Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. STUDY DESIGN: Cross-sectional. SETTING & PARTICIPANTS: 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis. PREDICTORS: Measurement of 448 known plasma metabolites. OUTCOMES: The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. ANALYTICAL APPROACH: Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." RESULTS: Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. LIMITATIONS: Lack of a second independent cohort for external validation of our findings. CONCLUSIONS: Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed. PLAIN-LANGUAGE SUMMARY: Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

Novel Genetic Variants Associated with Chronic Kidney Disease Progression.

SIGNIFICANCE STATEMENT: eGFR slope has been used as a surrogate outcome for progression of CKD. However, genetic markers associated with eGFR slope among patients with CKD were unknown. We aimed to identify genetic susceptibility loci associated with eGFR slope. A two-phase genome-wide association study identified single nucleotide polymorphisms (SNPs) in TPPP and FAT1-LINC02374 , and 22 of them were used to derive polygenic risk scores that mark the decline of eGFR by disrupting binding of nearby transcription factors. This work is the first to identify the impact of TPPP and FAT1-LINC02374 on CKD progression, providing predictive markers for the decline of eGFR in patients with CKD. BACKGROUND: The incidence of CKD is associated with genetic factors. However, genetic markers associated with the progression of CKD have not been fully elucidated. METHODS: We conducted a genome-wide association study among 1738 patients with CKD, mainly from the KoreaN cohort study for Outcomes in patients With CKD. The outcome was eGFR slope. We performed a replication study for discovered single nucleotide polymorphisms (SNPs) with P <10 -6 in 2498 patients with CKD from the Chronic Renal Insufficiency Cohort study. Several expression quantitative trait loci (eQTL) studies, pathway enrichment analyses, exploration of epigenetic architecture, and predicting disruption of transcription factor (TF) binding sites explored potential biological implications of the loci. We developed and evaluated the effect of polygenic risk scores (PRS) on incident CKD outcomes. RESULTS: SNPs in two novel loci, TPPP and FAT1-LINC02374 , were replicated (rs59402340 in TPPP , Pdiscovery =7.11×10 -7 , PCRIC =8.13×10 -4 , Pmeta =7.23×10 -8 ; rs28629773 in FAT1-LINC02374 , Pdiscovery =6.08×10 -7 , PCRIC =4.33×10 -2 , Pmeta =1.87×10 -7 ). The eQTL studies revealed that the replicated SNPs regulated the expression level of nearby genes associated with kidney function. Furthermore, these SNPs were near gene enhancer regions and predicted to disrupt the binding of TFs. PRS based on the independently significant top 22 SNPs were significantly associated with CKD outcomes. CONCLUSIONS: This study demonstrates that SNP markers in the TPPP and FAT1-LINC02374 loci could be predictive markers for the decline of eGFR in patients with CKD.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study.

BACKGROUND: Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake. OBJECTIVES: We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults. METHODS: Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785). RESULTS: In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS. CONCLUSIONS: We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

Whole-Genome Sequencing Association Analyses of Stroke and Its Subtypes in Ancestrally Diverse Populations From Trans-Omics for Precision Medicine Project.

BACKGROUND AND PURPOSE: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). METHODS: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. RESULTS: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance (P<5.00×10-9) and 4 novel loci at genome-wide significance (P<5.00×10-8), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke (P<3.11×10-6). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2, HDAC9, ZFHX3, and LRCH1. CONCLUSIONS: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

Rare coding variants in RCN3 are associated with blood pressure.

BACKGROUND: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. RESULTS: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7). CONCLUSIONS: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Trans-ethnic genome-wide association study of blood metabolites in the Chronic Renal Insufficiency Cohort (CRIC) study.

Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m2 in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.

Metabolomics study of blood pressure salt-sensitivity and hypertension.

BACKGROUND AND AIMS: Identify novel metabolite associations with blood pressure (BP) salt-sensitivity and hypertension. METHODS AND RESULTS: The Genetic Epidemiology Network of Salt Sensitivity (GenSalt) Replication study includes 698 Chinese participants who underwent a 3-day baseline examination followed by a 7-day low-sodium feeding and 7-day high-sodium feeding. Latent mixture models identified three trajectories of blood pressure (BP) responses to the sodium interventions. We selected 50 most highly salt-sensitive and 50 most salt-resistant participants for untargeted metabolomics profiling. Multivariable adjusted mixed logistic regression models tested the associations of baseline metabolites with BP salt-sensitivity. Multivariable adjusted mixed linear regression models tested the associations of BP salt-sensitivity with metabolite changes during the sodium interventions. Identified metabolites were tested for associations with hypertension among 1249 Bogalusa Heart Study (BHS) participants using multiple logistic regression. Fifteen salt-sensitivity metabolites were associated with hypertension in the BHS. Baseline values of serine, 2-methylbutyrylcarnitine and isoleucine directly associated with high salt-sensitivity. Among them, serine indirectly associated with hypertension while 2-methylbutyrylcarnitine and isoleucine directly associated with hypertension. Baseline salt-sensitivity status predicted changes in 14 metabolites when switching to low-sodium or high-sodium interventions. Among them, glutamate, 1-carboxyethylvaline, 2-methylbutyrylcarnitine, 3-methoxytyramine sulfate, glucose, alpha-ketoglutarate, hexanoylcarnitine, gamma-glutamylisoleucine, gamma-glutamylleucine, and gamma-glutamylphenylalanine directly associated with hypertension. Conversely, serine, histidine, threonate and 5-methyluridine indirectly associated with hypertension. Together, these metabolites explained an additional 7% of hypertension susceptibility when added to a model including traditional risk factors. CONCLUSIONS: Our findings contribute to the molecular characterization of BP response to sodium and provide novel biological insights into salt-sensitive hypertension.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Plasma Metabolomic Signatures of Healthy Dietary Patterns in the Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: In individuals with chronic kidney disease (CKD), healthy dietary patterns are inversely associated with CKD progression. Metabolomics, an approach that measures many small molecules in biofluids, can identify biomarkers of healthy dietary patterns. OBJECTIVES: We aimed to identify known metabolites associated with greater adherence to 4 healthy dietary patterns in CKD patients. METHODS: We examined associations between 486 known plasma metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and alternate Mediterranean diet (aMED) in 1056 participants (aged 21-74 y at baseline) in the Chronic Renal Insufficiency Cohort (CRIC) Study. Usual dietary intake was assessed using a semiquantitative FFQ. We conducted multivariable linear regression models to study associations between healthy dietary patterns and individual plasma metabolites, adjusting for sociodemographic characteristics, health behaviors, and clinical factors. We used principal component analysis to identify groups of metabolites associated with individual food components within healthy dietary patterns. RESULTS: After Bonferroni correction, we identified 266 statistically significant diet-metabolite associations (HEI: n = 60; AHEI: n = 78; DASH: n = 77; aMED: n = 51); 78 metabolites were associated with >1 dietary pattern. Lipids with a longer acyl chain length and double bonds (unsaturated) were positively associated with all 4 dietary patterns. A metabolite pattern low in saturated diacylglycerols and triacylglycerols, and a pattern high in unsaturated triacylglycerols was positively associated with intake of healthy food components. Plasmalogens were negatively associated with the consumption of nuts and legumes and healthy fat, and positively associated with the intake of red and processed meat. CONCLUSIONS: We identified many metabolites associated with healthy dietary patterns, indicative of food consumption. If replicated, these metabolites may be considered biomarkers of healthy dietary patterns in patients with CKD.

Pseudouridine and N-formylmethionine associate with left ventricular mass index: Metabolome-wide association analysis of cardiac remodeling.

BACKGROUND: Heart failure (HF) is the fastest growing form of cardiovascular disease both nationally and globally, underlining a need to phenotype subclinical HF intermediaries to improve primary prevention. OBJECTIVES: We aimed to identify novel metabolite associations with left ventricular (LV) remodeling, one upstream HF intermediary, among a community-based cohort of individuals. METHODS: We examined 1052 Bogalusa Heart Study participants (34.98% African American, 57.41% female, aged 33.6-57.5 years). Measures of LV mass and relative wall thickness (RWT) were obtained using two-dimensional-guided echocardiographic measurements via validated eqs. LV mass was indexed to height2.7 to calculate left ventricular mass index (LVMI). Untargeted metabolomic analysis of fasting serum samples was conducted. In combined and ethnicity-stratified analyses, multivariable linear and multinomial logistic regression models tested the associations of metabolites with the continuous LVMI and RWT and categorical LV geometry phenotypes, respectively, after adjusting for demographic and traditional cardiovascular disease risk factors. RESULTS: Pseudouridine (B = 1.38; p = 3.20 × 10-5) and N-formylmethionine (B = 1.65; 3.30 × 10-6) were significantly associated with LVMI in the overall sample as well significant in Caucasians, with consistent effect direction and nominal significance (p < .05) in African Americans. Upon exclusion of individuals with self-report myocardial infarction or congestive HF, we similarly observed a 1.33 g/m2.7 and 1.52 g/m2.7 higher LVMI for each standard deviation increase in pseudouridine and N-formylmethionine, respectively. No significant associations were observed for metabolites with RWT or categorical LV remodeling outcomes. CONCLUSIONS: The current analysis identified novel associations of pseudouridine and N-formylmethionine with LVMI, suggesting that mitochondrial-derived metabolites may serve as early biomarkers for LV remodeling and subclinical HF.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years. PREDICTORS: 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. OUTCOMES: Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). ANALYTICAL APPROACH: Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT. RESULTS: During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]). LIMITATIONS: Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. CONCLUSIONS: Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

Pooled cohort equations heart failure risk score predicts cardiovascular disease and all-cause mortality in a nationally representative sample of US adults.

BACKGROUND: Heart failure (HF) represents an accumulated burden of systemic vascular damage and is the fastest growing form of cardiovascular disease (CVD). Due to increasing HF-attributable mortality rates, we sought to assess the association of the new 2019 Pooled Cohort equations to Prevent Heart Failure (PCP-HF) risk score with CVD and all-cause mortality. METHODS: We linked data for 6333 black and white men and women aged 40-79 years, whom underwent electrocardiographic examination from the Third National Health and Nutrition Exam Survey, to National Death Index record matches. Sex- and race-specific PCP-HF risk scores were calculated using data on age, smoking, body mass index, systolic blood pressure, total cholesterol, HDL-cholesterol, fasting blood glucose, QRS complex duration, and antihypertensive and/or glucose-lowering medications. Cox regression estimated hazard ratios for the association of the PCP-HF risk score with CVD and all-cause mortality. RESULTS: Individuals were on average 54.9 years old (51.7% women, 25.4% black) and the median 10-year HF risk was 1.6% (Q1 = 0.5, Q3 = 4.8). There were 3178 deaths, 1116 from CVD, over a median follow-up time of 22.3 years. Black women had a higher 10-year HF risk compared to white women (2.1% vs. 1.1%; p < 0.01), while no significant difference was observed in predicted HF risk between black men and white men (2.3% vs. 2.1%, p = 0.16). A two-fold higher PCP-HF risk score was associated with a significant 58% (HR = 1.58; 95% CI, 1.48-1.70; p < 0.0001) and 38% (HR = 1.38; 95% CI, 1.32-1.46; p < 0.0001) greater risk of CVD and all-cause mortality, respectively. CONCLUSION: The PCP-HF risk score predicts CVD and all-cause mortality, in addition to the 10-year risk of incident HF among white and black men and women. These results underline the expanded utility of the PCP-HF risk score and suggest that its implementation in the clinical and population health settings may improve primary CVD prevention in the United States.

Branched-chain amino acids, history of gestational diabetes, and breastfeeding: The Bogalusa Heart Study.

BACKGROUND AND AIMS: To examine the associations between history of gestational diabetes mellitus (GDM) and breastfeeding with branched-chain amino acids (BCAA) and their metabolites in later life. METHODS AND RESULTS: 638 women (mean age 48.0 y) who had participated in the Bogalusa Heart Study and substudies of pregnancy history had untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy conducted by Metabolon© on serum samples. Metabolites were identified that were BCAA or associated with BCAA metabolic pathways. History of GDM at any pregnancy (self-reported, confirmed with medical records when possible) as well as breastfeeding were examined as predictors of BCAA using linear models, controlling for age, race, BMI, waist circumference, and menopausal status. None of the BCAA differed statistically by history of either GDM or breastfeeding, although absolute levels of each of the BCAA were higher with GDM and lower with breastfeeding. Of the 27 metabolites on the leucine, isoleucine and valine metabolism subpathway, 1-carboxyethylleucine, 1-carboxyethyvaline, and 3-hydroxy-2-ethylpropionate were higher in women with a history of GDM, but lower in women in women with a history of breastfeeding. Similar results were found for alpha-hydroxyisocaproate, 1-carboxyethylisoleucine, and N-acetylleucine. CONCLUSIONS: GDM and breastfeeding are associated in opposite directions with several metabolites on the BCAA metabolic pathway.

Serum metabolites associate with lipid phenotypes among Bogalusa Heart Study participants.

BACKGROUND AND AIMS: Dyslipidemia has been identified as a major risk factor for cardiovascular disease. We aimed to identify metabolites and metabolite modules showing novel association with lipids among Bogalusa Heart Study (BHS) participants using untargeted metabolomics. METHODS AND RESULTS: Untargeted ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to quantify serum metabolites of 1 243 BHS participants (816 whites and 427 African-Americans). The association of single metabolites with lipids was assessed using multiple linear regression models to adjust for covariables. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites and examine their covariable adjusted correlations with lipids. All analyses were conducted according to race and using Bonferroni-corrected α-thresholds to determine statistical significance. Thirteen metabolites with known biochemical identities showing novel association achieved Bonferroni-significance, p < 1.04 × 10-5, and showed consistent effect directions in both whites and African-Americans. Twelve were from lipid sub-pathways including fatty acid metabolism (arachidonoylcholine, dihomo-linolenoyl-choline, docosahexaenoylcholine, linoleoylcholine, oleoylcholine, palmitoylcholine, and stearoylcholine), monohydroxy fatty acids (2-hydroxybehenate, 2-hydroxypalmitate, and 2-hydroxystearate), and lysoplasmalogens [1-(1-enyl-oleoyl)-GPE (P-18:1) and 1-(1-enyl-stearoyl)-GPE (P-18:0)]. The gamma-glutamylglutamine, peptide from the gamma-glutamyl amino acid sub-pathway, were also identified. In addition, four metabolite modules achieved Bonferroni-significance, p < 1.39 × 10-3, in both whites and African-Americans. These four modules were largely comprised of metabolites from lipid sub-pathways, with one module comprised of metabolites which were not identified in the single metabolite analyses. CONCLUSION: The current study identified 13 metabolites and 4 metabolite modules showing novel association with lipids, providing new insights into the physiological mechanisms regulating lipid levels.

Leveraging linkage evidence to identify low-frequency and rare variants on 16p13 associated with blood pressure using TOPMed whole genome sequencing data.

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10-4) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.

Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge given its high global prevalence and associated risks of cardiovascular disease and progression to end stage renal disease. Although it is known that numerous metabolic changes occur in CKD patients, identifying novel metabolite associations with kidney function may enhance our understanding of the physiologic pathways relating to CKD. OBJECTIVES: The objective of this study was to elucidate novel metabolite associations with kidney function among participants of two community-based cohorts with carefully ascertained metabolomics, kidney function, and covariate data. METHODS: Untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry was used to detect and quantify blood metabolites. We used multivariate adjusted linear regression to examine associations between single metabolites and creatinine-based estimated glomerular filtration rate (eGFRcr) among 1243 Bogalusa Heart Study (BHS) participants (median eGFRcr: 94.4, 5th-95th percentile: 66.0-119.6 mL/min/1.73 m2). Replication, determined by statistical significance and consistent effect direction, was tested using gold standard measured glomerular filtration rate (mGFR) among 260 Multi-Ethnic Study of Atherosclerosis (MESA) participants (median mGFR: 72.0, 5th-95th percentile: 43.5-105.0 mL/min/1.73 m2). All analyses used Bonferroni-corrected alpha thresholds. RESULTS: Fifty-one novel metabolite associations with kidney function were identified, including 12 from previously unrelated sub-pathways: N6-carboxymethyllysine, gulonate, quinolinate, gamma-CEHC-glucuronide, retinol, methylmalonate, 3-hydroxy-3-methylglutarate, 3-aminoisobutyrate, N-methylpipecolate, hydroquinone sulfate, and glycine conjugates of C10H12O2 and C10H14O2(1). Significant metabolites were generally inversely associated with kidney function and smaller in mass-to-charge ratio than non-significant metabolites. CONCLUSION: The 51 novel metabolites identified may serve as early, clinically relevant, kidney function biomarkers.

Gut Microbiome Associates With Lifetime Cardiovascular Disease Risk Profile Among Bogalusa Heart Study Participants.

RATIONALE: Few studies have systematically assessed the influence of gut microbiota on cardiovascular disease (CVD) risk. OBJECTIVE: To examine the association between gut microbiota and lifetime CVD risk profile among 55 Bogalusa Heart Study participants with the highest and 57 with the lowest lifetime burdens of CVD risk factors. METHODS AND RESULTS: 16S ribosomal RNA sequencing was conducted on microbial DNA extracted from stool samples of the Bogalusa Heart Study participants. α Diversity, including measures of richness and evenness, and individual genera were tested for associations with lifetime CVD risk profile. Multivariable regression techniques were used to adjust for age, sex, and race (model 1), along with body mass index (model 2) and both body mass index and diet (model 3). In model 1, odds ratios (95% confidence intervals) for each SD increase in richness, measured by the number of observed operational taxonomic units, Chao 1 index, and abundance-based coverage estimator, were 0.62 (0.39-0.99), 0.61 (0.38-0.98), and 0.63 (0.39-0.99), respectively. Associations were consistent in models 2 and 3. Four genera were enriched among those with high versus low CVD risk profile in all models. Model 1 P values were 2.12×10(-3), 7.95×10(-5), 4.39×10(-4), and 1.51×10(-4) for Prevotella 2, Prevotella 7, Tyzzerella, and Tyzzerella 4, respectively. Two genera were depleted among those with high versus low CVD risk profile in all models. Model 1 P values were 2.96×10(-6) and 1.82×10(-4) for Alloprevotella and Catenibacterium, respectively. CONCLUSIONS: The current study identified associations of overall microbial richness and 6 microbial genera with lifetime CVD risk.