# paper.pdf ## Metadata - PDFFormatVersion=1.4 - Language=en-US - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Creator=Arbortext Advanced Print Publisher 9.1.510/W Unicode - ModDate=D:20260617163943+05'30' - Title=WNL-2025-207522 1..12 - Producer=Acrobat Distiller 7.0 (Windows) - CreationDate=D:20260617105546+05'30' - xmp:modifydate=2026-06-17T16:39:43+05:30 - xmp:metadatadate=2026-06-17T16:39:43+05:30 - xmp:createdate=2026-06-17T10:55:46+05:30 - xmp:creatortool=Arbortext Advanced Print Publisher 9.1.510/W Unicode - pdf:producer=Acrobat Distiller 7.0 (Windows) - dc:title=WNL-2025-207522 1..12 ## Contents ### Page 1 ![Figure]() RESEARCH ARTICLE OPEN ACCESS # Clinical Impact and Prognostic Value of AlzheimerDisease Biomarkers in the Very Old Chiara Ceriello,1,2 Olga H. Torres,1,3 Sara Rubio-Guerra,3,4,5 Jesus´ Garc´ıa-Castro,3,4,5 Judit Selma-Gonzalez,3,4,5 Isabel Sala,3,4,5 Mar´ıa Bel´en S´anchez-Saudinos,3,4,5 Laura Videla,3,4,5,6 Elena Vera-Campuzano,3,4,5 ´ Javier Arranz,3,4,5 ´Iñigo Rodr´ıguez-Baz,3,4,5 Luc´ıa Maure-Blesa,3,4,5 Oriol Dols-Icardo,3,4,5 S´ılvia Valldeneu,3,4,5Isabel Barroeta,3,4,5 Miguel Santos-Santos,3,4,5 Maria Carmona-Iragui,3,4,5,6 L´ıdia Vaqu´e-Alc´azar, 3,4,5 Esther Alvarez-Sanchez,3,4,5 Oriol Lorente,3,4,5 Mireia Carreras,3,4,5 Alexandre Bejanin,3,4,5 Olivia Belbin,3,4,5 ´ Alberto Lleo,3,4,5 Juan Fortea,3,4,5,6 Daniel Alcolea,3,4,5 and Ignacio Ill´an-Gala Neurology® 2026;107:e218180. doi:10.1212/WNL.0000000000218180 3,4,5 ## Abstract ### Background and Objectives Alzheimer disease (AD) is increasingly viewed as a clinical-biological continuum, but the utilityof biomarkers in individuals aged ≥80 years, the so-called “very old,” remains uncertain, becauseof concerns about its clinical usefulness. This study aimed to determine the clinical impact andprognostic value of AD biomarkers in very old individuals. ### Methods We performed a retrospective longitudinal cohort study within the Sant Pau Initiative onNeurodegeneration (SPIN, Barcelona, Spain), a memory clinic–based research cohort. Patientswere referred from primary care physicians or community neurologists for evaluation withina public universal health care catchment area. We included SPIN participants evaluated between October 2013 and May 2024 who were aged ≥80 years and had mild cognitive impairment (MCI) at the baseline visit. Participants underwent standardized clinical andneuropsychological assessments and had CSF and plasma biomarker measurements available.AD biology was defined by the CSF phosphorylated tau at threonine 181/amyloid-β 42 ratio.Plasma phosphorylated tau at threonine 217 (p-Tau217) was evaluated for (1) diagnosticaccuracy for AD biology and (2) prognostic associations with longitudinal cognitive change(Mini-Mental State Examination [MMSE]) and progression to dementia. Analyses used linearmixed-effects models for MMSE trajectories and Cox regression for dementia conversion. ### Results A total of 167 participants were included (mean age 82.3 years, 59% women) with a meanfollow-up of 35.8 months; the ones with AD biology (n = 116, 69%) had worse baselinecognitive performance, particularly in memory, compared with those without (Cohen d = 0.34,p = 0.03). Plasma p-Tau217 showed excellent diagnostic accuracy for detecting AD biology(0.93, 95% CI 0.88–0.98; cutoff 0.19 pg/mL; sensitivity 94.5%; specificity 84%). Over time,participants with AD biology declined faster on the MMSE than those without (−0.47 vs −0.18points per year; p < 0.01). Cox models showed an increased risk of progression to a dementiastage among individuals with higher plasma p-Tau217 concentrations (hazard ratio 1.49, 95%CI 1.05–2.13, p = 0.026). ### Discussion In very old individuals with MCI, AD biology (CSF) and plasma p-Tau217 identify individualsat higher risk of faster cognitive decline and progression to dementia. Limitations of this studyinclude the single-center design and the modest sample size. CorrespondenceDr. Ill´an-Gala MORE ONLINE #### Supplementary Material 1Geriatric Unit, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain;2Institut de Neuroci`encies, Universitat Aut onoma de Barcelona, Spain;3Institut de Recerca Sant Pau (IR SANT ` ´ PAU), Barcelona, Spain;4Sant Pau Memory Unit, IR SANT PAU, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain;5Centro de Investigaci ´ on Biom´edica en Red de Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain; and6Barcelona Down Medical Center, Fundacio Catalana S´ındrome de Down, Barcelona, Spain. The Article Processing Charge was funded by the authors. This is an open access article distributed under the terms of the, where it is permissible todownload and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. e218180(1) ### Page 2 ## Glossary Aβ42 = amyloid-β 42; AD = Alzheimer disease; ADNC = Alzheimer disease neuropathologic change; AUC = area under thecurve; CDR-SB = Clinical Dementia Rating Sum of Boxes; eGFR = estimated glomerular filtration rate; HR = hazard ratio;IDDD = Interview for Deterioration in Daily Living in Dementia; LATE = limbic-predominant age-related TDP-43encephalopathy; LME = linear mixed-effect; MCI = mild cognitive impairment; MMSE = Mini-Mental State Examination;NPV = negative predicted value; PPV = positive predicted value; p-Tau181 = phosphorylated tau at threonine 181; p-Tau217 =phosphorylated tau at threonine 217; ROC = receiver operating characteristics; SPIN = Sant Pau Initiative onNeurodegeneration; TBI = traumatic brain injury. ## Introduction Alzheimer disease (AD) is increasingly viewed as a clinicalbiological continuum that can be detected decades beforedementia onset through imaging and fluid biomarkers likeCSF and plasma analytes.1 Advances in these biomarkers havetransformed diagnostic practice and facilitated the development and approval of disease-modifying therapies forearly symptomatic stages. Notably, plasma phosphorylatedtau at threonine 217 (p-Tau217) has emerged as a minimallyinvasive, scalable, and highly accurate marker of AD biology,with diagnostic performance comparable with CSF and amyloid PET in younger and middle-aged population.2-6Reflecting this evidence, the latest criteria from the Alzheimer’s Association now recognize p-Tau217 as a standalone biomarker for AD diagnosis.7 With the advent ofdisease-modifying therapies, lecanemab and donanemab,early detection becomes increasingly critical to delay cognitiveand functional decline, reduce institutionalization, and potentially lower mortality.8-10 Despite this progress, biomarker-guided evaluation remainsuncommon in individuals aged ≥80 years, who now representthe fastest-growing segment of the population affected bycognitive impairment.11 Long-standing concerns originatefrom neuropathologic studies showing high prevalence ofamyloid plaques and tau tangles in the very old, often coexisting with other age-related pathologies.12-14 This has historically raised scepticism about the specificity and clinicalvalue of AD biomarkers in late life. However, recent evidencechallenges the notion that Alzheimer disease neuropathologicchange (ADNC) represents an incidental finding in advancedage.15-18 Several studies have shown that ADNC may contribute to cognitive decline even in the presence of multimorbidity, and growing data indicate that p-Tau217 maintainsexcellent diagnostic performance for AD pathology across theage spectrum.2,19,20 Given the ongoing demographic aging and the clinical needfor accurate risk stratification in very old adults (≥80 years), itis essential to clarify whether AD biomarkers remain clinicallyinformative in this population. Using the Sant Pau Initiativeon Neurodegeneration (SPIN) cohort,21 we evaluated ADbiomarkers in memory clinic patients aged ≥80 years withcognitive complaints, focusing on CSF-defined AD biologyand plasma p-Tau217. Specifically, we aimed to (1) assess the diagnostic performance of plasma p-Tau217 for CSF-definedAD biology; (2) examine associations between AD biologyand baseline cognition and medical comorbidity; and (3)determine whether AD biology and plasma p-Tau217 areassociated with longitudinal cognitive decline and progressionto dementia. ## Methods ### Study Participants Participants were recruited at the Sant Pau Memory Unit,within the Department of Neurology at Hospital de la SantaCreu i Sant Pau (Barcelona, Spain), a public, tertiary memoryclinic within Catalonia’s universal health care system that servesa defined reference population. Patients are typically referred byprimary care physicians and community neurologists for specialized diagnostic evaluation because of cognitive and/or behavioral symptoms; self-referral is not the usual pathway.Access to evaluation is covered by the public system (i.e., noout-of-pocket costs for patients within the reference area), andfirst appointments are generally scheduled within approximately 2–3 months. Following routine clinical assessment, eligible and consenting individuals are invited to participate in theSPIN,21 an umbrella cohort including individuals with a rangeof neurodegenerative conditions. From the SPIN database, weincluded all consecutive participants aged ≥80 years who metcriteria for mild cognitive impairment (MCI) at the baselinevisit, defined by objective cognitive decline with preservedfunctional independence.22 Eligible participants were evaluatedbetween October 2013 and May 2024, including both amnesticand nonamnestic MCI. For inclusion in this analysis, participants were required to have CSF and plasma biomarkermeasurements available from the diagnostic workup and tohave provided written informed consent. Key exclusion criteria include conditions that preclude reliableneuropsychological assessment (e.g., severe sensory deficits/illiteracy), contraindications to MRI and/or lumbar puncture,current use of medications that may substantially impair cognition, major neurologic disorders (e.g., stroke, epilepsy, structuralbrain lesions), major psychiatric illness, recent substance abuse,and active or insufficiently treated malignancy with potentialCNS involvement.21 Neuroimaging was obtained as part of theclinical evaluation; however, cerebrovascular comorbidity wasnot systematically excluded in this memory clinic–based cohort. Neurology | Volume 107, Number 1 | July 14, 2026 e218180(2) ### Page 3 ### Clinical Assessment Baseline was defined as each participant’s first visit at the SantPau Memory Unit (index visit), when clinical and biomarkerassessments were performed; thus, baseline occurred at different calendar dates across participants (October 2013–May2024). At first visit, all participants underwent a full clinical assessment, including collection of demographic data, comorbidities (toxic habits, hypertension, stroke, epilepsy, historyof traumatic brain injury or “TBI,” primary psychiatric disorder and neoplasia), biochemical analytical routinalparameters and ongoing pharmacologic therapies (withparticular attention to drugs with potential cognitive effect).The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease EpidemiologyCollaboration equation (version of 2009),23 without a racecoefficient. ### Neuropsychological Assessment Participants underwent a standardized neuropsychologicalbattery according to the SPIN protocol.21 Episodic memorywas assessed using the Free and Cued Selective RemindingTest (total free recall, total recall, delayed free recall, anddelayed total recall) and the Rey–Osterrieth Complex Figuredelayed recall. Language was assessed using the BostonNaming Test (percentage correct) and Digit Span forwardand backward. Attention and executive functions were evaluated using the Trail Making Test parts A and B, semantic(animals), and phonemic verbal fluency (letter P) tasks.Visuospatial abilities were evaluated with the Rey–OsterriethComplex Figure copy, the Poppelreuter test, and the VisualObject and Space Perception battery number location subtest.Domain-specific composite scores were calculated as themean of available standardized z-scores within each cognitivedomain. For neuropsychological outcomes, we used standardized scores derived from demographically adjustedamyloid-β–negative next-generation norms, generated usinggeneralized additive models for location, scale, and shape.This approach was used because Spanish normative references are not available as z-score metrics across the full age/education distribution. Next-generation norms–derivedstandardized scores were used to build domain compositemeasures for statistical analyses.24,25 The z scores were used solely to assess the correlation between biomarkers and cognition and were not used for participant classification. More details on this approach havebeen recently reported by our group.24,25 The Mini-MentalState Examination (MMSE)26 was used as a general measureof global cognition because of its wide availability duringroutine clinical follow-up and its high responsiveness toclinical decline during long follow-up periods in persons withAD biology.27 Functional status was assessed with the Interview for Deterioration in Daily Living in Dementia(IDDD) scale,28 specific for functional decline in dementia,and the global level of cognitive impairment was quantified by Clinical Dementia Rating Sum of Boxes (CDR-SB)29 andGlobal Deterioration Scale.30 ### CSF and Plasma Biomarkers CSF and plasma samples were collected according to theSPIN cohort protocol and following international recommendations.21 Briefly, CSF samples were obtained via lumbarpuncture, centrifuged, aliquoted, and stored at −80°C forsubsequent analysis. CSF levels of Aβ1–42, Aβ1–40, andphosphorylated tau at threonine 181 (p-Tau181) were measured using the Lumipulse G600II fully automated platformwith commercially available kits (Fujirebio Europe, Ghent,Belgium). All CSF measurements were scheduled twicea month and were used for routine diagnostic assessment ofpatients. Blood samples were drawn into EDTA-K2 tubes,processed within 2 hours of extraction, and centrifuged at2,000 rpm for 10 minutes at 4°C. The plasma was then aliquoted and stored at −80°C until analysis. Plasma p-Tau217concentrations were also measured in the Lumipulse fullyautomated platform G600II using the commercially availablekits. Plasma measurements were made by laboratory technicians blinded to clinical data, and treating physicians did nothave access to these measurements during follow-up. ### Genetic Analyses DNA was extracted from whole blood using standard procedures,21 and APOE genotyping was performed by direct sequencing of exon 4 as part of the routine assessment. ### Definition of AD AD biology was defined using the CSF p-Tau181/amyloid-β42 (Aβ42) ratio because it has shown the best diagnosticperformance for detecting amyloid PET positivity and thehighest accuracy in predicting ADNC in autopsy-provenmulticenter cohorts.31,32 Participants with a CSF p-Tau181/Aβ42 ratio ≥0.068 were classified as positive AD biology (ADpositive), otherwise negative.31 ### Clinical Follow-Up Per the SPIN protocol, participants in stages 2–6 werescheduled for annual follow-up visits, including neuropsychological assessment. Additional visits could be performed more frequently when clinically indicated, at thediscretion of the treating physician. These assessments aimed to document subtle cognitive decline over time, which may not have been detectable with briefcognitive tests or evident through functional decline. The fullbaseline neuropsychological battery was not systematicallyrepeated at follow-up visits; longitudinal cognition wastherefore assessed using MMSE. At each visit, clinicians recorded the most likely etiology basedon available clinical and biomarker information, including AD,frontotemporal lobar degeneration, Lewy body disease, otherneurodegenerative disease, non-neurodegenerative disease, oruncertain etiology. Data on clinical diagnosis, clinical stage, Neurology | Volume 107, Number 1 | July 14, 2026 e218180(3) ### Page 4 and MMSE scores were prospectively collected during eachvisit using Research Electronic Data Capture tool.33 ### Statistical Analysis Data were explored for normality using the Shapiro-Wilk test.Between-group differences were determined with analysis ofvariance or t test for normally distributed variables (with falsediscovery rate correction for multiple comparisons), MannWhitney or Wilcoxon tests for non-normally distributedvariables, and the χ2 for dichotomous or categorical data. Alltests were performed in R statistical software version 4.2.2.The α threshold was set at 0.05 for all analyses. The diagnostic accuracy of plasma p-Tau217 in discriminatingsubjects with positive AD biology (AD-positive) was assessedusing receiver operating characteristic (ROC) analysis. Logistic regression models were applied to test the associationbetween plasma p-Tau 217 and CSF-defined AD biology afteradjusting for age, sex, years of education, and estimated glomerular filtration rate. In additional analyses, we evaluated thediagnostic performance of baseline MMSE for CSF-definedAD biology using ROC curves and compared areas under thecurve (AUCs) for MMSE vs plasma p-Tau217 using theDeLong test. We also fit logistic regression models includingMMSE and p-Tau217 (adjusted for age, sex, education, andeGFR) to assess whether p-Tau217 provides incrementaldiscrimination beyond MMSE. Linear mixed-effects (LME) models were used to evaluatelongitudinal changes in MMSE scores over time in participants with AD biology. Time, age at first assessment, sex,years of education, estimated glomerular filtration rate, biomarker of interest (CSF or plasma p-Tau217), and the interaction of AD biology and time were included as fixedeffects. Random intercepts and slopes were specified to account for within-subject variability. We used bootstrapresampling to assess the robustness of the cutoffs. In survival analyses, survival time was defined as the timefrom the first visit at which stage 4 was recorded. Participantswho had not developed dementia by the time of analysiswere censored at their last follow-up visit. Cox proportionalhazards regression models were used to estimate the risk ofprogression to dementia, adjusted for age, sex, years of education, and estimated glomerular filtration rate as covariates. The assumption of proportional hazards was testedand confirmed. For illustrative purposes, survival curveswere generated using the ggsurvplot function from thesurvminer package in R. ### Standard Protocol Approvals, Registrations,and Participant Consents All procedures in the study were approved by the Sant Pauethics committee (protocol number IIBSP-DOW-201430) and conducted in accordance with the Declaration ofHelsinki. Written informed consent was obtained from allparticipants. ### Data Availability The data sets generated and analyzed during this study areavailable from the corresponding authors upon reasonablerequest. ## Results ### Sample Composition and Demographics Table 1 describes the baseline characteristics of the 167 participants with MCI included in the study stratified by ADbiology. A total of 116 participants (70%) had AD biology, asdetermined by the CSF p-Tau181/Aβ42 ratio. The age, sexdistribution, education, and marital/living status were similaracross participants with and without AD biology (Table 1).The mean follow-up in the analytic sample was 35.8 monthsand did not differ significantly between AD biology–positiveand AD biology–negative participants (38.1 vs 30.5 months,p = 0.13). Because the number of participants under observation decreased substantially beyond this point, figures weretruncated at 5 years. As expected, participants with AD biology had a higher frequency of APOE e4 carriership (34% vs4.5%, Cohen d = 0.71, p < 0.01). Despite showing similarfunctional impairment at baseline (as measured with IDDD),participants with AD biology showed slightly greater cognitivefunctional impairment, as measured with the CDR-SB (2.8 vs2.4, Cohen’s d = 0.34, p = 0.042) and the MMSE (24.7 vs 25.9,Cohen’s d = 0.37, p = 0.049). The frequency of the mostsignificant comorbidities and baseline pharmacologic treatments was also similar between participants with and withoutAD biology. The only notable exception was a history of TBI,which was reported more often in participants without ADbiology than in those with AD biology (7.8% vs 0.9%, respectively; Cram´er V = 0.18; p = 0.028). Additional details onparticipants’ comorbidities at baseline are provided in thesupplementary material (eTable 1). eTable 2 presents a panelof biochemical parameters used as part of the baselinescreening for cognitive impairment, but no statistically significant differences were observed. ### Cognitive Differences at Baseline by AD Biologyin the Very Old Baseline domain composite scores are summarized in Table 1.As illustrated in Figure 1, memory was the most impairedcognitive domain in participants with positive AD biology(−1.98 vs −1.65; Cohen d = −0.34; p = 0.03), followed byexecutive function, language, and visuospatial composites(−1.12; −0.75; −0.45, respectively), but no statistically significant differences were observed between the 2 groupscomparing the other domains (additional data are listed ineTables 3 and 4). ### Diagnostic Performance of Plasma p-Tau217for AD Biology in the Very Old Next, we explored the diagnostic accuracy of plasmap-Tau217 in identifying AD biology in the very old. As shownin Figure 2, the optimal cutoff defined by Youden Index for Neurology | Volume 107, Number 1 | July 14, 2026 e218180(4) ### Page 5 | Characteristic | All participants(N = 167)a | Participants without AD biology(n = 51)a | Participants with AD biology(n = 116)a | p Valueb | | - | - | - | - | - | | Sociodemographic characteristics | | Age, y | 82.3 (1.6) | 82.1 (1.7) | 82.4 (1.6) | 0.2 | | Age group | >0.9 | | 80–85 | 155 (93) | 48 (94) | 107 (92) | | >85 | 12 (7.2) | 3 (5.9) | 9 (7.8) | | Biologic sex, n (%) | 0.3 | | Male | 69 (41) | 24 (47) | 45 (39) | | Female | 98 (59) | 27 (53) | 71 (61) | | Years of education | 10.0 (4.2) | 10.1 (4.1) | 9.9 (4.2) | 0.8 | | Marital status | >0.9 | | Married | 108 (65) | 34 (67) | 74 (64) | | Divorced | 2 (1.2) | 0 (0) | 2 (1.7) | | Not married | 3 (1.8) | 1 (2.0) | 2 (1.7) | | Widow/er | 40 (24) | 12 (24) | 28 (24) | | First domicile | 0.4 | | Lives at home with a caregiver | 121 (72) | 37 (73) | 84 (72) | | Institutionalized | 1 (0.6) | 1 (2.0) | 0 (0) | | Lives alone at home | 38 (23) | 10 (20) | 28 (24) | | Total follow-up, mo | 35.8 (36.5) | 30.5 (36.0) | 38.1 (36.7) | 0.13 | | Etiologic diagnosis at last visit | <0.001 | | AD | 95 (57) | 0 (0) | 95 (82) | | FTLD | 11 (6.6) | 9 (18) | 2 (1.7) | | LBD | 17 (10) | 9 (18) | 8 (6.9) | | Others | 44 (26) | 33 (65) | 11 (9.5) | | APOE «4 (carriership), n (%) | 36 (25) | 2 (4.5) | 34 (34) | 0.001 | | Global function assessment | | CDR-SB | 2.7 (1.3) | 2.4 (1.2) | 2.8 (1.3) | 0.04 | | Total IDDD score | 38.8 (5.6) | 39.2 (5.6) | 38.7 (5.7) | 0.5 | | MMSE at first visit | 25.1 (3.5) | 25.9 (2.8) | 24.7 (3.7) | 0.04 | | AD biomarker status | | Plasma p-Tau217 | 0.44 (0.57) | 0.16 (0.19) | 0.56 (0.64) | <0.001 | | Baseline cognitive composite z-scores | | Memory | −1.88 (0.96) | −1.65 (1.01) | −1.98 (0.92) | 0.03 | | Executive | −1.03 (0.72) | −0.85 (0.76) | −1.11 (0.69) | 0.06 | | Language | −0.71 (0.79) | −0.63 (0.82) | −0.75 (0.78) | 0.13 | | Visuospatial | −0.33 (1.69) | −0.06 (1.85) | −0.44 (1.60) | 0.34 | Abbreviations: AD = Alzheimer disease; Aβ42 = amyloid‐β 42; CDR-SB = Clinical Dementia Rating, sum of boxes; FTLD = frontotemporal lobe degeneration;IDDD = Interview for Deterioration in Daily Living in Dementia; LBD = Lewy body disease; MMSE = Mini-Mental State Examination; p‐Tau181 = phosphorylatedtau at threonine 181; p-Tau217 = phosphorylated tau at threonine 217. The table shows the general characteristics of the 2 groups at the first visit. AD biology is defined by CSF p-Tau181/Aβ42 ratio (cutoff of ≥0.068). Values areexpressed as mean (SD) or n (%) as appropriate. Continuous variables were compared by Wilcoxon rank-sum test; categorical variables were compared usingPearson χ2 test of Fisher exact test when was required. a Mean (SD); n (%). b Wilcoxon rank sum test; Fisher exact test; Pearson χ2 test. Neurology | Volume 107, Number 1 | July 14, 2026 e218180(5) ### Page 6 identifying participants with AD biology was 0.19 pg/mL(sensitivity 94.5%, specificity 84%, positive predicted value[PPV] 93%, negative predicted value [NPV] 87%), and theresults indicate an excellent discriminatory capacity of plasmap-Tau217 (AUC = 0.93, 95% CI 0.88–0.98). Because renalfunction may affect circulating biomarker concentrations, allprimary models were adjusted for eGFR. Adjustment for age,sex, education, and eGFR did not materially change the diagnostic accuracy of plasma p-Tau217 for CSF-defined ADbiology (eTable 5). In exploratory analyses (eFigure 1),plasma p-Tau217 concentrations were higher at lower eGFR;however, separation between AD biology–positive and ADbiology–negative participants remained evident across renalfunction strata. Baseline MMSE showed limited discrimination for CSFdefined AD biology (AUC = 0.45, 95% CI 0.34–0.57), substantially lower than plasma p-Tau217 (AUC = 0.93, 95% CI0.88–0.98; p < 0.001 by DeLong test for AUC difference).In multivariable models, plasma p-Tau217 remained independently associated with AD biology after includingMMSE and covariates, and the combined model yielded an AUC of 0.95 (95% CI 0.89–1.00). More details are available inthe supplementary material (eTable 6). The distribution of final etiologic diagnoses is shown ineFigure 2. ### Relationship Between AD Biology andLongitudinal Change in MMSE Figure 3 illustrates MMSE trajectory predictions by AD biology, as estimated by the LME models (more details ineTable 6). Participants with AD biology (as defined with CSFbiomarkers) showed a faster cognitive decline over time(β = −3.95, 95% CI −5.99 to −1.90, p < 0.001) than participants without AD biology. Participants with a positive plasmap-Tau217 (using the 0.19 pg/mL cutoff) also presenteda faster cognitive decline over time (β = −0.5, 95% CI −0.98to −0.01, p = 0.044) compared with participants with a negative p-Tau217 result at baseline. When plasma p-Tau217levels were included as a continuous variable, higherp-Tau217 levels at baseline predicted a faster MMSE declineover time (β = −1.36, 95% CI −1.95 to −0.77, p < 0.001). Forillustrative purposes, trajectory plots were constructed using ![Figure]() Plots show the comparison of z-standardized composite scores in 4 cognitive domains (Memory, Executive Functions, Language, Visuospatial) betweenindividuals stratified by AD biology. The Wilcoxon rank sum test is applied for comparison between groups. Effect sizes are reported as Cohen d. Statisticallysignificant was set at p-values <0.05. AD = Alzheimer disease. Neurology | Volume 107, Number 1 | July 14, 2026 e218180(6) ### Page 7 ![Figure]() In panel A, the ROC curve shows the performance of p-Tau 217 in discriminating AD biology, as defined by CSF p-Tau181/Aβ42 ratio cutoff of ≥0.068 (goldstandard). The optimal cutoff was determined by the Youden Index, via bootstrapping resampling. The figure reports the AUC with its 95% CI. In panel B, theboxplot illustrates the distribution of p-Tau217 stratified by AD biology (CSF p-Tau181/Aβ42 ratio). The horizontal bar represents the group mean.The Wilcoxon rank-sum test is used for comparison between groups. Statistical significance was set at p < 0.05, and the corresponding p-value is shown in thefigure. Aβ42 = amyloid-β 42; AD = Alzheimer disease; AUC = area under the curve; GS = gold standard; p-Tau217 = phosphorylated tau at threonine 217;p-Tau181 = phosphorylated tau at threonine 181; ROC = receiver operating characteristics. the variable dichotomized at the 0.19 pg/mL threshold fromROC curves. Although the presence of increased risk genotypes of APOEe4 was tested as an additional covariate in preliminary models(β = −0.18, 95% CI −0.90 to 0.36, p = 0.589), we excluded itfrom the final model because it did not improve its performance. We could not find statistical significance in age, sex,years of education, or estimated glomerular filtration rate atbaseline in the cognitive trajectories. ### Relationship Between AD Biology andProgression to Dementia in the Very Old Finally, we applied Cox regression models to investigate theassociation between AD biology and the risk of clinical progression to dementia. The presence of AD biology in CSF wasassociated with an increased risk of clinical progression to dementia stages (hazard ratio [HR] 1.64, 95% CI 1.00–2.70, p =0.051). Participants with positive plasma p-Tau217 (>0.19 pg/mL as cutoff) also showed a higher risk of dementia progression(HR 1.64, 95% CI 0.98–2.74, p = 0.060). Similarly, increasedconcentrations of plasma p-Tau217 analyzed as a continuousvariable were significantly associated with a higher risk of dementia (HR 1.49, 95% CI 1.05–2.13, p = 0.026). Age, sex,education, and glomerular filtration rate did not show anystatistical significance in the models (eTable 7). As expected,the ability of AD biology in both CSF and plasma to predictprogression to dementia was significantly improved after excluding participants who progressed to non-AD dementiaduring follow-up from the analyses (Figure 4; eFigure 3). ## Discussion In this study, we demonstrate that AD biology retains diagnosticand prognostic value in individuals aged ≥80 years. Using longitudinal data from a memory clinic–based cohort, we found thatbaseline cognitive differences by AD biomarker status weremodest, whereas AD biology and higher plasma p-Tau217 wereassociated with faster cognitive decline and a higher risk ofprogression to dementia. These findings provide evidence thatAD biology continues to play a clinically meaningful role even inadvanced age, a population traditionally perceived as less responsive to biomarker-based stratification. Our first aim was to assess the diagnostic performance ofplasma p-Tau217 in this age group. We found that its ability todetect AD biology remained excellent, comparable with thatreported in younger and middle-aged populations.2,3,6,34Plasma biomarkers also offer practical advantages for implementation: Blood-based testing is minimally invasive, scalable,and may be more cost-effective than CSF or amyloid PET asan initial triage step. This is particularly relevant in very oldadults, in whom lumbar puncture, amyloid PET, and repeatedin-person follow-up can be logistically difficult. A pragmaticapproach is a 2-step workflow, using plasma p-Tau217 forinitial screening and reserving confirmatory testing forequivocal cases or when diagnostic uncertainty remains.35,36The biomarker also retained specificity in differentiating ADfrom other age-associated pathologies, such as primary agerelated tauopathy,20 reinforcing its value as a minimally invasive tool for clinical use in the very old. Neurology | Volume 107, Number 1 | July 14, 2026 e218180(7) ### Page 8 ![Figure]() In panel A, AD biology is defined by theCSF p-Tau181/Aβ42 ratio using thecutoff of ≥0.068. In panel B, AD biologyis based on binary value of plasmap-Tau217 (cutoff of >0.19 pg/mL, usingthe Youden Index). These predictionswere derived from linear mixed-effectsmodels, including AD biology, time offollow-up, age at first visit, years of education, sex, glomerular filtration rate,and the interaction between time andAD biology as fixed effects. Statisticalsignificance was set at p < 0.05. Aβ42 =amyloid-β 42; AD = Alzheimer disease;MMSE = Mini-Mental State Examination; p-Tau181 = phosphorylated tau atthreonine 181; p-Tau217 = phosphorylated tau at threonine 217. A biomarker-guided approach offers an important advantagein very old adults because neuropsychological phenotypesalone have limited discriminative power in this age group. Inour cohort, although participants with and without AD biology differed in cognitive performance, these differenceswere modest and did not consistently remain significant aftercorrection for multiple comparisons. This overlap underscores the need for molecular biomarkers to distinguish ADbiology from other common age-associated neurodegenerative pathologies, an increasingly important challenge in theera of disease-modifying therapies. Although AD biology wasassociated with a predominantly amnestic profile consistentwith early AD with milder executive dysfunction,37,38 ADnegative participants also exhibited substantial memory impairment. This pattern is compatible with non-AD etiologiescommon in advanced age, including limbic-predominant agerelated TDP-43 encephalopathy (LATE),12,39 which mayaccount for a meaningful proportion of memory-predominantpresentations in the absence of AD biomarkers.40,41 Another notable observation was the higher prevalence of TBIamong participants with MCI without AD biology. Althougheffect sizes were modest, this finding aligns with prior workidentifying TBI as a risk factor for dementia.42,43 It raises the possibility that alternative mechanisms, such as chronic traumatic encephalopathy, may contribute to cognitive decline inthe very old. In the absence of validated in vivo biomarkers ofTBI-related neurodegeneration, further research is needed,particularly given the high frequency of falls in very old adults.44 The second aim of our study was to evaluate the clinicalconsequences of AD biology in this age group. AlthoughAD-negative participants also showed deterioration, likelyreflecting comorbid pathologies and frailty,45 AD-positiveparticipants declined at nearly double the rate on MMSE andhad shorter dementia-free survival. Although the annual difference in MMSE points may seem modest, its cumulativeimpact is clinically meaningful and may determine conversionfrom MCI to dementia. These findings extend previousobservations in younger cohorts and align with studies inoctogenarians and nonagenarians,18,46 supporting the ideathat AD biology confers an independent risk of cognitivedeterioration even in very advanced age. In addition, plasmap-Tau217 retained prognostic value, with higher levels associated with poorer cognition and increased dementia risk,consistent with observations in younger populations.34However, in very old individuals, the mere presence of amyloid or CSF AD biology may not discriminate fast from slow Neurology | Volume 107, Number 1 | July 14, 2026 e218180(8) ### Page 9 ![Figure]() Kaplan-Meier survival curves represent the probability of progression to dementia stage over time for participants classified by AD biology, excluding whoprogressed to non-AD dementia during follow-up. In panel A, AD biology is defined by the CSF p-Tau181/Aβ42 ratio using the cutoff of ≥0.068 (log-rank p =0.003). In panel B, AD biology is based on binary value of plasma p-Tau217 (cutoff of >0.19 pg/mL, using the Youden Index) (log-rank p < 0.001). Shaded areasindicate 95% CIs. Aβ42 = amyloid-β 42; AD = Alzheimer disease; p-Tau181 = phosphorylated tau at threonine 181; p-Tau217 = phosphorylated tau at threonine217. progressors as effectively as p-Tau217, which may betteridentify active, clinically symptomatic disease. Overall, ourresults support the view that AD biology retains strongprognostic value, particularly after excluding non-AD neurodegenerative diseases. One important implication of ourfindings is that the results of AD biomarkers in the very oldshould be carefully interpreted, considering the clinical phenotype to exclude neurodegenerative diseases other than AD. Although recent studies have proposed the use of doublepredefined thresholds of p-Tau217 and even a 2-step strategyto classify patients into low, intermediate, and high probabilityof amyloid pathology,2,35,47 those cutoffs have been extrapolated from younger cohorts and may not perform well in veryold individuals. When applied to our sample, the proposedand previously validated in younger ages p-Tau217 cutoffs (<0.19 pg/mL, >0.39 pg/mL)2 correctly identified AD pathology with excellent specificity and PPV (97%), but shownreduced sensitivity (47%) and placed half of the sample intothe intermediate zone, limiting their clinical application. Ourfindings suggest that p-Tau217 cut-off values in very oldpatients may be lower than those proposed for youngerpopulations. This may reflect not only age-related differencesin the cerebral distribution of tau pathology48 but also thehigh burden of copathologies16,49 and lower cognitive reserve,which may contribute to cognitive decline. To date, no validated, age-adjusted p-Tau217 threshold exists, underscoringthe need for additional studies before a biomarker-basedstrategy can be applied in clinical practice. Collectively, these results carry important clinical implications. They challenge the assumption that biomarker testing is Neurology | Volume 107, Number 1 | July 14, 2026 e218180(9) ### Page 10 of limited value in very old adults,50 demonstrating thatidentifying AD pathology provides meaningful prognosticinformation. This may support more informed care planning,guide the use of disease-modifying therapies, and help allocateresources efficiently. Nonetheless, biomarkers should beinterpreted within a broader geriatric context. Non-AD pathologies, frailty, gait impairment, medical comorbidities, andpsychosocial factors remain highly prevalent among the veryand the oldest-old44,45,49 and are likely to interact with ADbiology. Renal impairment is common in very old adults and mayinfluence plasma biomarker concentrations. Consistent withthis, plasma p-Tau217 levels increased with worsening renalfunction in our cohort (eFigure 1). Importantly, adjusting foreGFR did not materially alter the discrimination of AD biology or the associations with longitudinal cognitive declineand progression to dementia, suggesting that our main findings are unlikely to be driven solely by renal comorbidity. Amultimodal approach, integrating biomarker testing withcomprehensive geriatric assessment, may be the most effectivestrategy for determining who benefits most from intensivediagnostic evaluation or targeted interventions.8 Our study also has some limitations. Our results are based ona monocentric study of a unique cohort with up to 10 years offollow-up, in which participants aged 80 years or older werenot systematically excluded from CSF biomarker testing. Although longitudinal follow-up was available, the sample sizewas modest, particularly for participants aged >85 years, whocomprised 12/167 (7%) of the cohort (3 AD-negative and9 AD-positive). This small subgroup precluded separateanalyses with adequate statistical power and warrants cautionwhen extrapolating our findings specifically to individualsaged >85 years. Postmortem confirmation was not available,and biomarkers of non-AD neurodegenerative pathologies(e.g., LATE, Lewy body disease) were not assessed. Futureresearch should focus on larger multicenter cohorts withneuropathologic validation and should investigate resilienceand protective factors in biomarker positive older adults whoremain clinically stable. In summary, AD biomarkers retain significant diagnostic andprognostic value in very old individuals with cognitive complaints. CSF and plasma measures of AD biology predict fastercognitive decline and increased dementia risk, and plasmap-Tau217 offers a scalable, noninvasive option for detectingAD pathology in this age group. In an era of emerging therapies and personalized medicine, excluding very old adultsfrom biomarker-based diagnostic pathways risks creatinginequities in access to care. Our findings help fill the criticalevidence gap surrounding this often-overlooked population. ### Acknowledgment The authors thank the patients and their relatives for theirsupport of this study. Neurology | Volume 107, Number 1 | July 14, 2026 ### Author Contributions C. Ceriello: drafting/revision of the manuscript for content,including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. O.H. Torres: drafting/revision of themanuscript for content, including medical writing for content.S. Rubio-Guerra: drafting/revision of the manuscript forcontent, including medical writing for content; major role inthe acquisition of data. J. Garc´ıa-Castro: drafting/revision ofthe manuscript for content, including medical writing forcontent; major role in the acquisition of data. J. Selma-Gonzalez: drafting/revision of the manuscript for content, including medical writing for content; major role in theacquisition of data. I. Sala: drafting/revision of the manuscriptfor content, including medical writing for content; major rolein the acquisition of data. M.B. S´anchez-Saudin´os: drafting/revision of the manuscript for content, including medicalwriting for content; major role in the acquisition of data. L.Videla: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. E. Vera-Campuzano: drafting/revision of themanuscript for content, including medical writing for content;major role in the acquisition of data. J. Arranz: drafting/revision of the manuscript for content, including medicalwriting for content; major role in the acquisition of data. ´I.Rodr´ıguez-Baz: drafting/revision of the manuscript for content, including medical writing for content; major role in theacquisition of data. L. Maure-Blesa: drafting/revision of themanuscript for content, including medical writing for content;major role in the acquisition of data. O. Dols-Icardo: drafting/revision of the manuscript for content, including medicalwriting for content; major role in the acquisition of data. S.Valldeneu: drafting/revision of the manuscript for content,including medical writing for content; major role in the acquisition of data. I. Barroeta: drafting/revision of the manuscript for content, including medical writing for content;major role in the acquisition of data. M. Santos-Santos:drafting/revision of the manuscript for content, includingmedical writing for content; major role in the acquisition ofdata. M. Carmona-Iragui: drafting/revision of the manuscriptfor content, including medical writing for content; major rolein the acquisition of data. L. Vaqu´e-Alc´azar: drafting/revisionof the manuscript for content, including medical writing forcontent; major role in the acquisition of data. E. AlvarezSanchez: drafting/revision of the manuscript for content,including medical writing for content; major role in the acquisition of data. O. Lorente: drafting/revision of the manuscript for content, including medical writing for content;major role in the acquisition of data. M. Carreras: drafting/revision of the manuscript for content, including medicalwriting for content; major role in the acquisition of data. A.Bejanin: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. O. Belbin: drafting/revision of the manuscriptfor content, including medical writing for content; major rolein the acquisition of data. A. Lle´o: drafting/revision of the e218180(10) ### Page 11 manuscript for content, including medical writing for content;major role in the acquisition of data. J. Fortea: drafting/revision of the manuscript for content, including medicalwriting for content; major role in the acquisition of data. D.Alcolea: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. I. Ill´an-Gala: drafting/revision of the manuscriptfor content, including medical writing for content; major rolein the acquisition of data; study concept or design; analysis orinterpretation of data. ### Study Funding We acknowledge the Support for Research Groups fundingfrom the Department of Research and Universities of theGeneralitat de Catalunya (2021 SGR 00979). The SPIN cohortwas funded by Instituto de Salud Carlos III (ISCIII) throughthe projects PI14/01126, PI17/01019, PI18/00335, PI19/00882, PI18/00435, PI22/00611, INT19/00016, INT23/00048, PI17/01896, PI22/00307, AC19/00103, PI22/00758,ICI23/00032, INT21/00073, PI20/01473, PI24/01087, PI23/01786, PI21/00791, PI2400598 and co-funded by the European Union. The SPIN cohort received additional supportfrom NIH grants (R01 AG056850-01A1, R21 AG056974, R01AG061566, and R01AG080470), Generalitat de Catalunya(2017-SGR-547, SLT006/17/125, SLT006/17/119, SLT002/16/408, SLT042/25/000032), “La Marat´o de TV3” foundation grants (20141210, 044412, and 20142610), Fundaci´oBancaria “La Caixa” (DABNI project), Fundaci´o CatalanaS´ındrome de Down, and Fundaci´o V´ıctor Gr´ıfols i Lucas. Thisstudy also received funding from the Centro de Investigaci´onBiom´edica en Red sobre Enfermedades Neurodegenerativas(CIBERNED) Program 1, jointly funded by the FondoEuropeo de Desarrollo Regional (FEDER) and the EuropeanUnion under the framework “Una manera de hacer Europa”/“Away to make Europe.” S. Rubio-Guerra received contractfunding from the Alzheimer’s Association (AACSF-21850193). J. Garc´ıa-Castro, J.E. Arriola-Infante, I. Rodr´ıguezBaz, and L. Maure Blesa are supported by a R´ıo Hortega grant(CM23/00176, CM22/00219, CM22/00052, CM23/00291)from the Carlos III National Institute of Health of Spain, partlyfunded by the European Social Fund. M. Carmona-Iragui isa senior Atlantic Fellow for Equity in Brain Health at the GlobalBrain Health Institute (GBHI) and reports grants from theAlzheimer’s Association (AARG-22-973966), Global BrainHealth Institute (GBHI) (GBHI_ALZ-18-543740), J´eromeLejeune Foundation (1913 cycle 2019B), and Societat Catalanade Neurologia (SCN2020). L. Vaqu´e-Alc´azar is supported bya Sara Borrell grant (CD23/00235) from the Carlos III National Institute of Health of Spain. Miguel Santos-Santosacknowledges support from the Spanish Institute of HealthCarlos III co-funded by the European Union (Juan Rod´es research grant JR18-00018; Fondo de investigaci´on sanitariagrant PI19/00882), the Department of Research and Universities from the Generalitat de Catalunya (2021 SGR 00979),the Alzheimer’s Association clinician scientist fellowship(AACSF-22-972945), and the NIH (R01AG080470). A.Bejanin acknowledges support from the Instituto de Salud Carlos III through the Miguel Servet grant “CP20/00038,”which is co-funded by the European Union, the Alzheimer’sAssociation (AARG-22-923680), and the Ajuntament de Barcelona, in collaboration with Fundaci´o La Caixa (23S06157001). O. Dols-Icardo receives funding from the Alzheimer’sAssociation (AARF-22-924456) and the Fondation J´eromeˆLejeune (PDC-2023-51; 202307). I. Ill´an-Gala is a senior Atlantic Fellow for Equity in Brain Health at the Global BrainHealth Institute (GBHI) and receives funding from the Alzheimer’s Association and the Alzheimer Society (GBHI ALZUK-21-720973 and AACSF-21-850193). I. Ill´an-Gala was alsosupported by the Juan Rod´es Contract (JR20/0018) from theCarlos III National Institute of Health of Spain, partly fundedby the European Social Fund. ### Disclosure S. Rubio-Guerra serves on the advisory board for educationalevents of Esteve Pharmaceuticals, S.A. J. Arranz reports receivingpersonal fees for service on advisory boards, speaker honoraria,or educational activities from Lilly, Roche Diagnostics, andEsteve, outside the submitted work. I. Barroeta reports receivingpersonal fees for educational activities from Adium. M. Carmona-Iragui reports receiving personal fees for service on advisory boards, speaker honoraria, or educational activities fromLilly, Roche, Adium Pharma, Esteve, and Neuraxpharm, outsidethe submitted work. O. Belbin reports holding a patent formarkers of synaptopathy in neurodegenerative disease (licensedto ADx NeuroSciences N.V., WO2019175379 Markers of synaptopathy in neurodegenerative diseases). A. Lle´o reported receiving personal fees for service on advisory boards or speakerhonoraria from Almirall, Beckman-Coulter, Biogen, Eisai, Esteve,Fujirebio-Europe, Grifols, KRKA, Lilly, Novartis, NovoNordisk,Nutricia, Otsuka Pharmaceutical, Roche, and Zamb´on. AL isco-author of a patent for markers of synaptopathy in neurodegenerative disease (licensed to ADx, EPI8382175.0) and onantibodies for amyloid precursor, methods and uses thereof,European priority (EP25382226). J. Fortea reported receivingpersonal fees for service on the advisory boards, adjudicationcommittees, or speaker honoraria from AC Immune, Adamed,Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, LaboratoriosCarnot, Life Molecular Imaging, Lilly, Novo Nordisk, Perha,Roche, Zamb´on, and outside the submitted work. He reportsholding a patent for markers of synaptopathy in neurodegenerative disease (licensed to ADx, EPI8382175.0). D. Alcoleareported receiving personal fees for service on advisory boards ofFujirebio-Europe, Roche Diagnostics, Grifols S.A., and Lilly, andreceived speaker honoraria from Fujirebio-Europe, RocheDiagnostics, Nutricia, Krka Farmac´eutica S.L., Zambon S.A.U.,Neuraxphar, and Esteve Pharmaceuticals S.A. D. Alcolea declaresa filed patent application (WO2019175379 A1 Markers ofsynaptopathy in neurodegenerative disease). I. Ill´an-Galareported receiving personal fees for service on advisory boards ofUCB and Nutricia, and received speaker honoraria fromAlmirall, Esteve Pharmaceuticals S.A., Kern Pharma, KrkaFarmac´eutica S.L., Lilly, Nutricia, and Zambon S.A.U. All otherauthors report no disclosures relevant to the manuscript. Go to for full disclosures. Neurology | Volume 107, Number 1 | July 14, 2026 e218180(11) ### Page 12 ### Publication History Received by Neurology® January 7, 2026. Accepted in final formApril 9, 2026. Submitted and externally peer reviewed. The handlingeditor was Associate Editor Linda Hershey, MD, PhD, FAAN. ## References - 1. Dubois B, Villain N, Schneider L, et al. Alzheimer disease as a clinical-biologicalconstruct: an international working group recommendation. JAMA Neurol. 2024;81(12):1304-1311. doi:10.1001/jamaneurol.2024.3770 - 2. Arranz J, Zhu N, Rubio-Guerra S, et al. Diagnostic performance of plasma pTau217,pTau181, Aβ1-42 and Aβ1-40 in the LUMIPULSE automated platform for the detection of Alzheimer disease. Alzheimers Res Ther. 2024;16(1):139. doi:10.1186/s13195-024-01513-9 - 3. Arranz J, Ferrer R, Zhu N, et al. Prospective evaluation of plasma pTau217 stability forthe detection of Alzheimer’s disease in a tertiary memory clinic. Alzheimers Res Ther.2025;17(1):150. doi:10.1186/s13195-025-01779-7 - 4. Ashton NJ, Brum WS, Di Molfetta G, et al. Diagnostic accuracy of a plasma phosphorylated tau 217 immunoassay for Alzheimer disease pathology. JAMA Neurol.2024;81(3):255-263. doi:10.1001/jamaneurol.2023.5319 - 5. Leuzy A, Mattsson‐Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood‐based biomarkers for Alzheimer’s disease. EMBO Mol Med. 2022;14(1):e14408. doi:10.15252/emmm.202114408 - 6. Therriault J, Janelidze S, Benedet AL, et al. Diagnosis of Alzheimer’s disease usingplasma biomarkers adjusted to clinical probability. Nat Aging. 2024;4(11):1529-1537.doi:10.1038/s43587-024-00731-y - 7. Jack CR, Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging ofAlzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers Dement. 2024;20(8):5143-5169. doi:10.1002/alz.13859 - 8. Paolacci L, Giannandrea D, Mecocci P, Parnetti L. Biomarkers for early diagnosis ofAlzheimer’s disease in the oldest old: yes or no? J Alzheimers Dis. 2017;58(2):323-335.doi:10.3233/JAD-161127 - 9. Rabinovici GD, Gatsonis C, Apgar C, et al. Association of amyloid positron emissiontomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia. JAMA. 2019;321(13):1286-1294. doi:10.1001/jama.2019.2000 - 10. Van Maurik IS, Broulikova HM, Mank A, et al. A more precise diagnosis by means ofamyloid PET contributes to delayed institutionalization, lower mortality, and reducedcare costs in a tertiary memory clinic setting. Alzheimers Dement. 2023;19(5):2006-2013. doi:10.1002/alz.12846 - 11. 2024 Alzheimer’s disease facts and figures. Alzheimers Dement. 2024;20(5):3708-3821. doi:10.1002/alz.13809 - 12. Corriveau-Lecavalier N, Botha H, Graff-Radford J, et al. Clinical criteria for a limbicpredominant amnestic neurodegenerative syndrome. Brain Commun. 2024;6(4):fcae183. doi:10.1093/braincomms/fcae183 - 13. Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old: an autopsy study.Acta Neuropathol (Berl). 2010;119(4):421-433. doi:10.1007/s00401-010-0654-5 - 14. Savva GM, Wharton SB, Ince PG, et al. Age, neuropathology, and dementia. N Engl JMed. 2009;360(22):2302-2309. doi:10.1056/NEJMoa0806142 - 15. Aevarsson O, Svanborg A, Skoog I. Seven-year survival rate after age 85 years: relationto Alzheimer disease and vascular dementia. Arch Neurol. 1998;55(9):1226-1232. doi:10.1001/archneur.55.9.1226 - 16. Lopez OL, Klunk WE, Mathis C, et al. Amyloid, neurodegeneration, and small vesseldisease as predictors of dementia in the oldest-old. Neurology. 2014;83(20):1804-1811. doi:10.1212/WNL.0000000000000977 - 17. Martino-Adami PV, Chatterjee M, Kleineidam L, et al. Prognostic value of Alzheimer’sdisease plasma biomarkers in the oldest-old: a prospective primary care-based study.Lancet Reg Health Eur. 2024;45:101030. doi:10.1016/j.lanepe.2024.101030 - 18. Rohde SK, Luimes MC, Lorenz LMC, et al. Amyloid-beta pathology and cognitiveperformance in centenarians. JAMA Neurol. 2025;82(8):837-847. doi:10.1001/jamaneurol.2025.1734 - 19. Grande G, Valletta M, Rizzuto D, et al. Blood-based biomarkers of Alzheimer’s diseaseand incident dementia in the community. Nat Med. 2025;31(6):2027-2035. doi:10.1038/s41591-025-03605-x - 20. Yu L, Boyle PA, Janelidze S, et al. Plasma p-tau181 and p-tau217 in discriminatingPART, AD and other key neuropathologies in older adults. Acta Neuropathol (Berl).2023;146(1):1-11. doi:10.1007/s00401-023-02570-4 - 21. Alcolea D, Clarim´on J, Carmona‐Iragui M, et al. The Sant Pau Initiative on Neurodegeneration (SPIN) cohort: a data set for biomarker discovery and validation inneurodegenerative disorders. Alzheimers Dement (NY). 2019;5(1):597-609. doi:10.1016/j.trci.2019.09.005 - 22. Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med. 2011;364(23):2227-2234. doi:10.1056/NEJMcp0910237 - 23. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerularfiltration rate. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9200905050-00006 - 24. Rubio-Guerra S, Sala I, S´anchez-Saudin´os MB, et al. Amyloid-negative neuropsychological norms: added value in the era of biomarkers and disease-modifyingtherapies. Alzheimers Dement (Amst). 2025;17(4):e70223. doi:10.1002/dad2.70223 - 25. Rubio‐Guerra S, S´anchez‐Saudin´os MB, Sala I, et al. Development of amyloid‐negative neuropsychological norms using GAMLSS. Alzheimers Dement (Amst). 2025;17(4):e70224. doi:10.1002/dad2.70224 - 26. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method forgrading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189-198. doi:10.1016/0022-3956(75)90026-6 - 27. McDougall F, Edgar C, Mertes M, et al. Psychometric properties of the clinicaldementia rating: sum of boxes and other cognitive and functional outcomes ina prodromal Alzheimer’s disease population. J Prev Alzheimers Dis. 2021;8(2):151-160. doi:10.14283/jpad.2020.73 - 28. Teunisse S, Derix MMA. The interview for deterioration in daily living activities indementia: agreement between primary and secondary caregivers. Int Psychogeriatr.1997;9(suppl 1):155-162. doi:10.1017/S1041610297004845 - 29. O’Bryant SE, Waring SC, Cullum CM, et al. Staging dementia using clinical dementiarating scale sum of boxes scores: a Texas Alzheimer’s Research Consortium Study.Arch Neurol. 2008;65(8):1091-1095. doi:10.1001/archneur.65.8.1091 - 30. Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale forassessment of primary degenerative dementia. Am J Psychiatry. 1982;139(9):1136-1139. doi:10.1176/ajp.139.9.1136 - 31. Alcolea D, Pegueroles J, Muñoz L, et al. Agreement of amyloid PET and CSF biomarkers for Alzheimer’s disease on Lumipulse. Ann Clin Transl Neurol. 2019;6(9):1815-1824. doi:10.1002/acn3.50873 - 32. Safransky M, Groh JR, Blennow K, et al. Lumipulse-measured cerebrospinal fluidbiomarkers for the early detection of Alzheimer disease. Neurology. 2024;103(11):e209866. doi:10.1212/WNL.0000000000209866 - 33. Harris PA, Taylor R, Minor BL, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95: - 103208. doi:10.1016/j.jbi.2019.103208 - 34. Selma-Gonzalez J, Rubio-Guerra S, Garc´ıa-Castro J, et al. Association of plasmaphosphorylated tau 217 with clinical deterioration across Alzheimer disease stages.Neurology. 2025;105(1):e213769. doi:10.1212/WNL.0000000000213769 - 35. Brum WS, Cullen NC, Janelidze S, et al. A two-step workflow based on plasmap-tau217 to screen for amyloid β positivity with further confirmatory testing onlyin uncertain cases. Nat Aging. 2023;3(9):1079-1090. doi:10.1038/s43587-02300471-5 - 36. Mattsson-Carlgren N, Collij LE, Stomrud E, et al. Plasma biomarker strategy forselecting patients with Alzheimer disease for antiamyloid immunotherapies. JAMANeurol. 2024;81(1):69-78. doi:10.1001/jamaneurol.2023.4596 - 37. Grober E, Sanders AE, Hall C, Lipton RB. Free and cued selective reminding identifiesvery mild dementia in primary care. Alzheimer Dis Assoc Disord. 2010;24(3):284-290.doi:10.1097/WAD.0b013e3181cfc78b - 38. Sala I, Ill´an-Gala I, Alcolea D, et al. Diagnostic and prognostic value of the combination of two measures of verbal memory in mild cognitive impairment due toAlzheimer’s disease. J Alzheimers Dis. 2017;58(3):909-918. doi:10.3233/JAD-170073 - 39. Wolk DA, Nelson PT, Apostolova L, et al. Clinical criteria for limbic-predominantage-related TDP-43 encephalopathy. Alzheimers Dement. 2025;21(1):e14202. doi:10.1002/alz.14202 - 40. Nelson PT, Dickson DW, Trojanowski JQ, et al. Limbic-predominant age-relatedTDP-43 encephalopathy (LATE): consensus working group report. Brain. 2019;142(6):1503-1527. doi:10.1093/brain/awz099 - 41. Nelson PT, Schneider JA, Jicha GA, Duong MT, Wolk DA. When Alzheimer’s isLATE: why does it matter? Ann Neurol. 2023;94(2):211-222. doi:10.1002/ana.26711 - 42. Barnes DE, Byers AL, Gardner RC, Seal KH, Boscardin WJ, Yaffe K. Association ofmild traumatic brain injury with and without loss of consciousness with dementia inUS military veterans. JAMA Neurol. 2018;75(9):1055-1061. doi:10.1001/jamaneurol.2018.0815 - 43. Smith DH, Johnson VE, Stewart W. Chronic neuropathologies of single and repetitiveTBI: substrates of dementia? Nat Rev Neurol. 2013;9(4):211-221. doi:10.1038/nrneurol.2013.29 - 44. Tian Q, Montero-Odasso M, Buchman AS, et al. Dual cognitive and mobilityimpairments and future dementia: setting a research agenda. Alzheimers Dement.2023;19(4):1579-1586. doi:10.1002/alz.12905 - 45. Petermann-Rocha F, Lyall DM, Gray SR, et al. Associations between physical frailtyand dementia incidence: a prospective study from UK Biobank. Lancet HealthyLongev. 2020;1(2):e58-e68. doi:10.1016/S2666-7568(20)30007-6 - 46. Melikyan ZA, Al-Darsani Z, Colcord KA, et al. Association of subjective memorycomplaints with neuropathologic changes at age 90 or older: the 90+ study. Neurology.2025;105(4):e213948. doi:10.1212/WNL.0000000000213948 - 47. Palmqvist S, Warmenhoven N, Anastasi F, et al. Plasma phospho-tau217 for Alzheimer’s disease diagnosis in primary and secondary care using a fully automatedplatform. Nat Med. 2025;31(6):2036-2043. doi:10.1038/s41591-025-03622-w - 48. Vogel JW, Young AL, Oxtoby NP, et al. Four distinct trajectories of tau depositionidentified in Alzheimer’s disease. Nat Med. 2021;27(5):871-881. doi:10.1038/s41591021-01309-6 - 49. Syrjanen JA, Campbell MR, Algeciras‐Schimnich A, et al. Associations of amyloid andneurodegeneration plasma biomarkers with comorbidities. Alzheimers Dement. 2022;18(6):1128-1140. doi:10.1002/alz.12466 - 50. Frisoni GB, Hansson O, Nichols E, et al. New landscape of the diagnosis of Alzheimer’s disease. Lancet. 2025;406(10510):1389-1407. doi:10.1016/S0140-6736(25)01294-2 Neurology | Volume 107, Number 1 | July 14, 2026 e218180(12)