.2015.121 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kitagawa K, Oku N, Kimura Y, et al. Relationship between cerebral blood flow and later cognitive decline in hypertensive patients with cerebral small vessel disease. Hypertens Res. 2009;32:816–820. https://doi.org/ 10.1038/hr.2009.100 [DOI] [PubMed] [Google Scholar]
- 17. Carlson NE, Moore MM, Dame A, et al. Trajectories of brain loss in aging and the development of cognitive impairment. Neurology. 2008;70:828–833. https://doi.org/ 10.1212/01.wnl.0000280577.43413.d9 [DOI] [PubMed] [Google Scholar]
- 18. Ferguson IT, Elbejjani M, Sabayan B, et al. N-terminal pro-brain natriuretic peptide and associations with brain magnetic resonance imaging (MRI) features in middle age: the CARDIA Brain MRI Study. Front Neurol. 2018;9:307. https://doi.org/ 10.3389/fneur.2018.00307 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Veugen MGJ, Henry RMA, Brunner-La Rocca H-P, et al. Cross-sectional associations between cardiac biomarkers, cognitive performance, and structural brain changes are modified by age. Arterioscler Thromb Vasc Biol. 2018;38:1948–1958. https://doi.org/ 10.1161/ATVBAHA.118.311082 [DOI] [PubMed] [Google Scholar]
- 20. Sabayan B, van Buchem MA, de Craen AJM, et al. N-terminal pro-brain natriuretic peptide and abnormal brain aging. Neurology. 2015;85:813–820. https://doi.org/ 10.1212/WNL.0000000000001885 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Williamson JD, Pajewski NM, Auchus AP, et al. ; SPRINT MIND Investigators for the SPRINT Research Group. Effect of intensive vs standard blood pressure control on probable dementia: a randomized clinical trial. JAMA. 2019;321:553–561. https://doi.org/ 10.1001/jama.2018.21442 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Ascher SB, Scherzer R, de Lemos JA, et al. Associations of high-sensitivity troponin and natriuretic peptide levels with serious adverse events in SPRINT. J Am Heart Assoc. 2022;11:e023314. https://doi.org/ 10.1161/JAHA.121.023314 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Berry JD, Nambi V, Ambrosius WT, et al. Associations of high-sensitivity troponin and natriuretic peptide levels with outcomes after intensive blood pressure lowering: findings from the SPRINT randomized clinical trial. JAMA Cardiol. 2021;6:1397–1405. https://doi.org/ 10.1001/jamacardio.2021.3187 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Ambrosius WT, Sink KM, Foy CG, et al. ; SPRINT Study Research Group. The design and rationale of a multicenter clinical trial comparing two strategies for control of systolic blood pressure: the systolic blood pressure intervention trial (SPRINT). Clin Trials. 2014;11:532–546. https://doi.org/ 10.1177/1740774514537404 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. The SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373:2103–2116. https://doi.org/ 10.1056/NEJMoa1511939 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. The SPRINT MIND Investigators for the SPRINT Research Group. Association of intensive vs standard blood pressure control with cerebral white matter lesions. JAMA. 2019;322:524–534. https://doi.org/ 10.1001/jama.2019.10551 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Rapp SR, Gaussoin SA, Sachs BC, et al. ; SPRINT Research Group. Effects of intensive versus standard blood pressure control on domain-specific cognitive function: a substudy of the SPRINT randomised controlled trial. Lancet Neurol. 2020;19:899–907. https://doi.org/ 10.1016/S1474-4422(20)30319-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Dolui S, Detre JA, Gaussoin SA, et al. Association of intensive vs standard blood pressure control with cerebral blood flow: secondary analysis of the SPRINT MIND randomized clinical trial. JAMA Neurol. 2022;79:380–389. https://doi.org/ 10.1001/jamaneurol.2022.0074 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Nasrallah IM, Gaussoin SA, Pomponio R, et al. ; SPRINT Research Group. Association of intensive vs standard blood pressure control with magnetic resonance imaging biomarkers of Alzheimer disease: secondary analysis of the SPRINT MIND randomized trial. JAMA Neurol. 2021;78:568–577. https://doi.org/ 10.1001/jamaneurol.2021.0178 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Gabrio A, Plumpton C, Banerjee S, Leurent B. Linear mixed models to handle missing at random data in trial-based economic evaluations. Health Econ. 2022;31:1276–1287. https://doi.org/ 10.1002/hec.4510 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Bertens AS, Sabayan B, de Craen AJM, Van der Mast RC, Gussekloo J. High sensitivity cardiac troponin t and cognitive function in the oldest old: the Leiden 85-Plus Study. J Alzheimers Dis. 2017;60:235–242. https://doi.org/ 10.3233/JAD-170171 [DOI] [PubMed] [Google Scholar]
- 32. Mirza SS, de Bruijn RFAG, Koudstaal PJ, 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;87:356–362. https://doi.org/ 10.1136/jnnp-2014-309968 [DOI] [PubMed] [Google Scholar]
- 33. Schneider ALC, Rawlings AM, Sharrett AR, et al. High-sensitivity cardiac troponin T and cognitive function and dementia risk: the atherosclerosis risk in communities study. Eur Heart J. 2014;35:1817–1824. https://doi.org/ 10.1093/eurheartj/ehu124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Broersen LHA, Siegerink B, Sperber PS, et al. High-sensitivity cardiac troponin t and cognitive function in patients with ischemic stroke. Stroke. 2020;51:1604–1607. https://doi.org/ 10.1161/STROKEAHA.119.028410 [DOI] [PubMed] [Google Scholar]
- 35. Pokharel Y, Mouhanna F, Schneider ALC, et al. High-sensitive troponin T, natriuretic peptide, and cognitive change. J Am Geriatr Soc. 2019;67:2353–2361. https://doi.org/ 10.1111/jgs.16092 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Haney D, Ma Y, Dalmacy D, et al. High-sensitivity troponin T, NT-proBNP, and cognitive outcomes in SPRINT. Hypertension. 2024;81:1956–1965. https://doi.org/ 10.1161/HYPERTENSIONAHA.124.22876 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Kochunov P, Coyle T, Lancaster J, et al. Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging. Neuroimage. 2010;49:1190–1199. https://doi.org/ 10.1016/j.neuroimage.2009.09.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Payabvash S, Souza LCS, Wang Y, et al. Regional ischemic vulnerability of the brain to hypoperfusion. Stroke. 2011;42:1255–1260. https://doi.org/ 10.1161/STROKEAHA.110.600940 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Jaarsma E, Nooyens A, Kok AAL, et al. Modifiable risk factors for accelerated decline in processing speed: results from three dutch population cohorts. J Prev Alzheimers Dis. 2023;11:108–116. https://doi.org/ 10.14283/jpad.2023.64 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. van Vliet P, Sabayan B, Wijsman LW, et al. NT-proBNP, blood pressure, and cognitive decline in the oldest old: The Leiden 85-plus Study. Neurology. 2014;83:1192–1199. https://doi.org/ 10.1212/WNL.0000000000000820 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Cediel G, Codina P, Spitaleri G, et al. Gender-related differences in heart failure biomarkers. Front Cardiovasc Med. 2021;7:617705. https://doi.org/ 10.3389/fcvm.2020.617705 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Dadu RT, Fornage M, Virani SS, et al. Cardiovascular biomarkers and subclinical brain disease in the atherosclerosis risk in communities study. Stroke. 2013;44:1803–1808. https://doi.org/ 10.1161/STROKEAHA.113.001128 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. de Kort FAS, Vinke EJ, van der Lelij EJ, et al. Cerebral white matter hyperintensity volumes: normative age- and sex-specific values from 15 population-based cohorts comprising 14 876 individuals. Neurobiol Aging. 2025;146:38–47. https://doi.org/ 10.1016/j.neurobiolaging.2024.11.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. https://doi.org/ 10.1136/bmj.c3666 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Cees De Groot J, De Leeuw F-E, Oudkerk M, et al. Cerebral white matter lesions and cognitive function: the Rotterdam scan study. Ann Neurol. 2000;47:145–151. https://doi.org/ [DOI] [PubMed] [Google Scholar]
- 46. Turken U, Whitfield-Gabrieli S, Bammer R, Baldo JV, Dronkers NF, Gabrieli JDE. Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage. 2008;42:1032–1044. https://doi.org/ 10.1016/j.neuroimage.2008.03.057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Weber M, Mitrovic V, Hamm C. B-type natriuretic peptide and N-terminal pro-B-type natriuretic peptide—diagnostic role in stable coronary artery disease. Exp Clin Cardiol. 2006;11:99–101. [PMC free article] [PubMed] [Google Scholar]
- 48. Saunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation. 2011;123:1367–1376. https://doi.org/ 10.1161/CIRCULATIONAHA.110.005264 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Kuznetsova KA, Maniega SM, Ritchie SJ, et al. Brain white matter structure and information processing speed in healthy older age. Brain Struct Funct. 2016;221:3223–3235. https://doi.org/ 10.1007/s00429-015-1097-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Ungvari Z, Toth P, Tarantini S, et al. Hypertension-induced cognitive impairment: from pathophysiology to public health. Nat Rev Nephrol. 2021;17:639–654. https://doi.org/ 10.1038/s41581-021-00430-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.