Neurology · Medical Literature
Adaptive pathways for multimodal community-based detection of cognitive impairment: the CogScreen I study.
Abstract
Early detection of cognitive impairment is essential for dementia prevention and timely care. However, implementation in primary care and community settings remains limited. Building dementia-prepared health systems requires scalable and adaptive pathways that integrate subjective, digital and biological indicators, while accounting for heterogeneity in risk, education and age. CogScreen I was a cluster-randomized trial conducted in Munich senior centers from March 2023 to March 2024. Recruitment focused on community-dwelling adults aged ≥ 60 years reporting subjective cognitive concerns. Centers were randomized to: (A) the Subjective Cognitive Decline Questionnaire (SCD-Q) only, (B) SCD-Q plus digital cognitive testing, or (C) SCD-Q plus digital testing plus blood biomarkers (Aβ1-42/1-40, pTau181, GFAP, NfL). The primary endpoints were feasibility and acceptability, assessed through structured questionnaires and follow-up interviews with participants and general practitioners. Secondary endpoints examined latent cognitive structures and biomarker associations. Exploratory factor and clustering analyses revealed multimodal subgroups across subjective, digital, and biological measures, which informed a hypothesis-generating three-tier adaptive detection framework (low, medium, high intensity). Among the 473 participants (mean age 74.1 ± 7.6 years; 66% female; 63% with tertiary education), both feasibility and acceptability were high: Participants described the assessments as personally relevant, clearly communicated, and medically meaningful, and appreciated the added value of digital testing and biomarkers. Digital cognitive testing and biomarkers captured distinct latent dimensions of learning/working memory, psychomotor attention, and glial and amyloid pathology. Exploratory multimodal clustering identified three dementia risk profiles-low concern, intermediate, and at risk-which informed an adaptive, tiered detection model. Notably, subjective cognitive concerns assessed by the SCD-Q were strongly associated with overall subjective symptom burden, supporting the use of brief self-report questionnaires as a pragmatic and scalable first-step stratification approach in community-based dementia detection. Community-based, tiered detection pathways for cognitive decline are feasible, acceptable, and meaningful to older adults. Exploratory multimodal profiling suggests that combining subjective, digital, and biological measures may support individualized assessment strategies. However, limited GP engagement highlights the need for stronger integration with primary care to ensure downstream diagnostics and prevention pathways. The proposed framework remains hypothesis-generating and requires prospective validation.Clinical trials registeredCogScreen has been registered at clinical trials (NCT06191952, 2023-12-20).
Clinical relevance
Early and scalable detection of cognitive impairment in community settings remains a critical unmet need in dementia prevention, particularly for older adults with subjective cognitive concerns who are not yet engaged with formal diagnostic pathways. CogScreen I addresses this gap by evaluating a multimodal, tiered screening framework combining subjective questionnaires, digital cognitive testing, and blood-based biomarkers (Aβ1-42/1-40, pTau181, GFAP, NfL) in a real-world community setting. With dementia prevalence rising globally, neurologists and primary care physicians urgently need evidence-based, scalable models that can stratify risk and guide earlier intervention in unselected community-dwelling populations.
Key findings
1. Study population: 473 community-dwelling adults (mean age 74.1 ± 7.6 years; 66% female; 63% with tertiary education) were enrolled across Munich senior centers in a cluster-randomized design (March 2023–March 2024), with three arms of escalating assessment intensity (SCD-Q alone; SCD-Q + digital testing; SCD-Q + digital testing + blood biomarkers). 2. Primary endpoints met: Both feasibility and acceptability were rated high across all three arms; participants characterized assessments as personally relevant, clearly communicated, and medically meaningful, supporting real-world implementability of this tiered approach. 3. Multimodal biomarker dimensionality: Exploratory factor analysis identified distinct latent dimensions—learning/working memory and psychomotor attention (captured by digital testing) and glial/amyloid pathology (captured by blood biomarkers: GFAP, NfL, Aβ1-42/1-40, pTau181)—confirming that subjective, digital, and biological measures are non-redundant and complementary. 4. Three dementia risk profiles identified: Unsupervised multimodal clustering yielded three distinct dementia risk subgroups—low concern, intermediate, and at risk—informing the hypothesis-generating three-tier adaptive detection framework (low, medium, and high intensity pathways). 5. SCD-Q performance: Subjective cognitive concerns assessed by the SCD-Q were strongly associated with overall subjective symptom burden, validating brief self-report questionnaires as a pragmatic and scalable first-step stratification tool in community-based settings. 6. Primary care integration gap: Limited GP engagement was identified as a significant structural barrier, with downstream diagnostic and prevention pathway linkage remaining insufficient despite high participant-level acceptability. Note: Specific quantitative effect sizes, confidence intervals, and p-values were not reported in the abstract; findings above reflect the precision of data available.
Practice implications
Neurologists and geriatricians should consider adopting a stepwise, adaptive screening approach beginning with a brief validated self-report tool (such as the SCD-Q) as a first-tier stratification instrument for community-dwelling adults ≥60 years with subjective cognitive concerns, reserving digital cognitive testing and blood-based biomarker panels (Aβ1-42/1-40, pTau181, GFAP, NfL) for intermediate- and high-risk tiers respectively to optimize resource utilization. Patient selection for higher-intensity multimodal assessment should account for heterogeneity in age, education level, and baseline risk profile, as these factors influence both digital and biomarker-derived cognitive dimensions. Critically, this framework remains hypothesis-generating and requires prospective longitudinal validation before clinical implementation; furthermore, robust integration with primary care workflows and GP engagement protocols must be co-designed to ensure that detected at-risk individuals are successfully referred into formal diagnostic and prevention pathways.