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Qu S, Tao H, Qin L, Zhang W, Han S, Zhang S, Huang J. Harmonization of distributed multi-center analysis based on dried blood spot reference materials supporting the screening of neonatal inherited metabolic disorders. J Clin Lab Anal 2023; 37:e24970. [PMID: 37837220 PMCID: PMC10681404 DOI: 10.1002/jcla.24970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/07/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND The standardization of quantification data is critical for ensuring the reliability and measurement traceability in the screening of neonatal inherited metabolic disorders. However, the availability of national certified reference materials is limited in China. METHODS In this study, we developed a series of dried blood spot (DBS) reference materials containing 9 amino acids (AA) and 10 acylcarnitines (AC) for neonatal screening. Four levels of the reference materials were measured with tandem mass spectrometry (MS/MS) by seven laboratories using different commercial In Vitro Diagnostic Device (IVD) kits. Then, 100 clinical samples were measured using both derivatization and non-derivatization methods by the same laboratory. RESULTS We found high homogeneity and stability at all levels of the reference materials, with the coefficient of variation (CV) of the analytes less than 15%. These reference materials can be used to assess the testing capabilities of different laboratories. Our test also revealed that the correction factors (CF) calculated by the reference materials, along with clinical samples, could increase the consistency for different kits. CONCLUSION The DBS reference materials proposed in this study provide reliability for the harmonization in multi-center analysis for the screening of neonatal inherited metabolic disorders. And applying our correction method for the screening could improve the data consistency of the DBS samples prepared by different methods.
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Affiliation(s)
- Shou‐Fang Qu
- Division of Diagnostic for Non‐infectious DiseaseNational Institutes for food and drug Control (NIFDC), Institute for in Vitro Diagnostic ControlBeijingChina
| | - Hao‐Ran Tao
- BGI GenomicsShenzhenChina
- College of Life Sciences, University of Chinese Academy of SciencesBeijingChina
| | | | - Wen‐Xin Zhang
- Division of Diagnostic for Non‐infectious DiseaseNational Institutes for food and drug Control (NIFDC), Institute for in Vitro Diagnostic ControlBeijingChina
| | - Shan Han
- GBI Biotech, BGI GenomicsBeijingChina
| | - Shen‐Yan Zhang
- BGI GenomicsShenzhenChina
- GBI Biotech, BGI GenomicsBeijingChina
| | - Jie Huang
- Division of Diagnostic for Non‐infectious DiseaseNational Institutes for food and drug Control (NIFDC), Institute for in Vitro Diagnostic ControlBeijingChina
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2
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Zöllner HJ, Thiel TA, Füllenbach ND, Jördens MS, Ahn S, Wilms LM, Ljimani A, Häussinger D, Butz M, Wittsack HJ, Schnitzler A, Oeltzschner G. J-difference GABA-edited MRS reveals altered cerebello-thalamo-cortical metabolism in patients with hepatic encephalopathy. Metab Brain Dis 2023; 38:1221-1238. [PMID: 36729261 PMCID: PMC10897767 DOI: 10.1007/s11011-023-01174-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/17/2023] [Indexed: 02/03/2023]
Abstract
Hepatic encephalopathy (HE) is a common neurological manifestation of liver cirrhosis and is characterized by an increase of ammonia in the brain accompanied by a disrupted neurotransmitter balance, including the GABAergic and glutamatergic systems. The aim of this study is to investigate metabolic abnormalities in the cerebello-thalamo-cortical system of HE patients using GABA-edited MRS and links between metabolite levels, disease severity, critical flicker frequency (CFF), motor performance scores, and blood ammonia levels. GABA-edited MRS was performed in 35 participants (16 controls, 19 HE patients) on a clinical 3 T MRI system. MRS voxels were placed in the right cerebellum, left thalamus, and left motor cortex. Levels of GABA+ and of other metabolites of interest (glutamine, glutamate, myo-inositol, glutathione, total choline, total NAA, and total creatine) were assessed. Group differences in metabolite levels and associations with clinical metrics were tested. GABA+ levels were significantly increased in the cerebellum of patients with HE. GABA+ levels in the motor cortex were significantly decreased in HE patients, and correlated with the CFF (r = 0.73; p < .05) and motor performance scores (r = -0.65; p < .05). Well-established HE-typical metabolite patterns (increased glutamine, decreased myo-inositol and total choline) were confirmed in all three regions and were closely linked to clinical metrics. In summary, our findings provide further evidence for alterations in the GABAergic system in the cerebellum and motor cortex in HE. These changes were accompanied by characteristic patterns of osmolytes and oxidative stress markers in the cerebello-thalamo-cortical system. These metabolic disturbances are a likely contributor to HE motor symptoms in HE. In patients with hepatic encephalopathy, GABA+ levels in the cerebello-thalamo-cortical loop are significantly increased in the cerebellum and significantly decreased in the motor cortex. GABA+ levels in the motor cortex strongly correlate with critical flicker frequency (CFF) and motor performance score (pegboard test tPEG), but not blood ammonia levels (NH3).
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Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | - Thomas A Thiel
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Nur-Deniz Füllenbach
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Markus S Jördens
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | | | - Lena M Wilms
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Dieter Häussinger
- Department of Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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3
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Dobri S, Chen JJ, Ross B. Insights from auditory cortex for GABA+ magnetic resonance spectroscopy studies of aging. Eur J Neurosci 2022; 56:4425-4444. [PMID: 35781900 DOI: 10.1111/ejn.15755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 06/21/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022]
Abstract
Changes in levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) may underlie aging-related changes in brain function. GABA and co-edited macromolecules (GABA+) can be measured with MEGA-PRESS magnetic resonance spectroscopy (MRS). The current study investigated how changes in the aging brain impact the interpretation of GABA+ measures in bilateral auditory cortices of healthy young and older adults. Structural changes during aging appeared as decreasing proportion of grey matter in the MRS volume of interest and corresponding increase in cerebrospinal fluid. GABA+ referenced to H2 O without tissue correction declined in aging. This decline persisted after correcting for tissue differences in MR-visible H2 O and relaxation times but vanished after considering the different abundance of GABA+ in grey and white matter. However, GABA+ referenced to creatine and N-acetyl aspartate (NAA), which showed no dependence on tissue composition, decreased in aging. All GABA+ measures showed hemispheric asymmetry in young but not older adults. The study also considered aging-related effects on tissue segmentation and the impact of co-edited macromolecules. Tissue segmentation differed significantly between commonly used algorithms, but aging-related effects on tissue-corrected GABA+ were consistent across methods. Auditory cortex macromolecule concentration did not change with age, indicating that a decline in GABA caused the decrease in the compound GABA+ measure. Most likely, the macromolecule contribution to GABA+ leads to underestimating an aging-related decrease in GABA. Overall, considering multiple GABA+ measures using different reference signals strengthened the support for an aging-related decline in auditory cortex GABA levels.
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Affiliation(s)
- Simon Dobri
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Bernhard Ross
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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4
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Song Y, Lally PJ, Yanez Lopez M, Oeltzschner G, Nebel MB, Gagoski B, Kecskemeti S, Hui SCN, Zöllner HJ, Shukla D, Arichi T, De Vita E, Yedavalli V, Thayyil S, Fallin D, Dean DC, Grant PE, Wisnowski JL, Edden RAE. Edited magnetic resonance spectroscopy in the neonatal brain. Neuroradiology 2022; 64:217-232. [PMID: 34654960 PMCID: PMC8887832 DOI: 10.1007/s00234-021-02821-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 10/20/2022]
Abstract
J-difference-edited spectroscopy is a valuable approach for the detection of low-concentration metabolites with magnetic resonance spectroscopy (MRS). Currently, few edited MRS studies are performed in neonates due to suboptimal signal-to-noise ratio, relatively long acquisition times, and vulnerability to motion artifacts. Nonetheless, the technique presents an exciting opportunity in pediatric imaging research to study rapid maturational changes of neurotransmitter systems and other metabolic systems in early postnatal life. Studying these metabolic processes is vital to understanding the widespread and rapid structural and functional changes that occur in the first years of life. The overarching goal of this review is to provide an introduction to edited MRS for neonates, including the current state-of-the-art in editing methods and editable metabolites, as well as to review the current literature applying edited MRS to the neonatal brain. Existing challenges and future opportunities, including the lack of age-specific reference data, are also discussed.
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Affiliation(s)
- Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Peter J Lally
- Department of Brain Sciences, Imperial College London, London, UK
| | - Maria Yanez Lopez
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Borjan Gagoski
- Department of Radiology, Division of Neuroradiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | | | - Steve C N Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Deepika Shukla
- Centre for Perinatal Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Tomoki Arichi
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - Enrico De Vita
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, St Thomas's Hospital, Westminster Bridge Road, Lambeth Wing, 3rd Floor, London, SE1 7EH, UK
| | - Vivek Yedavalli
- Division of Neuroradiology, Park 367G, The Johns Hopkins University School of Medicine, 600 N. Wolfe St. B-112 D, Baltimore, MD, 21287, USA
| | - Sudhin Thayyil
- Centre for Perinatal Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Daniele Fallin
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins University, Baltimore, USA.,Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Douglas C Dean
- Waisman Center, University of WI-Madison, Madison, WI, 53705, USA.,Department of Pediatrics, Division of Neonatology and Newborn Nursery, University of WI-Madison, School of Medicine and Public Health, Madison, WI, 53705, USA.,Department of Medical Physics, University of WI-Madison, School of Medicine and Public Health, Madison, WI, 53705, USA
| | - P Ellen Grant
- Department of Radiology, Division of Neuroradiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.,Department of Medicine, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica L Wisnowski
- Children's Hospital Los Angeles, Los Angeles, CA, 90027, USA.,Department of Radiology and Fetal and Neonatal Institute, CHLA Division of Neonatology, Department of Pediatrics, Children's Hospital of Los Angeles, University of Southern California, Los Angeles, CA, 90033, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. .,Division of Neuroradiology, Park 367G, The Johns Hopkins University School of Medicine, 600 N. Wolfe St. B-112 D, Baltimore, MD, 21287, USA.
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5
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Morton SU, Leyshon BJ, Tamilia E, Vyas R, Sisitsky M, Ladha I, Lasekan JB, Kuchan MJ, Grant PE, Ou Y. A Role for Data Science in Precision Nutrition and Early Brain Development. Front Psychiatry 2022; 13:892259. [PMID: 35815018 PMCID: PMC9259898 DOI: 10.3389/fpsyt.2022.892259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Multimodal brain magnetic resonance imaging (MRI) can provide biomarkers of early influences on neurodevelopment such as nutrition, environmental and genetic factors. As the exposure to early influences can be separated from neurodevelopmental outcomes by many months or years, MRI markers can serve as an important intermediate outcome in multivariate analyses of neurodevelopmental determinants. Key to the success of such work are recent advances in data science as well as the growth of relevant data resources. Multimodal MRI assessment of neurodevelopment can be supplemented with other biomarkers of neurodevelopment such as electroencephalograms, magnetoencephalogram, and non-imaging biomarkers. This review focuses on how maternal nutrition impacts infant brain development, with three purposes: (1) to summarize the current knowledge about how nutrition in stages of pregnancy and breastfeeding impact infant brain development; (2) to discuss multimodal MRI and other measures of early neurodevelopment; and (3) to discuss potential opportunities for data science and artificial intelligence to advance precision nutrition. We hope this review can facilitate the collaborative march toward precision nutrition during pregnancy and the first year of life.
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Affiliation(s)
- Sarah U Morton
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | | | - Eleonora Tamilia
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Rutvi Vyas
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Michaela Sisitsky
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Imran Ladha
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States
| | | | | | - P Ellen Grant
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital, Boston, MA, United States
| | - Yangming Ou
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital, Boston, MA, United States
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