López-Jiménez, Francisco

Introduction : Obesity is a common risk factor in cardiac rehabilitation (CR), yet many CR patients do not realize significant weight loss, despite engaging in regular exercise training while in CR. Hypothesis : We hypothesized that standard CR would demonstrate favorable body composition (BC) changes even in the absence of weight loss and sought to measure the performance characteristics of weight loss to predict BC changes. Methods : We included consecutive patients enrolled in early outpatient CR who underwent air displacement plethysmography with measurements of height, weight, and waist circumference at initiation and completion of CR. We calculated the accuracy of weight loss to identify significant improvements in BC. Results : Data from 112 patients were available for analysis; mean age 60±12 yr., 76% male, mean body mass index (BMI) 30.2±5.3 kg/m 2 . The indication for CR was myocardial infarction (n=33), percutaneous coronary intervention (n=29), coronary artery bypass or valve surgery (n=45), and heart transplant (n=5). Repeat testing occurred after a median of 81 days and 21 phase II CR sessions. There was no change in BMI (-0.53±1.3 kg/m 2 , p=0.43) and weight (-1.6±4.0 kg, p=0.51). Fat mass showed a borderline significant decrease of 2.9±3.9 kg (p=0.08). However, BC improved significantly as measured by body fat % (-2.5±3.2%, p=0.03) and waist circumference (-4.6±5.2 cm; p=0.01). There was a correlation between weight loss and fat mass loss (r=0.85, p=0.0001, positive predictive value=0.94), and % fat change (r=0.67, p=0.0001, positive predictive value=0.91). See table 1. However, the negative predictive value to detect no fat change for patients who did not lose weight was low (0.49, when assessed by % body fat loss, and 0.57, when assessed by fat mass loss). Among patients who did not lose weight, 29 (51%) had a greater than 1% body fat loss. Conclusions : The diagnostic performance of weight change to predict favorable BC changes is poor and highlights the potential utility of BC measurements in the CR setting. Table. Diagnostic Characteristics of Weight Loss as a Predictor of Improvements in Body Composition Weight Loss (kg) Body Fat % Loss ≤ 1% Fat Mass Loss (kg) > 1kg > 1% ≤ 1kg > 1kg 49 5 51 3 ≤ 1kg or gain 29 28 25 33 Sensitivity 0.63 0.67 Specificity 0.85 0.92 PPV * 0.91 0.94 NPV ** 0.49 0.57 * PPV = Positive Predictive Valve, ** NPV = Negative Predictive Value

Background : The significant bidirectional changes in cardiovascular(CV) risk factor prevalence make the net trend in incident CV risk unknown. We assessed the trends in the net 10-year risk for incident CV disease using the Framingham risk model and data from the National Health and Nutrition Examination Surveys(NHANES). Methods : We used data from NHANES II (1976–1980), NHANES III (1988–1994), and NHANES 1999–2004, including major CV risk factors, all collected in a standardized manner. People with a history of CV disease, pregnant women, people with missing data and those outside the Framingham age range were excluded. The Framingham risk function estimated the 10-year risk for CV disease. The slope of change or rate of change per year between NHANES II/III, and between NHANES III/1999–2004 were determined. The slope difference was calculated and compared to zero. Results : The mean age-adjusted 10-year CV risk decreased from 10 to 7.9% between NHANES II and III, with a significant slope (p<0.001). However, the mean age-adjusted risk decreased to a lesser extent between NHANES III and NHANES 1999–2004 from 7.9 to 7.4% (p<0.001). The slopes were different (p<0.001). In women, CV risk did not change between NHANES III and 1999–2004, 7.9 to 7.5%, (p=0.40). Predicted 10-year risk by age group (Figure ) shows that the decrease in CV risk was lower in the second period compared to the first one in all age groups, except in the elderly. Conclusion : The estimated net risk for CV disease in the US population decreased from 1976–1980 to 1988–1994 but minimally changed from 1988–1994 to 1999–2004, particularly in women. The increased prevalence of obesity may have offset the gain in CV risk reduction. Predicted 10-year Framingham Risk by Age Group

Background: Decreased testosterone (TTST) and sex hormone binding globin (SHBG) contribute to development of cardiometabolic disorders in obese men. We aimed to determine the effect of weight gain and weight loss on TTST and SHBG in healthy men and to examine their relationship with endothelial function. Method: 25 normal weight healthy men underwent an overfeeding protocol to gain 5% body weight over 8 weeks. Following weight gain, they underwent 8 weeks of weight loss. TTST, SHBG and endothelial function (brachial artery flow‐mediated dilation, FMD) were measured at baseline (T1), after weight gain (T2) and after weight loss (T3). Results: Volunteers gained 4 ± 1.3 kg and subsequently lost 3.2 ± 1.5 kg of body weight. TTST levels did not change (T1: 18.85 ± 6.2 nmol/L, T2: 19.3 ± 6.9 nmol/L, T3: 20.3 ± 7.6 nmol/L). However, SHBG decreased during weight gain (p=0.04) and increased during weight loss (p=0.0005) (T1: 22.8 ± 10.5 nmol/L, T2: 20.8 ± 11.1 nmol/L, T3: 25.58 ± 11.5 nmol/L). Furthermore, SHBG reduction during weight gain tended to correlate with decreases in FMD (p=0.06; R 2 =0.15). During weight loss, increases in SHBG correlate negatively with changes in FMD (p=0.008; R 2 =0.32). Conclusion : In normal weight healthy men, SHBG decreases with modest weight gain and increases with weight loss. TTST does not change. Changes in SHBG partly predict improvements in endothelial function with weight loss. Grant Funding Source : Supported by AHA 11SDG7260046, R21 DK81014, AHA 13POST16420009

Introduction: Body fat (BF) content better predicts adiposity-related cardiovascular risk than the body mass index (BMI). Accurate and accepted methods to assess BF are complex, expensive, and accessible only in clinical settings. Multi-sensor 3D body volume (3D-BV) measurement technology has been shown to accurately estimate BF. We assessed the hypothesis that 3D models generated from biplane imaging (i.e., front and side facing photographs) using mobile devices (App), could be used to predict body volume measurements and derive BF. Methods: We prospectively enrolled 196 subjects, who underwent 3D-BV (gold standard for body volume) within 24 hours, dual-energy X-ray Absorptiometry (iDEXA) (gold standard for BF) and photographs taken with an Ipad® App at a predefined distance and pose. These photos were post-processed with a computer-assisted algorithm to estimate body length, girth, and volume. These were used to calculate body density (BD) using the bicompartmental principles of body composition (BD=Body Mass/Volume) and derive BF% using the Siri equation [(4.95/BD-4.50) X 100]. Correlation indexes and residual plots were created to compare 3D-BV and iDEXA with the App. Mean differences were compared using a paired t-test. Results: Mean ±SD age was 31.9±9.15 years, 53% were women, weight 72.8±14.1 kg, BMI was 25.5±4.5 kg/m 2 .The App volume correlated with 3D-BV (R 2 =0.95,95%CI 0.90,0.95, p<.0001, Figure 1-A ) ; average difference between App volume vs 3D-BV volume was -1.2 Liters, 95%CI,0.03, 2.42, p=0.06, ( Figure 1-B ). App BF% correlated with iDEXA BF% (R 2 =0.92, 95%CI 0.90,0.94, p<.0001, Figure 1-C ) ; while average difference between App BF% vs iDEXA BF% was -0.13 %, 95%CI -0.6,0.4, p=0.6, ( Figure 1-D ). Conclusion: BF can be estimated using volume measurements obtained by biplane imaging from mobile devices and could serve as a home-based, portable, scalable, cost-effective, and convenient measure to assess BF and track changes in body composition over time.

Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P &lt; 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Objective: To identify an efficacious intervention on treating adolescents with overweight and obesity, this might result in health benefits. Methods: Adolescents with overweight or obesity aged 10–17 years with BMI percentile ≥85th were included in this historical observational analysis. Subjects used an entirely remote weight loss program combining mobile applications, frequent self-weighing, and calorie restriction with meal replacement. Body weight changes were evaluated at 42, 60, 90, and 120 days using different metrics including absolute body weight, BMI, and BMI z-score. Chi-square or Fisher exact tests (categorical variables) and Student's t -test (continuous variables) were used to compare subjects. Results: In total, 2,825 participants, mean age 14.4 ± 2.2 years, (54.8% girls), were included from October 27, 2016, to December 31, 2017, in mainland China; 1355 (48.0%) had a baseline BMI percentile ≥97th. Mean BMI and BMI z-score were 29.20 ± 4.44 kg/m 2 and 1.89 ± 0.42, respectively. At day 120, mean reduction in body weight, BMI, and BMI z-score was 8.6 ± 0.63 kg, 3.13 ± 0.21 kg/m 2 , and 0.42 ± 0.03; 71.4% had lost ≥5% body weight, 69.4% of boys and 73.2% of girls, respectively. Compared with boys, girls achieved greater reduction on BMI z-score at all intervals ( p < 0.004 for all comparisons). Higher BMI percentile at baseline and increased frequency of use of the mobile application were directly associated with more significant weight loss. Conclusions: An entirely remote digital weight loss program is effective in facilitating weight loss in adolescents with overweight or obesity in the short term and mid term.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

Abstract Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

Abstract Introduction: Cardiovascular disease is the most common cause of morbidity and mortality in the United States and in the world. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes. Methods: Data on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available CDC website from 2006-2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time. Results: Hospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58, P<0.001), atrial fibrillation (0.57, P<0.001), coronary heart disease (0.58, P<0.001), myocardial infarction (0.70, P<0.001), stroke (0.62, P<0.001), cardiac dysrhythmia (0.46, P<0.001)). All diseases studied showed a positive increase in correlation throughout the time period studied (P<0.05). All five of the cardiovascular risk factors studied showed strong correlation with online search data; diabetes (R=0.78, P<0.001), cigarette use (R=0.79, P<0.001), hypertension (R=0.81, P<0.001), high cholesterol (R=0.59, P<0.001), obesity (R=0.80, P<0.001). Three of the five showed an increasing correlation over time. Conclusion: The prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes nationwide and when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.

Abstract Aims/Hypothesis The need for understanding obesity as a chronic disease, its stigmatization, and the lack of actionability related to it demands a new approach. The adiposity‐based chronic disease (ABCD) model is based on adiposity amount, distribution, and function, with a three stage complication‐centric rather than a body mass index (BMI)‐centric approach. The prevalence rates and associated risk factors are presented. Methods In total, 2159 participants were randomly selected from Czechia. ABCD was established as BMI ≥ 25 kg/m 2 or high body fat percent, or abdominal obesity and then categorized by their adiposity‐based complications: Stage 0: none; Stage 1: mild/moderate; Stage 2: severe. Results ABCD prevalence was 62.8%. Stage 0 was 2.3%; Stage 1 was 31.4%; Stage 2 was 29.1%. Comparing with other classifiers, participants in Stage 2 were more likely to have diabetes, hypertension, and metabolic syndrome than those with overweight, obesity, abdominal obesity, and increased fat mass. ABCD showed the highest sensitivity and specificity to detect participants with peripheral artery disease, increased intima media, and vascular disease. Conclusion/Interpretation The ABCD model provides a more sensitive approach that facilitates the early detection and stratification of participants at risk compared to traditional classifiers.