Health profiles and lifestyles of those with cardiomyopathy vs. age-matched controls: a UK Biobank analysis

Cardiomyopathies are diseases of the heart muscle (ICD‑10 chapter IX, code I42). This study compared the health profiles of individuals with cardiomyopathy to age- and sex-matched controls in the UK Biobank prospective cohort to better understand health behaviours. Historical advice for patients to avoid exercise may have contributed to earlier heart failure; addressing these outdated perceptions could guide future recommendations to improve outcomes and reduce cardiovascular mortality.

Data from the UK Biobank were analysed, including physical activity behaviours, body mass index (BMI), waist circumference, body composition, hand-grip strength, and lifestyle factors, such as intake of fruit, processed/red meat, oily fish, alcohol and smoking, as well as PC-sitting and TV-viewing time. Linear and logistic regression assessed associations between these exposures and cardiomyopathy, adjusting for age, sex, and deprivation index.

The cohort comprised 442 individuals with cardiomyopathy and 173,429 matched controls. Significant differences were noted in age, deprivation index, alcohol intake, BMI, waist and hip circumference, physical activity levels, TV viewing, and sedentary time. Males had higher odds of cardiomyopathy than females (odds ratio [OR] 2.5, 95% confidence interval [CI] 2.04 to 3.05, p<0.0001). Obesity was strongly associated with cardiomyopathy (OR 3.7, 95%CI 2.88 to 4.76, p<0.0001). Sleep risk scores and type of physical activity risk scores were also significantly associated with cardiomyopathy.

In conclusion, individuals with cardiomyopathy demonstrated poorer health profiles and more sedentary behaviours than controls. These findings highlight the need for targeted interventions and updated exercise advice to improve clinical outcomes and reduce cardiovascular mortality in this population.

Introduction

Cardiomyopathies (CM) are heart conditions that cause functional or structural abnormalities in the ventricular myocardium. They are not associated with coronary artery pathology, valvular heart disease, hypertension, or congenital heart disease.¹ The disease presentation varies from asymptomatic individuals to those with abnormalities on investigation, syncope/presyncope symptoms, arrythmias, thromboembolic disease, or sudden cardiac death (SCD).¹ Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, and the leading cause of SCD from cardiomyopathy, with a prevalence of 1:500.¹ Dilated cardiomyopathy (DCM) has a prevalence of 1:2,500, while restrictive cardiomyopathy (RCM) represents a small percentage of cases. Other rare causes of cardiomyopathy include arrhythmogenic cardiomyopathy (ARCV) and takotsubo cardiomyopathy. In patients older than 35 years, sudden deaths have a wider aetiology, including epilepsy, asthma, anorexia, schizophrenia, Marfan syndrome with aortic dissection, and patients with alcohol-related pathologies.^2–3

Exercise-related concerns in individuals with CM revolve around the perceived risk of SCD.⁴ Excessive circulating levels of catecholamines and ventricular scarring leading to cardiac arrhythmias, have been hypothesised as mechanisms of SCD.⁵ However, recent studies indicate that individuals with CM may have a normal catecholamine response to vigorous exercise.⁶ Exercise advice for CM patients has been debated, with some studies suggesting a higher prevalence of SCD in student athletes (0.63/100,000 in normal student populations vs. 0.95/100,000),^7–9 while others report similar rates to the general population.¹⁰ HCM accounted for one-third of deaths in one study,¹¹ while other studies^12–15 found that HCM accounted for less than 10% of SCD during post-mortem examination, and that many of these deaths were more likely to occur in structurally ‘normal’ hearts. While previous literature recommended exercise restrictions,^16,17 recent evidence suggests that the benefits of exercise may outweigh the risks.¹⁸

Historically, individuals with CM were advised to engage only in low-demand sports and avoid vigorous activity.^16,17 These recommendations lack solid evidence and are often based on low-grade research or expert opinion. The influence of high-profile SCD cases in athletic populations has shaped perceptions and led to over-conservative advice. Also, SCD occurrences during sleep or off the field are less likely to be reported accurately, creating reporting bias. Studies have found that a significant portion of HCM-related deaths occur during routine activities or rest.^13,19

These attitudes have influenced the behaviour and health profiles of individuals with CM, leading to more sedentary lifestyles compared with the general population.²⁰ Reasons for this include health anxiety, misinformation, and conservative advice from clinicians.^20,21 These behaviours compound and contribute to earlier development of cardiometabolic disease and mortality in individuals with CM.²² Avoidance of exercise entirely is controversial advice based on the evidence provided. Recent evidence suggests that moderate exercise, and sometimes even vigorous exercise, can be safe and cardioprotective for individuals with CM.^23,24 Physiological pathways for heart remodelling initiated during exercise are thought to differ from pathological pathways, and can be beneficial for individuals partaking in exercise training. Exercise has numerous health benefits, reduces all-cause mortality, and lowers the risk of coronary artery disease,²⁴ a potential factor which increases SCD risk for CM patients.²⁵

This study aims to compare the health profiles of individuals with CM with age- and sex-matched controls from the UK Biobank prospective cohort study. Understanding the health-related behaviours of individuals with CM will facilitate targeted health interventions (e.g. dietary change) and messaging to improve outcomes, provide appropriate exercise advice, and contribute to the reduction of heart failure development and cardiovascular mortality. The hypothesis of this study is that individuals with CM will generally have worse health profiles and lead more sedentary lifestyles than age-matched controls.

Materials and method

Cohort

This cross-sectional study utilises data from the UK Biobank prospective cohort study. The UK Biobank study recruited more than 500,000 participants between 2006 and 2010 (5.5% response rate)²⁶ across 22 assessment centres throughout Scotland, England, and Wales. Participants had their data collected from 2006 to 2023. Participants involved in the UK Biobank completed electronic consent forms, touch-screen questionnaires, had physical measurements taken and biological samples collected.^27,28 Participants were aged 37–73 years at recruitment.

Outcome

The main outcomes, ‘cardiomyopathy’ and ‘heart failure’, were collected via self-reported electronic questionnaires. Participants self-reported at the baseline assessment if they have been medically diagnosed with any long-term condition (LTC).

Exposures

Multiple behavioural exposures were assessed. Physical activity was measured using the International Physical Activity Questionnaire (IPAQ) and a wrist-worn accelerometer (Axivity AX3) in a subset of participants (n=96,526). Moderate and vigorous physical activity were categorised based on minutes per week, and moderate-to-vigorous physical activity (MVPA) was calculated accordingly. Total physical activity was expressed as metabolic equivalent of task (MET)-minutes per week.^29–31 Body mass index (BMI) and waist circumference were measured, and body composition was assessed using bio-impedance measures. Hand-grip strength and VO₂ max data were obtained. Ethnicity, education achievement, alcohol intake, smoking status, dietary habits, television viewing, computer use, and sleep data were self-reported. Sleep data were also gathered from touch-screen questionnaires considering duration of sleep, and data on napping, dozing, snoring, sleeplessness and chronotype. To align with current physical activity (PA) guidelines,³² moderate PA was further divided into the following categories based on minutes per week. Similarly, vigorous PA was divided into categories based on minutes per week. Moderate-to-vigorous PA (MVPA) was calculated by summing the minutes per week of moderate PA and twice the minutes per week of vigorous PA. The total PA was determined by summing the minutes per week of light, moderate, and vigorous PA, expressed as MET-minutes per week. More details about the data collection and processing can be found elsewhere.³³

Covariates

Covariates were assessed during the initial visit from 2006 to 2010. Age was calculated using participants’ birth dates and the baseline assessment date. Sex was obtained from clinical records and recorded at baseline. The deprivation index, reflecting socio-economic status, was determined based on participants’ residential postal codes using the Townsend deprivation score.³⁴ All models were adjusted for these covariates.

Study approval

The study conducted on the UK Biobank was granted approval by the Northwest Multi-Centre Research Ethics Committee under reference number 11/NW/0382 on 17 June 2011. All participants in the UK Biobank study provided written informed consent before participating. The study protocol can be accessed online. This research utilised the UK Biobank resource and was conducted under application number 7155.³⁵

Statistical analysis

Data were presented as mean ± standard deviation (SD) for continuous variables and as a proportion for categorical variables. The association between the exposures of interest and CM was assessed using linear regression for continuous variables and logistic regression for binary outcomes. Results were reported as adjusted means for linear regression or odds ratio (OR) for logistic regressions and their 95% confidence interval (CI), respectively. Cohen’s D effect size analysis was conducted for each continuous variable to assess if differences in people with CM and matched controls were large (Cohen’s D≥0.8), modest (Cohen’s D≥0.5) or small (Cohen’s D<0.5).

All models were adjusted for age, sex, and deprivation index, except for the first model, categorising the incidence of CM by age when compared with individuals without the condition. All analyses were conducted in STATA MP version 17 software. Significant differences were set at p<0.05.

Results

A total number of 173,871 were included in the regression and logistic-regression analyses out of the total of the UK Biobank participants, however, only 96,512 of these had PA data available due to insufficient wear time (<72 hours). The number of those identified with the condition CM was 442, and 173,429 were used as age-matched controls for comparison and analysis. The mean age and SD of CM and control groups were similar (CM 58.4 ± 7.7, control 54.0 ± 8.1 years). Summary statistics display a mild difference in BMI between both groups (CM 29.2 vs. control 26.3 kg/m², p<0.0001) and a small difference is seen in body fat percentage (CM 31.6 ± 8.7 vs. control 29.7 ± 8.2) with both having higher than recommended levels of body fat (table 1). The CM group had higher levels of fat-free mass index (CM 19.1, 95%CI 18.93 to 19.27; control 18.33, 95%CI 18.32 to 18.34, p≤0.0001). When looking at the frequency of individuals with CM in table 1, it appears CM in this sample is a disease most prevalent in high deprivation backgrounds (39.4% of CM cases) versus middle deprivation (31.7%) and low deprivation (29.0%) in this sample group. There is a higher prevalence of CM in males than females (men 302, 68.3% of CM cases; women 140, 31.7% of CM cases, p≤0.0001).

Table 2 compares differences on demographic, lifestyle, anthropometric and physical activity variables between the two groups using adjusted regression analysis. Significant differences were observed for age, deprivation index, alcohol intake, BMI, all anthropometric variables excluding height, moderate physical activity (MPA) as measured by accelerometer, TV-viewing and total self-reported sedentary time. However, when the differences were assessed using Cohen’s D, only waist circumference presented large differences between the control group and those with CM. Moderate size differences were observed for age, BMI, waist and hip circumference, fat-free mass index, and device-based MPA.

The logistic-regression analysis revealed significant differences in various categorical outcomes between the two groups. Men were found to have a 2.5 times greater chance of having CM compared with women (OR 2.5, 95%CI 2.04 to 3.05, p≤0.0001). Individuals classified as obese based on BMI had 3.7 times higher odds of having CM when compared with those classified as having a healthy BMI (OR 3.7, 95%CI 2.88 to 4.76, p≤0.0001). Similarly, individuals classified as obese based on waist circumference, had a 2.76 times higher likelihood of having CM compared with those classified as ‘not obese’ based on waist circumference (OR 2.76, 95%CI 2.28 to 3.33, p≤0.0001) as indicated in table 3.

Sleep risk score was based on five sleep behaviours to determine a ‘healthy’ sleep score on sleep duration, chronotype, insomnia, snoring and excessive daytime sleepiness. Sleep risk score showed a significant association, with individuals having a 26% lower chance of CM for those who were considered normal sleepers (score =1) when compared with short/long sleepers (score =0) (OR 0.74, 95%CI 0.60 to 0.91, p=0.004). When analysing type of physical activity risk score, active participants (score =1) had a 65% lower likelihood of having CM than individuals who undertook no physical activity (score =0) (OR 0.35, 95%CI 0.26 to 0.47, p≤0.0001). For physical activity risk score, active participants (score =1) had a 46% lower likelihood of having CM than inactive participants (score =0) (OR 0.54, 95%CI 0.44 to 0.67, p≤0.0001). These findings suggest that individuals without CM exercise more than those who have the condition. On the other hand, individuals who watched more TV (>4 hours) had a 48% lower likelihood of having CM than those who watched <4 hours of TV (OR 0.52, 95%CI 0.44 to 0.64, p≤0.0001).

All other variables and risk scores were found to be non-significant, consistent with the trends observed in the regression analysis of continuous variables.

Discussion

An interesting observation from the above study is that the differences in health and lifestyle parameters of individuals with CM and the general UK population is smaller than expected. According to data gathered by World Obesity, the UK population is within the top 15% of global obesity rankings,³⁶ which may account for the relatively small differences between the two groups. The control sample in this study do not fit into the typical characteristics of a ‘healthy’ cohort. Despite this, statistical analysis alarmingly demonstrates a significant effect across a range of parameters, which would typically be associated with poorer health.

The BMI and body fat percentage in both groups exceed the healthy range. The control group’s BMI approaches the upper limit of the overweight category, while individuals in the CM group, on average, are classified as obese. However, the difference between the groups is not statistically significant, as shown in table 2. Nonetheless, this finding is relevant because obesity is directly associated with an increased risk of restrictive CM.³⁷ Other obesity indicators, such as waist and hip circumference, are significantly linked to a higher likelihood of having CM in this study. These results align with the notion that individuals with CM tend to have poorer health profiles, as these measurements serve as general indicators of overall health.⁷

Individuals with CM showed a significantly lower level of MPA, as measured by accelerometer data, compared with the control group. On average, this difference amounted to 15 minutes less. However, the CM group displayed more favourable outcomes in terms of fat-free mass index, which could be attributed to their generally heavier frames. It is worth noting that both groups fell significantly below the physical activity recommendations of the World Health Organization (WHO).³² The control group only achieved 5.5 minutes of vigorous physical activity, while the CM group had even lower levels at 3.8 minutes. Possible factors contributing to this could be related to previous exercise guidelines, recommendations, and individual health anxiety.^16,18

Encouragingly, there have been positive developments in physical activity recommendations for CM. A four-year observational study suggested that participating in vigorous competitive sports could be considered for individuals with CM.²⁴ Additionally, Saberi et al. (2017) conducted the first randomised-controlled trial (RCT) with 136 participants, demonstrating promising results from a 16-week prescribed moderate-intensity exercise programme. At the end of the 16 weeks, they observed a 6% absolute increase in the mean change in peak oxygen consumption compared with the control group, associated with an 8% lower risk of cardiovascular mortality.²¹

Performing a large RCT with sufficient power to ensure safety would be challenging. However, valuable insights on risk prediction can be gained from ongoing observational studies, such as the Lifestyle and Exercise in HCM trial (LIVE-HCM, NCT02549664), which has enrolled over 1,600 participants. The goal is to compare individuals engaged in vigorous or competitive sports with less active cohorts.³⁸

In another small pilot study, Klempfner et al. examined the benefits of increased exercise in 20 symptomatic patients with HCM. These patients attended cardiac rehabilitation sessions twice a week, following an exercise routine tailored to their heart rate reserve (HRR). The exercise intensity gradually increased from 50% to 85% of HRR. The study demonstrated a significant improvement in functional capacity, with 10 patients (50%) showing at least one grade improvement in New York Heart Association (NYHA) functional class. Importantly, no clinical deterioration or adverse events were observed during the study and the subsequent 12 months.³⁹

Dietary habits between both groups were not significantly different across the board, this would tie in with the general UK population not being much healthier than the CM group. There was a moderately higher alcohol intake, which was significant. The control group drinks on average three more units of alcohol per week, however, the effect size is barely noticeable when Cohen’s coefficient analysis was conducted. The lower rates of alcohol consumption in the CM group are in keeping with other studies.²⁰ In the study conducted by Reineck et al. (2013), participants with CM typically displayed similar or more favourable non-physical activity health behaviours, which may reflect psychosocial adaptations to living with a perceived increased risk of mortality. This may be an effort to reduce risk, or due to more regular input from health professionals.

Reviewing logistic analyses found that with respect to BMI risk scores, individuals with CM were at a 3.7 times higher likelihood to fall into the obese BMI category. This group also had a 2.76 times higher chance of being classified as obese by waist circumference. Males were also found to have a 2.5 times higher likelihood of having CM in this sample, which is in keeping with other studies.⁴⁰ CM is highest in areas with the highest deprivation categories, with the rate of prevalence in this sample falling between the rates seen for HCM and DCM. A weakness of this sample is that the type of CM is not subcategorised, as some of the evidence suggests that certain subtypes of CM have higher rates of SCD and, therefore, may guide individualised treatment approaches.⁶

A large advantage for athletic populations of individuals, in which much of the research around CM is conducted, is that semi-professional and professional athletes will often undergo cardiac screening as part of their pre-medical work-up on a routine basis.⁴¹ This allows for identification of individuals at risk of CM, and, therefore, SCD, which would lead to risk reduction measures, such as implantable cardioverter-defibrillator (ICD) implantation.⁴² While this would mean a reduction in risk for those participating in exercise, general non-athletic populations, such as in this group of participants, are unlikely to undergo regular cardiac screening in life, and may present at a much later stage of disease, with the earliest discovery occurring following an episode of SCD.¹ The sample group used in this study is also heterogenous and would not represent the typical health profile of the athletic population, making it difficult to make inferences about either group based on these data or previous literature performed in athletic individuals.²² Furthermore, comparing the health profiles of individuals with CM against age-matched controls in other nations may demonstrate larger differences between both groups than was demonstrated in this study. There is still room in the literature for international comparisons to be made.

Overall, this study highlights the significant associations between sex, obesity (based on BMI and waist circumference), sleep risk score, type of physical activity risk score, and TV risk score with the condition cardiomyopathy. These findings provide valuable insights into the potential risk factors and characteristics associated with CM.

Strengths and limitations

The study benefited from UK Biobank participants as a comparable control group, improving reliability. Accelerometer data enhanced the accuracy of physical activity measurements. The extensive data allowed for comprehensive comparisons of health and lifestyle behaviours between the groups. However, self-reported variables may be affected by recall bias. The cross-sectional design limited the depth of information compared with longitudinal studies. Barriers to physical activity participation were not explored, hindering insights into improving adherence. Additionally, the age range of the participants may limit the relevance of the findings for younger individuals with CM. The demographic homogeneity of the UK Biobank (predominantly White British participants) limits generalisability to other populations. The representativeness of UK Biobank, with a generally healthier and more affluent population, could impact the generalisability of the results to the wider UK population.

Conclusion

This study lends weight to the argument that individuals with CM typically have worse health profiles than age-matched controls.²⁰ The caveat to this study being that the age-matched controls were already significantly short of the standards of healthy living. Despite this, individuals with CM demonstrated significantly poorer health profiles in a variety of measures including physical activity. Physical activity guidelines in CM are evolving with studies such as the LIVE-HCM, HIIT-HCM and the Dallas HIIT-HCM^42,43 pilot studies aiming to demonstrate that moderate and vigorous physical activity are safe for patients with HCM, which would allow reduction in cardiovascular and all-cause mortality. Nevertheless, this study lays the groundwork for further studies to be conducted investigating the barriers to participation in physical activity for the UK population. With this information, any future recommendations for physical activity in patients with CM may be adopted with more efficacy, and interventions can include re-education and re-indorsement of exercise for CM patients to improve long-term health outcomes. Individualised approaches to exercise recommendations should take into consideration the subtype of CM, individual risk profile, and desired level of sports participation. This should be guided by a cardiologist and exercise specialist.

Key messages

• Exercise safety in cardiomyopathy (CM): historical guidelines advised against vigorous physical activity in CM due to fear of sudden cardiac death (SCD), whereas newer evidence suggests moderate and even vigorous exercise may be safe and cardioprotective in some individuals with CM
• Physical activity and lifestyle behaviours: individuals with CM are significantly more sedentary than controls, with lower moderate physical activity and higher sedentary time, possibly due to fear and anxiety regarding past guidelines
• Health profile comparisons – CM vs. controls: CM patients exhibit significantly worse health metrics, including higher body mass index (BMI), waist and hip circumference, and body fat percentage. Differences in some variables (e.g. waist circumference, BMI) were moderate to large, suggesting a genuine disparity in cardiometabolic risk

Conflicts of interest

None declared.

Funding

None.

Study approval

The study conducted on the UK Biobank was granted approval by the Northwest Multi-Centre Research Ethics Committee under reference number 11/NW/0382 on 17 June 2011. All participants in the UK Biobank study provided written informed consent before participating. The study protocol can be accessed online. This research utilised the UK Biobank resource and was conducted under application number 7155.³⁵

Supplemental material

UK Biobank. Anthropometry. Version 1.0, June 2014. Available from: https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/Anthropometry.pdf

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