ArticleCommunications medicine2025
Comparison of the additive value of blood-based and retinal biomarkers to clinical features in predicting cognitive decline.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Plasma proteomics of cerebrovascular disease, cognitive decline, and clinical outcomes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Association of Atrial Electrophysiological Abnormalities With Cognitive Decline and Cerebrovascular Disease.Journal of the American Heart Association · 2026Article
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Authors and funding
21 authors.
Funding
Abstract
backgroundRetinal imaging features, and blood-based biomarkers may provide insights into cognitive neuropathology. However, the combined utility of retinal imaging features, and blood-based biomarkers for predicting cognitive decline remains under-investigated.
methodsA cohort of memory-clinic subjects (age 71.86 ± 7.91 years, 48.4% male) were followed for 5 years, with annual Clinical Dementia Rating Scale Sum-of-Boxes(CDR-SB) assessments. Cognitive decline was defined as a CDR-SB ≥ 2 increment from baseline. Elastic net regression of 71 multimodal variables for predicting cognitive decline was performed comprising:8 clinical factors, 50 retinal fundoscopic features, and 13 blood-based biomarkers (Ergothioneine, pTau-181, NfL, insulin, leptin, IL-6, IL-8, TNF, NGF, thrombomodulin, GDF-15, hs-cTnT and NT-proBNP). This yielded a set of 13 variables(4 clinical factors, 5 blood-based biomarkers, and 4 retinal fundoscopic indices) which was used for model construction. Predictive models for cognitive decline were built using:1)Clinical variables, 2)Clinical+Retinal variables 3)Clinical+Blood variables, and 4)Clinical+Retinal+Blood variables. Predictive performance for cognitive decline was evaluated using cross-validated AUROC(area under receiver operating characteristic curve).
resultsHere we show, of 306 subjects followed-up for 60.0 months [Interquartile range 48.0-60.0], 104 (33.9%) display cognitive decline. The AUROC of the clinical model for cognitive decline is 0.691[0.629-0.754]. The addition of retinal features to the clinical model does not provide additive risk stratification(AUROC 0.680, [0.616-0.743],p = 0.486), but the addition of blood-based biomarkers improves the discriminative ability of the clinical model for cognitive decline(AUROC 0.748,[0.691-0.754],p = 0.016). A combination of clinical+retinal+blood variables yields the highest predictive ability for cognitive decline(AUROC 0.752 [0.695-0.809]).
conclusionsA multimodal model comprising blood, retinal and clinical variables may represent an accessible, non-invasive tool for predicting cognitive decline.
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