Medina-Inojosa, Betsy

Introduction: Metabolically obese normal weight (MONW) refers to metabolic abnormalities despite normal body mass index (BMI). Hypothesis: Because obesity is generally defined by BMI, it is assumed that people with MONW do not have excessive adiposity but still have the metabolic dysregulation associated with adiposity. Methods: We included participants aged 20-65 of the National Health and Nutrition Examination Surveys from 1999-2006 and 2011-2018 with normal BMI (18.5-25 kg/m 2 ), fasting blood samples, and DEXA data. According to the AHA criteria, MONW was defined having ≥3 of the following criteria, besides a normal BMI: Hypertriglyceridemia (≥ 150 mg/dL) or treatment for elevated triglycerides; HDL cholesterol level < 40 mg/dL in men or <50 mg/dL in women or treatment for reduced HDL-C; elevated blood pressure (systolic ≥ 130 mmHg and/or diastolic ≥ 85 mmHg) or antihypertensive treatment or history of hypertension; elevated fasting plasma glucose ≥ 100 mg/dL or insulin/oral hypoglycemic treatment and central obesity by waist circumference (≥ 94 cm in men and ≥ 80 cm in women). High fat percentage was defined as ≥25% in men, ≥35% in women. Weighted logistic regression was performed to test the relation between MONW and high fat%, central obesity. Results: Of the 7,887 participants (53.6% men; mean age±SE was 39.23±0.24 years), 2433 (33.4%) had normal BMI included in the analysis. Among these, 148 had MONW (5%). Mean Fat% was 26.08±0.36% in those with MONW versus 21.90±0.17% in those without MONW for men, and 37.02±0.35% versus 33.31±0.21% for women. MONW subjects were 2.21 times more likely to have either central obesity or high fat % (OR 12.09, 95%CI: 6.18-23.67, p<0.01) and 2.18 times more likely to have high fat % (OR=2.84, 95%CI: 1.72-4.67, p<0.01) (see Figure). Conclusions: Most individuals with MONW have either increased fat content, central deposition of fat, or both. That highlights people with normal BMI may still have metabolic complications of adiposity.

Introduction: The effect of obesity on physiologic aging has not been completely established. We previously developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm that predicts age and determines that the difference between AI-ECG age and chronological age (Age-Gap) may reflect accelerated aging, as it is associated with increased total and cardiovascular (CV) mortality. Hypothesis: Obesity is associated with an increased Age-Gap reflecting morbidity and accelerated aging. Methods: We included adults undergoing a wellness evaluation including, body fat percentage (BF%) determination by air displacement plethysmography (Bod-Pod®) and a 12-lead ECG within 2 years. We excluded those with coronary artery disease, congestive heart failure, stroke or being underweight defined as having a BMI<18.5 kg/m2. Logistic regression models tested the association between obesity by BMI (≥30 kg/m2) or BF% quartiles (≥35% for men and ≥25 in women) and being older by AI-ECG. Results: We included 1295 subjects (70% women), mean chronological age 46.7 ± 13.1 years. Mean Age-gap positive difference compared to those with BMI<25 was 1.39, 2.06 and 2.83 for those with BMI 25-29.9, 30-34.9, and >35 respectively (p<0.05). In the BF% group, Age-gap difference compared to those in the 1st quartile was 0.008, 0.37 and 1.52 for those in the 2nd, 3rd, and 4th quartile (p<0.05). Those>3 years older by ECG were more likely to have obesity by BMI and BF%, see figure. Conclusions: Obesity, either by BMI or BF%, is associated with increased physiologic age as measured by AI-ECG, highlighting the importance of obesity as a health threat.

Background: The role of body adiposity on metabolic dysregulation in subjects with normal body mass index (BMI) is still under debate with limited population-based evidence. Hypothesis: Body adiposity measured by dual x-ray absorptiometry (DEXA) is associated with metabolic disorders in normal-weight individuals. Methods: We evaluated a sample of adults aged 20-65 who underwent DEXA in the US National Health and Nutrition Examination Surveys from 1999-2006 and 2011-2018. We excluded those with history of cardiovascular disease, cancer, and chronic obstructive pulmonary disease. Weighted logistic regression was performed to test the association between body fat percentage (BF%) and metabolic disorders [e.g., insulin resistance (IR), metabolic syndrome (MS), dyslipidemia, type 2 diabetes mellitus (DM), hypertension]. Models were adjusted for potential confounders while considering complex survey design and were further stratified by sex and BF% as tertiles in normal-weight individuals. IR was tested as a secondary analysis to evaluate the effect on the predicted risk. Results: We included 20,613 individuals (51.5% men, 65.4% non-Hispanic whites), mean age ± SE was 39.42±0.22 years, and 6,424 were normal weight by BMI and represented our study sample. The overall BF% in these subjects measured by DEXA was 28.41±0.19%. In our cohort, metabolic disorders were prevalent in 2.1% to 40.4% of patients (Table 1). Every 10-unit % increase in total BF% was associated with a higher risk for IR, MS, dyslipidemia in the normal weight BMI group (Table 1). Metabolic disorders were associated with BF% tertiles (Figure 1). IR was significantly associated with body fat across all BF% tertiles for men and highest tertiles for women (Figure 2). Conclusion(s): Less than 1/3 of subjects in a representative national sample had a normal BMI. Even in these “normal weight” subjects, increases in body adiposity are significantly associated with increased cardiometabolic disease burden.

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.

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.