Combining plasma phospho-tau and accessible measures to evaluate progression to Alzheimer’s dementia in mild cognitive impairment patients

Prodromal Alzheimer’s disease (AD) is a common cause of mild cognitive impairment (MCI), but it is very difficult to clinically differentiate it from other causes of MCI [1]. Generally, between 20 to 40% of MCI patients will progress to AD dementia within a few years [2, 3]. Improving the identification of patients at greater risk of further cognitive decline is thus important for clinical practice for patients and families, in clinical trials to enroll patients having AD pathology, and in the future for selecting patients for treatment with disease-modifying drugs. The key protein causing AD, beta-amyloid (Aβ) and tau, can be measured either in the cerebrospinal fluid (CSF) with lumbar puncture or in the brain with positron emission tomography (PET). These can be used to help in determining the risk of progression to AD dementia in individuals with MCI [4‐7]. However, CSF collection may be regarded as invasive and PET scans are costly and have limited availability, which hampers the use of these methodologies in clinical practice from a global perspective. With the recent advent of blood-based biomarkers, we can now measure a variety of proteins related to AD in a time- and cost-effective manner and investigate how well such markers can inform disease diagnosis and prognosis [6]. The molecular pathways that can be investigated with plasma biomarkers also now extend beyond Aβ and tau. These include for example neurodegenerative markers such as neurofilament light (NfL) and glial activation biomarkers such as glial fibrillary acidic protein (GFAP) [6, 8]. Very promising results in non-demented patients suggest that plasma tau phosphorylated at threonine 217 (p-tau217), in combination with cognitive performance and apolipoprotein E (APOE) genotype, was the best marker to predict conversion to AD dementia within 4 years, with very high accuracy [1]. These recent results were derived from only two cohorts, and thus, we still need to validate the optimal markers of conversion in other independent cohorts and determine the most consistent results before we can implement such prognostic algorithms in clinical practice globally.

The current study focuses on a subset of amnestic MCI patients previously enrolled in a 3-year clinical trial, which was originally designed to determine the accuracy of [¹⁸F]flutemetamol PET to predict subsequent conversion to dementia [7]. We now investigated which combinations of key plasma biomarkers and other commonly used and accessible markers of AD were related to progression to AD dementia. First, we studied the accuracy of plasma biomarkers to identify MCI patients who are likely to progress to AD dementia. In this cohort, we quantified four plasma biomarkers: p-tau217, the ratio of Aβ42/Aβ40, as well as NfL and GFAP. Next, we considered whether combining the best performing plasma biomarkers with hippocampal volume, APOE genotype, and a composite cognitive score would further improve the discrimination between MCI patients who progressed to AD dementia and those who did not.

Aβ plaques, tau tangles, and neurodegeneration are the core pathophysiological alterations of AD, as conceptualized in the biomarker-driven AT(N) classification [18]. However, other pathophysiological pathways (X) are being investigated as potentially important in AD, resulting in the proposition of new ATX(N) classification, to be able to incorporate and adapt to new biomarkers [19]. One such pathway is neuroinflammation and glial activation, which can be tracked with novel fluid biomarkers like GFAP, YKL40, and TREM2 [20, 21]. We thus applied an ATX(N)-like framework in the MCI cohort to investigate which plasma biomarkers were most related to conversion to AD dementia. We selected the Aβ42/Aβ40 ratio (A), p-tau217 (T), NfL (N), and the increasingly studied astrocytic marker GFAP (X). Using a data-driven approach allowing all combinations of plasma biomarkers to derive the best models with conversion to AD dementia as outcome, it was clear that p-tau217 was consistently the best biomarker to discriminate MCI progressors from non-progressors. In fact, adding other plasma biomarkers in combination with p-tau217 did not result in improved model fit or better discrimination (no change in AICc or AUC, Table 2). This result further adds to the growing literature of plasma p-tau217 (or p-tau181) as important markers to track AD progression [1, 22, 23].

We did not observe added value of plasma Aβ42/Aβ40 when combined with plasma p-tau217 for either clinical progression or Aβ-PET status. However, we cannot exclude that this result is assay-specific, since Aβ42 and Aβ40 were measured using Simoa immunoassays, which have been shown to be less accurate than certain mass spectrometry-based Aβ assays [12]. Still, a recent study where the outcome was brain amyloidosis also found that at the MCI stage, plasma p-tau217 was the best biomarker to identify Aβ-positive participants, with or without plasma Aβ42/Aβ40 (quantified with mass spectrometry-based assay) as an additional predictor [12]. These results also align with the AD pathophysiology, with Aβ proteins starting to change and plateauing earlier in the AD continuum making them less informative for predicting much later cognitive decline, while p-tau continues to increase through the prodromal stage of the disease to the dementia stage, shown both in studies using CSF [24‐26] and plasma [27, 28] biomarkers. Accumulating evidence also suggested close relationship between Aβ and GFAP rather than between GFAP and p-tau [29‐32]. While plasma Aβ and GFAP might be more closely associated with Aβ pathology in earlier stages of the disease, p-tau is more associated with clinical progression.

With the idea of implementing a cost-effective predictive model, we also focused on combining p-tau217 with other easily accessible measures in AD. We used measures in line with those investigated in the initial clinical trial in combination with Aβ-PET [7], i.e., hippocampal volume, APOE4 genotype and a global score of cognition. Adding these three variables with p-tau217 resulted in the best identification of MCI progressors to AD dementia, with an AUC of 0.89. Previous studies with a similar outcome but using CSF or PET biomarkers rather than plasma also often found an added value of such additional non-biomarker measures [33‐35]. However, when comparing the best model that included all variables to the best subsequent combinations of variables, we found that p-tau217 and the global cognitive score mPACC were largely comparable to the full model. Model fits were similar, but the AUC was slightly lower (0.89 for full model vs. 0.87 for p-tau217 + mPACC, p = 0.07). Across all models combining p-tau217 with other variables, we should note that mPACC was always a significant contributor, while hippocampal volume or APOE4 genotype were often at trend-level with p-values around 0.1. Our approach for this study and the main results corroborate the findings from a recent large-scale study from our group where p-tau217, memory score, executive function, and APOE4 genotype was the best combination to determine conversion to AD-dementia within 4 years in cognitively normal older adults or MCI patients [1]. As with the current study, NfL, structural measures from MRI, and basic demographics only had little influence on model performance. Notably, model accuracy was similar in both studies, with an AUC of 0.91 in the large-scale study and 0.89 here. Overall, across very different datasets, there is converging evidence that plasma p-tau217 in combination with easily accessible AD markers have the highest potential to help detect individuals at risk of progression to dementia. To move the field forward and get closer to implementing the most promising markers more widely in clinical practice, it is important to validate results evaluating risk of conversion to AD in multiple samples. Further, in cases of smaller sample size as the current study where only minor differences between models existed, we propose that p-tau217 and global cognitive score would be sufficient predictors for a parsimonious, most easily accessible model predicting progression to AD dementia.

There are a few limitations to consider to this study. Unfortunately, the four biomarkers of interest were not available for all participants, due to limited amount of plasma to analyze for some individuals. We tried to circumvent this aspect by first selecting the plasma markers most related to conversion to AD, which allowed us to conduct further analyses in the full sample, in which p-tau217 level was measured in all participants. Still, the limited plasma quantity precluded us from measuring other p-tau isoforms or Aβ42 and Aβ40 using the most accurate mass spectrometry-based methods (which are superior to plasma Aβ immunoassays used here) [36]. Future studies should evaluate if combing p-tau isoforms with Aβ42/Aβ40 measured using mass spectrometry-based methods would offer improved performance in different stages of AD. Only plasma and no CSF was available in this sample; therefore, we were not able to test how well plasma p-tau levels reflect CSF level. Still, we hypothesize that CSF p-tau217 would have been a key marker related to progression to AD [37]. Given the somewhat small sample size, we also aimed to restrict the number of variables included in logistic regression models and opted for a global score of cognition instead of multiple neuropsychological tests. With memory and executive function being both important cognitive domains to predict AD dementia in the previous large-scale study [1], we derived a modified PACC (we were missing the Free and Cued Selective Reminding Test included in the original version), analogous to the PACC5, which encompassed both domains and is widely used [15, 16]. However, we acknowledge that the Free and Cued Selective Reminding Test might have provided sensitive memory measure to the composite score. Ten participants had missing APOE4 genotype, but we replicated the main results when restricting the variables of interest to p-tau217, cognition, and hippocampal volume. We should also mention the current study focused on determining conversion to AD dementia where all converters had brain amyloidosis, while the outcome of the original clinical trial was probable AD, relying on the clinical status from the clinical adjudication committee. Lastly, all participants were categorized as amnestic MCI, and thus generalization of the results to more diverse MCI patients should be determined.

In MCI patients, plasma p-tau217 was the biomarker most associated with risk of conversion to AD dementia within 3 years. Combining p-tau217 with a few commonly used markers of AD improved the discrimination between those who progressed to AD dementia or not. Aiming for a balance in terms of model fit, parsimony and easily accessible measures, plasma p-tau217, and a score of global cognition were the best markers to predict future decline in this cohort.