Plasma phospho-tau 181 and p-tau 217 can distinguish people with Alzheimer’s disease from controls most, but not all, of the time. To increase diagnostic accuracy, researchers are creating algorithms combining p-tau with other fluid biomarker and diagnostic data. In the May 24 Nature Medicine, researchers led by Oskar Hansson and Sebastian Palmqvist, both at Lund University, Sweden, reported just such an algorithm to predict AD. They report that among people with a subjective memory complaint, plasma p-tau, APOE genotype, executive function, and memory scores together predicted AD onset within two to six years with 90 percent accuracy. In the same cohort, clinicians were about 72 percent accurate. Alzforum first reported on the algorithm at this year’s International Conference on Alzheimer’s and Parkinson’s Diseases (Apr 2021 conference news).
- Algorithm predicts AD in people with memory complaint or MCI.
- It includes plasma p-tau181 or 217, APOE genotype, cognitive tests.
- AUCs are 0.90 and greater for dementia onset within two to six years.
To build and compare prediction algorithms, first author Palmqvist and colleagues analyzed data from 340 participants in the Biofinder cohort, of whom 164 had subjective cognitive decline and 176 had mild cognitive impairment. The researchers considered a number of variables, including demographics, ApoE4 genotype, cortical thickness, cognitive measures, and levels of plasma and cerebrospinal fluid markers. First, they prioritized algorithms that found the best compromise between fit and complexity; then they sequentially omitted variables to search for simpler ones that retained the same performance power.
Algorithm Design. Clinicians were least accurate in predicting Alzheimer’s disease dementia (black box). Plasma p-tau217 by itself did slightly better, while incorporating additional measures increased accuracy. The best model had an AUC of 0.92, but the most parsimonious one did as well with fewer variables. [Courtesy of Palmqvist et al., Nature Medicine, 2021.]
Which combination of variables worked best? Plasma p-tau217 alone predicted dementia within two, four, and six years with AUCs of 0.78, 0.83, and 0.85, respectively—AUC, aka Area Under the Curve, reflects the specificity and sensitivity of a measure, with a value of 1.0 being perfection and rarely achieved. A model that incorporated APOE genotype, executive function and memory scores did better, predicting AD within four years with an AUC of 0.90. Even better was adding plasma NfL and cortical thickness measured by MRI; these six variables combined brought the AUC to 0.92 (see image above and plot below).
Improving Prediction. Plasma p-tau217 (pink line) predicted Alzheimer’s disease over four years with an AUC of 0.81. Sequentially adding cognition (blue), and APOE genotype (green) improved accuracy, but the best fit (AUC 0.92) came from including plasma NFL and cortical thickness as well (red). [Courtesy of Palmqvist et al., Nature Medicine, 2021.]
Other models did better over shorter or longer time frames. The same six factors plus sex, years of education, and verbal ability gave the best two-year predictions, with an AUC of 0.91. The six plus plasma Aβ42/Aβ40 best predicted AD over six years, with an AUC of 0.94.
How did this compare to clinical diagnosis? In a subset of 285 Biofinder participants, memory clinic clinicians predicted AD within four years with an AUC of 0.72.
Next, the authors tested their algorithms using data from ADNI. Of the 543 volunteers in this cohort, 106 had enrolled with subjective cognitive decline and 437 with MCI. Palmqvist and colleagues tested similar algorithms here, though they had to exclude plasma Aβ42/Aβ40 because it was not available for all participants, and they had to swap plasma p-tau181 for p-tau217 because the latter was unavailable.
Again, the four-variable model with p-tau181, APOE4, executive function, and memory posted the same power as in Biofinder. It predicted AD over a four-year period with an AUC of 0.90, with the proviso that each algorithm used a different plasma p-tau isoform.
To run a direct comparison, the researchers converted continuous p-tau values to binary ones, i.e., positive or negative, based on cutoffs. They set cutoffs based on plasma p-tau levels in 215 and 547 Aβ-negative, cognitively normal participants in Biofinder and ADNI, respectively. Their rationale was that anyone Aβ-negative should have normal levels of p-tau since the latter has been found to rise only in Aβ-positive people (Aug 2019 news). The authors set the cutoffs for p-tau positivity at two standard deviations above the mean value in the Aβ-negative groups. This turned out to be 0.387 pg/mL for plasma p-tau217, and 38.2 pg/mL for p-tau181.
Even though binary values are by definition less informative than a continuous variable, in this case the cutoffs only marginally reduced the four-factor algorithm's accuracy, dropping the four-year AUC by 0.01 in Biofinder and by 0.04 in ADNI. In essence, plasma p-tau181 and p-tau217 gave almost identical results. “This implies that normalized, well-functioning assays measuring any of the p-tau epitopes could be used in this algorithm, making it widely applicable,” Hansson said. The scientists built an online tool for this modified algorithm.
How about using this in the clinic? That is a hot topic in the field right now. Co-author Kaj Blennow, University of Gothenburg, Sweden, thinks algorithms like this are best suited to gauge AD risk. “At present, I’m not ready to call these algorithms diagnostic tools, but they give very good risk scores for future AD,” he told Alzforum.
Hansson believes their algorithms can soon be implemented in memory clinics but need more work for use in primary care. “While the Biofinder and ADNI cohorts enrolled representative, heterogenous memory clinic populations, we need to validate our algorithm in multiple primary care cohorts,” Hansson told Alzforum. Palmqvist agreed, adding, “We also need to see how the algorithm works in a more ethnically diverse population.”
To this end, Palmqvist, Hansson, and colleagues last spring began recruiting older people in 15 Swedish primary care clinics. So far, they have included 140 of their 600-person goal.
Before such algorithms find use in routine diagnosis, assays for various p-tau isoforms need to be standardized, just as the field is currently doing for plasma Aβ assays.
In the meantime, p-tau plasma testing is already rolling out in some leading clinics to aid in diagnosis. “In our lab, we added plasma NfL as a clinical diagnostic last year and we plan to add p-tau, either 181 or 231, this year,” said Blennow.—Chelsea Weidman Burke
- Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Alzheimer’s Disease Neuroimaging Initiative, Dage JL, Stomrud E, Janelidze S, Mattsson-Carlgren N, Hansson O. Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med. 2021 May 24; PubMed.