Metabolomic profiling during ex situ normothermic perfusion before heart transplantation defines patterns of substrate utilization and correlates with markers of allograft injury

BACKGROUND

Cardiac metabolism is altered in heart failure and ischemia-reperfusion injury states. We hypothesized that metabolomic profiling during ex situ normothermic perfusion before heart transplantation (HT) would lend insight into myocardial substrate utilization and report on subclinical and clinical allograft dysfunction risk.

METHODS

Metabolomic profiling was performed on serial samples of ex situ normothermic perfusate assaying biomarkers of myocardial injury in lactate and cardiac troponin I (TnI) as well as metabolites (66 acylcarnitines, 15 amino acids, nonesterified fatty acids [NEFA], ketones, and 3-hydroxybutyrate). We tested for change over time in injury biomarkers and metabolites, along with differential changes by recovery strategy (donation after circulatory death [DCD] vs donation after brain death [DBD]). We examined associations between metabolites, injury biomarkers, and primary graft dysfunction (PGD). Analyses were performed using linear mixed models adjusted for recovery strategy, assay batch, donor-predicted heart mass, and time.

RESULTS

A total of 176 samples from 92 ex situ perfusion runs were taken from donors with a mean age of 35 (standard deviation 11.3) years and a median total ex situ perfusion time of 234 (interquartile range 84) minutes. Lactate trends over time differed significantly by recovery strategy, while TnI increased during ex situ perfusion regardless of DCD vs DBD status. We found fuel substrates were rapidly depleted during ex situ perfusion, most notably the branched-chain amino acids leucine/isoleucine, as well as ketones, 3-hydroxybutyrate, and NEFA (least squares [LS] mean difference from the first to last time point -1.7 to -4.5, false discovery rate q < 0.001). Several long-chain acylcarnitines (LCAC), including C16, C18, C18:1, C18:2, C18:3, C20:3, and C20:4, increased during the perfusion run (LS mean difference 0.42-0.67, q < 0.001). Many LCACs were strongly associated with lactate and TnI. The change over time of many LCACs was significantly different for DCD vs DBD, suggesting differential trends in fuel substrate utilization by ischemic injury pattern. Changes in leucine/isoleucine, arginine, C12:1-OH/C10:1-DC, and C16-OH/C14-DC were associated with increased odds of moderate-severe PGD. Neither end-of-run nor change in lactate or TnI was associated with PGD.

CONCLUSIONS

Metabolomic profiling of ex situ normothermic perfusion solution reveals a pattern of fuel substrate utilization that correlates with subclinical and clinical allograft dysfunction. This study highlights a potential role for interventions focused on fuel substrate modification in allograft conditioning during ex situ perfusion to improve allograft outcomes.

Associations among NMR-measured inflammatory and metabolic biomarkers and accelerated aging in cardiac catheterization patients

Research into aging has grown substantially with the creation of molecular biomarkers of biological age that can be used to determine age acceleration. Concurrently, nuclear magnetic resonance (NMR) assessment of biomarkers of inflammation and metabolism provides researchers with new ways to examine intermediate risk factors for chronic disease. We used data from a cardiac catheterization cohort to examine associations between biomarkers of cardiometabolic health and accelerated aging assessed using both gene expression (Transcriptomic Age) and DNA methylation (Hannum Age, GrimAge, Horvath Age, and Phenotypic Age). Linear regression models were used to associate accelerated aging with each outcome (cardiometabolic health biomarkers) while adjusting for chronological age, sex, race, and neighborhood socioeconomic status. Our study shows a robust association between GlycA and GrimAge (5.71, 95% CI = 4.36, 7.05, P = 7.94 × 10-16), Hannum Age (1.81, 95% CI = 0.65, 2.98, P = 2.30 × 10-3), and Phenotypic Age (2.88, 95% CI = 1.91, 3.87, P = 1.21 × 10-8). We also saw inverse associations between apolipoprotein A-1 and aging biomarkers. These associations provide insight into the relationship between aging and cardiometabolic health that may be informative for vulnerable populations.

Targeted Metabolomic Profiling of Dapagliflozin in Heart Failure With Preserved Ejection Fraction: The PRESERVED-HF Trial

BACKGROUND

Although sodium glucose co-transporter 2 inhibitors (SGLT2is) improve heart failure (HF)-related symptoms and outcomes in HF with preserved ejection fraction (HFpEF), underlying mechanisms remain unclear. In HF with reduced EF, dapagliflozin altered ketone and fatty acid metabolites vs placebo; however, metabolite signatures of SGLT2is have not been well elucidated in HFpEF.

OBJECTIVES

The goal of this study was to assess whether SGLT2i treatment altered systemic metabolic pathways and their relationship to outcomes in HFpEF.

METHODS

Targeted profiling of 64 metabolites was performed from 293 participants in PRESERVED-HF (Dapagliflozin in PRESERVED Ejection Fraction Heart Failure), a 12-week, placebo-controlled trial of dapagliflozin. Linear regression assessed changes in metabolite factors defined by principal components analysis (PCA) with dapagliflozin vs placebo. The relationship between changes in metabolite factors with changes in study endpoints was also assessed.

RESULTS

The mean age was 70 ± 11 years, 58% were female, and 29% were Black. There were no significant differences in 12 PCA-derived metabolite factors between treatment arms, including metabolites reflecting ketone, fatty acid, or branched-chain amino acid (BCAA) pathways. Combining treatment arms, changes in BCAAs and branched-chain ketoacids were negatively associated with changes in N-terminal pro-B-type natriuretic peptide; changes in medium-/long-chain acylcarnitines were positively associated with changes in N-terminal pro-B-type natriuretic peptide and negatively associated with changes in 6-minute walk test distance; and changes in ketones were negatively associated with changes in weight, without treatment interaction.

CONCLUSIONS

Leveraging targeted metabolomics in a placebo-controlled SGLT2i trial of HFpEF, dapagliflozin did not alter systemic metabolic as reflected by circulating metabolites, in contrast with reported effects in HF with reduced ejection fraction. Metabolite biomarkers reflecting BCAA, ketone, and fatty acid metabolism were associated with markers of disease severity, suggesting a role for potential novel treatment targets. (Dapagliflozin in PRESERVED Ejection Fraction Heart Failure [PRESERVED-HF]; NCT03030235).

Metabolite signature of diabetes remission in individuals with obesity undergoing weight loss interventions

OBJECTIVE

This observational study investigated metabolomic changes in individuals with type 2 diabetes (T2D) after weight loss. We hypothesized that metabolite changes associated with T2D-relevant phenotypes are signatures of improved health.

METHODS

Fasting plasma samples from individuals undergoing bariatric surgery (n = 71 Roux-en-Y gastric bypass [RYGB], n = 22 gastric banding), lifestyle intervention (n = 66), or usual care (n = 14) were profiled for 139 metabolites before and 2 years after weight loss. Principal component analysis grouped correlated metabolites into factors. Association of preintervention metabolites was tested with preintervention clinical features and changes in T2D markers. Association between change in metabolites/metabolite factors and change in T2D remission markers, homeostasis model assessment of β-cell function, homeostasis model assessment of insulin resistance, and glycated hemoglobin (HbA1c) was assessed.

RESULTS

Branched-chain amino acids (BCAAs) were associated with preintervention adiposity. Changes in BCAAs (valine, leucine/isoleucine) and branched-chain ketoacids were positively associated with change in HbA1c (false discovery rate q value ≤ 0.001) that persisted after adjustment for percentage weight change and RYGB (p ≤ 0.02). In analyses stratified by RYGB or other weight loss method, some metabolites showed association with non-RYGB weight loss.

CONCLUSIONS

This study confirmed known metabolite associations with obesity/T2D and showed an association of BCAAs with HbA1c change after weight loss, independent of the method or magnitude of weight loss.

Accelerated Epigenetic Aging Is Associated With Multiple Cardiometabolic, Hematologic, and Renal Abnormalities: A Project Baseline Health Substudy

BACKGROUND

Epigenetic clocks estimate chronologic age using methylation levels at specific loci. We tested the hypothesis that accelerated epigenetic aging is associated with abnormal values in a range of clinical, imaging, and laboratory characteristics.

METHODS

The Project Baseline Health Study recruited 2502 participants, including 1661 with epigenetic age estimates from the Horvath pan-tissue clock. We classified individuals with extreme values as having epigenetic age acceleration (EAA) or epigenetic age deceleration. A subset of participants with longitudinal methylation profiling was categorized as accelerated versus nonaccelerated. Using principal components analysis, we created phenoclusters using 122 phenotypic variables and compared individuals with EAA versus epigenetic age deceleration, and at one year of follow-up, using logistic regression models adjusted for sex (false discovery rate [Q] <0.10); in secondary exploratory analyses, we tested individual clinical variables.

RESULTS

The EAA (n=188) and epigenetic age deceleration (n=195) groups were identified as having EAA estimates ≥5 years or ≤-5 years, respectively. In primary analyses, individuals with EAA had higher values for phenoclusters summarizing lung function and lipids, and lower values for a phenocluster representing physical function. In secondary analyses of individual variables, neutrophils, body mass index, and waist circumference were significantly higher in individuals with EAA (Q<0.10). No phenoclusters were significantly different between participants with accelerated (n=148) versus nonaccelerated (n=112) longitudinal aging.

CONCLUSIONS

We report multiple cardiometabolic, hematologic, and physical function features characterizing individuals with EAA. These highlight factors that may mediate the adverse effects of aging and identify potential targets for study of mitigation of these effects.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT03154346.

The association of accelerated epigenetic age with all-cause mortality in cardiac catheterization patients as mediated by vascular and cardiometabolic outcomes

BACKGROUND

Epigenetic age is a DNA methylation-based biomarker of aging that is accurate across the lifespan and a range of cell types. The difference between epigenetic age and chronological age, termed age acceleration (AA), is a strong predictor of lifespan and healthspan. The predictive capabilities of AA for all-cause mortality have been evaluated in the general population; however, its utility is less well evaluated in those with chronic conditions. Additionally, the pathophysiologic pathways whereby AA predicts mortality are unclear. We hypothesized that AA predicts mortality in individuals with underlying cardiovascular disease; and the association between AA and mortality is mediated, in part, by vascular and cardiometabolic measures.

METHODS

We evaluated 562 participants in an urban, three-county area of central North Carolina from the CATHGEN cohort, all of whom received a cardiac catheterization procedure. We analyzed three AA biomarkers, Horvath epigenetic age acceleration (HAA), phenotypic age acceleration (PhenoAA), and Grim age acceleration (GrimAA), by Cox regression models, to assess whether AAs were associated with all-cause mortality. We also evaluated if these associations were mediated by vascular and cardiometabolic outcomes, including left ventricular ejection fraction (LVEF), blood cholesterol concentrations, angiopoietin-2 (ANG2) protein concentration, peripheral artery disease, coronary artery disease, diabetes, and hypertension. The total effect, direct effect, indirect effect, and percentage mediated were estimated using pathway mediation tests with a regression adjustment approach.

RESULTS

PhenoAA (HR = 1.05, P < 0.0001), GrimAA (HR = 1.10, P < 0.0001) and HAA (HR = 1.03, P = 0.01) were all associated with all-cause mortality. The association of mortality and PhenoAA was partially mediated by ANG2, a marker of vascular function (19.8%, P = 0.016), and by diabetes (8.2%, P = 0.043). The GrimAA-mortality association was mediated by ANG2 (12.3%, P = 0.014), and showed weaker evidence for mediation by LVEF (5.3%, P = 0.065).

CONCLUSIONS

Epigenetic age acceleration remains strongly predictive of mortality even in individuals already burdened with cardiovascular disease. Mortality associations were mediated by ANG2, which regulates endothelial permeability and angiogenic functions, suggesting that specific vascular pathophysiology may link accelerated epigenetic aging with increased mortality risks.

Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events

There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93-2.77, p < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 (p = 1.5 × 10-4). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality (p < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.

Prevalence and Phenotypic Burden of Monogenic Arrhythmias Using Integration of Electronic Health Records With Genetics

BACKGROUND

Inherited primary arrhythmia syndromes and arrhythmogenic cardiomyopathies can lead to sudden cardiac arrest in otherwise healthy individuals. The burden and expression of these diseases in a real-world, well-phenotyped cardiovascular population is not well understood.

METHODS

Whole exome sequencing was performed on 8574 individuals from the CATHGEN cohort (Catheterization Genetics). Variants in 55 arrhythmia-related genes (associated with 8 disorders) were identified and assessed for pathogenicity based on American College of Genetics and Genomics/Association for Molecular Pathology criteria. Individuals carrying pathogenic/likely pathogenic (P/LP) variants were grouped by arrhythmogenic disorder and matched 1:5 to noncarrier controls based on age, sex, and genetic ancestry. Long-term phenotypic data were annotated through deep electronic health record review.

RESULTS

Fifty-eight P/LP variants were found in 79 individuals in 12 genes associated with 5 arrhythmogenic disorders (arrhythmogenic right ventricular cardiomyopathy, Brugada syndrome, hypertrophic cardiomyopathy, LMNA-related cardiomyopathy, and long QT syndrome). The penetrance of these P/LP variants in this cardiovascular cohort was 33%, 0%, 28%, 83%, and 4%, respectively. Carriers of P/LP variants associated with arrhythmogenic disorders showed significant differences in ECG, imaging, and clinical phenotypes compared with noncarriers, but displayed no difference in survival. Carriers of novel truncating variants in FLNC, MYBPC3, and MYH7 also developed relevant arrhythmogenic cardiomyopathy phenotypes.

CONCLUSIONS

In a real-world cardiovascular cohort, P/LP variants in arrhythmia-related genes were relatively common (1:108 prevalence) and most penetrant in LMNA. While hypertrophic cardiomyopathy P/LP variant carriers showed significant differences in clinical outcomes compared with noncarriers, carriers of P/LP variants associated with other arrhythmogenic disorders displayed only ECG differences.

A randomized controlled trial of mobile health intervention in patients with heart failure and diabetes

BACKGROUND

Mobile health (mHealth) platforms can affect health behaviors but have not been rigorously tested in randomized trials.

OBJECTIVES

We sought to evaluate the effectiveness of a pragmatic mHealth intervention in patients with HF and DM Methods: We conducted a multicenter randomized trial in 187 patients with both HF and DM to assessing a mHealth intervention to improve physical activity and medication adherence compared to usual care. The primary endpoint was change in mean daily step count from baseline through 3 months. Other outcomes included medication adherence, health related quality of life, and metabolomic profiling.

RESULTS

The mHealth group had an increase in daily step count of 151 steps/day at 3 months whereas the usual care group had a decline of 162 steps/day (LS-mean between-group difference = 313 steps/day; 95% CI: 8, 619, p = 0.044). Medication adherence measured using the Voils Adherence Questionnaire did not change from baseline to 3 months (LS-mean change -0.08 in mHealth vs. -0.15 in usual care, p = 0.47). The mHealth group had an improvement in Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS) compared to the usual care group (LS-mean difference = 5.5 points, 95% CI: 1.4, 9.6, p = 0.009). Thirteen metabolites, primarily medium- and long-chain acylcarnitines, changed differently between treatment groups from baseline to 3 months (p < 0.05).

CONCLUSIONS

In patients with HF and DM, a 3-month mHealth intervention significantly improved daily physical activity, health related quality of life and metabolomic markers of cardiovascular health, but not medication adherence.

CLINICALTRIALS

gov Identifier: NCT02918175 Condensed Abstract: Heart failure (HF) and diabetes (DM) have overlapping biologic and behavioral risk factors. We conducted a multicenter randomized, clinical trial in 187 patients with both HF (regardless of ejection fraction) and DM to assess whether a mHealth intervention could improve physical activity and medication adherence. The mHealth group had an increase in mean daily step count and quality of life but not medication adherence. Medium- and long-chain acylcarnitines changed differently between treatment groups from baseline to 3 months (p < 0.05). These data have important implications for designing effective lifestyle interventions in HF and DM.

Multi-dimensional characterization of prediabetes in the Project Baseline Health Study

Background

We examined multi-dimensional clinical and laboratory data in participants with normoglycemia, prediabetes, and diabetes to identify characteristics of prediabetes and predictors of progression from prediabetes to diabetes or reversion to no diabetes.

Methods

The Project Baseline Health Study (PBHS) is a multi-site prospective cohort study of 2502 adults that conducted deep clinical phenotyping through imaging, laboratory tests, clinical assessments, medical history, personal devices, and surveys. Participants were classified by diabetes status (diabetes [DM], prediabetes [preDM], or no diabetes [noDM]) at each visit based on glucose, HbA1c, medications, and self-report. Principal component analysis (PCA) was performed to create factors that were compared across groups cross-sectionally using linear models. Logistic regression was used to identify factors associated with progression from preDM to DM and for reversion from preDM to noDM.

Results

At enrollment, 1605 participants had noDM; 544 had preDM; and 352 had DM. Over 4 years of follow-up, 52 participants with preDM developed DM and 153 participants reverted to noDM. PCA identified 33 factors composed of clusters of clinical variables; these were tested along with eight individual variables identified a priori as being of interest. Six PCA factors and six a priori variables significantly differed between noDM and both preDM and DM after false discovery rate adjustment for multiple comparisons (q < 0.05). Of these, two factors (one comprising glucose measures and one of anthropometry and physical function) demonstrated monotonic/graded relationships across the groups, as did three a priori variables: ASCVD risk, coronary artery calcium, and triglycerides (q < 10^–21 for all). Four factors were significantly different between preDM and noDM, but concordant or similar between DM and preDM: red blood cell indices (q = 8 × 10^-10), lung function (q = 2 × 10^-6), risks of chronic diseases (q = 7 × 10^-4), and cardiac function (q = 0.001), along with a priori variables of diastolic function (q = 1 × 10^-10), sleep efficiency (q = 9 × 10^-6) and sleep time (q = 6 × 10^-5). Two factors were associated with progression from prediabetes to DM: anthropometry and physical function (OR [95% CI]: 0.6 [0.5, 0.9], q = 0.04), and heart failure and c-reactive protein (OR [95% CI]: 1.4 [1.1, 1.7], q = 0.02). The anthropometry and physical function factor was also associated with reversion from prediabetes to noDM: (OR [95% CI]: 1.9 [1.4, 2.7], q = 0.02) along with a factor of white blood cell indices (OR [95% CI]: 0.6 [0.4, 0.8], q = 0.02), and the a priori variables ASCVD risk score (OR [95% CI]: 0.7 [0.6, 0.9] for each 0.1 increase in ASCVD score, q = 0.02) and triglycerides (OR [95% CI]: 0.9 [0.8, 1.0] for each 25 mg/dl increase, q = 0.05).

Conclusions

PBHS participants with preDM demonstrated pathophysiologic changes in cardiac, pulmonary, and hematology measures and declines in physical function and sleep measures that precede DM; some changes predicted an increased risk of progression to DM. A factor with measures of anthropometry and physical function was the most important factor associated with progression to DM and reversion to noDM. Future studies may determine whether these changes elucidate pathways of progression to DM and related complications and whether they can be used to identify individuals at higher risk of progression to DM for targeted preventive interventions.

Trial registration ClinicalTrials.gov NCT03154346

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01565-x.

Altered branched-chain α-keto acid metabolism is a feature of NAFLD in individuals with severe obesity

Hepatic de novo lipogenesis is influenced by the branched-chain α-keto acid dehydrogenase (BCKDH) kinase (BCKDK). Here, we aimed to determine whether circulating levels of the immediate substrates of BCKDH, the branched-chain α-keto acids (BCKAs), and hepatic BCKDK expression are associated with the presence and severity of nonalcoholic fatty liver disease (NAFLD). Eighty metabolites (3 BCKAs, 14 amino acids, 43 acylcarnitines, 20 ceramides) were quantified in plasma from 288 patients with bariatric surgery with severe obesity and scored liver biopsy samples. Metabolite principal component analysis factors, BCKAs, branched-chain amino acids (BCAAs), and the BCKA/BCAA ratio were tested for associations with steatosis grade and presence of nonalcoholic steatohepatitis (NASH). Of all analytes tested, only the Val-derived BCKA, α-keto-isovalerate, and the BCKA/BCAA ratio were associated with both steatosis grade and NASH. Gene expression analysis in liver samples from 2 independent bariatric surgery cohorts showed that hepatic BCKDK mRNA expression correlates with steatosis, ballooning, and levels of the lipogenic transcription factor SREBP1. Experiments in AML12 hepatocytes showed that SREBP1 inhibition lowered BCKDK mRNA expression. These findings demonstrate that higher plasma levels of BCKA and hepatic expression of BCKDK are features of human NAFLD/NASH and identify SREBP1 as a transcriptional regulator of BCKDK.

Proteomic profiling identifies CLEC4C expression as a novel biomarker of primary graft dysfunction after heart transplantation

PURPOSE

Clinical models to identify patients at high risk of primary graft dysfunction (PGD) after heart transplantation (HT) are limited, and the underlying pathophysiology of this common post-transplant complication remains poorly understood. We sought to identify whether pre-transplant levels of circulating proteins reporting on immune activation and inflammation are associated with incident PGD.

METHODS

The study population consisted of 219 adult heart transplant recipients identified between 2016 and 2020 at Duke University Medical Center, randomly divided into derivation (n = 131) and validation (n = 88) sets. PGD was defined using modified ISHLT criteria. Proteomic profiling was performed using Olink panels (n = 354 proteins) with serum samples collected immediately prior to transplantation. Association between normalized relative protein expression and PGD was tested using univariate and multivariable (recipient age, creatinine, mechanical circulatory support, and sex; donor age; ischemic time) models. Significant proteins identified in the derivation set (p < 0.05 in univariate models), were then tested in the validation set. Pathway enrichment analysis was used to test candidate biological processes. The predictive performance of proteins was compared to that of the RADIAL score.

RESULTS

Nine proteins were associated with PGD in univariate models in the derivation set. Of these, only CLEC4C remained associated with PGD in the validation set after Bonferroni correction (OR [95% CI] = 3.04 [1.74,5.82], p = 2.8 × 10^-4). Patterns of association were consistent for CLEC4C in analyses stratified by biventricular/left ventricular and isolated right ventricular PGD. Pathway analysis identified interferon-alpha response and C-type lectin signaling as significantly enriched biologic processes. The RADIAL score was a poor predictor of PGD (AUC = 0.55). CLEC4C alone (AUC = 0.66, p = 0.048) and in combination with the clinical covariates from the multivariable model (AUC = 0.69, p = 0.018) improved discrimination for the primary outcome.

CONCLUSIONS

Pre-transplantation circulating levels of CLEC4C, a protein marker of plasmacytoid dendritic cells (pDCs), may identify HT recipients at risk for PGD. Further studies are needed to better understand the potential role pDCs and the innate immune response in PGD.

Metabolomic profiling identifies complex lipid species and amino acid analogues associated with response to weight loss interventions

Obesity is an epidemic internationally. While weight loss interventions are efficacious, they are compounded by heterogeneity with regards to clinically relevant metabolic responses. Thus, we sought to identify metabolic biomarkers that are associated with beneficial metabolic changes to weight loss and which distinguish individuals with obesity who would most benefit from a given type of intervention. Liquid chromatography mass spectrometry-based profiling was used to measure 765 metabolites in baseline plasma from three different weight loss studies: WLM (behavioral intervention, N = 443), STRRIDE-PD (exercise intervention, N = 163), and CBD (surgical cohort, N = 125). The primary outcome was percent change in insulin resistance (as measured by the Homeostatic Model Assessment of Insulin Resistance [%ΔHOMA-IR]) over the intervention. Overall, 92 individual metabolites were associated with %ΔHOMA-IR after adjustment for multiple comparisons. Concordantly, the most significant metabolites were triacylglycerols (TAGs; p = 2.3e-5) and diacylglycerols (DAGs; p = 1.6e-4), with higher baseline TAG and DAG levels associated with a greater improvement in insulin resistance with weight loss. In tests of heterogeneity, 50 metabolites changed differently between weight loss interventions; we found amino acids, peptides, and their analogues to be most significant (4.7e-3) in this category. Our results highlight novel metabolic pathways associated with heterogeneity in response to weight loss interventions, and related biomarkers which could be used in future studies of personalized approaches to weight loss interventions.

The Pediatric Obesity Microbiome and Metabolism Study (POMMS): Methods, Baseline Data, and Early Insights

OBJECTIVE

The purpose of this study was to establish a biorepository of clinical, metabolomic, and microbiome samples from adolescents with obesity as they undergo lifestyle modification.

METHODS

A total of 223 adolescents aged 10 to 18 years with BMI ≥95th percentile were enrolled, along with 71 healthy weight participants. Clinical data, fasting serum, and fecal samples were collected at repeated intervals over 6 months. Herein, the study design, data collection methods, and interim analysis-including targeted serum metabolite measurements and fecal 16S ribosomal RNA gene amplicon sequencing among adolescents with obesity (n = 27) and healthy weight controls (n = 27)-are presented.

RESULTS

Adolescents with obesity have higher serum alanine aminotransferase, C-reactive protein, and glycated hemoglobin, and they have lower high-density lipoprotein cholesterol when compared with healthy weight controls. Metabolomics revealed differences in branched-chain amino acid-related metabolites. Also observed was a differential abundance of specific microbial taxa and lower species diversity among adolescents with obesity when compared with the healthy weight group.

CONCLUSIONS

The Pediatric Metabolism and Microbiome Study (POMMS) biorepository is available as a shared resource. Early findings suggest evidence of a metabolic signature of obesity unique to adolescents, along with confirmation of previously reported findings that describe metabolic and microbiome markers of obesity.

Metabolites and diabetes remission after weight loss

There is marked heterogeneity in the response to weight loss interventions with regards to weight loss amount and metabolic improvement. We sought to identify biomarkers predictive of type 2 diabetes remission and amount of weight loss in individuals with severe obesity enrolled in the Longitudinal Assessment of Bariatric Surgery (LABS) and the Look AHEAD (Action for Health in Diabetes) studies. Targeted mass spectrometry-based profiling of 135 metabolites was performed in pre-intervention blood samples using a nested design for diabetes remission over five years (n = 93 LABS, n = 80 Look AHEAD; n = 87 remitters), and for extremes of weight loss at five years (n = 151 LABS; n = 75 with high weight loss). Principal components analysis (PCA) was used for dimensionality reduction, with PCA-derived metabolite factors tested for association with both diabetes remission and weight loss. Metabolic markers were tested for incremental improvement to clinical models, including the DiaRem score. Two metabolite factors were associated with diabetes remission: one primarily composed of branched chain amino acids (BCAA) and tyrosine (odds ratio (95% confidence interval) [OR (95% CI)] = 1.4 [1.0–1.9], p = 0.045), and one with betaine and choline (OR [95% CI] = 0.7 [0.5–0.9], p = 0.02).These results were not significant after adjustment for multiple tests. Inclusion of these two factors in clinical models yielded modest improvements in model fit and performance: in a constructed clinical model, the C-statistic improved from 0.87 to 0.90 (p = 0.02), while the net reclassification index showed improvement in prediction compared to the DiaRem score (NRI = 0.26, p = 0.0013). No metabolite factors associated with weight loss at five years. Baseline levels of metabolites in the BCAA and trimethylamine-N-oxide (TMAO)-microbiome-related pathways are independently and incrementally associated with sustained diabetes remission after weight loss interventions in individuals with severe obesity. These metabolites could serve as clinically useful biomarkers to identify individuals who will benefit the most from weight loss interventions.

Identification of Undetected Monogenic Cardiovascular Disorders

BACKGROUND

Monogenic diseases are individually rare but collectively common, and are likely underdiagnosed.

OBJECTIVES

The purpose of this study was to estimate the prevalence of monogenic cardiovascular diseases (MCVDs) and potentially missed diagnoses in a cardiovascular cohort.

METHODS

Exomes from 8,574 individuals referred for cardiac catheterization were analyzed. Pathogenic/likely pathogenic (P/LP) variants associated with MCVD (cardiomyopathies, arrhythmias, connective tissue disorders, and familial hypercholesterolemia were identified. Electronic health records (EHRs) were reviewed for individuals harboring P/LP variants who were predicted to develop disease (G+). G+ individuals who did not have a documented relevant diagnosis were classified into groups of whether they may represent missed diagnoses (unknown, unlikely, possible, probable, or definite) based on relevant diagnostic criteria/features for that disease.

RESULTS

In total, 159 P/LP variants were identified; 2,361 individuals harbored at least 1 P/LP variant, of whom 389 G+ individuals (4.5% of total cohort) were predicted to have at least 1 MCVD. EHR review of 342 G+ individuals predicted to have 1 MCVD with sufficient EHR data revealed that 52 had been given the relevant clinical diagnosis. The remaining 290 individuals were classified as potentially having an MCVD as follows: 193 unlikely (66.6%), 50 possible (17.2%), 30 probable (10.3%), and 17 definite (5.9%). Grouping possible, probable, definite, and known diagnoses, 149 were considered to have an MCVD. Novel MCVD pathogenic variants were identified in 16 individuals.

CONCLUSIONS

Overall, 149 individuals (1.7% of cohort) had MCVDs, but only 35% were diagnosed. These patients represents a "missed opportunity," which could be addressed by greater use of genetic testing of patients seen by cardiologists.

Novel plasma biomarkers improve discrimination of metabolic health independent of weight

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10-4-3.6 × 10-123), as well as within most of the individual BMI categories (p = 8.1 × 10-3-1.4 × 10-49). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus

BACKGROUND

Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

METHODS

To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

RESULTS

None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

CONCLUSIONS

This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

Circulating MicroRNA Profiling in Non-ST Elevated Coronary Artery Syndrome Highlights Genomic Associations with Serial Platelet Reactivity Measurements

Changes in platelet physiology are associated with simultaneous changes in microRNA concentrations, suggesting a role for microRNA in platelet regulation. Here we investigated potential associations between microRNA and platelet reactivity (PR), a marker of platelet function, in two cohorts following a non-ST elevation acute coronary syndrome (NSTE-ACS) event. First, non-targeted microRNA concentrations and PR were compared in a case (N = 77) control (N = 76) cohort within the larger TRILOGY-ACS trial. MicroRNA significant in this analysis plus CVD-associated microRNAs from the literature were then quantified by targeted rt-PCR in the complete TRILOGY-ACS cohort (N = 878) and compared with matched PR samples. Finally, microRNA significant in the non-targeted & targeted analyses were verified in an independent post NSTE-ACS cohort (N = 96). From the non-targeted analysis, 14 microRNAs were associated with PR (Fold Change: 0.91–1.27, p-value: 0.004–0.05). From the targeted analysis, five microRNAs were associated with PR (Beta: −0.09–0.22, p-value: 0.004–0.05). Of the 19 significant microRNAs, three, miR-15b-5p, miR-93 and miR-126, were consistently associated with PR in the TRILOGY-ACS and independent Singapore post-ACS cohorts, suggesting the measurement of circulating microRNA concentrations may report on dynamic changes in platelet biology following a cardiovascular ischemic event.

Epigenome-Wide Association Study for All-Cause Mortality in a Cardiovascular Cohort Identifies Differential Methylation in Castor Zinc Finger 1 (CASZ1)

Background DNA methylation is implicated in many chronic diseases and may contribute to mortality. Therefore, we conducted an epigenome-wide association study (EWAS) for all-cause mortality with whole-transcriptome data in a cardiovascular cohort (CATHGEN [Catheterization Genetics]). Methods and Results Cases were participants with mortality≥7 days postcatheterization whereas controls were alive with≥2 years of follow-up. The Illumina Human Methylation 450K and EPIC arrays (Illumina, San Diego, CA) were used for the discovery and validation sets, respectively. A linear model approach with empirical Bayes estimators adjusted for confounders was used to assess difference in methylation (Δβ). In the discovery set (55 cases, 49 controls), 25 629 (6.5%) probes were differently methylated (P<0.05). In the validation set (108 cases, 108 controls), 3 probes were differentially methylated with a false discovery rate-adjusted P<0.10: cg08215811 (SLC4A9; log2 fold change=-0.14); cg17845532 (MATK; fold change=-0.26); and cg17944110 (castor zinc finger 1 [CASZ1]; FC=0.26; P<0.0001; false discovery rate-adjusted P=0.046-0.080). Meta-analysis identified 6 probes (false discovery rate-adjusted P<0.05): the 3 above, cg20428720 (intergenic), cg17647904 (NCOR2), and cg23198793 (CAPN3). Messenger RNA expression of 2 MATK isoforms was lower in cases (fold change=-0.24 [P=0.007] and fold change=-0.61 [P=0.009]). The CASZ1, NCOR2, and CAPN3 transcripts did not show differential expression (P>0.05); the SLC4A9 transcript did not pass quality control. The cg17944110 probe is located within a potential regulatory element; expression of predicted targets (using GeneHancer) of the regulatory element, UBIAD1 (P=0.01) and CLSTN1 (P=0.03), were lower in cases. Conclusions We identified 6 novel methylation sites associated with all-cause mortality. Methylation in CASZ1 may serve as a regulatory element associated with mortality in cardiovascular patients. Larger studies are necessary to confirm these observations.

Associations of osteopontin and NT-proBNP with circulating miRNA levels in acute coronary syndrome

The genomic regulatory networks underlying the pathogenesis of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) are incompletely understood. As intermediate traits, protein biomarkers report on underlying disease severity and prognosis in NSTE-ACS. We hypothesized that integration of dense microRNA (miRNA) profiling with biomarker measurements would highlight potential regulatory pathways that underlie the relationships between prognostic biomarkers, miRNAs, and cardiovascular phenotypes. We performed miRNA sequencing using whole blood from 186 patients from the TRILOGY-ACS trial. Seven circulating prognostic biomarkers were measured: NH₂-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein, osteopontin (OPN), myeloperoxidase, growth differentiation factor 15, monocyte chemoattractant protein, and neopterin. We tested miRNAs for association with each biomarker with generalized linear models and controlled the false discovery rate at 0.05. Ten miRNAs, including known cardiac-related miRNAs 25-3p and 423-3p, were associated with NT-proBNP levels (min. P = 7.5 × 10⁻⁴) and 48 miRNAs, including cardiac-related miRNAs 378a-3p, 20b-5p and 320a, -b, and -d, were associated with OPN levels (min. P = 1.6 × 10⁻⁶). NT-proBNP and OPN were also associated with time to cardiovascular death, myocardial infarction (MI), or stroke in the sample. By integrating large-scale miRNA profiling with circulating biomarkers as intermediate traits, we identified associations of known cardiac-related and novel miRNAs with two prognostic biomarkers and identified potential genomic networks regulating these biomarkers. These results, highlighting plausible biological pathways connecting miRNAs with biomarkers and outcomes, may inform future studies seeking to delineate genomic pathways underlying NSTE-ACS outcomes.

Evaluating DNA methylation age on the Illumina MethylationEPIC Bead Chip

DNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci, present on Illumina's 450k and 27k DNA methylation microarrays. With increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this aging measure to evaluate estimation differences due to array design. Of the requisite 353 loci, 17 are missing from the 850k microarray. Similarly, an alternate, 71 loci DNA methylation age assessment measure created by Hannum et al. is missing 6 requisite loci. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we compared each sample's epigenetic age estimated from all 353 loci required by the Horvath DNAm age calculator, and using only the 336 loci available on the 850k array. In 450k/27k data, removing loci not on the 850k array resulted in underestimation of Horvath's DNAm age. Underestimation of Horvath DNAm age increased from ages 0 to ~20, remaining stable thereafter (mean deviation = -3.46 y, SD = 1.13 for individuals ≥20 years). Underestimation of Horvath's DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating it is driven by missing probes. In analogous examination of Hannum's DNAm age, the magnitude and direction of epigenetic age misestimation varied with chronological age. In conclusion, inter-array deviations in DNAm age estimations may be largely driven by missing probes between arrays, despite default probe imputation procedures. Though correlations and associations based on Horvath's DNAm age may be unaffected, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.

GALC deletions increase the risk of primary open-angle glaucoma: the role of Mendelian variants in complex disease

DNA copy number variants (CNVs) have been reported in many human diseases including autism and schizophrenia. Primary Open Angle Glaucoma (POAG) is a complex adult-onset disorder characterized by progressive optic neuropathy and vision loss. Previous studies have identified rare CNVs in POAG; however, their low frequencies prevented formal association testing. We present here the association between POAG risk and a heterozygous deletion in the galactosylceramidase gene (GALC). This CNV was initially identified in a dataset containing 71 Caucasian POAG cases and 478 ethnically matched controls obtained from dbGAP (study accession phs000126.v1.p1.) (p = 0.017, fisher's exact test). It was validated with array comparative genomic hybridization (arrayCGH) and realtime PCR, and replicated in an independent POAG dataset containing 959 cases and 1852 controls (p = 0.021, OR (odds ratio) = 3.5, 95% CI -1.1-12.0). Evidence for association was strengthened when the discovery and replication datasets were combined (p = 0.002; OR = 5.0, 95% CI 1.6-16.4). Several deletions with different endpoints were identified by array CGH of POAG patients. Homozygous deletions that eliminate GALC enzymatic activity cause Krabbe disease, a recessive Mendelian disorder of childhood displaying bilateral optic neuropathy and vision loss. Our findings suggest that heterozygous deletions that reduce GALC activity are a novel mechanism increasing risk of POAG. This is the first report of a statistically-significant association of a CNV with POAG risk, contributing to a growing body of evidence that CNVs play an important role in complex, inherited disorders. Our findings suggest an attractive biomarker and potential therapeutic target for patients with this form of POAG.

Association of ALS with head injury, cigarette smoking and APOE genotypes

OBJECTIVE

An increased risk of ALS has been reported for US veterans, but the cause is unknown. Since head injury and cigarette smoking are two previously implicated environmental risk factors that are more common in military than civilian study populations, we tested their association with ALS in a US veteran study population.

METHODS

We used logistic regression to examine the association of ALS with head injury and cigarette smoking in 241 incident cases and 597 controls. Since APOE is a plausible ALS candidate gene, we also tested its main effect and its statistical interaction with these environmental exposures.

RESULTS

Cigarette smoking was not associated with ALS in this predominantly male and Caucasian population. Veterans who had experienced head injuries during the last 15years before the reference date had an adjusted odds ratio of 2.33 (95% confidence interval 1.18-4.61), relative to veterans without any head injuries. This association was strongest in APOE-4 carriers.

CONCLUSIONS

Our results add to the body of evidence suggesting that head injuries may be a risk factor for multiple neurodegenerative diseases, including ALS. We hypothesize that the strength of association between head injuries and ALS may depend upon APOE genotype.

A two-stage genome-wide association study of sporadic amyotrophic lateral sclerosis

The cause of sporadic amyotrophic lateral sclerosis (ALS) is largely unknown, but genetic factors are thought to play a significant role in determining susceptibility to motor neuron degeneration. To identify genetic variants altering risk of ALS, we undertook a two-stage genome-wide association study (GWAS): we followed our initial GWAS of 545 066 SNPs in 553 individuals with ALS and 2338 controls by testing the 7600 most associated SNPs from the first stage in three independent cohorts consisting of 2160 cases and 3008 controls. None of the SNPs selected for replication exceeded the Bonferroni threshold for significance. The two most significantly associated SNPs, rs2708909 and rs2708851 [odds ratio (OR) = 1.17 and 1.18, and P-values = 6.98 x 10(-7) and 1.16 x 10(-6)], were located on chromosome 7p13.3 within a 175 kb linkage disequilibrium block containing the SUNC1, HUS1 and C7orf57 genes. These associations did not achieve genome-wide significance in the original cohort and failed to replicate in an additional independent cohort of 989 US cases and 327 controls (OR = 1.18 and 1.19, P-values = 0.08 and 0.06, respectively). Thus, we chose to cautiously interpret our data as hypothesis-generating requiring additional confirmation, especially as all previously reported loci for ALS have failed to replicate successfully. Indeed, the three loci (FGGY, ITPR2 and DPP6) identified in previous GWAS of sporadic ALS were not significantly associated with disease in our study. Our findings suggest that ALS is more genetically and clinically heterogeneous than previously recognized. Genotype data from our study have been made available online to facilitate such future endeavors.

A powerful and flexible multilocus association test for quantitative traits

Association mapping of complex traits typically employs tagSNP genotype data to identify a trait locus within a region of interest. However, considerable debate exists regarding the most powerful strategy for utilizing such tagSNP data for inference. A popular approach tests each tagSNP within the region individually, but such tests could lose power as a result of incomplete linkage disequilibrium between the genotyped tagSNP and the trait locus. Alternatively, one can jointly test all tagSNPs simultaneously within the region (by using genotypes or haplotypes), but such multivariate tests have large degrees of freedom that can also compromise power. Here, we consider a semiparametric model for quantitative-trait mapping that uses genetic information from multiple tagSNPs simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared to existing multivariate approaches. We fit this model by using a dimension-reducing technique called least-squares kernel machines, which we show is identical to analysis using a specific linear mixed model (which we can fit by using standard software packages like SAS and R). Using simulated SNP data based on real data from the International HapMap Project, we demonstrate that our approach often has superior performance for association mapping of quantitative traits compared to the popular approach of single-tagSNP testing. Our approach is also flexible, because it allows easy modeling of covariates and, if interest exists, high-dimensional interactions among tagSNPs and environmental predictors.