Application of the IWG-2 Diagnostic Criteria for Alzheimer’s Disease to the ADNI

Abstract

Background:

The International Working Group (IWG) recently proposed the revised diagnostic criteria for Alzheimer’s disease (AD) to define and refine several types of AD, and to reclassify AD-related biomarkers into diagnostic and progression markers, but its performance is not known.

Objective:

This study was designed to describe the application of the revised IWG criteria in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and to ascertain whether diagnostic and progression markers show significant differences in their relationships to AD severity and progression.

Methods:

Based on the requirements of the refined criteria, 857 ADNI subjects with memory evaluation and at least one pathophysiological marker (CSF or amyloid imaging biomarkers) were eligible and reclassified in this study, and we calculated the associations of diagnostic (CSF and amyloid PET) and progression markers (MRI and fluorodeoxyglucose-PET) with AD severity and progression respectively.

Results:

The majority (84.2% ) of ADNI AD group (n = 117) and 173 MCI (37.4% ) subjects in ADNI met the definition of typical AD; and 105 cognitively normal (41.0% ) individuals were diagnosed as asymptomatic AD. Furthermore, diagnostic and progression markers showed significant differences when correlated to AD severity and progression.

Conclusion:

A large proportion of AD dementia subjects were categorized as typical AD, and the revised criteria could identify typical AD from MCI status as well as asymptomatic AD at the asymptomatic stage. Moreover, the significant differences between diagnostic and progression markers further supported the new biomarkers categorization in the refined criteria.

Keywords

Alzheimer’s disease Alzheimer’s disease neuroimaging Initiative diagnostic markers international working group-2 criteria progression markers

INTRODUCTION

Research into biomarkers has documented the crucial value of cerebrospinal fluid (CSF) proteins [1 –4], magnetic resonance imaging (MRI) [5 –7], 18-fluorodeoxyglucose (FDG) uptake on position emission tomography (PET) [8, 9], and amyloid PET [10 –14] in the diagnosis of Alzheimer’s disease (AD). In 2007 the International Working Group (IWG) for New Research Criteria for the Diagnosis of AD first identified pathophysiological and topographical biomarkers in the diagnosis, and conferred each marker an equal part in the algorithm [15]. Subsequently in 2011, the National Institute on Aging–Alzheimer’s Association (NIA-AA) recognized differences between amyloid-beta (Aβ_1-42) deposition and neuronal injury markers, but attributed equal utility to both in the diagnosis [16]. To address the problem of multiple related biomarkers and their assumed weight in the diagnosis [17 –19], the IWG in 2014 reconsidered the categorizations of these markers [20]. The new IWG framework considered pathophysiological markers (CSF proteins and amyloid PET) as diagnostic biomarkers, which reflect AD pathology, and are present at any stage of disease but mainly static; while topographic markers (MRI and FDG-PET) are viewed as progression biomarkers which lack specificity for AD, but are associated with disease severity and progression. However, the authors of the IWG-2 acknowledged that the new algorithm needs to be validated with adequate resources to determine its utility within clinical practice [20, 21].

To date, there are no reports about how the refined criteria perform within a clinical dataset, and no direct comparisons have been made between diagnostic and progression markers on their link to AD progression. Here we investigated the application of the new criteria in subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and explored the differences between diagnostic and progression markers on their association with AD severity and progression respectively.

MATERIALS AND METHODS

ADNI dataset

The ADNI is a large, multicenter, longitudinal neuroimaging study, launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, pharmaceutical companies, and nonprofit organizations [22]. The initial goal of ADNI was to recruit 800 subjects but the ADNI has been followed by ADNI-GO and ADNI-2 with additional recruitment. To date, the three protocols have recruited over 1,500 adults, ages 55 to 90, including cognitively normal older individuals (CN), patients with mild cognitive impairment (MCI), and patients with early dementia due to AD. The study was approved by the institutional review boards of all participating centers and written informed consent was obtained from all participants or authorized representatives after extensive description of the ADNI based on the 1975 Declaration of Helsinki.

Subjects

The participants used in this study were obtained from the ADNI database (http://adni.loni.usc.edu). Inclusion criteria for AD subjects included National Institute of Neurological and Communication Disorders/Alzheimer’s Disease and Related Disorders Association criteria for probable AD (NINCDS–ADRDA) [23] with a Mini-Mental State Examination score between 20 and 26, a global Clinical Dementia Rating of 0.5 or 1, a sum-of-boxes Clinical Dementia Rating of 1.0 to 9.0. All amnestic MCI subjects fulfilled a Mini-Mental State Examination score of 24 to 30 and a Memory Box score of at least 0.5. On the other hand, subjects who had any serious neurological disease other than possible AD, any history of brain lesions or head trauma, or psychoactive medication use (including antidepressants, neuroleptics, chronic anxiolytics, or sedative hypnotics) were excluded from this study.

Other details about the ADNI cohort can be foundonline (http://adni.loni.usc.edu/wp-content/uploads/2010/09/ADNI_GeneralProceduresManual.pdf).

Memory function test

The memory function was largely measured by Rey Auditory Verbal Learning Test (RAVLT) scale, and the memory tests and corresponding scores can be found on website (http://adni.loni.usc.edu/upload-data/). Each episodic memory score were converted into a Z score within the population. Although these memory tests are continuous measures, the new IWG criteria required the classification of every test as either normal or abnormal [24], and the cut-off values for these measurements were based on a cohort that was independent of the included subjects. For the purposes of this study, subjects below the tenth percentile of the cognitively normal sample (less than –1.25 SD) were considered to have episodic memory impairment and abnormal [18 , 25].

CSF proteins examination

The detailed methods of CSF proteins, amyloid-beta (Aβ_1-42), total tau (T-tau), and phosphorylated-tau (P-tau₁₈₁) measurement were described elsewhere [26]. The CSF data used here was from the University of Pennsylvania (UPENN), which covered baseline CSF samples in ADNI, ADNI-GO, and ADNI-2. Similarly, cutoff points were necessary to categorize these continuous protein concentrations into normal and abnormal levels. Based on Shaw et al., the cutoff point for CSF Aβ_1-42 was 192 pg/ml, which meant the levels of ≤192 pg/ml were considered abnormal, and the CSF T-tau and P-tau₁₈₁ cutoff level were 93 pg/ml and 23 pg/ml respectively [2, 27].

Neuroimaging methods

The PET imaging data with amyloid tracer, florbetapir (AV-45), were obtained from UC Berkeley – AV45 analysis dataset on website (http://adni.loni.usc.edu/upload-data/). The institute used a native-space MRI scan for each subject that is segmented with Freesurfer (version 4.5.0) to define cortical grey matter regions of interest (ROI) (frontal, anterior/posterior cingulate, lateral parietal, lateral temporal) that make up a summary cortical ROI. The cerebellum was defined as reference region. Each florbetapir scan was applied to the corresponding MRI and the mean florbetapir uptake calculated within the cortical and reference region. Finally, florbetapir standard uptake value ratios (SUVRs) were created by averaging across the 4 cortical regions and dividing this cortical summary ROI by the whole cerebellum. The florbetapir cutoff was determined as 1.11 using the whole cerebellum as reference region. Likewise, other neuroimaging data, such as regional volume on MRI, and cerebral metabolic rate for glucose (CMRgl) on FDG-PET were downloaded from the corresponding dataset in ADNI dataset. The detailed description on acquiring and treating imaging data from ADNI was previously described [28]. Referring to the evidence presented in the refined criteria, we used hippocampal atrophy as MRI-related markers and the CMRgl of bilateral posterior cingulate as FDG-PET markers for our analysis.

Statistical analysis

Given that subjects who had any serious neurological disease other than possible AD had been excluded from ADNI, the criteria for typical AD required both significant memory impairment and markers of Alzheimer’s pathology (either decreased Aβ_1-42 with elevated T-tau/P-tau₁₈₁ in CSF or elevated SUVR on amyloid PET). Asymptomatic AD required normal memory function with abnormal pathophysiological markers (CSF or amyloid imaging biomarkers). Subjects that were positive for cognitive impairment but negative for pathophysiological markers were MCI patients based on the IWG-2 criteria. The normal control group was defined as the individuals with normal cognitive function (including memory function) and without biomarkers of pathology.

Baseline differences between the three stages were analyzed using one-way analysis of variance (ANOVA) for continuous variables and Pearson chi2 analysis and logistic regression models for categorical variables. The Clinical Dementia Rating scale sum of boxes (CDRSB) represented the measure of cognitive impairment in our study [25]. In addition, we adopted a multiple linear regression model to identify correlation between biomarkers and AD severity. In this model, the levels of AD biomarkers were dependent variables, CDRSB was the independent variable, and age, gender, education, and APOE ɛ4 status were used as covariates to estimate coefficients and the 95% confidence interval (CI). Furthermore, to investigate whether topographical biomarkers changed over time, a regression model with change of topographic markers as explanatory variable, the types of population (typical AD and normal group) as independent variable, and follow-up time, age, gender, education, and APOE ɛ4 status as covariates was used. All statistical analyses were performed by Stata 12.0. The criterion for significant difference was p < 0.01.

RESULTS

IWG-2 categorization of ADNI subjects

Figure 1 lists the details of classification. A total of 1,735 individuals were recruited from ADNI database. In this sample 1,727 subjects (AD = 339, MCI = 870, CN = 518) were measured with the memory scales, 389 subjects (AD = 25, MCI = 257, CN = 107) underwent CSF biomarkers test, and 852 individuals (AD = 138, MCI = 458, CN = 256) received amyloid biomarkers examination. Given that only participants with episodic memory evaluation and at least a marker of AD pathology (CSF or amyloid imaging markers) were eligible for our investigation, 857 subjects (139 AD, 462 MCI, and 256 CN) entered into the categorization process. According to the IWG-2 criteria 290 subjects were diagnosed with typical AD, consisting of 117 ADNI AD (84.2% of AD group) and173 MCI (37.4% of MCI group) participants, and 105 CN subjects (41.0% ) were diagnosed as asymptomatic AD. The 289 MCI subjects who showed cognitive impairment without evidence of AD pathology were still considered as MCI group. In addition, 151 CN subjects were viewed as normal control group, defined as those with normal episodic memory and without markers of AD pathology. Finally, 22 subjects from ADNI AD dementia group (15.8% ) did not fulfill both asymptomatic AD and typical AD criteria. Among them, four patients with markers of AD pathology but without memory impairment should be considered as atypical AD, whereas the remaining 18 patients lacked evidence of AD pathology and should be considered as other types of dementia.

Demography, cognition, and neuroimaging at baseline

The baseline demographics, cognitive function, and neuroimaging markers levels at baseline are presented in Table 1. There were significant differences in age, gender, and education years between normal individuals, asymptomatic AD, MCI group, and typical AD. As expected, the ɛ4 allele of APOE gene substantially increased the risk of AD. Typical AD patients had the worst cognitive function, and normal group had the best cognitive performances based on CDRSB, ADAS-cog, RALVT, and MMSE scales. The three neuroimaging markers (hippocampus, entorhinal area, and medial temporal cortex) showed considerable atrophy in typical AD group than those in MCI, asymptomatic AD, and normal group; and typical AD patients had the largest ventricles across the four groups. Furthermore, FDG-PET examination detected that typical AD patients had the lowest CMRgl, followed by MCI, asymptomatic AD, and normal grouprespectively.

Diagnostic markers and Alzheimer’s disease

Based on the IWG-2 criteria, CSF proteins (Aβ_1-42, T-tau, and P-tau₁₈₁) and brain amyloid retention (SUVR) on PET imaging are diagnostic markers. Here, we tested the relationships between these diagnostic markers and the severity of dementia (via CDRSB) within the typical AD group in a multi-linear regression model where levels of diagnostic markers was regarded as dependent variable, the CDRSB scores as independent variable, and age, gender, education, and APOE ɛ4 allele as covariates. None of these diagnostic markers (Aβ_1-42: p = 0.013, T-tau: p = 0.144, P-tau₁₈₁: p = 0.35, SUVR: p = 0.05) showed significant association with CDRSB scores at the significant level of 0.01 (see Table 2).

Progression markers and Alzheimer’s disease

Topographic biomarkers were defined as progression markers by the revised IWG algorithm. Among the topographic markers, hippocampus atrophy is the most robust [29 –31]. Our study suggested that both left hippocampus (p = 0.003) and right hippocampus atrophy (p < 0.001) were positively correlated with the severity of dementia based on CDRSB scale within typical AD group in cross-section analysis, after controlling for age, gender, education and APOE ɛ4 allele (Fig. 2A, B). Furthermore, typical AD patients showed a trend of increasing atrophy on bilateral hippocampus (left hippocampus: p = 0.002, right hippocampus: p = 0.002) compared to normal control group in follow-up, after adjusting for follow-up time, age, gender, education, and APOE ɛ4 allele (Fig. 2C, D).

In addition, cortical hypometabolism on FDG-PET was sensitive to early dysfunction in AD and may follow disease progression. Based on previous evidence, the CMRgl in bilateral posterior cingulate were treated as the region of interest [32]. Using multiple regression analysis with age, gender, education, and APOE4 allele as covariates, we detected that FDG uptake on bilateral posterior cingulate was closely associated with the decline of cognition in typical AD group (p < 0.001) (Fig. 3A). Likewise, typical AD patients had decreasing FDG uptake (p = 0.01) compared to normal control group longitudinally, when corrected for follow-up time, age, gender, education, and APOE ɛ4 allele (Fig. 3B).

DISCUSSION

The IWG-2 research diagnostic criteria reinforce our concept of AD as a clinicobiological entity; that is to consider AD as a disease and not a syndrome (dementia or MCI), and allows one single algorithm to be used at any stage of AD [20]. This study implemented the refined IWG diagnostic criteria for AD in a multicenter, well-described, clinically diagnosed database, and provided vital implications on how the proposed criteria function in a research setting. Here, we reclassify these 857 subjects with memory scores and at least a pathophysiological marker using the revised criteria. Finally, we detected that 117 ADNI probable AD dementia patients (84.2% ) conformed to the revised definition of typical AD; 173 MCI subjects are newly diagnosed as typical AD based on the modified algorithm; and 105 CN individuals were categorized as asymptomatic AD with the new criteria. In addition, 22 probable AD cases (15.8% ) did not fulfill criteria for typical AD, among which 4 cases were negative for episodic memory impairment, and 18 for pathophysiological marker. These findings denoted that the majority of ADNI AD dementia patients were typical AD, and the refined algorithm not only could detect typical AD from dementia and MCI in the ADNI dataset, but also precisely diagnose asymptomatic AD at the asymptomaticstage.

Compared to the 2007 IWG [15] and 2011 NIA-AA criteria [16], the most important advantage within the revised IWG research diagnostic criteria is that they assigned different weight to these biomarkers to address the problem of the multitude of these markers. Based on the current criteria, pathophysiological markers (Aβ_1-42 with T-tau or P-tau₁₈₁ proteins in CSF and amyloid retention on PET) are viewed as diagnostic markers, which reflect in vivo pathology in brain (deposition of Aβ_1-42 protein and formation of neurofibrillary tangles). These markers are present at all stages of AD and provide in vivo evidence of AD pathology, even in the asymptomatic state, but may not be correlated with clinical severity. In this study, typical AD patients included 173 MCI subjects from ADNI, while asymptomatic AD patients included 151 CN individuals. This suggests diagnostic markers can be abnormal at any stages of AD, even in the asymptomatic stage. Moreover, these diagnostic biomarkers did not show a significant association with the severity of AD dementia in typical AD group, which support the hypothesis that these diagnostic markers are static at the symptomatic stage. On the other hand, topographical markers (MRI and FDG-PET markers) are considered progression markers, which lack the specificity for AD, but indicate clinical severity or disease progression, and might not be present in early stages. We treated the volume on hippocampus (MRI) [29 –31] and hypometabolism on bilateral posterior cingulate (FDG-PET) [32] as progression markers, and these neuroimaging markers both showed significant correlations with disease severity with increased atrophy on hippocampus (or deceased metabolism on posterior cingulate) correlating with more severe dementia. Furthermore, our longitudinal analysis for MRI and FDG-PET markers showed a declining trend over time suggesting that MRI and FDG-PET markers could reflect the progression of AD dementia. This was consistent with the previous findings that MRI and FDG-PET are better markers of clinical progression compared to CSF and PET-amyloid [33, 34]. The above evidence demonstrated the significant difference betweendiagnostic and progression markers in AD, and further supported the rationality and validity of separating AD biomarkers into two categories in the newcriteria [20].

To our knowledge, this is the first study that assessed the performance of the IWG-2 criteria in a large clinical database, and the simplified algorithm not only accurately diagnosed typical AD from dementia and MCI, but also detected asymptomatic AD from asymptomatic status. The findings were consistent with the previous studies, both of which applied these new criteria in clinical dataset and identified the good performance of the criteria in clinical practice [35, 36]. However, there were several potential limitations. First of all, ADNI studies are largely within a research setting and not a clinical setting. Our study, which demonstrated good performance in a research setting (ADNI dataset), does not indicate the new criteria will perform well in clinical practice. Secondly, both the variability between labs and the lack of agreement regarding the cut-offs for determining an abnormal level in the analysis for CSF biomarkers potentially generate a risk of incorporation bias. Moreover, 85 cases showed the discordant result among 383 subjects with both amyloid PET and CSF biomarkers tests, although the cases with one of the two abnormal markers (amyloid or CSF biomarkers) were considered to be positive for pathophysiological marker on the IWG-2 criteria. In addition, the refined algorithm has integrated atypical and mixed AD, but our assessment did not evaluate these forms of AD, due to the deficiency of clinical information. Finally, longitudinal follow up is imperative to verify that the MCI and CN subjects with abnormal pathological levels develop cognitive impairment, and our study did not investigate the prognostic status of asymptomatic AD owning to a deficit in follow-up data.

In conclusion, our study was the first to examine the application of the IWG-2 criteria in a large multi-center clinical dataset (ADNI), consisting of highly selected groups of MCI subjects, AD patients, and CN controls. We observed that a large proportion of AD dementia subjects in ADNI dataset were diagnosed as typical AD, and the revised criteria could identify typical AD from MCI status as well as asymptomatic AD at the asymptomatic stage. In addition, our study demonstrated the significant disparities between diagnostic and progression markers and their relationship to AD progression, providing further evidence for the new AD biomarkers categorization in the refinedcriteria.

Footnotes

ACKNOWLEDGMENTS

Data collection and sharing was funded by ADNI (National Institutes of Health U01 AG024904). ADNI is funded by the National Institute on Aging; the National Institute of Biomedical Imaging and Bioengineering; the Alzheimer’s Association; the Alzheimer’s Drug Discovery Foundation; BioClinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Co, F. Hoffmann-LaRoche Ltd and Genetech, Inc; GE Healthcare; Innogenetics, NV; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development LLC; Medpace, Inc; Merck & Co, Inc; Meso Scale Diagnostics, LLC; NeuroRx Research; Novartis Pharmaceuticals, Co, Pfizer, Inc; Piramal Imaging; Servier; Synarc Inc; and Takeda Pharmaceutical Co. 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 (). The grantee organization was the Northern California Institute for Research and Education, and the study was 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 California, Los Angeles.

This research was also supported by National Natural Science Foundation of China (81171209, 81371406, 81471309).

Authors’ disclosures available online ().

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