Multidisciplinary approach combining food metabolomics and epidemiology identifies meglutol as an important bioactive metabolite in tempe, an Indonesian fermented food

This study introduces a multidisciplinary approach to investigate bioactive food metabolites often overlooked due to their low concentrations. We integrated an in-house food metabolite library (n = 494), a human metabolite library (n = 891) from epidemiological studies, and metabolite pharmacological databases to screen for food metabolites with potential bioactivity. We identified six potential metabolites, including meglutol (3-hydroxy-3-methylglutarate), an understudied low-density lipoprotein (LDL)-lowering compound. We further focused on meglutol as a case study to showcase the range of characterizations achievable with this approach. Green pea tempe was identified to contain the highest meglutol concentration (21.8 ± 4.6 mg/100 g). Furthermore, we identified a significant cross-sectional association between plasma meglutol (per 1-standard deviation) and lower LDL cholesterol in two Hispanic adult cohorts (n = 1,628) (β [standard error]: -5.5 (1.6) mg/dl, P = 0.0005). These findings highlight how multidisciplinary metabolomics can serve as a systematic tool for discovering and enhancing bioactive metabolites in food, such as meglutol, with potential applications in personalized dietary approaches for disease prevention.

Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.

Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

BACKGROUND

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants.

METHODS

We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value.

FINDINGS

The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5).

INTERPRETATION

This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Tryptophan-kynurenine metabolic pathway and daytime dysfunction in women with HIV

Sleep disturbances are prevalent in women with HIV (WWH). Tryptophan-kynurenine (T-K) pathway metabolites are associated with alterations in actigraphy derived sleep measures in WWH, although may not always correlate with functional impairment. We investigated the relationship between T-K pathway metabolites and self-reported daytime dysfunction in WWH and women without HIV (WWoH). 141 WWH on stable antiretroviral therapy and 140 demographically similar WWoH enrolled in the IDOze Study had targeted plasma T-K metabolites measured using liquid chromatography-tandem mass spectrometry. We utilized the daytime dysfunction component of the Pittsburgh Sleep Quality Index (PSQI) to assess functional impairment across HIV-serostatus. Lower levels of 5-hydroxytryptophan and serotonin were associated with greater daytime dysfunction in all women. In WWH, daytime dysfunction was associated with increased kynurenic acid (R = 0.26, p < 0.05), and kynurenic acid-tryptophan (KA-T) ratio (R = 0.28, p < 0.01). WWH with daytime dysfunction had a 0.7 log fold increase in kynurenic acid compared to WWH without daytime dysfunction. Kynurenic acid levels and the KA-T ratio were associated with daytime dysfunction in WWH but not in WWoH. Longitudinal studies are needed to establish a causal relationship and directionality between T-K metabolic changes and sleep impairment in WWH.

Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: a randomized clinical trial

BACKGROUND

The health benefits of the Mediterranean diet (MedDiet) have been linked to the presence of beneficial gut microbes and related metabolites. However, its impact on the fecal metabolome remains poorly understood.

OBJECTIVES

Our goal was to investigate the weight-loss effects of a 1-y lifestyle intervention based on an energy-reduced MedDiet coupled with physical activity (intervention group), compared with an ad libitum MedDiet (control group), on fecal metabolites, fecal microbiota, and their potential association with cardiovascular disease risk factors.

METHODS

A total of 400 participants (200 from each study group), aged 55-75 y, and at high cardiovascular disease risk, were included. Dietary and lifestyle information, anthropometric measurements, blood biochemical parameters, and stool samples were collected at baseline and after 1 y of follow-up. Liquid chromatography-tandem mass spectrometry was used to profile endogenous fecal metabolites, and 16S amplicon sequencing was employed to profile the fecal microbiota.

RESULTS

Compared with the control group, the intervention group exhibited greater weight loss and improvement in various cardiovascular disease risk factors. We identified intervention effects on 4 stool metabolites and subnetworks primarily composed of bile acids, ceramides, and sphingosines, fatty acids, carnitines, nucleotides, and metabolites of purine and the Krebs cycle. Some of these were associated with changes in several cardiovascular disease risk factors. In addition, we observed a reduction in the abundance of the genera Eubacterium hallii group and Dorea, and an increase in alpha diversity in the intervention group after 1 y of follow-up. Changes in the intervention-related microbiota profiles were also associated with alterations in different fecal metabolite subnetworks and some cardiovascular disease risk factors.

CONCLUSIONS

An intervention based on an energy-reduced MedDiet and physical activity promotion, compared with an ad libitum MedDiet, was associated with improvements in cardiometabolic risk factors, potentially through modulation of the fecal microbiota and metabolome. This trial was registered at https://www.isrctn.com/ as ISRCTN89898870 (https://doi.org/10.1186/ISRCTN89898870).

The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes

AIMS/HYPOTHESIS

Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk.

METHODS

We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk.

RESULTS

We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant.

CONCLUSIONS/INTERPRETATION

The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.

Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Olive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD.

METHODS

The discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training-validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models.

RESULTS

We identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence.

CONCLUSIONS

This study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence.

TRIAL REGISTRATION

The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).

APOE4 impairs the microglial response in Alzheimer's disease by inducing TGFβ-mediated checkpoints

The APOE4 allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). The contribution of microglial APOE4 to AD pathogenesis is unknown, although APOE has the most enriched gene expression in neurodegenerative microglia (MGnD). Here, we show in mice and humans a negative role of microglial APOE4 in the induction of the MGnD response to neurodegeneration. Deletion of microglial APOE4 restores the MGnD phenotype associated with neuroprotection in P301S tau transgenic mice and decreases pathology in APP/PS1 mice. MGnD-astrocyte cross-talk associated with β-amyloid (Aβ) plaque encapsulation and clearance are mediated via LGALS3 signaling following microglial APOE4 deletion. In the brains of AD donors carrying the APOE4 allele, we found a sex-dependent reciprocal induction of AD risk factors associated with suppression of MGnD genes in females, including LGALS3, compared to individuals homozygous for the APOE3 allele. Mechanistically, APOE4-mediated induction of ITGB8-transforming growth factor-β (TGFβ) signaling impairs the MGnD response via upregulation of microglial homeostatic checkpoints, including Inpp5d, in mice. Deletion of Inpp5d in microglia restores MGnD-astrocyte cross-talk and facilitates plaque clearance in APP/PS1 mice. We identify the microglial APOE4-ITGB8-TGFβ pathway as a negative regulator of microglial response to AD pathology, and restoring the MGnD phenotype via blocking ITGB8-TGFβ signaling provides a promising therapeutic intervention for AD.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals

Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined.

Broadcasters, receivers, functional groups of metabolites and the link to heart failure progression using polygenic factors

In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Multiset correlation and factor analysis enables exploration of multi-omics data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Lactate limits CNS autoimmunity by stabilizing HIF-1α in dendritic cells

Dendritic cells (DCs) have a role in the development and activation of self-reactive pathogenic T cells1,2. Genetic variants that are associated with the function of DCs have been linked to autoimmune disorders3,4, and DCs are therefore attractive therapeutic targets for such diseases. However, developing DC-targeted therapies for autoimmunity requires identification of the mechanisms that regulate DC function. Here, using single-cell and bulk transcriptional and metabolic analyses in combination with cell-specific gene perturbation studies, we identify a regulatory loop of negative feedback that operates in DCs to limit immunopathology. Specifically, we find that lactate, produced by activated DCs and other immune cells, boosts the expression of NDUFA4L2 through a mechanism mediated by hypoxia-inducible factor 1α (HIF-1α). NDUFA4L2 limits the production of mitochondrial reactive oxygen species that activate XBP1-driven transcriptional modules in DCs that are involved in the control of pathogenic autoimmune T cells. We also engineer a probiotic that produces lactate and suppresses T cell autoimmunity through the activation of HIF-1α-NDUFA4L2 signalling in DCs. In summary, we identify an immunometabolic pathway that regulates DC function, and develop a synthetic probiotic for its therapeutic activation.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFAs) is implicated in the pathogenesis of obesity-associated diseases. However, there are no scalable approaches to comprehensively assess the diverse FFAs circulating in human plasma. Furthermore, assessing how FFA-mediated processes interact with genetic risk for disease remains elusive. Here, we report the design and implementation of fatty acid library for comprehensive ontologies (FALCON), an unbiased, scalable, and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids associated with decreased membrane fluidity. Furthermore, we prioritized genes that reflect the combined effects of harmful FFA exposure and genetic risk for type 2 diabetes (T2D). We found that c-MAF-inducing protein (CMIP) protects cells from FFA exposure by modulating Akt signaling. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Engineered probiotics limit CNS autoimmunity by stabilizing HIF-1α in dendritic cells

Dendritic cells (DCs) control the generation of self-reactive pathogenic T cells. Thus, DCs are considered attractive therapeutic targets for autoimmune diseases. Using single-cell and bulk transcriptional and metabolic analyses in combination with cell-specific gene perturbation studies we identified a negative feedback regulatory pathway that operates in DCs to limit immunopathology. Specifically, we found that lactate, produced by activated DCs and other immune cells, boosts NDUFA4L2 expression through a mechanism mediated by HIF-1α. NDUFA4L2 limits the production of mitochondrial reactive oxygen species that activate XBP1-driven transcriptional modules in DCs involved in the control of pathogenic autoimmune T cells. Moreover, we engineered a probiotic that produces lactate and suppresses T-cell autoimmunity in the central nervous system via the activation of HIF-1α/NDUFA4L2 signaling in DCs. In summary, we identified an immunometabolic pathway that regulates DC function, and developed a synthetic probiotic for its therapeutic activation.

Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease

For decades, variability in clinical efficacy of the widely used inflammatory bowel disease (IBD) drug 5-aminosalicylic acid (5-ASA) has been attributed, in part, to its acetylation and inactivation by gut microbes. Identification of the responsible microbes and enzyme(s), however, has proved elusive. To uncover the source of this metabolism, we developed a multi-omics workflow combining gut microbiome metagenomics, metatranscriptomics and metabolomics from the longitudinal IBDMDB cohort of 132 controls and patients with IBD. This associated 12 previously uncharacterized microbial acetyltransferases with 5-ASA inactivation, belonging to two protein superfamilies: thiolases and acyl-CoA N-acyltransferases. In vitro characterization of representatives from both families confirmed the ability of these enzymes to acetylate 5-ASA. A cross-sectional analysis within the discovery cohort and subsequent prospective validation within the independent SPARC IBD cohort (n = 208) found three of these microbial thiolases and one acyl-CoA N-acyltransferase to be epidemiologically associated with an increased risk of treatment failure among 5-ASA users. Together, these data address a longstanding challenge in IBD management, outline a method for the discovery of previously uncharacterized gut microbial activities and advance the possibility of microbiome-based personalized medicine.

Abstract P201: Plasma Proteomic Signatures of Angptl3/8 and 4/8 Before and After Exercise Training

Introduction: Angiopoietin like protein (ANGPTL) complexes 3/8 and 4/8 are established inhibitors of lipoprotein lipase and modifiable by regular exercise. However, the molecular biomarkers related to their exercise responses have not been fully elucidated. The purpose of this study was to examine the associations between plasma proteins and ANGPTL3/8 and 4/8 before and after exercise training.

Methods: Measurements were taken before and after 20 weeks of exercise training in 630 adults (36% Black, 56% women, mean age 35 yrs) of the HERITAGE Family Study. Meso Scale Discovery immunoassays were used to measure ANGPTL3/8 and ANGPTL4/8 complexes in serum. Plasma proteins (n=4979 aptamers) were quantified using SomaScan. Linear mixed models tested the association between plasma proteins and each trait at baseline and post- training with full covariate adjustment. Significance was set to FDR<0.05.

Results: A total of 862 and 126 proteins were significantly associated with ANGPTL3/8 at baseline or post-training, respectively, with 80 proteins associated at both time points, including PCSK9 and PLTP ( Figure 1A ). Conversely, 46 proteins were only associated with ANGPTL3/8 at post-training, 23 of which whose levels significantly changed with training including NRP1, ghrelin, HHIP, and APOA1. A total of 433 and 71 proteins were significantly associated with ANGPTL4/8 at baseline or post-training, respectively, with 46 proteins associated with both time points ( Figure 1B ). A total of 25 proteins were only associated with ANGPTL4/8 at post-training, 12 of which whose levels significantly changed with training including ghrelin, COL6A2, and MMP16.

Conclusions: We identified a subset of proteins uniquely associated with exercise-induced changes in ANGPTL3/8 and 4/8. Thus, the responsiveness of ANGPTL3/8 and 4/8 to regular exercise may be related to cholesterol esterification, lipoprotein remodeling, inflammatory response, and regulation of fibrinolysis and coagulation among other biological pathways.

Abstract MP22: Plasma Metabolomic Profiles Associated With Mortality and Longevity in a Prospective Study of 13,401 Individuals

Background: Recent advances in metabolomic studies have shown promise in elucidating the biological pathways underpinning aging processes and mortality in animal models, but data in humans are lacking. We aimed to evaluate the associations between metabolite profiles, all-cause and cause-specific mortality, and longevity.

Methods: Within three prospective cohorts (Nurses’ Health Study [NHS], NHSII, and Health Professional’s Follow-up Study), we measured plasma metabolites from 11,523 participants (mean age 54 years, 86% female) using high-throughput liquid chromatography-mass spectrometry. Participants were free of cardiovascular disease and cancer at blood collection. Metabolome-wide association analyses were conducted for all-cause, cardiovascular, and cancer mortality using Cox proportional hazards regression and longevity (attaining 85 years of age) using logistic regression. Both pre-defined and data-driven metabolite groups were also evaluated. We further developed a multi-metabolite profile score for all-cause mortality using an elastic-net regularized Cox model and assessed its associations with mortality and longevity. Results for all-cause mortality were replicated among 1878 participants (mean age 67 years, 58% female) from the PREDIMED trial.

Results: We documented 4252 deaths (including 864 cardiovascular deaths and 1070 cancer deaths) and 3048 achieving longevity over a median follow-up of 22.6 years in the NHS/NHSII/HPFS, and 126 deaths during a follow-up of 4.7 years in the PREDIMED. Higher levels of three nucleosides (N2,N2-dimethylguanosine, pseudouridine, and N4-acetylcytidine), 4-acetamidobutanoic acid, triacylglycerols with ≤56 carbons and ≤3 double bonds, and several other lipids were associated with increased risk of all-cause mortality and corresponding decreased likelihood of longevity; whereas L-serine, L-glutamine, and TAGs with >56 carbons or >3 double bonds were associated with lower mortality risk and higher odds of achieving longevity. A multi-metabolite profile score comprising 73 metabolites was positively associated with all-cause (HR per 1-SD increment=1.25 [95% CI: 1.21, 1.30] in NHS/NHSII/HPFS and 1.47 [95% CI: 1.22, 1.78] in PREDIMED), cardiovascular (HR=1.34 [95% CI:1.24, 1.45]), and cancer mortality (HR=1.15 [95% CI:1.08, 1.24]) and inversely associated with longevity (OR=0.79 [95% CI: 0.74, 0.86]).

Conclusions: We identified multiple metabolites associated with mortality and longevity. Our findings provide insights into the biological pathways that lead to death and open up new avenues to incorporate these metabolomic markers in clinical and research settings.

Abstract P203: Healthy Lifestyle Plasma Metabolite Profile and Risk of Mortality in US Prospective Cohort Studies

Background: A healthy lifestyle is associated with a lower risk of premature death. Metabolic pathways of a healthy lifestyle and their association with mortality remain to be understood. This study aimed to identify the metabolomic profile of a healthy lifestyle score and examine its prospective association with all-cause and cause-specific mortality, including death from cardiovascular disease (CVD) and cancer.

Methods: The population included 12,146 participants from the Nurses’ Health Study (NHS), NHS II and Health Professionals Follow-Up Study (HPFS)(83% women, 97% white, aged 55±9y). Plasma metabolites were profiled using high-throughput liquid chromatography mass-spectrometry at baseline (NHS:1989-1990; NHSII:1996-1999; HPFS:1993-1995). The healthy lifestyle score was computed by summing the total number of healthy lifestyle factors participants adhered to from validated questionnaires at baseline: healthy diet (Alternative Healthy Eating Index, upper 40%), moderate alcohol intake (women: 5-15 g/d; men: 5-30 g/d), moderate-to-vigorous physical activity (≥30min/d), never smoking and normal BMI (18.5-24.9kg/m 2 ). Deaths were ascertained with death certificates and medical records. The metabolite profile was identified using elastic net regressions with train test validation split (70-30%). Metabolic pathways were determined using Metabolite Set Enrichment Analysis (MSEA). Multivariable-adjusted Cox proportional hazards regressions were used to estimate hazard ratios and 95% confidence intervals (HR[CI]) per unit of score of the healthy lifestyle metabolite profile with mortality risk.

Results: The identified profile included 88 metabolites and correlated with the healthy lifestyle score (Pearson r=0.43-0.44; p<0.001). Triglyceride and diglyceride metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were directly associated (p<0.001). Among individual lifestyle factors, the profile was most strongly correlated with normal BMI (r pb =0.43; p<0.001). Over 32y of follow-up, there were 3,851 deaths, including 749 deaths from CVD and 994 from cancer. Participants with a higher healthy lifestyle metabolite profile score had lower risk of all-cause (HR=0.79[0.73, 0.85]) and CVD mortality (HR=0.77[0.58, 0.95]), but not cancer (HR=0.91[0.77, 1.05]). Significant associations persisted after further adjustment for the healthy lifestyle score.

Conclusions: In US adults, we identified a metabolite profile related to a healthy lifestyle largely reflecting lipid metabolism pathways. A higher metabolite score was associated with lower subsequent all-cause mortality risk, specifically from CVD. Findings provide novel insights into potential metabolic pathways underlying the association between a healthy lifestyle and lower premature mortality.

Abstract MP58: A Plasma Metabolite Score Related to Psychological Distress and Future Diabetes Risk: A Nested Case-Control Study in US Women

Introduction: Various forms of psychological distress, such as depression and anxiety, have been identified as risk factors for diabetes. However, there is limited evidence from population-based, epidemiologic studies investigating potential molecular mechanisms (e.g., metabolic dysregulation) linking psychological distress and diabetes development.

Hypothesis: We assessed the hypothesis that a metabolite score reflecting psychological distress-related metabolic dysregulation is predictive of future diabetes risk in women.

Methods: We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses’ Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 controls matched on age, race, fasting status, and time/date of blood collection. Blood samples were collected between 1989-1990 and incident diabetes was diagnosed between 1990-2012. Based on the metabolomic signature of chronic psychological distress we previously identified and validated in women (including coefficient estimates for individual metabolite associations), we calculated a weighted plasma metabolomic score of psychological distress comprised of 19 metabolites (e.g., serotonin, threonine, hippurate, biliverdin, glutamine, etc.). We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% CI for diabetes risk across quintiles of the distress-related metabolite score.

Results: After adjusting for sociodemographic factors, family history of diabetes and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the distress-related metabolite score was 1.00 (reference) for Q1, 1.11 (0.77, 1.61) for Q2, 1.64 (1.14, 2.35) for Q3, 2.07 (1.42, 3.01) for Q4, and 2.35 (1.60, 3.47) for Q5. Each 1-unit increase in the score was associated with 26% higher diabetes risk (95% CI: 1.15, 1.38; p-trend<0.0001). This association was moderately attenuated after additional adjustment for baseline body mass index (OR per 1-unit increase: 1.13; 95% CI: 1.02, 1.25; p-trend=0.02). Further, psychological distress assessed via self-report (defined as presence of depression or anxiety) was associated with modestly increased diabetes risk in the same sample (OR: 1.25; 95% CI: 1.01, 1.55). The metabolite score mediated 20.7% (95% CI: 5.1, 56.1) of the association between psychological distress and diabetes risk (p=0.0065).

Conclusions: Our results suggest that the distress-related metabolite score was significantly associated with diabetes risk in women and partly mediated the association between self-reported psychological distress and diabetes risk. These findings provide supporting evidence that metabolic dysregulation may be an important mechanism underlying the observed association between psychological distress and diabetes risk.

Abstract P205: Association of Angptl3/8 and 4/8 With Plasma Metabolites Before and After Exercise Training

Introduction: Angiopoietin like protein (ANGPTL) complexes 3/8 and 4/8 are established inhibitors of lipoprotein lipase and modifiable by regular exercise. However, the molecular underpinning of these novel biomarkers has not been fully elucidated. The purpose of this study was to examine the associations between plasma metabolites and ANGPTL3/8 and 4/8 before and after exercise training.

Methods: Measurements were taken before and after 20 weeks of exercise training in 630 adults (36% Black, 56% women, mean age 35 yrs) of the HERITAGE Family Study. Meso Scale Discovery immunoassays were used to measure ANGPTL3/8 and 4/8 in serum. Plasma metabolites (n=300 named) were measured using LC-MS and the HILIC-pos method. Linear mixed models tested the association between metabolites and each trait at baseline and post-training with full covariate adjustment. Significance was set to FDR<0.05.

Results: A total of 111 and 93 metabolites were significantly associated with ANGPTL3/8 at baseline or post-training, respectively ( Figure 1A ). A total of 69 metabolites were associated with ANGPTL3/8 at both time points, including ketone bodies, acylcarnitines, and DMGV, while 24 metabolites were only associated at post-training including citric acid, methionine, and glycine. A total of 89 and 40 metabolites were significantly associated with ANGPTL4/8 at baseline or post-training, respectively ( Figure 1B ). A total of 13 metabolites were associated with ANGPTL4/8 at both time points including glycine and lysophospholipids, whereas 27 metabolites were associated only at post-training including hydroxyproline, N-Lignoceroyl Taurine, and methylguanidine.

Conclusions: We identified several plasma metabolites associated with ANGPTL3/8 and 4/8 before and after exercise training. Our findings indicate potential metabolic pathways related to the exercise responsiveness of ANGPTL3/8 and 4/8, including glucose-alanine cycle, urea cycle, and glycine and methionine metabolism.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFA) is implicated in the pathogenesis of obesity-associated diseases. However, studies to date have assumed that a few select FFAs are representative of broad structural categories, and there are no scalable approaches to comprehensively assess the biological processes induced by exposure to diverse FFAs circulating in human plasma. Furthermore, assessing how these FFA- mediated processes interact with genetic risk for disease remains elusive. Here we report the design and implementation of FALCON (Fatty Acid Library for Comprehensive ONtologies) as an unbiased, scalable and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids (MUFAs) with a distinct lipidomic profile associated with decreased membrane fluidity. Furthermore, we developed a new approach to prioritize genes that reflect the combined effects of exposure to harmful FFAs and genetic risk for type 2 diabetes (T2D). Importantly, we found that c-MAF inducing protein (CMIP) protects cells from exposure to FFAs by modulating Akt signaling and we validated the role of CMIP in human pancreatic beta cells. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Highlights

FALCON (Fatty Acid Library for Comprehensive ONtologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effectsFALCON is applicable to many and diverse cell typesA subset of monounsaturated FAs (MUFAs) equally or more toxic than canonical lipotoxic saturated FAs (SFAs) leads to decreased membrane fluidityNew approach prioritizes genes that represent the combined effects of environmental (FFA) exposure and genetic risk for diseaseC-Maf inducing protein (CMIP) is identified as a suppressor of FFA-induced lipotoxicity via Akt-mediated signaling.

Sleep duration, plasma metabolites, and obesity and diabetes: a metabolome-wide association study in US women

Short and long sleep duration are associated with adverse metabolic outcomes, such as obesity and diabetes. We evaluated cross-sectional differences in metabolite levels between women with self-reported habitual short (<7 h), medium (7-8 h), and long (≥9 h) sleep duration to delineate potential underlying biological mechanisms. In total, 210 metabolites were measured via liquid chromatography-mass spectrometry in 9207 women from the Nurses' Health Study (NHS; N = 5027), the NHSII (N = 2368), and the Women's Health Initiative (WHI; N = 2287). Twenty metabolites were consistently (i.e. praw < .05 in ≥2 cohorts) and/or strongly (pFDR < .05 in at least one cohort) associated with short sleep duration after multi-variable adjustment. Specifically, levels of two lysophosphatidylethanolamines, four lysophosphatidylcholines, hydroxyproline and phenylacetylglutamine were higher compared to medium sleep duration, while levels of one diacylglycerol and eleven triacylglycerols (TAGs; all with ≥3 double bonds) were lower. Moreover, enrichment analysis assessing associations of metabolites with short sleep based on biological categories demonstrated significantly increased acylcarnitine levels for short sleep. A metabolite score for short sleep duration based on 12 LASSO-regression selected metabolites was not significantly associated with prevalent and incident obesity and diabetes. Associations of single metabolites with long sleep duration were less robust. However, enrichment analysis demonstrated significant enrichment scores for four lipid classes, all of which (most markedly TAGs) were of opposite sign than the scores for short sleep. Habitual short sleep exhibits a signature on the human plasma metabolome which is different from medium and long sleep. However, we could not detect a direct link of this signature with obesity and diabetes risk.

Liver-specific FGFR4 knockdown in mice on an HFD increases bile acid synthesis and improves hepatic steatosis

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with increased risk in patients with metabolic syndrome. There are no FDA-approved treatments, but FXR agonists have shown promising results in clinical studies for NAFLD management. In addition to FXR, fibroblast growth factor receptor FGFR4 is a key mediator of hepatic bile acid synthesis. Using N-acetylgalactosamine-conjugated siRNA, we knocked down FGFR4 specifically in the liver of mice on chow or high-fat diet and in mouse primary hepatocytes to determine the role of FGFR4 in metabolic processes and hepatic steatosis. Liver-specific FGFR4 silencing increased bile acid production and lowered serum cholesterol. Additionally, we found that high-fat diet-induced liver steatosis and insulin resistance improved following FGFR4 knockdown. These improvements were associated with activation of the FXR-FGF15 axis in intestinal cells, but not in hepatocytes. We conclude that targeting FGFR4 in the liver to activate the intestinal FXR-FGF15 axis may be a promising strategy for the treatment of NAFLD and metabolic dysfunction.

Abstract A052: Systematic dissection of transcriptional states in pancreatic cancer

Transcriptional states in pancreatic cancer can stratify patients by response to chemotherapy and clinical outcomes. These include the classical and basal-like states as well as a newly identified neural-like progenitor (NRP) state, which we have previously found to be enriched in primary patient tumors treated with neoadjuvant chemotherapy and radiotherapy. While several transcription factor drivers of classical and basal-like identity have been described, key regulators of the NRP state are unknown. Through in silico approaches, we identified candidate transcription factors of the NRP state, including GLIS3, a Krüppel-like zinc finger protein that mediates neuroendocrine fate during pancreatic development and differentiation of human embryonic stem cells into posterior neural progenitor cells. Our understanding of biologic and clinically-relevant attributes of transcriptional cell states remains limited by state-specific biases in various preclinical models. Existing human cell lines maintained as two-dimensional cultures tend to preferentially represent the basal-like state, whereas human three-dimensional organoid models grown in standard culture conditions best reflect the classical state. These phenotypes are therefore impacted by culture conditions as well as underlying genetic features. Furthermore, most murine pancreatic cancer models do not fully reflect the classical vs. basal-like state heterogeneity observed in humans. To enable systematic study of the classical, basal-like and NRP phenotypes, we developed isogenic KP (KrasG12D/+;Trp53FL/FL) murine organoids with a germline dCas9-VPR system to enable facile overexpression of state-specific transcription factors through CRISPR activation approaches. Quantitative PCR, RNA-sequencing, and proteomics confirmed Gata6, deltaN Trp63, and Glis3 as drivers of classical, basal-like, and NRP identity, respectively. DeltaN Trp63 organoids were further differentiated by loss of luminal morphology. Pairwise comparisons of global transcriptional alterations suggest the greatest similarities between the Gata6- and Glis3-overexpressed models, which is consistent with enhanced associations between classical and NRP states in patient tumors. Finally, although basal-like and NRP states are associated with poorer response to multi-agent chemotherapy, state-specific therapeutic sensitivities to other treatments remain incompletely defined. We therefore performed drug sensitivity assays with a panel of targeted therapies and unveiled state-specific sensitivities. These data were corroborated by drug sensitivity profiling of human patient-derived organoids and cell lines. Taken together, these results suggest a framework for defining cell state-specific vulnerabilities that may aid in stratifying and treating pancreatic cancer patients with new therapies.

Citation Format: Jimmy A. Guo, Jennifer Su, Ananya Jambhale, Julien Dilly, Connor J. Hennessey, Carina Shiau, Patrick Yu, Steven Wang, Junning Wang, Laleh Abbassi, James Neiswender, Tate Bertea, Annan Yang, Qijia Yu, Peter Westcott, Jason Schenkel, Daniel Y. Kim, Hannah I. Hoffman, Grissel Cervantes Jaramillo, Giselle A. Uribe, Westley W. Wu, Arnav Mehta, David Ting, Julian A. Pacheco, Amy Deik, Clary Clish, Francisca Vazquez, Brian Wolpin, Aviv Regev, William A. Freed-Pastor, Joseph D. Mancias, Tyler Jacks, William L. Hwang, Andrew J. Aguirre. Systematic dissection of transcriptional states in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A052.

The IDOze Study: The Link Between Sleep Disruption and Tryptophan-Kynurenine Pathway Activation in Women With Human Immunodeficiency Virus

BACKGROUND

Poor sleep is associated with human immunodeficiency virus (HIV), particularly among women with HIV (WWH), although mechanisms are unclear. We explored cross-sectional associations between sleep disruption and tryptophan-kynurenine (T/K) pathway activation, measured by the kynurenine-to-tryptophan ratio (K:T).

METHODS

HIV-uninfected women (HIV-) and WWH aged 35-70 years and on stable antiretroviral therapy were included. Sleep metrics were measured using wrist actigraphy. Plasma T/K pathway metabolites were measured using liquid chromatography-tandem mass spectrometry. Multivariate linear regression models examined relationships between K:T and actigraphy-based sleep metrics by HIV status.

RESULTS

WWH (n = 153) and HIV- women (n = 151) were demographically similar. Among WWH, median CD4 was 751 cells/µL; 92% had undetectable HIV RNA. Compared to HIV- women, WWH had higher K:T (P < .001) and kynurenine (P = .01) levels but similar tryptophan levels (P = .25). Higher K:T was associated with more wake bouts (P = .001), more time awake after sleep onset (P = .01), and lower sleep efficiency (P = .03) in WWH only.

CONCLUSIONS

HIV infection was associated with T/K pathway activation; this activation was associated with poorer sleep efficiency and more fragmented sleep. While longitudinal studies are needed to elucidate the directionality of these associations, these findings may help identify treatments to reduce sleep disruption in WWH by targeting residual inflammation and T/K pathway activation.

Metabolomic and transcriptomic signatures of influenza vaccine response in healthy young and older adults

Seasonal influenza causes mild to severe respiratory infections and significant morbidity, especially in older adults. Transcriptomic analysis in populations across multiple flu seasons has provided insights into the molecular determinants of vaccine response. Still, the metabolic changes that underlie the immune response to influenza vaccination remain poorly characterized. We performed untargeted metabolomics to analyze plasma metabolites in a cohort of younger and older subjects before and after influenza vaccination to identify vaccine-induced molecular signatures. Metabolomic and transcriptomic data were combined to define networks of gene and metabolic signatures indicative of high and low antibody response in these individuals. We observed age-related differences in metabolic baselines and signatures of antibody response to influenza vaccination and the abundance of α-linolenic and linoleic acids, sterol esters, fatty-acylcarnitines, and triacylglycerol metabolism. We identified a metabolomic signature associated with age-dependent vaccine response, finding increased tryptophan and decreased polyunsaturated fatty acids (PUFAs) in young high responders (HRs), while fatty acid synthesis and cholesteryl esters accumulated in older HRs. Integrated metabolomic and transcriptomic analysis shows that depletion of PUFAs, which are building blocks for prostaglandins and other lipid immunomodulators, in young HR subjects at Day 28 is related to a robust immune response to influenza vaccination. Increased glycerophospholipid levels were associated with an inflammatory response in older HRs to flu vaccination. This multi-omics approach uncovered age-related molecular markers associated with influenza vaccine response and provides insight into vaccine-induced metabolic responses that may help guide development of more effective influenza vaccines.

Prediagnostic Plasma Metabolomics and the Risk of Exfoliation Glaucoma

Purpose

The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG.

Methods

We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR).

Results

Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P < 0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR < 0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05).

Conclusions

In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.

0030 Development and Validation of a Metabolomic Risk Score for Obstructive Sleep Apnea across Race/Ethnicities

Introduction

Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of upper airway obstruction during sleep resulting in oxygen desaturation and sleep fragmentation, and associated with increased risk of adverse health outcomes. Metabolites are being increasingly used for biomarker discovery and evaluation of disease processes and progression. We aimed to develop a metabolomic risk score (MRS) for OSA and identify individual metabolites associated with OSA to provide insights into the pathogenesis of OSA.

Methods

We studied 219 metabolites and their associations the apnea hypopnea index (AHI) and with OSA, defined as AHI , in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (n=3507) using two methods: (1) association analysis of individual metabolites, and (2) least absolute shrinkage and selection operator (LASSO) regression to identify a subset of metabolites that are jointly associated with OSA, and develop an MRS. We then validated the results in Multi-Ethnic Study of Atherosclerosis (MESA) (n=475), an independent dataset.

Results

HCHS/SOL participants were 41.72 years old on average, 50.7% female, and 10.2% had OSA. MESA individuals were 68.45 years old on average, 56.2% females, and 46.7% had OSA. When assessing the associations between OSA/AHI and individual metabolites, we identified seven metabolites significantly and positively associated with OSA in HCHS/SOL (FDR p&lt;0.05), of which four associations - glutamate, oleoyl-linoleoyl-glycerol (18:1/18:2) (DAG(36:3)), linoleoyl-linoleoyl-glycerol (18:2/18:2) (DAG(36:4)) and phenylalanine, replicated in MESA (one sided-p value&lt;0.05). The OSA-MRS, composed of 14 metabolites, was associated with 52% increase of risk for moderate to severe OSA (OR=1.52 [95% CI: 1.23-1.87] per 1 SD of OSA-MRS, p&lt;.001) in the discovery dataset of HCHS/SOL and 44% increased risk (OR=1.44 [95% CI: 1.03-2.03] per 1 SD of OSA-MRS, p=0.034) in the validation dataset of MESA, both adjusted for demographic, lifestyle, and comorbidities. Similar albeit less significant associations were observed for AHI modeled continuously.

Conclusion

We developed an MRS that replicated in an independent multi-ethnic dataset, demonstrating the robustness of metabolomic-based OSA risk score to population heterogeneity. Replicated metabolite associations may provide insights into OSA-related molecular and metabolic mechanisms.

Support (If Any)

Support for metabolomics data was graciously provided by the JLH Foundation (Houston, Texas). The Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the Office of Dietary Supplements. The authors thank the staff and participants of HCHS/SOL for their important contributions. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420. MESA Family is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258, R01HL071259, and by the National Center for Research Resources, Grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). ). Metabolomics for “NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)” (phs001416) was performed at Broad Institute and Beth Israel Metabolomics Platform (HHSN268201600038I). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study was supported by NHLBI grant R35HL135818.

Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Comprehensive metabolomic profiling of plasma from patients (pts) with metastatic urothelial carcinoma (mUC) receiving immune checkpoint inhibitors (ICI) or platinum-based chemotherapy (PBC)

565

Background: Metabolomic profiling of plasma from mUC pts has not been comprehensively examined. Plasma metabolomics may capture the effects of interactions between the malignancy, host and therapy. We hypothesized that Identifying metabolites in plasma from patients with mUC receiving an ICI or PBC may shed valuable insights regarding tumor biology and mechanisms of resistance. Methods: We obtained 0.2 ml plasma before and after starting therapy from pts with mUC receiving an ICI or PBC at the Dana-Farber Cancer Institute. Plasma metabolomic profiling was conducted at the Broad Institute using 3 complementary liquid chromatography tandem mass spectrometry (LC-MS)-based metabolomics platforms. We measured 648 metabolites at baseline prior to starting ICI/PBC and at a second time point in each subject following initiation of ICI/PBC. Metabolite levels were assumed to be normally distributed with log transformation to transform distributions to be approximately symmetric. We performed Wilcoxon-rank sum test to compare the levels of metabolites before and after initiation of the ICI or PBC (significance at p < 0.05). Results: Plasma was available at baseline and during therapy in 53 mUC pts (ICI: n = 43; PBC: n = 10). The median age was 68 (range: 39-86) years and 42 (82.3%) were male. The median time from baseline to the second time point was 4.7 months (range: 0.7-90.2). The ICIs administered were atezolizumab (n = 20), pembrolizumab (n = 16), nivolumab (n = 5), and durvalumab + tremelimumab (n = 1). We identified 20 metabolites that were significantly increased in post-PBC plasma samples (vs. pre-PBC) and 19 metabolites increased in post-ICI (vs. pre-ICI) samples (p < 0.05). All altered metabolites except one (Uracil) were exclusive for each treatment group. The most significant metabolites that increased following initiation of the ICI and PBC are shown in the Table. Evaluation of the association of plasma metabolomics with clinical outcomes and toxicities is ongoing. Conclusions: This is the first report, to our knowledge, of comprehensive metabolomic plasma profiling of pre- and post-ICI and PBC pts with mUC. The metabolomic changes after ICI appear distinct from those seen after PBC. Furthermore, our study sheds insights on potential mechanisms of resistance and new therapeutic targets in pts with mUC.[Table: see text]

Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: Results from the Nurses' Health Study

BACKGROUND

Metabolomics profiles were consistently associated with type 2 diabetes (T2D) risk, but evidence on long-term metabolite changes and T2D incidence is lacking. We examined the associations of 10-year plasma metabolite changes with subsequent T2D risk.

METHODS

We conducted a nested T2D case-control study (n=244 cases, n=244 matched controls) within the Nurses' Health Study. Repeated metabolomics profiling (170 targeted metabolites) was conducted in participant blood specimens from 1989/1990 and 2000/2001, and T2D occurred between 2002 and 2008. We related 10-year metabolite changes (Δ-values) to subsequent T2D risk using conditional logistic models, adjusting for baseline metabolite levels and baseline levels and concurrent changes of BMI, diet quality, physical activity, and smoking status.

FINDINGS

The 10-year changes of thirty-one metabolites were associated with subsequent T2D risk (false discovery rate-adjusted p-values [FDR]<0.05). The top three high T2D risk-associated 10-year changes were (odds ratio [OR] per standard deviation [SD], 95%CI): Δisoleucine (2.72, 1.97-3.79), Δleucine (2.53, 1.86-3.47), and Δvaline (1.93, 1.52-2.44); other high-risk-associated metabolite changes included alanine, tri-/diacylglycerol-fragments, short-chain acylcarnitines, phosphatidylethanolamines, some vitamins, and bile acids (ORs per SD between 1.31and 1.82). The top three low T2D risk-associated 10-year metabolite changes were (OR per SD, 95% CI): ΔN-acetylaspartic acid (0.54, 0.42-0.70), ΔC20:0 lysophosphatidylethanolamine (0.68, 0.56-0.82), and ΔC16:1 sphingomyelin (0.68, 0.56-0.83); 10-year changes of other sphingomyelins, plasmalogens, glutamine, and glycine were also associated with lower subsequent T2D risk (ORs per SD between 0.66 and 0.78).

INTERPRETATION

Repeated metabolomics profiles reflecting the long-term deterioration of amino acid and lipid metabolism are associated with subsequent risk of T2D.

Tricarboxylic acid cycle related-metabolites and risk of atrial fibrillation and heart failure

BACKGROUND

Tricarboxylic acid (TCA) cycle deregulation may predispose to cardiovascular diseases, but the role of TCA cycle-related metabolites in the development of atrial fibrillation (AF) and heart failure (HF) remains unexplored. This study sought to investigate the association of TCA cycle-related metabolites with risk of AF and HF.

METHODS

We used two nested case-control studies within the PREDIMED study. During a mean follow-up for about 10 years, 512 AF and 334 HF incident cases matched by age (±5 years), sex and recruitment center to 616 controls and 433 controls, respectively, were included in this study. Baseline plasma levels of citrate, aconitate, isocitrate, succinate, malate and d/l-2-hydroxyglutarate were measured with liquid chromatography-tandem mass spectrometry. Multivariable conditional logistic regression models were fitted to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for metabolites and the risk of AF or HF. Potential confounders included smoking, family history of premature coronary heart disease, physical activity, alcohol intake, body mass index, intervention groups, dyslipidemia, hypertension, type 2 diabetes and medication use.

RESULTS

Comparing extreme quartiles of metabolites, elevated levels of succinate, malate, citrate and d/l-2-hydroxyglutarate were associated with a higher risk of AF [OR_{Q4 vs. Q1} (95% CI): 1.80 (1.21-2.67), 2.13 (1.45-3.13), 1.87 (1.25-2.81) and 1.95 (1.31-2.90), respectively]. One SD increase in aconitate was directly associated with AF risk [OR (95% CI): 1.16 (1.01-1.34)]. The corresponding ORs (95% CI) for HF comparing extreme quartiles of malate, aconitate, isocitrate and d/l-2-hydroxyglutarate were 2.15 (1.29-3.56), 2.16 (1.25-3.72), 2.63 (1.56-4.44) and 1.82 (1.10-3.04), respectively. These associations were confirmed in an internal validation, except for aconitate and AF.

CONCLUSION

These findings underscore the potential role of the TCA cycle in the pathogenesis of cardiac outcomes.

TAMI-05. FATTY ACID SYNTHESIS IS REQUIRED FOR HER2+ BREAST CANCER BRAIN METASTASIS

Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2-positive breast cancer and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, resulting in site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase reduces human epidermal growth factor receptor 2-positive breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.

Plasma Metabolomic Markers of Insulin Resistance and Diabetes and Rate of Incident Parkinson's Disease

BACKGROUND

Although there is evidence of shared dysregulated pathways between diabetes and Parkinson's disease, epidemiologic research on an association between the two diseases has produced inconsistent results.

OBJECTIVE

We aimed to assess whether known metabolomic markers of insulin resistance and diabetes are also associated with Parkinson's disease development.

METHODS

We conducted a nested case-control study among Nurses' Health Study and Health Professionals Follow-up Study participants who had provided blood samples up to twenty years prior to Parkinson's diagnosis. Cases were matched to risk-set sampled controls by age, sex, fasting status, and time of blood collection. Participants provided covariate information via regularly collected cohort questionnaires. We used conditional logistic regression models to assess whether plasma levels of branched chain amino acids, acylcarnitines, glutamate, or glutamine were associated with incident development of Parkinson's disease.

RESULTS

A total of 349 case-control pairs were included in this analysis. In the primary analyses, none of the metabolites of interest were associated with Parkinson's disease development. In investigations of the association between each metabolite and Parkinson's disease at different time intervals prior to diagnosis, some metabolites showed marginally significant association but, after correction for multiple testing, only C18 : 2 acylcarnitine was significantly associated with Parkinson's disease among subjects for whom blood was collected less than 60 months prior to case diagnosis.

CONCLUSIONS

Plasma levels of diabetes-related metabolites did not contribute to predict risk of Parkinson's disease. Further investigation of the relationship between pre-diagnostic levels of diabetes-related metabolites and Parkinson's disease in other populations is needed to confirm these findings.

Metabolic modeling of single Th17 cells reveals regulators of autoimmunity

Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.

Cycling cancer persister cells arise from lineages with distinct programs

Non-genetic mechanisms have recently emerged as important drivers of cancer therapy failure¹, where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment². Although most cancer persisters remain arrested in the presence of the drug, a rare subset can re-enter the cell cycle under constitutive drug treatment. Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drugs. To study this rare, transiently resistant, proliferative persister population, we developed Watermelon, a high-complexity expressed barcode lentiviral library for simultaneous tracing of each cell's clonal origin and proliferative and transcriptional states. Here we show that cycling and non-cycling persisters arise from different cell lineages with distinct transcriptional and metabolic programs. Upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation are associated with persister proliferative capacity across multiple cancer types. Impeding oxidative stress or metabolic reprogramming alters the fraction of cycling persisters. In human tumours, programs associated with cycling persisters are induced in minimal residual disease in response to multiple targeted therapies. The Watermelon system enabled the identification of rare persister lineages that are preferentially poised to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

Circulating blood metabolite trajectories and risk of rheumatoid arthritis among military personnel in the Department of Defense Biorepository

OBJECTIVES

We sought to identify metabolic changes potentially related to rheumatoid arthritis (RA) pathogenesis occurring in the blood prior to its diagnosis.

METHODS

In a US military biorepository, serum samples collected at two timepoints prior to a diagnosis of RA were identified. These were matched to controls who did not develop RA by subject age, race and time between sample collections and RA diagnosis time to stored serum samples. Relative abundances of 380 metabolites were measured using liquid chromatography-tandem mass spectrometry. We determined whether pre-RA case versus control status predicted metabolite concentration differences and differences over time (trajectories) using linear mixed models, assessing for interactions between time, pre-RA status and metabolite concentrations. We separately examined pre-RA and pre-seropositive RA cases versus matched controls and adjusted for smoking. Multiple comparison adjustment set the false discovery rate to 0.05.

RESULTS

291 pre-RA cases (80.8% pre seropositive RA) were matched to 292 controls, all with two serum samples (2.7±1.6 years; 1.0±0.9 years before RA/matched date). 52.0% were women; 52.8% were White, 26.8% Black and 20.4% other race. Mean age was 31.2 (±8.1) years at earliest blood draw. Fourteen metabolites had statistically significant trajectory differences among pre-RA subjects versus controls, including sex steroids, amino acid/lipid metabolism and xenobiotics. Results were similar when limited to pre seropositive RA and after adjusting for smoking.

CONCLUSIONS

In this military case-control study, metabolite concentration trajectory differences in pre-RA cases versus controls implicated steroidogenesis, lipid/amino acid metabolism and xenobiotics in RA pathogenesis. Metabolites may have potential as biomarkers and/or therapeutic targets preceding RA diagnosis.

Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease

The intestinal microbiome is a key determinant of responses to biologic therapy in inflammatory bowel disease (IBD). However, diverse therapeutics and variable responses among IBD patients have posed challenges in predicting clinical therapeutic success. In this prospective study, we profiled baseline stool and blood in patients with moderate-to-severe Crohn's disease or ulcerative colitis initiating anti-cytokine therapy (anti-TNF or -IL12/23) or anti-integrin therapy. Patients were assessed at 14 weeks for clinical remission and 52 weeks for clinical and endoscopic remission. Baseline microbial richness indicated preferential responses to anti-cytokine therapy and correlated with the abundance of microbial species capable of 7α/β-dehydroxylation of primary to secondary bile acids. Serum signatures of immune proteins reflecting microbial diversity identified patients more likely to achieve remission with anti-cytokine therapy. Remission-associated multi-omic profiles were unique to each therapeutic class. These profiles may facilitate a priori determination of optimal therapeutics for patients and serve as targets for newer therapies.

Choline Metabolism and Risk of Atrial Fibrillation and Heart Failure in the PREDIMED Study

BACKGROUND

Few studies have examined the associations of trimethylamine-N-oxide (TMAO) and its precursors (choline, betaine, dimethylglycine, and L-carnitine) with the risk of atrial fibrillation (AF) and heart failure (HF). This study sought to investigate these associations.

METHODS

Prospective associations of these metabolites with incident AF and HF were examined among participants at high cardiovascular risk in the PREDIMED study (PREvención con DIeta MEDiterránea) after follow-up for about 10 years. Two nested case-control studies were conducted, including 509 AF incident cases matched to 618 controls and 326 HF incident cases matched to 426 controls. Plasma levels of TMAO and its precursors were semi-quantitatively profiled with liquid chromatography tandem mass spectrometry. Odds ratios were estimated with multivariable conditional logistic regression models.

RESULTS

After adjustment for classical risk factors and accounting for multiple testing, participants in the highest quartile vs. the lowest quartile of baseline choline and betaine levels had a higher risk of AF [OR (95% CI): 1.85 (1.30-2.63) and 1.57 (1.09-2.24), respectively]. The corresponding OR for AF for extreme quartiles of dimethylglycine was 1.39 (0.99-1.96). One SD increase in log-transformed dimethylglycine was positively associated with AF risk (OR, 1.17; 1.03-1.33). The corresponding ORs for HF for extreme quartiles of choline, betaine, and dimethylglycine were 2.51 (1.57-4.03), 1.65 (1.00-2.71) and 1.65 (1.04-2.61), respectively. TMAO and L-carnitine levels were not associated with AF or HF.

CONCLUSIONS

Our findings support the role of the choline metabolic pathway in the pathogenesis of AF and HF.

Abstract 022: Plasma Metabolome Related To Plant-based Diets And The Association With Type 2 Diabetes: A Prospective Cohort Study

Background: Plant-based diets have been associated with a lower risk of type 2 diabetes. However, the underlying mechanisms are not completely understood, and the evidence using objective approaches to assess the adherence of plant-based diets is limited.

Methods: In the Nurses’ Health Study (NHS), NHSII, and Health Professionals Follow-Up Study, we characterized the plasma metabolome related to plant-based diets and examined its association with the incidence of type 2 diabetes among 10 699 participants. Plasma metabolomic profiling was conducted by liquid chromatography-tandem mass spectrometry. Adherence to plant-based diets was assessed by three plant-based diet indices derived from the food frequency questionnaire: an overall plant-based diet index (PDI), a healthful PDI (hPDI), and an unhealthful PDI (uPDI). Metabolomic signatures reflecting the adherence to plant-based diets were created using elastic net regression, and their associations with risk of type 2 diabetes were subsequently evaluated using multivariable Cox proportional hazards regression.

Results: Among 263 metabolites measured, nearly half were significantly associated with PDI (41.4%, 109 of 263), hPDI (51.7%, 136 of 263), and uPDI (40.3%, 106 of 263) after Bonferroni correction. We developed a metabolomic signature comprising 53 metabolites for PDI, 76 metabolites for hPDI, and 88 metabolites for uPDI, each robustly correlated with the corresponding diet index (r=0.34-0.36 for PDI, 0.43-0.44 for hPDI, and 0.36-0.37 for uPDI). We observed an inverse association of PDI metabolomic signature (HR 0.86, 95% CI 0.79-0.93 per one standard deviation) and hPDI metabolomic signature (0.79, 0.72-0.86) with type 2 diabetes risk after adjustment for body mass index and other potential confounders. These two inverse associations remained significant even further adjusting for the corresponding diet index PDI and hPDI. The metabolomic signature for uPDI was not associated with type 2 diabetes risk (1.00, 0.93-1.09).

Conclusion: Plasma metabolome can robustly reflect adherence and metabolic response to plant-based diets. Metabolomic signatures reflecting greater adherence to an overall plant-based diet, especially a healthful plant-based diet, were associated with a lower risk of type 2 diabetes. These findings support and provide mechanistic insights on the important role of healthful plant-based diets in diabetes prevention.

Baseline Metabolomic Profile as Potential Biomarker for Weight Change After Roux-en-Y Gastric Bypass Surgery

Introduction: Weight loss surgery (WLS) has emerged as an effective treatment for severe obesity (BMI ≥ 40 kg/m2 in adults) and Type 2 diabetes (T2D). There is a wide spectrum of long-term response, both in weight change and resolution of T2D after WLS. Younger age at surgery, white race and the extent of weight loss prior to surgery are the known traits associated with favorable outcomes. The aim of this study was to investigate untargeted metabolite profile prior to surgery as a potential biomarker for long-term weight change response to WLS.

Methods: Latent class growth mixture modeling (LCGMM) was used to classify the longitudinal weight change trajectories in a cohort of individuals who underwent Roux-en-Y gastric bypass (RYGB). Untargeted metabolite profile was done on a 4-module Liquid Chromatography/ Mass Spectroscopy (LC-MS) platform on the pre-surgery fasting plasma samples from subjects with weight regain or sustained weight loss. Metabolite wide association studies followed by pathway analysis was undertaken using Mummichog and GSEA algorithms. Partial least-square discriminant analysis (PLS-DA), a supervised classification framework used for datasets with thousands of correlated variables and a small number of samples that performs variable selection and classification as a one-step procedure, was used to identify the informative features that defined the two groups.

Results: LCGMM identified 3-classes of weight change in a cohort of 1589 subjects who had undergone RYGB – a) typical trajectory with significant weight loss by 12 months with plateau at ~80% weight loss (n= 1357, 85.4%), b) sustained weight loss without plateau (SWL, n=116, 7.3%) c) weight regain (RGN, 116, 7.3%). Samples from 80 subjects each with RGN or SWL (age 42.5 ± 10 years, 55% F, Excess body weight 221 ± 40 lbs) were used for untargeted profiling of 37,570 metabolite features (564 known). After QC and adjusting for age, sex, race and fasting time, 1920 features (37 known) were associated with the weight category at nominal significance (p <0.05). Amongst the known metabolites, the pathways represented in RGN were amino acid metabolism, branched chain and other essential amino acids that have been previously identified as markers of insulin resistance and T2D, while those with SWL were from sphingolipid metabolism. Dimethylguanidino valeric acid, a marker of liver fat and predictor of T2D was higher in individuals with SWL. Pathway analysis of the known and unknown metabolites together revealed pathways in urea cycle, pyrimidine, glutamate, essential amino acids, and butyrate metabolism. Features identified by PLS-DA overlapped with these pathways.

Conclusions: Untargeted baseline metabolites may serve as predictive biomarkers for weight change after RYGB. Future work will focus on developing a metabolite risk score and replication in other cohorts.

Metabolomic Markers of Southern Dietary Patterns in the Jackson Heart Study

SCOPE

New biomarkers are needed that are representative of dietary intake.

METHODS AND RESULTS

We assess metabolites associated with Southern dietary patterns in 1401 Jackson Heart Study participants. Three dietary patterns are empirically derived using principal component analysis: meat and fast food, fish and vegetables, and starchy foods. We randomly select two subsets of the study population: two-third sample for discovery (n = 934) and one-third sample for replication (n = 467). Among the 327 metabolites analyzed, 14 are significantly associated with the meat and fast food dietary pattern, four are significantly associated with the fish and vegetables dietary pattern, and none are associated with the starchy foods dietary pattern in the discovery sample. In the replication sample, nine remain associated with the meat and fast food dietary pattern [indole-3-propanoic acid, C24:0 lysophosphatidylcholine (LPC), N-methyl proline, proline betaine, C34:2 phosphatidylethanolamine (PE) plasmalogen, C36:5 PE plasmalogen, C38:5 PE plasmalogen, cotinine, hydroxyproline] and three remain associated with the fish and vegetables dietary pattern [1,7-dimethyluric acid, C22:6 lysophosphatidylethanolamine, docosahexaenoic acid (DHA)].

CONCLUSION

Twelve metabolites are discovered and replicated in association with dietary patterns detected in a Southern U.S. African-American population, which could be useful as biomarkers of Southern dietary patterns.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.