Blood-Based Cardiac Biomarkers and the Risk of Cognitive Decline, Cerebrovascular Disease, and Clinical Events

Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features

The objective of this study was to identify a female-specific prognostic biomarker for peripheral artery disease (PAD) and develop a prediction model for 2-year major adverse limb events (MALE). Patients with/without PAD were recruited (n=461). Plasma concentrations of 68 circulating proteins were measured and patients were followed for 2 years. The primary outcome was MALE (composite of vascular intervention, major amputation, or acute/chronic limb threatening ischemia). We trained a random forest model using: 1) clinical characteristics, 2) female-specific PAD biomarker, and 3) clinical characteristics and female-specific PAD biomarker. Galectin-9 was the only protein to be significantly elevated in females compared to males in the discovery/validation analyses. The random forest model achieved the following AUROC's: 0.72 (clinical features), 0.83 (Galectin-9), and 0.86 (clinical features + Galectin-9). We identified Galectin-9 as a female-specific PAD biomarker and developed an accurate prognostic model for 2-year MALE using a combination of clinical features and plasma Galectin-9 levels.

Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data

Purpose

Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data.

Methods

We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers.

Results

The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84.

Conclusion

Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.

Inflammatory Biomarkers to Predict Major Adverse Cardiovascular Events in Patients with Carotid Artery Stenosis

Background and Objectives: Inflammatory proteins and their prognostic value in patients with carotid artery stenosis (CAS) have not been adequately studied. Herein, we identified CAS-specific biomarkers from a large pool of inflammatory proteins and assessed the ability of these biomarkers to predict adverse events in individuals with CAS. Materials and Methods: Samples of blood were prospectively obtained from 336 individuals (290 with CAS and 46 without CAS). Plasma concentrations of 29 inflammatory proteins were determined at recruitment, and the patients were followed for 24 months. The outcome of interest was a major adverse cardiovascular event (MACE; composite of stroke, myocardial infarction, or death). The differences in plasma protein concentrations between patients with vs. without a 2-year MACE were determined using the independent t-test or Mann-Whitney U test to identify CAS-specific prognostic biomarkers. Kaplan-Meier and Cox proportional hazards analyses with adjustment for baseline demographic and clinical characteristics were performed to assess the prognostic value of differentially expressed inflammatory proteins in predicting a 2-year MACE in patients with CAS. Results: The mean age of the cohort was 68.8 (SD 10.2) years and 39% were female. The plasma concentrations of two inflammatory proteins were significantly higher in individuals with a 2-year MACE relative to those without a 2-year MACE: IL-6 (5.07 (SD 4.66) vs. 3.36 (SD 4.04) pg/mL, p = 0.03) and CD163 (233.825 (SD 230.306) vs. 159.673 (SD 175.669) pg/mL, p = 0.033). Over a follow-up period of 2 years, individuals with elevated levels of IL-6 were more likely to develop MACE (HR 1.269 (95% CI 1.122-1.639), p = 0.042). Similarly, over a 2-year period, patients with high levels of CD163 were more likely to develop MACE (HR 1.413 (95% CI 1.022-1.954), p = 0.036). Conclusions: The plasma levels of inflammatory proteins IL-6 and CD163 are independently associated with adverse outcomes in individuals with CAS. These CAS-specific prognostic biomarkers may assist in the risk stratification of patients at an elevated risk of a MACE and subsequently guide further vascular evaluation, specialist referrals, and aggressive medical/surgical management, thereby improving outcomes for patients with CAS.

High-Sensitivity Cardiac Troponin T and Cognitive Function Over 12 Months After Stroke-Results of the DEMDAS Study

BACKGROUND

Subclinical myocardial injury in form of hs-cTn (high-sensitivity cardiac troponin)  levels has been associated with cognitive impairment and imaging markers of cerebral small vessel disease (SVD) in population-based and cardiovascular cohorts. Whether hs-cTn is associated with domain-specific cognitive decline and SVD burden in patients with stroke remains unknown.

METHODS AND RESULTS

We analyzed patients with acute stroke without premorbid dementia from the prospective multicenter DEMDAS (DZNE [German Center for Neurodegenerative Disease]-Mechanisms of Dementia after Stroke) study. Patients underwent neuropsychological testing 6 and 12 months after the index event. Test results were classified into 5 cognitive domains (language, memory, executive function, attention, and visuospatial function). SVD markers (lacunes, cerebral microbleeds, white matter hyperintensities, and enlarged perivascular spaces) were assessed on cranial magnetic resonance imaging to constitute a global SVD score. We examined the association between hs-cTnT (hs-cTn T levels) and cognitive domains as well as the global SVD score and individual SVD markers, respectively. Measurement of cognitive and SVD-marker analyses were performed in 385 and 466 patients with available hs-cTnT levels, respectively. In analyses adjusted for demographic characteristics, cardiovascular risk factors, and cognitive status at baseline, higher hs-cTnT was negatively associated with the cognitive domains "attention" up to 12 months of follow-up (beta-coefficient, -0.273 [95% CI, -0.436 to -0.109]) and "executive function" after 12 months. Higher hs-cTnT was associated with the global SVD score (adjusted odds ratio, 1.95 [95% CI, 1.27-3.00]) and the white matter hyperintensities and lacune subscores.

CONCLUSIONS

In patients with stroke, hs-cTnT is associated with a higher burden of SVD markers and cognitive function in domains linked to vascular cognitive impairment.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01334749.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Blood-based biomarkers of cerebral small vessel disease

Age-associated cerebral small vessel disease (CSVD) represents a clinically heterogenous condition, arising from diverse microvascular mechanisms. These lead to chronic cerebrovascular dysfunction and carry a substantial risk of subsequent stroke and vascular cognitive impairment in aging populations. Owing to advances in neuroimaging, in vivo visualization of cerebral vasculature abnormities and detection of CSVD, including lacunes, microinfarcts, microbleeds and white matter lesions, is now possible, but remains a resource-, skills- and time-intensive approach. As a result, there has been a recent proliferation of blood-based biomarker studies for CSVD aimed at developing accessible screening tools for early detection and risk stratification. However, a good understanding of the pathophysiological processes underpinning CSVD is needed to identify and assess clinically useful biomarkers. Here, we provide an overview of processes associated with CSVD pathogenesis, including endothelial injury and dysfunction, neuroinflammation, oxidative stress, perivascular neuronal damage as well as cardiovascular dysfunction. Then, we review clinical studies of the key biomolecules involved in the aforementioned processes. Lastly, we outline future trends and directions for CSVD biomarker discovery and clinical validation.

Cardiac Troponin Serum Concentration Measurement Is Useful Not Only in the Diagnosis of Acute Cardiovascular Events

Cardiac troponin serum concentration is the primary marker used for the diagnosis of acute coronary syndrome. Moreover, the measurement of cardiac troponin concentration is important for risk stratification in patients with pulmonary embolism. The cardiac troponin level is also a general marker of myocardial damage, regardless of etiology. The purpose of this study is to conduct a literature review and present the most important information regarding the current state of knowledge on the cardiac troponin serum concentration in patients with chronic cardiovascular disease (CVD), as well as on the relationships between cardiac troponin serum concentration and features of subclinical cardiovascular dysfunction. According to research conducted to date, patients with CVDs, such as chronic coronary syndrome, chronic lower extremities' ischemia, and cerebrovascular disease, are characterized by higher cardiac troponin concentrations than people without a CVD. Moreover, the literature data indicate that the concentration of cardiac troponin is correlated with markers of subclinical dysfunction of the cardiovascular system, such as the intima-media thickness, pulse wave velocity, ankle-brachial index, coronary artery calcium index (the Agatston score), and flow-mediated dilation. However, further research is needed in various patient subpopulations and in different clinical contexts.

Maintenance of brain health: The role of social determinants of health and other non-traditional cardiovascular risks

Brain health is the complete functioning of the brain across the life course to support the full physical, mental, social, and spiritual well-being and quality of life of an individual towards attaining and maintaining the epitome of a meaningful, impactful, purposeful, and productive life. The determinants of brain health are complex and include at least in part, non-traditional risks such as interactions among social, economic, physical, and internal factors (e.g., emotions and adaptations to changing life experiences), and external factors such as environment, geography, and climate change. Thus, social determinants of health (e.g., where we work, live, and play) are those non-medical factors that influence health outcomes, and as non-traditional cardiovascular factors, may influence the development of traditional cardiovascular risks. Examples of the non-traditional cardiovascular factors include environmental stressors (e.g., climate change, air pollution), and psychological and physical abuse. In this article, we provide a discussion of social determinants of health and other non-traditional cardiovascular risks as they relate to brain health.

Cerebral hypometabolism mediates the effect of stroke volume on cognitive impairment in heart failure patients

AIMS

The present study aimed to phenotype the cerebral structural and glucose metabolic alterations in patients with heart failure (HF) using simultaneous positron emission tomography (PET)/magnetic resonance (MR) and to investigate their relationship to cardiac biomarkers and cognitive performance.

METHODS AND RESULTS

Forty-two HF patients caused by ischaemic heart disease (mean age 67.2 ± 10.4, 32 males) and 32 age- and sex-matched healthy volunteers (mean age 61.3 ± 4.8, 18 males) were included in this study. Participants underwent simultaneous cerebral fluorine-18 (18 F) fluorodeoxyglucose PET/MR followed by cardiac MR scan, and neuropsychological scores were obtained to assess cognitive performance. The grey matter volume (GMV) and standardized uptake value ratio (SUVR) were calculated to examine cerebral structural and metabolic alterations. Cardiac biomarkers included cardiac MR parameters and cardiac serum laboratory tests. Mediation analysis was performed to explore the associations among cerebral alterations, cardiac biomarkers, and cognitive performance. HF patients demonstrated notable cognitive impairment compared with normal controls (P < 0.001). Furthermore, HF patients exhibited regional brain hypometabolism in the bilateral calcarine cortex, caudate nucleus, thalamus, hippocampus, precuneus, posterior cingulate cortex, lingual and olfactory cortex, and GMV reduction in bilateral thalamus and hippocampus (cluster level at P < 0.05, Gaussian random field correction). The SUVR of the hypometabolic brain regions was correlated with the Montreal Cognitive Assessment (MoCA) scores (r = 0.55, P = 0.038) and cardiac stroke volume (r = 0.49, P = 0.002). Cerebral hypometabolism played a key role in the relationship between the decreased stroke volume and MoCA scores, with a mediation effect of 33.2%.

CONCLUSIONS

HF patients suffered cerebral metabolic and structural alterations in regions associated with cognition. The observed correlation between cardiac stroke volume and cognitive impairment underscored the potential influence of cerebral hypometabolism, suggesting that cerebral hypometabolism due to chronic systemic hypoperfusion may significantly contribute to cognitive impairment in HF patients.

Chronic cerebral hypoperfusion: a critical feature in unravelling the etiology of vascular cognitive impairment

Vascular cognitive impairment (VCI) describes a wide spectrum of cognitive deficits related to cerebrovascular diseases. Although the loss of blood flow to cortical regions critically involved in cognitive processes must feature as the main driver of VCI, the underlying mechanisms and interactions with related disease processes remain to be fully elucidated. Recent clinical studies of cerebral blood flow measurements have supported the role of chronic cerebral hypoperfusion (CCH) as a major driver of the vascular pathology and clinical manifestations of VCI. Here we review the pathophysiological mechanisms as well as neuropathological changes of CCH. Potential interventional strategies for VCI are also reviewed. A deeper understanding of how CCH can lead to accumulation of VCI-associated pathology could potentially pave the way for early detection and development of disease-modifying therapies, thus allowing preventive interventions instead of symptomatic treatments.

Association of Interleukin-6 and Interleukin-8 with Cognitive Decline in an Asian Memory Clinic Population

BACKGROUND

Neuroinflammation has been postulated to play an important role in cognitive impairment, cognitive decline, and dementia. Inflammatory biomarkers such as interleukin-6 (IL-6) and IL-8 are found to be associated with the neuro-inflammatory process and worse cognitive function. However, it is unknown whether these interleukins are associated with long-term cognitive function.

OBJECTIVE

To investigate the association of baseline IL-6 and IL-8 with cognitive function at baseline as well as its association with cognitive decline over five-year follow-up.

METHODS

387 patients were recruited from an ongoing memory clinic-based study who underwent comprehensive physical, medical, neuropsychological and blood assessments together with brain MRI. IL-6 and IL-8 were measured using LUMINEX assays. The National Institute of Neurological Disorders and Stroke-Canadian Stroke Network neuropsychological battery was used to assess cognitive decline across multiple domains.

RESULTS

Among the 387 (mean age = 72.9 years and 53.7% males) participants, 322 had at least two follow-up assessments and were included in the longitudinal analysis. Negative linear trend associations were found between tertiles of IL-8 with baseline global cognition (p-trend< 0.001), attention (p-trend = 0.005), executive function (p-trend< 0.001), and visuospatial function (p-trend = 0.002) domains. No association was found between baseline IL-8 and cognitive decline. IL-6 was not associated with both baseline and follow-up cognition.

CONCLUSION

IL-8 was associated with worse cognition especially in attention, executive function, and visuospatial function, suggesting the role of neuroinflammation in cognitive impairment. Hence, blood inflammatory biomarkers may be useful indicators in identifying patients at risk of cognitive impairment and warrant consideration for inclusion in treatment trials.

Association of Arterial Spin Labeling Parameters With Cognitive Decline, Vascular Events, and Mortality in a Memory-Clinic Sample

BACKGROUND

Cognitive decline in older adults has been attributed to reduced cerebral blood flow (CBF). Recently, the spatial coefficient of variation (sCoV) of ASL has been proposed as a proxy marker of cerebrovascular insufficiency. We investigated the association between baseline ASL parameters with cognitive decline, incident cerebrovascular disease, and risk of vascular events and mortality.

DESIGN, SETTING, AND PARTICIPANTS

About 368 memory-clinic patients underwent three-annual neuropsychological assessments and brain MRI scans at baseline and follow-up. MRIs were graded for white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), cortical infarcts, and intracranial stenosis. Baseline gray (GM) and white matter (WM) CBF and GM-sCoV were obtained with ExploreASL from 2D-EPI pseudo-continuous ASL images. Cognitive assessment was done using a validated neuropsychological battery. Data on incident vascular events (heart disease, stroke, transient ischemic attack) and mortality were obtained.

RESULTS

Higher baseline GM-sCoV was associated with decline in the memory domain over 3 years of follow-up. Furthermore, higher GM-sCoV was associated with a decline in the memory domain only in participants without dementia. Higher baseline GM-sCoV was associated with progression of WMH and incident CMBs. During a mean follow-up of 3 years, 29 (7.8%) participants developed vascular events and 18 (4.8%) died. Participants with higher baseline mean GM-sCoV were at increased risk of vascular events.

CONCLUSIONS

Higher baseline GM-sCoV of ASL was associated with a decline in memory and risk of cerebrovascular disease and vascular events, suggesting that cerebrovascular insufficiency may contribute to accelerated cognitive decline and worse clinical outcomes in memory clinic participants.

Cardiac Troponin, Cognitive Function, and Dementia: A Systematic Review

Elevated cardiac troponin, a biomarker of myocardial injury, has been found in individuals with brain damage and lower cognitive function. We conducted a systematic review to examine the association of troponin with cognitive function, incidence of dementia and dementia-related outcomes. PubMed, Web of Science and EMBASE were searched from inception to August 2022. Inclusion criteria were: (i) population-based cohort studies; (ii) troponin measured as determinant; and (iii) cognitive function in any metric or diagnosis of any type of dementia or dementia-related measures as outcomes. Fourteen studies were identified and included, with a combined total of 38,286 participants. Of these studies, four examined dementia-related outcomes, eight studies examined cognitive function, and two studies examined both dementia-related outcomes and cognitive function. Studies report higher troponin to be associated with higher prevalence of cognitive impairment (n=1), incident dementia (n=1), increased risk of dementia hospitalization (specifically due to vascular dementia) (n=1), but not with incident Alzheimer's Disease (n=2). Majority of studies on cognitive function found elevated troponin also associated with worse global cognitive function (n=3), attention (n=2), reaction time (n=1) and visuomotor speed (n=1), both cross-sectionally and prospectively. Evidence regarding the association between higher troponin and memory, executive function, processing speed, language and visuospatial function was mixed. This was the first systematic review on the association between troponin, cognitive function, and dementia. Higher troponin is associated with subclinical cerebrovascular damage and might act as a risk-marker of cognitive vulnerability.

The prognostic utility of CSF neurogranin in predicting future cognitive decline in the Alzheimer's disease continuum: A systematic review and meta-analysis with narrative synthesis

Core cerebrospinal fluid (CSF) biomarkers (Aβ42, T-tau, P-tau) were included as supporting diagnostic criteria for Alzheimer's Disease (AD), but they lack the power to predict AD progression. On the other hand, a new biomarker CSF Neurogranin (Ng) has been shown to predict cognitive decline. This systematic review aims to synthesise the prognostic utility of CSF Ng in predicting cognitive decline in the AD continuum. Seven databases were searched systematically from inception to 30 September 2020. Participants were 55 years or older, who had baseline and at least one follow-up cognitive assessments. Risk of bias was assessed using the Quality in Prognosis Studies tool. Meta-analysis was conducted by pooling standardised beta coefficients and adjusted hazard ratios. Thirteen studies were included and high-quality evidence suggests that CSF Ng predicts Mini-Mental State Examination (MMSE) decline in Aβ⁺ mild cognitive impairment (MCI). Moderate quality evidence showed that CSF Ng could predict the decline of memory and executive function in MCI. Narrative synthesis found that CSF Ng/Aβ42 was also likely to predict cognitive decline. More studies are required to validate the use of CSF Ng as an AD prognostic marker and its application in future development of drug treatment and diagnosis.

Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.