Impact of fasting status and storage time on plasma Amyloid-β 42 and 40, p-tau181, p-tau231, GFAP and NfL measurements

Impact of fasting status and storage time on plasma Amyloid-β 42 and 40, p-tau181, p-tau231, GFAP and NfL measurements | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of fasting status and storage time on plasma Amyloid-β 42 and 40, p-tau181, p-tau231, GFAP and NfL measurements Helena Blasco-Forniés, Javier Torres-Torronteras, Armand González-Escalante, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND : Pre-analytical factors may influence plasma biomarker levels reflecting Alzheimer’s disease (AD) pathology and neurodegeneration. We evaluated the impact of fasting and long-term storage at -80ºC on plasma biomarkers (Amyloid-β[Aβ]40, Aβ42, phospho-tau181, p-tau231, GFAP and NfL). METHODS : Biomarkers were measured in ALFA cohort using Simoa technology. Fasting effects were assessed using 16 paired samples. Long-term storage at -80ºC was evaluated in 623 samples stored up to 10 years. RESULTS : Fasting lowered Aβ peptides levels, but it was mitigated with the Aβ42/40 ratio. Long-term storage showed no effect on Aβ42/40 or NfL. However, Aβ peptides, p-tau181, p-tau231 and GFAP levels were higher in samples stored longer than 6 years. CONCLUSION : Fasting only influences Aβ peptides levels. The variability created by long-term storage in Aβs, p-tau181, p-tau231 and GFAP was below 6%. The results suggest that fasting does not influence biomarkers measurements and storage time might be considered, especially in longitudinal studies. Alzheimer Disease pre-analytical factors fasting storage time plasma Figures Figure 1 Figure 2 BACKGROUND AD is pathologically characterized by the accumulation of amyloid-β (Aβ) in extracellular plaques and hyperphosphorylated tau (p-tau) in intracellular neurofibrillary tangles. These pathological changes can be detected using reference standard methods, namely positron emission tomography (PET) imaging of amyloid and tau pathology or measurement of Aβ and tau proteins in cerebrospinal fluid (CSF) 1 . In recent years, blood-based biomarkers for amyloid accumulation (Aβ40 and Aβ42), tau pathology (different Tau phosphorylated forms, such as p-tau181, p-tau217 and p-tau231), neurodegeneration (Neurofilament Light Chain, NfL) and astroglial reactivity (glial fibrillary acidic protein, GFAP) have become a less invasive and cost-effective alternative to reference standard methods. Their levels vary along the Alzheimer’s continuum , making them valuable for AD early diagnosis 2 – 4 . These blood-based biomarkers are widely used in research settings and are being adopted in clinical practice as supportive tools for AD diagnosis 2 , 5 – 7 . Recent developments of new therapies have prompted their implementation in clinical trials to assess participant eligibility and to monitor treatment effectiveness. In preclinical AD, biomarker changes over time are subtle compared to symptomatic stages. As a result, preanalytical and other confounding factors can influence measured levels 8 , potentially masking true disease-related effects and hampering the findings reproducibility. This underscores the need to evaluate factors during sample collection, processing, and storage that may affect biomarker measurements. Empirical results obtained from different laboratories contribute to consensus guidelines and standard operating procedures for blood sample handling 7 , 9 . Still, data on the impact of some factors, such as fasting conditions before sample collection or long-term storage at -80ºC, are scarce and should be analysed from different perspectives due to their complexity. This study aims to assess the impact of fasting conditions and long-term storage at -80ºC on plasma biomarkers for AD pathology (Aβ40, Aβ42, Aβ42/Aβ40 ratio, p-tau181, p-tau231), glial reactivity (GFAP) and neurodegeneration (NfL) using samples from the ALFA cohort 10 , 11 . METHODS Sample collection and selection Plasma samples were obtained from cognitively unimpaired (CU) participants from the ALFA cohort at the Barcelonaβeta Brain Research Center (BBRC; Barcelona, Spain) 10 , 11 . Plasma from each participant was collected three times along 10 years, with about ~ 3 years distance within visits (2013-14, 2016-19, 2019-23). Aβ status was defined based on the CSF Aβ42/Aβ40 ratio, applying a cutoff of 0.071 to discriminate between Aβ-positive and Aβ-negative individuals 3 . Plasma was collected in Ethylenediaminetetraacetic acid (EDTA) tubes (Vacutainer K2EDTA; BD Diagnostics). Due to historical protocol changes, samples collected in the first visit were centrifuged at 1459g for 15 minutes at room temperature (RT) (Orto Arlesa, Unicen 21model), and 1.5mL were aliquoted in 2 mL polypropylene tubes (V9637, Merck) (75% of filling volume); whereas the other samples were centrifuged at 2000g and 4ºC for 10 minutes (Eppendorf, 5702R model), and 0.5 mL of plasma was aliquoted in 0.5 mL polypropylene tubes (72.730.711, Sarstedt) (100% of filling volume). After centrifugation, all samples were immediately frozen at -80ºC. All participants signed an informed consent. Biomarker analysis Aliquots were thawed at RT for 1 hour, vortexed and centrifuged for 10 minutes at 4000g at RT. To reduce experimental variability, all samples were randomized before the measurements and analysed at the same time. Biomarker concentrations were quantified using the SIMOA HD-X Analyzer at the Fluid Biomarker Facility of BBRC. Aβ40 and Aβ42, GFAP and NfL were analysed with the Neurology 4-PLEX E (N4PE) Advantage (103670, Quanterix) and p-tau181 with the p-tau-181 Advantage v2.1 (104112, Quanterix) commercial kits according to the manufacturer’s instructions. Plasma pTa231 was analysed using a validated homebrew assay througly described in previous studies 4 , 12 . Quality controls (QCs) were included in every analysis to ensure the reliability of the measurements. The coefficient of variation (CV) intraplate and interplate of the QCs analysed along the experiments are below 10% and 15%, respectively, which are within the accepted range. Sample selection To assess the impact of fasting on plasma biomarker levels, paired samples from 16 individuals were collected under two conditions: fasting, after a minimum fasting period of 8 hours, and non-fasting, when no medical indication for fasting was present (mean age 64 years old; 69% female, 50% amyloid positive; Table 1 ). Fasting was confirmed by serum glucose levels (< 115 mg/dl). The average time between paired samples collection was 54 days. Table 1 Study demographics of the fasting study. Fasting was confirmed when serum glucose levels fell within the reference range (< 115 mg/dl). Plasma biomarker concentrations are expressed as mean ± SD. Aβ, Amyloid Beta; GFAP, glial fibrillary acid protein; NfL, Neurofilament Light Chain. N.A., not applicable. FASTING NON-FASTING Participants' information Sample size 16 16 Age, y (range) 63.8 (55.8–70.8) 63.8 (55.8–70.8) Sex, F (%) 68.8 68.8 CSF Amyloid positive (%) 50 50 Sample information Time between fasting and non-fasting blood sampling, mean days (range) 54 (15–100) 54 (15–100) Serum glucose, mg/dl (range) 90.7 (83.0-107.0) N.A. Plasma Aβ40, pg/mL 97.35 ± 26.06 104.6 ± 25.5 Plasma Aβ42, pg/mL 5.21 ± 1.36 5.66 ± 1.54 Plasma Aβ42/Aβ40 0.05 ± 0.01 0.05 ± 0.01 Plasma pTau181, pg/mL 23.18 ± 10.25 24.02 ± 15.57 Plasma pTau231, pg/mL 2.55 ± 1.08 2.74 ± 2.03 Plasma GFAP, pg/mL 107.20 ± 46.72 124.7 ± 66.02 Plasma Nf-L, pg/mL 14.22 ± 5.28 14.81 ± 6.81 For the storage time analysis, samples from different individuals collected along the 10 years were chosen. The final number of samples selected was 623, from which 211 samples were stored for up to 10 years, and the rest for 2-6.5 years (Table 2 ). Samples at different time points were balanced in terms of age, sex and APOE-ε4 carriership using MatchIt package (4.7.2 version). All biomarkers were measured in samples subjected to less than 3 freeze-thaw cycles (FTC). For the storage time, 91% of the samples in p-tau181 and N4PE had 2 FTC and 9% had 3 FTC; for p-tau231, 99% had 2 and 1% had 3 FTC, respectively. Table 2 Demographic table of participants included in the storage time study with samples stored up to 10 years. Plasma biomarker concentrations are expressed as mean ± SD. Aβ, Amyloid Beta; GFAP, glial fibrillary acid protein; NfL, Neurofilament Light Chain. SAMPLES INFORMATION Sample size 623 Age, y (range) 63.1 (50.6–73.4) Sex, F (%) 65.3 APOE-ε4, carrier (%) 37.9 Plasma Aβ40, pg/mL 108.87 ± 21.47 Plasma Aβ42, pg/mL 6.44 ± 1.48 Plasma Aβ42/Aβ40 0.06 ± 0.01 Plasma pTau181, pg/mL 20.92 ± 7.09 Plasma pTau231, pg/mL 2.29 ± 0.88 Plasma GFAP, pg/mL 96.31 ± 47.44 Plasma Nf-L, pg/mL 15.55 ± 6.74 Storage time N4PE, mean years (range) 5.51 (0.69–10.35) Storage time pTau181, mean years (range) 5.17 (0.41–9.96) Storage time pTau231, mean years (range) 6.75 (1.96–11.50) Statistical analysis GraphPad Prism version 10.2.3 (403) was used to perform the statistical analysis for the fasting study. Plasma biomarker concentrations under both fasting conditions were compared using the parametric paired t-test for normally distributed data, and the non-parametric Wilcoxon signed-rank test when normality assumptions were not met. Differences between conditions were represented using Bland-Altman plots. The performance to detect Aβ-positivity under both conditions was evaluated using receiver operating characteristic (ROC) curves. De Long’s test (with IBM SPSS Statistics) was used to compare the area under the curve (AUC) between two ROC Curves. The p-value indicates whether there are significant differences in the AUC. RStudio software (2024.04.0 + 735) using R (4.3.1 (2023-06-16)) was used to analyse the impact of long-term storage time. Multiple regression modelling was used to evaluate the effect of storage time on biomarker concentrations. We checked whether the data for each biomarker fitted better a quadratic or linear model using the lmtest package (0.9.40 version). When the quadratic model was the best option (p-value < 0.05), the storage time variable was used in quadratic terms. In all the cases, to minimize the influence of other factors, the models were corrected by age, sex, and APOE-ε4 status. Model assumptions (presence of outliers, homoscedasticity, normality, multicollinearity and autocorrelation) were assessed using the performance package (0.15.2 version). When necessary, data was normalized using Box-Cox transformation ( car package version 3.1.3). Results were plotted with the geffects package (2.3.1 version). To determine the variance explained by the storage time, the partial.R2 function was applied ( asbio package, 1.11 version). RESULTS Effect of fasting status on plasma biomarkers concentration Plasma Aβ40 peptide levels were decreased under fasting conditions, although the effect size was modest (p 0.05, Fig. 1 A). Bland-Altman plots showed good concordance between measurements, with a bias 0.05, Suppl. Figure 1). Effect of storage time on plasma biomarkers concentration Storage time influenced plasma concentrations on Aβ peptides, with increased levels in samples stored for longer time (Aβ40: β = 0.0021, p-value = 0.0155; Aβ42: β = 0.1357, p-value = < 0.001). This effect was not observed with the ratio Aβ42/Aβ40 (β=-4.594-05, p-value = 0.7608, Fig. 2 and Suppl. Table 1). According to our regression model, the variability explained by the storage time (partial.R2) was 11.17% and 7.71% for Aβ40 and Aβ42, respectively, and only 0.02% for the ratio (Suppl. Table 1). Plasma p-tau181 and p-tau231 concentrations fluctuated over time, with significant lower levels in samples stored for 6 years (p-tau181: β = 0.0015, p-value = < 0.001; p-tau231: β = 0.0063, p-value = < 0.001). Plasma GFAP levels decreased in samples stored more than 6 years (β = 0.0088, p-value = < 0.001). However, the variability of the biomarker’s concentration explained by storage time was below 6% for all these biomarkers. We did not observe changes in NfL over time (β = 0.0010, p-value = 0.2199). Considering that some pre-analytical factors, such as the centrifugation settings and storage conditions differed in those samples stored for up to 10 years due to historical protocols, we performed a sensitivity analysis including only those following the same procedure (i.e., samples stored for up to 6.5 years; Suppl. Table 2 and Suppl. Figure 2). Among all the biomarkers, only p-tau231 (β=-0.0277, p-value = 0.0004; Supp. Table 3) and GFAP (β=-0.0499, p-value = < 0.001) showed decreased levels in samples stored for longer periods, but similarly, the variability on biomarker concentrations explained by the storage time was below 5%. DISCUSSION Understanding the impact of pre-analytical factors on fluid biomarker measurements is essential for their optimal implementation in clinical practice and trials 13 – 15 . Here, we evaluated the impact of fasting conditions and long-term storage at -80ºC on plasma concentration of Aβ40, Aβ42, Aβ42/Aβ40, p-tau181, p-tau231, GFAP and NfL. Given the variation in haematological analytes after a food intake 16 , 17 , it is crucial to evaluate the impact of fasting conditions on plasma biomarker concentration. To this end, we analysed paired plasma samples from participants that were asked or not to fast before venipuncture. Blood samples under both conditions were collected within a short period of time (mean: 54 days) and the duration of blood processing for plasma separation was consistent across samples (less than one hour). Samples were analysed at the same time, reducing experimental variability. Our findings showed that plasma Aβ peptide concentrations were lower under fasting conditions which, in line with other studies, was mitigated by using Aβ42/Aβ40 18 . The use of the ratio has been previously recommended 17 , 18 as it minimizes the surface tube adsorption effect on Aβ42 levels and the interindividual variability 17 , 18 . The other biomarkers were not by fasting status, neither at their absolute levels, nor on their performance to detect amyloid positivity, suggesting that fasting does not have a significant effect on these biomarker levels. Previous studies reported no changes on plasma Aβ40 and Aβ42 levels after and before food intake 19 , but a recent study demonstrated significant changes in all the biomarkers analysed (Aβ40, Aβ42, p-tau181, p-tau231, GFAP and NfL) in postprandial conditions 20 . Of note, this study was conducted in a cohort of obese individuals following a specific diet, and thus under specific pathophysiological conditions. Our study analysed the impact of non-fasting conditions mimicking the routine practice, where there is no control of the patient’s diet and last food intake. We compared paired samples from the same individuals under different fasting conditions. The inclusion of different participants in each group (fasting/non-fasting) in previous studies 18 , 21 may also explain the discrepancies observed. Thus, our results suggest that fasting status is not relevant for the biomarkers’ performance, although some comorbidities such as obesity might be considered for biomarkers’ interpretation. Another relevant factor scarcely investigated is the impact of long-term storage at -80ºC on plasma biomarker levels 22 , 23 . The analysis of biomarkers in samples stored for long periods is essential for increasing sample size and statistical power; and in longitudinal studies to identify prognostic and monitoring biomarkers 24 . Few studies have been performed assessing the effect of storage time on plasma biomarkers concentration. There are several approaches described in literature, some of which measure samples from the same individuals at different time points 22 – 24 . A study on CSF made predictions based on the Arrhenius Eq. 2 5 ; another one performed repeated measurements of the same sample for 26 months 26 . However, these approximations cannot differentiate the effect caused by the storage time from the age effect. We took another approach by analysing the distribution of samples from different individuals stored for up to 10 years within the same immunoassay, to discard any potential batch effect that may impact biomarker measurements 27 . Individuals were matched for factors known to influence biomarker levels such as sex, age and APOE-ε4 genotype 28 – 31 . We observed that samples stored for longer periods presented higher levels of Aβ40 and Aβ42 with a variability of 11.17 and 7.7% respectively, which was mitigated by using the amyloid ratio. There were also fluctuations over time on plasma p-tau181 and p-tau231 concentrations, with lower levels in samples stored for 6 years. However, such variability was below 6%, which is within the coefficient of variation of the immunoassay and may thus not have a major impact on biomarker interpretation. Similar effects were seen for plasma GFAP, with decreased levels in samples stored for 6 years or more. Plasma NfL was the only biomarker stable along time. Previous studies have shown long-term stability of plasma Aβ40, Aβ42, GFAP and NfL within periods ranging from 5 to 20 years 22 – 24 . However, in line with our findings, the stability of p-tau181 appeared to be more variable and sensitive to storage conditions in CU individuals 24 .To further investigate these discrepancies, we performed a sensitivity analysis using the samples stored for up to 6.5 years, as those had gone through the same collection procedures (centrifugation and storage). Plasma Aβ peptides showed stability for up to 6 years (Suppl. Figure 2); p-tau231 and GFAP still had lower levels in samples stored longer, but the variability observed was under 5%. The impact of such variability on biomarker interpretation depends on the analytical robustness of the assay, which is high for Simoa assays of plasma GFAP and p-tau181 (similar performance with simulated variations > 20%), but low for plasma Aβs 32 . Overall, our results suggest that whether significant changes on some plasma biomarkers over time were detected, these will likely have limited effect for cross-sectional studies using samples stored for long periods of time. For longitudinal studies, the variability detected should be considered in the interpretation of the results. For amyloid peptides, the use of the ratio rather than the single peptides would be recommended. Despite this study characterized two preanalytical factors relevant for the implementation of AD plasma biomarkers, there are several limitations to consider: i) participants were not asked to be in a non-fasting condition, and thus we cannot guarantee a postprandial state. Still, this simulates the clinical diagnosis scenario in which the non-fasting status is not specifically required for AD plasma biomarker analysis; ii) samples had either undergone two or three FTC; however, previous investigations demonstrated Aβ peptides to be stable up to 3 FTC 9 , 13 , and the other biomarkers up to 4 FTC 9 , 33 , 34 and, therefore, it is not expected to influence our results; and iii) protocols for plasma collection and storage, including specific material, have historically changed across centers. In our case, centrifugation settings and storage conditions of samples stored longer were different. Despite a prior study suggests that the centrifugation settings (speed, temperature) do not affect the stability of biomarkers 9 , smaller surface/volume ratios could lead to smaller concentrations of AD biomarkers 35 – 37 . However, differences on the surface/volume ratio have only been studied in CSF, with an effect on Aβ levels 35 , 36 , whereas p-tau181 remained stable 35 . In summary, our findings suggest that fasting status does not impact biomarker measurements, although comorbid conditions, including obesity, might be considered in the interpretation of plasma biomarker levels. Even storage at -80ºC for up to 10 years may influence the Aβ peptides, p-tau181, p-tau231 and GFAP levels, its effect was below 6%. Plasma amyloid peptides showed the strongest effect, but this was overcome using the amyloid ratio. Since some biomarkers levels changed across storage time, this factor should be considered in the analysis and interpretation of the results, especially in longitudinal studies. Abbreviations Aβ Amyloid-β AD Alzheimer’s disease AUC Area under the curve CSF Cerebrospinal fluid CU Cognitively unimpaired CV Coefficient of variation EDTA Ethylenediaminetetraacetic acid FTC Freeze thaw cycle GFAP Glial fibrillary acidic protein NfL Neurofilament Light Chain PET Positron emission tomography pTau Phospho-Tau QC Quality control ROC analysis Receiver operating characteristic analysis RT Room temperature Declarations FUNDING The ALFA + study receives funding from “la Caixa” Foundation, under agreement LCF/PR/SC22/68000001 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa17519007). HB receives funding from the European Union – NextGenerationEU – and Generalitat de Catalunya (2023 INV-2 00038). MdD receives funding from the European Union – NextGenerationEU – and Generalitat de Catalunya (2023 INV-2 00038). FA receives funding from the JDC2022-049347-I grant, funded by the MCIU/AEI/10.13039/501100011033and the European Union NextGenerationEU/PRTR and from the BrightFocus Foundation Alzheimer’s Disease Research Program (grant A2025004F). MdC receives funding from the MCIN/AEI/10.13039/501100011033through the grants RYC2023-043831-I (co-funded by the FSE+) and PID2023-153312OB-I00 (co-funded by the FEDER, EU). MS-C received funding from the ERC under the EU’s Horizon 2020 research and innovation program (grant no. 948677), ERA PerMed-ERA NET and the Generalitat de Catalunya (Departament de Salut) through project no. SLD077/21/000001, from the Instituto de Salud Carlos III (ISCIII) and co-funded by the EU (FEDER) through projects PI19/00155 and PI22/00456, and from ‘la Caixa’ Foundation fellowship (ID 100010434) and the EU’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant no. 847648 (LCF/BQ/PR21/11840004)). Author Contribution HB, JT and MdC wrote the main manuscript. AG and FA contributed to the statistical analysis.EJ, PO and MdD contributed to the sample analysis.MS and CM substantively revised the manuscript.All authors reviewed the manuscript. ACKNOWLEDGMENTS This publication is part of the ALFA study (ALzheimers and FAmilies). The authors would like to express their most sincere gratitude to the ALFA project participants and relatives without whom this research would not have been possible. We thank the collaborators of the ALFA Study: Clara Abadías, Müge Akinci, Andrea Ambite, Ricardo Aquite, Sara Aragó, Eider Arenaza Urquijo, Kahina Baouche, Ricardo Berbería, Annabella Beteta, Marco Bianchi, Anna Brugulat-Serrat, Raffaele Cacciaglia, Jordi Camí, Fernanda Campos Strazzi, Lidia Canals Gispert, Alba Cañas, Diego Cascales, José Contador, Marta Crous-Bou, Irene Cumplido, Rafael Dal-Ré, Neus de la Cruz-Sanchez, Carme Deulofeu, Ruth Dominguez, Maria Emilio, Isabel Estragués, Tavia Evans, Carles Falcón, Karine Fauria, Marta Félez, Aida Fernandez, Alba Fernández Bonet, Ana Fernández-Arcos, Elisabeth Ferrer i Mairal, Jordi Freixa, Sherezade Fuentes, Clara Gallay, Marina García, Manuel Garfia, Fernando Gaston Rossi, Patricia Genius, Juan Domingo Gispert, José María González de Echávarri, Xavier Gotsens, Nina Gramunt Fombuena, Oriol Grau Rivera, Laura Gusó, Ana Harris, Laura Hernandez, Felipe Hernández-Villamizar, Gema Huesa, Jordi Huguet, Laura Iglesias, Michalis Kassinopoulos, Iva Knezevic, Maria León, Aldana Lizarraga, David López-Martos, Ferran Lugo, Paula Marne, Carlota Medina, Francisco Javier Meléndez, Tania Menchón, Marta Milà Alomà, José Luis Molinuevo, Cristina Mustata, Irene Navalpotro, Grégory Operto, Paula Ortiz, Eva Palacios, Eleni Palpatzis, Wiesje Pelkmans, Jordi Peña-Casanova, Isabel Perez, Aitana Plaza, Albina Polo, Clara Porta-Mas, Sandra Pradas, Aleix Puig, Andreea Rădoi, Jaume Roca Alcaraz, Albert Rodrigo-Pares, Noelia Rodríguez de Guzmán Gallego, Blanca Rodríguez-Fernández, Maria Roman, Sarata Sall Sall, Gemma Salvadó, Mireia Sánchez, Pau Sánchez, Gonzalo Sánchez-Benavides, Sabrina Segundo, Mahnaz Shekari, Lluis Solsona, Anna Soteras, Laura Stankeviciute, Pilar Tartière-González, Laia Tenas, Núria Tort-Colet, Elisabet Zhan Travesset Muntada, David Vállez, Montserrat Vilà, Marc Vilanova, Natalia Vilor-Tejedor. CONFLICTS OF INTEREST MS-C has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Biogen, Beckman Coulter, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli 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, Eli Lilly, Fujirebio, Janssen Research & Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization. MC has been an invited speaker at Eisai and Novonordisk. She is an associate editor at Alzheimer´s Research & Therapy and has been an invited writer for Springer Healthcare. She is part of the DATA-PD advisory team of the Michael J Fox Foundation. The other authors have nothing to disclose. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Simrén J, Leuzy A, Karikari TK, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimer’s Dement. 2021;17(7):1145–56. 10.1002/alz.12283 . Teunissen CE, Verberk IMW, Thijssen EH, et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol Elsevier Ltd. 2022;21(1):66–77. 10.1016/S1474-4422(21)00361-6 . Milà-Alomà M, Salvadó G, Gispert JD, et al. Amyloid beta, tau, synaptic, neurodegeneration, and glial biomarkers in the preclinical stage of the Alzheimer’s continuum. Alzheimer’s Dement. 2020;16(10):1358–71. 10.1002/alz.12131 . Milà-Alomà M, Ashton NJ, Shekari M, et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer’s disease. Nat Med. 2022;28(9):1797–801. 10.1038/s41591-022-01925-w . Palmqvist S, Whitson HE, Allen LA, et al. Alzheimer’s Association Clinical Practice Guideline on the use of blood-based biomarkers in the diagnostic workup of suspected Alzheimer’s disease within specialized care settings. Alzheimer’s Dement. 2025;21(7). 10.1002/alz.70535 . Palmqvist S, Warmenhoven N, Anastasi F, et al. Plasma phospho-tau217 for Alzheimer’s disease diagnosis in primary and secondary care using a fully automated platform. Nat Med. 2025;31(6):2036–43. 10.1038/s41591-025-03622-w . Verberk IMW, Gouda M, Antwi-Berko D, et al. Evidence‐based standardized sample handling protocol for accurate blood‐based Alzheimer’s disease biomarker measurement: Results and consensus of the Global Biomarker Standardization Consortium. Alzheimer’s Dement. 2025;21(10). 10.1002/alz.70752 . Torres-Torronteras J, Gouda M, Teunissen C, Verberk I, del Campo M. Impact of Pre-analytical Factors on Fluid Biomarker Measurements in Alzheimer Disease. In: Tarawneh R, ed. Biomarkers of Neurodegenerative Disorders: Current Progress and Future Directions . 2025, p 475–496. Verberk IMW, Misdorp EO, Koelewijn J, et al. Characterization of pre-analytical sample handling effects on a panel of Alzheimer’s disease–related blood-based biomarkers: Results from the Standardization of Alzheimer’s Blood Biomarkers (SABB) working group. Alzheimer’s Dement. 2022;18(8):1484–97. 10.1002/alz.12510 . Molinuevo JL, Gramunt N, Gispert JD, et al. The ALFA project: A research platform to identify early pathophysiological features of Alzheimer’s disease. Alzheimer’s Dementia: Translational Res Clin Interventions. 2016;2(2):82–92. 10.1016/j.trci.2016.02.003 . Vilor-Tejedor N, Genius P, Rodríguez-Fernández B, et al. Genetic characterization of the ALFA study: Uncovering genetic profiles in the Alzheimer’s continuum. Alzheimer’s Dement. 2024;20(3):1703–15. 10.1002/alz.13537 . Ashton NJ, Pascoal TA, Karikari TK, et al. Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol. 2021;141(5):709–24. 10.1007/s00401-021-02275-6 . Rózga M, Bittner T, Batrla R, Karl J. Preanalytical sample handling recommendations for Alzheimer’s disease plasma biomarkers. Alzheimer’s Dementia: Diagnosis Assess Disease Monit. 2019;11:291–300. 10.1016/j.dadm.2019.02.002 . Hansson O, Mikulskis A, Fagan AM, et al. The impact of preanalytical variables on measuring cerebrospinal fluid biomarkers for Alzheimer’s disease diagnosis: A review. Alzheimer’s Dementia Elsevier Inc. 2018;14(10):1313–33. 10.1016/j.jalz.2018.05.008 . Fourier A, Portelius E, Zetterberg H, Blennow K, Quadrio I, Perret-Liaudet A. Pre-analytical and analytical factors influencing Alzheimer’s disease cerebrospinal fluid biomarker variability. Clinica Chim Acta Elsevier. 2015;449:9–15. 10.1016/j.cca.2015.05.024 . Lippi G, Lima-Oliveira G, Salvagno GL, et al. Influence of a light meal on routine haematological tests. Blood Transfus. 2010;8(2):94–9. 10.2450/2009.0142-09 . Lima-Oliveira G, Salvagno GL, Lippi G, et al. Infuence of a regular, standardized meal on clinical chemistry analytes. Ann Lab Med. 2012;32(4):250–6. 10.3343/alm.2012.32.4.250 . Wu Z, Mielke MM, Murray AM, et al. The impact of pre-analytical factors on plasma biomarkers for Alzheimer’s disease: The ASPREE Healthy Ageing Biobank. J Prev Alzheimers Dis. 2025;12(4):100058. 10.1016/j.tjpad.2025.100058 . Hansson O, Zetterberg H, Vanmechelen E, et al. Evaluation of plasma Aβ40 and Aβ42 as predictors of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neurobiol Aging. 2010;31(3):357–67. 10.1016/j.neurobiolaging.2008.03.027 . Huber H, Ashton NJ, Schieren A, et al. Levels of Alzheimer’s disease blood biomarkers are altered after food intake—A pilot intervention study in healthy adults. Alzheimer’s Dement. 2023;19(12):5531–40. 10.1002/alz.13163 . Bouteloup V, Pellegrin I, Dubois B, Chene G, Planche V, Dufouil C. Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia. Neurology. 2024;102(8):e209219. 10.1212/WNL.0000000000209219 . Chiu MJ, Lue LF, Sabbagh MN, Chen TF, Chen HH, Yang SY. Long-Term Storage Effects on Stability of Aβ1–40, Aβ1–42, and Total Tau Proteins in Human Plasma Samples Measured with Immunomagnetic Reduction Assays. Dement Geriatr Cogn Dis Extra. 2019;9(1):77–86. 10.1159/000496099 . Schubert CR, Paulsen AJ, Pinto AA, Merten N, Cruickshanks KJ. Effect of Long-Term Storage on the Reliability of Blood Biomarkers for Alzheimer’s Disease and Neurodegeneration. J Alzheimer’s Disease. 2022;85(3):1021–9. 10.3233/JAD-215096 . Zhao L, Zhang M, Li Q, et al. Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases. Neural Regen Res. 2025;20(8):2373–81. 10.4103/NRR.NRR-D-23-01983 . Schoonenboom NSM, Mulder C, Vanderstichele H, et al. Effects of processing and storage conditions on amyloid β (1–42) and tau concentrations in cerebrospinal fluid: Implications for use in clinical practice. Clin Chem. 2005;51(1):189–95. 10.1373/clinchem.2004.039735 . Bjerke M, Portelius E, Minthon L, et al. Confounding factors influencing amyloid beta concentration in cerebrospinal fluid. Int J Alzheimers Dis Published online. 2010. 10.4061/2010/986310 . Zeng X, Chen Y, Sehrawat A, et al. Alzheimer blood biomarkers: practical guidelines for study design, sample collection, processing, biobanking, measurement and result reporting. Mol Neurodegener. 2024;19(1). 10.1186/s13024-024-00711-1 . Zhu D, Montagne A, Zhao Z. Alzheimer’s pathogenic mechanisms and underlying sex difference. Cellular and Molecular Life Sciences . Springer Science and Business Media Deutschland GmbH . 2021;78(11):4907–4920. 10.1007/s00018-021-03830-w Hara Y, McKeehan N, Fillit HM. Translating the biology of aging into novel therapeutics for Alzheimer disease. Neurology Lippincott Williams Wilkins. 2019;92(2):84–93. 10.1212/WNL.0000000000006745 . Liu Y, Tan Y, Zhang Z, Yi M, Zhu L, Peng W. The interaction between ageing and Alzheimer’s disease: insights from the hallmarks of ageing. Transl Neurodegener BioMed Cent Ltd. 2024;13(1). 10.1186/s40035-024-00397-x . Troutwine BR, Hamid L, Lysaker CR, Strope TA, Wilkins HM. Apolipoprotein E and Alzheimer’s disease. Acta Pharm Sin B Chinese Acad Med Sciences. 2022;12(2):496–510. 10.1016/j.apsb.2021.10.002 . Benedet AL, Brum WS, Hansson O, et al. The accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimers Res Ther. 2022;14(1). 10.1186/s13195-021-00942-0 . Kurz C, Stöckl L, Schrurs I, et al. Impact of pre-analytical sample handling factors on plasma biomarkers of Alzheimer’s disease. J Neurochem. 2023;165(1):95–105. 10.1111/jnc.15757 . Van Lierop ZYGJ, Verberk IMW, Van Uffelen KWJ, et al. Pre-analytical stability of serum biomarkers for neurological disease: Neurofilament-light, glial fibrillary acidic protein and contactin-1. Clin Chem Lab Med. 2022;60(6):842–50. 10.1515/cclm-2022-0007 . Delaby C, Muñoz L, Torres S, et al. Impact of CSF storage volume on the analysis of Alzheimer’s disease biomarkers on an automated platform. Clin Chim Acta. 2019;490:98–101. 10.1016/j.cca.2018.12.021 . Toombs J, Foiani MS, Wellington H, et al. Amyloid β peptides are differentially vulnerable to preanalytical surface exposure, an effect incompletely mitigated by the use of ratios. Alzheimer’s Dementia: Diagnosis Assess Disease Monit. 2018;10:311–21. 10.1016/j.dadm.2018.02.005 . Toombs J, Paterson RW, Lunn MP, et al. Identification of an important potential confound in CSF AD studies: Aliquot volume. Clin Chem Lab Med. 2013;51(12):2311–7. 10.1515/cclm-2013-0293 . Additional Declarations No competing interests reported. Supplementary Files ADDITIONALFILE1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8107685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549946608,"identity":"942c033c-bfea-4c27-899a-e0a8fff97ba4","order_by":0,"name":"Helena Blasco-Forniés","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Helena","middleName":"","lastName":"Blasco-Forniés","suffix":""},{"id":549946609,"identity":"21b6077b-3038-4e70-ba26-f09ebee8b561","order_by":1,"name":"Javier Torres-Torronteras","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Torres-Torronteras","suffix":""},{"id":549946610,"identity":"c547b844-d048-4c4b-ab6c-53a91cdda58a","order_by":2,"name":"Armand González-Escalante","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Armand","middleName":"","lastName":"González-Escalante","suffix":""},{"id":549946611,"identity":"ad023bd0-cb8b-460d-b425-4963cad6ce07","order_by":3,"name":"Federica Anastasi","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Anastasi","suffix":""},{"id":549946612,"identity":"3b91d0de-1a16-4474-a632-637502f4f71e","order_by":4,"name":"Esther Jiménez-Moyano","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Jiménez-Moyano","suffix":""},{"id":549946613,"identity":"28764c54-3c4f-436e-8e2b-23cde815f808","order_by":5,"name":"Paula Ortiz-Romero","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Ortiz-Romero","suffix":""},{"id":549946614,"identity":"9f87ecd8-f5ad-4e0a-97f8-b7a0135da047","order_by":6,"name":"Marina de Diego-Osaba","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"de Diego-Osaba","suffix":""},{"id":549946615,"identity":"da5baa30-4849-4684-a7a6-f1d66910be4b","order_by":7,"name":"Carolina Minguillón","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Minguillón","suffix":""},{"id":549946616,"identity":"5d022000-1415-493f-afff-a56269b74df1","order_by":8,"name":"Marc Suárez-Calvet","email":"","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Suárez-Calvet","suffix":""},{"id":549946617,"identity":"9d5a9e8d-8a3e-4ded-9113-2c713b040c13","order_by":9,"name":"Marta del Campo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYPACmwQw9QCIDYjUkgbRkgDWwkyUlsMkaNFtP/x0c0HF+Tz+2YcfPkiouCNvzt5/8ANDxT27BhxazM6kmd2eceZ2scS5NGODhDPPDHf2HGaWYDhTnIxTyw0Gs9u8bbcTG87wsEkkth1m3HAjmUGCsS0hGZfDzG6wfwNqOZc4/wwP+w+gFvsN9x8z/8CvhQdky4HEDUBbGIBaEjfcYGYD2WKHU8uZnDKgX5KLDc+wGUsknDmcvLMn2cwi4UxCAk4tx49vu11QYZcnd4b54YcPFYdtt7MffHzjQ0WCPS4tIIAlHoBWJDaQpgUI8NoyCkbBKBgFIwoAAEZgYLZD1VQPAAAAAElFTkSuQmCC","orcid":"","institution":"Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"del","lastName":"Campo","suffix":""}],"badges":[],"createdAt":"2025-11-13 16:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8107685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8107685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97023989,"identity":"7137bcd3-1e67-45b2-ab42-368567ff7997","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4839732,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPT.docx","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/99737c8105aca50b0ef4bed6.docx"},{"id":97023983,"identity":"21805a53-3fe0-4572-99db-78bed2e2e8eb","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9909,"visible":true,"origin":"","legend":"","description":"","filename":"ec82a60d05da416f9cafb2ce2b359146.json","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/bd303e99ea8f06a48becf33c.json"},{"id":97139753,"identity":"d3eb5cc0-e1b8-4586-91c4-d979de3cd356","added_by":"auto","created_at":"2025-12-01 10:02:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":643200,"visible":true,"origin":"","legend":"","description":"","filename":"ADDITIONALFILE1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/c24fb21ac2f067fbe85c2ca6.docx"},{"id":97023987,"identity":"c39d1de8-b8b0-47d9-aec0-90895c5cc05f","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110583,"visible":true,"origin":"","legend":"","description":"","filename":"ec82a60d05da416f9cafb2ce2b3591461enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/2c59f301e3366ffb63456e85.xml"},{"id":97139760,"identity":"357f492a-890c-434b-8be0-813f5f2a2ffa","added_by":"auto","created_at":"2025-12-01 10:02:21","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":282824,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/22646994175f68a8e06727ce.jpeg"},{"id":97139374,"identity":"22a04416-0407-4562-98a5-d60fea90e4c2","added_by":"auto","created_at":"2025-12-01 10:00:10","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":378290,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/be208d299a1f8c9bcb38a15f.jpeg"},{"id":97023993,"identity":"38c99ea9-faf5-4e32-aa41-4c4b0cae4e3b","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110477,"visible":true,"origin":"","legend":"","description":"","filename":"ec82a60d05da416f9cafb2ce2b3591461structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/0cb387062ac311444f4b2657.xml"},{"id":97023991,"identity":"bb484fa9-0f5e-466d-afb8-8bf6a0cd7bfe","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121614,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/9e605b82424693e57d24ba91.html"},{"id":97023985,"identity":"b36dbcc0-eef6-4a07-b1d0-0b3d4f3a99c3","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46846,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Paired t-test of fasting and non-fasting conditions. For the Aβs, GFAP and NfL a parametric paired t-test was performed, and for p-tau181 and p-tau231 a Wilcoxon test; (B) Bland- Altman plots for assessing the concordance between the measurements under fasting (F) and non-fasting (NF) conditions. The patient’s mean concentrations are plotted in the x-axis and the difference between measurements are in the y-axis. The blue line indicates the mean difference (bias) between measurements, and the dashed lines correspond to the limits of agreement.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/36cc13026e139fcdf2bc13ef.png"},{"id":97023984,"identity":"390b6a45-f723-486b-9e24-cb15be215540","added_by":"auto","created_at":"2025-11-28 20:19:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53013,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of the relationship between the storage time and the biomarkers concentration. Depicted curves are fitted with a Loess. Regression models were performed and corrected by age, sex and \u003cem\u003eAPOE-ε4\u003c/em\u003e carriership. The βs and p-values correspond to the model corrected. *p-value below significance threshold of 0.05. Aβ, Amyloid Beta; GFAP, glial fibrillary acid protein; NfL, Neurofilament Light Chain.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/3493236d31b11c101c0dc64c.png"},{"id":99307075,"identity":"5e36d805-a3e0-4a9d-8f43-f1e6f4a3d562","added_by":"auto","created_at":"2025-12-31 16:05:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":802320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/5bb6ac9f-8d82-4c07-944d-27a6743eeb4b.pdf"},{"id":97139923,"identity":"cc4413ef-c6b1-4029-a1f3-ae3ff47cabf0","added_by":"auto","created_at":"2025-12-01 10:03:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":643200,"visible":true,"origin":"","legend":"","description":"","filename":"ADDITIONALFILE1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8107685/v1/5f39d42d1721ecdd46b2ca52.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of fasting status and storage time on plasma Amyloid-β 42 and 40, p-tau181, p-tau231, GFAP and NfL measurements","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAD is pathologically characterized by the accumulation of amyloid-β (Aβ) in extracellular plaques and hyperphosphorylated tau (p-tau) in intracellular neurofibrillary tangles. These pathological changes can be detected using reference standard methods, namely positron emission tomography (PET) imaging of amyloid and tau pathology or measurement of Aβ and tau proteins in cerebrospinal fluid (CSF)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent years, blood-based biomarkers for amyloid accumulation (Aβ40 and Aβ42), tau pathology (different Tau phosphorylated forms, such as p-tau181, p-tau217 and p-tau231), neurodegeneration (Neurofilament Light Chain, NfL) and astroglial reactivity (glial fibrillary acidic protein, GFAP) have become a less invasive and cost-effective alternative to reference standard methods. Their levels vary along the Alzheimer\u0026rsquo;s \u003cem\u003econtinuum\u003c/em\u003e, making them valuable for AD early diagnosis\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These blood-based biomarkers are widely used in research settings and are being adopted in clinical practice as supportive tools for AD diagnosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Recent developments of new therapies have prompted their implementation in clinical trials to assess participant eligibility and to monitor treatment effectiveness.\u003c/p\u003e\u003cp\u003eIn preclinical AD, biomarker changes over time are subtle compared to symptomatic stages. As a result, preanalytical and other confounding factors can influence measured levels\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, potentially masking true disease-related effects and hampering the findings reproducibility. This underscores the need to evaluate factors during sample collection, processing, and storage that may affect biomarker measurements. Empirical results obtained from different laboratories contribute to consensus guidelines and standard operating procedures for blood sample handling\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Still, data on the impact of some factors, such as fasting conditions before sample collection or long-term storage at -80\u0026ordm;C, are scarce and should be analysed from different perspectives due to their complexity. This study aims to assess the impact of fasting conditions and long-term storage at -80\u0026ordm;C on plasma biomarkers for AD pathology (Aβ40, Aβ42, Aβ42/Aβ40 ratio, p-tau181, p-tau231), glial reactivity (GFAP) and neurodegeneration (NfL) using samples from the ALFA cohort\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample collection and selection\u003c/h2\u003e\u003cp\u003ePlasma samples were obtained from cognitively unimpaired (CU) participants from the ALFA cohort at the Barcelonaβeta Brain Research Center (BBRC; Barcelona, Spain)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Plasma from each participant was collected three times along 10 years, with about\u0026thinsp;~\u0026thinsp;3 years distance within visits (2013-14, 2016-19, 2019-23). Aβ status was defined based on the CSF Aβ42/Aβ40 ratio, applying a cutoff of 0.071 to discriminate between Aβ-positive and Aβ-negative individuals\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePlasma was collected in Ethylenediaminetetraacetic acid (EDTA) tubes (Vacutainer K2EDTA; BD Diagnostics). Due to historical protocol changes, samples collected in the first visit were centrifuged at 1459g for 15 minutes at room temperature (RT) (Orto Arlesa, Unicen 21model), and 1.5mL were aliquoted in 2 mL polypropylene tubes (V9637, Merck) (75% of filling volume); whereas the other samples were centrifuged at 2000g and 4\u0026ordm;C for 10 minutes (Eppendorf, 5702R model), and 0.5 mL of plasma was aliquoted in 0.5 mL polypropylene tubes (72.730.711, Sarstedt) (100% of filling volume). After centrifugation, all samples were immediately frozen at -80\u0026ordm;C. All participants signed an informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBiomarker analysis\u003c/h3\u003e\n\u003cp\u003eAliquots were thawed at RT for 1 hour, vortexed and centrifuged for 10 minutes at 4000g at RT. To reduce experimental variability, all samples were randomized before the measurements and analysed at the same time.\u003c/p\u003e\u003cp\u003eBiomarker concentrations were quantified using the SIMOA HD-X Analyzer at the Fluid Biomarker Facility of BBRC. Aβ40 and Aβ42, GFAP and NfL were analysed with the Neurology 4-PLEX E (N4PE) \u003cb\u003eAdvantage\u003c/b\u003e (103670, Quanterix) and p-tau181 with the p-tau-181 Advantage v2.1 (104112, Quanterix) commercial kits according to the manufacturer\u0026rsquo;s instructions. Plasma pTa231 was analysed using a validated homebrew assay througly described in previous studies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Quality controls (QCs) were included in every analysis to ensure the reliability of the measurements. The coefficient of variation (CV) intraplate and interplate of the QCs analysed along the experiments are below 10% and 15%, respectively, which are within the accepted range.\u003c/p\u003e\n\u003ch3\u003eSample selection\u003c/h3\u003e\n\u003cp\u003eTo assess the impact of fasting on plasma biomarker levels, paired samples from 16 individuals were collected under two conditions: fasting, after a minimum fasting period of 8 hours, and non-fasting, when no medical indication for fasting was present (mean age 64 years old; 69% female, 50% amyloid positive; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Fasting was confirmed by serum glucose levels (\u0026lt;\u0026thinsp;115 mg/dl). The average time between paired samples collection was 54 days.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy demographics of the fasting study. Fasting was confirmed when serum glucose levels fell within the reference range (\u0026lt;\u0026thinsp;115 mg/dl). Plasma biomarker concentrations are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Aβ, Amyloid Beta; GFAP, glial fibrillary acid protein; NfL, Neurofilament Light Chain. N.A., not applicable.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFASTING\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNON-FASTING\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eParticipants' information\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.8 (55.8\u0026ndash;70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.8 (55.8\u0026ndash;70.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, F (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSF Amyloid positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eSample information\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime between fasting and non-fasting blood sampling, mean days (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (15\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (15\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum glucose, mg/dl (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.7 (83.0-107.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN.A.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ40, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.35\u0026thinsp;\u0026plusmn;\u0026thinsp;26.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ42, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ42/Aβ40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma pTau181, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.02\u0026thinsp;\u0026plusmn;\u0026thinsp;15.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma pTau231, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma GFAP, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.20\u0026thinsp;\u0026plusmn;\u0026thinsp;46.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;66.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Nf-L, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the storage time analysis, samples from different individuals collected along the 10 years were chosen. The final number of samples selected was 623, from which 211 samples were stored for up to 10 years, and the rest for 2-6.5 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Samples at different time points were balanced in terms of age, sex and \u003cem\u003eAPOE-ε4\u003c/em\u003e carriership using \u003cem\u003eMatchIt\u003c/em\u003e package (4.7.2 version). All biomarkers were measured in samples subjected to less than 3 freeze-thaw cycles (FTC). For the storage time, 91% of the samples in p-tau181 and N4PE had 2 FTC and 9% had 3 FTC; for p-tau231, 99% had 2 and 1% had 3 FTC, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic table of participants included in the storage time study with samples stored up to 10 years. Plasma biomarker concentrations are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Aβ, Amyloid Beta; GFAP, glial fibrillary acid protein; NfL, Neurofilament Light Chain.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAMPLES INFORMATION\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.1 (50.6\u0026ndash;73.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, F (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPOE-ε4, carrier (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ40, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108.87\u0026thinsp;\u0026plusmn;\u0026thinsp;21.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ42, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Aβ42/Aβ40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma pTau181, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma pTau231, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma GFAP, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.31\u0026thinsp;\u0026plusmn;\u0026thinsp;47.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma Nf-L, pg/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStorage time N4PE, mean years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.51 (0.69\u0026ndash;10.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStorage time pTau181, mean years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.17 (0.41\u0026ndash;9.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStorage time pTau231, mean years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.75 (1.96\u0026ndash;11.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eGraphPad Prism version 10.2.3 (403) was used to perform the statistical analysis for the fasting study. Plasma biomarker concentrations under both fasting conditions were compared using the parametric paired t-test for normally distributed data, and the non-parametric Wilcoxon signed-rank test when normality assumptions were not met. Differences between conditions were represented using Bland-Altman plots. The performance to detect Aβ-positivity under both conditions was evaluated using receiver operating characteristic (ROC) curves. De Long\u0026rsquo;s test (with IBM SPSS Statistics) was used to compare the area under the curve (AUC) between two ROC Curves. The p-value indicates whether there are significant differences in the AUC.\u003c/p\u003e\u003cp\u003eRStudio software (2024.04.0\u0026thinsp;+\u0026thinsp;735) using R (4.3.1 (2023-06-16)) was used to analyse the impact of long-term storage time. Multiple regression modelling was used to evaluate the effect of storage time on biomarker concentrations. We checked whether the data for each biomarker fitted better a quadratic or linear model using the \u003cem\u003elmtest\u003c/em\u003e package (0.9.40 version). When the quadratic model was the best option (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the storage time variable was used in quadratic terms. In all the cases, to minimize the influence of other factors, the models were corrected by age, sex, and \u003cem\u003eAPOE-ε4\u003c/em\u003e status. Model assumptions (presence of outliers, homoscedasticity, normality, multicollinearity and autocorrelation) were assessed using the \u003cem\u003eperformance\u003c/em\u003e package (0.15.2 version). When necessary, data was normalized using Box-Cox transformation (\u003cem\u003ecar\u003c/em\u003e package version 3.1.3). Results were plotted with the \u003cem\u003egeffects\u003c/em\u003e package (2.3.1 version). To determine the variance explained by the storage time, the partial.R2 function was applied (\u003cem\u003easbio\u003c/em\u003e package, 1.11 version).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEffect of fasting status on plasma biomarkers concentration\u003c/h2\u003e\u003cp\u003ePlasma Aβ40 peptide levels were decreased under fasting conditions, although the effect size was modest (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Such differences were not observed with the Aβ42/Aβ40 ratio. Plasma p-tau181, p-tau231, GFAP and NfL did not differ between conditions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Bland-Altman plots showed good concordance between measurements, with a bias\u0026thinsp;\u0026lt;\u0026thinsp;15% for all the biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The performance of each plasma biomarker to detect amyloid positivity did not differ between fasting and non-fasting conditions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Suppl. Figure\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEffect of storage time on plasma biomarkers concentration\u003c/h3\u003e\n\u003cp\u003eStorage time influenced plasma concentrations on Aβ peptides, with increased levels in samples stored for longer time (Aβ40: β\u0026thinsp;=\u0026thinsp;0.0021, p-value\u0026thinsp;=\u0026thinsp;0.0155; Aβ42: β\u0026thinsp;=\u0026thinsp;0.1357, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This effect was not observed with the ratio Aβ42/Aβ40 (β=-4.594-05, p-value\u0026thinsp;=\u0026thinsp;0.7608, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Suppl. Table\u0026nbsp;1). According to our regression model, the variability explained by the storage time (partial.R2) was 11.17% and 7.71% for Aβ40 and Aβ42, respectively, and only 0.02% for the ratio (Suppl. Table\u0026nbsp;1). Plasma p-tau181 and p-tau231 concentrations fluctuated over time, with significant lower levels in samples stored for 6 years (p-tau181: β\u0026thinsp;=\u0026thinsp;0.0015, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau231: β\u0026thinsp;=\u0026thinsp;0.0063, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Plasma GFAP levels decreased in samples stored more than 6 years (β\u0026thinsp;=\u0026thinsp;0.0088, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the variability of the biomarker\u0026rsquo;s concentration explained by storage time was below 6% for all these biomarkers. We did not observe changes in NfL over time (β\u0026thinsp;=\u0026thinsp;0.0010, p-value\u0026thinsp;=\u0026thinsp;0.2199). Considering that some pre-analytical factors, such as the centrifugation settings and storage conditions differed in those samples stored for up to 10 years due to historical protocols, we performed a sensitivity analysis including only those following the same procedure (i.e., samples stored for up to 6.5 years; Suppl. Table\u0026nbsp;2 and Suppl. Figure\u0026nbsp;2). Among all the biomarkers, only p-tau231 (β=-0.0277, p-value\u0026thinsp;=\u0026thinsp;0.0004; Supp. Table\u0026nbsp;3) and GFAP (β=-0.0499, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed decreased levels in samples stored for longer periods, but similarly, the variability on biomarker concentrations explained by the storage time was below 5%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUnderstanding the impact of pre-analytical factors on fluid biomarker measurements is essential for their optimal implementation in clinical practice and trials\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Here, we evaluated the impact of fasting conditions and long-term storage at -80\u0026ordm;C on plasma concentration of Aβ40, Aβ42, Aβ42/Aβ40, p-tau181, p-tau231, GFAP and NfL.\u003c/p\u003e\u003cp\u003eGiven the variation in haematological analytes after a food intake\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, it is crucial to evaluate the impact of fasting conditions on plasma biomarker concentration. To this end, we analysed paired plasma samples from participants that were asked or not to fast before venipuncture. Blood samples under both conditions were collected within a short period of time (mean: 54 days) and the duration of blood processing for plasma separation was consistent across samples (less than one hour). Samples were analysed at the same time, reducing experimental variability. Our findings showed that plasma Aβ peptide concentrations were lower under fasting conditions which, in line with other studies, was mitigated by using Aβ42/Aβ40\u003csup\u003e18\u003c/sup\u003e. The use of the ratio has been previously recommended\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e as it minimizes the surface tube adsorption effect on Aβ42 levels and the interindividual variability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The other biomarkers were not by fasting status, neither at their absolute levels, nor on their performance to detect amyloid positivity, suggesting that fasting does not have a significant effect on these biomarker levels. Previous studies reported no changes on plasma Aβ40 and Aβ42 levels after and before food intake\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, but a recent study demonstrated significant changes in all the biomarkers analysed (Aβ40, Aβ42, p-tau181, p-tau231, GFAP and NfL) in postprandial conditions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Of note, this study was conducted in a cohort of obese individuals following a specific diet, and thus under specific pathophysiological conditions. Our study analysed the impact of non-fasting conditions mimicking the routine practice, where there is no control of the patient\u0026rsquo;s diet and last food intake. We compared paired samples from the same individuals under different fasting conditions. The inclusion of different participants in each group (fasting/non-fasting) in previous studies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e may also explain the discrepancies observed. Thus, our results suggest that fasting status is not relevant for the biomarkers\u0026rsquo; performance, although some comorbidities such as obesity might be considered for biomarkers\u0026rsquo; interpretation.\u003c/p\u003e\u003cp\u003eAnother relevant factor scarcely investigated is the impact of long-term storage at -80\u0026ordm;C on plasma biomarker levels\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The analysis of biomarkers in samples stored for long periods is essential for increasing sample size and statistical power; and in longitudinal studies to identify prognostic and monitoring biomarkers\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Few studies have been performed assessing the effect of storage time on plasma biomarkers concentration. There are several approaches described in literature, some of which measure samples from the same individuals at different time points\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A study on CSF made predictions based on the Arrhenius Eq.\u0026nbsp;2\u003csup\u003e5\u003c/sup\u003e; another one performed repeated measurements of the same sample for 26 months\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, these approximations cannot differentiate the effect caused by the storage time from the age effect. We took another approach by analysing the distribution of samples from different individuals stored for up to 10 years within the same immunoassay, to discard any potential batch effect that may impact biomarker measurements\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Individuals were matched for factors known to influence biomarker levels such as sex, age and \u003cem\u003eAPOE-ε4\u003c/em\u003e genotype\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We observed that samples stored for longer periods presented higher levels of Aβ40 and Aβ42 with a variability of 11.17 and 7.7% respectively, which was mitigated by using the amyloid ratio. There were also fluctuations over time on plasma p-tau181 and p-tau231 concentrations, with lower levels in samples stored for 6 years. However, such variability was below 6%, which is within the coefficient of variation of the immunoassay and may thus not have a major impact on biomarker interpretation. Similar effects were seen for plasma GFAP, with decreased levels in samples stored for 6 years or more. Plasma NfL was the only biomarker stable along time. Previous studies have shown long-term stability of plasma Aβ40, Aβ42, GFAP and NfL within periods ranging from 5 to 20 years\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, in line with our findings, the stability of p-tau181 appeared to be more variable and sensitive to storage conditions in CU individuals\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.To further investigate these discrepancies, we performed a sensitivity analysis using the samples stored for up to 6.5 years, as those had gone through the same collection procedures (centrifugation and storage). Plasma Aβ peptides showed stability for up to 6 years (Suppl. Figure\u0026nbsp;2); p-tau231 and GFAP still had lower levels in samples stored longer, but the variability observed was under 5%. The impact of such variability on biomarker interpretation depends on the analytical robustness of the assay, which is high for Simoa assays of plasma GFAP and p-tau181 (similar performance with simulated variations\u0026thinsp;\u0026gt;\u0026thinsp;20%), but low for plasma Aβs\u003csup\u003e32\u003c/sup\u003e. Overall, our results suggest that whether significant changes on some plasma biomarkers over time were detected, these will likely have limited effect for cross-sectional studies using samples stored for long periods of time. For longitudinal studies, the variability detected should be considered in the interpretation of the results. For amyloid peptides, the use of the ratio rather than the single peptides would be recommended.\u003c/p\u003e\u003cp\u003eDespite this study characterized two preanalytical factors relevant for the implementation of AD plasma biomarkers, there are several limitations to consider: i) participants were not asked to be in a non-fasting condition, and thus we cannot guarantee a postprandial state. Still, this simulates the clinical diagnosis scenario in which the non-fasting status is not specifically required for AD plasma biomarker analysis; ii) samples had either undergone two or three FTC; however, previous investigations demonstrated Aβ peptides to be stable up to 3 FTC\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and the other biomarkers up to 4 FTC\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and, therefore, it is not expected to influence our results; and iii) protocols for plasma collection and storage, including specific material, have historically changed across centers. In our case, centrifugation settings and storage conditions of samples stored longer were different. Despite a prior study suggests that the centrifugation settings (speed, temperature) do not affect the stability of biomarkers\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, smaller surface/volume ratios could lead to smaller concentrations of AD biomarkers\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, differences on the surface/volume ratio have only been studied in CSF, with an effect on Aβ levels\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, whereas p-tau181 remained stable\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn summary, our findings suggest that fasting status does not impact biomarker measurements, although comorbid conditions, including obesity, might be considered in the interpretation of plasma biomarker levels. Even storage at -80\u0026ordm;C for up to 10 years may influence the Aβ peptides, p-tau181, p-tau231 and GFAP levels, its effect was below 6%. Plasma amyloid peptides showed the strongest effect, but this was overcome using the amyloid ratio. Since some biomarkers levels changed across storage time, this factor should be considered in the analysis and interpretation of the results, especially in longitudinal studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAβ\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmyloid-β\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCSF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCerebrospinal fluid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCU\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCognitively unimpaired\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoefficient of variation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEDTA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEthylenediaminetetraacetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eFTC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFreeze thaw cycle\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGFAP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNfL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeurofilament Light Chain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePET\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePositron emission tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003epTau\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhospho-Tau\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eQC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuality control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROC analysis\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoom temperature\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eThe ALFA\u0026thinsp;+\u0026thinsp;study receives funding from \u0026ldquo;la Caixa\u0026rdquo; Foundation, under agreement LCF/PR/SC22/68000001 and the Alzheimer\u0026rsquo;s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa17519007).\u003c/p\u003e\n\u003cp\u003eHB receives funding from the European Union \u0026ndash; NextGenerationEU \u0026ndash; and Generalitat de Catalunya (2023 INV-2 00038).\u003c/p\u003e\n\u003cp\u003eMdD receives funding from the European Union \u0026ndash; NextGenerationEU \u0026ndash; and Generalitat de Catalunya (2023 INV-2 00038).\u003c/p\u003e\n\u003cp\u003eFA receives funding from the JDC2022-049347-I grant, funded by the MCIU/AEI/10.13039/501100011033and the European Union NextGenerationEU/PRTR and from the BrightFocus Foundation Alzheimer\u0026rsquo;s Disease Research Program (grant A2025004F).\u003c/p\u003e\n\u003cp\u003eMdC receives funding from the MCIN/AEI/10.13039/501100011033through the grants RYC2023-043831-I (co-funded by the FSE+) and PID2023-153312OB-I00 (co-funded by the FEDER, EU).\u003c/p\u003e\n\u003cp\u003eMS-C received funding from the ERC under the EU\u0026rsquo;s Horizon 2020 research and innovation program (grant no. 948677), ERA PerMed-ERA NET and the Generalitat de Catalunya (Departament de Salut) through project no. SLD077/21/000001, from the Instituto de Salud Carlos III (ISCIII) and co-funded by the EU (FEDER) through projects PI19/00155 and PI22/00456, and from \u0026lsquo;la Caixa\u0026rsquo; Foundation fellowship (ID 100010434) and the EU\u0026rsquo;s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant no. 847648 (LCF/BQ/PR21/11840004)).\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eHB, JT and MdC wrote the main manuscript. AG and FA contributed to the statistical analysis.EJ, PO and MdD contributed to the sample analysis.MS and CM substantively revised the manuscript.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eThis publication is part of the ALFA study (ALzheimers and FAmilies). The authors would like to express their most sincere gratitude to the ALFA project participants and relatives without whom this research would not have been possible.\u003c/p\u003e\n\u003cp\u003eWe thank the collaborators of the ALFA Study: Clara Abad\u0026iacute;as, M\u0026uuml;ge Akinci, Andrea Ambite, Ricardo Aquite, Sara Arag\u0026oacute;, Eider Arenaza Urquijo, Kahina Baouche, Ricardo Berber\u0026iacute;a, Annabella Beteta, Marco Bianchi, Anna Brugulat-Serrat, Raffaele Cacciaglia, Jordi Cam\u0026iacute;, Fernanda Campos Strazzi, Lidia Canals Gispert, Alba Ca\u0026ntilde;as, Diego Cascales, Jos\u0026eacute; Contador, Marta Crous-Bou, Irene Cumplido, Rafael Dal-R\u0026eacute;, Neus de la Cruz-Sanchez, Carme Deulofeu, Ruth Dominguez, Maria Emilio, Isabel Estragu\u0026eacute;s, Tavia Evans, Carles Falc\u0026oacute;n, Karine Fauria, Marta F\u0026eacute;lez, Aida Fernandez, Alba Fern\u0026aacute;ndez Bonet, Ana Fern\u0026aacute;ndez-Arcos, Elisabeth Ferrer i Mairal, Jordi Freixa, Sherezade Fuentes, Clara Gallay, Marina Garc\u0026iacute;a, Manuel Garfia, Fernando Gaston Rossi, Patricia Genius, Juan Domingo Gispert, Jos\u0026eacute; Mar\u0026iacute;a Gonz\u0026aacute;lez de Ech\u0026aacute;varri, Xavier Gotsens, Nina Gramunt Fombuena, Oriol Grau Rivera, Laura Gus\u0026oacute;, Ana Harris, Laura Hernandez, Felipe Hern\u0026aacute;ndez-Villamizar, Gema Huesa, Jordi Huguet, Laura Iglesias, Michalis Kassinopoulos, Iva Knezevic, Maria Le\u0026oacute;n, Aldana Lizarraga, David L\u0026oacute;pez-Martos, Ferran Lugo, Paula Marne, Carlota Medina, Francisco Javier Mel\u0026eacute;ndez, Tania Mench\u0026oacute;n, Marta Mil\u0026agrave; Alom\u0026agrave;, Jos\u0026eacute; Luis Molinuevo, Cristina Mustata, Irene Navalpotro, Gr\u0026eacute;gory Operto, Paula Ortiz, Eva Palacios, Eleni Palpatzis, Wiesje Pelkmans, Jordi Pe\u0026ntilde;a-Casanova, Isabel Perez, Aitana Plaza, Albina Polo, Clara Porta-Mas, Sandra Pradas, Aleix Puig, Andreea Rădoi, Jaume Roca Alcaraz, Albert Rodrigo-Pares, Noelia Rodr\u0026iacute;guez de Guzm\u0026aacute;n Gallego, Blanca Rodr\u0026iacute;guez-Fern\u0026aacute;ndez, Maria Roman, Sarata Sall Sall, Gemma Salvad\u0026oacute;, Mireia S\u0026aacute;nchez, Pau S\u0026aacute;nchez, Gonzalo S\u0026aacute;nchez-Benavides, Sabrina Segundo, Mahnaz Shekari, Lluis Solsona, Anna Soteras, Laura Stankeviciute, Pilar Tarti\u0026egrave;re-Gonz\u0026aacute;lez, Laia Tenas, N\u0026uacute;ria Tort-Colet, Elisabet Zhan Travesset Muntada, David V\u0026aacute;llez, Montserrat Vil\u0026agrave;, Marc Vilanova, Natalia Vilor-Tejedor.\u003c/p\u003e\n\u003cp\u003eCONFLICTS OF INTEREST\u003c/p\u003e\n\u003cp\u003eMS-C has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Biogen, Beckman Coulter, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli 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, Eli Lilly, Fujirebio, Janssen Research \u0026amp; Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization.\u003c/p\u003e\n\u003cp\u003eMC has been an invited speaker at Eisai and Novonordisk. She is an associate editor at Alzheimer\u0026acute;s Research \u0026amp; Therapy and has been an invited writer for Springer Healthcare. She is part of the DATA-PD advisory team of the Michael J Fox Foundation.\u003c/p\u003e\n\u003cp\u003eThe other authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSimr\u0026eacute;n J, Leuzy A, Karikari TK, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s Dement. 2021;17(7):1145\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.12283\u003c/span\u003e\u003cspan address=\"10.1002/alz.12283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeunissen CE, Verberk IMW, Thijssen EH, et al. Blood-based biomarkers for Alzheimer\u0026rsquo;s disease: towards clinical implementation. Lancet Neurol Elsevier Ltd. 2022;21(1):66\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(21)00361-6\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(21)00361-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMil\u0026agrave;-Alom\u0026agrave; M, Salvad\u0026oacute; G, Gispert JD, et al. Amyloid beta, tau, synaptic, neurodegeneration, and glial biomarkers in the preclinical stage of the Alzheimer\u0026rsquo;s continuum. Alzheimer\u0026rsquo;s Dement. 2020;16(10):1358\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.12131\u003c/span\u003e\u003cspan address=\"10.1002/alz.12131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMil\u0026agrave;-Alom\u0026agrave; M, Ashton NJ, Shekari M, et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer\u0026rsquo;s disease. Nat Med. 2022;28(9):1797\u0026ndash;801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-022-01925-w\u003c/span\u003e\u003cspan address=\"10.1038/s41591-022-01925-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmqvist S, Whitson HE, Allen LA, et al. Alzheimer\u0026rsquo;s Association Clinical Practice Guideline on the use of blood-based biomarkers in the diagnostic workup of suspected Alzheimer\u0026rsquo;s disease within specialized care settings. Alzheimer\u0026rsquo;s Dement. 2025;21(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.70535\u003c/span\u003e\u003cspan address=\"10.1002/alz.70535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmqvist S, Warmenhoven N, Anastasi F, et al. Plasma phospho-tau217 for Alzheimer\u0026rsquo;s disease diagnosis in primary and secondary care using a fully automated platform. Nat Med. 2025;31(6):2036\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-025-03622-w\u003c/span\u003e\u003cspan address=\"10.1038/s41591-025-03622-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerberk IMW, Gouda M, Antwi-Berko D, et al. Evidence‐based standardized sample handling protocol for accurate blood‐based Alzheimer\u0026rsquo;s disease biomarker measurement: Results and consensus of the Global Biomarker Standardization Consortium. Alzheimer\u0026rsquo;s Dement. 2025;21(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.70752\u003c/span\u003e\u003cspan address=\"10.1002/alz.70752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorres-Torronteras J, Gouda M, Teunissen C, Verberk I, del Campo M. Impact of Pre-analytical Factors on Fluid Biomarker Measurements in Alzheimer Disease. In: Tarawneh R, ed. \u003cem\u003eBiomarkers of Neurodegenerative Disorders: Current Progress and Future Directions\u003c/em\u003e. 2025, p 475\u0026ndash;496.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerberk IMW, Misdorp EO, Koelewijn J, et al. Characterization of pre-analytical sample handling effects on a panel of Alzheimer\u0026rsquo;s disease\u0026ndash;related blood-based biomarkers: Results from the Standardization of Alzheimer\u0026rsquo;s Blood Biomarkers (SABB) working group. Alzheimer\u0026rsquo;s Dement. 2022;18(8):1484\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.12510\u003c/span\u003e\u003cspan address=\"10.1002/alz.12510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMolinuevo JL, Gramunt N, Gispert JD, et al. The ALFA project: A research platform to identify early pathophysiological features of Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s Dementia: Translational Res Clin Interventions. 2016;2(2):82\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trci.2016.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.trci.2016.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVilor-Tejedor N, Genius P, Rodr\u0026iacute;guez-Fern\u0026aacute;ndez B, et al. Genetic characterization of the ALFA study: Uncovering genetic profiles in the Alzheimer\u0026rsquo;s continuum. Alzheimer\u0026rsquo;s Dement. 2024;20(3):1703\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.13537\u003c/span\u003e\u003cspan address=\"10.1002/alz.13537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshton NJ, Pascoal TA, Karikari TK, et al. Plasma p-tau231: a new biomarker for incipient Alzheimer\u0026rsquo;s disease pathology. Acta Neuropathol. 2021;141(5):709\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00401-021-02275-6\u003c/span\u003e\u003cspan address=\"10.1007/s00401-021-02275-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR\u0026oacute;zga M, Bittner T, Batrla R, Karl J. Preanalytical sample handling recommendations for Alzheimer\u0026rsquo;s disease plasma biomarkers. Alzheimer\u0026rsquo;s Dementia: Diagnosis Assess Disease Monit. 2019;11:291\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dadm.2019.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.dadm.2019.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHansson O, Mikulskis A, Fagan AM, et al. The impact of preanalytical variables on measuring cerebrospinal fluid biomarkers for Alzheimer\u0026rsquo;s disease diagnosis: A review. Alzheimer\u0026rsquo;s Dementia Elsevier Inc. 2018;14(10):1313\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jalz.2018.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jalz.2018.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFourier A, Portelius E, Zetterberg H, Blennow K, Quadrio I, Perret-Liaudet A. Pre-analytical and analytical factors influencing Alzheimer\u0026rsquo;s disease cerebrospinal fluid biomarker variability. Clinica Chim Acta Elsevier. 2015;449:9\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2015.05.024\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2015.05.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLippi G, Lima-Oliveira G, Salvagno GL, et al. Influence of a light meal on routine haematological tests. Blood Transfus. 2010;8(2):94\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2450/2009.0142-09\u003c/span\u003e\u003cspan address=\"10.2450/2009.0142-09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLima-Oliveira G, Salvagno GL, Lippi G, et al. Infuence of a regular, standardized meal on clinical chemistry analytes. Ann Lab Med. 2012;32(4):250\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3343/alm.2012.32.4.250\u003c/span\u003e\u003cspan address=\"10.3343/alm.2012.32.4.250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Z, Mielke MM, Murray AM, et al. The impact of pre-analytical factors on plasma biomarkers for Alzheimer\u0026rsquo;s disease: The ASPREE Healthy Ageing Biobank. J Prev Alzheimers Dis. 2025;12(4):100058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tjpad.2025.100058\u003c/span\u003e\u003cspan address=\"10.1016/j.tjpad.2025.100058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHansson O, Zetterberg H, Vanmechelen E, et al. Evaluation of plasma Aβ40 and Aβ42 as predictors of conversion to Alzheimer\u0026rsquo;s disease in patients with mild cognitive impairment. Neurobiol Aging. 2010;31(3):357\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neurobiolaging.2008.03.027\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2008.03.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuber H, Ashton NJ, Schieren A, et al. Levels of Alzheimer\u0026rsquo;s disease blood biomarkers are altered after food intake\u0026mdash;A pilot intervention study in healthy adults. Alzheimer\u0026rsquo;s Dement. 2023;19(12):5531\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.13163\u003c/span\u003e\u003cspan address=\"10.1002/alz.13163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouteloup V, Pellegrin I, Dubois B, Chene G, Planche V, Dufouil C. Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia. Neurology. 2024;102(8):e209219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000209219\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000209219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiu MJ, Lue LF, Sabbagh MN, Chen TF, Chen HH, Yang SY. Long-Term Storage Effects on Stability of Aβ1\u0026ndash;40, Aβ1\u0026ndash;42, and Total Tau Proteins in Human Plasma Samples Measured with Immunomagnetic Reduction Assays. Dement Geriatr Cogn Dis Extra. 2019;9(1):77\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000496099\u003c/span\u003e\u003cspan address=\"10.1159/000496099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchubert CR, Paulsen AJ, Pinto AA, Merten N, Cruickshanks KJ. Effect of Long-Term Storage on the Reliability of Blood Biomarkers for Alzheimer\u0026rsquo;s Disease and Neurodegeneration. J Alzheimer\u0026rsquo;s Disease. 2022;85(3):1021\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3233/JAD-215096\u003c/span\u003e\u003cspan address=\"10.3233/JAD-215096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao L, Zhang M, Li Q, et al. Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases. Neural Regen Res. 2025;20(8):2373\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/NRR.NRR-D-23-01983\u003c/span\u003e\u003cspan address=\"10.4103/NRR.NRR-D-23-01983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchoonenboom NSM, Mulder C, Vanderstichele H, et al. Effects of processing and storage conditions on amyloid β (1\u0026ndash;42) and tau concentrations in cerebrospinal fluid: Implications for use in clinical practice. Clin Chem. 2005;51(1):189\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1373/clinchem.2004.039735\u003c/span\u003e\u003cspan address=\"10.1373/clinchem.2004.039735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBjerke M, Portelius E, Minthon L, et al. Confounding factors influencing amyloid beta concentration in cerebrospinal fluid. Int J Alzheimers Dis Published online. 2010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4061/2010/986310\u003c/span\u003e\u003cspan address=\"10.4061/2010/986310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng X, Chen Y, Sehrawat A, et al. Alzheimer blood biomarkers: practical guidelines for study design, sample collection, processing, biobanking, measurement and result reporting. Mol Neurodegener. 2024;19(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13024-024-00711-1\u003c/span\u003e\u003cspan address=\"10.1186/s13024-024-00711-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu D, Montagne A, Zhao Z. Alzheimer\u0026rsquo;s pathogenic mechanisms and underlying sex difference. \u003cem\u003eCellular and Molecular Life Sciences\u003c/em\u003e.\u003cem\u003eSpringer Science and Business Media Deutschland GmbH\u003c/em\u003e. 2021;78(11):4907\u0026ndash;4920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-021-03830-w\u003c/span\u003e\u003cspan address=\"10.1007/s00018-021-03830-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHara Y, McKeehan N, Fillit HM. Translating the biology of aging into novel therapeutics for Alzheimer disease. Neurology Lippincott Williams Wilkins. 2019;92(2):84\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000006745\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000006745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Tan Y, Zhang Z, Yi M, Zhu L, Peng W. The interaction between ageing and Alzheimer\u0026rsquo;s disease: insights from the hallmarks of ageing. Transl Neurodegener BioMed Cent Ltd. 2024;13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40035-024-00397-x\u003c/span\u003e\u003cspan address=\"10.1186/s40035-024-00397-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTroutwine BR, Hamid L, Lysaker CR, Strope TA, Wilkins HM. Apolipoprotein E and Alzheimer\u0026rsquo;s disease. Acta Pharm Sin B Chinese Acad Med Sciences. 2022;12(2):496\u0026ndash;510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apsb.2021.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.apsb.2021.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenedet AL, Brum WS, Hansson O, et al. The accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimers Res Ther. 2022;14(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13195-021-00942-0\u003c/span\u003e\u003cspan address=\"10.1186/s13195-021-00942-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurz C, St\u0026ouml;ckl L, Schrurs I, et al. Impact of pre-analytical sample handling factors on plasma biomarkers of Alzheimer\u0026rsquo;s disease. J Neurochem. 2023;165(1):95\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jnc.15757\u003c/span\u003e\u003cspan address=\"10.1111/jnc.15757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Lierop ZYGJ, Verberk IMW, Van Uffelen KWJ, et al. Pre-analytical stability of serum biomarkers for neurological disease: Neurofilament-light, glial fibrillary acidic protein and contactin-1. Clin Chem Lab Med. 2022;60(6):842\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/cclm-2022-0007\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2022-0007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelaby C, Mu\u0026ntilde;oz L, Torres S, et al. Impact of CSF storage volume on the analysis of Alzheimer\u0026rsquo;s disease biomarkers on an automated platform. Clin Chim Acta. 2019;490:98\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2018.12.021\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2018.12.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToombs J, Foiani MS, Wellington H, et al. Amyloid β peptides are differentially vulnerable to preanalytical surface exposure, an effect incompletely mitigated by the use of ratios. Alzheimer\u0026rsquo;s Dementia: Diagnosis Assess Disease Monit. 2018;10:311\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dadm.2018.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.dadm.2018.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToombs J, Paterson RW, Lunn MP, et al. Identification of an important potential confound in CSF AD studies: Aliquot volume. Clin Chem Lab Med. 2013;51(12):2311\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/cclm-2013-0293\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2013-0293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer Disease, pre-analytical factors, fasting, storage time, plasma","lastPublishedDoi":"10.21203/rs.3.rs-8107685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8107685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e: Pre-analytical factors may influence plasma biomarker levels reflecting Alzheimer’s disease (AD) pathology and neurodegeneration. We evaluated the impact of fasting and long-term storage at -80ºC on plasma biomarkers (Amyloid-β[Aβ]40, Aβ42, phospho-tau181, p-tau231, GFAP and NfL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e: Biomarkers were measured in ALFA cohort using Simoa technology. Fasting effects were assessed using 16 paired samples. Long-term storage at -80ºC was evaluated in 623 samples stored up to 10 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e: Fasting lowered Aβ peptides levels, but it was mitigated with the Aβ42/40 ratio. Long-term storage showed no effect on Aβ42/40 or NfL. However, Aβ peptides, p-tau181, p-tau231 and GFAP levels were higher in samples stored longer than 6 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION\u003c/strong\u003e: Fasting only influences Aβ peptides levels. The variability created by long-term storage in Aβs, p-tau181, p-tau231 and GFAP was below 6%. The results suggest that fasting does not influence biomarkers measurements and storage time might be considered, especially in longitudinal studies.\u003c/p\u003e","manuscriptTitle":"Impact of fasting status and storage time on plasma Amyloid-β 42 and 40, p-tau181, p-tau231, GFAP and NfL measurements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 20:19:03","doi":"10.21203/rs.3.rs-8107685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"942573a5-f1fc-4e60-8037-99efd39a970f","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 20:19:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8107685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8107685","identity":"rs-8107685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}