Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Ritchie, Scott C.; Surendran, Praveen; Karthikeyan, Savita; Bolton, Thomas; Pennells, Lisa; Danesh, John; Angelantonio, Emanuele Di; Butterworth, Adam S.; Inouye, Michael

doi:10.1038/s41597-023-01949-y

Cited by 58 publications

(32 citation statements)

References 33 publications

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“…We classified the Nightingale metabolite measurements and the clinical biomarkers into the following 10 different biological classes or physiological systems based on known biology: lipids, amino acids, liver, glucose-related/glycolysis, renal, hormones, ketone bodies, bone and joint, inflammatory, and immune ( Tables S1 A and S1B). The Nightingale metabolite measurements that are available for 121,695 UKB participants mainly include lipoprotein lipids, fatty acids and their compositions, and various other low-molecular-weight metabolites 24 ( Table S1 A). The clinical biomarkers, which have been measured in nearly all the UKB participants, are more diverse and relate to glucose and lipid metabolism, renal and liver function, and others ( Table S1 B).…”

Section: Resultsmentioning

confidence: 99%

“…Concentrations of 168 blood metabolites (107 “non-derived” metabolites and 61 composite metabolites) and 81 metabolite ratios, pertaining to lipoprotein lipids, fatty acids and their compositions, and various other low-molecular-weight metabolites, were measured in a subset of randomly selected 121,695 UKB participants by Nightingale Health using nuclear magnetic resonance (NMR) spectroscopy 24 ( Table S1 A). Metabolite measurements were normalized for known sources of technical variation using the ukbnmr R package ( https://github.com/sritchie73/ukbnmr/ ).…”

Section: Methodsmentioning

confidence: 99%

“…We also derived an additional 76 metabolite ratios that were not present in the original Nightingale data but were suggested by the authors of the ukbnmr package as having potential biological significance. 24 Thus, in total, 325 metabolite measurements (107 non-derived metabolites, 61 composite metabolites, 81 metabolite ratios, and 76 ukbnmr-derived metabolite ratios) were analyzed in this study. Additionally, we also analyzed routine clinical blood biomarkers (n = 30) related to glucose and lipid metabolism, renal and liver function, that were measured in the majority of the 500,000 UKB participants ( Table S1 B).…”

Section: Methodsmentioning

confidence: 99%

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Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Nag¹,

Dhindsa²,

Middleton³

et al. 2023

The American Journal of Human Genetics

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Nag¹,

Dhindsa²,

Middleton³

et al. 2023

The American Journal of Human Genetics

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“…A random subset of 118 466 individuals was profiled for plasma metabolites using a high-throughput 1H-NMR metabolomics (Nightingale Health; biomarker quantification version 2020), which includes 249 metabolites. A natural logarithm transformation of each metabolite was performed for the analysis.…”

Section: Methodsmentioning

confidence: 99%

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Amin

Bonnechère

et al. 2023

JAMA Psychiatry

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ImportanceMetabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression.ObjectiveTo identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD.Design, Setting and ParticipantsThis cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021.Main Outcomes and MeasuresOutcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform.ResultsThe study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], −0.07 [0.02]; FDR = 4 × 10−04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not.Conclusions and RelevanceThe study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

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“…These data are now available to approved researchers through the UK Biobank Showcase for all aspects of public health research. Many studies are already using these biomarker data, spanning applications related to, for instance, risk prediction, causal analyses, genetic discovery and drug target validation [9][10][11][12][13][14][15][16][17][18] .…”

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confidence: 99%

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

Julkunen¹,

Cichońska²,

Tiainen³

et al. 2023

Nat Commun

174

100

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Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases (nightingalehealth.com/atlas). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

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Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Cited by 58 publications

References 33 publications

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

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