Disparities by Sex, Race, and Ethnicity in Use of Left Ventricular Assist Devices and Heart Transplants Among Patients With Heart Failure With Reduced Ejection Fraction.

BACKGROUND: The extent to which sex, racial, and ethnic groups receive advanced heart therapies equitably is unclear. We estimated the population rate of left ventricular assist device (LVAD) and heart transplant (HT) use among (non-Hispanic) White, Hispanic, and (non-Hispanic) Black men and women who have heart failure with reduced ejection fraction (HFrEF). METHODS AND RESULTS: We used a retrospective cohort design combining counts of LVAD and HT procedures from 19 state inpatient discharge databases from 2010 to 2018 with counts of adults with HFrEF. Our primary outcome measures were the number of LVAD and HT procedures per 1000 adults with HFrEF. The main exposures were sex, race, ethnicity, and age. We used Poisson regression models to estimate procedure rates adjusted for differences in age, sex, race, and ethnicity. In 2018, the estimated population of adults aged 35 to 84 years with HFrEF was 69 736, of whom 44% were women. Among men, the LVAD rate was 45.6, and the HT rate was 26.9. Relative to men, LVAD and HT rates were 72% and 62% lower among women (P<0.001). Relative to White men, LVAD and HT rates were 25% and 46% lower (P<0.001) among Black men. Among Hispanic men and women and Black women, LVAD and HT rates were similar (P>0.05) or higher (P<0.01) than among their White counterparts. CONCLUSIONS: Among adults with HFrEF, the use of LVAD and HT is lower among women and Black men. Health systems and policymakers should identify and ameliorate sources of sex and racial inequities.

COT: an efficient and accurate method for detecting marker genes among many subtypes.

Motivation: Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results: We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods. Availability and implementation: The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Comparative assessment and novel strategy on methods for imputing proteomics data.

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy-convex analysis of mixtures-for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes.

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

QRS duration is associated with all-cause mortality in type 2 diabetes: The diabetes heart study.

BACKGROUND: QRS-duration predicts mortality in patients with heart failure and, to a lesser extent, the general population. However, in patients with diabetes, its prognostic significance is unknown. To better understand how QRS-duration relates to mortality among those with diabetes, we explored survival as a function of QRS-duration in the Diabetes Heart Study. METHODS: The study population included 1335 participants. Cox proportional hazards modeling was used to evaluate the relationship between QRS-duration and all-cause mortality, comparing those with QRS-duration ≤120 vs. >120 (ms). Multivariable models adjusted for age, sex, race, hypertension, smoking, years with diabetes, BMI, systolic blood pressure, cholesterol, triglycerides, glomerular filtration rate, and hemoglobin A1c. RESULTS AND CONCLUSIONS: Participants were: mean age 61 ± 9, 55% women, 83% white; 99 participants (7.5%) had a QRS-duration >120. After 11,000 person-years of follow-up (median 8.5 years; maximum 13.9 years), 266 participants had died (20%). Participants with baseline QRS-duration >120 had an adjusted hazard ratio for all-cause mortality of 1.56 (95% CI 1.05-2.24; p = 0.027). Modeling QRS-duration as a continuous variable, we found an 11% increase in all-cause mortality for each 10 ms increase in QRS-duration. In conclusion, QRS-duration is associated with subsequent all-cause mortality among those with type 2 diabetes-participants with QRS-duration >120 ms had a 56% increase in all-cause mortality, even after adjustment for conventional risk factors. Given the ubiquitous presence of ECG data in the medical record, QRS-duration may prove to be a useful prognostic measure, especially among those with diabetes.

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis.

BACKGOUND: The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. METHODS: Here we describe the human arterial proteome and proteomic features strongly associated with early atherosclerosis based on mass spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling, and bioinformatic analyses defined the composition, network rewiring, and likely regulatory features of the protein networks associated with early atherosclerosis and how they vary across 2 anatomic distributions. RESULTS: The data document significant differences in mitochondrial protein abundance between coronary and aortic samples (coronary>>aortic), and between atherosclerotic and normal tissues (atherosclerotic<

Heart rate is an independent predictor of all-cause mortality in individuals with type 2 diabetes: The diabetes heart study.

AIM: To assess the association of resting heart rate with all-cause and cardiovascular disease (CVD) mortality in the Diabetes Heart Study (DHS). METHODS: Out of a total of 1443 participants recruited into the DHS, 1315 participants with type 2 diabetes who were free of atrial fibrillation and supraventricular tachycardia during the baseline exam were included in this analysis. Heart rate was collected from baseline resting electrocardiogram and mortality (all-cause and CVD) was obtained from state and national death registry. Kaplan-Meier (K-M) and Cox proportional hazard analyses were used to assess the association. RESULTS: The mean age, body mass index (BMI) and systolic blood pressure (SBP) of the cohort were 61.4 ± 9.2 years, 32.0 ± 6.6 kg/m2, and 139.4 ± 19.4 mmHg respectively. Fifty-six percent were females, 85% were whites, 15% were blacks, 18% were smokers. The mean ± SD heart rate was 69.8 (11.9) beats per minute (bpm). After a median follow-up time of 8.5 years (maximum follow-up time is 14.0 years), 258 participants were deceased. In K-M analysis, participants with heart rate above the median had a significantly higher event rate compared with those below the median (log-rank P = 0.0223). A one standard deviation increase in heart rate was associated with all-cause mortality in unadjusted (hazard ratio 1.16, 95%CI: 1.03-1.31) and adjusted (hazard ratio 1.20, 95%CI: 1.05-1.37) models. Similar results were obtained with CVD mortality as the outcome of interest. CONCLUSION: Heart rate is an independent predictor of all-cause mortality in this population with type 2 diabetes. In this study, a 1-SD increase in heart rate was associated with a 20% increase in risk suggesting that additional prognostic information may be gleaned from this ubiquitously collected vital sign.

Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis.

OBJECTIVE: Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for ß-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants. RESEARCH DESIGN AND METHODS: We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes. RESULTS: We observed a significant association of total zinc intake with lower fasting glucose levels (ß-coefficient ± SE per 1 mg/day of zinc intake: -0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (ß-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant. CONCLUSIONS: Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.

OBJECTIVE: Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS: Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. RESULTS: Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (ß [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. CONCLUSIONS: Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.