Objectives: The main goal of machine learning approaches to classify people into healthy, increased Alzheimer's disease (AD) risk, and AD is the identification of valuable predictors for valid classification, prediction of conversion, and automatization of the process. While biomarkers from cerebrospinal fluid (CSF) are the best-established predictors for AD, other less invasive, easy-to-assess candidate predictors have been identified. Here, we evaluated the predictive value of such less invasive, predictors separately and in different combinations for classification of healthy controls (HC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and mild AD. Methods: We evaluated the predictive value of personality scores, geriatric anxiety and depression scores, a resting-state functional magnetic resonance imaging (fMRI) marker (mPerAF), apoliprotein E (ApoE), and CSF markers (tTau, pTau181, Abeta 42/40 ratio) separately and in different combinations in multi-class support vector machine classification. Participants (189 HC, 338 SCD, 132 MCI, 74 mild AD) were recruited from the multi-center DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Results: HC were best predicted by a feature set comprised of personality, anxiety, and depression scores, while participants with AD were best predicted by a feature set containing CSF markers. Both feature sets had equally high overall decoding accuracy. However, all assessed feature sets performed relatively poorly in the classification of SCD and MCI. Conclusion: Our results highlight that SCD and MCI are heterogeneous groups, pointing out the importance of optimizing their diagnosis criteria. Moreover, CSF biomarkers, personality, depression, and anxiety indicate complementary value for class prediction, which should be followed up on in future studies.

F. Jessen received fees for consultations and presentations between 2019 and 2022 from AC Immune, Biogen, Danone/Nutricia, Eisai, GE Healthcare, Grifols, Janssen, Lilly, MSD, Novo Nordisk, and Roche. E. Düzel is cofounder of neotiv GmbH. The remaining authors report no disclosures relevant to the manuscript.

https://drks.de/search/de/trial/DRKS00007966

The study was funded by the German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen [DZNE]), reference number BN012.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study protocol was approved by Institutional Review Boards of all participating study centers of the DZNE. The process was led and coordinated by the ethical committee of the medical faculty of the University of Bonn (registration number 117/13).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

All scripts used to perform the analyses are available under https://github.com/jmkizilirmak/DELCODE162. Data can be made available to cooperation partners of the DZNE after setting up appropriate data sharing contracts.