Physical activity and sedentary behaviour changes during and after cardiac rehabilitation:Can patients be clustered?

Objective: To identify clusters of patients according to changes in their physical behaviour during and after cardiac rehabilitation, and to predict cluster membership. Methods: The study included 533 patients (mean age 57.9 years; 18.2% females) with a recent acute coronary syndrome who participated in a 12-week multi-disciplinary cardiac rehabilitation programme, within a cohort study design. Physical behaviour (light physical activity, moderate-to vigorous physical activity, step count, and sedentary behaviour) was measured using accelerometry at 4 time-points. To identify clusters of patients according to changes in physical behaviour during and after cardiac rehabilitation, latent class trajectory modelling was applied. Baseline factors to predict cluster membership were assessed using multinomial logistic regression. Results: During and after cardiac rehabilitation, 3 separate clusters were identified for all 4 physical behaviour outcomes: patients with steady levels (comprising 68–83% of the patients), and improving (6–21%) or deteriorating (4–23%) levels. Main predictor for membership to a specific cluster was baseline physical behaviour. Patients with higher starting physical behaviour were more likely to be a member of clusters with deteriorating levels. Conclusion: Separate clusters of physical behaviour changes during and after cardiac rehabilitation could be identified. Clusters were mainly distinguis-hed by baseline physical behaviour level.</p

Physical activity and sedentary behaviour changes during and after cardiac rehabilitation:Can patients be clustered?

Objective: To identify clusters of patients according to changes in their physical behaviour during and after cardiac rehabilitation, and to predict cluster membership. Methods: The study included 533 patients (mean age 57.9 years; 18.2% females) with a recent acute coronary syndrome who participated in a 12-week multi-disciplinary cardiac rehabilitation programme, within a cohort study design. Physical behaviour (light physical activity, moderate-to vigorous physical activity, step count, and sedentary behaviour) was measured using accelerometry at 4 time-points. To identify clusters of patients according to changes in physical behaviour during and after cardiac rehabilitation, latent class trajectory modelling was applied. Baseline factors to predict cluster membership were assessed using multinomial logistic regression. Results: During and after cardiac rehabilitation, 3 separate clusters were identified for all 4 physical behaviour outcomes: patients with steady levels (comprising 68–83% of the patients), and improving (6–21%) or deteriorating (4–23%) levels. Main predictor for membership to a specific cluster was baseline physical behaviour. Patients with higher starting physical behaviour were more likely to be a member of clusters with deteriorating levels. Conclusion: Separate clusters of physical behaviour changes during and after cardiac rehabilitation could be identified. Clusters were mainly distinguis-hed by baseline physical behaviour level.</p

Healthy lifestyle in older adults and life expectancy with and without heart failure

Several lifestyle factors have been linked to risk for heart failure (HF) and premature mortality. The aim of this study was to estimate the impact of a healthy lifestyle on life expectancy with and without HF among men and women from a general population. This study was performed among 6113 participants (mean age 65.8 ± 9.7 years; 58.9% women) from the Rotterdam Study, a large prospective population-based cohort study. A continuous lifestyle score was created based on five lifestyle factors: smoking status, alcohol consumption, diet quality, physical activity and weight status (assessed 1995–2008). The lifestyle score was categorized into three levels: unhealthy (reference), intermediate and healthy. Gompertz regression and multistate life tables were used to estimate the effects of lifestyle on life expectancy with and without HF in men and women separately at ages 45, 65 and 85 years (follow-up until 2016). During an average follow-up of 11.3 years, 699 incident HF events and 2146 deaths occurred. At the age of 45 years, men in the healthy lifestyle category had a 4.4 (95% CI: 4.1–4.7) years longer total life expectancy than men in the unhealthy lifestyle category, and a 4.8 (95% CI: 4.4–5.1) years longer life expectancy free of HF. Among women, the difference in total life-expectancy at the age of 45 years was 3.4 (95% CI: 3.2–3.5) years and was 3.4 (95% CI: 3.3–3.6) years longer for life expectancy without HF. This effect persisted also at older ages. An overall healthy lifestyle can have a positive impact on total life expectancy and life expectancy free of HF

Physical activity and sedentary behaviour changes during and after cardiac rehabilitation: Can patients be clustered?

Objective: To identify clusters of patients according to changes in their physical behaviour during and after cardiac rehabilitation, and to predict cluster membership. Methods: The study included 533 patients (mean age 57.9 years; 18.2% females) with a recent acute coronary syndrome who participated in a 12-week multi-disciplinary cardiac rehabilitation programme, within a cohort study design. Physical behaviour (light physical activity, moderate-to vigorous physical activity, step count, and sedentary behaviour) was measured using accelerometry at 4 timepoints. To identify clusters of patients according to changes in physical behaviour during and after cardiac rehabilitation, latent class trajectory modelling was applied. Baseline factors to predict cluster membership were assessed using multinomial logistic regression. Results: During and after cardiac rehabilitation, 3 separate clusters were identified for all 4 physical behaviour outcomes: patients with steady levels (comprising 68–83% of the patients), and improving (6–21%) or deteriorating (4–23%) levels. Main predictor for membership to a specific cluster was baseline physical behaviour. Patients with higher starting physical behaviour were more likely to be a member of clusters with deteriorating levels. Conclusion: Separate clusters of physical behaviour changes during and after cardiac rehabilitation could be identified. Clusters were mainly distinguished by baseline physical behaviour level. LAY ABSTRACT Physical behaviour is a construct including both physical activity and sedentary behaviour. Healthy levels of physical behaviour are important for cardiac patients. Cardiac rehabilitation programs are designed to promote a heart-healthy lifestyle for this group. Nevertheless, not all patients perform sufficient physical activity after cardiac rehabilitation. It is important to identify patients at risk for disappointing physical behaviour outcomes at an early stage to provide additional care. Outcomes of the current study show that cardiac patients can be clustered according to their change in physical behavior during and after cardiac rehabilitation. The majority showed steady levels and no improvements, but we could also identify groups of patients with improving and deteriorating levels. Patients with higher starting physical activity levels or low sedentary behaviour levels were more likely to be a member of clusters with deteriorating levels. These patients could benefit of additional interventions

Plasma circulating microRNAs associated with obesity, body fat distribution, and fat mass: the Rotterdam Study

Background: MicroRNAs (miRNAs) represent a class of small non-coding RNAs that regulate gene expression post-transcriptionally and are implicated in the pathogenesis of different diseases. Limited studies have investigated the association of circulating miRNAs with obesity and body fat distribution and their link to obesity-related diseases using population-based data. Methods: We conducted a genome-wide profile of circulating miRNAs in plasma, collected between 2002 and 2005, in 1208 participants from the population-based Rotterdam Study cohort. Obesity and body fat distribution were measured as body mass index (BMI), waist-to-hip ratio (WHR), android-fat to gynoid-fat ratio (AGR), and fat mass index (FMI) measured by anthropometrics and Dual X-ray Absorptiometry. Multivariable linear regression models were used to assess the association of 591 miRNAs well-expressed in plasma with these traits adjusted for potential covariates. We further sought for the association of identified miRNAs with cardiovascular and metabolic diseases in the Rotterdam study and previous publications. Results: Plasma levels of 65 miRNAs were associated with BMI, 40 miRNAs with WHR, 65 miRNAs with FMI, and 15 miRNAs with AGR surpassing the Bonferroni-corrected P < 8.46 × 10 −5. Of these, 12 miRNAs were significantly associated with all traits, while four miRNAs were associated only with WHR, three miRNAs only with FMI, and miR-378i was associated only with AGR. The most significant association among the overlapping miRNAs was with miR-193a-5p, which was shown to be associated with type 2 diabetes and hepatic steatosis in the Rotterdam Study. Moreover, five of the obesity-associated miRNAs and two of the body fat distribution miRNAs have been correlated previously to cardiovascular disease. Conclusions: This study indicates that plasma levels of several miRNAs are associated with obesity and body fat distribution which could help to better understand the underlying mechanisms and may have the biomarker potential for obesity-related diseases

Plasma circulating microRNAs associated with obesity, body fat distribution, and fat mass:the Rotterdam Study

BACKGROUND: MicroRNAs (miRNAs) represent a class of small non-coding RNAs that regulate gene expression posttranscriptionally and are implicated in the pathogenesis of different diseases. Limited studies have investigated the association of circulating miRNAs with obesity and body fat distribution and their link to obesity-related diseases using population-based data. METHODS: We conducted a genome-wide profile of circulating miRNAs in plasma, collected between 2002 and 2005, in 1208 participants from the population-based Rotterdam Study cohort. Obesity and body fat distribution were measured as body mass index (BMI), waist-to-hip ratio (WHR), android-fat to gynoid-fat ratio (AGR), and fat mass index (FMI) measured by anthropometrics and Dual X-ray Absorptiometry. Multivariable linear regression models were used to assess the association of 591 miRNAs wellexpressed in plasma with these traits adjusted for potential covariates. We further sought for the association of identified miRNAs with cardiovascular and metabolic diseases in the Rotterdam study and previous publications. RESULTS: Plasma levels of 65 miRNAs were associated with BMI, 40 miRNAs with WHR, 65 miRNAs with FMI, and 15 miRNAs with AGR surpassing the Bonferroni-corrected P < 8.46 Å~ 10−5. Of these, 12 miRNAs were significantly associated with all traits, while four miRNAs were associated only with WHR, three miRNAs only with FMI, and miR-378i was associated only with AGR. The most significant association among the overlapping miRNAs was with miR-193a-5p, which was shown to be associated with type 2 diabetes and hepatic steatosis in the Rotterdam Study. Moreover, five of the obesity-associated miRNAs and two of the body fat distribution miRNAs have been correlated previously to cardiovascular disease. CONCLUSIONS: This study indicates that plasma levels of several miRNAs are associated with obesity and body fat distribution which could help to better understand the underlying mechanisms and may have the biomarker potential for obesity-related diseases

Heart failure and promotion of physical activity before and after cardiac rehabilitation (HF-aPProACH)

Aims: Lifestyle changes, such as increasing physical activity (PA), are a cornerstone of treatment of patients with chronic heart failure (HF). However, improving PA in HF patients is challenging, and low participation rates for cardiac rehabilitation (CR) as well as relapse to low PA levels after CR are major issues. We designed a randomized controlled trial to investigate if PA monitoring with motivational feedback before and after centre-based CR in HF patients with reduced ejection fraction (HFrEF) will lead to a clinically meaningful increase in physical fitness. Methods and results: A randomized controlled trial will be conducted in a sample of 180 HFrEF patients (New York Heart Association Class II/III) who are referred to 12-week standard CR. Patients will be randomized (2:1) to (1) standard of care (SoC) plus wearing a PA monitoring device (Fitbit Charge 3) with personalized step goals, feedback and motivation or (2) SoC only. The intervention lasts ±7 months: 4–5 weeks before CR, 12 weeks during CR and 12 weeks after CR. Measurements will take place at three time points. The primary endpoint is the change in the distance in 6-min walking test (6MWT) over the entire study period. Other endpoints include step count, grip strength, quality of life and all-cause mortality or hospitalization. Conclusions: HF-aPProACH will provide novel information on the effectiveness of remote PA stimulation and feedback before, during and after standard CR using a commercially available device to improve physical fitness in HFrEF patients.</p