Spatial multi-omic map of human myocardial infarction.

Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

Recognition of psychological comorbidity and psychotherapeutic treatment status of ventricular assist device patients.

BACKGROUND: Due to its high impact on quality of life and mental health, close monitoring and often psychotherapy is recommended for patients with a ventricular assist device (VAD). This study investigates the psychological comorbidity and the corresponding psychotherapeutic treatment situation of VAD patients. Special attention is also given to the professional perspective VAD team (assistant and senior cardiologists and specialized nurses). METHODS: We conducted a cross-sectional observational study. Data from 50 VAD patients (mean age = 53.52, standard deviation = 13.82 years, 84.0% male) and their VAD team were analyzed. The presence of a psychological disorder was evaluated by structured clinical interviews for DSM-IV (SCID-I-Interviews). Patients answered a questionnaire regarding their current psychotherapeutic treatment status and their attitude towards psychotherapy. The VAD team answered a questionnaire about the patients' needs for psychotherapy and indicated whether they addressed this topic with the patient. Data were analyzed descriptively, by analysis of variance and t-test. RESULTS: A total of 58% of VAD patients suffered from at least one significant psychological disorder, 79.3% of those were not in psychotherapy. The VAD team could not identify the patients who suffered from a psychological disorder (F = 1.90; p = 0.18). They perceived more need for psychotherapy than they addressed with their patients (T = 3.39; p < 0.001). CONCLUSIONS: While there is a high psychological morbidity among VAD patients, only few receive psychotherapy. Psychological comorbidity is not easily detected by the VAD team. Standardized psychosocial care could be implemented by regular psychological assessments and further information of patients and their VAD teams.

Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study.

AIMS: Although highly heritable, the genetic etiology of calcific aortic stenosis (AS) remains incompletely understood. The aim of this study was to discover novel genetic contributors to AS and to integrate functional, expression, and cross-phenotype data to identify mechanisms of AS. METHODS AND RESULTS: A genome-wide meta-analysis of 11.6 million variants in 10 cohorts involving 653 867 European ancestry participants (13 765 cases) was performed. Seventeen loci were associated with AS at P ≤ 5 × 10-8, of which 15 replicated in an independent cohort of 90 828 participants (7111 cases), including CELSR2-SORT1, NLRP6, and SMC2. A genetic risk score comprised of the index variants was associated with AS [odds ratio (OR) per standard deviation, 1.31; 95% confidence interval (CI), 1.26-1.35; P = 2.7 × 10-51] and aortic valve calcium (OR per standard deviation, 1.22; 95% CI, 1.08-1.37; P = 1.4 × 10-3), after adjustment for known risk factors. A phenome-wide association study indicated multiple associations with coronary artery disease, apolipoprotein B, and triglycerides. Mendelian randomization supported a causal role for apolipoprotein B-containing lipoprotein particles in AS (OR per g/L of apolipoprotein B, 3.85; 95% CI, 2.90-5.12; P = 2.1 × 10-20) and replicated previous findings of causality for lipoprotein(a) (OR per natural logarithm, 1.20; 95% CI, 1.17-1.23; P = 4.8 × 10-73) and body mass index (OR per kg/m2, 1.07; 95% CI, 1.05-1.9; P = 1.9 × 10-12). Colocalization analyses using the GTEx database identified a role for differential expression of the genes LPA, SORT1, ACTR2, NOTCH4, IL6R, and FADS. CONCLUSION: Dyslipidemia, inflammation, calcification, and adiposity play important roles in the etiology of AS, implicating novel treatments and prevention strategies.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction.

BACKGROUND: Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles. METHODS: We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature. RESULTS: We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance. CONCLUSIONS: We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease.

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04-1.18; P 8.43 × 10-4) and 1.13 (95% CI, 1.07-1.20; P 4.51 × 10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03-1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95-1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99-1.14; P = 0.07) and 1.07 (1.01-1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery.

BACKGROUND: Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. METHODS: We defined all-cause HF among 488 010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30 201). RESULTS: We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci (P<1×10-6), the majority linked to upstream HF risk factors, ie, coronary artery disease (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy (BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-of-function variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10-5). CONCLUSIONS: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk factors and that are associated with subclinical left ventricular dysfunction.

Pharmacogenetics of hypoglycemia associated with sulfonylurea therapy in usual clinical care.

Hypoglycemia is a common complication among type 2 diabetes mellitus (T2DM) patients receiving sulfonylurea therapy. The aim of this study was to determine if genetic contributions to sulfonylurea pharmacokinetics or pharmacodynamics substantially affect the risk of hypoglycemia in these patients. In a retrospective case-control study in European American patients with T2DM, we examined the potential association between CYP2C9 reduced-function variants and sulfonylurea-related hypoglycemia. We also explored the relationship between sulfonylurea-related hypoglycemia and several candidate genetic variants previously reported to alter the response to sulfonylureas. We detected no evidence of association between CYP2C9 reduced-function alleles or any of the candidate genetic variants and sulfonylurea-related hypoglycemia. In conclusion, we identified no clinically significant predictors of hypoglycemia among genes associated with sulfonylurea pharmacokinetics or pharmacodynamics.

Big Data Approaches in Heart Failure Research.

PURPOSE OF REVIEW: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential. RECENT FINDINGS: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Relationship between very low low-density lipoprotein cholesterol concentrations not due to statin therapy and risk of type 2 diabetes: A US-based cross-sectional observational study using electronic health records.

BACKGROUND: Observations from statin clinical trials and from Mendelian randomization studies suggest that low low-density lipoprotein cholesterol (LDL-C) concentrations may be associated with increased risk of type 2 diabetes mellitus (T2DM). Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations in increased risk of T2DM. METHODS AND FINDINGS: We used de-identified electronic health records (EHRs) at Vanderbilt University Medical Center to compare the risk of T2DM in a cross-sectional study among individuals with very low (≤60 mg/dl, N = 8,943) and normal (90-130 mg/dl, N = 71,343) LDL-C levels calculated using the Friedewald formula. LDL-C levels associated with statin use, hospitalization, or a serum albumin level < 3 g/dl were excluded. We used a 2-phase approach: in 1/3 of the sample (discovery) we used T2DM phenome-wide association study codes (phecodes) to identify cases and controls, and in the remaining 2/3 (validation) we identified T2DM cases and controls using a validated algorithm. The analysis plan for the validation phase was constructed at the time of the design of that component of the study. The prevalence of T2DM in the very low and normal LDL-C groups was compared using logistic regression with adjustment for age, race, sex, body mass index (BMI), high-density lipoprotein cholesterol, triglycerides, and duration of care. Secondary analyses included prespecified stratification by sex, race, BMI, and LDL-C level. In the discovery cohort, phecodes related to T2DM were significantly more frequent in the very low LDL-C group. In the validation cohort (N = 33,039 after applying the T2DM algorithm to identify cases and controls), the risk of T2DM was increased in the very low compared to normal LDL-C group (odds ratio [OR] 2.06, 95% CI 1.80-2.37; P < 2 × 10-16). The findings remained significant in sensitivity analyses. The association between low LDL-C levels and T2DM was significant in males (OR 2.43, 95% CI 2.00-2.95; P < 2 × 10-16) and females (OR 1.74, 95% CI 1.42-2.12; P = 6.88 × 10-8); in normal weight (OR 2.18, 95% CI 1.59-2.98; P = 1.1× 10-6), overweight (OR 2.17, 95% CI 1.65-2.83; P = 1.73× 10-8), and obese (OR 2.00, 95% CI 1.65-2.41; P = 8 × 10-13) categories; and in individuals with LDL-C < 40 mg/dl (OR 2.31, 95% CI 1.71-3.10; P = 3.01× 10-8) and LDL-C 40-60 mg/dl (OR 1.99, 95% CI 1.71-2.32; P < 2.0× 10-16). The association was significant in individuals of European ancestry (OR 2.67, 95% CI 2.25-3.17; P < 2 × 10-16) but not in those of African ancestry (OR 1.09, 95% CI 0.81-1.46; P = 0.56). A limitation was that we only compared groups with very low and normal LDL-C levels; also, since this was not an inception cohort, we cannot exclude the possibility of reverse causation. CONCLUSIONS: Very low LDL-C concentrations occurring in the absence of statin treatment were significantly associated with T2DM risk in a large EHR population; this increased risk was present in both sexes and all BMI categories, and in individuals of European ancestry but not of African ancestry. Longitudinal cohort studies to assess the relationship between very low LDL-C levels not associated with lipid-lowering therapy and risk of developing T2DM will be important.

Genome-wide association and pathway analysis of left ventricular function after anthracycline exposure in adults.

BACKGROUND: Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. METHODS: We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 patients identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with P values less than 1×10 were independently tested for replication in a cohort of 181 anthracycline-exposed patients from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. RESULTS: Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism near PR domain containing 2, with ZNF domain (PRDM2), rs7542939, had a combined P value of 6.5×10 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. CONCLUSION: Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.

Replication and fine-mapping of genetic predictors of lipid traits in African-Americans.

Circulating lipid concentrations are among the strongest modifiable risk factors for coronary artery disease (CAD). Most genetic studies have focused on Caucasian populations with little information available for populations of African ancestry. Using a cohort of ~2800 African-Americans (AAs) from a biobank at Vanderbilt University (BioVU), we sought to trans-ethnically replicate genetic variants reported by the Global Lipids Genetics Consortium to be associated with lipid traits in Caucasians, followed by fine-mapping those loci using all available variants on the MetaboChip. In AAs, we replicated one of 56 SNPs for total cholesterol (TC) (rs6511720 in LDLR, P=2.15 × 10-8), one of 63 SNPs for high-density lipoprotein cholesterol (HDL-C) (rs3764261 in CETP, P=1.13 × 10-5), two of 46 SNPs for low-density lipoprotein cholesterol (LDL-C) (rs629301 in CELSR2/SORT1, P=1.11 × 10-5 and rs6511720 in LDLR, P=2.47 × 10-5) and one of 34 SNPs for TG (rs645040 in MSL2L1, P=4.29 × 10-4). Using all available variants on MetaboChip for fine mapping, we identified additional variants associated with TC (APOE), HDL-C (LPL and CETP) and LDL-C (APOE). Furthermore, we identified two loci significantly associated with non-HDL-C: APOE/APOC1/TOMM40 and PCSK9. In conclusion, the genetic architecture of lipid traits in AAs differs substantially from that in Caucasians and it remains poorly characterized.

Genetic analysis of the localization of APOBEC3F to human immunodeficiency virus type 1 virion cores.

UNLABELLED: Members of the APOBEC3 family of cytidine deaminases vary in their proportions of a virion-incorporated enzyme that is localized to mature retrovirus cores. We reported previously that APOBEC3F (A3F) was highly localized into mature human immunodeficiency virus type 1 (HIV-1) cores and identified that L306 in the C-terminal cytidine deaminase (CD) domain contributed to its core localization (C. Song, L. Sutton, M. Johnson, R. D'Aquila, J. Donahue, J Biol Chem 287:16965-16974, 2012, http://dx.doi.org/10.1074/jbc.M111.310839). We have now determined an additional genetic determinant(s) for A3F localization to HIV-1 cores. We found that one pair of leucines in each of A3F's C-terminal and N-terminal CD domains jointly determined the degree of localization of A3F into HIV-1 virion cores. These are A3F L306/L368 (C-terminal domain) and A3F L122/L184 (N-terminal domain). Alterations to one of these specific leucine residues in either of the two A3F CD domains (A3F L368A, L122A, and L184A) decreased core localization and diminished HIV restriction without changing virion packaging. Furthermore, double mutants in these leucine residues in each of A3F's two CD domains (A3F L368A plus L184A or A3F L368A plus L122A) still were packaged into virions but completely lost core localization and anti-HIV activity. HIV virion core localization of A3F is genetically separable from its virion packaging, and anti-HIV activity requires some core localization. IMPORTANCE: Specific leucine-leucine interactions are identified as necessary for A3F's core localization and anti-HIV activity but not for its packaging into virions. Understanding these signals may lead to novel strategies to enhance core localization that may augment effects of A3F against HIV and perhaps of other A3s against retroviruses, parvoviruses, and hepatitis B virus.

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study.

BACKGROUND: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.

A novel scatterplot-based method to detect copy number variation (CNV).

Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.

Microbiome-based risk prediction in incident heart failure: a community challenge.

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

The development and use of decision support framework for informing selection of select agent toxins with modelling studies to inform permissible toxin amounts.

Many countries have worked diligently to establish and implement policies and processes to regulate high consequence pathogens and toxins that could have a significant public health impact if misused. In the United States, the Antiterrorism and Effective Death Penalty Act of 1996 (Public Law 104-132, 1996), as amended by the Bioterrorism Preparedness and Response Act of 2002 (Public Law 107-188, 2002) requires that the Department of Health and Human Services (HHS) [through the Centers for Disease Control and Prevention (CDC)] establish a list of bacteria, viruses, and toxins that have the potential to pose a severe threat to public health and safety. Currently, this list is reviewed and updated on a biennial basis using input from subject matter experts (SMEs). We have developed decision support framework (DSF) approaches to facilitate selection of select toxins and, where toxicity data are known, conducted modelling studies to inform selection of toxin amounts that should be excluded from select agent regulations. Exclusion limits allow laboratories to possess toxins under an established limit to support their research or teaching activities without the requirement to register with the Federal Select Agent Program. Fact sheets capturing data from a previously vetted SME workshop convened by CDC, literature review and SME input were developed to assist in evaluating toxins using the DSF approach. The output of the DSF analysis agrees with the current select toxin designations, and no other toxins evaluated in this study were recommended for inclusion on the select agent and toxin list. To inform the selection of exclusion limits, attack scenarios were developed to estimate the amount of toxin needed to impact public health. Scenarios consisted of simulated aerosol releases of a toxin in high-population-density public facilities and the introduction of a toxin into a daily consumable product supply chain. Using published inhalation and ingestion median toxic dose (TD50) and median lethal dose (LD50) values, where available, a range of toxin amounts was examined to estimate the number of people exposed to these amounts in these scenarios. Based on data generated by these models, we proposed toxin exclusion values corresponding to levels below those that would trigger a significant public health response (i.e., amounts estimated to expose up to ten people by inhalation or one hundred people by ingestion to LD50 or TD50 levels of toxin in the modeled scenarios).

Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making.

The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding "by eye" may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes.

OBJECTIVE: Body mass index (BMI) is the most commonly used predictor of weight-related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology. METHODS: Use of BMI or height-weight interaction in prediction models for established weight-related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high-density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2, and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps. RESULTS: Compared to BMI, the maximally flexible height-weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2, and Somers' Dxy rank correlation, for event-free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range. CONCLUSIONS: Because alternative measures of body composition such as waist-to-hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk.

Heart failure clinical care analysis uncovers risk reduction opportunities for preserved ejection fraction subtype.

Heart failure (HF) has no cure and, for HF with preserved ejection fraction (HFpEF), no life-extending treatments. Defining the clinical epidemiology of HF could facilitate earlier identification of high-risk individuals. We define the clinical epidemiology of HF subtypes (HFpEF and HF with reduced ejection fraction [HFrEF]), identified among 2.7 million individuals receiving routine clinical care. Differences in patterns and rates of accumulation of comorbidities, frequency of hospitalization, use of specialty care, were defined for each HF subtype. Among 28,156 HF cases, 8322 (30%) were HFpEF and 11,677 (42%) were HFrEF. HFpEF was the more prevalent subtype among older women. 177 Phenotypes differentially associated with HFpEF versus HFrEF. HFrEF was more frequently associated with diagnoses related to ischemic cardiac injury while HFpEF was associated more with non-cardiac comorbidities and HF symptoms. These comorbidity patterns were frequently present 3 years prior to a HFpEF diagnosis. HF subtypes demonstrated distinct patterns of clinical co-morbidities and disease progression. For HFpEF, these comorbidities were often non-cardiac and manifested prior to the onset of a HF diagnosis. Recognizing these comorbidity patterns, along the care continuum, may present a window of opportunity to identify individuals at risk for developing incident HFpEF.

BMI Is Causally Associated With Pulmonary Artery Pressure But Not Hemodynamic Evidence of Pulmonary Vascular Remodeling.

BACKGROUND: There is an unclear relationship of obesity to the pathogenesis and severity of pulmonary arterial hypertension (PAH) and pulmonary venous hypertension (PVH). RESEARCH QUESTION: Is BMI casually associated with pulmonary artery pressure (PAP) and/or markers of pulmonary vascular remodeling? STUDY DESIGN AND METHODS: The study design was a two-sample inverse-variance weighted Mendelian randomization. We constructed two BMI genetic risk scores from genome-wide association study summary data and deployed them in nonoverlapping cohorts of subjects referred for right heart catheterization (RHC) or echocardiography. A BMI highly polygenic risk score (hpGRS) optimally powered to detect shared genetic architecture of obesity with other traits was tested for association with RHC parameters including markers of pulmonary vascular remodeling. A BMI strict genetic risk score (sGRS) composed of high-confidence genetic variants was used for Mendelian randomization analyses to assess if higher BMI causes higher PAP. RESULTS: Among all subjects, both directly measured BMI and hpGRS were positively associated with pulmonary arterial pressures but not markers of pulmonary vascular remodeling. Categorical analyses revealed BMI and hpGRS were associated with PVH but not PAH. Mendelian randomization of the sGRS supported that higher BMI is causal of higher systolic pulmonary artery pressure (sPAP). Sensitivity analyses showed sPAP-BMI sGRS relationship was preserved when either individuals with PAH or PVH were excluded. In the echocardiographic cohort, BMI and hpGRS were positively associated with estimated PAP and markers of left heart remodeling. INTERPRETATION: BMI is a modifier of pulmonary hypertension severity in both PAH and PVH but is only involved in the pathogenesis of PVH.