Citations

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1 *04 subtypes best accounted for the association, strongest with HLA-DRB1 *04:04 and HLA-DRB1 *04:07, and intermediary with HLA-DRB1 *04:01 and HLA-DRB1 *04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1 *04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1 *04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

AbstractGynecological malignancies are a significant cause of morbidity and mortality across the globe. Due to delayed presentation, gynecological cancer patients are often referred late in the disease's course, resulting in poor outcomes. A considerable number of patients ultimately succumb to chemotherapy‐resistant disease, which reoccurs at advanced stages despite treatment interventions. Although efforts have been devoted to developing therapies that demonstrate reduced resistance to chemotherapy and enhanced toxicity profiles, current clinical outcomes remain unsatisfactory due to treatment resistance and unfavorable off‐target effects. Consequently, innovative biological and nanotherapeutic approaches are imperative to strengthen and optimize the therapeutic arsenal for gynecological cancers. Advancements in nanotechnology‐based therapies for gynecological malignancies offer significant advantages, including reduced toxicity, expanded drug circulation, and optimized therapeutic dosing, ultimately leading to enhanced treatment effectiveness. Recent advances in nucleic acid therapeutics using microRNA, small interfering RNA, and messenger RNA provide novel approaches for cancer therapeutics. Effective single‐agent and combinatorial nucleic acid therapeutics for gynecological malignancies have the potential to transform cancer treatment by giving safer, more tailored approaches than conventional therapies. This review highlights current preclinical studies that effectively exploit these approaches for the treatment of gynecological malignant tumors and malignant ascites.

Abstract Objectives We investigated coronavirus disease 2019 (COVID-19) vaccine safety in pregnant and breastfeeding women with autoimmune diseases (AID) in the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. Methods Delayed-onset (>7 days) vaccine-related adverse events (AE), disease flares and AID-related treatment modifications were analysed upon diagnosis of AID vs healthy controls (HC) and the pregnancy/breastfeeding status at the time of at least one dose of vaccine. Results Among the 9201 participants to the self-administered online survey, 6787 (73.8%) were women. Forty pregnant and 52 breastfeeding patients with AID were identified, of whom the majority had received at least one dose of COVID-19 vaccine (100% and 96.2%, respectively). AE were reported significantly more frequently in pregnant than in non-pregnant patients (overall AE 45% vs 26%, P = 0.01; minor AE 40% vs 25.9%, P = 0.03; major AE 17.5% vs 4.6%, P < 0.01), but no difference was found in comparison with pregnant HC. No difference was observed between breastfeeding patients and HC with respect to AE. Post-vaccination disease flares were reported by 17.5% of pregnant and 20% of breastfeeding patients, and by 18.3% of age- and disease-matched non-pregnant and non-breastfeeding patients (n = 262). All pregnant/breastfeeding patients who experienced a disease flare were managed with glucocorticoids; 28.6% and 20% of them required initiation or change in immunosuppressants, respectively. Conclusion This study provides reassuring insights into the safety of COVID-19 vaccines administered to women with AID during the gestational and post-partum periods, helping overcome hesitant attitudes, as the benefits for the mother and for the fetus by passive immunization appear to outweigh potential risks.

Objective: To compare conventional low-temperature storage of transplant donor livers [static cold storage (SCS)] with storage of the organs at physiological body temperature [normothermic machine perfusion (NMP)]. Background: The high success rate of liver transplantation is constrained by the shortage of transplantable organs (eg, waiting list mortality >20% in many centers). NMP maintains the liver in a functioning state to improve preservation quality and enable testing of the organ before transplantation. This is of greatest potential value with organs from brain-dead donor organs (DBD) with risk factors (age and comorbidities), and those from donors declared dead by cardiovascular criteria (donation after circulatory death). Methods: Three hundred eighty-three donor organs were randomized by 15 US liver transplant centers to undergo NMP (n = 192) or SCS (n = 191). Two hundred sixty-six donor livers proceeded to transplantation (NMP: n = 136; SCS: n = 130). The primary endpoint of the study was “early allograft dysfunction” (EAD), a marker of early posttransplant liver injury and function. Results: The difference in the incidence of EAD did not achieve significance, with 20.6% (NMP) versus 23.7% (SCS). Using exploratory, “as-treated” rather than “intent-to-treat,” subgroup analyses, there was a greater effect size in donation after circulatory death donor livers (22.8% NMP vs 44.6% SCS) and in organs in the highest risk quartile by donor risk (19.2% NMP vs 33.3% SCS). The incidence of acute cardiovascular decompensation at organ reperfusion, “postreperfusion syndrome,” as a secondary outcome was reduced in the NMP arm (5.9% vs 14.6%). Conclusions: NMP did not lower EAD, perhaps related to the inclusion of lower-risk liver donors, as higher-risk donor livers seemed to benefit more. The technology is safe in standard organ recovery and seems to have the greatest benefit for marginal donors.

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.

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.

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: Cardiac rehabilitation (CR) is a comprehensive secondary cardiovascular disease (CVD) program which integrates structured lifestyle interventions to reduce morbidity and mortality. The Life’s Simple 7 (LS7) is an objective measure of cardiovascular health (CVH) which incorporates health-promoting behaviors and clinical factors however, it has not been rigorously evaluated in the CR setting. Methods: Retrospective cohort study of 937 patients who attended center-based CR at Mayo Clinic from Jan 2018 -Sept 2020. Patient characteristics and LS7 component metrics at baseline (pre-CR) and completion (post-CR) were collected: 3 clinical factors (blood pressure [BP], cholesterol, and hemoglobin A1c [HbA1c]), and 4 health behaviors (smoking, body mass index [BMI], physical activity [PA] and diet). LS7 score was computed as a composite of each LS7 component by assigning 2-points for ideal, 1-point for intermediate, or 0-points for poor CVH ranging from 0-14 points. LS7 score was further categorized as poor 0-6, intermediate 7-8 and ideal 9 -14. Results: Patients were 64±13 years old, 34% were female, and attended a mean of 11±12 CR sessions, Table 1. At baseline, the LS7 components with the highest proportion within the poor category were HbA1c (201/495, 41%) and BMI (465/936, 50%), Table 2. From pre to post-CR, there was a reduction in proportion of patients classified within the poor category for each LS7 component except HbA1c ( Figure 1 ), and the proportion of patients with LS7 scores in the ideal range increased from 6.4% to 19% (P<0.001). There was a statistically significant improvement in LS7 score from 5.5±1.8 to 6.7± 2.4 (+Δ1.6±2.4, P<.0001), Table 2 . Conclusions: Patients attending CR demonstrated an improvement in LS7 score and almost all LS7 components suggesting an overall positive impact of CR on CVH. Further research is needed to better understand the impact of the individual LS7 components on CV outcomes in diverse populations participating in CR.