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Transforming cerebrospinal fluid Aβ42 measures into calculated Pittsburgh compound B units of brain Aβ amyloid
Author(s) -
Weigand Stephen D.,
Vemuri Prashanthi,
Wiste Heather J.,
Senjem Matthew L.,
Pankratz Ver S.,
Aisen Paul S.,
Weiner Michael W.,
Petersen Ronald C.,
Shaw Leslie M.,
Trojanowski John Q.,
Knopman David S.,
Jack Clifford R.
Publication year - 2011
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2010.08.230
Subject(s) - pittsburgh compound b , cerebrospinal fluid , neuroimaging , positron emission tomography , linear regression , cognitive impairment , nuclear medicine , medicine , alzheimer's disease , lumbar puncture , alzheimer's disease neuroimaging initiative , psychology , pathology , disease , neuroscience , machine learning , computer science
Background Positron‐emission tomography (PET) imaging of amyloid with Pittsburgh Compound B (PIB) and Aβ42 levels in the cerebrospinal fluid (CSF Aβ42) demonstrate a highly significant inverse correlation. Both these techniques are presumed to measure brain Aβ amyloid load. The objectives of this study were to develop a method to transform CSF Aβ42 measures into calculated PIB measures (PIBcalc) of Aβ amyloid load, and to partially validate the method in an independent sample of subjects. Methods In all, 41 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent PIB PET imaging and lumbar puncture (LP) at the same time. This sample, referred to as the “training” sample (nine cognitively normal subjects, 22 subjects with mild cognitive impairment, and 10 subjects with Alzheimer's disease), was used to develop a regression model by which CSF Aβ42 (with apolipoprotein E ɛ 4 carrier status as a covariate) was transformed into units of PIB PET (PIBcalc). An independent “supporting” sample of 362 ADNI subjects (105 cognitively normal subjects, 164 subjects with mild cognitive impairment, and 93 subjects with Alzheimer's disease) who underwent LP but not PIB PET imaging had their CSF Aβ42 values converted to PIBcalc. These values were compared with the overall PIB PET distribution found in the ADNI subjects (n = 102). Results A linear regression model demonstrates good prediction of actual PIB PET from CSF Aβ42 measures obtained in the training sample ( R 2 = 0.77, P < .001). PIBcalc data (derived from CSF Aβ42) in the supporting sample of 362 ADNI subjects who underwent LP but not PIB PET imaging demonstrate group‐wise distributions that are highly consistent with the larger ADNI PIB PET distribution and with published PIB PET imaging studies. Conclusion Although the precise parameters of this model are specific for the ADNI sample, we conclude that CSF Aβ42 can be transformed into PIBcalc measures of Aβ amyloid load. Brain Aβ amyloid load can be ascertained at baseline in therapeutic or observational studies by either CSF or amyloid PET imaging and the data can be pooled using well‐established multiple imputation techniques that account for the uncertainty in a CSF‐based PIBcalc value.