Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum:a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process.Summary:The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease. [Figure not available: see fulltext.]</p

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease. [Figure not available: see fulltext.

Longitudinal associations of air pollution and green space with cardiometabolic risk factor clustering among children in the Netherlands

BACKGROUND: This study examines longitudinal associations of air pollution and green space with cardiometabolic risk among children in the Netherlands. METHODS: Three Dutch prospective cohorts with a total of 13,822 participants aged 5 to 17 years were included: (1) the Amsterdam Born Children and their Development (ABCD) study from Amsterdam (n = 2,547), (2) the Generation R study from Rotterdam (n = 5,431), and (3) the Lifelines study from northern Netherlands (n = 5,844). Air pollution (PM2.5, PM10, NO2, and elemental carbon (EC)) and green space exposures (density in multiple Euclidean buffer sizes) from 2006 to 2017 at home address level were used. Cardiometabolic risk factor clustering was assessed by a MetScore, which was derived from a confirmatory factor analysis of six cardiometabolic risk factors to assess the overall risk. Linear regression models with change in Metscore as the dependent variable, adjusted for multiple confounders, were conducted for each cohort separately. Meta-analyses were used to pool cohort-specific estimates. RESULTS: Exposure to higher levels of NO2 and EC was significantly associated with increases in MetScore in Lifelines (per SD higher exposure: βNO2 = 0.006, 95 % CI = 0.001 to 0.010; βEC = 0.008, 95 % CI = 0.002 to 0.014). In the other two cohort studies, these associations were in the same direction but these were not significant. Higher green space density in 500-meter buffer zones around participants' residential addresses was not significantly associated with decreases of MetScore in all three cohorts. Higher green space density in 2000-meter buffer zones was significantly associated with decreases of MetScore in ABCD and Lifelines (per SD higher green space density: βABCD = -0.008, 95 % CI = -0.013 to -0.003; βLifelines = -0.002, 95 % CI = -0.003 to -0.00003). The pooled estimates were βNO2 = 0.003 (95 % CI = -0.001 to 0.006) for NO2, βEC = 0.003 (95 % CI = -0.001, 0.007) for EC, and β500m buffer = -0.0014 (95 % CI = -0.0026 to -0.0001) for green space. CONCLUSIONS: More green space exposure at residence was associated with decreased cardiometabolic risk in children. Exposure to more NO2 and EC was also associated with increased cardiometabolic risk

The implementation of integrated care: the empirical validation of the Development Model for Integrated Care

Background: Integrated care is considered as a strategy to improve the delivery, efficiency, client outcomes and satisfaction rates of health care. To integrate the care from multiple providers into a coherent client-focused service, a large number of activities and agreements have to be implemented like streamlining information flows and patient transfers. The Development Model for Integrated care (DMIC) describes nine clusters containing in total 89 elements that contribute to the integration of care. We have empirically validated this model in practice by assessing the relevance, implementation and plans of the elements in three integrated care service settings in The Netherlands: stroke, acute myocardial infarct (AMI), and dementia. Methods. Based on the DMIC, a survey was developed for integrated care coordinators. We invited all Dutch stroke and AMI-services, as well as the dementia care networks to participate, of which 84 did (response rate 83%). Data were collected on relevance, presence, and year of implementation of the 89 elements. The data analysis was done by means of descriptive statistics, Chi Square, ANOVA and Kruskal-Wallis H tests. Results: The results indicate that the integrated care practice organizations in all three care settings rated the nine clusters and 89 elements of the DMIC as highly relevant. The average number of elements implemented was 50 18, 42 13, and 45 22 for stroke, acute myocardial infarction, and dementia care services, respectively. Although the dementia networks were significantly younger, their numbers of implemented elements were comparable to those of the other services. The analyses of the implementation timelines showed that the older integrated care services had fewer plans for further implementation than the younger ones. Integrated care coordinators stated that the DMIC helped them to assess their integrated care development in practice and supported them in obtaining ideas for expanding their integrated car

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Correction: Volume53, Issue5 Page 762-762 DOI: 10.1038/s41588-021-00832-z Published MAY 2021Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to similar to 1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequencyPeer reviewe

Pyruvate: immunonutritional effects on neutrophil intracellular amino or alpha-keto acid profiles and reactive oxygen species production

For the first time the immunonutritional role of pyruvate on neutrophils (PMN), free α-keto and amino acid profiles, important reactive oxygen species (ROS) produced [superoxide anion (O2−), hydrogen peroxide (H2O2)] as well as released myeloperoxidase (MPO) acitivity has been investigated. Exogenous pyruvate significantly increased PMN pyruvate, α-ketoglutarate, asparagine, glutamine, aspartate, glutamate, arginine, citrulline, alanine, glycine and serine in a dose as well as duration of exposure dependent manner. Moreover, increases in O2− formation, H2O2-generation and MPO acitivity in parallel with intracellular pyruvate changes have also been detected. Regarding the interesting findings presented here we believe, that pyruvate fulfils considerably the criteria for a potent immunonutritional molecule in the regulation of the PMN dynamic α-keto and amino acid pools. Moreover it also plays an important role in parallel modulation of the granulocyte-dependent innate immune regulation. Although further research is necessary to clarify pyruvate’s sole therapeutical role in critically ill patients’ immunonutrition, the first scientific successes seem to be very promising

Psychosocial factors and cancer incidence (PSY-CA):Protocol for individual participant data meta-analyses

OBJECTIVES: Psychosocial factors have been hypothesized to increase the risk of cancer. This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer.METHODS: The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2).CONCLUSION: PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses.</p