June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
A multi-regression model for improving the diagnostic performance of OCT to discriminate mild cognitive impairment and Alzheimer’s disease
Author Affiliations & Notes
  • Leopold Schmetterer
    Singapore Eye Research Institute, Singapore, Singapore
  • Jacqueline Chua
    Singapore Eye Research Institute, Singapore, Singapore
  • Chi Li
    Singapore Eye Research Institute, Singapore, Singapore
  • Christopher Chen
    Memory Aging and Cognition Centre, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Leopold Schmetterer None; Jacqueline Chua None; Chi Li None; Christopher Chen None
  • Footnotes
    Support  National Medical Research Council (CG/C010A/2017; OFIRG/0048/2017; OFLCG/004c/2018; and TA/MOH-000249-00/2018), A*STAR (A20H4b0141), the Duke-NUS Medical School (Duke-NUS-KP(Coll)/2018/0009A), the SERI-Lee Foundation (LF1019-1) Singapore.
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 3352 – F0161. doi:
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    • Get Citation

      Leopold Schmetterer, Jacqueline Chua, Chi Li, Christopher Chen; A multi-regression model for improving the diagnostic performance of OCT to discriminate mild cognitive impairment and Alzheimer’s disease. Invest. Ophthalmol. Vis. Sci. 2022;63(7):3352 – F0161.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Optical coherence tomography (OCT) has been proposed as a retinal biomarker for Alzheimer's disease (AD), however, current diagnostic performance is limited but may be improved by reducing the inter-individual variability of circumpapillary retinal nerve fiber layer (cpRNFL) measurements. We developed a multiple-regression framework including compensation for anatomical confounders, and combination of the cpRNFL and macular layers to increase discrimination between cognitively normal participants, mild cognitive impairment (MCI) and AD.

Methods : This cross-sectional study included 62 AD (n=92 eyes), 108 MCI (n=158 eyes), and 55 cognitively normal control (n=86 eyes) participants. Using an Early Treatment Diabetic Retinopathy Study circle at the macular level, thicknesses of the macular RNFL (mRNFL), macular ganglion cell layer (mGCL), macular inner plexiform layer (mIPL) were determined. Circumpapillary (cpRNFL) measurements were compensated for potential confounders including demographics (ethnicity and age), refractive error, optic disc (ratio, orientation, and area), fovea (distance and angle), retinal vessel density and signal strength. Thickness measurements of each layer and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between the groups.

Results : Age and gender did not differ among groups. In the multivariate analysis, MCI/AD participants showed significantly thinner measured and compensated cpRNFL, mRNFL, mGCL, mIPL, and altered retinal vessel density (p<0.05). Compensated RNFL outperformed measured RNFL for discrimination of MCI (AUC=0.74 vs 0.68; p=0.02) and AD (AUC=0.79 vs 0.71; p=0.025) from controls. Combining macular and compensated cpRNFL parameters further improved the detection of MCI (AUC = 0.79 vs 0.68; p<0.001) and AD (AUC=0.87 vs 0.71; p<0.001).

Conclusions : This study suggests that accounting for interindividual variations of ocular anatomical features in cpRNFL thickness measurements and incorporating information from macular layers may allow for a improve the identification of high-risk individuals with early cognitive impairment and dementia.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Steps to account for ocular factors from circumpapillary retinal nerve fiber layer (cpRNFL) measurement.

Steps to account for ocular factors from circumpapillary retinal nerve fiber layer (cpRNFL) measurement.

 

Receiver operating characteristic (ROC) curves and corresponding areas under the ROC curve (AUC).

Receiver operating characteristic (ROC) curves and corresponding areas under the ROC curve (AUC).

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