Work

Comparison of the additive value of blood-based and retinal biomarkers to clinical features in predicting cognitive decline

Year: 2025

Type: article

Abstract: Retinal 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. A 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). Here 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]). A multimodal model comprising blood, retinal and clinical variables may represent an accessible, non-invasive tool for predicting cognitive decline. Combining clinical, retinal and blood-based biomarkers for the prediction of cognitive decline has not been previously explored. We examined this in a longitudinal Asian memory clinic cohort of 306 subjects, with 104 incident cases of cognitive decline, followed up for 5 years. Supervised machine learning was used to develop predictive models for cognitive decline, using 71 baseline predictor variables (8 clinical variables, 13 blood-based biomarkers, and 50 retinal fundoscopic features). We found that a predictive model combining retinal, blood-based and clinical variables yielded the greatest prediction for cognitive decline. However, a combination of clinical, and blood-based biomarkers achieved a desirable balance between predictive performance and resource utilisation. Our findings suggest future scalable models for the prediction of cognitive decline. Sim, Tham et al. explore the combined predictive ability of clinical, retinal, and blood biomarkers for the prediction of cognitive decline in Southeast Asian memory clinic participants. Authors find that combining blood tests, eye markers, and clinical data may be a potential noninvasive tool for early risk detection for cognitive decline.

Source: Communications Medicine

DOI: 10.1038/s43856-025-01237-3