OBJECTIVES: To identify prognostic surrogate markers for change in cognitive states of HIV-infected patients. DESIGN: Longitudinal cerebrospinal fluid (CSF) samples were collected from 98 HIV-infected patients identified by temporal change in cognitive states classified as normal, stably impaired, improving and worsening. METHODS: The metabolic composition of CSF was analysed using H nuclear magnetic resonance (H NMR) spectroscopy that focused on energy metabolites. Metabolic biomarkers for cognitive states were identified using multivariate partial least squares regression modelling of the acquired spectra, combined with nonparametric analyses of metabolites with clinical features. RESULTS: Multivariate modelling and cross-validated recursive partitioning identified several energy metabolites that, when combined with clinical variables, classified patients based on change in neurocognitive states. Prognostic identification for worsening was achieved with four features that included no change in a detectable plasma viral load, elevated citrate and acetate; decreased creatine, to produce a model with a predictive accuracy of 92%, sensitivity of 88% and 96% specificity. Prognosis for improvement contained seven features that included first visit age less than 47 years, new or continued use of antiretrovirals, elevated glutamine and glucose; decreased myo-inositol, β-glucose and creatinine to generate a model with a predictive accuracy of 92%, sensitivity of 100% and specificity of 84%. CONCLUSION: These CSF metabolic results suggest that worsening cognitive status in HIV-infected patients is associated with increased aerobic glycolysis, and improvements in cognitive status are associated with a shift to anaerobic glycolysis. Dietary, lifestyle and pharmacologic interventions that promote anaerobic glycolysis could protect the brain in setting of HIV infection with combined antiretroviral therapy.
OBJECTIVES: To identify prognostic surrogate markers for change in cognitive states of HIV-infectedpatients. DESIGN: Longitudinal cerebrospinal fluid (CSF) samples were collected from 98 HIV-infectedpatients identified by temporal change in cognitive states classified as normal, stably impaired, improving and worsening. METHODS: The metabolic composition of CSF was analysed using H nuclear magnetic resonance (H NMR) spectroscopy that focused on energy metabolites. Metabolic biomarkers for cognitive states were identified using multivariate partial least squares regression modelling of the acquired spectra, combined with nonparametric analyses of metabolites with clinical features. RESULTS: Multivariate modelling and cross-validated recursive partitioning identified several energy metabolites that, when combined with clinical variables, classified patients based on change in neurocognitive states. Prognostic identification for worsening was achieved with four features that included no change in a detectable plasma viral load, elevated citrate and acetate; decreased creatine, to produce a model with a predictive accuracy of 92%, sensitivity of 88% and 96% specificity. Prognosis for improvement contained seven features that included first visit age less than 47 years, new or continued use of antiretrovirals, elevated glutamine and glucose; decreased myo-inositol, β-glucose and creatinine to generate a model with a predictive accuracy of 92%, sensitivity of 100% and specificity of 84%. CONCLUSION: These CSF metabolic results suggest that worsening cognitive status in HIV-infectedpatients is associated with increased aerobic glycolysis, and improvements in cognitive status are associated with a shift to anaerobic glycolysis. Dietary, lifestyle and pharmacologic interventions that promote anaerobic glycolysis could protect the brain in setting of HIV infection with combined antiretroviral therapy.
Authors: Ase B Andersen; Ian Law; Karen S Krabbe; Helle Bruunsgaard; Sisse R Ostrowski; Henrik Ullum; Liselotte Højgaard; Annemette Lebech; Jan Gerstoft; Andreas Kjaer Journal: J Neuroinflammation Date: 2010-02-14 Impact factor: 8.322
Authors: David S Wishart; Craig Knox; An Chi Guo; Roman Eisner; Nelson Young; Bijaya Gautam; David D Hau; Nick Psychogios; Edison Dong; Souhaila Bouatra; Rupasri Mandal; Igor Sinelnikov; Jianguo Xia; Leslie Jia; Joseph A Cruz; Emilia Lim; Constance A Sobsey; Savita Shrivastava; Paul Huang; Philip Liu; Lydia Fang; Jun Peng; Ryan Fradette; Dean Cheng; Dan Tzur; Melisa Clements; Avalyn Lewis; Andrea De Souza; Azaret Zuniga; Margot Dawe; Yeping Xiong; Derrick Clive; Russ Greiner; Alsu Nazyrova; Rustem Shaykhutdinov; Liang Li; Hans J Vogel; Ian Forsythe Journal: Nucleic Acids Res Date: 2008-10-25 Impact factor: 16.971
Authors: Claire L Waterman; Richard A Currie; Lisa A Cottrell; Jacky Dow; Jayne Wright; Catherine J Waterfield; Julian L Griffin Journal: BMC Genomics Date: 2010-01-06 Impact factor: 3.969
Authors: B Lee Peterlin; Michelle M Mielke; Alex M Dickens; Subroto Chatterjee; Paul Dash; Guillermo Alexander; Rebeca V A Vieira; Veera Venkata Ratnam Bandaru; Joelle M Dorskind; Gretchen E Tietjen; Norman H Haughey Journal: Neurology Date: 2015-09-09 Impact factor: 9.910
Authors: Deanna Saylor; Alex M Dickens; Ned Sacktor; Norman Haughey; Barbara Slusher; Mikhail Pletnikov; Joseph L Mankowski; Amanda Brown; David J Volsky; Justin C McArthur Journal: Nat Rev Neurol Date: 2016-03-11 Impact factor: 42.937
Authors: Christine Fennema-Notestine; Tricia A Thornton-Wells; Todd Hulgan; Scott Letendre; Ronald J Ellis; Donald R Franklin; Albert M Anderson; Robert K Heaton; Cinnamon S Bloss; Igor Grant; Asha R Kallianpur Journal: Brain Imaging Behav Date: 2020-10 Impact factor: 3.978
Authors: Samuel S Bailin; Cathy A Jenkins; Christopher Petucci; Jeffrey A Culver; Bryan E Shepherd; Joshua P Fessel; Todd Hulgan; John R Koethe Journal: AIDS Res Hum Retroviruses Date: 2018-05-02 Impact factor: 2.205
Authors: Jessica M Illenberger; Steven B Harrod; Charles F Mactutus; Kristen A McLaurin; Asha Kallianpur; Rosemarie M Booze Journal: J Neuroimmune Pharmacol Date: 2020-06-12 Impact factor: 4.147