Cardiovascular rate pressure product is associated with NfL in older adults at risk for AD

Abstract

INTRODUCTION

Elevated cardiovascular rate pressure product (RPP) has been shown to predict cardiovascular mortality and is associated with poor cognitive test performance among older adults. However, it is unclear how RPP is related to the cerebrospinal fluid (CSF) biomarkers of neurodegeneration and neuroinflammation.

METHODS

RPP was cross‐sectionally evaluated as a predictor of CSF biomarker levels in a cohort of 310 cognitively unimpaired late‐middle‐aged adults at risk for Alzheimer's disease. The primary outcomes were CSF levels of α‐Synuclein, glial fibrillary acidic protein, neurofilament light (NfL), soluble triggering receptor expressed in myeloid cells 2, and total tau. Further analyses examined amyloid beta (Aβ)42/Aβ40, phosphorylated tau 181 (pTau181), and pTau181/Aβ4.

RESULTS

RPP was positively associated with NfL (β = 0.006, R ² = 0.411, p = 0.012, but Bonferroni‐corrected p ≤ 0.006) and not with other CSF biomarkers of neurodegeneration and neuroinflammation investigated in this sample.

DISCUSSION

A high myocardial oxygen demand at rest may be related to neuronal death and axonal degeneration in cognitively unimpaired late‐middle‐aged adults.

Highlights

• We explored the relationship between RPP and CSF analytes.

• Higher RPP was associated with higher NfL but not other measured CSF biomarkers.

• HR was positively associated with NfL, whereas SBP was not.

1. BACKGROUND

Alzheimer's disease (AD) is mainly a disease of aging, particularly affecting those 65 years and older. Currently, individuals aged 65 and older make up 18% of the US population; however, this number is projected to increase substantially by 2030. ¹ Similarly, as the proportion of older Americans increases, the prevalence of AD is projected to double by 2060. ² AD is characterized by abnormal amyloid beta (Aβ) and tau protein aggregations, causing amyloid plaques and neurofibrillary tangles. ³ These lead to eventual neurodegeneration, a process that leads to irreversible neuronal damage and death. ⁴ Neuroinflammation is also present when the brain's innate immune system is activated in response to an inflammatory challenge ⁵ and is implicated in contributing to AD, ⁶ particularly as a tissue response to Aβ pathology. ⁷

Cardiovascular diseases (CVDs) are common in older adults, and studies have identified similar risk factors for both CVDs and neurodegenerative diseases such as AD. ⁸ , ⁹ , ¹⁰ Additionally, high blood pressure has been associated with neuroinflammation and neurodegeneration. ¹¹ Cardiovascular rate pressure product (RPP) is a simple, indirect assessment of resting cardiac workload and myocardial oxygen consumption that incorporates basal systolic blood pressure (SBP) and heart rate (HR). ¹² , ¹³ Elevated RPP is predictive of cardiovascular mortality ¹⁴ , ¹⁵ and associated with poor cognitive test performance among older adults. ¹⁶ , ¹⁷ There is also evidence of a relationship between RPP and cognitive function among persons with neocortical amyloid aggregation. ¹² However, it is unclear how RPP may be related to the cerebrospinal fluid (CSF) biomarkers of neurodegeneration and neuroinflammation.

CSF biomarkers that characterize neurodegeneration include α‐Synuclein, neurofilament light (NfL), and total tau (tTau), and those that characterize neuroinflammation include glial fibrillary acidic protein (GFAP) and soluble triggering receptor expressed in myeloid cells 2 (sTREM2). ¹⁸ GFAP measures astroglial activation or astrocytosis, a well‐known pathological process that is commonly found surrounding Aβ plaques, ¹⁹ sTREM2 regulates the activation of microglial cells, which are the immune cells of the central nervous system, ²⁰ and neurofilament light (NfL) is a non‐specific marker of neuroaxonal injury that rises upon neuroaxonal damage and is released into the blood and CSF. ²¹ NfL levels are related to neuronal death and axonal degeneration ²² and have also been associated with cognitive decline and brain atrophy. ²³ Accumulation of misfolded α‐Synuclein defines multiple forms of neural degeneration; however, the normal function remains poorly understood. ²⁴

Therefore, the objective of this study was to assess the relationship between RPP and CSF biomarkers of neurodegeneration and neuroinflammation in a cohort of cognitively normal late‐middle‐aged adults at risk for AD. We hypothesized that those with worse vascular profiles would also have worse CSF core (Aβ42/Aβ40, pTau181, and pTau181/Aβ42) and non‐AD (NfL, tTau, GFAP, sTREM2) biomarker profiles.

2. METHODS

2.1. Participants

Three hundred and ten cognitively unimpaired adults from the Wisconsin Alzheimer's Disease Research Center (WADRC) and the Wisconsin Registry for Alzheimer's Prevention (WRAP) were included in this study. WRAP is a longitudinal cohort study consisting of > 1500 late‐middle‐aged adults who were cognitively normal and free of major medical, psychiatric, and neurological conditions at study entry. ²⁵ , ²⁶ WADRC is a longitudinal cohort study consisting of both cognitively healthy and impaired participants. Cognitive normality was determined using comprehensive neuropsychological testing, clinical examination, and a consensus conference. ²⁵ , ²⁶ By design, both cohorts are enriched with individuals at risk for AD due to parental history of AD or a genetic predisposition based on carriage of one or both apolipoprotein E (APOE) ε4 alleles. ²⁵ , ²⁶ All study procedures were approved by the University of Wisconsin Institutional Review Board, and each subject provided informed consent prior to participation. In this study, only cognitively unimpaired participants who completed a lumbar puncture to collect CSF were included.

2.2. CSF collection and analysis

A lumbar puncture for the collection of CSF was performed in the morning after an 8‐ to 12‐h fast. CSF was extracted from the interspace of L3/L4 or L4/L5 using a Sprotte 24‐ or 25‐gauge atraumatic spinal needle and gentle extraction into polypropylene syringes. Approximately 22 mL of CSF was extracted, combined, mixed, and centrifuged at 2000 × g for 10 min. Supernatants were frozen in 0.5 mL aliquots in polypropylene tubes and stored at −80°C. ²¹ CSF samples were immunoassayed for proteins related to the NeuroToolKit, a panel of robust prototype biomarker assays, and run on cobas^® analyzers (Roche Diagnostics International, Rotkreuz, Switzerland). ²⁷ The assays were performed by board‐certified laboratory technicians who were blinded to clinical data and used protocols accredited by the Swedish Board of Accreditation and Conformity Assessment, as previously described. ²⁸

2.3. RPP

RPP was calculated using the following equation: (SBP × HR) / 100. ¹² HR and blood pressure data were obtained during the same visit but before the lumbar puncture per protocol, after participants had been in a resting, seated position for a minimum of 5 min. The measurements were acquired using a GE Dinamap Pro 400 V2 Vital Signs Monitor with a GE Critikon blood pressure cuff. Blood pressure readings were obtained three consecutive times, with the average of the second and third readings deemed the final value and, therefore, used in the equation. A single HR reading was collected and used in the equation. ²⁹

RESEARCH IN CONTEXT

1. Systematic review: We reviewed the literature using traditional (eg, PubMed) sources, meeting abstracts, and presentations. Cardiovascular RPP is a simple, indirect assessment of myocardial oxygen consumption that incorporates basal SBP and HR. Elevated RPP has been associated with poor cognitive test performance among older adults; however, to our knowledge, this is the first study looking at the relationship of RPP with CSF analytes.

2. Interpretation: There was a positive association between RPP and the axonal biomarker NfL, where higher RPP was related to higher levels of NfL (p = 0.012; Bonferroni correction p ≤ 0.006).

3. Future directions: Given the novel nature of this study, our finding could form the basis for further longitudinal inquiries into this relationship in a larger cohort of cognitively unimpaired versus impaired participants. It will also be important to look at the influence of possible antecedents (eg, physical activity or fitness) on RPP.

2.4. Statistical analysis

A Shapiro–Wilk test was applied to assess whether biomarkers were normally distributed. All biomarkers violated normality test assumptions; Blom transformation was applied to each to remedy the violation. We tested a linear regression model with non‐AD (NfL, tTau, GFAP, sTREM2) and CSF core (Aβ42/Aβ40, pTau181, and pTau181/Aβ42) biomarkers of AD as outcomes predicted by RPP, while adjusting for age, sex, and APOE ε4. For significant associations, we repeated the analyses separately for SBP and HR to assess their unique contributions. Similarly, a secondary analysis was performed to investigate whether any observed associations differed by age, APOE ε4 status, and sex. After stratification for age, APOE ε4 status and sex were included in the model as covariates. Similarly, when the sample was stratified by APOE ε4 status, age and sex were included as covariates. We also repeated the analysis using interaction terms and standardization of continuous variables ³⁰ , ³¹ instead of stratifying. All analyses were conducted using IBM SPSS, version 29. ³²

3. RESULTS

3.1. Participants

Participants had a mean age of 64.6 years and were mostly female (68.7%) and non‐Hispanic White (90.3%). Further, 44.5% carried at least one APOE ε4 allele. On average, the participants had a HR of 63.7 bpm and a SBP of 130.5 mmHg, which is classified as Stage 1 hypertension. ³³ Additional participant characteristics are found in Table 1.

TABLE 1.

Participant characteristics.

Characteristic	Value *
Demographics
Age, years	64.6 (7.1)
Race
American Indian or Alaska Native, %	2.6
Asian, %	0.3
Black or African American, %	6.1
Non‐Hispanic White, %	90.3
Unknown	0.7
Hispanic, %	1.9
APOE ε4‐positive, %	44.5
Education, years	15.8 (2.7)
Female, %	68.7
Normal clinical diagnosis % Cardiac factors	100
Diastolic blood pressure, mmHg	78.1 (8.2)
Systolic blood pressure, mmHg	130.5 (17.8)
Resting HR, bpm	63.7 (10.4)
Rate pressure product	83.2 (18.3)
CSF biomarkers
α‐Synuclein	181.5 (64.5)
GFAP	9.9 (3.4)
NfL	107.1 (36.0)
sTREM2	9.2 (2.8)
tTau	201.9 (63.1)

Abbreviations: APOE ε4, ε4 allele of apolipoprotein E gene; bpm, beats per minute; GFAP, glial fibrillary acidic protein; HR, heart rate; mmHg, millimeters of mercury; MMSE, Mini‐Mental State Examination; NfL, neurofilament light; sTREM2, soluble triggering receptor expressed in myeloid cells 2; tTau, total tau.

^*Values represented as mean (SD) unless otherwise noted.

3.2. RPP and CSF biomarkers

As presented in Table 2, higher RPP was significantly associated with higher NfL (β = 0.006, _R ² = 0.411, p = 0.012) but not with other measured CSF biomarkers if the findings were considered significant at p < 0.05. No relationships between RPP and CSF biomarkers would be considered significant after applying conservative Bonferroni correction for multiple comparisons (p ≤ 0.006). To further determine whether the components of RPP were differentially related to CSF biomarkers, we repeated the NfL analysis separately for SBP and HR to assess their unique contributions. HR was positively associated with NfL (β = 0.011, R ² = 0.412, p = 0.010) whereas SBP was not (β = 0.003, R ² = 0.402, p = 0.231).

TABLE 2.

Linear regressions of CSF biomarkers on RPP.

CSF biomarker a	β (SE)	R 2	t	p
α‐Synuclein	0.003 (0.003)	0.062	1.025	0.306
GFAP	0.002 (0.003)	0.263	0.709	0.479
NfL	0.006 (0.002)	0.411	2.539	0.012 *
pTau	0.002 (0.003)	0.140	0.515	0.607
sTREM2	−0.001 (0.003)	0.121	−0.291	0.771
tTau	0.002 (.003)	0.130	0.602	0.547
Aβ42/ Aβ40	−0.001 (0.003)	0.181	−0.343	0.732
pTau181/ Aβ42	0.000 (0.003)	0.185	0.087	0.931

Abbreviations: β, unstandardized beta; Aβ40, amyloid beta 1‐40; Aβ42, amyloid beta 1‐42; CSF, cerebrospinal fluid; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; pTau181, phosphorylated tau181; RPP, rate pressure product; SE, standard error; sTREM2, soluble triggering receptor expressed in myeloid cells 2; tTau, total tau.

^a The models included age, sex, and APOE ε4 status as covariates.

^* p < 0.05, n = 310 for all variables.

p ≤ 0.006 if Bonferroni‐corrected.

To further assess whether this association between RPP and NfL might be pathognomonic for an underlying amyloid pathology, we tested for associations between RPP and CSF core AD biomarkers (Table 2). None of the associations reached statistical significance (all p ≥ 0.05).

3.3. Post hoc analyses for NfL as predicted by RPP: stratification by age, APOE ε4, and sex

As shown in Table 3, stratified analyses revealed that the relationship between RPP and NfL reached statistical significance in younger (<65 years of age; NfL: β = 0.009, R ² = 0.099, p = 0.018), female (NfL: β = 0.007, R ² = 0.381, p = 0.017), and APOE ε4+ (NfL: β = 0.010, R ² = 0.389, p = 0.022) participants. When the analyses were repeated using interaction terms and standardization to reduce multicollinearity ³⁰ , ³¹ (as opposed to stratifying), the effect of RPP on NfL no longer varied significantly by age (β = 0.047, R ² = 0.414, p = 0.284), gender (β = 0.003, R ² = 0.412, p = 0.563), or APOE ε4+ status (β = 0.006, R ² = 0.414, p = 0.256), even though the relationship between RPP and NfL remained statistically significant (β = 0.006, R ² = 0.411, p = 0.012; Table 4).

TABLE 3.

Secondary analysis: Linear regression of NfL on RPP stratified by age, APOE ε4, and sex.

Characteristics	Categories	N	β (SE)	R 2	t	p
Age	<65	158	0.009 (0.004)	0.099	2.394	0.018 *
	≥65	152	0.007 (0.004)	0.083	1.870	0.063
APOE ε4	Negative	172	0.004 (0.003)	0.438	1.330	0.185
	Positive	138	0.010 (0.004)	0.389	2.323	0.022 *
Sex	Male	97	0.004 (0.004)	0.361	0.926	0.357
	Female	213	0.007 (0.003)	0.381	2.396	0.017 *

Abbreviation: APOE ε4, ε4 allele of apolipoprotein E gene.

^* p < 0.05, n = 310 for all variables.

p ≤ 0.006 if Bonferroni‐corrected.

TABLE 4.

Linear regressions of NfL on RPP (not stratified).

Model	β (SE)	R 2	t	p
RPP	0.006 (0.002)	0.411	2.539	0.012 *
Age	0.079 (0.006)	0.411	12.65	<.001 **
APOE ε4	−0.020 (0.088)	0.411	−0.225	0.822
Gender	−0.497 (0.094)	0.411	−5.265	<.001 **
Gender*Age	0.001 (0.014)	0.411	0.059	0.953
Ages*RPPs	0.047 (0.044)	0.414	1.074	0.284
Gender*RPP	0.003 (0.005)	0.412	0.579	0.563
APOE ε4*RPP	0.006 (0.005)	0.414	1.138	0.256

Abbreviations: β, unstandardized beta; NfL, neurofilament light; RPP, rate pressure product; SE, standard error.

^a n = 310 for all variables. The models included age, sex, APOE ε4 status, and interaction terms for RPP*age, RPP*gender, RPP*APOE ε4, and gender*age.

^* p ≤ 0.05.

^** p ≤ 0.006 if Bonferroni‐corrected.

4. DISCUSSION

4.1. Primary results

The purpose of this study was to assess the relationship between RPP and CSF biomarkers of core AD neuropathology, neurodegeneration, and neuroinflammation in a cognitively unimpaired cohort at risk for AD. We report no significant associations between CSF core AD biomarkers and RPP. RPP is positively associated with NfL, not with other measures of neurodegeneration or neuroinflammation in this sample. There is a significant relationship between RPP and NfL, however, that does not survive a conservative Bonferroni correction for multiple comparisons (p ≤ 0.006). Nonetheless, we considered this finding significant at p < 0.05 (two‐tailed) given that the lack of significance after correcting for multiple comparisons is likely, at least in part, due to the small sample size and the limited representativeness of the sample. Given the exploratory nature of the present study, we believe that further investigations with larger samples and longitudinal designs are warranted.

Previous studies looking at CVD markers, neurodegeneration, and neuroinflammation focused on metrics such as cardiac troponins, cardiovascular risk scores, and blood pressure. ¹¹ , ³⁴ , ³⁵ , ³⁶ The prevailing theory behind these studies is that CVD plays a role in increasing small vessel cerebrovascular diseases through arteriolosclerosis. ³⁷ Similarly, neuroinflammation has been identified as a mechanism linking hypertension with increased risk of neurodegenerative diseases like AD. ³⁸ Animal studies have demonstrated that chronically elevated blood pressure leads to adverse glial activation and increased brain inflammatory mediators. ³⁸ These cause damage to cerebral microvasculature and locally activate the renin–angiotensin system, which are the key pathogenetic mechanisms linking hypertension to neuroinflammation and the accompanying neurodegeneration. ³⁸

One study, which looked at CVD and plasma biomarkers, found an independent association between greater cardiovascular risk factors and elevated plasma NfL but not Aβ42/Aβ40_. ³⁹ This association is believed to be reflective of neurodegeneration rather than AD‐specific pathways, as NfL is a non‐specific marker of axonal damage and may be elevated in other neurodegenerative disorders. To our knowledge, this is the first study looking at the relationship of RPP with CSF analytes. We believe that the observed linkage between RPP and NfL may not be pathognomonic of an underlying amyloid pathology as well, given the null association with core AD markers.

4.2. Post hoc analyses

Also, to our knowledge, no studies directly examined the components of RPP (SBP and HR) with CSF analytes. When RPP was segregated into its components in this study, a positive relationship between HR and CSF NfL was noted, whereas SBP failed to be associated with NfL. Plasma NfL has been reported to be elevated in patients with atrial fibrillation (a condition characterized by significantly increased HR). The markedly elevated HRs resulted in changes to the blood supply to the brain, causing brain injury from cerebral hypoperfusion. ⁴⁰ Although results are variable in the available literature, studies that have examined RPP and blood flow restrictions have identified HR as the dominant factor over SBP. ⁴¹ , ⁴²

Further post hoc analyses revealed that the relationship between RPP and NfL did not vary based on age, gender, or APOE ε4+ status of the participants. This is not consistent with an existing report of plasma NfL levels being significantly greater in APOE ε4 carriers. ⁴³ It is worth noting that there are differences between this and our study sample, which may account for the disparate results. For example, a study conducted on plasma ⁴³ had a greater proportion of older and male participants compared to our cohort.

4.3. Limitations

This study has some limitations. Our cohort was composed mainly of highly educated, non‐Hispanic White individuals. This limits the generalizability of our results, as this is not an accurate representation of the US population. Moreover, racial and ethnic minoritized groups are at higher risk of developing CVD and neurodegenerative diseases, ⁴⁴ , ⁴⁵ so it will be important to look into the observed associations within such populations in the future. The cross‐sectional design of the study does not allow for the distinction of causality versus co‐occurrence in our findings, so future longitudinal studies will be necessary for fully investigating the temporality of the observed relationship. While the observed effect is small in this study, we think it could be due to the variability in CSF NfL between subjects. ⁴⁶ , ⁴⁷ We believe that other factors, not accounted for in our model, could help strengthen the relationships tested. For instance, data on comorbidities, medication use that may affect the measurements of RPP, and cardiovascular history were unavailable at the time of our analyses and thus were not included in the model. We, however, consider them to be important covariates for subsequent studies pending availability.

4.4. Future directions

Given the novel nature of this study, our finding could form the basis for further longitudinal inquiries into this relationship in a larger cohort of cognitively unimpaired versus impaired participants. It will also be important to look at the impact of RPP on other relevant outcomes (eg, cognition) as well as the influence of possible antecedents (eg, physical activity or fitness) on RPP.

4.5. Conclusion

In summary, we report that higher RPP is related to higher levels of axonal biomarker NfL. Our findings suggest that RPP may be associated with axonal degeneration, which in turn may be driven by alterations in basal HR. Although our findings do not survive corrections for multiple comparisons, we do believe that they merit further investigation, both in larger samples and longitudinally.

CONFLICT OF INTEREST STATEMENT

H.Z. has served on scientific advisory boards and/or as a consultant for AbbVie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). K.B. has served as a consultant and on advisory boards for AC Immune, Acumen, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte., Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai, and Roche Diagnostics; and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. S.C.J. has served as a consultant to Roche Diagnostics, AlzPath, and Enigma Biomedical. The NeuroToolKit robust prototype assays are for investigational purposes and are not approved for clinical use. cobas and ELECSYS are trademarks of Roche. All other product names and trademarks are the property of their respective owners. C.O., J.S., S.L., Y.M., C.G., B.B., C.C., N.C., S.A., J.P.M., and O.O. have nothing to disclose. Author disclosures are available in the Supporting Information.

CONSENT STATEMENT

All study procedures were approved by the University of Wisconsin–Madison Institutional Review Board, and each subject provided informed consent prior to participation.

Supporting information

Supporting Information

Odo CC, Strong J, Lose SR, et al. Cardiovascular rate pressure product is associated with NfL in older adults at risk for AD. Alzheimer's Dement. 2025;17:e70086. 10.1002/dad2.70086