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Novel CSF Biomarker of Metabolic Dysfunction Predicts AD‐like Associations across the Alzheimer's Spectrum

2016· article· en· W4389008639 on OpenAlexaboutno aff
Kelsey E. McLimans, Auriel A. Willette

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthNational Institute of Biomedical Imaging and BioengineeringIowa State University
KeywordsAutotaxinInternal medicineCognitive declineInsulin resistanceBiomarkerEndocrinologyAlzheimer's Disease Neuroimaging InitiativeAtrophyCerebrospinal fluidDementiaPsychologyInsulinMedicineChemistryDiseaseLysophosphatidic acidBiochemistry

Abstract

fetched live from OpenAlex

Obesity and insulin resistance(IR) are associated with brain atrophy and cognitive decline, particularly in Alzheimer's disease (AD). IR is correlated with temporal and frontal amyloid deposition and brain atrophy in late middle‐aged participants at risk for AD, as well as less glucose metabolism in cognitively normal elders and AD participants. Because insulin‐degrading enzyme complicates the measurement of insulin levels in cerebrospinal fluid (CSF), an alternative biomarker for metabolic dysregulation in CSF should be used to draw conclusions between central hyper insulinemia and associations with brain atrophy and hypometabolism. Ecto‐nucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), also known as autotaxin, is studied in tumor cell mobility and is produced by beige adipose tissue. ENPP2 regulates energy metabolism, and is higher in AD prefrontal cortex (PFC). Additionally, ENPP2 stimulates the formation of lysophosphatidic acid, which promotes the generation of tissue fibrosis in vivo and in vitro . We studied Alzheimer's Disease Neuroimaging Initiative(ADNI) participants who were cognitively normal (CN; n=86) or had Mild Cognitive impairment (MCI; n=135) or AD (n=66). Statistical analyses were conducted using SPSS software. Multinomial regression analyses tested if higher ENPP2 was associated with higher relative risk ratios for MCI or AD diagnosis, relative to the CN reference group. Mixed model analyses were used to regress ENPP2 against metabolic, MRI, FDG‐PET and cognitive outcomes. Then on‐parametric Spearman's statistic was used to correlate ENPP2 and CSF biomarker values. T1‐weighted MRI images were preprocessed using FreeSurfer 4.3, and cortical thickness was examined in8 bilateral regions of interest. CSF ENPP2 levels strongly corresponded with CSF insulin‐like growth factor binding protein 2, an established biomarker of glucoregulatory function, and systemic fasting glucose. ENPP2 levels were higher in MCI and AD, and each point increase in log‐based ENPP2 values corresponded to a 3.5 to 5 times increase in the odds of having some degree of clinically relevant memory impairment. Higher ENPP2 predicted thinner PFC cortical thickness in most regions of interest in AD such as medialorbitofrontal cortex, and pars orbital is in MCI. In addition, less bilateral prefrontal cortex glucose metabolism was seen in AD. ENPP2 was also positively correlated with levels of total tau, p‐Tau181, total tau/Aβ1‐42, as well as pTau‐181/Aβ1‐42 to a marginal degree. This pattern suggests that CSF ENPP2 predicts AD neuropathology in a manner similar to dysmetabolism. Future studies should examine the potential benefit of decreasing ENPP2 levels in individuals with MCI or AD. Support or Funding Information This study was funded by Iowa State University and NIH K99 AG047282. Neither funding source had any involvement in the report. Data collection and sharing for this project were funded by the ADNI (National Institutes of Health Grant U01‐AG‐024904) and Department of Defense ADNI (award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private‐sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.264
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes1
Has abstractyes

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