Impact of Social Determinants of Health and Lifestyle on Association Between Lipoprotein(a) and Cardiovascular Events.

Background: In European cohorts, healthier lifestyle either attenuated or associated with lower cardiovascular risk despite elevated lipoprotein(a) [Lp(a)]. Objectives: The purpose of this study was to test if social determinants of health (SDOH) and Life's Simple 7 (LS7) scores impact the association of Lp(a) with cardiovascular events in U.S. cohorts. Methods: We performed a sequential multivariable Cox proportional hazard analysis using the ARIC (Atherosclerosis Risk In Communities) and MESA (Multi-Ethnic Study of Atherosclerosis) cohorts. We first adjusted for age, gender, non-high-density lipoprotein-cholesterol, race, and ethnicity, then sequentially added SDOH and LS7 scores. The primary outcomes were time until first myocardial infarction (MI) or stroke. Results: ARIC (n = 15,072; median Lp(a) = 17.3 mg/dL) had 16.2 years and MESA (n = 6,822; median Lp(a) = 18.3 mg/dL) had 12.3 years of average follow-up. In age, gender, race, and ethnicity, and non-high-density lipoprotein-cholesterol adjusted analyses, Lp(a) was associated with MI in ARIC (HR: 1.10, P < 0.001) and MESA (HR: 1.11, P = 0.001), and stroke in ARIC (HR: 1.07, P < 0.001) but not MESA (HR: 0.97, P = 0.53). In models with SDOH and LS7, associations of Lp(a) remained similar with MI (ARIC, HR: 1.08, P < 0.001; MESA, HR: 1.10, P = 0.001) and stroke (ARIC, HR: 1.06, P = 0.002; MESA, HR: 0.96, P = 0.37). Each additional SDOH correlated positively with MI (ARIC, HR: 1.04, P = 0.01; MESA, HR: 1.08, P = 0.003) and stroke in ARIC (HR: 1.08, P = 0.00) but not MESA (HR: 1.03, P = 0.41). Each additional LS7 point correlated negatively with MI (ARIC, HR: 0.88, P < 0.001; MESA, HR: 0.85, P < 0.001) and stroke (ARIC, HR: 0.91, P < 0.001; MESA, HR: 0.86, P < 0.001). Conclusions: SDOH and lifestyle factors associated with risk for MI and stroke but did not largely impact the association between Lp(a) and cardiovascular events.

Projected Changes in Statin and Antihypertensive Therapy Eligibility With the AHA PREVENT Cardiovascular Risk Equations.

Importance: Since 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) have recommended the pooled cohort equations (PCEs) for estimating the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). An AHA scientific advisory group recently developed the Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations, which incorporated kidney measures, removed race as an input, and improved calibration in contemporary populations. PREVENT is known to produce ASCVD risk predictions that are lower than those produced by the PCEs, but the potential clinical implications have not been quantified. Objective: To estimate the number of US adults who would experience changes in risk categorization, treatment eligibility, or clinical outcomes when applying PREVENT equations to existing ACC and AHA guidelines. Design, Setting, and Participants: Nationally representative cross-sectional sample of 7765 US adults aged 30 to 79 years who participated in the National Health and Nutrition Examination Surveys of 2011 to March 2020, which had response rates ranging from 47% to 70%. Main Outcomes and Measures: Differences in predicted 10-year ASCVD risk, ACC and AHA risk categorization, eligibility for statin or antihypertensive therapy, and projected occurrences of myocardial infarction or stroke. Results: In a nationally representative sample of 7765 US adults aged 30 to 79 years (median age, 53 years; 51.3% women), it was estimated that using PREVENT equations would reclassify approximately half of US adults to lower ACC and AHA risk categories (53.0% [95% CI, 51.2%-54.8%]) and very few US adults to higher risk categories (0.41% [95% CI, 0.25%-0.62%]). The number of US adults receiving or recommended for preventive treatment would decrease by an estimated 14.3 million (95% CI, 12.6 million-15.9 million) for statin therapy and 2.62 million (95% CI, 2.02 million-3.21 million) for antihypertensive therapy. The study estimated that, over 10 years, these decreases in treatment eligibility could result in 107 000 additional occurrences of myocardial infarction or stroke. Eligibility changes would affect twice as many men as women and a greater proportion of Black adults than White adults. Conclusion and Relevance: By assigning lower ASCVD risk predictions, application of the PREVENT equations to existing treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults.

Shared plasma metabolomic profiles of cognitive and mobility decline predict future dementia.

Experiencing decline in both cognition and mobility is associated with a substantially higher dementia risk than cognitive decline only. Metabolites associated with both cognitive and mobility declines may be early predictors of dementia and reveal specific pathways to dementia. We analyzed data from 2450 participants initially free of dementia who had 613 metabolites measured in plasma in 1998-1999 (mean age = 75.2 ± 2.9 years old, 37.8% Black, 50% women) from the Health, Aging and Body Composition study. Dementia diagnosis was determined by race-specific decline in 3MS scores, medication use, and hospital records through 2014. Cognition and mobility were repeatedly measured using 3MS and a 20-m walking test up to 10 years, respectively. We examined metabolite associations with changes in 3MS (n = 2046) and gait speed (n = 2019) using multivariable linear regression adjusted for age, sex, race, and baseline performance and examined metabolite associations with dementia risk using Cox regression. During a mean follow-up of 9.3 years, 534 (21.8%) participants developed dementia. On average, 3MS declined 0.47/year and gait declined 0.04 m/sec/year. After covariate adjustment, 75 metabolites were associated with cognitive decline, and 111 metabolites were associated with gait decline (FDR-adjusted p < 0.05). Twenty-six metabolites were associated with both cognitive and gait declines. Eighteen of 26 metabolites were associated with dementia risk (p < 0.05), notably amino acids, glycerophospholipids (lysoPCs, PCs, PEs), and sphingolipids. Results remained similar after adjusting for cardiovascular disease or apolipoprotein E ɛ4 carrier status. During aging, metabolomic profiles of cognitive decline and mobility decline show distinct and shared signatures. Shared metabolomic profiles suggest that inflammation and deficits in mitochondria and the urea cycle in addition to the central nervous system may play key roles in both cognitive and mobility declines and predict dementia. Future studies are warranted to investigate longitudinal metabolite changes and metabolomic markers with dementia pathologies.

Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations.

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Metabolic liability for weight gain in early adulthood.

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

Proteomics, Human Environmental Exposure, and Cardiometabolic Risk.

BACKGROUND: The biological mechanisms linking environmental exposures with cardiovascular disease pathobiology are incompletely understood. We sought to identify circulating proteomic signatures of environmental exposures and examine their associations with cardiometabolic and respiratory disease in observational cohort studies. METHODS: We tested the relations of >6500 circulating proteins with 29 environmental exposures across the built environment, green space, air pollution, temperature, and social vulnerability indicators in ≈3000 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults) across 4 centers using penalized and ordinary linear regression. In >3500 participants from FHS (Framingham Heart Study) and JHS (Jackson Heart Study), we evaluated the prospective relations of proteomic signatures of the envirome with cardiovascular disease and mortality using Cox models. RESULTS: Proteomic signatures of the envirome identified novel/established cardiovascular disease-relevant pathways including DNA damage, fibrosis, inflammation, and mitochondrial function. The proteomic signatures of the envirome were broadly related to cardiometabolic disease and respiratory phenotypes (eg, body mass index, lipids, and left ventricular mass) in CARDIA, with replication in FHS/JHS. A proteomic signature of social vulnerability was associated with a composite of cardiovascular disease/mortality (1428 events; FHS: hazard ratio, 1.16 [95% CI, 1.08-1.24]; P=1.77×10-5; JHS: hazard ratio, 1.25 [95% CI, 1.14-1.38]; P=6.38×10-6; hazard ratio expressed as per 1 SD increase in proteomic signature), robust to adjustment for known clinical risk factors. CONCLUSIONS: Environmental exposures are related to an inflammatory-metabolic proteome, which identifies individuals with cardiometabolic disease and respiratory phenotypes and outcomes. Future work examining the dynamic impact of the environment on human cardiometabolic health is warranted.

Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition.

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.

Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults.

BACKGROUND: The circulating proteome may encode early pathways of diabetes susceptibility in young adults for surveillance and intervention. Here, we define proteomic correlates of tissue phenotypes and diabetes in young adults. METHODS: We used penalized models and principal components analysis to generate parsimonious proteomic signatures of diabetes susceptibility based on phenotypes and on diabetes diagnosis across 184 proteins in >2000 young adults in the CARDIA (Coronary Artery Risk Development in Young Adults study; mean age, 32 years; 44% women; 43% Black; mean body mass index, 25.6±4.9 kg/m2), with validation against diabetes in >1800 individuals in the FHS (Framingham Heart Study) and WHI (Women's Health Initiative). RESULTS: In 184 proteins in >2000 young adults in CARDIA, we identified 2 proteotypes of diabetes susceptibility-a proinflammatory fat proteotype (visceral fat, liver fat, inflammatory biomarkers) and a muscularity proteotype (muscle mass), linked to diabetes in CARDIA and WHI/FHS. These proteotypes specified broad mechanisms of early diabetes pathogenesis, including transorgan communication, hepatic and skeletal muscle stress responses, vascular inflammation and hemostasis, fibrosis, and renal injury. Using human adipose tissue single cell/nuclear RNA-seq, we demonstrate expression at transcriptional level for implicated proteins across adipocytes and nonadipocyte cell types (eg, fibroadipogenic precursors, immune and vascular cells). Using functional assays in human adipose tissue, we demonstrate the association of expression of genes encoding these implicated proteins with adipose tissue metabolism, inflammation, and insulin resistance. CONCLUSIONS: A multifaceted discovery effort uniting proteomics, underlying clinical susceptibility phenotypes, and tissue expression patterns may uncover potentially novel functional biomarkers of early diabetes susceptibility in young adults for future mechanistic evaluation.

Automated Deep Learning Segmentation of Cardiac Inflammatory FDG PET.

Background: Fluorodeoxyglucose positron emission tomography (FDG PET) with glycolytic metabolism suppression plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets is critical and myocardial segmentation enables consistent image scaling and quantification. However, both are challenging and labor intensive. We developed a 3D U-Net deep learning (DL) algorithm for automated myocardial segmentation in cardiac sarcoidosis FDG PET. Methods: The DL model was trained on 316 patients' FDG PET scans, and left ventricular contours derived from perfusion datasets. Qualitative analysis of clinical readability was performed to compare DL segmentation with the current automated method on a 50-patient test subset. Additionally, left ventricle displacement and angulation, as well as SUVmax sampling were compared to inter-user reproducibility results. Results: DL segmentation enhanced readability scores in over 90% of cases compared to the standard segmentation currently used in the software. DL segmentation performed similarly to a trained technologist, surpassing standard segmentation for left ventricle displacement and angulation, as well as correlation of SUVmax. Conclusion: The DL-based automated segmentation tool presents a marked improvement in the processing of cardiac sarcoidosis FDG PET, promising enhanced clinical workflow. This tool holds significant potential for accelerating clinical practice and improving consistency and quality. Further research with varied datasets is warranted to broaden its applicability.

Implications of metabolism on multi-systems healthy aging across the lifespan.

Aging is increasingly thought to involve dysregulation of metabolism in multiple organ systems that culminate in decreased functional capacity and morbidity. Here, we seek to understand complex interactions among metabolism, aging, and systems-wide phenotypes across the lifespan. Among 2469 adults (mean age 74.7 years; 38% Black) in the Health, Aging and Body Composition study we identified metabolic cross-sectionally correlates across 20 multi-dimensional aging-related phenotypes spanning seven domains. We used LASSO-PCA and bioinformatic techniques to summarize metabolome-phenome relationships and derive metabolic scores, which were subsequently linked to healthy aging, mortality, and incident outcomes (cardiovascular disease, disability, dementia, and cancer) over 9 years. To clarify the relationship of metabolism in early adulthood to aging, we tested association of these metabolic scores with aging phenotypes/outcomes in 2320 participants (mean age 32.1, 44% Black) of the Coronary Artery Risk Development in Young Adults (CARDIA) study. We observed significant overlap in metabolic correlates across the seven aging domains, specifying pathways of mitochondrial/cellular energetics, host-commensal metabolism, inflammation, and oxidative stress. Across four metabolic scores (body composition, mental-physical performance, muscle strength, and physical activity), we found strong associations with healthy aging and incident outcomes, robust to adjustment for risk factors. Metabolic scores for participants four decades younger in CARDIA were related to incident cardiovascular, metabolic, and neurocognitive performance, as well as long-term cardiovascular disease and mortality over three decades. Conserved metabolic states are strongly related to domain-specific aging and outcomes over the life-course relevant to energetics, host-commensal interactions, and mechanisms of innate immunity.

Ejection fraction and ventricular volumes on rubidium positron emission tomography: Validation against cardiovascular magnetic resonance.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is the non-invasive gold standard for non-invasively determining left ventricular volumes (LVVs) and ejection fraction (EF). We aimed to assess the accuracy of LVV and left ventricular ejection fraction measured by positron emission tomography (PET) as compared to CMR. METHODS: Patients who underwent both PET and CMR within 1 year were identified from prospective institutional registries. Analysis was performed to evaluate the agreement between the raw and body-surface-area-normalized left ventricular volume (LVV) and EF derived from PET vs. those derived from CMR. RESULTS: The study population consisted of 669 patients (mean age 62 ± 13 years, 65% male). The median (interquartile range [IQR]) duration between CMR and PET imaging was 36 (7-118) days. The median (IQR) EF values were 52% (38-63%) on CMR and 53% (37-65%) on PET (mean difference: 0.53% ± 9.1, P = 0.129) with a strong correlation (Spearman rho = 0.84, P < 0.001; Intraclass Correlation Coefficient 0.84, 95% confidence interval [CI]: 0.82-0.86, P < 0.001; Lin's concordance correlation coefficient was 0.844, 95% CI: 0.822 to 0.865). Results were similar with LVV, normalized LVV/EF, and in subgroups of patients with reduced EF, coronary artery disease scar, and LV hypertrophy as well as in patients with defibrillators. However, PET tended to underestimate LVV compared to CMR. CONCLUSION: Our analysis showed a strong correlation of EF and LVV by PET against a reference standard of CMR, whereas PET significantly underestimated LVV, but not EF, compared to CMR.

Branched-chain amino acids and type 2 diabetes: a bidirectional Mendelian randomization analysis.

OBJECTIVE: Genetic studies have suggested that the branched-chain amino acids (BCAAs) valine, leucine, and isoleucine have a causal association with type 2 diabetes (T2D). However, inferences are based on a limited number of genetic loci associated with BCAAs. METHODS: Instrumental variables (IVs) for each BCAA were constructed and validated using large well-powered data sets and their association with T2D was tested using a two-sample inverse-variance weighted Mendelian randomization approach. Sensitivity analyses were performed to ensure the accuracy of the findings. A reverse association was assessed using instrumental variables for T2D. RESULTS: Estimated effect sizes between BCAA IVs and T2D, excluding outliers, were as follows: valine (ß = 0.14 change in log-odds per SD change in valine, 95% CI: -0.06 to 0.33, p = 0.17), leucine (ß = 0.15, 95% CI: -0.02 to 0.32, p = 0.09), and isoleucine (ß = 0.13, 95% CI: -0.08 to 0.34, p = 0.24). In contrast, T2D IVs were positively associated with each BCAA, i.e., valine (ß = 0.08 per SD change in levels per log-odds change in T2D, 95% CI: 0.05 to 0.10, p = 1.8 × 10-9 ), leucine (ß = 0.06, 95% CI: 0.04 to 0.09, p = 4.5 × 10-8 ), and isoleucine (ß = 0.06, 95% CI: 0.04 to 0.08, p = 2.8 × 10-8 ). CONCLUSIONS: These data suggest that the BCAAs are not mediators of T2D risk but are biomarkers of diabetes.

Choline metabolites and incident cardiovascular disease in a prospective cohort of adults: Coronary Artery Risk Development in Young Adults (CARDIA) Study.

BACKGROUND: The potential role for choline metabolite trimethylamine N-oxide (TMAO) in cardiovascular disease (CVD) has garnered much attention, but there have been limited data from diverse population-based cohorts. Furthermore, few studies have included circulating choline and betaine, which can serve as precursors to TMAO and may independently influence CVD. OBJECTIVE: We quantified prospective associations between 3 choline metabolites and 19-y incident CVD in a population-based cohort and tested effect modification of metabolite-CVD associations by kidney function. METHODS: Data were from the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort with recruitment from 4 US urban centers (year 0: 1985-1986, n = 5115, ages 18-30). The analytic sample included 3444 White and Black males and females, aged 33 to 45, who attended the year 15 follow-up exam and did not have prevalent CVD. TMAO, choline, and betaine were quantitated from stored plasma (-70°C) using liquid-chromatography mass-spectrometry. Nineteen-year incident CVD events (n = 221), including coronary heart disease and stroke, were identified through adjudicated hospitalization records and linkage with the National Death Register. RESULTS: Plasma choline was positively associated with CVD in Cox proportional hazards regression analysis adjusted for demographics, health behaviors, CVD risk factors, and metabolites (hazard ratio: 1.24; 95% CI: 1.09, 1.40 per standard deviation-unit choline). TMAO and betaine were not associated with CVD in an identically adjusted analysis. There was statistical evidence for effect modification by kidney function with CVD positively associated with TMAO and negatively associated with betaine at lower values of estimated glomerular filtration rate (interaction P values: 0.0046 and 0.020, respectively). CONCLUSIONS: Our findings are consistent with a positive association between plasma choline and incident CVD. Among participants with lower kidney function, TMAO was positively, and betaine negatively, associated with CVD. These results further our understanding of the potential role for choline metabolism on CVD risk.

Cytomegalovirus infection is associated with impaired myocardial flow reserve after heart transplantation.

BACKGROUND: Cardiac allograft vasculopathy (CAV) limits long-term survival after heart transplantation (HT). This study evaluates the relationship between clinically significant cytomegalovirus infection (CS-CMVi) and CAV using cardiac positron emission tomography (PET). METHODS: We retrospectively evaluated HT patients from 2005 to 2019 who underwent cardiac PET for CAV evaluation. Multivariable linear and logistic regression models were used to evaluate the association between CS-CMVi and myocardial flow reserve (MFR). Kaplan-Meier and Cox regression analyses were used to assess the relationship between CS-CMV, MFR, and clinical outcomes. RESULTS: Thirty-two (31.1%) of 103 HT patients developed CS-CMVi at a median 9 months after HT. Patients with CS-CMVi had a significantly lower MFR at year 1 and 3, driven by reduction in stress myocardial blood flow. Patients with CS-CMVi had a faster rate of decline in MFR compared to those without infection (-0.10 vs -0.06 per year, p < 0.001). CS-CMVi was an independent predictor of abnormal MFR (<2.0) (odds ratio: 3.8, 95% confidence intervals (CI): 1.4-10.7, p = 0.001) and a lower MFR (ß = -0.39, 95% CI: -0.63 to -0.16, p = 0.001) at year 3. In adjusted survival analyses, both abnormal MFR (log-rank p < 0.001; hazard ratio [HR]: 5.7, 95% CI: 4.2-7.2) and CS-CMVi (log-rank p = 0.028; HR: 3.3, 95% CI: 1.8-4.8) were significant predictors of the primary outcome of all-cause mortality, retransplantation, heart failure hospitalization, and acute coronary syndrome. CONCLUSIONS: CS-CMVi is an independent predictor of reduced MFR following HT. These findings suggest that CMV infection is an important risk factor in the development and progression of CAV.

Metabolomic characterization of vigor to frailty among community-dwelling older Black and White men and women.

Older women and Black individuals are more likely to experience frailty. A metabolomic characterization of frailty may help inform more effective interventions aimed at improving health, reducing disparities, and preventing frailty with aging. We sought to identify metabolites and pathways associated with vigor to frailty and determine whether associations differed by sex and/or race among n = 2189 older Black and White men and women from the Health, Aging, and Body Composition (Health ABC) study. Fasting plasma metabolites were measured using liquid chromatography-mass spectrometry. Vigor to frailty was based on weight change, physical activity, gait speed, grip strength, and usual energy. We used linear regression of a single metabolite on vigor to frailty, adjusting for age, sex, race, study site, and multiple comparisons using a Bonferroni correction. Among 500 metabolites, 113 were associated with vigor to frailty (p < 0.0001). Associations between metabolites and vigor to frailty did not differ significantly by race and/or sex. Lower amino acids, glycerophospholipids, sphingolipids, and dehydroepiandrosterone sulfate and higher acylcarnitines, fatty acids, amino acid derivatives, organic acids, carbohydrates, citric acid cycle metabolites, and trimethylamine oxide were associated with frailer scores. Pathway analyses identified the citric acid cycle as containing more frailty-associated metabolites than expected by chance (p = 0.00005). Calories and protein intake did not differ by vigor to frailty. Frailer Health ABC participants may have lower utilization of energy pathways, potentially as a result of less demand and less efficient utilization of similar amounts of nutrients when compared to more vigorous participants.

Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

Deep neural network for identification of impaired microvascular and vasomotor function from stress electrocardiography.

Impaired microvascular and vasomotor function is a common consequence of aging, diabetes, and other risk factors, and is associated with adverse cardiac outcomes. Such impairments are not readily identified by standard clinical methods of cardiovascular testing such as coronary angiography and noninvasive single photon emission tomography (SPECT) myocardial perfusion imaging (MPI). We hypothesized that signals embedded within stress electrocardiograms (ECGs) identify individuals with microvascular and vasomotor dysfunction. Methods: We developed and validated a novel convolutional neural network (CNN) using stress and rest ECG data (ECG-Flow) to identify patients with impaired myocardial flow reserve (MFR) on quantitative positron emission tomography (PET) MPI (N=3887). Diagnostic accuracy was validated with an internal holdout set of patients undergoing stress PET MPI (N=963). The prognostic association of ECG-Flow with mortality was then evaluated in a separate cohort of patients undergoing SPECT MPI (N=5102). Results: ECG-Flow achieved good diagnostic accuracy for impaired MFR in the holdout PET cohort (AUC, sensitivity, specificity: 0.737, 71.1%, 65.7%). Abnormal ECG-Flow was found to be significantly associated with mortality in both PET holdout and SPECT MPI cohorts (adjusted HR 2.12 [95 ρ CI 1.45, 2.10], ρ = 0.0001, and 2.07 [1.82, 2.36], ρ < 0.0001, respectively). Conclusion: Signals predictive of microvascular and vasomotor dysfunction are embedded in stress ECG waveforms. These signals can be identified by deep learning methods and are related to prognosis in patients undergoing both stress PET and SPECT MPI.

Integrated myocardial flow reserve (iMFR) assessment: optimized PET blood flow quantification for diagnosis of coronary artery disease.

PURPOSE: Distinguishing obstructive epicardial coronary artery disease (CAD) from microvascular dysfunction and diffuse atherosclerosis would be of immense benefit clinically. However, quantitative measures of absolute myocardial blood flow (MBF) integrate the effects of focal epicardial stenosis, diffuse atherosclerosis, and microvascular dysfunction. In this study, MFR and relative perfusion quantification were combined to create integrated MFR (iMFR) which was evaluated using data from a large clinical registry and an international multi-center trial and validated against invasive coronary angiography (ICA). METHODS: This study included 1,044 clinical patients referred for 82Rb rest/stress positron emission tomography myocardial perfusion imaging and ICA, along with 231 patients from the Flurpiridaz 301 trial (clinicaltrials.gov NCT01347710). MFR and relative perfusion quantification were combined to create an iMFR map. The incremental value of iMFR was evaluated for diagnosis of obstructive stenosis, adjusted for patient demographics and pre-test probability of CAD. Models for high-risk anatomy (left main or three-vessel disease) were also constructed. RESULTS: iMFR parameters of focally impaired perfusion resulted in best fitting diagnostic models. Receiver-operating characteristic analysis showed a slight improvement compared to standard quantitative perfusion approaches (AUC 0.824 vs. 0.809). Focally impaired perfusion was also associated with high-risk CAD anatomy (OR 1.40 for extent, and OR 2.40 for decreasing mean MFR). Diffusely impaired perfusion was associated with lower likelihood of obstructive CAD, and, in the absence of transient ischemic dilation (TID), with lower likelihood of high-risk CAD anatomy. CONCLUSIONS: Focally impaired perfusion extent derived from iMFR assessment is a powerful incremental predictor of obstructive CAD while diffusely impaired perfusion extent can help rule out obstructive and high-risk CAD in the absence of TID.

Proteomics and Precise Exercise Phenotypes in Heart Failure With Preserved Ejection Fraction: A Pilot Study.

BACKGROUND: While exercise impairments are central to symptoms and diagnosis of heart failure with preserved ejection fraction (HFpEF), prior studies of HFpEF biomarkers have mostly focused on resting phenotypes. We combined precise exercise phenotypes with cardiovascular proteomics to identify protein signatures of HFpEF exercise responses and new potential therapeutic targets. METHODS AND RESULTS: We analyzed 277 proteins (Olink) in 151 individuals (N=103 HFpEF, 48 controls; 62±11 years; 56% women) with cardiopulmonary exercise testing with invasive monitoring. Using ridge regression adjusted for age/sex, we defined proteomic signatures of 5 physiological variables involved in HFpEF: peak oxygen uptake, peak cardiac output, pulmonary capillary wedge pressure/cardiac output slope, peak pulmonary vascular resistance, and peak peripheral O2 extraction. Multiprotein signatures of each of the exercise phenotypes captured a significant proportion of variance in respective exercise phenotypes. Interrogating the importance (ridge coefficient magnitude) of specific proteins in each signature highlighted proteins with putative links to HFpEF pathophysiology (eg, inflammatory, profibrotic proteins), and novel proteins linked to distinct physiologies (eg, proteins involved in multiorgan [kidney, liver, muscle, adipose] health) were implicated in impaired O2 extraction. In a separate sample (N=522, 261 HF events), proteomic signatures of peak oxygen uptake and pulmonary capillary wedge pressure/cardiac output slope were associated with incident HFpEF (odds ratios, 0.67 [95% CI, 0.50-0.90] and 1.43 [95% CI, 1.11-1.85], respectively) with adjustment for clinical factors and B-type natriuretic peptides. CONCLUSIONS: The cardiovascular proteome is associated with precision exercise phenotypes in HFpEF, suggesting novel mechanistic targets and potential methods for risk stratification to prevent HFpEF early in its pathogenesis.