Cardiovascular Disease and Alzheimer's Disease: The Heart–Brain Axis

ABSTRACT

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in aging adults across the United States. Prior studies indicate that the presence of atherosclerosis, the pathogenic basis of CVD, is linked with dementias. Alzheimer's disease (AD) and AD‐related dementias are a major public health challenge in the United States. Recent studies indicate that ≈3.7 million Americans ≥65 years of age had clinical AD in 2017, with projected increases to 9.3 million by 2060. Treatment options for AD remain limited. Development of disease‐modifying therapies are challenging due, in part, to the long preclinical window of AD. The preclinical incubation period of AD starts in midlife, providing a critical window for identification and optimization of AD risk factors. Studies link AD with CVD risk factors such as hypertension, inflammation, and dyslipidemia. Both AD and CVD are progressive diseases with decades‐long development periods. CVD can clinically manifest several years earlier than AD, making CVD and its risk factors a potential predictor of future AD. The current review focuses on the state of literature on molecular and metabolic pathways modulating the heart–brain axis underlying the potential association of midlife CVD risk factors and their effect on AD and related dementias. Further, we explore potential CVD/dementia preventive strategies during the window of opportunity in midlife and the future of research in the field in the multiomics and novel biomarker use era.

Nonstandard Abbreviations and Acronyms

Aβ

amyloid beta

A plethora of shared pathophysiological processes link the cardiovascular and the cerebrovascular system forming the heart–brain axis. Abnormalities in the heart–brain axis are likely associated with the incidence of cardiovascular disease (CVD) and Alzheimer's disease (AD), 2 of the leading aging‐related chronic diseases. The precise mechanisms and molecular processes that modulate the heart–brain axis remain elusive. However, there are several common CVD risk factors ¹ , ² , ³ , ⁴ , ⁵ that are increasingly linked with AD dementia and AD‐related dementias incidence.

The links between CVD and AD have been confirmed in observational cohorts as well as experimental data. The presence of coronary artery disease is independently associated with circulating amyloid beta (Aβ1‐40) levels among adults with normal cognitive function or patients with AD. ⁶ , ⁷ , ⁸ Individuals with more favorable levels of cardiovascular health have a significantly lower risk for several of the leading causes of death, including coronary heart disease, AD, and death. ⁹ , ¹⁰ Thus, the increasing efforts of the American Heart Association and American Stroke Association in areas of brain health are also well poised to drive toward improvement in the leading causes of death and disability (including AD, depression, and substance abuse) that directly influence years of life lost and years of disability. ⁹ , ¹¹ , ¹²

As such, efforts to maintain and improve cardio and cerebrovascular health for curtailing mortality and disability in the United States, and globally, is a shared fundamental priority from a public health domain. ⁹ Early prediction and detection of CVD and AD are far from optimal at present ¹³ although it is reasonable to conclude that CVD prevention strategies are years ahead compared with AD prevention currently.

Better understanding of the shared mechanisms and specific metabolic pathways that modulate the heart–brain axis could inform the development of early risk stratification algorithms for both AD and CVD. Further, the identification of targets for prevention and interventions can promote healthier aging. Contemporary advances in biomarkers for AD using mass spectrometry‐based assays for plasma Aβ and phosphorylated‐tau 181 and use of multiomics technologies are an exciting promise for the near future. Literature focusing on the recent developments in the inquiry of common risk factors using multiomics data in the heart–brain axis is limited. This article examines the current state and future risk stratification strategies, integrating multiomic data with phenotypic, genetic, as well as cutting‐edge biofluid markers and state‐of the art brain pathology imaging, for the heart–brain axis alterations and mechanism of pathology.

The Heart of the Problem: Atherosclerosis Versus Arteriosclerosis?

Approximately >10% of adults ≥65 years are affected by AD dementia making it one of the leading causes of death worldwide. ¹⁴ Although the pathophysiologic mechanisms for AD have not been fully elucidated, studies link AD with CVD manifested by hypertension ¹⁵ , ¹⁶ and intra‐ and extracranial atherosclerosis and arteriosclerosis. ¹⁷ Both AD and CVD are progressive diseases with decades‐long incubation periods before clinical manifestation. ¹⁸ Although aging is the greatest risk factor, AD and CVD also share several modifiable risk factors, such as smoking, lack of physical exercise, hyperlipidemia, and hypertension. ¹⁹ , ²⁰ , ²¹ Furthermore, recent studies have suggested that subclinical CVD in midlife may be associated with incidence of dementia, including AD dementia, in late life. ²² , ²³ , ²⁴

Atherosclerosis is the deposition of fibrofatty lesions in the arterial walls, and arteriosclerosis is the stiffening of the media of the arterial wall as a result of degeneration of connective tissue, particularly elastin. ²⁵ , ²⁶ Although both atherosclerosis and arteriosclerosis commonly occur together, they are thought to have differing causes and classical risk factors. The pathogenesis of atherosclerosis is centralized to the collection of lipoproteins (mainly low‐density lipoprotein particles in the subendothelial intima). ²⁷ The smaller and cholesterol enriched lipoprotein particles easily cross the arterial wall and undergo modification via oxidation, acetylation, and aggregation. ²⁸ These modifications allow an easier capture by macrophages and smooth muscle cells, which then form foam cells inducing an inflammatory cascade response. Under normal physiological conditions, the high‐density lipoprotein prevents the accumulation of cholesterol in macrophages by promoting cholesterol efflux and its return to the liver. ²⁹ , ³⁰ Clinical events in arteriosclerosis are postulated to be secondary to the systolic hypertension that results from aortic stiffening as well as other adverse hemodynamic effects.

One‐third of AD‐related dementias are attributable to modifiable atherosclerotic CVD risk factors, such as hypertension, which promote the accumulation of amyloid, the hallmark pathologic misfolded protein in the brains of patients with AD. Atherosclerosis itself is associated with hypoxia, inflammation, oxidative stress, and the accumulation of advanced glycation end products, all of which are factors that can enhance the deposition and/or reduce clearance of amyloid in the brain as described in Figure 1. This may explain, in part, the elevation in plasma Aβ levels in preclinical AD. For decades, researchers have observed mixed association between atherosclerosis and AD. ¹ , ¹⁷ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶ Recent data have shown cerebrovascular atherosclerosis may be of a significantly higher incidence in AD compared with normal aging and those with other neurodegenerative diseases. Significantly, a strong association in community samples between circle of Willis atherosclerosis and AD has also been demonstrated. ³⁷ , ³⁸ , ³⁹

Figure 1. Amyloid beta and associated disease states.

Pathophysiological mechanisms underlying elevated amyloid beta levels and associated disease states.

How exactly does atherosclerosis or arteriosclerosis lead to AD or AD‐related dementias independently of ischemic brain lesions or neurodegenerative pathology? Atherosclerosis could contribute to brain dysfunction and axonal damage by a subtle reduction in microvascular perfusion without causing overt ischemic lesions. Blood‐flow‐independent aspects of neurovascular function, such as blood–brain barrier permeability, neurotrophic support by endothelial cells or neuroimmune modulation, could also be involved. ⁴⁰ Cerebral endothelial dysfunction is also linked to cognitive impairment without compromising cerebral blood flow supports this hypothesis. ⁴¹ , ⁴² Atherosclerosis has been independently linked to protein expression changes indicative of reduced synaptic function, excess myelination, and accumulation of the neuronal scaffolding proteins neurofilament light and medium chain suggesting a role in axonal injury. Among individuals with CVD, less AD pathology (ie, plaques and tangles) is seen for a given clinical severity, and cerebral infarctions increase the odds of clinical dementia in people with AD pathology. ⁴³ , ⁴⁴

Arteriosclerosis, marked by measures of pulse wave velocity (among others), is associated with cognitive impairment. ⁴⁵ The brain, which is both a high flow and low impedance organ, is susceptible to damage from increased pulse pressures. ⁴⁶ Increased pulse pressure is also associated with cerebrospinal fluid Aβ and tau levels. ⁴⁷ Hypertension, a primary factor in arteriosclerosis formation, is associated with in vivo measures of Aβ deposition ⁴⁸ , ⁴⁹ and Aβ interacts with vascular risk factors to increase cortical thinning. ⁵⁰

Shared Risk Factors

Traditional risk factors of CVD, including hypertension, ⁵¹ hyperlipidemia, ⁵² metabolic syndrome, ⁵³ and cigarette smoking, ⁵⁴ may predispose individuals to AD by promotion of amyloid accumulation in the brain. ⁶ In addition, subclinical atherosclerosis in coronary, carotid, and femoral arteries has been linked with dementia and AD incidence. ⁵⁵ , ⁵⁶ This association is particularly strong in the setting of specific genetic variants, such as in APOε4 (apolipoprotein E4 polymorphism) carriers. ¹⁷ , ²⁰ At a molecular level, endothelial dysfunction, inflammation, and oxidation, which are precursors of atherosclerosis, ⁵⁷ are associated with disequilibrium in Aβ clearance and degradation. ⁵⁸ Dysregulated Aβ clearance has been demonstrated in adults with CVD risk factors and clinical CVD (myocardial infarction and acute coronary syndromes) but no cognitive dysfunction. ⁷ , ⁵⁸ The shared risk factors between AD and CVD are summarized in Figure 2.

Figure 2. Alzheimer's disease and cardiovascular disease: risk factors and mechanisms.

Shared risk factors between Alzheimer's disease pathology and cardiovascular diseases including myocardial infarction, stroke, and cardiac death. APOE4 indicates apolipoprotein E4.

Physical Activity

Lack of physical activity is linked to a higher risk of CVD, and women remain less active than men at all ages. Both being inactive and sitting for long periods of time can increase CVD and AD risk. ⁵⁹ The 2019 American College of Cardiology/American Heart Association CVD prevention guidelines ⁶⁰ emphasize that a comprehensive lifestyle intervention encompasses regular self‐monitoring of food intake, physical activity, and weight. It is recommended to engage in aerobic moderate‐intensity physical activities, such as brisk walking, for a minimum of 150 minutes per week. This equates to at least 30 minutes of activity on most days of the week. However, to sustain weight loss, individuals are encouraged to pursue higher levels of physical activity, typically ranging from ≈200 to 300 minutes per week.

Smoking

Smoking and smokeless tobacco (chewing tobacco and other forms) use are a well‐known cause of CVD and all‐cause mortality. ¹¹ A reduction in smoking has previously shown ≈13% decline in the incidence of atherosclerotic CVD risk. ⁵⁸ , ⁶¹ Cigarette smoking also exacerbates some of the typical neuropathological alterations associated with AD in vivo. ⁶² Midlife heavy exposure to smoking is associated with a higher risk of dementia, including AD, in later life. ⁶³ Possible mechanisms by which smoking increases both AD and CVD risk include increase of oxidative stress, impairments of the proteostasis machinery, and higher inflammatory burden.

Hypertension

Hypertension has been called the harbinger of stroke, dementia, and CVD. Approximately 46% of US adults have hypertension (defined as systolic blood pressure [SBP] ≥130 mm Hg or diastolic blood pressure [DBP] ≥80 mm Hg). The prevalence of hypertension is higher in Black individuals as compared with White, Asian, and Hispanic counterparts in the United States and rises noticeably with older age. It is also important to note that the race‐ or ethnicity‐related differences in hypertension exist in the context of socioeconomic and access‐related disparities. ⁶⁴ Reduction in SBP has proven beneficial in prevention of CVD and mild cognitive impairment ⁶⁵ ; however, there is an increased risk of renal function decline with stringent blood pressure control. ⁶⁶

Hypertension also rises noticeably with older age and is higher in older women than in older men. ⁶⁷ The association of blood pressure with AD in older adults has been described as U‐shaped in several studies in the older adults. ⁶⁶ In the large prospective HUNT Study (Trøndelag Health Study) in Norway, 24 638 participants (60 years or older), SBP was inversely associated with all‐cause dementia and AD but not with vascular dementia. Conversely, among middle‐aged subjects (<60 years old), elevated SBP and pulse pressures were associated with eventual AD in participants who reported using antihypertensive medication. ⁶⁸

Hypercholesterolemia

Hypercholesterolemia has been best known to cause atherosclerosis leading to CVD. ⁶⁹ , ⁷⁰ , ⁷¹ , ⁷² , ⁷³ , ⁷⁴ , ⁷⁵ , ⁷⁶ Metabolism of cholesterol and mechanisms to protect cells from excess cholesterol accumulation. Cholesterol metabolism includes, in general, de novo synthesis, uptake, and efflux of cholesterol. Cholesterol can be synthesized in almost all mammalian cells from acetyl‐CoA through a series of reactions.

In the past 2 decades, the relationship between cholesterol and AD has been intensively investigated. Several studies have reported a cognitive decline seen with hypercholesterolemia. ⁹ , ¹¹ Zambon et al reported a higher incidence of cognitive impairment in familial hypercholesterolemia. ⁷⁷ In a Mendelian randomization study using 380 genetic variants associated with low‐density lipoprotein‐cholesterol levels as instrumental variables also suggested that low plasma low‐density lipoprotein‐cholesterol levels have a causal effect in reducing the risk of AD. ⁷⁸ Although plasma cholesterol cannot directly affect neurons, animal studies have found that hypercholesterolemia may damage the integrity of the blood–brain barrier, can alter the clearance of Αβ in the brain, and also increase Αβ production as cholesterol in lipid rafts promotes the activity of g‐secretase (the enzyme responsible for Αβ production). ³⁴ Plasmalogen species, derivatives of ether lipids, have been shown to affect AD‐related dementias ⁷⁹ , ⁸⁰ , ⁸¹ modulated by an anti‐inflammatory effect. We have recently shown that 3 plasmalogen species are inversely associated with longitudinal CVD ⁸² pending replicative studies.

Diabetes, smoking, air pollution, depression, social isolation, and education levels have also been identified as potentially modifiable late‐life dementia and CVD risk factors. Diabetes and physical inactivity are correlated with adverse vascular profiles as well as increased inflammation and oxidative stress in the brain. Inflammatory responses and thus, worsening CVD and AD risk, have also been associated with smoking and air pollution, which enhance reactive oxygen and inflammatory responses. Depression and social isolation are also associated with accelerated brain and cardiovascular aging and poor health behaviors.

Risk Stratification

Novel Biomarkers

Biomarkers with predictive value for CVD ⁸³ such as peripheral inflammation (hs‐CRP [high‐sensitivity C‐reactive protein]), subclinical myocardial injury (hs‐cTnT [high‐sensitivity cardiac troponin T]), fibrosis (galectin‐3), and cardiac myocytes stretch response hormone (NT‐proBNP [N‐terminal pro‐brain natriuretic peptide]) ⁸⁴ , ⁸⁵ are evident several years before clinical onset of CVD events. ⁷¹ , ⁸⁶ , ⁸⁷ , ⁸⁸ , ⁸⁹ , ⁹⁰ An association of hs‐CRP with postmortem AD brains has previously been identified. ⁹¹

Recently identified plasma biomarkers of AD (including plasma Aβ40, Aβ42, and phosphorylated‐tau 181 and 217) are surrogate indicators of brain pathology of AD and may improve the risk prediction. ⁹² , ⁹³ , ⁹⁴ , ⁹⁵ , ⁹⁶ , ⁹⁷ It has also been demonstrated that levels of reactive astrocyte markers (GFAP [glial fibrillary acidic protein] and YKL40 [chitinase‐3‐like protein 1]) mediate the effects of Aβ and tau pathologies on hippocampal atrophy and cognitive impairment. ⁹⁸ Both GFAP and YKL40 are strongly correlated with several neuroinflammatory proteins previously linked with AD progression. In addition, neurofilament light chain has prognostic and diagnostic utility to detect clinical and preclinical AD risk. ⁹² , ⁹⁹ , ¹⁰⁰ , ¹⁰¹

Such an advance in biomarkers opens several potential mechanistic pathways to study the interplay between the 2 diseases and identify individuals at high risk for AD earlier in life. It remains imperative to explore whether midlife CVD biomarkers are associated with elevated AD biomarkers, which can predate the tau and Aβ deposition in positron emission tomography scans in late life. This can more succinctly identify those at the highest risk of AD or vice versa.

Metabolomics

Metabolomics, a study used to measure levels of small molecule metabolites in biological samples, represents one of the best omics platforms for the diagnosis and prognosis of sporadic AD. Metabolomics can generate explainable readouts defining the entire biological continuum of a disease through the parallel quantification of several hundred to thousands of small molecules (<1500 Da). ¹⁰² , ¹⁰³ The metabolome reflects interactions between our genetics, protein profiles, and environmental influence. ¹⁰⁴ , ¹⁰⁵ Deficiencies in several lipid classes, such as phospholipid, phosphatidylcholins, phosphatidylinositol, sphingolipid, lysophospholipid, sphingomyelin, and sterols, have been reported in brain tissue, cerebrospinal fluid, and blood in patients with dementia. ¹⁰³ , ¹⁰⁶

In recent metabolomic studies of neurotypical older adults with preclinical AD, researchers found depletion of glycerophospholipids and alterations of metabolites involved in cellular energy metabolism, suggesting a shift in energy metabolism from oxidative phosphorylation to fatty acid β oxidation. ¹⁰⁷ , ¹⁰⁸ , ¹⁰⁹ Mapstone et al have shown that lipid rafts, the highly dynamic membrane domains rich in cholesterol, glycosphingolipids, and glycosyl‐phosphatidylinositol glycosylphosphatidylinositol‐anchored proteins, ³⁶ could facilitate the aggregation of Aβ, which may indicate loss of membrane function and neurodegeneration in mild cognitive impairment. ¹ , ² , ¹¹⁰

Investigating the potential overlaps between blood and brain metabolites is important both scientifically for precise risk stratification and scientific discovery. First, the current literature has little information regarding the relationship between circulating CVD or AD metabolites and brain metabolites from same individuals. Understanding the potential overlap or interaction between peripheral and central metabolism will enhance our understanding of the complex mechanisms underpinning the heart–brain axis. Second, for AD and CVD at an individual level, the metabolomic and proteomic data can help identify potential therapeutic targets. Specifically, circulating metabolites or proteomic data that are assessed in community samples be used as biomarkers for AD/CVD risk prediction, and meanwhile metabolites in postmortem brain tissue could help us understand disease mechanisms. Classifying the overlapping metabolites in the blood and brain may be a likely pathway to novel mechanistic markers of AD and CVD.

Proteomics

During the past decade, proteomic throughput capabilities have increased noticeably as a result of technological developments (including sample preparation, mass spectrometry‐based analysis, database searching, and bioinformatics techniques that facilitate data interpretation). The translation of proteomic discoveries to meaningful clinical applications in cardiovascular and cerebrovascular medicine has the primary benefit of providing an unbiased evaluation of complex protein mixtures.

Recently validated targeted proteomic signature composed primarily of inflammatory markers (cytokines and chemokines) in older neurotypical adults with AD identified prevalent and incident AD with very high accuracy. Wingo et al ¹¹¹ examined a brain proteomic study in community‐based cohorts with mild cognitive impairment and AD dementia. Atherosclerosis noted in large intracranial arteries was associated with mild cognitive impairment–AD independent of 8 other brain pathologies examined in postmortem specimens, including Aβ, neurofibrillary tangles, atherosclerosis of the circle of Willis and proximal arteries, and gross and micro infarcts. ¹¹¹

Transcriptomics

Gene expression profiling, also known as transcriptomics, has been revolutionized in the era of rapidly progressive next‐generation sequencing techniques. ¹¹² , ¹¹³ Transcriptomic expression of genes in diseased and nondiseased states can identify individual genes or genetic signature composed of multiple expression changes associated with disease. Transcriptomic are also being used to assess patients' precise treatment responses in CVD ¹¹⁴ ; recently, a therapeutic response to beta‐blocker therapy in heart failure was discovered through a transcriptomic analysis of molecular pathways in endomyocardial biopsies. ¹¹⁵ Transcriptome profiling of AD brains has also discovered possible mechanistic pathways involved in progression of AD pathology. ¹¹⁶ Recently, Liu and coworkers identified 74 differentially expressed genes and largely immune‐related pathways shared in AD pathology and ischemic stroke. ¹¹⁷

Transcriptomics, therefore, remains a highly important in expanding our understanding of shared AD and CVD pathological processes but also in identifying novel biomarkers for risk stratification and potential treatment targets.

Image‐omics

At the crossroads of biology and machine learning is the emerging field of imageomics, which uses artificial intelligence to extract biological information directly from images. ¹¹⁸ Leveraging the power of artificial intelligence for pattern recognition for disease prediction with routine imaging is a promising, albeit challenging, avenue specifically for the pathologies in the heart–brain axis. Low‐dose computed tomography scans ¹¹⁹ , ¹²⁰ , ¹²¹ , ¹²² and ultrasound measurement of carotid intima media thickness ¹²³ have already been used predictors of atherosclerotic CVD. Although ultrasonography is inexpensive, convenient, and noninvasive, operator‐related reproducibility is a challenge. Magnetic resonance imaging is increasingly being used in population‐based studies because it provides high tissue contrast without using ionizing radiation unlike computed tomography scans. Magnetic resonance imaging has generated substantial scientific knowledge about the human brain in population studies, such as the UK Biobank prospective epidemiological study, ¹²⁴ the Rotterdam Study, ¹²⁵ and the 1000BRAINS study. ¹²⁶ Deep learning models that combine neuroimaging and genomics data are in early stages in diagnosis and prediction of AD. ¹²⁷ , ¹²⁸ Pending necessary and comprehensive replication studies, these artificial intelligence‐derived models can potentially develop target‐specific therapeutics for personalized medicine.

The ongoing population‐based Aging Imageomics Study aims to build a repository of imaging data sets from advanced structural and functional whole‐body magnetic resonance imaging to enable the analysis of associations between imaging biomarkers and biopsychosocial parameters, cardiovascular indexes, and multiomics data with age‐related variables. ¹¹⁸ These data may inform more precise biological aging quantification methods using multifaceted data points. Thus, addition of imageomics to the currently available diagnostic armamentarium could not only lead to development of advanced imaging biomarkers to identify biopsychosocial risks associated with aging and improved clinical outcomes but also further support hypothesis generation for discovery purposes in the heart–brain axis.

Window of Opportunity for Preventive Strategies

Primordial and Primary Prevention

There is enough evidence to show that subclinical pathologies of CVD and AD start at least decades before the clinical events (including myocardial infarction, revascularization need, cognitive impairment, or death). Thus, there is a prolonged window of opportunity during which preventive interventions can reduce disease burden. Research strongly indicates curtailing risk of clinical CVD and AD will require primordial and primary prevention efforts. ⁸

Management of modifiable CVD risk factors including adhering to a heart‐healthy lifestyle, following the American Heart Association's Life's Essential 8 ¹²⁹ and optimization of hypertension, diabetes, and hyperlipidemia could not only reduce the incidence of coronary artery disease, heart failure, and stroke but could also affect the development and progression of AD. Specifically, these interventions would be most beneficial if started in midlife to encourage primordial prevention focused at preventing development of risk factors. ¹³

Given recent advances in the field, we suspect that the time is near where we may be able to create more precise risk stratification models that incorporate genomics, proteomics, and metabolomics, either at midlife or even earlier. Cross‐collaborative clinical settings with neurology and cardiology input to evaluate and risk stratify at‐risk middle and elderly individuals for primary and primordial prevention may help decrease the AD and CVD burden globally.

Recognition of high‐risk individuals in midlife (either based on family history of dementia or CVD or APOε4 genotype carriers) could be followed in specialized clinical settings, or Healthy Heart and Brain clinics (Figure 3). These clinics could use plasma and brain biomarkers, subclinical disease imaging, and multiomics data as an integrated approach for risk stratification and prevention strategies, with aggressive midlife risk factor modification aimed at APOε4 carriers to delay or counterweigh the risk of AD. In addition to traditional nutritional counseling, social stimulation, physical/cognitive training, and management of vascular risk factors, patients from these clinical settings may also provide epidemiological research data that could then provide critical insights on healthy agers among those who were initially at high risk of AD and CVD.

Figure 3. The Heart–Brain clinic.

Proposed midlife evaluation of patients at risk of cardiovascular and neurocognitive diseases in the heart–brain clinic. APOE4 indicates apolipoprotein E4; CVD, cardiovascular disease; and FDG‐PET, ¹⁸F‐fluorodeoxyglucose‐positron emission tomography.

Conclusions

Our knowledge of the interaction of heart and brain has expanded and deepened significantly over the past few decades. Instead of being siloed systems, the heart and brain are fundamentally interconnected by neurovascular and humoral pathways that form the heart–brain axis. However, the role of midlife CVDs in the development and progression of brain pathologies and late‐life AD has not been clearly defined. Further efforts to elucidate this relationship require more investigation into several critical questions. The application of state‐of‐the‐art neuroimaging, cardiac and neurofibrillary biomarkers, and proteomic and metabolomic techniques presents an unprecedented capability for more precise risk prediction of CVD and AD. The contemporary ability to dissect the cellular and molecular crosstalk mining the heart–brain axis' molecular pathways is indeed an unparalleled opportunity for scientific discovery. To study these underlying pathophysiological mechanisms to develop better diagnostic tools and define the vulnerable at‐risk populations will require cross‐collaboration across multiple disciplines and specialists, including neurologists, neurobiologists, and cardiologists. The innovative findings resulting from these efforts would not only provide novel research on prevention but could inform us with more efficient ways to treat patients with AD and related dementias as well as CVD.

Sources of Funding

None.

Disclosures

Dr Anum Saeed is supported by American Heart Association award number: 23CDA105548. The remaining authors have no financial disclosures to report.