Summary of study ST001098

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000734. The data can be accessed directly via it's Project DOI: 10.21228/M8SX1Q This work is supported by NIH grant, U2C- DK119886.

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Study IDST001098
Study TitleMetabolomics of Metabolic Risk in Patients Taking Atypical Antipsychotics (part II)
Study SummarySTUDY OBJECTIVE Patients with schizophrenia are known to have higher rates of metabolic disease than the general population. Contributing factors likely include lifestyle and atypical antipsychotic (AAP) use, but the underlying mechanisms are unknown. The objective of this study was to identify metabolomics variability in adult patients with schizophrenia who were taking AAPs and grouped by fasting insulin concentration, our surrogate marker for metabolic risk. DESIGN Metabolomics analysis. PARTICIPANTS Ninety-four adult patients with schizophrenia who were taking an AAP for at least 6 months, with no changes in their antipsychotic regimen for the previous 8 weeks, and who did not require treatment with insulin. Twenty age- and sex-matched nonobese (10 subjects) and obese (10 subjects) controls without cardiovascular disease or mental health diagnoses were used to match the body mass index range of the patients with schizophrenia to account for metabolite concentration differences attributable to body mass index. MEASUREMENTS AND MAIN RESULTS Existing serum samples were used to identify aqueous metabolites (to differentiate fasting insulin concentration quartiles) and fatty acids with quantitative nuclear magnetic resonance (NMR) and gas chromatography (GC) methods, respectively. To exclude metabolites from our pathway mapping analysis that were due to variability in weight, we also subjected serum samples from the nonobese and obese controls to the same analyses. Patients with schizophrenia had a median age of 47.0 (interquartile range 41.0-52.0) years. Using a false discovery rate threshold of <25%, 10 metabolites, not attributable to weight, differentiated insulin concentration quartiles in patients with schizophrenia and identified variability in one-carbon metabolism between groups. Patients with higher fasting insulin concentrations (quartiles 3 and 4) also trended toward having higher levels of saturated fatty acids compared with patients with lower fasting insulin concentrations (quartiles 1 and 2). CONCLUSION These results illustrate the utility of metabolomics to identify pathways underlying variable fasting insulin concentration in patients with schizophrenia. Importantly, no significant difference in AAP exposure was observed among groups, suggesting that current antipsychotic use may not be a primary factor that differentiates middle-aged adult patients with schizophrenia by fasting insulin concentration. This article is protected by copyright. All rights reserved. As published in Pharmacotherapy. 2018 Jun;38(6):638-650.
Institute
University of Michigan
Last NameStringer
First NameKathleen
Address428 Church St
EmailNMRmetabolomics@umich.edu
Phone7346474775
Submit Date2018-11-15
Analysis Type DetailMS
Release Date2019-01-22
Release Version1
Kathleen Stringer Kathleen Stringer
https://dx.doi.org/10.21228/M8SX1Q
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR000734
Project DOI:doi: 10.21228/M8SX1Q
Project Title:Metabolomics of Metabolic Risk in Patients Taking Atypical Antipsychotics
Project Type:Quantitative NMR and TLC-GC fatty acid analysis
Project Summary:STUDY OBJECTIVE Patients with schizophrenia are known to have higher rates of metabolic disease than the general population. Contributing factors likely include lifestyle and atypical antipsychotic (AAP) use, but the underlying mechanisms are unknown. The objective of this study was to identify metabolomic variability in adult patients with schizophrenia who were taking AAPs and grouped by fasting insulin concentration, our surrogate marker for metabolic risk. DESIGN Metabolomics analysis. PARTICIPANTS Ninety-four adult patients with schizophrenia who were taking an AAP for at least 6 months, with no changes in their antipsychotic regimen for the previous 8 weeks, and who did not require treatment with insulin. Twenty age- and sex-matched nonobese (10 subjects) and obese (10 subjects) controls without cardiovascular disease or mental health diagnoses were used to match the body mass index range of the patients with schizophrenia to account for metabolite concentration differences attributable to body mass index. MEASUREMENTS AND MAIN RESULTS Existing serum samples were used to identify aqueous metabolites (to differentiate fasting insulin concentration quartiles) and fatty acids with quantitative nuclear magnetic resonance (NMR) and gas chromatography (GC) methods, respectively. To exclude metabolites from our pathway mapping analysis that were due to variability in weight, we also subjected serum samples from the nonobese and obese controls to the same analyses. Patients with schizophrenia had a median age of 47.0 (interquartile range 41.0-52.0) years. Using a false discovery rate threshold of <25%, 10 metabolites, not attributable to weight, differentiated insulin concentration quartiles in patients with schizophrenia and identified variability in one-carbon metabolism between groups. Patients with higher fasting insulin concentrations (quartiles 3 and 4) also trended toward having higher levels of saturated fatty acids compared with patients with lower fasting insulin concentrations (quartiles 1 and 2). CONCLUSION These results illustrate the utility of metabolomics to identify pathways underlying variable fasting insulin concentration in patients with schizophrenia. Importantly, no significant difference in AAP exposure was observed among groups, suggesting that current antipsychotic use may not be a primary factor that differentiates middle-aged adult patients with schizophrenia by fasting insulin concentration. This article is protected by copyright. All rights reserved. As published in Pharmacotherapy. 2018 Jun;38(6):638-650.
Institute:University of Michigan
Department:Clinical Pharmacy
Laboratory:University of Michigan NMR Metabolomics Core
Last Name:Stringer
First Name:Kathleen
Address:428 Church St
Email:NMRmetabolomics@umich.edu
Phone:734-647-4775
Funding Source:Funding for this work was supported in part by grants from the following centers: the University of Michigan Claude D. Pepper Older Americans Independence Center (National Institute on Aging [NIA] grantAGA024824); the University of Michigan’s Nutrition Obesity Research Center (grant DK089503) and Weight Management Program, and the Michigan Regional Comprehensive Metabolomics Resource Core (grant DK097153),the Michigan Center for Diabetes Translational Research(grant P30DK092926), and the A. Alfred Taubman Medical Institute and the Robert C. and Veronica Atkins Foundation. This work was also supported in part by a metabolomics supplement to a grant from the National Institute of Mental Health (NIMH; grant MH082784; Dr. Ellingrod).Dr. Stringer’s effort is supported in part by a grant from the National Institute of General Medical Sciences (NIGMS; grant GM111400). Dr. Rothberg’s effort is supported in part by DK089503.
Publications:Pharmacotherapy. 2018 Jun;38(6):638-650
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