Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Heart waitlist survival in adults with an intra-aortic balloon pump relative to other Status 2, Status 1, and inotrope Status 3 patients.

BACKGROUND: Intra-aortic balloon pump (IABP) utilization has significantly outpaced other Status 2 eligibility criteria for heart transplant. The risk of waitlist mortality of IABP-supported patients relative to other Status 2 listed patients has not been described. METHODS: We performed a retrospective analysis of all adult patients listed Status 2 for heart transplantation under the current U.S. allocation policy, using data from the United Network for Organ Sharing. Patients listed status 1 and status 3 for high-dose inotropes were included for reference. Mortality and waitlist decompensation were modeled as a function of time-varying status in cause-specific Cox survival models. RESULTS: We identified 3638 Status 2 listings, of whom 1676 (46%) were Status 2 due to IABP. Relative to patients supported with IABP, status 2 patients with ventricular tachycardia/fibrillation [VT/VF] (HR 4.0, p < .001), right-or-biventricular assist device configurations (HR 2.3, p = .002), or temporary surgical left ventricular assist devices [LVAD] (HR 2.6, p = .003) had greater risk of waitlist mortality and decompensation. Other Status 2 subgroups had mortality comparable to IABP Status 2. Risk of waitlist mortality and decompensation for IABP Status 2 was similar to Status 3 patients listed for high-dose inotropes (HR 1.2, p = .27) and lower than Status 1 patients (HR 0.7, p = .002). CONCLUSIONS: Waitlist mortality varies significantly by Status 2 eligibility criteria and is highest among patients listed for VT/VF, right-or-biVAD configurations, or temporary surgical LVADs. IABP-supported patients were among those with the lowest Status 2 waitlist mortality risk and comparable to Status 3 inotrope-supported patients.

Interrupted Time Series Analysis of Donor Heart Use Before and After the 2018 UNOS Heart Allocation Policy Change.

BACKGROUND: Donor heart scarcity remains the fundamental barrier to increased transplant access. We examined whether 2018 United Network for Organ Sharing (UNOS) policy changes have had an impact on donor heart acceptance rates. METHODS AND RESULTS: We performed an interrupted time series analysis in UNOS to evaluate for abrupt changes in donor heart-acceptance rates associated with the new policy. All adult donor offers were evaluated between 2015 and 2021 (n = 66,654 donors). Donor volumes and transplants increased during this period, but the donor acceptance rate declined significantly from 31% in quarter 3 of 2018 to 26% acceptance in quarter 3 of 2021 (slope change -0.4% per quarter; P < 0.001). We identified 2 trends associated with this decline: (1) a growing number of donors with high-risk features, and (2) decreased acceptance of donors with certain high-risk features in the new allocation system. CONCLUSIONS: Heart transplant volumes have increased in recent years as a result of increased donor volumes, but donor heart acceptance rates began decreasing under the current allocation system. Changes in the donor pool and acceptance patterns for certain donor-risk features may explain this shift and warrant further evaluation to maximize donor heart use.

The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies.

Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman's rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.