Medina-inojosa, Jose

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.

Introduction: The presence of chronic somatic and mental health conditions is complex and challenging for patients and the health care system due to the health deficit accumulation, worsening outcomes, and increased health care utilization. While previous studies on multimorbidity have focused on somatic chronic conditions, we aim to assess the relation between somatic, mental, and combined somatic-mental multimorbidity (SMM) and impact on Major Adverse Cardiac Events (MACE) in patients enrolled in phase II outpatient cardiac rehabilitation (CR). Methods: Using the Rochester Epidemiology Project records-linkage system, we identified patients from Olmsted County, Minnesota who were 18 years old and attended (≥1) CR sessions at Mayo Clinic Rochester. The prevalence of 18 (somatic=13; mental=5) chronic conditions defined by the US Department of Health and Human Services was ascertained electronically. Results: We included 618 patients; 24.4% were female. Patients were 61.5±11.0 years old with 38.0% ≥65 years old. Overall median number of chronic conditions was 6 (Range 0-13) with 98.8% of patients having ≥2 conditions. The prevalence of combined SMM was 44.6%. Mean follow-up was 7.3± 4.5 years, 147 (23.7%) patients had MACE (number): acute coronary syndrome (46), percutaneous coronary intervention (33), heart failure (16), stroke (8), coronary artery bypass grafting (4), ventricular arrhythmias (4), or death (65). Risk of MACE in patients with SMM was significantly higher compared to patients with only somatic conditions (HR: 1.62, 95% CI: 1.14-2.30, p=0.01 ), Figure 1. The association remained significant after adjustment for age and sex ( p=0.006 ). Conclusions: Our results show that somatic-mental multimorbidity (SMM) in CR patients is highly prevalent and imposes a higher risk of MACE compared with patients who have only somatic conditions highlighting the importance of individualized assessment and care plans to reduce adverse events and mortality.

Introduction: Cardiac rehabilitation (CR) is associated with improvements in exercise capacity (peak oxygen uptake (VO 2 peak)); however, 23% of CR patients do not improve VO 2 peak following CR. CR adherence (i.e. number of CR sessions attended) has been proposed to play an important role in VO 2 peak improvement following CR, but has not been investigated. Identifying clinical predictors of VO 2 peak improvement are critical as the degree of VO 2 peak improvement is associated with improved long-term outcomes in these patients. Purpose: To determine if clinical characteristics and number of CR sessions attended were predictive of patients who exhibited improvement in VO 2 peak following CR. Methods: Using the Rochester Epidemiology Project records-linkage system, we identified all consecutive patients >18 years old from Olmsted County, Minnesota who underwent cardiopulmonary exercise testing prior to and following CR from 1999 to 2017. Regression models were created to assess clinical predictors of VO 2 peak improvement (>0% baseline) following CR. Results: The analysis included 804 patients (75% men) with a mean age of 60.7±12.4 years. Of the 804 patients, 609 (76%) patients exhibited VO 2 peak improvement following CR. Significant univariate predictors of VO 2 peak improvement included younger age (OR: 0.99, 95% CI: 0.97-1.00), lower pre-CR VO 2 peak (OR: 0.97, 95% CI: 0.94-0.99), history of peripheral artery disease (OR: 0.48, 95% CI: 0.32-0.73), and smoking (OR: 0.63, 95% CI: 0.45-0.90). Significant independent predictors of VO 2 peak improvement from a multivariate analysis included younger age (OR: 0.98, 95% CI: 0.96-1.00), number of CR sessions (OR: 1.02, 95% CI: 1.01-1.03), lower pre-CR VO 2 peak (OR: 0.94, 95% CI: 0.91-0.96), history of peripheral artery disease (OR: 0.42, 95% CI: 0.27-0.66), and smoking (OR: 0.56, 95% CI: 0.39-0.82). Conclusions: These findings demonstrate the importance of CR adherence and individual clinical characteristics in influencing VO 2 peak improvement following CR in patients with CVD. These findings may influence the clinical practice by providing specific clinical characteristics to account for when personalizing the CR treatment plan to maximize positive outcomes.