López-Jiménez, Francisco

Abstract Aims This study aims to identify whether adding peripheral microvascular dysfunction (PMED), a marker of atherosclerosis to established risk scores has an incremental prognostic value for major adverse cardiovascular events (MACE). Methods and results This is a retrospective study of patients who underwent measuring peripheral arterial tonometry from 2006 to 2020. The optimal cut-off value of the reactive hyperaemia index (RHI) that had maximal prognostic value associated with MACE was calculated. Peripheral microvascular endothelial dysfunction was defined as the RHI lower than the cut-off. Traditional cardiovascular risk factors such as age, sex, congestive heart failure, hypertension, diabetes, stroke, and vascular disease were determined to calculate the CHA2DS2-Vasc score. The outcome was MACE defined as myocardial infarction, heart failure hospitalization, cerebrovascular events, and all-cause mortality. A total of 1460 patients were enrolled (average age 51.4 ± 13.6, 64.1% female). The optimal cut-off value of the RHI was 1.83 in the overall population and in females and males was 1.61 and 1.8, respectively. The risk of MACE during 7 [interquartile range (IQR): 5,11] years of follow-up was 11.2%. Kaplan–Meier analysis showed that lower RHI is associated with worse MACE-free survival (P < 0.001). Multivariate Cox proportional hazard analysis, controlling for classic cardiovascular risk factors or risk scores such as CHA2DS2-Vasc and Framingham risk score revealed that PMED is an independent predictor of MACE. Conclusion Peripheral microvascular dysfunction predicts cardiovascular events. Non-invasive assessment of peripheral endothelial function may be useful in early detection and improving the stratification of high-risk patients for cardiovascular events. Lay summary Key findings

Introduction: We have previously shown that ECG-derived age (ECG-Age) predicts all-cause and cardiovascular (CV) mortality, reflecting physiologic age. We hypothesized that ECG-Age minus chronological age (Age-Gap) would be associated with coronary artery calcium (CAC) assessed by computer tomography. Methods: We developed a neural network model to predict ECG-Age using 12 lead ECGs from 399,750 training, 99,977 validation and 275,056 testing patients. We then applied the algorithm to 41,202 consecutive patients that from 1997-2020 underwent clinically indicated CAC and ECG within 1 year. Major modifiable CV risk factors were collected as part of preventive cardiology or general medical evaluations. Exclusion criteria were statin therapy, pacemakers, or history of coronary artery disease. Results: We included 41,202 subjects (68.5% males, 83.6% Caucasian), mean chronological age was 55.2±9.1 yrs and the ECG-Age was 56.01±9.3 yrs. The mean Age-Gap was -0.78±7 yrs, R 2 =0.78, p<0.0001. Mean CAC score was 140.2±532.3, and 21,483 (52%) had CAC >0. After adjustment for age and sex, those older by ECG (defined as having a positive Age-Gap) were more likely to have CAC>0 (OR 1.35, 95% CI 1.34-2.37 for those with >1 but <2 SD of a positive Age-Gap; and OR 1.80, 95% CI 1.34-2.37 for those with ≥2 SD of a positive Age-Gap), while those younger by ECG were less likely to have any CAC (OR 0.70, 95% CI 0.57-0.87 for those with >1 but <2 SD of a negative Age-Gap; and OR 0.75, 95% CI 0.67-0.84 for those with ≥2 SD of a negative Age-Gap), respectively. This association remained significant after multivariate adjustment, Fig. A and increased with CAC burden, Fig. B , all P for trend <0.01. Conclusions: The difference between physiologic AI-ECG age and chronologic age, something likely representing rate of biological aging, is associated with presence of CAC, further supporting the hypothesis that AI-ECG age can identify people more likely to have subclinical coronary atherosclerosis.

Background: The body Mass Index (BMI) is commonly used in clinical practice to screen for obesity and resulting cardiometabolic conditions. Nonetheless, BMI has limited utility as it cannot differentiate body fat (BF). Hypothesis: We tested the hypothesis that BF content measured by air displacement plethysmography (Bod-Pod®) had a stronger association with prevalent hypertension (HTN), diabetes mellitus (DM) and dyslipidemia (DLP) when compared to BMI. Methods: We performed a community-based cross-sectional study on adults attending an employee wellness center from January 2007-December 2011 who had had a body composition assessment with Bod-Pod®. We excluded subjects with history of myocardial infarction, coronary artery disease, congestive heart failure, stroke at baseline. The prevalence of HTN, DM and DLP was assessed across quartiles of BF% and BMI categories. Their association with BF% and BMI was evaluated using linear regression models that adjusted for age, sex, and white race. Results: We included 3,052 individuals; 68 % were women, and the mean age ± SD was 42.09±12.8, BMI was 27.9±6.2; BF% 31.88±10.69, 892 (29.2%) were obese by BMI. Cardiometabolic conditions were prevalent in 496 (16.2%) with HTN, 378 (12.4%) with DM and 743 (24.4%) with DLP. While the prevalence of DM, HTN and DLP increased across BF% quartiles (all p for linearity>0.01), this was not always the case across BMI categories (p for linearity<0.05 for HTN and DLP, and 0.04 for DM) see Figure A . Elevated BF %, better identified individuals with HTN (AUC 0.766 vs 0.625; p<0.001), DM (AUC 0.643 vs. 0.568; p<0.001), DLP (AUC 0.758 vs. 0.593; p<0.001). Conclusion: BF content is better associated with a higher prevalence of HTN, DM, and DLP. Highlighting the role of body composition assessment in primary prevention. Dyslipidemia.

Evaluating the Electrocardiographic Characteristics of Transgender Patients Undergoing Gender-affirming Hormone Therapy with Artificial Intelligence Introduction: An artificial intelligence (AI) electrocardiogram (ECG) algorithm has been demonstrated to identify a patient’s birth designated sex with 90.4% accuracy with an AUC = 0.97. However, the algorithm has not been evaluated in transgender and gender diverse individuals. We aimed to determine how the algorithm output varies among transgender individuals seen at the Mayo Clinic Transgender and Intersex Specialty Care Clinic (TISCC) who are undergoing gender-affirming hormonal treatment (GAHT). Methods: We applied the algorithm (which assessed the probability of “male” between 0 and 1, with < 0.5 considered as “female” and > 0.5 as “male”) to ECGs obtained from patients in the TISCC before and after the initiation of GAHT. GAHT frequently involves the use of testosterone in transgender men, and estrogen and anti-androgen agents in transgender women. Patient characteristics, including the date of the initiation of GAHT, were collected through chart review. Results: Among transgender women, the AI model probability of “male” decreased from 0.84± 0.25 (12/86 classified as males) to 0.59±0.36 (68/173 classified as male) with GAHT (p < 7.8e-10). Among transgender men, the AI model probability of “male” increased from 0.16 ± 0.28 (7/47 classified as male) to 0.41 ±0.38 (22/53 classified as male) with GAHT (p < 2.4e-4). Conclusion: After GAHT initiation, the Mayo Clinic AI ECG algorithm assigns a higher probability of being male among transgender men and a lower probability of being male among transgender women. These findings warrant further investigation into the electrocardiographic differences across genders and the effects of GAHT on the human heart.

Background: We developed a deep learning algorithm to predict elevated coronary artery calcium (CAC) score from 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that this CAC-ECG algorithm will independently predict long-term survival. Methods: We leveraged a historical cohort of 43,210 consecutive patients who from the years 1997-2020 underwent clinically indicated ECG-gated unenhanced chest computed tomography (CT) to identify and quantify CAC and had an ECG within 1 year of the CT. Data on cardiovascular risk (CV) factors, and to calculate the Pooled Cohort Equation (PCE) for ASCVD was collected as part of preventive cardiology or general medical evaluations. We used the oldest CAC in record and excluded those taking statins at the time of the CAC. The algorithm was trained in 60% of the cohort, and the association between CAC-ECG and survival was evaluated with multivariate cox proportional hazard models in 40% of the remaining observations. Results: Of the 17,284 evaluated patients, mean ± SD age 55.9 ± 9.9, 33% female, 3,714 (21%) had elevated CAC ≥ 300. During an average of 15±5.9 years follow-up, 848 (5%) patients died. The algorithm’s area under the receiving operating characteristics curve (ROC), sensitivity, specificity, and accuracy to detect a CAC ≥ 300 were 0.83, 0.90%, 0.56%, 0.60%. Those with elevated CAC had a nearly two-fold risk of death, a value similar to those deemed positive by the ECG-CAC algorithm ( Figure A ). Risk of death increased with CAC-ECG probability quartiles ( Figure A ). The CAC-ECG algorithm enhanced predicted capabilities of the PCE across all ASCVD risk subgroups ( Figure B ), as well as in those with no CAC and elevated CAC ( Figure C ), all p for trend <0.001. Conclusions: A deep learning-enabled CAC-ECG algorithm was independently associated with long-term survival and enhanced current risk prediction paradigms. The CAC-ECG algorithm could help identify individuals at risk in primary prevention of CV disease.

Introduction: Increasing evidence unravels the role of loneliness and social connection in cardiovascular diseases potentially mediating accelerated biological aging. The present study aims to explore the association between social connection and biological aging as determinate by artificial intelligence-enabled electrocardiography (ECG). Methods: This is an observational retrospective cohort of adults aged ≥18 seen at Mayo Clinic from 2019-2022 who completed the social determinants of health questionnaire and had a 12-lead ECG within one year of completing the questionnaire. Five questions were asked to understand each patient’s social connection . The answers were scored to assess their level of social connection from socially isolated (score of 0) to socially connected (score of 4). The biological age was predicted from ECGs using the previously developed convolutional neural network (AI-ECG age). Delta age (Δage) was defined as AI-ECG age minus chronological age, where positive values reflect an older than expected age. Results: We included 280,323 subjects (chronological age 59.7±16.4 years, 51% female). The average AI-ECG age was 59.5±13.5 year. Better social connection status correlated with lower delta age (β=-0.33 (95%CI, -0.38 and -0.28, p<0.001, adjusted to chronological age and sex). Conversely, individuals reporting the least social connection were an average of 2 years older than expected whereas those with the most social connection were 2 years younger than expected by AI-ECG (Figure 1). Conclusions: Social connection is strongly associated with slower biological aging compared to chronological aging, independent of the conventional cardiovascular risk factors. This observation underscores the need to address social connection as a healthcare determinant.

Introduction: The physiological changes associated with aging have deleterious effects on cardiovascular function. In addition, increasing evidence showed the effect of exercise on improving cardiovascular health and reducing the aging process. The present study sought to demonstrate the association between exercise and biological age, estimated by our previously reported method using artificial intelligence-enabled electrocardiography (ECG). Methods: This retrospective observational study enrolled adults aged ≥18 years who have been evaluated at Mayo Clinic between 2019-2022 and completed the exercise questionnaire. The total time spent in moderate to strenuous exercise was calculated by multiplying the number of days engaging in exercise per week by the average minutes per day. The biological age was determined by applying our previously developed convolutional neural network model on the 12-lead ECG records (AI-ECG). The ECG records were within one year of subjects’ visits (in case of multiple visits, the closest one to the questionnaire was chosen). Positive Age-Gap (AI-ECG minus chronological age) reflects a person identified as being older by AI-ECG compared to his/her chronological age. Results: A total of 268,002 subjects (51% female) were recruited (mean age 59.7±16.4 years). Of those, 25.7% were not engaged in any exercise. The median day of engaging in exercise was 3 (Interquartile range, 2,5) per week, and the average time of exercising was 31.3±30 minutes/day. Age-Gap diminished progressively with increased duration of moderate to strenuous exercise (Figure 1). Linear regression analysis revealed exercise is an independent predictor of Age-Gap, (β= -0.043, 95%CI, -0.045 and -0.041, p<0.001). Conclusions: Engaging in moderate to strenuous exercise is associated with slower biological aging relative to chronological age as determined by AI-enabled ECG.

Introduction: Peripheral microvascular endothelial dysfunction (PMED) is a marker and precursor of atherosclerosis and is associated with an increased risk of cardiovascular events. The present study aims to identify the best cut-off value of PMED, assessed by the reactive hyperemia index, in predicting major adverse cardiovascular events (MACE). Additionally, we investigate whether integrating PMED to CHA2DS2-Vasc score improves the predicting accuracy of cardiovascular disease. Methods: this is a retrospective study of patients who underwent reactive hyperemia peripheral arterial tonometry (PAT). The optimal cut-off value of reactive hyperemia index associated with MACE was calculated using the area under the receiver-operating curve and the maximum Youden index. Traditional cardiovascular risk factors such as age, sex, congestive heart failure, hypertension, diabetes, stroke, vascular disease were determined to calculate the CHA2DS2-VASc score. MACE was defined as myocardial infarction, heart failure hospitalization, and cerebrovascular events. Results: 1,690 patients were enrolled (average age 51.1±13.9, 64.1% female). The optimal cut-off value of the reactive hyperemia index was 2.0 in the overall population and females and 2.17 in males. The risk of MACE during 10 (Interquartile range: 4,16) years of follow-up was 15.3%. Multivariable logistic regression revealed that PMED as an independent predictor of MACE (PMED: OR, 3.6, 95% CI, 2.6-4.9, p<0.001) (Table1). Furthermore, integrating PMED to CHA2DS2-VASc significantly improved the predictive accuracy (area under the curve, 0.747 vs. 0.708, p<0.001). Conclusion: PMED is a strong predictor of cardiovascular events. Non-invasive assessment of peripheral endothelial function may be useful in early detection and improving the stratification of high-risk patients for cardiovascular events.