A biomarker signature that identifies people on the brink of Alzheimer’s disease would help clinicians recruit the most appropriate volunteers for prevention trials. Researchers have mostly focused on select markers known to be associated with AD—from imaging, cerebrospinal fluid (CSF), or plasma—but nothing has emerged that predicts onset with high sensitivity and specificity. In an effort to find a more robust signal, scientists led by Markus Britschgi, F. Hoffmann–La Roche Ltd., Basel, Switzerland, took an unbiased sampling of more than 200 markers in the CSF, blood, and brains of participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had mild cognitive impairment (MCI) to see if a combination of marker types could better predict AD. “We took a holistic approach, asking if a combination of different clinical readouts together with imaging and fluid markers could predict progression better than individual or small sets of markers,” Britschgi told Alzforum. Trumping the stalwarts, Aβ and tau, his group found a combination of six factors from the plasma and CSF that predicted who would progress to Alzheimer’s disease (AD) in three years with 80 percent accuracy.
“The identification of predictive markers for progression from MCI to AD is important,” said Sid O’Bryant, University of North Texas Health Science Center, Fort Worth, Texas, who was not involved in the research. “This is an interesting article that leverages a tremendous dataset in ADNI and this preliminary work certainly warrants further investigation.” Henrik Zetterberg, University of Gothenburg, Sweden, also praised the effort. “We need tau- and Aβ-independent markers of AD,” he wrote to Alzforum. “They would be useful to evaluate treatment effects in tau- and Aβ-targeted treatment trials alongside the standard markers.” However, they and other researchers contacted for this article cautioned that the analysis was not repeated in an independent cohort, causing them to question whether the results could be replicated.
ADNI provides the biggest publicly available sample of people with MCI. Previously, researchers have used this data to look for a biomarker signature that would predict progression to disease within three years. Scientists have tried using imaging measures, CSF proteins, and their combination with limited success, but nothing has proved reliable (Young et al., 2013; Trzepacz et al,. 2014; Eskildsen et al., 2013). Britschgi and colleagues hoped to find a marker signature that predicted conversion with higher specificity and sensitivity.
First author Benoit Lehallier, Stanford University School of Medicine, California, included data from 928 people in ADNI who had MCI at enrollment. He and colleagues considered 249 baseline variables that might predict disease onset, including clinical and demographic characteristics, cognitive scores, MRI data, genetic status, and fluid markers. Because complete data sets were available for only a few volunteers, the researchers selected a subgroup to maximize both sample size and the number of markers. They settled on 94 people for whom data on 224 variables were available. The researchers analyzed, year by year, which markers best separated those who had stayed cognitively stable and those who had progressed to AD over a six-year period. The sample size shrank each year, due to missing data. Sixteen subjects were included in the final year-six analysis.
Without a data set comparable to ADNI with which to replicate this analysis, the researchers used a statistical method that cross-validates within the sample set. In a nutshell, they tested the sample 1,000 times, each time dividing the participants into learning and test groups a different way. For each iteration, the algorithm—called elastic net—selected the markers most highly associated with progression. Elastic net is a standard methodology used when the number of predictors is much bigger than the number of observations. In the end, the top-performing biomarkers were those that cropped up most often using this algorithm. These markers were then used in a forward classification strategy to find out how well they predicted, at baseline, progression from MCI to AD. The authors found that marker levels two and three years before diagnosis gave the best sensitivity and specificity. This time window is the most relevant for clinical trials.
They found that the typical Alzheimer’s markers, such as Aβ42, total tau, and phosphorylated tau, made unreliable predictors in this data set. Instead, a combination of six different factors—two in plasma and four in CSF—predicted progression within three years with 88 percent sensitivity and 70 percent specificity. Those who converted to AD had high apolipoprotein A-II and cortisol levels in the blood, as well as high fibroblast growth factor 4 (FGF-4) and low tumor necrosis factor–related apoptosis-inducing ligand receptor 3 (TRAIL-R3) in the CSF. They also had slightly higher calcitonin and heart-type fatty acid binding protein (FABP-heart) in the CSF. The last two factors, while not significant on their own, added to the model in combination with the other factors.
While Britschgi said he is unsure what cortisol and these five proteins say about AD or neurodegeneration in general, they point to biological pathways that previous studies have tied to Alzheimer’s. Scientists have found that levels of CSF FABP-heart and TRAIL-R3 levels, as well as plasma cortisol and ApoA-II, vary in AD (Olsson et al., 2013; Craig-Schapiro et al., 2011; Csernansky et al., 2006; Song et al., 2012). In one study, Britschgi and colleagues found that CSF levels of FABP-heart were associated with tau and p-tau levels in AD patients and controls (Britschgi et al., 2011).
The authors wrote that the lack of replication in an independent data set and the relatively small sample size limit the study, but they contend that their statistical methods make the best use of the limited data available. However, some scientists commented privately to Alzforum that this kind of analysis run on a small sample still runs great risk of detecting error or noise in the data instead of finding true progression markers, and they predicted replication would prove difficult.
Aside from a paucity of samples, William Hu, Emory University, Atlanta, pointed out that another challenge faced by the field is that results from many protein assays are highly variable in what and how much they detect, and how they perform in different labs. Many scientists from different institutions collect data for ADNI, so the data are prone to variability, and this makes results from panel analyses hard to replicate, he said. “The multicentered nature of ADNI makes it a better validation set than a discovery set,” he told Alzforum. In his experience, fewer than a third of CSF and plasma analytes found to be associated with AD in ADNI are validated. He said that people often look past how the assays perform if these tests identify markers previously associated with AD. However, he noted that the assays used in ADNI were designed to pick up proteins tied to AD in previous reports, so those associations are already likely (see full comment below).
“If this approach is independently cross-validated in AIBL [the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing] or some other cohort, that would be tremendous and would set the stage for a prospective clinical study of newly diagnosed MCI cases,” O’Bryant wrote to Alzforum. “Validation is the key next step for this sort of work.” Britschgi agreed that these unvalidated markers are not ready for prime time, but said that other researchers might now explore whether the proteins help stratify their own patient samples. He also suggested other researchers analyze large data sets in a similar, integrative way using this type of algorithm.—Gwyneth Dickey Zakaib
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