Acute Myocardial Infarction and Risk of Cognitive Impairment and Dementia: A Review

Cardiovascular health and its association with dementia, Parkinson's Disease, and mortality among UK older adults

Background

Previous research has primarily examined individual factors of cardiovascular health (CVH) and disease in PD and dementia, but no study has examined CVH measures with PD, dementia, and mortality simultaneously while accounting for potentially confounding factors.

Objectives

To examine the relationship between CVH, all-cause dementia, Parkinson's disease (PD), and mortality, focusing on associations and health transitions from a large population-based study.

Methods

We investigated these relationships using Cox Proportional Hazards and multistate parametric models with Weibull regression from the UK Biobank data (n = 269,816, Age = 50 + y individuals, ≤15y follow-up, 2006-2021).

Results

Full Cox models found poor CVH (measured with standardized reverse-coded Life's Essential 8 total score, LE8zrev), to be associated with increased risks for all-cause dementia (Hazard Ratio (HR) = 1.14, 95 % CI: 1.11-1.18, P < 0.001) and all-cause mortality (HR = 1.31, 95 % CI: 1.29-1.33, P < 0.001). Unlike "Healthy to PD" and "Dementia→Death" transitions, PD→Death (Weibull full model: HR = 1.18, 95 % CI: 1.06-1.31, P = 0.002), Healthy→dementia (HR = 1.15, 95 % CI: 1.12-1.19, P < 0.001), and Healthy→Death (HR = 1.33, 95 % CI: 1.32-1.35, P < 0.001) exhibited a positive relationship with poor CVH.

Conclusions

Poor CVH is directly associated with an increased risk of mortality from PD, transition into Dementia, and all-cause mortality without dementia or PD occurrence. Clinicians should aggressively screen for and manage CVH risk measures to reduce the risk of poor cognitive health outcomes.

Prevalence of Depression, Anxiety and Post-Traumatic Stress Disorder (PTSD) After Acute Myocardial Infarction: A Systematic Review and Meta-Analysis

Background: Mental illnesses following an acute myocardial infarction (AMI) are a growing concern, as they are associated with worse outcomes for AMI patients. Our understanding of the prevalence of mental illnesses after an AMI is incomplete, as most studies investigate depression while overlooking other conditions like anxiety and PTSD. Existing studies often rely on patient-reported questionnaires for mental illness diagnoses, a method that can be subjective. To address this, we conducted a systematic review and meta-analysis to determine the prevalence and risk factors of depression, anxiety, and PTSD after AMI, including only studies with formal mental illness diagnoses. Methods: Searches in MEDLINE, EMBASE, and PsycINFO up to 23 January 2025 identified 23 qualifying studies that assessed the prevalence of depression, anxiety, and PTSD after AMI, with cases defined exclusively by formal diagnoses established through psychiatrist-administered structured interviews according to the Diagnostic and Statistical Manual for Mental Disorders (DSM) criteria (versions III to V). For each outcome, the pooled prevalence was estimated using meta-analyses of proportions with random-effects models. If significant heterogeneity was detected, subgroup analyses and meta-regression were performed to explore the factors contributing to this heterogeneity. Results: A total of 25 studies were included in the meta-analysis. Among the 20 studies included, the pooled prevalence of depression after AMI was 23.58% (95% CI: 22.86%; 24.32%). When stratified by time since AMI, the prevalence was 19.46% (95% CI: 15.47%; 24.19%) for those assessed within 3 months and 14.87% (95% CI: 9.55%; 22.43%) for those assessed after 3 months. The pooled prevalence of anxiety (seven studies) and PTSD (three studies) was 11.96% (95% CI: 6.15; 21.96%) and 10.26% (95% CI: 5.49%; 18.36%), respectively. Further pooled prevalence subgroup analysis of depression and anxiety revealed significantly higher rates in the female sex (29.89%, 95% CI: 21.85; 39.41%), in those with hypertension (25.01%, 95% CI: 21.68; 28.67%), diabetes (25.01%, 95% CI: 21.68; 28.67%), or hyperlipidemia (28.96% 95% CI: 23.44; 35.17%), and in smokers (25.23%., 95% CI: 19.48; 32.00%), while the pooled prevalence of depression is higher in unmarried (35.44%, 95% CI: 19.61; 55.26%) than married individuals (28.63%, 95% CI: 18.67; 41.20%) and in those with a history of depression (57.41%, 95% CI: 31.47; 78.92%). The results of the meta-regression indicated that a prior history of depression was a significant predictor of depression prevalence (p = 0.0035, regression coefficient 1.54). Conclusions: The prevalence of mental illnesses, including depression, anxiety, and PTSD, is notable following an AMI. Identified risk factors encompass female sex, hypertension, diabetes mellitus, hyperlipidemia, smoking, a history of depressive illness, and social context.

Cognitive performance classification of older patients using machine learning and electronic medical records

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. This study aims to address challenges in current systems by leveraging readily available electronic medical record (EMR) data to simplify and enhance the classification of cognitive impairment. The analysis includes 283 older adults, categorized into three groups: 144 individuals with mild cognitive impairment (MCI), 38 with dementia, and 101 healthy controls. Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. Key predictors for distinguishing healthy controls from individuals with MCI are identified. These are history of myocardial infarction, vitamin D3 levels, the Instrumental Activities of Daily Living (IADL) scale, age, and sodium levels. The nonlinear Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieve the best performance for MCI classification, with an accuracy of 69%, an AUC of 0.75, and a Matthews Correlation Coefficient (MCC) of 0.43. For distinguishing healthy controls from those with dementia, the most influential factors include the IADL scale, the Activities of Daily Living (ADL) scale, education, vitamin D3 levels, and age. Here, the Random Forest algorithm demonstrates superior performance, achieving 84% accuracy, an AUC of 0.96, and an MCC of 0.71. These two models consistently outperform other ML techniques, such as K-Nearest Neighbors, Multi-Layer Perceptron, linear SVM, Naive Bayes, Quadratic Discriminant Analysis, Linear Discriminant Analysis, AdaBoost, and Gaussian Process Classifiers. The findings suggest that EMR data can be an effective resource for the initial classification of cognitive impairments. Integrating these ML-driven approaches into primary care settings may facilitate the early identification of older patients who could benefit from further cognitive assessments.

Risk factors of long-term brain health outcomes after hospitalization for critical illness

BACKGROUND

Brain health may be impaired years after hospitalization for critical illness, and similar impairments occur after hospitalization for COVID-19. However, it remains unclear which patients are most likely to experience long-term brain health consequences and whether these adverse events differ between non-COVID critical illness and COVID-19.

METHODS

In a prospective observational study, we enrolled patients hospitalized for (1) non-COVID critical illness (pneumonia, myocardial infarction, or ICU-requiring conditions) or for (2) COVID-19, from March 2020 to June 2021. Brain health was assessed at 18-month follow-up with cognitive, psychiatric, and neurological tests. We used both logistic regression and prediction models to test for associations between different variables and brain health.

RESULTS

We included 245 patients: 125 hospitalized for non-COVID critical illness and 120 for COVID-19 [mean age 61.2 (± 13.6) years, 42% women]. Brain health was impaired in 76% of patients (72% critical illness, 81% COVID-19; p = 0.14) at 18-month follow-up. The strongest predictive factors associated with impaired brain health were education < 13 years, age ≥ 70 years, and neuroticism traits in the best performing model (AUC = 0.63). When analyzing non-COVID critical illness and COVID-19 patients separately, low education was one of the few factors associated with impaired brain health in both groups (AUCs for best models: 0.66 and 0.69).

CONCLUSION

Brain health is comparably impaired after hospitalization for critical illness and COVID-19. Factors like higher age, lower education and neuroticism may help identifying vulnerable individuals, who could benefit from close monitoring to improve brain health after critical illness, regardless of the underlying disease etiology.

Sleep quality associate with the increased prevalence of cognitive impairment in coronary artery disease patients: A retrospective case-control study

Background

The pathogenesis of cognitive impairment (CI) in coronary artery disease (CAD) patients is still unclear and numerous influence factors could affect the CI status. The current studies suggest that sleep quality and behavior pattern are significant influence factors associated with CAD susceptibility.

Methods

A total of 223 participants including 90 CAD patients with CI and 133 controls were enrolled into this retrospective study. Demographic information, laboratory test results, clinical diagnostic data, and questionnaire survey were collected to recognize the influencing factors of CI in CAD patients. Appropriate statistical methods are used to analyze these collected data.

Results

Univariate analysis results of demographic information, laboratory test results, and questionnaire survey data revealed that the differences in fatigue symptom, age, HDL, TG, and sleep quality were statistically significant (p = 0.006, p = 0.000, p = 0.019, p = 0.028, and p = 0.037, respectively). Logistic regression analysis showed that age, fatigue, and sleep quality were the influence factors for CI in CAD population (p = 0.000, p = 0.035, and p = 0.017).

Conclusions

Sleep quality, fatigue, and age were associated with the increased susceptibility of CI in CAD patients. Both CI state and its related factors were involved in the pathological process of CAD, these findings could offer additional information for the prevention and control of CAD.

The Long-Term Impact of Cardiac Rehabilitation on Cognitive Function in Older Patients With Myocardial Infarction: A Systematic Review

Myocardial Infarction (MI) is an obstruction in the coronary arteries, resulting in restricted blood flow and oxygen supply to the heart, leading to damage to the heart's tissues. Beyond the cardiovascular system, the impact of MI extends to potentially affecting cognitive abilities, especially in elderly populations. To optimize patient recovery and long-term outcomes, timely cardiac interventions and subsequent rehabilitation programs are essential. This systematic review aims to assess the potential benefits of cardiac rehabilitation (CR) in enhancing cognitive function among elderly individuals who have experienced an MI. The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and utilizes PubMed, PubMed Central, Cochrane, Google Scholar, and ScienceDirect databases. Studies included in the review encompass meta-analyses, controlled trials, systematic/narrative reviews, randomized/nonrandomized trials, observational studies, and research articles published within the past five years. Only accessible, full-text English-language studies meeting the inclusion criteria are selected, while books, documents over five years old, animal studies, and individuals under 65 are excluded. Following a predefined template, the initial search identifies 4,915 studies. From this pool, 27 free full-text articles are then selected for quality appraisal based on relevance. After performing a quality assessment on each survey, 12 high-quality studies are included in this systematic review. The research studies demonstrate notable cognitive improvements among elderly patients who have experienced an MI and participated in CR programs. Additional clinical trial studies are recommended to substantiate these findings further and advance our understanding.

Analysis and prevention strategies of risk factors for postoperative stroke complications in cardiac surgery

OBJECTIVE

To analyze the risk factors associated with postoperative stroke complications in cardiac surgery.

METHODS

A retrospective analysis was conducted on the clinical data of 549 patients who underwent cardiac surgery. Among these patients, 501 did not experience a stroke postoperatively (non-stroke group), while 48 developed a postoperative stroke (stroke group). Patients who experienced a stroke postoperatively were divided into two groups based on the type of surgery: those who underwent surgery with cardiopulmonary bypass (18 patients) and those without cardiopulmonary bypass (30 patients). The clinical characteristics of the two groups of patients with postoperative strokes were compared, and the risk factors influencing the occurrence of postoperative stroke complications in cardiac surgery were analyzed.

RESULTS

① Clinical findings: Cardiopulmonary bypass group had lower cortical infarction rates but higher large-area and bilateral infarction rates compared to the non-cardiopulmonary bypass group (p < 0.05). No significant gender, age, or infarction type differences were observed (p > 0.05). ② Univariate analysis: No significant differences were found in gender, smoking, alcohol, lipids, or glucose levels (p > 0.05). However, age, education, hypertension, diabetes, hypotension, and atrial fibrillation showed significant differences (p < 0.05). ③ Multivariate Logistic regression: Age, education, hypertension, diabetes, hypotension, and atrial fibrillation were independent risk factors for postoperative stroke complications (p < 0.05).

CONCLUSION

Cardiopulmonary bypass increases risk of large-area and bilateral strokes; non-bypass surgery associates with cortical strokes. Age, educational level, hypertension, diabetes, postoperative hypotension, and atrial fibrillation are all factors independently associated with the occurrence of postoperative cerebral infarctions in cardiac surgery. Early interventions may reduce postoperative strokes.

The Impact of Geriatric Conditions in Elderly Patients with Coronary Heart Disease: A State-of-the-Art Review

The growing geriatric population presenting with coronary artery disease poses a primary challenge for healthcare services. This is a highly heterogeneous population, often underrepresented in studies and clinical trials, with distinctive characteristics that render them particularly vulnerable to standard management/approaches. In this review, we aim to summarize the available evidence on the treatment of acute coronary syndrome in the elderly. Additionally, we contextualize frailty, comorbidity, sarcopenia, and cognitive impairment, common in these patients, within the realm of coronary artery disease, proposing strategies for each case that may assist in therapeutic approaches.

Modulating Mitochondrial Dynamics Mitigates Cognitive Impairment in Rats with Myocardial Infarction

BACKGROUND

We have previously demonstrated that oxidative stress and brain mitochondrial dysfunction are key mediators of brain pathology during myocardial infarction (MI).

OBJECTIVE

To investigate the beneficial effects of mitochondrial dynamic modulators, including mitochondrial fission inhibitor (Mdivi-1) and mitochondrial fusion promotor (M1), on cognitive function and molecular signaling in the brain of MI rats in comparison with the effect of enalapril.

METHODS

Male rats were assigned to either sham or MI operation. In the MI group, rats with an ejection Fraction less than 50% were included, and then they received one of the following treatments for 5 weeks: vehicle, enalapril, Mdivi-1, or M1. Cognitive function was tested, and the brains were used for molecular study.

RESULTS

MI rats exhibited cardiac dysfunction with systemic oxidative stress. Cognitive impairment was found in MI rats, along with dendritic spine loss, blood-brain barrier (BBB) breakdown, brain mitochondrial dysfunction, and decreased mitochondrial and increased glycolysis metabolism, without the alteration of APP, BACE-1, Tau and p-Tau proteins. Treatment with Mdivi-1, M1, and enalapril equally improved cognitive function in MI rats. All treatments decreased dendritic spine loss, brain mitochondrial oxidative stress, and restored mitochondrial metabolism. Brain mitochondrial fusion was recovered only in the Mdivi-1-treated group.

CONCLUSION

Mitochondrial dynamics modulators improved cognitive function in MI rats through a reduction of systemic oxidative stress and brain mitochondrial dysfunction and the enhancement of mitochondrial metabolism. In addition, this mitochondrial fission inhibitor increased mitochondrial fusion in MI rats.