Abstract
Dementia diagnosis increasingly relies on blood-based biomarkers, yet their performance in diverse populations remains insufficiently characterized. Latin America, with substantial genetic and environmental heterogeneity, is particularly underrepresented in biomarker research. Here we show that plasma AT(N) biomarkers can distinguish Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) in a multinational Latin American cohort (N = 605). Aβ42/Aβ40 amyloid-β ratios were reduced and levels of phosphorylated tau (p-tau217, p-tau181) and neurofilament light chain (NfL) were elevated in both disorders, with NfL showing greater increases in FTLD. Classification models achieved receiver operating characteristic areas under the curve (ROC AUCs) of 83% for AD and 88% for FTLD. Meta-analyses confirmed consistency across countries, and these markers correlated with executive, memory and global cognitive impairment. Biomarker alterations combined with disease-specific neuroimaging patterns and cognitive measures further improved accuracy (ROC AUCs of 89% for AD and 95% for FTLD). These findings indicate that plasma AT(N) biomarkers, combined with neuroimaging and clinical assessments, can enhance dementia diagnosis across diverse Latin American populations.
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Data availability
Processed data supporting the findings of this study are available via GitHub (https://github.com/DuranLab2/Redlat_paper). Raw AT(N) biomarkers and neuropsychological, neuroimaging and additional clinical data are not publicly available due to participant privacy and institutional restrictions. Access to the ReDLat dataset is available upon reasonable request and application (contact A.I. at agustin.ibanez@gbhi.org). A formal data-sharing agreement is required prior to release. The approval process may typically take up to 12 weeks, depending on the nature of the request and compliance with institutional guidelines.
Code availability
The code used to perform machine learning and linear regression analyses can be accessed at https://github.com/DuranLab2/Redlat_paper.
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Acknowledgements
Data in this manuscript were collected by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), supported by National Institutes of Health (NIH) research grant R01 AG057234, funded by the National Institute on Aging (NIA) and the Fogarty International Center (FIC); an Alzheimer’s Association grant (SG-20-725707-ReDLat); the Rainwater Charitable Foundation; and the Global Brain Health Institute, with additional support from the Bluefield Project to Cure Frontotemporal Dementia, an NIH contract (75N95022C00031) and the NIA under award numbers R01 AG075775, R01 AG082056 and R01 AG083799. C.D.-A. is supported by ANID/FONDECYT Regular (1210622 and 1250091), ANID/NAM22I0007, ANID/PIA/ANILLOS ACT210096, the Alzheimer’s Association (AARGD-24-1310017), ANID/FOVI240065 and ANID/Proyecto Exploracion 13240170. A.I. is supported by grants from ANID/FONDECYT Regular (1250091, 1210176 and 1220995), ANID/FONDAP/15150012, Takeda CW2680521 and ReDLat. C.D.-A. and A.I. are supported by grant ANID/PIA/ANILLO ACT210096. The contents of this publication are solely the responsibility of the authors and do not represent the official views of these institutions. H.Z. is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council, supported by grants from the Swedish Research Council (2023-00356, 2022-01018 and 2019-02397), the European Union’s Horizon Europe Research and Innovation Program under grant agreement number 101053962, Swedish State Support for Clinical Research (ALFGBG-71320), the Alzheimer Drug Discovery Foundation (201809-2016862), the AD Strategic Fund and the Alzheimer’s Association (ADSF-21-831376-C, ADSF-21-831381-C, ADSF-21-831377-C and ADSF-24-1284328-C), the European Partnership on Metrology (co-financed by the European Union’s Horizon Europe Research and Innovation Program and by the Participating States (NEuroBioStand, 22HLT07)), the Bluefield Project to Cure Frontotemporal Dementia, the Cure Alzheimer’s Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor (Hjärnfonden, Sweden (FO2022-0270)), the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie grant agreement number 860197 (MIRIADE), the European Union Joint Program–Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre and the UK Dementia Research Institute at UCL (UKDRI-1003). K.H. is partially supported by NIH grants RF1AG059867, RF1AG064312 and R01AG083799. A.M.G. is partially supported by the NIA of the NIH (R01AG075775, R01AG083799 and 2P01AG019724); ANID (FONDECYT Regular 1250317 and 1250091); DICYT-USACH (032351GDAS); Agencia Nacional de Promoción Científica y Tecnológica (01-PICTE-2022-05-00103); and ReDLat, which is supported by the FIC, the NIH/NIA (R01AG057234, R01AG075775, R01AG21051 and CARDS−NIH), the Alzheimer’s Association (SG-20-725707), the Rainwater Charitable Foundation’s Tau Consortium, the Bluefield Project to Cure Frontotemporal Dementia and the Global Brain Health Institute. F.R.F. is supported by the Alzheimer’s Association (AARF-21-848281). This research was supported in part by the Intramural Research Program of the NIH/NIA, Department of Health and Human Services, project number ZIAAG000534. M.S.-C. receives funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement number 948677); ERA PerMed-ERA NET; the Generalitat de Catalunya (Departament de Salut) through project SLD077/21/000001 and projects PI19/00155 and PI22/00456, funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union; a fellowship from ‘la Caixa’ Foundation (ID 100010434); and the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie grant agreement number 847648 (LCF/BQ/PR21/11840004). A.S. is partially supported by ANID/Fondap/15150012, ANID/Fondecyt/1231839, ANID/FONDEF/ID22I10251 and ANID/Proyectos de Exploración 13220082. The content is solely the responsibility of the authors and does not represent the official views of the institutions.
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Study conception and design: A.I., C.D.-A., A.C. and F.C.M. Plasma sample collection: A.S., D.A., D.L.M., E.R., F.L., J.A.F., M.I.B., M.B. and N.C. Data collection, curation and analysis: A.C., F.C.M., P.O., H.H., F.H., R.G.-G. and D.C. Machine learning methods: F.C., H.H., A.I. and H.S.M. Imaging analyses: R.G.-G. and C.C.-O. Writing—original draft: A.C., F.C., P.O., H.H., C.G.-S., C.D.-A. and A.I. Writing—review and editing: all authors. Project administration and funding: C.D.-A. and A.I. Accessed and verified data: A.C., P.O., H.H. and R.G.-G.
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H.Z. has served on scientific advisory boards and/or as a consultant for AbbVie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics and Wave; has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk and Roche; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Not related to the current work, K.H. is the Honorary Lifetime Co-founder of iFutureLab Inc., serves on the scientific advisory board of the company and has received consulting fees from the company. Z.L.’s participation in this project was part of a competitive contract awarded to DataTecnica LLC by the NIH to support open science research. S.M.D.B. has served on scientific advisory boards and/or as a consultant for Biogen, Lilly, Novo Nordisk and Roche and has received a research productivity grant from the National Council for Scientific and Technological Development (Ministry of Science and Technology−Brazil). M.S.-C. has received consultancy/speaker fees (paid to the institution) in the past 36 months from Almirall, Lilly, Quanterix, Novo Nordisk and Roche Diagnostics. He has received consultancy fees from or served on advisory boards (paid to the institution) of Lilly, Grifols, Novo Nordisk and Roche Diagnostics. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Lilly, Fujirebio, Janssen Research & Development, Meso Scale Discovery and Roche Diagnostics. M.S.-C. did not receive any personal compensation from these organizations or any other for-profit organization. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Comparison of plasma AT(N) biomarker levels in cognitively normal (CN), Alzheimer’s disease (AD), and frontotemporal lobar degeneration (FTLD) participants after removing outliers.
Levels of (a) Aβ42/Aβ40 ratio, (b) p-tau217, (c) p-tau181 and (d) NfL after outlier removal using the z-score method (|z | > 3). Group differences in panels (a–d) were assessed using one-way Kruskal–Wallis tests followed by Dunn’s multiple-comparisons post hoc tests. Aβ42/Aβ40 ratio: CN vs. AD, p < .001; CN vs. FTLD, p = .001; AD vs. FTLD, p = 0.366. p-tau217: CN vs. AD, p < .001; CN vs. FTLD, p < .001; AD vs. FTLD, p = 0.021. p-tau181: CN vs. AD, p < .001; CN vs. FTLD p < .001; AD vs. FTLD, p = 0.067. NfL: CN vs. AD, p < .001; CN vs. FTLD p < .001; AD vs. FTLD, p < .001. ANCOVA analyses adjusted for sex, age, and education are shown in panel (e), and in panel (f) after outlier removal. Aβ42/Aβ40 ratio: CN vs. AD, p < .001; CN vs. FTLD p = .001; AD vs. FTLD, p = 0.253. p-tau217: CN vs. AD, p < .001; CN vs. FTLD p < .001; AD vs. FTLD, p = 0.017. p-tau181: CN vs. AD, p < .001; CN vs. FTLD p < .001; AD vs. FTLD, p = 0.058. NfL: CN vs. AD, p < .001; CN vs. FTLD p < .001; AD vs. FTLD, p < .001. P values: ***p < .001, *p < .05. CN: cognitively normal individuals; AD: Alzheimer disease patients; FTLD: frontotemporal lobar degeneration patients.
Extended Data Fig. 2 Diagnostic performance of individual plasma AT(N) biomarkers for pairwise classifications.
Receiver Operating Characteristic area under the curve (ROC AUC) for (a) CN vs. AD, (b) CN vs. FTLD, and (c) FTLD vs. AD for each AT(N) biomarker. Each data point represents a participant (CN: n = 202; AD: n = 223; FTLD: n = 134). ROC-AUC data are presented as mean values ± SEM. The bottom panels display Youden Index analyses for the top-performing biomarkers: p-tau217 (CN vs. AD) and NfL (CN vs. FTLD). Green dashed lines indicate optimal biomarker thresholds, and red dashed lines represent probability decision thresholds. CN: cognitively normal individuals; AD: Alzheimer’s disease patients; FTLD: frontotemporal lobar degeneration patients; SEM: Standard Error of the Mean.
Extended Data Fig. 3 Performance of AT(N) biomarkers for classification of AD vs. FTLD.
Receiver Operating Characteristic area under the curve (ROC AUC) for FTLD vs. AD for models trained with AT(N) biomarkers. Permutation tests ranked AT(N) biomarkers based on their impact on model output. Each data point represents a participant (AD: n = 202; FTLD: n = 134). ROC-AUC data are presented as mean values ± SEM. Feature importance magnitudes represent the mean decrease in model performance (AUC-ROC) when a feature is permuted. The direction of the association (positive or negative bar) indicates whether a feature is predictive of class 1 (AD) or class 0 (FTLD). In contrast, features with a mean importance score ≤ 0.005 are considered non-predictive. AUC: Area Under the Curve; CN: cognitively normal individuals; AD: Alzheimer’s disease patients; FTLD: frontotemporal lobar degeneration patients; SEM: Standard Error of the Mean.
Extended Data Fig. 4 Associations of AT(N) plasma biomarkers and countries with cognitive domains.
Ridge regression models were used to predict each cognitive domain using AT(N) biomarkers and country in (a) CN (n = 186) + AD (n = 148), (b) CN (n = 186) + FTLD (n = 93), and (c) CN + AD + FTLD. Plots display R2, f2 and F values for each analysis. Saturated colors highlight significant (p < .05) associations for each predictor. Top predictors for CN + AD (p-value): Executive function: p-tau217 = 1.4×10−8, NfL = 5.4×10−5; Memory: p-tau217 = 2.4×10−10; Global cognition: p-tau217 = 9.4×10−13, NfL = 1.7×10−4; Functionality: p-tau217 = 7.3×10−7, NfL = 3×10−3. Top predictor for CN + FTLD (p-value): Executive function: NfL = 1.2×10−7, p-tau217 = 6.7×10−6; Memory: p-tau217 = 7×10−5, NfL = 3.1×10−3; Global cognition: p-tau217 = 6.7×10−13, NfL = 8.5×10−10; Functionality: p-tau217 = 7.8×10−6, NfL = 2.1×10−5. Top predictor for CN + AD + FTLD (p-value): Executive function: p-tau217 = 9.6×10−9, NfL = 1.3×10−8; Memory: p-tau217 = 4.2×10−9, NfL = 1.1×10−2; Global cognition: p-tau217 = 8.9×10−14, NfL = 1.2×10−8; Functionality: p-tau217 = 1.8×10−5, NfL = 1.7×10−4. CN: cognitively normal individuals; AD: Alzheimer’s disease patients; FTLD: frontotemporal lobar degeneration patients.
Extended Data Fig. 5 Feature importance across different feature sets in pairwise classification models.
Permutation-based feature importance analyses for models distinguishing (a) CN vs. AD and (b) CN vs. FTLD using different combinations of plasma AT(N) biomarkers, neuropsychological tests, and MRI-derived variables. Each panel shows the ranked contribution of features based on their impact on model output. N = 104 for CN individuals, n = 95 for AD patients, and n = 58 for FTLD patients. MRI: magnetic resonance imaging; Cog: Cognition; Hipp: hippocampus; R: right; L: left; TMT-B: trail making test part B; Benson: Benson Complex Figure test; CN: cognitively normal individuals; AD: Alzheimer disease patients; FTLD: frontotemporal lobar degeneration patients.
Extended Data Fig. 6 Ancestry structure, regression analyses, and biomarker distributions by ApoE ε4 status.
(a) Principal component (PC) projection of genetic data showing ancestry assignment. Most participants (n = 300) were classified as AMR, with six as EUR and one as AFR. (b) Distribution of AT(N) biomarkers stratified by ApoE rs429358 ε4 carrier status (presence vs. absence of at least one ε4 allele). Statistical significance was assessed using the two-sided Mann–Whitney U test. Aβ42/Aβ40 ratio: ε4- vs ε4 + , p < .001; p-tau217: ε4- vs ε4 + , p < .001; p-tau181: ε4- vs ε4 + , p < .001; NfL: ε4- vs ε4 + , p = .0394. Significance levels: *p < .05, ***p < .001. Ridge regression analysis was performed for each cognitive domain based on plasma levels of AT(N) biomarkers and ApoE rs429358 ε4 genotype (presence vs. absence of at least one ε4 allele) in CN (n = 165) and AD patients (n = 126) (c), and in CN (n = 165) and FTLD patients (n = 79) (d). The plots display R², f², F, and significant p-values for each regression analysis. Saturated colors highlight significant associations (p < .05) for each predictor. Top predictor for CN + AD (p-value): Executive function: p-tau217 = 1.0×10-5, NfL = 1.7×10-4; Memory: p-tau217 = 1.1×10-7, ApoE ε4 = 9.2×10-3; Global cognition: p-tau217 = 4.8×10-10, NfL = 7.7×10-5; Functionality: p-tau217 = 2.7×10-4, NfL = 3×10-2. Top predictor for CN + FTLD (p-value): Executive function: NfL = 2.9×10-8, p-tau217 = 4.5×10-6; Memory: p-tau217 = 5.3×10-4, NfL = 1.6×10-3; Global cognition: p-tau217 = 6.0×10-12, NfL = 2.4×10-9; Functionality: p-tau217 = 2.6×10-6, NfL = 1.2×10-5. EUR: European; EAS: East Asian; AMR: Amerindian; SAS: South Asian; AFR: African; ε4-: ApoE ε4 non carriers; ε4 + : ApoE ε4 carriers; CN: cognitively normal individuals; AD: Alzheimer disease patients; FTLD: frontotemporal lobar degeneration patients.
Extended Data Fig. 7 Sensitivity analyses.
Receiver Operating Characteristic (ROC) curve area under the curve (AUC) for CN vs AD (a) and CN vs FTLD (b) for models trained with AT(N) biomarkers after matching samples by sex, age, and education Each data point represents a participant. ROC-AUC data are presented as mean values ± SEM. Permutation test values for features used to train the models, ranked based on their impact on model output for AD vs CN (a) and FTLD vs CN (b). Feature importance magnitudes represent the mean decrease in model performance (AUC-ROC) when a feature is permuted. The direction of the association indicates whether a feature is predictive of class 1 (AD or FTLD) or class 0 (CN), while features with a mean importance score ≤ .005 are considered non-predictive. (c-d) Ridge regression analysis was performed for each cognitive domain based on plasma levels of AT(N) biomarkers and diagnosis in CN and AD patients after matching by sex, age, and education (c), and in CN and FTLD patients (d). The plots display R², f², F, and significant p-values for each regression analysis. Saturated colors highlight significant associations (p < .05) for each predictor within the model. Top predictor for CN + AD (p-value): Executive function: p-tau217 = 7.4×10−6, NfL = 2.8×10−2; Memory: p-tau217 = 1.7×10−8; Global cognition: p-tau217 = 3.3×10−6, Aβ42/Aβ40 ratio = 2.2×10−2, NfL = 4.4×10−2; Functionality: p-tau217 = 2.6×10−5, NfL = 3.3×10−2. Top predictor for CN + FTLD (p-value): Executive function: NfL = 1.0×10−5, and p-tau217 = 5.0×10−5; Memory: p-tau217 = 1.2×10−4, NfL = 7.6×10−3; Global cognition: p-tau217 = 2.1×10−10, NfL = 4.2×10−7; Functionality: p-tau217 = 3.5×10−5, NfL = 1.2×10−3. CN: cognitively normal individuals; AD: Alzheimer’s disease patients; FTLD: frontotemporal lobar degeneration patients; SEM: Standard Error of the Mean.
Extended Data Fig. 8 Effect of comorbidities on AT(N) biomarkers.
Pie chart showing comorbidity percentages in the ReDLat cohort. (a). Receiver Operating Characteristic (ROC) curve area under the curve (AUC) for CN vs AD (b) and CN vs FTLD (c) for models trained with AT(N) biomarkers and comorbidities. Each data point represents a participant (CN: n = 202; AD: n = 223; FTLD: n = 134). ROC-AUC data are presented as mean values ± SEM. Permutation test values for features used to train the models, ranked based on their impact on model output for AD vs CN (b) and FTLD vs CN (c). Feature importance magnitudes represent the mean decrease in model performance (AUC-ROC) when a feature is permuted. The direction of the association indicates whether a feature is predictive of class 1 (AD or FTLD) or class 0 (CN), while features with a mean importance score ≤ .005 are considered non-predictive. Ridge regression analysis was performed for each cognitive domain using plasma levels of AT(N) biomarkers and comorbidities in CN and AD patients (d) and in CN and FTLD patients (e). The plots display R², f², and F values for each regression analysis. Saturated colors highlight significant associations (p < .05) for each predictor within the model. Top predictor for CN + AD (p-value): Executive function: p-tau217 = 4.5×10−8, NfL = 1.7×10−3; Memory: p-tau217 = 7.9×10−12; Global cognition: p-tau217 = 8.2×10−12, NfL = 1.5×10−4; Functionality: p-tau217 = 2.2×10−6, NfL = 1.5×10−2. Top predictor for CN + FTLD (p-values): Executive function: NfL = 5.9×10−9, p-tau217 = 1.7×10−6; Memory: p-tau217 = 8.6×10−5, NfL = 8.9×10−4; Global cognition: p-tau217 = 1.1×10−12, NfL = 2.2×10−10; Functionality: p-tau217 = 3.1×10−6, NfL = 5.4×10−6. Comorb: Comorbidities; CN: cognitively normal individuals; AD: Alzheimer’s disease patients; FTLD: frontotemporal lobar degeneration patients; SEM: Standard Error of the Mean.
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Caviedes, A., Cabral-Miranda, F., Orellana, P. et al. Blood-based AT(N) biomarkers for Alzheimer’s disease and frontotemporal lobar degeneration in Latin America. Nat Aging (2026). https://doi.org/10.1038/s43587-025-01061-3
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DOI: https://doi.org/10.1038/s43587-025-01061-3
