Therneau, Terry M.

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.

A clinical trial represents a large commitment from all individuals involved and a huge financial obligation given its high cost; therefore, it is wise to make the most of all collected data by learning as much as possible. A multistate model is a generalized framework to describe longitudinal events; multistate hazards models can treat multiple intermediate/final clinical endpoints as outcomes and estimate the impact of covariates simultaneously. Proportional hazards models are fitted (one per transition), which can be used to calculate the absolute risks, that is, the probability of being in a state at a given time, the expected number of visits to a state, and the expected amount of time spent in a state. Three publicly available clinical trial datasets, colon, myeloid, and rhDNase, in the survival package in R were used to showcase the utility of multistate hazards models. In the colon dataset, a very well-known and well-used dataset, we found that the levamisole+fluorouracil treatment extended time in the recurrence-free state more than it extended overall survival, which resulted in less time in the recurrence state, an example of the classic “compression of morbidity.” In the myeloid dataset, we found that complete response (CR) is durable, patients who received treatment B have longer sojourn time in CR than patients who received treatment A, while the mutation status does not impact the transition rate to CR but is highly influential on the sojourn time in CR. We also found that more patients in treatment A received transplants without CR, and more patients in treatment B received transplants after CR. In addition, the mutation status is highly influential on the CR to transplant transition rate. The observations that we made on these three datasets would not be possible without multistate models. We want to encourage readers to spend more time to look deeper into clinical trial data. It has a lot more to offer than a simple yes/no answer if only we, the statisticians, are willing to look for it.

Abstract BACKGROUND We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E ( APOE ) ε 4 genotype, amyloid PET, and plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p‐tau217. The outcome was a change in a memory composite measure. RESULTS All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p‐tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person‐level prediction intervals were wide. DISCUSSION Plasma p‐tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level.

AbstractBackgroundKnowledge of the temporal evolution among plasma Alzheimer’s biomarkers and PET will inform disease pathophysiology and appropriate implementation of plasma biomarkers (Therneau, NeuroImage 2021; Betthauser, Brain 2022). This study investigates the relative timing of progression of plasma p‐tau181, p‐tau217, GFAP, and amyloid PET on individual and population levels.MethodWe included 957 participants from the Mayo Clinic Study of Aging with clinical diagnoses of cognitively unimpaired (n = 732), MCI (n = 205), and Alzheimer’s clinical syndrome dementia (n = 20). An accelerated failure time (AFT) model was fit with amyloid (PiB) PET, plasma p‐tau217 (Meso Scale Discovery, Lilly), plasma p‐tau181, and GFAP (Simoa, Quanterix HD‐X) as quad‐variate endpoints, and sex, APOE genotype status, and education as covariates. The model output included a per subject adjustment or time‐shift for each biomarker indicating how much earlier or later it was estimated to progress relative to the average participant, the correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing. Secondly, we estimated progression at a given age for selected cutpoints.ResultTemporal trajectories for each biomarker are shown in Figure 1. Individual‐level adjustments between plasma p‐tau217 and amyloid PET were highly correlated, R = 0.69(0.64‐0.74); how early or late an individual progressed on plasma p‐tau217 explained 48% of the variation in relative timing of amyloid PET progression. Associations of plasma p‐tau181 and GFAP with amyloid PET individual adjustments were moderate and low, respectively (Figure 2). Using the chosen cutpoints, the estimated order of biomarker progression was GFAP, amyloid PET, plasma p‐tau181, and plasma p‐tau217 (Figure 3).ConclusionOn the individual level, the strong association of the relative timing of progression of plasma p‐tau217 and amyloid PET supports that these processes are mechanistically related. The relatively lower associations of the timing of p‐tau181 and GFAP with amyloid PET likely reflect a lower biological association and greater measurement noise for p‐tau181. The individual adjustments, the primary output of this model, are independent of cutpoints. On the population level, relative timing of biomarker change is highly dependent on the cutpoints applied. This true for any analysis that employs cutpoints, regardless of the approach.

Abstract INTRODUCTION The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co‐evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p‐tau217, p‐tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS Individual timing of plasma p‐tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p‐tau, and tau PET temporal meta‐regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION Plasma p‐tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p‐tau and Aβ PET was the strongest. Highlights Plasma p‐tau progression was more strongly associated with Aβ than tau PET. Progression on plasma p‐tau was associated with Aβ PET and GFAP progression. P‐tau181 and p‐tau217 become abnormal after Aβ PET and before tau PET. GFAP became abnormal first, before plasma p‐tau and Aβ PET.

Background and Aims: The presence of at-risk NASH is associated with an increased risk of cirrhosis and complications. Therefore, noninvasive identification of at-risk NASH with an accurate biomarker is a critical need for pharmacologic therapy. We aim to explore the performance of several magnetic resonance (MR)-based imaging parameters in diagnosing at-risk NASH. Approach and Results: This prospective clinical trial (NCT02565446) includes 104 paired MR examinations and liver biopsies performed in patients with suspected or diagnosed NAFLD. Magnetic resonance elastography-assessed liver stiffness (LS), 6-point Dixon-derived proton density fat fraction (PDFF), and single-point saturation-recovery acquisition-calculated T1 relaxation time were explored. Among all predictors, LS showed the significantly highest accuracy in diagnosing at-risk NASH [AUCLS: 0.89 (0.82, 0.95), AUCPDFF: 0.70 (0.58, 0.81), AUCT1: 0.72 (0.61, 0.82), z-score test z >1.96 for LS vs any of others]. The optimal cutoff value of LS to identify at-risk NASH patients was 3.3 kPa (sensitivity: 79%, specificity: 82%, negative predictive value: 91%), whereas the optimal cutoff value of T1 was 850 ms (sensitivity: 75%, specificity: 63%, and negative predictive value: 87%). PDFF had the highest performance in diagnosing NASH with any fibrosis stage [AUCPDFF: 0.82 (0.72, 0.91), AUCLS: 0.73 (0.63, 0.84), AUCT1: 0.72 (0.61, 0.83), |z| <1.96 for all]. Conclusion: Magnetic resonance elastography-assessed LS alone outperformed PDFF, and T1 in identifying patients with at-risk NASH for therapeutic trials.

INTRODUCTION: Intrahepatic cholangiocarcinoma (iCCA) is a primary liver malignancy with poor prognosis. Current prognostic methods are most accurate for patients with surgically resectable disease. However, a significant proportion of patients with iCCA are not surgical candidates. We aimed to develop a generalizable staging system based on clinical variables to determine prognosis of all patients with iCCA. METHODS: The derivation cohort included 436 patients with iCCA seen between 2000 and 2011. For external validation, 249 patients with iCCA seen from 2000 to 2014 were enrolled. Survival analysis was performed to identify prognostic predictors. All-cause mortality was the primary end point. RESULTS: Eastern Cooperative Oncology Group status, tumor number, tumor size, metastasis, albumin, and carbohydrate antigen 19-9 were incorporated into a 4-stage algorithm. Kaplan-Meier estimates for 1-year survival were 87.1% (95% confidence interval [CI] 76.1–99.7), 72.7% (95% CI 63.4–83.4), 48.0% (95% CI 41.2–56.0), and 16% (95% CI 11–23.5), respectively, for stages I, II, III, and IV. Univariate analysis yielded significant differences in risk of death for stages II (hazard ratio [HR] 1.71; 95% CI 1.0–2.8), III (HR 3.32; 95% CI 2.07–5.31), and IV (HR 7.44; 95% CI 4.61–12.01) compared with stage I (reference). Concordance indices showed the new staging system was superior to the TNM staging for predicting mortality in the derivation cohort, P < 0.0001. In the validation cohort, however, the difference between the 2 staging systems was not significant. DISCUSSION: The proposed independently validated staging system uses nonhistopathologic data to successfully stratify patients into 4 stages. This staging system has better prognostic accuracy compared with the TNM staging and can assist physicians and patients in treatment of iCCA.

Abstract Staging the severity of Alzheimer’s disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer’s clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1–2, 3–4, 5–6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1–42 and Aβ1–40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78–0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72–0.85 versus C = 0.73–0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer’s disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

AbstractBackgroundAlzheimer’s disease (AD) pathologic changes (e.g. amyloid and tau aggregation), and cerebrovascular disease often coexist, and it remains unclear whether these are mechanistically linked processes (Graff‐Radford, Brain 2019; Provenzano, JAMA Neurol 2013). Most studies evaluating these associations are cross‐sectional; however, establishing temporal relationships may provide additional insight into potential mechanistic associations. One method to assess the temporal evolution of a biomarker is the Accelerated Failure Time (AFT) model, which assumes a common pattern of progression for a given biomarker that is shifted earlier or later in time for each individual (Therneau, Neuroimage 2021). In this work we investigate the relationship of disease trajectories of cerebrovascular disease (via white matter hyperintensity, WMH, volume) and AD biomarkers (via amyloid and tau PET).MethodsWe included Mayo Clinic Study of Aging (MCSA) and Mayo Alzheimer’s Disease Research Center (ADRC) participants with amyloid PET, tau PET, and MRI, age > 50 years, and a clinical diagnosis of cognitively unimpaired (CU), mild cognitive impairment (MCI) or Alzheimer’s disease dementia. The AFT model was fit with amyloid PET (PiB) global meta‐ROI SUVR, tau PET (flortaucipir) temporal meta‐ROI SUVR, and WMH volume as the tri‐variate endpoints, with APOE genotype, sex, education, total intracranial volume, and referral/population subject as covariates. A per subject/endpoint random effect represents individual adjustments for each biomarker to indicate if the subject's biomarker became abnormal earlier or later relative to their demographic peers, accounting for the covariates.ResultsThe study included 1144 participants (Table 1). There was a strong correlation between earlier onset of amyloid and earlier onset of tau (r=0.64, CI=0.60‐0.67) (Figure 1, Table 2); early/late amyloid timing explained nearly half (0.642 = 41%) of the variation in tau onset. There were only weak associations between an individual’s WMH and amyloid PET onset adjustments (r=0.10, 1.0%) and WMH and tau PET onset adjustments (r=‐0.07, 0.5%).ConclusionThis analysis supports a strong mechanistic relationship between amyloid and tau aggregation, as reported by others. However, our findings do not support a mechanistic association between WMH and amyloid or tau PET.