Background

The marked excess in cardiovascular mortality that results from uremia remains poorly understood.

Methods and results

In 2 independent, nested case-control studies, we applied liquid chromatography-mass spectrometry-based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with 100 individuals who survived for at least 1 year (controls), matched for age, sex, and race. Four highly intercorrelated long-chain acylcarnitines achieved the significance threshold adjusted for multiple testing (P<0.0003). Oleoylcarnitine, the long-chain acylcarnitine with the strongest association with cardiovascular mortality in unadjusted analysis, remained associated with 1-year cardiovascular death after multivariable adjustment (odds ratio per SD 2.3 [95% confidence interval, 1.4 to 3.8]; P=0.001). The association between oleoylcarnitine and 1-year cardiovascular death was then replicated in an independent sample (n=300, odds ratio per SD 1.4 [95% confidence interval, 1.1 to 1.9]; P=0.008). Addition of oleoylcarnitine to clinical variables improved cardiovascular risk prediction using net reclassification (NRI, 0.38 [95% confidence interval, 0.20 to 0.56]; P<0.0001). In physiologic profiling studies, we demonstrate that the fold change in plasma acylcarnitine levels from the aorta to renal vein and from pre- to posthemodialysis samples exclude renal or dialytic clearance of long-chain acylcarnitines as confounders in our analysis.

Conclusions

Our data highlight clinically meaningful alterations in acylcarnitine homeostasis at the time of dialysis initiation, which may represent an early marker, effector, or both of uremic cardiovascular risk.

Background

Whereas several longitudinal metabolomics studies have been conducted in individuals with normal estimated glomerular filtration rate (eGFR) at baseline, disease progression among individuals with established chronic kidney disease (CKD) has not been rigorously examined.

Methods

We performed a nested case-control study of rapid CKD progression in the Chronic Renal Insufficiency Cohort Study, profiling baseline plasma from 200 individuals each with eGFR slope <-3 ml/min/1.73 m2/year (cases) or between -1 and +1 ml/min/1.73 m2/year (controls), matched on baseline eGFR and proteinuria. To directly assess how the kidney modulates circulating metabolites, we profiled plasma from the aorta and renal vein of 25 hospital-based individuals.

Results

At baseline, cases and controls had a mean eGFR of 41.7 ± 13.3 and 45.0 ± 14.5 ml/min/1.73 m2, respectively. Ten plasma metabolites were nominally associated with CKD progression in logistic regression models adjusted for age, sex, race/ethnicity, hypertension, systolic and diastolic blood pressure, diabetes, eGFR and proteinuria; no metabolite achieved the Bonferroni-adjusted significance threshold (p < 0.0003). In a cross-sectional analysis, all 6 of the metabolites that were higher in cases than controls were significantly associated with eGFR at baseline. By contrast, threonine, methionine and arginine were lower in cases than in controls and had no association with baseline eGFR. Furthermore, in the hospital-based cohort that underwent renal arteriovenous sampling, these 3 metabolites were net released from the kidney. Combining these metabolites into a panel of markers further strengthened their association with CKD progression.

Conclusion

Our results motivate interest in arginine, methionine and threonine as potential indicators of renal metabolic function and markers of renal prognosis.

Objective

To identify prediagnostic plasma metabolomic biomarkers associated with amyotrophic lateral sclerosis (ALS).

Methods

We conducted a global metabolomic study using a nested case-control study design within 5 prospective cohorts and identified 275 individuals who developed ALS during follow-up. We profiled plasma metabolites using liquid chromatography-mass spectrometry and identified 404 known metabolites. We used conditional logistic regression to evaluate the associations between metabolites and ALS risk. Further, we used machine learning analyses to determine whether the prediagnostic metabolomic profile could discriminate ALS cases from controls.

Results

A total of 31 out of 404 identified metabolites were associated with ALS risk (p < 0.05). We observed inverse associations (n = 27) with plasma levels of diacylglycerides and triacylglycerides, urate, purine nucleosides, and some organic acids and derivatives, while we found positive associations for a cholesteryl ester, 2 phosphatidylcholines, and a sphingomyelin. The number of significant associations increased to 67 (63 inverse) in analyses restricted to cases with blood samples collected within 5 years of onset. None of these associations remained significant after multiple comparison adjustment. Further, we were not able to reliably distinguish individuals who became cases from controls based on their metabolomic profile using partial least squares discriminant analysis, elastic net regression, random forest, support vector machine, or weighted correlation network analyses.

Conclusions

Although the metabolomic profile in blood samples collected years before ALS diagnosis did not reliably separate presymptomatic ALS cases from controls, our results suggest that ALS is preceded by a broad, but poorly defined, metabolic dysregulation years before the disease onset.

The microbiota modulates gut immune homeostasis. Bacteria influence the development and function of host immune cells, including T helper cells expressing interleukin-17A (T_H17 cells). We previously reported that the bile acid metabolite 3-oxolithocholic acid (3-oxoLCA) inhibits T_H17 cell differentiation¹. Although it was suggested that gut-residing bacteria produce 3-oxoLCA, the identity of such bacteria was unknown, and it was unclear whether 3-oxoLCA and other immunomodulatory bile acids are associated with inflammatory pathologies in humans. Here we identify human gut bacteria and corresponding enzymes that convert the secondary bile acid lithocholic acid into 3-oxoLCA as well as the abundant gut metabolite isolithocholic acid (isoLCA). Similar to 3-oxoLCA, isoLCA suppressed T_H17 cell differentiation by inhibiting retinoic acid receptor-related orphan nuclear receptor-γt, a key T_H17-cell-promoting transcription factor. The levels of both 3-oxoLCA and isoLCA and the 3α-hydroxysteroid dehydrogenase genes that are required for their biosynthesis were significantly reduced in patients with inflammatory bowel disease. Moreover, the levels of these bile acids were inversely correlated with the expression of T_H17-cell-associated genes. Overall, our data suggest that bacterially produced bile acids inhibit T_H17 cell function, an activity that may be relevant to the pathophysiology of inflammatory disorders such as inflammatory bowel disease.

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A, and trans interactions between LAG3 and B2M. A LAG3-associated protein network has significant associations with HDL-C and mortality.

Background

Alterations in gut microbiota and blood metabolomic profiles have been implicated in HIV infection and cardiovascular disease. However, it remains unclear whether alterations in gut microbiota may contribute to disrupted host blood metabolomic profiles in relation to atherosclerosis, especially in the context of HIV infection.

Methods

We analyzed cross-sectional associations between gut microbiota features and carotid artery plaque in 361 women with or at high risk of HIV (67% HIV+), and further integrated plaque-associated microbial features with plasma lipidomic/metabolomic profiles. Furthermore, in 737 women and men, we examined prospective associations of baseline gut bacteria-associated lipidomic and metabolomic profiles with incident carotid artery plaque over 7-year follow-up.

Results

We found 2 potentially pathogenic bacteria, Fusobacterium and Proteus, were associated with carotid artery plaque; while the beneficial butyrate producer Odoribacter was inversely associated with plaque. Fusobacterium and Proteus were associated with multiple lipids/metabolites which were clustered into 8 modules in network. A module comprised of 9 lysophosphatidylcholines and lysophosphatidylethanolamines and a module comprised of 9 diglycerides were associated with increased risk of carotid artery plaque (risk ratio [95% CI], 1.34 [1.09-1.64] and 1.24 [1.02-1.51] per SD increment, respectively). Functional analyses identified bacterial enzymes in lipid metabolism associated with these plasma lipids. In particular, phospholipase A1 and A2 are the key enzymes in the reactions producing lysophosphatidylcholines and lysophosphatidylethanolamines.

Conclusions

Among individuals with or at high risk of HIV infection, we identified altered gut microbiota and related functional capacities in the lipid metabolism associated with disrupted plasma lipidomic profiles and carotid artery atherosclerosis.

Fibroblast growth factor 23 (FGF23) is a bone-derived hormone that controls blood phosphate levels by increasing renal phosphate excretion and reducing 1,25-dihydroxyvitamin D3 [1,25(OH)2D] production. Disorders of FGF23 homeostasis are associated with significant morbidity and mortality, but a fundamental understanding of what regulates FGF23 production is lacking. Because the kidney is the major end organ of FGF23 action, we hypothesized that it releases a factor that regulates FGF23 synthesis. Using aptamer-based proteomics and liquid chromatography-mass spectrometry-based (LC-MS-based) metabolomics, we profiled more than 1600 molecules in renal venous plasma obtained from human subjects. Renal vein glycerol-3-phosphate (G-3-P) had the strongest correlation with circulating FGF23. In mice, exogenous G-3-P stimulated bone and bone marrow FGF23 production through local G-3-P acyltransferase-mediated (GPAT-mediated) lysophosphatidic acid (LPA) synthesis. Further, the stimulatory effect of G-3-P and LPA on FGF23 required LPA receptor 1 (LPAR1). Acute kidney injury (AKI), which increases FGF23 levels, rapidly increased circulating G-3-P in humans and mice, and the effect of AKI on FGF23 was abrogated by GPAT inhibition or Lpar1 deletion. Together, our findings establish a role for kidney-derived G-3-P in mineral metabolism and outline potential targets to modulate FGF23 production during kidney injury.

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

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.

Background

Racial differences in metabolomic profiles may reflect underlying differences in social determinants of health by self-reported race and may be related to racial disparities in coronary heart disease (CHD) among women in the United States. However, the magnitude of differences in metabolomic profiles between Black and White women in the United States has not been well-described. It also remains unknown whether such differences are related to differences in CHD risk.

Methods

Plasma metabolomic profiles were analyzed using liquid chromatography-tandem mass spectrometry in the WHI-OS (Women's Health Initiative-Observational Study; 138 Black and 696 White women), WHI-HT trials (WHI-Hormone Therapy; 156 Black and 1138 White women), MESA (Multi-Ethnic Study of Atherosclerosis; 114 Black and 219 White women), JHS (Jackson Heart Study; 1465 Black women with 107 incident CHD cases), and NHS (Nurses' Health Study; 2506 White women with 136 incident CHD cases). First, linear regression models were used to estimate associations between self-reported race and 472 metabolites in WHI-OS (discovery); findings were replicated in WHI-HT and validated in MESA. Second, we used elastic net regression to construct a racial difference metabolomic pattern (RDMP) representing differences in the metabolomic patterns between Black and White women in the WHI-OS; the RDMP was validated in the WHI-HT and MESA. Third, using conditional logistic regressions in the WHI (717 CHD cases and 719 matched controls), we examined associations of metabolites with large differences in levels by race and the RDMP with risk of CHD, and the results were replicated in Black women from the JHS and White women from the NHS.

Results

Of the 472 tested metabolites, levels of 259 (54.9%) metabolites, mostly lipid metabolites and amino acids, significantly differed between Black and White women in both WHI-OS and WHI-HT after adjusting for baseline characteristics, socioeconomic status, lifestyle factors, baseline health conditions, and medication use (false discovery rate <0.05); similar trends were observed in MESA. The RDMP, composed of 152 metabolites, was identified in the WHI-OS and showed significantly different distributions between Black and White women in the WHI-HT and MESA. Higher RDMP quartiles were associated with an increased risk of incident CHD (odds ratio=1.51 [0.97-2.37] for the highest quartile comparing to the lowest; P_trend=0.02), independent of self-reported race and known CHD risk factors. In race-stratified analyses, the RDMP-CHD associations were more pronounced in White women. Similar patterns were observed in Black women from the JHS and White women from the NHS.

Conclusions

Metabolomic profiles significantly and substantially differ between Black and White women and may be associated with CHD risk and racial disparities in US women.