Metabolomic profiling identifies novel metabolites associated with cardiac dysfunction.

Metabolic comorbidities, such as obesity and diabetes, are associated with subclinical alterations in both cardiac structure/function and natriuretic peptides prior to the onset of heart failure (HF). Despite this, the exact metabolic pathways of cardiac dysfunction which precede HF are not well-defined. Among older individuals without HF in the Multi-Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of 47 circulating metabolites measured by 1H-NMR with echocardiographic measures of cardiac structure and function. We then evaluated associations of significant metabolites with circulating N-terminal pro-B-type natriuretic peptide (NT-proBNP). In a separate cohort, we evaluated differences between top metabolites in patients with HF with preserved ejection fraction (HFpEF) and comorbidity-matched controls. Genetic variants associated with top metabolites (mQTLs) were then related to echocardiographic measures and NT-proBNP. Among 3440 individuals with metabolic and echocardiographic data in MESA (62 ± 10 years, 52% female, 38% White), 10 metabolites broadly reflective of glucose and amino acid metabolism were associated with at least 1 measure of cardiac structure or function. Of these 10 metabolites, 4 (myo-inositol, glucose, dimethylsulfone, carnitine) were associated with higher NT-proBNP and 2 (d-mannose, acetone) were associated with lower NT-proBNP. In a separate cohort, patients with HFpEF had higher circulating myo-inositol levels compared with comorbidity-matched controls. Genetic analyses revealed that 1 of 6 known myo-inositol mQTLs conferred risk of higher NT-proBNP. In conclusion, metabolomic profiling identifies several novel metabolites associated with cardiac dysfunction in a cohort at high risk for HF, revealing pathways potentially relevant to future HF risk.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts. Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs. Methods: An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa. Results: The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78. Conclusion: Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Eicosapentaenoic acid and Arachidonic acid Protection Against Left Ventricle Pathology: the Multi-Ethnic Study of Atherosclerosis.

Background: We have shown that ω3 polyunsaturated fatty acids (PUFAs) reduce risk for heart failure, regardless of ejection fraction status. Ventricular remodeling and reduced ventricular performance precede overt hear failure, however there is little insight into how PUFAs contribute to maladaptive signaling over time. PUFAs are agonists for regulatory activity at g-protein coupled receptors such as Ffar4, and downstream as substrates for monooxygenases (e.g lipoxygenase, cytochrome p450, or cyclooxygenase (COX)) which mediate intracellular adaptive signaling. Methods: Plasma phospholipid PUFA abundance at Exam 1 as mass percent EPA, DHA, and arachidonic acid (AA) from the Multi-Ethnic Study of Atherosclerosis (MESA) were evaluated using pathway modeling to determine the association with time-dependent changes in left ventricular (LV) mass (LVM), end-diastolic LV volume (EDV), and end-systolic volume (ESV) measured by cardiac MRI at Exams 1 and 5. Ejection fraction (EF) and mass:volume (MV) were calculated posteriorly from the first three. Results: 2,877 subjects had available MRI data. Participants with low AA and EPA had accelerated age-dependent declines in LVM. Males with low AA and EPA also had accelerated declines in EDV, but among females there was no PUFA association with EDV declines and exam 5 EDV status was positively associated with AA. Both sexes had nearly the same positive association of AA with changes in ESV. Conclusion: Plasma phospholipid AA and EPA are prospectively associated with indices of heart remodeling, including ventricular remodeling and performance. Combined AA and EPA scarcity was associated with the most accelerated age-related changes and exam 5 status, while the greatest benefits were found among participants with both PUFAs. This suggests that both PUFAs are required for optimal slowing of age-related declines in ventricular function.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

Relationship Between Sarcopenia and Intensive Blood Pressure Control Efficacy and Safety: A Secondary Analysis of SPRINT.

BACKGROUND: Sarcopenia and hypertension are independently associated with worse cardiovascular disease (CVD) risk and survival. While individuals with sarcopenia may benefit from intensive blood pressure (BP) control, the increased vulnerability of this population raises concerns for potential harm. This study aimed to evaluate clinical and safety outcomes with intensive (target <120 mm Hg) versus standard (<140 mm Hg) systolic BP targets in older hypertensive adults with sarcopenia compared with nonsarcopenic counterparts in the SPRINT (Systolic Blood Pressure Intervention Trial). METHODS: Sarcopenia was defined using surrogates of the lowest sex-stratified median of the sarcopenia index (serum creatinine/cystatin C×100) for muscle wasting and gait speed ≤0.8 m/s for muscle weakness. Outcomes included CVD events, all-cause mortality, and serious adverse events. RESULTS: Of 2571 SPRINT participants with sarcopenia index and gait speed data available (aged ≥75 years), 502 (19.5%) met the criteria for sarcopenia, which was associated with higher risks of CVD events (adjusted hazard ratio, 1.49 [95% CI, 1.15-1.94]; P=0.003) and all-cause mortality (adjusted hazard ratio, 1.46 [95% CI, 1.09-1.94]; P=0.010). In participants with sarcopenia, intensive (versus standard) BP control nearly halved the risk of CVD events (adjusted hazard ratio, 0.57 [95% CI, 0.36-0.88]; P=0.012) without increasing serious adverse events. Similar risk reduction was seen for all-cause mortality in participants with sarcopenia (adjusted hazard ratio, 0.66 [95% CI, 0.41-1.08]; P=0.102), but the effect was only significant in those without chronic kidney disease. CONCLUSIONS: Older hypertensive adults with sarcopenia randomized to intensive BP control experienced a lower risk of CVD without increased adverse events compared with standard BP control. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01206062.

LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes.

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians.

BACKGROUND: South Asians are at higher risk for type 2 diabetes (T2D) than many other race/ethnic groups. Ectopic adiposity, specifically hepatic steatosis and visceral fat may partially explain this. Our objective was to derive metabolite risk scores for ectopic adiposity and assess associations with incident T2D in South Asians. METHODS: We examined 550 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 years without known cardiovascular disease or T2D and with metabolomic data. Computed tomography scans at baseline assessed hepatic attenuation and visceral fat area, and fasting serum specimens at baseline and after 5 years assessed T2D. LC-MS-based untargeted metabolomic analysis was performed followed by targeted integration and reporting of known signals. Elastic net regularized linear regression analyses was used to derive risk scores for hepatic steatosis and visceral fat using weighted coefficients. Logistic regression models associated metabolite risk score and incident T2D, adjusting for age, gender, study site, BMI, physical activity, diet quality, energy intake and use of cholesterol-lowering medication. RESULTS: Average age of participants was 55 years, 36% women with an average body mass index (BMI) of 25 kg/m2 and 6% prevalence of hepatic steatosis, with 47 cases of incident T2D at 5 years. There were 445 metabolites of known identity. Of these, 313 metabolites were included in the MET-Visc score and 267 in the MET-Liver score. In most fully adjusted models, MET-Liver (OR 2.04 [95% CI 1.38, 3.03]) and MET-Visc (OR 2.80 [1.75, 4.46]) were associated with higher odds of T2D. These associations remained significant after adjustment for measured adiposity. CONCLUSIONS: Metabolite risk scores for intrahepatic fat and visceral fat were strongly related to incident T2D independent of measured adiposity. Use of these biomarkers to target risk stratification may help capture pre-clinical metabolic abnormalities.

Assessment of Prevalence, Clinical Characteristics, and Risk Factors Associated With "Low Flow State" Using Cardiac Magnetic Resonance.

Objective: To assess prevalence, clinical characteristics, and risk factors associated with low flow state (LFS) in a multiethnic population with normal left ventricular ejection fraction (LVEF). Patients and Methods: The study included 4398 asymptomatic participants undergoing cardiac magnetic resonance from July 17, 2000, to August 29, 2002. Left ventricular (LV) mass, volume, and myocardial contraction fraction were assessed. Low flow state was defined as stroke volume index (SVi of <35 mL/m2). Clinical characteristics, cardiac risk factors, and cardiac magnetic resonance findings were compared between LFS and normal flow state (NFS) groups (NFS: SVi of ≥35 mL/m2). Results: There were significant differences in the prevalence of LFS in different ethnic groups. Individuals with LFS were older (66±9.6 vs 61±10 years; P<.0001). The prevalence of LFS was 19% in the group aged older than 70 years. The logistic multivariable regression analysis found that age was independently associated with LFS. The LFS group had significantly higher prevalence of diabetes (30% vs 24%; P=.001), LV mass-volume ratio (1.13±0.22 vs 0.91±0.15; P<.0001), inflammatory markers, a lower LV mass index (59±10 vs 65±11 kg/m2; P<.001), lower myocardial contraction fraction (58.1±10.6% vs 75.7±13%; P<.001), and a lower left atrial size index (32.2±4.6 vs 36.7±5.9 mm/m2; P<.0001) than NFS. Conclusion: Low flow state may be considered an under-recognized clinical entity associated with increasing age, multiple risk factors, increased inflammatory markers, a lower LV mass index, and suboptimal myocardial performance despite the presence of normal LVEF and absence of valvular disease.

Association of lipoprotein subfractions with incidence of type 2 diabetes among five U.S. Race and Ethnic groups: The Mediators of Atherosclerosis in South Asians Living in America (MASALA) and Multi-Ethnic study of Atherosclerosis (MESA).

AIMS: We examined associations between lipoprotein subfractions and prevalent and incident T2D in two race/ethnically diverse cohort studies. METHODS: Adults self-identifying as White, Black, Chinese, Hispanic and South Asian-American without cardiovascular disease, with fasting serum, demographic, and clinical data at enrollment and after 5 years of follow-up were included. Lipoprotein subfractions were measured at enrollment using NMR spectrometry. LASSO regularized logistic regression models adjusted for age, sex, race/ethnicity, lipid-lowering agent use, and waist circumference assessed odds of incident T2D in pooled analyses. RESULTS: There were 4474 participants with lipoprotein subfraction data at enrollment and 3839 participants without prevalent diabetes, mean age 62 years, 51 % women, with 234 incident T2D cases at 5 years. Triglycerides in small, dense LDL-5 [OR 1.26 (95 % CI 1.11,1.43)], VLDL triglycerides 1.30** [1.16,1.46] and phospholipids in VLDL-1 [OR 1.31 (1.17,1.47)] were associated with higher odds of incident T2D, while free cholesterol in large HDL-1 [OR 0.75 (95 % CI 0.63,0.89)] was inversely associated. The results were similar for prevalent diabetes and did not vary by race/ethnic group. CONCLUSIONS: Composition of lipoprotein subfractions is differentially associated with prevalent and incident T2D without difference by race/ethnic group. Assessment of lipoprotein composition may enhance targeted risk reduction for T2D.

CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.

Clinical heterogeneity remains a challenge in the practice of medicine and is an underlying motivation for much of biomedical research. Unfortunately, despite an abundance of technologies capable of producing millions of discrete data elements with information about a patient's health status or disease prognosis, our ability to translate those data into meaningful improvements in understanding of clinical heterogeneity is limited. To address this gap, we have applied newer approaches to manifold learning and developed additional and complementary techniques to interrogate and interpret complex, high dimensional omics data. The central premise is that there exist manifolds embedded in high dimensional data that represent fundamental biologic processes that may help address the challenges of clinical heterogeneity. Preliminary evidence from several real-world data sets suggests that these techniques can identify coherent and reproducible manifolds embedded in higher dimensional omics data. Work is currently ongoing to determine the clinical informativeness of these novel data structures.

Untargeted metabolomic analysis investigating links between unprocessed red meat intake and markers of inflammation.

BACKGROUND: Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships. OBJECTIVE: To identify whether any metabolites associated with red meat intake are also associated with inflammation. METHODS: A cross-sectional analysis of observational data from older adults (52.84% women, mean age 63 ± 0.3 y) participating in the Multi-Ethnic Study of Atherosclerosis (MESA). Dietary intake was assessed by food-frequency questionnaire, alongside C-reactive protein (CRP), interleukin-2, interleukin-6, fibrinogen, homocysteine, and tumor necrosis factor alpha, and untargeted proton nuclear magnetic resonance (1H NMR) metabolomic features. Associations between these variables were examined using linear regression models, adjusted for demographic factors, lifestyle behaviors, and body mass index (BMI). RESULTS: In analyses that adjust for BMI, neither processed nor unprocessed forms of red meat were associated with any markers of inflammation (all P > 0.01). However, when adjusting for BMI, unprocessed red meat was inversely associated with spectral features representing the metabolite glutamine (sentinel hit: ß = -0.09 ± 0.02, P = 2.0 × 10-5), an amino acid which was also inversely associated with CRP level (ß = -0.11 ± 0.01, P = 3.3 × 10-10). CONCLUSIONS: Our analyses were unable to support a relationship between either processed or unprocessed red meat and inflammation, over and above any confounding by BMI. Glutamine, a plasma correlate of lower unprocessed red meat intake, was associated with lower CRP levels. The differences in diet-inflammation associations, compared with diet metabolite-inflammation associations, warrant further investigation to understand the extent that these arise from the following: 1) a reduction in measurement error with metabolite measures; 2) the extent that which factors other than unprocessed red meat intake contribute to glutamine levels; and 3) the ability of plasma metabolites to capture individual differences in how food intake is metabolized.

Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Avocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes. OBJECTIVES: To identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia. METHODS: Baseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an ∼18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined. RESULTS: Three highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 ∗ 10-7) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (ß = -0.07 +/- 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m2). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (ß = -0.22 +/- 0.02, P < 2.0 ∗ 10-16), lower fasting insulin (ß = -0.17 +/- 0.02, P < 2.0 ∗ 10-16), and a lower incidence of T2D (hazard ratio: 0.68; 0.63-074, P < 2.0 ∗ 10-16), even when adjusting for BMI. CONCLUSIONS: Highly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.

Metabolomic Profiling of Cholesterol Efflux Capacity in a Multiethnic Population: Insights From MESA.

BACKGROUND: Impaired cholesterol efflux capacity (CEC) is a novel lipid metabolism trait associated with atherosclerotic cardiovascular disease. Mechanisms underlying CEC variation are unknown. We evaluated associations of circulating metabolites with CEC to advance understanding of metabolic pathways involved in cholesterol efflux regulation. METHODS: Participants enrolled in the MESA (Multi-Ethnic Study of Atherosclerosis) who underwent nuclear magnetic resonance metabolome profiling and CEC measurement (N=3543) at baseline were included. Metabolite associations with CEC were evaluated using standard linear regression analyses. Repeated ElasticNet and multilayer perceptron regression were used to assess metabolite profile predictive performance for CEC. Features important for CEC prediction were identified using Shapley Additive Explanations values. RESULTS: Greater CEC was significantly associated with metabolite clusters composed of the largest-sized particle subclasses of VLDL (very-low-density lipoprotein) and HDL (high-density lipoprotein), as well as their constituent apo A1, apo A2, phospholipid, and cholesterol components (ß=0.072-0.081; P<0.001). Metabolite profiles had poor accuracy for predicting in vitro CEC in linear and nonlinear analyses (R2<0.02; Spearman ρ<0.18). The most important feature for CEC prediction was race, with Black participants having significantly lower CEC compared with other races. CONCLUSIONS: We identified independent associations among CEC, the largest-sized particle subclasses of VLDL and HDL, and their constituent apolipoproteins and lipids. A large proportion of variation in CEC remained unexplained by metabolites and traditional clinical risk factors, supporting further investigation into genomic, proteomic, and phospholipidomic determinants of CEC.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. Results: We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementation: The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

Implications of Social Disadvantage Score in Cardiovascular Outcomes and Risk Assessment: Findings From the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Social determinants of health contribute to disparate cardiovascular outcomes, yet they have not been operationalized into the current paradigm of cardiovascular risk assessment. METHODS: Data from the Multi-Ethnic Study of Atherosclerosis, which includes participants from 6 US field centers, were used to create an index of baseline Social Disadvantage Score (SDS) to explore its association with incident atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality and impact on ASCVD risk prediction. SDS, which ranges from 0 to 4, was calculated by tallying the following social factors: (1) household income less than the federal poverty level; (2) educational attainment less than a high school diploma; (3) single-living status; and (4) experience of lifetime discrimination. Cox models were used to examine the association between SDS and each outcome with adjustment for traditional cardiovascular risk factors. Changes in the discrimination and reclassification of ASCVD risk by incorporating SDS into the pooled cohort equations were examined. RESULTS: A total of 6434 participants (mean age, 61.9±10.2 years; female 52.8%; non-white 60.9%) had available SDS 1733 (26.9%) with SDS 0; 2614 (40.6%) with SDS 1; 1515 (23.5%) with SDS 2; and 572 (8.9%) with SDS ≥3. In total, 775 incident ASCVD events and 1573 deaths were observed over a median follow-up of 17.0 years. Increasing SDS was significantly associated with incident ASCVD and all-cause mortality after adjusting for traditional risk factors (ASCVD: per unit increase in SDS hazard ratio, 1.15 [95% CI, 1.07-1.24]; mortality: per unit increase in SDS hazard ratio, 1.13 [95% CI, 1.08-1.19]). Adding SDS to pooled cohort equations components in a Cox model for 10-year ASCVD risk prediction did not significantly improve discrimination (P=0.208) or reclassification (P=0.112). CONCLUSIONS: Although SDS is independently associated with incident ASCVD and all-cause mortality, it does not improve 10-year ASCVD risk prediction beyond pooled cohort equations.

Metabolomic Profile of the Healthy Eating Index-2015 in the Multiethnic Study of Atherosclerosis.

BACKGROUND: Poor diet quality is a risk factor for type 2 diabetes and cardiovascular disease. However, knowledge of metabolites marking adherence to Dietary Guidelines for Americans (2015 version) are limited. OBJECTIVES: The goal was to determine a pattern of metabolites associated with the Healthy Eating Index (HEI)-2015, which measures adherence to the Dietary Guidelines for Americans. METHODS: The analysis examined 3557 adult men and women from the longitudinal cohort Multiethnic Study of Atherosclerosis (MESA), without known cardiovascular disease and with complete dietary data. Fasting serum specimens and diet and demographic questionnaires were assessed at baseline. Untargeted 1H 1-dimensional nuclei magnetic resonance spectroscopy (600 MHz) was used to generate metabolomics and lipidomics. A metabolome-wide association study specified each spectral feature as outcomes, HEI-2015 score as predictor, adjusting for age, sex, race, and study site in linear regression analyses. Subsequently, hierarchical clustering defined the discrete groups of correlated nuclei magnetic resonance features associated with named metabolites, and the linear regression analysis assessed for associations with HEI-2015 total and component scores. RESULTS: The sample included 50% women with an mean age of 63 years, with 40% identifying as White, 23% as Black, 24% as Hispanic, and 13% as Chinese American. The mean HEI-2015 score was 66. The metabolome-wide association study identified 179 spectral features significantly associated with HEI-2015 score. The cluster analysis identified 7 clusters representing 4 metabolites; HEI-2015 score was significantly associated with all. HEI-2015 score was associated with proline betaine [ß = 0.12 (SE = 0.02); P = 4.70 × 10-13] and was inversely related to proline [ß = -0.13 (SE = 0.02); P = 4.45 × 10-14], 1,5 anhydrosorbitol [ß = -0.08 (SE = 0.02); P = 4.37 × 10-7] and unsaturated fatty acyl chains [ß = 0.08 (SE = 0.02); P = 8.98 × 10-7]. Intake of total fruit, whole grains, and seafood and plant proteins was associated with proline betaine. CONCLUSIONS: Diet quality is significantly associated with unsaturated fatty acyl chains, proline betaine, and proline. Further analysis may clarify the link between diet quality, metabolites, and pathogenesis of cardiometabolic disease.