70-year legacy of the Framingham Heart Study

The design and implementation of natural population cohort study Biobank: A multiple-center project cooperation with medical consortia in Southwest China

Objective

The West China Hospital of Sichuan University collaborated with regional medical consortia in Sichuan Province to launch a natural population cohort study (NPCS) to investigate the health status of residents and collect public health data in southwest China.

Methods

Up to 80,000 participants will be enrolled by the NPCS from 11 regional medical consortia over five years. Individuals are invited to visit one of 11 participating medical consortia to fill out questionnaires, receive a free health exam, and donate biospecimens upon enrolment. All participating medical facilities adhered to standard operating procedures for collecting and processing biospecimens to ensure uniformity (serum, lithium heparinized plasma, ethylene diamine tetraacetie acid plasma, and buffy coat). The Electronic Data Capture System, Picture Archiving and Communication System, Laboratory Information Management System, Biospecimen Quality Control System, Biobank Information Management System, and will be used to sort and classify clinical indices, imaging data, laboratory parameters, pre-analytical variables, and biospecimen information, respectively. All quality assurance and quality control procedures in the NPCS biobank adhered to the "DAIDS Guidelines for Good Clinical Laboratory Practice Standards". This project will integrate high-dimensional multi-omics data, laboratory data, clinical data, questionnaire data, and environmental risk factors.

Results

An estimated 2,240,000 aliquots of the sample will be stored by the end of the study. These samples are linked with comprehensively collected clinical indices, imaging data, and laboratory parameters. Big data analysis can be implemented to create predictive algorithms, explore pathogenesis mechanisms, uncover potential biomarkers, and provide information on public health.

Conclusions

NPCS will provide an integrative approach to research risk factors and pathogenesis of major chronic or endemic diseases in Sichuan Province and provide key scientific evidence to support the formulation of health management policies in China.

Hipertensión arterial y riesgo cardiovascular

Introducción: la hipertensión arterial es una de las principales enfermedades a nivel mundial y constituye una importante causa de morbilidad y mortalidad para países de bajos y medianos ingresos. Objetivo: determinar la importancia epidemiológica de la hipertensión arterial como factor de riesgo cardiovascular en diferentes estudios realizados a nivel mundial, en Latinoamérica y Colombia. Metodología: se realizó una búsqueda de la literatura científica en las bases de datos de PudMed/Medline, Scielo, LILACS, así como también en revistas médicas y textos publicados por el Ministerio de Salud y Protección Social de Colombia. Discusión y conclusiones: más de 90% de los pacientes hipertensos padecen la forma primaria de la enfermedad, la cual está asociada con un aumento de la resistencia vascular periférica. Las características socioeconómicas de los países y el nivel educativo individual se relacionan con la prevalencia y el manejo adecuado de esta patología. El aumento en la prevalencia de las enfermedades crónicas, sumado a eventos históricos de importancia, fueron determinantes para el desarrollo de estudios epidemiológicos mundiales como el Framingham Heart Study. En América Latina y en Colombia se han realizado diferentes estudios que permiten establecer datos relacionados con la hipertensión arterial, demostrando cifras alarmantes en cuanto al conocimiento, tratamiento y control de esta condición, por lo cual, surge la necesidad de establecer programas para la detección de pacientes hipertensos con el fin de generar estrategias que disminuyan de manera significativa las enfermedades cardiovasculares.

Examine the public health impacts of functional somatic disorders using the DanFunD study

Background: Persistent physical symptoms (e.g. pain, fatigue) are prevalent in the population and some persons may develop a functional somatic disorder (FSD). We still need to explore the limits between general bodily sensations and FSD, and great controversies exist as regard delimitation, occurrence, risk factors, prognosis, and costs of FSD in the general population. This is mainly due to the lack of focused, sufficient powered, population-based epidemiological studies. Material and Methods: The DanFunD study is the largest focused population-based study on FSD and has the potential to answer these crucial questions regarding the FSD disorders. DanFunD has its origin in the Copenhagen area of Denmark and was initiated in 2009 by an interdisciplinary team of researchers including basic scientists, clinical researchers, epidemiologists, and public health researchers. A population-based cohort of nearly 10,000 people have filled in detailed questionnaires, gone through a thorough health examination, and a biobank is established. The cohort was re-examined after five years. Results:The prevalence of FSD in the Danish population is about 10-15% and is twice as common in women as in men. Persons with FSD report impaired daily activities and low self-perceived health, which qualifies FSD as a major public health problem. The research plan to unravel the risk factors for FSD employs a bio-psycho-social approach according to a detailed plan. Preliminary results are presented, and work is in progress. Likewise, plans for assessing prognosis and health care costs are provided. Conclusion: We invite researchers in the field to collaborate on this unique data material.

Estimating the Incidence and Key Risk Factors of Cardiovascular Disease in Patients at High Risk of Imminent Fracture Using Routinely Collected Real-World Data From the UK

The objective of this work was to estimate the incidence rate of cardiovascular disease (CVD) events (myocardial infarction, stroke, or CVD death) at 1 year among three cohorts of patients at high risk of fracture (osteoporosis, previous fracture, and anti-osteoporosis medication) and to identify the key risk factors of CVD events in these three cohorts. To do so, this prospective cohort study used data from the Clinical Practice Research Datalink, a primary care database from United Kingdom. Major adverse cardiovascular events (MACE, a composite outcome for the occurrence of either myocardial infarction [MI], stroke, or CVD death) were identified in patients aged 50 years or older at high or imminent fracture risk identified in three different cohorts (not mutually exclusive): recently diagnosed with osteoporosis (OST, n = 65,295), incident fragility fracture (IFX, n = 67,065), and starting oral bisphosphonates (OBP, n = 145,959). About 1.90%, 4.39%, and 2.38% of the participants in OST, IFX, and OBP cohorts, respectively, experienced MACE events. IFX was the cohort with the higher risk: MACE incidence rates (cases/1000 person-years) were 19.63 (18.54-20.73) in OST, 52.64 (50.7-54.5) in IFX, and 26.26 (25.41-27.12) in OBP cohorts. Risk of MACE events at 1 year was predicted in the three cohorts. Models using a set of general, CVD, and fracture candidates selected by lasso regression had a good discrimination (≥70%) and internal validity and generally outperformed the models using only the CVD risk factors of general population listed in QRISK tool. Main risk factors common in all MACE models were sex, age, smoking, alcohol, atrial fibrillation, antihypertensive medication, prior MI/stroke, established CVD, glomerular filtration rate, systolic blood pressure, cholesterol levels, and number of concomitant medicines. Identified key risk factors highlight the differences of patients at high risk of fracture versus general population. Proposed models could improve prediction of CVD events in patients with osteoporosis in primary care settings. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Population-based epidemiology: The Glostrup Population Studies 1964–2021

The Glostrup Population Studies are population-based cohorts undertaken in the south-western part of Greater Copenhagen since 1964. The participants were randomly selected from the adult general population. The first cohort was established to assess cardiovascular risk factors and, since, the objectives have been broadened to describe and analyse the health of the general population. The studies are health-examination studies with clinical and biochemical data in addition to data from self-administered questionnaires and, in some studies, interviews. Fasting blood and urine samples were collected and stored in our biobank for further studies. Several of the cohorts were performed according to standardized methods in international consortia, hence data have been pooled with other, both Danish and international, cohorts. To date more than 30,000 individuals, both men and women, aged 15–85 years, have participated in The Glostrup Population Studies and participants have been re-examined up to eight times. The data can be used for disease-specific epidemiology, social epidemiology, genetic epidemiology, ageing, lifestyle and health interventions nested within the cohorts. The Glostrup Population Studies represent a great resource; the possibility of merging the different cohorts enables large datasets, as well as trends over time. Furthermore, the long follow-up in both the national registers and with follow-up examinations is unique. The purpose of this commentary is to inform about The Glostrup Population Studies and to invite collaborations to continue utilizing this great resource to combat current and future challenges within health promotion and disease prevention.

Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach

INTRODUCTION

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.

METHODS

A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics.

RESULTS

Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively.

DISCUSSION

The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

Towards a More Evidence-Based Risk Assessment for People in the Criminal Justice System: the Case of OxRec in the Netherlands

Risk assessment tools are widely used throughout the criminal justice system to assist in making decisions about sentencing, supervision, and treatment. In this article, we discuss several methodological and practical limitations associated with risk assessment tools currently in use. These include variable predictive performance due to the exclusion of important background predictors; high costs, including the need for regular staff training, in order to use many tools; development of tools using suboptimal methods and poor transparency in how they create risk scores; included risk factors being based on dated evidence; and ethical concerns highlighted by legal scholars and criminologists, such as embedding systemic biases and uncertainty about how these tools influence judicial decisions. We discuss the potential that specific predictors, such as living in a deprived neighbourhood, may indirectly select for individuals in racial or ethnic minority groups. To demonstrate how these limitations and ethical concerns can be addressed, we present the example of OxRec, a risk assessment tool used to predict recidivism for individuals in the criminal justice system. OxRec was developed in Sweden and has been externally validated in Sweden and the Netherlands. The advantages of OxRec include its predictive accuracy based on rigorous multivariable testing of predictors, transparent reporting of results and the final model (including how the probability score is derived), scoring simplicity (i.e. without the need for additional interview), and the reporting of a wide range of performance measures, including those of discrimination and calibration, the latter of which is rarely reported but a key metric. OxRec is intended to be used alongside professional judgement, as a support for decision-making, and its performance measures need to be interpreted in this light. The reported calibration of the tool in external samples clearly suggests no systematic overestimation of risk, including in large subgroups.

Epigenetic regulation in cardiovascular disease: mechanisms and advances in clinical trials

Epigenetics is closely related to cardiovascular diseases. Genome-wide linkage and association analyses and candidate gene approaches illustrate the multigenic complexity of cardiovascular disease. Several epigenetic mechanisms, such as DNA methylation, histone modification, and noncoding RNA, which are of importance for cardiovascular disease development and regression. Targeting epigenetic key enzymes, especially the DNA methyltransferases, histone methyltransferases, histone acetylases, histone deacetylases and their regulated target genes, could represent an attractive new route for the diagnosis and treatment of cardiovascular diseases. Herein, we summarize the knowledge on epigenetic history and essential regulatory mechanisms in cardiovascular diseases. Furthermore, we discuss the preclinical studies and drugs that are targeted these epigenetic key enzymes for cardiovascular diseases therapy. Finally, we conclude the clinical trials that are going to target some of these processes.

Strategies of inclusion: The tradeoffs of pursuing "baked in" diversity through place-based recruitment

US funding agencies have begun to institutionalize expectations that biomedical studies achieve defined thresholds for diversity among research participants, including in precision medicine research (PMR). In this paper, we examine how practices of recruitment have unfolded in the wake of these diversity mandates. We find that a very common approach to seeking diverse participants leverages understandings of spatial, geographic, and site diversity as proxies and access points for participant diversity. That is, PMR investigators recruit from a diverse sampling of geographic areas, neighborhoods, sites, and institutional settings as both opportunistic but also meaningful ways to "bake in" participant diversity. In this way, logics of geographic and institutional diversity shift the question from who to recruit, to where. However, despite seeing geographic and site diversity as social and scientific 'goods' in the abstract and as key to getting diverse participants, PMR teams told us that working with diverse sites was often difficult in practice due to constraints in funding, time, and personnel, and inadequate research infrastructures and capacity. Thus, the ways in which these geographic and institutional diversity strategies were implemented resulted ultimately in limiting the meaningful inclusion of populations and organizations that had not previously participated in biomedical research and reproduced the inclusion of institutions that are already represented. These prevailing assumptions about and practices of "baked-in" diversity in fact exacerbate and produce other forms of inequity, in research capacity and research representation. These findings underscore how structural inequities in research resources must be addressed for diversity to be achieved in both research sites and research participants.

Additive genetic effect of GCKR, G6PC2, and SLC30A8 variants on fasting glucose levels and risk of type 2 diabetes

Impaired glucose tolerance is a major risk factor for type 2 diabetes (T2D) and several cardiometabolic disorders. To identify genetic loci underlying fasting glucose levels, we conducted an analysis of 9,232 individuals of European ancestry who at enrollment were either nondiabetic or had untreated type 2 diabetes. Multivariable linear mixed models were used to test for associations between fasting glucose and 7.9 million SNPs, with adjustment for age, body mass index (BMI), sex, significant principal components of the genotypes, and cryptic relatedness. Three previously discovered loci were genome-wide significant, with the lead SNPs being rs1260326, a missense variant in GCKR (p = 1.06×10⁻⁸); rs560887, an intronic variant in G6PC2 (p = 3.39×10⁻¹¹); and rs13266634, a missense variant in SLC30A8 (p = 4.28×10⁻¹⁰). Fine mapping, genome-wide conditional analysis, and functional annotation indicated that the three loci were independently associated with fasting glucose. Each copy of an alternate allele at any of these three SNPs was associated with a reduction of 0.012 mmol/L in fasting glucose levels (p = 8.0×10⁻²⁸), and this association was replicated in trans-ethnic analysis of 14,303 individuals (p = 2.2×10⁻¹⁶). The three SNPs were jointly associated with significantly reduced T2D risk, with an odds ratio (95% CI) of 0.93 (0.88, 0.98) per protective allele. Our findings implicate additive effects across pathophysiological pathways involved in type 2 diabetes, including glycolysis, gluconeogenesis, and insulin secretion. Since none of the individuals homozygous for the alternate alleles at all three loci has T2D, it might be possible to use a genetic predictor of fasting glucose levels to identify individuals at low vs. high risk of developing type 2 diabetes.

Relation between periodontitis and risk of cardiovascular disease: Insights from The Tromsø Study

BACKGROUND

Few large-scale studies have investigated the association between periodontitis and cardiovascular risk estimated by risk assessment models; moreover, this association remains unexplored in never-smokers. We aimed to examine the relationship between periodontitis and cardiovascular risk in a Norwegian general population, with a focus on never-smokers and the impact of sex and age.

METHODS

The present study included 2623 participants from the seventh survey of the Tromsø Study (Tromsø7, 2015-2016) aged 45-74 years and without previous myocardial infarction or stroke. Periodontitis was defined according to the 2017 AAP/EFP classification system. Participants were categorised by grade based on percentage bone loss/age as no periodontitis/grade A (low progression rate) and grade B/C (moderate-rapid progression rate). Low, medium, and high cardiovascular risk was defined based on the Norwegian risk model NORRISK 2. We used ordered logistic regression analysis to examine the association between periodontitis and cardiovascular risk, adjusting for education, toothbrushing frequency, body mass index, and diabetes. Subanalyses included stratification by sex and age (45-54, 55-64, 65-74 years) and a separate analysis of never-smokers.

RESULTS

Periodontitis grade B/C was associated with higher cardiovascular risk than no periodontitis/grade A (odds ratio [OR] 2.13, 95% confidence interval [CI]: 1.75-2.61). This association was significant in both men and women, all age groups, and never-smokers. However, when never-smokers were stratified by age, the association remained significant only in those aged 65-74 years (OR 3.00, 95% CI 1.50-5.99).

CONCLUSION

Periodontitis grade B/C was associated with higher cardiovascular risk overall, and in never-smokers aged 65-74 years. This article is protected by copyright. All rights reserved.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Efficacy and Safety of Proprotein Convertase Subtilisin/Kexin Type 9 Inhibitors as Adjuvant Treatments for Patients with Hypercholesterolemia Treated with Statin: A Systematic Review and Network Meta-analysis

Background: The proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors are potent LDL-C lowering agents. However, few head-to-head studies evaluated the efficacy on the lowering in other atherogenic apolipoproteins and safety of PCSK9 inhibitors at different dosages as an add-on statins therapy in hypercholesterolemia patients.

Methods: This study is a systematic review and network meta-analysis of randomized control trials to compare the efficacy of lipid reduction and adverse events of PCSK9 inhibitors in statin-treated hypercholesterolemia patients. PubMed, EMBASE, and Cochrane Library databases were searched till April 20, 2021, for randomized controlled trials. Random-effect network meta-analyses were undertaken to compare the differences in the percent reduction in low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (ApoB), and lipoprotein (a) [Lp(a)] levels and the risk of AEs among different PCSK9 inhibitors.

Results: A total of 22 articles with 42,786 patients were included. The lipid reductions in LDL-C, ApoB, and Lp(a) with add-on PCSK9 inhibitors vs. placebo in statin-treated patients across all trials were 50–63%, 43–52%, and 23–31%, respectively. Evolocumab 140 mg Q2W was ranked the best among all treatment strategies for lowering LDL-C, ApoB, and Lp(a) levels, and the treatment difference was 68.05% (95% confidence interval (CI), 62.43% to 73.67) in LDL-C reduction, 54.95% (95% CI, 49.55% to 60.35%) in ApoB reduction, and 34.25% (95% CI, 27.59% to 40.91%) in Lp(a) reduction compared with the placebo. No significant risk difference of adverse events between PCSK9 inhibitors and placebo was found.

Conclusion: PCSK9 inhibitors showed a significant effect on the reduction in LDL-C, ApoB, and Lp(a) levels in statin-treated patients. Evolocumab 140 mg Q2W showed significantly larger degrees of LDL-C, ApoB, and Lp(a) reduction.

Recent Experimental Studies of Maternal Obesity, Diabetes during Pregnancy and the Developmental Origins of Cardiovascular Disease

Globally, cardiovascular disease remains the leading cause of death. Most concerning is the rise in cardiovascular risk factors including obesity, diabetes and hypertension among youth, which increases the likelihood of the development of earlier and more severe cardiovascular disease. While lifestyle factors are involved in these trends, an increasing body of evidence implicates environmental exposures in early life on health outcomes in adulthood. Maternal obesity and diabetes during pregnancy, which have increased dramatically in recent years, also have profound effects on fetal growth and development. Mounting evidence is emerging that maternal obesity and diabetes during pregnancy have lifelong effects on cardiovascular risk factors and heart disease development. However, the mechanisms responsible for these observations are unknown. In this review, we summarize the findings of recent experimental studies, showing that maternal obesity and diabetes during pregnancy affect energy metabolism and heart disease development in the offspring, with a focus on the mechanisms involved. We also evaluate early proof-of-concept studies for interventions that could mitigate maternal obesity and gestational diabetes-induced cardiovascular disease risk in the offspring.

Using psychosis biotypes and the Framingham model for parsing psychosis biology

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has invested in the collection and use of multiple biomarkers in individuals with psychosis. We expect psychosis biology and its distinctive types to be reflected in the biomarkers, as they are the 'behaviors' of the brain. Like infectious diseases, we expect the etiologies of these biomarker-driven entities to be multiple and complex. Biomarkers have not yet been annotated with disease characteristics and need to be. As a model, we seek to adopt aspects of the Framingham Heart Study (FHS) to guide and organize these observations.

Cardiovascular risk prediction: from classical statistical methods to machine learning approaches

Nowadays, cardiovascular risk prediction scores are commonly used in primary prevention settings. Estimating the cardiovascular individual risk is of crucial importance for effective patient management and optimal therapy identification, with relevant consequences on secondary prevention settings. To reach this goal, a plethora of risk scores have been developed in the past, most of them assuming that each cardiovascular risk factor is linearly dependent on the outcome. However, the overall accuracy of these methods often remains insufficient to solve the problem at hand. In this scenario, machine learning techniques have repeatedly proved successful in improving cardiovascular risk predictions, being able to capture the non-linearity present in the data. In this concern, we present a detailed discussion concerning the application of classical versus machine learning-based cardiovascular risk scores in the clinical setting. This review aimed to give an overview of the current risk scores based on classical statistical approaches and machine learning techniques applied to predict the risk of several cardiovascular diseases, comparing them, discussing their similarities and differences, and highlighting their main drawbacks to aid the physician having a more critical understanding of these tools.

Cardiovascular health and healthy longevity in people with and without cardiometabolic disease: A prospective cohort study

BACKGROUND

Existing evidence suggest an association of cardiovascular health (CVH) level with cardiometabolic disease (CMD) and mortality, but the effect of CVH on life expectancy, particularly survival years in CMD patients, has not been well-established. This study aimed to investigate the association of CVH defined using the 7-item tool from the American Heart Association (AHA) with life expectancy in people with and without CMD.

METHODS

Between 2006 and 2010, a total of 341,331 participants (age 37-73 years) in the UK Biobank were examined and thereafter followed up to 2020. The CVH raised by the AHA included 4 behavioral (smoking, diet, physical activity, body mass index) and 3 biological (fasting glucose, blood cholesterol, blood pressure) metrics, coded on a three-point scale (0, 1, 2). The CVH score was the sum of 7 metrics (score range 0-14) and was then categorized into poor (scores 0-6), intermediate (7-11), and ideal (12-14) CVH. The flexible parametric survival models were applied to estimate life expectancy.

FINDINGS

During a median follow-up of 11.4 years, 18,420 (5.4%) deaths occurred. The multivariable-adjusted hazard ratio (HRs) of all-cause mortality were 2.21 (95% CI: 1.77 to 2.75) for male and 2.63 (95% CI: 2.22 to 3.12) for female with prevalent CMD and a poor CVH compared with CMD-free and ideal CVH group, an ideal CVH attenuated the CMD-related risk of mortality by approximately 62% for male and 53% for female. In CMD patients, an ideal CVH compared to poor CVH was associated with additional life years gain of 5.50 (95% CI: 3.94-7.05) for male 4.20 (95% CI: 2.77-5.62) for female at the age of 45 years. Corresponding estimates in those without CMD were 4.55 (95% CI: 3.62-5.48) and 4.89 (95% CI: 3.99-5.79), respectively. Ideal smoking status, fasting glucose and physical activity for male and ideal smoking status, cholesterol level and physical activity for female contributed to the greatest survival benefit.

INTERPRETATION

An ideal CVH is associated with a lower risk of premature mortality and longer life expectancy whether in general population or CMD patients. Our study highlights the benefits of maintaining better CVH across the life course and calls attention to the need for comprehensive strategies (healthy behavioral lifestyle and biological phenotypes) to preserve and restore a higher CVH level.

FUNDING

Scientific Research Foundation for Scholars of HZNU (Grant No. 4265C50221204119).

Human genetics and its impact on cardiovascular disease

Cardiovascular disease (CVD) is a major cause of death worldwide. Given that CVD is a highly heritable trait, researchers have attempted to fully understand the genetic basis of CVD for a long time. The human genome comprises 3,100 Mbp per haploid genome and 6,200 Mbp in total (diploid genome). However, there is a tendency for rare genetic variations to exhibit a large effect size, whereas common genetic variations have a small effect on diseases, because of natural selection. In this sense, dividing genetic variations into two groups based on allele frequency (and effect sizes on diseases) is a good idea. We know there are several important genes (especially lipid-related genes) in which rare genetic variations are apparently associated with CVD risk, while a polygenic risk score comprising common genetic variations appears to work quite well among general populations. That information can be used not only for risk stratification but also for discoveries for novel pharmacologic targets. In this review article, we provide the important and simple idea that human genetics is important for CVD because it is a highly heritable trait, and we believe that it will lead to precision medicine in this field.

A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features

Background

Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved some progress, they were based on clinical or genetic features alone. Here, we have developed a deep learning framework, HFmeRisk, using both 5 clinical features and 25 DNA methylation loci to predict the early risk of HFpEF in the Framingham Heart Study Cohort.

Results

The framework incorporates Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting-based feature selection, as well as a Factorization-Machine based neural network-based recommender system. Model discrimination and calibration were assessed using the AUC and Hosmer–Lemeshow test. HFmeRisk, including 25 CpGs and 5 clinical features, have achieved the AUC of 0.90 (95% confidence interval 0.88–0.92) and Hosmer–Lemeshow statistic was 6.17 (P = 0.632), which outperformed models with clinical characteristics or DNA methylation levels alone, published chronic heart failure risk prediction models and other benchmark machine learning models. Out of them, the DNA methylation levels of two CpGs were significantly correlated with the paired transcriptome levels (R < −0.3, P < 0.05). Besides, DNA methylation locus in HFmeRisk were associated with intercellular signaling and interaction, amino acid metabolism, transport and activation and the clinical variables were all related with the mechanism of occurrence of HFpEF. Together, these findings give new evidence into the HFmeRisk model.

Conclusion

Our study proposes an early risk assessment framework for HFpEF integrating both clinical and epigenetic features, providing a promising path for clinical decision making.

Purpose, Partnership, and Possibilities: The Implementation of the Dog Aging Project Biobank

Background:

Biobanks have been supporting longitudinal prospective and retrospective studies by providing standardized services for the acquisition, transport, processing, storage, and distribution of high-quality biological material and associated data. Here, we describe how the Dog Aging Project (DAP), a large-scale longitudinal study of the domestic dog ( Canis familiaris) with translational applications for humans, developed a biobank of canine biospecimens and associated data.

Design and methods:

This was accomplished by working with the Cornell Veterinary Biobank, the first biobank in the world to receive accreditation to ISO 20387:2018—General Requirements for Biobanking. The biobank research team was involved in the early collection stages of the DAP, contributing to the development of appropriate workflows and processing fit-for-purpose biospecimens. In support of a dynamic strategy for real-time adjustment of processes, a pilot phase was implemented to develop, test, and optimize the biospecimen workflows, followed by an early phase of collection, processing, and banking of specimens from DAP participants.

Results:

During the pilot and early phases of collection, the DAP Biobank stored 164 aliquots of whole blood, 273 aliquots of peripheral blood mononuclear cells, 130 aliquots of plasma, and 70 aliquots of serum, and extracted high molecular weight genomic DNA suitable for whole-genome sequencing from 109 whole blood specimens. These specimens, along with their associated preanalytical data, have been made available for distribution to researchers.

Conclusion:

We discuss the challenges and opportunities encountered during the implementation of the DAP Biobank, along with novel strategies for promoting biobanking sustainability such as partnering with a DAP quality assurance manager and a DAP marketing and communication specialist and developing a pilot grant structure to fund small innovative research projects.

Personalizing treatments for patients based on cardiovascular phenotyping

Introduction

Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes.

Areas covered

With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient's unique phenotype.

Expert opinion

Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.

Nonlinear relationship between HbA1c and coronary artery calcium score progression: a secondary analysis based on a retrospective cohort study

OBJECTIVE

Coronary artery calcium score and glycated hemoglobin(HbA1c) are both considered risk factors for coronary heart disease. However, the relationship between coronary artery calcium score and HbA1c is still unclear. Consequently, the present study was undertaken to explore HbA1c association with coronary artery calcium score progression in South Korea.

METHODS

This study is a secondary analysis based on a retrospective cohort study in which 8151 participants received Health examination kits at the Health Promotion Center, Samsung Medical Center in Seoul, South Korea, from March 1, 2003-December 31, 2013. Cox proportional-hazards regression model was then used to evaluate the independent relationship between HbA1c and coronary artery calcium score progression.

RESULTS

After adjusting potential confounding factors (age, sex, BMI, height, weight, SBP, DBP, TC, LDL-C, HDL-C, triglycerides, smoking status, alcohol consumption, reflux esophagitis status, hypertension, diabetes, dyslipidemia, ischemic heart disease and cerebrovascular disease), it was revealed that there was a nonlinear relationship between HbA1c and coronary artery calcium score progression, while the scoring point was 5.8%. The effect size was 2.06 to the left of the inflection point, while the 95% CI was 1.85 to 2.29. Whereas, the effect size was 1.04, on the right side of the inflection point while 95% CI was 0.99 to1.10.

CONCLUSION

The relationship between HbA1c and coronary artery calcium score progression is nonlinear. HbA1c is positively related to coronary artery calcium score progression when HbA1c level was less than 5.8%.

Communication approaches to enhance patient motivation and adherence in cardiovascular disease prevention

Preventive cardiology visits have traditionally focused on educating patients about disease risk factors and the need to avoid and manage them through lifestyle changes and medications. However, long-term patient adherence to the recommended interventions remains a key unmet need. In this review we discuss the rationale and potential benefits of a paradigm shift in the clinician-patient encounter, from focusing on education to explicitly discussing key drivers of individual motivation. This includes the emotional, psychological, and economic mindset that patients bring to their health decisions. Five communication approaches are proposed that progress clinician-patient preventive cardiology conversations, from provision of information to addressing values and priorities such as common health concerns, love for the family, desire of social recognition, financial stressors, and desire to receive personalized advice. Although further research is needed, these approaches may facilitate developing deeper, more effective bonds with patients, enhance adherence to recommendations and ultimately, improve cardiovascular outcomes.

Editorial: It's Difficult To Make Predictions, Especially About the Future: Risk Calculators Come of Age in Child Psychiatry

A quote attributed to many people, from the Nobel prize-winning Quantum physicist Niels Bohr to legendary baseball player (and philosopher) Yogi Berra states: "It is difficult to make predictions, especially about the future." As though any other prediction would matter; but this is exactly what parents want when they bring their child to the doctor for any concern, ranging from a bump or bruise to whether the child has bipolar disorder. They want the doctor to use both the science and art of medicine to answer key questions: What is wrong with my child? What tests or workup is needed to figure this out? What is the best treatment for this problem? Will my child get better?

Management of Ischemic Heart Disease in Pregnancy

PURPOSE OF REVIEW

Cardiovascular disease is an escalating cause of maternal morbidity and mortality. Women are at risk for acute myocardial infarction (MI), and more are living with risk factors for ischemic heart disease (IHD). The purpose of this review is to describe the evaluation and management of women at risk for and diagnosed with IHD in pregnancy.

RECENT FINDINGS

Pregnancy can provoke MI which has been estimated as occurring in 1.5-10/100, 000 deliveries or 1/12,400 hospitalizations, with a high inpatient mortality rate of approximately 5-7%. An invasive strategy may or may not be preferred, but fetal radiation exposure is less of a concern in comparison to maternal mortality. Common medications used to treat IHD may be continued successfully during pregnancy and lactation, including aspirin, which has an emerging role in pregnancy to prevent preeclampsia, preterm labor, and maternal mortality. Hemodynamics can be modulated during pregnancy, labor, and postpartum to mitigate risk for acute decompensation in women with IHD. Cardiologists can successfully manage IHD in pregnancy with obstetric partners and should engage women in a lifetime of cardiovascular care.

Roadmap for translating results from the micronucleus assay into clinical practice: From observational studies to randomized controlled trials

According to the definition delivred by the WHO, a biomarker, independently from its role that may be indicative of exposure, response or effect, is inevitably linked to a clinical outcome or to a disease. The presence of a continuum from early biological events to therapy, and prognosis is the unifying mechanism that justifies this conclusion. Traditionally, the technical and inter-individual variability of the assays, together with the long duration between early pathogenetic events and the disease, prevented clinical applications to these biomarkers. These limitations became less important with the emerging of personalized preventive medicine because of the focus on disease prediction and prevention, and the recommended use of all data concerning measurable patient's features. Several papers have been published on the best validation procedures for translating biomarkers to real life. The history of cholesterol concentration is extensively discussed as a reliable example of a biomarker that - after a long and controversial validation process - is currently used in clinical practice. The frequency of micronucleated cells is a reliable biomarker for the pathogenesis of cancer and other non-communicable diseases, and the link with clinical outcomes is substantiated by epidemiological evidence and strong mechanistic basis. Available literature concerning the use of the micronucleus assay in clinical studies is discussed, and a suitable three-levels road-map driving this biomarker towards clinical practice is presented. Under the perspective of personalized medicine, the use of the micronucleus assays can play a decisive role in addressing preventive and therapeutic strategies of chronic diseases. In many cases the MN assay is either currently used in clinical practice or classified as adequate to consider translation into practice. The roadmap to clinical validation of the micronucleus assay finds inspiration from the history of biomarkers such as cholesterol, which clearly showed that the evidence from prospective studies or RCTs is critical to achieve the required level of trust from the healthcare profession. (307 words).

Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort

AIM

Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.

METHODS

Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants' Mini-Mental State Examination (MMSE) scores.

RESULTS

Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].

CONCLUSIONS

Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.

Economic evaluation of nutrition interventions: Does one size fit all?

BACKGROUND

Nutrition interventions have specific features that might warrant modifications to the methods used for economic evaluations of healthcare interventions.

AIM

The aim of the article is to identify these features and when they challenge the use of cost-utility analysis (CUA).

METHODS

A critical review of the literature is conducted and a 2 by 2 classification matrix for nutrition interventions is proposed based on 1) who the main party responsible for the implementation and funding of the intervention is; and 2) who the target recipient of the intervention is. The challenges of conducting economic evaluations for each group of nutrition interventions are then analysed according to four main aspects: attribution of effects, measuring and valuing outcomes, inter-sectorial costs and consequences and equity considerations.

RESULTS AND CONCLUSIONS

CUA is appropriate for nutrition interventions when they are funded from the healthcare sector, have no (or modest) spill-overs to other sectors of the economy and have only (or mainly) health consequences. For other interventions, typically involving different government agencies, with cost implications for the private sector, with important wellbeing consequences outside health and with heterogeneous welfare effects across socio-economic groups, other economic evaluation methods need to be developed in order to offer valid guidance to policy making. For these interventions, checklists for critical appraisal of economic evaluations may require some substantial changes.

Diagnostic Ability of Peguero-Lo Presti Electrocardiographic Left Ventricular Hypertrophy Criterion in Severe Aortic Stenosis

Traditional electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH), introduced in the pre-echocardiographic era of diagnosis, have a relatively low sensitivity (usually not exceeding 25–40%) in detecting LVH. A novel Peguero-Lo Presti ECG-LVH criterion was recently shown to exhibit a higher sensitivity than the traditional ECG-LVH criteria in hypertension. Our aim was to test the diagnostic ability of the novel Peguero-Lo Presti ECG-LVH criterion in severe aortic stenosis. We retrospectively analyzed 12-lead ECG tracings and echocardiographic records from the index hospitalization of 50 patients with isolated severe aortic stenosis (mean age: 77 ± 10 years; 30 women and 20 men). Exclusion criteria included QRS > 120 ms, bundle branch blocks or left anterior fascicular block, a history of myocardial infarction, more than mild aortic or mitral regurgitation, and significant LV dysfunction by echocardiography. We compared the agreement of the novel Peguero-Lo Presti criterion and traditional ECG-LVH criteria with echocardiographic LVH (LV mass index > 95 g/m² in women and >115 g/m² in men). Echocardiographic LVH was found in 32 out of 50 study patients. The sensitivity of the Peguero-Lo Presti criterion in detecting LVH was improved (55% vs. 9–34%) at lower specificity (72% vs. 78–100%) in comparison to 8 single traditional ECG-LVH criteria. Additionally, the positive predictive value (77% vs. 72%), positive likelihood ratio (2.0 vs. 1.5), and odds ratio (3.2 vs. 2.4) were higher for the Peguero-Lo Presti criterion versus the presence of any of these 8 traditional ECG-LVH criteria. Cohen’s Kappa, a measure of concordance between ECG and echocardiography with regard to LVH, was 0.24 for the Peguero-Lo Presti criterion, −0.01–0.13 for single traditional criteria, and 0.20 for any traditional criterion. However, by the receiver operating characteristics (ROC) curve analysis, the overall ability to discriminate between patients with and without LVH was insignificantly lower for the Peguero-Lo Presti versus Cornell voltage as a continuous variable (area under the ROC curve: 0.65 (95% CI, 0.48–0.81) vs. 0.71 (0.55–0.86), p = 0.5). In conclusion, our preliminary results suggest a slightly better, albeit still low, agreement of the novel Peguero-Lo Presti ECG criterion compared to the traditional ECG-LVH criteria with echocardiographic LVH in severe aortic stenosis.

Assessment of Incidence and Risk Factors of Intracerebral Hemorrhage Among Participants in the Framingham Heart Study Between 1948 and 2016

Importance

Intracerebral hemorrhage (ICH) has the highest mortality of all stroke types and is the most serious complication of anticoagulation. Data regarding trends in ICH incidence and location-specific risk factors on the population level are conflicting.

Objective

To assess long-term population-based trends in the incidence of ICH, examine incidence rates stratified by deep and lobar locations, and characterize location-specific risk factors.

Design, Setting, and Participants

This longitudinal prospective community-based cohort study comprised 10 333 original participants (n = 5209; age range, 28-62 years) and offspring participants (n = 5124; age range, 5-70 years) from the Framingham Heart Study who were followed up from January 1, 1948, to December 31, 2016. Original and offspring patient cohorts were confirmed to have experienced a spontaneous ICH event through imaging or pathologic testing. A total of 129 participants were identified with a primary incident of ICH. After exclusions, the remaining 99 patients were divided into 2 nested case-control samples, which were created by stratifying the first incident of ICH by brain region (lobar ICH or deep ICH), with 55 patients included in the lobar ICH sample and 44 patients included in the deep ICH sample. Patients were matched by age and sex (1:4 ratio) with 396 individuals without any stroke event (the control group). No participant in the patient samples was excluded or approached for consent, as their initial consent to participate in the Framingham Heart Study included consent to follow-up of cardiovascular outcomes. Data were analyzed in October 2019.

Main Outcomes and Measures

The unadjusted and age-adjusted ICH incidence rates, assessed in 3 periods (period 1, from 1948-1986; period 2, from 1987-1999; and period 3, from 2000-2016) to study incidence trends. Nested case-control samples were used to examine baseline risk factors and medication exposures with the incidence of ICH events located in the lobar and deep brain regions within the 10 years before participants experienced a stroke event.

Results

Of 10 333 original and offspring participants in the Framingham Heart Study, 129 patients (72 women [55.8%]; mean [SD] age, 77 [11] years) experienced a primary ICH incident during a follow-up period of 68 years (301 282 person-years), with an incidence rate of 43 cases per 100 000 person-years. The unadjusted incidence rate increased over time, but the age-adjusted incidence rate decreased slightly between periods 2 and 3. An age-stratified analysis indicated a continued increase in ICH incidence among patients 75 years and older, reaching 176 cases per 100 000 person-years in period 3. A concurrent 3-fold increase in the use of anticoagulant medications was observed, from 4.4% in period 2 to 13.9% in period 3. The incidence rate increased substantially with age for both lobar and deep ICH. Higher systolic and diastolic blood pressure and statin medication use (odds ratio [OR], 4.07; 95% CI, 1.16-14.21; P = .03) were associated with the incidence of deep ICH. Higher systolic blood pressure and apolipoprotein E ε4 allele homozygosity (OR, 3.66; 95% CI, 1.28-10.43; P = .02) were associated with the incidence of lobar ICH.

Conclusions and Relevance

This study found that the incidence of ICH increased in the oldest patients. Hypertension is a treatable risk factor for both deep and lobar ICH, while the use of statin medications is associated with the risk of a deep ICH event.

Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data

BACKGROUND

There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention.

OBJECTIVE

To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses.

METHODS

This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association.

RESULTS

Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names.

CONCLUSION

Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

Framingham Heart Study: JACC Focus Seminar, 1/8

The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study.

Increased Endogenous Activity of the Renin-Angiotensin System Reduces Infarct Size in the Rats with Early Angiotensin II-dependent Hypertension which Survive the Acute Ischemia/Reperfusion Injury

We investigated the role of the interaction between hypertension and the renin-angiotensin system in the pathophysiology of myocardial ischemia/reperfusion injury. We hypothesized that in the early phase of angiotensin II (ANG II)-dependent hypertension with developed left ventricular hypertrophy, cardioprotective mechanism(s) are fully activated. The experiments were performed in transgenic rats with inducible hypertension, noninduced rats served as controls. The early phase of ANG II-dependent hypertension was induced by five-days (5 days) dietary indole-3-carbinol administration. Cardiac hypertrophy, ANG II and ANG 1-7 levels, protein expression of their receptors and enzymes were determined. Separate groups were subjected to acute myocardial ischemia/reperfusion injury, and infarct size and ventricular arrhythmias were assessed. Induced rats developed marked cardiac hypertrophy accompanied by elevated ANG levels. Ischemia/reperfusion mortality was significantly higher in induced than noninduced rats (52.1 and 25%, respectively). The blockade of AT1 receptors with losartan significantly increased survival rate in both groups. Myocardial infarct size was significantly reduced after 5 days induction (by 11%), without changes after losartan treatment. In conclusion, we confirmed improved cardiac tolerance to ischemia/reperfusion injury in hypertensive cardiohypertrophied rats and found that activation of AT1 receptors by locally produced ANG II in the heart was not the mechanism underlying infarct size reduction.

Reduction of Cardio-Metabolic Risk and Body Weight through a Multiphasic Very-Low Calorie Ketogenic Diet Program in Women with Overweight/Obesity: A Study in a Real-World Setting

BACKGROUND

The prevention and treatment of obesity and its cardio-metabolic complications are relevant issues worldwide. Among lifestyle approaches, very low-calorie ketogenic diets (VLCKD) have been shown to lead to rapid initial weight loss, resulting in better long-term weight loss maintenance. As no information on VLCKD studies carried on in a real-world setting are available, we conducted this multi-centre study in a real-world setting, aiming at assessing the efficacy and the safety of a specific multiphasic VLCKD program in women with overweight or obesity.

METHODS

A multi-center, prospective, uncontrolled trial was conducted in 33 outpatient women (age range 27-60 y) with overweight or obesity (BMI: 30.9 ± 2.7 kg/m²; waist circumference: 96.0 ± 9.4 cm) who started a VLCKD dietary program (duration: 24 weeks), divided into four phases. The efficacy of VLCKD was assessed by evaluating anthropometric measures and cardiometabolic markers; liver and kidney function biomarkers were assessed as safety parameters.

RESULTS

The VLCKD program resulted in a significant decrease of body weight and BMI (-14.6%) and waist circumference (-12.4%). At the end of the protocol, 33.3% of the participants reached a normal weight and the subjects in the obesity range were reduced from 70% to 16.7%. HOMA-IR was markedly reduced from 3.17 ± 2.67 to 1.73 ± 1.23 already after phase 2 and was unchanged thereafter. Systolic blood pressure decreased after phase 1 (-3.5 mmHg) and remained unchanged until the end of the program. Total and LDL cholesterol and triglycerides were significantly reduced by VLCKD along with a significant HDL cholesterol increase. Liver, kidney and thyroid function markers did not change and remained within the reference range.

CONCLUSIONS

The findings of a multi-center VLCKD program conducted in a real-world setting in a cohort of overweight/obese women indicate that it is safe and effective, as it results in a major improvement of cardiometabolic parameters, thus leading to benefits that span well beyond the mere body weight/adiposity reduction.

Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

Erectile Dysfunction Is a Hallmark of Cardiovascular Disease: Unavoidable Matter of Fact or Opportunity to Improve Men's Health?

Erectile dysfunction (ED) is an early manifestation of cardiovascular (CV) disease. For this reason, men with ED should be carefully assessed for CV risk factors in order to prevent future major adverse CV events (MACE). Traditional risk factors are not found in all subjects at high CV risk. In fact, a relevant proportion of MACE occurs in men who are apparently risk factor free. In men with ED, it is important to take into account not only traditional risk factors but also unconventional ones. Several parameters that derive from good clinical assessment of subjects with ED have proven to be valuable predictors of MACE. These include family history of cardiometabolic events, alcohol abuse, fatherhood, decreased partner’s sexual interest, severe impairment in erection during intercourse or during masturbation, impaired fasting glucose, increased triglycerides, obesity even without metabolic complications, decreased penile blood flows or impaired response to an intra-cavernosal injection test. Recognizing these risk factors may help in identifying, among subjects with ED, those who merit stricter lifestyle or pharmacological interventions to minimize their CV risk. Effective correction of risk factors in ED men considered as high risk, besides reducing CV risk, is also able to improve erectile function.

Risk of cardiovascular disease according to the Framingham score in patients with high blood pressure from Píllaro, Ecuador. 2017-2018

Introduction: Cardiovascular disease (CVD) is the main cause of morbidity and mortality worldwide. The use of the Framingham Risk Score is of great importance for predicting CVD risk. Objective: To estimate the 10-year CVD risk in adult patients diagnosed high blood pressure (HBP) who visited the outpatient service of the San Miguelito de Píllaro Health Center, in Tungurahua, Ecuador, using the Framingham Risk Score (2008). Materials and methods: Cross-sectional, observational, prospective and descriptive study conducted in 120 HBP patients aged 30 to 74 years who visited the outpatient service between January and October 2017. Data were obtained from the review of medical records, which were in turn updated during the execution of the study. The Framingham risk score was used to calculate the 10-year CVD risk. A descriptive analysis of the data was performed in Epi Info 7, using absolute frequencies and percentages. Results: Of the 120 patients, 59.17% were women. Furthermore, 15% of the participants had been diagnosed with type 2 diabetes mellitus, 13.33% had a history of smoking, 47.50% had elevated systolic blood pressure, and 39.17% had hypercholesterolemia. CVD risk was low (≤ 1% Framingham score), intermediate (10-19%), and high (≥ 20%) in 15%, 29.16%, and 59.16% of participants, respectively. None of them had a very low CVD risk (≤1%). Conclusion: The Framingham risk score was useful to estimate CVD risk in the study population treated in the primary health care setting. Consequently, more extensive use of this instrument in different health units is recommended to obtain better estimates of CVD risk and, as a result, achieve the implementation of health prevention and health care actions that improve the prognosis in the medium and long term, and thus the quality of life of these patients.

Modeling genome-wide by environment interactions through omnigenic interactome networks

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

Aging-induced isoDGR-modified fibronectin activates monocytic and endothelial cells to promote atherosclerosis

BACKGROUND AND AIMS

Aging is the primary risk factor for cardiovascular disease (CVD), but the mechanisms underlying age-linked atherosclerosis remain unclear. We previously observed that long-lived vascular matrix proteins can acquire 'gain-of-function' isoDGR motifs that might play a role in atherosclerotic pathology.

METHODS

IsoDGR-specific mAb were generated and used for ELISA-based measurement of motif levels in plasma samples from patients with coronary artery diseases (CAD) and non-CAD controls. Functional consequences of isoDGR accumulation in age-damaged fibronectin were determined by bioassay for capacity to activate monocytes, macrophages, and endothelial cells (signalling activity, pro-inflammatory cytokine expression, and recruitment/adhesion potential). Mice deficient in the isoDGR repair enzyme PCMT1 were used to assess motif distribution and macrophage localisation in vivo.

RESULTS

IsoDGR-modified fibronectin and fibrinogen levels in patient plasma were significantly enhanced in CAD and further associated with smoking status. Functional assays demonstrated that isoDGR-modified fibronectin activated both monocytes and macrophages via integrin receptor 'outside in' signalling, triggering an ERK:AP-1 cascade and expression of pro-inflammatory cytokines MCP-1 and TNFα to drive additional recruitment of circulating leukocytes. IsoDGR-modified fibronectin also induced endothelial cell expression of integrin β1 to further enhance cellular adhesion and matrix deposition. Analysis of murine aortic tissues confirmed accumulation of isoDGR-modified proteins co-localised with CD68+ macrophages in vivo.

CONCLUSIONS

Age-damaged fibronectin features isoDGR motifs that increase binding to integrins on the surface of monocytes, macrophages, and endothelial cells. Subsequent activation of 'outside-in' signalling elicits a range of potent cytokines and chemokines that drive additional leukocyte recruitment to the developing atherosclerotic matrix.

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.