Medina‐Inojosa, José R.

Abstract Aims To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS). Methods and results We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m2, and 30.2% had MS and an MS severity z-score of −0.2 ± 0.9. Features β coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity z-score of −0.8, and an AUC of 0.88. Conclusion The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

Background: The Metabolically Obese Normal Weight (MONW) phenotype has been described as a unique subgroup of individuals with metabolic dysregulation similar to obese individuals, but not considered obese per se . In theory, these individuals have an adiposity-independent substrate predisposing them to metabolic dysregulation. We tested whether central obesity explains the metabolic abnormalities in persons with MONW in a prospective population-based cohort in the Czech Republic. Methods: We used data from Kardiovize Brno 2030 study, a prospective community-based cohort in Central Europe. We defined obesity using World Health Organization categories for body mass index (BMI) and central obesity by waist circumference (WC≥ 94 cm in men and ≥ 80 cm in women). Metabolically obese (MO) was defined using AHA criteria by having three or more of the following: Hypertriglyceridemia (≥ 1.7 mmol/l); HDL cholesterol level <1.0 mmol/l in men or <1.3 mmol/l in women; elevated blood pressure (systolic ≥ 130 mmHg and/or diastolic ≥ 85 mmHg) or antihypertensive drug treatment or history of hypertension; elevated fasting plasma glucose ≥ 5.6 mmol/l; or drug treatment and central obesity. Results: Of the 2,056 participants (46% men; mean age= 47.2 years, SD=11.2) included in the current analysis, 975 (47.4%) had normal weight and 696 (33.8%) were overweight. Prevalence of MO in the overall sample was 20.6% (n=424), 3.7% (n=36) among those with normal BMI and 9.9% (n=204) among overweight individuals. From the 36 subjects with MONW, 33 (91.6%) had central obesity by WC, while 201/204 (98.5%) with metabolically obese overweight, had central obesity by WC. Remarkably, in a subgroup of people with no metabolic obesity factors nobody had central obesity, see Figure . Conclusions: Metabolic dysregulation in people labeled as MONW can be explained by excessive visceral adiposity and not by an inherent predisposition, independent of adiposity.These findings underscore the importance of identifying central obesity in clinical practice even if BMI is below the obese criteria.

Background Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions to reduce sedentary behavior. Methods and Results A randomized cross‐over clinical trial was conducted at an employee wellness center. Four office settings were evaluated on 4 consecutive days: stationary or sitting station on day 1 (referent), and 3 subsequent active workstations (standing, walking, or stepper) in randomized order. Neurocognitive function (Selective Attention, Grammatical Reasoning, Odd One Out, Object Reasoning, Visuospatial Intelligence, Limited‐Hold Memory, Paired Associates Learning, and Digit Span) and fine motor skills (typing speed and accuracy) were tested using validated tools. Average scores were compared among stations using linear regression with generalized estimating equations to adjust standard errors. Bonferroni method adjusted for multiple comparisons. Healthy subjects were enrolled (n=44), 28 (64%) women, mean±SD age 35±11 years, weight 75.5±17.1 kg, height 168.5±10.0 cm, and body mass index 26.5±5.2 kg/m 2 . When comparing active stations to sitting, neurocognitive test either improved or remained unchanged, while typing speed decreased without affecting typing errors. Overall results improved after day 1, suggesting habituation. We observed no major differences across active stations, except decrease in average typing speed 42.5 versus 39.7 words per minute with standing versus stepping ( P =0.003). Conclusions Active workstations improved cognitive performance, suggesting that these workstations can help decrease sedentary time without work performance impairment. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06240286.

Abstract Background and Aims Severe aortic stenosis (AS) is the guideline-based indication for aortic valve replacement (AVR), which has markedly increased with transcatheter approaches, suggesting possible increasing AS incidence. However, reported secular trends of AS incidence remain contradictory and lack quantitative Doppler echocardiographic ascertainment. Methods All adults residents in Olmsted County (MN, USA) diagnosed over 20 years (1997–2016) with incident severe AS (first diagnosis) based on quantitatively defined measures (aortic valve area ≤ 1 cm2, aortic valve area index ≤ 0.6 cm2/m2, mean gradient ≥ 40 mmHg, peak velocity ≥ 4 m/s, Doppler velocity index ≤ 0.25) were counted to define trends in incidence, presentation, treatment, and outcome. Results Incident severe AS was diagnosed in 1069 community residents. The incidence rate was 52.5 [49.4–55.8] per 100 000 patient-year, slightly higher in males vs. females and was almost unchanged after age and sex adjustment for the US population 53.8 [50.6–57.0] per 100 000 residents/year. Over 20 years, severe AS incidence remained stable (P = .2) but absolute burden of incident cases markedly increased (P = .0004) due to population growth. Incidence trend differed by sex, stable in men (incidence rate ratio 0.99, P = .7) but declining in women (incidence rate ratio 0.93, P = .02). Over the study, AS clinical characteristics remained remarkably stable and AVR performance grew and was more prompt (from 1.3 [0.1–3.3] years in 1997–2000 to 0.5 [0.2–2.1] years in 2013–16, P = .001) but undertreatment remained prominent (&gt;40%). Early AVR was associated with survival benefit (adjusted hazard ratio 0.55 [0.42–0.71], P &lt; .0001). Despite these improvements, overall mortality (3-month 8% and 3-year 36%), was swift, considerable and unabated (all P ≥ .4) throughout the study. Conclusions Over 20 years, the population incidence of severe AS remained stable with increased absolute case burden related to population growth. Despite stable severe AS presentation, AVR performance grew notably, but while declining, undertreatment remained substantial and disease lethality did not yet decline. These population-based findings have important implications for improving AS management pathways.

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Methods: Consecutive adults admitted to a Mayo Clinic or Mayo Clinic Health System hospital or to Olmsted Community Hospital for ischemic stroke between January,1,2000 and June,30,2022 were identified using the Rochester Epidemiology Project. Recurrent ischemic strokes were identified after reviewing medical records and imaging studies. We excluded those with known AFib or without ECGs within 3 years prior to the first ischemic stroke. The AI-ECG network was previously developed using 649,931 ECGs from > 180,000 subjects. The primary endpoint was recurrent ischemic stroke; secondary endpoint was incident AFib. Cox model adjusted for age, sex, and CHA2DS2-VASc2 score variables. Results: We included 4,816 subjects, mean age 70±15 yr, 54% male, 92% white, median CHA2DS2-VASc2 score 4 [3-5], median follow-up 3 [1-7] yr. 280 subjects (6%) had a second stroke and 366 (8%) were diagnosed with incident AFib over follow-up. Those with a positive AI-ECG AFib had an increased risk for recurrent ischemic stroke(HR 1.54, 95% CI 1.21-1.96, P<0.001 Figure A) This association remained significant after adjustment for CHA2DS2-VASc2 score(HR 1.37 95% CI 1.07-1.76, P=0.01). These associations were not affected by anticoagulation use over follow-up. AI-ECG AFib was also associated with incident AFib(HR 2.42 95% CI 1.96-2.98, P<0.001 Figure B) Conclusions: AI-ECG AFib predicts recurrent ischemic stroke in the community, possibly reflecting undiagnosed AFib that can be detected by AI-ECG, as it also predicts incident AFib. These results underscore the potential utility of AI-ECG for risk stratification of patients with ischemic stroke.

Introduction: Common genetic variation has been linked to an increased risk of developing dilated cardiomyopathy (DCM). However, the phenotypic profile, outcomes, and biological mechanisms by which such genetic background may predispose individuals to DCM are still unknown. Hypothesis: The primary objectives of this study were to develop a DCM polygenic risk score (PRS) based on prior case control-DCM GWAS and to apply this PRS to a well clinical characterized general population. Methods: The DCM-PRS was derived from 13 DCM risk alleles that were assayed in an unselected cohort (n=1897) of unrelated Olmsted County residents age >45 years who did not have a history of heart failure (HF) or CM and underwent physical examination, echocardiography, and blood analyses. Results: The entire population was equally divided into quartiles based on the DCM-PRS. No significant differences were present in terms of age (median 62 years) and sex (48% male). However, a significant correlation was detected between the DCM-PRS and left ventricle ejection fraction (LVEF) (Beta: -2.68, p<0.001), LV end-systolic diameter (Beta: 0.22, p<0.001), and LV end-diastolic diameter (Beta: 0.14, p=0.01). During a follow-up of 16 years (IQR: 15-17), 47 subjects developed DCM. Using a 3:1 non-DCM vs DCM matched cohort, a logistic regression analysis, found a significant correlation between median DCM-PRS and DCM development [OR=2.27 (1.06, 4.85), p=0.03]. However, there was no association between the DCM-PRS and HF biomarkers (aldosterone, natriuretic peptides, ST2, Galectin-3). Conclusions: DCM-PRS correlates with decreased LVEF, increased LV dimensions, and DCM development in a general population without significant differences in baseline clinical features. The absence of correlation between the DCM-PRS and available HF biomarkers suggests that this genetic background affects LV traits and DCM development independent of these biological mechanisms.

Background: Depression is prevalent in almost half of patients with cardiovascular disease (CVD) and is associated with CVD and mortality. We used an artificial intelligence algorithm using ECG that predicts physiological age (ECG-Age) that has been linked to increased total and CVD mortality and CVD risk factors. We hypothesized that depressive symptoms would be associated with physiologic aging, as measured by AI-ECG Methods: A previously validated algorithm was applied to determine ECG-Age to 105,732 consecutive ECGs of individuals age ≥18 who sought care at Mayo Clinic between June 2019 and January 2022 and filled the Patient Health Questionnaire-9 (PHQ-9) within a month of ECG. PHQ-9 ascertains severity and frequency of depressive symptoms with values from 0 - 27 and were categorized as (None≤4, Mild 5-9, Moderate 10-14, Moderately severe 15-19 and Severe ≥20). Delta-Age was defined as ECG-Age minus chronological age and accelerated aging was defined as a Delta-Age >1SD above the mean. Logistic regression analyses associated PHQ-9 scores with positive Delta-Age while adjusting for CVD risk factors Results: Mean chronological age was 57±17 yr and mean ECG-Age 58±14 yr (R2 0.85, P < 0.0001), Delta-Age 0.71±9.40 yr, 58% women, average PHQ-9 5±6, 61% had none, 20% mild, 10 % moderate, 5%moderately severe and 4% had severe depressive symptoms. Delta-Age was 2.10 years higher in those with depressive symptoms, reflecting accelerated aging. Compared to those with no depressive symptoms, those with severe symptoms were 1.5 times more likely to have Delta-Age >1SD (OR 1.52 CI 95%1.73-1.33), independently of CV risk factors and the Charlson comorbidity index. Figure. There is significant attenuation of the association once accounting for age, sex and CVD risk factors Conclusions: Depressive symptoms are associated with physiologic aging, highlighting the role of depression in physiologic aging and the potential of preventive strategies in patients with depression and CVD

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.