1
|
Hwangbo N, Zhang X, Raftery D, Gu H, Hu SC, Montine TJ, Quinn JF, Chung KA, Hiller AL, Wang D, Fei Q, Bettcher L, Zabetian CP, Peskind E, Li G, Promislow DEL, Franks A. A Metabolomic Aging Clock Using Human Cerebrospinal Fluid. J Gerontol A Biol Sci Med Sci 2021; 77:744-754. [PMID: 34382643 PMCID: PMC8974344 DOI: 10.1093/gerona/glab212] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Indexed: 01/13/2023] Open
Abstract
Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.
Collapse
Affiliation(s)
- Nathan Hwangbo
- Department of Statistics and Applied Probability, University of California, Santa Barbara, USA
| | - Xinyu Zhang
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Haiwei Gu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Shu-Ching Hu
- Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA,Department of Neurology, University of Washington School of Medicine, Seattle, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Joseph F Quinn
- Portland Veterans Affairs Medical Center, Oregon, USA,Department of Neurology, Oregon Health and Science University, Portland, USA
| | - Kathryn A Chung
- Portland Veterans Affairs Medical Center, Oregon, USA,Department of Neurology, Oregon Health and Science University, Portland, USA
| | - Amie L Hiller
- Portland Veterans Affairs Medical Center, Oregon, USA,Department of Neurology, Oregon Health and Science University, Portland, USA
| | - Dongfang Wang
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Qiang Fei
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Lisa Bettcher
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, USA
| | - Cyrus P Zabetian
- Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA,Department of Neurology, University of Washington School of Medicine, Seattle, USA
| | - Elaine Peskind
- Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, USA
| | - Gail Li
- Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, USA
| | - Daniel E L Promislow
- Department of Biology, University of Washington, Seattle, USA,Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, USA
| | - Alexander Franks
- Department of Statistics and Applied Probability, University of California, Santa Barbara, USA,Address correspondence to: Alexander Franks, PhD, Department of Statistics and Applied Probability, University of California, Santa Barbara, UCSB Statistics Department, 5607A South Hall, Santa Barbara, CA 93106, USA. E-mail:
| |
Collapse
|
2
|
Ulvik A, McCann A, Midttun Ø, Meyer K, Godfrey KM, Ueland PM. Quantifying Precision Loss in Targeted Metabolomics Based on Mass Spectrometry and Nonmatching Internal Standards. Anal Chem 2021; 93:7616-7624. [PMID: 34014078 DOI: 10.1021/acs.analchem.1c00119] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In mass spectrometry, reliable quantification requires correction for variations in ionization efficiency between samples. The preferred method is the addition of a stable isotope-labeled internal standard (SIL-IS). In targeted metabolomics, a dedicated SIL-IS for each metabolite of interest may not always be realized due to high cost or limited availability. We recently completed the analysis of more than 70 biomarkers, each with a matching SIL-IS, across four mass spectrometry-based platforms (one GC-MS/MS and three LC-MS/MS). Using data from calibrator and quality control samples added to 60 96-well trays (analytical runs), we calculated analytical precision (CV) retrospectively. The use of integrated peak areas for all metabolites and internal standards allowed us to calculate precision for all matching analyte (A)/SIL-IS (IS) pairs as well as for all nonmatching A/IS pairs within each platform (total n = 1442). The median between-run precision for matching A/IS across the four platforms was 2.7-5.9%. The median CV for nonmatching A/IS (corresponding to pairing analytes with a non-SIL-IS) was 2.9-10.7 percentage points higher. Across all platforms, CVs for nonmatching A/IS increased with increasing difference in retention time (Spearman's rho of 0.17-0.93). The CV difference for nonmatching vs matching A/IS was often, but not always, smaller when analytes and internal standards were close structural analogs.
Collapse
Affiliation(s)
- Arve Ulvik
- Bevital, Laboratoriebygget, 9 etg., Jonas Lies veg 87, 5021 Bergen, Norway
| | - Adrian McCann
- Bevital, Laboratoriebygget, 9 etg., Jonas Lies veg 87, 5021 Bergen, Norway
| | - Øivind Midttun
- Bevital, Laboratoriebygget, 9 etg., Jonas Lies veg 87, 5021 Bergen, Norway
| | - Klaus Meyer
- Bevital, Laboratoriebygget, 9 etg., Jonas Lies veg 87, 5021 Bergen, Norway
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton SO17 1BJ, United Kingdom
| | - Per M Ueland
- Bevital, Laboratoriebygget, 9 etg., Jonas Lies veg 87, 5021 Bergen, Norway
| |
Collapse
|
3
|
Chen L, Zhong F, Zhu J. Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches. Metabolites 2020; 10:E348. [PMID: 32867165 PMCID: PMC7570162 DOI: 10.3390/metabo10090348] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/19/2020] [Accepted: 08/23/2020] [Indexed: 01/11/2023] Open
Abstract
This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.
Collapse
Affiliation(s)
- Li Chen
- Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA;
- James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Fanyi Zhong
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA;
| | - Jiangjiang Zhu
- Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA;
- James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
4
|
Shi X, Wang S, Jasbi P, Turner C, Hrovat J, Wei Y, Liu J, Gu H. Database-Assisted Globally Optimized Targeted Mass Spectrometry (dGOT-MS): Broad and Reliable Metabolomics Analysis with Enhanced Identification. Anal Chem 2019; 91:13737-13745. [PMID: 31556994 DOI: 10.1021/acs.analchem.9b03107] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Xiaojian Shi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Shuai Wang
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
- Department of Vascular Surgery, The First Hospital of Jilin University, Jilin University, Changchun, Jilin Province 130021, People’s Republic of China
| | - Paniz Jasbi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Cassidy Turner
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Jonathan Hrovat
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Yiping Wei
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province 330006, People’s Republic of China
| | - Jingping Liu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
- Key Laboratory of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, People’s Republic of China
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| |
Collapse
|