Lifestyle advice and interventions for cardiovascular risk reduction: A systematic review of guidelines

Khanji, Mohammed Y; Waardhuizen, Claudia N. van; Bicalho, Vinícius V.S.; Ferket, Bart S.; Hunink, M. G. Myriam; Petersen, Steffen E.

doi:10.1016/j.ijcard.2018.02.094

Cited by 43 publications

(28 citation statements)

References 28 publications

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“…Studies assessing the role for cholesterol modification therapy during illness or hospital admission could be undertaken to assess potential impact on outcomes (Greenhalgh et al, 2020). Longer term and wider emphasis on tackling obesity and dyslipidaemia, which already features in many cardiovascular disease risk algorithms (Khanji et al, 2016), through lifestyle advice and interventions should be made a priority as part of a prevention strategy (Khanji et al, 2018).…”

Section: Clinical Relevancementioning

confidence: 99%

Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study

et al. 2020

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Background Cross-sectional observational studies have reported obesity and cardiometabolic co-morbidities as important predictors of coronavirus disease 2019 (COVID-19) hospitalization. The causal impact of these risk factors is unknown at present. Methods We conducted multivariable logistic regression to evaluate the observational associations between obesity traits (body mass index [BMI], waist circumference [WC]), quantitative cardiometabolic parameters (systolic blood pressure [SBP], serum glucose, serum glycated hemoglobin [HbA1c], low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol and triglycerides [TG]) and SARS-CoV-2 positivity in the UK Biobank cohort. One-sample MR was performed by using the genetic risk scores of obesity and cardiometabolic traits constructed from independent datasets and the genotype and phenotype data from the UK Biobank. Two-sample MR was performed using the summary statistics from COVID-19 host genetics initiative. Cox proportional hazard models were fitted to assess the risk conferred by different genetic quintiles of causative exposure traits. Results The study comprised 1,211 European participants who were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 387,079 participants who were either untested or tested negative between 16 March 2020 to 31 May 2020. Observationally, higher BMI, WC, HbA1c and lower HDL-cholesterol were associated with higher odds of COVID-19 infection. One-sample MR analyses found causal associations between higher genetically determined BMI and LDL cholesterol and increased risk of COVID-19 (odds ratio [OR]: 1.15, confidence interval [CI]: 1.05–1.26 and OR: 1.58, CI: 1.21–2.06, per 1 standard deviation increment in BMI and LDL cholesterol respectively). Two-sample MR produced concordant results. Cox models indicated that individuals in the higher genetic risk score quintiles of BMI and LDL were more predisposed to COVID-19 (hazard ratio [HR]: 1.24, CI: 1.03–1.49 and HR: 1.37, CI: 1.14–1.65, for the top vs the bottom quintile for BMI and LDL cholesterol, respectively). Conclusion We identified causal associations between BMI, LDL cholesterol and susceptibility to COVID-19. In particular, individuals in higher genetic risk categories were predisposed to SARS-CoV-2 infection. These findings support the integration of BMI into the risk assessment of COVID-19 and allude to a potential role of lipid modification in the prevention and treatment.

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Section: Clinical Relevancementioning

confidence: 99%

Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study

et al. 2020

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“…The rise in obesity, diabetes and an aging population raises concern for future incidence and prevalence of cardiovascular disease (CVD). Primary prevention of CVD through risk factor reduction may reduce future burden [123]. Innovative measures to improve cardiovascular health are needed with nine potentially modifiable risk factors accounting for over 90% of the population attributable risk of a first myocardial infarction based on the findings from the INTERHEART Study [4].…”

Section: Introductionmentioning

confidence: 99%

Personalized E-Coaching in Cardiovascular Risk Reduction: A Randomized Controlled Trial

Khanji

Balawon

Boubertakh

et al. 2019

Annals of Global Health

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Objectives: To assess whether electronic (e-) coaching, using personalized web-based lifestyle and risk factor counselling with additional email prompts, provides additional risk reduction when added to standard of care (SOC) in individuals at increased risk. Methods: Between June 2013 and May 2015, 402 participants were allocated 1:1 to e-coaching and SOC versus SOC. Participants free of manifest cardiovascular disease, with internet access, and a 10-year QRISK2 cardiovascular risk of ≥10% were enrolled. Change in oscillometric carotid-femoral pulse wave velocity (PWV) from baseline to six months was the primary endpoint. Secondary outcomes included change in blood pressure (BP), weight, and risk scores. Analysis was by intention to treat. Results: Mean (±SD) age was 65.5 (5.6) years with 37% females. Primary outcome data were available for 94%. There was no difference in PWV reductions between e-coaching and standard of care groups (–0.16 m/s vs. –0.25 m/s, 95% confidence interval –0.39 to 0.22, p = 0.56). There were no differences in the improvement between groups for BP, weight, Framingham, or QRISK2 scores. Pulse wave velocity change was more favorable in those with a higher level of education (p = 0.04), but was not associated with age, gender, presence of diabetes, baseline QRISK2 score, or logins to the website. Conclusions: In individuals at increased cardiovascular risk, a comprehensive ‘health check’ program modestly reduced future risk. Personalized e-coaching did not provide added risk reduction. Currently there is no evidence to routinely recommend e-coaching in cardiovascular health check programs. Trial registration: HAPPY London ClinicalTrials.gov : NCT01911910

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“…An estimated 1 million COPD patients in the UK alone are undiagnosed [31]. The inclusion of COPD could potentially improve risk estimation, provision of lifestyle advise and intervention [53], and promote the early diagnosis of COPD through increased usage of pulmonary function testing and the availability of pulmonary function data on primary and secondary care databases.…”

Section: Discussionmentioning

confidence: 99%

Chronic Obstructive Pulmonary Disease as a Predictor of Cardiovascular Risk: A Case-Control Study

Khanji

Stone

Boubertakh

et al. 2019

COPD: Journal of Chronic Obstructive Pulmonary Disease

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Chronic obstructive pulmonary disease (COPD) is a complex multi-morbid disorder with significant cardiac mortality. Current cardiovascular risk prediction models do not include COPD. We investigated whether COPD modifies future cardiovascular risk to determine if it should be considered in risk prediction models. Case-control study using baseline data from two randomized controlled trials performed between 2012 and 2015. Of the 90 eligible subjects, 26 COPD patients with lung hyperinflation were propensity matched for 10-year global cardiovascular risk score (QRISK2) with 26 controls having normal lung function. Patients underwent cardiac magnetic resonance imaging, arterial stiffness and lung function measurements. Differences in pulse wave velocity (PWV), total arterial compliance (TAC) and aortic distensibility were main outcome measures. PWV (mean difference 1.0 m/s, 95% CI 0.02-1.92; p ¼ 0.033) and TAC (mean difference -0.27 mL/ m 2 /mmHg, 95% CI 0.39-0.15; p < 0.001) were adversely affected in COPD compared to the control group. The PWV difference equates to an age, sex and risk-factor adjusted increase in relative risk of cardiovascular events and mortality of 14% and 15%, respectively. There were no differences in aortic distensibility. In the whole cohort (n ¼ 90) QRISK2 (b ¼ 0.045, p ¼ 0.005) was associated with PWV in multivariate analysis. The relationship between QRISK2 and PWV were modified by COPD, where the interaction term reached significance (p ¼ 0.014). FEV1 (b ¼ 0.055 (0.027), p ¼ 0.041) and pulse (B ¼ -0.006 (0.002), p ¼ 0.003) were associated with TAC in multivariate analysis. Markers of cardiovascular outcomes are adversely affected in COPD patients with lung hyperinflation compared to controls matched for global cardiovascular risk. Cardiovascular risk algorithms may benefit from the addition of a COPD variable to improve risk prediction and guide management.HAPPY London ClinicalTrials.gov: NCT01911910 and HZC116601; ClinicalTrials.gov: NCT01691885 ARTICLE HISTORY

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Lifestyle advice and interventions for cardiovascular risk reduction: A systematic review of guidelines

Abstract: Current guidelines agree on the importance of lifestyle in the prevention of CVD with consensus on most factors including physical activity, smoking cessation and diet, which should be actively integrated in cardiovascular risk reduction programs aiming to improve clinical outcomes.

Cited by 43 publications

References 28 publications

Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study

Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study

Personalized E-Coaching in Cardiovascular Risk Reduction: A Randomized Controlled Trial

Chronic Obstructive Pulmonary Disease as a Predictor of Cardiovascular Risk: A Case-Control Study

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