Heart Stroke Research Articles

SVEGFR3 alleviates myocardial ischemia/reperfusion injury through regulating mitochondrial homeostasis and immune cell infiltration.

Recent studies have suggested that sVEGFR3 is involved in cardiac diseases by regulating lymphangiogenesis; however, results are inconsistent. The aim of this study was to investigate the function and mechanism of sVEGFR3 in myocardial ischemia/reperfusion injury (MI/RI). sVEGFR3 effects were evaluated in vivo in mice subjected to MI/RI, and in vitro using HL-1 cells exposed to oxygen-glucose deprivation/reperfusion. Echocardiography, TTC-Evans blue staining, ELISA, electron microscopy, immunofluorescence, western blotting, and flow cytometry were used to investigate whether sVEGFR3 attenuates I/R injury. Transcriptome sequencing was used to investigate the downstream mechanism of sVEGFR3. Results showed that, in vivo, sVEGFR3 pretreatment reduced cardiac dysfunction, infarct area, and myocardial injury indicators by reducing ROS production, AIF expression, and apoptosis. In vitro, sVEGFR3 restored mitochondrial homeostasis by stabilizing the mitochondrial membrane potential (MMP) and preventing the opening of mitochondrial permeability transition pores (mPTP). And sVEGFR3 inhibits mitochondrial apoptosis through the Ras/MEK/ERK pathway. Furthermore, I/R injury increased the proportion of M1 macrophages and CD4 + T cells in myocardial tissue, as well as serum IFN-γ and TNF-α levels, whereas sVEGFR3 treatment attenuated these effects. sVEGFR3 attenuates MI/RI by regulating mitochondrial homeostasis and immune cell infiltration, and reduces intrinsic ROS-mediated mitochondrial apoptosis via the Ras/MEK/ERK pathway.

BMI-mediated association between glyphosate exposure and increased risk of atherosclerotic heart disease: A large-scale cross-sectional study.

Glyphosate, as the main component of glyphosate pesticides, has been shown to have toxic effects on multiple human systems. However, the association between glyphosate and atherosclerotic cardiovascular disease (ASCVD) remains unclear. This study aims to explore the effect of glyphosate exposure on ASCVD. This study involved 1,602 participants and employed various statistical techniques, such as multivariable logistic regression, linear fitting, mediation analysis, subgroup analysis, and sensitivity analysis, to elucidate the association between glyphosate exposure and cardiovascular diseases. Compared with the low level of glyphosate exposure, the risk of ASCVD increased significantly with high level of glyphosate exposure (OR = 2.05, 95%CI = 1.17-3.58, p<0.05, p for trend<0.05), which showed a linear upward trend. Further analysis found that exposure to high levels of glyphosate and angina pectoris (OR = 2.84, 95%CI = 1.00-8.04, p<0.05, p for trend<0.05), coronary heart disease (OR = 3.76, 95%CI = 1.80-7.83, p<0.01, p for trend<0.05), heart attack (OR = 2.65, 95%CI = 1.35-5.23, p<0.01, p for trend = 0.09) were associated. Mediating analysis found that BMI mediated the association between glyphosate exposure and ASCVD, with an indirect effect of 0.002405(95%CI: 0.000182-0.01, p = 0.02) and a direct effect of 0.039336(95%CI: 0.000391-0.09), total effect of 0.041741(95%CI: 0.002112-0.10). Increased exposure to glyphosate is associated with an increased risk of ASCVD, and BMI plays a mediating role in this association. In addition, glyphosate exposure is associated with a higher risk of angina, coronary heart disease, and heart attack.

Relationship between Death Anxiety and Resilience among Myocardial Infarction Patients: Moderated By Social Support

The current research aimed to assess the relationship between death anxiety and resilience among myocardial infarction patients, moderated by social support, and to find out the difference of death anxiety, resilience, and social support about the demographic variables (gender, age, qualification, marital status and number of heart attacks). A sample of 300 patients from different hospitals (CMH, AIMS, Cardiac hospital &amp; AMI)in Muzaffarabad was taken. Death Anxiety Scale (DAS), Nicholson McBride Resilience Questionnaire (NMRQ), and Social Support Questionnaire- Shortened Version were used. Total 45items of questionnaires were given to the participants from different hospitals of Muzaffarabad. Data analysis was performed using SPSS (Statistical Package for Social Sciences) software version 21, using the analysis of correlation, regression, moderation, t-test, and ANOVA. Correlation yielded that there is no significant relationship between death anxiety and resilience among MI patients. Social support plays the role of moderator in the relationship between death anxiety and resilience. Statistical analysis has shown that there exists no significant difference in levels of death anxiety and resilience of MI patients based on age, gender, qualification, family income, and number of heart attacks. Marital status has a significant impact on death anxiety but not on the resilience of these patients. Our research shows that the study of these demographics has a significant impact on the level of social support. A significant difference was noted in values of social support based on gender, qualification, and number of heart attacks. The study can provide insight that could guide interventions and support for those dealing with death anxiety and the vital role of social support.

ECMO against the background of endovascular intervention in a patient with acute coronary syndrome in conditions of resuscitation measures

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) may be a critically important method for supporting patients with acute STsegment elevation coronary syndrome (STEMI) undergoing percutaneous coronary intervention (PCI) in high-risk settings.Objective: to demonstrate the effectiveness of ECMO in a patient with severe cardiovascular insufficiency against the background of myocardialischemia in conditions of ongoing intensive careClinical observations: a case of the use of VA-ECMO before PCI in a 63-year-old patient with multiple occlusive stenotic lesions of the coronary bed is described. Due to the high risk of complications during planned PCI and ongoing CPR, it was decided to start VA-ECMO to provide mechanical circulatory support. Balloon angioplasty was performed with stenting of the LCA trunk and permanent residence with 2 stents in conditions of extracorporeal life support. After stenting, an effective sinus rhythm was independently restored. On the 4th day, decannulation and excommunication from ECMO were performed. On the 7th day, the patient was transferred from the ICU to the cardiology department. The patient was discharged on the 18th day.Conclusion: the use of ECMO contributed to the stabilization of the patient’s vital signs in conditions of acute myocardial ischemia and made it possible to perform effective recanalization against the background of resuscitation measures.

Diagnosis and management of type 2 diabetes mellitus in patients with ischaemic heart disease and acute coronary syndromes - a review of evidence and recommendations.

Type 2 diabetes mellitus (T2DM) represents a major healthcare condition of the 21st century. It is characterised by persistently elevated blood glucose occurring as a result of peripheral insulin resistance and reduced insulin production which may lead to multiple long-term health conditions such as retinopathy, neuropathy, and nephropathy. The estimated number of individuals suffering from diabetes mellitus (DM) is expected to rise to 591 million by the year 2035 with 4.4 million in the United Kingdom (UK) alone, 90% of which is attributed to T2DM. Moreover, a significant proportion of individuals may have undetected diabetes mellitus, especially among those presenting with symptoms of ischaemic heart disease (IHD). This is particularly important in those individuals presenting with acute coronary syndromes (ACS) who are at the highest risk of complications and sudden cardiac death. Identifying abnormal levels of common biochemical markers of diabetes, such as capillary blood glucose or glycated haemoglobin (HbA1c) in these patients is important for early diagnosis, which will then allow for timely intervention to improve outcomes. However, a significant proportion of individuals who meet the criteria for the diagnosis of diabetes remain undiagnosed, representing missed opportunities for early intervention. This may result in a prolonged period of untreated hyperglycaemia, which can result resulting in significant further microvascular and macrovascular complications. There is an increased risk of IHD, heart failure, cerebrovascular accidents (CVA), and peripheral artery disease (PVD). These account accounting for 50% of deaths in patients with T2DM. Cardiovascular diseases in the context of diabetes particular represent a significant cause of morbidity and mortality with a two to three times higher risk of cardiovascular disease in individuals with T2DM than in those without the condition normo-glycaemia. In the United Kingdom UK alone, around 120 amputations, 770 CVA, 590 heart attacks, and more than 2300 presentations with heart failure per week are attributed to diabetes DM. with One 1 in six 6 hospital beds and around 10% of the healthcare budget may be being spent on managing diabetes DM or its complications. Therefore, it represents a significant burden on our healthcare system.

Deficiency of Caveolin-1 Aggravates Cardiac Infarction Injury by Disturbing the Endothelial Homeostasis

Deficiency of Caveolin-1 Aggravates Cardiac Infarction Injury by Disturbing the Endothelial Homeostasis

Three-dimensional standard electrocardiogram: A first approach based on precordial leads.

Traditional electrocardiography (ECG) is limited to two-dimensional (2D) representations, which restricts its ability to capture the full complexity of cardiac electrical activity. We propose a novel three-dimensional (3D) ECG methodology directly derived from standard 2D recordings, providing enhanced spatial insights without requiring additional hardware modifications. Additionally, we introduce a new formulation, which we term "almost-curvature", to optimize the detection of variations in acute ischemic states, which is one of the leading causes of mortality and exhibits sensitivity issues in diagnosis using traditional ECG methods. To introduce the methodology for developing 3D ECG and evaluate its diagnostic utility in detecting morphological changes associated with acute myocardial ischemia through perimeter, curvature, and almost-curvature metrics. We developed a methodology based on spherical-to-Cartesian coordinate transformations applied to standard ECG data from the PhysioNet database. For validation, we utilized datasets from patients with induced myocardial ischemia. We extracted perimeter, curvature, and almost-curvature metrics from both 3D and 2D ECG and compared them across different ischemic states. From method implementation to clinical validation, we used clustering analyses and statistical tests such as Anderson-Darling, Kolmogorov-Smirnov, Mantel, Shapiro-Wilk, Wilcoxon, and the Permutation test. Statistical analysis revealed a correlation between the plane presenting standard deflections in the 3D ECG and the plane displaying the novel loops generated by our methodology. The almost-curvature metric demonstrated an enhanced capacity to detect variations between ischemic states, surpassing the diagnostic performance of traditional curvature metrics. While 2D analyses showed a reduction in curvature and perimeter during ischemia progression, 3D ECG analysis revealed an increase in these metrics, underscoring its ability to capture morphological changes that may be overlooked by conventional methods. 3D ECG analysis shows potential to enhance the detection of ischemic alterations, offering a more detailed spatial representation of cardiac electrical activity compared to traditional methods. By leveraging spherical-to-Cartesian transformations, our methodology integrates temporal and voltage dynamics into a unified framework, potentially revealing subtle morphological changes associated with ischemia. Although the methodology remains in its early developmental stage and requires further refinement, its promising diagnostic utility suggests it could significantly enhance cardiac diagnostics. Further studies are essential to validate its clinical applicability and address current limitations.

The interplay of senescence and MMPs in myocardial infarction: implications for cardiac aging and therapeutics.

Aging is associated with a marked increase in cardiovascular diseases, such as myocardial infarction (MI). Cellular senescence is also a crucial factor in the development of age-related MI. Matrix metalloproteinases (MMPs) interaction with cellular senescence is a critical determinant of MI development and outcomes, most notably in the aged heart. After experiencing a heart attack, senescent cells exhibit a Senescence-Associated Secretory Phenotype (SASP) and are involved in tissue regeneration and chronic inflammation. MMPs are necessary for extracellular matrix proteolysis and have a biphasic effect, promoting early heart healing and detrimental change if overexpressed shortly. This review analyses the complex connection between senescence and MMPs in MI and how it influences elderly cardiac performance. Critical findings suggest that increasing cellular senescence in aged hearts elevates MMP activity and aggravates extended ventricular remodeling and dysfunction. Additionally, we explore potential therapeutics that address MMPs and senescence to enhance old MI patient myocardial performance and regeneration.

Analysis of Artificial Intelligence Methods in Classifying Heart Attack Risk: Black-Box Models vs. Glass-Box Models

Artificial Intelligence (AI) is becoming more and more involved in human life day by day. Healthcare is one of the areas where AI is widely used, such as in the diagnosis prediction, and/or classification of diseases. Techniques such as machine learning provide high-accuracy results, but many algorithms have black-box structures, where the reasoning behind the predictions is not known. Explainable AI emerges to address this by providing explanations for complex models. While interpretable ("glass-box") models are desirable, they may have lower accuracy than complex ("black-box") models. Finding the right balance is crucial, especially in critical areas such as healthcare. It is also important to provide individual explanations for the predictions. This study uses patient data to explore a model to predict heart attack risk. Therefore, we compare glass-box models (logistic regression, naive Bayes, decision tree, and explainable boosting) with black-box models (random forest, support vector machine, multi-layer perceptron, gradient boosting, and stochastic gradient boosting). The results show that explainable boosting achieves the highest accuracy. To delve into individual explanations on a patient basis, the explainable boosting algorithm is compared with the random forest algorithm, which gives the best results among the black-box models. Here, LIME and SHAP are used to provide interpretability of random forests. As a result, it is concluded that the random forest algorithm has differences in the importance weights of the variables compared to the explainable boosting algorithm. Both results provide valuable tools for healthcare stakeholders to choose the most appropriate model.

Knowledge, attitudes, and beliefs regarding acute coronary syndrome in patients with type 2 diabetes: a cross-sectional study

IntroductionCardiovascular diseases are one of the leading causes of mortality and disability among individuals with diabetes. Cardiac mortality in these patients is often associated with the prevalence of silent ischemia. Increasing awareness and changing patient behaviors and attitudes can reduce disease-related mortality. Therefore, this study aimed to determine the knowledge, attitudes, and beliefs regarding acute coronary syndrome in patients with type 2 diabetes mellitus (T2DM).MethodsThis cross-sectional study was conducted on 303 diabetic patients attending the Velayat and Bou Ali hospitals in Qazvin, Iran, in 2024. Inclusion criteria included a willingness to participate in the study, mental health status (based on existing records), and no history of myocardial infarction (MI). Data collection tools comprised a demographic questionnaire and the ACS Response questionnaire. Data analysis was performed using descriptive and analytical statistics, with a significance level of <0.05 for all tests.ResultsThe mean (standard deviation) scores for knowledge, attitudes, and performance of patients with T2DM were 67.46 (6.3), 62.11 (28.2), and 97.22 (5.4), respectively. In the knowledge section, the highest percentage of correct responses was related to awareness of facilities in the hospital to reduce heart attack-related damages (62.3%) and familiarity with the symptoms of arm or hand numbness (43.41%). Patients had a low attitude towards heart disease, with approximately 36.4% being somewhat or completely confident in their ability to assist others with a heart attack. Regarding performance, 45.7% of individuals believed that they should promptly go to the hospital during a heart attack. Additionally, 44.2% preferred others to transport them to the hospital during a heart attack instead of using an ambulance.ConclusionOur findings indicate that patients with T2DM lack sufficient knowledge, attitudes, and beliefs regarding the symptoms of acute coronary syndrome (ACS). Although ACS manifests through various symptoms, patients in this study primarily identified chest pain, arm pain, and shortness of breath as indicators of ACS. At the same time, over half of the participants were unaware of other symptoms. Therefore, it is essential to educate individuals about the full range of potential ACS symptoms.

Association between weight-adjusted-waist index and long-term prognostic outcomes in cardiovascular disease patients: results from the NHANES 1999–2018 study

BackgroundAs cardiovascular disease (CVD) morbidity and mortality increase yearly, this study aimed to explore the potential of the weight-adjusted-waist index (WWI) and its relation to long-term mortality in patients with CVD.MethodsThe diagnosis of CVD was based on standardized medical condition questionnaires that incorporated participants’ self-reported physician diagnoses. WWI (cm/√kg) is a continuous variable and calculated as waist circumference (WC, cm) divided by square root of body weight (kg). For analysis purposes, the participants were divided into four groups based on the quartiles (Q1 – Q4) of the WWI. The study’s primary outcome was all-cause mortality in patients with CVD, with cardiovascular mortality as the secondary outcome, and sample weights and complex survey designs were used to ensure reliable, accurate results.ResultsThe final analysis included 4,445 study participants. In the fully adjusted model, the highest quartile (WWI > 12.05 cm/√ kg) showed a higher all-cause mortality rate compared with the lowest quartile (WWI < 11.03 cm/√ kg) (HR = 1.37, 95% CI: 1.03, 1.82, P < 0.05). The risk of all-cause mortality increased with WWI and showed a linear association in patients with congestive heart failure, heart attack (P-overall < 0.05, P − nonlinear > 0.05); WWI was nonlinearly associated with the risk of all-cause mortality in patients with coronary heart disease and angina (P-overall < 0.05, P − nonlinear < 0.05). Survival curve analysis further showed that all cause and cardiovascular mortality were higher in the high WWI group (Q4) (P < 0.001). The time-dependent receiver operating characteristic (ROC) curve showed that WWI’s area under the curves (AUC) for 5- and 10-year survival rates were 0.76 and 0.792 for all-cause mortality and 0.734 and 0.757 for CVD mortality. WWI’s AUC were higher than those of body mass index (BMI) and WC (all P < 0.01).ConclusionOur findings indicate that a high WWI is positively associated with an increased risk of all-cause mortality. Additionally, the high AUC values for WWI strengthen its potential as a meaningful prognostic marker, underscoring its utility in clinical practice for assessing long-term survival risk in patients with CVD.

The Correlation Between the Type of Fluid Administration and the Incidence of Hypotension in Patients Undergoing Spinal Anesthesia at RS Wijayakusuma Purwokerto

Background: Spinal anesthesia is a type of regional anesthesia that involves injecting a local anesthetic into the subarachnoid area. The effect of sympathetic blockade in spinal anesthesia can cause dilation of blood vessels, suppress systemic vascular resistance and result in hypotension [1]. The effects of hypotension are pulmonary aspiration, cardiac arrest, respiratory depression, and loss of consciousness. A sudden drop in blood pressure can also cause a heart attack, a complication of spinal anesthesia. Method: A quantitative approach using a correlational observational design with research variables, namely the administration of fluid types with the incidence of hypotension in patients undergoing spinal anesthesia. The research was conducted in the IBS room at Wijayakusuma Hospital, Purwokerto, with samples was calculated obtained 53 respondents who were selected using a purposive sampling technique. The relationship between the two variables tested was carried out using the non-parametric chi square statistical test with the Fisher exact test Results: Among the respondents, the type of fluid administered was crystalloids in 98.1% of cases, with 70.2% of respondents not experiencing hypotension. The chi-square test yielded a p-value of 1.000, indicating no significant correlation between the type of fluid administered and the incidence of hypotension in patients undergoing spinal anesthesia. Conclusion: There is no significant correlation between the administration of crystalloid or colloid fluids and the incidence of hypotension in patients undergoing spinal anesthesia at RST Wijayakusuma Purwokerto.

An improved stacking model for predicting myocardial infarction risk in imbalanced data.

Early diagnosis and treatment of myocardial infarction (MI) can significantly reduce the severity of the disease. Disease data are often imbalanced, which can lead to poor prediction outcomes when using conventional models. Therefore, developing a risk prediction model for MI with imbalanced datasets has become challenging. This paper presents a novel model called 2GDNN-FL-Stacked, which aims to address the issue of predicting the risk of MI in imbalanced data. Our group mitigates the impact of data imbalance on the model by employing random under-sampling and cost-sensitive techniques. We improve the model's identification capabilities by stacking and combining 2GDNN-FL, CatBoost, RandomForest, and LightGBM. Our model's Matthews Correlation Coefficient(MCC), F1-score, and Area Under the ROC Curve(AUC) scores increased by 0.87% 15.70%, 0.55% 9.81%, and 0.75% 8.11% respectively, compared to some baseline models, which represent a significant improvement over the performance of a single model on imbalanced datasets. This paper demonstrates the effectiveness of each component through ablation experiments, showing that removing either component affects model performance and proves the efficacy of all components. The method offers new insights into predicting heart attack risks and has the potential to offer potent assistance in making clinical decisions.

Evaluating the effects of hands-only CPR training on knowledge, attitudes, and altruistic behavior among school children in rural India

A significant increase in sudden heart attacks among Indian men under 50 years old has been observed nationwide. Survival from cardiac arrest largely depends on how quickly CPR is started and the quality of CPR given. People who receive effective bystander CPR are 2-3 times more likely to survive, but many people lack the knowledge to recognize or respond to a cardiac arrest emergency especially in rural India. This research examines the effectiveness of Hands-Only CPR training in enhancing schoolchildren's knowledge, attitudes, and responses in rural India. To investigate effects of Hands-Only CPR training on CPR knowledge in school girls, investigate effects of Hands-Only CPR training on the willingness, attitudes, &amp; intentions toward helping others and performing CPR and assess the potential of targeted educational interventions at schools, to improve community response in cardiac emergencies and improving survival rates. Setting- girls’ High school in Barela, a rural town with limited resources in the Jabalpur District. The Cross-sectional Pre and Post intervention study took place at P.M. SHRI Government Higher Secondary School for Girls in Barela, Jabalpur, Madhya Pradesh, a rural town with limited resources in the Jabalpur District. Study Design- The Cross-sectional Pre and Post intervention study to obtain quantitative data on the effectiveness of the educational intervention of Hands Only CPR. Pre-training and post-training assessments were used to measure changes in the participants' knowledge, attitudes, and willingness to perform CPR. None of the participants knew what CPR or Basic Life Support was prior to the training. Significant improvement was noted on knowledge towards recognizing signs of life, steps of Hands only CPR amongst the participants. Comfort on performing CPR and willingness to help during cardiac emergencies after the CPR training significantly improved Our study identifies a notable gap in the awareness and preparedness of the students, highlighting the need for comprehensive CPR training programs at a high school level in rural India, to improve public health outcomes. Our study suggest that targeted educational interventions could empower communities and reduce the impact of cardiac emergencies.

Heart Attack Risk Assessment Using Deep Learning with Feature Optimization

Heart Attack Risk Assessment Using Deep Learning with Feature Optimization

Blockchain-enabled digital twin system for brain stroke prediction

A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.

A Pilot Study of Hemoglobin A1C Levels in Patients with Type 2 Diabetes after Creation of a Patient Assistance Program Enrollment Committee at a Student-Run Free Clinic

Background: Our student-run free clinic (SRFC) treats uninsured patients with type 2 diabetes (T2D) in a medically underserved region. Mississippi has the second highest diabetes prevalence in the nation. Increasing access for patients with diabetes to affordable medication is challenging. Some studies provide encouraging results for lowering hemoglobin A1C and increasing medication adherence through patient assistance programs (PAP). None have examined a student-run PAP committee’s impact on diabetes outcomes. Objective: To compare A1C levels for patients with diabetes enrolled in PAPs by our committee with those not enrolled and to describe clinical outcomes. Methods: A retrospective review of patients with T2D at our SRFC between 2015 and 2023 was performed. The primary outcome was change in A1C within a 4-9 month follow-up window. Secondary outcomes were emergency department (ED) visits and hospital admissions. Results: Twenty-five patients with T2D were enrolled in PAPs, while 77 were not. The PAP group had a higher baseline A1C (10.9% vs. 8.7%). The difference in A1C between groups was not statistically significant (P=0.68), even with adjustment for covariates (P=0.59). ED visit and hospital admission frequency was similar between groups. Neuropathy was the most common diabetic complication. ED visits and hospital admissions for heart attacks occurred only in the non-PAP group. Conclusion: While patients enrolled in PAPs showed a greater average reduction in A1C, the difference was not statistically significant. The higher baseline A1C in the PAP group carries greater reduction potential. A prospective study is necessary to better evaluate PAP enrollment outcomes for uninsured patients with diabetes.

Multimorbidity patterns and cognitive change among Mexican older adults: findings from the Mexican Health and Aging Study

Introduction: Mexico faces unique challenges due to the confluence of population ageing, an increasing burden of chronic conditions and limited resources to address these concerns. Substantial evidence links both independent and co-occurring chronic conditions to the risk of cognitive decline, but the association between common multimorbidity patterns and change in cognitive function has not been examined among older adults in Mexico. Objective: The objective of our research was to identify the most common multimorbidity patterns among ageing Mexican adults in 2012, then examine the association between these multimorbidity patterns and change in cognitive function with advancing age. Methodology: Data from the Mexican Health and Aging Study (2012–21; n = 6,082) was used to identify multimorbidity patterns in 2012 (including hypertension, diabetes, cancer, lung disease, heart attack, stroke, arthritis and depressive symptoms), then latent growth modelling was used to examine associations between the mean cognitive trajectory and the baseline multimorbidity patterns. Results: Several baseline multimorbidity patterns were associated with variation in cognitive function. Diabetes and depressive symptoms were present in the majority of multimorbidity patterns associated with lower cognitive function at baseline, and respondents with either lone diabetes or hypertension+diabetes+arthritis at baseline experienced more rapid cognitive decline than those reporting no conditions at baseline. Conclusion: Our findings suggest that prevention and management of diabetes may reduce cognitive gaps that manifest prior to older adulthood and protect against rapid cognitive decline among older Mexican adults. Further examination of depressive symptoms as a component of multimorbidity and cognitive change is also warranted.

Mental Health Conditions of Adults with Chronic Obstructive Pulmonary Disease and Comorbidities

Although the association is not well understood, people with COPD are likely to suffer from mental health issues such as depression and anxiety. Due to this occurrence, the aim of the present study is to better understand the mental health experience and COPD comorbidities of U.S. adults with a COPD diagnosis. The final sample comprised 1,676 U.S. adults who were surveyed in the 2019 National Health Interview Survey adult public file. Logistic and ordinal regression were used to calculate adjusted odds ratios for comorbid conditions predicting depression, anxiety, and the number of mental health issues, respectively. Depressive symptoms were more common in adults with COPD who also suffered from diabetes, heart disease, stroke, or asthma. Anxiety symptoms were more common in adults with COPD who also suffered from diabetes, heart disease, stroke, asthma or hypertension. For people with COPD, there was a significant difference seen between the prevalence of diabetes, heart attack, stroke, and asthma and the total number of mental health issues. These findings have implications for how mental health and primary care professionals treat COPD and COPD-related comorbidities. Additionally, they have implications for providing individualized treatments for persons living with COPD and related comorbidities.

ANALYSIS OF THE USE OF ROY'S ADAPTATION NURSING THEORY IN HEART DISEASE PATIENTS

Background: The heart as an important organ cannot be protected from diseases that can attack it. One of the deadliest diseases in the world is heart attack. Objective: This research aims to determine the behavioral response of heart disease patients which gives rise to individual adaptive responses to maintain individual adaptation in heart disease patients. Method: This research uses a qualitative method with a qualitative descriptive approach. The sampling technique is purposive sampling. Data collection methods were in-depth interviews and observation sheets from Calista Roy. Data analysis using the content analysis method. The informants in this study with heart disease in the Siloam Hospital Palembang Inpatient Room were 4 people (2 men, 2 women) and 1 Key Informant. Results: The results of this study emerged 7 themes, namely lack of knowledge about heart disease, signs and symptoms felt by patients, heart patients' efforts to treat their disease, compliance with treatment regimens, healthy diet and lifestyle, obstacles in maintaining their health, and hospital facilities and actions for heart disease patients. The research results show that there is an adaptive response from heart disease patients which can improve patient integrity and be able to carry out activities that are favorable to survival. However, it was still found that participants lacked knowledge regarding their disease, so there was still non-compliance with the treatment regimen. Conclusions: The use of Roy's adaptive nursing theory in providing nursing care to patients with heart disease makes it easier for nurses to detect the patient's situation in adapting to heart disease.