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Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer’s disease

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Abstract

Purpose

It has been suggested that neuronal energy metabolism may be involved in Alzheimer’s disease (AD). In this view, the finding of increased cerebrospinal fluid (CSF) lactate levels in AD patients has been considered the result of energetic metabolism dysfunction. Here, we investigated the relationship between neuronal energy metabolism, as measured via CSF lactate levels, and cerebral glucose metabolism, as stated at the 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography ([18F]FDG PET) in AD patients.

Methods

AD patients underwent lumbar puncture to measure CSF lactate levels and [18F]FDG PET to assess brain glucose metabolism. CSF and PET data were compared to controls. Since patients were studied at rest, we specifically investigated brain areas active in rest-condition owing to the Default Mode Network (DMN). We correlated the CSF lactate concentrations with the [18F]FDG PET data in brain areas owing to the DMN, using sex, age, disease duration, Mini Mental State Examination, and CSF levels of tau proteins and beta-amyloid as covariates.

Results

AD patients (n = 32) showed a significant increase of CSF lactate levels compared to Control 1 group (n = 28). They also showed brain glucose hypometabolism in the DMN areas compared to Control 2 group (n = 30). Within the AD group we found the significant correlation between increased CSF lactate levels and glucose hypometabolism in Broadman areas (BA) owing to left medial prefrontal cortex (BA10, mPFC), left orbitofrontal cortex (BA11, OFC), and left parahippocampal gyrus (BA 35, PHG).

Conclusion

We found high CSF levels of lactate and glucose hypometabolism within the DMN in AD patients. Moreover, we found a relationship linking the increased CSF lactate and the reduced glucose consumption in the left mPFC, OFC and PHG, owing to the anterior hub of DMN. These findings could suggest that neural glucose hypometabolism may affect the DMN efficiency in AD, also proposing the possible role of damaged brain energetic machine in impairing DMN.

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References

  1. Lambert MP, Barlow AK, Chromy BA, Edwards C, Freed R, Liosatos M, et al. Diffusible, nonfibrillar ligands derived from Abeta1-42 are potent central nervous system neurotoxins. Proc Natl Acad Sci U S A. 1998;95(11):6448–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ballatore C, Lee VM, Trojanowski JQ. Tau-mediated neurodegeneration in Alzheimer’s disease and related disorders. Nat Rev Neurosci. 2007;8(9):663–72.

    Article  CAS  PubMed  Google Scholar 

  3. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack Jr CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Leuner K, Müller WE, Reichert AS. From mitochondrial dysfunction to amyloid beta formation: novel insights into the pathogenesis of Alzheimer’s disease. Mol Neurobiol. 2012;46(1):186–93.

    Article  CAS  PubMed  Google Scholar 

  5. Leuner K, Hauptmann S, Abdel-Kader R, Scherping I, Keil U, Strosznajder JB, et al. Mitochondrial dysfunction: the first domino in brain aging and Alzheimer’s disease? Antioxid Redox Signal. 2007;9(10):1659–75.

    Article  CAS  PubMed  Google Scholar 

  6. Mao P, Reddy PH. Aging and amyloid beta-induced oxidative DNA damage and mitochondrial dysfunction in Alzheimer’s disease: implications for early intervention and therapeutics. Biochim Biophys Acta. 2011;1812(11):1359–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yamada K, Toribe Y, Yanagihara K, Mano T, Akagi M, Suzuki Y. Diagnostic accuracy of blood and CSF lactate in identifying children with mitochondrial diseases affecting the central nervous system. Brain Dev. 2012;34(2):92–7.

    Article  PubMed  Google Scholar 

  8. Parnetti L, Gaiti A, Polidori MC, Brunetti M, Palumbo B, Chionne F. Increased cerebrospinal fluid pyruvate levels in Alzheimer’s disease. Neurosci Lett. 1995;199(3):231–3.

    Article  CAS  PubMed  Google Scholar 

  9. Parnetti L, Reboldi GP, Gallai V. Cerebrospinal fluid pyruvate levels in Alzheimer’s disease and vascular dementia. Neurology. 2000;54(3):735–7.

    Article  CAS  PubMed  Google Scholar 

  10. Redjems-Bennani N, Jeandel C, Lefebvre E, Blain H, Vidailhet M, Guéant JL. Abnormal substrate levels that depend upon mitochondrial function in cerebrospinal fluid from Alzheimer patients. Gerontology. 1998;44(5):300–4.

    Article  CAS  PubMed  Google Scholar 

  11. Liguori C, Stefani A, Sancesario G, Sancesario GM, Marciani MG, Pierantozzi M. CSF lactate levels, τ proteins, cognitive decline: a dynamic relationship in Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2015;86(6):655–9.

    Article  CAS  PubMed  Google Scholar 

  12. Calkins MJ, Manczak M, Mao P, Shirendeb U, Reddy PH. Impaired mitochondrial biogenesis, defective axonal transport of mitochondria, abnormal mitochondrial dynamics and synaptic degeneration in a mouse model of Alzheimer’s disease. Hum Mol Genet. 2011;20(23):4515–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Manczak M, Anekonda TS, Henson E, Park BS, Quinn J, Reddy PH. Mitochondria are a direct site of A beta accumulation in Alzheimer’s disease neurons: implications for free radical generation and oxidative damage in disease progression. Hum Mol Genet. 2006;15(9):1437–49.

    Article  CAS  PubMed  Google Scholar 

  14. Leuner K, Pantel J, Frey C, et al. Enhanced apoptosis, oxidative stress and mitochondrial dysfunction in lymphocytes as potential biomarkers for Alzheimer’s disease. J Neural Transm Suppl. 2007b;(72):207–15.

  15. Hirai K, Aliev G, Nunomura A, et al. Mitochondrial abnormalities in Alzheimer’s disease. J Neurosci. 2001;21(9):3017–23.

    CAS  PubMed  Google Scholar 

  16. Chiaravalloti A, Pagani M, Cantonetti M, et al. Brain metabolic changes in Hodgkin disease patients following diagnosis and during the disease course: an 18F-FDG PET/CT study. Oncol Lett. 2015;9(2):685–90.

    PubMed  Google Scholar 

  17. Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. European Association of Nuclear Medicine Neuroimaging Committee. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36(12):2103–10.

    Article  PubMed  Google Scholar 

  18. Chiaravalloti A, Martorana A, Koch G, Toniolo S, di Biagio D, di Pietro B, et al. Functional correlates of t-Tau, p-Tau and Aβ1−42 amyloid cerebrospinal fluid levels in Alzheimer’s disease: a 18F-FDG PET/CT study. Nucl Med Commun. 2015;36(5):461–8.

    CAS  PubMed  Google Scholar 

  19. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. 2014;12(4):575–93.

    Article  PubMed  Google Scholar 

  20. Bennett CM, Wolford GL, Miller MB. The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci. 2009;4(4):417–22.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lancaster JL, Rainey LH, Summerlin JL, Freitas CS, Fox PT, Evans AC, et al. Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp. 1997;5(4):238–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10(3):120–31.

    Article  CAS  PubMed  Google Scholar 

  23. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100(1):253–8.

    Article  CAS  PubMed  Google Scholar 

  24. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex. 2009;19(1):72–8.

    Article  PubMed  Google Scholar 

  25. Toussaint PJ, Maiz S, Coynel D, Doyon J, Messé A, de Souza LC, et al. Characteristics of the default mode functional connectivity in normal ageing and Alzheimer’s disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. Neuroimage. 2014;101:778–86.

    Article  PubMed  Google Scholar 

  26. Kikuchi M, Hirosawa T, Yokokura M, Yagi S, Mori N, Yoshikawa E, et al. Effects of brain amyloid deposition and reduced glucose metabolism on the default mode of brain function in normal aging. J Neurosci. 2011;31(31):11193–9.

    Article  CAS  PubMed  Google Scholar 

  27. Schmahmann JD, Doyon J, McDonald D, Holmes C, Lavoie K, Hurwitz AS, et al. Three-dimensional MRI atlas of the human cerebellum in proportional stereotaxic space. Neuroimage. 1999;10(3 Pt 1):233–60.

    Article  CAS  PubMed  Google Scholar 

  28. Soonawala D, Amin T, Ebmeier KP, Steele JD, Dougall NJ, Best J, et al. Statistical parametric mapping of (99m)Tc-HMPAO-SPECT images for the diagnosis of Alzheimer’s disease: normalizing to cerebellar tracer uptake. Neuroimage. 2002;17(3):1193–202.

    Article  PubMed  Google Scholar 

  29. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19(3):1233–9.

    Article  PubMed  Google Scholar 

  30. Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab. 1984;4(4):484–99.

    Article  CAS  PubMed  Google Scholar 

  31. Lee DS, Kang H, Kim H, Park H, Oh JS, Lee JS, et al. Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur J Nucl Med Mol Imaging. 2008;35(9):1681–91.

    Article  PubMed  Google Scholar 

  32. Di X, Biswal BB. Dynamic brain functional connectivity modulated by resting-state networks. Brain Struct Funct. 2015;220(1):37–46.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Herholz K. PET studies in dementia. Ann Nucl Med. 2003;17(2):79–89.

    Article  PubMed  Google Scholar 

  35. Mosconi L. Glucose metabolism in normal aging and Alzheimer’s disease: methodological and physiological considerations for PET studies. Clin Transl Imaging. 2013. doi:10.1007/s40336-013-0026-y.

  36. Rocher AB, Chapon F, Blaizot X, Baron JC, Chavoix C. Resting-state brain glucose utilization as measured by PET is directly related to regional synaptophysin levels: a study in baboons. Neuroimage. 2003;20(3):1894–8.

    Article  PubMed  Google Scholar 

  37. Laird AR, Eickhoff SB, Li K, Robin DA, et al. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J Neurosci. 2009;29(46):14496–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004;101(13):4637–42.

  39. Balthazar ML, de Campos BM, Franco AR, Damasceno BP, Cendes F. Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer’s disease. Psychiatry Res. 2014;221(1):37–42.

    Article  PubMed  Google Scholar 

  40. Koch W, Teipel S, Mueller S, Benninghoff J, Wagner M, Bokde AL, et al. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiol Aging. 2012;33(3):466–78.

    Article  PubMed  Google Scholar 

  41. Li R, Wu X, Fleisher AS, Reiman EM, Chen K, Yao L. Attention-related networks in Alzheimer’s disease: a resting functional MRI study. Hum Brain Mapp. 2012;33(5):1076–88.

    Article  PubMed  Google Scholar 

  42. Jones DT, Machulda MM, Vemuri P, McDade EM, et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology. 2011;77(16):1524–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38.

    Article  PubMed  Google Scholar 

  44. Petrella JR, Sheldon FC, Prince SE, Calhoun VD, Doraiswamy PM. Default mode network connectivity in stable vs progressive mild cognitive impairment. Neurology. 2011;76(6):511–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25(34):7709–17.

    Article  CAS  PubMed  Google Scholar 

  46. Celebi O, Uzdogan A, Oguz KK, Has AC, Dolgun A, Cakmakli GY, et al. Default mode network connectivity is linked to cognitive functioning and CSF Aβ1-42 levels in Alzheimer’s disease. Arch Gerontol Geriatr. 2016;62:125–32.

    Article  CAS  PubMed  Google Scholar 

  47. Li X, Li TQ, Andreasen N, Wiberg MK, Westman E, Wahlund LO. Ratio of Aβ42/P-tau181p in CSF is associated with aberrant default mode network in AD. Sci Rep. 2013;3:1339.

    PubMed  PubMed Central  Google Scholar 

  48. Walsh DM, Selkoe DJ. Deciphering the molecular basis of memory failure in Alzheimer’s disease. Neuron. 2004;44(1):181–93.

    Article  CAS  PubMed  Google Scholar 

  49. Koch K, Myers NE, Göttler J, Pasquini L, et al. Disrupted intrinsic networks link amyloid-β pathology and impaired cognition in prodromal Alzheimer’s disease. Cereb Cortex. 2015;25(12):4678–88.

    Article  PubMed  Google Scholar 

  50. Amadoro G, Corsetti V, Sancesario GM, Lubrano A, et al. Cerebrospinal fluid levels of a 20–22 kDa NH2 fragment of human tau provide a novel neuronal injury biomarker in Alzheimer’s disease and other dementias. J Alzheimers Dis. 2014;42(1):211–26.

    CAS  PubMed  Google Scholar 

  51. Vlassenko AG, Vaishnavi SN, Couture L, Sacco D, Shannon BJ, Mach RH, et al. Spatial correlation between brain aerobic glycolysis and amyloid-beta (Abeta) deposition. Proc Natl Acad Sci U S A. 2010;107(41):17763–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P. Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: an fMRI study. Hum Brain Mapp. 2005;26(4):231–9.

    Article  PubMed  Google Scholar 

  53. Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, Li Y, et al. FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009;36(5):811–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Toussaint PJ, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, Doyon J, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer’s disease using conjoint univariate and independent component analyses. Neuroimage. 2012;63(2):936–46.

    Article  PubMed  Google Scholar 

  55. Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin. 2014;6:445–54.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Perani D, Cerami C, Caminiti SP, Santangelo R, Coppi E, Ferrari L, et al. Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting. Eur J Nucl Med Mol Imaging. 2016;43(3):499–508.

    Article  CAS  PubMed  Google Scholar 

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Authors' contributions

Claudio Liguori: study concept, acquisition of data, data analysis and interpretation, statistical analysis, drafting the manuscript.

Agostino Chiaravalloti: PET data acquisition and analysis, statistical analysis, drafting the manuscript.

Giuseppe Sancesario: study supervision, critical revision of the manuscript for important intellectual content.

Alessandro Stefani: critical revision of the manuscript for important intellectual content

Giulia Maria Sancesario: data analysis

Nicola Biagio Mercuri: study supervision

Orazio Schillaci: critical revision of the manuscript for important intellectual content

Mariangela Pierantozzi: study concept and supervision, data analysis and interpretation, statistical analysis, drafting the manuscript.

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Correspondence to Claudio Liguori.

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Claudio Liguori, Agostino Chiaravalloti, Giuseppe Sancesario, Alessandro Stefani, Giulia Maria Sancesario, Orazio Schillaci, Nicola Biagio Mercuri, Mariangela Pierantozzi report no financial discosures/fundings or conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Claudio Liguori and Agostino Chiaravalloti contributed equally to this work.

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Liguori, C., Chiaravalloti, A., Sancesario, G. et al. Cerebrospinal fluid lactate levels and brain [18F]FDG PET hypometabolism within the default mode network in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 43, 2040–2049 (2016). https://doi.org/10.1007/s00259-016-3417-2

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