Algeciras‐Schimnich, Alicia

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.

AbstractBackgroundVascular contributions to cognitive impairment and dementia (VCID) are often comorbid with Alzheimer’s disease and increase the risk of dementia. Blood‐based biomarkers may be promising for identifying individuals at high risk for VCID due to small vessel disease (SVD).MethodWe included 1709 participants from the Mayo Clinic Study of Aging who had concurrent MRI, plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) measured on the HD‐X Simoa Quanterix platform, and clinical dementia rating scale (CDR). We categorized participants into SVD vs. not using WMH/TIV≥1.3% which corresponds to Fazekas>=2 cutoff and cognitively impaired (CI) vs. not using CDR≥0.5. We obtained four groups using SVD and cognitively normal and impaired classifications: cognitively unimpaired without SVD (CU), CU with SVD (Vas‐CU), CI with SVD (VCI), and CI without SVD (Non‐vas‐CI). Univariate and multivariable logistic regression models were used to obtain odds ratios, confidence intervals and ROC curves to evaluate the association between plasma markers and each disease group compared to CU. We also evaluated these analyses in amyloid positive and negative participants.ResultThere was a high proportion of CU (Vas‐CU/(CU+Vas‐CU)=18%) and CI (VCI/(VCI+ Non‐vas‐CI)=47%) participants with SVD in the population‐based sample. Higher plasma levels were associated with greater vascular burden and differed by amyloid status (Figure 1). ROC analyses showed that both NfL and GFAP provided discrimination between Vas‐CU and CU groups (AUC=0.83) as well as VCI and CU groups (AUC=0.88) (Figure 2, Table 1). Though GFAP and NfL are non‐specific and may also reflect non‐vascular damage, we found that these plasma markers did not clearly separate NonVas‐CI from the CU group in the overall cohort (AUC=0.69). When amyloid status was available, the discriminative power of NfL and GFAP in separating the 4 groups was very low in amyloid positive participants. On the other hand, NfL and GFAP separated Vas‐CU and VCI groups from CI reasonably well in the amyloid negative group.ConclusionPlasma biomarkers associated with SVD‐burden provided reliable discriminative power in predicting VCID, particularly in the absence of amyloid, suggesting their utility in the prognosis and stratification for clinical trials.

AbstractBackgroundUnderstanding the relationship between AβPET and AβCSF biomarkers will define their potential utility in Aβ treatment. Few longitudinal or neuropathological comparisons have been reported. We assessed the relationship of AβPET and AβCSF biomarkers in a large community cohort.MethodParticipants 50+ years with coincident AβPET and AβCSF biomarkers (pTau181/Aβ42, n=505, and Aβ42/40, n=54) were included. Of these participants, a subgroup was identified that had neuropathology data (n=47) of which many had longitudinal AβPET and CSF testing. The relationships of AβPET and CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in those with autopsy data. Analyses were performed using AβPET Meta‐ROI and AβPET early‐Aβ‐deposition ROIs (ROIs with early Aβ deposition: fusiform, angular, inferior temporal, middle temporal, middle occipital, and calcarine).ResultIn biomarker tests performed at the same time, more participants were AβPET+ and CSF‐ than AβPET‐ and CSF+ using Meta‐ROI AβPET. This discordance occurred more frequently when using early‐Aβ‐deposition regions (Figure 1). Of 47 participants with autopsy data, 35 had Thal stage ≥1. The sensitivity for earliest detection of cortical Aβ neuropathology by AβPET was 89% (31/35) and by CSF was 66%, (23/35). Eight of these 35 had positive AβPET findings and negative coincident CSF tests at the first time tested. Of these, 5/8 were Meta‐ROI and early AβPET ROI positive. The other 3/8 were only positive on early‐Aβ AβPET ROIs. Four of the 8 with negative CSF and positive AβPET had subsequent testing and 2/4 became positive on CSF tests after 1.5 years while 2/4 continued to be negative on subsequent testing.ConclusionAβPET is more sensitive for detection of early cortical Aβ deposition than CSF biomarkers in cross‐sectional or autopsy data. While the assessment of AβPET by standard Meta‐ROI AβPET methods showed cortical Aβ deposition occurring when not indicated by CSF biomarkers, AβPET ROIs for early Aβ deposition detected additional participants that were AβPET positive and AβCSF negative. These findings have implications for detecting early Aβ neuropathology among CU participants in Aβ prevention trials when robust detection is needed. Further work is needed to compare Aβ plasma biomarkers and AβPET using these methods.

AbstractBackgroundThe quantification of neurofilament light chain (NfL) in blood and cerebrospinal fluid (CSF) has proved useful in many contexts, for the diagnosis and prognosis of various neurological disorders. There is, however, a diversity of practices between centers, essentially linked to the context of use (COU), analytical methods, consideration of comorbidities, determination of cut‐points or use of interpretation scales. Finally, for the same biochemical profile, the interpretation and reporting of results may differ from one center to another, raising the question of test commutability. To date, no consensus has been reached between the different laboratories involved to define the most appropriate conclusions/comments based on COU and cut‐points. This work is an essential step towards consensual harmonization of the clinical use of NfL after CSF and/or blood analysis in various neurological contexts, as advocated by the Alzheimer's Association "Biofluid Based Biomarkers PIA" working group.MethodThis international project involves 58 clinical laboratories in 16 countries, specializing in the biochemical diagnosis of neurological disorders. By means of a questionnaire, we obtained a description of the COU, pre‐analytical and analytical (biological fluid and method used to quantify NfL) protocols of all the centers involved.ResultsOf the centers, 42% quantified NfL in CSF, 29% in serum and 28% in plasma, and 1% in dried blood spot. The COUs were as follows: Frontotemporal dementia (FTD, 17%), Alzheimer's disease (AD, 16%), multiple sclerosis (MS, 16%), amyotrophic lateral sclerosis (ALS, 11%), psychiatric syndrome (PS, 10%), Creutzfeldt‐Jakob disease (CJD, 8%), Parkinson's disease (PD, 8%), peripheral neuropathy (PN, 7%) and traumatic brain injury (TBI, 7%). Most centers define pathological cut‐points based on published literature and take age into account (50%).ConclusionOur initial results highlight the state of the art in terms of the clinical use of NfL analysis in CSF and blood in the context of different neurological diseases. We have now defined a coordinator for each COU subgroup and are organizing consensus meetings to harmonize the use and reporting of NfL measurements for the identified clinical applications. The results of these next steps will be presented.

AbstractBackgroundChronic kidney disease (CKD) has been associated with increased plasma phosphorylated Tau217 (pTau217) concentrations, potentially confounding its utility in the evaluation of Alzheimer's disease (AD). We assessed the association of estimated glomerular filtration rate (eGFR) with plasma pTau217 concentrations measured by various assays.MethodWe included 195 participants from the Mayo Clinic Study of Aging or the Alzheimer’s Disease Research Center with diagnoses of cognitively unimpaired (n=114), mild cognitive impairment (n=70), and AD dementia (n=11). Amyloid‐PET was performed with Pittsburgh 11C‐Compound B; abnormal amyloid (A+) was defined as SUVR ≥1.52 (Centiloid ≥25). eGFR was determined based on serum creatinine concentrations. pTau217 was quantified using ALZpath Simoa pTau217 immunoassay, Fujirebio Lumipulse G pTau217(N‐Terminal) immunoassay, and a pTau217 mass spectrometry (MS) assay (C2N Diagnostics). Additionally, a pTau217 ratio (pTau217 to non‐phosphorylated [npTau217]) was calculated using the MS assay. Linear regression was used to assess associations of continuous eGFR with plasma pTau217 and amyloid PET. Differences in mean plasma concentrations between eGFR groups by amyloid PET status were evaluated with one‐way ANOVA tests and associated confidence intervals for each sample mean.ResultMedian pTau217 concentrations in the A+ and A‐ groups were higher in individuals with eGFR <60 mL/min/1.73 m2 compared to those with eGFR ≥60 in all assays except for the A+ group when using the pTau217/npTau217 ratio. A 10‐unit worsening of eGFR was associated with higher pTau217 mean concentrations as follows: 24% (95% CI, 13‐36%) for C2N pTau217, 16% (9‐24%) for ALZpath pTau217, and 12% (5‐20%) for Lumipulse pTau217. eGFR was not significantly associated with the pTau217/npTau217 ratio or amyloid‐PET.ConclusionCKD was associated with increased pTau217 concentrations when measuring pTau217 but not when using the pTau217 ratio. Additional research is needed to better understand the confounding effects of CKD in the clinical interpretation of plasma pTau217 assays.

AbstractBackgroundTau phosphorylated at threonine 217 (pTau217) is one of the most promising blood‐based biomarkers of Alzheimer’s disease (AD). This study compares the performance of two plasma pTau217 immunoassays on the Simoa platform for detection of abnormal amyloid‐PET.MethodPlasma samples from 112 individuals without cognitive impairment and 114 with mild cognitive impairment (MCI) or AD related dementia were evaluated. Testing was performed on the Quanterix Simoa® HD‐X Analyzer using the ALZpath Simoa® pTau217 and the Simoa® ACC pTau217 (Janssen antibodies) commercial assays. Amyloid‐PET imaging was performed using 11C‐Pittsburgh Compound B and a standard uptake value ratio (SUVR) cutoff of ≥1.52 (≥25 Centiloid) was used for abnormal (n= 147) vs. normal (n= 79) classification. Passing‐Bablok regression analysis and Spearman’s correlation coefficient (rho) were used to estimate bias and correlation between the assays, respectively. Correlation of each assay with Amyloid‐PET SUVR was evaluated. Amyloid‐PET status as the outcome and plasma pTau217 as predictor was used to estimate the area under the receiver operating characteristic (ROC) curve (AUC).ResultThe pTau217 concentrations between the two assays showed an excellent correlation (Spearman’s rho= 0.94 [95% CI= 0.93 to 0.96, p<0.0001]). However, the Passing‐Bablok regression analysis equation for ALZpath (x‐axis) and ACC (y‐axis) was y= 0.1324x ‐ 0.0019, showing that measured pTau217 concentrations with the ALZpath assay were higher (∼87%) than with the ACC assay (concentrations ranged 0.057 to 3.5 pg/mL and 0.003 to 0.5 pg/mL, respectively). Correlations between amyloid‐PET SUVR and the two pTau217 immunoassays were almost identical with Spearman’s rho= 0.70 (95% CI= 0.62 to 0.76, p<0.0001) for ALZpath and 0.69 (95% CI= 0.61 to 0.75, p<0.0001) for ACC. The AUC for detection of an abnormal amyloid‐PET among the MCI/AD group was 0.88 (95% CI= 0.81 to 0.95) for ALZpath, and 0.89 (95% CI= 0.83 to 0.96) for ACC.ConclusionAnalytically, the two Simoa® pTau217 assays correlate well with each other, but results are not interchangeable due to a significant proportional bias. Clinically, the performance of the assays was largely equivalent for the detection of abnormal amyloid‐PET by AUC analysis based on their almost identical correlations with amyloid‐PET SUVR.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total ß‐amyloid Aß42 relative to Aß40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

Abstract Context Diagnosing adrenal insufficiency (AI) often requires complex testing, which can be time-consuming and expensive. Dehydroepiandrosterone sulfate (DHEAS) is a promising marker of hypothalamic-pituitary-adrenal axis function; however, its diagnostic performance has not yet been evaluated in a large-scale study. Objective Evaluate the performance of DHEAS and baseline cortisol in assessing AI. Design Single-center retrospective cohort study. Setting Referral center. Patients Adults who underwent cosyntropin stimulation testing (CST) between 2005 and 2023 and had DHEAS measured within 3 months prior to CST. Main Outcome Measures Area under receiver operating characteristic curve (AUROC) for DHEAS and baseline cortisol. Prevalence of AI based on various DHEAS and baseline cortisol concentrations. Results Among 1135 patients, 195 (17.2%) had AI. Both baseline cortisol and DHEAS independently had good diagnostic performance with AUROC 0.81 [95% confidence interval (CI) 0.77-0.84 and 0.81 (95% CI 0.78-0.85)], respectively. Time of CST performance had no significant effect on diagnostic accuracy of baseline cortisol while recent glucocorticoid use decreased diagnostic performance of DHEAS (AUROC 0.72 vs 0.83). Only 1.2% of patients with baseline cortisol ≥10 mcg/dL had AI based on CST. Among patients with baseline cortisol between 5 and 9.9 mcg/dL, only 1.3% had AI if DHEAS was ≥60 mcg/dL. Conversely, the majority (72.2%) of patients with both baseline cortisol &lt;5 mcg/dL and DHEAS &lt;25 mcg/dL were found to have AI. Conclusion DHEAS has good diagnostic performance in assessing AI. Measuring both baseline cortisol and DHEAS concentrations may eliminate the need for further dynamic testing in many patients.

Abstract Background Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself. Methods Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined. Results Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9–4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements. Conclusions An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.

AbstractINTRODUCTIONUnderstanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population‐based or neuropathologic comparisons have been reported.METHODSParticipants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p‐tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in autopsy data.RESULTSCross‐sectionally, more participants were Aβ PET+ versus Aβ CSF− than Aβ PET− versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p‐tau181/Aβ42 (64%).DISCUSSIONAβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF− findings are several‐fold greater using regional Aβ PET analyses and in peri‐threshold‐standardized uptake value ratio participants.Highlights Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population‐based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers.