Age is the main risk factor for Alzheimer’s Disease (AD) and most neurodegenerative diseases. However, the mechanisms underlying this association are still poorly understood (Fjell et al., 2014). Both normal aging and AD are associated with region-specific cerebral morphological changes characterized by the occurrence of atrophy (Bakkour et al., 2013; Fjell et al., 2014). Both aging and AD have differential and partially overlapping effects on specific regions of the cerebral cortex like, for instance, the dorsolateral prefrontal cortex (Bakkour et al., 2013; Fjell et al., 2014; Pichet Binette et al., 2020). Conversely, some regions are predominantly affected by age (e.g. calcarine cortex) and some others are predominantly affected by AD (e.g. medial temporal cortex; Bakkour et al., 2013). A better understanding of the mechanistic links between the brain aging process and neurodegenerative diseases is an urgent priority to develop effective strategies to deal with their rising burden amid an ageing population (Franke and Gaser, 2019). Therefore, a growing amount of research is focusing on using neuroimaging techniques to develop a biomarker of biological brain aging. In this framework, the concept of brain-age has emerged as an appealing comprehensive marker that enables determining on an individual basis, the risk for age-associated brain diseases (Cole and Franke, 2017a; Cole et al., 2017c; Franke et al., 2010; Franke and Gaser, 2019). However, this is a challenging task because, even though the cerebral structural changes related to aging are well established, the older population is characterized by substantial variation in neurobiological aging trajectories (Cole et al., 2018; Fjell et al., 2014).

Recently, machine learning techniques have gained popularity as brain-age prediction models (Cole et al., 2017b; Dafflon et al., 2020; de Lange et al., 2019; Franke and Gaser, 2019), due to their ability in identifying relevant data-driven patterns within complex data (Zhavoronkov et al., 2019). These models learn the association between chronological age and cerebral morphological features derived from structural magnetic resonance imaging (MRI) in healthy individuals, yielding a predicted brain-age for each individual. Individuals with a predicted brain-age higher than their chronological age may have an ‘older’ brain than expected, whereas an individual with an estimated brain-age lower than their chronological age has a ‘younger’ brain. Subtracting chronological age from estimated brain-age hence provides an estimate of accelerated brain aging, namely the brain-age delta. Recent literature has shown the adequacy of using a brain-age predicted measurement in the assessment of the clinical severity of AD, by finding higher brain-age deltas in AD and individuals with mild cognitive impairment (MCI) with respect to cognitively unimpaired (CU) individuals (Beheshti et al., 2018; Kaufmann et al., 2019). A higher brain-age delta has also been reported in other diseases, such as multiple sclerosis, epilepsy and psychiatric disorders, with respect to healthy controls (Beheshti et al., 2018; Kaufmann et al., 2019). In addition, brain-age delta has also been associated with other biological measures such as: lifestyle factors (Cole, 2020), cognition (Beheshti et al., 2018; Cole, 2020) hypertension (de Lange et al., 2020a) and prediction of mortality (Cole et al., 2018).

Even though these studies support the association of brain-age delta as a biomarker of biological aging with relevance to various brain diseases, it is of interest to further validate this measurement in association with specific biological markers of AD pathology (i.e. Amyloid-β[Aβ] and tau pathology), neurodegeneration and cerebrovascular disease in the earliest stages of AD. This is a very relevant aspect since the recent AD research framework criteria defines AD as a biological construct, namely the presence of both abnormal Aβ (A+) and tau (T+) biomarkers, regardless of clinical manifestations (Jack et al., 2018). The term ‘Alzheimer’s pathological change’ is proposed whenever there is evidence of Aβ but not tau pathology (A+T-). The umbrella term ‘Alzheimer’s continuum’ includes both ‘Alzheimer’s pathological change’ (A+T-) and ‘Alzheimer’s Disease’ (A+T + ). Under this definition, A-T +individuals would not fall into the AD continuum. Then, under this framework, neurodegeneration biomarkers (N) and cognitive status (i.e. CU, MCI and dementia syndromes) are used to stage disease progression.

Recent literature has studied the associations between brain-age delta and the above-mentioned biomarkers with different aims. Brain-age delta was recently associated with plasma biomarkers of neurodegeneration and with imaging biomarkers of cerebrovascular disease in the 1946 British Birth Cohort Wagen et al., 2022; however, it was not associated with CSF biomarkers of neurodegeneration (Millar et al., 2022). Moreover, although brain-age delta was significantly associated with AD biomarkers of amyloid and tau in MCI individuals, these associations were not found in CU individuals (Millar et al., 2022; Wagen et al., 2022). Conversely, Another study focusing on the impact of training the brain-age prediction model in individuals with Aβ pathology (Aβ+) showed that CU Aβ+individuals had a higher brain-age delta than CU Aβ- individuals (Ly et al., 2020). Other lines of research have employed brain-age delta to predict conversion to different disease stages. For instance, it was previously studied the impact of using brain-age, alone and in combination with several biomarkers, to predict progression from MCI to AD (Popescu et al., 2020). In addition, a recent study used brain-age measurements to identify amnestic MCI (aMCI), the typical clinical presentation of prodromal AD, from other individuals with MCI, by studying the association with AD risk factors such as apolipoprotein Ε (APOE) and Aβ (Huang et al., 2021). The association of brain-age with these biomarkers have also been shown in other diseases, by which brain-age was associated with Aβ deposition in Down syndrome (Cole et al., 2017b). Nonetheless, there remains a need to replicate some of these results and to study the associations between brain-age prediction and abnormal biomarkers of AD and neurodegeneration in preclinical and prodromal AD stages in different and independent cohorts and in a larger sample size. These results would be particularly informative to potentially inform therapeutic interventions.

Moreover, given that female individuals have a higher AD prevalence compared to males (Nebel et al., 2018) and display different lifetime trajectories in the brain morphological features (Gennatas et al., 2017), it is of interest to determine the effect of sex on brain age delta and its interaction with AD biomarkers. Literature describes sex differences in AD biomarkers, such as that females with abnormal Aβ who are APOE-ε4 carriers show greater subsequent increase in cerebrospinal fluid (CSF) tau than their male counterparts (Buckley et al., 2019), or that females with higher Aβ burden show higher entorhinal cortical tau than their male counterparts (Buckley et al., 2019). Conversely, levels of the neurodegeneration biomarker CSF neurofilament light (NfL) have been widely reported to be higher in males than in females (Mielke, 2020; Milà-Alomà et al., 2020). In line with this, sex differences in the brain-age delta have been also reported. For instance, AD risk factors have been associated with greater brain aging in women than men (Sanford et al., 2022; Subramaniapillai et al., 2021) and a lower brain aging has been found in the lower in the prefrontal cortex and insula in females (Sanford et al., 2022). These results need to be replicated and further studied in different cohorts.

Therefore, in the present study, we aim to validate brain-age delta as a clinically relevant marker of brain aging, which can be impacted by AD pathology and neurodegeneration even in non-demented individuals. For this purpose, we determine the association between the predicted structural brain-age delta with biomarkers and risk factors for AD and neurodegeneration in non-demented individuals, as well as to study the effect of sex on these associations. We trained a model to predict the brain-age separately for females and males, using machine learning on imaging-derived measures of cortical thickness, cortical volume, and subcortical volume from the UK Biobank cohort (N=22,661). Using this model, we then estimated brain-age in four independent cohorts: ALFA+ (N=380), ADNI (N=719), EPAD (N=808), and OASIS (N=407). In each cohort, we studied the associations of brain-age delta with biomarkers of AD pathology (CSF Aβ and p-tau as continuous values, as well as categorized in AT stages), the APOE-ε4 genotype which is the main genetic risk factor for AD, neurodegeneration (CSF and plasma NfL), and small vessel disease (White Matter Hyperintensities [WMH]). Finally, we studied the sex differences in brain age prediction and the sex effects with these biomarkers on brain-age delta.

In this study, we show that, in non-demented individuals, the predicted brain-age delta is associated with specific AD biomarkers (amyloid-$\beta$ PET, CSF Aβ42 and CSF pTau) and risk factors (APOE genotype), as well as with unspecific neurodegeneration biomarkers (plasma NfL), and markers of cerebrovascular disease (WMH volume). However, our results do not show that variation in cross-sectional brain-age delta was able to capture longitudinal atrophy and, therefore, it is solely a cross-sectional analysis. Our results also indicate that there might be sex differences in the development of brain aging trajectories, which must be further characterized. Taken together, our findings validate the use of machine learning predicted brain-age delta as a non-invasive biomarker of brain aging, which is associated to AD pathology in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury. The capacity of brain-age delta to de detect accelerated aging must be further studied.

We have studied the associations between brain-age delta and different biomarkers of AD pathology and neurodegeneration in CU individuals. We are aware of the complexity of disentangling the effects of aging and pathology in brain aging. In this work, we do not aim to disentangle to what extent the brain structural differences are caused by AD pathology (as measured by the biomarkers that we study) or aging. Here, we show that an unspecific estimation of biological brain aging, agnostic of the underlying mechanisms is associated with the specific biological process of AD.

Regarding the associations with AD biomarkers and risk factors, regression analyses revealed significant positive associations of brain-age delta with increased Aβ pathology and with AT stages for CU and MCI individuals. We also found significant associations with APOE status in the CU and MCI individuals, in which larger brain-age deltas were associated with the presence of APOE-ε4 allele. This result is in line with previous literature that has shown that APOE-ε4 carriership may accelerate AD-related brain atrophy (Evans et al., 2014; Filippini et al., 2011), as accelerated brain aging has also been found in MCI and AD patients (Beheshti et al., 2018; Kaufmann et al., 2019). The association of brain-age delta with APOE-ε4 was also previously studied, for which significant associations were found in MCI individuals (Cole et al., 2018; Löwe et al., 2016). Taken together, our results advocate for an effect of APOE-ε4 in physiological brain aging, albeit of a lesser magnitude than when AD pathology is present. These results with AD biomarkers and risk factors were highly reproducible in within-cohort analyses.

With the aim of studying the associations between brain-age delta and neurodegeneration, we computed the associations with NfL, a marker of neuro-axonal damage (Khalil et al., 2018) which can be measured both in CSF and in plasma, and with longitudinal changes in the aging signature composite as marker of age-related brain atrophy. The particular use of NfL in this context is supported by its correlation with age throughout the lifespan, as well as its strong association with all-cause mortality in the elderly (Kaeser et al., 2021). We found significant positive associations between brain-age delta and plasma NfL both in CU and MCI individuals, but we did not find significant associations between CSF NfL and CU brain-age delta. Even though we found NfL to be positively associated with chronological age in CU individuals, in line with previous studies (Beheshti et al., 2018; Kaufmann et al., 2019; Khalil et al., 2020; Milà-Alomà et al., 2020), the expected annual change of NfL in CU individuals whose mean age range was 65 years old is around 3.5% (Khalil et al., 2020). Therefore, we expected to find weak associations with brain-age delta. Still, we found a significant interaction effect of age and CSF NfL on CU brain-age delta, for which the individuals with larger brain-age delta had increased CSF NfL over the years, whereas the decelerated ones remained more stable. Taken together, a strong association between brain-age delta and plasma NfL was observed across all individuals whereas the association on CU brain-age delta and CSF NfL was milder and could only be detected as an interaction with age. Overall, these mild associations between brain-age delta and NfL suggest that the morphological effects of aging in the brain are not fully driven by neurodegeneration. In this regard, it is worth noting that cortical thinning with age has also been linked to loss of volume of the neuropil and other non-neuronal processes which are not necessarily implicated in neurodegeneration (Vidal-Pineiro et al., 2020). Conversely, brain-age delta was not associated with longitudinal change in aging signature. This might indicate, as previously shown (Vidal-Pineiro et al., 2020), that brain-age delta might not reflect accelerated brain aging but instead early life factors. However, the lack of this association might also be influenced by the short amount of time between time visits (average of 3 years) or the measurement used to measure longitudinal atrophy (cortical aging signature). Therefore, further research is needed to determine the relative contribution of early life factors and accelerated aging to brain-age delta estimates.

We also studied the associations between brain-age delta and cerebrovascular disease. Regression analyses revealed significant associations of brain-age delta with increased WMH for both CU and MCI individuals. These results were expected, as the increase in WMH with age has been previously studied (Maniega et al., 2015) and it has been shown that individuals with high WMH burden display spatial patterns of atrophy that partially overlap with those of brain aging (Brugulat-Serrat et al., 2020a; Habes et al., 2016). In addition, WMH have been linked to cognitive dysfunction and dementia (Brugulat-Serrat et al., 2020b; Brugulat-Serrat et al., 2020a; Maniega et al., 2015) and a potential pathway has been proposed, in which small vessel cerebrovascular disease affects cognition by promoting neurodegenerative changes (Rizvi et al., 2018). In summary, our results support an effect of cerebrovascular disease in physiological brain aging.

Brain structure aging-associated changes have been widely studied (Bakkour et al., 2013; Fjell et al., 2014). In our study, the brain regions that had highest impact on the machine learning prediction were similar to regions previously mentioned in literature (Arenaza-Urquijo et al., 2019; Bakkour et al., 2013). We found an overlap between some of our selected regions and regions included in the aging signature for both females and males, such as the precentral sulcus, insula, superior frontal and rostral middle frontal regions. In addition, the effect of sex on age-related changes in brain structure has also been studied in the recent years, with some studies reporting age–sex interactions in volumes of certain brain structures (Coffey et al., 1998; DeCarli et al., 2005), and others not finding such interactions (Greenberg et al., 2008). In our study, we found that, even though most of the regions with highest impact were the same for males and females, there were some regions that were sex specific. In particular, we found reduction in the superior-frontal, isthmus-cingulate and pars orbitalis regions within males and regions such as inferior-parietal, pars triangularis and paracentral within females. Most of these sex-specific regions were in concordance with previous studies (Armstrong et al., 2019; Podgórski et al., 2021). The mechanisms underlying these sex-specific brain aging differences are not well-known. Sexual hormones such as estrogen, progesterone and androgen could play a role in brain atrophy Armstrong et al., 2019; in the WHIMS-MRI study (Resnick et al., 2009), women under menopausal hormone therapy were associated with greater brain atrophy. Others, however, have proposed that estrogen and progesterone may play a protective effect in women (Armstrong et al., 2019; Green and Simpkins, 2000). Other possible biological mechanisms influencing these results could be developmental (Baron-Cohen et al., 2005) or the influence of a greater presence of adverse lifestyle-related factors in men (DeCarli et al., 2005).

In line with the effect of sex on age-related changes in brain structure, we studied the effect of sex on the associations between brain-age delta and the above-mentioned variables. Regarding the AD biomarkers and risk factors, we found that the association between brain-age delta and a larger proportion of A+T + was only seen in females for the CU individuals, but the interaction effect of sex and AT stages on brain-age delta was not significant. Regarding the neurodegeneration variables, we found a positive association between brain-age delta and plasma NfL in CU and in MCI females. These associations were not seen in their male counterparts. However, the interaction effect of sex and CSF and plasma NfL on CU and MCI brain-age delta was not significant. In addition, we found a significant interaction effect of age and CSF and plasma NfL on brain-age delta, for which the CU females with larger brain-age delta had increased CSF and plasma NfL over the years, whereas the decelerated ones remained more stable. These results were expected, as females have higher chances of undergoing neurodegeneration and have showed to undergo faster cognitive decline than males (Ferretti et al., 2018). Although the role of sex hormones still needs to be clarified, it has been suggested that the menopausal drop of estrogen increases vulnerability to neurological events (Green and Simpkins, 2000; Maioli et al., 2021). On the contrary, results suggest that morphological effects of aging in the CU males’ brain are not fully driven by neurodegeneration. Lastly, regarding the cerebrovascular disease biomarkers, we found a positive association between brain-age delta and WMH for both CU females and males, while no interaction effect of sex was found. Conversely, in MCI individuals, we only found positive associations between brain-age delta and WMH on males, but no interaction effect of sex was found in MCI individuals. Overall, we found cross-sectional sex differences in the associations between brain-age delta and markers of neurodegeneration and cerebrovascular disease. NfL was only positively associated with brain-age delta in females, while WMH were positively associated with both CU females and males. Positive associations between NfL and WMH have been previously demonstrated, for both CU and MCI (Osborn et al., 2018), and the different AD stages (Walsh et al., 2021). Moreover, it has been proposed that WMH may reflect two different pathological pathways, one including amyloid aggregation and another including axonal injury (Osborn et al., 2018). Our results may suggest that brain aging in males might be driven more strongly by the former pathway, while brain aging in females might be driven more by the latter one. However, the lack of significant brain-age delta-by-sex interactions in our analyses reflect the limitations in the evidence for the above-mentioned sex-differences. Further work exploring these sex differences in detail is warranted.

Our purpose was to study the clinical validity of using the brain-age delta as a proxy biomarker of brain aging associated to AD and neurodegeneration. Therefore, our main aim was studying the characteristics of the individuals whose brain age is more accelerated or decelerated. One of the strengths of this study was the robustness of the brain-age delta measurement using a widely used segmentation atlas such as the Desikan-Killiany. Notably, we demonstrated the robustness of our method by training our model with one cohort and testing independently on four independent cohorts. The similar results obtained in all cohorts allowed us to seek associations in a large sample of participants with biomarker data and to further stratify the data by sex. This aspect is critical for this type of analyses as the effects of biological aging are necessarily very small, particularly in CU individuals of limited age range. This may explain why we could not detect significant effects versus longitudinal brain atrophy, as the available sample size for these analyses was smaller since this variable was not available in all cohorts. Another strength of our work was that we were able to include a wide range of different biomarkers of AD pathology allowing us to perform an in-depth analysis of the effect of these measurements with the brain-age delta. Conversely, our model used a smaller number of features and a training set with a more limited age range than other models seen in literature recently (Liem et al., 2017; Peng et al., 2021), leading to a performance which cannot be compared against these state-of-the-art models. However, the performance of age prediction was similar to other publications that used similar methodologies (Beheshti et al., 2018; Dafflon et al., 2020; de Lange et al., 2020a) and, most importantly, was successful in studying the utility of brain-age delta as biomarker of brain aging in non-demented individuals, which is impacted by abnormal levels of biomarkers of AD and neurodegeneration. Moreover, using a relatively restricted number of features allowed us to visualize and interpret in an easier way the contribution of different brain regions to the brain age prediction by means of SHAP values. This ensured that the model had neurobiological interpretability. However, it is important to note that SHAP values may not reflect the true relationship between the features and the model’s prediction in the presence of correlated features. This is an important consideration to bear in mind for correctly interpreting our results. In addition, future work should also focus on developing a model with larger number of features or a 3D model and should study the effect of these validation measurements for AD and neurodegeneration, as well as longitudinal changes, with the brain-age delta more in depth.

In conclusion, we validated that machine-learning based brain age prediction obtained from a widely used segmentation atlas can be used as a biomarker of biological brain aging associated with AD pathology, risk factors, and neurodegeneration. Moreover, our results confirm the presence of sex-related brain aging structural changes and suggest the prevalence of different neuropathological pathways involved in brain aging within females and males. Therefore, these results indicate the importance on considering different approaches for assessing aging and neurodegeneration differently for each sex.

UK Biobank data availability at https://www.ukbiobank.ac.uk/ ADNI data availability at https://adni.loni.usc.edu/ EPAD data availability at https://ep-ad.org/ ALFA: data availability through GAAIN at https://www.gaaindata.org/partners/online.html Fully steps and instructions on data access can be found in the following links: UK Biobank: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ADNI: https://adni.loni.usc.edu/data-samples/access-data/EPAD: https://ep-ad.org/open-access-data/access/ ALFA: https://www.gaaindata.org/partner/ALFA.

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Author details

1. Irene Cumplido-Mayoral

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. Universitat Pompeu Fabra, Barcelona, Spain

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1967-7025

2. Marina García-Prat

   Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain

   Contribution

   Data curation, Software, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

3. Grégory Operto

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
   3. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France

   Contribution

   Resources, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

4. Carles Falcon

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
   3. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain

   Contribution

   Resources, Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Mahnaz Shekari

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. Universitat Pompeu Fabra, Barcelona, Spain
   3. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain

   Contribution

   Resources, Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Raffaele Cacciaglia

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
   3. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France

   Contribution

   Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Marta Milà-Alomà

   1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
   2. Universitat Pompeu Fabra, Barcelona, Spain
   3. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
   4. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France

   Contribution

   Resources, Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Luigi Lorenzini

   Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Resources, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

9. Silvia Ingala

   Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Resources, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

10. Alle Meije Wink

    Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Contribution

    Resources, Data curation, Software, Writing – review and editing

    Competing interests

    No competing interests declared

11. Henk JMM Mutsaerts

    Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Contribution

    Validation, Investigation, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0894-0307

12. Carolina Minguillón

    1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
    2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
    3. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France

    Contribution

    Resources, Data curation, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

13. Karine Fauria

    1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
    2. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France

    Contribution

    Resources, Data curation, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

14. José Luis Molinuevo

    Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain

    Present address

    H.Lundbeck A/S, Copenhagen, Denmark

    Contribution

    Validation, Investigation, Writing – review and editing

    Competing interests

    is currently a full-time employee of H. Lundbeck A/S and previously has served as a consultant or on advisory boards for the following for-profit companies or has given lectures in symposia sponsored by the following for-profit companies: Roche Diagnostics, Genentech, Novartis, Lundbeck, Oryzon, Biogen, Lilly, Janssen, Green Valley, MSD, Eisai, Alector, BioCross, GE Healthcare, and ProMIS Neurosciences

15. Sven Haller

    CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland

    Contribution

    Resources, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7433-0203

16. Gael Chetelat

    Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain, Cyceron, France

    Contribution

    Resources, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Adam Waldman

    Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Resources, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Adam J Schwarz

    Takeda Pharmaceutical Company Ltd, Cambridge, United States

    Contribution

    Resources, Validation, Investigation, Writing – review and editing

    Competing interests

    Employee of Takeda Pharmaceutical Company Ltd

    "This ORCID iD identifies the author of this article:" 0000-0002-9743-6171

19. Frederik Barkhof

    1. Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    2. Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom

    Contribution

    Resources, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

20. Ivonne Suridjan

    Roche Diagnostics International Ltd, Rotkreuz, Switzerland

    Contribution

    Resources, Writing – review and editing

    Competing interests

    Is a full-time employee and shareholder of Roche Diagnostics International Ltd

21. Gwendlyn Kollmorgen

    Roche Diagnostics GmbH, Penzberg, Germany

    Contribution

    Resources, Writing – review and editing

    Competing interests

    Is a full-time employee of Roche Diagnostics GmbH

22. Anna Bayfield

    Roche Diagnostics GmbH, Penzberg, Germany

    Contribution

    Resources, Writing – review and editing

    Competing interests

    Is a full-time employee and shareholder of Roche Diagnostics GmbH

23. Henrik Zetterberg

    1. Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
    2. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
    3. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom
    4. Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
    5. UK Dementia Research Institute at UCL, London, United Kingdom

    Contribution

    Resources, Data curation, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

24. Kaj Blennow

    1. Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
    2. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Contribution

    Resources, Data curation, Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

25. Marc Suárez-Calvet

    1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
    2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
    3. CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, France
    4. Servei de Neurologia, Hospital del Mar, Barcelona, Spain

    Contribution

    Conceptualization, Data curation, Validation, Investigation, Writing – original draft, Writing – review and editing

    Competing interests

    Has served as a consultant and at advisory boards for Roche Diagnostics International Ltd and has given lectures in symposia sponsored by Roche Diagnostics, S.L.U, Roche Farma, S.A and Roche Sistemas de Diagnósticos, Sociedade Unipessoal, Lda

26. Verónica Vilaplana

    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain

    Contribution

    Conceptualization, Supervision, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    veronica.vilaplana@upc.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6924-9961

27. Juan Domingo Gispert

    1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
    2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
    3. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain

    Contribution

    Conceptualization, Supervision, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    jdgispert@barcelonabeta.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6155-0642

28. ALFA study

    Contribution

    Resources

    Competing interests

    No competing interests declared

29. EPAD study

    Contribution

    Resources

    Competing interests

    No competing interests declared

30. ADNI study

    Contribution

    Resources

    Competing interests

    No competing interests declared

31. OASIS study

    Contribution

    Resources

    Competing interests

    No competing interests declared

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