Dataset Information

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

ABSTRACT: Alzheimer's disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI.

SUBMITTER: St John-Williams L

PROVIDER: S-EPMC6797798 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

St John-Williams Lisa L Mahmoudiandehkordi Siamak S Arnold Matthias M Massaro Tyler T Blach Colette C Kastenmüller Gabi G Louie Gregory G Kueider-Paisley Alexandra A Han Xianlin X Baillie Rebecca R Motsinger-Reif Alison A AA Rotroff Daniel D Nho Kwangsik K Saykin Andrew J AJ Risacher Shannon L SL Koal Therese T Moseley M Arthur MA Tenenbaum Jessica D JD Thompson J Will JW Kaddurah-Daouk Rima R

Scientific data 20191017 1

Alzheimer's disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consor ...[more]

PMID: 31624257

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Project description:Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Project description:The present experiment was conducted to determine the effect of bile acids (BAs) supplementation on growth performance, BAs profile, fecal microbiome, and serum metabolomics in growing-finishing pigs. A total of 60 pigs [Duroc × (Landrace × Yorkshire)] with an average body weight of 27.0 ± 1.5 kg were selected and allotted into one of 2 groups (castrated male to female ratio = 1:1), with 10 replicates per treatment and 3 pigs per replicate. The 2 treatments were the control group (control) and a porcine bile extract-supplemented group dosed at 0.5 g/kg feed (BA). After a 16-wk treatment, growth performance, BAs profiles in serum and feces, and fecal microbial composition were determined. An untargeted metabolomics approach using gas chromatography with a time-of-flight mass spectrometer was conducted to identify the metabolic pathways and associated metabolites in the serum of pigs. We found that BAs supplementation had no effect on the growth performance of the growing-finishing pig. However, it tended to increase the gain-to-feed ratio for the whole period (P = 0.07). BAs supplementation resulted in elevated serum concentrations of secondary bile acids, including hyodeoxycholic acid (HDCA), glycoursodeoxycholic acid, and tauro-hyodeoxycholic acid, as well as fecal concentration of HDCA (P < 0.05). Fecal microbiota analysis revealed no differences in alpha and beta diversity indices or the relative abundance of operational taxonomic units (OTUs) at both phylum and genus levels between groups. Metabolic pathway analysis revealed that the differential metabolites between control and BA groups are mainly involved in purine metabolism, ether lipid metabolism, glycerophospholipid metabolism, and amino sugar and nucleotide sugar metabolism, as well as primary bile acid biosynthesis. Our findings indicate that BAs supplementation tended to improve the feed efficiency, and significantly altered the BA profile in the serum and feces of growing-finished pigs, regardless of any changes in the gut microbial composition. The altered metabolic pathways could potentially play a vital role in improving the feed efficiency of growing-finished pigs with BAs supplementation.

Dataset Information

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

Publications

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

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