Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

AIMS: Proteomic profiling offers an expansive approach to biomarker discovery and mechanistic hypothesis generation for LV remodelling, a critical component of heart failure (HF). We sought to identify plasma proteins cross-sectionally associated with left ventricular (LV) size and geometry in a diverse population-based cohort without known cardiovascular disease (CVD).

METHODS AND RESULTS: Among participants of the Multi-Ethnic Study of Atherosclerosis (MESA), we quantified plasma abundances of 1305 proteins using an aptamer-based platform at exam 1 (2000-2002) and exam 5 (2010-2011) and assessed LV structure by cardiac magnetic resonance (CMR) at the same time points. We used multivariable linear regression with robust variance to assess cross-sectional associations between plasma protein abundances and LV structural characteristics at exam 1, reproduced findings in later-life at exam 5, and explored relationships of associated proteins using annotated enrichment analysis. We studied 763 participants (mean age 60 ± 10 years at exam 1; 53% female; 19% Black race; 31% Hispanic ethnicity). Following adjustment for renal function and traditional CVD risk factors, plasma levels of 3 proteins were associated with LV mass index at both time points with the same directionality (FDR < 0.05): leptin (LEP), renin (REN), and cathepsin-D (CTSD); 20 with LV end-diastolic volume index: LEP, NT-proBNP, histone-lysine N-methyltransferase (EHMT2), chordin-like protein 1 (CHRDL1), tumour necrosis factor-inducible gene 6 protein (TNFAIP6), NT-3 growth factor receptor (NTRK3), c5a anaphylatoxin (C5), neurogenic locus notch homologue protein 3 (NOTCH3), ephrin-B2 (EFNB2), osteomodulin (OMD), contactin-4 (CNTN4), gelsolin (GSN), stromal cell-derived factor 1 (CXCL12), calcineurin subunit B type 1 (PPP3R1), insulin-like growth factor 1 receptor (IGF1R), bone sialoprotein 2 (IBSP), interleukin-11 (IL-11), follistatin-related protein 1 (FSTL1), periostin (POSTN), and biglycan (BGN); and 4 with LV mass-to-volume ratio: RGM domain family member B (RGMB), transforming growth factor beta receptor type 3 (TGFBR3), ephrin-A2 (EFNA2), and cell adhesion molecule 3 (CADM3). Functional annotation implicated regulation of the PI3K-Akt pathway, bone morphogenic protein signalling, and cGMP-mediated signalling.

CONCLUSIONS: We report proteomic profiling of LV size and geometry, which identified novel associations and reinforced previous findings on biomarker candidates for LV remodelling and HF. If validated, these proteins may help refine risk prediction and identify novel therapeutic targets for HF.

Adverse cardiovascular events are emerging with the use of immune checkpoint therapies in oncology. Using datasets in the Trans-Omics for Precision Medicine program (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study [JHS], and Framingham Heart Study), we examined the association of immune checkpoint plasma proteins with each other, their associated protein network with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), and the association of HDL-C- and LDL-C-associated protein networks with all-cause mortality risk. Plasma levels of LAG3 and HAVCR2 showed statistically significant associations with mortality risk. Colocalization analysis using genome wide-association studies of HDL-C or LDL-C and protein quantitative trait loci from JHS and the Atherosclerosis Risk in Communities identified TFF3 rs60467699 and CD36 rs3211938 variants as significantly colocalized with HDL-C; in contrast, none colocalized with LDL-C. The measurement of plasma LAG3, HAVCR2, and associated proteins plus targeted genotyping may identify patients at increased mortality risk.

BACKGROUND: Post-traumatic stress disorder (PTSD) is characterized by severe distress and associated with cardiometabolic diseases. Studies in military and clinical populations suggest that dysregulated metabolomic processes may be a key mechanism. Prior work identified and validated a metabolite-based distress score (MDS) linked with depression and anxiety and subsequent cardiometabolic diseases. Here, we assessed whether PTSD shares metabolic alterations with depression and anxiety and if additional metabolites are related to PTSD.

METHODS: We leveraged plasma metabolomics data from three subsamples nested within the Nurses' Health Study II, including 2835 women with 2950 blood samples collected across three time points (1996-2014) and 339 known metabolites assayed by mass spectrometry-based techniques. Trauma and PTSD exposures were assessed in 2008 and characterized as follows: lifetime trauma without PTSD, lifetime PTSD in remission, and persistent PTSD symptoms. Associations between the exposures and the MDS or individual metabolites were estimated within each subsample adjusting for potential confounders and combined in random-effects meta-analyses.

RESULTS: Persistent PTSD symptoms were associated with higher levels of the previously developed MDS. Out of 339 metabolites, we identified 29 metabolites (primarily elevated glycerophospholipids and glycerolipids) associated with persistent symptoms (false discovery rate < 0.05; adjusting for technical covariates). No metabolite associations were found with the other PTSD-related exposures.

CONCLUSIONS: As the first large-scale, population-based metabolomics analysis of PTSD, our study highlighted shared and distinct metabolic differences linked to PTSD versus depression or anxiety. We identified novel metabolite markers associated with PTSD symptom persistence, suggesting further connections with metabolic dysregulation that may have downstream consequences for health.

Angiopoietin-like 3 (ANGPTL3) is a key regulator of lipoprotein metabolism, known for its potent inhibition on intravascular lipoprotein and endothelial lipase activities. Recent studies have shed light on the cellular functions of ANGPTL3. However, the precise mechanism underlying its regulation of cellular lipid metabolism remains elusive. We recently reported that ANGPTL3 interacts with the chromatin regulator SMARCAL1, which plays a pivotal role in maintaining cellular lipid homeostasis. Here, through a combination of in vitro and in vivo functional analyses, we provide evidence that ANGPTL3 indeed influences cellular lipid metabolism. Increased expression of Angptl3 prompted the formation of lipid droplets (LDs) in response to slow growth conditions. Notably, under the conditions, Angptl3 accumulated within cytoplasmic peroxisomes, where it interacts with SmarcAL1, which translocated from nucleus as observed previously. This translocation induced changes in gene expression favoring triglyceride (TG) accumulation. Indeed, ANGPTL3 gene knockout (KO) in human cells increased the expression of key lipid genes, which could be linked to elevated nuclear localization of SMARCAL1, whereas the expression of these genes decreased in SMARCAL1 KO cells. Consistent with these findings, the injection of Angptl3 protein to mice led to hepatic fat accumulation derived from circulating blood, a phenotype likely indicative of its long-term effect on blood TG, linked to SmarcAL1 activities. Thus, our results suggest that the Angptl3-SmarcAL1 pathway may confer the capacity for TG storage in cells in response to varying growth states, which may have broad implications for this pathway in regulating energy storage and trafficking.

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants (pQTL) and phenotypes. Here, we examined 2,157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1,930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2,157 proteins followed a bimodal distribution (median r=0.30). Protein measures from each platform were associated with genetic variants (pQTLs), and one-third of the pQTL signals were driven by protein-altering variants (PAVs). We highlight 80 proteins that correlate differently across ancestry groups likely due to differing PAV frequencies by ancestry. Furthermore, adjustment for PAVs with opposite directions of effect by platform improved inter-platform protein measure correlation and resulted in more concordant genetic and phenotypic associations. Hence, PAVs need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

Metabolome-based biomarkers contribute to identify mechanisms of disease and to a better understanding of overall mortality. In a long-term follow-up subsample (n = 1878) of the PREDIMED trial, among 337 candidate baseline plasma metabolites repeatedly assessed at baseline and after 1 year, 38 plasma metabolites were identified as predictors of all-cause mortality. Gamma-amino-butyric acid (GABA), homoarginine, serine, creatine, 1-methylnicotinamide and a set of sphingomyelins, plasmalogens, phosphatidylethanolamines and cholesterol esters were inversely associated with all-cause mortality, whereas plasma dimethylguanidino valeric acid (DMGV), choline, short and long-chain acylcarnitines, 4-acetamidobutanoate, pseudouridine, 7-methylguanine, N6-acetyllysine, phenylacetylglutamine and creatinine were associated with higher mortality. The multi-metabolite signature created as a linear combination of these selected metabolites, also showed a strong association with all-cause mortality using plasma samples collected at 1-year follow-up in PREDIMED. This association was subsequently confirmed in 4 independent American cohorts, validating the signature as a consistent predictor of all-cause mortality across diverse populations.

BACKGROUND: Metabolic-inflammatory states are central to multiorgan mechanisms of aging, but precise functional biomarkers of physiological aging remain less clear.

METHODS: In the Health, Aging and Body Composition study, we defined metabolomic profiles of the Healthy Aging Index (HAI), a composite of cardiovascular, lung, cognitive, metabolic, and renal function (0-10, with higher scores indicating poorer health) in a split set design from 2015 older participants (mean age 73.6 years; 50% women; 35% Black). We used standard regression to identify metabolomic correlates of Year 1 and Year 10 HAI, change in HAI over time, and mortality. A metabolite score of HAI was developed using LASSO regression.

RESULTS: We identified 42 metabolites consistently associated with Year 1 and Year 10 HAI, as well as change in HAI: 13 lipids, 4 amino acids, and 4 metabolites of other classes were associated with worse and worsening HAI while 20 lipids and 1 amino acid was associated with better and improving HAI. Most of these associations were no longer significant after additionally adjusting for inflammation biomarkers. A higher metabolite score of Year 1 HAI was associated with greater HAI deterioration over time (hold-out "test" set beta 0.40 [0.15-0.65]) and higher mortality (hold-out "test" set hazard ratio: 1.43 [1.23-1.67]).

CONCLUSIONS: A multi-organ healthy aging phenotype was linked to lipid metabolites, suggesting potential pathways related to mitochondrial function, oxidative stress and inflammation. Metabolomics of HAI at older age were related to worsening health and mortality, suggesting potential links between metabolism and accelerated physiological aging.

BACKGROUND: The APOL1 high-risk haplotype has been associated with chronic kidney disease (CKD) and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated.

METHODS: We compared methylation (N = 3,191; 715 [22%] carriers), proteomic (N = 1,240; 169 [14%] carriers), and metabolomic (N = 6,309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and non-carriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the PAGE Consortium and UK BioBank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 CpG sites, 2,923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size weighted meta-analyses.

RESULTS: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1 - 4 and MYH9. All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1-associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. Additionally, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein (β = 1.12, PFDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z = -4.50, PFDR = 4.83e-3).

CONCLUSIONS: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.