Medina‐Inojosa, José R.

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.

In contrast to the decreasing burden related to cardiovascular disease (CVD), the burden related to dysglycemia and adiposity complications is increasing in Czechia, and local drivers must be identified. A comprehensive literature review was performed to evaluate biological, behavioral, and environmental drivers of dysglycemia and abnormal adiposity in Czechia. Additionally, the structure of the Czech healthcare system was described. The prevalence of obesity in men and diabetes in both sexes has been increasing over the past 30 years. Possible reasons include the Eastern European eating pattern, high prevalence of physical inactivity and health illiteracy, education, and income-related health inequalities. Despite the advanced healthcare system based on the compulsory insurance model with free-for-service healthcare and a wide range of health-promoting initiatives, more effective strategies to tackle the adiposity/dysglycemia are needed. In conclusion, the disease burden related to dysglycemia and adiposity in Czechia remains high but is not translated into greater CVD. This discordant relationship likely depends more on other factors, such as improvements in dyslipidemia and hypertension control. A reconceptualization of abnormal adiposity and dysglycemia into a more actionable cardiometabolic-based chronic disease model is needed to improve the approach to these conditions. This review can serve as a platform to investigate causal mechanisms and secure effective management of cardiometabolic-based chronic disease.

Background: This is the first systematic review and meta-analysis to investigate the effect of resistance training (RT) on survival and other cardiovascular outcomes including ischemic heart disease events and stroke. Methods: An experienced librarian searched databases up to September 25 th , 2017, for randomized trials and cohort studies that evaluated the effect of RT on survival and cardiovascular events in the general population. The databases included Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus. Two investigators conducted the screening process independently and in duplicate. Cochrane tools were used to assess the risk of bias in clinical trials and observational studies. We calculated hazard ratios and 95% confidence intervals using RevMan and fixed and random effect models and had a subgroup analysis based on doses of RT and for the combination of RT and aerobic exercise (AE) vs no exercise. Results: The search identified 1429 studies from which 10 (one randomized trial) met the inclusion criteria, including 338,254 participants with a mean follow up of 8.14 years. The meta-analysis showed that RT, in comparison with no exercises, is associated with 24% lower all-cause mortality and 48% lower mortality when combined with AE. Based on subgroup analysis, performing 1-2 sessions of RT/week is associated with lower all-cause mortality by 28% (HR 0.72, 95% CI 0.66-0.78) whereas > 5 sessions of RT/week has no association with all-cause mortality (HR 0.99, 95% CI 0.76-1.31). Further, RT alone or combined with AE is associated with lower CV mortality compared to no exercise (Figures). Finally, RT alone also showed a borderline association with lower all-cancer mortality. Heterogeneity was present for several comparisons, and subsequent analysis will explore sources of this variability. Using study design-specific Cochrane risk of bias tools, no major sources of bias were identified in the included studies. One cohort study looked at the effect of RT on coronary heart disease events and found 23% risk reduction in men, while no study specifically assessed the effect of RT on cerebrovascular outcomes. Conclusion: RT is associated with lower all-cause, CV and all-cancer mortality. RT appears to have an additive effect when combined with AE.

Background Increases in circulating free‐fatty acids (FFAs) contribute to obesity‐associated cardiometabolic pathology and may be considered a surrogate marker for adipose tissue dysfunction. However, alterations in FFA metabolism resulting from modest weight gain have not been previously evaluated. Objective Examine the changes in FFAs during modest weight gain in healthy individuals and to investigate the relationship between regional fat depots. Study Design We evaluated the effects of overfeeding in 8 healthy, non‐obese participants (4 men; age 29 ± 5.7 years; BMI: 25.1 ± 2.1 kg/m2). FFA concentrations were measured using liquid chromatography/mass spectrometry under fasting conditions and during oral‐glucose tolerance test (OGTT) at baseline and after 8 weeks of weight gain. Prior to the baseline and weight gain visits, subjects consumed isocaloric macro‐nutrient stable diet for three days (50% carbohydrate, 35% fat and 15% protein). Body composition and regional fat depots were assessed using dual‐energy X‐ray absorptiometry and abdominal computed tomography scans. Results Overfeeding was accompanied by 3.9 ± 1.6 kg weight gain (p=0.01) resulting from 2.9 ± 0.9 kg (p=0.01) increase in fat mass and 1.0 ± 0.9 kg (p=0.04) increase in fat‐free mass. Compared to baseline, fasting FFA (439 ± 162 μM vs. 368 ± 123 μM, p=0.55) and area under the curve (AUC) for FFA during OGTT (23319 ± 11686 μM. min−1 vs. 20495 ± 4847 μM.min−1, p=0.55) did not change with weight gain. However, at 120 min. of the OGTT FFA were significantly greater after weight gain (60 ± 18 μM vs. 77 ± 34 μM, p=0.04). We found a negative relationship between relative changes in visceral fat area and FFA at 120 min of glucose challenge (rho=−0.76, p=0.03). Furthermore, relative changes in fasting FFA and AUC for FFA during OGTT with weight gain were associated with baseline total fat mass (fasting FFA: rho=0.76, p=0.03; AUC FFA: rho=0.81; p=0.02) and baseline abdominal fat area (fasting FFA: rho=0.76, p=0.03; AUC FFA: rho=0.79; p=0.02). Conclusions Modest weight gain does not alter fasting FFA or overall FFA response to oral glucose challenge. However, our data suggest development of early stages of adipose tissue insulin resistance which is apparent via lesser suppression of FFA at 120 min. of OGTT. Furthermore, the relationships between fasting FFA, AUC‐FFA, and baseline fat mass suggest that individuals with high baseline adiposity may be more susceptible to develop adipose tissue insulin resistance with weight gain. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Objective To compare the prevalence and patterns of anomalous coronary artery origin from the opposite sinus (ACAOS) in patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV). Methods Retrospective review of consecutive patients with surgically excised BAV and TAV was performed from 1994 to 2015. Clinical notes, echocardiograms, coronary angiograms, CT angiographies, and pathology reports were reviewed. ACAOS included right coronary artery from the left cusp, left circumflex artery from the right cusp and left main or left anterior descending artery from the right cusp. Results 2371 (years 1994–2015) and 1679 (years 2009–2015) consecutive patients with pathology-confirmed BAV and TAV, respectively, and defined preoperative coronary anatomy were identified. A left dominant coronary circulation was present in 386 (18%) patients with BAV and 179 (11%) patients with TAV (p<0.001). ACAOS was identified in 43 (1.8%) patients with BAV and 15 (0.9%) patients with TAV, p=0.02. Among patients with BAV and ACAOS, the most common phenotype was right-left fusion (n=34, 79%) with present raphe (n=36, 84%), with no association between BAV phenotype and ACAOS type. On multivariate analysis, BAV status and size of the mid-ascending aorta were independently associated with ACAOS (OR 3.29; CI 1.26 to 8.6; p=0.02; OR 0.93; CI 0.87 to 0.98; p=0.01; respectively). Only two patients with ACAOS, one with BAV and one with TAV, had a perioperative coronary ischaemic event. Conclusions The prevalence of the potentially malignant ACAOS is significantly higher (threefold higher odds) in patients with BAV as compared with TAV, yet remains uncommon in absolute terms. Most patients with BAV and ACAOS had right-left cusp fusion and present raphe. Perioperative coronary events are rare in patients with ACAOS.

Purpose The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

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.

Introduction: Spontaneous coronary artery dissection (SCAD) is an important cause of myocardial infarction in otherwise healthy and most times presumably fit young women. In the absence of evidence for benefit or harm, exercise prescription (EXP) after SCAD remains arbitrary. We assessed EXP patterns among participants in a large multinational SCAD registry. Methods: Participants with SCAD as confirmed by coronary angiography completed surveys detailing exercise (EX) habits prior to SCAD (initial survey), and EXP from their provider on initial and subsequent visits after SCAD (follow-up survey). Demographic and clinical characteristics were collected by electronic health record review by trained nurse abstractors. EXP was categorized according to EX components that include: type, intensity, frequency, time/session, and external conditions. Results: Among 950 participants, mean ± age was 46.8 ± 9.5 years, 96% were women and 77% reported attending ≥1 Cardiopulmonary Rehabilitation session. Prior to SCAD, 49% were aerobically exercising ≥ 3times per week, and only 32% performed strength-building EX regularly. Initial and subsequent post-SCAD visit details in 299/950 participants showed that most (93%) patients received some form of EX (Figure 1-A) counseling, namely: EXP, unspecific advice or were discouraged from any EX (Figure 1-B) . Limits regarding EX type and intensity were the most common advice received among EXP group (Figure 1-C) . Conclusions: Insights from our study show that only 40% of patients were achieving current exercise recommendations for adults prior to SCAD (vs 53% adult U.S population; CDC, 2018) which suggests that SCAD patients may not be as active as previously described. Most patients receive some form of EX counseling after SCAD; however, a structured EXP is needed to avoid overly cautious approaches that could lead to uncertainty, deconditioning, impaired quality of life, or increase risks associated with a sedentary lifestyle.

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 describe the process and outcome of creating a patient cohort in the early stages of the COVID-19 pandemic in order to better understand the process of and predict the outcomes of COVID-19. Patients and Methods A total of 1169 adults aged 18 years of age or older who tested positive in Mayo Clinic Rochester or the Mayo Clinic Midwest Health System between January 1 and May 23 of 2020. Results Patients were on average 43.9 years of age and 50.7% were female. Most patients were white (69.0%), and Blacks (23.4%) and Asians (5.8%) were also represented in larger numbers. Hispanics represented 16.3% of the sample. Just under half of patients were married (48.4%). Common comorbid conditions included: cardiovascular diseases (25.1%), dyslipidemia (16.0%), diabetes mellitus (11.2%), chronic obstructive pulmonary disease (6.6%), asthma (7.5%), and cancer (5.1%). All other comorbid conditions were less the 5% in prevalence. Data on 3 comorbidity indices are also available including the: DHHS multi-morbidity score, Charlson Comorbidity Index, and Mayo Clinic COVID-19 Risk Factor Score. Conclusion In addition to managing the ever raging pandemic and growing death rates, it is equally important that we develop adequate resources for the investigation and understanding of COVID-19-related predictors and outcomes.