López-Jiménez, Francisco

The aim of the present study was to evaluate the association between television viewing/physical activity (TVV/PA) interactions and cardiometabolic risk in an adult European population. A total of 2155 subjects (25–64 years) (45.2% males), a random population-based sample were evaluated in Brno, Czechia. TVV was classified as low (<2 h/day), moderate (2–4), and high (≥4). PA was classified as insufficient, moderate, and high. To assess the independent association of TVV/PA categories with cardiometabolic variables, multiple linear regression was used. After adjustments, significant associations were: High TVV/insufficient PA with body mass index (BMI) (β = 2.61, SE = 0.63), waist circumference (WC) (β = 7.52, SE = 1.58), body fat percent (%BF) (β = 6.24, SE = 1.02), glucose (β = 0.25, SE = 0.12), triglycerides (β = 0.18, SE = 0.05), and high density lipoprotein (HDL-c) (β = −0.10, SE = 0.04); high TVV/moderate PA with BMI (β = 1.98, SE = 0.45), WC (β = 5.43, SE = 1.12), %BF (β = 5.15, SE = 0.72), triglycerides (β = 0.08, SE = 0.04), total cholesterol (β = 0.21, SE = 0.10), low density protein (LDL-c) (β = 0.19, SE = 0.08), and HDL-c (β = −0.07, SE = 0.03); and moderate TVV/insufficient PA with WC (β = 2.68, SE = 1.25), %BF (β = 3.80, SE = 0.81), LDL-c (β = 0.18, SE = 0.09), and HDL-c (β = −0.07, SE = 0.03). Independent of PA levels, a higher TVV was associated with higher amounts of adipose tissue. Higher blood glucose and triglycerides were present in subjects with high TVV and insufficient PA, but not in those with high PA alone. These results affirm the independent cardiometabolic risk of sedentary routines even in subjects with high-levels of PA.

Introduction Obesity is a global health issue that impacts quality of life. A calorie-restricted diet with high-intensity consultation provided via the internet may be an effective way to lose weight. The objective of this study was to assess the effectiveness of a practitioner-guided, mobile internet-based low-energy dietary intervention in overweight and obese populations in China. Methods and analysis This open-label randomised controlled trial enrolled 220 overweight and obese adults aged 18–70 years who met the inclusion criteria. Participants were assigned to the control group (n=110) or trial group (n=110). The trial group will be enrolled in the MetaWell programme, a weight loss programme using diet replacement products, wireless scales and a mobile phone app. Participants in the control group will receive paper material containing a sample diet for weight loss. The follow-up period will be 1 year, and measurements will occur at 3, 6 and 12 months. Dual-emission X-ray absorptiometry and abdominal quantitative CT will be performed to estimate the percentage of overall body fat and areas of visceral and subcutaneous fat, alongside several cardiometabolic measurements. The primary outcome of this study is the change in body mas index (BMI) at 6 months after enrolment. A mixed-effects model will be used to compare BMI and body fat changes between the two groups. Ethics and dissemination This study was approved by the ethics committee of the Hospital of Chengdu Office of the People’s Government of the Tibetan Autonomous Region. Advertisements for recruitment will be sent via official accounts using WeChat. The results will be disseminated via publications in academic journals and our clinic. Our study group will maintain contact with the participants to inform them of the study findings. Trial registration number ChiCTR1900021630.

Abstract We here posit that measurements of midlife cognition can be instructive in understanding cognitive disorders. Even though molecular events signal possible onset of cognitive disorders decades prior to their clinical diagnoses, cognition and its possible early changes in midlife remain poorly understood. We characterize midlife cognition in a cognitively healthy population‐based sample using the Cogstate Brief Battery and test for associations with cardiovascular, adiposity‐related, lifestyle‐associated, and psychosocial variables. Learning and working memory showed significant variability and vulnerability to psychosocial influences in midlife. Furthermore, midlife aging significantly and progressively increased prevalence of suboptimal cognitive performance. Our findings suggest that physiological changes in cognition, measured with simple tests suitable for use in everyday clinical setting, may signal already in midlife the first clinical manifestations of the presymptomatic biologically defined cognitive disorders. This pilot study calls for longitudinal studies investigating midlife cognition to identify clinical correlates of biologically defined cognitive disorders.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Background: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis. Methods: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy. Results: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20–1.42]; P <0.001). In the population as a whole, each 10% increase in AI-probability was associated with a 12% greater risk of death (hazard ratio, 1.12 [95% CI, 1.08–1.17]; P <0.001) during long-term follow-up, which was no longer significant after adjustments for baseline characteristics. Conclusions: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.

Ideal cardiovascular health (CVH) is defined for the presence of ideal behavioral and health metrics known to prevent cardiovascular disease (CVD). The association of circulatory phospho- and sphingo-lipids to primary reduction in cardiovascular risk is unclear. Our aim was to determine the association of CVH metrics with the circulating lipid profile of a population-based cohort. Serum sphingolipid and phospholipid species were extracted from 461 patients of the randomly selected prospective Kardiovize study based on Brno, Czech Republic. Lipids species were measured by a hyphenated mass spectrometry technique, and were associated with poor CVH scores, as defined by the American Heart Association. Phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE) species were significantly lower in ideal and intermediate scores of health dietary metric, blood pressure, total cholesterol and blood fasting glucose compared to poor scores. Current smokers presented higher levels of PC, PE and LPE individual species compared to non-smokers. Ceramide (Cer) d18:1/14:0 was altered in poor blood pressure, total cholesterol and fasting blood glucose metrics. Poor cardiovascular health metric is associated with a specific phospho- and sphingolipid pattern. Circulatory lipid profiling is a potential biomarker to refine cardiovascular health status in primary prevention strategies.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Background There is wide variability in cardiac rehabilitation (CR) dose (ie, number of sessions) delivered, and no evidence‐based recommendations regarding what dose to prescribe. We aimed to test what CR dose impacts major adverse cardiovascular events (MACEs). Methods and Results This is an historical cohort study of all patients who had coronary artery disease and who initiated supervised CR between 2002 and 2012 from a single major CR center. CR dose was defined as number of visits including exercise and patient education. Follow‐up was performed using record linkage from the Rochester Epidemiology Project. MACEs included acute myocardial infarction, unstable angina, ventricular arrhythmias, stroke, revascularization, or all‐cause mortality. Dose was analyzed in several ways, including tertiles, categories, and as a continuous variable. Cox models were adjusted for factors associated with dose and MACE. The cohort consisted of 2345 patients, who attended a mean of 12.5±11.1 of 36 prescribed sessions. After a mean follow‐up of 6 years, 695 (29.65%) patients had a MACE, including 231 who died. CR dose was inversely associated with MACE (hazard ratio, 0.66 [95% CI]; 0.55–0.91) in those completing ≥20 sessions, when compared with those not exposed to formal exercise sessions (≤1 session; log‐rank P =0.007). We did not find evidence of nonlinearity ( P ≥0.050), suggesting no minimal threshold nor ceiling. Each additional session was associated with a lower rate of MACE (fully adjusted hazard ratio, 0.98 [95% CI, 0.97–0.99]). Greater session frequency was also associated with lower MACE risk (fully adjusted hazard ratio, 0.74 [95% CI, 0.58–0.94]). Conclusions CR reduces MACEs, but the benefit appears to be linear, with greater risk reduction with higher doses, and no upper threshold.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.

Abstract Aims Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. Methods and results We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, &lt;45%, and &lt;50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF &lt; 45%), and 0.87 (LVEF &lt; 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. Conclusions An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.