Environmental Determinants of Cardiovascular Disease

Effects of Changes in Seasonal Weather Patterns on the Subjective Well-Being in Patients with CAD Enrolled in Cardiac Rehabilitation

Objective: We examined whether seasonal and monthly variations exist in the subjective well-being of weather-sensitive patients with coronary artery disease (CAD) during cardiac rehabilitation. Methods: In this cross-sectional study, 865 patients (30% female, age 60 ± 9) were recruited within 2−3 weeks of treatment for acute coronary syndrome and during cardiac rehabilitation. The patients completed the Palanga self-assessment diary for weather sensitivity (PSAD-WS) daily, for an average of 15.5 days. PSAD-WS is an 11-item (general) three-factor (psychological, cardiac, and physical symptoms) questionnaire used to assess weather sensitivity in CAD patients. Weather data were recorded using the weather station “Vantage Pro2 Plus”. Continuous data were recorded eight times each day for the weather parameters and the averages of the data were linked to the respondents’ same-day diary results. Results: Weather-sensitive (WS) patients were found to be more sensitive to seasonal changes than patients who were not WS, and they were more likely to experience psychological symptoms. August (summer), December (winter), and March (spring) had the highest numbers of cardiac symptoms (all p < 0.001). In summary, peaks of symptoms appeared more frequently during the transition from one season to the next. Conclusion: This study extends the knowledge about the impact of atmospheric variables on the general well-being of weather-sensitive CAD patients during cardiac rehabilitation.

Obesogenic environments and cardiovascular disease: a path analysis using US nationally representative data

INTRODUCTION

People living in obesogenic environments, with limited access to healthful food outlets and exercise facilities, generally have poor health. Previous research suggests that behavioral risk factors and indicators of physiological functioning may mediate this link; however, no studies to date have had the requisite data to investigate multi-level behavioral and physiological risk factors simultaneously. The present study conducted serial and parallel mediation analyses to examine behavioral and physiological pathways explaining the association between environmental obesogenicity and cardiovascular disease (CVD).

METHODS

This cross-sectional observational study used data from the 2012-2016 Health and Retirement Study, a representative survey of US older adults (n = 12,482, mean age 65.9). Environmental obesogenicity was operationalized as a combined score consisting of nine environmental measures of food and physical activity. CVD and health-compromising behaviors (diet, alcohol consumption, smoking, and exercise) were self-reported. Physiological dysregulation was assessed with measured blood pressure, heart rate, HbA1c, cholesterol levels, BMI, and C-reactive protein. The Hayes Process Macro was used to examine serial and parallel paths through health-compromising behaviors and physiological dysregulation in the environmental obesogenicity-CVD link.

RESULTS

People living in more obesogenic environments had greater odds of self-reported CVD (odds ratio = 1.074, 95% confidence interval (CI): 1.028, 1.122), engaged in more health-compromising behaviors (β = 0.026, 95% CI: 0.008, 0.044), and had greater physiological dysregulation (β = 0.035, 95% CI: 0.017, 0.054). Combined, health-compromising behaviors and physiological dysregulation accounted for 7% of the total effects of environmental obesogenicity on CVD.

CONCLUSION

Behavioral and physiological pathways partially explain the environmental obesogenicity-CVD association. Obesogenic environments may stymie the success of cardiovascular health-promotion programs by reducing access to resources supporting healthy lifestyles.

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

Association between Altitude and the Framingham Risk Score: A Cross-Sectional Study in the Peruvian Adult Population

To determine the association between altitude and the Framingham risk score in the Peruvian population, we performed a cross-sectional analytical study of data collected by the 2017–2018 Food and Nutrition Surveillance by Life Stages survey. The outcome of this study was the Framingham 10-year cardiovascular disease event risk prediction, which is composed of six modifiable and non-modifiable coronary risk factors. A generalized linear model (GLM) of the gamma family and log link function was used to report the crude and adjusted β coefficients. Several sensitivity analyses were performed to assess the association of interest. Data from a total of 833 surveyed participants were included. After adjusting for educational level, poverty level, alcohol consumption, physical activity level, the presence of any limitation, obesity, and area of residence, it was observed that altitude ≥ 2500 m above sea level (β = −0.42 [95% CI: −0.69 to −0.16]) was negatively and significantly associated with a decrease in the Framingham 10-year risk score. High altitude was significantly and negatively associated with Framingham 10-year risk scores. Our results will allow prevention strategies considering modifiable risk factors to avoid the development of cardiovascular diseases, especially in people living at low altitudes.

Nonlaboratory-based risk assessment model for coronary heart disease screening: Model development and validation

BACKGROUND

Identifying groups at high risk of coronary heart disease (CHD) is important to reduce mortality due to CHD. Although machine learning methods have been introduced, many require laboratory or imaging parameters, which are not always readily available; thus, their wide applications are limited.

OBJECTIVE

The aim of this study was to develop and validate a simple, efficient, and joint machine learning model for identifying individuals at high risk of CHD using easily obtainable nonlaboratory parameters.

METHODS

This prospective study used data from the Henan Rural Cohort Study, which was conducted in rural areas of Henan Province, China, between July 2015 and September 2017. A joint machine learning model was developed by selecting and combining four base machine learning algorithms, including logistic regression (LR), artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM). We used readily accessible variables, including demographics, medical and family history, lifestyle and dietary factors, and anthropometric data, to inform the model. The model was also externally validated by a cohort of individuals from the Dongfeng-Tongji cohort study. Model discrimination was assessed by using the area under the receiver operating characteristic curve (AUC), and calibration was measured by using the Brier score (BS).

RESULTS

A total of 38 716 participants (mean [SD] age, 55.64[12.19] years; 23449[60.6%] female) from the Henan Rural Cohort Study and 17 958 subjects (mean [SD] age, 62.74 [7.59] years; 10,076 [56.1%] female) from the Dongfeng-Tongji cohort study were included in the analysis. Age, waist circumference, pulse pressure, heart rate, family history of CHD, education level, family history of type 2 diabetes mellitus (T2DM), and family history of dyslipidaemia were strongly associated with the development of CHD. In regard to internal validation, the model we built demonstrated good discrimination (AUC, 0.844 (95% CI 0.828-0.860)) and had acceptable calibration (BS, 0. 066). In regard to external validation, the model performed well with clearly useful discrimination (AUC, 0.792 (95% CI 0.774-0.810)) and robust calibration (BS, 0.069).

CONCLUSIONS

In this study, the novel and simple, machine learning-based model comprising readily accessible variables accurately identified individuals at high risk of CHD. This model has the potential to be widely applied for large-scale screening of CHD populations, especially in medical resource-constrained settings.

TRIAL REGISTRATION

The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register. (Trial registration: ChiCTR-OOC-15006699. Registered 6 July 2015 - Retrospectively registered) http://www.chictr.org.cn/showproj.aspx?proj=11375.

Prenatal Hypoxia Affects Foetal Cardiovascular Regulatory Mechanisms in a Sex- and Circadian-Dependent Manner: A Review

Prenatal hypoxia during the prenatal period can interfere with the developmental trajectory and lead to developing hypertension in adulthood. Prenatal hypoxia is often associated with intrauterine growth restriction that interferes with metabolism and can lead to multilevel changes. Therefore, we analysed the effects of prenatal hypoxia predominantly not associated with intrauterine growth restriction using publications up to September 2021. We focused on: (1) The response of cardiovascular regulatory mechanisms, such as the chemoreflex, adenosine, nitric oxide, and angiotensin II on prenatal hypoxia. (2) The role of the placenta in causing and attenuating the effects of hypoxia. (3) Environmental conditions and the mother's health contribution to the development of prenatal hypoxia. (4) The sex-dependent effects of prenatal hypoxia on cardiovascular regulatory mechanisms and the connection between hypoxia-inducible factors and circadian variability. We identified that the possible relationship between the effects of prenatal hypoxia on the cardiovascular regulatory mechanism may vary depending on circadian variability and phase of the days. In summary, even short-term prenatal hypoxia significantly affects cardiovascular regulatory mechanisms and programs hypertension in adulthood, while prenatal programming effects are not only dependent on the critical period, and sensitivity can change within circadian oscillations.

Polystyrene bead ingestion promotes adiposity and cardiometabolic disease in mice

Vast amounts of plastic materials are produced in the modern world and despite recycling efforts, large amounts are disposed in water systems and landfills. Under these storage conditions, physical weathering and photochemical processes break down these materials into smaller particles of the micro- and nano-scale. In addition, ecosystems can be contaminated with plastic particles which are manufactured in these size ranges for commercial purposes. Independent of source, microplastics are abundant in the environment and have found their way into water supplies and the food cycle where human exposure is inevitable. Nevertheless, the health consequences of microplastic ingestion, inhalation, or absorption are largely unknown. In this study we sought to determine if ingestion of microplastics promoted pre-clinical cardiovascular disease (CVD). To do this, we supplied mice with normal drinking water or that supplemented with polystyrene beads of two different sizes (0.5 µm and 5 µm) and two different doses (0.1 μg/ml and 1 μg/ml) each for 12 weeks and measured several indices of metabolism and glucose homeostasis. As early as 3 weeks of consumption, we observed an accelerated weight gain with a corresponding increase in body fat for some exposure groups versus the control mice. Some exposure groups demonstrated increased levels of fasting plasma glucose. Those mice consuming the smaller sized beads (0.5 µm) at the higher dose (1 μg/ml), had increased levels of fasting plasma insulin and higher homeostatic model assessment of insulin resistance (HOMA-IR) scores as well. This was accompanied by changes in the gut microbiome consistent with an obese phenotype. Using samples of perivascular adipose tissue collected from the same group, we observed changes in gene expression consistent with increased adipogenesis. These results suggest that ingestion of polystyrene beads promotes a cardiometabolic disease phenotype and thus may be an unrecognized risk factor for CVD.

Pan-risk factor for a comprehensive cardiovascular health management

Cardiovascular diseases (CVDs) have become the leading cause of death in China. CVDs are mainly caused by multiple well-known modifiable risk factors that are affected by socioeconomic and environmental determinants, lifestyle and behavioral choices, and familial and genetic predispositions. With more risk factors proved to be associated with CVD occurrence, the concept "pan-risk factor" is proposed in this review to indicate all discovered and yet-to-be-discovered CVD risk factors for comprehensive primary prevention of CVD. Recognizing more factors and their roles in CVD development and progression is the first step in reducing the ever-increasing burden of CVD. This review is an overview of the pan-risk factor whose associations with CVD outcomes have been established. Along with the accumulation of scientific evidence, an increasing number of risk factors will be discovered and included in the list of pan-risk factors.

Spatiotemporal patterns and inequity of urban green space accessibility and its relationship with urban spatial expansion in China during rapid urbanization period

Equitable access to urban green spaces (UGS) is an important component of social justice and can be quantified using indices such as urban green space accessibility (UGSA). However, the spatiotemporal patterns and inequity of UGSA among cities with different developments during rapid urbanization are unclear, especially lack evidence at a macroscopic national scale during rapid urbanization. Therefore, we evaluated the UGSA in 366 cities of China during 1990-2015 by the Gaussian-based two-step floating catchment area method (Gaussian-based 2SFCA). Then, the inequity pattern of UGSA among cities with different economic developments was analyzed by the concentration curve and concentration index. Finally, the relationship between UGSA and urban spatial expansion was explored quantitatively by the spatial econometric model. The results showed that: (1) The overall UGSA in China declined significantly by nearly 57.23% during 1990-2015. From the regional perspective, the UGSA in the southeastern region was always lower than that in the northwestern region, the Eastern zone presented a downward trend. From the perspective of different sizes cities, the UGSA of the megacities kept decreasing during 1990-2015, while UGSA of the large, medium, and small cities had turned to increase since 2010. (2) During rapid urbanization, the equity of UGSA among the cities gradually improved, while the cities with low economic developments tended to have higher UGSA. (3) Urban spatial expansion led to the decrease of UGSA during 1990-2015, while the impact had spatiotemporal heterogeneity, and UGSA had a positive spatial spillover effect. Our research provides a comparative baseline for the improvement of UGSA from a macroscopic perspective for China's urbanization policy in the future and novel insights into the green justice issue. The results can be compared with the development of UGS in other countries at different urbanization stages to promote UGS design and policy.

European Society of Cardiology: cardiovascular disease statistics 2021

AIMS

This report from the European Society of Cardiology (ESC) Atlas Project updates and expands upon the widely cited 2019 report in presenting cardiovascular disease (CVD) statistics for the 57 ESC member countries.

METHODS AND RESULTS

Statistics pertaining to 2019, or the latest available year, are presented. Data sources include the World Health Organization, the Institute for Health Metrics and Evaluation, the World Bank, and novel ESC sponsored data on human and capital infrastructure and cardiovascular healthcare delivery. New material in this report includes sociodemographic and environmental determinants of CVD, rheumatic heart disease, out-of-hospital cardiac arrest, left-sided valvular heart disease, the advocacy potential of these CVD statistics, and progress towards World Health Organization (WHO) 2025 targets for non-communicable diseases. Salient observations in this report: (i) Females born in ESC member countries in 2018 are expected to live 80.8 years and males 74.8 years. Life expectancy is longer in high income (81.6 years) compared with middle-income (74.2 years) countries. (ii) In 2018, high-income countries spent, on average, four times more on healthcare than middle-income countries. (iii) The median PM2.5 concentrations in 2019 were over twice as high in middle-income ESC member countries compared with high-income countries and exceeded the EU air quality standard in 14 countries, all middle-income. (iv) In 2016, more than one in five adults across the ESC member countries were obese with similar prevalence in high and low-income countries. The prevalence of obesity has more than doubled over the past 35 years. (v) The burden of CVD falls hardest on middle-income ESC member countries where estimated incidence rates are ∼30% higher compared with high-income countries. This is reflected in disability-adjusted life years due to CVD which are nearly four times as high in middle-income compared with high-income countries. (vi) The incidence of calcific aortic valve disease has increased seven-fold during the last 30 years, with age-standardized rates four times as high in high-income compared with middle-income countries. (vii) Although the total number of CVD deaths across all countries far exceeds the number of cancer deaths for both sexes, there are 15 ESC member countries in which cancer accounts for more deaths than CVD in males and five-member countries in which cancer accounts for more deaths than CVD in females. (viii) The under-resourced status of middle-income countries is associated with a severe procedural deficit compared with high-income countries in terms of coronary intervention, ablation procedures, device implantation, and cardiac surgical procedures.

CONCLUSION

Risk factors and unhealthy behaviours are potentially reversible, and this provides a huge opportunity to address the health inequalities across ESC member countries that are highlighted in this report. It seems clear, however, that efforts to seize this opportunity are falling short and present evidence suggests that most of the WHO NCD targets for 2025 are unlikely to be met across ESC member countries.

Greenspace, Inflammation, Cardiovascular Health, and Cancer: A Review and Conceptual Framework for Greenspace in Cardio-Oncology Research

Cardiovascular disease (CVD) is a leading cause of global morbidity and mortality. Cancer survivors have significantly elevated risk of poor cardiovascular (CV) health outcomes due to close co-morbid linkages and shared risk factors between CVD and cancer, as well as adverse effects of cancer treatment-related cardiotoxicity. CVD and cancer-related outcomes are exacerbated by increased risk of inflammation. Results from different pharmacological interventions aimed at reducing inflammation and risk of major adverse cardiovascular events (MACEs) have been largely mixed to date. Greenspaces have been shown to reduce inflammation and have been associated with CV health benefits, including reduced CVD behavioral risk factors and overall improvement in CV outcomes. Greenspace may, thus, serve to alleviate the CVD burden among cancer survivors. To understand pathways through which greenspace can prevent or reduce adverse CV outcomes among cancer survivors, we review the state of knowledge on associations among inflammation, CVD, cancer, and existing pharmacological interventions. We then discuss greenspace benefits for CV health from ecological to multilevel studies and a few existing experimental studies. Furthermore, we review the relationship between greenspace and inflammation, and we highlight forest bathing in Asian-based studies while presenting existing research gaps in the US literature. Then, we use the socioecological model of health to present an expanded conceptual framework to help fill this US literature gap. Lastly, we present a way forward, including implications for translational science and a brief discussion on necessities for virtual nature and/or exposure to nature images due to the increasing human-nature disconnect; we also offer guidance for greenspace research in cardio-oncology to improve CV health outcomes among cancer survivors.

Seasonal Variation of Atrial Fibrillation Admission and Quality of Care in the United States

Background

Currently, little is known regarding seasonal variation for atrial fibrillation (AF) in the United States and whether quality of care for AF varies between seasons.

Methods and Results

The GWTG‐AFib (Get With The Guidelines–AFib) registry was initiated by the American Heart Association to enhance national guideline adherence for treatment and management of AF. Our analyses included 61 291 patients who were admitted at 141 participating hospitals from 2014 to 2018 across the United States. Outcomes included numbers of AF admissions and quality‐of‐care measures (defect‐free care, defined as a patient’s receiving all eligible measures). For quality‐of‐care measures, generalized estimating equations accounting for within‐site correlations were used to estimate odds ratios (ORs) with 95% CIs, adjusting patient and hospital characteristics. The proportion of AF admissions for each season was similar, with the highest percentage of AF admissions being observed in the fall (spring 25%, summer 25%, fall 27%, and winter 24%). Overall, AF admissions across seasons were similar, with no seasonal variation observed. No seasonal variation was observed for incident AF. There were no seasonal differences in care quality (multivariable adjusted ORs and 95% CIs were 0.93 (0.87–1.00) for winter, 1.09 (1.01–1.18) for summer, and 1.08 (0.97–1.20) for fall, compared with spring).

Conclusions

In a nationwide quality improvement registry, no seasonal variation was observed in hospital admissions for AF or quality of care for AF.

Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank

Background

Understanding the interplay between educational attainment and genetic predictors of cardiovascular risk may improve our understanding of the aetiology of educational inequalities in cardiovascular disease.

Methods

In up to 320 120 UK Biobank participants of White British ancestry (mean age = 57 years, female 54%), we created polygenic scores for nine cardiovascular risk factors or diseases: alcohol consumption, body mass index, low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes and stroke. We estimated whether educational attainment modified genetic susceptibility to these risk factors and diseases.

Results

On the additive scale, higher educational attainment reduced genetic susceptibility to higher body mass index, smoking, atrial fibrillation and type 2 diabetes, but increased genetic susceptibility to higher LDL-C and higher systolic blood pressure. On the multiplicative scale, there was evidence that higher educational attainment increased genetic susceptibility to atrial fibrillation and coronary heart disease, but little evidence of effect modification was found for all other traits considered.

Conclusions

Educational attainment modifies the genetic susceptibility to some cardiovascular risk factors and diseases. The direction of this effect was mixed across traits considered and differences in associations between the effect of the polygenic score across strata of educational attainment was uniformly small. Therefore, any effect modification by education of genetic susceptibility to cardiovascular risk factors or diseases is unlikely to substantially explain the development of inequalities in cardiovascular risk.

Effect of vinyl chloride exposure on cardiometabolic toxicity

Vinyl chloride (VC) is an organochlorine mainly used to manufacture its polymer polyvinyl chloride, which is extensively used in the manufacturing of consumer products. Recent studies suggest that chronic low dose VC exposure affects glucose homeostasis in high fat diet-fed mice. Our data suggest that even in the absence of high fat diet, exposure to VC (0.8 ppm, 6 h/day, 5 day/week, for 12 weeks) induces glucose intolerance (1.0 g/kg, i.p.) in male C57BL/6 mice. This was accompanied with the depletion of hepatic glutathione and a modest increase in lung interstitial macrophages. VC exposure did not affect the levels of circulating immune cells, endothelial progenitor cells, platelet-immune cell aggregates, and cytokines and chemokines. The acute challenge of VC-exposed mice with LPS did not affect lung immune cell composition or plasma IL-6. To examine the effect of VC exposure on vascular inflammation and atherosclerosis, LDL receptor-KO mice on C57BL/6 background maintained on western diet were exposed to VC for 12 weeks (0.8 ppm, 6 h/day, 5 day/week). Unlike the WT C57BL/6 mice, VC exposure did not affect glucose tolerance in the LDL receptor-KO mice. Plasma cytokines, lesion area in the aortic valve, and markers of lesional inflammation in VC-exposed LDL receptor-KO mice were comparable with the air-exposed controls. Collectively, despite impaired glucose tolerance and modest pulmonary inflammation, chronic low dose VC exposure does not affect surrogate markers of cardiovascular injury, LPS-induced acute inflammation in C57BL/6 mice, and chronic inflammation and atherosclerosis in the LDL receptor-KO mice.

Trends in the Incidence of New-Onset Hypertensive Disorders of Pregnancy Among Rural and Urban Areas in the United States, 2007 to 2019

Background

Hypertensive disorders of pregnancy are growing public health problems that contribute to maternal morbidity, mortality, and future risk of cardiovascular disease. Given established rural‐urban differences in maternal cardiovascular health, we described contemporary trends in new‐onset hypertensive disorders of pregnancy in the United States.

Methods and Results

We conducted a serial, cross‐sectional analysis of 51 685 525 live births to individuals aged 15 to 44 years from 2007 to 2019 using the Centers for Disease Control and Prevention Natality Database. We included gestational hypertension and preeclampsia/eclampsia in individuals without chronic hypertension and calculated the age‐adjusted incidence (95% CI) per 1000 live births overall and by urbanization status (rural or urban). We used Joinpoint software to identify inflection points and calculate rate of change. We quantified rate ratios to compare the relative incidence in rural compared with urban areas. Incidence (95% CI) of new‐onset hypertensive disorders of pregnancy increased from 2007 to 2019 in both rural (48.6 [48.0–49.2] to 83.9 [83.1–84.7]) and urban (37.0 [36.8–37.2] to 77.2 [76.8–77.6]) areas. The rate of annual increase in new‐onset hypertensive disorders of pregnancy was more rapid after 2014 with greater acceleration in urban compared with rural areas. Rate ratios (95% CI) comparing incidence of new‐onset hypertensive disorders of pregnancy in rural and urban areas decreased from 1.31 (1.30–1.33) in 2007 to 1.09 (1.08–1.10) in 2019.

Conclusions

Incidence of new‐onset hypertensive disorders of pregnancy doubled from 2007 to 2019 with persistent rural‐urban differences highlighting the need for targeted interventions to improve the health of pregnant individuals and their offspring.

Interaction between walkability and fine particulate matter on risk of ischemic stroke: A prospective cohort study in China

Living in walkable neighborhoods has been reported to be associated with a lower risk of cardiovascular disease. Features of walkable neighborhoods, however, may be related to particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5), which could increase risk of cardiovascular disease. The interaction effect between walkability and PM_2.5 on risk of ischemic stroke remains to be elucidated. In this study, we recruited a total of 27,375 participants aged ≥40 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations of walkability and PM_2.5 with risk of ischemic stroke. We used amenity categories and decay functions to evaluate walkability and high-spatiotemporal-resolution land-use regression models to assess PM_2.5 concentrations. We used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). During a median follow-up of 4.08 years, we identified a total of 637 incident cases of ischemic stroke in the entire cohort. Higher walkability was associated with a lower risk of ischemic stroke (quartile, Q4 vs. Q1 walkability: HR = 0.59, 95% CI: 0.47-0.75), whereas PM_2.5 was positively associated with risk of ischemic stroke (Q4 vs. Q1 PM_2.5: HR = 1.70, 95% CI: 1.29-2.25). Furthermore, we observed a significant interaction between walkability and PM_2.5 on risk of ischemic stroke. Walkability was inversely associated with risk of ischemic stroke at lower PM_2.5 concentrations, but this association was attenuated with increasing PM_2.5 concentrations. Although walkable neighborhoods appear to decrease the risk of ischemic stroke, benefits may be offset by adverse effects of PM_2.5 exposure in the most polluted areas. These findings are meaningful for future neighborhood design, air pollution control, and stroke prevention.

Changes in Impaired Fasting Glucose and Borderline High Low-Density Lipoprotein-Cholesterol Status Alter the Risk of Cardiovascular Disease: A 9-Year Prospective Cohort Study

BACKGROUND

We aimed to characterize the relationships of the changes in impaired fasting glucose (IFG) and borderline high low-density lipoprotein-cholesterol (LDL-C) status with cardiovascular disease (CVD).

METHODS

A total of 36,537 participants who did not have previous CVD, diabetes mellitus, or high LDL-C (≥ 4.1 mmol/L), nor were taking lipid-lowering drugs were recruited from the Kailuan study. The participants were allocated to six groups according to their baseline and follow-up fasting blood glucose (FBG) and LDL-C concentrations: (1) both were normal; (2) both normal at baseline, one abnormality subsequently; (3) both normal at baseline, both abnormal subsequently; (4) at least one abnormality that became normal; (5) at least one abnormality at baseline, a single abnormality subsequently; and (6) at least one abnormality, two abnormalities subsequently. The outcomes were CVD and subtypes of CVD (myocardial infarction and stroke). Multiple Cox regression models were used to calculate adjusted hazard ratio (HR) and confidence interval (95% CI).

RESULTS

During a median follow-up period of 9.00 years, 1,753 participants experienced a CVD event. After adjustment for covariates, participants with IFG in combination with a borderline high LDL-C status at baseline and follow-up had higher risks of CVD (HR: 1.52; 95% CI: 1.04-2.23 and HR: 1.38, 95% CI: 1.13-1.70, respectively) compared with those with normal fasting blood glucose and LDL-C. Compared with participants that remained normal, those who changed from normality to having two abnormalities were at a higher risk of CVD (HR: 1.26; 95% CI: 0.98-1.61), as were those who changed from at least one abnormality to two abnormalities (HR: 1.48, 95% CI: 1.02-2.15).

CONCLUSION

Changes in IFG and borderline high LDL-C status alter the risk of CVD and its subtype, implying that it is important to focus on such individuals for the prevention and control of CVD.

Social determinants of health and cognitive performance of older adults living in rural communities: The Three Villages Study

OBJECTIVES

There is limited information on factors associated with poor cognitive performance in rural settings of Low- and Middle-Income Countries. Using the Three Villages Study Cohort, we assessed whether social determinants of health (SDH) play a role in cognitive performance among older adults living in rural Ecuador.

METHODS

Atahualpa, El Tambo and Prosperidad residents aged ≥60 years received measurement of SDH by means of the Gijon Scale together with a Montreal Cognitive Assessment (MoCA). The association between SDH and cognitive performance (dependent variable) was assessed by generalized linear models, adjusted for demographics, years of education, cardiovascular risk factors, symptoms of depression and biomarkers of structural brain damage.

RESULTS

We included 513 individuals (mean age: 67.9 ± 7.3 years; 58% women). The mean score on the Gijon scale was 9.9 ± 2.9 points, with 237 subjects classified as having a high social risk (≥10 points). The mean MoCA score was 19.6 ± 5.4 points. Locally weighted scatterplot smoothing showed an inverse linear relationship between SDH and MoCA scores. SDH and MoCA scores were inversely associated in linear models adjusted for clinical covariates (β: -0.17; 95% C.I.: -0.32 to -0.02; p = 0.020), neuroimaging covariates (β: -0.17; 95% C.I.: -0.31 to -0.03; p = 0.018), as well as in the most parsimonious model (β: -0.16; 95% C.I.: -1.30 to -0.02; p = 0.026).

CONCLUSIONS

Study results provide robust evidence of an inverse association between SDH and cognitive performance. Interventions and programs aimed to reduce disparities in the social risk of older adults living in underserved rural populations may improve cognitive performance in these individuals.

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.

The association between urinary metabolites of polycyclic aromatic hydrocarbons (PAHs) and cardiovascular diseases and blood pressure: a systematic review and meta-analysis of observational studies

Although epidemiological studies have discerned the association between polycyclic aromatic hydrocarbons (PAHs) exposure and hypertension and/or cardiovascular disease in the general population, the possible mechanisms for this association are not well understood. We sought to examine the association between urinary metabolites of PAHs and cardiovascular diseases (CVDs) and blood pressure in adults, by conducting a meta-analysis of observational studies. We searched PubMed, Scopus, Embase, and Web of science, up to July 2021, for observational studies that investigated the association between urinary metabolites of PAHs and CVDs and blood pressure in adults. Nine prospective studies, including 27,280 participants, were included. Based on overall pooled results, there was a significant positive association between all types of urinary metabolites of PAH and blood pressure (OR: 1.32; 95%, CI: 1.19 to 1.48, p < 0.0001) (I2 = 62.4%, p < 0.0001). There was no significant association between any urinary metabolite of PAH and CHD (OR: 0.93; 95%, CI: 0.83 to 1.03, p = 0.174) (I2 = 0%, p = 0.653). Overall, there was a significant positive association between all urinary metabolites of PAH and CVD (OR: 1.23; 95%, CI: 1.16 to 1.30, p < 0.0001) (I2 = 59.7%, p < 0.0001). The results of the present meta-analysis suggest that different metabolites PAHs are associated with an increased risk of CVD and HTN. Further studies, including randomized clinical trials, are needed to confirm the veracity of our findings.

Polygenic risk for coronary artery disease in the Scottish and English population

Background

Epidemiological studies have repeatedly observed a markedly higher risk for coronary artery disease (CAD) in Scotland as compared to England. Up to now, it is unclear whether environmental or genetic factors might explain this phenomenon.

Methods

Using UK Biobank (UKB) data, we assessed CAD risk, based on the Framingham risk score (FRS) and common genetic variants, to explore the respective contribution to CAD prevalence in Scotland (n = 31,963) and England (n = 317,889). We calculated FRS based on sex, age, body mass index (BMI), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), antihypertensive medication, smoking status, and diabetes. We determined the allele frequency of published genome-wide significant risk CAD alleles and a weighted genetic risk score (wGRS) for quantifying genetic CAD risk.

Results

Prevalence of CAD was 16% higher in Scotland as compared to England (8.98% vs. 7.68%, P < 0.001). However, the FRS only predicted a marginally higher CAD risk (less than 1%) in Scotland (12.5 ± 10.5 vs.12.6 ± 10.6, P = 0.03). Likewise, the overall number of genome-wide significant variants affecting CAD risk (157.6 ± 7.7 and 157.5 ± 7.7; P = 0.12) and a wGRS for CAD (2.49 ± 0.25 in both populations, P = 0.14) were remarkably similar in the English and Scottish population. Interestingly, we observed substantial differences in the allele frequencies of individual risk variants. Of the previously described 163 genome-wide significant variants studied here, 35 variants had higher frequencies in Scotland, whereas 37 had higher frequencies in England (P < 0.001 each).

Conclusions

Neither the traditional risk factors included in the FRS nor a genetic risk score (GRS) based on established common risk alleles explained the higher CAD prevalence in Scotland. However, we observed marked differences in the distribution of individual risk alleles, which emphasizes that even geographically and ethnically closely related populations may display relevant differences in the genetic architecture of a common disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02398-4.

Environmental factors and risks of cognitive impairment and dementia: A systematic review and meta-analysis

BACKGROUND

Dementia is a challenging neurodegenerative disease. This systematic review aimed to summarize natural, physical, and social environmental factors that are associated with age-related cognitive impairment and dementia.

METHODS

We systematically searched PubMed, EMBASE, Web of Science, and PsychINFO till January 11, 2021 for observational studies. The hazard ratio (HR), relative risk (RR), and odds ratio (OR) with 95% confidence interval (CI) were aggregated using random-effects methods. The quality of evidence for each association was evaluated.

RESULTS

Of the 48,399 publications identified, there were 185 suitable for review across 44 environmental factors. Meta-analyses were performed for 22 factors. With high-to-moderate quality of evidence, risks were suggested in exposure to PM_2.5 (HR=1.24, 95%CI: 1.17-1.31), NO₂ (HR=1.07, 95%CI: 1.02-1.12), aluminum (OR=1.35, 95%CI: 1.14-1.59), solvents (OR=1.14, 95%CI: 1.07-1.22), road proximity (OR=1.08, 95%CI: 1.04-1.12) and other air pollutions, yet more frequent social contact (HR=0.82, 95%CI: 0.76-0.90) and more greenness (OR=0.97, 95%CI: 0.95-0.995) were protective. With low-to-very low quality, electromagnetic fields, pesticides, SO₂, neighborhood socioeconomic status, and rural living were suggested risks, but more community cultural engagement might be protective. No significant associations were observed in exposure to PM₁₀, NO_x, noise, silicon, community group, and temperature. For the remaining 22 factors, only a descriptive analysis was undertaken as too few studies or lack of information.

CONCLUSIONS

This review highlights that air pollutions, especially PM_2.5 and NO₂ play important role in the risk for age-related cognitive impairment and dementia.

Prenatal and postnatal exposure to PFAS and cardiometabolic factors and inflammation status in children from six European cohorts

Developing children are particularly vulnerable to the effects of exposure to per- and polyfluoroalkyl substances (PFAS), a group of endocrine disrupting chemicals. We hypothesized that early life exposure to PFASs is associated with poor metabolic health in children. We studied the association between prenatal and postnatal PFASs mixture exposure and cardiometabolic health in children, and the role of inflammatory proteins. In 1,101 mothers-child pairs from the Human Early Life Exposome project, we measured the concentrations of PFAS in blood collected in pregnancy and at 8 years (range = 6-12 years). We applied Bayesian Kernel Machine regression (BKMR) to estimate the associations between exposure to PFAS mixture and the cardiometabolic factors as age and sex- specific z-scores of waist circumference (WC), systolic and diastolic blood pressures (BP), and concentrations of triglycerides (TG), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) cholesterol. We measured thirty six inflammatory biomarkers in child plasma and examined the underlying role of inflammatory status for the exposure-outcome association by integrating the three panels into a network. Exposure to the PFAS mixture was positively associated with HDL-C and systolic BP, and negatively associated with WC, LDL-C and TG. When we examined the independent effects of the individual chemicals in the mixture, prenatal PFHxS was negatively associated with HDL-C and prenatal PFNA was positively associated with WC and these were opposing directions from the overall mixture. Further, the network consisted of five distinct communities connected with positive and negative correlations. The selected inflammatory biomarkers were positively, while the postnatal PFAS were negatively related with the included cardiometabolic factors, and only prenatal PFOA was positively related with the pro-inflammatory cytokine IL-1beta and WC. Our study supports that prenatal, rather than postnatal, PFAS exposure might contribute to an unfavorable lipidemic profile and adiposity in childhood.

Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain

Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ²  = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.

Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study

Background

Stroke risk assessment is an important means of primary prevention, but the applicability of existing stroke risk assessment scales in the Chinese population has always been controversial. A prospective study is a common method of medical research, but it is time-consuming and labor-intensive. Medical big data has been demonstrated to promote disease risk factor discovery and prognosis, attracting broad research interest.

Objective

We aimed to establish a high-precision stroke risk prediction model for hypertensive patients based on historical electronic medical record data and machine learning algorithms.

Methods

Based on the Shenzhen Health Information Big Data Platform, a total of 57,671 patients were screened from 250,788 registered patients with hypertension, of whom 9421 had stroke onset during the 3-year follow-up. In addition to baseline characteristics and historical symptoms, we constructed some trend characteristics from multitemporal medical records. Stratified sampling according to gender ratio and age stratification was implemented to balance the positive and negative cases, and the final 19,953 samples were randomly divided into a training set and test set according to a ratio of 7:3. We used 4 machine learning algorithms for modeling, and the risk prediction performance was compared with the traditional risk scales. We also analyzed the nonlinear effect of continuous characteristics on stroke onset.

Results

The tree-based integration algorithm extreme gradient boosting achieved the optimal performance with an area under the receiver operating characteristic curve of 0.9220, surpassing the other 3 traditional machine learning algorithms. Compared with 2 traditional risk scales, the Framingham stroke risk profiles and the Chinese Multiprovincial Cohort Study, our proposed model achieved better performance on the independent validation set, and the area under the receiver operating characteristic value increased by 0.17. Further nonlinear effect analysis revealed the importance of multitemporal trend characteristics in stroke risk prediction, which will benefit the standardized management of hypertensive patients.

Conclusions

A high-precision 3-year stroke risk prediction model for hypertensive patients was established, and the model's performance was verified by comparing it with the traditional risk scales. Multitemporal trend characteristics played an important role in stroke onset, and thus the model could be deployed to electronic health record systems to assist in more pervasive, preemptive stroke risk screening, enabling higher efficiency of early disease prevention and intervention.

A dry immersion model of microgravity modulates platelet phenotype, miRNA signature, and circulating plasma protein biomarker profile

Ground based research modalities of microgravity have been proposed as innovative methods to investigate the aetiology of chronic age-related conditions such as cardiovascular disease. Dry Immersion (DI), has been effectively used to interrogate the sequelae of physical inactivity (PI) and microgravity on multiple physiological systems. Herein we look at the causa et effectus of 3-day DI on platelet phenotype, and correlate with both miRomic and circulating biomarker expression. The miRomic profile of platelets is reflective of phenotype, which itself is sensitive and malleable to the exposome, undergoing responsive transitions in order to fulfil platelets role in thrombosis and haemostasis. Heterogeneous platelet subpopulations circulate at any given time, with varying degrees of sensitivity to activation. Employing a DI model, we investigate the effect of acute PI on platelet function in 12 healthy males. 3-day DI resulted in a significant increase in platelet count, plateletcrit, platelet adhesion, aggregation, and a modest elevation of platelet reactivity index (PRI). We identified 15 protein biomarkers and 22 miRNA whose expression levels were altered after DI. A 3-day DI model of microgravity/physical inactivity induced a prothrombotic platelet phenotype with an unique platelet miRNA signature, increased platelet count and plateletcrit. This correlated with a unique circulating protein biomarker signature. Taken together, these findings highlight platelets as sensitive adaptive sentinels and functional biomarkers of epigenetic drift within the cardiovascular compartment.

Cardiometabolic, Lifestyle, and Nutritional Factors in Relation to Varicose Veins: A Mendelian Randomization Study

Background We conducted a 2-sample Mendelian randomization study to assess the associations of cardiometabolic, lifestyle, and nutritional factors with varicose veins. Methods and Results Independent single-nucleotide polymorphisms associated with height (positive control), body mass index, type 2 diabetes, diastolic and systolic blood pressure, smoking, alcohol and coffee consumption, 7 circulating vitamins (A, B6, B9, B12, C, 25-hydroxyvitamin D, and E), and 5 circulating minerals (calcium, iron, magnesium, selenium, and zinc) at the genome-wide significance level were used as instrumental variables. Summary-level data for the genetic associations with varicose veins were obtained from the UK Biobank (8763 cases and 352 431 noncases) and the FinnGen consortium (13 928 cases and 153 951 noncases). Genetically predicted higher height, body mass index, smoking, and circulating iron levels were associated with an increased risk of varicose veins. The odds ratios (ORs) per 1-SD increase in the exposure were 1.34 (95% CI, 1.25-1.43) for height, 1.39 (95% CI, 1.27-1.52) for body mass index, 1.12 (95% CI, 1.04-1.22) for the prevalence of smoking initiation, and 1.24 (95% CI, 1.16-1.33) for iron. Higher genetically predicted systolic blood pressure and circulating calcium and zinc levels were associated with a reduced risk of varicose veins, whereas the association for systolic blood pressure did not persist after adjustment for genetically predicted height. The OR was 0.75 (95% CI, 0.62-0.92) per 1-SD increase in calcium levels and 0.97 (95% CI, 0.95-0.98) for zinc. Conclusions This study identified several modifiable risk factors for varicose veins.

Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

Background

Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.

Methods

In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.

Results

Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.

Conclusion

The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

Association of Seafood Consumption and Mercury Exposure With Cardiovascular and All-Cause Mortality Among US Adults

IMPORTANCE

Although seafood is known to contain heart-healthy omega-3 fatty acids, many people choose to limit their seafood consumption because of fear of mercury exposure from seafood. It is imperative to clarify the potential health effects of current mercury exposure in contemporary populations.

OBJECTIVE

To examine the association of seafood consumption and mercury exposure with all-cause and cardiovascular disease (CVD)-related mortality in the US general population.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study included adults 20 years or older who participated in the 2003 to 2012 cycles of the National Health and Nutrition Examination Survey; data were linked to mortality records through December 31, 2015. Data analysis was performed from January to March 10, 2021.

EXPOSURES

Seafood consumption was assessed through two 24-hour dietary recalls, and mercury exposure was assessed by blood mercury levels.

MAIN OUTCOMES AND MEASURES

All-cause and CVD-related mortality. Multivariable Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs of mortality associated with usual seafood consumption and blood mercury concentration quartiles.

RESULTS

This study included 17 294 participants (mean [SD] age, 45.9 [17.1] years; 9217 [53.3%] female) with a mean (SD) blood mercury concentration of 1.62 (2.46) μg/L. During 131 276 person-years of follow-up, 1076 deaths occurred, including 181 deaths from CVD. The multivariable-adjusted HR for an increase in seafood consumption of 1 oz equivalent per day and all-cause mortality was 0.84 (95% CI, 0.66-1.07) and for CVD-related mortality was 0.89 (95% CI, 0.54-1.47). Blood mercury level was not associated with all-cause or CVD-related mortality. Comparing the highest with the lowest quartile of blood mercury concentration, the multivariable-adjusted HRs were 0.82 (95% CI, 0.66-1.05) for all-cause mortality and 0.90 (95% CI, 0.53-1.52) for CVD-related mortality.

CONCLUSIONS AND RELEVANCE

In this cohort study of US adults, seafood consumption and mercury exposure with the current seafood consumption level were not significantly associated with the risk of all-cause or CVD-related mortality. These findings may inform future public health guidelines regarding mercury exposure, seafood consumption, and cardiovascular health promotion.

Preliminary Analysis of the Risk Factor Identification Embedding Model for Cardiovascular Disease

Cardiovascular Disease (CVD) is responsible for a large part of healthcare costs every year, but susceptibility to it is affected by complex biological and physiological variables including patients' genetics and lifestyles. There has not been much work to develop a framework that incorporates these important and clinically relevant risk factors into a comprehensive model for CVD research. Moreover, the data labeling required to do so, such as annotating gene functions, is an extremely challenging, tedious, and time-consuming process. In this work, our goal was to develop and validate a risk factor embedding model, which incorporates genotype, phenotype without pre-labeled information to identify various risk factors of CVD. We hypothesize that (1) the knowledge background that does not require data labeling could be gathered from published abstract data, (2) the phenotype, genotype risk factors could be represented in an embedding vector space. We collected 1,363,682 published abstracts from PubMed using the keyword "heart" and 19,264 human gene names, then trained our model using the collected abstracts. We evaluated our CVD risk factor identification model using both intrinsic and extrinsic evaluations: for the intrinsic evaluation, we examined whether or not the captured top-10 words and genes have references related to the input query "myocardial infarction", as one of CVDs, and our model correctly identified them. For the extrinsic evaluation, we used our model to the dimensionality reduction task for classifications, and our method outperformed other popular methods. These results show the feasibility of our approach for disease-associated risk factors of CVD which incorporates genotype, phenotype.Clinical Relevance-Our model provides a comprehensive tool to incorporate various risk factors without any a priori data labeling knowledge for CVD. Our approach shows a potential to provide discovered knowledge that contributes to better understanding and treatment of CVD.

Relationship between Cardiovascular Disease Risk and Neck Circumference Shown in the Systematic Coronary Risk Estimation (SCORE) Risk Model

INTRODUCTION

The most important way to reduce CVD-related mortality is to apply appropriate treatment according to the risk status of the patients. For this purpose, the SCORE risk model is used in Europe. In addition to these risk models, some anthropometric measurements are known to be associated with CVD risk and risk factors.

OBJECTIVES

This study aimed to investigate the association of these anthropometric measurements, especially neck circumference (NC), with the SCORE risk chart.

METHODS

This was planned as a cross-sectional study. The study population were classified according to their SCORE risk values. The relationship of NC and other anthropometric measurements with the total cardiovascular risk indicated by the SCORE risk was investigated.

RESULTS

A total of 232 patients were included in the study. The patients participating in the study were analysed in four groups according to the SCORE ten-year total cardiovascular mortality risk. As a result, the NC was statistically significantly lower among the SCORE low and moderate risk group than all other SCORE risk groups (low-high and very high 36(3)-38(4) (IQR) p: 0.026, 36(3)-39(4) (IQR) p < 0.001, 36(3)-40(4) (IQR) p < 0.001), (moderate-high and very high 38(4) vs. 39(4) (IQR) p: 0.02, 38(4) vs. 40(4) (IQR) p < 0.001, 39(4) vs. 40(4) (IQR) p > 0.05). NC was found to have the strongest correlation with SCORE than the other anthropometric measurements.

CONCLUSIONS

Neck circumference correlates strongly with the SCORE risk model which shows the ten-year cardiovascular mortality risk and can be used in clinical practice to predict CVD risk.

Gene-Environment Interactions for Cardiovascular Disease

PURPOSE OF REVIEW

We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field.

RECENT FINDINGS

Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.

Cardiovascular Effects of Particulate Air Pollution

Inhalation of fine particulate matter (PM_2.5), produced by the combustion of fossil fuels, is an important risk factor for cardiovascular disease. Exposure to PM_2.5 has been linked to increases in blood pressure, thrombosis, and insulin resistance. It also induces vascular injury and accelerates atherogenesis. Results from animal models corroborate epidemiological evidence and suggest that the cardiovascular effects of PM_2.5 may be attributable, in part, to oxidative stress, inflammation, and the activation of the autonomic nervous system. Although the underlying mechanisms remain unclear, there is robust evidence that long-term exposure to PM_2.5 is associated with premature mortality due to heart failure, stoke, and ischemic heart disease. Expected final online publication date for the Annual Review of Medicine, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

On-Treatment Blood Pressure and Cardiovascular Outcomes in Adults With Hypertension and Left Ventricular Hypertrophy

BACKGROUND

Benefits of intensive blood pressure lowering on health outcomes have been demonstrated in high-risk patients. However, little is known about such benefits in patients with left ventricular hypertrophy (LVH).

OBJECTIVES

This study sought to investigate the association of on-treatment blood pressure with cardiovascular disease (CVD) risk in adults with hypertension and LVH.

METHODS

From a nationwide health examination database, this study identified 95,545 participants aged 40-79 years who were taking antihypertensive medication and had LVH on baseline electrocardiography. Using Cox models, HRs and 95% CIs for CVD events were calculated according to systolic blood pressure (SBP) or diastolic blood pressure (DBP).

RESULTS

Over a median follow-up of 11.5 years, 12,035 new CVD events occurred. An SBP of <130 mm Hg and DBP of <80 mm Hg were associated with the lowest risk for CVD events in cubic spline models. When the group with SBP of 120-129 mm Hg was the reference, multivariable-adjusted HRs were 1.31 (95% CI: 1.24-1.38) in the ≥140 mm Hg group, 1.08 (95% CI: 1.02-1.15) in the 130-139 mm Hg group, and 1.03 (95% CI: 0.93-1.15) in the <120 mm Hg group. Likewise, when the group with DBP of 70-79 mm Hg was the reference, multivariable-adjusted HRs were 1.30 (95% CI: 1.24-1.37) in the ≥90 mm Hg group, 1.06 (95% CI: 1.01-1.12) in the 80-89 mm Hg group, and 1.08 (95% CI: 0.96 to 1.20) in the <70 mm Hg group.

CONCLUSIONS

In adults with hypertension and LVH, the risk for CVD events was the lowest at SBP <130 mm Hg and DBP <80 mm Hg. Further randomized trials are warranted to establish optimal blood pressure-lowering strategies for these patients.

Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review

INTRODUCTION

Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants.

METHODS

A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included.

RESULTS

Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health.

DISCUSSION

Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.

Metabolic Dysfunction-Associated Fatty Liver Disease and Incident Cardiovascular Disease Risk: A Nationwide Cohort Study

BACKGROUND & AIMS

An international expert panel proposed a new definition for metabolic dysfunction-associated fatty liver disease (MAFLD) as a name change from nonalcoholic fatty liver disease (NAFLD). The clinical impact of this change on the assessment of cardiovascular disease (CVD) risk is yet unknown. We evaluated the prevalence of fatty liver disease (FLD) and the associated CVD risk using each of these definitions.

METHODS

From a nationwide health screening database, we included 9,584,399 participants (48.5% male) aged 40-64 years between 2009 and 2010. Participants were categorized by presence of NAFLD and MAFLD, separately, and by the combination of the 2 definitions-Neither-FLD, NAFLD-only, MAFLD-only, or Both-FLD. The primary outcome was a composite CVD event, including myocardial infarction, ischemic stroke, heart failure, or CVD-related death.

RESULTS

The prevalence of NAFLD and MAFLD was 28.0% and 37.3%, respectively. After excluding those with prior CVD, 8,962,813 participants were followed for a median of 10.1 years. NAFLD and MAFLD were each associated with significantly higher risk for CVD events. When the Neither-FLD group was the reference, multivariable-adjusted hazard ratios (95% confidence interval) for CVD events were 1.09 (1.03-1.15) in the NAFLD-only group, 1.43 (1.41-1.45) in the MAFLD-only group, and 1.56 (1.54-1.58) in the Both-FLD group.

CONCLUSIONS

A considerable proportion of middle-aged Korean adults have MAFLD without satisfying the former definition of NAFLD. The change from NAFLD to MAFLD criteria may identify a greater number of individuals with metabolically complicated fatty liver and increased risk for CVD.

Exposure to Normobaric Hypoxia Combined with a Mixed Diet Contributes to Improvement in Lipid Profile in Trained Cyclists

This study aimed to analyze the effects of live high-train low method (LH-TL) and intermittent hypoxic training (IHT) with a controlled mixed diet on lipid profile in cyclists. Thirty trained male cyclists at a national level with at least six years of training experience participated in the study. The LH-TL group was exposed to hypoxia (FiO2 = 16.5%) for 11-12 h a day and trained under normoxia for 3 weeks. In the IHT group, participants followed the IHT routine three times a week under hypoxia (FiO2 = 16.5%) at lactate threshold intensity. The control group (N) lived and trained under normoxia. The results showed that the 3-week LH-TL method significantly improved all lipid profile variables. The LH-TL group showed a significant increase in HDL-C by 9.0% and a decrease in total cholesterol (TC) by 9.2%, LDL-C by 18.2%, and triglycerides (TG) by 27.6%. There were no significant changes in lipid profiles in the IHT and N groups. ∆TG and ∆TC were significantly higher in the LH-TL group compared to the N group. In conclusion, hypoxic conditions combined with a mixed diet can induce beneficial changes in lipid profile even in highly trained athletes. The effectiveness of the hypoxic stimulus is closely related to the hypoxic training method.

Green Space and Health in Mainland China: A Systematic Review

Non-communicable diseases (NCDs) have become a major cause of premature mortality and disabilities in China due to factors concomitant with rapid economic growth and urbanisation over three decades. Promoting green space might be a valuable strategy to help improve population health in China, as well as a range of co-benefits (e.g., increasing resilience to climate change). No systematic review has so far determined the degree of association between green space and health outcomes in China. This review was conducted to address this gap. Five electronic databases were searched using search terms on green space, health, and China. The review of 83 publications that met eligibility criteria reports associations indicative of various health benefits from more green space, including mental health, general health, healthier weight status and anthropometry, and more favorable cardiometabolic and cerebrovascular outcomes. There was insufficient evidence to draw firm conclusions on mortality, birth outcomes, and cognitive function, and findings on respiratory and infectious outcomes were inconsistent and limited. Future work needs to examine the health benefits of particular types and qualities of green spaces, as well as to take advantage of (quasi-)experimental designs to test greening interventions within the context of China's rapid urbanization and economic growth.

Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors

Heart disease remains a significant human health burden worldwide with a significant fraction of morbidity attributable to environmental exposures. However, the extent to which the thousands of chemicals in commerce and the environment may contribute to heart disease morbidity is largely unknown, because in contrast to pharmaceuticals, environmental chemicals are seldom tested for potential cardiotoxicity. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes have become an informative in vitro model for cardiotoxicity testing of drugs with the availability of cells from multiple individuals allowing in vitro testing of population variability. In this study, we hypothesized that a panel of iPSC-derived cardiomyocytes from healthy human donors can be used to screen for the potential cardiotoxicity hazard and risk of environmental chemicals. We conducted concentration-response testing of 1029 chemicals (drugs, pesticides, flame retardants, polycyclic aromatic hydrocarbons (PAHs), plasticizers, industrial chemicals, food/flavor/fragrance agents, etc.) in iPSC-derived cardiomyocytes from 5 donors. We used kinetic calcium flux and high-content imaging to derive quantitative measures as inputs into Bayesian population concentration-response modeling of the effects of each chemical. We found that many environmental chemicals pose a hazard to human cardiomyocytes in vitro with more than half of all chemicals eliciting positive or negative chronotropic or arrhythmogenic effects. However, most of the tested environmental chemicals for which human exposure and high-throughput toxicokinetics data were available had wide margins of exposure and, thus, do not appear to pose a significant human health risk in a general population. Still, relatively narrow margins of exposure (<100) were estimated for some perfuoroalkyl substances and phthalates, raising concerns that cumulative exposures may pose a cardiotoxicity risk. Collectively, this study demonstrated the value of using a population-based human in vitro model for rapid, high-throughput hazard and risk characterization of chemicals for which little to no cardiotoxicity data are available from guideline studies in animals.

Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran

Background

Myocardial Infarction (MI) is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the incidence of MI to identify potential clusters of the incidence of MI patterns across rural areas in Zanjan province, Iran.

Materials & methods

This was a retrospective and geospatial analysis study of the incidence of MI data from nine hospitals during 2014–2018. Three different spatial analysis methods (Spatial autocorrelation, hot spot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of the incidence of MI at the study area.

Results

Three thousand eight hundred twenty patients were registered at Zanjan hospitals due to MI during 2014–2018. The overall age-adjusted incidence rate of MI was 343 cases per 100,000 person which was raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person-year (a 30% increase, P < 0.001). Golabar region had the highest age-adjusted incidence rate of MI (515 cases per 100,000 person). Five hot spots and one high-high cluster were detected using spatial analysis methods.

Conclusion

This study showed that there is a great deal of spatial variations in the pattern of the incidence of MI in Zanjan province. The high incidence rate of MI in the study area compared to the national average, is a warning to local health authorities to determine the possible causes of disease incidence and potential drivers of high-risk areas. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the incidence of MI and allocate health resource to unmet need areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11695-8.

Prevalence, awareness, treatment and control of hypertension among Ngawa Tibetans in China: a cross-sectional study

OBJECTIVES

To explore the prevalence, awareness, treatment and control rate of hypertension and analyse the potential social environment factors among Ngawa Tibetans in China.

DESIGN

This was a cross-sectional observational study.

SETTING

The investigation based on a multistage stratified cluster sampling was conducted in the Ngawa area, Sichuan Province, Southwest China. Tibetan residents were selected by random sampling method from one city and six counties in Ngawa.

METHODS

Basic demographical information, physical activity and blood pressure were collected. In addition, the participants completed the questionnaire. A multivariate logistic regression analysis was used to examine the association between the prevalence, awareness, treatment and control rate of hypertension and the potential risk factors.

PARTICIPANTS

The sample comprised 2228 Ngawa Tibetan residents (age 18-80 years) from September 2018 to June 2019.

RESULTS

The prevalence rate of hypertension was 24.6%. The control rate was 6.2%, while the awareness rate (32.3%) and treatment rate (21.7%) of hypertension had been significantly improved.

CONCLUSION

The prevalence of hypertension among Ngawa Tibetans was high. The awareness and treatment were improved in recent years. But the control rate was low. The government needs to strengthen the basic medical care and health education for Ngawa Tibetans.

A Simple Model for Predicting 10-Year Cardiovascular Risk in Middle-Aged to Older Chinese: Guangzhou Biobank Cohort Study

The aim of this paper is to develop 10-year cardiovascular disease (CVD) risk prediction models for the contemporary Chinese populations based on the Guangzhou Biobank Cohort Study (GBCS) and to compare its performance with models based on Framingham's general cardiovascular risk profile and the Prediction for Atherosclerotic CVD Risk in China (China-PAR) project. Subjects were randomly classified into the training (n = 15,000) and validation (n = 12,721) sets. During an average of 12.0 years' follow-up, 3,732 CVD events occurred. A 10-year sex-specific CVD risk prediction model including age, systolic blood pressure, use of antihypertensive medication, smoking, and diabetes was developed. Compared with the Framingham and China-PAR models, the GBCS model had a better discrimination in both women (c-statistic 0.72, 95% CI 0.71-0.73) and men (c-statistic 0.68, 95% CI 0.67-0.70), and the risk predicted was closer to the actual risk. This prediction model would be useful for identifying individuals at higher risks of CVD in contemporary Chinese populations.

Demographic and socioeconomic inequalities in ideal cardiovascular health: A systematic review and meta-analysis

Background

In 2010, the American Heart Association introduced a new concept of ideal cardiovascular health (CVH) defined as the simultaneous presence of 7 favorable CVH metrics (smoking, diet, physical activity, body mass index, blood pressure, total cholesterol, and fasting blood glucose). The objective of this study was to conduct a systematic literature review and meta-analysis of studies examining the prevalence of ideal CVH, and each of the ideal CVH metrics as well as the relationship between socio-demographic determinants and ideal CVH.

Methods

A comprehensive literature search was conducted in Medline and Scopus databases for studies published between 1 January 2010 and 30 June 2020. A total of 50 studies including 2,148,470 participants were analyzed. Associations were estimated using DerSimonian-Laird random-effect models. Heterogeneity was investigated through subgroup analyses, Q-test, and I² statistics.

Results

This study showed a low prevalence of ideal CVH defining as 6 and 7 ideal metrics (3.3%). Among seven ideal CVH metrics, smoking was the best metric (71%), while the poorest CVH metric was a healthy diet (5.8%). Gender was a statistically significant moderator of ideal smoking (81% in females and 60% in males) and ideal blood pressure (42% in females and 30% in males). Females and young adults had better CVH status compared to males and older adults. Also, more educated and better-off individuals had a greater number of ideal CVH metrics.

Conclusions

To the best of our knowledge, this is the first systematic review on the relationship between participants’ socioeconomic status and ideal CVH. The results suggest that the prevalence of ideal CVH and most metrics was unsatisfactory. In order to achieve the improvement of the CVH metrics and the overall ideal CVH, nationwide prevention efforts at the population and individual levels are urgently needed.

Resilience and Equity in a Time of Crises: Investing in Public Urban Greenspace Is Now More Essential Than Ever in the US and Beyond

The intersecting negative effects of structural racism, COVID-19, climate change, and chronic diseases disproportionately affect racial and ethnic minorities in the US and around the world. Urban populations of color are concentrated in historically redlined, segregated, disinvested, and marginalized neighborhoods with inadequate quality housing and limited access to resources, including quality greenspaces designed to support natural ecosystems and healthy outdoor activities while mitigating urban environmental challenges such as air pollution, heat island effects, combined sewer overflows and poor water quality. Disinvested urban environments thus contribute to health inequity via physical and social environmental exposures, resulting in disparities across numerous health outcomes, including COVID-19 and chronic diseases such as cancer and cardiovascular diseases (CVD). In this paper, we build off an existing conceptual framework and propose another conceptual framework for the role of greenspace in contributing to resilience and health equity in the US and beyond. We argue that strategic investments in public greenspaces in urban neighborhoods impacted by long term economic disinvestment are critically needed to adapt and build resilience in communities of color, with urgency due to immediate health threats of climate change, COVID-19, and endemic disparities in chronic diseases. We suggest that equity-focused investments in public urban greenspaces are needed to reduce social inequalities, expand economic opportunities with diversity in workforce initiatives, build resilient urban ecosystems, and improve health equity. We recommend key strategies and considerations to guide this investment, drawing upon a robust compilation of scientific literature along with decades of community-based work, using strategic partnerships from multiple efforts in Milwaukee Wisconsin as examples of success.

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

Cobalt exposure in relation to cardiovascular disease in the United States general population

Cobalt exposure has adverse health effects on the cardiovascular system in occupational and laboratory studies, but these effects have not been assessed in the general population. We aimed to determine whether serum cobalt levels had relationship with the prevalence of cardiovascular disease (CVD) in the general population. Using data from the National Health and Nutrition Examination Survey (NHANES) (2015-2016), we performed the cross-sectional study. We analyzed the baseline characteristics of 3389 participants (1623 men and 1766 women). Generalized linear models and restricted cubic spline plots curve were undertaken to elucidate the relationship. Stratified subgroup analysis was tested to exclude interaction between different variates and cobalt. Our results showed that the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for CVD prevalence across the quartiles of cobalt were 0.94 (0.67, 1.30), 1.55 (1.15, 2.10), and 1.74 (1.28, 2.35) compared with lowest quartile. The restricted cubic spline curve also suggested nonlinear and positive association between cobalt and CVD (P for nonlinearity = 0.007). In summary, our cross-sectional results verify that higher cobalt levels are associated with a higher prevalence of cardiovascular disease.

Weather-related subjective well-being in patients with coronary artery disease

One of the particularly vulnerable groups for adverse weather conditions is people with heart disease. Most of the studies analyzed the association between certain weather conditions and increased mortality, morbidity, hospital admissions, calls, or visits to the emergency department and used as statistical data. This study evaluated associations between daily weather conditions and daily weather-related well-being in patients with coronary artery disease (CAD). From June 2008 to October 2012, a total of 865 consecutive patients with CAD (mean age 60 years; 30% of women) were recruited from the cardiac rehabilitation program at the Hospital Palanga Clinic, Lithuania. To evaluate the well-being, all patients filled in Palanga self-assessment diary for weather sensitivity every day from 8 to 21 days (average 15 ± 3 days) about their well-being (psychological, cardiac, and physical symptoms) on the last day. The weather data was recorded in the database eight times every day with a 3-hour interval using the weather station "Vantage Pro2 Plus" which was located in the same Clinic. The daily averages of the eight time records for weather parameters were calculated and were linked to the same-day diary data. We found that the well-being of patients with CAD was associated with weather parameters; specifically, general well-being was better within the temperature range 9-15 °C and worse on both sides of this range. Worsened general well-being was also associated with higher relative humidity and lower atmospheric pressure. Weather parameters can explain from 3 to 8% of the variance of well-being in patients with CAD.

Time spent outdoors and risk of myocardial infarction and stroke in middle and old aged adults: Results from the UK Biobank prospective cohort

BACKGROUND

Time spent outdoors has been previously related to several cardiovascular risk factors, implying that it may confer either beneficial or harmful effects on cardiovascular health. However, no large population-based studies have examined the relation between time spent outdoors and myocardial infarction and stroke.

OBJECTIVES

We aimed to investigate the longitudinal relation between time spent outdoors and myocardial infarction and stroke in large UK population-based cohort.

METHODS

A total of 446,648 participants from UK Biobank were included in the study of which 431,146 participants (56% females and 44% males with a mean age of 56.4 ± 8.1 years) were followed for a median time of 7 years. Time spent outdoors was self-reported and participants were stratified into quantiles (less than 1.5 [reference group]; 1.5 to 2.4; 2.5 to 3.5 and more than 3.5 h per day outdoors). Myocardial infarction and stroke events were either collected from hospital records and death registries or were self-reported by the participants. Cox proportional hazard regression was used for the analysis. In addition to age and sex, analyses were adjusted for potential demographic (TDI, ethnic background, current employment status), lifestyle (alcohol intake frequency, current tobacco use, sedentary time and moderate-to-vigorous physical activity), health related factors (BMI, systolic and diastolic blood pressure) and environmental indicators (NO₂, NO_x, PM₁₀, PM_2.5-10, PM_2,5, noise pollution, % greenspace, % natural environment and % water).

RESULTS

A 20% increased risk for myocardial infarction incidence was observed among participants who reported spending more than 3.5 h/day outdoors (HR: 1.20, 95% CI: 1.06-1.36) compared to the reference group. A trend was also observed for stroke (HR: 1.14, 95% CI: 0.97-1.34).

CONCLUSION

Findings from the present study indicate that spending more than 3.5 h/day outdoors is a risk factor for myocardial infarction and stroke. Future research is needed to further understand the relation between time spent outdoors and cardiovascular disease.

Does four-week consecutive, dawn-to-sunset intermittent fasting during Ramadan affect cardiometabolic risk factors in healthy adults? A systematic review, meta-analysis, and meta-regression

AIMS

This study aimed to evaluate the effects of Ramadan diurnal intermittent fasting (RDIF; 29-30 days) on cardiometabolic risk factors (CMRF) in healthy adults, and examine the effect of various cofactors on the outcomes using sub-group meta-regression.

DATA SYNTHESIS

We conducted a systematic review and meta-analysis to measure the effect sizes of changes in CMRF in healthy adult Muslims observing RDIF. Ten scientific databases (EBSCOhost, CINAHL, Cochrane, EMBASE, PubMed/MEDLINE, Scopus, Google Scholar, ProQuest Medical, ScienceDirect, and Web of Science) were searched from the date of inception (1950) to the end of November 2020. The CMRF searched and analyzed were total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), diastolic blood pressure (DBP), and heart rate (HR). We identified 91 studies (4431 adults aged 18-85 years) conducted between 1982 and 2020 in 23 countries distributed over four continents. RDIF-induced effect sizes for CMRF were: TC (no. of studies K = 77, number of subjects N = 3705, Hedge's g = -0.092, 95% confidence interval (CI): -0.168, 0.016); TG (K = 74, N = 3591, Hedge's g = -0.127, 95% CI: -0.203, 0.051); HDL-C (K = 68, N = 3528, Hedge's g = 0.138, 95% CI: 0.051, 0.224); LDL-C (K = 65, N = 3354, Hedge's g = -0.115, 95% CI: -0.197, -0.034); VLDL-C (K = 13, N = 648, Hedge's g = -0.252, 95% CI: -0.431, 0.073), DBP (K = 32, N = 1716, Hedge's g = -0.255, 95% CI: -0.363, 0.147), and HR (K = 12, N = 674, Hedge's g = -0.082, 95% CI: -0.300, 0.136). Meta-regression revealed that the age of fasting people was a significant moderator of changes in both HDL-C (P = 0.02) and VLDL-C (P = 0.01). Male sex was the only significant moderator of changes in LDL-C (P = 0.055). Fasting time duration was the only significant moderator of HDL-C (P = 0.001) at the end of Ramadan.

CONCLUSIONS

RDIF positively impacts CMRF, which may confer short-term transient protection against cardiovascular disease among healthy people.