10.5061/DRYAD.5HQBZKH57
Bellaver, Bruna
Federal University of Rio Grande do Sul
Ferrari-Souza, João Pedro
Federal University of Rio Grande do Sul
Uglione da Ros, Lucas
Federal University of Rio Grande do Sul
F. Carter, Stephen
University of Cambridge
Rodriguez-Vieitez, Elena
Karolinska Institute
Nordberg, Agneta
Karolinska Institute
Pellerin, Luc
0000-0002-1016-1970
University of Poitiers
Rosa-Neto, Pedro
McGill University
Teixeira Leffa, Douglas
Hospital de Clínicas de Porto Alegre
R. Zimmer, Eduardo
Federal University of Rio Grande do Sul
Supplemental material: Astrocyte biomarkers in Alzheimer’s disease: a
systematic review and meta-analysis
Dryad
dataset
2021
FOS: Clinical medicine
National Council for Scientific and Technological Development
https://ror.org/03swz6y49
460172/2014-0
Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
https://ror.org/05k49za97
16/2551-0000475-7
Instituto Nacional de Ciência e Tecnologia em Excitotoxicidade e Neuroproteção
https://ror.org/03tydf244
465671/2014-4
Instituto Serrapilheira
https://ror.org/050q5pk40
Serra-1912-31365
2022-03-24T00:00:00Z
2022-03-24T00:00:00Z
en
1778506 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Objective: To perform a systematic review and meta-analysis to determine
whether fluid and imaging astrocyte biomarkers are altered in
Alzheimer's disease (AD). Methods: PubMed and Web of Science
databases were searched for articles reporting fluid or imaging astrocyte
biomarkers in AD. Pooled effect sizes were determined with mean
differences (SMD) using the Hedge’s G method with random-effects to
determine biomarker performance. Adapted questions from QUADAS-2 were
applied for quality assessment. A protocol for this study has been
previously registered in PROSPERO (registration number: CRD42020192304).
Results: The initial search identified 1,425 articles. After exclusion
criteria were applied, 33 articles (a total of 3,204 individuals)
measuring levels of GFAP, S100B, YKL-40 and AQP4 in the blood and
cerebrospinal fluid (CSF), as well as MAO-B, indexed by positron emission
tomography 11C-deuterium-L-deprenyl ([11C]-DED), were included. GFAP (SMD
= 0.94; 95% CI = 0.71-1.18) and YKL-40 (SMD = 0.76; CI 95% = 0.63-0.89)
levels in the CSF, S100B levels in the blood (SMD = 2.91; CI 95% =
1.01-4.8) were found significantly increased in AD patients.
Conclusions: Despite significant progress, applications of astrocyte
biomarkers in AD remain in their early days. The meta-analysis
demonstrated that astrocyte biomarkers are consistently altered in AD and
supports further investigation for their inclusion in the AD clinical
research framework for observational and interventional studies.
Search strategy Two databases were searched: PubMed and Web of
Science. Complete search terms are in Supplemental File 1. No language,
study design restrictions or date of publication limit were applied. The
search was conducted by one author (BB) in November 17th, 2019. The list
of included studies was screened (BB, JPFS and LUDR) in order to identify
additional articles for inclusion. An active search was performed by BB in
May 2020 in a meta-analysis of CSF and blood biomarkers in AD. Briefly,
this meta-analysis evaluated only two astrocyte biomarkers: GFAP (2
cohorts) and YKL-40 (6 cohorts). We also conducted additional search in
book chapters related to the theme. No grey literature (i.e. preprint
databases) were searched. The authors were contacted in case of absent
data or questions about data extraction. If there was no reply after two
contacts occurring at least 10 days apart, the study was not included in
the meta-analysis. Data screening, inclusion and exclusion criteria All
articles identified in our search were downloaded into an online software
program, Rayyan QCRI. Study inclusion and exclusion were performed with a
pre-screening based on title and abstract. If there were no evident
exclusion criteria observed (i.e. reviews or editorials, studies not
related to AD or conducted using in vitro, animal models
or post-mortem analysis), a full-text analysis was performed. Both were
conducted by two authors, independently, and blinded to each other's
decisions (JPFS and LUDR). Any disagreement was discussed and resolved
with two other authors (BB and DTL). All studies reporting astrocyte
biomarkers in the blood (serum/plasma), cerebrospinal fluid (CSF) or brain
imaging of AD versus CU individuals were included. Astrocyte biomarkers
were selected based on a recent review update by Carter and colleagues The
following exclusion criteria were applied: studies with less than 10
participants, control group with inflammatory conditions, or with
neurologic or psychiatric diagnosis and biomarkers measured by
non-quantitative methods. Only studies presenting data as mean and
standard deviation (SD) or mean and standard error of the mean (SEM)
and using established criteria for AD diagnosis, including clinically
defined (NINCDS-ADRDA) and biomarker-defined (NIAA-AA and IWG) criteria,
were selected for the meta-analysis. Data were included from
cross-sectional or baseline measurements in longitudinal studies. Data
extraction Data extraction from included articles was performed
independently by two authors (JPFS and LUDR). The data extracted were:
sample size, gender (percentage of male in each group), age, AD diagnostic
criteria, method, imaging or fluid, biomarker mean and SD or SEM. To avoid
erroneous data collection, BB checked all the data extracted searching for
discrepancies. When data were reported only in graphs, a digital ruler was
used to estimate the values from graphs, as previously described. If both
methods were not possible, the authors were contacted. Additionally, when
two different AD cohorts shared the same CU control group, the number of
individuals of the control group was divided by the number of comparisons
and rounded down, in order to avoid overestimation of the effect. Genetic
background was not considered in this analysis since the majority of
articles did not provide genetic information. In particular, out of 33
articles analysed, 8 provided APOEε4 status and only 1 cohort described
familial autosomal-dominant AD mutations. Due to the paucity of genetic
information, cohorts were classified as early-onset AD (EOAD, patients
< 65 years old) and late-onset AD (LOAD, patients > 65 years
old). A total of 11 studies did not stratify patients by age, and
therefore were identified in this meta-analysis as mixed cohorts. Bias
assessment Risk of bias was assessed by three authors independently (BB,
JPFS and LUDR) following the revised Quality Assessment of Diagnostic
Accuracy Studies (QUADAS-2), a tool recommended for use in systematic
reviews by the Agency for Healthcare Research and Quality, Cochrane
Collaboration, and the U.K. National Institute for Health and Clinical
Excellence. The 12 questions adapted from QUADAS-2 and applied in this
study are listed in Supplemental File 1. Only studies with
biomarker-supported diagnosis were considered as low risk of bias in its
assessment section. In case of control groups with different ages, only
the age-matched group was used in the meta-analysis. If the overall age of
controls did not match with AD groups, the study was considered with a
high-risk of bias in its specific section of the assessment. Publication
bias was tested by visual inspection of funnel plots and by the Egger’s
regression test obtained using Stata version 14. Statistical analysis
Studies were grouped according to the astrocyte biomarker used - for
imaging biomarkers, an additional subdivision was used based on brain
regions - and a meta-analysis was conducted for each of them. In order to
conduct a meta-analysis, a minimum of 2 cohorts were necessary. To
consider intrinsic variability, Hedge´s G method with random-effect models
were used to pool effect sizes in order to determine standardized mean
differences (SMD) with 95% confidence interval (CI). The significance of
pooled effect sizes was measured using the Z-test. False discovery rate
(FDR), using a q value of 5%, was applied in order to correct for multiple
comparisons and results were considered to be statistically significant if
the corrected p-value was < 0.05. Individual study weights were
estimated using the inverse of the variance. The heterogeneity among
studies was identified using the Chi2 and quantified by I2 [I2 = (Q -
df/Q) x 100, where Q = Chi2 test results]. A p-value ≤ 0.1 was considered
significant for the Chi2 test. I2 values of 25%, 50%, and 75%, represent
low, moderate, and high heterogeneity, respectively. SMD and heterogeneity
analysis were obtained using Stata version 14. Sensitivity analyses
Sensitivity analysis was performed to identify whether any specific
article or group of articles included in this meta-analysis, in addition
to any main methodological decision, might have significantly skewed the
analyses performed. For that, the following tests were implemented: (1)
the jackknife method, in order to determine the influence of each article
on the SMD and heterogeneity; (2) exclusion of studies exhibiting a
concerning risk of bias, defined as less than 8 categories presenting low
risk of bias in the quality assessment; (3) exclusion of cohorts composed
only by EOAD patients and (4) stratifying cohorts by the AD diagnosis used
in the study. Sensitivity analyses were performed for biomarkers
presenting a minimum of 3 studies.