BACKGROUND: This study examined the association between American Heart Association\'s (AHA) cardiovascular health (CVH) metrics -Life\'s Essential 8 (LE8)- and predicted heart age among U.S. adults.
METHODS: The sample comprised 7,075 participants aged 30-74 years without CVD and/or stroke from the National Health and Nutrition Examination Survey (NHANES) 2015-March 2020. LE8 was measured according to AHA\'s metrics (overall score ranging from 0 to 100 points), and nonlaboratory-based Framingham Risk Score was used to estimate predicted heart age. Analyses were completed in June 2024.
RESULTS: Median LE8 scores were 62.8 for men and 66.0 for women. Over 80% of participants had less than optimal CVH scores, affecting 141.5 million people and 1-in-6 participants had a low CVH score, impacting 30.0 million people. Mean predicted heart age and excess heart age (EHA, difference between actual and predicted heart age) were 56.6 (95% CI 56.1-57.1) and 8.6 (8.1-9.1) years for men and 54.0 (53.4-54.7) and 5.9 (5.2-6.5) years for women. Participants in the low CVH group (scores<50), had an EHA that was 20.7 years higher than those in the high CVH group (score 80-100). Compared to the high CVH group, participants in low CVH group had 15 times (for men) and 44 times (for women) higher risk of having EHA ≥10 years. The pattern of differences in predicted heart age, EHA, and prevalence of EHA ≥10 years by LE8 groups remained largely consistent across subpopulations.
CONCLUSIONS: These findings highlight the importance of maintaining a healthy lifestyle to improve cardiovascular health and reduce excess heart age.

UNASSIGNED: Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age.
UNASSIGNED: Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice.
UNASSIGNED: A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.

BACKGROUND: The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings.
METHODS: A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a \'heart age\' tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002).
CONCLUSIONS: This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour.
BACKGROUND: The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.

UNASSIGNED: There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.
UNASSIGNED: We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].
UNASSIGNED: In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.
UNASSIGNED: Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

BACKGROUND: It is well documented that individuals struggle to understand cardiovascular disease (CVD) percentage risk scores, which led to the development of heart age as a means of communicating risk. Developed for clinical use, its application in raising public awareness of heart health as part of a self-directed digital test has not been considered previously.
OBJECTIVE: This study aimed to understand who accesses England\'s heart age test (HAT) and its effect on user perception, knowledge, and understanding of CVD risk; future behavior intentions; and potential engagement with primary care services.
METHODS: There were 3 sources of data: routinely gathered data on all individuals accessing the HAT (February 2015 to June 2020); web-based survey, distributed between January 2021 and March 2021; and interviews with a subsample of survey respondents (February 2021 to March 2021). Data were used to describe the test user population and explore knowledge and understanding of CVD risk, confidence in interpreting and controlling CVD risk, and effect on future behavior intentions and potential engagement with primary care. Interviews were analyzed using reflexive thematic analysis.
RESULTS: Between February 2015 and June 2020, the HAT was completed approximately 5 million times, with more completions by men (2,682,544/4,898,532, 54.76%), those aged between 50 to 59 years (1,334,195/4,898,532, 27.24%), those from White ethnic background (3,972,293/4,898,532, 81.09%), and those living in the least deprived 20% of areas (707,747/4,898,532, 14.45%). The study concluded with 819 survey responses and 33 semistructured interviews. Participants stated that they understood the meaning of high estimated heart age and self-reported at least some improvement in the understanding and confidence in understanding and controlling CVD risk. Negative emotional responses were provoked among users when estimated heart age did not equate to their previous risk perceptions. The limited information needed to complete it or the production of a result when physiological risk factor information was missing (ie, blood pressure and cholesterol level) led some users to question the credibility of the test. However, most participants who were interviewed mentioned that they would recommend or had already recommended the test to others, would use it again in the future, and would be more likely to take up the offer of a National Health Service Health Check and self-reported that they had made or intended to make changes to their health behavior or felt encouraged to continue to make changes to their health behavior.
CONCLUSIONS: England\'s web-based HAT has engaged large number of people in their heart health. Improvements to England\'s HAT, noted in this paper, may enhance user satisfaction and prevent confusion. Future studies to understand the long-term benefit of the test on behavioral outcomes are warranted.

BACKGROUND: Communicating cardiovascular risk to the general population requires forms of communication that can enhance risk perception and stimulate lifestyle changes associated with reduced cardiovascular risk.
OBJECTIVE: The aim of this study was to evaluate the motivational potential of a novel lifestyle risk assessment (\"Life Age\") based on factors predictive of both premature mortality and psychosocial well-being.
METHODS: A feasibility study with a single-arm repeated measures design was conducted to evaluate the potential efficacy of Life Age on motivating lifestyle changes. Participants were recruited via social media, completed a web-based version of the Life Age questionnaire at baseline and at follow-up (8 weeks), and received 23 e-newsletters based on their Life Age results along with a mobile tracker. Participants\' estimated Life Age scores were analyzed for evidence of lifestyle changes made. Quantitative feedback of participants was also assessed.
RESULTS: In total, 18 of 27 participants completed the two Life Age tests. The median baseline Life Age was 1 year older than chronological age, which was reduced to -1.9 years at follow-up, representing an improvement of 2.9 years (P=.02). There were also accompanying improvements in Mediterranean diet score (P=.001), life satisfaction (P=.003), and sleep (P=.05). Quantitative feedback assessment indicated that the Life Age tool was easy to understand, helpful, and motivating.
CONCLUSIONS: This study demonstrated the potential benefit of a novel Life Age tool in generating a broad set of lifestyle changes known to be associated with clinical risk factors, similar to \"Heart Age.\" This was achieved without the recourse to expensive biomarker tests. However, the results from this study suggest that the motivated lifestyle changes improved both healthy lifestyle risks and psychosocial well-being, consistent with the approach of Life Age in merging the importance of a healthy lifestyle and psychosocial well-being. Further evaluation using a larger randomized controlled trial is required to fully evaluate the impact of the Life Age tool on lifestyle changes, cardiovascular disease prevention, and overall psychosocial well-being.

OBJECTIVE: \"Biological age\" calculators are widely used as a way of communicating health risk. This study evaluated the behaviour change techniques (BCTs) within such tools, underlying algorithm differences and suitability for people with varying health literacy.
METHODS: Two authors entered terms into Google (eg, biological/heart age) and recorded the first 50 results. A standard patient profile was entered into eligible biological age calculators. Evaluation was based on Michie et al\'s BCT taxonomy and a readability calculator.
RESULTS: From 4000 search results, 20 calculators were identified: 11 for cardiovascular age, 7 for general biological age and 2 for fitness age. The calculators gave variable results for the same 65-year-old profile: biological age ranged from younger to older (57-87 years), while heart age was always older (69-85+ years). Only 11/20 (55%) provided a reference explaining the underlying algorithm. The average reading level was Grade 10 (range 8.7-12.4; SD 1.44). The most common BCTs were salience of consequences, information about health consequences and credible source.
CONCLUSIONS: Biological age tools have highly variable results, BCTs and readability.
CONCLUSIONS: Developers are advised to use validated models, explain the result at the average Grade 8 reading level, and incorporate a clear call to action using evidence-based behaviour change techniques.

UNASSIGNED: Dietary factor plays an important role in the prevention of cardiovascular disease (CVD). The healthy eating index-2015 (HEI-2015), an indicator of the overall dietary quality, has been introduced to reflect adherence to the 2015-2020 Dietary Guidelines for Americans (DGA). This study aims to explore the associations of the HEI-2015 with predicted 10-year CVD risk and heart age among United States adults aged 30-74 years old using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014.
UNASSIGNED: We conducted a cross-sectional analysis among 6,614 participants aged 30-74 years old. The HEI-2015 scores were calculated from 2-days 24-h dietary recall interviews. The 10-year CVD risk and heart age were derived from the sex-specific Framingham general cardiovascular disease risk score. We defined high cardiovascular disease risk as a predicted 10-year cardiovascular disease risk of > 20%. Multiple linear regression and binary logistic regression models were used to investigate the associations of the HEI-2015 with predicted 10-year CVD risk and heart age. Compared with participants in the lowest HEI-2015 quartile, those in the highest quartile had lower predicted 10-year CVD risk (β = -2.37, 95% CI: -3.09 to -1.65, P < 0.0001), lower heart age (β = -2.63, 95% CI: -3.29 to -1.96, P < 0.0001) and lower odds for high risk of CVD (OR = 0.62, 95% CI: 0.49 to 0.80, P-trend < 0.0001) after adjusting for multiple covariates.
UNASSIGNED: Higher adherence to the 2015-2020 Dietary Guidelines for Americans is associated with lower predicted 10-year cardiovascular disease risk and lower heart age among United States adults.

BACKGROUND: Shared decision-making is an essential principle for the prevention of cardiovascular disease (CVD), where asymptomatic people consider lifelong medication and lifestyle changes.
OBJECTIVE: This study aims to develop and evaluate the first literacy-sensitive CVD prevention decision aid (DA) developed for people with low health literacy, and investigate the impact of literacy-sensitive design and heart age.
METHODS: We developed a standard DA based on international standards. The standard DA was based on our existing general practitioner DA. The literacy-sensitive DA included simple language, supporting images, white space, and a lifestyle action plan. The control DA used Heart Foundation materials. A randomized trial included 859 people aged 45-74 years using a 3 (DA: standard, literacy-sensitive, control) ×2 (heart age: heart age + percentage risk, percentage risk only) factorial design, with outcomes including prevention intentions and behaviors, gist and verbatim knowledge of risk, credibility, emotional response, and decisional conflict. We iteratively improved the literacy-sensitive version based on end-user testing interviews with 20 people with varying health literacy levels.
RESULTS: Immediately after the intervention (n=859), there were no differences in any outcome among the DA groups. The heart age group was less likely to have a positive emotional response, perceived the message as less credible, and had higher gist and verbatim knowledge of heart age risk but not percentage risk. After 4 weeks (n=596), the DA group had better gist knowledge of percentage risk than the control group. The literacy-sensitive DA group had higher fruit consumption, and the standard DA group had better verbatim knowledge of percentage risk. Verbatim knowledge was higher for heart age than for percentage risk among those who received both.
CONCLUSIONS: The literacy-sensitive DA resulted in increased knowledge of CVD risk and increased fruit consumption in participants with varying health literacy levels and CVD risk results. Adding heart age did not increase lifestyle change intentions or behavior but did affect psychological outcomes, consistent with previous findings. This tool will be integrated with additional resources to improve other lifestyle outcomes.
BACKGROUND: Australian New Zealand Clinical Trials Registry ACTRN12620000806965; https://tinyurl.com/226yhk8a.

Background: Osteoarthritis (OA) and cardiovascular disease (CVD) are prevalent in India. However, there is dearth of literature among Indians studying the relationship between the two. This study was carried out to assess various cardiovascular (CV) risk factors in patients with knee OA with an objective to investigate their association, screening and management.  Methods: In total, 225 patients were included in this cross-sectional study. Participants were diagnosed with knee OA on the basis of the Kellgren and Lawrence (K-L) classification of their radiograph. Participants were also assessed for CV risk factors (age, body mass index, systolic blood pressure, diabetes mellitus, total cholesterol, high-density lipoprotein, smoking) with the help of the Joint British Society QRisk3 calculator (JBS3) a comprehensive risk score calculator as well as a screening tool, which produces three more variables, namely 10-years risk of developing CVD, physiological heart age and life expectancy. Chi Square, Fishers exact test and one-way ANOVA tests were used to compare the categorical and quantitative variables, respectively. Pearson\'s correlation coefficient was used to assess the relationship between CV risk factors and knee OA. Multiple regression analysis was done to adjust the multiple con-founders and determine their significance. Results: Patients with severe knee OA had a statistically significantly higher prevalence of CV risk factors (p<0.05). Grade 4 knee OA patients were found to have a mean JBS3 risk of 38%, heart age of 82 years and life expectancy of 77 years as compared to grade 2 patients who had a mean JBS3 risk of 11%, heart age of 63 years and life expectancy of 82 years.  Conclusions: Our study concluded that there is a strong positive correlation between knee OA and CVD, with CV risk score being directly proportional to the severity of OA.