Figures
Abstract
Objectives
The purpose of this study was to carry out systematic review of the literature and meta-analysis to evaluate the diagnostic utility of cerebrospinal fluid (CSF) levels of the 42 amino acid form of amyloid-beta (Aβ1–42) as a biomarker for differentiating Alzheimer’s disease (AD) from non-AD dementia.
Methods
Design. Systematic literature review was used to evaluate the effectiveness of the Aβ for the diagnosis of AD. The Scottish Intercollegiate Guidelines Network (SIGN) tool was used to evaluate independently the quality of the studies.
Data sources. The literature review covered from January 1, 2004, to October 22, 2013, and searched eight domestic databases including Korea Med and international databases including Ovid-MEDLINE, EMBASE, and Cochrane Library.
Data Extraction and Synthesis. Primary criteria for inclusion were valid studies on (i) patients with mild cognitive impairment with confirmed or suspected AD and non-AD dementia, and (ii) assessment of Aβ1–42 levels using appropriate comparative tests.
Results
A total of 17 diagnostic evaluation studies were identified in which levels of CSF Aβ1–42 were assessed. Meta-analysis was performed on 11 robust studies that compared confirmed AD (n = 2211) with healthy individuals (n = 1030), 10 studies that compared AD with non-AD dementias (n = 627), and 5 studies that compared amnestic mild cognitive impairment (n = 1133) with non-amnestic type subjects (n = 1276). Overall, the CSF Aβ1–42 levels were reduced in AD compared to controls or non-AD dementia. The effectiveness of test was evaluated for diagnostic accuracy (pooled sensitivity, 0.80 (95% CI 0.78–0.82); pooled specificity, 0.76 (95% CI 0.74–0.78).
Citation: Mo J-A, Lim J-H, Sul A-R, Lee M, Youn YC, Kim H-J (2015) Cerebrospinal Fluid β-Amyloid1–42 Levels in the Differential Diagnosis of Alzheimer’s Disease—Systematic Review and Meta-Analysis. PLoS ONE 10(2): e0116802. https://doi.org/10.1371/journal.pone.0116802
Academic Editor: Rosanna Squitti, Fatebenefratelli Foundation for Health Research and Education, ITALY
Received: June 21, 2014; Accepted: December 15, 2014; Published: February 24, 2015
Copyright: © 2015 Mo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All sources are from the database of Korean National Evidence-based Healthcare Collaborating Agency (http://www.neca.re.kr/eng/). Specific locations of the data set are listed in Supporting Information.
Funding: This study was supported by research funds from the Ministry of Health and Welfare. This work was supported by the research fund of Hanyang University (HY-2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
A substantial proportion of current therapeutic development in AD focuses on therapies targeting the Aβ peptide or Aβ aggregates, the core pathology of AD [1,2]. However, large-scale clinical trials of Aβ removal by immunological or pharmacologic means have yielded no reproducible benefits [2]. There are two routes to resolve this dilemma. First, anti-Aβ therapies (and perhaps anti-tau therapies) might be conducted on minimally affected individuals (secondary prevention in stages 1/2). A second strategy is to develop therapies that are likely to be of benefit in symptomatic patients (i.e., in a preclinical stage 3 or prodromal AD) [2]. Therefore, further development of AD therapeutics will require the establishment of biomarkers that accurately reflect the progression of AD pathology, thereby permitting early diagnosis of AD and facilitating drug trials selectively targeting the early predementia stages of the disease [3].
The sampling of cerebrospinal fluid (CSF) represents the most direct and convenient methods to study the biochemical changes occurring in the central nervous system. Aβ1–42, tau, and phosphorylated forms of tau have emerged as attractive diagnostic and prognostic CSF biomarkers for ongoing AD research [4,5]. Decreased CSF Aβ1–42 has been proposed as an useful diagnostic tool for AD [4]. It has been reported that the mean level of Aβ1–42 in the CSF are reduced to around 50% in subjects with AD relative to age-matched controls against initial prediction [4], and diagnosis of AD has evolved towards separate categories of preclinical and overt dementia based on levels of CSF Aβ1–42 [6]. However, CSF Aβ1–42 levels have been reported to fluctuate over time in a cohort of old and young individuals [7], and no absolute threshold has been identified that would differentiate between mild cognitive impairment and AD in mildly symptomatic individuals [8].
In the present study we aimed to review systematically the reported association between CSF Aβ1–42 and AD with a view to evaluating the clinical usefulness of CSF Aβ1–42 in the differential diagnosis of AD versus non-AD cognitive impairment.
Methods
Systematic literature review was performed according to the reporting guidelines of the Arbitration Act Handbook (Hoggins and Green) as proposed by the Cochrane Union (Cochrane collaboration) and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) group [9]. In this study all researchers were recommended by the Korean Medical Association: these comprised a specialist of the Korean Ministry of Health and Welfare, two experts in laboratory medicine, two neurologists, and one neurological surgeon. Six meetings of all experts were held (three times in writing, three times in person) to (i) establish selection criteria, (ii) review studies selected for inclusion, (iii) overview data extraction, (iv) refine and validate the conclusions of the study.
1. Systematic literature review
Systematic literature searching was performed in the Ovid-MEDLINE, EMBASE, and Cochrane Library data bases, as well as Korea Med, and was completed on October 22, 2013. Medline searching was conducted to locate all studies published in English and Korean from January 2004 to March 2013 using MeSH terms ‘Alzheimer disease/diagnosis’ [Mesh] AND ‘sensitivity and specificity’ [Mesh] AND (imaging OR biomarkers) and (‘dementia/diagnosis’ [Mesh] AND ‘biological markers/cerebrospinal fluid’ [Mesh]) OR ‘AD/diagnosis’ [Mesh]) AND ‘([beta or amyloid] adj2 42). mp.OR (amyloid adj2 [beta or 42]).mp.)’ in Ovid-EMBASE (S1 Table). All 369 abstracts were reviewed using a combination of the search terms. The Patients—Intervention—Comparators—Outcomes (PICO) and search strategy was drafted. Study groups included patients with suspected mild cognitive impairment and/or AD, and study selection focused on reports that included measurements of Aβ levels. The reference standard was clinical diagnosis with medical results being followed up for more than 1 year. Literature searches using MEDLINE and EMBASE are summarized in S1 Table. One report (Swedish Council on Technology Assessment 2008) was identified by searching the Cochrane Library and other databases for ‘Aβ1–42’.
2. Inclusion and exclusion criteria for selected documents
- Inclusion criteria
- Research on mild cognitive impairment (MCI) or patients with suspected or confirmed AD
- National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria [10] and Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) [11] for AD
- Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV)[12]for MCI and Other dementia
- Studies using Aβ1–42 testing
- Comparative research using appropriate tests
- For predictive accuracy of reporting, studies with more than 1 year follow-up
- Research paper using appropriate inspection techniques (eg, diagnostic tools as ELISA immunoassay, amyloid PET, biopsy or autopsy)
- Research paper since 2004
- Exclusion criteria
- Reports restricted to treatment or preclinical animal studies
- Unpublished studies
- Non-research articles (non-systematic reviews, editorials, letters, comments, opinion pieces, congress or conference material, guidelines, notes, news articles, abstracts)
- Studies published only as abstracts or case reports
Searching through the literature identified 1515 documents; a further 62 documents were identified using hand searching. Among these, 1097 documents met our exclusion criteria. 451duplicated data from other reports were also excluded. A total of 17studies were included in the final evaluation (Fig. 1).
Searching through the literature identified 1515 documents; a further 62 documents were identified using hand searching. Of these, 1097 documents met our exclusion criteria. 451documents duplicated data from other reports and were also excluded. A total of 17studieswere included in the final evaluation.
3. Quality of documents
The quality assessment tool selected for literature selection was adopted from the UK Scottish Intercollegiate Guidelines (SIGN) ‘Methodology Checklist’ (2004 March). SIGN is a systematic evaluation tool for the quality of the original literature and divides reports into randomized controlled trials, cohort studies, case—control studies, diagnostic assessments, and economic evaluation studies. Most of the literature on health technology assessment comprises non-randomized clinical trials and observational studies, and selection criteria were adapted accordingly (Table 1). Each stage of categorization was performed independently by two evaluators; their joint recommendations graded reports as summarized in Table 2. The present study excluded ‘The Swedish Council on Technology Assessment in Health Care Study’ in view of limitations as follows: (i) the study did not fulfil PICO standards; (ii) database searching was based on the references of pre-selected literature; (iii) the study included diverse controls ranging from non-AD dementias to other psychiatric or neurological disorders.
4. Data Extraction
Because documents put forward for evaluation comprised more than one type of study, data extraction was repeated several times and analyzed by two evaluators. Selection and categorization were performed in consultation with other researchers who advised on problem resolution. The data were then categorized according to type of data, study characteristics, and the reliability of the techniques employed. Final extraction of data from validated primary sources was performed by two evaluators.
5. Statistical Analyses
Funnel plot was used to address publication bias. Sensitivity testing was also conducted to assess the magnitude of publication bias, which was determined using a fail-safe number, defined as the minimum number of patients with non- significant findings that are needed to overturn the conclusion of a meta-analysis [13–15]. Larger fail-safe numbers indicate that the results are less prone to publication bias. For each outcome we tested the heterogeneity of results across the studies using “I2”. If significant heterogeneity was observed (p<.10), a random effects model-which assigns a weight to each study based on individual study variance as well as between study variance- was used to pool the results together. Also Mann-Whitney test was used to compare numerical values of β-amyloid levels between different reports in same disease categories (χ2). Confidence intervals were determined using the means and standard deviations reported in each document. Meta-analysis was performed to assess the overall diagnostic accuracy of the pooled reports based on the random effects model. In addition, the fail-safe Number was calculated manually with EXCEL, suggested by Corwin [16]. SPSS (Statistical Package for the Social Sciences) 21.0 (SPSS/IBM Inc, New York) was used to recalculate the reported the χ2 values. Revman 5.0 Meta DiSc 1.4 version (Hospital Universtario Ramony Cajal, Madrid, Spain) was subsequently used for meta-analysis of the entire dataset.
Results
Following systematic analysis of the literature and retrieval of primary data, meta-analysis was performed on eleven robust studies that compared Aβ1–42 levels in AD (n = 2211) with healthy individuals (n = 1030), 10 studies that compared AD with non-AD dementias (n = 627), and five studies that compared a-MCI (amnestic mild cognitive impairment) (n = 1133) with na-MCI (non-amnestic mild cognitive impairment) subjects (n = 1276). The present evaluation is therefore based on the results of 17 published studies (Fig. 1). The primary documents and the extracted data are listed in Table 3. All selected paper used ELISA Kit of Innotest kind as a test tools, despite not limited to scan tool and the type of the selected documents. Range of test was 125 ~ 2000pg/mL, respectively and threshold was varied from 290 to 679pg/mL according to each document.
1. Results of systematic literature review
The diagnostic efficacy of CSF Aβ1–42 in AD and healthy controls was reported in eleven documents. CSF Aβ1–42 levels in AD ranged from 194±88.7 to 545±230 pg/ml, whereas levels in the healthy control group ranged from 383.5±101.8 to 1020±230 pg/ml (p <.001) (Figs. 2, 3 and Table 3). Five papers reported diagnostic efficacy of CSF Aβ1–42 for amnestic type MCI (a-MCI) patients and non-amnestic MCI (na-MCI). CSF Aβ1–42 levels ranged from 172.6±53.5 to 622.9±275.6 pg/ml in a-MCI, whereas levels in na-MCI ranged from 228±37.35 to 789.9±38.12 pg/ml (p = .003) (Figs. 2, 3 and Table 3). Diagnostic efficacy of CSF Aβ1–42 in non-AD dementias and AD was reported in 10 studies. CSF Aβ1–42 levels in AD ranged from 194±88.7 to 426.8±119.5pg/ml whereas levels in non-AD dementias ranged from 184.5±121 to 800±174 pg/ml (p <. 0001) (Figs. 2, 3 and Table 3). CSF Aβ1–42 level with 95% confidence intervals in AD was 382.2±102.0 pg/ml (95% CI 336.9–427.4) whereas levels in the healthy control group was 755.6±209.1 pg/ml (95% CI 651.5–859.6). However, CSF Aβ1–42 levels in non-AD (589.0 ± 217.5, 95% CI 105.4–977.2 pg/ml), a-MCI (434.4 ± 200.6, 95% CI 162.4–740.8 pg/ml) and na-MCI (577.9 ± 244.6, 95% CI 217.5–842.5 pg/ml) frequently overlapped (Figs. 2, 3 and Table 3).
CSF Aβ1–42 levels in AD was 382.2 ± 102.0 pg/ml (95% CI 336.9–427.4) whereas levels in the healthy control group was 755.6 ± 209.1 pg/ml (95% CI 651.5–859.6). However, CSF Aβ1–42 levels in non-AD (589.0 ± 217.5, 95% CI 105.4–977.2 pg/ml), a-MCI (434.4 ± 200.6, 95% CI 162.4–740.8 pg/ml) and na-MCI (577.9 ± 244.6, 95% CI 217.5–842.5 pg/ml) frequently overlapped. Abbreviations: a-MCI, amnestic mild cognitive impairment; na-MCI, non-amnestic mild cognitive impairment; AD, Alzheimer’s disease; non-AD, non-AD dementia.
Pooled mean difference (MD) analysis of CSF Aβ1–42 levels revealed that overall levels were significantly lower in AD patients than in healthy controls. However, there was significant heterogeneity and the ranges frequently overlapped. Abbreviations: a-MCI, amnestic mild cognitive impairment; na-MCI, non-amnestic mild cognitive impairment; AD, Alzheimer’s disease; non-AD, non-AD dementia.
2. Meta-analysis
A funnel plot confirming heterogeneity of studies is presented in Fig. 4. Pooled mean difference (MD) analysis of CSF Aβ1–42 levels revealed that overall levels were significantly lower in AD patients than in healthy controls. However, there was significant heterogeneity and the ranges frequently overlapped: pooled MD was -367.32 (95%CI–422.70~–311.94), p < 0.001, I2 = 85%, effect Z = 13.00 (Fig. 3).
There was significant heterogeneity between AD and healthy controls. Abbreviations: a-MCI, amnestic mild cognitive impairment; na-MCI, non-amnestic mild cognitive impairment; AD, Alzheimer’s disease; non-AD, non-AD dementia.
Diagnostic accuracy was evaluated on the basis of ten documents: pooled sensitivity (SN) was 0.84 (95% CI 0.82–0.86), χ2 = 24.39, p = 0.0112, I2 = 54.9%, and pooled specificity (SP) was 0.84 (95% CI 0.82–0.87), χ2 = 13.48, p = 0.2630, I2 = 18.4%. The SROC AUC (Summary Receiver Operating Characteristic Area Under the Curve (SROC AUC) value was 0.9066±0.0083 (Fig. 5).
Diagnostic accuracy was evaluated on the basis of ten documents: pooled sensitivity (SN) was 0.84 (95% CI 0.82–0.86), χ2 = 24.39, p = 0.0112, I2 = 54.9%, and pooled specificity (SP) was 0.84 (95% CI 0.82–0.87), χ2 = 13.48, p = 0.263, I2 = 18.4%. The SROC AUC (Summary Receiver Operating Characteristic Area Under the Curve (SROC AUC) value was 0.9066±0.0083. Forest plot of sensitivities and specificity and Receiver operating characteristics (ROC) curve for a-MCI and na-MCI (B). Diagnostic accuracy was evaluated on the basis of ten documents: pooled sensitivity (SN) was 0.77 (95% CI 0.73–0.81), χ2 = 30.72, p = 0.0001, I2 = 77.2%, and pooled specificity (SP) was 0.65 (95% CI 0.61–0.69), χ2 = 27.13, p = 0.003, I2 = 74.2%. The SROC AUC (Summary Receiver Operating Characteristic Area Under the Curve (SROC AUC) value was 0.7899±0.0478. Forest plot of sensitivities and specificity and Receiver operating characteristics (ROC) curve for AD and non-AD dementia (C). Diagnostic accuracy was evaluated on the basis of ten documents: pooled sensitivity (SN) was 0.80 (95% CI 0.78–0.82), χ2 = 52.88, p = 0.0000, I2 = 71.6%, and pooled specificity (SP) was 0.76 (95% CI 0.74–0.78), χ2 = 98.74, p = 0.0000, I2 = 84.8%. The SROC AUC (Summary Receiver Operating Characteristic Area Under the Curve (SROC AUC) value was 0.8668±0.0167. Abbreviations: AD, Alzheimer’s disease; non-AD, non-AD dementia; df, differences; SROC, Summary Receiver-Operating Characteristic curve; AUC, area under curve; SE, Standard Error; Q*, Heterogeneity statistic.
Pooled MD analysis showed statistically significant higher CSF Aβ1–42 levels in na-MCI compared a-MCI groups, although highly heterogeneity was apparent: pooled MD was -145.91 (95%CI–241.67~–50.16), p = 0.003, I2 = 97%, effect Z = 2.99) (Fig. 3).
The diagnostic accuracy of CSF Aβ1–42 levels was evaluated on the basis of 8published reports. Pooled SN of CSF Aβ1–42 levels was 0.77 (95% CI 0.73–0.81), χ2 = 30.72, p = .0001, I2 = 77.2% and pooled SP was 0.65 (95% CI 0.61–0.69), χ2 = 27.13, p = 0.0003, I2 = 74.2%. The SROC AUC value was 0.7899±0.0478 (Fig. 5)
Pooled MD analysis demonstrated that CSF Aβ1–42 levels were significantly lower in patients with AD versus non-AD dementia, but the results were significantly heterogeneous: the pooled MD was -212.40 (95% CI -299.09~–125.72), p<.00001, I2 = 88%, effect Z = 4.80 (Fig. 3).
Diagnostic accuracy was evaluated based on 16 reports. Pooled SN was 0.80 (95% CI 0.78–0.82), χ2 = 52.88 (p = .0000), I2 = 71.6%, and pooled SP was 0.76 (95% CI 0.74–0.78), χ2 = 98.74 (p = .0000), I2 = 84.8%. The SROC AUC value was 0.8668±0.0167 (Fig. 5).
Additionally, a sub-analysis according to age and MMSE was performed to determine the cause of the heterogeneity within the effect size of the difference between AD and non-AD. There were no significant findings (S1 Fig.).
The diagnostic accuracy of CSF Aβ1–42 levels in a-MCI versus AD, and a-MCI versus healthy controls, was only reported in one document and meta-analysis could therefore not be performed.
Discussion
In this study we have evaluated the clinical utility of CSF Aβ1–42 levels in the diagnosis of AD versus healthy controls and non-AD dementias. Data retrieved from systematic literature review did not identify threshold CSF Aβ1–42 levels that can distinguish between healthy controls and subjects with AD because there was highly significant heterogeneity and the ranges frequently overlapped. The fact that there is not a threshold, in other words a cut off, which can distinguish AD from healthy controls, as well from the other categories analyzed should be highlighted and it is a result of the meta-analysis along with those reported. However, this meta-analysis confirms that, overall, CSF Aβ1–42 levels in AD are significantly lower than in healthy controls.
Although meta-analysis was unable to differentiate reliably between a-MCI and healthy controls, several reports have attested to the clinical utility of CSF Aβ1–42 levels in MCI. Maruyama et al. reported that CSF Aβ1–42 levels did not differ significantly between the healthy control group and MCI [17]. Another study showed the values of CSF Aβ1–42 were significantly lower in the progressive MCI group than in the control subjects and the stable MCI group [18]. CSF Aβ1–42 concentration has a high diagnostic accuracy for correct allocation of AD patients in case—control studies and, together with CSF tau levels, can predict incipient AD in patients with MCI [19]. Values of CSF Aβ1–42 differed according to sample state (fresh versus frozen samples), but overall values were lower in AD patients than in MCI patients [20]. However, a threshold value discriminating between a-MCI and healthy controls could not be established. Instead, other studies have employed the ratio of CSF Aβ1–42 to either Aβ1–40, total tau, or phosphorylated tau as a potential measure of the evolution of MCI to AD [19,21–24].
In the present analysis there were significant differences between the a-MCI and na-MCI groups. CSF Aβ1–42 levels were lower in a-MCI (range 172.6±53.5 to 622.9±275.6 pg/ml) than in na-MCI (range 228.0±37.35 to 789.9±38.12pg/ml), and the pooled MD between groups was significant (pooled MD, 59.77 pg/ml). However, there was highly significant heterogeneity (I2 = 66%), and calculated diagnostic accuracy for MCI alone gave SN and SP values, respectively, of 0.52–0.83 and 0.50–0.84.
Significant discriminatory power was also seen in AD versus non-AD dementia. CSF Aβ1–42 levels in AD (range 194.0±88.7 to 545.0±230.0 pg/ml) were significantly below those reported in non-AD dementia (range 184.5±121.0 to 800.0 ± 174.0 pg/ml). The pooled MD value between groups was significantly lower in AD (pooled MD, 187.21 pg/ml). However, there was also significant heterogeneity (I2 = 66%), and the calculated diagnostic accuracy of AD versus non-AD dementia gave SN and SP values, respectively, of 0.71–0.91 and 0.44–0.82.
These findings may be summarized as follows. First, in patients with probable AD, CSF Aβ1–42 levels are of value in differential diagnosis of AD from other dementias and from healthy controls. The mean concentration of Aβ1–42 in the CSF is significantly reduced by around 50%, in subjects with AD relative to age-matched controls [4,25]. There are debates about whether the Aβ1–42 alone is useful or not in differentiating AD from non-AD dementias including frontotemporal dementia, vascular dementia, and dementia with Lewy bodies (DLB). Because concurrent presence of fibrillar Aβ deposits occurs in the majority of patients with DLB, it is possible that the reduced Aβ1–42 levels in the CSF have also been documented in patients with other dementia [4]. However, meta-analytic study indicates that CSF Aβ1–42 can serve as a diagnostic and surrogate biomarker for Aβ deposition in the brain [26]. Second, the ranges of Aβ1–42 levels partially overlap between AD and a-MCI, and it is therefore not possible to establish a cut-off value that discriminates between the two groups. Moreover, it is possible that a-MCI is an extension of AD pathology, and it has been suggested that a-MCI might be redefined to as a-MCI due to AD [6]. There might be the following several reasons; Some outstanding prospective CSF studies in MCI subjects would be particularly useful to add strength to this claim [27,28]. However, we decided to enroll papers published since 2004, because the criteria for MCI were revised to encompass other patterns of cognitive impairment in addition to memory loss [29]. In this paper we analyzed CSF results according to a-MCI and na-MCI. The other is considerable intra- or inter-laboratory variability of CSF analyses, which may influence the diagnostic classification of dementia according to results of CSF [30]. The intra- and inter-laboratory variability in CSF results from differences in pre-analytical and analytical procedures, lot-to-lot variation of analytical kits, freezing conditions and storage time [31–33]. It is necessary for research community to overcome this confusing situation that CSF variability was largest for Aβ1–42.
In summary, this meta-analysis establishes that reduced Aβ1–42 levels are of diagnostic utility in AD, and relatively high CSF levels of Aβ1–42 are indicative of non-AD pathology (e.g., na-MCI, non-AD dementias). However, CSF Aβ1–42 levels alone are insufficient for reliable differential diagnosis of AD. Further research on the use of combinations of biomarkers, for example Aβ1–42 levels in conjunction with other markers (e.g., total Aβ, Aβ1–40, tau, phosphorylated tau), will be necessary in order to develop CSF biochemical measurements permitting reliable diagnosis of AD versus other non-AD cognitive impairments.
Supporting Information
S1 Fig. Sub-group analysis by age and MMSE in the groups of AD and non-AD.
A sub-analysis according to age and MMSE has performed to determine the cause of the heterogeneity within the effect size of the difference between AD and non-AD. There were no significant findings. Abbreviations: AD, Alzheimer’s disease; non-AD, non-AD dementia.
https://doi.org/10.1371/journal.pone.0116802.s001
(TIF)
S1 PRISMA Checklist. For meta-analyses and systematic reviews, a PRISMA checklist.
https://doi.org/10.1371/journal.pone.0116802.s002
(DOC)
S1 Table. Ovid-MEDLINE and EMBASE Search Strategy.
Literature searches using MEDLINE and EMBASE. Abbreviation: PICO, Patients—Intervention—Comparators—Outcomes.
https://doi.org/10.1371/journal.pone.0116802.s003
(DOCX)
Author Contributions
Conceived and designed the experiments: JAM YCY HJK. Performed the experiments: JAM JHL. Analyzed the data: YCY HJK. Contributed reagents/materials/analysis tools: JAM JHL ML ARS. Wrote the paper: JAM HJK. Obtained funding: ML.
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