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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., (© 2024 The Author(s). Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.)

Hypoglycemia triggers autonomic and endocrine counter-regulatory responses to restore glucose homeostasis, a response that is impaired in patients with diabetes and its long-term complication hypoglycemia-associated autonomic failure (HAAF). We show that insulin-evoked hypoglycemia is severely aggravated in mice lacking the cation channel proteins TRPC1, TRPC4, TRPC5, and TRPC6, which cannot be explained by alterations in glucagon or glucocorticoid action. By using various TRPC compound knockout mouse lines, we pinpointed the failure in sympathetic counter-regulation to the lack of the TRPC5 channel subtype in adrenal chromaffin cells, which prevents proper adrenaline rise in blood plasma. Using electrophysiological analyses, we delineate a previously unknown signaling pathway in which stimulation of PAC1 or muscarinic receptors activates TRPC5 channels in a phospholipase-C-dependent manner to induce sustained adrenaline secretion as a crucial step in the sympathetic counter response to insulin-induced hypoglycemia. By comparing metabolites in the plasma, we identified reduced taurine levels after hypoglycemia induction as a commonality in TRPC5-deficient mice and HAAF patients., (© 2024. The Author(s).)

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., (©Rebecca T Levinson, Cinara Paul, Andreas D Meid, Jobst-Hendrik Schultz, Beate Wild. Originally published in JMIR Cardio (https://cardio.jmir.org), 23.07.2024.)

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., (© 2023. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Qiao, Levinson, Chen, Engelter, Erhart, Gaynor, McArdle, Schlicht, Krawczak, Stenman, Lindgren, Cole and Grond-Ginsbach.)

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., Competing Interests: Conflict of interest Scott M. Damrauer receives research support (to the University of Pennsylvania) from RenalytixAI and personal fees from Caico Ibs, both outside the scope of the present work. SMD is also named as a co-inventor on a government-owned US Patent application related to the use of genetic risk prediction for venous thromboembolic disease filed by the US Department of Veterans Affairs in accordance with Federal regulatory requirements. SMD is named as a co-inventor on a Government-owned US Patent application related to the use of PDE3B inhibition for preventing cardiovascular disease filed by the US Department of Veterans Affairs in accordance with Federal regulatory requirements. Stefan Söderberg has received speaker honoraria and consulting fees from Actelion Ltd. George Thanassoulis has received consulting fees from Ionis Pharmaceuticals and has participated in advisory boards for Amgen, Sanofi, Novartis, HLS Therapeutics and Silence. Morten Salling Olesen has received 5.000.000 dkrfra Sundhedsdonationer.Journalnr. 2022-0243. David O. Arnar has received travel support from Pfizer to attend the ESC 2022 Scientific Meeting in Barcelona and has stock options in Sidekick Health Digital Therapeutics. Henning Bundgaard has received lecture fees from Amgen, MSD, Sanofi-Avensis, BMS and grants from NordForsk, Innovation Fond, Denmark, The Capital Regions Research Foundation. Alex Hoerby Christensen—Novo Nordisk Foundation NNF20OC0065799. Romaine Capoulade has received an Honorarium for one lecture from Novartis. Robert Clarke has received support from BAYER (China Kadoorie Biobank). Unnur Thorsteinsdottir’s research is funded by deCODE genetics/Amgen. Daniel F. Gudbjartsson receives funds from deCODE Genetics/Amgen. Until 1 June 2022, Gudmundur Thorgeirsson was a part time employee of deCode Genetics that is owned by Amgen. Hilma Holm is an employee of deCODE genetics/Amgen Inc. Anna Helgadottir is an employee of deCODE genetics/Amgen Inc., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Myocardial infarction is a leading cause of death worldwide 1 . Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . 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., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

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., (© 2021. The Author(s).)

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., Competing Interests: J. Beckman declares potential conflicts with Amgen, Bayer, Glaxo Smith Kline, Janssen, Novartis, and Sanofi in the past 12months. All other authors declare no conflicts of interest., (© 2021 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

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., (© 2019. The Author(s).)

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.

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., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

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.

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.

Importance: Aortic stenosis (AS) has no approved medical treatment. Identifying etiological pathways for AS could identify pharmacological targets., Objective: To identify novel genetic loci and pathways associated with AS., Design, Setting, and Participants: This genome-wide association study used a case-control design to evaluate 44 703 participants (3469 cases of AS) of self-reported European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort (from January 1, 1996, to December 31, 2015). Replication was performed in 7 other cohorts totaling 256 926 participants (5926 cases of AS), with additional analyses performed in 6942 participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Follow-up biomarker analyses with aortic valve calcium (AVC) were also performed. Data were analyzed from May 1, 2017, to December 5, 2019., Exposures: Genetic variants (615 643 variants) and polyunsaturated fatty acids (ω-6 and ω-3) measured in blood samples., Main Outcomes and Measures: Aortic stenosis and aortic valve replacement defined by electronic health records, surgical records, or echocardiography and the presence of AVC measured by computed tomography., Results: The mean (SD) age of the 44 703 GERA participants was 69.7 (8.4) years, and 22 019 (49.3%) were men. The rs174547 variant at the FADS1/2 locus was associated with AS (odds ratio [OR] per C allele, 0.88; 95% CI, 0.83-0.93; P = 3.0 × 10-6), with genome-wide significance after meta-analysis with 7 replication cohorts totaling 312 118 individuals (9395 cases of AS) (OR, 0.91; 95% CI, 0.88-0.94; P = 2.5 × 10-8). A consistent association with AVC was also observed (OR, 0.91; 95% CI, 0.83-0.99; P = .03). A higher ratio of arachidonic acid to linoleic acid was associated with AVC (OR per SD of the natural logarithm, 1.19; 95% CI, 1.09-1.30; P = 6.6 × 10-5). In mendelian randomization, increased FADS1 liver expression and arachidonic acid were associated with AS (OR per unit of normalized expression, 1.31 [95% CI, 1.17-1.48; P = 7.4 × 10-6]; OR per 5-percentage point increase in arachidonic acid for AVC, 1.23 [95% CI, 1.01-1.49; P = .04]; OR per 5-percentage point increase in arachidonic acid for AS, 1.08 [95% CI, 1.04-1.13; P = 4.1 × 10-4])., Conclusions and Relevance: Variation at the FADS1/2 locus was associated with AS and AVC. Findings from biomarker measurements and mendelian randomization appear to link ω-6 fatty acid biosynthesis to AS, which may represent a therapeutic target.

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Rationale: Red cell distribution width (RDW) is a prognostic factor in many diseases; however, its clinical utility remains limited because the relative value of RDW as a biomarker across disease states has not been established. Objectives: To establish an unbiased RDW disease hierarchy to guide the clinical use of RDW and to assess its relationship to cardiovascular hemodynamic and structural parameters. Methods: We performed phenome-wide association studies for RDW in discovery and replication cohorts derived from a deidentified electronic health record in nonanemic individuals. RDW values obtained within 30 days of echocardiogram or right heart catheterization were tested for association with structural and hemodynamic variables. Results: RDW was associated with 263 phenotypes in both men and women in the discovery cohort ( n  = 121,530), 48 of which replicated in an independent cohort ( n  = 2,039). The strongest associations were observed with pulmonary arterial hypertension (odds ratio [OR], 2.1; 95% confidence interval [CI], 1.9-2.3), chronic pulmonary heart disease (OR, 2.0; 95% CI, 1.9-2.2), and congestive heart failure (OR, 1.9; 95% CI, 1.8-2.0); P  < 1 × 10 -74 for all. By echocardiography, RDW was higher in the setting of right ventricular dysfunction than left ventricular dysfunction ( P  < 0.001). Measured invasively, mean pulmonary arterial pressure was associated with RDW (21 vs. 33 mm Hg at 25th vs. 75th percentile RDW; P  < 1 × 10 -7 ) and remained strongly significant even when controlling for mean pulmonary capillary wedge pressure (21 vs. 29 mm Hg at 25th vs. 75th percentile RDW; P  < 1 × 10 -7 ). Conclusions: Among 1,364 coded medical conditions, increased RDW was strongly associated with pulmonary hypertension and heart failure. Hemodynamic and echocardiographic phenotyping confirmed these associations and underscored that the most clinically relevant phenotype associated with RDW was pulmonary hypertension. These hypothesis-generating findings highlight the potential shared pathophysiology of pulmonary hypertension and elevated RDW. Elevated RDW in the absence of anemia should alert clinicians to the potential for underlying cardiopulmonary disease.

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.

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., Competing Interests: The authors have declared that no competing interests exist.

Diastolic dysfunction (DD), an abnormality in cardiac left ventricular (LV) chamber compliance, is associated with increased morbidity and mortality. Although DD has been extensively studied in older populations, co-morbidity patterns are less well characterized in middle-aged subjects. We screened 156,434 subjects with transthoracic echocardiogram reports available through Vanderbilt's electronic heath record and identified 6,612 subjects 40 to 55 years old with an LV ejection fraction ≥50% and diastolic function staging. We tested 452 incident and prevalent clinical diagnoses for associations with early-stage DD (n = 1,676) versus normal function. There were 44 co-morbid diagnoses associated with grade 1 DD including hypertension (odds ratio [OR] = 2.02, 95% confidence interval [CI] 1.78 to 2.28, p <5.3 × 10-29), type 2 diabetes (OR 1.96, 95% CI 1.68 to 2.29, p = 2.1 × 10-17), tachycardia (OR 1.38, 95% CI 0.53 to 2.19, p = 2.9 × 10-6), obesity (OR 1.76, 95% CI 1.51 to 2.06, p = 1.7 × 10-12), and clinical end points, including end-stage renal disease (OR 3.29, 95% CI 2.19 to 4.96, p = 1.2 × 10-8) and stroke (OR 1.5, 95% CI 1.12 to 2.02, p = 6.9 × 10-3). Among the 60 incident diagnoses associated with DD, heart failure with preserved ejection fraction (OR 4.63, 95% CI 3.39 to 6.32, p = 6.3 × 10-22) had the most significant association. Among subjects with normal diastolic function and blood pressure at baseline, a blood pressure measurement in the hypertensive range at the time of the second echocardiogram was associated with progression to stage 1 DD (p = 0.04). In conclusion, DD was common among subjects 40 to 55 years old and was associated with a heavy burden of co-morbid disease., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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.

Aim: Sympathetic activation suppresses insulin secretion via pancreatic ADRA2A.  Because sympathetic activity and insulin demand increase during pregnancy, we tested the hypothesis  that ADRA2A variants are associated with gestational diabetes (GDM)., Patients & Methods: Among Caucasian pregnant women without pre-existing diabetes, we genotyped 458 who had GDM and 1537 without GDM for seven ADRA2A variants., Results: rs1800038 (OR: 2.34; p = 0.020) and rs3750625 (OR: 1.56; p = 0.010) increased the risk of GDM, and rs11195418 decreased it (OR: 0.62; p = 0.025). The associations remained significant after adjustment for maternal age, maternal BMI, parity and a genetic risk score that included variants previously associated with Type 2 diabetes mellitus and GDM., Conclusion: ADRA2A genetic variation contributes independently to the risk of GDM in Caucasian women.

Objective: Gestational diabetes (GDM) is characterized by maternal glucose intolerance that manifests during pregnancy. Because GDM resembles type 2 diabetes (T2DM), shared genetic predisposition is likely but has not been established. We tested the hypothesis that a genetic risk score (GRS) that included variants known to be associated with T2DM is associated with GDM., Study Design: We conducted a case-control study using the Vanderbilt Medical Center biobank (BioVU) and calculated a simple-count GRS using 34 variants previously associated with T2DM or fasting glucose in the general population, or with GDM or glucose intolerance in pregnancy. We assessed the association of the GRS with GDM adjusting for maternal age, parity, and body mass index (BMI) and calculated the area under the curve for the receiver-operating characteristic curve (c-statistic)., Study Population: Among Caucasian women, we identified 458 cases of GDM and 1538 pregnant controls with normal glucose tolerance., Results: Cases of GDM had a higher number of risk alleles compared to controls (38.9±4.0 vs 37.4±4.0 risk alleles, P=1.6×10 -11 ). The GRS was significantly associated with GDM; the adjusted odds ratio associated with each additional risk allele was 1.10 (95% CI: 1.07-1.13, P=6×10 -11 ). Clinical variables predicted the risk of GDM (c-statistic 0.67, 95% CI: 0.64-0.70), and adding the GRS modestly improved prediction (0.70, 95% CI: 0.67-0.73)., Conclusions: Among Caucasian women, a GRS that included common T2DM genetic risk variants was associated with increased risk of GDM but showed limited utility in the identification of GDM cases., (© 2017 John Wiley & Sons Ltd.)

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.

The alpha 1B (α 1B )-adrenergic receptors contribute to vasoconstriction in humans. We tested the hypothesis that variation in the ADRA1B gene contributes to interindividual variability and ethnic differences in adrenergic vasoconstriction. We measured dorsal hand vein responses to increasing doses of phenylephrine in 64 Caucasians and 41 African Americans and genotyped 34 ADRA1B variants. We validated findings in another model of catecholamine-induced vasoconstriction, the increase in mean arterial pressure (ΔMAP) during a cold pressor test (CPT). One ADRA1B variant, rs10070745, present in 14 African-American heterozygotes but not in Caucasians, was associated with a lower phenylephrine ED 50 (geometric mean (95% confidence interval), 144 (69-299) ng ml -1 ) compared with 27 African-American non-carriers (208 (130-334) ng ml -1 ; P=0.015) and contributed to the ethnic differences in ED 50 . The same variant was also associated with a greater ΔMAP during CPT (P=0.008). In conclusion, ADRA1B rs10070745 was significantly associated with vasoconstrictor responses after adrenergic stimulation and contributed to the ethnic difference in phenylephrine sensitivity.

Objective: Blood type A and the A1 allele have been associated with increased ovarian cancer risk. With only two small studies published to date, evidence for an association between ABO blood type and ovarian cancer survival is limited., Methods: We conducted a retrospective cohort study of Tumor Registry confirmed ovarian cancer cases from the Vanderbilt University Medical Center with blood type from linked laboratory reports and ABO variants from linked Illumina Exome BeadChip data. Associations with overall survival (OS) were quantified by hazard ratios (HR) and confidence intervals (CI) from proportional hazards regression models; covariates included age, race, stage, grade, histologic subtype, and year of diagnosis., Results: ABO phenotype (N = 694) and/or genotype (N = 154) data were available for 713 predominantly Caucasian (89.3%) cases. In multivariable models, blood type A had significantly better OS compared to either O (HR: 0.75, 95% CI: 0.60-0.93) or all non-A (HR: 0.77, 95% CI: 0.63-0.94) cases. Similarly, missense rs1053878 minor allele carriers (A2) had better OS (HR: 0.50, 95% CI: 0.25-0.99). Among Caucasians, this phenotype association was strengthened, but the genotype association was attenuated; instead, four variants sharing moderate linkage disequilibrium with the O variant were associated with better OS (HR: 0.62, 95% CI: 0.39-0.99) in unadjusted models., Conclusions: Blood type A was significantly associated with longer ovarian cancer survival in the largest such study to date. This finding was supported by genetic analysis, which implicated the A2 allele, although O related variants also had suggestive associations. Further research on ABO and ovarian cancer survival is warranted.

Statins (HMG-CoA reductase inhibitors) lower low-density lipoprotein cholesterol (LDL-C) and prevent cardiovascular disease. However, there is wide individual variation in LDL-C response. Drugs targeting proprotein convertase subtilin/kexin type 9 (PCSK9) lower LDL-C and will be used with statins. PCSK9 mediates the degradation of LDL receptors (LDLRs). Therefore, a greater LDL-C response to statins would be expected in individuals with PCSK9 loss-of-function (LOF) variants because LDLR degradation is reduced. To examine this hypothesis, the effect of 11 PCSK9 functional variants on statin response was determined in 669 African Americans. One LOF variant, rs11591147 (p.R46L) was significantly associated with LDL-C response to statin (P=0.002). In the three carriers, there was a 55.6% greater LDL-C reduction compared with non-carriers. Another functional variant, rs28362261 (p.N425S), was marginally associated with statin response (P=0.0064).The effect of rs11591147 was present in individuals of European ancestry (N=2388, P=0.054). The therapeutic effect of statins may be modified by genetic variation in PCSK9.

Greater genetic variability in an individual is protective against recessive disease. However, existing quantifications of autozygosity, such as runs of homozygosity (ROH), have proved highly sensitive to genotyping density and have yielded inconclusive results about the relationship of diversity and disease risk. Using genotyping data from three data sets with >43,000 subjects, we demonstrated that an alternative approach to quantifying genetic variability, the heterozygosity ratio, is a robust measure of diversity and is positively associated with the nondisease trait height and several disease phenotypes in subjects of European ancestry. The heterozygosity ratio is the number of heterozygous sites in an individual divided by the number of nonreference homozygous sites and is strongly affected by the degree of genetic admixture of the population and varies across human populations. Unlike quantifications of ROH, the heterozygosity ratio is not sensitive to the density of genotyping performed. Our results establish the heterozygosity ratio as a powerful new statistic for exploring the patterns and phenotypic effects of different levels of genetic variation in populations., (Copyright © 2016 by the Genetics Society of America.)

Background.  Herpes zoster, or shingles, is a common, painful reactivation of latent varicella zoster virus infection. Understanding host factors that predispose to herpes zoster may permit development of more effective prevention strategies. Our objective was to examine mitochondrial haplogroups as a potential host factor related to herpes zoster incidence. Methods.  Study participants were drawn from BioVU, a deoxyribonucleic acid (DNA) biobank connected to deidentified electronic medical records (EMRs) from Vanderbilt University Medical Center. Our study used 9691 Caucasian individuals with herpes zoster status determined by International Classification of Diseases, Ninth Revision codes 053-053.9. Cases and controls were matched on sex and date of birth within 5 years. Mitochondrial haplogroups were defined from mitochondrial DNA variants genotyped on the Illumina 660W or Illumina Infinium Human-Exome Beadchip. Sex and date of birth were extracted from the EMR. Results.  European mitochondrial haplogroup H had a protective association with herpes zoster status (odds ratio [OR] = .82; 95% confidence interval [CI], .71-.94; P = .005), whereas haplogroup clade IWX was a risk factor for herpes zoster status (OR = 1.38; 95% CI, 1.07-1.77; P = .01). Conclusions.  Mitochondrial haplogroup influences herpes zoster risk. Knowledge of a patient's mitochondrial haplogroup could allow for a precision approach to the management of herpes zoster risk through vaccination strategies and management of other modifiable risk factors.

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., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

Objective: We explored associations between mitochondrial DNA (mtDNA) haplogroups, epidermal nerve fiber density (ENFD), and HIV-associated sensory neuropathy (HIV-SN) in a randomized trial of Thai patients initiating antiretroviral therapy (ART)., Design: The South East Asia Research Collaboration with Hawaii 003 study evaluated toxicity of nucleoside reverse transcriptase inhibitors (stavudine vs. zidovudine vs. tenofovir). We present secondary analyses of mtDNA haplogroups and ENFD changes., Methods: ENFD, peripheral blood mononuclear cell mitochondrial complex I and IV, and 8-oxo-deoxyguanine (8-oxo-dG) were quantified. Peripheral blood mononuclear cell mtDNA sequences were obtained for haplogroup determination. Multivariate regression of ENFD change was performed., Results: Paired ENFD was available from 118 patients. Median age, CD4 cell count, and height at entry were 34 years, 172 cells/μl, and 162 cm, respectively. Major haplogroups included M (42%), F (21%), and B (16%). Baseline ENFD, CD4 cell count, randomized ART, and biomarkers did not differ by haplogroup. Haplogroup B patients were older (P=0.02) at baseline, and had an increase in median ENFD (+1.5 vs. -2.9 fibers/mm; P=0.03) and 8-oxo-dG break frequency (+0.05 vs. 0.00; P=0.05) compared to other haplogroups. In a multivariate model, haplogroup B was associated with increased ENFD (β=3.5, P=0.009) at week 24, whereas older age (P=0.02), higher baseline CD4 cell count, (P=0.03), higher complex I level (P=0.03), and higher ENFD (P<0.001) at baseline were all associated with decreased ENFD. Three of the six HIV-SN cases were haplogroup B (P=0.05)., Conclusions: Thai persons belonging to mtDNA haplogroup B had increased ENFD and 8-oxo-dG on ART, and were more likely to develop HIV-SN. These results suggest that mtDNA variation influences early oxidative damage and ENFD changes.

Light chain (AL) amyloidosis is characterized by the misfolding of immunoglobulin light chains, accumulating as amyloid fibrils in vital organs. Multiple reports have indicated that amyloidogenic light chains internalize into a variety of cell types, but these studies used urine-derived proteins without indicating any protein sequence information. As a result, the role of somatic mutations in amyloidogenic protein internalization has not been yet studied. We characterized the internalization of AL-09, an AL amyloidosis protein into mouse cardiomyocytes. We also characterized the internalization of the germline protein κI O18/O8, devoid of somatic mutations, and three AL-09 restorative mutations (I34N, Q42K, and H87Y) previously characterized for their role in protein structure, stability, and amyloid formation kinetics. All proteins shared a common internalization pathway into lysosomal compartments. The proteins caused different degrees of lysosomal expansion. Oregon green (OG) labeled AL-09 showed the most rapid internalization, while OG-Q42K presented the slowest rate of internalization.