Brain changes related to Alzheimer’s disease (AD) exist years before manifestation of any cognitive decline.1 Mild cognitive impairment (MCI) is impairment in cognition with preserved independence and functional abilities. MCI is considered a risk factor for dementia, however, not all MCI conditions are due to AD pathology. Although ultimate diagnosis of AD is made at autopsy,2 more accurate in vivo diagnosis can be achieved by combining biomarkers in combination with clinical tests and history.3 Understanding how combinations of biomarkers improve diagnosis and assessment of the impact of novel therapeutics is important.
Proteins such as τ, Aβ1-42, Pτ181P, which are aggregated as neurofibrillary tangles and amyloid plaques and are also present in the cerebrospinal fluid (CSF), are considered as AD biomarkers.4 Decreased levels of CSF Aβ42 and increased levels of τ and phosphorylated τ (p-τ) are considered biomarkers indicating underlying AD pathology.5 Moreover, AD is associated with changes in brain structure, including changes to cortical thickness (CT) and cortical and subcortical volumes.6 Specifically, reduced volume of hippocampus is accepted as an AD biomarker.5
Although CSF and imaging biomarkers are frequently analyzed in isolation, the current study uses a well-characterized data set to test the utility of biomarkers both in isolation and in combination, in a large population, and a unique statistical approach. The aims are to (1) differentiate groups including AD, MCI, and healthy participants with normal cognitive status (NCS) and (2) assess relationship of imaging (CT and V) and fluid biomarker (CSF) with cognition.
Participants and Materials
Here we present a cross-sectional study on 822 individuals (mean age=77 years, SD=6.9 years) including healthy controls with NCS and patients diagnosed with MCI or AD. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, ADNI 1 (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Diagnoses were made following ADNI protocol (adni.loni.usc.edu). NCS participants showed no sign of depression, MCI, or dementia. MCI participants reported a subjective memory concern despite maintaining daily activities, and their performance on Wechsler Memory Scale Logical Memory II. Participants would be labeled as AD if they met the NINCDS/ADRDA criteria for probable AD.7
We used the Mini-mental State Examination (MMSE) data to assess overall cognition. CSF levels of total τ, Aβ1-42, and Pτ181P proteins were analyzed and structural measures of brain regions of interest (ROI) were computed using freesurfer version 4.3, downloaded on April 18, 2016 (https://surfer.nmr.mgh.harvard.edu/). Regions were selected to include currently accepted biomarkers and regions known to be affected early in the AD process.8 These measures included CT of 38 regions (ROIs) as well as total gray matter. ROIs were the left and the right hippocampus, anterior cingulate cortex, insula, inferior parietal cortex, middle frontal cortex, inferior temporal cortex, precentral cortex, postcentral cortex, caudate middle frontal cortex, and frontal poles, inferior frontal gyrus, superior frontal gyrus, inferior parietal gyrus, inferior temporal gyrus, middle temporal gyrus, postcentral cortex, caudate middle frontal cortex, entorhinal cortex, precuneus, and fusiform. Moreover, the volume measures of (V) of 20 ROIs were the right and the left inferior parietal, entorhinal cortex, middle temporal gyrus, temporal lobe, thalamus, hippocampus, middle temporal gyrus, putamen, caudate, and amygdala.
The Pearson correlations were calculated to measure the association between CSF protein levels, CT of brain ROIs, and Vs of brain ROIs with MMSE scores. To correct for the probability of accepting null hypotheses due to multiple comparisons, family-wise error rate Bonferroni correction was used to adjust P-values. For the whole population, regression models including every individual predictor were used to determine the variables explaining the highest variance of outcome of MMSE. All variables that explained >10% of variance were entered in separate stepwise regression models: specifically, a linear regression model involving the CSF variables, another model involving CT variables and a third model including volumetric variables. Using variable inflation factor (VIF), collinearity was checked among variables. Predictors that showed collinearity were not included in the regression model. Predictors that could significantly contribute in explaining the variance of outcome of MMSE were put in a single stepwise linear regression model. A forward stepwise logistic binary analysis was performed with the variables explaining the variance of outcome of cognitive impairment, to estimate prediction of correct diagnosis (AD, MCI, and NCS).
Individual regressions showed that CSF τ, Aβ1-42, and pτ181P were significantly associated with MMSE and explained 7% to 11% of its variance. Regression models combining CT and V variables, showed that V of the left hippocampus and both entorhinal cortices and CT of the right middle temporal gyrus, as well as the left entorhinal cortex and the inferior parietal gyrus explained about 20% of variance in MMSE. Stepwise regression of a model including CSF Aβ1-42, as well as the above regions explained 50% of variance in MMSE. Table 1 summarizes the association of CSF proteins, ROI’s V, and CT with MMSE.
CSF Aβ1-42 was also associated with MCI diagnosis. Aβ1-42, volume of the right entorhinal cortex and the CT of the left inferior parietal gyrus, and the entorhinal cortex were associated with AD diagnosis. Normal levels of CSF Aβ1-42 τ combined with no structural changes to the temporal lobes confirmed normal status.
Table 1 summarizes the association of CSF proteins, ROI’s V, and CT with cognitive impairment.
Combining imaging and CSF results created a more sensitive biomarker associate of cognitive function, validating previous reports.4 The importance of biomarkers for diagnosis was also confirmed. Higher level of CSF Aβ1-42 showed association with MCI (Table 1). It has been argued that a positive CSF Aβ1-42 without any evidence of neuroanatomical injury has an intermediate likelihood that MCI is due to AD.5 This is while lower levels of CSF Aβ1-42 are used to differentiate AD from other dementia.3,5 Consistent with the literature,4 combinations of CSF and magnetic resonance imaging biomarkers offer stronger support for accurate diagnosis, as compared with individual biomarkers. CSF Aβ1-42 levels, combined with CT and V measures of hippocampus and entorhinal cortices were strongly associated with AD diagnosis (Table 1). These results are consistent with previous studies on diagnostic biomarkers studies3: normal levels of CSF Aβ1-42 and τ and normal volume of the left entorhinal and the hippocampus as well as normal thickness of the right middle temporal gyrus significantly were associated with NCS (Table 1).
In line with previous studies,9 in isolation, various CSF levels showed significant but weak associations with MMSE scores, Aβ1-42 showing the strongest association (Table 2). Imaging biomarkers also related to cognition: temporal lobe measures, including the hippocampus and the entorhinal cortex, as well as the parietal gyrus were associated with MMSE (Table 2). Changes to these regions are known to be the early signs of cognitive impairment.10 Given the early course of AD in these regions impacts new memory formation, these results were expected.
This study showed that combined CSF, CT, and V measures offer promise for early AD diagnosis and tracing of cognitive decline in well-defined cohorts. However, their ability to predict outcome needs to be tested prospectively in typical clinical population and with autopsy confirmed AD pathology. Further, CSF measures are invasive. More research is required to reach noninvasive but rapid, inexpensive, and reliable biomarkers.
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