Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

The Genetic Architecture of Biological Age in Nine Human Organ Systems

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10 -8 ) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture 1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine .

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Echocardiographic measures of the left heart and cerebrospinal fluid biomarkers of Alzheimer's disease pathology in cognitively intact adults: The CABLE study

INTRODUCTION

This study delineated the interrelationships between subclinical alterations in the left heart, cerebrospinal fluid (CSF), Alzheimer's disease (AD) biomarkers, and cognition.

METHODS

Multiple linear regressions were conducted in 1244 cognitively normal participants (mean age = 65.5; 43% female) who underwent echocardiography (left atrial [LA] and left ventricular [LV] morphologic or functional parameters) and CSF AD biomarkers measurements. Mediating effects of AD pathologies were examined. Differences in cardiac parameters across ATN categories were tested using analysis of variance (ANOVA) and logistic regressions.

RESULTS

LA or LV enlargement (characterized by increased diameters and volumes) and LV hypertrophy (increased interventricular septal or posterior wall thickness and ventricular mass) were associated with higher CSF phosphorylated (p)-tau and total (t)-tau levels, and poorer cognition. Tau pathologies mediated the heart-cognition relationships. Cardiac parameters were higher in stage 2 and suspected non-Alzheimer's pathology groups than controls.

DISCUSSION

These findings suggested close associations of subclinical cardiac changes with tau pathologies and cognition.

HIGHLIGHTS

Various subclinical alterations in the left heart related to poorer cognition. Subclinical cardiac changes related to tau pathologies in cognitively normal adults. Tau pathologies mediated the heart-cognition relationships. Subclinical cardiac changes related to the AD continuum, especially to stage 2. The accumulation of cardiac alterations magnified their damage to the brain.

Three-dimensional aortic geometry: clinical correlates, prognostic value and genetic architecture

Aortic structure and function impact cardiovascular health through multiple mechanisms. Aortic structural degeneration increases left ventricular afterload, pulse pressure and promotes target organ damage. Despite the impact of aortic structure on cardiovascular health, aortic 3D-geometry has yet to be comprehensively assessed. Using a convolutional neural network (U-Net) combined with morphological operations, we quantified aortic 3D-geometric phenotypes (AGPs) from 53,612 participants in the UK Biobank and 8,066 participants in the Penn Medicine Biobank. AGPs reflective of structural aortic degeneration, characterized by arch unfolding, descending aortic lengthening and luminal dilation exhibited cross-sectional associations with hypertension and cardiac diseases, and were predictive for new-onset hypertension, heart failure, cardiomyopathy, and atrial fibrillation. We identified 237 novel genetic loci associated with 3D-AGPs. Fibrillin-2 gene polymorphisms were identified as key determinants of aortic arch-3D structure. Mendelian randomization identified putative causal effects of aortic geometry on the risk of chronic kidney disease and stroke.

Identification of Circulating Plasma Proteins as a Mediator of Hypertension-Driven Cardiac Remodeling: A Mediation Mendelian Randomization Study

BACKGROUND

This study focused on circulating plasma protein profiles to identify mediators of hypertension-driven myocardial remodeling and heart failure.

METHODS

A Mendelian randomization design was used to investigate the causal impact of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure on 82 cardiac magnetic resonance traits and heart failure risk. Mediation analyses were also conducted to identify potential plasma proteins mediating these effects.

RESULTS

Genetically proxied higher SBP, DBP, and pulse pressure were causally associated with increased left ventricular myocardial mass and alterations in global myocardial wall thickness at end diastole. Elevated SBP and DBP were linked to increased regional myocardial radial strain of the left ventricle (basal anterior, mid, and apical walls), while higher SBP was associated with reduced circumferential strain in specific left ventricular segments (apical, mid-anteroseptal, mid-inferoseptal, and mid-inferolateral walls). Specific plasma proteins mediated the impact of blood pressure on cardiac remodeling, with FGF5 (fibroblast growth factor 5) contributing 2.96% (P=0.024) and 4.15% (P=0.046) to the total effect of SBP and DBP on myocardial wall thickness at end diastole in the apical anterior segment and leptin explaining 15.21% (P=0.042) and 23.24% (P=0.022) of the total effect of SBP and DBP on radial strain in the mid-anteroseptal segment. Additionally, FGF5 was the only mediator, explaining 4.19% (P=0.013) and 4.54% (P=0.032) of the total effect of SBP and DBP on heart failure susceptibility.

CONCLUSIONS

This mediation Mendelian randomization study provides evidence supporting specific circulating plasma proteins as mediators of hypertension-driven cardiac remodeling and heart failure.

FPLS-DC: functional partial least squares through distance covariance for imaging genetics

MOTIVATION

Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues.

RESULTS

We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies.

AVAILABILITY AND IMPLEMENTATION

The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.

Shexiang Tongxin dropping pills protect against ischemic stroke-induced cerebral microvascular dysfunction via suppressing TXNIP/NLRP3 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Patients with ischemic stroke (IS) often continue to exhibit cerebral microcirculatory dysfunction even after receiving thrombolytic therapy. Enhancing the function of cerebral microvascular endothelia represents a pivotal advancement in the therapeutic strategy for ischemic microcirculatory disturbances. A traditional Chinese medicinal formulation named Shexiang Tongxin Dropping Pills (STDP), has been clinically employed to ameliorate microcirculatory abnormalities. Existing literature attests to the beneficial role of STDP on endothelial cells (ECs). Nevertheless, specific impacts and underlying mechanisms of STDP in rectifying IS-induced cerebral microvascular dysfunction warrant further exploration.

AIM OF THE STUDY

This investigation seeks to delineate the effects of STDP on cerebral microvascular endothelial damage induced by ischemic stroke and to elucidate the underlying mechanism involved.

MATERIALS AND METHODS

Middle cerebral artery occlusion and reperfusion (MCAO/R) technique was employed to established ischemic stroke model in mice. The therapeutic efficacy of STDP on cerebral microvascular function was assessed through laser speckle contrast imaging, behavioral assays, and histological evaluations. Biochemical markers in the brain tissue, including GSH, SOD, MDA, and ROS, were quantified using specific assay kits. In vitro study, oxygen-glucose deprivation and reperfusion (OGD/R) was performed in bEnd.3 cells. The cytoprotective potential of STDP was then evaluated by measuring cell viability, LDH activity, endothelial permeability, and oxidative stress parameters. Important targets in critical pathway were verified by immunoblotting and immunofluorescence both in mice brain slices and bEnd.3 cells.

RESULTS

STDP decrease brain infarct size, repaired microvascular cerebral blood flow and attenuated neurological deficiency in MCAO/R mice. Moreover, STDP abolished MCAO/R-induced oxidative stress which was reflected by rescuing GSH content, restoration of SOD activity and T-AOC, reduction of MDA and ROS. Ex vivo, STDP increased cerebral microvascular endothelial cells viability, abolished oxidative stress and decreased their permeability after ODG/R. Mechanistically, STDP significantly suppressed endothelial ROS-TXNIP mediated the activation of NLRP3 inflammasome in vivo and in vitro.

CONCLUSION

STDP improves ischemic stroke-induced cerebral microcirculatory deficits by regulating cerebral microvascular endothelial ROS/TXNIP/NLRP3 signaling pathway.

Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation

Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.

Transesophageal echocardiography: A bibliometric analysis from 1979 to 2022

OBJECTIVE

Heart disease poses a significant global health challenge. Transesophageal echocardiography (TEE) has gained prominence in clinical practice because of advancements in visual medicine. The present bibliometric analysis provides an overview of TEE research, identifies trends, and highlights emerging topics.

METHODS

A comprehensive search of TEE-related literature from the establishment of the Web of Science Core Collection (WOSCC) until 2022 was conducted. Utilizing the CiteSpace software, we performed an in-depth analysis of the literature data encompassing disciplines, publication years, countries, institutions, authors, journals, cited references, and keywords.

RESULTS

A total of 17 032 TEE-related articles were included in this study. The most active disciplines in TEE research were Cardiac & Cardiovascular Systems, Anesthesiology, and Respiratory System. The number of publications displayed a consistent upward trajectory over the years. Notably, research contributions predominantly originated from developed countries, mainly Europe and North America, with the United States, Germany, Italy, and Japan leading the way. Analysis of institutions, authors, and journals revealed the United States' significant role in TEE research. Furthermore, the analysis of cited references and keywords identified the treatment of patent foramen ovale and its association with stroke as emerging hot topics in recent years.

CONCLUSIONS

This study highlights that TEE remains a research hotspot, with the United States at the forefront. Future research should investigate the relationship between heart disease and brain function.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

The interaction effects of age, APOE and common environmental risk factors on human brain structure

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study

Objective

To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods

A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.

Results

CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)].

Conclusion

pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

Effects of diabetes mellitus and glycemic traits on cardiovascular morpho-functional phenotypes

BACKGROUND

The effects of diabetes on the cardiac and aortic structure and function remain unclear. Detecting and intervening these variations early is crucial for the prevention and management of complications. Cardiovascular magnetic resonance imaging-derived traits are established endophenotypes and serve as precise, early-detection, noninvasive clinical risk biomarkers. We conducted a Mendelian randomization (MR) study to examine the association between two types of diabetes, four glycemic traits, and preclinical endophenotypes of cardiac and aortic structure and function.

METHODS

Independent genetic variants significantly associated with type 1 diabetes, type 2 diabetes, fasting insulin (FIns), fasting glucose (FGlu), 2 h-glucose post-challenge (2hGlu), and glycated hemoglobin (HbA1c) were selected as instrumental variables. The 96 cardiovascular magnetic resonance imaging traits came from six independent genome-wide association studies. These traits serve as preclinical endophenotypes and offer an early indication of the structure and function of the four cardiac chambers and two aortic sections. The primary analysis was performed using MR with the inverse-variance weighted method. Confirmation was achieved through Steiger filtering and testing to determine the causal direction. Sensitivity analyses were conducted using the weighted median, MR-Egger, and MR-PRESSO methods. Additionally, multivariable MR was used to adjust for potential effects associated with body mass index.

RESULTS

Genetic susceptibility to type 1 diabetes was associated with increased ascending aortic distensibility. Conversely, type 2 diabetes showed a correlation with a reduced diameter and areas of the ascending aorta, as well as decreased distensibility of the descending aorta. Genetically predicted higher levels of FGlu and HbA1c were correlated with a decrease in diameter and areas of the ascending aorta. Furthermore, higher 2hGlu levels predominantly showed association with a reduced diameter of both the ascending and descending aorta. Higher FIns levels corresponded to increased regional myocardial-wall thicknesses at end-diastole, global myocardial-wall thickness at end-diastole, and regional peak circumferential strain of the left ventricle.

CONCLUSIONS

This study provides evidence that diabetes and glycemic traits have a causal relationship with cardiac and aortic structural and functional remodeling, highlighting the importance of intensive glucose-lowering for primary prevention of cardiovascular diseases.

The Impact of the Nervous System on Arteries and the Heart: The Neuroimmune Cardiovascular Circuit Hypothesis

Three systemic biological systems, i.e., the nervous, the immune, and the cardiovascular systems, form a mutually responsive and forward-acting tissue network to regulate acute and chronic cardiovascular function in health and disease. Two sub-circuits within the cardiovascular system have been described, the artery brain circuit (ABC) and the heart brain circuit (HBC), forming a large cardiovascular brain circuit (CBC). Likewise, the nervous system consists of the peripheral nervous system and the central nervous system with their functional distinct sensory and effector arms. Moreover, the immune system with its constituents, i.e., the innate and the adaptive immune systems, interact with the CBC and the nervous system at multiple levels. As understanding the structure and inner workings of the CBC gains momentum, it becomes evident that further research into the CBC may lead to unprecedented classes of therapies to treat cardiovascular diseases as multiple new biologically active molecules are being discovered that likely affect cardiovascular disease progression. Here, we weigh the merits of integrating these recent observations in cardiovascular neurobiology into previous views of cardiovascular disease pathogeneses. These considerations lead us to propose the Neuroimmune Cardiovascular Circuit Hypothesis.

Cardiovascular disease risk models and dementia or cognitive decline: a systematic review

Background

Health cognitive promotion and protection is a critical topic. With the world's aging population and rising life expectancy, there will be many people living with highly age-related dementia illnesses. Cardiovascular disease (CVD) and dementia share the same risk factors, such as unhealthy lifestyles and metabolic factors. These recognized risks associated with CVD and dementia frequently co-occur. CVD risk models may have a close association with dementia and cognitive decline. So, this systematic review aimed to determine whether CVD risk models were connected with dementia or cognitive decline and compare the predictive ability of various models.

Methods

PubMed, Web of Science, PsychINFO, Embase, Cochrane Library, CNKI, Sinomed, and WanFang were searched from 1 January 2014 until 16 February 2023. Only CVD risk models were included. We used the Newcastle-Ottawa scale (NOS) for the quality assessment of included cohort studies and the Agency for Healthcare Research and Quality (AHRQ) for cross-sectional studies. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement's guidelines were followed in this systematic study.

Results

In all, 9,718 references were screened, of which 22 articles were included. A total of 15 CVD risk models were summarized. Except for the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) health index, the other 14 CVD risk models were associated with dementia and cognitive decline. In comparison, different CVD risk models and domain-specific cognitive function correlation variation depended on cohort characteristics, risk models, cognitive function tests, and study designs. Moreover, it needed to be clarified when comparing the predicting performance of different CVD risk models.

Conclusion

It is significant for public health to improve disease risk prediction and prevention and mitigate the potential adverse effects of the heart on the brain. More cohort studies are warranted to prove the correlation between CVD risk models and cognitive function. Moreover, further studies are encouraged to compare the efficacy of CVD risk models in predicting cognitive disorders.

Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

Genetic heart-brain connections

Multiorgan imaging unveils the intertwined nature of the human heart and brain.