The resampling simulation analyses were performed in the NEO study. This study has been extensively described elsewhere [20 (link)] and in Appendix A . The NEO study was accepted by the Medical Ethics committee of the Leiden University Medical Center under protocol P08.109. The study is also registered at clinicaltrials.gov under number NL21981.058.08/P08.109. All participants gave written informed consent [20 (link)]. Fasting state serum samples from a sub-population (n = 599) of the NEO study were sent for untargeted metabolomics measurements at Metabolon Inc. (Durham, NC, USA) using their Metabolon™ Discovery HD4 platform. In brief, this process involves four independent ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS/MS) platforms [21 (link),22 (link)]. Two platforms used positive ionization reverse phase chromatography, one used negative ionization reverse phase chromatography, and one used hydrophilic interaction liquid chromatography (HILIC) negative ionization [22 (link)]. In total, 1365 serum metabolites were measured which included 840 endogenous, 296 unannotated, and 229 xenobiotic metabolites.
Discovery hd4 platform
Manufactured by Metabolon
Sourced in United States
The Discovery HD4 platform is a comprehensive metabolomics solution designed for high-throughput, high-resolution analysis of small molecules. It combines advanced mass spectrometry technology with robust data processing and interpretation capabilities to enable the identification and quantification of a wide range of metabolites from biological samples.
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19 protocols using discovery hd4 platform
1
Untargeted Serum Metabolomics Analysis
2
Maternal Metabolic Profiling in Pregnancy
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Self-administered questionnaires were used to collect sociodemographic information (age, race/ethnicity, education, insurance type, and marital status), lifestyle (cigarette smoking and alcohol use during pregnancy), parity, and medical history at enrollment. Self-reported height and weight prior to pregnancy were collected at enrollment and used to calculate pre-pregnancy BMI as weight (in kilograms) divided by the square of height (in meters).
The nonfasting maternal plasma samples collected in the second trimester (at recruitment) and stored at −80°C were used to conduct prenatal metabolomics profiling. The untargeted metabolomics analysis was performed using the Metabolon Discovery HD4™ Platform (Metabolon Inc., Morrisville, NC), which includes four ultra-high-performance liquid chromatography-mass spectrometry methods (Supplementary Materials ). A total of 949 metabolites with known structural identity (named biochemicals) were identified in the study samples. After excluding 69 metabolites with missing/below-the-detection-limit > 80% of the samples, 880 metabolites were included in the present study.
The nonfasting maternal plasma samples collected in the second trimester (at recruitment) and stored at −80°C were used to conduct prenatal metabolomics profiling. The untargeted metabolomics analysis was performed using the Metabolon Discovery HD4™ Platform (Metabolon Inc., Morrisville, NC), which includes four ultra-high-performance liquid chromatography-mass spectrometry methods (
3
Maternal Metabolic Profiling in Pregnancy
4
Metabolomic Analysis of Regional Adiposity
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Plasma succinate and 12,13-diHOME levels were quantified by ultra-performance LC coupled with tandem MS (DiscoveryHD4 platform, Metabolon) as described [15 ]. Funding was available to undertake metabolomic screening in 2,248 participants. Individuals were selected based on their DXA-determined fat distribution with the aim of generating metabolomic data from a group of individuals with a wide range of regional adiposity representative of the general population. Raw area counts were normalised to the median value of the run day to correct for inter-day variation in the measurements. After log10 transformation, outlier values (>4 SDs from the mean) were removed.
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Tryptophan Metabolomic Profiling Across Cohorts
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Cross-Sectional and Prospective Serum Metabolomics
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A subset of 825 participants had available metabolomics profiling of visit 2 fasting serum samples; these participants were selected for metabolomics profiling based on availability of gut microbiome samples collected within 30 d of visit 2. After restricting by our exclusion criteria (Suplementary Figure S1), 700 participants remained for cross-sectional metabolomics analysis at visit 2. Additionally, a subset of 6,180 participants had available metabolomics profiling of visit 1 fasting serum samples; 3,978 were a random subsample, while the remainder were selected based on participation in the echocardiographic ancillary study of HCHS/SOL or based on decline in eGFR from visits 1 to 2 (the random subsample and pre-selected samples were measured in different batches, “batch 1” and “batch 2,” which are pooled here). After restricting by our exclusion criteria (Suplementary Figure S1), 3,635 participants remained for prospective analysis of visit 1 metabolomics data. Using the discoveryHD4 platform at Metabolon Inc., quantification of serum metabolites was achieved by using an untargeted LC-MS-based metabolomic quantification protocol, as previously described59 (link). We imputed values below detection as half the minimum value per metabolite.
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Plasma Metabolomic Profiling of PMS
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Plasma was isolated from 54 participants (n = 29 unaffected controls; n = 11 class I mutations; n = 14 class II mutations) by centrifugation of blood samples in EDTA tubes for 30 min at 1,500 × g. Notably, 17 PMS participants and 12 unaffected controls had paired peripheral blood transcriptomic data. Separated plasma aliquots of 0.5 mL were stored immediately at −80°C until transport in dry ice for global metabolomic profiling using the analytical DiscoveryHD4 platform by Metabolon, as described previously.30 (link),31 (link) Raw data were extracted and signature chromatographic peaks and relative ion concentrations for metabolites detected were identified for each sample. Spectrometry data were analyzed using the quantify individual components in a sample method.31 (link) Metabolite identification was performed by matching each metabolite aggregate to an annotated reference chemical library containing >4,000 metabolites with well-defined chemical profiles. Peaks were quantified using the area under the curve. Metabolite data were then normalized in terms of raw peak area counts and re-scaled to set the median equal to one. Subsequently, any missing values, which constituted ∼8% of the entire data frame, were imputed with the minimum. Finally, we removed metabolites with low standard deviation (SD < 0.01) across the entire cohort, yielding 1,045 metabolites.
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Untargeted Plasma Metabolomics: Comprehensive Profiling
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Plasma samples from all cohorts underwent untargeted metabolomic analysis at a core laboratory using a well-validated mass spectrometry platform (Discovery HD4 platform, Metabolon Inc., Morrisville, NC, USA) between August 2019 and January 202127 (link),28 (link). The platform combines four complementary sample preprocessing protocols and a comprehensive reference library, resulting in quantitative estimates of metabolite abundance (mass spectral counts) for metabolites covering a broad spectrum of chemical classes, including amino acids, carbohydrates, lipids, nucleotides, peptides and vitamins, but also xenobiotic substances such as pharmaceutical and food preservative compounds. A detailed description of the analytical protocol, metabolite identification, and normalization procedures is included in the Supplementary Methods. Samples were randomized across the platform run with quality control samples spaced evenly among the injections. Metabolite abundance was estimated from the area under the curve for annotated peaks in the mass spectrogram (mass spectral counts) and was normalized for batch and run day in each cohort. All analyses were conducted in individual cohorts and not integrated.
9
Untargeted Metabolomics Profiling
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Metabolites were measured as part of Metabolon Inc.’s untargeted Discovery HD4 platform (Metabolon, Morrisville, NC, USA), as previously described in detail [23 (link)]. Briefly, methanol extraction of biochemicals followed by a non-targeted relative quantitative liquid chromatography–tandem mass spectrometry (LC-MS/MS) were performed. The Metabolon Discovery HD4 platform was applied to assay named and unnamed metabolites. A total of 1098 unique metabolites were included in statistical analysis. The classification of the metabolites was based on the Human Metabolome Database (http://www.hmdb.ca , accessed on 1 June 2022).
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Untargeted Metabolomic Profiling of Fasting Serum
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Fasting serum samples were collected from the HCHS/SOL baseline visit for metabolomic profiling and stored at −70 °C since collection. The profiling was performed at Metabolon (Durham, NC, USA) using the Discovery HD4 platform in 2017 [20 (link)]. Untargeted liquid chromatography–mass spectrometry (LC-MS) protocol was utilized to semi-quantify metabolites [53 (link),54 (link),55 (link)]. In total, 1136 metabolites were discovered, including 782 known and 354 unknown metabolites. Finally, 640 analyzable metabolites were verified as only known metabolites with missing rates ≤ 25% were regarded for quality control. Missing data for the metabolites were imputed to half of the lowest value [56 (link),57 (link)]. Additional details are provided in the Supplemental Methods (Table S8) .
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