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Background We carried out a study of the aptamer proteomic assay, SomaScan V4, to evaluate the analytical and biological variability of the assay in plasma samples of patients with moderate to severe chronic kidney disease (CKD). Methods Plasma samples were selected from 2 sources: (a) 24 participants from the Chronic Renal Insufficiency Cohort (CRIC) and (b) 49 patients from the Brigham and Women’s Hospital–Kidney/Renal Clinic. We calculated intra-assay variability from both sources and examined short-term biological variability in samples from the Brigham clinic. We also measured correlations of aptamer measurements with traditional biomarker assays. Results A total of 4656 unique proteins (4849 total aptamer measures) were analyzed in all samples. Median (interquartile range [IQR] intra-assay CV) was 3.7% (2.8–5.3) in CRIC and 5.0% (3.8–7.0) in Brigham samples. Median (IQR) biological CV among Brigham samples drawn from one individual on 2 occasions separated by median (IQR) 7 (4–14) days was 8.7% (6.2–14). CVs were independent of CKD stage, diabetes, or albuminuria but were higher in patients with systemic lupus erythematosus. Rho correlations between aptamer and traditional assays for biomarkers of interest were cystatin C = 0.942, kidney injury model-1 = 0.905, fibroblast growth factor-23 = 0.541, tumor necrosis factor receptors 1 = 0.781 and 2 = 0.843, P < 10−100 for all. Conclusions Intra-assay and within-subject variability for SomaScan in the CKD setting was low and similar to assay variability reported from individuals without CKD. Intra-assay precision was excellent whether samples were collected in an optimal research protocol, as were CRIC samples, or in the clinical setting, as were the Brigham samples.

We aimed to evaluate associations of baseline telomere length with overall and annual change in estimated glomerular filtration rate (eGFR) and trajectory of kidney function during an 8-year follow-up. A total of 3 964 participants of the Health and Retirement Study were included. We identified 3 trajectory groups of kidney function: consistently normal (n = 1 163 or 29.3%), normal to impaired (n = 2 306 or 58.2%), and consistently impaired groups (n = 495 or 12.5%). After controlling for age, sex, race, education, smoking, drinking, diabetes, heart disease, blood pressure, body mass index, total cholesterol, and hemoglobin A1c, participants with longer telomere length were 20% less likely (odds ratio = 0.80, 95% confidence interval: 0.69–0.93, p = .003) to have a normal to impaired kidney function trajectory than a consistently normal function trajectory. Telomere length was not associated with changing rate of eGFR over 8 years (p = .45). Participants with longer telomere length were more likely to have consistently normal kidney function.

African Americans (AAs) experience a high burden of hypertension but have been underrepresented in genetic studies of blood pressure. We performed common and rare variant genome-wide association studies (GWAS) of systolic (SBP) and diastolic (DBP) blood pressure, pulse pressure and hypertension in 95,457 AAs from the Million Veteran Program and Continental Origins and Genetic Epidemiology Network (COGENT) consortium with replication in up to 41,014 African-ancestry participants. GWAS meta-analysis confirmed 26 previously reported loci including 15 with SBP, four with DBP, 10 with pulse pressure and five with hypertension. Predicted gene expression analysis identified 25 genes newly associated with blood pressure. We provide validated African-ancestry polygenic scores for SBP and DBP that are strongly associated with hypertension. This work provides evidence for similar genetic architectures of hypertension and blood pressure across racial/ethnic groups and demonstrates the utility of predicted expression analysis in identifying novel genes beyond GWAS alone.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

INTRODUCTION: We hypothesize that an untargeted metabolomics study will identify novel mechanisms underlying smoking associated weight loss. METHODS: We performed cross-sectional analyses among 1,252 participants in the Bogalusa Heart Study and assessed 1,202 plasma metabolites for mediation effects on smoking-BMI associations. Significant metabolites were tested for associations with smoking genetic risk scores (GRSs) among a subset of participants (n=654) with available genomic data, followed by direction dependence analyses (DDA) to investigate causal relationships between the metabolites and smoking and BMI. All analyses controlled for age, sex, race, education, alcohol drinking and physical activity. RESULTS: Compared to never smokers, current and former smokers had 3.31 kg/m(2) and 1.77 kg/m(2) lower BMI after adjusting for all covariables. Twenty-two xenobiotics and 94 endogenous metabolites were significantly associated with current smoking. Eight xenobiotics were also associated with former smoking. Forty metabolites mediated the smoking-BMI associations, and five showed causal relationships with both smoking and BMI. These metabolites, including 1-oleoyl-GPE (18:1), 1-linoleoyl-GPE (18:2), 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4), alpha-ketobutyrate, and 1-palmitoyl-GPE (16:0) mediated 26.0% of the association between current smoking and BMI. CONCLUSIONS: We catalogued plasma metabolites altered by cigarette smoking and identified 5 metabolites that partially mediated the association between current smoking and BMI.

We conducted a metabolome-wide association study to identify blood pressure (BP)-related metabolites among 825 white and 436 African-American Bogalusa Heart Study (BHS) participants. After quality control, 1,202 metabolites were tested for association with systolic BP (SBP), diastolic BP (DBP), and hypertension in race-stratified analyses. Multiple regressions were used to adjust for age, sex, education, cigarette smoking, alcohol drinking, physical activity, and body mass index. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites that associated with covariable adjusted BP phenotypes. After Bonferroni corrections, 16 metabolites from amino acid, cofactor and vitamin, lipid, carbohydrate, and peptide pathways in whites and one from the carbohydrate pathway in African-Americans significantly associated with BP phenotypes. Two novel and one previously reported metabolite consistently associated with hypertension across race groups ( Table ). Among whites, modules consisting primarily of lipid metabolites including phosphatidylcholines, phosphatidylinositol and lysophospholipids ( P =1.0х10 -3 and 6.0х10 -4 for SBP and DBP, respectively), monoacylglycerols and lysophospholipids ( P =3.0х10 -4 and 7.0х10 -5 for SBP and DBP, respectively), and phosphatidylethanolamines, mono- and diacylglycerols, and lysophospholipids ( P =4.0х10 -4 and 4.9х10 -4 for SBP and hypertension, respectively) were identified. In addition, modules consisting primarily of amino acids and peptides such as gamma-glutamyl amino acid metabolites and those involved in leucine, isoleucine and valine metabolism were significant in both African-Americans (P= 2.0х10 -3 and 1.5х10 -3 for DBP and hypertension, respectively) and whites ( P =7.1х10 -4 for hypertension). In aggregate, the current analysis identified novel metabolites and metabolite modules involved in BP regulation. These data add to the accumulating evidence that serum metabolites may play an important role in BP.

Background: Large scale untargeted metabolomics studies are needed to understand the mechanisms of arterial stiffness. Methods: We performed untargeted metabolomics profiling among 1,239 participants of the biracial Bogalusa Heart Study. After quality control, 1,202 metabolites were evaluated for associations with augmentation index (AI) and pulse-wave velocity (PWV) overall and by race, adjusting for age, sex, education, smoking, drinking, body mass index, and physical activity. Weighted correlation network analysis (WGCNA) was applied to build metabolites modules among all participants. Bonferroni correction was used to determine significant metabolites. Significant metabolites should also have P Results: We identified 4 and 17 novel metabolites associated with AI and PWV, respectively ( Table ). We also replicated associations of 12 previously reported metabolites with PWV in the overall sample, including 1,5-anhydroglucitol ( P =5.55E-9), glucose ( P =8.19E-14), glutamic acid ( P =1.56E-8), glycine ( P =9.87E-6), serine ( P =0.003), urea ( P =0.03), uridine ( P =0.002), glutamine ( P =0.001), 3-phenylpropionate ( P =0.004), trans-4-hydroxyproline ( P =0.001), pyruvate ( P =0.002), and lysine ( P =0.007). WGCNA identified two modules in significant associations with both AI ( P =3E-4 and 8E-4, respectively) and PWV ( P =2E-6 and 7E-5, respectively). One module was composed of metabolites of glycerolipids recycling pathway. The other module consisted of amino acids involved in glutamate, leucine, isoleucine, valine, methionione, systein, taurine, and alanine metabolisms. WGCNA also identified a network of sphingolipid metabolism for PWV ( P =0.002). Investigation to hub metabolites of these modules identified 3 novel metabolites for AI and 4 novel metabolites for PWV ( Table ). Conclusions: The current study identified important metabolites and metabolites networks associated with AI and PWV.

Background: A history of asthma from childhood has been related to various risk factors affecting left ventricular (LV) remodeling; however, no prospective study has analyzed the impact of asthma on markers of LV remodeling. Methods: Prospective analyses were performed among 1177 Bogalusa Heart Study participants (average age at follow-up: 36.3±8.3), with a baseline history of self-reported asthma collected since childhood (mean first diagnosed age: 25.2±10.3). LV mass was assessed using 2-dimensional guided M-mode echocardiography, and indexed for body height (m 2.7 ) as LV mass index (LVMI, g/m 2.7 ). A multivariate linear mixed model was fitted for the repeated measures. Results: After an average of 9.1 years follow-up, participants with a history of asthma showed significantly greater LV mass (166.9 vs. 156.3, p=0.007) and LVMI (40.1 vs. 37.1, p=0.002) than those without asthma, with adjustment for age, gender, race, smoking status, antihypertensive medicine, heart rate and systolic blood pressure (SBP) at follow-up. Also, we found significant interactions between SBP and asthma on LV mass and LVMI (p for interaction= 0.008 and 0.006). The associations between asthma and LV measures appeared to be stronger among prehypertensive participants (SBP≥130 mm/Hg) compared with those with normal SBP ( Conclusions: Our findings indicate that a history of asthma is associated with higher LV mass, and such association is stronger among individuals with prehypertension.

Recent advances in sequencing technology have made it feasible to characterize human gut microbial communities (or microbiota) and examine their associations with disease outcomes, including cardiovascular disease (CVD) related traits. However, there is a paucity of research examining how the human gut microbiome may influence overall CVD risk in population based studies. The objective of the current study was to identify composite measures of microbiota diversity and specific microbial genera that associate with CVD risk among participants of the Bogalusa Heart Study (BHS). The current BHS cohort includes 1,257 participants who have been screened at least two times in childhood and two times in adulthood for CVD risk factors. For the microbiome study, we selected the 55 participants at highest CVD risk and 57 participants at lowest CVD risk using a z-score based data reduction technique, leveraging longitudinal mean measures of systolic BP, low-density lipoprotein cholesterol, and fasting plasma glucose levels. Next generation 16S rRNA sequencing of the V4 hypervariable region was conducted using genomic bacterial DNA extracted from stool samples of the 112 participants. The Wilcoxon rank sum test was used to examine differences in measures of alpha diversity, including observed operational taxonomic units (OTUs) and the Chao1 index, along with specific microbial taxa between CVD risk groups. Permutation multivariate analysis of variance (PERMANOVA) was used to test compositional differences based on unweighted Unifrac distance as a measure of beta diversity. We observed significantly less alpha diversity in those at high versus those at low CVD risk (P=0.04 and 0.02 for observed OTU and Chao1 index measures, respectively). The PERMANOVA test indicated marginal significance of compositional differences between the two groups (P=0.07). In addition, 10 microbial genera significantly differed between high and low CVD risk participants, including Faecalibacterium (P=0.0014), Subdoligranulum (P=0.01), Turicibacter (P=0.01), Rothia (P=0.01), Lachnospira (P=0.01), Haemophilus (P=0.03), Dialister (P=0.03), Bifidobacterium (P=0.03), Oscillibacter (P=0.04), and Megamonas (P=0.05). In summary, we provide empirical evidence of lower microbial enrichment among BHS participants at high versus those at low CVD risk, along with promising findings of differences in specific microbial genera between CVD risk groups.

Higher blood pressure (BP) response to cold pressor test (CPT) is associated with increased risk of hypertension and cardiovascular diseases. However, it is unknown whether BP response to CPT is a stable and reproducible trait. We repeated the CPT among 568 Han Chinese who participated in the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) four years after the original study. The same CPT protocol was applied in the original and repeated studies. BP was measured prior to and at 0, 1, 2, and 4 minutes after the participants immersed their hand in ice water (3 o C to 5 o C) for 1 minute using a standard mercury sphygmomanometer. On average, study participants were 39.0 years old and 54.0% of them were male. The mean body mass index was 23.6 kg/m 2 , systolic BP was 117.8 mmHg, and diastolic BP was 74.5 mmHg at baseline among study participants. The mean (standard deviation) of systolic BP responses at time 0 and 1 minutes, maximum responses, and area-under-the-curve during CPT were 13.3 (10.1), 4.2 (6.0), 13.6 (9.8) and 10.8 (17.6) mmHg in the original study and 11.1 (9.5), 4.1 (6.0), 11.7 (8.9), and 10.1 (17.1) mmHg in the repeated study. BP responses in the original and repeated studies were highly correlated. For example, the correlation coefficients for systolic BP responses to CPT were 0.4137 at time 0 minute, 0.3711 at time 1 minute, 0.4221 for the maximum responses, and 0.3641 for area-under-the-curve during CPT (all p